eLife assessment

Using unbiased transcriptional profiling, the study reports a fundamental discovery of a novel hepatic lncRNA, FincoR, which regulates FXR. The convincing findings have therapeutic implications in the treatment of MASH. The authors use state-of-the-art methodology and use unbiased transcriptomic profiling and epigenetic profiling, including validation in mouse models and human samples.

Author Response

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

eLife assessment:

The authors report a novel hepatic lncRNA FincoR regulating FXR with therapeutic implications in the treatment of MASH. The findings are important and use an appropriate methodology in line with the current state-of-the-art, with convincing support for the claims.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

In the article titled "Hammerhead-type FXR agonists induce an eRNA FincoR that ameliorates nonalcoholic steatohepatitis in mice," the authors explore the role of the Farnesoid X Receptor (FXR) in treating metabolic disorders like NASH. They identify a new liver-specific long non-coding RNA (lncRNA), FincoR, regulated by FXR, notably induced by agonists such as tropifexor. The study shows that FincoR plays a significant role in enhancing the efficacy of tropifexor in mitigating liver fibrosis and inflammation associated with NASH, suggesting its potential as a novel therapeutic target. The study makes a promising contribution to understanding the role of FincoR in alleviating liver fibrosis in NASH, providing initial insights into the mechanisms involved. While it offers a valuable starting point, there is potential for further exploration into the functional roles of FincoR and their specific actions in human NASH cases. Building upon the current findings to elucidate more detailed mechanistic pathways through which FincoR exerts its therapeutic effects in liver disease would elevate the research's significance and potential impact in the field.

Strengths:

This study stands out for its comprehensive and unbiased approach to investigating the role of FincoR, a liver-specific lncRNA, in the treatment of NASH. Key strengths include: 1) The application of advanced sequencing methods like GRO-seq and RNA-seq offered a comprehensive and unbiased view of the transcriptional changes induced by tropifexor, particularly highlighting the role of FincoR. 2) Utilizing a genetic mouse model of FXR KO and a FincoR liver-specific knockdown (FincoR-LKD) mouse model provided a controlled and relevant environment for studying NASH, allowing for precise assessment of tropifexor's therapeutic effects. 3) The inclusion of tropifexor, an FDAapproved FXR agonist, adds significant clinical relevance to the study. It bridges the gap between experimental research and potential therapeutic application, providing a direct pathway for translating these findings into real-world clinical benefits for NASH patients. 4) The study's rigorous experimental design, incorporating both negative and positive controls, ensured that the results were specifically attributable to the action of FincoR and tropifexor.

Weaknesses:

The study presents several notable weaknesses that could be addressed to strengthen its findings and conclusions: 1) The authors focus on FincoR, but do not extensively test other lncRNAs identified in Figure 1A. A more comprehensive approach, such as rescue experiments with these lncRNAs, would provide a better understanding of whether similar roles are played by other lncRNAs in mitigating NASH. 2) FincoR was chosen for further study primarily because it is the most upregulated lncRNA induced by GW4064. Including another GW4064-induced lncRNA as a control in functional studies would strengthen the argument for FincoR's unique role in NASH. 3) The study does not conclusively demonstrate whether FincoR is specifically expressed in hepatocytes or other liver cell types. Conducting FincoR RNA-FISH with immunofluorescent experiments or RT-PCR, using markers for different liver cell types, would clarify its expression profile. 4) Understanding the absolute copy number of FincoR is crucial. Determining whether there are sufficient copies of FincoR to function as proposed would lend more credibility to its suggested role. 5) The manuscript, although technically proficient, does not thoroughly address the relevance of these findings to human NASH. Questions like the conservation of FincoR in humans and its potential role in human NASH should be discussed.

Reviewer #2 (Public Review):

Summary:

Nonalcoholic fatty liver disease (NASH), recently renamed as metabolic dysfunctionassociated steatohepatitis (MASH) is a leading cause of liver-related death. Farnesoid X receptor (FXR) is a promising drug target for treating NASH and several drugs targeting FXR are under clinical investigation for their efficacy in treating NASH. The authors intended to address whether FXR mediates its hepatic protective effects through the regulation of lncRNAs, which would provide novel insights into the pharmacological targeting of FXR for NASH treatment. The authors went from an unbiased transcriptomics profiling to identify a novel enhancer-derived lncRNA FincoR enriched in the liver and showed that the knockdown of FincoR in a murine NASH model attenuated part of the effect of tropifexor, an FXR agonist, namely inflammation and fibrosis, but not steatosis. This study provides a framework for how one can investigate the role of noncoding genes in pharmacological intervention targeting known protein-coding genes. Given that many disease-associated genetic variants are located in the non-coding regions, this study, together with others, may provide useful information for improved and individualized treatment for metabolic disorders.

Strengths:

The study leverages both transcriptional profile and epigenetic signatures to identify the top candidate eRNA for further study. The subsequent biochemical characterization of FincoR using FXR-KO mice combined with Gro-seq and Luciferase reporter assays convincingly demonstrates this eRNA as a FXR transcriptional target sensitive to FXR agonists. The use of in vitro culture cells and the in vivo mouse model of NASH provide multi-level evaluation of the context-dependent importance of the FincoR downstream of FXR in the regulation of functions related to liver dysfunction.

Weaknesses:

As discussed, future work to dissect the mechanisms by which FincoR facilitates the action of FXR and its agonists is warranted. It would be helpful if the authors could base this on the current understanding of eRNA modes of action and the observed biochemical features of FincoR to speculate potential molecular mechanisms explaining the observed functional phenotype. It is unclear if this eRNA is conserved in humans in any way, which will provide relevance to human disease. Additionally, the eRNA knockdown was achieved by deletion of an upstream region of the eRNA transcription. A more direct approach to alter eRNA levels, e.g., overexpression of FincoR in the liver would provide important data to interpret its functional regulation.

We thank the Editor and Reviewers for their constructive comments. We believe we have addressed all of the issues (detailed below) and the revisions have greatly strengthened the manuscript.

Reviewer 1:

The study presents several notable weaknesses that could be addressed to strengthen its findings and conclusions:

(1) The authors focus on FincoR, but do not extensively test other lncRNAs identified in Figure 1A. A more comprehensive approach, such as rescue experiments with these lncRNAs, would provide a better understanding of whether similar roles are played by other lncRNAs in mitigating NASH.

(2) FincoR was chosen for further study primarily because it is the most upregulated lncRNA induced by GW4064. Including another GW4064-induced lncRNA as a control in functional studies would strengthen the argument for FincoR's unique role in NASH.

(3) The study does not conclusively demonstrate whether FincoR is specifically expressed in hepatocytes or other liver cell types. Conducting FincoR RNA-FISH with immunofluorescent experiments or RT-PCR, using markers for different liver cell types, would clarify its expression profile.

(4) Understanding the absolute copy number of FincoR is crucial. Determining whether there are sufficient copies of FincoR to function as proposed would lend more credibility to its suggested role.

Response to 1 - 4): We thank Reviewer 1 for the positive comments on the strength of our work, including the open-ended approach, the novel eRNA FincoR and its strong relevance to liver disease. We also value the constructive feedback provided by the reviewer and agree that additional studies are important to fully understand the mechanisms of FincoR and the functional significance of other FXR-induced lncRNAs. In this manuscript we report the discovery and initial characterization of FincoR, as well as its potential function in FXR action in response to hammerhead agonists, but a number of interesting questions are raised. Future experiments, as suggested by reviewer, will be needed to examine the role of other FXR-induced lncRNAs, the potential role of FincoR induction by other nuclear receptors with binding sites at FincoR, whether FincoR is expressed in liver cell types in addition to hepatocytes, and the expression abundance of FincoR. These are all excellent suggestions for future experimentation which we feel are beyond the scope of the present report. For example, generating a genetic CRISPR/Cas9 of another lncRNA is not trial as it takes a significant amount of work with murine models. Also, we did not mean to exclude if other lncRNAs induced by FXR also bear functions. Technically, rescue experiment is not possible as FincoR RNA can be potentially very long (~10 kb if estimated by RNA-seq pattern in Fig.1C), and it is not feasible now to properly express it by exogenous vectors to ensure the expression levels are similar to endogenous ones. We therefore consider that these important questions are more suitable for future work to fully address. Our belief is that a comprehensive exploration of FXR-regulated lncRNAs holds the potential to unveil novel insights crucial for the development of therapies targeting NASH and other metabolic diseases. The study of FincoR is the beginning of this area of research.

(5) The manuscript, although technically proficient, does not thoroughly address the relevance of these findings to human NASH. Questions like the conservation of FincoR in humans and its potential role in human NASH should be discussed.

Response: These are important questions. To respond to the reviewer’s comment, new experiments are presented in our final revised manuscript in which we utilized mouse models of NAFLD/NASH and cholestatic liver injury to determine FincoR’s role in these diseases. Hepatic FincoR levels were significantly increased in mice fed with high fat diet (HFD) for 12 weeks (Supplementary Figure S1A) and in mice fed a HFD with high fructose (HFHF) in drinking water for 12 weeks (Supplementary Figure S1B). Elevated hepatic FincoR levels were also observed in mice treated with α-naphthylisothiocyanate (ANIT), a chemical inducer of liver cholestasis (Supplementary Figure S1C), and in mice with bile duct ligation (BDL), a surgical method to induce cholestatic liver injury (Supplementary Figure S1D).

In terms of the human relevance, we have provided additional information and figures showing that there is sequence similarity between mouse FincoR and a human loci. FincoR sequence is moderately conserved between mice and humans as displayed in the UCSC genome browser (Supplementary Figure S1E). Annotation of these conserved human sequences revealed that they overlap with a functionally uncharacterized human lncRNA XR_007061585.1 (Supplementary Figure S1F). Further, we conducted qRT-PCR experiment from human patient’s RNA samples, which demonstrated that hepatic lncRNA XR_007061585.1 levels are elevated in patients with NAFLD and PBC, but not in severe NASH-fibrosis patients (Supplementary Figure S1G, H). These results demonstrate that hepatic levels of a potential human analog of FincoR are elevated in NAFLD and PBC patients, which is consistent with FincoR’s upregulation in mouse models of chronic liver disease with hepatic inflammation and liver injury. Whether human lncRNA XR_007061585.1 is entirely analogous to mouse FincoR in terms of functions and mechanisms, and whether the elevation of this human lncRNA has a role in liver disease progression or is an adaptive response to liver injury remains to be determined.

Reviewer #2 (Recommendations For The Authors):

(1) In the introduction Line 96, "..., while the vast majority are transcribed into ncRNAs" may not be accurate. Please refer to Pointing and Haerty Annu Rev 2022 for a related discussion.

Response: We would like to thank the reviewer for pointing out this inaccurate information in the introduction. We have changed the content in the text, “While a significant portion of the genome was initially thought to be "junk DNA", it has been established that many non-coding regions give rise to functional non-coding RNAs.”

(2) Figure 5: the authors should provide a clear illustration demonstrating the sequence targeted by the sgRNA in relation to the transcriptional and epigenetic profile (i.e., RNAseq and H3K27ac ChIP-seq data).

Response: The illustration (Figure 5-figure supplement 1A, right panel) demonstrating the sequence targeted by the sgRNA has been updated as suggested by the reviewer.

In this model, the upstream of FincoR is deleted, leading to the inhibition of FincoR transcription. Does the deleted region include FXR binding sites? If so, would the phenotype be due to the deletion of these binding sequences, rather than the decreased FincoR transcripts? Accordingly, the limitation or alternative interpretation should be discussed.

Response: The reviewer made a good point. The deleted region includes FXR binding sites so that we cannot rule out decreased binding of FXR or decreased transcription of the region per se, in addition to the decreased levels of FincoR, to bear a role in the phenotypic changes we observed. In the final revision, we have added discussion of this alternative (6th paragraph in the revised discussion section).

(3) Figure 6C, the images should be accompanied by quantification. It appears the FincoR-KD shows a visible difference as compared to Tropifexor-treated control mice, which does not match entirely what is written in the results.

Response: The quantitation of Oil Red O staining has been done as suggested by the reviewer (Figure 6C). The result is consistent with the triglyceride result showing that tropifexor treatment markedly reduced neutral lipids determined by Oil Red O staining of liver sections (Figure 6C) and liver TG levels (Figure 6D) and these beneficial effects on reducing fatty liver were not altered by FincoR.

(4) Figure 7, does AST show the same pattern as ALT? As indicated from Line 335, "tropifexor treatment reduced mRNA levels of several genes that promote fibrosis (Col1a1, Col1a2, ...)". Fig. 7D does not seem to match the description of Col1a1. Authors may need to modify the results.

Response: AST has been measured and has the same pattern as ALT. The new data have been added to Figure 7B. Col1a1 expression has been re-measured and the results have been updated in Figure 7D.

(5) Is FincoR level reduced in NASH conditions?

Response: We thank the Reviewer for this question. We now added new data to examine the levels of FincoR in mouse liver disease models and also examined levels of a potential human analog of FincoR in human liver specimens from PBC, NAFLD, and NASH patients. Please see our new data and description above in the response to comment 5 by Reviewer 1 (most data now included in the new Supplementary Figure S1).

(6) Please provide information on the conservation of FincoR (DNA and RNA) in humans. This would be important to provide the human disease relevance.

Response: As described above in the response to comment 5 of reviewer 1, a human loci shows sequence similarity to mouse FincoR and this conserved region has an annotated uncharacterized human lncRNA. We also examined the levels of this human homolog in human diseased liver samples. Our new results demonstrate that hepatic levels of a potential human analog of FincoR are elevated in NAFLD and PBC patients, which is consistent with FincoR’s upregulation in mouse models of chronic liver disease with hepatic inflammation and liver injury. Whether human lncRNA XR_007061585.1 is entirely analogous to mouse FincoR in terms of functions and mechanisms, and whether the elevation of this human lncRNA has a role in liver disease progression or is an adaptive response to liver injury remains to be determined.

(7) Several discussion points for the authors' consideration:

(7.1) human-mouse conservation as alluded to in #6;

Response: Potential human-mouse conservation is discussed with new data in the last paragraph of the Results section.

(7.2) potential molecular mechanism involved in FincoR-regulated hepatocyte function;

Response: We thank Reviewer for this comment. We have added more discussion as shown below: “RNA inside the cells usually associates with different RNA-binding proteins (RBPs). To predict those potential binding proteins of FincoR. Additional bioinformatic analysis identified proteins that potentially binding FincoR, including KHDRBS1, RBM38, YBX2 and YBX3 (Supplemental Table S5). These findings and potential functions of the binding proteins are discussed in the 5th paragraph of the discussion section in the final revised manuscript. Whether these predicted RBPs interact with FincoR and the underlying mechanisms will need to be investigated in future experimentation to understand the mechanisms involved in FincoR-regulated hepatocyte function.”

(7.3) any disease-associated SNPs in the FincoR locus.

Response: No SNPs were noted in the annotation of the human loci with sequence similarity to mouse FincoR in the NCBI genome data viewer.

(7.4) the in vitro induction of FincoR is transient but in vivo this occurs after 12 days of drug treatment. How do the authors reconcile the differential induction patterns?

Response: To clarify, the induction of FincoR after a single dose of GW4064 in vivo was transient, peaked within 1 h and then declined gradually (Figure 1-figure Supplement 1C). In the tropifexor treatment protocol (also in vivo), the mice were treated daily with tropifexor for 12 days so that the multiple doses maintained FincoR induction. The beneficial effect of tropifexor by inducing FincoR, therefore, accumulated over the 12 days.

It is worthy to note that we failed to see induction of FincoR in isolated primary mouse hepatocytes treated with GW4064 in vitro. We can only detect FincoR in primary hepatocytes isolated from GW4064-treated mice liver. This may be due to the loss of key factors mediating FincoR induction in the cultured primary hepatocytes.

Reviewer #1 (Public Review):

Summary:

In their article, the authors delve into the therapeutic potential of a newly identified liver-specific lncRNA, FincoR, regulated by the Farnesoid X Receptor (FXR) and induced by the agonist tropifexor, in treating nonalcoholic steatohepatitis (NASH). They demonstrate that FincoR significantly enhances tropifexor's effectiveness in reducing liver fibrosis and inflammation in NASH, presenting it as a promising therapeutic target. The manuscript revisions broaden the study to include both mouse and human data, showing elevated FincoR levels in various mouse models of liver disease and identifying a similar lncRNA in humans, potentially indicating a conserved therapeutic mechanism. This research offers valuable insights into FincoR's role in NASH and suggests further exploration into its functions and mechanisms in liver disease treatment.

Strengths:

This study enhances our understanding of FincoR, a liver-specific lncRNA, and its therapeutic potential in treating NASH through a multifaceted research approach. The revised manuscript further strengthens this contribution by incorporating additional experiments and human relevance, summarized as follows: 1) The use of GRO-seq and RNA-seq technologies has provided an in-depth and unbiased view of the transcriptional alterations driven by the FXR agonist tropifexor, especially emphasizing FincoR's pivotal role. 2) The research expands on the original findings by including diverse mouse models of NAFLD/NASH and cholestatic liver injury. These models demonstrate significant increases in hepatic FincoR levels across various conditions, such as diets high in fat and fructose, chemical induction of liver cholestasis with ANIT, and surgical induction via bile duct ligation. This broadened scope underscores FincoR's involvement in liver disease mechanisms beyond the initial models of FXR knockout (KO) and FincoR liver-specific knockdown (FincoR-LKD). 3) Incorporation of tropifexor, an FDA-approved FXR agonist, alongside these experimental models bridges experimental findings to potential therapeutic applications for NASH patients. 2) The manuscript revision includes promising data on the sequence similarity between mouse FincoR and a human locus, identifying a partially conserved human lncRNA (XR_007061585.1) with elevated levels in NAFLD and PBC patients. This addition enhances the study's relevance to human health. 3) The study's design, with the inclusion of both negative and positive controls and now enriched with a wider array of mouse models and human data, ensures that the observed therapeutic effects can be confidently attributed to FincoR's modulation by tropifexor.

Weaknesses:

The authors acknowledge that certain questions remain unanswered within the scope of this study on FincoR, due to feasibility and technical challenges. While it's important to note that such limitations are rooted in the practical and technical complexities, these unresolved issues might limit the study's immediate impact. The decision to focus on the discovery and initial characterization of FincoR, is strategically but not scientifically justified.

Reviewer #2 (Public Review):

Summary:

Nonalcoholic fatty liver disease (NASH), recently renamed as metabolic dysfunction-associated steatohepatitis (MASH) is a leading cause of liver-related death. Farnesoid X receptor (FXR) is a promising drug target for treating NASH and several drugs targeting FXR is under clinical investigation for its efficacy in treating NASH. The authors intended to address whether FXR mediates its hepatic protective effects through regulation of lncRNAs, which would provide novel insights into the pharmacological targeting of FXR for NASH treatment. The authors went from an unbiased transcriptomics profiling to identify a novel enhancer-derived lncRNA FincoR enriched in the liver and showed that the knockdown of FincoR in a murine NASH model attenuated part of the effect of tropifexor, an FXR agonist, namely inflammation and fibrosis, but not steatosis. This study provides a framework how one can investigate the role of noncoding genes in pharmacological intervention targeting a known protein coding genes. Given that many disease-associated genetic variants are located in the non-coding regions, this study, together with others, may provide useful information for improved and individualized treatment for metabolic disorders.

Strengths:

The study leverages both transcriptional profile and epigenetic signatures to identify the top candidate eRNA for further study. The subsequent biochemical characterization of FincoR using FXR-KO mice combined with Gro-seq and Luciferase reporter assays convincingly demonstrates this eRNA as a FXR transcriptional targets sensitive to FXR agonists. The use of in vitro culture cells and the in vivo mouse model of NASH provide multi-level evaluation of the context-dependent importance of the FincoR downstream of FXR in regulation of functions related to liver dysfunction.

Weaknesses:

Future work to dissect the detailed mechanisms by which FincoR facilitates action of FXR and its agonists is warranted. A more direct approach to alter eRNA levels, e.g., overexpression of FincoR in the liver would provide important data to interpret its functional regulation.

Author Response

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

Response to Public Reviewer Comments

We again thank the reviewers for the time and effort they clearly put into reviewing our manuscript. We have revised our manuscript to take into account the majority of their suggestions, primary among them being refinements of our model and classification approach, detailed sensitivity analysis of our model, and several new simulations. Their very constructive feedback has resulted in what we feel is a much-improved paper. In what follows, we respond to each of their points.

Reviewer #1:

COMMENT: The reviewer suggested that our control policy classification thresholds should be increased, especially if the behavioral labels are to be subsequently used to guide analyses of neural data which “is messy enough, but having trials being incorrectly labeled will make it even messier when trying to quantify differences in neural processing between strategies.”

REPLY: We appreciate the observation and agree with the suggestion. In the revised manuscript, we simplified the model (as another reviewer suggested), which allowed for better training of the classifier. This enabled an increase in the threshold to 95% to have more confidence in the identified control strategies. Figures 7 and 8 were regenerated based on the new threshold.

COMMENT: The reviewer asked if we could discuss what one might expect to observe neurally under the different control policies, and also suggested that an extension of this work could be to explore perturbation trials, which might further distinguish between the two control policies.

REPLY: It is indeed interesting to speculate what neural activity could underlie these different behavioral signatures. As this task is novel to the field, it is difficult to predict what we might observe once we examine neural activity through the lens of these control regimes. We hope this will be the topic of future studies, and one aspect worthy of investigation is how neural activity prior to the start of the movement may reflect two different control objectives. Previous work has shown that motor cortex is highly active and specific as monkeys prepare for a cued movement and that this preparatory activity can take place without an imposed delay period (Ames et al., 2014; Cisek & Kalaska, 2005; Dekleva et al., 2018; Elsayed et al., 2016; Kaufman et al., 2014; Lara et al., 2018; Perich et al., 2018; Vyas et al., 2018; Zimnik & Churchland, 2021). It seems possible that the control strategies we observed correspond to different preparatory activity in the motor cortex. We added these speculations to the discussion.

The reviewer’s suggestion to introduce perturbations to probe sensory processing is very good and was also suggested by another reviewer. We therefore conducted additional simulations in which we introduced perturbations (Supplementary Material; Figure S10). Indeed, in these model simulations the two control objectives separated more. However, testing these predictions via experiments must await future work.

COMMENT: “It seems like a mix of lambda values are presented in Figure 5 and beyond. There needs to be some sort of analysis to verify that all strategies were equally used across lambda levels. Otherwise, apparent differences between control strategies may simply reflect changes in the difficulty of the task. It would also be useful to know if there were any trends across time?”

REPLY: We appreciate and agree with the reviewer’s suggestion. We have added a complementary analysis of control objectives with respect to task difficulty, presented in the Supplementary Material (Figures S7 and S8). We demonstrate that, overall, the control objectives remain generally consistent throughout trials and difficulty levels. Therefore, it can be concluded that the difference in behavior associated with different control objectives does not depend on the trial sequence or difficulty of the task. A statement to this extent was added to the main text.

COMMENT: “Figure 2 highlights key features of performance as a function of task difficulty. …However, there is a curious difference in hand/cursor Gain for Monkey J. Any insight as to the basis for this difference?”

REPLY: The apparently different behavior of Monkey J in the hand/cursor RMS ratio could be due to subject-to-subject variability. Given that we have data from only two monkey subjects, we examined inter-individual variations between human subjects in the Supplementary Material by presenting individual hand/cursor gain data for all individual human subjects (Figure S1). As can be seen, there was indeed variability, with some subjects not exhibiting the same clear trend with task difficulty. However, on average, the RMS ratio shows a slight decrease as trials grow more difficult, as was earlier shown in Figure 2. We added a sentence about the possibility of inter-individual variations to address the difference in behavior of monkey J with reference to the supplementary material.

Reviewer #2:

(Reviewer #2's original review is with the first version of the Reviewed Preprint. Below is the authors' summary of those comments.)

COMMENT: The reviewer commends the care and effort taken to characterize control policies that may be used to perform the CST, via dual human and monkey experiments and model simulations, noting the importance of doing so as a precursor to future neural recordings or BMI experiments. But the reviewer also wondered if it is all that surprising that different subjects might choose different strategies: “... it makes sense that different subjects might choose to favor different objectives, and also that they can do so when instructed. But has this taught us something about motor control or simply that there is a natural ambiguity built into the task?”

REPLY: The redundancy in the task that allowed different solutions to achieve the task was deliberate, and the motivation for choosing this task for this study. We therefore did not regard the resulting subject-to-subject variability as a finding of our study. Rather, redundancy and inter-individual variability are features ubiquitous in all everyday actions and we explicitly wanted to examine behavior that is closer to such behavior. As commended by the reviewers, CST is a rich task that extends our research beyond the conventional highly-constrained reaching task. The goal of our study was to develop a computational account to identify and classify such differences to better leverage future neural analyses of such more complex behaviors. This choice of task has now been better motivated in the Introduction of the revised manuscript.

COMMENT: The reviewer asks about our premise that subjects may use different control objectives in different trials, and whether instead a single policy may be a more parsimonious account for the different behavioral patterns in the data, given noise and instability in the system. In support of this view, the reviewer implemented a simple fixed controller and shared their own simulations to demonstrate its ability to generate different behavioral patterns simply by changing the gain of the controller. The reviewer concludes that our data “are potentially compatible with any of these interpretations, depending on which control-style model one prefers.”

REPLY: We first address the reviewer’s concern that a simple “fixed” controller can account for the two types of behavioral patterns observed in Experiment 2 (instructed groups) by a small change in the control gain. We note that our controller is also fixed in terms of the plant, the actuator, and the sensory feedback loop; the only change we explore is in the relative weights of position vs. velocity in the Q matrix. This determines whether it is deviations in position or in velocity that predominate in the cost function. This, in turn, generates changes in the gain vector L in our model, since the optimal solution (i.e. the gains L that minimize the cost function) depends on the Q matrix as well as the dynamics of the plant (specifically, the lambda value). Hence, one could interpret the differences arising from changes in the control objective (the Q matrix) as changes in the gains of our “fixed” controller.

More importantly, while the noise and instability in the system may indeed occasionally result in distinct behavioral patterns (and we have observed such cases in our simulations as well), these factors are far from giving an alternative account for the structural differences in the behavior that we attribute to the control objective. To substantiate this point, we performed additional simulations that are provided in the Supplementary Material (Figures S4—6). These simulations show that neither a change in noise nor in the relative cost of effort can account for the two distinct types of behavior. These differences are more consistently attributed to a change in the control objective.

In addition, our approach provides a normative account of the control gains needed to simulate the observed data, as well as the control objectives that underlie those gains. As such, the two control policies in our model (Position and Velocity Control) resulted in control gains that captured the differences in the experimental groups (Experiment 2), both at the single trial and aggregate levels and across different task difficulties. Figure S9 in the Supplementary Material shows how the control gains differ between Position and Velocity Control in our model across different difficulty levels.

We agree,with the reviewer’s overall point, that there are no doubt many models that can exhibit the variability observed in our experimental data, our simulations, or the reviewer’s simulations. Our study aimed to explore in detail not only the model’s ability to generate the variable behavior observed in experimental data, but also to match experimental results in terms of performance levels, gains, lags and correlations across a wide range of lambda values, wherein the only changes in the model were the lambda value and the control objective. Without the details of the reviewer’s model, we are unable to perform a detailed analysis of that model. Even so, we are not claiming that our model is the ‘ground truth,’ only that it is certainly a reasonable model, adopted from the literature, that provides intuitive and normative explanation about the performance of humans and monkeys over a range of metrics, system dynamics, and experimental conditions.

Finally, we understand the reviewer’s concern regarding whether the trial-by-trial identification of control strategy in Figure 8 suggests that (uninstructed) subjects constantly switch control objectives between Position and Velocity. Although it is not unreasonable to imagine that individuals would intuitively try different strategies between ‘keeping the cursor still’ and ‘keeping the cursor at the center’ across trials, we agree that it is generally difficult to determine such trial-to-trial changes, especially when the behavior lies somewhere in between the two control objectives. In such cases, as we originally discussed in the manuscript, an alternative explanation could be a mixed control objective that generates behavior at the intersection of Position and Velocity Control, i.e., between the two slopes in Figure 8. We believe, however, that our modeling approach is still helpful in cases where performance is predominantly based on Position or Velocity Control. After all, the motivation for this study was to parse neural data into two classes associated with each control objective to potentially better identify structure underlying these behaviors.

We clarified these points in the main text by adding further explanation in the Discussion section.

COMMENT: The reviewer suggested additional experiments, such as perturbation trials, that might be useful to further explore the separability of control objectives. They also suggested that we temper our conclusion that our approach can reliably discriminate amongst different control policies on individual trials. Finally, the reviewer suggested that we modify our Introduction and/or Discussion to note past human/monkey research as well as investigations of minimization of velocity-error versus position-error in the smooth pursuit system.

REPLY: We have expanded our simulations to investigate the effects of perturbation on the separability of different control objectives (Figure S10 in Supplementary Materials). We demonstrated that introducing perturbations more clearly differentiated between Position and Velocity Control. These results provide a good basis for further experimental verifications of the control objectives, but we defer these for future work.

We also appreciate the additional past work that bridges human and monkey research that the reviewer highlights, including the related discussions in the eye movement literature on position versus velocity control. We have modified our Introduction and Discussion accordingly.

Reviewer #3:

COMMENT: The reviewer asked whether the observed differences in behavior might be due to some other factors besides the control policy, such as motor noise or effort cost, and suggested that we more systematically ruled out that possibility.

REPLY: We appreciate and have heeded the reviewer’s suggestion. The revised manuscript now includes additional simulations in which the control objective was fixed to either Position or Velocity Control, while other parameters were systematically varied. Specifically, we examined the influence of the relative effort cost, the sensory delay, and motor noise, on performance. The results of these sensitivity analyses are presented in the Supplementary Material, Figures S4—6. In brief, we found that changing the relative effort cost, delay, or noise levels, mainly affected the success rate in performance (as expected), but did not affect the behavioral features originally associated with control objectives. We include a statement about this result in the main text with reference to the details provided in the Supplementary Material.

COMMENT: The reviewer questioned our choice of classification features (RMS position and velocity) and wondered if other features might yield better class separation, such as the hand/cursor gain. In a similar vein, reviewer 2 suggested in their recommendations that we examine the width of the autocorrelation function as a potentially better feature.

REPLY: We note first that our choice of cursor velocity and position stems from a dynamical systems perspective, where position-velocity phase-space analysis is common. However, we also explored other features as suggested. We found that they, too, exhibited overlap between the two different control objectives, and did not provide any significant improvement in classification performance (Figures S2 and S3; Supplementary Materials). Of course, that is not to say that a more exhaustive examination of features may not find ones that yield better classification performance than those we investigated, but that is beyond the scope of our study. We refer to this consideration of alternative metrics in the discussion.

COMMENT: The reviewer notes that “It seems that the classification problem cannot be solved perfectly, at least on a single-trial level.” To address this point, the reviewer suggested that we conduct additional simulations under the two different control objectives, and quantify the misclassifications.

REPLY: We appreciate the reviewer’s suggestion, and have conducted the additional simulations as suggested, the results of which are included in the revised manuscript.

COMMENT: “The problem of inferring the control objective is framed as a dichotomy between position control and velocity control. In reality, however, it may be a continuum of possible objectives, based on the relative cost for position and velocity. How would the problem differ if the cost function is framed as estimating a parameter, rather than as a classification problem?”

REPLY: A blended control strategy, formulated as a cost function that is a weighted combination of position and velocity costs, is indeed a possibility that we briefly discussed in the original manuscript. This possibility arises particularly for individuals whose performance metrics lie somewhere between the purely Position or purely Velocity Control. While our model allows for a weighted cost function, which we will explore in future work, we felt in this initial study that it was important to first identify the behavioral features unique to each control objective.

Response to Recommendations for the Authors:

Reviewer #1 (Recommendations For The Authors):

None beyond those stated above.

Reviewer #2 (Recommendations For The Authors):

COMMENT: Line 166 states "According to equation (1), this behavior was equivalent to reducing the sum (𝑝 + 𝑥) when 𝜆 increased, so as to prevent rapid changes in cursor velocity". This doesn't seem right. In equation 1, velocity (not acceleration) depends on p+x. So a large p+x doesn't create a "rapid change in cursor velocity", but rather a rapid change in cursor position.

REPLY: The reviewer is correct and we have corrected this misworded sentence; thank you for catching that.

COMMENT: The reviewer points out the potential confusion readers may have, given our unclear use of ‘control strategy’ vs. ‘control policy’ vs. ‘control objective’. The reviewer suggests that “It would be helpful if this could be spelled out early and explicitly. 'Control strategy' seems perilously close to 'control policy', and it would be good to avoid that confusion. The authors might prefer to use the term 'cost function', which is really what is meant. Or they might prefer 'control objective', a term that they introduce as synonymous with 'control strategy'.”

REPLY: We thank the reviewer for noting this ambiguity. We have clarified the language in the Introduction to explicitly note that by strategy, we mean the objective or cost function that subjects attempt to optimize. We then use ‘control objective’ consistently and removed the term ‘policy’ from the paper to avoid confusion. We also now use Position Control and Velocity Control as the labels for our two control objectives.

COMMENT: The reviewer notes that in Figure 2B and the accompanying text in the manuscript, we need to be clearer about what is being correlated; namely, cursor and hand position.

REPLY: Thank you for pointing out this lack of clarity, which we have corrected as suggested.

COMMENT: The reviewer questions our attribution of decreasing lag with task difficulty as a consequence of subjects becoming more attentive/responsive when the task is harder, and points out that our model doesn’t include this possible influence yet the model reproduces the change in lag. The reviewer suggests that a more likely cause is due to phase lead in velocity compared to position, with velocity likely increasing with task difficulty, resulting in a phase advance in the response.

REPLY: Our attribution of the decrease in lag with task difficulty being due to attention/motivation was a recapitulation of this point made in the paper by Quick et al. [2018]. But as noted by the reviewer, this potential influence on lag is not included in our model. Accordingly, the change in lag is more likely a reflection of the phase response of the closed loop system, which does change with task difficulty since the optimal gains depend upon the plant dynamics (i.e., the value of lambda). We have, therefore, deleted the text in question.

COMMENT: “The Methods tell us rather a lot about the dynamics of the actual system, and the cost functions are also well defined. However, how they got from the cost function to the controller is not described. I was also a bit confused about the controller itself. Is the 50 ms delay assumed when deriving the controller or only when simulating it (the text seems to imply the latter, which might make sense given that it is hard to derive optimal controllers with a hard delay)? How similar (or dissimilar) are the controllers for the two objectives? Is the control policy (the matrix that multiplies state to get u) quite different, or only subtly?”

REPLY: Thanks for pointing this out. For brevity, we had omitted the details and referred readers to the original paper (Todorov, 2005). However, we now revised the manuscript to now include all the details in the Methods section. Hence, the entire section on the model is new. This also necessitated updating all data figures (Figures 3, 4, 5, 6, 7, 8) as they contain modeling results.

COMMENT: “Along similar lines, I had some minor to moderate confusions regarding the OFC model as described in the main text. Fig 3 shows a model with a state estimator, but it isn't explained how this works. …Here it isn't clear whether there is sensory noise, or a delay. The methods say a delay was included in the simulation (but perhaps not when deriving the controller?). Noise appears to have been added to u, but I'm guessing not to x or x'? The figure legend indicates that sensory feedback contains only some state variables, and that state estimation is used to estimate the rest. Presumably this uses a Kalman filter? Does it also use efference copy, as would be typical? My apologies if this was stated somewhere and I missed it. Either way, it would be good to add a bit more detail to the figure and/or figure legend.”

REPLY: As the lack of detail evidently led to some confusion, we now more clearly spell out the details of the model in the Methods, including the state estimation procedure.

COMMENT: The reviewer wondered why we chose to plot mean velocity vs. mean position as in Figure 5, noting that, “ignoring scale, all scatter plots would be identical if the vertical axis were final position (because mean velocity determines final position). So what this plot is really examining is the correlation between final position and average position. Under position control, the autocorrelation of position is short, and thus final position tends to have little to do with average position. Under velocity control, the autocorrelation of position is long, and thus final position tends to agree with average position. Given this, why not just analyze this in terms of the autocorrelation of position? This is expected to be much broader under velocity control (where they are not corrected) than under position control (where they are, and thus disappear or reverse quickly). To me, thinking of the result in terms of autocorrelation is more natural.”

REPLY: The reviewer is correct that the scatter plots in Fig. 5 would be the same (to within a scale factor of the vertical axis) had we plotted final position vs. mean position instead of mean velocity vs. mean position as we did. Our preference for mean velocity vs. mean position stems from a dynamical systems perspective, where position-velocity phase-space analysis is common. We now mention these perspectives in the revised manuscript for the benefit of the reader.

As suggested, we also investigated the width of the (temporal) autocorrelation function (acf) of cursor position for 200 simulated position control trials and 200 simulated velocity control trials, at four different lambda values (50 simulated trials per lambda). Figs. S2A and B (Supplementary Materials) show example trials and histograms of the acf width, respectively. As the reviewer surmised, velocity control trials tend to have wider acfs than position control trials. However, as with the metrics we chose to analyze, there is overlap and there is no visible benefit for the classification.

COMMENT: “I think equation ten is incorrect, but would be correct if the identity matrix were added? Also, why is the last term of B set to 1/(Tau*M). What is M? Is it mass (which above was lowercase m)? If so, mass should also be included in A (it would be needed in two places in the last column). Or if we assume m = 1, then just ignore mass everywhere, including here and equation 5. Or perhaps I'm confused, and M is something else?”

REPLY: Thanks for pointing this out. The Matrix A shown in the paper is for the continuous-time representation of the model. However, as the reviewer correctly mentioned, for the discrete-time implementation of the model, a modification (identity matrix) was added in our simulations. We have now clarified this in the Methods section of the revised manuscript. Also, as correctly pointed out, M is the mass of the hand, which depending on whether the hand acceleration (d^2 p/dt^2) or hand force (F) are taken as the state, it can be included in the A matrix. In our case, the A matrix is modified according to the state vector. Similarly, the B matrix is also modified. This is now clarified in the Methods section of the manuscript.

Reviewer #3 (Recommendations For The Authors):

COMMENT: “Equations 4-8 are written in continuous time, but Equation 9 is written in discrete time. Then Equation 10 is in discrete time. This needs to be tidied up. … I would suggest being more detailed and systematic, perhaps formulating the control problem in continuous time and then converting to discrete time.”

REPLY: Thank you for this helpful suggestion. The model section in the Methods has been expanded to provide further details of the equation of motion, the discretization process, the control law calculation and the state estimation process.

COMMENT: “It seems slightly odd for the observation to include only position and velocity of the cursor. Presumably participants can also observe the state of their own hand through proprioception (even if it were occluded). How would it affect the model predictions if the other states were observable?”

REPLY: Thanks for pointing this out. We initially included only cursor position and velocity since we felt that was the most prominent state feedback, and the system is observable in that case. Nevertheless, we revised the manuscript and repeated all simulations using a full observability matrix. Our findings and conclusions remain unchanged. With the changes in the modeling, the figures were also updated (Fig.3, 4, 5, 6, 7, 8).

COMMENT: “It seems unnecessary to include the acceleration of the cursor in the formulation of the model. …the acceleration is not even part of the observed state according to line 668… I think the model could therefore be simplified by omitting cursor acceleration from the state vector.”

REPLY: We agree. We have simplified the model, and generated new simulations and figures. Our results and conclusions were unchanged by this modification. With the changes in the modeling, the figures were also updated (Fig.3, 4, 5, 6, 7, 8).

COMMENT: “In the cost function, it's not clear why any states other than position and velocity of the cursor need to have non-zero values. …The choice to have the cost coefficient for these other states be 1 is completely arbitrary… If the point is that the contribution of these other costs should be negligible, then why not just set them to 0?”

REPLY: We agree, and have made this change in the Methods section. Our findings and conclusions were unaffected.

COMMENT: “It seems that the cost matrices were specified after transforming to discrete-time. It is possible however (and perhaps recommended) to formulate in continuous time and convert to discrete time. This can be done cleanly and quite straightforwardly using matrix exponentials. Depending on the discretization timestep, this can also naturally lead to non-zero costs for other states in the discrete-time formulation even if they were zero under continuous time. … A similar comment applies to discretization of the noise.”

REPLY: Thanks for the suggestion. We have expanded on the discretization process in our Methods section, which uses a common approximation of the matrix exponentiation method.

COMMENT: “Most of the parameters of the model seem to be chosen arbitrarily. I think this is okay as the point is to illustrate that the kinds of behaviors observed are within the scope of the model. However, it would be helpful to provide some rationale as to how the parameters were chosen. e.g. Were they taken directly from prior literature, or were they hand-tuned to approximately match observed behavior?”

REPLY: We have revised the manuscript to more clearly note that the noise parameters, as well as parameters of the mechanical system (mass, muscle force, time scale, etc) in our model were taken from previous publications (Todorov, 2005, Cluff et al. 2019). As described in the manuscript, the parameter values of the cost function (Q matrix) were obtained by tuning the parameters to achieve a similar range of success rate with the model as observed in the experimental data. This is now clarified in the Methods section.

COMMENT: “The ‘true’ cost function for this task is actually a 'well' in position space - zero cost within the screen and very high cost elsewhere. In principle, it might be possible to derive the optimal control policy for this more veridical cost function. It would be interesting to consider whether or not this model might reproduce the observed behaviors.”

REPLY: This is indeed a very interesting suggestion, but difficult to implement based on the current optimal feedback control framework. However, this is interesting to consider in future work.

Minor Comments:

COMMENT: “In Figs 4 and 5, the data points are drawn from different conditions with varying values of lambda. How did the structure of this data depend on lambda? Might it be possible to illustrate in the figure (e.g. the shade/color of each dot) what the difficulty was for each trial?”

REPLY: We performed additional analyses to show the effects of task difficulty on the choice of control objective. Overall, we found that the main behavioral characteristics of the control objective remained fairly unchanged across different task difficulties or across time. The results of this analysis are included in Fig. S7 and S8 of the Supplementary Materials.

COMMENT: “Should mention trial duration (6s) in the main narrative of the intro/results.”

REPLY: We now mention this detail when we describe the task for the first time.

COMMENT: “As an alternative to training on synthetic data (which might not match behavior that precisely, and was also presumably fitted to subject data at some level) it might be worth considering to do a cross-validation analysis, i.e. train the classifier on subsets of the data with one participant removed each time, and classify on the held-out participant.”

REPLY: This is indeed a valid point. The main reason to train the classifier based on model simulations was two-fold: first, to have confidence in the training data, as the experimental data was limited and noisy, which would result in less reliable classifications; and second, the model simulations are available for different contexts and conditions, where experimental data is not necessarily available. The latter is a more practical reason to be able to identify control objectives for any subject (who received no instructions), without having to collect training data from matching control subjects who received explicit instructions. Nonetheless, we appreciate the reviewer’s recommendation and will consider that for our future studies.

COMMENT: “line 690 - Presumably the optimal policy was calculated without factoring in any delay (this would be tricky to do), but the 50ms delay was incorporated at the time of simulation?”

REPLY: The discretization of the system equations allowed us to incorporate the delay in the system dynamics and solve for the optimal controller with the delay present. This was done simply by system augmentation (e.g., Crevecoeur et al., 2019), where the states of the system in the current time-step were augmented with the states from the 5 preceding time-steps to form the new state vector x(t)_aug =[x(t) , x(t-1) , … , x(t-d) ]. Similarly, the matrices A, B, and H from the system dynamics could be expanded accordingly to form the new dynamical system:

$$x(t+1){aug} = A{aug} * x(t){aug} + B{aug} * u$$

Then, the optimal control was implemented on the new (augmented) system dynamics.

We have revised the manuscript (Methods) to clarify this issue.

eLife assessment

This study represents a step towards integrating human and non-human primate research towards a broader understanding of the neural control of motor strategies. It could offer valuable insights into how humans and non-human primates (Rhesus monkeys) manage visuomotor tasks, such as stabilizing an unstable virtual system, potentially leading to discoveries in neural behaviour mechanisms. While the evidence is mostly solid, some results, particularly from the binary classification of control strategies for non instructed behaviour, require further validation before it could be conclusively interpreted.

Reviewer #3 (Public Review):

This paper considers a challenging motor control task - the critical stability task (CST) - that can be performed equally well by humans and macaque monkeys. This task is of considerable interest since it is rich enough to potentially yield important novel insights into the neural basis of behavior in more complex tasks that point-to-point reaching. Yet it is also simple enough to allow parallel investigation in humans and monkeys, and is also easily amenable to computational modeling. The paper makes a compelling argument for the importance of this type of parallel investigation and the suitability of the CST for doing so.

Behavior in monkeys and in human subjects suggests that behavior seems to include two qualitatively different kinds of behavior - in some cases, the cursor oscillates about the center of the screen, and in other cases, it drifts more slowly in one direction. The authors argue that these two behavioral regimes can be reliably induced by instructing human participants to either maintain the cursor in the center of the screen (position control objective), or keep the cursor still anywhere in the screen (velocity control objective) - as opposed to the usual 'instruction' to just not let the cursor leave the screen. A computational model based on optimal feedback control can reproduce the different behaviors under these two instructions.

Overall, this is a creative study that leverages experiments in humans and computational modeling to gain insight into the nature of individual differences in behavior across monkeys (and people). The authors convincingly demonstrate that they can infer the control objectives from participants who were instructed how to perform the task to emphasize either position or velocity control, based on the RMS cursor position and RMS cursor velocity. The authors show that, while other behavioral metrics do contain similar information about the control objective, RMS position and velocity are sufficient, and their approach classifies control objectives for simulated data with high accuracy (~95%).

The authors also convincingly show that the range of behaviors observed in the CST task cannot be explained as emerging from variations in effort cost, motor execution noise, or sensorimotor delays.

One significant issue, however relates to framing the range of possible control objectives as a simple dichotomy between 'position' and 'velocity' objectives. The authors do clearly state that this is a deliberate choice made in order to simplify their first attempts at solving this challenging problem. However, I do think that the paper at times gives a false impression that this dichotomous view of the control objectives was something that emerged from the data, rather than resulting from a choice to simplify the modeling/inference problem. For instance, line 115: "An optimal control model was used to simulate different control objectives, through which we identified two different control objectives in the experimental data of humans and monkeys."

In the no-instruction condition - which is the starting point and which the ultimate goal of the paper is to understand - there is a lot of variability in behavior across trials (even within an individual) and generally no clear correspondence to either the position or velocity objective. This variability is largely interpreted as the monkeys (and people) switching between control objectives on a trial-to-trial basis. If the behavior were truly a bimodal mixture of these two different behaviors, this might be a convincing interpretation. However, there are a lot of trials that fall in-between the patterns of behavior expected under the position and velocity control objectives. The authors do mention this issue in the discussion. However, it's not clearly examined whether these are simply fringe trials that are ambiguous (like some trials generated by the model are), or whether they reflect a substantial proportion of trials that require some other explanation (whether that is blended position/velocity control, or something else). The existence of these 'in-between' trials (which possibly amount to more than a third of all trials) makes the switching hypothesis a lot less plausible.

Overall, while I think the paper introduces a promising approach and overall helps to improve our understanding of the behavior in this task, I'm not fully convinced that the core issue of explaining the variability in behavior in the no-instruction condition (in monkeys especially) has been resolved. The main explanation put forward is that the monkeys are switching between control objectives on a trial-by-trial basis, but there is no real evidence in the data for this, and I don't think there is yet a good explanation of what is occurring in the 'in-between' trials that aren't explained well by velocity or position objectives.

Reviewer #1 (Public Review):

The present study examines whether one can identify kinematic signatures of different motor strategies in both humans and non-human primates (NHP). The Critical Stability Task (CST) requires a participant to control a cursor with complex dynamics based on hand motion. The manuscript includes datasets on performance of NHPs collected from a previous study, as well as new data on humans performing the same task. Further human experiments and optimal control models highlight how different strategies lead to different patterns of hand motion. Finally, classifiers were developed to predict which strategy individuals were using on a given trial.

There are several strengths to this manuscript. I think the CST task provides a very useful behavioural task to explore the neural basis of voluntary control. While reaching is an important basic motor skill and commonly studied, there is much to learn by looking at other motor actions to address many fundamental issues on the neural basis of voluntary control.

I also think the comparison between human and NHP performance is important as there is a common concern that NHPs can be overtrained in performing motor tasks leading to differences in their performance as compared to humans. The present study highlights that there are clear similarities in motor strategies of humans and NHPs. While the results are promising, I would suggest that the actual use of these paradigms and techniques likely need some improvement/refinement. Notably, the threshold or technique to identify which strategy an individual is using on a given trial needs to be more stringent given the substantial overlap in hand kinematics between different strategies.

The most important goal of this study is to set up future studies to examine how changes in motor strategies impact neural processing. The revised manuscript has improved the technique for identifying which strategy appears to be performed by the individual. A pivotal assumption is that one can identify control strategies from differences in behaviour. As I'm sure the authors know, this inversion of the control problem is not trivial and so success requires that there are only a few 'reasonable' strategies to solve the control problem, and that these strategies lead to distinct patterns of behavior. Many of the concerns raised by myself and the other reviewers relate to this challenge. The revised manuscript now uses a more strict criteria which is good improvement.

One of the values of this paper is to start to develop the tools and approaches to address neural basis of control. The strength of the present manuscript is that it includes modelling, explicit strategy instructions in humans, and then analysis of free-form performance in humans and non-human primates. Given the novelty of this question and approach, there likely are many ways that the techniques and approaches could be improved, but I think they've done a great start. Their approach is quite clever and provides an important blueprint for future studies.

One weakness at this point is that there is still substantial overlap in behavoural performance predicted between strategies, as some human participants given an explicit strategy were almost equally categorized as reflecting the other strategy. I'm glad to see the addition of the model performance on perturbation trials as this additional figure clearly highlights much greater separation in performance than when observing natural behavior. While it is not reasonable to expand beyond this for the present manuscript, I think it is essential for this group to develop the perturbation paradigm (and potentially other approaches) that can better isolate behavioral signatures of different control strategies. I think future work will be strengthened by having multiple experimental angles to interpret the neural activity.

Author Response

Reviewer #1 (Public Review):

Summary:

The present study's main aim is to investigate the mechanism of how VirR controls the magnitude of MEV release in Mtb. The authors used various techniques, including genetics, transcriptomics, proteomics, and ultrastructural and biochemical methods. Several observations were made to link VirR-mediated vesiculogenesis with PG metabolism, lipid metabolism, and cell wall permeability. Finally, the authors presented evidence of a direct physical interaction of VirR with the LCP proteins involved in linking PG with AG, providing clues that VirR might act as a scaffold for LCP proteins and remodel the cell wall of Mtb. Since the Mtb cell wall provides a formidable anatomical barrier for the entry of antibiotics, targeting VirR might weaken the permeability of the pathogen along with the stimulation of the immune system due to enhanced vesiculogenesis. Therefore, VirR could be an excellent drug target. Overall, the study is an essential area of TB biology.

Strengths:

The authors have done a commendable job of comprehensively examining the phenotypes associated with the VirR mutant using various techniques. Application of Cryo-EM technology confirmed increased thickness and altered arrangement of CM-L1 layer. The authors also confirmed that increased vesicle release in the mutant was not due to cell lysis, which contrasts with studies in other bacterial species.

Another strength of the manuscript is that biochemical experiments show altered permeability and PG turnover in the mutant, which fits with later experiments where authors provide evidence of a direct physical interaction of VirR with LCP proteins.

Transcriptomics and proteomics data were helpful in making connections with lipid metabolism, which the authors confirmed by analyzing the lipids and metabolites of the mutant.

Lastly, using three approaches, the authors confirm that VirR interacts with LCP proteins in Mtb via the LytR_C terminal domain.

Altogether, the work is comprehensive, experiments are designed well, and conclusions are made based on the data generated after verification using multiple complementary approaches.

Weaknesses:

The major weakness is that the mechanism of VirR-mediated EV release remains enigmatic. Most of the findings are observational and only associate enhanced vesiculogenesis observed in the VirR mutant with cell wall permeability and PG metabolism. The authors suggest that EV release occurs during cell division when PG is most fragile. However, this has yet to be tested in the manuscript - the AFM of the VirR mutant, which produces thicker PG with more pore density, displays enhanced vesiculogenesis. No evidence was presented to show that the PG of the mutant is fragile, and there are differences in cell division to explain increased vesiculogenesis. These observations, counterintuitive to the authors' hypothesis, need detailed experimental verification.

Response: We thank the reviewer for this comments. We would like to convince this reviewer about the fact that the VirR mutant is truly caring a more fragile PG. We will perfume additional experiments that would support this notion. We will determine the degree of PG release to the extracellular space and run additional mass spectrometry data on isolated PG.

Transcriptomic data only adds a little substantial. Transcriptomic data do not correlate with the proteomics data. It remains unclear how VirR deregulates transcription. TLCs of lipids are not quantitative. For example, the TLC image of PDIM is poor; quantitative estimation needs metabolic labeling of lipids with radioactive precursors. Further, change in PDIMs is likely to affect other lipids (SL-1, PAT/DAT) that share a common precursor (propionyl- CoA).

Response: We agree with the reviewer that TLC analysis is not quantitative. Additional TLCs will be run to investigate other lipids sharing common precursors. At the present time, we can not run radioactive experiments on the lab.

The connection of cholesterol with cell wall permeability is tenuous. Cholesterol will serve as a carbon source and contribute to the biosynthesis of methyl-branched lipids such as PDIM, SL-1, and PAD/DAT. Carbon sources also affect other aspects of physiology (redox, respiration, ATP), which can directly affect permeability and import/export of drugs. Authors should investigate whether restoration of the normal level of permeability and EV release is not due to the maintenance of cell wall lipid balance upon cholesterol exposure of the VirR mutant.

Response: We concur with the reviewer that cholesterol as sole carbon source is introducing many changes in Mtb cells beside permeability. Our central hypothesis regarding this data is that cholesterol will make Mtb cell membrane less fluid and this fact will make Ev release to be reduced. We will try to measure membrane fluidity in the presence and absence of cholesterol. However, permeability changes in Mtb cells can be manifested at different levels of the cell envelope. This would suggest that the increased permeability observed in the VirR mutant, could be different than that of observed upon TRZ treatment. The main point on this is that vesiculogenesis could be a general process responding to changes in permeability regardless of the cell envelope compartment affected. We need to define experiments here, but we will try to demonstrate this.

Finally, protein interaction data is based on experiments done once without statistical analysis. If the interaction between VirR and LCP protein is expected on the mycobacterial membrane, how the SPLIT_GFP system expressed in the cytoplasm is physiologically relevant. No explanation was provided as to why VirR interacts with the truncated version of LCP proteins and not with the full-length proteins.

Response: Split-GFP has been previously used with cell membrane proteins with success. However, we will repeat the experiments and perform statistics.

Reviewer #2 (Public Review):

Summary:

In this work, Vivian Salgueiro et al. have comprehensively investigated the role of VirR in the vesicle production process in Mtb using state-of-the-art omics, imaging, and several biochemical assays. From the present study, authors have drawn a positive correlation between cell membrane permeability and vasculogenesis and implicated VirR in affecting membrane permeability, thereby impacting vasculogenesis.

Strengths:

The authors have discovered a critical factor (i.e. membrane permeability) that affects vesicle production and release in Mycobacteria, which can broadly be applied to other bacteria and may be of significant interest to other scientists in the field. Through omics and multiple targeted assays such as targeted metabolomics, PG isolation, analysis of Diaminopimelic acid and glycosyl composition of the cell wall, and, importantly, molecular interactions with PG-AG ligating canonical LCP proteins, the authors have established that VirR is a central scaffold at the cell envelope remodelling process which is critical for MEV production.

Response: We thank the reviewer for this kind words.

Weaknesses:

Throughout the study, the authors have utilized a CRISPR knockout of VirR. VirR is a non-essential gene for the growth of Mtb; a null mutant of VirR would have been a better choice for the study.

Response: We thank the reviewer for bringing up this issue. Contrary to predictions, we believe that virR is an essential gene as we have tried to delete it several times with no success. We used in the study a transposon mutant and its complementing strain since they have been the base of previous studies to establish their genetic implications in vesiculogenesis in Mtb. The choice of CRISPRi was run similar experiments in a background different from transposon mutagenesis. Our data, support similar phenotypes in term of vesicle release.

eLife assessment

In this important study, the authors set out to investigate the biogenesis of extracellular vesicles in mycobacteria and provide several observations to link VirR with vesiculogenesis, PG metabolism, lipid metabolism, and cell wall permeability. Whilst some of the evidence provided is convincing, data to support the proposed mechanism are somewhat incomplete. The work will be of interest to bacteriologists.

Reviewer #1 (Public Review):

Summary:

The present study's main aim is to investigate the mechanism of how VirR controls the magnitude of MEV release in Mtb. The authors used various techniques, including genetics, transcriptomics, proteomics, and ultrastructural and biochemical methods. Several observations were made to link VirR-mediated vesiculogenesis with PG metabolism, lipid metabolism, and cell wall permeability. Finally, the authors presented evidence of a direct physical interaction of VirR with the LCP proteins involved in linking PG with AG, providing clues that VirR might act as a scaffold for LCP proteins and remodel the cell wall of Mtb. Since the Mtb cell wall provides a formidable anatomical barrier for the entry of antibiotics, targeting VirR might weaken the permeability of the pathogen along with the stimulation of the immune system due to enhanced vesiculogenesis. Therefore, VirR could be an excellent drug target. Overall, the study is an essential area of TB biology.

Strengths:

The authors have done a commendable job of comprehensively examining the phenotypes associated with the VirR mutant using various techniques. Application of Cryo-EM technology confirmed increased thickness and altered arrangement of CM-L1 layer. The authors also confirmed that increased vesicle release in the mutant was not due to cell lysis, which contrasts with studies in other bacterial species.

Another strength of the manuscript is that biochemical experiments show altered permeability and PG turnover in the mutant, which fits with later experiments where authors provide evidence of a direct physical interaction of VirR with LCP proteins.

Transcriptomics and proteomics data were helpful in making connections with lipid metabolism, which the authors confirmed by analyzing the lipids and metabolites of the mutant.

Lastly, using three approaches, the authors confirm that VirR interacts with LCP proteins in Mtb via the LytR_C terminal domain.

Altogether, the work is comprehensive, experiments are designed well, and conclusions are made based on the data generated after verification using multiple complementary approaches.

Weaknesses:

The major weakness is that the mechanism of VirR-mediated EV release remains enigmatic. Most of the findings are observational and only associate enhanced vesiculogenesis observed in the VirR mutant with cell wall permeability and PG metabolism. The authors suggest that EV release occurs during cell division when PG is most fragile. However, this has yet to be tested in the manuscript - the AFM of the VirR mutant, which produces thicker PG with more pore density, displays enhanced vesiculogenesis. No evidence was presented to show that the PG of the mutant is fragile, and there are differences in cell division to explain increased vesiculogenesis. These observations, counterintuitive to the authors' hypothesis, need detailed experimental verification.

Transcriptomic data only adds a little substantial. Transcriptomic data do not correlate with the proteomics data. It remains unclear how VirR deregulates transcription. TLCs of lipids are not quantitative. For example, the TLC image of PDIM is poor; quantitative estimation needs metabolic labeling of lipids with radioactive precursors. Further, change in PDIMs is likely to affect other lipids (SL-1, PAT/DAT) that share a common precursor (propionyl- CoA).

The connection of cholesterol with cell wall permeability is tenuous. Cholesterol will serve as a carbon source and contribute to the biosynthesis of methyl-branched lipids such as PDIM, SL-1, and PAD/DAT. Carbon sources also affect other aspects of physiology (redox, respiration, ATP), which can directly affect permeability and import/export of drugs. Authors should investigate whether restoration of the normal level of permeability and EV release is not due to the maintenance of cell wall lipid balance upon cholesterol exposure of the VirR mutant.

Finally, protein interaction data is based on experiments done once without statistical analysis. If the interaction between VirR and LCP protein is expected on the mycobacterial membrane, how the SPLIT_GFP system expressed in the cytoplasm is physiologically relevant. No explanation was provided as to why VirR interacts with the truncated version of LCP proteins and not with the full-length proteins.

Reviewer #2 (Public Review):

Summary:

In this work, Vivian Salgueiro et al. have comprehensively investigated the role of VirR in the vesicle production process in Mtb using state-of-the-art omics, imaging, and several biochemical assays. From the present study, authors have drawn a positive correlation between cell membrane permeability and vasculogenesis and implicated VirR in affecting membrane permeability, thereby impacting vasculogenesis.

Strengths:

The authors have discovered a critical factor (i.e. membrane permeability) that affects vesicle production and release in Mycobacteria, which can broadly be applied to other bacteria and may be of significant interest to other scientists in the field. Through omics and multiple targeted assays such as targeted metabolomics, PG isolation, analysis of Diaminopimelic acid and glycosyl composition of the cell wall, and, importantly, molecular interactions with PG-AG ligating canonical LCP proteins, the authors have established that VirR is a central scaffold at the cell envelope remodelling process which is critical for MEV production.

Weaknesses:

Throughout the study, the authors have utilized a CRISPR knockout of VirR. VirR is a non-essential gene for the growth of Mtb; a null mutant of VirR would have been a better choice for the study.

Author Response

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

Reviewer #1

More details about the classification and how it is trained

We included a sentence in the introduction to clarify which data we are using: "In order to demonstrate this improvement, we apply our methods to two classification datasets: a synthetic dataset and a public clinical dataset where the predicted outcome is the survival of the patient"

And about how the classifier is trained in the "Results" section: "we used the default parameters of the classifier, since our focus is comparing the different imputation methods"

Availability of the code

Now the code is publicly available in a github repository https://github.com/AstraZeneca/dpp_imp/ (see Availability of Data and Code section)

Reviewer #2

Clarifying that Determinantal Point Processes and their deterministic version have been introduced before but are applied for the first time for data imputation in this work:

We added explanation in the 6th paragraph of the introduction that we use pre-existing DPP and deterministic-DPP algorithms for our imputation methods and include the references to avoid confusion

We also added a paragraph at the end of the introduction to summarize this work's contribution

Explaining the claim about the computational advantage of using quantum determinantal point processes for the imputation methods:

In the fourth paragraph of the "Discussion" section (page 8), we give an imputation example by numerically comparing the classical and quantum algorithms running time for DPP sampling, which shows the advantage of using the quantum algorithm.

Regarding running time for classical DPP and quantum DPP sampling algorithms:

We included Table VIII (page 13) that compares the preprocessing and sampling complexities for both classical and quantum DPP algorithms, we consider the case where we sample d rows from an (n,d) matrix and n=O(d) which is usually the case for our DPP-Random Forest algorithm

We added some details regarding the quantum advantage in the first paragraph of page 12

Regarding the comment about the modest improvement of the DPP methods and questions about their practical benefit:

As mentioned in the third paragraph of the "Discussion" section, we point out that the consistency of the improvement and the removal of variance as a result of using the DPP and deterministic DPP methods make our methods very beneficial to use on clinical data. Further exploration with different data sets can provide a more result in a more complete understanding of the practical advantages of the methods

Algorithmic complexity of the deterministic DPP algorithm:

Detailed in the last sentence of the "Determinantal Point Processes" subsection of the "Methods" section: O(N^2 d) for the preprocessing step and O(Nd^3) for the sampling step

Running time for the quantum deterministic DPP sampling and how it is done in practice:

While it is difficult to assess the real running time for the quantum detDPP algorithm for large circuits (100 or more qubits), due to the unavailability of such devices, we give more details about our practical implementation in the last paragraph of the "Methods" section. In our case (up to 10 qubits) we used 1000 shots to sample the highest probability elements.

On which quantum simulator was used

We point out in the first paragraph of page 5 that we employ the qiskit noiseless simulator

eLife assessment

The methods presented in this work provide modest yet consistent accuracy improvements for data classification tasks where certain data are missing. The authors also present a way to use quantum computers for this task. The methodology and results for the classical (non-quantum) case are solid, although evidence for the practical quantum advantage via their approach in 'next generation' quantum computers remains incomplete. The results are valuable and should interest data scientists, life scientists and anyone working in quantum computing.

Reviewer #1 (Public Review):

Summary:

The article written by Kazdaghli et al. proposes a modification of imputation methods, to better account and exploit the variability of the data. The aim is to reduce the variability of the imputation results.<br /> The authors propose two methods, one that still includes some imputation variability, but accounts for the distribution of the data points to improve the imputation. The other one proposes a determinantal sampling, that presents no variation in the imputation data, but it seems to be, that they measure the variation in the classification task, instead. As these methods grow easily in computation requirements and time, they also propose an algorithm to run these methods in quantum processors.

Strengths:

The sampling method for imputing missing values that account for the variability of the data seems to be accurate.

Weaknesses:

The authors state "Ultimately, the quality and reliability of imputations can be measured by the performance of a downstream predictor, which is usually the AUC (area under the receiver operating curve) for a classification task." but there is no citation of other scientists doing this. I think the authors could have evaluated the imputations directly, as they mention in the introduction, I understand that the final goal in the task is to have a better classification. In a real situation, they would have data that would be used for training the algorithm, and then new data that needs to be imputed and classified. Is there any difference between imputing all the data together and training the algorithm, versus doing the imputation, training a classifier, then imputing new data (for the testing set), and then testing the classification?<br /> I wonder if there could be some spurious interaction between the imputation and the classification methods, that could bias the data in the sense of having a better classification, but not imputing the real values; in particular when the deterministic DPP is used.

Author Response

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

eLife assessment

This important study identifies the gene mamo as a new regulator of pigmentation in the silkworm Bombyx mori, a function that was previously unsuspected based on extensive work on Drosophila where the mamo gene is involved in gamete production. The evidence supporting the role of Bm-nano in pigmentation is convincing, including high-resolution linkage mapping of two mutant strains, expression profiling, and reproduction of the mutant phenotypes with state-of-the-art RNAi and CRISPR knock-out assays. While the discussion about genetic changes being guided or accelerated by the environment is extremely speculative and has little relevance for the findings presented, the work will be of interest to evolutionary biologists and geneticists studying color patterns and evolution of gene networks.

Response: Thank you very much for your careful work. In the revised version, we conducted a comparative genomic analysis of the upstream regions of the Bm-mamo gene in 51 wild silkworms and 171 domesticated local silkworms. The analysis of nucleotide diversity (pi) and the fixation index (FSTs) of the Bm-mamo genome sequences in the wild and domesticated silkworm populations were also performed. The results showed that the Bm-mamo genome sequence of local silkworms was relatively conserved, while the upstream sequence of wild silkworms exhibited high nucleotide diversity. This finding suggested a high degree of variability in the regulatory region of the Bm-mamo gene, in wild strains. Additionally, the sequence in this region may have been fixed by domestication selection. We have optimized the description in the discussion section.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

This papers performs fine-mapping of the silkworm mutants bd and its fertile allelic version, bdf, narrowing down the causal intervals to a small interval of a handful of genes. In this region, the gene orthologous to mamo is impaired by a large indel, and its function is later confirmed using expression profiling, RNAi, and CRISPR KO. All these experiments are convincingly showing that mamo is necessary for the suppression of melanic pigmentation in the silkworm larval integument.

The authors also use in silico and in vitro assays to probe the potential effector genes that mamo may regulate.

Strengths:

The genotype-to-phenotype workflow, combining forward (mapping) and reverse genetics (RNAi and CRISPR loss-of-function assays) linking mamo to pigmentation are extremely convincing.

This revision is a much improved manuscript and I command the authors for many of their edits.

Response: Thank you very much for your careful work. With the help of reviewers and editors, we have revised the manuscript to improve its readability.

I find the last part of the discussion, starting at "It is generally believed that changes in gene expression patterns are the result of the evolution of CREs", to be confusing.

In this section, I believe the authors sequentially:

• emphasize the role of CRE in morphological evolution (I agree)

• emphasize that TF, and in particular their own CRE, are themselves important mutational targets of evolution (I agree, but the phrasing need to insist the authors are here talking about the CRE found at the TF locus, not the CRE bound by the TF).

• use the stickleback Pel enhancer as an example, which I think is a good case study, but the authors also then make an argument about DNA fragility sites, which is hard to connect with the present study.

• then continue on "DNA fragility" using the peppered moth and butterfly cortex locus. There is no evidence of DNA fragility at these loci, so the connection does not work. "The cortex gene locus is frequently mutated in Lepidoptera", the authors say. But a more accurate picture would be that the cortex locus is repeatedly involved in the generation of color pattern variants. Unlike for Pel fragile enhancer, we don't know if the causal mutations at this locus are repeatedly the same, and the haplotypes that have been described could be collateral rather than causal. Overall, it is important to clarify the idea that mutation bias is a possible factor explaining "genetic hotspots of evolution" (or genetic parallelism sensu 10.1038/nrg3483), but it is also possible that many genetic hotspots are repeated mutational targets because of their "optimal pleiotropy" (e.g. hub position in GRNs, such as mamo might be), or because of particularly modular CRE region that allow fine-tuning. Thus, I find the "fragility" argument misleading here. In fact the finding that "bd" and "bdf" alleles are different in nature is against the idea of a fragility bias (unless the authors can show increased mutation rates at this locus in a wild silkmoth species?). These alleles are also artificially-selected ie. they increased in frequency by breeding rather than natural selection in the wild, so while interesting for our understand of the genotype-phenotype map, they are not necessarily representative of the mutations that may underlie evolution in the wild.

Response: Thank you very much for your careful work. DNA fragility is an interesting topic, but some explanations for DNA fragility are confusing. One study measured the rate of DNA double-strand breaks (DSBs) in yeast artificial chromosomes (YACs), which are chromosomes containing marine Pel that broke ~25 to 50 times more frequently than did the control. These authors believe that the increase in the mutation rate is caused by DNA sequence characteristics, particularly TG-dinucleotide repeats. Moreover, they found that adding a replication origin on the opposite side of Pel did not cause the fungus to switch fragile, making the forward sequence stable and the reverse complement fragile. Thus, Pel fragility is also dependent on the direction of DNA replication. In summary, they suggested that the special DNA sequence is the cause of DNA fragility. In addition, the sequence features associated with DNA fragility in the Pel region are also found in thousands of other positions in the stickleback and human genomes (Xie KT et al, 2019, science).

In yeast artificial chromosomes (YACs), the characteristics of DNA sequences, such as TG-dinucleotide repeat sequences, may be important reasons for DNA fragility, and these breaks occur during DNA replication. However, the inserted sequence of YAC often undergoes deletion or recombination during cultivation and passage. In addition, yeast is a single-celled organism. Therefore, the results in yeast cannot represent the situation in multicellular organisms. If multicellular organisms are like this, there are several issues as follows:

(1) The DNA replication process occurs separately in different multicellular organisms. Because DNA breakage and repair are independent, they can lead to the presence of different alleles in different cells. This can potentially lead to the occurrence of extensive chimeric organisms. However, we have not found such a situation in the genome sequencing of many multicellular organisms.

(2) If the DNA sequence, TG-dinucleotide repeats, is the determining factor, the mutations near the sequence lose their strong correlation with environmental changes. The researchers conducted yeast artificial chromosome experiments in the same environment and found that the frequency of DNA breaks containing TG dinucleotide repeat sequences was 25 to 50 times greater than that of the control group. This means that, whether in the marine population or the lake population, this part of the sticklebacks’ genome has undergone frequent mutations. However, according to related research, populations of lake sticklebacks, rather than marine populations, often exhibit a decrease in the pelvic phenotype.

(3) Researchers have found thousands of loci in the genome of sticklebacks and humans that contain such sequences (TG-dinucleotide repeats). This means that thousands of sites undergo frequent mutations during DNA replication. Unless these sites do not possess functionality, they will have some impact on the organism, even causing damage. Even if they are not functional sequences, these sequences will gradually be discarded or replaced during frequent mutations rather than being present in large quantities in the genome.

Therefore, the study of DNA fragility in yeast cannot explain the situation in multicellular organisms.

As you noted, we want to express that the frequent variation in the cortex gene should be regulated by targeted regulation involving the GRN in Lepidoptera. In addition, studies on specific epigenetic modifications discovered through the referenced fragile DNA sites suggest that DNA fragility is not determined by the DNA sequence (Ji F, 2020, Cell Res) but rather by other factors, such as epigenetic factors. The sequence features discovered at fragile DNA sites are traces of frequent mutations, not causes.

In this revision, we analyzed the nucleotide diversity of the mamo genome in 51 wild and 171 domestic silkworms. We found high nucleic acid diversity from the third exon to the upstream region of this gene in wild silkworms. We randomly selected 12 wild silkworms and 12 domestic silkworms and compared their upstream sequences to approximately 1 kb. In wild silkworms, there is significant diversity in their upstream sequences. In domestic silkworms, the sequences are highly conserved, but in some silkworms, a long interspersed nuclear element (LINE) is inserted. This finding suggested that there is frequent variation in the sequence of this region in wild silkworms, while fixation occurs in domesticated silkworms. These genomic data are sourced from the pangenome of silkworms (Tong X, 2022, Nat Commun.). In the pangenomic research, 1078 strains (205 local strains, 194 improved strains, 632 mutant strains, and 47 wild silkworms), which included 545 third-generation sequencing genomes, were obtained. An online website was built to utilize these data (http://silkmeta.org.cn/). We warmly welcome you to use these data.

In summary, for clearer expression, we have rewritten this section.

Xie KT, Wang G, Thompson AC, Wucherpfennig JI, Reimchen TE, MacColl ADC, Schluter D, Bell MA, Vasquez KM, Kingsley DM. DNA fragility in the parallel evolution of pelvic reduction in stickleback fish. Science. 2019 Jan 4;363(6422):81-84. doi: 10.1126/science.aan1425.

Ji F, Liao H, Pan S, Ouyang L, Jia F, Fu Z, Zhang F, Geng X, Wang X, Li T, Liu S, Syeda MZ, Chen H, Li W, Chen Z, Shen H, Ying S. Genome-wide high-resolution mapping of mitotic DNA synthesis sites and common fragile sites by direct sequencing. Cell Res. 2020 Nov;30(11):1009-1023. doi: 10.1038/s41422-020-0357-y.

Tong X, Han MJ, Lu K, Tai S, Liang S, Liu Y, Hu H, Shen J, Long A, Zhan C, Ding X, Liu S, Gao Q, Zhang B, Zhou L, Tan D, Yuan Y, Guo N, Li YH, Wu Z, Liu L, Li C, Lu Y, Gai T, Zhang Y, Yang R, Qian H, Liu Y, Luo J, Zheng L, Lou J, Peng Y, Zuo W, Song J, He S, Wu S, Zou Y, Zhou L, Cheng L, Tang Y, Cheng G, Yuan L, He W, Xu J, Fu T, Xiao Y, Lei T, Xu A, Yin Y, Wang J, Monteiro A, Westhof E, Lu C, Tian Z, Wang W, Xiang Z, Dai F. High-resolution silkworm pan-genome provides genetic insights into artificial selection and ecological adaptation. Nat Commun. 2022 Sep 24;13(1):5619. doi: 10.1038/s41467-022-33366-x.

Lu K, Pan Y, Shen J, Yang L, Zhan C, Liang S, Tai S, Wan L, Li T, Cheng T, Ma B, Pan G, He N, Lu C, Westhof E, Xiang Z, Han MJ, Tong X, Dai F. SilkMeta: a comprehensive platform for sharing and exploiting pan-genomic and multi-omic silkworm data. Nucleic Acids Res. 2024 Jan 5;52(D1):D1024-D1032. doi: 10.1093/nar/gkad956.

Curiously, the last paragraph ("Some research suggests that common fragile sites...") elaborate on the idea that some sites of the genome are prone to mutation. The connection with mamo and the current article are extremely thin. There is here an attempt to connect meiotic and mitotic breaks to Bm-mamo, but this is confusing: it seems to propose Bm-mamo as a recruiter of epigenetic modulators that may drive higher mutation rates elsewhere. Not only I am not convinced by this argument without actual data, but this would not explain how the mutations at the Bm-mamo itself evolved.

Response: Thank you very much for your careful work. This section mainly illustrates that DNA fragility is not determined by sequence but is regulated by other factors in animals. In fruit flies, they found that mamo is an important candidate gene for recombination hotspot setting in meiosis. First, we evaluated PRDM9, which plays an important role in setting recombination hotspots during meiosis. Our purpose in mentioning this information is to illustrate that chromosome recombination is a process of programmed double strand breaks and to answer another reviewer's question about programmed events in the genome. In summary, we suggest that some variations in DNA sequences are procedural results. We have optimized the description of this section in this version.

On a more positive note, I find it fascinating that the authors identified a TF that clearly articulates or orchestrate larval pattern development, and that when it is deleted, can generate healthy individuals. In other words, while it is a TF with many targets, it is not too pleiotropic. This idea, that the genetically causal modulators of developmental evolution are regulatory genes, has been described elsewhere (e.g. Fig 4c in 10.1038/s41576-020-0234-z, and associated refs). To me, the beautiful findings about Bm-mamo make sense in the general, existing framework that developmental processes and regulatory networks "shape" the evolutionary potential and trajectories of organisms. There is a degree of "programmability" in the genomes, because some loci are particularly prone to modulate a given type of trait. Here, Bm-mamo, as a potentially regulator of both CPs and melanin pathway genes, appear to be a potent modulator of epithelial traits. Claiming that there are inherent mutational biases behind this is unwarranted.

Response: Thank you very much for your careful work. I completely agree with your statement that the genome exhibits a certain degree of programmability. On the one hand, some transcription factors can precisely control the spatiotemporal expression levels of some structural genes (such as pigment synthesis genes). On the other hand, these transcription factors are also subject to strict expression regulation. Because the color pattern is complex, changes in single or minority structural genes result in incomplete or imprecise changes in coloring patterns. Nevertheless, several regulatory factors can regulate multiple downstream target genes. Changes in their expression patterns can lead to holistic and significant changes in color patterns. There are long intergenic regions upstream of many important transcription factors, dozens of kilobase pairs (Kb) to hundreds of Kb, which may contain many different regulatory elements for better control of their expression patterns. Therefore, gene regulatory networks can directly regulate transcription factors to modulate a given type of trait. Transcription factors and their downstream target genes can form a functional module, which is similar to a functional module in software or operating systems. This regulation of transcription factors is simpler in terms of steps, which are similar to a single click switch button. The gene regulatory network regulates these modules in response to environmental changes and is widely recognized.

Some people do not agree that genetic variations can also be regulated. They claim that this is completely random. The infinite monkey theorem (Félix-Édouard-Justin-Émile Borel, 1909) states that if an infinite number of monkeys were given typewriters and an infinite amount of time, they would eventually produce the complete works of Shakespeare. Although this theory advocates randomness on the surface, its conclusions are full of inevitability (tail event). In nature, some things we observe do not have obvious regularity because they involve relatively complex factors, and the underlying logic is obscure and difficult to understand. We often name them random. However, as we gradually understand the logic behind this complex event, we can also recognize the procedural nature of this randomness.

Previously, chromosomal recombination during meiosis was believed to be a random event. However, currently, it is believed that the process is procedural. The occurrence of meiotic recombination mentioned earlier indicates that the genome has the ability to self-set the position of double-strand breaks to form new allelic forms. Because meiotic recombination is programmed, transcription factors that recognize DNA sites, enzymes that cleave double strands, and DNA repair systems exist, programming can also introduce genetic variation. A study in plants has provided insights into this programmed mutation (Monroe JG, 2023, nature). Frequent changes in the expression patterns of some transcription factors occur between and/or within species. In this article, we only discuss the possible reasons for variations in the expression patterns of some transcription factors in a general manner and simple reasoning. We have added an analysis of the response of wild silkworms and improved the relevance of the discussion.

Monroe JG, Srikant T, Carbonell-Bejerano P, Becker C, Lensink M, Exposito-Alonso M, Klein M, Hildebrandt J, Neumann M, Kliebenstein D, Weng ML, Imbert E, Ågren J, Rutter MT, Fenster CB, Weigel D. Mutation bias reflects natural selection in Arabidopsis thaliana. Nature. 2022 Feb;602(7895):101-105. doi: 10.1038/s41586-021-04269-6. Epub 2022 Jan 12. Erratum in: Nature. 2023 Aug;620(7973):

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

• Please structure your Discussion with section headers.

Response: Thank you very much for your careful work. We have added relevant section headers.

• As explained in my public review, I found the two last sections of the Discussion to be dispersed and confusing. I also must say that I carefully read the Response to Reviewers on this, which helped me to better understand the authors' intentions here. Please consider the revision of this Discussion as this feels extremely speculative difficult to connect with Bm-mamo.

Response: Thank you very much for your careful work. We have rewritten this part of the content.

• typo: were found near the TTS of yellow --> TSS

Response: Thank you very much for your careful work. We have made these modifications.

• l. 234 :"expression level of the 18 CP genes in the integument". Consider adding a mention of Figure 7 here, as only Fig. S10 is cited here.

Response: Thank you very much for your careful work. We have made these modifications.

• Editorial comment on the second half of the Abstract:

Wu et al : "We found that Bm-mamo can comprehensively regulate the expression of related pigment synthesis and cuticular protein genes to form color patterns. This indicates that insects have a genetic basis for coordinate regulation of the structure and shape of the cuticle, as well as color patterns. This genetic basis provides the possibility for constructing the complex appearances of some insects. This study provides new insight into the regulation of color patterns."

I respectfully suggest a more accurate rephrasing, where the methods are mentioned, and where the logical argument is more straightforward. For example

"Using RNAi and CRISPR we show that Bm-mamo is a repressor or dark melanin patterns in the larval epithelium. Using in-vitro binding assays and gene expression profiling in wild-type and mutant larvae, we also show that Bm-mamo likely regulate the expression of related pigment synthesis and cuticular protein genes in a coordinated manner to mediate its role in color pattern formation. This mechanism is consistent with a dual role of this transcription factor in regulating both the structure and shape of the cuticle and pigments that are embedded within it. This study provides new insight into the regulation of color patterns as well as in the construction more complex epithelial features in some insects."

I hope this let the ideas of the original version transpire as the authors intended.

Response: Thank you very much for your careful work. We have made these modifications.

Joint Public Review:

This papers performs fine-mapping of the silkworm mutants bd and its fertile allelic version, bdf, narrowing down the causal intervals to a small interval of a handful of genes. In this region, the gene orthologous to mamo is impaired by a large indel, and its function is later confirmed using expression profiling, RNAi, and CRISPR KO. All these experiments are convincingly showing that mamo is necessary for the suppression of melanic pigmentation in the silkworm larval integument.

The authors also use in silico and in vitro assays to probe the potential effector genes that mamo may regulate.

The genotype-to-phenotype workflow, combining forward (mapping) and reverse genetics (RNAi and CRISPR loss-of-function assays) linking mamo to pigmentation are extremely convincing.

Comments on latest version:

This second revision took into account all the reviewers' comments. The authors added an interesting analysis of nucleotide diversity at the Bm-mamo locus, using available sequence data from 51 wild silkworms and 171 domesticated silkworms.<br /> The last paragraph added to the discussion, starting with "It has often been believed that changes in CREs are caused by random mutations", is speculative. There is currently no evidence that the mutation rate is biased at the Bm-mamo locus.

Author Response

Public Reviews

We thank both reviewers for taking the time and effort to think critically about our paper and point out areas where it can be improved. In this document, we do our best to clarify any misunderstandings with the hope that further consideration about the strengths and weaknesses of our approach will be possible. Our responses are in bold.

Reviewer #1 (Public Review):

Summary:

In their manuscript, Schmidlin, Apodaca, et al try to answer fundamental questions about the evolution of new phenotypes and the trade-offs associated with this process. As a model, they use yeast resistance to two drugs, fluconazole and radicicol. They use barcoded libraries of isogenic yeasts to evolve thousands of strains in 12 different environments. They then measure the fitness of evolved strains in all environments and use these measurements to examine patterns in fitness trade-offs. They identify only six major clusters corresponding to different trade-off profiles, suggesting the vast genotypic landscape of evolved mutants translates to a highly constrained phenotypic space. They sequence over a hundred evolved strains and find that mutations in the same gene can result in different phenotypic profiles.

Overall, the authors deploy innovative methods to scale up experimental evolution experiments, and in many aspects of their approach tried to minimize experimental variation.

We thank the reviewer for this positive assessment of our work. We are happy that the reviewer noted what we feel is a unique strength of our approach: we scaled up experimental evolution by using DNA barcodes and by exploring 12 related selection pressures. Despite this scaling up, we still see phenotypic convergence among the 744 adaptive mutants we study.

The environments we study represent 12 different concentrations or combinations of two drugs, radicicol and fluconazole. Our hope is that this large dataset (774 mutants x 12 environments) will be useful, both to scientists who are generally interested in the genetic and phenotypic underpinnings of adaptation, and to scientists specifically interested in the evolution of drug resistance.

Weaknesses:

(1) One of the objectives of the authors is to characterize the extent of phenotypic diversity in terms of resistance trade-offs between fluconazole and radicicol. To minimize noise in the measurement of relative fitness, the authors only included strains with at least 500 barcode counts across all time points in all 12 experimental conditions, resulting in a set of 774 lineages passing this threshold. This corresponds to a very small fraction of the starting set of ~21 000 lineages that were combined after experimental evolution for fitness measurements.

This is a misunderstanding that we will work to clarify in the revision. Our starting set did not include 21,000 adaptive lineages. The total number of unique adaptive lineages in this starting set is much lower than 21,000 for two reasons.

First, ~21,000 represents the number of single colonies we isolated in total from our evolution experiments. Many of these isolates possess the same barcode, meaning they are duplicates. Second, and more importantly, most evolved lineages do not acquire adaptive mutations, meaning that many of the 21,000 isolates are genetically identical to their ancestor. In our revised manuscript, we will explicitly state that these 21,000 isolated lineages do not all represent unique, adaptive lineages. In figure 2 and all associated text, we will change the word “lineages” to “isolates,” where relevant.

More broadly speaking, several previous studies have demonstrated that diverse genetic mutations converge at the level of phenotype, and have suggested that this convergence makes adaptation more predictable (PMID33263280, PMID37437111, PMID22282810, PMID25806684). Our study captures mutants that are overlooked in previous studies, such as those that emerge across subtly different selection pressures (e.g., 4 𝜇g/ml vs. 8 𝜇g/ml flu) and those that are undetectable in evolutions lacking DNA barcodes. Thus, while our experimental design misses some mutants (see next comment), it captures many others. Note that 774 adaptive lineages is more than most previous studies. Thus, we feel that “our work – showing that 774 mutants fall into a much smaller number of groups” is important because it “contributes to growing literature suggesting that the phenotypic basis of adaptation is not as diverse as the genetic basis (lines 161 - 162).”

As the authors briefly remark, this will bias their datasets for lineages with high fitness in all 12 environments, as all these strains must be fit enough to maintain a high abundance.

The word “briefly” feels a bit unfair because we discuss this bias on 3 separate occasions (on lines 146 - 147, 260 - 264, and in more detail on 706 - 714). We even walk through an example of a class of mutants that our study misses. We say, “our study is underpowered to detect adaptive lineages that have low fitness in any of the 12 environments. This is bound to exclude large numbers of adaptive mutants. For example, previous work has shown some FLU resistant mutants have strong tradeoffs in RAD (Cowen and Lindquist 2005). Perhaps we are unable to detect these mutants because their barcodes are at too low a frequency in RAD environments, thus they are excluded from our collection of 774.”

In our revised version, we will add more text to the first mention of these missing mutants (lines 146 - 147) so that the implications are more immediately made apparent.

While we “miss” some classes of mutants, we “catch” other classes that may have been missed in previous studies of convergence. For example, we observe a unique class of FLU-resistant mutants that primarily emerged in evolution experiments that lack FLU (Figure 3). Thus, we think that the unique design of our study, surveying 12 environments, allows us to make a novel contribution to the study of phenotypic convergence.

One of the main observations of the authors is phenotypic space is constrained to a few clusters of roughly similar relative fitness patterns, giving hope that such clusters could be enumerated and considered to design antimicrobial treatment strategies. However, by excluding all lineages that fit in only one or a few environments, they conceal much of the diversity that might exist in terms of trade-offs and set up an inclusion threshold that might present only a small fraction of phenotypic space with characteristics consistent with generalist resistance mechanisms or broadly increased fitness. This has important implications regarding the general conclusions of the authors regarding the evolution of trade-offs.

We discussed these implications in some detail in the 16 lines mentioned above (146 - 147, 260 - 264, 706 - 714). To add to this discussion, we will also add the following sentence to the end of the paragraph on lines 697 - 714: “This could complicate (or even make impossible) endeavors to design antimicrobial treatment strategies that thwart resistance”.

We will also add a new paragraph that discusses these implications earlier in our manuscript. This paragraph will highlight the strengths of our method (e.g., that we “catch” classes of mutants that are often overlooked) while being transparent about the weaknesses of our approach (e.g., that we “miss” mutants with strong tradeoffs).

(2) Most large-scale pooled competition assays using barcodes are usually stopped after ~25 to avoid noise due to the emergence of secondary mutations.

The rate at which new mutations enter a population is driven by various factors such as the mutation rate and population size, so choosing an arbitrary threshold like 25 generations is difficult.

We conducted our fitness competition following previous work using the Levy/Blundell yeast barcode system, in which the number of generations reported varies from 32 to 40 (PMID33263280, PMID27594428, PMID37861305, see PMID27594428 for detailed calculation of the fraction of lineages biased by secondary mutations in this system).

The authors measure fitness across ~40 generations, which is almost the same number of generations as in the evolution experiment. This raises the possibility of secondary mutations biasing abundance values, which would not have been detected by the whole genome sequencing as it was performed before the competition assay.

We understand how the reviewer came to this misunderstanding and will adjust our revised manuscript accordingly. Previous work has demonstrated that, in this particular evolution platform, most of the mutations actually occur during the transformation that introduces the DNA barcodes (PMID25731169). In other words, these mutations do not accumulate during the 40 generations of evolution, they are already there. So the observation that we collect a genetically diverse pool of adaptive mutants after 40 generations of evolution is not evidence that 40 generations is enough time for secondary mutations to bias abundance values.

(3) The approach used by the authors to identify and visualize clusters of phenotypes among lineages does not seem to consider the uncertainty in the measurement of their relative fitness. As can be seen from Figure S4, the inter-replicate difference in measured fitness can often be quite large. From these graphs, it is also possible to see that some of the fitness measurements do not correlate linearly (ex.: Med Flu, Hi Rad Low Flu), meaning that taking the average of both replicates might not be the best approach.

This concern, and all subsequent concerns, seem to be driven by either (a) general concerns about the noisiness of fitness measurements obtained from large-scale barcode fitness assays or (b) general concerns about whether the clusters obtained from our dimensional reduction approach capture this noise as opposed to biologically meaningful differences.

We will respond to each concern point-by-point, but want to start by generally stating that (a) our particular large-scale barcode fitness assay has several features that diminish noise, and (b) we devote 4 figures and 200 lines of text to demonstrating that these clusters capture biologically meaningful differences between mutants (and not noise).

In terms of this specific concern, we performed an analysis of noise in the submitted manuscript: Our noisiest fitness measurements correspond to barcodes that are the least abundant and thus suffer the most from stochastic sampling noise. These are also the barcodes that introduce the nonlinearity the reviewer mentions. We removed these from our dataset by increasing our coverage threshold from 500 reads to 5,000 reads. The clusters did not collapse, which suggests that they were not capturing noise (Figure S7 panel B). But we agree with the reviewer that this analysis alone is not sufficient to conclude that the clusters distinguish groups of mutants with unique fitness tradeoffs.

Because the clustering approach used does not seem to take this variability into account, it becomes difficult to evaluate the strength of the clustering, especially because the UMAP projection does not include any representation of uncertainty around the position of lineages.

To evaluate the strength of the clustering, we performed numerous analyses including whole genome sequencing, growth experiments, reclustering, and tracing the evolutionary origins of each cluster (Figures 5 - 8). All of these analyses suggested that our clusters capture groups of mutants that have different fitness tradeoffs. We will adjust our revised manuscript to make clear that we do not rely on the results of a clustering algorithm alone to draw conclusions about phenotypic convergence.

We are also grateful to the reviewer for helping us realize that, as written, our manuscript is not clear with regard to how we perform clustering. We are not using UMAP to decide which mutant belongs to which cluster. Recent work highlights the importance of using an independent clustering method (PMID37590228). Although this recent work addresses the challenge of clustering much higher dimensional data than we survey here, we did indeed use an independent clustering method (gaussian mixture model). In other words, we use UMAP for visualization but not clustering. We also confirm our clustering results using a second independent method (hierarchical clustering; Figure S8). And in our revised manuscript, will confirm with a third method (PCA, see below). We will adjust the main text and the methods section to make these choices clearer.

This might paint a misleading picture where clusters appear well separate and well defined but are in fact much fuzzier, which would impact the conclusion that the phenotypic space is constricted.

The salient question is whether the clusters are so “fuzzy” that they are not meaningful. That interpretation seems unreasonable. Our clusters group mutants with similar genotypes, evolutionary histories, and fitness tradeoffs (Figures 5 - 8). Clustering mutants with similar behaviors is important and useful. It improves phenotypic prediction by revealing which mutants are likely to have at least some phenotypic effects in common. And it also suggests that the phenotypic space is constrained, at least to some degree, which previous work suggests is helpful in predicting evolution (PMID33263280, PMID37437111, PMID22282810, PMID25806684).

(4) The authors make the decision to use UMAP and a gaussian mixed model to cluster and represent the different fitness landscapes of their lineages of interest. Their approach has many caveats. First, compared to PCA, the axis does not provide any information about the actual dissimilarities between clusters. Using PCA would have allowed a better understanding of the amount of variance explained by components that separate clusters, as well as more interpretable components.

The components derived from PCA are often not interpretable. It’s not obvious that each one, or even the first one, will represent some intuitive phenotype, like resistance to fluconazole.

Moreover, we see many non-linearities in our data. For example, fitness in a double drug environment is not predicted by adding up fitness in the relevant single drug environments. Also, there are mutants that have high fitness when fluconazole is absent or abundant, but low fitness when mild concentrations are present. These types of nonlinearities can make the axes in PCA very difficult to interpret, plus these nonlinearities can be missed by PCA, thus we prefer other clustering methods.

We will adjust our revised manuscript to explain these reasons why we chose UMAP and GMM over PCA.

Also, we will include PCA in the supplement of our revised manuscript. Please find below PC1 vs PC2, with points colored according to the cluster assignment in figure 4 (i.e. using a gaussian mixture model). It appears the clusters are largely preserved.

Author response image 1.

Second, the advantages of dimensional reduction are not clear. In the competition experiment, 11/12 conditions (all but the no drug, no DMSO conditions) can be mapped to only three dimensions: concentration of fluconazole, concentration of radicicol, and relative fitness. Each lineage would have its own fitness landscape as defined by the plane formed by relative fitness values in this space, which can then be examined and compared between lineages.

We worry that the idea stems from apriori notions of what the important dimensions should be. It also seems like this would miss important nonlinearities such as our observation that low fluconazole behaves more like a novel selection pressure than a dialed down version of high fluconazole.

Also, we believe the reviewer meant “fitness profile” and not “fitness landscape”. A fitness landscape imagines a walk where every “step” is a mutation. Most lineages in barcoded evolution experiments possess only a single adaptive mutation. A single-step walk is not enough to build a landscape, though others are expanding barcoded evolution experiments beyond the first step (PMID34465770, PMID31723263), so maybe one day this will be possible.

Third, the choice of 7 clusters as the cutoff for the multiple Gaussian model is not well explained. Based on Figure S6A, BIC starts leveling off at 6 clusters, not 7, and going to 8 clusters would provide the same reduction as going from 6 to 7. This choice also appears arbitrary in Figure S6B, where BIC levels off at 9 clusters when only highly abundant lineages are considered.

We agree. We did not rely on the results of BIC alone to make final decisions about how many clusters to include. We thank the reviewer for pointing out this gap in our writing. We will adjust our revised manuscript to explain that we ultimately chose to describe 6 clusters that we were able to validate with follow-up experiments. In figures 5, 6, 7, and 8, we use external information to validate the clusters that we report in figure 4. And in lines 697 – 714, we explain that there are may be additional clusters beyond those we tease apart in this study.

This directly contradicts the statement in the main text that clusters are robust to noise, as more a stringent inclusion threshold appears to increase and not decrease the optimal number of clusters. Additional criteria to BIC could have been used to help choose the optimal number of clusters or even if mixed Gaussian modeling is appropriate for this dataset.

We are under the following impression: If our clustering method was overfitting, i.e. capturing noise, the optimal number of clusters should decrease when we eliminate noise. It increased. In other words, the observation that our clusters did not collapse (i.e. merge) when we removed noise suggests these clusters were not capturing noise.

More generally, our validation experiments, described below, provide additional evidence that our clusters capture meaningful differences between mutants (and not noise).

(5) Large-scale barcode sequencing assays can often be noisy and are generally validated using growth curves or competition assays.

Some types of bar-seq methods, in particular those that look at fold change across two time points, are noisier than others that look at how frequency changes across multiple timepoints (PMID30391162). Here, we use the less noisy method. We also reduce noise by using a stricter coverage threshold than previous work (e.g., PMID33263280), and by excluding batch effects by performing all experiments simultaneously (PMID37237236).

The main assay we use to measure fitness has been previously validated (PMID27594428). No subsequent study using this assay validates using the methods suggested by the reviewer (see PMID37861305, PMID33263280, PMID31611676, PMID29429618, PMID37192196, PMID34465770, PMID33493203).

More to the point, bar-seq has been used, without the reviewer’s suggested validation, to demonstrate that the way some mutant’s fitness changes across environments is different from other mutants (PMID33263280, PMID37861305, PMID31611676, PMID33493203, PMID34596043). This is the same thing that we use bar-seq to demonstrate.

For all of these reasons, we are hesitant to confirm bar-seq itself as a valid way to infer fitness. It seems this is already accepted as a standard in our field.

Having these types of results would help support the accuracy of the main assay in the manuscript and thus better support the claims of the authors.

We don’t agree that fitness measurements obtained from this bar-seq assay generally require validation. But we do agree that it is important to validate whether the mutants in each of our 6 clusters indeed are different from one another in meaningful ways, in particular, in that they have different fitness tradeoffs. We have four figures (5 - 8) and 200 lines of text dedicated to validating whether our clusters capture reproducible and biologically meaningful differences between mutants. Happily, one of these figures (Fig 7) includes growth curves, which are exactly the type of validation experiment asked for by the reviewer.

Below, we walk through the different types of validation experiments that are present in our original manuscript, and additional validation experiments that we plan to include in the revised version. We are hopeful that these validation experiments are sufficient, or at the very least, that this list empowers reviewers to point out where more work is needed.

(1) Mutants from different clusters have different growth curves: In our original manuscript, we measured growth curves corresponding to a fitness tradeoff that we thought was surprising. Mutants in clusters 4 and 5 both have fitness advantages in single drug conditions. While mutants from cluster 4 also are advantageous in the double drug conditions, mutants from cluster 5 are not! We validated these different behaviors by studying growth curves for a mutant from each cluster (Figures 7 and S10).

(2) Mutants from different clusters have different evolutionary origins: In our original manuscript, we came up with a novel way to ask whether the clusters capture different types of adaptive mutants. We asked whether the mutants in each cluster originate from different evolution experiments. Indeed they often do (see pie charts in Figures 6, 7, 8). This method also provides evidence supporting each cluster’s differing fitness tradeoffs.

For example, mutants in cluster 5 appear to have a tradeoff in a double drug condition (described above). They rarely originate from that evolution condition, unlike mutants in nearby cluster 4 (see Figure 7).

(3) Mutants from each cluster often fall into different genes: In our original manuscript, we sequenced many of these mutants and show that mutants in the same gene are often found in the same cluster. For example, all 3 IRA1 mutants are in cluster 6 (Fig 8), both GPB2 mutants are in cluster 4 (Figs 7 & 8), and 35/36 PDR mutants are in either cluster 2 or 3 (Figs 5 & 6).

(4) Mutants from each cluster have behaviors previously observed in the literature: In our original manuscript, we compared our sequencing results to the literature and found congruence. For example, PDR mutants are known to provide a fitness benefit in fluconazole and are found in clusters that have high fitness in fluconazole (lines 457 - 462). Previous work suggests that some mutations to PDR have different tradeoffs than others, which is what we see (lines 540 - 542). IRA1 mutants were previously observed to have high fitness in our “no drug” condition, and are found in the cluster that has the highest fitness in the “no drug” condition (lines 642 - 646). Previous work even confirms the unusual fitness tradeoff we observe where IRA1 and other cluster 6 mutants have low fitness only in low concentrations of fluconazole (lines 652 - 657).

(5) Mutants largely remain in their clusters when we use alternate clustering methods: In our original manuscript, we performed various different reclustering and/or normalization approaches on our data (Fig 6, S5, S7, S8, S9). The clusters of mutants that we observe in figure 4 do not change substantially when we recluster the data. We will add PCA (see above) to these analyses in our revised manuscript.

(6) We will include additional data showing that mutants in different clusters have different evolutionary origins: Cluster 1 is defined by high fitness in low fluconazole that declines with increasing fluconazole (see Fig 4E and Fig 5C). In our revised manuscript, we will show that cluster 1 lineages were overwhelmingly sampled from evolutions conducted in our lowest concentration of fluconazole (see figure panel A below). No other cluster’s evolutionary history shows this pattern (figures 6, 7, and 8).

(7) We will include additional data showing that mutants in different clusters have different growth curves: Cluster 1 lineages are unique in that their fitness advantage is specific to low flu and trades off in higher concentrations of fluconazole. We obtained growth curves for three cluster 1 mutants (2 SUR1 mutants and 1 UPC2 mutant). We compared them to growth curves for three PDR mutants (from clusters 2 and 3). Cluster 1 mutants appear to have the highest growth rates and reach the higher carrying capacity in low fluconazole (see red and green lines in Author response image 2 panel B below). But the cluster 1 mutants are negatively affected by higher concentrations of fluconazole, much more so than the mutants from clusters 2 and 3 (see Author response image 2 panel C below). This is consistent with the different fitness tradeoffs we observe for each cluster (figures 4 and 5). We will include a more detailed version of this analysis and the figures below in our revised manuscript.

Author response image 2.

Validation experiments demonstrate that cluster 1 mutants have uniquely high fitness in only the lowest concentration of fluconazole. (A) The mutant lineages in cluster 1 were largely sampled from evolution experiments performed in low flu. This is not true of other clusters (see pie charts in main manuscript). (B) In low flu (4 𝜇g/ml), Cluster 1 lineages (red/UPC2 and green/SUR1) grow faster and achieve higher density than lineages from clusters 2 and 3 (blue/PDR). This is consistent with barseq measurements demonstrating that cluster 1 mutants have the highest fitness in low flu. (C) Cluster 1 lineages are sensitive to increasing flu concentrations (SUR1 and UPC2 mutants, middle and rightmost graphs). This is apparent in that the gray (8 𝜇g/ml flu) and light blue (32 𝜇g/ml flu) growth curves rise more slowly and reach lower density than the dark blue curves (4 𝜇g/ml flu). But this is not the case for the PDR mutants from clusters 2 and 3 (leftmost graph). These observations are consistent with the bar-seq fitness data presented in the main manuscript (Fig 4E).

With all of these validation efforts combined, we are hopeful that the reviewer is now more convinced that our clusters capture groups of mutants with different fitness tradeoffs (as opposed to noise). We want to conclude by saying that we are grateful to the reviewer for making us think deeply about areas where we can include additional validation efforts as well as areas where we can make our manuscript clearer.

Reviewer #2 (Public Review):

Summary:

Schmidlin & Apodaca et al. aim to distinguish mutants that resist drugs via different mechanisms by examining fitness tradeoffs across hundreds of fluconazole-resistant yeast strains. They barcoded a collection of fluconazole-resistant isolates and evolved them in different environments with a view to having relevance for evolutionary theory, medicine, and genotypephenotype mapping.

Strengths:

There are multiple strengths to this paper, the first of which is pointing out how much work has gone into it; the quality of the experiments (the thought process, the data, the figures) is excellent. Here, the authors seek to induce mutations in multiple environments, which is a really large-scale task. I particularly like the attention paid to isolates with are resistant to low concentrations of FLU. So often these are overlooked in favour of those conferring MIC values >64/128 etc. What was seen is different genotype and fitness profiles. I think there's a wealth of information here that will actually be of interest to more than just the fields mentioned (evolutionary medicine/theory).

We are very grateful for this positive review. This was indeed a lot of work! We are happy that the reviewer noted what we feel is a unique strength of our manuscript: that we survey adaptive isolates across multiple environments, including low drug concentrations.

Weaknesses:

Not picking up low fitness lineages - which the authors discuss and provide a rationale as to why. I can completely see how this has occurred during this research, and whilst it is a shame I do not think this takes away from the findings of this paper. Maybe in the next one!

We thank the reviewer for these words of encouragement and will work towards catching more low fitness lineages in our next project.

In the abstract the authors focus on 'tradeoffs' yet in the discussion they say the purpose of the study is to see how many different mechanisms of FLU resistance may exist (lines 679-680), followed up by "We distinguish mutants that likely act via different mechanisms by identifying those with different fitness tradeoffs across 12 environments". Whilst I do see their point, and this is entirely feasible, I would like a bit more explanation around this (perhaps in the intro) to help lay-readers make this jump. The remainder of my comments on 'weaknesses' are relatively fixable, I think:

We think that phrasing the “jump” as a question might help lay readers get from point A to point B. So, in the introduction of our revised manuscript, we will add a paragraph roughly similar to this one: “If two groups of drug-resistant mutants have different fitness tradeoffs, does it mean that they provide resistance through different underlying mechanisms? Alternatively, it could mean that both provide drug resistance via the same mechanism, but some mutations come with a cost that others don’t pay. However, another way to phrase this alternative is to say that both groups of mutants affect fitness through different suites of mechanisms that are only partially overlapping. And so, by identifying groups of mutants with different fitness tradeoffs, we argue that we will be uncovering sets of mutations that impact fitness through different underlying mechanisms. The ability to do so would be useful for genotype-phenotype mapping endeavors.”

In the introduction I struggle to see how this body of research fits in with the current literature, as the literature cited is a hodge-podge of bacterial and fungal evolution studies, which are very different! So example, the authors state "previous work suggests that mutants with different fitness tradeoffs may affect fitness through different molecular mechanisms" (lines 129-131) and then cite three papers, only one of which is a fungal research output. However, the next sentence focuses solely on literature from fungal research. Citing bacterial work as a foundation is fine, but as you're using yeast for this I think tailoring the introduction more to what is and isn't known in fungi would be more appropriate. It would also be great to then circle back around and mention monotherapy vs combination drug therapy for fungal infections as a rationale for this study. The study seems to be focused on FLU-resistant mutants, which is the first-line drug of choice, but many (yeast) infections have acquired resistance to this and combination therapy is the norm.

In our revised manuscript, we will carefully review all citations. The issue may stem from our attempt to reach two different groups of scientists. We ourselves are broadly interested in the structure of the genotype-phenotype-fitness map (PMID33263280, PMID32804946). Though the 3 papers the reviewer mentions on lines 132 - 133 all pertain to yeast, we cite them because they are studies about the complexity of this map. Their conclusions, in theory, should apply broadly, beyond yeast. Similarly, the reason we cite papers from yeast, as well as bacteria and cancer, is that we believe general conclusions about the genotype-phenotype-fitness map should apply broadly. For example, the sentence the reviewer highlights, “previous work suggests that mutants with different fitness tradeoffs may affect fitness through different molecular mechanisms” is a general observation about the way genotype maps to fitness. So we cited papers from across the tree of life to support this sentence.

On the other hand, because we study drug resistant mutations, we also hope that our work is of use to scientists studying the evolution of resistance. We agree with the reviewer that in this regard, some of our findings may be especially pertinent to the evolution of resistance to antifungal drugs. We will consider this when reviewing the citations in our revised manuscript and add some text to clarify these points.

Methods: Line 769 - which yeast? I haven't even seen mention of which species is being used in this study; different yeast employ different mechanisms of adaptation for resistance, so could greatly impact the results seen. This could help with some background context if the species is mentioned (although I assume S. cerevisiae).

In the revised manuscript, we will make clear that we study S. cerevisiae.

In which case, should aneuploidy be considered as a mechanism? This is mentioned briefly on line 556, but with all the sequencing data acquired this could be checked quickly?

We like this idea and we are working on it, but it is not straightforward. The reviewer is correct in that we can use the sequencing data that we already have. But calling aneuploidy with certainty is tough because its signal can be masked by noise. In other words, some regions of the genome may be sequenced more than others by chance. Given this is not straightforward, at least not for us, this analysis will likely have to wait for a subsequent paper.

I think the authors could be bolder and try and link this to other (pathogenic) yeasts. What are the implications of this work on say, Candida infections?

Perhaps because our background lies in general study of the genotype-phenotype map, we did not want to make bold assertions about how our work might apply to pathogenic yeasts. But we see how this could be helpful and will add some discussion points about this. Specifically, we will discuss which of the genes and mutants we observe are also found in Candida. We will also investigate whether our observation that low fluconazole represents a seemingly unique challenge, not just a milder version of high fluconazole, has any corollary in the Candida literature.

eLife assessment

This study provides valuable new insights into the trade-offs associated with the evolution of drug resistance. The authors use a solid approach to evolve and phenotype hundreds of independent strains. They identify distinct phenotypic clusters based on growth across defined conditions that suggest that tradeoffs are diverse but at the same time could be limited to a few classes based on the underlying resistance mechanisms. The methodologies used align with the current state-of-the-art, and the data and analysis are solid as they broadly support the claims, with only minor weaknesses. This work will interest molecular biologists working on the evolution of new phenotypes and microbiologists studying multi-drug therapy.

Reviewer #1 (Public Review):

Summary:<br /> In their manuscript, Schmidlin, Apodaca, et al try to answer fundamental questions about the evolution of new phenotypes and the trade-offs associated with this process. As a model, they use yeast resistance to two drugs, fluconazole and radicicol. They use barcoded libraries of isogenic yeasts to evolve thousands of strains in 12 different environments. They then measure the fitness of evolved strains in all environments and use these measurements to examine patterns in fitness trade-offs. They identify only six major clusters corresponding to different trade-off profiles, suggesting the vast genotypic landscape of evolved mutants translates to a highly constrained phenotypic space. They sequence over a hundred evolved strains and find that mutations in the same gene can result in different phenotypic profiles.

Overall, the authors deploy innovative methods to scale up experimental evolution experiments, and in many aspects of their approach tried to minimize experimental variation.

Weaknesses:<br /> (1) One of the objectives of the authors is to characterize the extent of phenotypic diversity in terms of resistance trade-offs between fluconazole and radicicol. To minimize noise in the measurement of relative fitness, the authors only included strains with at least 500 barcode counts across all time points in all 12 experimental conditions, resulting in a set of 774 lineages passing this threshold. This corresponds to a very small fraction of the starting set of ~21 000 lineages that were combined after experimental evolution for fitness measurements. As the authors briefly remark, this will bias their datasets for lineages with high fitness in all 12 environments, as all these strains must be fit enough to maintain a high abundance. One of the main observations of the authors is phenotypic space is constrained to a few clusters of roughly similar relative fitness patterns, giving hope that such clusters could be enumerated and considered to design antimicrobial treatment strategies. However, by excluding all lineages that fit in only one or a few environments, they conceal much of the diversity that might exist in terms of trade-offs and set up an inclusion threshold that might present only a small fraction of phenotypic space with characteristics consistent with generalist resistance mechanisms or broadly increased fitness. This has important implications regarding the general conclusions of the authors regarding the evolution of trade-offs.

(2) Most large-scale pooled competition assays using barcodes are usually stopped after ~25 to avoid noise due to the emergence of secondary mutations. The authors measure fitness across ~40 generations, which is almost the same number of generations as in the evolution experiment. This raises the possibility of secondary mutations biasing abundance values, which would not have been detected by the whole genome sequencing as it was performed before the competition assay.

(3) The approach used by the authors to identify and visualize clusters of phenotypes among lineages does not seem to consider the uncertainty in the measurement of their relative fitness. As can be seen from Figure S4, the inter-replicate difference in measured fitness can often be quite large. From these graphs, it is also possible to see that some of the fitness measurements do not correlate linearly (ex.: Med Flu, Hi Rad Low Flu), meaning that taking the average of both replicates might not be the best approach. Because the clustering approach used does not seem to take this variability into account, it becomes difficult to evaluate the strength of the clustering, especially because the UMAP projection does not include any representation of uncertainty around the position of lineages. This might paint a misleading picture where clusters appear well separate and well defined but are in fact much fuzzier, which would impact the conclusion that the phenotypic space is constricted.

(4) The authors make the decision to use UMAP and a gaussian mixed model to cluster and represent the different fitness landscapes of their lineages of interest. Their approach has many caveats. First, compared to PCA, the axis does not provide any information about the actual dissimilarities between clusters. Using PCA would have allowed a better understanding of the amount of variance explained by components that separate clusters, as well as more interpretable components. Second, the advantages of dimensional reduction are not clear. In the competition experiment, 11/12 conditions (all but the no drug, no DMSO conditions) can be mapped to only three dimensions: concentration of fluconazole, concentration of radicicol, and relative fitness. Each lineage would have its own fitness landscape as defined by the plane formed by relative fitness values in this space, which can then be examined and compared between lineages. Third, the choice of 7 clusters as the cutoff for the multiple Gaussian model is not well explained. Based on Figure S6A, BIC starts leveling off at 6 clusters, not 7, and going to 8 clusters would provide the same reduction as going from 6 to 7. This choice also appears arbitrary in Figure S6B, where BIC levels off at 9 clusters when only highly abundant lineages are considered. This directly contradicts the statement in the main text that clusters are robust to noise, as more a stringent inclusion threshold appears to increase and not decrease the optimal number of clusters. Additional criteria to BIC could have been used to help choose the optimal number of clusters or even if mixed Gaussian modeling is appropriate for this dataset.

(5) Large-scale barcode sequencing assays can often be noisy and are generally validated using growth curves or competition assays. Having these types of results would help support the accuracy of the main assay in the manuscript and thus better support the claims of the authors.

Reviewer #2 (Public Review):

Summary:<br /> Schmidlin & Apodaca et al. aim to distinguish mutants that resist drugs via different mechanisms by examining fitness tradeoffs across hundreds of fluconazole-resistant yeast strains. They barcoded a collection of fluconazole-resistant isolates and evolved them in different environments with a view to having relevance for evolutionary theory, medicine, and genotype-phenotype mapping.

Strengths:<br /> There are multiple strengths to this paper, the first of which is pointing out how much work has gone into it; the quality of the experiments (the thought process, the data, the figures) is excellent. Here, the authors seek to induce mutations in multiple environments, which is a really large-scale task. I particularly like the attention paid to isolates with are resistant to low concentrations of FLU. So often these are overlooked in favour of those conferring MIC values >64/128 etc. What was seen is different genotype and fitness profiles. I think there's a wealth of information here that will actually be of interest to more than just the fields mentioned (evolutionary medicine/theory).

Weaknesses:<br /> Not picking up low fitness lineages - which the authors discuss and provide a rationale as to why. I can completely see how this has occurred during this research, and whilst it is a shame I do not think this takes away from the findings of this paper. Maybe in the next one!

In the abstract the authors focus on 'tradeoffs' yet in the discussion they say the purpose of the study is to see how many different mechanisms of FLU resistance may exist (lines 679-680), followed up by "We distinguish mutants that likely act via different mechanisms by identifying those with different fitness tradeoffs across 12 environments". Whilst I do see their point, and this is entirely feasible, I would like a bit more explanation around this (perhaps in the intro) to help lay-readers make this jump. The remainder of my comments on 'weaknesses' are relatively fixable, I think:

In the introduction I struggle to see how this body of research fits in with the current literature, as the literature cited is a hodge-podge of bacterial and fungal evolution studies, which are very different! So example, the authors state "previous work suggests that mutants with different fitness tradeoffs may affect fitness through different molecular mechanisms" (lines 129-131) and then cite three papers, only one of which is a fungal research output. However, the next sentence focuses solely on literature from fungal research. Citing bacterial work as a foundation is fine, but as you're using yeast for this I think tailoring the introduction more to what is and isn't known in fungi would be more appropriate. It would also be great to then circle back around and mention monotherapy vs combination drug therapy for fungal infections as a rationale for this study. The study seems to be focused on FLU-resistant mutants, which is the first-line drug of choice, but many (yeast) infections have acquired resistance to this and combination therapy is the norm.

Methods: Line 769 - which yeast? I haven't even seen mention of which species is being used in this study; different yeast employ different mechanisms of adaptation for resistance, so could greatly impact the results seen. This could help with some background context if the species is mentioned (although I assume S. cerevisiae). In which case, should aneuploidy be considered as a mechanism? This is mentioned briefly on line 556, but with all the sequencing data acquired this could be checked quickly?

I think the authors could be bolder and try and link this to other (pathogenic) yeasts. What are the implications of this work on say, Candida infections?

Author Response

We would like to thank the reviewers for their thoughtful feedback on our work. One important point that they bring up is a potential issue with our method for accounting for excess NCO events that are detected due to increased marker resolution in the introgressed regions. The method we chose was to simulate average sized NCO tracts over both introgressed and non-introgressed windows to determine the expected increase in NCO detection due to marker density. We then took that expected increase and used it to correct our per-window NCO counts in all windows. We used these corrections for all results and analysis involving genomic windows (maps and genomewide comparisons) but did not include them when focusing on introgression-specific characteristics (e.g. analyzing fine-scale sequence differences around NCO tracts in introgressed regions). We chose this method based on previous work in the field and after some additional analyses on our own data that we did not include in the final manuscript. We will attempt to better communicate our decision making process and include some of the exploratory results that guided us in our revised manuscript. We look forward to responding to all comments and highlighting additional aspects of our findings that we think are of interest to the evolution and recombination communities, including significant changes to the recombination landscape between closely related strains and the impact of introgression on allelic shuffling.

eLife assessment

The authors provide useful information, confirming previous observations that heterologous sequecnes affect crossing-over frequency. Surprisingly, they conclude that heterozygous introgressed regions, with greater levels of heterology, have greater noncrossover levels than non-introgressed regions with much lower levels of heterology. As the evidence for this conclusion is incomplete and potentially biased, the significance of these findings relative to previous knowledge in the field remains to be determined.

Reviewer #1 (Public Review):

Summary:

The authors investigated how the presence of interspecific introgressions in the genome affects the recombination landscape. This research was intended to inform about genetic phenomena influencing the evolution of introgressed regions, although it should be noted that the research itself is based on examining only one generation, which limits the possibility of drawing far-reaching evolutionary conclusions. In this work, yeast hybrids with large (from several to several dozen percent of the chromosome length) introgressions from another yeast species were crossed. Then, the products of meiosis were isolated and sequenced, and on this basis, the genome-wide distribution of both crossovers (COs) and noncrossovers (NCOs) was examined. Carrying out the analysis at different levels of resolution, it was found that in the regions of introduction, there is a very significant reduction in the frequency of COs and a simultaneous increase in the frequency of NCOs. Moreover, it was confirmed that introgressions significantly limit the local shuffling of genetic information, and NCOs are only able to slightly contribute to the shuffling, thus they do not compensate for the loss of CO recombination.

Strengths:

- Previously, experiments examining the impact of SNP polymorphism on meiotic recombination were conducted either on the scale of single hotspots or the entire hybrid genome, but the impact of large introgressed regions from another species was not examined. Therefore, the strength of this work is its interesting research setup, which allows for providing data from a different perspective.

- Good quality genome-wide data on the distribution of CO and NCO were obtained, which could be related to local changes in the level of polymorphism.

Weaknesses:

- The research is based on examining only one generation, which limits the possibility of drawing far-reaching evolutionary conclusions. Moreover, meiosis is stimulated in hybrids in which introgressions occur in a heterozygous state, which is a very unlikely situation in nature. Therefore, I see the main value of the work in providing information on the CO/NCO decision in regions with high sequence diversification, but not in the context of evolution.

- The work requires greater care in preparing informative figures and, more importantly, re-analysis of some of the data (see comments below).

More specific comments:

- The authors themselves admit that the detection of NCO, due to the short size of conversion tracts, depends on the density of SNPs in a given region. Consequently, more NCOs will be detected in introgressed regions with a high density of polymorphisms compared to the rest of the genome. To investigate what impact this has on the analysis, the authors should demonstrate that the efficiency of detecting NCOs in introgressed regions is not significantly higher than the efficiency of detecting NCOs in the rest of the genome. If it turns out that this impact is significant, analyses should be presented proving that it does not entirely explain the increase in the frequency of NCOs in introgressed regions.

- CO and NCO analyses performed separately for individual regions rarely show statistical significance (Figures 3 and 4). I think that the authors, after dividing the introgressed regions into non-overlapping windows of 100 bp (I suggest also trying 200 bp, 500 bp, and 1kb windows), should combine the data for all regions and perform correlations to SNP density in each window for the whole set of data. Such an analysis has a greater chance of demonstrating statistically significant relationships. This could replace the analysis presented in Figure 3 (which can be moved to Supplement). Moreover, the analysis should also take into account indels.

- In Arabidopsis, it has been shown that crossover is stimulated in heterozygous regions that are adjacent to homozygous regions on the same chromosome (http://dx.doi.org/10.7554/eLife.03708.001, https://doi.org/10.1038/s41467- 022-35722-3). This effect applies only to class I crossovers, and is reversed for class II crossovers (https://doi.org/10.15252/embj.2020104858, https://doi.org/10.1038/s41467-023-42511-z). This research system is very similar to the system used by the authors, although it likely differs in the level of DNA sequence divergence. The authors could discuss their work in this context.

Reviewer #2 (Public Review):

Summary:

Schwartzkopf et al characterized the meiotic recombination impact of highly heterozygous introgressed regions within the budding yeast Saccharomyces uvarum, a close relative of the canonical model Saccharomyces cerevisiae. To do so, they took advantage of the naturally occurring Saccharomyces bayanus introgressions specifically within fermentation isolates of S. uvarum and compared their behavior to the syntenic regions of a cross between natural isolates that do not contain such introgressions. Analysis of crossover (CO) and noncrossover (NCO) recombination events shows both a depletion in CO frequency within highly heterozygous introgressed regions and an increase in NCO frequency. These results strongly support the hypothesis that DNA sequence polymorphism inhibits CO formation, and has no or much weaker effects on NCO formation. Eventually, the authors show that the presence of introgressions negatively impacts "r", the parameter that reflects the probability that a randomly chosen pair of loci shuffles their alleles in a gamete.

The authors chose a sound experimental setup that allowed them to directly compare recombination properties of orthologous syntenic regions in an otherwise intra-specific genetic background. The way the analyses have been performed looks right, although this reviewer is unable to judge the relevance of the statistical tests used. Eventually, most of their results which are elegant and of interest to the community are present in Figure 2.

Strengths:

Analysis of crossover (CO) and noncrossover (NCO) recombination events is compelling in showing both a depletion in CO frequency within highly heterozygous introgressed regions and an increase in NCO frequency.

Weaknesses:

The main weaknesses refer to a few text issues and a lack of discussion about the mechanistic implications of the present findings.

- Introduction

The introduction is rather long. I suggest specifically referring to "meiotic" recombination (line 71) and to "meiotic" DSBs (line 73) since recombination can occur outside of meiosis (ie somatic cells).

From lines 79 to 87: the description of recombination is unnecessarily complex and confusing. I suggest the authors simply remind that DSB repair through homologous recombination is inherently associated with a gene conversion tract (primarily as a result of the repair of heteroduplex DNA by the mismatch repair (MMR) machinery) that can be associated or not to a crossover. The former recombination product is a crossover (CO), the latter product is a noncrossover (NCO) or gene conversion. Limited markers may prevent the detection of gene conversions, which erase NCO but do not affect CO detection.

In addition, "resolution" in the recombination field refers to the processing of a double Holliday junction containing intermediates by structure-specific nucleases. To avoid any confusion, I suggest avoiding using "resolution" and simply sticking with "DSB repair" all along the text.

Note that there are several studies about S. cerevisiae meiotic recombination landscapes using different hybrids that show different CO counts. In the introduction, the authors refer to Mancera et al 2008, a reference paper in the field. In this paper, the hybrid used showed ca. 90 CO per meiosis, while their reference to Liu et al 2018 in Figure 2 shows less than 80 COs per meiosis for S. cerevisiae. This shows that it is not easy to come up with a definitive CO count per meiosis in a given species. This needs to be taken into account for the result section line 315-321.

In line 104, the authors refer to S. paradoxus and mention that its recombination rate is significantly different from that of S. cerevisiae. This is inaccurate since this paper claims that the CO landscape is even more conserved than the DSB landscape between these two species, and they even identify a strong role played by the subtelomeric regions. So, the discussion about this paper cannot stand as it is.

Line 150, when the authors refer to the anti-recombinogenic activity of the MMR, I suggest referring to the published work from Martini et al 2011 rather than the not-yet-published work from Copper et al 2021, or both, if needed.

Results

The clear depletion in CO and the concomitant increase in NCO within the introgressed regions strongly suggest that DNA sequence polymorphism triggers CO inhibition but does not affect NCO or to a much lower extent. Because most CO likely arises from the ZMM pathway (CO interference pathway mainly relying on Zip1, 2, 3, 4, Spo16, Msh4, 5, and Mer3) in S. uvarum as in S. cerevisiae, and because the effect of sequence polymorphism is likely mediated by the MMR machinery, this would imply that MMR specifically inhibits the ZMM pathway at some point in S. uvarum.

The weak effect or potential absence of the effect of sequence polymorphism on NCO formation suggests that heteroduplex DNA tracts, at least the way they form during NCO formation, escape the anti-recombinogenic effect of MMR in S. uvarum. A few comments about this could be added.

The same applies to the fact that the CO number is lower in the natural cross compared to the fermentation cross, while the NCO number is the same. This suggests that under similar initiating Spo11-DSB numbers in both crosses, the decrease in CO is likely compensated by a similar increase in inter-sister recombination.

Introgressions represent only 10% of the genome, while the decrease in CO is at least 20%. This is a bit surprising especially in light of CO regulation mechanisms such as CO homeostasis that tends to keep CO constant. Could the authors comment on that?

Finally, the frequency of NCOs in introgressed regions is about twice the frequency of CO in non-introgressed regions. Both CO and NCO result from Spo11-initiating DSBs. This suggests that more Spo11-DSBs are formed within introgressed regions and that such DSBs specifically give rise to NCO. Could this be related to the lack of homolog engagement which in turn shuts down Spo11-DSB formation as observed in ZMM mutants by the Keeney lab? Could this simply result from better detection of NCO in introgressed regions related to the increased marker density, although the authors claim that NCO counts are corrected for marker resolution?

What could be the explanation for chromosome 12 to have more shuffling in the natural cross compared to the fermentation cross which is deprived of the introgressed region?

Technical points:

- In line 248, the authors removed NCO with fewer than three associated markers.<br /> What is the rationale for this? Is the genotyping strategy not reliable enough to consider events with only one or two markers? NCO events can be rather small and even escape detection due to low local marker density.

- Line 270: The way homology is calculated looks odd to this reviewer, especially the meaning of 0.5 homology. A site is either identical (1 homology) or not (0 homology).

- Line 365: beware that the estimates are for mitotic mismatch repair (MMR). Meiotic MMR may work differently.

- Figure 1: there is no mention of potential 4:0 segregations. Did the authors find no such pattern? If not, how did they consider them?

Reviewer #3 (Public Review):

When members of two related but diverged species mate, the resulting hybrids can produce offspring where parts of one species' genome replace those of the other. These "introgressions" often create regions with a much greater density of sequence differences than are normally found between members of the same species. Previous studies have shown that increased sequence differences, when heterozygous, can reduce recombination during meiosis specifically in the region of increased difference. However, most of these studies have focused on crossover recombination, and have not measured noncrossovers. The current study uses a pair of Saccharomyces uvarum crosses: one between two natural isolates that, while exhibiting some divergence, do not contain introgressions; the other is between two fermentation strains that, when combined, are heterozygous for 9 large regions of introgression that have much greater divergence than the rest of the genome. The authors wished to determine if introgressions differently affected crossovers and noncrossovers, and, if so, what impact that would have on the gene shuffling that occurs during meiosis.

While both crossovers and noncrossovers were measured, assessing the true impact of increased heterology (inherent in heterozygous introgressions) is complicated by the fact that the increased marker density in heterozygous introgressions also increases the ability to detect noncrossovers. The authors used a relatively simple correction aimed at compensating for this difference, and based on that correction, conclude that, while as expected crossovers are decreased by increased sequence heterology, counter to expectations noncrossovers are substantially increased. They then show that, despite this, genetic shuffling overall is substantially reduced in regions of heterozygous introgression. However, it is likely that the correction used to compensate for the effect of increased sequence density is defective, and has not fully compensated for the ascertainment bias due to greater marker density. The simplest indication of this potential artifact is that, when crossover frequencies and "corrected" noncrossover frequencies are taken together, regions of introgression often appear to have greater levels of total recombination than flanking regions with much lower levels of heterology. This concern seriously undercuts virtually all of the novel conclusions of the study.

Until this methodological concern is addressed, the work will not be a useful contribution to the field.

eLife assessment

This study addresses the long-standing question as to how different functional pools of synaptic vesicles are organized in presynaptic terminals to mediate different modes of neurotransmitter release. Based on imaging of active synapses with recycling synaptic vesicles labeled by FM-styryl dyes, the authors provide data that are compatible with the hypothesis that two separate reserve pools of vesicles – slowly vs. rapidly mobilizing – feed two distinct releasable pools – reluctantly vs. rapidly releasing. Overall, this study represents a valuable contribution to the field of synapse biology, specifically to presynaptic dynamics and plasticity. The authors' methodological approach of using bulk FM-styryl dye destaining as a readout of precise vesicle arrangements and pools in a population of functionally very diverse synapses has limitations. Consequently, the evidence that directly supports the authors' two-pool-interpretation of their data is incomplete, and alternative interpretations of the data remain possible.

eLife assessment

This important paper sheds light on the role of expectations in perceptual decision-making. Sophisticated analyses of human EEG data provide convincing evidence that both motor preparation and sensory processing were affected by expectations, albeit with different time courses. These findings will be of interest to scientists interested in perception and decision-making.

Author Response

Reviewer #1 (Public Review):

Summary:

The study investigates the role of cylicin-1 (CYLC1) in sperm acrosome-nucleus connections and its clinical relevance to male infertility. Using mouse models, the researchers demonstrate that cylicin-1 is specifically expressed in the post acrosomal sheath-like region in spermatids and plays a crucial role in mediating acrosome-nucleus connections. Loss of CYLC1 results in severe male subfertility, characterized by acrosome detachment and aberrant head morphology in sperm. Further analysis of a large cohort of infertile men reveals CYLC1 variants in patients with sperm head deformities. The study provides valuable insights into the role of CYLC1 in male fertility and proposes CYLC1 variants as potential risk factors for human male infertility, emphasizing the importance of mouse models in understanding the pathogenicity of such variants.

We appreciate the comprehensive summary of reviewer 1.

Strengths:

This article demonstrates notable strengths in various aspects. Firstly, the clarity and excellent writing style contribute to the accessibility of the content. Secondly, the employed techniques are not only relevant but also complementary, enhancing the robustness of the study. The precision in their experimental design and the meticulous interpretation of results reflect the scientific rigor maintained throughout the study. Furthermore, the decision to create a second mouse model with the exact CYLC1 mutation found in humans adds significant qualitative value to the research. This approach not only validates the clinical relevance of the identified variant but also strengthens the translational impact of the findings.

We appreciate the positive comment of reviewer 1.

Weaknesses:

There are no obvious weaknesses. While a few minor refinements, as suggested in the recommendations to authors, could enhance the overall support for the data and the authors' messages, these suggested improvements in no way diminish the robustness of the already presented data.

In the recommendation for the authors, reviewer 1 mentioned a recent study (Schneider et al., eLife, 2023) showing that Cylc1-KO mice exhibits a reduced sperm count, an observation not noted in our current study. We would like to comment that that main and most important phenotype of Cylc1-KO mice in both studies is quite similar, including male subfertility and abnormal head morphology. We think the different targeting strategy and mouse strain may cause this discrepancy. In Schneider’s and our current studies, the total motility abnormality of Cylc1-KO mice are not observed. We appreciate the suggestion of reviewer 1 to further examine the detailed parameters of motility such as VCL, VSL, and ALH. Given that the head deformation is the most obvious phenotype of Cylc1-KO mice and the focus of our study, we feel sorry that this detailed analysis of sperm motility was not performed in the current stage. Reviewer 1 also asked whether Cylc1-KO female mice are fertile or not. Given that Cylc1 is an X chromosome gene and Cylc1-KO (Cylc1-/Y) mice are severely subfertile, we do not obtain enough Cylc1-KO female mice to examine their fecundity. We also would like to thank reviewer 1 to point out several inaccurate descriptions.

Reviewer #2 (Public Review):

Summary:

To verify the function of PT-associated protein CYLC1, the authors generated a Cylc1-KO mouse model and revealed that loss of cylicin-1 leads to severe male subfertility as a result of sperm head deformities and acrosome detachment. Then they also identified a CYLC1 variant by WES analysis from 19 infertile males with sperm head deformities. To prove the pathogenicity of the identified mutation site, they further generated Cylc1-mutant mice that carried a single amino acid change equivalent to the variant in human CYLC1. The Cylc1-mutant mice also exhibited male subfertility with detached acrosomes of sperm cells.

We appreciate the comprehensive summary of reviewer 2.

Strengths:

The phenotypes observed in the Cylc1-KO mice provide strong evidence for the function of CYLC1 as a PT-associated protein in spermatogenesis and male infertility. Further mechanistic studies indicate that loss of cylicin-1 in mice may disrupt the connections between the inner acrosomal membrane and acroplaxome, leading to detached acrosomes of sperm cells.

We appreciate the positive comment of reviewer 2.

Weaknesses:

The authors identified a missense mutation (c.1377G>T/p. K459N) from 19 infertile males with sperm head deformities. The information for the variant in Table 1 is insufficient to determine the pathogenicity and reliability of the mutation site. More information should be added, including all individuals in gnomAD, East Asians in gnomAD, 1000 Genomes Project for allele frequency in the human population; MutationTaster, M-CAP, FATHMM, and more other tools for function prediction. Then, the expression of CYLC1 in the spermatozoa from men with CYLC1 mutation should be explored by qPCR, Western blot, or IF staining analyses. Although 19 infertile males were found carrying the same missense mutation (c.1377G>T/p. K459N), their phenotypes are somewhat different. For example, sperm concentrations for individuals AAX765, BBA344, and 3086 are extremely low but this is not observed in other infertile males. Then, progressive motility for individuals AAT812, 3165, 3172, 3203, and 3209 are extremely low but this is also not observed in other infertile males. It is worth considering why different phenotypes are observed in probands carrying the same mutation.

We appreciate the suggestion of reviewer 2. First, Table 1 shows the information of the variant identified in CYLC1 gene, including allele frequency in gnomAD and functional prediction by SIFT, PolyPhen-2, and CADD. Given that mutant mice is a gold standard to confirm the pathogenicity of a variant, we generate Cylc1-mutant mice and Cylc1-mutant mice exhibit male subfertility with sperm acrosome detachment. The animal evidence is much more solid than bioinformatics prediction to confirm the pathogenicity of the identified variant in the CYLC1 gene. Second, the expression of CYLC1 in the spermatozoa from patients have been examined by IF staining (Fig. 5B). Unfortunately, the patients declined to continue in the project to donate more semen for qPCR and Western blot analyses. Third, the reviewer 2 asks why not all patients with CYLC1 gene mutation show the identical phenotype. Although some patients exhibit low sperm count or reduced motility, sperm head deformities are the shared phenotype of 19 patients. Many factors, such as way of life, may affect sperm quality. Perfectly identical phenotype of all 19 patients carrying the CYLC1 mutation is idealistic and will not always happen in clinical diagnosis. We also appreciate other suggestions from reviewer 2.

eLife assessment

Spermiogenesis is a complex process allowing the emergence of specific sperm organelle, including the acrosome, a sperm giant vesicle of secretion. This important study reports the key role of Cylicin-1 in acrosome biogenesis and identifies the molecular partners necessary for acrosome anchoring. The compelling demonstration is based on infertile patient samples and two animal models. Overall, this provides results that will be invaluable to the male reproduction community, including scientists and andrologists.

Reviewer #1 (Public Review):

Summary:

The study investigates the role of cylicin-1 (CYLC1) in sperm acrosome-nucleus connections and its clinical relevance to male infertility. Using mouse models, the researchers demonstrate that cylicin-1 is specifically expressed in the post acrosomal sheath-like region in spermatids and plays a crucial role in mediating acrosome-nucleus connections. Loss of CYLC1 results in severe male subfertility, characterized by acrosome detachment and aberrant head morphology in sperm. Further analysis of a large cohort of infertile men reveals CYLC1 variants in patients with sperm head deformities. The study provides valuable insights into the role of CYLC1 in male fertility and proposes CYLC1 variants as potential risk factors for human male infertility, emphasizing the importance of mouse models in understanding the pathogenicity of such variants.

Strengths:

This article demonstrates notable strengths in various aspects. Firstly, the clarity and excellent writing style contribute to the accessibility of the content. Secondly, the employed techniques are not only relevant but also complementary, enhancing the robustness of the study. The precision in their experimental design and the meticulous interpretation of results reflect the scientific rigor maintained throughout the study. Furthermore, the decision to create a second mouse model with the exact CYLC1 mutation found in humans adds significant qualitative value to the research. This approach not only validates the clinical relevance of the identified variant but also strengthens the translational impact of the findings.

Weaknesses:

There are no obvious weaknesses. While a few minor refinements, as suggested in the recommendations to authors, could enhance the overall support for the data and the authors' messages, these suggested improvements in no way diminish the robustness of the already presented data.

Reviewer #2 (Public Review):

Summary:

* To verify the function of PT-associated protein CYLC1, the authors generated a Cylc1-KO mouse model and revealed that loss of cylicin-1 leads to severe male subfertility as a result of sperm head deformities and acrosome detachment.

* Then they also identified a CYLC1 variant by WES analysis from 19 infertile males with sperm head deformities.

* To prove the pathogenicity of the identified mutation site, they further generated Cylc1-mutant mice that carried a single amino acid change equivalent to the variant in human CYLC1. The Cylc1-mutant mice also exhibited male subfertility with detached acrosomes of sperm cells.

Strengths:

* The phenotypes observed in the Cylc1-KO mice provide strong evidence for the function of CYLC1 as a PT-associated protein in spermatogenesis and male infertility.

* Further mechanistic studies indicate that loss of cylicin-1 in mice may disrupt the connections between the inner acrosomal membrane and acroplaxome, leading to detached acrosomes of sperm cells.

Weaknesses:

* The authors identified a missense mutation (c.1377G>T/p. K459N) from 19 infertile males with sperm head deformities. The information for the variant in Table 1 is insufficient to determine the pathogenicity and reliability of the mutation site. More information should be added, including all individuals in gnomAD, East Asians in gnomAD, 1000 Genomes Project for allele frequency in the human population; MutationTaster, M-CAP, FATHMM, and more other tools for function prediction. Then, the expression of CYLC1 in the spermatozoa from men with CYLC1 mutation should be explored by qPCR, Western blot, or IF staining analyses.

* Although 19 infertile males were found carrying the same missense mutation (c.1377G>T/p. K459N), their phenotypes are somewhat different. For example, sperm concentrations for individuals AAX765, BBA344, and 3086 are extremely low but this is not observed in other infertile males. Then, progressive motility for individuals AAT812, 3165, 3172, 3203, and 3209 are extremely low but this is also not observed in other infertile males. It is worth considering why different phenotypes are observed in probands carrying the same mutation.

Reviewer #1 (Public Review):

In this work, the authors provide a comprehensive description of transcriptional regulation in Pseudomonas syringae by investigating the binding characteristics of various transcription factors. They uncover the hierarchical network structure of the transcriptome by identifying top-, middle-, and bottom-level transcription factors that govern the flow of information in the network. Additionally, they assess the functional variability and conservation of transcription factors across different strains of P. syringae by studying DNA-binding characteristics. These findings notably expand our current knowledge of the P. syringae transcriptome.

The findings associated with crosstalk between transcription factors and pathways, and the diversity of transcription factor functions across strains provide valuable insights into the transcriptional regulatory network of P. syringae. However, these results are at times underwhelming as their significance is unclear. This study would benefit from a discussion of the implications of transcription factor crosstalk on the functioning of the organism as a whole. Additionally, the implications of variability in transcription factor functions on the phenotype of the strains studied would further this analysis.

Overall, this manuscript serves as a key resource for researchers studying the transcriptional regulatory network of P. syringae.

eLife assessment

This work advances our understanding of transcriptional regulation of virulence and metabolic pathways in plant pathogenic bacteria. Solid evidence for the claims is provided by computational analysis of the newly generated data on the genome-wide binding of 170 transcription factors to their target genes, together with experimental validation of the biological functions of some of these transcription factors. The findings and resources from this study will be valuable to researchers in the fields of systems biology, bacteriology, and plant-microbe interactions.

Reviewer #2 (Public Review):

Summary:

The phytopathogenic bacterium Pseudomonas syringae is comprised of many pathovars with different host plant species and has been used as a model organism to study bacterial pathogenesis in plants. Transcriptional regulation is key to plant infection and adaptation to host environments by this bacterium. However, researchers have focused on a limited number of transcription factors (TFs) that regulate virulence-related pathways. Thus, a comprehensive, systems-level understanding of regulatory interactions between transcription factors in P. syringae has not been achieved.

This study by Sun et al performed ChIP-seq analysis of 170 out of 301 TFs in P. syringae pv. syringae 1448A and used this unique dataset to infer transcriptional regulatory networks in this bacterium. The network analyses revealed hierarchical interactions between TFs, various network motifs, and co-regulation of target genes by TF pairs, which collectively mediate information flow. As discussed, the structure and properties of the P. syringae transcriptional regulatory networks are somewhat different from those identified in humans, yeast, and E. coli, highlighting the significance of this study. Further, the authors made use of the P. syringae transcriptional regulatory networks to find TFs of unknown functions to be involved in virulence-related pathways. For some of these TFs, their target specificity and biological functions, such as motility and biofilm formation, were experimentally validated. Of particular interest is the finding that despite conservation of TFs between P. syringae pv. syringae 1448A, P. syringae pv. tomato DC3000, P. syringae pv. syringae B728a, and P. syringae pv. actinidiae C48, some of the conserved TFs show different repertoires of target genes in these four P. syringae strains.

Strengths:

This study presents a systems-level analysis of transcriptional regulatory networks in relation to P. syringae virulence and metabolism, and highlights differences in transcriptional regulatory landscapes of conserved TFs between different P. syringae strains, and develops a user-friendly database for mining the ChIP-seq data generated in this study. These findings and resources will be valuable to researchers in the fields of systems biology, bacteriology, and plant-microbe interactions.

Weaknesses:

No major weaknesses were found, but some of the results may need to be interpreted with caution. ChIP-seq was performed with bacterial strains overexpressing TFs. This may cause artificial binding of TFs to promoters which may not occur when TFs are expressed at physiological levels. Another caution is applied to the interpretation of the biological functions of TFs. The biological roles of the tested TFs are based on in vitro experiments. Thus, functional relevance of the tested TFs during plant infection and/or survival under natural environmental conditions remains to be demonstrated.

Reviewer #3 (Public Review):

Summary:

This study aims to understand gene regulation of the plant bacterial pathogen Pseudomonas syringae. Although the function of some TFs has been characterized in this strain, a global picture of the gene regulatory network remains elusive. The authors conducted a large-scale ChIP-seq analysis, covering 170 out of 301 TFs of this strain, and revealed gene regulatory hierarchy with functional validation of some previously uncharacterized TFs.

Strengths:

- This study provides one of the largest ChIP-seq datasets for a single bacterial strain, covering more than half of its TFs. This impressive resource enabled comprehensive systems-level analysis of the TF hierarchy.

- This study identified novel gene regulation and function with validations through biochemical and genetic experiments.

- The authors attempted on broad analyses including comparisons between different bacterial strains, providing further insights into the diversity and conservation of gene regulatory mechanisms.

Weaknesses:

(1) Some conclusions are not backed by quantitative or statistical analyses, and they are sometimes overinterpreted.

(2) Some figures and analyses are not well explained, and I was not able to understand them.

(3) The Method section lacks depth, especially in data analyses. It is strongly recommended that the authors share their analysis codes so that others can reproduce the analyses.

Author Response

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

Reviewer 1

Major points:

R1C1: I appreciate that the data are aligned, in some points, with related studies of this niche. However, it would help the reader to have this alignment explored more extensively in the Discussion as well.

Answer: We acknowledge that the discussion would benefit from additional comparisons to the available datasets. We thus add the following comment after the first paragraph of the discussion: “Previous studies of the different sub-populations of SVZ progenitors were carried out using transcriptomic approaches based on the expression of various more or less specific markers. These approaches have made it possible to identify quiescent and activated neural stem cells as well as mature neuroblasts, but have been faced with the strong influence of the cell cycle on cell clustering. Indeed, neural progenitors in these studies cycling have been gathered in either “mitotic” clusters (Llorens et al. 2015, Zywitza et al. 2018, Cebrian et al. 2021) or “neural progenitor cells” clusters (Dulken et al. 2017) that had no clear biological significance and hindering identification of subtypes of SVZ cycling progenitors. Our study, combining, for the first time, characterization of Facs-isolated cells and an irradiation-based model of sequential regeneration, allowed to clearly distinguish the molecular profiles of TAP and iNB among cycling progenitors reflecting differences in their in vitro and in vivo respective potentials”.

R1C2: The data on multilineage differentiation, both in culture and upon engraftment, would be greatly strengthened by quantification. What is the relative yield of TUJ1/DCX-positive cells versus the other marker combinations? Specifically regarding the multilineage differentiation in vitro - because different media conditions are used to generate each lineage, it may be difficult to determine relative yield. Could a differentiation system that allows production of all 3 lineages be used instead?

If the fraction of non-DCX/TUJ1-labeled progeny is low, particularly in vivo, this might suggest that while multilineage differentiation is possible, it is a much less likely cellular state outcome than production of mature neuroblasts. Some suggested references with examples of the culture conditions, experimental conditions, and discussions highlighted in the public review: Culture conditions that allow simultaneous trilineage differentiation. PMID: 17615304 Influence of culture conditions on potency: similar to issues covered in PMID: 21549325.

Answer: We agree with the reviewer that quantification of a multilineage differentiation in vitro would improve the characterization of the relative potencies of the different SVZ progenitor.

According to PMID: 17615304 and PMID: 21549325, and in agreement with our own experience, the only culture condition that allows neurosphere-derived neural progenitors to differentiate in vitro into the three lineages is the removal of mitogens from the culture medium. However, this does not work on freshly isolated SVZ cells, which remain in an undifferentiated state in this condition.

This is why we chose to use specific differentiation media for each of the 3 lineages as in Figure 1C. It is also for this reason that we performed as many experiments as possible in vivo rather than in vitro as in Figure S2. In the new version, we have added a quantitative analysis of stainings by antibodies against GFAP, CNPase or DCX of GFP-positive cells persisting at IS, where high number of grafted cells were found in Figure S2B. This was performed by using the NIS software measuring eGFP-, GFAP-, CNPase- and DCX-positive areas. The intersection between each marker and eGFP areas was then determined as a percentage of staining (Figure S2C). The results showed that approximately one third of GFP+ cells expressed GFAP or DCX. The quantitative analysis of CNPase expression was complicated by CNPase-positive host cells, but the stronger CNPase staining in eGFP-positive areas clearly revealed the expression of CNPase by a significant proportion of eGFP-positive cells.

R1C3: Additionally, for claims similar to what is currently made in the text, it would be extremely valuable to confirm the purity of the sort for each population - for example by fixing and staining the sorted fraction with additional antibodies that confirm cell identity.

Answer: We have previously shown in Daynac et al. 2013 that s-iNB expressed the neuroblast markers CD24 and DCX, but also markers of neural progenitors such as Mash1, a basic helix-loop-helix transcription factor. As suggested by the reviewer, we have further investigated the expression of other markers of neural progenitors by sorted cells. The results showed that the proportion of DLX2+ cells a marker of proliferating progenitors (Doetsch et al. 2002) was very high in aNSC/TAP (98%) and progressively decreased in iNB (82%) and mNB (25%). Similarly, the expression of the transcription factor SOX2 that plays an essential role in the maintenance of neural progenitors (PMID: 25126380) accounted for 78% of aNSC/TAP, 70% of iNB and 17% of mNB.

Altogether, these new data confirmed the identity of the different cell populations and particularly that of iNB. They are commented at the beginning of the Results and shown in Figure S1.

R1C4: Line 125: GFAP alone doesn't necessarily indicate a "conversion to NSCs" - this conclusion could be greatly strengthened by inclusion of more markers, particularly at the protein level, or cyto-architectural studies.

Answer: We agree with the reviewer that GFAP expression alone is not sufficient to evidence the presence of NSC in the SVZ. We have thus modified the text accordingly: “Importantly, eGFP+ cells were present in the SVZ of all the animals transplanted with eGFP+s-iNB and eGFP+s-NSC/TAP (Fig. 1Db, Fig. 1Dc), some of them expressing GFAP indicating the generation of astrocytes, and therefore possibly NSC”.

R1C5: Could these cellular states be reflective of preferential translation of DCX? It would be very helpful to see the flow cytometry sort data for iNBs / mNBs used in Figure 6, particularly if these cells were also fixed and stained directly for DCX protein.

Answer: As suggested by the reviewer, freshly FAC-sorted iNB and mNB were fixed and labelled with an anti-DCX monoclonal antibody after permeabilization. As shown in the figure below, we found a higher level of DCX expression in mNB than in iNB. Therefore, this result tends to indicate that the proliferation capacity is somehow related to the level of DCX expression. However, because of the relatively low importance of this result, we decided not to include them in the manuscript.

Author response image 1.

Modal histogram representation of DCX expression level in unstained, iNB and mNB cells determined by flow cytometry (FlowJo).

<R1C6: Figure S8 is all zeroes, showing the GFP+Dcxhigh NBs do not retain proliferative capacity. But we don't get a direct experimental comparison to EGFPnegative/lowDcxlow iNB engraftment, which would strengthen the conclusions of the paper.

Answer: Unfortunately, there is no method available to analyse the eGFPnegative/lowDcxlow iNB engraftment: by definition, these cells do not express eGFP and the use of a tracker is not appropriate for long periods of time — and thus a high number of cell divisions — after engraftment. However, to us, this control is not needed to conclude that GFP+Dcxhigh iNB have no (or at least a lower) stem cell potential in vivo considering that we have shown in Figure 1 and Table 1 that the whole iNB population is able to generate the different types of neural cells.

R1C7: Transplant data in Table 1 - a relatively small proportion of transplant derived cells are in OB, etc. Given that A cells are thought to cycle at least once in vivo, is this expected?

Answer: The reviewer is right considering that a relatively small proportion of transplant derived cells were found in the OB. However, we should consider that we used immunocompetent mice as receivers, which could have significantly reduced the engraftment efficiency, and the migration of engrafted cells outside the injection site.

R1C8: A caveat is that there is not much functional testing of the proposed model, especially for the interconversion of iNB states suggested by the diagram in Figure 7. The text is relatively restrained in proposing this model, so it is reasonable to keep - but perhaps should be noted that this part of the model will need additional testing.

Answer: Data presented in Figure 6 clearly suggest that Dcxhigh iNB have similar in vitro potential than Dcxlow iNB, whereas they don’t have such potential in vivo (Figure S10). This suggests that, providing they are in appropriate conditions, Dcxhigh iNB could reacquire stem/progenitor properties. However, we agree that this hypothesis requires further investigation. Therefore, as suggested by the reviewer, we have added in the Figure 7 legend: “Possible interconversion of iNB states would require further experimental confirmation.”

Additional minor points:

R1C9: Introduction: the SVZ is described as "the lateral wall" - however, several works in the mouse have also examined the medial wall and callosal roof, as cited later in the intro. Suggest rephrasing the second sentence (line 48) and later sentence (line 66) to clarify that "the SVZ" encompasses all of these subregions, they are not necessarily separate niches. Answer: As indicated by the reviewer, the SVZ encompasses distinct subdomains, with NSCs having a regional identity based on their location in the lateral or septal wall of the ventricle and generating different types of neuronal and glial progeny (PMID:34259628.). To address the reviewer concern about possible confusion and clearly indicate that SVZ encompass several subdomains, we have modified the sentence line 66 as follows: “Since then, the single cell RNA-sequencing has revolutionized the field and has made it possible to precisely elucidate the transcriptome of SVZ cells present in the LW and in the septal wall which also harbors NSC niches”.

However, we did not modify the line 48, since in this sentence we just indicate that the largest neurogenic niche in the adult brain reside in the LW of the SVZ.

R1C10: Line 77: "exposure" not "exposition"

Answer: The error has been corrected in the revised manuscript.

R1C11: As noted in the Public Review - the use of the term "D1/D2" cells seems likely to confuse readers who are also versed in dentate gyrus neurogenesis. Recommend removing this term from the manuscript.

Answer: We agree that the D1/D2 terminology could bring confusion, D cells referring to Tanycytes in the hypothalamus. We now refer to iNB1 for DcxLow iNB and iNB2 for DcxHigh iNB in the revised manuscript.

Reviewer 2

Major comments:

Lack of rigor

R2C1: There is a lack of appropriate normalization controls for the microarray data. As there is a decreased level of transcription in quiescent NSCs, there needs to be a cell number control (spike-ins based on cell numbers). Without this normalization, the readout can be greatly skewed.

Answer: We agree that qNSC are marked by a decreased level of transcription due to quiescence. To overcome this problem in the Clariom assays, we thus chose to calibrate each population, with a fixed amount of cRNA and cDNA using Hela cells as internal control. We totally agree that this method is not optimal but it appears to be efficient in the end. Indeed, it should be noticed that it has been adopted, thus with the same rigor, in other microarray studies published in the field (PMID: 24811379) and also on skeletal muscle cells (PMID: 29273087). Moreover, interestingly the transcriptomic signature of qNSC matches perfectly with those from other studies and particularly to those of related clusters in single cell experiments (including ours, Figure S5). This is probably linked to the fact that more importantly that the number of cells, the main characteristic of these cells is the lack of expression of genes involved in cell proliferation and metabolism. Whatever so, these data confirming previously published are not the main information of our manuscript, which is mainly dedicated to the characterization of proliferating cells, which is not impaired by our choices of normalization.

R2C2: The absolute segregation of clusters in the single-cell analysis is currently entirely in agreement with the cell cycle stage. This suggests that in the author's analysis, the clustering in 3F is entirely shaped by the cell cycle, making that the defining characteristic of the author's definitions for their cell types. Has an analysis been done that regresses out cell cycle-associated genes to see if there are clusters for different cell states/types that are identified in the absence of cell cycle stage being the defining factor? (Barron and Li, 2016). For example, just as you would see a difference in cluster if you are a quiescent or activated NSC as compared to a neuroblast for example, even without the contribution of cell cycle. These are different cell types.

Answer: We agree that cell cycle regression would theoretically allow for further discrimination between cycling cells along successive neurogenic stages. We have already performed regression using several methods, including regressing using S- and G2/M-score regression as indicated in the Seurat workflow, removing cell cycle-related PCs from UMAP calculation as used in the Cebrian-Sylla study, and using alternative gene sets such as the ones provided by the tricycle method (PMID: 35101061). These regression methods have all been used on our datasets, the original Cebrian-Sylla datasets and a combination of our datasets with the Cebrian-Sylla original datasets to increase cell number and clustering resolution. However, none of these methods modified the clustering of cycling cells.

In fact, the strong influence of the cell cycle over clustering highlights the relevance of our depletion/replenishment approaches to decipher the molecular changes masked by the cell cycle, as discussed below.

R2C3: The use of the DCX-CreERT2 line is a lineage tracing line. Once DCX is expressed, Cre recombines the DNA to allow for fluorescence. It is binary, on or off associated with DCX expression. And once on, it is always on, whether the cell is currently expressing DCX or not. As the authors had previously described a DCXlow condition, the eGFP- cells would not reflect DCXlow, but no DCX at all. And the eGFP+ cells may not be currently expressing DCX anymore. The authors should have used a system where the DCX promoter itself drives fluorescence.

Answer: We took advantage of the DCX-CreERT2 line to demonstrate that some neural cells that have recently acquired DCX expression (i.e. eGFP+ iNB) could keep (or recover) the potential of neural progenitors in vitro. Of course, some of these GFP+ cells could have stopped to express DCX. This is probably the case when they differentiate into astrocytes and oligodendrocytes in vitro as shown in Figure 6.

Whatever so, the use of the Dcx promoter as a direct driver of eGFP fluorescence would have totally impeded our capacity to demonstrate such changes in cell fate in vivo because of the impossibility to track oligodendrocytes or astrocytes derived from iNB because of the loss of Dcx expression.

R2C4: The lack of analysis of images (differentiation, for example) limits the conclusions of the in-vitro data, and the images with unclear staining, limit the conclusions of the in-vivo experiments.

Answer: This comment is similar to that of R1C2. We have now added a quantification in Figure S2.

R2C5: The cited difference in splicing differences in cell types was interesting (though did not show up in the transcriptome enrichment analyses Fig S2) and would be something to further pursue, however, this was a very limited analysis. There was no further study of these splicing mediators beyond single-cell data.

Answer: We now show enrichments of GO terms corresponding to mRNA splicing isoforms in the different types of sorted SVZ cells (Figure S4). This analysis clearly revealed that spliced genes in SVZ cells are mainly involved in neuron development and neurogenesis. Interestingly this also showed that qNSC logically differed from the other cell types by splicing concerning genes involved in mitosis and cell cycle, consistently with their quiescent state. More importantly, GO annotations of differentially spliced isoforms further confirmed that s-TAP and s-iNB have distinct features. We agree with the reviewer that further analysis of splicing mediators would be very important for understanding molecular changes involved in neurogenesis. However, we think that it is largely beyond the scope of this study.

R2C6: Fig 1C - Show values, not just pictures. You may need to shift your current differentiation paradigm to do so by removing growth factors instead of unique differentiation conditions.

Answer: See the answer to R1C2.

R2C7: Fig S1A - Stainings for GFAP and DCX are not clear. It is very hard to distinguish which cells are associated with these signals.

Answer: This figure (now Figure S2A) shows an eGFP+iNB cell (white arrow) that has reached the rostral migratory stream and expressed DCX (inset a3), but not GFAP (inset a2). This is now indicated in the figure legend. We have also moved the arrow for more clarity.

R2C8: Fig S1B2 - There is red staining everywhere, so it is very hard to see a specific CNPase signal.

Answer: We have added a new figure (Fig S2B) distinguishing eGFP+CNPase+ cells (yellow arrows) from eGFP+CNPase- cells (white arrow).

R2C9: Line 174 - It's the mRNA that you are detecting is being downregulated - be more specific as you are not showing protein downregulation.

Answer: We specified, "encoding" a major splicing repressor in the Line 174 text to refer to the mRNA: “Interestingly, Ptbp1, encoding a major splicing repressor”.

R2C10: Line 189 - text in this line have some clusters not shown in the figure - (clusters 6 and 15, DCX+ Ki67+ neuroblasts) - which would be an important thing to visualize. As is shown now, the authors are only showing that iNBs are similar to mitotic TAPs.

Answer: Clusters 6 and 15 have been added to Figure S5.

R2C11: Fig 3D-E - Why is cluster 17 called aNSCs (3E) when it has the highest GFAP (Fig 3D). Typically, the highest GFAP cells are qNSCs or astrocytes, not aNSCs.

Answer: We previously reported that the level of gfap mRNA expression in neural stem cells (quiescent and activated) did not exactly reflect the amount of protein in these cells. This is the reason why we also used the Slc1a3 marker (Glast), which is highly expressed both at the RNA and protein levels in quiescent NSCs (Daynac et al. 2013).

R2C12: Line 216 - You said in line 216 cluster 13 were astrocytes, then you said in line 227 that cluster 13 was s-qNSC. Which is it?

Answer: This is due to the fact that we performed two distinct analyses.

In the first one (line 216), cells were scored based on datasets provided by Cebrian et al. with one dataset containing genes enriched in astrocytes, and another one, genes enriched in quiescent B-cells. Therefore, cluster 13 was shown to contain 73% cells expressing astrocyte markers, whereas cluster 4 gathered cells expressing both qNSC (B-cells, 48%) and astrocyte (52%) genes.

In the second one (line 227), cells were scored using our transcriptomic signatures of FAC-sorted SVZ cells, which do not include differentiated astrocytes. We demonstrated that the cluster 13 cells only expressed s-qNSC genes.

R2C13: Line 214 - While other clusters were all named in lines 214-221 that were then further discussed in lines 227-230, clusters 15 and 19 were not. You associate both of those clusters with s-iNB - what was it associated with in the above section?

Answer: Lines 219-221 have been reworded as follows: Clusters 10, 5, 15, 12, and 8 were defined as cycling progenitors based on the expression of proliferative markers such as Top2a, Mki67, Ascl1. Clusters 1, 3, 7 and 9 were identified as mNB due to the loss of Mki67, Top2 a and Ascl1 expressions and the expression of Robo2 and Dcx. Cluster 19 that have lost Ascl1 but still expressing Top2a and Mki67 together with Robo2 and Dcx appears at the transition between iNB and mNB.

R2C14: Fig 3I-J - 5 days after irradiation, I would like to see from tissue slices how many cells are dividing compared to 1day post-irradiation and controls. In other paradigms, such as temozolomide experiments (Kalamakis et al), by 5 days we should see less cells in quiescence and more of those quiescent cells exiting quiescence into the cell cycle. Why would there be more cells in quiescence in the irradiated brain? Even if they are radiation resistant, the base number should be comparative between controls and irradiated, which is not what you show in Fig 3I-J. And R2C14)

Line 234-235 - the text says normalized to numbers of qNSCs which is supposed to be the same (which I agree should be the same). However, your graph in 3I and J shows more qNSCs in irradiated conditions, which would influence greatly and is currently hard to interpret.

Answer: As stated by the reviewer, there is no increase in the absolute number of quiescent cells in the irradiated SVZ. The reconstitution of SVZ cell populations after 4Gy irradiation has already been studied by our group (Daynac et al. 2013, see Fig. 3F), showing that s-iNB and s-mNB are still under-represented after 5 days, while qNSC are in similar numbers as in unirradiated SVZ. Therefore, this led to an over-representation of quiescent cells and early SVZ progenitors in Figure 3J as compared in Figure 3I.

R2C15: Fig 6A - the authors show a significant difference in neurospheres between eGFP- (DCX-) and eGFP+ (DCX+) iNBs - as would be expected as DCX suggests a further commitment towards neurogenic fates, yet your population doubling is the same.

Answer: To determine the population doublings, the medium was changed and cells numbered every 7 days. This condition masked the differences between two cell populations reaching the plateau phase at different time, explaining why eGFP-iNB and eGFP+iNB could not be clearly distinguished by this technique.

R2C16: Fig 6C - Differentiation data (in-vitro) should be quantified in 6C, just as was mentioned for 1C. These values should be done for both of the populations (eGFP-iNB, and eGFP+iNB) and not just compared to the previous pictures which were on total iNB. Again, numbers are required, not just picture examples.

Answer: Quantitative data have been given in Figure 6D showing that approximately 60-80% of cells eGFP+iNB are able to differentiate in either neurons, oligodendrocytes or astrocytes. We did not analyze the differentiation of eGFP-iNB since it would not add any supplementary information.

R2C17: Fig S8 - The authors did not show if the lack of engraftment of eGFP+ cells is due to the transplant (previously you showed only 2/3 worked in a similar paradigm). It would be helpful if the authors would have some means to visualize the DCX low cells to confirm they worked as before in the transplantation (another color? Another type of mouse (Thy1 antigen differences)?) Answer: Unfortunately, the Thy1 antigen has not been documented in mouse subventricular zone progenitors, but only in neurons (PMID: 10813783). Thy1 antigen has also been described in bipotent glial progenitor cell (GCP) from the developing human brain giving rise to oligodendrocytes (PMID: 36931245).

As shown, in Figure S10 we have performed 5 grafts with s-iNB eGFP+ cells, 2 alone and 3 mixed with eGFP- cells and never found any eGFP+ cells 5 weeks after grafting. Moreover, we did not find any eGFP+ cells in the brains of 3 other animals 2 weeks after grafting with s-iNB eGFP+ cells (These data have been added to Figure S10). As compared to the results described in Figure 1 this clearly shows that iNB DCXhigh are not able to generate persistent cells in the grafted brains similarly as mNB.

R2C18: Fig S8 - Why were there no eGFP cells even at the injection site? DCX expression promotes migration, indeed DCX expression becomes very high in cells in the SVZ as they begin to exit to go to the migratory stream. If one didn't see migration, one would expect you would still have survival. Currently, the authors show no cells at 5 weeks, however, they would need to show earlier timepoints as well to determine what is happening with these cells. It is possible these GFP+ cells are not even expressing DCX anymore (see above).

Answer: As stated above, we did not find any GFP+ cells in the brains of 3 other animals 2 weeks after grafting with s-iNB eGFP+ cells (see Figure S10).

R2C19: Line 320 - the authors suggest a subpopulation of NEURONS continues to divide and cite 2 works from the 1990s showing proliferating SVZ cells can differentiate. Our knowledge of this system has come dramatically forward since the 1990s as well as technologically, and to date, neurons have not been shown to divide.

Answer: We apologize for this lack of clarity, as we agree that neurons correspond to differentiated non-cycling cells, but we used the terminology used in these articles. The incorrect part of the sentence Line 320 has thus been deleted from the text.

R2C20: Fig 7 - The whole figure is based on changing levels of RSR genes which were not confirmed in any way to be involved in any of these stages, only descriptively in single-cell analyses.

Answer: As stated above, in our opinion, further characterization of the involvement of RSR genes in neurogenesis is largely beyond the scope of our manuscript. Nevertheless, we think that the role of RSR genes in neurogenesis is an important question that should be addressed in further studies.

Overstatement of findings

R2C21: Fig 1 - Authors did not compare all cell types in each condition but made overstatements about their relationships to each other between graphs. There should also be separate graphs showing all cell types at 4% and a separate one at 20%.

Answer: In the revised version, Figure 1 shows the graph comparing all cell types at 4%O2 and a separate one at 20% as requested by the reviewer. The graphs clearly shows that 4%O2 promotes iNB proliferation compared to the 20% condition.

R2C22: Fig 1D-b2 - Why does DCX look nuclear? One can't say they are only NSCs if they are GFAP as astrocytes also express GFAP. The authors would need another marker to separate those populations. In the text, the authors say expressing GFAP (line 124) which means NSC, but then in line 127 expressing GFAP means astrocytes - which further shows you need additional markers to validate those 2 different cell types. Answer: DCX nuclear translocation has been shown to improve cellular proliferation (PMID:32050972).

As indicated in R1C4. The text has been modified as follows: “Importantly, eGFP+ cells were present in the SVZ of all the animals transplanted with s-iNB eGFP+ and s-NSC/TAP eGFP+ (Fig. 1Db, 1Dc), some of them expressing GFAP indicating the generation of astrocytes, and therefore possibly NSC”.

R2C23: Fig S2 - The transcriptome signature for s-iNBs is very similar to s-TAP, basically suggesting the iNBs are further along in cell cycle.

Answer: This is now the Figure S3. Functional enrichment analysis of individual transcriptome signatures revealed that both s-TAP and s-iNB are enriched in genes related to the cell cycle although with different GO terms enrichments. Indeed, s-TAP are enriched in genes related to G1, G1/S and S phase (but with low -log10 adjusted p-values) and s-iNB with genes related to cell cycle mitosis and M phase (with high -log10 adjusted p-values).

We have previously shown that around 33 % s-iNB have DNA content>2N, versus around 26% of s-TAP and s- aNSC (Daynac et al. 2013), which is in accordance with GO terms enrichments. However, these data have also shown that most s-iNB and s-TAP are in G1, indicating that siNB are not just further along mitosis than TAP.

Moreover, our transcriptomic data clearly show that s-iNB are distinct from s-TAP: 1) according to principal component analyses (Figure 2B et C), the whole transcriptome of s-TAP is closer to that of s-aNSCs than to that of s-iNB (10% variations in PCA2), 2) the heatmap in Figure 2D shows that they have different RSR genes expression profiles, 3) the new Figure S4 shows that GO annotations of differentially spliced isoforms further confirmed that s-TAP and s-iNB have distinct features, and 5) Figure S5 shows that s-iNB expressed genes associated to either TAP or NB that have been described in previous studies, whereas s-TAP did not express genes associated to NB, but look closer to aNSC. Finally, scRNAsq cell clusters related to s-iNB are distinct from the cluster related to s-TAP as shown 1) in Figure 3D and 2) in Figure 4.

R2C24: Fig 3 - The lack of information about timepoint 0 after irradiation, and when proliferation and cell cycle entry begins again following irradiation, limits our interpretation of the single-cell irradiated data.

Answer: We have previously reported the relative abundance of each SVZ neural progenitors in the young adult mouse brain in several papers. Particularly, we based our interpretation on our SVZ irradiation model reported in Daynac et al. 2013 demonstrating a radio resistance of qNSC re-entering into the cell cycle as early as 2 days after 4Gy irradiation successively regenerating aNSC, TAP then iNB and mNB.

R2C25: Fig S3 - These results effectively show that the s-aNSCs and s-TAPs are actually less specific when compared to that same identity in other studies, and that the iNBs are most similar to mitotic TAPs. This supports what was mentioned above, which is that the transcriptional signatures are very similar between the s-TAPs and i-NBs, showing these are not a unique cell state, but just a bit further along mitosis within the TAP cell state.

Answer: This is now the Figure S5. In this figure, we show that s-iNB expressed genes associated to either TAP or NB that have been described in previous studies, whereas s-TAP did not express genes associated to NB, but look like closer to aNSC. As indicated above in R2C23, s-iNB are not just a bit further along mitosis within the TAP cell state. Indeed, we give several data showing that s-iNB and s-TAP have different transcriptomic profiles.

R2C26: Fig 4B - The focus on Ptbp1 as being associated with the iNB cluster border to mNB is expected as all previous studies of Ptbp1 have focused on its role in the progression of other cell types through the cell cycle, its control of cell cycle regulators, and a cell cycle mRNA regulon (Monzon-Casanova et al, 2018, 2019, 2020). This further supports these analyses are specifically defined by cell cycle stages.

Answer: We totally agree that Ptbp1 expression distinguishes cycling cells from postmitotic neuroblasts in accordance with previously published paper, and that based on this unique gene we cannot find any differences between cycling cells ie. aNSC, TAP and iNB. However, as shown in the manuscript and stated above (R2C23 and 25), these cells can be distinguished by their respective expression of many other genes, including other RSR genes.

R2C27: Line 281-282 is an overstatement - the authors suggest that this is a new type of cycling neural progenitor - when all studies point to it being the end of mitosis TAPs as they go on their way to mNBs. This clearly shows a trajectory and not a defined, binary cell type.

Answer: We agree with this statement that the use of the word "type" was misleading, and changed it to "stage" to better reflect that s-iNB are a distinct stage along the differentiation process according to our pseudotime cell-trajectory analysis.

Author response image 2.

Pseudotime analysis using Monocle 3 (excluding the cluster 13 corresponding to astrocytes and starting from s-qNSC) revealed two branches starting from s-TAP, one towards cell cycle the other towards neuronal differentiation.

minor comments:

R2C28: Fig 3D - For ease, please define what you called the clusters in 3D - not just cluster numbers

Answer: We chose not to call the clusters in 3D because their identification (Group names) is based on data presented after in Figures 3E, F and G.

R2C29: Fig 3E-F - Show astrocytes by text in 3E and F

Answer: As discussed above, astrocytes cannot be shown in these figures because they are based on our signatures which did not include astrocyte signature.

eLife assessment

This useful manuscript presents an intriguing potential refinement of models for adult SVZ neurogenesis, and highlights the role of RNA splicing at specific stages in the lineage. Unfortunately, the evidence does not fully support the claims, leaving it currently incomplete. The proposed role of RNA splicing in neuronal differentiation, though interesting, remains unexplored and would benefit significantly from targeted gene manipulation studies.

Reviewer #1 (Public Review):

In this study, the authors use prospective sorting and microarray analyses, extended by single-cell RNA sequencing, in the neural stem cell niche of the subventricular zone (SVZ) to identify and refine a series of states along the continuum from quiescent neural stem cells to mature progeny. Of note, changes in the levels and subgroups of RNA splicing regulators are detailed across this continuum. Using in vitro proliferation and differentiation assays, coupled with in vivo engraftment of some prospectively sorted subsets, the authors argue that a stage they define as immature neuroblasts (iNBs) retain proliferative and multilineage differentiation capacity that is not seen in the mature neuroblast population, and is unexpected based on prior models for lineage progression in this system. This iNB stage is accompanied by a change in RNA splicing regulator expression, which is of interest due to the emerging roles for RNA processing and preferential translation within this niche.

The central tension driving the discussion between authors and reviewers, in my view, is what is required to define cells as a "molecularly distinct population" in a lineage. Is it transcript expression, in vitro potential, or more? The authors argue that sorted immature neuroblasts are a defined, separate step in the neurogenic lineage. An alternative possibility is that this population is simply cycling transit-amplifying progenitors that have initiated a transcriptional program associated with neuroblast fates - that these cells are an intermediate point on a continuum between stem cells, transit-amplifying progeny, and commitment to a neuronal (or other) fate. Despite some additions in response to initial reviews, it is still the case that much of the data presented would be equally or more effective in supporting the second model. For example, the differentially spliced gene sets in Figure S4, which are put forward by the authors to support a different molecular identity for immature neuroblasts, show that the terms enriched for immature neuroblasts are largely also found in transit amplifying progenitors (generation of neurons, neurogenesis, cell projection organization, neuron development) and/or mature neuroblasts (cell projection organization, generation of neurons), suggesting that "immature neuroblasts" are transiting between these two states and that one of their most relevant features is that they are still cycling.

These data complement several additional sc-RNAseq studies of this stem cell niche, and use a different, but similar, sorting strategy to isolate and profile subpopulations of stem/progenitor cells and neuroblast progeny. The claim that immature neuroblasts retain multipotency - the ability to generate glia and neurons - is surprising and somewhat controversial given that this has largely not been reported before under homeostatic conditions. Some factors to consider when interpreting these data are that the "immature neuroblast" populations are studied in some experiments using a transcriptional signature and a functional assay, namely the timing of reappearance of these cells after use of agents that kill rapidly dividing cells (in this case, radiation), leading to reconstitution of the lineage by previously quiescent stem cells. In a separate set of experiments, a tamoxifen-inducible labeling system is used in combination with cell-surface markers to prospectively isolate and study the differentiation potential of neuroblast populations that are assumed to be equivalent to those found in transcriptional experiments. It would be of interest in the future to confirm that the exact sorted populations (using CD24/EGFR/DCX-CreERT2::CAG) have the same transcriptional profile as those studied in earlier experiments within the paper and to confirm the purity of the sorted populations. Finally, while use is made of engraftment of sorted populations to study the differentiation and lineage potential of these immature neuroblasts, a remaining question is the relative abundance of each lineage (neurons/astrocytes/oligodendrocytes) produced by the engrafted cells - is production of glia rare, or common? Could this be due to factors such as alteration of lineage potential due to culture conditions, a disconnect between transcript expression and protein expression, or an incompletely purified starter population?

Overall, this manuscript presents an intriguing possible refinement of models for SVZ neurogenesis, and highlights the role of RNA splicing at specific stages in the lineage. It will be of interest to see if additional groups confirm these findings and whether multiplexed immunostaining, highly multiplexed flow cytometry, or other approaches focused at the proteomic level extend these findings, particularly given recent data in the developing brain that suggest transcript and protein levels are relatively poorly correlated in stem/progenitor populations.

Reviewer #3 (Public Review):

Summary:

Bernou et al. propose the existence of a distinct neuroblast population with increased regenerative and differentiation potential. Their claims are based on the analysis of a sorted population identified as LeX-EGFR+CD24low, which they refer to as "immature NeuroBlasts, iNB". This population is defined by transcriptomics features that have been assessed through bulk microarray studies of sorted cells and single cell RNA sequencing of the whole SVZ- lineage. Analysis of these data sets leads to the identification of these iNBs as cycling cells with a specific expression pattern of RNA splicing machinery components. On these grounds, they propose that RNA splicing plays a key role in neuronal differentiation. Although the authors bring an innovative point to the table, their claims are not fully supported by their results.

Strengths:

Interesting Hypothesis

Weaknesses:

The comparison of their microarray data to published single-cell RNA sequencing datasets (scRNAseq) highlights the cycling nature of the iNB population. Moreover, their own cell cycle analysis on their scRNAseq data attributes G2M/S-phase stages to clusters classified as iNBs, while clusters identified as TAPs are assigned to a restricted G1/S-phase stage. However, it would be expected that TAPs, as cycling progenitors, would go through all cell cycle stages and not just the beginning of it. Thus, authors should consider the possibility that their iNB population entails a major fraction of transit amplifying progenitors (TAP) and a couple neuroblasts, as described in numerous previous studies.

Authors regard the iNB population as neuroblasts due to the capacity of their sorted population to proliferate and differentiate into diverse neural cell types (neurons, oligodendrocytes and astrocytes) in vitro. It cannot be discarded that the sorted population (LeX-EGFR+CD24low) may not be pure and may be composed of a mixture of cells in different stages, including TAPs. Such a mixture of different cell types is unavoidable in sorted populations analyzed as bulk and is precisely one of the issues solved by single cell transcriptomics. Thus, the analysis of single cells resolves transition states at higher resolution and should be preferred over bulk analysis to prevent biases in analysis.

To align the authors' findings with the existing body of literature and earlier characterizations of the SVZ niche, it is advisable to combine their single-cell RNA sequencing data with datasets that have already been published. Such integration will enable precise understanding of the identity of their iNB cells.

On another note, the role of RNA splicing on neurogenesis lacks experimental validation. Unless manipulation of RNA splicing factors is conducted, the key role of this machinery in adult neurogenesis cannot be claimed.

Author Response

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

We appreciate the insightful feedback provided by the editors and reviewers who have recognized the novelty of our study. We have mapped the spatial distribution of six endogenous somatic histone H1 variants within the nuclei of several human cell lines using specific antibodies, which strongly suggest functional differences between variants. We are submitting a revised version of the manuscript to accommodate the reviewers comments and recommendations.

Reviewer #1 (Recommendations For The Authors):

Minor Comments:

(1) In Figure 1C, since H1.4 is uniformly distributed among the four sections (A1-A4), its levels are not expected to be significant among the four sections as depicted. Even the violin plots shown do not seem to be significantly different from each other. This requires an explanation.

We agree with this reviewer that significant differences of H1.4 abundance within areas A1 to A4 seem to not exist, either looking at the images or the data violin plots, as discussed in the manuscript. Nonetheless, statistical testing gave this as significant, due to small differences and the elevated sample N of the analysis. It is clear that H1.4 does not show a relevant peripheral enrichment as shown for the other variants.

(2) At the end, it would be better to include a figure panel depicting chart/table/pictorial representation, depicting the summary of the work done with respect to all the histone variants, as there are several histone H1 variants studied under different conditions and contexts.

A table summarizing the location and characteristics of the different H1 variants has been included in the manuscript (Figure 6).

Reviewer #2 (Recommendations For The Authors):

(1) The authors may consider adding controls for the specificity of the antibodies used for the studies. While the antibodies used here are commercial, it does not guarantee the quality for immunofluorescence, especially considering their unreliability in the past. The authors may consider including peptide/ recombinant protein-based adsorption controls in addition to knockdown or knockout controls. Having these data will strengthen the exciting observations presented in this MS and significantly increase the impact of the presented findings.

We totally agree with the reviewers that the use of commercially available antibodies does not guarantee their quality and specificity. As this issue was crucial for our studies, we extensively assayed performance and specificity of the antibodies, using different approaches. The validations were shown in our previous publications where these antibodies where successfully used for ChIP-seq (Serna-Pujol et al. 2022 NAR 50:3892; Salinas-Pena et al. 2024 NAR doi 10.1093/nar/gkae014). In summary, performance of H1.0 (05-629l, Millipore), H1.2 (ab4086, abcam), H1.4 (702876; Invitrogen), H1.5 (711912, Invitrogen) and H1X (ab31972; abcam) antibodies was tested by Western-Blot, ChIP and proteomic analyses (all the results are included in Supplem. Figure 1 in Serna-Pujol et al. 2022 NAR 50:3892). Concretely, we tested specificity using inducible KDs for the depletion of each of the somatic H1 variants in T47D. We also checked that the antibodies did not recognize additional H1 variants using recombinant proteins or cell lines naturally lacking some of the variants. All the experiments confirmed that antibodies were variant-specific. In addition, when the corresponding epitope was absent, the antibodies did not gain new cross-reactivity with other variants. More recently, validation of the specificicity of the H1.3 antibody (ab203948) was performed following the same experimental approaches described for the rest of antibodies (Supplem. Figure 1 in Salinas-Pena et al. 2024 NAR doi 10.1093/nar/gkae014).

(2) Histone H1 is overexpressed in several cancers. While the authors do not use an overexpression strategy, the cells used in this study are all cancer cell lines. The study would benefit greatly if some of the findings- primarily regarding the spatial distribution of the H1 were to reproduce in non-tumorigenic, diploid cells.

We have also studied and discussed the spatial distribution of H1 variants in nontumorogenic cell lines 293T and IMR-90, and we have added this in the revised manuscript (Figure 5D and Figure 5-figure supplement 3). The nuclear radiality of H1.4 in 293T cells is also shown (Figure 5-figure supplement 4A).

Reviewer #3 (Recommendations For The Authors):

This is an interesting paper that provides convincing evidence of distinct distributions to individual histone H1 variants. There are several aspects of the study that leave me unconvinced that the study accurately captures histone H1 variant distributions.

(1) Antibody accessibility: (see PMID: 32505195). One means to address this is to express a fluorescent protein-tagged version of histone H1 and demonstrate that the antibody can detect that tagged version of histone H1 independent of its location in the nucleus. In general, these FP-tagged H1s show a much more even distribution than what is observed here. Of course, that could reflect artifacts related to the fusion or the expression of the exogenous construct. However, even if all of the above are true, this will test the ability of the antibodies to recognize their epitopes in different chromatin environments. The fluorescent protein tag enables unambiguous knowledge of the presence or absence of the H1 histone.

We have used cells expressing HA-tagged H1.0 variant and performed immunofluorescence with HA and H1.0 antibody to investigate co-localization, to test whether an H1 antibodiy recognize all the tagged protein in different chromatin environments or irrespective of its location in the nucleus. A very high correlation between the two antibodies has been found (Figure 1-figure supplement 1B).

(2) At high concentrations, the fluorescence signal intensity can be quenched. For example, this is common with high-affinity histone H3 serine 10 phosphorylation antibodies in late interphase/prophase nuclei. The artifact can be minimized by serial dilution of the antibody and identifying the minimum usable concentration for immunofluorescence. While I am not certain that this is taking place here, the rate and manner that the intensity drops off from the periphery in the peripheral H1 variant distribution are very similar in appearance. There are biological explanations related to constraints on diffusion that one could imagine also explaining the data so I'm not stating that this must be an artefact. However, I am concerned that it might be. An improved staining may reveal the same result but more convincingly.

We have performed immunofluorescence with serial dilutions of the H1.3 antibody to show that peripheral distribution was not due to fluorescence signal intensity quenching (Figure 1figure supplement 1A).

(3) Histone H1 is highly mobile and there is some concern that they could reorganize during the relatively long period of time that it takes to fully fix the cells for both ChIP and immunofluorescence. This should be acknowledged in the manuscript.

We have added this reviewer’ concern in the Discussion section.

(4) The paper would benefit from a more rigorous quantification of histone H1 subtypes. Mass spectrometry would be ideal but more classical techniques such as 2D AU-SDS PAGE, HPLC, etc...would be an improvement over immunoblotting. The authors did not explain the quantification of the immunoblots and the assignment of relative contributions of H1 subtypes to the individual coommassie bands in the Image J section of methods, which is referred to as the method of quantification in the immunoblotting methods.

We have further explained how the relative quantification of H1 variants in different cell lines was performed (Methods section). We agree that more sophisticated mass spectrometrybased quantification is desirable and we are collaborating to do this using internal H1 peptide controls (Parallel Reaction Monitoring), but this is out of the scope of this manuscript as the observed patterns of distribution of H1 variants do not depend on mild differences in variants abundance. Only the absence of H1.3 and H1.5 in some cell lines alters the distribution of other variants.

Additional author responses to the Public Review comments made by some Reviewer:

(1) Respect to the functional significance of the results presented here, we want to stress that as a consequence of the differential distribution and abundance of H1 variants among cell types, depletion of different variants has different consequences. For example, H1.2 depletion but not others has a great impact on chromatin compaction. Besides, cell lines lacking H1.3/H1.5 expression present a basal up-regulation of some Interferon stimulated genes (ISGs) and particular repetive elements, as it was previously described upon induced depletion of H1.2/H1.4 in a breast cancer cell line or in pancreatic adenocarcinomas with lower levels of replication-dependent H1 variants (Izquierdo et al. 2017 NAR 45:11622). So, our results reinforce the existing link between H1 content and immune signature. We have added this data in the revised manuscript (Figure 5-figure supplement 5).

Moreover, we also analyzed the chromatin structural changes upon combined depletion of H1.2 and H1.4. Combined H1.2/H1.4 depletion triggers a global chromatin decompaction, which supports previous observations from ATAC-Seq and Hi-C experiments in these cells (Izquierdo et al. 2017 NAR 45:11622; Serna-Pujol et al. 2022 NAR 50:3892). Although H1 content is more compromised in these cells (30% total H1 reduction) compared to single H1 KDs, the phenotype observed could not be recapitulated when other H1 KD combinations, in which total H1 content was reduced similarly, were investigated (Izquierdo et al. 2017 NAR 45:11622), supporting that the deleterious defects were due to the non-redundant role of H1.2 and H1.4 proteins. Indeed, this manuscript supports this notion, as H1.2 and H1.4 show a different genomewide and nuclear distribution.

(2) Our immunofluorescence data, together with ChIP-seq data, do not discard binding of H1 variants to a great variety of chromatin, but show enrichment or preferential binding to certain regions or chromatin types. Our data on the interphase nuclei does not suggest at all any type of quenching or saturation. Obviously, detection with antibodies depends on epitope accessibility, just like all immunofluorescence data ever published, and we have acknowledged that post-translational modifications of H1 may occlude antibody accessibility as some phospho-H1 antibodies give distribution patterns different than total/unmodified H1 antibodies. Thus, we cannot exclude that specific modified-H1s exhibit particular distribution patterns that are not being recapitulated in our data. This represents another layer of complexity in H1 diversity and we agree that exploration of the repertoire of H1 PTMs and their functional roles are an interesting matter of study that needs to be addressed. Still, our data is highly relevant as it demonstrates for the first time the unique distribution patterns of H1 variants among multiple cell lines and it does not use overexpression of tagged H1 variants that in our experience may produce mislocalization of H1s.

(3) We do have investigated co-localization of H1 variants with HP1alpha protein and we have added this data in the revised version of this manuscript (Figure 1-figure supplement 1C-D).

eLife assessment

This manuscript is an important advance in the study of Histone H1s, finding distinct distributions of various H1 variants in the genome. The controls presented by the authors provide convincing evidence to demonstrate a heterogenous distribution of H1 which might reflect functional regulation of chromatin accessibility by linker histones. This work will be of interest to the genome organization field, and could additionally provide a framework for understanding H1 mis-regulation observed in cancer cells.

Reviewer #1 (Public Review):

In this manuscript, authors have performed extensive imaging analysis of six human histone H1 variants, their enrichment and localization, their differential dynamics during interphase and mitosis, and their association with lamina-associated domains (LADs) or nucleolus-associated domains. The manuscript is well-written with high-quality confocal and super-resolution images. Various interesting observations are made on distribution patterns of H1 variants. H1.2, H1.3, and H1.5 are shown to be universally enriched at the nuclear periphery whereas H1.4 and H1X are found to be distributed throughout the nucleus. Interestingly, H1X was the only H1 variant found to be abundant in nucleoli. Depletion of H1 variants has been shown to affect chromatin structure in a variant-specific manner, with H1.2 knock-down resulting in global chromatin decompaction. Overall, the study presents several interesting insights on H1 variants conducted in a large number of cell lines.

Reviewer #2 (Public Review):

Summary:

The manuscript by Salinas-Pena et. al examines the distribution of a subgroup of histone H1 variants primarily with the use of high-resolution microscopy. The authors find that while some H1s have a universal distribution pattern, some display a preference for discrete regions within the nuclear landscape namely, the periphery, the center, or the nucleolus. They also show using that the various H1s within a cell did not colocalize significantly with each other, rather, they occupy discrete 'nanodomains' throughout the nucleus which is visualized as a punctate signal.<br /> The authors present evidence towards a long-standing question in the field regarding the spatial distribution of the different H1 variants. Since reliable, specific antibodies toward the variants were unavailable, this question was unable to elicit a definitive answer. This study uses more recently available antibodies against endogenous H1s to put together a systematic and comprehensive view of a group of H1 variant distribution inside a nucleus and ties it with previously generated genome wide data to demonstrate localization and some functional heterogeneity.

Strengths of the study.

(1) First systematic, high-resolution view of H1 variants providing a significant advance towards the long hypothesized functional differences between H1 variants.

(2) The use of endogenous antibodies allows the authors to bypass the need to use tagged proteins or overexpression strategies to study H1 distribution.

(3) The availability of genome wide H1 distribution data for the variants using the endogenous H1 antibodies to strengthen the presented visual data.

Weakness of the study.

One of the major reasons for slow progress in deciphering variant specific function has been the dearth of quality, specific, antibodies. This study is heavily dependent on the antibody function and its ability to accurately report on the distribution. The authors have cited previous validations of the antibodies used using H1 knockdown, immunoblotting and ChIP-seq. For the scope of this study, the controls are adequate.

Impact:

This study sets the stage for an exciting avenue of H1 study where variant-specific cellular functions can be explored which has otherwise been severely understudied.

Reviewer #3 (Public Review):

Summary:

This paper uses indirect immunofluorescence, superresolution fluorescence microscopy, and X-ChIP to demonstrate radial distribution profiles of all histone H1 somatic variants with the exception of histone H1.1. The results support earlier work from chromatin immunoprecipitation experiments that revealed biases for active versus repressed states of chromatin. The previous studies provided some support for the subtle sequence variation found primarily within the C-terminus of histone H1 variants conferred preferences in the type of DNA (e.g. methylated DNA) or chromatin bound. The current study significantly strengthens that argument. Importantly, this was shown across multiple cell lines and reveals conserved properties of localization of histone H1 variants.

Strengths:

The strength of the manuscript is the combined use of quantitative analysis of indirect immunofluorescence and X-ChIP. The results generally support the polar organization of the genome and a corresponding distribution of histone H1 variants that reflect this polar organization. AT-rich chromatin is positioned near the lamina and is found to be enriched in H1.2, H1.3, and H1.5. H1.4 and H1.X were more biased towards the GC-rich intranuclear chromatin.

There is emerging functional evidence for variant-specific properties to histone H1 subtypes. This work provides an important building block in understanding how different histone H1 variants may have specific functional consequences. The histone H1 variant that is most abundant in most cell types, H1.2, was found to decrease the area of the immunofluorescent slice that was chromatin-free when depleted, suggesting a more important role in global chromatin organization.

Weaknesses:

While histone H1 variants may show biases in their distributions, it is unlikely that these are more than biases. That is, it is unlikely that specific H1 variants are unable to bind to nucleosomes in regions where they are depleted. Fluorescence recovery after photobleaching experiments have demonstrated differences in binding affinity but the capacity to bind a range of chromatin structures, including highly acetylated chromatin, for histone H1 variants. Thus, it is critical in assessing this data to have accurate quantitative information on the relative abundance of the different histone variants amongst the cell lines tested here. The paper relies upon quantification by immunoblotting.

Another uncertainty in both the ChIP and immunofluorescence datasets is the accessibility of the epitope. This weakness is highlighted by the apparent loss of H1.2 and H1.4 in mitotic chromosomes that is revealed to be false by the detection of the phosphorylated species. The distributions relative to the surface of chromosomes in mitosis and the depletion of H1.2, H1.3, and H1.5 from the central regions of interphase nuclei reveals an unusual dissipation of the staining that is suggestive of antibody accessibility problems. The overall image quality of the immunofluorescence images is poor, further complicating analysis.

Author Response

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

eLife assessment

The authors analyzed the causative association between circulating immune cells and periodontitis, and reported three risk immune cells related to periodontitis. The significance of the findings is fundamental, which substantially advances our understanding of periodontitis. The strength of evidence is convincing.

Reviewer #1 (Public Review):

Ye et al. used Mendelian randomization method to evaluate the causative association between circulating immune cells and periodontitis and finally screened out three risk immune cells related to periodontitis. Overall, this is an important and novel piece of work that has the potential to contribute to our understanding of the causal relationship between circulating immune cells related to periodontitis. However, there are still some concerns that need to be addressed.

We sincerely appreciate the constructive feedback from the editor and reviewers, which has been instrumental in enhancing the quality of our manuscript.

(1) The authors used 1e-9 as the threshold to select effective instrumental variables (IVs), which should give the corresponding references. Meanwhile, the authors should test and discuss the potential impact of inconsistent thresholds for exposure (1e-9, 5e-6 were selected by the author respectively) and outcome IVs (5e-8) on the robustness of the results.

Thank you for your insightful comments. We have selected two GWAS databases as the data sources for the exposure group: the BCC Consortium with a sample size of 563,946, and the Sardinian cohort of 3,757. The considerable disparity in sample size between them may result in variations in outcomes, primarily showcased in the differences in positive SNP numbers. We, therefore, adopted an unconventional (non 5e-8) yet rigorously controlled screening strategy, an approach that is widely accepted in MR studies (Li et al., 2022; Liu et al., 2023). We believe that the present thresholds are sufficiently rigorous to guarantee the validity of the subsequent Mendelian randomization analysis.

However, employing two distinct methods in exposure screening is not typical, and we posit that this method can be viewed as an innovative strategy, providing a reference for future research dealing with two databases with significant discrepancies (Huang et al., 2023; Kong et al., 2023). As you perceptively noted, we acknowledge that this strategy may exert a certain influence on the research outcomes, and we have factored this potential limitation into our manuscript. “Third, the considerable variation in sample size between the two exposure databases contributes to the discrepancies in the number of positive SNPs. Despite our exploration of multiple selection thresholds for IVs, the inconsistency in screening methods and the discrepancy in the included SNPs could potentially introduce bias.” (Page 14)

As for the "outcome IVs with 5e-8" you mentioned, we didn't implement this screening threshold in the outcome IVs. Indeed, we applied the same screening criteria as specified at 5e-06 (refer to Stable 2). Is the statement that you're referring to the following: "Additionally, SNPs that displayed a direct association with the outcome would also be excluded to uphold the third MR assumption (P < 5e-8)" (Page 6)? In this context, we adopted a standard criterion in the IVs screening process to remove SNPs directly associated with the outcome.

Reference

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(2) What is the reference for selecting Smoking, Fasting plasma glucose, and BMI as covariates? They do not seem to be directly related to immune cells as confounding factors.

The variables of Smoking, Fasting Plasma Glucose (FPG), and Body Mass Index (BMI) are commonly used as covariates in multivariable Mendelian randomization studies (Kong et al., 2023; Liu et al., 2023). The association between Smoking, FPG, and BMI with immune cells may not be immediately apparent. However, these factors have been identified as potential confounders that could impact overall health, which in turn may indirectly modulate systemic immune responses, susceptibility, and inflammation.

(1) . Smoking: It has been well-documented that smoking can cause inflammation and impair immune function, thereby increasing individual's susceptibility to infections and diseases (Shiels et al., 2014). As such, smoking is recognized as a covariate that could potentially influence the outcomes of an investigation into immune cells.

(2) FPG: Elevated FPG levels indicate poor glycemic control, potentially leading to conditions like diabetes (Choi et al., 2018). Consequently, studies have demonstrated that elevated FPG levels can compromise the immune system's ability to combat infections.

(3) BMI: It is a measure of body fat that takes into account a person's weight and height. Both obesities, characterized by a high BMI, and underweights, characterized by a low BMI, have been associated with a range of health issues, inclusive of a compromised immune system (Piñeiro-Salvador et al., 2022). Consequently, BMI is factored in as a covariate in this study.

We have thus incorporated these factors as covariates in our study to mitigate their potential confounding effects. The selection of these covariates is primarily guided by previous research and established knowledge concerning the potential influences on immune function. We appreciate your query and will ensure to clarify this point in our revised manuscript. “We have incorporated covariates, including the number of cigarettes smoked, fasting plasma glucose (FPG) levels, and body mass index (BMI) into the MVMR analysis, given that these factors could indirectly affect systemic immune responses and inflammation (Liu et al., 2023).” (Page 6-7)

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Choi S-C, Titov AA, Abboud G, Seay HR, Brusko TM, Roopenian DC, Salek-Ardakani S, Morel L. 2018. Inhibition of glucose metabolism selectively targets autoreactive follicular helper T cells. Nat Commun 9:4369. doi:10.1038/s41467-018-06686-0

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Liu Y, Lai H, Zhang R, Xia L, Liu L. 2023. Causal relationship between gastro-esophageal reflux disease and risk of lung cancer: insights from multivariable Mendelian randomization and mediation analysis. Int J Epidemiol 52:1435–1447. doi:10.1093/ije/dyad090

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Shiels MS, Katki HA, Freedman ND, Purdue MP, Wentzensen N, Trabert B, Kitahara CM, Furr M, Li Y, Kemp TJ, Goedert JJ, Chang CM, Engels EA, Caporaso NE, Pinto LA, Hildesheim A, Chaturvedi AK. 2014. Cigarette smoking and variations in systemic immune and inflammation markers. J Natl Cancer Inst 106:dju294. doi:10.1093/jnci/dju294

(3) It is not entirely clear about the correction of P-value for the total number of independent statistical tests.

In our study, we used the Bonferroni correction to adjust the P-values for multiple comparisons. The adjusted P-value is calculated as the original P-value times the total number of independent statistical tests. Specifically, we applied multiple corrections in the following two aspects: First, we corrected the results of the FUSION algorithm in TWAS, with a correction value of P < 6.27 ×10-6 (0.05/7,890 genes) (Page 8). Second, we performed multiple corrections on the initial results of MR (P < 0.05/17 traits = 0.003). However, none of the results met the criteria after the correction, which is one of the limitations detailed in the discussion section of our study (Page 14).

(4) The author used whole blood data to apply FUSION algorithm. Although whole blood is a representative site, the authors should add FUSION testing of periodontally relevant tissues, such as oral mucosa.

We appreciate your insightful comments and suggestions. We concur that employing periodontally relevant tissues, like oral mucosa, for FUSION testing might yield more precise and pertinent results. However, in the Genotype-Tissue Expression project (GTEx) database, we could not find transcriptome data related to oral tissues, such as gums, oral mucosa, and alveolar bone (Review Table 1). Owing to the limitations of the database, in the context of our study, we primarily relied on whole blood data, given its availability and the extensive precedent documented in the literature for its utilization (Xu et al., 2023; Yuan et al., 2022).

We acknowledge that this is a limitation of our study and will certainly consider incorporating periodontally relevant tissues in our future research. In the revised manuscript, we have explicitly stated this limitation and underscored the necessity for additional studies to corroborate our findings with periodontally relevant tissues. Fifth, we relied on the whole blood data For FUSION algorithm due to the lack of transcriptome data associated with oral tissues (such as gums, oral mucosa, and alveolar bone) in the GTEx database. “Fifth, we relied on the whole blood data For FUSION algorithm due to the lack of transcriptome data associated with oral tissues (such as gums, oral mucosa, and alveolar bone) in the GTEx database. This has led to an excessive focus on systemic immunological changes, thereby overlooking the significance of alterations in local periodontal tissue immunity. Such an oversight could potentially compromise the precision and pertinence of our research findings.” (Page 15)

Author response table 1.

Organizations and Samplesize in the GTEx database

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(5) The authors chose gingival hyperplasia as a secondary validation phenotype of periodontitis in this study. However, gingival recession, as another important phenotype associated with periodontitis, should also be tested and discussed.

We appreciate your insightful feedback highlighting the significance of incorporating gingival recession as a phenotype in periodontitis studies. Our emphasis on gingival hyperplasia in the study was primarily dictated by the initial study design and the data available from FinnGen R9K11. Notwithstanding the lack of gingival recession data in the available databases, we identified chronic gingivitis data in an earlier version of the Finnish database (FinnGen R5K11) as an alternative. We performed a Mendelian Randomization analysis on this dataset, with the results integrated into Supplementary Table 10. Concurrently, Table 1, Supplementary Table 1, Figure 4, and the corresponding descriptions in the manuscript were updated. We trust this adjustment can address the limitations identified in our research. We are confident that this not only augments the comprehensiveness of our study but also fosters a more holistic comprehension of periodontal disease.

(6) This study used GLIDE data as a replicated validation, but the results were inconsistent with FinnGen's dataset.

Thank you for your insightful comments and for bringing this issue to our attention. Indeed, it is of utmost importance to ensure the validity and reliability of our findings across various datasets. The observed inconsistency between the GLIDE data and FinnGen's dataset could be attributed to several reasons.

Firstly, this discrepancy might originate from the differences in population composition. The former is grounded on a comprehensive meta-analysis of cohorts focusing on periodontitis, whereas the latter utilizes a dataset from a full-phenotype cohort. In the former, the ratio of periodontitis to the control groups is approximately 1:2. In contrast, the ratio in the latter seems to be minuscule. The sample size in the FinnGen data may not suffice to detect the effects observed in the GLIDE dataset, given that larger exposure sizes enhance the ability to detect genuine associations.

Moreover, the heterogeneity of periodontitis can potentially result in variable outcomes. Phenotypic definition methods differ between the two databases. The GLIDE database diagnoses based on the criteria of Centers for Disease Control and Prevention/American Academy of Periodontology (CDC/AAP) and Community Periodontal Index (CPI) for physical signs. While the FinnGen database adopts the International Classification of Diseases (ICD) 10 standard for a comprehensive diagnosis. The former database employs a more practical yet broader standard for periodontitis, which might encompass pseudo-periodontitis.

Finally, the observed differences could be attributed to the variations in immune responses at distinct stages of periodontitis. During the initial stages of periodontitis, neutrophils and macrophages primarily mediate the immune response. With the progression of the disease, the involvement of T cells and B cells increases, thereby leading to a more intricate immune response (Darveau, 2010). Besides, the immune system's response to these oral health conditions is not uniform and can be influenced by multiple factors, including the individual's overall health, genetics, and lifestyle, potentially impacting the results (Hung et al., 2023).

Reference

Darveau RP. 2010. Periodontitis: a polymicrobial disruption of host homeostasis. Nat Rev Microbiol 8:481–490. doi:10.1038/nrmicro2337

Hung M, Kelly R, Mohajeri A, Reese L, Badawi S, Frost C, Sevathas T, Lipsky MS. 2023. Factors Associated with Periodontitis in Younger Individuals: A Scoping Review. J Clin Med 12:6442. doi:10.3390/jcm12206442

Reviewer #2 (Public Review):

This manuscript presents a well-designed study that combines multiple Mendelian randomization analyses to investigate the causal relationship between circulating immune cells and periodontitis. The main conclusions of the manuscript are appropriately supported by the statistics, and the methodologies used are comprehensive and rigorous.

These findings have significant implications for periodontal care and highlight the potential for systemic immunomodulation management on periodontitis, which is of interest to readers in the fields of periodontology, immunology, and epidemiology.

We greatly appreciate the positive feedback and valuable insights provided by the reviewer, which have significantly contributed to the improvement of our manuscript.

Reviewer #2 (Recommendations for The Authors):

*Abstract

Line 30-32: "Two-sample bidirectional univariable MR followed by sensitivity testing, multivariable MR, subgroup analysis, and the Bayesian model averaging (MR-BMA) were performed to explore the causal association between them. " What does the term "them" refer to here, please clarify it. The research method here is unclear, please reorganize it.

Line 39: "S100A9 and S100A12" here should be italic.

We appreciate your meticulous suggestions and have revised the methods section accordingly. Additionally, the two genes have been highlighted in italics for emphasis.

"Univariable MR, multivariable MR, subgroup analysis, reverse MR, and Bayesian model averaging (MR-BMA) were utilized to investigate the causal relationships. Furthermore, transcriptome-wide association study (TWAS) and colocalization analysis were deployed to pinpoint the underlying genes." (Page 1)

Introduction

Line 78-80: "As reported, the number of immune cells in periodontal tissue changes as periodontitis progresses, featuring an increase in monocytes, and B cells and a decrease in T cells." Does the author mean that both monocytes and B cells increase as periodontitis progresses?

We are grateful for your meticulous reading and perceptive inquiries. We would like to confirm the accuracy of your understanding. In lines 78-80, our intended message was to communicate that with the progression of periodontitis, there is an increase in both monocytes and B cells in the periodontal tissue. This represents a typical immune response to the infection, where these cells play a pivotal role in counteracting periodontal pathogens. To enhance clarity, we have revised these lines in the manuscript as follows:

"With the progression of periodontitis, there is a significant alteration in the quantity of immune cells present within the periodontal tissue. Specifically, an increase in the count of both monocytes and B cells is observed, whereas a decrease is noted in the count of T cells." (Page 3)

Method

Line 164-165: "As the main test, the MVMR-IVW method, offered by the MVMR-least absolute shrinkage and selection operator (MVMR-LASSO), and the MVMR-Egger method were chosen." The author's expression here is ambiguous.

In response to your comment on the ambiguity in lines 164-165, we have revised the sentence for clarity. We hope this addresses your concern and clarifies our point more effectively.

"The MVMR-IVW method was utilized as the primary test, supplemented by the MVMR-least absolute shrinkage and selection operator (MVMR-LASSO) and the MVMR-Egger method." (Page 7)

Table 1: FinnGen has a greater sample size and more SNPs than GLIDE; why do authors choose the latter as the primary analysis?

Our choice to utilize GLIDE as the primary analysis tool, instead of FinnGen, was mainly guided by the specific research question we aimed to address. Despite FinnGen offering a larger sample size and more SNPs, GLIDE offers a more specialized and targeted dataset that suits the unique requirements of our study. In most MR studies, a similar strategy is adopted, wherein a large database of disease GWAS meta is utilized for exploration, followed by validation in full phenotype cohort (such as UKBiobank and FinnGen) (Liu et al., 2023; Yuan et al., 2023). To summarize, the reasons may primarily include the following:

Firstly, GLIDE offers a concentrated and targeted methodology for examining genetic data pertinent to periodontitis. This dataset is grounded in a comprehensive meta-analysis of cohorts centered on periodontitis, wherein the ratio of periodontitis cases to control groups is approximately 1:2. Conversely, the proportion in FinnGen seems to be negligible, given that it employs a dataset derived from a comprehensive phenotype cohort. Consequently, employing the GLIDE database as a primary investigative tool can generate more abundant genetic information associated with periodontitis.

Furthermore, the methodological facets of GLIDE align more accurately with the analytical framework of our study. For instance, the diagnostic criteria methods vary between the two databases. The GLIDE database derives its basis from the Centers for Disease Control and Prevention/American Academy of Periodontology (CDC/AAP) and Community Periodontal Index (CPI) for physical indicators. In contrast, the FinnGen database employs the International Classification of Diseases (ICD) 10 standard for an exhaustive diagnosis. The former adopts a more pragmatic, yet broader, standard for diagnosing periodontitis. The latter continues to use concepts of diseases such as "chronic periodontitis", which have been replaced by "periodontitis" in the latest disease classification from the "2017 World Workshop on the Classification of Periodontal and Peri-Implant Diseases and Conditions" in the periodontal field (Caton et al., 2018).

Reference

Caton JG, Armitage G, Berglundh T, Chapple ILC, Jepsen S, Kornman KS, Mealey BL, Papapanou PN, Sanz M, Tonetti MS. 2018. A new classification scheme for periodontal and peri-implant diseases and conditions - Introduction and key changes from the 1999 classification. J Clin Periodontol 45 Suppl 20:S1–S8. doi:10.1111/jcpe.12935

Liu Y, Lai H, Zhang R, Xia L, Liu L. 2023. Causal relationship between gastro-esophageal reflux disease and risk of lung cancer: insights from multivariable Mendelian randomization and mediation analysis. Int J Epidemiol 52:1435–1447. doi:10.1093/ije/dyad090

Yuan S, Xu F, Li X, Chen J, Zheng J, Mantzoros CS, Larsson SC. 2023. Plasma proteins and onset of type 2 diabetes and diabetic complications: Proteome-wide Mendelian randomization and colocalization analyses. Cell Rep Med 4:101174. doi:10.1016/j.xcrm.2023.101174

Result

Line 224: "The observed significant results remained robust after removing pleiotropic SNPs." It is not clear what the authors mean by "remain robust".

Line 229-231: "The causal relationship between neutrophils and periodontitis remained stable with no evidence of heterogeneity or pleiotropy." It is also not clear what the authors mean by "remain stable". How does the author get to the conclusion that there is no evidence of heterogeneity or pleiotropy?

Figure S5: Please offer a brief explanation on how to investigate outlier or influential changes using scatter plots and Cochran's Q test and Cook's distance.

Line 224: We apologize for the confusion caused by the term "remain robust". In the revised manuscript, we clarified this by stating, "The observed significant results are considered 'robust' if the effect of sensitivity analyses was identical to that of Inverse Variance Weighted (IVW) method, yielding a P-value less than 0.05." (Page 6)

Line 229-231: We used the terms "remain stable" and "remain robust" interchangeably to express the same idea. To clarify, we have now unified the expression in the revised manuscript. As for the conclusion of "no evidence of heterogeneity or pleiotropy", it is derived from the results of Cochran's Q and Egger's intercept tests (P<0.05). We have added this explanation to the revised manuscript for better clarity.

Figure S5: In the revised manuscript and Table, we have provided a succinct explanation regarding the investigation of outliers or influential changes as follows: " A genetic variant was defined as either an outlier or an influential variant if it possessed a q-value exceeding 10 or if its Cook's distance surpassed the median of the corresponding F-distribution. " (Page 7)

We have made all the necessary changes in the revised manuscript based on your comments. We hope our responses and revisions adequately address your concerns.

Discussion

I have consulted several pieces of literature to ensure a thorough explanation, which may be helpful for your writing.

(1) Hajishengallis G, Li X, Divaris K, Chavakis T. Maladaptive trained immunity and clonal hematopoiesis as potential mechanistic links between periodontitis and inflammatory comorbidities. Periodontol 2000. 2022;89(1):215-230. doi:10.1111/prd.12421

(2) Hajishengallis G, Chavakis T. Mechanisms and Therapeutic Modulation of Neutrophil-Mediated Inflammation. J Dent Res. 2022;101(13):1563-1571. doi:10.1177/00220345221107602

We appreciate your valuable feedback and the additional references you provided to enrich our manuscript. Upon receiving your comments, we have meticulously reviewed and incorporated the suggested literature into our revised manuscript. These references have furnished insightful information, which has been assimilated into the revised manuscript (Page 12) to enhance the explanation of the mechanisms of neutrophil-mediated inflammation and the potential association between periodontitis and inflammatory comorbidities.

"The quantity and functionality of neutrophils both act as critical indicators of inflammation severity. The reduction in neutrophil count and inflammatory mediators, observed after successful periodontitis treatment, suggests a reduction in systemic inflammation (Hajishengallis , 2022)." (Page 12)

"Trained myeloid cells have the potential to amplify the functionality of neutrophils, thereby fortifying the body's defense against subsequent infections. Nevertheless, within the framework of chronic inflammation, these cells could potentially intensify tissue damage (Hajishengallis and Chavakis, 2022)." (Page 12)

Author Response

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

We sincerely thank the reviewers for their constructive feedback. We have revised our manuscript to address some important concerns. The main changes are summarized as follows:

(1) A major concern as reflected in the eLife assessment and reviewer comments, was that the “evidence supporting the conclusion that striatal neurons encode single-limb gait is incomplete.” We have now provided an expanded analysis of gait phase-locking to different limbs in Figure 2 – figure supplement 1. The analysis reveals three key new insights: 1) most striatal neurons are significantly entrained to only one or two limbs; 2) for neurons entrained to two limbs, most limb pairs are diagonal pairs, whose phases are closely aligned; 3) the strength of phase-locking, as measured by the mean vector length, is biased toward a single limb. From these results we conclude that striatal neurons are indeed better correlated with single-limb (as opposed to multiple limbs’) gait. However, we speculate that because of the inherently correlated motion across limbs, some neurons also display significant phaselocking to multiple limbs, particularly to diagonal pairs.

(2) Reviewer 2 noted the lack of a manipulation experiment which would help establish the striatum’s relationship to gait control. We have therefore included the results of new experimental data in Figure 6 – figure supplement 2, in which we show that optogenetically activating D2 MSNs alters both some measures of whole-body motion and single-limb gait. We recognize that these experiments are not ideal, for example, the optical stimulation was not entrained to limb phase. Nevertheless, they hopefully allay any concern that the striatum is incapable of influencing gait performance.

(3) We have further characterized the relationship between vector length and firing rate, and firing rate between D1 and D2 MSNs. We now show that: 1) vector length is negatively correlated with session-wide firing rate (Figure 2 – figure supplement 1E); 2) session-wide firing rates are similar between D1 and D2 MSNs in both healthy and dopamine lesioned animals (Figure 4D and Figure 6H). Thus, the imbalance in the vector length between D1 and D2 MSNs following dopamine lesions is unlikely to be explained by changes in the overall firing rates of these cells.

(4) We have added new data similar to Figure 1 with distributions of stride frequency, duration, and length to illustrate the difference between sham and 6OHDA mice (Figure 5 – figure supplement 1B,C).

(5) We have expanded the Discussion section to discuss a number of important points raised by the reviewers. These include: 1) speculating on the origins of gait coding in the striatum; 2) discussion of some literature which reported similar levels of D1/D2 MSN start coding in contrast to our results in healthy mice; 3) discussion of the finding that almost all phase-locked cells also have a firing rate related to speed or start/stop signals; 4) discussion of one of the limitations of the unilateral 6OHDA model, namely, the strong turning bias, and its potential implications for our results.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

Yang et al combine high-speed video tracking of the limbs of freely moving mice with in vivo electrophysiology to demonstrate how striatal neurons encode single-limb gait. They also examine encoding other well-known aspects of locomotion, such as movement velocity and the initiation/termination of movement. The authors show that striatal neurons exhibit rhythmic firing phase-locked with mouse gait, while mice engage in spontaneous locomotion in an open field arena. Moreover, they describe gait deficits induced by severe unilateral dopamine neuron degeneration and associate these deficits with a relative strengthening of gait-modulation in the firing of D2-expressing MSNs. Although the source and function of this gait-modulation remain unclear, this manuscript uncovers an important physiological correlate of striatal activity with gait, which may have implications for gait deficits in Parkinson's Disease.

Strengths:

While some previous work has looked at the encoding of gait variables in the striatum and other basal ganglia nuclei, this paper uses more careful quantification of gait with video tracking. In addition, few if any papers do this in combination with optically-labeled recordings as were performed here.

Weaknesses:

The data collected has a great richness at the physiological and behavioral levels, and this is not fully described or explored in the manuscript. Additional analysis and display of data would greatly expand the interest and interpretability of the findings.

There are also some caveats to the interpretation of the analyses presented here, including how to compare encoding of gait variables when animals have markedly different behaviors (eg comparing sham and unilaterally 6-OHDA treated mice), or how to interpret the loss of gait modulation when single unit activity is overall very low.

(1) The authors use circular analysis to quantify the degree to which striatal neurons are phaselocked to individual limbs during gait. The result of this analysis is shown as the proportion of units phase-locked to each limb, vector length, and vector angle (Fig 2H-K; Fig 4E-F; Fig 6E-F). Given that gait is a cyclic oscillation of the trajectories of all four limbs, one could expect that if one unit is phase-locked to one limb, it will also be phase-locked to the other three limbs but at a different phase. Therefore, it is not clear in the manuscript how the authors determine to which limb each unit is locked, and how some units are locked to more than one limb (Fig 2H). More methodological/analytical detail would be especially helpful.

We thank the reviewer for raising this important issue, which was not sufficiently explored in our original manuscript. This relates to a major concern that “evidence supporting the conclusion that striatal neurons encode single-limb gait is incomplete.” We have now prepared a new figure supplement to address whether neurons are preferentially entrained to only one or multiple limbs (Figure 2 – figure supplement 1, panels A-C).

Author response image 1.

Panels A-C. Phase-locking to different limbs.

Panel A shows the percentage of striatal neurons (all neurons including untagged cells) with significant phase-locking to only 1, 2, 3, or all 4 limbs. The results indicate that most phaselocked cells are entrained to either only 1, or only 2 limbs, as opposed to 3 or all 4 limbs. We next looked more closely at the cells which were entrained to only 2 limbs: Panel B shows that a significant majority of those cells were coupled to diagonal limb pairs. This finding is insightful because diagonal limb pairs move at nearly the same phase during walking, thus some overlap in phase-locking to these limbs is to be expected. Finally, Panel C shows the mean vector length per neuron ranked from the highest to lowest value. The results reveal that the vector length is significantly biased toward the highest ranked limb. This bias would be absent if neurons were entrained to all 4 limbs with similar strength. Together, these results support the conclusion that striatal neuron spiking is preferentially coupled to single limbs as opposed to multiple limbs. However, we speculate that because of the inherently correlated motion across limbs, some neurons also display significant phase-locking to multiple limbs, particularly to diagonal pairs.

(2) In Figures 2 and 3, the authors describe the modulation of striatal neurons by gait, velocity, and movement transitions (start/end), with most of their examples showing firing rates compatible with rates typical of striatal interneurons, not MSNs. In order to have a complete picture of the relationship between striatal activity and gait, a cell type-specific analysis should be performed. This could be achieved by classifying units into putative MSN, FS interneurons, and TANs using a spike waveform-based unit classification, as has been done in other papers using striatal single-unit electrophysiology. An example of each cell type's modulation with gait, as well as summary data on the % modulation, would be especially helpful.

We appreciate the reviewer’s suggestion to analyze our data after classifying units into different putative cell types (MSN, FSI, TAN). Indeed, we have frequently adopted this practice in our other publications (e.g., Bakhurin & Masmanidis 2016, 2017; Lee & Masmanidis 2019). However, this study already relies on a more rigorous method – optogenetic tagging – to identify D1 and D2 MSNs. We felt that adding a second, more subjective and therefore less rigorous identification method based on spike waveforms would add unnecessary confusion in how the results are presented and interpreted. For example, we were unsure how to address the situation where an opto-tagged D1 or D2 MSN may be classified as a putative FSI or TAN according to spike waveform criteria. For this reason, we decided not to perform an analysis by putative MSN, FSI, and TAN. Finally, we have made all our electrophysiological data available should someone want to perform this analysis themselves.

(3) By normalizing limb trajectories to the nose-tail axis, the analysis ignores whether the mouse is walking straight, or making left/right turns. Is the gait-modulation of striatal activity shaped by ipsi- and contralateral turning? This would be especially important to understand changes in the unilateral disease model, given the imbalance in turning of 6-OHDA mice.

This is an important question, which our data are unfortunately underpowered to address. Lesioned mice turn sharply for nearly the entire duration of walking, while healthy mice walk in a nearly straight line, with occasional brief turning bouts. Thus, we do not have sufficient stride numbers during healthy turning to enable a rigorous analysis of gait phase locking during left/right turns. This raises some questions about the interpretation of the higher D2 MSN vector length in dopamine lesioned mice – does the higher vector length relate to the impaired gait, or the higher incidence of turning in this PD model? We have acknowledged this issue in the Discussion section as a limitation of the unilateral 6OHDA model. And, in future work we hope to investigate turning effects in more detail using behavioral arenas which force animals to turn left or right at specific locations.

(4) It looks like the data presented in Figure 4 D-F comes from all opto-identified D1- and D2MSNs. How many of these are gait-modulated? This information is missing (line 110). Pooling all units may dilute differences specific to gait-modulated units, therefore a similar analysis only on gait-modulated units should be performed.

The reviewer is correct that the data presented in Figure 4 comes from all optogenetically tagged cells. We have now included a new panel, Figure 4H, which shows the proportion of D1 and D2 MSNs which encode limb phase, body speed, or start/stop. The reviewer suggested that a similar analysis only gait-modulated units should be performed. We prefer to stick to our current approach (of using all cells, regardless of whether they show significant gait modulation) because it is less biased. For example, even cells which do not pass our threshold for statistical significance may display weak but visible gait modulation.

(5) Since 6-OHDA lesions are on the right hemisphere, we would expect left limbs to be more affected than right limbs (although right limbs may also compensate). It is therefore surprising that RF and RR strides seem slightly shorter than LF and LR (Fig 5G), and no differences in other stride parameters (Fig 5H-J). Could the authors comment on that? It may be that this is due to rotational behavior. One interesting analysis would be to compare activity during similar movements in healthy and 6-OHDA mice, eg epochs in which mice are turning right (which should be present in both groups) or walking a few steps straight ahead (which are probably also present in both groups).

Unilateral 6OHDA lesions are associated with ipsiversive turning (in this case, toward the right). The reviewer noted that the stride length is shorter for the two right compared to the two left limbs (Figure 5G), which is consistent with a right turning bias. In line with this observation, the stride speed for the right limbs also seemed slower than for the left limbs (Figure 5I), though we agree this is a bit difficult to see in the plot due to the choice of y-axis range. We appreciate the reviewer’s suggestion to analyze activity during similar movements in healthy and lesioned mice. As discussed in reply to their third comment above, our data did not contain sufficient bouts of straight walking in lesioned mice, or turning in healthy mice, to make such analysis possible. We have acknowledged this issue in the Discussion section as a limitation of the unilateral 6OHDA model. And, in future work we hope to investigate turning effects in more detail using behavioral arenas which force animals to turn left or right at specific locations.

(6) Multiple publications have shown that firing rates of D1-MSN and D2-MSN are dramatically changed after dopamine neuron loss. Is it possible that changes observed in gait-modulation might be biased by changes in firing rates? For example, dMSNs have exceptionally low overall activity levels after dopamine depletion (eg Parker...Schnitzer, 2018; Ryan...Nelson, 2018; Maltese...Tritsch, 2021); this might reduce the ability to detect modulation in the firing of dMSNs as compared to iMSNs, which have similar or increased levels of activity in dopamine depleted mice. Does vector length correlate with firing rate? In addition, the normalization method used (dividing firing rate by minimum) may amplify very small changes in absolute rates, given that the firing rates for MSN are very low. The authors could show absolute values or Z-score firing rates (Figure 6 A, D).

The reviewer asked a number of important questions here. First, is it possible that changes in gait modulation are biased by changes in firing rates? We have included a new analysis comparing the average session-wide firing rate of D1 and D2 MSNs (Figure 6D & 6H). This showed that firing rates were statistically similar between D1 and D2 MSNs for both sham and dopamine lesioned mice. Thus, it seems unlikely that the imbalance in vector length is purely due to changes in firing rate. The reviewer referenced some literature (e.g. Parker & Schnitzer; Ryan & Nelson; Maltese & Tritsch) which does appear to show significant changes in the relative firing levels of D1/D2 MSNs after dopamine lesions. While we can only speculate about the reason for the discrepancy (e.g., differences in measurement method, behavioral task, or analysis method), we note that not all prior literature has reported such changes (e.g., Ketzef & Silberberg 2017).

Author response image 2.

Panels D & H. No difference in firing between D1 and D2 MSNs.

Second, does vector length correlate with firing rate? Interestingly, we found that indeed it does. We now show that vector length is negatively correlated with firing rate (Figure 2 – figure supplement 1E), implying that cells with higher overall firing rates tend to have weaker phaselocking to the gait cycle. Though not shown in the manuscript, we found a similar negative correlation for D1 and D2 MSNs in both healthy and dopamine lesioned mice.

Author response image 3,

Panel E. Vector length is negatively correlated to firing rate.

Third, the reviewer asked about our normalization method in Figure 6A etc, in which we divide by the minimum rate. We would like to clarify that this normalization method was only used for visualizing our data, but not for calculating the vector length. Therefore, we chose to leave the plots as they are.

(7) The analysis shown in Fig 3C should also be done for opto-identified D1- and D2-MSNs (and for waveform-based classified units as noted above).

We have now performed the same analysis for optogenetically tagged D1 and D2 MSNs from healthy mice (Figure 4H). As with our original analysis, both populations showed a similar proportion of neurons which encoded limb phase, start of movement, body speed, and the combination of these. We did not perform this analysis for waveform-based classified units as per our reason outlined in reply to the reviewer’s second comment above.

Author response image 4.

Panel H. Venn diagrams showing the percentage of D1 and D2 MSNs with significant responses to limb phase of at least one limb, body speed, and start and/or stop of motion.

(8) Discussion: the origin of the gait-modulation as well as the possible mechanisms driving the alterations observed in 6-OHDA mice should be discussed in more detail.

Our Discussion section includes the following paragraph speculating on the origin of gait modulation: “Movement-related neural activity is widespread in many brain areas, and it is plausible that the striatum receives both motor and sensory signals involved in gait generation. For example, the primary motor cortex, which projects to dorsal striatum, has been shown to exhibit rhythmic spiking activity consistent with gait phase coding (Armstrong & Drew 1984), suggesting a shared mechanism underlying the production of this code.” We appreciate the request to also discuss the possible mechanisms driving the alterations in 6OHDA mice. But this is a very complex topic which our study is not aimed at addressing. The range of possible mechanisms uncovered in the literature is vast – from synaptic changes in striatal microcircuits, to altered intrinsic excitability of D1/D2 MSNs, and network-level alterations. Therefore, we preferred to keep the discussion focused on gait and movement coding.

Reviewer #2 (Public Review):

Summary:

Yang et al. recorded the activity of D1- and D2-MSNs in the dorsal striatum and analyzed their firing activity in relation to single-limb gait in normal and 6-OHDA lesioned mice. Although some of the observations of striatal encoding are interesting, the novelty and implications of this firing activity in relation to gait behavior remain unclear. More specifically, the authors made two major claims. First, the striatal D1- and D2-MSNs were phase-locked to the walking gait cycles of individual limbs. Second, dopamine lesions led to enhanced phase-locking between D2-MSN activity and walking gait cycles. The second claim was supported by the increase of vector length in D2-MSNs after unilateral 6-OHDA administration to the medial forebrain bundle. However, for the first claim, the authors failed to convincingly demonstrate that striatal MSNs were more phase-locked to gait with single-limb and step resolution than to the global gait cycles.

We thank the reviewer for their feedback and for their comment that “the authors failed to convincingly demonstrate that striatal MSNs were more phase-locked to gait with single-limb and step resolution than to the global gait cycles.” We now present new analysis demonstrating that neurons are more phase-locked to single-limb gait rather than multiple limbs (Figure 2 – figure supplement 1, panels A-C). These results are discussed in detail in response to Reviewer #1’s first comment. For conciseness we will not repeat the same response here but instead refer the reviewer to Reviewer #1, comment #1.

Strengths:

It is a technically advanced study.

Weaknesses:

(1) The authors focused on striatal encoding of gait information in current studies. However, it remains unclear whether the part of the striatum for which the authors performed neuronal recording is really responsible for or contributing to gait control. A lesion or manipulation experiment disrupting the part of the striatum recorded seems a necessary step to test or establish its relationship to gait control.

We agree that our study – like many others which employ recordings – is largely correlative, and that a direct causal relationship was lacking. We have therefore decided to present some data which, despite some caveats, shows that the striatum is in principle capable of altering gait performance (Figure 6 – figure supplement 2).

Author response image 5.

Optogenetic activation of D2 MSNs alters whole-body movement and single-limb gait.

These new results are from healthy mice (n=4) receiving optogenetic stimulation of D2 MSNs over a 5 minute period. Panels A-E show changes in a variety of whole-body measures of motion, mostly replicating the results of Kravitz & Kreitzer 2010. Panels F-I show changes (statistically significant or trending) in a variety of gait parameters, with the greatest effects found on the single-limb stride duration and stride speed. Interestingly, Kravitz & Kreitzer 2010 actually examined effects of this stimulation on gait; quoting from their paper: “we examined gait parameters in D1-ChR2 and D2-ChR2 mice in response to illumination, using a treadmill equipped with a high-speed camera. We quantified multiple gait parameters with the laser on and off, and found no significant differences in the average or variance of stride length, stance width, stride frequency, stance duration, swing duration, paw angle and paw area on belt for either line….This indicates that activation of direct and indirect pathways in the dorsomedial striatum regulates the pattern of motor activity, without changing the coordination of ambulation itself.” We wonder therefore if the reviewer’s comment about causality may have stemmed from the negative result in Kravitz & Kreitzer 2010. In any event, we now present results which firmly show a link between striatal D2 MSNs and gait. To be clear, we are not claiming that Kravitz & Kreitzer’s study was fundamentally flawed, but that perhaps their ability to resolve gait changes using a commercial treadmill system, or their choice of dorsomedial as opposed to more lateral regions of the striatum may have contributed to the negative result.

It is also important to acknowledge a limitation of our optogenetic stimulation experiment. Our optical stimulation was not phase-locked to the gait cycle; thus, technically, we did not address whether the phase code per se is involved in producing gait. We mention this caveat in the manuscript. Despite this, we believe the new data address the reviewer’s concern about lack of causality.

(2) The authors attributed one of the major novelties to phase-locking of striatal neural activities with single-limb gait cycles. The claim was not clearly supported, as the authors did not demonstrate that phase-locking to single-limb gaits was more significant than phase-locking to global walking gait cycles. In rhythmic walking, the LR and RF limbs were roughly anti-phase with the LF and RR limbs (Fig. 1D, E). In line with this relationship, striatal neurons were mainly in-phase with LR and RF limbs and anti-phase with LF and RR limbs (Fig. 2J, K). One could instead interpret this as the striatal neurons spanned all the phases of the global walking gait cycles (Fig. 3D). To demonstrate phase-locking with individual limb movements, the authors need to show that neural activities were better correlated with a specific limb than to the global gait cycles.

We sincerely appreciate the reviewer’s comment. As described above we now present new analysis demonstrating that neurons are more phase-locked to single-limb gait rather than multiple limbs (Figure 2 – figure supplement 1, panels A-C). These results are discussed in detail in response to Reviewer #1’s first comment. For conciseness we will not repeat the same response here but instead refer the reviewer to Reviewer #1, comment #1.

(3) The observation of the enhancement of coupling between D2 MSN firing and the gait cycles was interesting, but the physiological interpretation was not clear (as the authors also noted in the Discussion), which hampers the significance of the observation.

In the Discussion we comment on the potential behavioral significance of our findings, keeping in mind the reviewer’s earlier concern about the correlative nature of recordings. For example, we speculate that the increase in D2 MSN limb phase-locking strength contributes to bradykinetic symptoms, specifically the production and maintenance of a normal gait cycle and rhythm. We respectfully disagree with the reviewer about the limited significance of the observations, as this is the first study to describe striatal gait phase coding in detail, noting that gait impairments are a major motor symptom in PD. We believe that progress in better understanding and eventually treating PD will be made through a combination of correlative observations (i.e., neural recordings) and causal manipulations. There are both advantages and disadvantages to correlative as well as causal experiments.

(4) Due to the lack of causality experiments as mentioned in the first comment above, the observations of coupling between striatal neuronal activity and gait control might well result from a third brain region/factor serving as the common source to both, whether in normal or dopamine lesioned brain. If this is the case, the significance and implications of current findings will be greatly limited.

As mentioned above we have included new data to address this concern (Figure 6 – figure supplement 2). Please refer to Reviewer #2, comment #4 for a detailed discussion of these results and their caveats.

Reviewer #3 (Public Review):

In this study, Yang et al. address a fundamental question of the role of dorsal striatum in neural coding of gait. The authors study the respective roles of D1 and D2 MSNs by linking their balanced activity to detailed gait parameters. In addition, they put in parallel the striatal activity related to whole-body measures such as initiation/cessation of movement or body speed. They are using an elegant combination of high-resolution single-limb motion tracking, identification of bouts of movements, and electrophysiological recordings of striatal neurons to correlate those different parameters. Subpopulations of striatal output neurons (D1 and D2 expressing neurons) are identified in neural recordings with optogenetic tagging. Those complementary approaches show that a subset of striatal neurons have phase-locked activity to individual limbs. In addition, more than a third of MSNs appear to encode all three aspects of motor behavior addressed here, initiation/cessation of movement, body speed, and gait. This activity is balanced between D1 and D2 neurons, with a higher activity of D1 neurons only for movement initiation. Finally, alterations of gait, and the associated striatal activity, are studied in a mouse model of Parkinson's Disease, using 6-OHDA lesions in the medial forebrain bundle (MFB). In the 6OHDA mice, there is an imbalance toward D2 activity.

Strengths:

There is a long-standing debate on the respective role of D1 and D2 MSNs on the control of movement. This study goes beyond prior work by providing detailed quantification of individual limb kinematics, in parallel with whole-body motion, and showing a high proportion of MSNs to be phase-locked to precise gait cycle and also encoding whole-body motion. The temporal resolution used here highlights the preferential activity of D1 MSN at the movement starts, whereas previous studies described a more balanced involvement. Finally, they reveal neural mechanisms of dopamine depletion-induced gait alterations, with a preponderant phase-locked activity of D2 neurons. The results are convincing, and the methodology supports the conclusions presented here.

Weaknesses:

Some more detailed explanations would improve the clarity of the results in the corresponding section. Analysis of the 6OHDA experiments could be expanded to extract more relevant information.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

(1) Panels I and J from Figure 6 are referred to in the text (line 158) but they don't exist.

Thank you, we have corrected this in the text.

(2) For the classification of striatal units into putative MSN, FS interneurons, and TANs, see Gage et al. DOI: 10.1016/j.neuron.2010.06.034 or Thorn et al. DOI: 10.1523/JNEUROSCI.178213.2014.

As explained in the Public Reviews, Reviewer #1 comment #2 we opted not to perform an analysis by putative MSN, FSI, and TAN. We have performed analysis of different putative cell types in several of our other publications (e.g., Bakhurin & Masmanidis 2016, 2017; Lee & Masmanidis 2019). However, this study already relies on a more rigorous method – optogenetic tagging – to identify D1 and D2 MSNs. We felt that adding a second, more subjective and therefore less rigorous identification method based on spike waveforms would add unnecessary confusion in how the results are presented and interpreted. For example, we were unsure how to address the situation where an opto-tagged D1 or D2 MSN may be classified as a putative FSI or TAN according to spike waveform criteria. For this reason, we decided not to perform an analysis by putative MSN, FSI, and TAN. Finally, we have made all our electrophysiological data available should someone want to perform this analysis themselves.

(3) The discussion section could be improved by elaborating on the origin and function of these gait signals in the striatum, as well as the mechanisms underlying changes in the 6-OHDA model. In addition, it would be important to discuss the limitations of this model, since unilateral 6-OHDA lesions may not accurately recapitulate parkinsonian gait deficits, as it results in a very asymmetric gait.

Our Discussion section includes a paragraph speculating on the origin of gait modulation in the striatum, and another paragraph addressing the limitation that unilateral 6OHDA lesions induce gait asymmetry. We appreciate the request to also discuss the possible mechanisms driving the alterations in 6OHDA mice. But this is a very complex topic which our study is not aimed at addressing. The range of possible mechanisms uncovered in the literature is vast – from synaptic changes in striatal microcircuits, to altered intrinsic excitability of D1/D2 MSNs, and network-level alterations. Therefore, we preferred to keep the discussion focused on gait and movement coding.

Reviewer #2 (Recommendations For The Authors):

(1) The authors denoted the limb movement sequences as LR-LF-RR-RF, with limbs on the same left/right side moving first. However, considering multiple gait cycles, the sequence could also be described as RF-LR-LF-RR, with movements of the diagonal limbs temporally closer to each other, which was more intuitive from the visual inspection of Fig. 1D. The LR-LF-RR-RF denotation would make more sense if the authors could demonstrate that a walking bout almost always started from LR, as seen in the two examples in Fig. 1D.

We designated the sequence as LR-LF-RR-RF to illustrate the lateral sequence pattern. But the reviewer is correct that a shifted version of this sequence, such as RF-LR-LF-RR, is also valid. We are not making any claim that the LR limb is always the first to move in a walking bout, but rather, that limbs on the same side of the body move one after the other, followed by the limbs on the opposite side. We have edited the text to hopefully clarify this point: “Mice walked with a lateral sequence gait pattern (e.g., LRLFRRRF), with the limbs on the same side of the body moving one after the other, followed by movement of limbs on the opposite side (Figure 1E).”

(2) The study identified a biased D1-MSN activation at movement initiation, which was not reported in previous studies that relied on measuring calcium dynamics. The authors attributed the difference to the temporal resolution of electrophysiological versus optic methods. The authors would probably notice that in some previous studies that relied also on optic-tagging and electrophysiological recordings, start/stop activity was not found to be different between direct and indirect pathway MSNs. The authors should discuss these studies and offer some possible explanations.

This is an oversight on our part, and we thank the reviewer for noting this. We are aware of one such study (Jin & Costa 2014); we apologize if other studies were missed. The Discussion has been updated as follows to discuss this paper: “We also note that another study employing optogenetic tagging did not find significant D1/D2 MSN differences is start/stop activity (Jin & Costa 2014). However, the movement being measured was an instrumental action (rewardguided lever pressing), as opposed to self-initiated motion examined in our work. This suggests either that imbalances between D1 and D2 MSN start activity may be more pronounced under specific behavioral conditions, or that results vary depending on how movement initiation and cessation events are identified.”

(3) The authors could add some denotations to the peak firing rates in Fig. 3D to aid visualization, so that readers could get a sense of the distribution of neurons preferring each phase of the movements.

We appreciate this suggestion. We tried adding various colored lines to denote the peak firing rates, but ultimately, we felt the lines were not helpful and potential deleterious for some readers. We thus decided not to add any lines to the plot.

(4) Although the relative strength of D1/D2-MSN coding of body speed and movement cessation was found after dopamine lesion, it seemed that D1-MSNs cessation coding, as well as D1- and D2-MSN speed coding, were all altered after dopamine lesion (Fig. S3). The authors could mention these to avoid misunderstandings.

We thank the reviewer for their observation. In the Results, we now mention that “while speed coding remained balanced between D1 and D2 MSNs, there was a substantial reduction in the speed coding score of both cell types after dopamine lesions.” The stop modulation index did not change appreciably.

Reviewer #3 (Recommendations For The Authors):

(1) A suggestion would be to put more emphasis in the title on the first parts of the study, i.e. detailed correlation between striatal activity and quantified motion, and not only focus on the dopamine depletion model.

We considered other titles, but felt that our current choice is appropriate given that the study’s climax is with the dopamine lesion results in Figures 5 & 6.

(2) The calculation and the significance of the vector length should be more detailed in the results as it is used all along as a measure of "the strength of neural entrainment to the gait cycle".

We have added the following statement in the Results section to clarify the significance of vector length: “The vector length is a unitless parameter which can theoretically vary from 0 to 1, with 0 representing a neuron whose spikes occur at random limb phases, and 1 representing a neuron which always spikes at the same phase. Thus, higher vector length indicates a stronger entrainment of spiking activity to a specific limb phase.” For details on how vector length is calculated we refer readers to our Methods, specifically the section entitled “Gait phase coding analysis.”

(3) There is no difference in the ipsi- or contralateral limbs while recordings are made only in the right hemisphere. Given that MSNs receive inputs from IT and PT neurons from the motor cortex, would it not be expected to have differences in the phase-locked activity to right versus left limbs? This is a question also with the dopamine depletion model which is performed with unilateral 6OHDA injections.

This is something we also wondered and were somewhat surprised by the lack of a contralateral bias in the phase locking vector length, as shown in Figure 2 – figure supplement 1D. We have two hypotheses as to why there is no ipsi/contra-lateral bias. First, it is possible that striatal neurons receive similar levels of synaptic input signaling ipsi/contra-lateral limb movements. Second, the strongly correlated motion of diagonally opposed limbs may give the appearance that neurons that are phase-locked to one limb (e.g., LF) are also locked to the diagonally opposite limb (i.e., RR). We see evidence of this diagonal limb coupling in Figure 2 – figure supplement 1B.

(4) Among the 45% of striatal neurons that display significant phase-locking to at least one limb, it would be interesting to describe the % of neurons being phase-locked to several limbs and whether they are specific subtypes. Are there animals with more phase-locked cells in several limbs?

This is indeed a very interesting and important point which relates to the major concern that “evidence supporting the conclusion that striatal neurons encode single-limb gait is incomplete.” As described above we now present new analysis demonstrating that neurons are more phaselocked to single-limb gait rather than multiple limbs (Figure 2 – figure supplement 1, panels AC). These results are discussed in detail in response to Reviewer #1’s first comment. For conciseness we will not repeat the same response here but instead refer the reviewer to Reviewer #1, comment #1. With regard to whether there are specific subtypes, we performed the same analysis on optogenetically identified D1/D2 MSNs and found similar trends, but did not show these results in the manuscript to avoid redundancy.

(5) The Venn diagram in Fig. 3C shows ~40% of striatal cells encoding body speed, single-limb and start/stop information. Nevertheless, this percentage is limited by the number of single-limb phase-locked cells as almost all have a firing rate related to body speed and start/stop signals. This could be discussed.

This is a very interesting observation. Basically, the reviewer is noting that almost all the phaselocked cells also encode start/stop and/or speed. We have now updated the Discussion to specifically discuss this observation: “We found a different percentage of striatal neurons which encoded limb phase, movement initiation or cessation, and speed (Figure 3). Among these three categories, limb phase coding cells represented the smallest population with ~45% of neurons, as opposed to ~90% for start/stop or speed. In addition, nearly all phase coding cells were also significantly responsive to start/stop or speed, whereas a sizable proportion of start/stop or speed coding cells were not entrained to limb phase. It is unclear, however, whether these population size differences reflect a proportionally smaller role for the striatum in regulating single-limb gait as opposed to whole-body movement initiation, cessation or speed.”

(6) D1/D2 analysis:

For optogenetic identification of D1 and D2 neurons, 39 D1 neurons and 40 D2 neurons were extracted from the total of 274 recorded neurons while 222 neurons were optogenetically tagged according to the mat and meth. Were there any technical difficulties that made it difficult to identify more neurons?

The low yield of optogenetic tagging is quite common in the literature due to the rigorous criteria which must be satisfied in order to qualify as a tagged neuron (e.g., Kvitsiani & Kepecs 2013). The number 222 neurons quoted in the methods reflects the entirety of optogenetically tagged neurons in this study. Our study contained 33 mice, thus the average number of tagged units per animal was 222/33 ~ 6.7 units/animal. This is actually comparable to or slightly better than the yield reported in some other striatal literature (see for example, Figure 1 of Ryan & Nelson 2018).

It is mentioned that "a subset" of these were phase-locked to a single limb. It would be interesting to specify the exact percentage of those neurons for D1 and D2 populations.

Phase-locking of D2 neurons seems less sharp than D1 neurons, with a lower firing rate (Fig. 4D), please comment. Also difference in vector length for LR while none for other limbs, why? There is a balanced activity of D1 and D2 MSNs during walking (speed) and single-limb movements, but more D1 MSNs active at movement initiation. Is it also true for stop signals? Are they separated based on the speed threshold of 20 mm/s?

As mentioned above, our new analysis specifically examines the percentage of all neurons which are phase locked to a single limb (Figure 2 – figure supplement 1, panels A-C). We have performed the same analysis on optogenetically tagged D1/D2 MSNs and found similar trends, but not show these results in the manuscript to avoid redundancy. With regard to whether phase-locking of D2 is less sharp than D1 MSNs, the “sharpness” of phase-locking is characterized by the mean vector length. And we show that on average, the vector length is statistically the same for D1 and D2 MSNs in healthy mice (Figure 4F). The reviewer noted that the D2 vector length in Figure 4F appears visibly higher for LR while not for other limbs, however, this difference is not statistically significant. With regard to whether more D1 MSNs are active during movement cessation, we show that both sham and dopamine lesioned mice have similar levels of D1/D2 MSN activity during stop (Figure 6 – figure supplement 1, panels A & B). Details of how start, stop, and speed are calculated are provided in the Methods.

The relationship between firing and body speed (Fig. 4H) displays differences between D1 and D2. If a speed inferior to 20 mm/s, corresponds to "start or stop signal" as mentioned in the mat and meth, then early difference would correspond to start, but still there is a difference between 20 and 100 mm/s and after 150 mm/s. These results should be commented on.

The reviewer is correct that in the plot of firing rate vs body speed (Figure 4J), there visibly appears to be a difference between D1 and D2 MSNs at low speeds. However, according to our pre-determined measure of speed coding which relies on the correlation coefficient between firing rate and speed, D1 and D2 MSNs have similar speed coding indices. Since there is a precedent for using the correlation coefficient to quantify speed coding (Fobbs & Kravitz 2020; Kropff & Moser 2015), we prefer to stick with this measure despite some caveats. Furthermore, the apparent difference between D1 and D2 MSNs in Figure 4J is not seen in either sham or dopamine lesioned mice (Figure 6 – figure supplement 1, panels D & E). Taken together, we do not believe the apparent speed coding difference in Figure 4J rises to the level of a consistent result.

(7) The timing of normalized firing rate in relation to start/stop signals might be also quite interesting to comment on. D1 neurons have stronger activation for start signals and it seems that it is also earlier, with D2 activated after the onset of the movement (Fig. 4G).

We appreciate the observation that D1 neurons appear to fire a little earlier than D2 neurons in Figure 4I. However, this did not rise to the level of a statistically significant result by our attempted quantitative analysis (not shown). Furthermore, the earlier timing of D1 is not apparent in sham lesioned animals in Figure 6I, thus overall we cannot make any confident statements about earlier timing of D1 start signals.

In dopamine lesion experiments, in sham mice, it seems that both D1 and D2 have higher activity after the onset of the movement and that the peak of D2 activity is earlier (Fig. 6G). In 6OHDA mice, both peaks are after the onset of the movement although they are much less clearly defined.

Both peaks become less sharp after 6OHDA lesions, but in terms of amplitude the main effect is a reduction in the D1 start signal. This is reflected in the reduced D1 start modulation index whereas the D2 index remains relatively constant.

(8) 6OHDA model displays much fewer walking bouts with lower speed and initiation rate. It would be important to include in the figure a similar representation to Fig.1 with distributions of stride frequency, duration, and length to illustrate the difference between control and 6OHDA mice. On average, how many walking bouts were analyzed in control and 6OHDA animals?

We have added new data similar to Figure 1 with distributions of stride frequency, duration, and length to illustrate the difference between sham and 6OHDA mice (Figure 5 – figure supplement 1, panels B & C). We also added the following information on the number of walking bouts: “The mean number of walking bouts per session was reduced from 124 ± 42 in sham to 47 ± 19 in dopamine lesioned mice (mean ± SD).”

The initiation rate is particularly low in 6OHDA animals, 3-4 per minute, did the authors make longer behavioral recordings to extract enough initiation/stop signals for neural correlation analysis?

All of our recordings were of the same duration (30 minutes). This duration was pre-determined at the beginning of the study to ensure consistency.

The stride length seems smaller on the right limbs in 6OHDA mice and vector length in D2 neurons as well, while there is no change in D1 neurons. Is it a significant effect? If yes, it would be important to comment on this.

The ANOVA test in those figures was not designed to perform post-hoc multiple comparisons between different limbs. However, if one changes the ANOVA design then the effect for stride length is significant. This is probably related to the ipsiversive turning bias in the unilateral 6OHDA lesion model. Though we have not changed the ANOVA design, in the Discussion we do comment on the shorter stride length on the right limbs in 6OHDA mice in Figure 5G. There is no significant difference in D2 vector length between different limbs.

eLife assessment

This valuable work extends previous studies showing that the striatum multiplexes various aspects of locomotion, including velocity and movement transitions, by demonstrating that striatal neurons also encode single-limb gait. The authors present solid evidence to show that gait deficits induced by severe unilateral dopamine depletion are associated with an imbalance in the gait modulation of striatal firing. Although the source and function of this gait modulation remain unclear, this manuscript uncovers a role of striatal activity in gait, which may have implications for understanding gait disturbances in Parkinson's Disease.

Reviewer #1 (Public Review):

Summary:

The authors combined high-speed video tracking of the limbs of freely moving mice with in vivo electrophysiology to demonstrate how striatal neurons encode single-limb gait. They also examine encoding other well-known aspects of locomotion, such as movement velocity and the initiation/termination of movement. The authors show that striatal neurons exhibit firing phase-locked with mouse gait at the single limb but also multi-limb level. Moreover, they describe gait deficits induced by severe unilateral dopamine neuron degeneration, and associate these deficits with a relative strengthening of gait-modulation in the firing of D2-expressing MSNs. Although the source and function of this gait-modulation remain unclear, this manuscript uncovers an important physiological correlate of striatal activity with gait, which may have implications for gait deficits in Parkinson's Disease.

Strengths:

While some previous work has looked at the encoding of gait variables in the striatum and other basal ganglia nuclei, this paper uses more careful quantification of gait with video tracking, comparing healthy and 6-OHDA-treated mice in the open field. The authors have collected a relatively large dataset of optically-identified striatal recordings to shed light on similarities and differences in the encoding of gait by striatal direct and indirect pathway neurons

Weaknesses:

There are some caveats to the interpretation of the analyses presented here, including how to compare encoding of gait variables when animals have markedly different behaviors (eg comparing sham and unilaterally 6-OHDA treated mice). The authors now address this caveat in the Discussion.

In an effort to causally link striatal firing to gait, the authors have added data from N=4 mice in which D2-expressing MSNs are optogenetically activated, and measured the resulting changes in gait parameters. As the authors note, this experiment does not directly get at the question of whether gait modulation of firing in the striatum contributes to the kinematics of gait (an experiment in which they altered the pattern of firing, to reduce modulation, would likely be needed). Given that this experiment has very low N and there are no included controls (eg mice expressing a control construct with optical stimulation), I do not think this data should be included in the manuscript. I think commenting in the Discussion that causal experiments will be needed in the future is adequate.

Many of the examples, as well as the average firing rates shown, are higher than typical for MSNs as reported in the literature. This is true even of the optically identified units that are shown in Figure 4. This may reflect the inclusion of neurons with interneuron-type properties (the authors report that there were some optically identified units with interneuron properties), the inclusion of some multi-unit activity in some recordings, or differences in recording/spike sorting techniques.

Reviewer #2 (Public Review):

Yang et al. recorded the activity of D1- and D2-MSNs in the dorsal striatum and analyzed their firing activity in relation to single-limb gait in normal and 6-OHDA lesioned mice. The authors provided evidence that the striatal D1- and D2-MSNs were phase-locked to the walking gait cycles of individual limbs, and dopamine lesions led to enhanced phase-locking between D2-MSN activity and walking gait cycles.

Comments on revised version:

The authors addressed my largest concern, which questioned if D1 and D2 MSNs phase-locked to single limbs better than the global gait cycles.

As to my second major concern, which questioned the causal significance of single limb gait coding in D1 and D2 MSNs on gait control, they performed additional optogenetic experiments to establish evidence that D2 activity is causally relevant for gait pattern control. The additional experiments also closed the logic gap between dopamine lesion, D2 activity and gait control, supporting the hypothesis that dopamine affects gait control and global movement pattern via increasing D2 MSN activity.

Reviewer #3 (Public Review):

In this study, Yang et al. address a fundamental question of the role of dorsal striatum in neural coding of gait. The authors study the respective role of D1 and D2 MSNs by linking their balanced activity to detailed gait parameters. In addition, they put in parallel the striatal activity related to whole-body measures such as initiation/cessation of movement or body speed. They are using an elegant combination of high-resolution single-limb motion tracking, identification of bouts of movements and electrophysiological recordings of striatal neurons to correlate those different parameters. Subpopulations of striatal output neurons (D1 and D2 expressing neurons) are identified in neural recordings with optogenetic tagging. Those complementary approaches show that a subset of striatal neurons have phase-locked activity to individual limbs. In addition, more than a third of MSNs appear to encode all three aspects of motor behavior addressed here, initiation/cessation of movement, body speed and gait. This activity is balanced between D1 and D2 neurons, with a higher activity of D1 neurons only for movement initiation. Finally, alterations of gait, and the associated striatal activity, is studied in a mouse model of Parkinson's Disease, using 6-OHDA lesions in the medial forebrain bundle (MFB). In the 6OHDA mice, there is an imbalance toward D2 activity.

Strengths:

The study combines elegant approaches to correlate cell-specific striatal activity with specific aspects of motion and how it is affected in a PD model. The results are convincing, and the methodology supports the conclusions presented here.

Weaknesses:

All the data were not fully exploited or explained in the first version of the manuscript and the present version has been significantly improved.

There is a long-standing debate on the respective role of D1 and D2 MSNs on the control of movement. This study goes beyond prior work by providing detailed quantification of individual limb kinematics, in parallel of whole-body motion, and showing high proportion of MSNs to be phase-locked to precise gait cycle and also encoding whole-body motion. The temporal resolution used here highlights preferential activity of D1 MSN at the movement starts, where previous studies described a more balanced involvement. Finally they reveal neural mechanisms of dopamine depletion induced gait alterations, with a preponderant phase-locked activity of D2 neurons.

Author Response

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

eLife assessment

This manuscript describes valuable information on how the extraocular muscles (EOM) are preserved in a mouse model of familial Amyotrophic lateral sclerosis (ALS) that carries a G93A mutation in the Sod1 gene. The authors provide convincing evidence of how the integrity of neuromuscular junction is preserved in EOM but not in limb and diaphragm muscles of G93A mice. Overall, this interesting work provides new evidence regarding the etiopathogenesis of ALS and insights for the development of therapeutic targets to slow the loss of neuromuscular function in ALS.

Public Reviews:

Reviewer#1 (Public Review):

Summary:

The study explores the mechanisms that preserve satellite cell function in extraocular muscles (EOMs) in a mouse model of familial Amyotrophic lateral sclerosis (ALS) that carries the G93A mutation in the Sod1 gene. ALS is a fatal neuromuscular disorder driven by motor neuron degeneration, leading to progressive wasting of most skeletal muscles but not EOM. The study first established that integrity of neuromuscular junction (NMJ) is preserved in EOM but not in limb and diaphragm muscles of G93A mice, and sodium butyrate (NaBu) treatment partially improves NMJ integrity in limb and diaphragm muscles of G93A mice. They also found a loss of synaptic satellite cells and renewability of cultured myoblasts in hindlimb and diaphragm muscles of G93A mice, but not in EOM, and NaBu treatment restores myoblast renewability. Using RNA-seq analysis, they identify that exon guidance molecules, particularly Cxcl12, are highly expressed in EOM myoblasts, along with more sustainable renewability. Using a neuromuscular co-culture model, they convincingly show that AAV-mediated Cxcl12 expression in G93A myotubes enhances motor axon extension and innervation. Strikingly, NaBu-mediated preservation of NMJ in limb muscles of G93A mice is associated with elevated expression of Cxcl12 in satellite cells and improved renewability of myoblasts. These results together offer molecular insights into genes critical for maintaining satellite cell function and revealing a mechanism through which NaBu ameliorates ALS.

Strengths:

Combination of in vivo and cell culture models. Nice imaging of NMJ and associated satellite cells. Using motoneuron-myotube coculture to establish the mechanism. Tested and illustrated a mechanism through which a clinically used drug ameliorates ALS.

Weaknesses:

Data presentation could be improved (see details in the Recommendation for Authors).

It would have been nice to have included G93A motoneurons in the coculture study.

This is indeed a plan of our future study. In the revised version, we discussed the limitation of not including G93A motor neurons in the coculture assay. (Page 11, Line 445-448)

“However, it is possible that motor neurons carrying ALS mutations will respond differently to Cxcl12 mediated axon guidance than WT motor neurons. This is a limitation of the current study which will be investigated in future co-culture studies.”

Reviewer #2 (Public Review):

Summary:

The work is potentially interesting as it outlines the role of satellite cells in supporting the functional decline of skeletal muscle due to the denervation process. In this context the authors analyze the functional and molecular characteristics of satellite cells in different muscle types differently affected by the degenerative process in the ALS model.

Strengths:

The work illustrates a relevant aspect of the differences in stem cell potential in different skeletal muscles in a mouse model of the disease through a considerable amount of data and experimental models.

Weaknesses:

However, there are some criticisms of the structuring of the results:

It is not clear how many animals were used in each experimental group (Figs 1 and 2, Fig. 2-9). In particular, it is unclear whether the dots in the histograms represent biological or technical replicates. Furthermore, the gender used in experimental groups is never specified. This last point appears to be important considering the gender differences observed in the SOD1G93A mouse model.

The original quantification data and mouse gender specification were actually listed in the corresponding supplementary tables. We now added the gender specification and number of the mice used in all corresponding figure legends. The number of mice used for sorting SCs from different muscles were also specified in the Methods section in the revised manuscript. (Page 12, Line 489-493).

We also added one more supplementary figure (Figure 1-figure supplement 2) to compare the innervation status between male and female mice. The following description has been added in the updated manuscript (Page 3-4; Line 125-130):

“The data shown in Figure 1B has also been replotted to compare the innervation status between male and female mice (Figure 1- figure supplement 2). In terms of well- or partially- innervated ratios, there are no significant gender difference observed in our experimental condition, in which the muscle samples were collected at the end stage of the disease, although there is marginally lower “poorly innervated ratio” in the EDL muscle of G93A female mice compared to G93A male mice.”

However, we acknowledge that the current study has limitations to fully detect cross-gender differences in our experiments due to low “n” numbers per gender. We hope this is understandable as we have to split limited resource of ALS G93A mice between different kinds of experiments, including NMJ integrity assessment, peri-nuclear SC abundance assessment, whole muscle-qPCR, cell sorting for imaging, cell sorting for RNA-Seq, cell-sorting for qPCR, cell-sorting for neuromuscular co-culture, etc., in this pioneer study. However, we do intend to gradually build up “n” numbers for characterization of cross-gender difference in our ongoing studies.

As to what the dots in each plot represent, we have inserted the description in each relevant figure legend as detailed below:

For Fig 1, each dot represents quantification result from a single mouse. Please see Figure 1-figure supplement 1, Figure 1-figure supplement 2 and Figure 1-table supplement 1 for NMJs measured per muscle type per gender. Briefly, EDL, soleus and diaphragm muscles were from 4 male and 6 female mice per group; WT EOM group was from 4 male and 4 female mice; G93A EOM group was from 3 male and 4 female mice; G93A EOM with NaBu feeding group was from 6 female mice.

For Fig 2, each dot represents quantification result from a single mouse. Please see Figure 2-table supplement 1 for NMJs measured per muscle type per gender. Briefly, WT EDL group was from 2 male and 2 female mice; G93A EDL group was from 3 male and 3 female mice; G93A EDL with NaBu feeding group was from 2 male and 4 female mice; WT soleus group was from 2 male and 3 female mice; G93A soleus group was from 3 male and 2 female mice; G93A soleus with NaBu feeding group was from 1 male and 4 female mice; WT diaphragm group was from 1 male and 4 female mice; G93A diaphragm group was from 1 male and 4 female mice; G93A diaphragm with NaBu feeding group was from 4 female mice; WT EOM group was from 1 male and 3 female mice; G93A EOM group was from 5 female mice; G93A EOM with NaBu feeding group was from 1 male and 3 female mice.

For Fig 3, each dot in the box-and-dot plots represents result from one round of sorting. WT HL SCs were from 8 male and 6 female mice; G93A HL SCs were from 9 male and 5 female mice; WT diaphragm SCs were from 6 male and 3 female mice; G93A diaphragm SCs were from 12 male and 5 female mice. WT EOM SCs were from 6 batches of male and 1 batch of female mice (each batch contains 5-6 mice of the same gender). G93A EOM SCs were from 5 batches of male and 2 batches of female mice.

*Please note these results were from sorting in which the FACS profiles were recorded. Not all rounds of sorting were with FACS profile recorded.

For Fig 4A, each dot in the box-and-dot plots represents one image analyzed. For WT HL SCs, 94 images from 3 rounds of sorting; For WT Dia SCs, 107 images from 3 rounds of sorting; For WT EOM SCs, 75 images from 3 rounds of sorting; For G93A HL SCs, 96 images from 3 rounds of sorting; For G93A Dia SCs, 62 images from 3 rounds of sorting; For G93A EOM SCs, 79 images from 3 rounds of sorting. For the 3 rounds of sorting, 1 was from male and 2 were from female mice.

*Please note that the number of mice used for sorting SCs in different muscles were specified in the Method Section in the revised manuscript. (Page 12, Line 489-493)

For Fig 4B, each dot in the box-and-dot plots represents one image analyzed. For WT HL SCs, 52 images from 3 rounds of sorting; For WT Dia SCs, 51 images from 3 rounds of sorting; For WT EOM SCs, 51 images from 3 rounds of sorting; For G93A HL SCs, 52 images from 3 rounds of sorting; For G93A Dia SCs, 47 images from 3 rounds of sorting; For G93A EOM SCs, 56 images from 3 rounds of sorting. For the 3 rounds of sorting, 1 was from male and 2 were from female mice.

For Fig 5A, each dot in the box-and-dot plots represents one replicate of culture. HL SCs were from male mice.

For Fig 5B, each dot in the box-and-dot plots represents one image analyzed. For G93A HL SCs, 52 images from 3 rounds of sorting; 1-day NaBu treatment, 45 images from 3 rounds of sorting; 3-day NaBu treatment, 51 images from 3 rounds of sorting; For G93A Dia SCs, 47 images from 3 rounds of sorting; 1-day NaBu treatment, 60 images from 3 rounds of sorting; 3-day NaBu treatment, 57 images from 3 rounds of sorting. For the 3 rounds of sorting, 2 were from male and 1 was from female mice.

For Fig 6, all samples used for bulk RNA-Seq were from female mice.

For Fig 7C, each dot in the box-and-dot plots represents one replicate of culture. RNA samples were collected from 3-6 rounds of sorting and sorted cells were seeded into 3 dishes as replicates. WT HL SCs were from 3 male and 1 female mice. WT diaphragm SCs were from 2 male and 2 female mice; WT EOM SCs were from 3 male mice; G93A HL SCs were from 4 male and 2 female mice. G93A diaphragm SCs were from 1 male and 3 female mice; G93A EOM SCs were from 3 male mice.

For Fig 7D, each dot in the box-and-dot plots represents one replicate of culture. RNA samples were collected from 6 rounds of sorting and sorted cells were seeded into 3 dishes as replicates. G93A HL SCs were from 4 male and 2 female mice; G93A diaphragm SCs were from 2 male and 4 female mice.

For Fig 8D, each dot in the box-and-dot plot represents one neurite measured. HL and EOM SCs used for co-culture experiments were all from male mice.

For Fig 9D, each dot in the box-and-dot plot represents one image analyzed. HL and EOM SCs used for co-culture experiments were all from male mice.

For Figure 1-figure supplement 1, each dot in the box-and-dot plots represents quantification result from one mouse. Please also see Figure 1-table supplement 2. Briefly, muscles in WT and G93A groups were from 3 male and 3 female mice per group; G93A EDL with NaBu feeding group was from 3 male and 3 female mice. G93A soleus with NaBu feeding group was from 2 male and 3 female mice; G93A diaphragm with NaBu feeding group was from 2 male and 4 female mice; G93A EOM with NaBu feeding group was from 4 male and 2 female mice.

The first paragraph of the results lacks a functional analysis of the motor decline of the animals after the administration of sodium butyrate. The authors, in fact, administered NaBu around 90 days of age while in previous work the drug had been administered at a pre-symptomatic age. It would therefore be useful, to make the message more effective, to characterize the locomotor functions of the treated animals in parallel with the histological evidence of the integrity of the NMJ.

We are still in the process of collecting locomotor function data for G93A mice with and without NaBu treatment. We plan to report them in a future manuscript while this manuscript focuses on the molecular and histological aspect. Additionally, in the revised manuscript, we revised the rationale of the NaBu treatment starting after the disease onset. (Page 4, Line 131-134)

“In the previous study, NaBu treatment initiated at a pre-symptomatic age delayed disease progression in G93A mice. As treatment of ALS patients is initiated after symptoms appear, we further tested whether NaBu treatment started after disease onset (at the age of 3 months, 2% NaBu in water for 1 month) was effective in preserving NMJ integrity.”

Figure 5 should be completed with the administration of NaBu also to the satellite cells isolated from the WT mouse, the same for figure 9 where AAV-CMV-Cxcl12 transduction of WT myotubes is missing. We appreciate the reviewer’s suggestion of conducting the additional experiment with AAV-delivery of CXCL12 into the myotubes derived from the WT mice. Extensive studies by other investigators have been performed with butyrate on satellite cells derived from WT mice. To name a few here: Fiszman et al., 1980 (DOI: 10.1016/0014-4827(80)90467-X); Johnston et al., 1992 (DOI: 10.1128/mcb.12.11.5123-5130.1992); Lezzi et al., 2002 (DOI: 10.1073/pnas.112218599). To avoid performing redundant experiments, we focus on the effect of butyrate on the proliferation and differentiation of SCs derived from G93A mice. Thanks to the reviewer’s comment, we added additional discussion in the Results section (Page 6, line 216-217). Regarding the effect of Cxcl12, published studies have demonstrated its role in promoting axon growth. To name a few here: Negro et al., 2017 (DOI: 10.15252/emmm.201607257); Lieberam et al., 2005 (DOI: 10.1016/j.neuron.2005.08.011); Whitman et al., 2018 (DOI: 10.1167/iovs.18-25190). (Page 10, line 434, 440-442).

In the experiment illustrated in Figure 8, treatment of cell cultures with NaBu would improve the outcome as well as the interference of Cxcl12 expression in myotubes derived from G93A EOM SC (Fig.9) would strengthen the specificity of this protein in axon guidance in this NMJ typical of a spared muscle in ALS.

This is a great suggestion. Our study demonstrated the overexpression of CXCL12 in G93A myotube can enhance the axonal guidance and innervation of the co-cultured myotube/moto-neurons. We have also demonstrated the NaBu treatment can enhance the expression of CXCL12 and slow ALS progression. Combining NaBu treatment with CXCL12 overexpression may indeed have additive therapeutic benefits to slow ALS progression. We have added this statement in the revised Discussion. (Page 11, Line 466-468)

In the "materials and methods" section the paragraph relating to the methods used for statistical analysis is missing.

We have added it accordingly. (Page 15, Line 631-636)

Reviewer #3 (Public Review):

Summary:

In their paper, Li et al. investigate the transcriptome of satellite cells obtained from different muscle types including hindlimb, diaphragm, and extraocular muscles (EOM) from wild-type and G93A transgenic mice (end-stage ALS) in order to identify potential factors involved in the maintenance of the neuromuscular junction. The underlying hypothesis is that since EOMs are largely spared from this debilitating disease, they may secrete NMJ-protective factors. The results of their transcriptome analysis identified several axon guidance molecules including the chemokine Cxcl12, which are particularly enriched in EOM-derived satellite cells. Transduction of hindlimb-derived satellite cells with AAV encoding Cxcl12 reverted hindlimb-derived myotubes from the G93A mice into myotubes sharing phenotypic characteristics similar to those of EOM-derived satellite cells. Additionally, the authors were able to demonstrate that EOM-derived satellite cell myotube cultures are capable of enhancing axon extensions and innervation in co-culture experiments.

Strengths:

The strength of the paper is that the authors successfully isolated and purified different populations of satellite cells, compared their transcriptomes, identified specific factors released by EOM-derived satellite cells, overexpressed one of these factors (the chemokine Cxcl12) by AAV-mediated transduction of hindlimb-derived satellite cells. The transduced cells were then able to support axon guidance and NMJ integrity. They also show that administration of Na butyrate to mice decreased NMJ denervation and satellite cell depletion of hind limbs. Furthermore, the addition of Na Butyrate to hindlimb-derived satellite cell myotube cultures increased Cxcl12 expression. These are impressive results providing important insights for the development of therapeutic targets to slow the loss of neuromuscular function characterizing ALS.

Weaknesses:

Several important aspects have not been addressed by the authors, these include the following points which weaken the conclusions and interpretation of the results.

(a) Na Butyrate was shown to extend the survival of G93A mice by Zhang et al. Na butyrate has a variety of biological effects, for example, anti-inflammatory effects inhibit mitochondrial oxidative stress, positively influence mitochondrial function, is a class I / II HDAC inhibitor, etc. What is the mechanism underlying its beneficial effects both in the context of mouse muscle function in the ALS G93A mice and in the in vitro myotube assay? Cytokine quantification as well as histone acetylation/methylation can be assessed experimentally and this is an important point that has not been appropriately investigated.

Great suggestion by the reviewer.

Our previous publications (DOI: 10.3390/biom12020333; DOI: 10.3390/ijms22147412) have shown the beneficial roles of NaBu in ameliorating mitochondrial function in both motor neuron-like cells and adult muscle fibers. A focus of the current study is to test whether NaBu treatment also affect the SCs by regulating their gene transcription. Regarding the potential on HDAC/acetylation modification, there are previous studies by other investigators. We have added these references in the Discussion (Page 11, line 466-468).

(b) In the context of satellite cell characterization, on lines 151-152 the authors state that soleus muscles were excluded from further studies since they have a higher content of slow twitch fibers and are more similar to the diaphragm. This justification is not valid in the context of ALS as well as many other muscle disorders. Indeed, soleus and diaphragm muscles contain a high proportion of slow twitch fibers (up to 80% and 50% respectively) but soleus muscles are more spared than diaphragm muscles. What makes soleus muscles (and EOMs) more resistant to ALS NMJ injury? Satellite cells from soleus muscles need to be characterized in detail as well.

We agree with the reviewer’s comment that our original statement is misleading regarding the difference between soleus and diaphragm muscles in terms of the content of slow twitch fibers. Our histological studies revealed similar defects in denervation of diaphragm and soleus muscles derived from the G93A mice. Most importantly, the degree of NMJ degeneration and atrophy is less severe in soleus compared to other hindlimb muscles, such as EDL, during ALS progression. We have cited related studies such as Valdez et al., 2012 (DOI: 10.1371/journal.pone.0034640), Atkin et al., 2005 (DOI: 10.1016/j.nmd.2005.02.005). To avoid any confusion, we have removed the original statement and revised the paragraph (Page 4, line 159-162).

“The three groups were determined because they represent the most severely affected, moderately affected and least affected muscles by ALS progression, respectively. Soleus was not included in the hindlimb SCs pool because its less affected than other hindlimb muscles based on our study and others [6,42].”

Furthermore, EOMs are complex muscles, containing many types of fibers and expressing different myosin heavy chain isoforms and muscle proteins. The fact that in mice both the globular layer and orbital layers of EOMs express slow myosin heavy chain isoform as well as myosin heavy chain 2X, 2A, and 2B (Zhou et al., 2010 IOVIS 51:6355-6363) also indicates that the sparing is not directly linked to the fast or slow twitch nature of the muscle fiber. This needs to be considered.

We greatly appreciate your suggestions and have included these points in the revised Discussion. “It is known that EOMs are complex muscles. Besides the developmental myosin isoforms, EOMs also express both adult fast and slow myosin contractile elements (Zhou et al., 2010 IOVIS 51:6355-6363), suggesting that the sparing may not be solely linked to the fast or slow twitch nature of the muscle fiber, rather the changes in SCs may play a pivotal role in preserving the EOM function during the progression of ALS. ” (Page 9, line 389-392)

(c) In the context of myotube formation from cultured satellite cells on lines 178-179 the authors stained the myotubes for myosin heavy chain. Because of the diversity of myosin heavy chain isoforms and different muscle origins of the satellite cells investigated, the isoform of myosin heavy chain expressed by the myotubes needs to be tested and described. It is not sufficient to state anti-MYH.

We used the pan-anti-MYH antibody (MF20 from DSHB) for the immunostaining of myosin heavy chain for identification of the differentiated myotubes. As described in the commercial website: https://dshb.biology.uiowa.edu/MF-20), FM20 recognizes all myosin heavy chain isoforms. We are happy to examine whether specific myosin heavy chain isoforms may contribute to the differences observed in future studies.

(d) The original RNAseq results have not been deposited and while it is true that the authors have analyzed the results and described them in Figures 6 and 7 and relative supplements, the original data needs to be shown both as an xls list as a Volcano plots (q value versus log2 fold change). This will facilitate the independent interpretation of the results by the readers as some transcripts may not be listed. As presented it is rather difficult to identify which transcripts aside from Cxcl12 are commonly upregulated. Can the data be presented in a more visual way?

We have uploaded the Fastq files and the text files containing TPM values to the Gene Expression Omnibus (GEO) database and included the GEO access number GSE249484 in the revised text. Per recommendation of the reviewer, we have added supplementary tables for Figure 6, to list the top 20 differentially expressed genes (ranked by Log2FC, both the upregulated and downregulated) comparing 1) EOM SCs to their hindlimb and diaphragm counterparts (Figure 6-table supplement 1); 2) G93A SCs to WT SCs of the same muscle origin (Figure 6-table supplement 2); 3) G93A hindlimb and diaphragm SCs with 3 day-NaBu treatment to those without (Figure 6-table supplement 3). (Page 6, Line 237-257)

(e) There is no section describing the statistical analysis methods used. In many figures, more than 2 groups are compared so the authors need to use an ANOVA followed by a post hoc test.

Thank for the comments. We have added it accordingly. (Page 15, Line 631-636)

The authors have achieved their aim in showing that satellite cells derived from EOMs have a distinct transcriptome and that this may be the basis of their sparing in ALS. Furthermore, this work may help develop future therapeutic interventions for patients with ALS.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

The prevailing hypothesis of ALS is that motoneuron degeneration subsequently induces muscle atrophy and wasting. However, evidence also suggests that ALS is a muscle disease independent of motoneuron degeneration. The results from the current study support the latter. The RNA-seq data from cultured myoblasts (without innervation) suggest cell cell-autonomous effect of G93A on muscle cells. While the current analyses in this study identify axon guidance pathways in EOM satellite cells that may underlie their unique gene program that enhances motoneuron function, the powerfulness of the RNA-seq data is underutilized. I suggest that the authors explore the RNA-seq further by comparing genes and pathways altered by G93A in various muscles to better pinpoint how G93A influences satellite cell function.

Thanks for the comments and advice. Further analysis of the RNA-seq data is planned. As our original sequencing provider became unavailable to us since last year, we are currently negotiating with other sequencing providers. We have deposited the raw data files into the GEO database (GSE249484) to foster further analyses by other researcher teams.

To address the reviewer’s concern, we have added three more supplementary tables for Figure 6, which list the top 20 differentially expressed genes (DEG) (ranked by Log2FC, both the upregulated and downregulated) comparing 1) EOM SCs to their hindlimb and diaphragm counterparts (new Figure 6-table supplement 1); 2) G93A SCs to WT SCs of the same muscle origin (new Figure 6-table supplement 2); 3) G93A hindlimb and diaphragm SCs with and without 3 day-NaBu treatment (new Figure 6-table supplement 3). These three DEG lists are discussed in the results section of the revised manuscript as following (Page 6, Line 237-257).

Figure 4 presentation could be improved by adopting a similar comparison (WT vs G93A) as used in Figure 1-3. The current comparison is not straightforward. In addition, a magnified image of panel A would demonstrate the loss of myoblast homeostasis more clearly. (AKA Figure 2B)

The WT vs G93A comparison was presented in the supplementary figure of Figure 4 (Figure 4-figure supplement 1 in the previous version, and now in Figure 4-figure supplement 2 in the revised version).

As requested, we have added magnified single channel representative images of cultured SCs in the new Figure 4-figure supplement 1 in the revised manuscript.

Co-culture results in Figure 8 are very impressive. It would be nice if the data were quantified. The figure legend states that panel D is the quantification, but I don't see panel D. As the study used rat motoneurons (presumably SOD1 wildtype), it is unknown if G93A motoneurons would respond to muscle-derived CXCL12 similarly to the wildtype motoneurons. This information is crucial for understanding whether the SOD1 mutant ALS1 is a motoneuron disease or muscle disease or both. Some discussion should be provided to reflect the limitation (of not including G93A motoneurons in the coculture).

Panel D (the quantification data) was presented in the original figure setting (but may not be obvious). We have now revised Figure 8 to enlarge panel D to clearly present the quantification data.

We acknowledge the limitation of not including mutant G93A motor neurons in the coculture assay, and have added this important point (and our future plans to do so) in the discussion section of the revised manuscript: (Page 11, Line 445-448)

“However, it is possible that motor neurons carrying ALS mutations may respond differently to Cxcl12 mediated axon guidance than WT motor neurons. This is a limitation of the current study, which will be investigated in future co-culture studies.”

Reviewer #2 (Recommendations For The Authors):

Line 108. The sentence: "Z-stack scans of glycerol-cleared 109 whole muscles were obtained using a high working distance lens in a confocal microscope. The z-stacks were compacted into 2D images by maximal intensity projection" and should be moved to the material and methods section.

Removed from the Result section and added to the Method section as recommended (Page 13, Line 564-568).

Linea 113. The sentence: " In order to quantify the extent of denervation in a categorical manner, NMJs were arbitrarily defined as "well innervated" if SYP staining was present in >60% of the BTX positive area, "partially innervated" if between 60% and 30%, and "poorly innervated" if SYP staining corresponded to less than 30% of the BTX positive area" has already been written in the figure legend.

Thanks for the advice. We have rephrased the sentence to remove the redundant part.

In lines 445-7, it would be better to indicate the enzymatic units instead of the concentrations.

We included enzymatic units for the four enzymes in the Methods Section of revised manuscript (Page 12, Line 497-499).

Reviewer #3 (Recommendations for The Authors):

There are several points that need to be addressed by the authors including:

(a) The authors need to provide experimental evidence as to the mode of action of Na Butyrate and more specifically whether its beneficial effect is mediated by its anti-inflammatory action, inhibition of HDACs, or the combination of several mechanisms. Additionally, it should be clearer why Na Butyrate was administered. The sentence referring to reference 36 is not sufficient and some mechanistic insight needs to be provided in the results section.

Thanks for the great suggestion. We have revised the Results section accordingly to clarify the rationale for NaBu usage (please also see our detailed response to your suggestion above). (Page 4, line 131-134)

(b) Their reason for excluding soleus-derived-satellite cells from the analysis is not valid. Soleus muscles are "more" speared than diaphragm muscles and analysis may help shed light on this observation.

Please see our response to your question (b) in the above public review section.

(c) DATA AVAILABILITY: The RNAseq raw untransformed data has not been provided and Volcano plots are also not shown. I find it quite difficult to follow the results of the RNAseq experiments and this is central to the interpretation of the paper's results. Ideally, one should be able to look at the data and draw his/her own conclusions but as it stands this is difficult to do.

We have uploaded the raw FastQ files and the excel files containing TPM values to the GEO database with the access number GSE249484.

(d) A detailed description of all statistical tests that were used needs to be provided.

Yes, this has been added to the revised manuscript.

(e) Many figure legends are incomplete and some panels are not described appropriately, indicating that the authors need to thoroughly revise all aspects of the manuscript.

We have extensively edited the figure legends to address the issues raised by reviewers.

(f) Line 96-98: it is unlikely that muscles from ALS patients will be biopsied frequently. Furthermore, what biomarkers exactly could be followed in patients in response to therapy? This is unclear.

While it is true that it is not generally part of the diagnostic workup for ALS, muscle biopsy is increasingly being used pre- and post-treatment in ALS clinical trials to examine responses to potential new therapies. Muscle biopsy is also being explored in several ongoing studies as a potential ALS-relevant peripheral tissue amenable to biopsy (as opposed to brain or spinal cord) for predictive, pharmacodynamic, and prognostic biomarkers. This includes studies attempting to recapitulate pathophysiological patient clusters observed in CNS autopsy tissues and studies to detect aberrant TDP-43 aggregates in intramuscular nerve twigs, among others. Indeed, Dr. Ostrow’s clinical duties include performing muscle biopsies and interpreting muscle pathology, and he is involved in several ongoing studies attempting to correlate postmortem CNS and muscle analyses for these purposes.

To avoid potential controversy on the feasibility of multiple biopsies, we rephrased the sentence as follows (Page 3, Line 96-98)

“Characterizing the distinct EOM SC transcriptomic pattern could provide clues for identifying potential biomarkers in therapeutic trials in both ALS patients and animal models, in addition to identifying therapeutic targets.”

(g) Line 388-389. What do the authors mean by this sentence? It is not clear.

Thanks for the comment, we have expended the discussion to make it clearer in the revision. (Page 10, Line 428-431)

“It is possible that the more frequent self-renewal and spontaneous activation of EOM SCs contribute to higher rate of mitochondrial DNA replication, leading to accelerated spreading of mitochondrial DNA defects, resulting in higher proportion of COX-deficient myofibers than other muscles”.

(h) Were the experimenters blinded as to the results shown in Figures 2, 7, 8, and 9?

We endeavored to blind experiments whenever possible. Not all experiments were blinded due to logistic complexity and the clear difference in microscopic and gross appearances of wild-type and mutant muscle. The differences observed in Figures 2, 7, 8, 9 are qualitative (ie more than just quantitative), which should minimize the impact of possible human bias. Additionally, we employed multiple different experimental approaches to assess our hypotheses.

For Fig 2, the physical appearance is notably different between G93A and WT muscles. The different innervation status (Fig 2A) is also not amenable to blinding.

For Fig 7, the expression level of Hmga2, Notch3 and Cxcl12 detected by the qPCR assay are substantially greater in EOM derived SCs than counterparts from other muscles, and these results are also consistent with RNA-Seq, immunofluorescence assays. For Fig 8, the overexpression of Cxcl12 and the coculture with EOM SC derived myotubes not only increased the length of the longest neurites but also promoted axon branching, which can be easily observed.

For Fig 9, only the EOM SC derived myotubes were capable of aligning the neurites along with them on a global scale. This qualitative difference is easy to appreciate, even under low magnification.

(i) Line 64 -65 The authors refer to a very old paper by Fischer et al in 2002 for the expression profile of EOMs. There are more recent papers including that of Eckhardt et al. (eLife 2023, 12:e83618) showing the differences in proteome between EOMs and soleus and EOMs and EDL muscles. There are more than 2000 (and not 300!!) differentially expressed proteins.

Thank you for the newly published reference. We have revised the Introduction section to include this new proteomic study. (Page 2, Line 64-69)

(j) Figure 7 C. The Y axis is mislabeled as they should be log2 fold change and not the growth conditions.

Thank you for catching this. We have fixed it.

(k) In all figures, if each symbol represents the results obtained on 1 mouse, this needs to be clearly stated. What do the panels on the right of Figures 4 and 5B show?

Thanks for the comments. For Figure 1B and 2C, as well as Figure 1-figure supplement 1B, one dot in the box-and-dot plots represents result obtained from 1 mouse. For Figure 3B, one dot represents one round of sorting. Generally, one mouse was euthanized for each round of sorting for HL and diaphragm SCs. But the sorting of EOM SCs could take up to 6 mice (as the EOMs are much smaller). For Figure 4 and 5B, each dot represents one image analyzed. All images were collected from three rounds of sorting. For Figure 5A, each dot represents one replicate of culture. For Figure 5B, each dot represents one image analyzed. All images were collected from three rounds of sorting. We have indicated those details in the revision.

Please also see our response to the 1st question of Reviewer 2 in the public review section.

(l) Figure 6 Table supplement 3 does NOT show the FDR but only the log2 fold change. Please amend.

We have amended the supplementary table accordingly.

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.

Reviewer #3 (Public Review):

Summary:

In their paper Li et al. investigate the transcriptome of satellite cells obtained from different muscle types including hindlimb, diaphragm and extraocular muscles (EOM) from wild type and G93A transgenic mice (end stage ALS) in order to identify potential factors involved in the maintenance of the neuromuscular junction. The underlying hypothesis being that since EOMs are largely spared from this debilitating disease, they may secrete NMJ-protective factors. The results of their transcriptome analysis identified several axon guidance molecules including the chemokine Cxcl12, which are particularly enriched in EOM-derived satellite cells. Transduction of hindlimb-derived satellite cells with AAV encoding Cxcl12 reverted hindlimb-derived myotubes from the G93A mice into myotubes sharing phenotypic characteristics similar to those of EOM-derived satellite cells. Additionally, the authors were able to demonstrate that EOM-derived satellite cell myotube cultures are capable of enhancing axon extensions and innervation in co-culture experiments.

Strengths:

The strength of the paper is that the authors successfully isolated and purified different populations of satellite cells, compared their transcriptomes, identified specific factors release by EOM-derived satellite cells, overexpressed one of these factors (the chemokine Cxcl12) by AAV-mediated transduction of hindlimb-derived satellite cells. The transduced cells were then able to support axon guidance and NMJ integrity. They also show that administration of Na butyrate to mice decreased NMJ denervation and satellite cell-depletion of hind limbs. Furthermore, addition of Na Butyrate to hindlimb derived satellite cell myotube cultures increased Cxcl12 expression. These are impressive results providing important insights for the development of therapeutic targets to slow the loss on neuromuscular function characterizing ALS.

Weaknesses:

Several important aspects have not been addressed by the authors, these include the following points which weaken the conclusions and interpretation of the results.<br /> (a) Na Butyrate was shown to extend the survival of G93A mice by Zhang et al. Na butyrate has a variety of biological effects. For example, anti-inflammatory effects, inhibits mitochondrial oxidative stress, positively influences mitochondrial function, is a class I / II HDAC inhibitor etc. What is the mechanism underlying its beneficial effects both in the context of mouse muscle function in the ALS G93A mice and in the in vitro myotube assay? Cytokine quantification as well as histone acetylation/methylation can be assessed experimentally and this is an important point that has not been appropriately investigated.<br /> (b) In the context of satellite cell characterization, on line 151-152 the authors state that soleus muscles were excluded from further studies since they have a higher content of slow twitch fibers and are more similar to diaphragm. This justification is not valid in the context of ALS as well as many other muscle disorders. Indeed, soleus and diaphragm muscles contain a high proportion of slow twitch fibers (up to 80% and 50% respectively) but soleus muscles are more spared than diaphragm muscles. What makes soleus muscles (and EOMs) more resistant to ALS NMJ injury? Satellite cells from soleus muscles need to be characterized in detail as well.<br /> Furthermore, EOMs are complex muscles, containing many types of fibers and expressing different myosin heavy chain isoforms and muscle proteins. The fact that in mouse both the globular layer and orbital layers of EOMs express slow myosin heavy chain isoform as well as myosin heavy chain 2X, 2A and 2B (Zhou et al., 2010 IOVIS 51:6355-6363) also indicates that the sparing is not directly linked to the fast or slow twitch nature of the muscle fiber. This needs to be considered.<br /> (c) In the context of myotube formation from cultured satellite cells on line 178-179 the authors stained the myotubes for myosin heavy chain. Because of the diversity of myosin heavy chain isoforms and different muscle origin of the satellite cells investigated, the isoform of myosin heavy chain expressed by the myotubes needs to be tested and described. It is not sufficient to state anti-MYH.<br /> (d) The original RNAseq results have not been deposited and while it is true that the authors have analyzed the results and described them in Figures 6 and 7 and relative supplements, the original data needs to be shown both as an xls list as a Volcano plots (q value versus log2 fold change). This will facilitate the independent interpretation of the results by the readers as some transcripts may not be listed. As presented it is rather difficult to identify which transcripts aside Cxcl12 are commonly upregulated. Can the data be presented in a more visual way?<br /> (e) There is no section describing the statistical analysis methods used. In many figures more than 2 groups are compared so the authors need to use an ANOVA followed by a post hoc test.

The authors have achieved their aim in showing that satellite cells derived from EOMs have a distinct transcriptome and that this may be the basis of their sparing in ALS. Furthermore, this work may help develop future therapeutic interventions for patients with ALS.

Reviewer #4 (Public Review):

Summary:

In this work, the authors have used a mouse model of familial Amyotrophic lateral sclerosis (ALS) that carries a G93A mutation in the Sod1 gen to understand how the extraocular muscles (EOM) are preserved in ALS while other muscles undergo degeneration. Interestingly, the authors demonstrate that the integrity of neuromuscular junctions (NMJ) is affected by ALS in the limb and diaphragm muscles of G93A mice, while EOM is mostly preserved. The authors also further demonstrate that NaBu treatment partially restores the integrity of NMJ in the limb and diaphragm muscles of G93A mice. The results also indicate that chemokine Cxcl12 is expressed at higher levels in EOM myoblasts, and transduction with AAV encoding Cxcl12 improved the phenotypic characteristics of hindlimb-derived satellite cells.

Strengths:

The authors have used both in vivo and cell culture models. The findings have a translational potential.

Weaknesses:

The use of NaBu could be an issue as it has multiple effects and targets in ALS.

The sample size of animal experiments still needs to be improved.

The molecular mechanism of how Cxcl12 improved the phenotypic characteristics of hindlimb-derived satellite cells is still being determined.

Author Response

We would like to thank the editors for giving us an opportunity to address the insightful comments made by the referees. In our response to the comments, we provide a guide to important information that may have been overlooked, and hope to elaborate on the context for better evaluating this study.

As mentioned in the introduction of our manuscript, mosquito-transmitted diseases cause nearly a million deaths every year and significant worldwide morbidity. Moreover, the geographical range of mosquito vectors is rapidly expanding due to climate change and mosquito-borne disease risks are emerging in new parts of the world. DEET was discovered in the 1940s and has remained the primary insect repellent for >70 years in the developed world. The US Environmental Protection Agency (EPA) regulates mosquito repellents, and DEET-based commercial products are typically assigned protection times that vary with concentration. Products with lower concentration need repeated applications, whereas those with higher concentrations feel oily and cost more.

We also mentioned that DEET inhibits mammalian cation channels and human acetylcholinesterase. The latter is a target of carbamate insecticides that are commonly used in disease-endemic areas, raising additional concerns about prolonged use of DEET. DEET is also a solvent and damages several forms of plastics, synthetic fabrics, and painted . Unfortunately, DEET has been of little value in disease control in Africa and Asia. Even in developed countries, a natural, cosmetically pleasant alternative could benefit millions of people who currently avoid repellents.

Innovation in finding new repellents has been slow due to limitations in current research approaches and high costs for EPA registration (specially for synthetic compounds). Since DEET only five additional actives have been approved by the EPA for repellent products. In the 20+ years since discovery of insect odorant receptors from genomes, not a single novel repellent compound has been identified registered by the EPA. Thus, there is a both a strong need for new approaches to find insect repellents and need for new active ingredients that are safe and strategically effective. In fact, this goal of finding new mosquito repellents has been the topic of multiple Gates Foundation Grand Challenge grants, and numerous NIH funded grants to many research groups around the world.

Reviewer #1 (Public Review):

Summary:

In this manuscript, the authors set up a pipeline to predict insect repellents that are pleasant and safe for humans. This is done by daisy-chaining a new classification model based on predicting repellents with a published model on predicting human perception. Models use a feature-engineered selection of chemical features to make their predictions. The predicted molecules are then validated against a proxy humanoid (heated brick) and its safety is tested by molecular assays of human cells. The humanistic approach to modeling these authors have taken (which considers cosmetic/aesthetic appeal and safety) is novel and a necessary step for consumer usage. However, the importance of pleasantness over effectiveness is still up for debate (DEET is unpleasant but still used often) and the generalization of safety tests is unknown and assumed. The effectiveness of the prediction models is also still warranted. They pass the authors' own behavioral tests, but their contribution to the field is unknown as both models (new and published) have not been rigorously benchmarked to previous models. Moreover, the author's breadth of literature in this field is sparse, ignoring directly related studies.

Strengths:

Humanistic approach to modeling considers pleasantness and safety. Chaining models can help limit the candidate odorants from the vastness of odor space.

Weaknesses:

The current models need to be bench-marked against leading models predicting similar outcomes. Similarly, many of these papers need to be addressed and discussed in the introduction. The authors might even consider their data sources for model training to increase performance and lexical categorization for interoperability. For instance, the Dravnikes data lexicon, currently used in the human perception lexicon, has been highly criticized for its overlapping and hard-to-interpret descriptive terms ("FRAGRANT", "AROMATIC").

Human Perception:

Khan, R. M., Luk, C. H., Flinker, A., Aggarwal, A., Lapid, H., Haddad, R., & Sobel, N. (2007). Predicting odor pleasantness from odorant structure: pleasantness as a reflection of the physical world. Journal of Neuroscience, 27(37), 10015-10023.

Keller, A., Gerkin, R. C., Guan, Y., Dhurandhar, A., Turu, G., Szalai, B., ... & Meyer, P. (2017). Predicting human olfactory perception from chemical features of odor molecules. Science, 355(6327), 820-826.

Gutiérrez, E. D., Dhurandhar, A., Keller, A., Meyer, P., & Cecchi, G. A. (2018). Predicting natural language descriptions of mono-molecular odorants. Nature communications, 9(1), 4979.

Lee, B. K., Mayhew, E. J., Sanchez-Lengeling, B., Wei, J. N., Qian, W. W., Little, K. A., ... & Wiltschko, A. B. (2023). A principal odor map unifies diverse tasks in olfactory perception. Science, 381(6661), 999-1006.

Author Response: The human perception predictions were performed using models that we had reported in two earlier publications: Kowalewski & Ray, iScience (2020b) and Kowalewski, Huynh & Ray, Chem. Senses (2021). Three of the four references pointed out by the referee were cited in these prior studies, which involved computational validation by predicting on a test set of the data which was left out of training (as typically done), and also predicting across different human studies with a high degree of success. A rigorous benchmarking of the odor perception models was done in Kowalewski, Huynh & Ray, Chem. Senses (2021) and a mini-review published in the same issue of the journal by Gerkin, Chem. Senses, (2021). This included a favorable comparison with the two references indicated by the referee: Keller et al. Science (2017) as well as the Gutiérrez et. al. Nat. Communication (2018). The 4th reference, Lee et al, Science (2023) describes a neural network approach and was published much after our mosquito behavior studies were completed. Although using an advanced Neural network model Lee et al. worked with 2-D structures of compounds in contrast to our 3-D approach. They also did not report cross-study validations or comparisons with Keller et al, 2017 or benchmark to past studies, so it is difficult to compare advances if any.

The intent of the current study was to move beyond testing approaches, of which there are many, and instead work on a practical use case. As we see it, it is not necessarily the prediction of fragrance character or quality alone that matters but overlap with other predicted bioactivities. From the perspective of human use, a molecule with a pleasing scent that also repels insects is likely to be far more useful than one with an unappealing scent. Accordingly, our task in this study was to select molecules that fit into specific use categories: display strong insect repellency, have pleasing scent profiles, are natural in origin and are potentially repurposed from flavors and fragrances.

Insect Repellents:

Wright, R. H. (1956). Physical basis of insect repellency. Nature, 178(4534), 638-638.

Katritzky, A. R., Wang, Z., Slavov, S., Tsikolia, M., Dobchev, D., Akhmedov, N. G., ... & Linthicum, K. J. (2008). Synthesis and bioassay of improved mosquito repellents predicted from chemical structure. Proceedings of the National Academy of Sciences, 105(21), 7359-7364.

Bernier, U. R., & Tsikolia, M. (2011). Development of Novel Repellents Using Structure− Activity Modeling of Compounds in the USDA Archival Database. In Recent Developments in Invertebrate Repellents (pp. 21-46). American Chemical Society.

Author response: The Katritzky et. al. PNAS (2008) paper is cited in our study, and we have indicated that the chemical analogs reported therein are part of the training data set in our study. We thank the reviewer for pointing us to the book chapter by Bernier & Tsikolia (2011), which reviews the QSAR approaches taken for repellent discovery and in large measure focuses on the Katritzky et. al. PNAS (2008) paper. We did cite two relevant studies by Uli Bernier, but agree that citation of the book chapter would make a nice addition.

The current study assumes that insect repellents repel via their odor valence to the insect, but this is not accurate. Insect repellents also mask the body odor of humans making them hard to locate. The authors need to consult the literature to understand the localization and landing mechanisms of insects to their hosts. Here, they will understand that heat alone is not the attractant as their behavioral assay would have you believe. I suggest the authors test other behaviour assays to show more convincing evidence of effectiveness. See the following studies:

De Obaldia, M. E., Morita, T., Dedmon, L. C., Boehmler, D. J., Jiang, C. S., Zeledon, E. V., ... & Vosshall, L. B. (2022). Differential mosquito attraction to humans is associated with skin-derived carboxylic acid levels. Cell, 185(22), 4099-4116.

McBride, C. S., Baier, F., Omondi, A. B., Spitzer, S. A., Lutomiah, J., Sang, R., ... & Vosshall, L. B. (2014). Evolution of mosquito preference for humans linked to an odorant receptor. Nature, 515(7526), 222-227.

Wei, J. N., Vlot, M., Sanchez-Lengeling, B., Lee, B. K., Berning, L., Vos, M. W., ... & Dechering, K. J. (2022). A deep learning and digital archaeology approach for mosquito repellent discovery. bioRxiv, 2022-09.

Author response: In this study we took an unbiased approach to compile the training data set, including several known insect repellents of varying chemical structures and volatility, for most of which there is no information on how they are sensed by insects. Not surprisingly, the repellents we identified are varied in structure and in functional groups, and are likely detected in more than one way by the mosquitoes, using olfactory and/or gustatory systems. We did not consider “masking” of skin attraction as a factor in the training data set in this study, which precluded the need to discuss the papers pointed out by the referee in any detail. In fact there is an extremely vast and rich body of literature regarding human skin odor, CO2 and breath emanations, which includes our own contributions of research and review articles that are not discussed in the current paper.

We did in fact conduct human arm-in-cage experiments with a few of the compounds reported in this study using female Aedes aegypti mosquitoes; a preprint describes the smaller scale analysis, the results of which show strong repellency, in Boyle et. al. bioRxiv (2016) https://doi.org/10.1101/060178 (Figure 4). However, heat offers a practical proxy for evaluating prospective repellents in a high-throughput manner. It would certainly be desirable to further evaluate additional candidates from the heat attraction assay with human subjects in the future.

We thank the reviewer for pointing out the preprint by Wei, et. al. bioRxiv (2022). Our approaches differ in that Wei et al do not consider properties such as fragrance and toxicity. We also cannot assume that their newer neural network model is superior because although the model uses a large training dataset, it does not use 3D chemical structures that are extremely relevant for biological activity. While very little information is available for the actives reported in Wei et. al., we independently evaluated their top compounds similar or better than DEET (CAS#3731-16-6, 4282-32-0, 2040-04-2, 32940-15-1 and 3446-90-0) and could not find information about toxicity, smell, or natural source. In contrast, the top repellents that we identify here as similar or better than DEET (N=8) are all classified as GRAS (Generally Regarded as Safe) compounds by the Flavor and Extract Manufacturers (FEMA), are all naturally occurring (plum, jasmin, mushroom, grapes, etc), and have pleasant smells. The Dermal toxicity values in rabbits are known for six of our compounds and are at the best possible levels (5000mg/kg).

Reviewer #2 (Public Review):

Summary:

This is an interesting study that seeks to identify novel mosquito repellents that smell attractive to humans.

Strengths:

The combination of standard machine learning methods with mosquito behavioral tests is a strength.

Weaknesses:

The study would be strengthened by describing how other modern ML approaches (RF, decision trees) would classify and identify other potential repellents.

Author response: The current approach already shows a success rate >85% for repellency coefficient >0.5 and identifies eight naturally occurring GRAS compounds with repellency as strong as or greater than DEET. This substantially expands the repertoire of strong natural repellents. Since the 1950s only six active ingredients have been registered by US EPA for use in topical repellents, of which only two are natural in origin (Oil of lemon eucalyptus and catmint oil) and they typically do not protect as well as DEET does. That being said, we have since explored other predictive algorithms, for instance Neural Networks. The experimental evaluation of these newer pipelines will take significant resources and time and will be the focus of future grants.

A comparison in the repellent activity between DEET and the top ten hits identified in this new study indicates little change in repellent activity (~3%), suggesting that DEET remains the gold standard. Without additional toxicity tests, the study is arguably incremental. The study's novelty should be better clarified.

Author response: There is an urgent need to find new insect repellents that have better chances of being adopted by people who avoid DEET, such as in Africa and Asia. Having more natural actives that are effective, expands the tools against disease transmitting mosquitoes. As mentioned above, the top repellents that we identified as similar to or better than DEET (N=8) are all classified as GRAS (Generally Regarded as Safe) compounds by the Flavor and Extract Manufacturers (FEMA), are all naturally occurring (plum, jasmin, mushroom, grapes), and have pleasant smells. The Dermal toxicity values in rabbits are known for six and they are of the best possible levels (5000mg/kg).

The Methods in the repellency tests are sparse, and more information would be useful. Testing the top repellents at low doses (<<1%) and for long periods (2-12 h) would strengthen the manuscript. Without this information, the manuscript is lacking in depth.

Author response: The US Environmental Protection Agency (EPA) regulates mosquito repellents, and DEET-based commercial products are typically assigned protection times that vary with concentration (10% ~2 hrs, 30% ~5hrs, 100% ~8hrs). These would be the relevant concentrations for testing protection times on human volunteers, not lower as suggested. Such studies fall within the realm of EPA registration efforts, involving extensive GLP-testing for safety, physical chemistry, and Human Subjects Board approvals. This is outside the scope of the current study and is typically accomplished during development efforts.

Testing human subjects on their olfactory perceptions of the repellents would also increase the depth and utility of the manuscript. Without additional experiments, the authors' conclusions lack support and have limited impact on the state-of-the-art.

This manuscript is a mix of different approaches, which makes it lack cohesion. There is the ML method for classifying new repellents that smell good, but no testing of the repellents on human volunteers. The repellents are not tested at realistic concentrations and durations. And the calcium mobilization test is strange and makes little sense in the context of the other experiments and framing of the manuscript.

Author response: The human olfaction validation that we present in this paper is consistent with most current publications in the field (for example, Keller et al, Gutiérrez et al.). More systematic validation of the human odor character prediction pipelines used was presented in two previous papers Kowalewski & Ray, iScience (2020b) and Kowalewski, Huynh & Ray, Chem. Senses (2021) and a mini-review published in the same issue of the journal by Gerkin, Chem. Senses, (2021).

Reviewer #3 (Public Review):

While I am not a specialist in this field, I do have some knowledge of the subject matter and the computational aspects involved. The authors employ simple machine learning techniques (such as SVM) for the following purposes:

(a) Prediction of aversive valence.

(b) Predicting anti-repellent chemicals.

(c) Predicting calcium mobilization.

The approach is commonplace in chemoinformatics literature.

Weaknesses:

• All the above models are presented discretely, making it difficult to discern experiment design principles and connectedness.

• The ML work is rudimentary, lacking adequate details. Chemoinformatics has reached great heights, and SVM does not seem contemporary.

• There is significant existing research on finding repellents.

Author response: In the current study, we aimed to showcase how computational research may be combined with basic science to create scalable pipelines that address real world problems, rather than to demonstrate methodological novelty of chemoinformatics approaches. Specifically we wanted to use different predictive models to identify compounds that display strong insect repellency, have pleasing scent profiles, are natural in origin and are potentially repurposed from flavors and fragrances. Unfortunately, there is very little existing research on insect repellents that have these types of properties, which would make them better candidates for EPA registration. Most tested compounds are synthetic, and are often analogs of known repellents like DEET, and necessitate substantial time and resources to register. Moreover the identities of chemosensory receptors that are responsible for repellency to DEET and other compounds, and that are conserved across Anopheles, Aedes and Culex mosquitoes are not known.

It is true that the field of cheminformatics has experimented with a variety of newer approaches, based in part on neural networks (e.g., Graph Neural Networks and graph embeddings to encode chemical structure rather than a more conventional Extended Connectivity Fingerprint (ECFP)). Importantly, however, novelty does not imply usefulness. The mosquito behavior experiments that we present show a very high success rate (>85%), validating our approach and identifying several excellent candidates already.

Strengths:

• Authors attempt to make a case for calcium mobilization in the context of repellency. This aspect sounds interesting but is not surprising.

• Behavioral profiling of repellents could be useful.

Author Comment: We thank the referee for this comment. We have indeed done behavioral profiling for several repellents that evoke calcium mobilization, but we do not see any clear correlation thus far.

Reviewer #3 (Public Review):

While I am not a specialist in this field, I do have some knowledge of the subject matter and the computational aspects involved.

The authors employ simple machine learning techniques (such as SVM) for the following purposes:

a. Prediction of aversive valence.<br /> b. Predicting anti-repellent chemicals.<br /> c. Predicting calcium mobilization.

The approach is commonplace in chemoinformatics literature.

Weaknesses:

- All the above models are presented discretely, making it difficult to discern experiment design principles and connectedness.<br /> - The ML work is rudimentary, lacking adequate details. Chemoinformatics has reached great heights, and SVM does not seem contemporary.<br /> - There is significant existing research on finding repellents.

Strengths:

- Authors attempt to make a case for calcium mobilization in the context of repellency. This aspect sounds interesting but is not surprising.<br /> - Behavioral profiling of repellents could be useful.

eLife assessment

This useful study uses a chemoinformatics pipeline to identify a list of candidate mosquito repellants that may be pleasant to smell and safe for humans. The computational methodology is solid, but insufficiently benchmarked against other leading models. At the high concentrations tested, there may also be off-target effects of the repellents on the mosquitoes that are not considered. This paper may be of interest to specialists interested in the discovery of new mosquito repellents.

Reviewer #1 (Public Review):

Summary:

In this manuscript, the authors set up a pipeline to predict insect repellents that are pleasant and safe for humans. This is done by daisy-chaining a new classification model based on predicting repellents with a published model on predicting human perception. Models use a feature-engineered selection of chemical features to make their predictions. The predicted molecules are then validated against a proxy humanoid (heated brick) and its safety is tested by molecular assays of human cells. The humanistic approach to modeling these authors have taken (which considers cosmetic/aesthetic appeal and safety) is novel and a necessary step for consumer usage. However, the importance of pleasantness over effectiveness is still up for debate (DEET is unpleasant but still used often) and the generalization of safety tests is unknown and assumed. The effectiveness of the prediction models is also still warranted. They pass the authors' own behavioral tests, but their contribution to the field is unknown as both models (new and published) have not been rigorously benchmarked to previous models. Moreover, the author's breadth of literature in this field is sparse, ignoring directly related studies.

Strengths:

Humanistic approach to modeling considers pleasantness and safety. Chaining models can help limit the candidate odorants from the vastness of odor space.

Weaknesses:

The current models need to be bench-marked against leading models predicting similar outcomes. Similarly, many of these papers need to be addressed and discussed in the introduction. The authors might even consider their data sources for model training to increase performance and lexical categorization for interoperability. For instance, the Dravnikes data lexicon, currently used in the human perception lexicon, has been highly criticized for its overlapping and hard-to-interpret descriptive terms ("FRAGRANT", "AROMATIC").

Human Perception

Khan, R. M., Luk, C. H., Flinker, A., Aggarwal, A., Lapid, H., Haddad, R., & Sobel, N. (2007). Predicting odor pleasantness from odorant structure: pleasantness as a reflection of the physical world. Journal of Neuroscience, 27(37), 10015-10023.

Keller, A., Gerkin, R. C., Guan, Y., Dhurandhar, A., Turu, G., Szalai, B., ... & Meyer, P. (2017). Predicting human olfactory perception from chemical features of odor molecules. Science, 355(6327), 820-826.

Gutiérrez, E. D., Dhurandhar, A., Keller, A., Meyer, P., & Cecchi, G. A. (2018). Predicting natural language descriptions of mono-molecular odorants. Nature communications, 9(1), 4979.

Lee, B. K., Mayhew, E. J., Sanchez-Lengeling, B., Wei, J. N., Qian, W. W., Little, K. A., ... & Wiltschko, A. B. (2023). A principal odor map unifies diverse tasks in olfactory perception. Science, 381(6661), 999-1006.<br /> Related cleaned data: https://github.com/BioMachineLearning/openpom

Insect Repellents:

Wright, R. H. (1956). Physical basis of insect repellency. Nature, 178(4534), 638-638.

Katritzky, A. R., Wang, Z., Slavov, S., Tsikolia, M., Dobchev, D., Akhmedov, N. G., ... & Linthicum, K. J. (2008). Synthesis and bioassay of improved mosquito repellents predicted from chemical structure. Proceedings of the National Academy of Sciences, 105(21), 7359-7364.

Bernier, U. R., & Tsikolia, M. (2011). Development of Novel Repellents Using Structure− Activity Modeling of Compounds in the USDA Archival Database. In Recent Developments in Invertebrate Repellents (pp. 21-46). American Chemical Society.

Wei, J. N., Vlot, M., Sanchez-Lengeling, B., Lee, B. K., Berning, L., Vos, M. W., ... & Dechering, K. J. (2022). A deep learning and digital archaeology approach for mosquito repellent discovery. bioRxiv, 2022-09.

The current study assumes that insect repellents repel via their odor valence to the insect, but this is not accurate. Insect repellents also mask the body odor of humans making them hard to locate. The authors need to consult the literature to understand the localization and landing mechanisms of insects to their hosts. Here, they will understand that heat alone is not the attractant as their behavioral assay would have you believe. I suggest the authors test other behaviour assays to show more convincing evidence of effectiveness. See the following studies:

De Obaldia, M. E., Morita, T., Dedmon, L. C., Boehmler, D. J., Jiang, C. S., Zeledon, E. V., ... & Vosshall, L. B. (2022). Differential mosquito attraction to humans is associated with skin-derived carboxylic acid levels. Cell, 185(22), 4099-4116.

McBride, C. S., Baier, F., Omondi, A. B., Spitzer, S. A., Lutomiah, J., Sang, R., ... & Vosshall, L. B. (2014). Evolution of mosquito preference for humans linked to an odorant receptor. Nature, 515(7526), 222-227.

Wei, J. N., Vlot, M., Sanchez-Lengeling, B., Lee, B. K., Berning, L., Vos, M. W., ... & Dechering, K. J. (2022). A deep learning and digital archaeology approach for mosquito repellent discovery. bioRxiv, 2022-09.

Reviewer #2 (Public Review):

Summary:<br /> This is an interesting study that seeks to identify novel mosquito repellents that smell attractive to humans.

Strengths:<br /> The combination of standard machine learning methods with mosquito behavioral tests is a strength.

Weaknesses:<br /> The study would be strengthened by describing how other modern ML approaches (RF, decision trees) would classify and identify other potential repellents.

A comparison in the repellent activity between DEET and the top ten hits identified in this new study indicates little change in repellent activity (~3%), suggesting that DEET remains the gold standard. Without additional toxicity tests, the study is arguably incremental. The study's novelty should be better clarified.

The Methods in the repellency tests are sparse, and more information would be useful. Testing the top repellents at low doses (<<1%) and for long periods (2-12 h) would strengthen the manuscript. Without this information, the manuscript is lacking in depth.

Testing human subjects on their olfactory perceptions of the repellents would also increase the depth and utility of the manuscript. Without additional experiments, the authors' conclusions lack support and have limited impact on the state-of-the-art.

This manuscript is a mix of different approaches, which makes it lack cohesion. There is the ML method for classifying new repellents that smell good, but no testing of the repellents on human volunteers. The repellents are not tested at realistic concentrations and durations. And the calcium mobilization test is strange and makes little sense in the context of the other experiments and framing of the manuscript.

Reviewer #2 (Public Review):

Summary:

In this manuscript, Liu et al. identified an important pathway regulating the nuclear translocation of the key transcriptional factor FOG1 during human hematopoiesis. The authors show that heat shock cognate B (HSCB) can interact with and promote the proteasomal degradation of TACC3, and this function is independent of its role in iron-sulfur cluster (ISC) biogenesis. TACC3 represses the activity of FOG1 by sequestering it in the cytoplasm. Therefore, HSCB can promote the nuclear translocation of FOG1 through down-regulating TACC3. The authors further show that the phosphorylation of HSCB by PI3K downstream of the EPO signaling pathway is important for its role in regulating the nuclear translocation of FOG1. The data are solid and the manuscript is overall well written. The findings of this manuscript provide new knowledge to the fields of hematopoiesis and cell biology.

Strengths:

This study uses a multi-pronged approach that combines techniques from a number of fields to convincingly demonstrate the pathway regulating the nuclear translocation of FOG1 during hematopoiesis.

Weaknesses:

This study only uses cell models. The significance of this work may be broadened by further studies using animal models.

eLife assessment

This fundamental work significantly advances our understanding of how FOG1 nuclear localization is regulated during erythropoiesis and megakaryopoiesis, including the role of EPO and MPL/TPO signaling in this process. The authors provide compelling evidence using both K562 and CD34+ cells that heat shock cognate B (HSCB) can promote the proteasomal degradation of TACC3 to regulate the nuclear localization of FOG1, and that this function is independent of its role in iron-sulfur cluster (ISC) biogenesis. The conclusions would be strengthened in the future by the use of in vivo model systems, however, as written, this work will be of broad interest to cell biologists.

Reviewer #1 (Public Review):

Summary:

In the paper entitled "PI3K/HSCB axis facilitates FOG1 nuclear translocation to promote erythropoiesis and megakaryopoiesis", the authors sought to determine the role of HSCB, a known regulator of iron-sulfur cluster transfer, in the generation of erythrocytes and megakaryocytes. They utilized a human primary cell model of hematopoietic differentiation to identify a novel mechanism whereby HSCB is necessary for the activation of erythroid and megakaryocytic gene expression through regulation of the nuclear localization of FOG-1, an essential transcription co-regulator of the GATA transcription factors. Their work establishes this novel regulatory axis as a mechanism by which cytokine signaling through EPO-R and MPL drives the lineage-specification of hematopoietic progenitors to erythrocytes and megakaryocytes, respectively.

Impact:

The major impact of this work is in a greater understanding of how cytokine signaling through EPO/TPO functions to promote lineage specification of hematopoietic stem/progenitor cells. While the major kinase cascades downstream of the EPO/TPO receptors have been elucidated, how those cascades affect gene expression to promote a specific differentiation program is poorly understood. For this work, we now understand that nuclear localization of FOG is a critical regulatory node by which EPO/TPO signaling is required to launch FOG-dependent gene expression. However, these cytokine receptors have many overlapping and redundant targets, so it still remains to be elucidated how signaling through the different receptors promotes divergent gene expression programs. Perhaps similar regulatory mechanisms exist for other lineage-specifying transcription factors.

Strengths:

The authors use two different cellular models of erythroid differentiation (K562 and human primary CD34+ cells) to elucidate the multi-factorial mechanism controlling FOG-1 nuclear localization. The studies are well-controlled and rigorously establish their mechanism through complementary approaches. The differentiation effects are established through cell surface marker expression, protein expression, and gene expression analyses. Novel protein interactions discovered by proteomics analyses were validated through bi-directional co-IP experiments in multiple experimental systems. Protein cellular localization findings are supported by both immunofluorescence and cell fractionation immunoblot analyses. The robustness of their experimental findings gives great confidence in the likelihood that the methods and findings can be reproduced in future work based on their conclusions.

Weaknesses:

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.

One weakness of the study design is that no in vivo experiments are conducted. The authors comment that the HSCB mouse phenotype is too dramatic to permit studies of erythropoiesis in vivo; however, a conditional approach could have been pursued.

It should also be noted that a previous study had already shown that TACC3 regulates the nuclear localization of FOG-1, so this portion of the mechanism is not entirely novel. However, the role of HSCB and the proteasomal degradation of TACC3 is entirely novel to my knowledge.

eLife assessment

In this important study, the authors manually assessed randomly selected images published in eLife between 2012 and 2020 to determine whether they were accessible for readers with deuteranopia, the most common form of color vision deficiency. They then developed an automated tool designed to classify figures and images as either "friendly" or "unfriendly" for people with deuteranopia. While such a tool could be used by publishers, editors or researchers to monitor accessibility in the research literature, the evidence supporting the tools' utility was incomplete. The tool would benefit from training on an expanded dataset that includes different image and figure types from many journals, and using more rigorous approaches when training the tool and assessing performance. The authors also provide code that readers can download and run to test their own images. This may be of most use for testing the tool, as there are already several free, user-friendly recoloring programs that allow users to see how images would look to a person with different forms of color vision deficiency. Automated classifications are of most use for assessing many images, when the user does not have the time or resources to assess each image individually.

Reviewer #1 (Public Review):

Summary:<br /> The authors of this study developed a software application, which aims to identify images as either "friendly" or "unfriendly" for readers with deuteranopia, the most common color-vision deficiency. Using previously published algorithms that recolor images to approximate how they would appear to a deuteranope (someone with deuteranopia), authors first manually assessed a set of images from biology-oriented research articles published in eLife between 2012 and 2022. The researchers identified 636 out of 4964 images as difficult to interpret ("unfriendly") for deuteranopes. They claim that there was a decrease in "unfriendly" images over time and that articles from cell-oriented research fields were most likely to contain "unfriendly" images.<br /> The researchers used the manually classified images to develop, train, and validate an automated screening tool. They also created a user-friendly web application of the tool, where users can upload images and be informed about the status of each image as "friendly" or "unfriendly" for deuteranopes.

Strengths:<br /> The authors have identified an important accessibility issue in the scientific literature: the use of color combinations that make figures difficult to interpret for people with color-vision deficiency. The metrics proposed and evaluated in the study are a valuable theoretical contribution. The automated screening tool they provide is well-documented, open source, and relatively easy to install and use. It has the potential to provide a useful service to the scientists who want to make their figures more accessible. The data are open and freely accessible, well documented, and a valuable resource for further research. The manuscript is well written, logically structured, and easy to follow.

Weaknesses:<br /> (1) The authors themselves acknowledge the limitations that arise from the way they defined what constitutes an "unfriendly" image. There is a missed chance here to have engaged deuteranopes as stakeholders earlier in the experimental design. This would have allowed to determine to what extent spatial separation and labelling of problematic color combinations responds to their needs and whether setting the bar at a simulated severity of 80% is inclusive enough. A slightly lowered barrier is still a barrier to accessibility.

(2) The use of images from a single journal strongly limits the generalizability of the empirical findings as well as of the automated screening tool itself. Machine-learning algorithms are highly configurable but also notorious for their lack of transparency and for being easily biased by the training data set. A quick and unsystematic test of the web application shows that the classifier works well for electron microscopy images but fails at recognizing red-green scatter plots and even the classical diagnostic images for color-vision deficiency (Ishihara test images) as "unfriendly". A future iteration of the tool should be trained on a wider variety of images from different journals.

(3) Focusing the statistical analyses on individual images rather than articles (e.g. in figures 1 and 2) leads to pseudoreplication. Multiple images from the same article should not be treated as statistically independent measures, because they are produced by the same authors. A simple alternative is to instead use articles as the unit of analysis and score an article as "unfriendly" when it contains at least one "unfriendly" image. In addition, collapsing the counts of "unfriendly" images to proportions loses important information about the sample size. For example, the current analysis presented in Fig. 1 gives undue weight to the three images from 2012, two of which came from the same article. If we perform a logistic regression on articles coded as "friendly" and "unfriendly" (rather than the reported linear regression on the proportion of "unfriendly" images), there is still evidence for a decrease in the frequency of "unfriendly" eLife articles over time. Another issue concerns the large number of articles (>40%) that are classified as belonging to two subdisciplines, which further compounds the image pseudoreplication. Two alternatives are to either group articles with two subdisciplines into a "multidisciplinary" group or recode them to include both disciplines in the category name.

(4.)The low frequency of "unfriendly" images in the data (under 15%) calls for a different performance measure than the AUROC used by the authors. In such imbalanced classification cases the recommended performance measure is precision-recall area under the curve (PR AUC: https://doi.org/10.1371%2Fjournal.pone.0118432) that gives more weight to the classification of the rare class ("unfriendly" images).

Reviewer #2 (Public Review):

Summary:<br /> An analysis of images in the biology literature that are problematic for people with a color-vision deficiency (CVD) is presented, along with a machine learning-based model to identify such images and a web application that uses the model to flag problematic images. Their analysis reveals that about 13% of the images could be problematic for people with CVD and that the frequency of such images decreased over time. Their model yields 0.89 AUC score. It is proposed that their approach could help making biology literature accessible to diverse audiences.

Strengths:<br /> The manuscript focuses on an important yet mostly overlooked problem, and makes contributions both in expanding our understanding of the extent of the problem and in developing solutions to mitigate the problem. The paper is generally well-written and clearly organized. Their CVD simulation combines five different metrics. The dataset has been assessed by two researchers and is likely to be of high-quality. Machine learning algorithm used (convolutional neural network, CNN) is an appropriate choice for the problem. The evaluation of various hyperparameters for the CNN model is extensive.

Weaknesses:<br /> The focus seems to be on one type of CVD (deuteranopia) and it is unclear whether this would generalize to other types. The dataset consists of images from eLife articles. While this is a reasonable starting point, whether this can generalize to other biology/biomedical articles is not assessed. "Probably problematic" and "probably okay" classes are excluded from the analysis and classification, and the effect of this exclusion is not discussed. Machine learning aspects can be explained better, in a more standard way. The evaluation metrics used for validating the machine learning models seem lacking (e.g., precision, recall, F1 are not reported). The web application is not discussed in any depth.

Reviewer #3 (Public Review):

Summary:<br /> This work focuses on accessibility of scientific images for individuals with color vision deficiencies, particularly deuteranopia. The research involved an analysis of images from eLife published in 2012-2022. The authors manually reviewed nearly 5,000 images, comparing them with simulated versions representing the perspective of individuals with deuteranopia, and also evaluated several methods to automatically detect such images including training a machine-learning algorithm to do so, which performed the best. The authors found that nearly 13% of the images could be challenging for people with deuteranopia to interpret. There was a trend toward a decrease in problematic images over time, which is encouraging.

Strengths:<br /> The manuscript is well organized and written. It addresses inclusivity and accessibility in scientific communication, and reinforces that there is a problem and that in part technological solutions have potential to assist with this problem.

The number of manually assessed images for evaluation and training an algorithm is, to my knowledge, much larger than any existing survey. This is a valuable open source dataset beyond the work herein.

The sequential steps used to classify articles follow best practices for evaluation and training sets.

Weaknesses:<br /> I do not see any major issues with the methods. The authors were transparent with the limitations (the need to rely on simulations instead of what deuteranopes see), only capturing a subset of issues related to color vision deficiency, and the focus on one journal that may not be representative of images in other journals and disciplines.

eLife assessment

This study reports a potentially important discovery that testosterone-induced metabolic changes in seminal vesicle epithelial cells lead to the production of oleic acids in seminal plasma to enhance sperm fertility. The evidence to support metabolic changes in seminal vesicles and the identification of oleic acid as a key factor in seminal plasma is solid. However, the evidence for how oleic acids support enhanced sperm fertility in vivo is not well supported, thus currently remains incomplete, and requires further study.

Reviewer #1 (Public Review):

Summary:

In this report, the authors investigated the effects of reproductive secretions on sperm function in mice. The authors attempt to weave together an interesting mechanism whereby a testosterone-dependent shift in metabolic flux patterns in the seminal vesicle epithelium supports fatty acid synthesis, which they suggest is an essential component of seminal plasma that modulates sperm function by supporting linear motility patterns.

Strengths:

The topic is interesting and of general interest to the field. The study employs an impressive array of approaches to explore the relationship between mouse endocrine physiology and sperm function mediated by seminal components from various glandular secretions of the male reproductive tract.

Weaknesses:

Unfortunately, support for the proposed mechanism is not convincingly supported by the data, and the experimental design and methodology need more rigor and details, and the presence of numerous (uncontrolled) confounding variables in almost every experimental group significantly reduce confidence in the overall conclusions of the study.

The methodological detail as described is insufficient to support replication of the work. Many of the statistical analyses are not appropriate for the apparent designs (e.g. t-tests without corrections for multiple comparisons). This is important because the notion that different seminal secretions will affect sperm function would likely have a different conclusion if the correct controls were selected for post hoc comparison. In addition, the HTF condition was not adjusted to match the protein concentrations of the secretion-containing media, likely resulting in viscosity differences as a major confounding factor on sperm motility patterns.

There is ambiguity in many of the measurements due to the lack of normalization (e.g. all Seahorse Analyzer measurements are unnormalized, making cell mass and uniformity a major confounder in these measurements). This would be less of a concern if basal respiration rates were consistently similar across conditions and there were sufficient independent samples, but this was not the case in most of the experiments.

The observation that oleic acid is physiologically relevant to sperm function is not strongly supported. The cellular uptake of 10-100uM labeled oleic acid is presumably due to the detergent effects of the oleic acid, and the authors only show functional data for nM concentrations of exogenous oleic acid. In addition, the effect sizes in the supporting data were not large enough to provide a high degree of confidence given the small sample sizes and ambiguity of the design regarding the number of biological and technical replicates in the extracellular flux analysis experiments.

Overall, the most confident conclusion of the study was that testosterone affects the distribution of metabolic fluxes in a cultured human seminal vesicle epithelial cell line, although the physiological relevance of this observation is not clear.

In the introduction, the authors suggest that their analyses "reveal the pathways by which seminal vesicles synthesize seminal plasma, ensure sperm fertility, and provide new therapeutic and preventive strategies for male infertility." These conclusions need stronger or more complete data to support them.

Reviewer #2 (Public Review):

Summary:

Using a combination of in vivo studies with testosterone-inhibited and aged mice with lower testosterone levels, as well as isolated mouse and human seminal vesicle epithelial cells, the authors show that testosterone induces an increase in glucose uptake. They find that testosterone induces differential gene expression with a focus on metabolic enzymes. Specifically, they identify increased expression of enzymes that regulate cholesterol and fatty acid synthesis, leading to increased production of 18:1 oleic acid.

Strength:

Oleic acid is secreted by seminal vesicle epithelial cells and taken up by sperm, inducing an increase in mitochondrial respiration. The difference in sperm motility and in vivo fertilization in the presence of 18:1 oleic acid and the absence of testosterone is small but significant, suggesting that the authors have identified one of the fertilization-supporting factors in seminal plasma.

Weaknesses:

Further studies are required to investigate the effect of other seminal vesicle components on sperm capacitation to support the author's conclusions. The author's experiments focused on potential testosterone-induced changes in the rate of seminal vesicle epithelial cell glycolysis and oxphos, however, provide conflicting results and a potential correlation with seminal vesicle epithelial cell proliferation should be confirmed by additional experiments.

Reviewer #3 (Public Review):

Summary:

Male fertility depends on both sperm and seminal plasma, but the functional effect of seminal plasma on sperm has been relatively understudied. The authors investigate the testosterone-dependent synthesis of seminal plasma and identify oleic acid as a key factor in enhancing sperm fertility.

Strengths:

The evidence for changes in cell proliferation and metabolism of seminal vesicle epithelial cells and the identification of oleic acid as a key factor in seminal plasma is solid.

Weaknesses:

The evidence that oleic acids enhance sperm fertility in vivo needs more experimental support, as the main phenotypic effect in vitro provided by the authors remains simply as an increase in the linearity of sperm motility, which does not necessarily correlate with enhanced sperm fertility.

eLife assessment

This work is potentially useful because it has generated a mineable yield of new candidate immune inhibitory receptors, which can serve both as drug targets and as subjects for further biological investigation. It is noted however that the work is rather incomplete, in that it does little to validate the putative new receptors, and instead makes a study of their putative distribution across cell types. Experimental follow-up to demonstrate the claimed properties for the proteins identified, or mining existing experimental data sources on gene expression across tissues to at least show that the pipeline correctly identified genes likely to be specific to immune cells, would make this work more complete.

Reviewer #1 (Public Review):

This manuscript proposes a new bioinformatics approach identifying several hundreds of previously unknown inhibitory immunoreceptors. When expressed in immune cells (such as neutrophils, monocytes, CD8+, CD4+, and T-cells), such receptors inhibit the functional activity of these cells. Blocking inhibitory receptors represents a promising therapeutic strategy for cancer treatment.

As such, this is a high-quality and important bioinformatics study. One general concern is the absence of direct experimental validation of the results. In addition to the fact that the authors bioinformatically identified 51 known receptors, providing such experimental evaluation (of at least one, or better few identified receptors) would, in my opinion, significantly strengthen the presented evidence.

I will now briefly summarize the results and give my comments.

First, using sequence comparison analysis, the authors identify a large set of putative receptors based on the presence of immunoreceptor tyrosine-based inhibitory motifs (ITIMs), or immunoreceptor tyrosine-based switch motifs (ITSMs). They further filter the identified set of receptors for the presence of the ITIMs or ITSMs in an intracellular domain of the protein. Second, using AlphaFold structure modeling, the authors select only receptors containing ITIMs/ITSMs in structurally disordered regions. Third, the evaluation of gene expression profiles of known and putative receptors in several immune cell types was performed. Fourth, the authors classified putative receptors into functional categories, such as negative feedback receptors, threshold receptors, threshold disinhibition, and threshold-negative feedback. The latter classification was based on the available data from Nat Rev Immunol 2020. Fifth, using publicly available single-cell RNA sequencing data of tumor-infiltrating CD4+ and CD8+ cells from nearly twenty types of cancer, the authors demonstrate that a significant fraction of putative receptors are indeed expressed in these datasets.

In summary, in my opinion, this is an interesting, important, high-quality bioinformatics work. The manuscript is clearly written and all technical details are carefully explained.

One comment/suggestion regarding the methodology of evaluating gene expression profiles of putative receptors: perhaps it might be important to look at clusters of genes that are co-expressed with putative inhibitory receptors.

Reviewer #2 (Public Review):

Summary:

The authors developed a bioinformatic pipeline to aid the screening and identification of inhibitory receptors suitable as drug targets. The challenge lies in the large search space and lack of tools for assessing the likelihood of their inhibitory function. To make progress, the authors used a consensus protein membrane topology and sequence motif prediction tool (TOPCOS) combined with both a statistical measure assessing their likelihood function and a machine learning protein structural prediction model (AlphaFold) to greatly cut down the search space. After obtaining a manageable set of 398 high-confidence known and putative inhibitory receptors through this pipeline, the authors then mapped these receptors to different functional categories across different cell types based on their expression both in the resting and activated state. Additionally, by using publicly available pan-cancer scRNA-seq for tumor-infiltrating T-cell data, they showed that these receptors are expressed across various cellular subsets.

Strengths:

The authors presented sound arguments motivating the need to efficiently screen inhibitory receptors and to identify those that are functional. Key components of the algorithm were presented along with solid justification for why they addressed challenges faced by existing approaches. To name a few:

• TOPCON algorithm was elected to optimize the prediction of membrane topology.<br /> • A statistical measure was used to remove potential false positives.<br /> • AlphaFold is used to filter out putative receptors that are low confidence (and likely intrinsically disordered).

To examine receptors screened through this pipeline through a functional lens, the authors proposed to look at their expression of various immune cell subsets to assign functional categories. This is a reasonable and appropriate first step for interpreting and understanding how potential drug targets are differentially expressed in some disease contexts.

Weaknesses:

The paper has strength in the pipeline they presented, but the weakness, in my opinion, lies in the lack of concrete demonstration on how this pipeline can be used to at least "rediscover" known targets in a disease-specific manner. For example, the result that both known and putative immune inhibitory receptors are expressed across a wide variety of tumor-infiltrating T-cell subsets is reassuring, but this would have been more informative and illustrative if the authors could demonstrate using a disease with known targets, as opposed to a pan-cancer context. Additionally, a discussion that contrasts the known and putative receptors in the context above would help readers better identify use cases suitable for their research using this pipeline. Particularly,<br /> • For known receptors, does the pipeline and the expression analysis above rediscover the known target in the disease of interest?<br /> • For putative receptors, what do the functional category mapping and the differential expression across various tumor-infiltrating T-cell subsets imply on a potential therapeutic target?

Author Response

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

eLife assessment

This manuscript presents a valuable approach to exploring CD4+ T-cell response in mice across stimuli and tissues through the analysis of their T-cell receptor repertoires. The authors use a transgenic mouse model, in which the possible diversity of the T-cell receptor repertoire is reduced, such that each of a diverse set of immune exposures elicits more detectably consistent T-cell responses across different individuals. However, whereas the proposed experimental system could be utilized to study convergent T-cell responses, the analyses done in this manuscript are incomplete and do not support the claims due to limitations in the statistical analyses and lack of data/code access.

We worked to address the reviewers' concerns below, point-by-point.

All data on immune repertoires are deposited here: https://figshare.com/articles/dataset/Convergence_plasticity_and_tissue_residence_of_regulatory_and_effector_T_cell_response/22226155

We added the Data availability statement to the manuscript.

Public Reviews:

Reviewer #1 (Public Review):

The authors investigate the alpha chain TCR landscape in conventional vs regulatory CD4 T cells. Overall I think it is a very well thought out and executed study with interesting conclusions. The authors have investigated CDR3 alpha repertoires coupled with a transgenic fixed CDR3beta in a mouse system.

Strengths:

• One of a kind evidence and dataset.

• State-of-the-art analyses using tools that are well-accepted in the literature.

• Interesting conclusions on the breadth of immune response to challenges across different types of challenges (tumor, viral and parasitic).

Thank you for the positive view.

Weaknesses:

• Some conclusions regarding the eCD4->eTreg transition are not so strong using only the data.

The overlaps between the top-nucleotide clones in both LLC and PYMT challenges are prominently above the average, and this result is reproducible in lungs and skin, so we have no doubts based on these data. Further experiments with different methods, including tracking the clonal fates, should clarify and confirm/correct/disprove our findings.

• Some formatting issues.

We are working on the manuscript to correct minor errors and formatting.

Reviewer #2 (Public Review):

This study investigates T-cell repertoire responses in a mouse model with a transgenic beta chain, such that all T-cells in all mice share a fixed beta chain, and repertoire diversity is determined solely by alpha chain rearrangements. Each mouse is exposed to one of a few distinct immune challenges, sacrificed, and T-cells are sampled from multiple tissues. FACS is used to sort CD4 and Treg cell populations from each sample, and TCR repertoire sequencing from UMI-tagged cDNA is done.

Various analyses using repertoire diversity, overlap, and clustering are presented to support several principal findings: 1) TCR repertoires in this fixed beta system have highly distinct clonal compositions for each immune challenge and each cell type, 2) these are highly consistent across mice, so that mice with shared challenges have shared clones, and 3) induction of CD4-to-Treg cell type transitions is challenge-specific.

The beta chain used for this mouse model was previously isolated based on specificity for Ovalbumin. Because the beta chain is essential for determining TCR antigen specificity, and is highly diverse in wildtype mice, I found it surprising that these mice are reported to have robust and consistently focused clonal responses to very diverse immune challenges, for which a fixed OVA-specific beta chain is unlikely to be useful. The authors don't comment on this aspect of their findings, but I would think it is not expected a priori that this would work. If this does work as reported, it is a valuable model system: due to massively reduced diversity, the TCR repertoire response is much more stereotyped across individual samples, and it is much easier to detect challenge-specific TCRs via the statistics of convergent responses.

This was to some extent expected, since these mice live almost normally and have productive adaptive immune responses and protection. In real life, there are frequent TCR-pMHC interactions where the TCR-alpha chain dominates (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5701794/; https://pubmed.ncbi.nlm.nih.gov/37047500/). On the fixed TCR-beta background this mechanics starts working full-fledged, essentially substituting TCR-beta diversity, at the extent of relatively simplified TCRab repertoire and probably higher cross-reactivity.

We agree that this is a valuable model, for sure, and indicated this in the last sentence of our Discussion. Now we are also adding this point to the abstract.

While the data and analyses present interesting signals, they are flawed in several ways that undermine the reported findings. I summarize below what I think are the most substantive data and analysis issues.

(1) There may be systematic inconsistencies in repertoire sampling depth that are not described in the manuscript. Looking at the supplementary tables (and making some plots), I found that the control samples (mice with mock challenge) have consistently much shallower sampling-in terms of both read count and UMI count-compared with the other challenge samples. There is also a strong pattern of lower counts for Treg vs CD4 cell samples within each challenge.

The immune response of control mice is less extensive, as it should be. Just like the fact that the number of Tregs in tissues is lower than CD4, this is normal. So this all follows the expectations. But please note that we were very accurate everywhere with appropriate data normalisation, using all our previous extensive experience (https://pubmed.ncbi.nlm.nih.gov/29080364/).

In particular (now adding more relevant details to Methods):

For diversity metrics calculations, we randomly sampled an equal number of 1000 UMI from each cloneset. Samples with UMI < 700 were excluded from analysis.

For amino acid overlap metrics calculations, we selected top-1000 largest clonotypes from each cloneset. Samples with clonotype counts < 700 were excluded from analysis.

For nucleotide overlaps metrics calculations (eCD4-eTreg), we selected top-100 clonotypes from each cloneset. Samples with clonotypes < 100 were excluded from analysis.

The top N clonotypes were selected as the top N clonotypes after randomly shuffling the sequences and aligning them in descending order. This was done in order to get rid of the alphabetical order for clonotypes with equal counts (e.g. count = 1 or 2).

Downsampling was carried out using software vdjtools v.1.2.1.

(2) FACS data are not reported. Although the graphical abstract shows a schematic FACS plot, there are no such plots in the manuscript. Related to the issue above, it would be important to know the FACS cell counts for each sample.

Yes, we agree that this is valuable information that should be provided. Unfortunately, this data has not been preserved.

(3) For diversity estimation, UMI-wise downsampling was performed to normalize samples to 1000 random UMIs, but this procedure is not validated (the optimal normalization would require downsampling cells). What is the influence of possible sampling depth discrepancies mentioned above on diversity estimation? All of the Treg control samples have fewer than 1000 total UMIs-doesn't that pose a problem for sampling 1000 random UMIs?

Indeed, I simulated this procedure and found systematic effects on diversity estimates when taking samples of different numbers of cells (each with a simulated UMI count) from the same underlying repertoire, even after normalizing to 1000 random UMIs. I don't think UMI downsampling corrects for cell sampling depth differences in diversity estimation, so it's not clear that the trends in Fig 1A are not artifactual-they would seem to show higher diversity for control samples, but these are the very same samples with an apparent systematic sampling depth bias.

We evaluated this approach through all our work, and summarised in the ref: https://pubmed.ncbi.nlm.nih.gov/29080364/. Altogether, normalising to the same count of randomly sampled UMI seems to be the best approach (although, preferably, the initial sequencing depth should be essentially higher for all samples than the sampling threshold used). Initial sorting of identical numbers of cells and ideally uniform library preparation and sequencing is generally not realistic and does not work in the real world, while UMI downsampling does the same work much better.

(4) The Figures may be inconsistent with the data. I downloaded the Supplementary Table corresponding to Fig 1 and made my own version of panels A-C. This looked quite different from the diversity estimations depicted in the manuscript. The data does not match the scale or trends shown in the manuscript figure.

There was a wrong column for Chao1, now correcting. Also, please note that we only used samples with > 700 UMI. Supplementary Table now corrected accordingly. Also, please note that Figure 1 shows the results for lung samples only.

(5) For the overlap analysis, a different kind of normalization was performed, but also not validated. Instead of sampling 1000 UMIs, the repertoires were reduced to their top 1000 most frequent clones. It is not made clear why a different normalization would be needed here. There are several samples (including all Treg control samples) with only a couple hundred clones. It's also likely that the noted systematic sampling depth differences may drive the separation seen in MDS1 between Treg and CD4 cell types. I also simulated this alternative downsampling procedure and found strong effects on MDS clustering due to sampling effects alone.

That’s right, for the overlap analysis (which values are mathematically proportional to the clonotype counts in both compared repertoires, so the difference in the counts causes major biases) the right way to do it is to choose the same number of clonotypes. See Ref. https://pubmed.ncbi.nlm.nih.gov/29080364/.

We kept only samples with > 700 for the overlap analyses. Some relatively poor samples are present in all challenges, while MDS1 localization has clear reproducible logic, so we are confident in these results.

It is not made clear how the overlap scores were converted to distances for MDS. It's hard to interpret this without seeing the overlap matrix.

This is a built-in feature in VDJtools software (https://pubmed.ncbi.nlm.nih.gov/26606115/). See also here: https://vdjtools-doc.readthedocs.io/en/master/overlap.html.

(6) The cluster analysis is superficial, and appears to have been cherry-picked. The clusters reported in the main text have illegibly small logo plots, and no information about V/J gene enrichments. More importantly, as the caption states they were chosen from the columns of a large (and messier-looking) cluster matrix in the supplementary figure based on association with each specific challenge. There's no detail about how this association was calculated, or how it controlled for multiple tests. I don't think it is legitimate to simply display a set of clusters that visually correlate; in a sufficiently wide random matrix you will find columns that seem to correlate with any given pattern across rows.

Particular CDR3 sequences and VJ segments do not mean much for the results of this manuscript. Logos are given just for visual explanation of how the consensus motifs of the clusters look like.

We now add two more Supplementary Tables and a Supplementary Figure with full information about clusters.

We disagree that the Supplementary Figure 1 (representing all the clusters) looks “messy”. Vice versa, it is surprisingly “digital”, showing the clear patterns of responses and homings. This becomes clear if you visually study it for a while. But yes, it is too big to let the reader focus on this or that aspect. That is why we need to select TCR clusters to illustrate this or that aspect discussed in the work, but they were selected from the overall already structured picture.

(7) The findings on differential plasticity and CD4 to Treg conversion are not supported. If CD4 cells are converting to Tregs, we expect more nucleotide-level overlap of clones. This intuition makes sense. But it seems that this section affirms the consequent: variation in nucleotide-level clone overlap is a readout of variation in CD4 to Treg conversion. It is claimed, based on elevated nucleotide-level overlap, that the LLC and PYMT challenges induce conversion more readily than the other challenges. It is not noted in the textual interpretations, but Fig 4 also shows that the control samples had a substantially elevated nucleotide-level overlap. There is no mention of a null hypothesis for what we'd expect if there was no induced conversion going on at all. This is a reduced-diversity mouse model, so convergent recombination is more likely than usual, and the challenges could be expected to differ in the parts of TCR sequence space they induce focus on. They use the top 100 clones for normalization in this case, but don't say why (this is the 3rd distinct normalization procedure).

Your point is absolutely correct: “This is a reduced-diversity mouse model, so convergent recombination is more likely than usual”. Distinct normalisation procedure was required to focus on the most expanded clonotypes to avoid the tail of (presumably cross-reactive) and identical TCRs present in all repertoires in these limited-repertoire mice. So we downsampled as strictly as possible to minimise this background signal of nucleotide overlap, and only this strict downsampling to the top-100 clonotypes allowed us to visualise the difference between the challenges. This is a sort of too complicated explanation that would overload the manuscript. But your comments and our answers will be available to the reader who wants to go into all the details.

The observed (at this strict downsampling) overlaps between the top-nucleotide clones in both LLC and PYMT challenges are prominently above the average, and this result is reproducible in lungs and skin, so we have no doubts in interpretations based on these data. Further experiments with different methods, including tracking the clonal fates, should clarify and confirm/correct/disprove our findings.

Although interpretations of the reported findings are limited due to the issues above, this is an interesting model system in which to explore convergent responses. Follow-up experimental work could validate some of the reported signals, and the data set may also be useful for other specific questions.

Yes, thank you for your really thorough analysis. We fully agree with your conclusion.

Reviewer #3 (Public Review):

Nakonechnaya et al present a valuable and comprehensive exploration of CD4+ T cell response in mice across stimuli and tissues through the analysis of their TCR-alpha repertoires.

The authors compare repertoires by looking at the relative overlap of shared clonotypes and observe that they sometimes cluster by tissue and sometimes by stimulus. They also compare different CD4+ subsets (conventional and Tregs) and find distinct yet convergent responses with occasional plasticity across subsets for some stimuli.

The observed lack of a general behaviour highlights the need for careful comparison of immune repertoires across cell subsets and tissues in order to better understand their role in the adaptive immune response.

In conclusion, this is an important paper to the community as it suggests several future directions of exploration.

Unfortunately, the lack of code and data availability does not allow the reproducibility of the results.

Thank you for your positive view.

All data on immune repertoires are deposited here: https://figshare.com/articles/dataset/Convergence_plasticity_and_tissue_residence_of_regulatory_and_effector_T_cell_response/22226155

We added the Data availability statement to the manuscript.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

• In the manuscript at "yielding 13,369 {plus minus} 1,255 UMI-labeled TCRα cDNA molecules and 3233 {plus minus} 310 TCRα CDR3 clonotypes per sample" I'm not sure how can there be fewer unique DNA molecules than clonotypes in each sample.

That was our mistake for sure, now corrected.

• In the manuscript at "This indicates that the amplitude and focused nature of the effector and regulatory T cell response in lungs is generally comparable."

I'm not sure it's possible to conclude that a drop in diversity in all conditions necessarily signals a focused nature. Since at this stage, the nature of the colotypes was not compared between conditions, it is not possible to claim a focused nature of the response.

We have softened the wording:

"This could indicate that the amplitude and focused nature of the effector and regulatory T cell response in lungs is generally comparable."

• What are your thoughts on why there is such a large overlap between Treg and Teff in the Lung in control? For some replicates it is almost as much as a post-LLC challenge!

There is some natural dispersion in the data, which is generally expectable. The overlaps between the top-nucleotide clones in both LLC and PYMT challenges are prominently above the average, and this result is reproducible in lungs and skin, so we have no doubts based on these data. Further experiments with different methods, including tracking the clonal fates, should clarify and confirm/correct/disprove our findings.

• In the manuscript at "These results indicate that distinct antigenic specificities are generally characteristic for eTreg cells that preferentially reside in particular lymphatic niches" I'm not sure we can conclude this from the Figure. Wouldn't you expect the samples to be grouped by color (the different challenges)? Maybe I'm not understanding the sentence!

This is a different story, about resident Tregs, irrespective of the challenge.

The whole explanation is here in the text:

“Global CDR3α cluster analysis revealed that characteristic eTreg TCR motifs were present in distinct lymphatic tissues, including spleen and thymus, irrespective of the applied challenge (Supplementary Fig. 1). To better illustrate this phenomenon, we performed MDS analysis of CDR3α repertoires for distinct lymphatic tissues, excluding the lungs due to their otherwise dominant response to the current challenge. This analysis demonstrated close proximity of eTreg repertoires obtained from the same lymphatic tissues upon all lung challenges and across all animals (Fig. 5a, b). These results indicate that distinct antigenic specificities are generally characteristic for eTreg cells that preferentially reside in particular lymphatic niches. Notably, the convergence of lymphatic tissue-resident TCR repertoires was less prominent for the eCD4 T cells (Fig. 5c, d).”

And in the abstract:

“Additionally, our TCRα repertoire analysis demonstrated that distinct antigenic specificities are characteristic for eTreg cells residing in particular lymphatic tissues, regardless of the challenge, revealing the homing-specific, antigen-specific resident Treg populations. ”

• In the manuscript at " Notably, the convergence of lymphatic tissue-resident TCR repertoires was less prominent for the eCD4 T cells ":

5b and 5d seem to have the same pattern: Spleen and MLN group together, AxLN and IgLN together and thymus is separate. Do you mean to say that the groups are more diffuse? I feel like the pattern really is the same and it's likely due to some noise in the data…

Yes, we just mean here that eTreg groups are less diffuse - means more convergent.

• I'm not sold on the eCD4 to eTreg conversion evidence. Why only limit to the top 100 clones? The top 1000 clones were used in previous analyses! Moreover, the authors claim that calculating relative overlap (via F2) of matching CDR3+V+J genes is evidence of a conversion between eCD4 and eTreg. I think to convince myself of a real conversion, I would track the cells between groups, unfortunately, I'm not sure how to track this.. Maybe looking at the thymus population? For example, what is the overlap in the thymus vs. after the challenge? I don't have an answer on how to verify but I feel that this conclusion is a bit on the weaker end.

Distinct normalisation procedure was required to focus on the most expanded clonotypes to avoid the tail of (presumably cross-reactive) and identical TCRs present in all repertoires in these limited-repertoire mice. So we downsampled as strictly as possible to minimise this background signal of nucleotide overlap, and only this strict downsampling to the top-100 clonotypes allowed us to visualise the difference between the challenges. This is a sort of too complicated explanation that would overload the manuscript. But your comments and our answers will be available to the reader who wants to go into all the details.

The observed (at this strict downsampling) overlaps between the top-nucleotide clones in both LLC and PYMT challenges are prominently above the average, and this result is reproducible in lungs and skin, so we have no doubts in interpretations based on these data. Further experiments with different methods, including tracking the clonal fates, should clarify and confirm/correct/disprove our findings.

• There is a nuance in the analysis between Figure 3 and Figure 5 which I think I am not grasping. Both Figures use the same method and the same data but what is different? I think the manuscript would benefit from making this crystal clear. The conclusions will likely be more evident as well!

As explained in the text and above, on Figure 5 “we performed MDS analysis of CDR3α repertoires for distinct lymphatic tissues, excluding the lungs due to their otherwise dominant response to the current challenge.”

The idea of this mini-chapter of the manuscript is to reveal tissue-resident Tregs, distinct for distinct tissues, resident there in all these mice, irrespectively of the challenge we applied. And they are really there (!).

• Do the authors plan to share their R scripts?

All calculations were performed in VDJtools. R was only used to build figures. Corrected this in Methods.

Minor typos and formatting issues to address:

• Typo in Figure 2a the category should read "worm" instead of "warm"

Corrected.

• Figure 2a heatmap is missing a color bar indicating the value ranges

The detailed information can be found in additional Supplementary materials.

• Figure 2f is never mentioned in the manuscript!

Corrected.

• "eTreg repertoire upon lung challenge is reflected in the draining lymph node" - the word upon is of a lower size

Corrected.

• The authors should make the spelling of eTreg uniform across the manuscript (reg in subscript vs just lower case letters. Same goes for CDR3a vs CDR3\alpha

Corrected.

• Figure 4a-d p-values annotations are not shown. Is it because they are not significant?

Corrected.

• The spelling of FACS buffer should be uniform (FACs vs FACS, see methods)

Corrected.

• In the gating strategy, I would make a uniform annotation for the cluster of differentiation, for example, "CD44 high" vs "CD44^{hi}", pos vs + etc.

Corrected.

• Citation for MIGEC software (if available) is missing from methods

Present in the text so probably sufficient.

Reviewer #2 (Recommendations For The Authors):

I noticed the data was made available via Figshare in the preprint, but there is no data availability statement in the current ms.

We provided Data availability statement.

The methods state that custom scripts were written to perform the various analyses. Those should be made available in a code repository, and linked in the ms.

All calculations were performed in VDJtools. R was only used to build figures. Corrected this in Methods.

The title mentioned "TCR repertoire prism", so I thought "prism" was the name of a new method or software. But then the word "prism" didn't appear anywhere in the ms.

We just mean viewing or understanding something from a different perspective or through a lens that reveals different aspects or nuances.

Figure 1D lacks an x-axis label.

Worked on the figures in general.

Reviewer #3 (Recommendations For The Authors):

• The paper is very concise, possibly a bit too much. It could use additional explanations to properly affirm its relevance, for example:

why the choice of fixing the CDR3beta background?

To make repertoire more similar across the mice, and to track all the features of repertoire using only one chain.

to what it is fixed?

As explained in Methods:

“C57BL/6J DO11.10 TCRβ transgenic mice (kindly provided by Philippa Marrack) and crossed to C57BL/6J Foxp3eGFP TCRa-/- mice.”

What do you expect to see and not to see in this specific system and why it is important?

As stated above: we expected repertoire to be more similar across the mice, and it is important to find antigen-specific TCR clusters across mice, and to be able to track all the features of the TCR repertoire using only one chain.

Does this system induce more convergent responses? If so, can we extrapolate the results from this system to the full alpha-beta response?

Such a model, compared to conventional mice, is much more powerful in terms of the ability of monitoring convergent TCR responses. At the same time, it behaves natural, mice live almost normally, so we believe it reflects natural behaviour of the full fledged alpha-beta T cell repertoire.

• Is the lack of similarity of other tissues to Lung/MLN due to a lack of a response?

As indicated in the title of the corresponding mini-chapter: “eTreg repertoire upon lung challenge is reflected in the draining lymph node”. And conclusion of this mini-chapter is that “these results demonstrate the selective tissue localization of the antigen-focused Treg response. ”

Can you do a dendrogram like 2a for the other tissues to better clarify what is going on there? There is space in the supplementary material.

We built lots of those, but in such single dimension mostly they are less informative compared to 2D MDS plots.

• Figure 5 seems a bit out of place as it looks more related to Figure 2. It could maybe be integrated there, sent to supplementary or become Figure 3?

This is a different story, about resident Tregs, irrespective of the challenge.

The whole explanation is here in the text:

“Global CDR3α cluster analysis revealed that characteristic eTreg TCR motifs were present in distinct lymphatic tissues, including spleen and thymus, irrespective of the applied challenge (Supplementary Fig. 1). To better illustrate this phenomenon, we performed MDS analysis of CDR3α repertoires for distinct lymphatic tissues, excluding the lungs due to their otherwise dominant response to the current challenge. This analysis demonstrated close proximity of eTreg repertoires obtained from the same lymphatic tissues upon all lung challenges and across all animals (Fig. 5a, b). These results indicate that distinct antigenic specificities are generally characteristic for eTreg cells that preferentially reside in particular lymphatic niches. Notably, the convergence of lymphatic tissue-resident TCR repertoires was less prominent for the eCD4 T cells (Fig. 5c, d).”

And in the abstract:

“Additionally, our TCRα repertoire analysis demonstrated that distinct antigenic specificities are characteristic for eTreg cells residing in particular lymphatic tissues, regardless of the challenge, revealing the homing-specific, antigen-specific resident Treg populations. ”

• Have you explored more systematically the role of individual variability? If you stratify by individual, do you observe any trend? If not this is also an interesting observation to highlight and discuss.

This is inside the calculations and figures/ one dot = 1 mice, so this natural variation is there inside.

• Regarding the MDS plots: why are 2 dimensions the right amount? Maybe with 3, you can see both tissue specificity and stimuli contributions. Can you do a stress vs # dimensions plot to check what should be the right amount of dimensions to more accurately reproduce the distance matrix?

Tissue specificity and stimuli contribution is hard to distinguish without focussing on appropriate samples, as we did on Fig. 3 and 5. The work is already not that simple as is, and attempting to analyse this in multidimensional space is far beyond our current abilities. But this is an interesting point for future work, thank you.

• Figure 2: A better resolution is needed in order to properly resolve the logo plots at the bottom.

Yes, we worked on Figures, and also provide new Supplementary Figure with all the logos.

• No code or data are made available. There is also a lack of supplementary figures that complement and expand the results presented in the main text.

We believe that the main text, although succinct, contains lots of information to analyse and conclusions (preliminary) to make. So we do not see it rational to overload it further.

eLife assessment

This manuscript presents a valuable approach to exploring CD4+ T-cell response in mice across stimuli and tissues through the analysis of their T-cell receptor repertoires. The authors use a transgenic mouse model with reduced diversity of the T-cell receptor repertoire to elicit more consistent T-cell responses across individuals, demonstrating challenge-specific and tissue-specific responses of regulatory T-cells. The evidence for the authors' conclusions is solid, and the work will be of interest to immunologists studying T cell responses.

Reviewer #1 (Public Review):

The authors investigate the alpha chain t cell receptor landscape in conventional vs regulatory CD4 T cells. Overall I think it is a very well thought out and executed study with interesting conclusions. Findings are valuable and are supported by convincing evidence. This work will be of interest for immunologists studying T cells.

Strengths:

- One of a kind evidence and dataset.

- State of the art analyses using well accepted in the literature tools.

- Interesting conclusions on the breadth of immune response to challenges across different types of challenges (tumor, viral and parasitic).

Reviewer #3 (Public Review):

This study presents a valuable exploration of CD4+ T cell response in a fixed TCRβ chain FoxP3-GFP mouse model across stimuli and tissues through the analysis of their TCRα repertoires. This is an insightful paper for the community as it suggests several future directions of exploration.

The authors compare Treg and conventional CD4+ repertoires by looking at diversity measures and the relative overlap of shared clonotypes to characterize similarity across different tissues and antigen challenges. They find distinct yet convergent responses with occasional plasticity across subsets for some stimuli. The observed lack of a general behavior highlights the need for careful comparison of immune repertoires across cell subsets and tissues. Such comparisons are crucial in order to better understand the heterogeneity of the adaptive immune response. This mouse model demonstrates its utility for this task due to the reduced diversity of the TCRα repertoire and the ability to track a single chain.

The revised manuscript has significantly improved in terms of clarity of explanations and presentations of the results.

Author Response

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

Overall Response

We thank the reviewers for reviewing our manuscript, recognizing the significance of our study, and offering valuable suggestions. Based on the reviewer’s comments and the updated eLife assessment, we would like to chose the current version of our manuscript as the Version of Record of our manuscript.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

Given knowledge of the amino acid sequence and of some version of the 3D structure of two monomers that are expected to form a complex, the authors investigate whether it is possible to accurately predict which residues will be in contact in the 3D structure of the expected complex. To this effect, they train a deep learning model which takes as inputs the geometric structures of the individual monomers, per-residue features (PSSMs) extracted from MSAs for each monomer, and rich representations of the amino acid sequences computed with the pre-trained protein language models ESM-1b, MSA Transformer, and ESM-IF. Predicting inter-protein contacts in complexes is an important problem. Multimer variants of AlphaFold, such as AlphaFold-Multimer, are the current state of the art for full protein complex structure prediction, and if the three-dimensional structure of a complex can be accurately predicted then the inter-protein contacts can also be accurately determined. By contrast, the method presented here seeks state-of-the-art performance among models that have been trained end-to-end for inter-protein contact prediction.

Strengths:

The paper is carefully written and the method is very well detailed. The model works both for homodimers and heterodimers. The ablation studies convincingly demonstrate that the chosen model architecture is appropriate for the task. Various comparisons suggest that PLMGraph-Inter performs substantially better, given the same input, than DeepHomo, GLINTER, CDPred, DeepHomo2, and DRN-1D2D_Inter.

The authors control for some degree of redundancy between their training and test sets, both using sequence and structural similarity criteria. This is more careful than can be said of most works in the field of PPI prediction.

As a byproduct of the analysis, a potentially useful heuristic criterion for acceptable contact prediction quality is found by the authors: namely, to have at least 50% precision in the prediction of the top 50 contacts.

We thank the reviewer for recognizing the strengths of our work!

Weaknesses:

The authors check for performance drops when the test set is restricted to pairs of interacting proteins such that the chain pair is not similar as a pair (in sequence or structure) to a pair present in the training set. A more challenging test would be to restrict the test set to pairs of interacting proteins such that none of the chains are separately similar to monomers present in the training set. In the case of structural similarity (TM-scores), this would amount to replacing the two "min"s with "max"s in Eq. (4). In the case of sequence similarity, one would simply require that no monomer in the test set is in any MMSeqs2 cluster observed in the training set. This may be an important check to make, because a protein may interact with several partners, and/or may use the same sites for several distinct interactions, contributing to residual data leakage in the test set.

We thank the reviewer for the suggestion! In the case of protein-protein prediction (“0D prediction”) or protein-protein interfacial residue prediction(“1D prediction”), we think making none of the chains in the test set separately similar to monomers in the training set is necessary, as the reviewer pointed out that a protein may interact with several partners, and may even use the same sites for the interactions. Since the task of this study is predicting the inter-protein residue-residue contacts (“2D prediction”), even though a protein uses the same site to interact with different partners, as long as the interacting partners are different, the inter-protein contact maps would be different. Therefore, we don’t think that in our task, making this restriction to the test set is necessary.

The training set of AFM with v2 weights has a global cutoff of 30 April 2018, while that of PLMGraph-Inter has a cutoff of March 7 2022. So there may be structures in the test set for PLMGraph-Inter that are not in the training set of AFM with v2 weights (released between May 2018 and March 2022). The "Benchmark 2" dataset from the AFM paper may have a few additional structures not in the training or test set for PLMGraph-Inter. I realize there may be only few structures that are in neither training set, but still think that showing the comparison between PLMGraph-Inter and AFM there would be important, even if no statistically significant conclusions can be drawn.

We thank the reviewer for the suggestion! It is not enough to only use the date cutoff to remove the redundancy, since similar structures can be deposited in the PDB in different dates. Because AFM does not release the PDB codes of its training set, it is difficult for us to totally remove the redundancy. Therefore, we think no rigorous conclusion can be drawn by including these comparisons in the manuscript. Besides, the main point of this study is to demonstrate that the integration of multiple protein language models using protein geometric graphs can dramatically improve the model performance for inter-protein contact prediction, which can provide some important enlightenments for the future development of more powerful protein complex structure prediction methods beyond AFM, rather than providing a tool which can beat AFM at this moment. We think including too many stuffs in the comparison with AFM may distract the readers. Therefore, we choose to not include these comparisons in the manuscript.

Finally, the inclusion of AFM confidence scores is very good. A user would likely trust AFM predictions when the confidence score is high, but look for alternative predictions when it is low. The authors' analysis (Figure 6, panels c and d) seems to suggest that, in the case of heterodimers, when AFM has low confidence, PLMGraph-Inter improves precision by (only) about 3% on average. By comparison, the reported gains in the "DockQ-failed" and "precision-failed" bins are based on knowledge of the ground truth final structure, and thus are not actionable in a real use-case.

We agree with the reviewer that more studies are needed for providing a model which can well complement or even beat AFM. The main point of this study is to demonstrate that the integration of multiple protein language models using protein geometric graphs can dramatically improve the model performance for inter-protein contact prediction, which can provide some important enlightenments for the future development of more powerful protein complex structure prediction methods beyond AFM.

Reviewer #2 (Public Review):

This work introduces PLMGraph-Inter, a new deep learning approach for predicting inter-protein contacts, which is crucial for understanding proteinprotein interactions. Despite advancements in this field, especially driven by AlphaFold, prediction accuracy and efficiency in terms of computational cost still remains an area for improvement. PLMGraph-Inter utilizes invariant geometric graphs to integrate the features from multiple protein language models into the structural information of each subunit. When compared against other inter-protein contact prediction methods, PLMGraph-Inter shows better performance which indicates that utilizing both sequence embeddings and structural embeddings is important to achieve high-accuracy predictions with relatively smaller computational costs for the model training.

We thank the reviewer for recognizing the strengths of our work!

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

• I recommend renaming the section "Further potential redundancies removal between the training and the test" to "Further potential redundancies removal between the training and the test sets"

Changed.

• In lines 768-769, the sentence seems to end prematurely in "to use more stringent threshold in the redundancy removal"

Corrected.

• In Eq. (4), line 789, there are many instances of dashes that look like minus signs, creating some confusion.

Corrected.

• I think I may have mixed up figure references in my first review. When I said (Recommendations to the authors): "p. 22, line 2: from the figure, I would have guessed "greater than or equal to 0.7", not 0.8", I think I was referring to what is now lines 423-424, referring to what is now Figure 5c. The point stands there, I think.

Corrected.

• A couple of new grammatical mishaps have been introduced in the revision. These could be rectified.

We carefully rechecked our revisions, and corrected the grammatical issues we found.

Reviewer #2 (Recommendations For The Authors):

Most of my concerns were resolved through the revision. I have only one suggestion for the main figure.

The current scatter plots in Figure 2 are hard to understand as too many different methods are abstracted into a single plot with multiple colors. I would suggest comparing their performances using box plot or violin plot for the figure 2.

We thank the reviewer for the suggestion! In the revision, we tried violin plot, but it does not look good since too many different methods are included in the plot. Besides, we chose the scatter plot as it can provide much more details. We also provided the individual head-to-head scatter plots as supplementary figures, we think which can also be helpful for the readers to capture the information of the figures.

---

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

Overall Response

We would like to thank the reviewers for reviewing our manuscript, recognizing the significance of our study, and offering valuable suggestions. We have carefully revised the manuscript to address all the concerns and suggestions raised by the reviewers.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

Given knowledge of the amino acid sequence and of some version of the 3D structure of two monomers that are expected to form a complex, the authors investigate whether it is possible to accurately predict which residues will be in contact in the 3D structure of the expected complex. To this effect, they train a deep learning model that takes as inputs the geometric structures of the individual monomers, per-residue features (PSSMs) extracted from MSAs for each monomer, and rich representations of the amino acid sequences computed with the pre-trained protein language models ESM-1b, MSA Transformer, and ESM-IF. Predicting inter-protein contacts in complexes is an important problem. Multimer variants of AlphaFold, such as AlphaFold-Multimer, are the current state of the art for full protein complex structure prediction, and if the three-dimensional structure of a complex can be accurately predicted then the inter-protein contacts can also be accurately determined. By contrast, the method presented here seeks state-of-the-art performance among models that have been trained end-to-end for inter-protein contact prediction.

Strengths:

The paper is carefully written and the method is very well detailed. The model works both for homodimers and heterodimers. The ablation studies convincingly demonstrate that the chosen model architecture is appropriate for the task. Various comparisons suggest that PLMGraph-Inter performs substantially better, given the same input than DeepHomo, GLINTER, CDPred, DeepHomo2, and DRN-1D2D_Inter. As a byproduct of the analysis, a potentially useful heuristic criterion for acceptable contact prediction quality is found by the authors: namely, to have at least 50% precision in the prediction of the top 50 contacts.

We thank the reviewer for recognizing the strengths of our work!

Weaknesses:

My biggest issue with this work is the evaluations made using bound monomer structures as inputs, coming from the very complexes to be predicted. Conformational changes in protein-protein association are the key element of the binding mechanism and are challenging to predict. While the GLINTER paper (Xie & Xu, 2022) is guilty of the same sin, the authors of CDPred (Guo et al., 2022) correctly only report test results obtained using predicted unbound tertiary structures as inputs to their model. Test results using experimental monomer structures in bound states can hide important limitations in the model, and thus say very little about the realistic use cases in which only the unbound structures (experimental or predicted) are available. I therefore strongly suggest reducing the importance given to the results obtained using bound structures and emphasizing instead those obtained using predicted monomer structures as inputs.

We thank the reviewer for the suggestion! In the revision, to emphasize the performance of PLMGraph-Inter using the predicted monomer structures, we moved the evaluation results based on the predicted monomer from the supplementary to the main text (see the new Table 1 and Figure 2 in the revised manuscript) and re-organized the two subsections “Evaluation of PLMGraph-Inter on HomoPDB and HeteroPDB test sets” and “Impact of the monomeric structure quality on contact prediction” in the main text.

In particular, the most relevant comparison with AlphaFold-Multimer (AFM) is given in Figure S2, not Figure 6. Unfortunately, it substantially shrinks the proportion of structures for which AFM fails while PLMGraph-Inter performs decently. Still, it would be interesting to investigate why this occurs. One possibility would be that the predicted monomer structures are of bad quality there, and PLMGraph-Inter may be able to rely on a signal from its language model features instead. Finally, AFM multimer confidence values ("iptm + ptm") should be provided, especially in the cases in which AFM struggles.

We thank the reviewer for the suggestion! It is worth noting that AFM automatically searches monomer templates in the prediction, and when we checked our AFM runs, we found that 99% of the targets in our study (including all the targets in the four datasets: HomoPDB, HeteroPDB, DHTest and DB5.5) at least 20 templates were identified (AFM employed the top 20 templates in the prediction), and 87.8% of the targets employed the native templates (line 455-462 in page 25 in the subsection of “Comparison of PLMGraph-Inter with AlphaFold-Multimer”). Therefore, we think Figure 6 not Figure S5 (the original Figure S2) shows a fairer comparison. Besides, it is also worth noting the targets used in this study would have a large overlap with the training set of AlphaFold-Multimer, since AFM used all protein complex structures in PDB deposited before 2018-04-30 in the model training, which would further cause the overestimation of the performance of AFM (line 450-455 in page 24-25 in the subsection of “Comparison of PLMGraph-Inter with AlphaFold-Multimer”).

To mimic the performance of AlphaFold2 in real practice and produce predicted monomeric structures with more diverse qualities, we only used the MSA searched from Uniref100 protein sequence database as the input to AlphaFold2 and set to not use the template (line 203~210 in page 12 in the subsection of “Evaluation of PLMGraph-Inter on HomoPDB and HeteroPDB test sets”). Since some of the predicted monomer structures are of bad quality, it is reasonable that the performance of PLMGraph-Inter drops when the predicted monomeric structures are used in the prediction. We provided a detailed analysis of the impact of the monomeric structure quality on the prediction performance in the subsection “Impact of the monomeric structure quality on contact prediction” in the main text.

We provided the analysis of the AFM multimer confidence values (“iptm + ptm”) in the revision (Figure 6, Figure S5 and line 495-501 in page 27 in the subsection of “Comparison of PLMGraph-Inter with AlphaFold-Multimer”).

Besides, in cases where any experimental structures - bound or unbound - are available and given to PLMGraph-Inter as inputs, they should also be provided to AlphaFold-Multimer (AFM) as templates. Withholding these from AFM only makes the comparison artificially unfair. Hence, a new test should be run using AFM templates, and a new version of Figure 6 should be produced. Additionally, AFM's mean precision, at least for top-50 contact prediction, should be reported so it can be compared with PLMGraph-Inter's.

We thank the reviewers for the suggestion, and we are sorry for the confusion! In the AFM runs to predict protein complex structures, we used the default setting of AFM which automatically searches monomer templates in the prediction. When we checked our AFM runs, we found that 99% of the targets in our study (including all the targets in the four datasets: HomoPDB, HeteroPDB, DHTest and DB5.5) employed at least 20 templates in their predictions (AFM only used the top 20 templates), and 87.8% of the targets employed the native template. We further clarified this in the revision (line 455462 in page 25 in the subsection of “Comparison of PLMGraph-Inter with AlphaFoldMultimer”). We also included the mean precisions of AFM (top-50 contact prediction) in the revision (Table S5 and line 483-484 in page 26 in the subsection of “Comparison of PLMGraph-Inter with AlphaFold-Multimer”).

It's a shame that many of the structures used in the comparison with AFM are actually in the AFM v2 training set. If there are any outside the AFM v2 training set and, ideally, not sequence- or structure-homologous to anything in the AFM v2 training set, they should be discussed and reported on separately. In addition, why not test on structures from the "Benchmark 2" or "Recent-PDB-Multimers" datasets used in the AFM paper?

We thank the reviewer for the suggestion! The biggest challenge to objectively evaluate AFM is that as far as we known, AFM does not release the PDB ids of its training set and the “Recent-PDB-Multimers” dataset. “Benchmark 2” only includes 17 heterodimer proteins, and the number would be further decreased after removing targets redundant to our training set. We think it is difficult to draw conclusions from such a small number of targets.

It is also worth noting that the AFM v2 weights have now been outdated for a while, and better v3 weights now exist, with a training cutoff of 2021-09-30.

Author response image 1.

The head-to-head comparison of qualities of complex predicted by AlphaFold-Multimer (2.2.0) and AlphaFold-Multimer (2.3.2) for each target PPI.

We thank the reviewer for reminding the new version of AFM. The only difference between AFM V3 and V2 is the cutoff date of the training set. During the revision, we also tested the new version of AFM on the datasets of HomoPDB and HeteroPDB, but we found the performance difference between the two versions of AFM is actually very little (see the figure above, not shown in the main text). One reason might be that some targets in HomoPDB and HeteroPDB are redundant with the training sets of the two version of AFM. Since our test sets would have more overlaps with the training set of AFM V3, we keep using the AFM V2 weights in this study.

Another weakness in the evaluation framework: because PLMGraph-Inter uses structural inputs, it is not sufficient to make its test set non-redundant in sequence to its training set. It must also be non-redundant in structure. The Benchmark 2 dataset mentioned above is an example of a test set constructed by removing structures with homologous templates in the AF2 training set. Something similar should be done here.

We thank the reviewer for the suggestion! In the revision, we explored the performance of PLMGraph-Inter when using different thresholds of fold similarity scores of interacting monomers to further remove potential redundancies between the training and test sets (i.e. redundancy in structure ) (line 353-386 in page 19-21 in the subsection “Ablation study”; line 762-797 in page 41-43 in the subsection “Further potential redundancies removal between the training and the test”). We found that for heteromeric PPIs (targets in HeteroPDB), the further removal of potential redundancy in structure has little impact on the model performance (~3%, when TM-score 0.5 is used as the threshold). However, for homomeric PPIs (targets in HomoPDB), the further removal of potential redundancy in structure significantly reduce the model performance (~18%, when TM-score 0.5 is used as the threshold) (see Table 2). One possible reason for this phenomenon is that the binding mode of the homomeric PPI is largely determined by the fold of its monomer, thus the does not generalize well on targets whose folds have never been seen during the training.

Whether the deep learning model can generalize well on targets with novel folds is a very interesting and important question. We thank the reviewer for pointing out this! However, to the best of our knowledge, this question has rarely been addressed by previous studies including AFM. For example, the Benchmark 2 dataset is prepared by ClusPro TBM (bioRxiv 2021.09.07.459290; Proteins 2020, 88:1082-1090) which uses a sequence-based approach (HHsearch) to identify templates not structure-based. Therefore, we don’t think this dataset is non-redundant in structure.

Finally, the performance of DRN-1D2D for top-50 precision reported in Table 1 suggests to me that, in an ablation study, language model features alone would yield better performance than geometric features alone. So, I am puzzled why model "a" in the ablation is a "geometry-only" model and not a "LM-only" one.

Using the protein geometric graph to integrate multiple protein language models is the main idea of PLMGraph-Inter. Comparing with our previous work (DRN-1D2D_Inter), we consider the building of the geometric graph as one major contribution of this work. To emphasize the efficacy of this geometric graph, we chose to use the “geometry-only” model as the base model.

Reviewer #1 (Recommendations For The Authors):

Some sections of the paper use technical terminology which limits accessibility to a broad audience. An obvious example is in the section "Results > Overview of PLMGraph-Inter > The residual network module": the average eLife reader is not a machine learning expert and might not be familiar with a "convolution with kernel size of 1 * 1". In general, the "Overview of PLMGraph-Inter" is a bit heavy with technical details, and I suggest moving many of these to Methods. This overview section can still be there but it should be shorter and written using less technical language.

We thank the reviewer for the suggestion! We moved some technical details to the Methods section in the revision (line 184-185 in page 11; line 729-735 in page 39).

List of typos and minor issues (page number according to merged PDF):

• p. 3. line -3: remove "to"

Corrected (line 36, page 3)

• p. 5, line 7: "GINTER" should be "GLINTER"

Corrected (line 64, page 5)

• p. 6, line -4: "Given structures" -> "Given the structures"

Corrected (line 95, page 6)

• p. 6, line -2: "with which encoded"... ?

We rephrased this sentence in revision. (line 97, page 6)

• p. 9, line 1: "principal" -> "principle"

Corrected (line 142, page 9)

• p. 13, line 1: "has" -> "but have"

Corrected (line 231, page 13)

• p. 14, lines 6-7: "As can be seen from the figure that the predicted" -> "As can be seen from the figure, the predicted"

We rephrased this paragraph, and the sentence was deleted in the revision (line 257-259 in page 15).

• p. 18, line 1: the "five models" are presumably models a-e? If so, say "of models a-e"

Corrected (line 310, page 17)

• p. 22, line 2: from the figure, I would have guessed "greater than or equal to 0.7", not 0.8

Based the Figure 3C, we think 0.8 is a more appropriate cutoff, since the precision drops significantly when the DTM-score is within 0.7~0.8.

• p. 23, lines 2-3: "worth to making" -> "worth making"

Corrected (line 443, page 24)

• p. 24, line -5: "predict" -> "predicted"

Corrected (line 484, page 26)

• p 28, line -5: Please clarify what you mean by "We doubt": are you saying that you don't think these rearrangements exist in nature? If not, then reword.

Corrected (line 566, page 30)

• Figure 2, panel c, "DCPred" in the legend should be "CDPred"

Corrected

• Figures 3 and 5: Please improve the y-axis title in panel C. "Percent" of what?

We changed the “Percent” to “% of targets” in the revision.

We thank the reviewer for carefully reading our manuscript!

Reviewer #2 (Public Review):

This work introduces PLMGraph-Inter, a new deep-learning approach for predicting inter-protein contacts, which is crucial for understanding proteinprotein interactions. Despite advancements in this field, especially driven by AlphaFold, prediction accuracy and efficiency in terms of computational cost) still remains an area for improvement. PLMGraph-Inter utilizes invariant geometric graphs to integrate the features from multiple protein language models into the structural information of each subunit. When compared against other inter-protein contact prediction methods, PLMGraph-Inter shows better performance which indicates that utilizing both sequence embeddings and structural embeddings is important to achieve high-accuracy predictions with relatively smaller computational costs for the model training.

The conclusions of this paper are mostly well supported by data, but test examples should be revisited with a more strict sequence identity cutoff to avoid any potential information leakage from the training data. The main figures should be improved to make them easier to understand.

We thank the reviewer for recognizing the significance of our work! We have carefully revised the manuscript to address the reviewer’s concerns.

(1) The sequence identity cutoff to remove redundancies between training and test set was set to 40%, which is a bit high to remove test examples having homology to training examples. For example, CDPred uses a sequence identity cutoff of 30% to strictly remove redundancies between training and test set examples. To make their results more solid, the authors should have curated test examples with lower sequence identity cutoffs, or have provided the performance changes against sequence identities to the closest training examples.

We thank the reviewer for the valuable suggestion! The “40 sequence identity” is a widely used threshold to remove redundancy when evaluating deep-learning based protein-protein interaction and protein complex structure prediction methods, thus we also chose this threshold in our study (bioRxiv 2021.10.04.463034, Cell Syst. 2021 Oct 20;12(10):969-982.e6). In the revision, we explored whether PLMGraph-inter can keep its performance when more stringent thresholds (30%,20%,10%) is applied (line 353386 in page 20-21 in the subsection of “Ablation study” and line 762-780 in page 40 in the subsection of “Further potential redundancies removal between the training and the test”). The result shows that even when using “10% sequence identity” as the threshold, mean precisions of the predicted contacts only decreases by ~3% (Table 2).

(2) Figures with head-to-head comparison scatter plots are hard to understand as scatter plots because too many different methods are abstracted into a single plot with multiple colors. It would be better to provide individual head-tohead scatter plots as supplementary figures, not in the main figure.

We thank the reviewer for the suggestion! We will include the individual head-to-head scatter plots as supplementary figures in the revision (Figure S1 and Figure S2 in the supplementary).

(3) The authors claim that PLMGraph-Inter is complementary to AlphaFoldmultimer as it shows better precision for the cases where AlphaFold-multimer fails. To strengthen the point, the qualities of predicted complex structures via protein-protein docking with predicted contacts as restraints should have been compared to those of AlphaFold-multimer structures.

We thank the reviewer for the suggestion! We included this comparison in the revision (Figure S7).

(4) It would be interesting to further analyze whether there is a difference in prediction performance depending on the depth of multiple sequence alignment or the type of complex (antigen-antibody, enzyme-substrates, single species PPI, multiple species PPI, etc).

We thank the reviewer for the suggestion! We analyzed the relationship between the prediction performance and the depth of MSA in the revision (Figure S4 and Line 253264 in page 15 in the subsection of “Evaluation of PLMGraph-Inter on HomoPDB and HeteroPDB test sets” and line 798-806 in page 42 in the subsection of “Calculating the normalized number of the effective sequences of paired MSA”).

Reviewer #2 (Recommendations For The Authors):

I have the following suggestions in addition to the public review.

(1) Overall, the manuscript is well-written; however, I recommend a careful review for minor grammar corrections to polish the final text.

We carefully checked the manuscript and corrected all the grammar issues and typos we found in the revision.

(2) It would be better to indicate that single sequence embeddings, MSA embeddings, and structure embeddings are ESM-1b, ESM-MSA & PSSM, and ESM-IF when they are first mentioned in the manuscript e.g. single sequence embeddings from ESM-1b, MSA embeddings from ESM-MSA and PSSM, and structural embeddings from ESM-IF.

We revised the manuscript according to the reviewer’s suggestion (line 86-88 in page 6; line 99-101 in page 7).

(3) I don't think "outer concatenation" is commonly used. Please specify whether it's outer sum, outer product, or horizontal & vertical tiling followed by concatenation.

It is horizontal & vertical tiling followed by concatenation. We clarified this in the revision (line 129-130 in page 8).

(4) 10th sentence on the page where the Results section starts, please briefly mention what are the other 2D pairwise features.

We clarified this in the revision (line 131-132 in page 8).

(5) In the result section, it states edges are defined based on Ca distances, but in the method section, it says edges are determined based on heavy atom distances. Please correct one of them.

It should be Ca distances. We are sorry for the carelessness, and we corrected this in the revision (line 646 in page 35).

(6) For the sentence, "Where ESM-1b and ESM-MSA-1b are pretrained PLMs learned from large datasets of sequences and MSAs respectively without label supervision,", I'd suggest replacing "without label supervision" with "with masked language modeling tasks" for clarity.

We revised the manuscript according to the reviewer’s suggestion (line 150-151 in page 9).

(7) It would be better to briefly explain what is the dimensional hybrid residual block when it first mentioned.

We explained the dimensional hybrid residue block when it first mentioned in the revision (line 107 in page 7).

(8) Please include error bars for the bar plots and standard deviations for the tables.

We thank the reviewer for the suggestion! Our understanding is the error bars and standard deviations are very informative for data which follow gaussian-like distributions, but our data (precisions of the predicted contacts) are obviously not this type. Most previous studies in protein contact prediction and inter-protein contact prediction also did not include these in their plots or tables. In our case, including these elements requires a dramatic change of the styles of our figures and tables, but we would like to not change our figures and tables too much in the revision.

(9) Please indicate whether the chain break is considered to generate attention map features from ESM-MSA-1b. If it's considered, please specify how.

The paired sequences were directly concatenated without using any letter to connect them, which means we did not consider chain break in generating the attention maps from ESM-MSA-1b.

Reviewer #1 (Public Review):

Summary:

Given knowledge of the amino acid sequence and of some version of the 3D structure of two monomers that are expected to form a complex, the authors investigate whether it is possible to accurately predict which residues will be in contact in the 3D structure of the expected complex. To this effect, they train a deep learning model which takes as inputs the geometric structures of the individual monomers, per-residue features (PSSMs) extracted from MSAs for each monomer, and rich representations of the amino acid sequences computed with the pre-trained protein language models ESM-1b, MSA Transformer, and ESM-IF. Predicting inter-protein contacts in complexes is an important problem. Multimer variants of AlphaFold, such as AlphaFold-Multimer, are the current state of the art for full protein complex structure prediction, and if the three-dimensional structure of a complex can be accurately predicted then the inter-protein contacts can also be accurately determined. By contrast, the method presented here seeks state-of-the-art performance among models that have been trained end-to-end for inter-protein contact prediction.

Strengths:

The paper is carefully written and the method is very well detailed. The model works both for homodimers and heterodimers. The ablation studies convincingly demonstrate that the chosen model architecture is appropriate for the task. Various comparisons suggest that PLMGraph-Inter performs substantially better, given the same input, than DeepHomo, GLINTER, CDPred, DeepHomo2, and DRN-1D2D_Inter.<br /> The authors control for some degree of redundancy between their training and test sets, both using sequence and structural similarity criteria. This is more careful than can be said of most works in the field of PPI prediction.<br /> As a byproduct of the analysis, a potentially useful heuristic criterion for acceptable contact prediction quality is found by the authors: namely, to have at least 50% precision in the prediction of the top 50 contacts.

Weaknesses:

The authors check for performance drops when the test set is restricted to pairs of interacting proteins such that the chain pair is not similar *as a pair* (in sequence or structure) to a pair present in the training set. A more challenging test would be to restrict the test set to pairs of interacting proteins such that *none* of the chains are separately similar to monomers present in the training set. In the case of structural similarity (TM-scores), this would amount to replacing the two "min"s with "max"s in Eq. (4). In the case of sequence similarity, one would simply require that no monomer in the test set is in any MMSeqs2 cluster observed in the training set. This may be an important check to make, because a protein may interact with several partners, and/or may use the same sites for several distinct interactions, contributing to residual data leakage in the test set.

The training set of AFM with v2 weights has a global cutoff of 30 April 2018, while that of PLMGraph-Inter has a cutoff of March 7 2022. So there may be structures in the test set for PLMGraph-Inter that are not in the training set of AFM with v2 weights (released between May 2018 and March 2022). The "Benchmark 2" dataset from the AFM paper may have a few additional structures not in the training or test set for PLMGraph-Inter. I realize there may be only few structures that are in neither training set, but still think that showing the comparison between PLMGraph-Inter and AFM there would be important, even if no statistically significant conclusions can be drawn.

Finally, the inclusion of AFM confidence scores is very good. A user would likely trust AFM predictions when the confidence score is high, but look for alternative predictions when it is low. The authors' analysis (Figure 6, panels c and d) seems to suggest that, in the case of heterodimers, when AFM has low confidence, PLMGraph-Inter improves precision by (only) about 3% on average. By comparison, the reported gains in the "DockQ-failed" and "precision-failed" bins are based on knowledge of the ground truth final structure, and thus are not actionable in a real use-case.

Reviewer #2 (Public Review):

This work introduces PLMGraph-Inter, a new deep learning approach for predicting inter-protein contacts, which is crucial for understanding protein-protein interactions. Despite advancements in this field, especially driven by AlphaFold, prediction accuracy and efficiency in terms of computational cost still remains an area for improvement. PLMGraph-Inter utilizes invariant geometric graphs to integrate the features from multiple protein language models into the structural information of each subunit. When compared against other inter-protein contact prediction methods, PLMGraph-Inter shows better performance which indicates that utilizing both sequence embeddings and structural embeddings is important to achieve high-accuracy predictions with relatively smaller computational costs for the model training.

Author Response

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

Reviewer #1 (Public Review):

The manuscript investigates the role of membrane contact sites (MCSs) and sphingolipid metabolism in regulating vacuolar morphology in the yeast Saccharomyces cerevisiae. The authors show that tricalbin (1-3) deletion leads to vacuolar fragmentation and the accumulation of the sphingolipid phytosphingosine (PHS). They propose that PHS triggers vacuole division through MCSs and the nuclear-vacuolar junction (NVJ). The study presents some solid data and proposes potential mechanisms underlying vacuolar fragmentation driven by this pathway. Although the manuscript is clear in what the data indicates and what is more hypothetical, the story would benefit from providing more conclusive evidence to support these hypothesis. Overall, the study provides valuable insights into the connection between MCSs, lipid metabolism, and vacuole dynamics.

We thank the positive review from the Reviewer #1. We hope that our hypotheses are supported by the "Author Response to Recommendations" and by further research in the future.

Reviewer #2 (Public Review):

This manuscript explores the mechanism underlying the accumulation of phytosphingosine (PHS) and its role in initiating vacuole fission. The study posits the involvement of membrane contact sites (MCSs) in two key stages of this process. Firstly, MCSs tethered by tricalbin between the endoplasmic reticulum (ER) and the plasma membrane (PM) or Golgi regulate the intracellular levels of PHS. Secondly, the amassed PHS triggers vacuole fission, most likely through the nuclear-vacuolar junction (NVJ). The authors propose that MCSs play a regulatory role in vacuole morphology via sphingolipid metabolism. While some results in the manuscript are intriguing, certain broad conclusions occasionally surpass the available data. Despite the authors' efforts to enhance the manuscript, certain aspects remain unclear. It is still uncertain whether subtle changes in PHS levels could induce such effects on vacuolar fission. Additionally, it is regrettable that the lipid measurements are not comparable with previous studies by the authors. Future advancements in methods for determining intracellular lipid transport and levels are anticipated to shed light on the remaining uncertainties in this study.

We thank the careful comment from Reviewer #2. As Reviewer #2 pointed out, the mechanism of how slight changes in PHS levels can induce the vacuolar fission event is still uncovered in this manuscript. We sincerely consider that this issue has to be resolved in further study.

Reviewer #3 (Public Review):

In this manuscript, the authors investigated the effects of deletion of the ER-plasma membrane/Golgi tethering proteins tricalbins (Tcb1-3) on vacuolar morphology to demonstrate the role of membrane contact sites (MCSs) in regulating vacuolar morphology in Saccharomyces cerevisiae. Their data show that tricalbin deletion causes vacuolar fragmentation possibly in parallel with TORC1 pathway. In addition, their data reveal that levels of various lipids including ceramides, long-chain base (LCB)-1P, and phytosphingosine (PHS) are increased in tricalbin-deleted cells. The authors find that exogenously added PHS can induce vacuole fragmentation and by performing analyses of genes involved in sphingolipid metabolism, they conclude that vacuolar fragmentation in tricalbin-deleted cells is due to the accumulated PHS in these cells. Importantly, exogenous PHS- or tricalbin deletion-induced vacuole fragmentation was suppressed by loss of the nucleus vacuole junction (NVJ), suggesting the possibility that PHS transported from the ER to vacuoles via the NVJ triggers vacuole fission. Of note, the authors find that hyperosmotic shock increases intracellular PHS levels, suggesting a general role of PHS in vacuole fission in response to physiological vacuolar division-inducing stimuli. This work provides valuable insights into the relationship between MCS-mediated sphingolipid metabolism and vacuole morphology. The conclusions of this paper are mostly supported by their results, but inclusion of direct evidence indicating increased transport of PHS from the ER to vacuoles via NVJ in response to vacuolar division-inducing stimuli would have strengthened this study. There is another weakness in their claim that the transmembrane domain of Tcb3 contributes to the formation of the tricalbin complex which is sufficient for tethering ER to the plasma membrane and the Golgi complex. Their claim is based only on the structural simulation, but not on by biochemical experiments such as co-immunoprecipitation and pull-down.

We appreciate the careful feedback from Reviewer #3. We have responded in the "Recommendations to Authors" section and hope it can partially support the weakness in our claim regarding the physical interaction between Tcb1, 2, and 3.

Reviewer #1 (Recommendations For The Authors):

I would suggest that the authors include some of the data (e.g., Tcb interactions) that they refer to in the response to the reviewers. I think that this could enhance the message in this manuscript. Also, maybe it's a typo and you were referring to some other image panel, but in the rebuttal letter a "Fig. S3B" is mentioned, but I could not find it.

Following the suggestions of reviewers #1 and #3, we have added the data of co-immunoprecipitation which confirmed that Tcb3 binds to both Tcb1 and Tcb2 as Supplemental Figure 2. With this change, the person (Ms. Saku Sasaki) who performed this analysis was also added as a co-author.

Also, we appreciate the careful remark and apologize for the mistake. In the previous Author's response, we mentioned the vacuole observation using SD medium, but this data was Fig 5C, not Fig S3B.

Reviewer #3 (Recommendations For The Authors):

I would recommend that the authors include the IP data mentioned in their rebuttal letter to show the interactions among Tcb1-3. Also, the authors should quantify all lipid species in Fig 5B, as shown in Fig 3A.

Following the suggestions of reviewers #1 and #3, we have added the co-immunoprecipitation data (Fig S2). In a further study, we would like to test if the transmembrane domain of Tcb3 is sufficient for the interaction among Tcb1-3. Also, we quantified all lipid species and replaced the data in Fig 5B.

Minor points:

(1) The function of vps4 is not mentioned in the manuscript.

(2) The function of Sur2p is not mentioned in the manuscript. It should be clearly mentioned that DHS is converted to PHS by Sur2p.

(1) We have added text sections which mention that VPS4 is needed for normal ESCRT function, and its deletion is an example for inhibition of GFP-Cps1p transport into the vacuole.

(2) We have added the text in the manuscript that states Sur2p is the hydroxylase that catalysis the conversion of DHS to PHS.

eLife assessment

This manuscript presents valuable findings that contribute to our understanding of how sphingolipids and membrane contact sites, formed by the tethering protein family tricalbins, are involved in regulating vacuolar morphology in S. cerevisiae. The evidence supporting the authors' claims is largely solid: while the reported correlation between sphingolipid levels and vacuole homeostasis is intriguing, the data do not completely substantiate the proposed mechanism. This study will be of interest to cell biologists focusing on intracellular organization and lipid metabolism.

eLife assessment

This manuscript presents valuable findings that contribute to our understanding of how sphingolipids and membrane contact sites, formed by the tethering protein family tricalbins, are involved in regulating vacuolar morphology in S. cerevisiae. The evidence supporting the authors' claims is largely solid. While the reported correlation between sphingolipid levels and vacuole homeostasis is interesting and intriguing, more work is needed to thoroughly substantiate the proposed mechanism. This study will be of interest to cell biologists focusing on intracellular organization and lipid metabolism.

Reviewer #1 (Public Review):

The manuscript investigates the role of membrane contact sites (MCSs) and sphingolipid metabolism in regulating vacuolar morphology in the yeast Saccharomyces cerevisiae. The authors show that tricalbin (1-3) deletion leads to vacuolar fragmentation and the accumulation of the sphingolipid phytosphingosine (PHS). They propose that PHS triggers vacuole division through MCSs and the nuclear-vacuolar junction (NVJ). The study presents some solid data and proposes potential mechanisms underlying vacuolar fragmentation driven by this pathway. Although the manuscript is clear in what the data indicates and what is more hypothetical, the story would benefit from providing more conclusive evidence to support these hypothesis. Overall, the study provides valuable insights into the connection between MCSs, lipid metabolism, and vacuole dynamics.

Reviewer #2 (Public Review):

This manuscript explores the mechanism underlying the accumulation of phytosphingosine (PHS) and its role in initiating vacuole fission. The study posits the involvement of membrane contact sites (MCSs) in two key stages of this process. Firstly, MCSs tethered by tricalbin between the endoplasmic reticulum (ER) and the plasma membrane (PM) or Golgi regulate the intracellular levels of PHS. Secondly, the amassed PHS triggers vacuole fission, most likely through the nuclear-vacuolar junction (NVJ). The authors propose that MCSs play a regulatory role in vacuole morphology via sphingolipid metabolism.

While some results in the manuscript are intriguing, certain broad conclusions occasionally surpass the available data. Despite the authors' efforts to enhance the manuscript, certain aspects remain unclear. It is still uncertain whether subtle changes in PHS levels could induce such effects on vacuolar fission. Additionally, it is regrettable that the lipid measurements are not comparable with previous studies by the authors. Future advancements in methods for determining intracellular lipid transport and levels are anticipated to shed light on the remaining uncertainties in this study.

Reviewer #3 (Public Review):

In this manuscript, the authors investigated the effects of deletion of the ER-plasma membrane/Golgi tethering proteins tricalbins (Tcb1-3) on vacuolar morphology to demonstrate the role of membrane contact sites (MCSs) in regulating vacuolar morphology in Saccharomyces cerevisiae. Their data show that tricalbin deletion causes vacuolar fragmentation possibly in parallel with TORC1 pathway. In addition, their data reveal that levels of various lipids including ceramides, long-chain base (LCB)-1P, and phytosphingosine (PHS) are increased in tricalbin-deleted cells. The authors find that exogenously added PHS can induce vacuole fragmentation and by performing analyses of genes involved in sphingolipid metabolism, they conclude that vacuolar fragmentation in tricalbin-deleted cells is due to the accumulated PHS in these cells. Importantly, exogenous PHS- or tricalbin deletion-induced vacuole fragmentation was suppressed by loss of the nucleus vacuole junction (NVJ), suggesting the possibility that PHS transported from the ER to vacuoles via the NVJ triggers vacuole fission. Of note, the authors find that hyperosmotic shock increases intracellular PHS levels, suggesting a general role of PHS in vacuole fission in response to physiological vacuolar division-inducing stimuli.

This work provides valuable insights into the relationship between MCS-mediated sphingolipid metabolism and vacuole morphology. The conclusions of this paper are mostly supported by their results, but inclusion of direct evidence indicating increased transport of PHS from the ER to vacuoles via NVJ in response to vacuolar division-inducing stimuli would have strengthened this study.

There is another weakness in their claim that the transmembrane domain of Tcb3 contributes to the formation of the tricalbin complex which is sufficient for tethering ER to the plasma membrane and the Golgi complex. Their claim is based only on the structural simulation, but not on by biochemical experiments such as co-immunoprecipitation and pull-down.

Author Response

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

Overall, the magnitude of the effect size due to FNDC5 deficiency in both male and female mice is rather modest. Looking at the data from a qualitative perspective, it is clear that knockout females still lose bone during lactation and on the low calcium diet (LCD). It is difficult to assess the physiologic consequence of the modest quantitative 'protection' seen in FNDC5 mutants since the mutants still show clear and robust effects of lactation and LCD on all parameters measured. Similarly, the magnitude of the 'increased' cortical bone loss in FNDC5 mutant males is also modest and perhaps could be related to the fact that these mice are starting with slightly more cortical bone. Since the authors do not provide a convincing molecular explanation for why FNDC5 deficiency causes these somewhat subtle changes, I would like to offer a suggestion for the authors to consider (below, point #2) which might de-emphasize the focus of the manuscript on FNDC5. If the authors chose not to follow this suggestion, the manuscript could be strengthened by addressing the consequences of the modest changes observed in WT versus FNDC5 KO mice.

Response: We agree that the magnitude of the effect size due to FNDC5 deficiency is modest with regards to the quantitative cortical bone parameters. However, if one examines the changes in osteocyte lacunar size and the mechanical properties of these bones, the differences are greater. As shown in Figure 3 E, the lacunar area of the WT females on a low calcium diet increases by over 30% and the KO by less than 20%, while in the males it is approximately 38% in WT compared to 46% in KO mice. According to Sims and Buenzli (PMID: 25708054) a potential total loss of ~16,000 mm3 (16 mL) of bone occurs through lactation in the human skeleton. This was based on our measurements in lactation-induced murine osteocytic osteolysis (Qing et al PMID: 22308018). They used our 2D section of tibiae from lactating mice showing an increase in lacunar size from 38 to 46 um2. In that paper we also showed that canalicular width is increased with lactation. Therefore, this would suggest a dramatic decrease in intracortical porosity due to the osteocyte lacunocanalicular system in female KO on a low calcium diet compared to WT females and a dramatic increase in KO males compared to WT males. Also, PTH was higher in the serum of female WT compared to female KO mice on a low calcium diet, the opposite for males in order to maintain normal calcium levels (See Table 1). Based on this data, using the FNDC5 null animals, we would speculate that the product of FNDC5, irisin, is having a highly significant effect on the ultrastructure of bone in both males and females challenged with a low calcium diet.

(2) The bone RNA-seq findings reported in Figures 4-6 are quite interesting. Although Youlten et al previously reported that the osteocyte transcriptome is sex-dependent, the work here certainly advances that notion to a considerable degree and likely will be of high interest to investigators studying skeletal biology and sexual dimorphism in general. To this end, one direction for the authors to consider might be to refocus their manuscript toward sexually-dimorphic gene expression patterns in osteocytes and the different effects of LCD on male versus female mice. This would allow the authors to better emphasize these major findings, and to then use FNDC5 deficiency as an illustrative example of how sexually-dimorphic osteocytic gene expression patterns might be affected by deletion of an osteocyte-acting endocrine factor. Ideally, the authors would confirm RNA-seq data comparing male versus female mice in osteocytes using in situ hybridization or immunostaining.

Response: Thank you for this suggestion. We have compared the different effects of LCD on male versus female mice in our revised version and have added a figure containing this information.

(3) Along the lines of point #2 (above), the presentation of the RNA-seq studies in Figures 4-6 is somewhat confusing in that the volcano plot titles seem to be reversed. For example, Figure 4A is titled "WT M: WT F", but the genes in the upper right quadrant appear to be up-regulated in female cortical bone RNA samples. Should this plot instead be titled "WT F: WT M"? If so, then all other volcano plots should be re-titled as well.

Response: We have now insured that the plots are appropriately labeled.

(4) Have the authors compared male versus female transcriptomes of LCD mice?

Response: We have now compared the male vs female transcriptomes of LCD mice and added an additional figure.

(5) It would be appreciated if the authors could provide additional serum parameters (if possible) to clarify incomplete data in both lactation and low-calcium diet models: RANKL/OPG ratio, Ctx, PTHrP, and 1,25-dihydroxyvitamin D levels.

Response: It is not possible to quantitate each of these as the serum has been exhausted. We have checked the RANKL/OPG ratio in the RNA seq and qPCR data using osteocyte enriched bone chips and found no difference.

(6) Lastly, the data that overexpressing irisin improved bone properties in Fig 2G was somewhat confusing. Based on Kim et al.'s (2018) work, irisin injection increased sclerostin gene expression and serum levels, thus reducing bone formation. Were sclerostin levels affected by irisin overexpression in this study? Was irisin's role in modulating sclerostin levels attenuated with additional calcium deficiency?

Response: We have not observed any differences in the osteocyte Sost mRNA expression between WT and KO normal and low-calcium-diet male and female mice in our RNAseq and qPCR data. As such, we did not check the Sost levels for the 2G experiment.

Reviewer #2 (Public Review):

Summary:

The goal of this study was to examine the role of FNDC5 in the response of the murine skeleton to either lactation or a calcium-deficient diet. The authors find that female FNDC5 KO mice are somewhat protected from bone loss and osteocyte lacunar enlargement caused by either lactation or a calcium-deficient diet. In contrast, male FNDC5 KO mice lose more bone and have a greater enlargement of osteocyte lacunae than their wild-type controls. Based on these results, the authors conclude that in males irisin protects bone from calcium deficiency but that in females it promotes calcium removal from bone for lactation.

While some of the conclusions of this study are supported by the results, it is not clear that the modest effects of FNDC5 deletion have an impact on calcium homeostasis or milk production.

Specific comments:

(1) The authors sometimes refer to FNDC5 and other times to irisin when describing causes for a particular outcome. Because irisin was not measured in any of the experiments, the authors should not conclude that lack of irisin is responsible. Along these lines, is there any evidence that either lactation or a calcium-deficient diet increases the production of irisin in mice?

therefore we have extrapolated that the observed effects are due to a lack of circulating irisin. However, this does not rule out that Fndc5 itself could have a function, but this would have to be most likely in muscle and not in the osteocyte as we do not detect significant levels of irisin in either primary osteoblasts nor primary osteocytes compared to muscle and C2C12 cells. As such, we concluded that the phenotypical differences we saw in our experiments are due to a lack of irisin. We now address the reviewer’s point in the discussion. The measurement of irisin in the circulation with lactation or with low calcium diet of normal mice has not been performed.

(2) The results of the irisin-rescue experiment shown in figure 2G cannot be appropriately interpreted without normal diet controls. In addition, some evidence that the AAV8-irisin virus actually increased irisin levels in the mice would strengthen the conclusion.

Response: We do not have the normal diet controls at this time. We have quantitate tagged irisin in other AAV experiments and found highly significant expression

(3) There is insufficient evidence to support the idea that the effect of FNDC5 on bone resorption and osteocytic osteolysis is important for the transfer of calcium from bone to milk. Previous studies by others have shown that bone resorption is not required to maintain milk or serum calcium when dietary calcium is sufficient but is critical if dietary calcium is low (Endo. 156:2762-73, 2015). To support the conclusions of the current study, it would be necessary to determine whether FNDC5 is required to maintain calcium levels when lactating mice lack sufficient dietary calcium.

Response: We agree that it would be important to measure calcium levels in the milk to test the hypothesis that FNDC5 is important to maintain calcium levels in milk. However, as the calcium levels are normal in the serum, we are assuming they are normal in milk. This would require future experiments.

(4) The amount of cortical bone loss due to lactation is very similar in both WT and FNDC5 KO mice. The results of the statistical analysis of the data presented in figure 1B are surprising given the very similar effect size of lactation. The key result from the 2-way ANOVA is whether there is an effect of genotype on the effect size of lactation (genotype-lactation interaction). The interaction terms were not provided. Similar concerns are noted for the results shown in figure 1G and H.

Response: We agree, thanks. We will now add the interaction terms in the figure legends.

(5) It is not clear what justifies the term 'primed' or 'activated' for resorption. Is there evidence that a certain level of TRAP expression lowers the threshold for osteocytic osteolysis in response to a stimulus?

Response: The number of TRAP positive osteocytes in female KO mice are lower than in female WT. The number of TRAP positive osteocytes are lower in WT males compared to WT females. We propose that irisin plays a role in the number of TRAP positive osteocytes in normal, WT females by readying or preparing these cells to rapidly respond to low calcium. We will use the term ‘primed’ and will not use the term ‘activated’. We are open to any terminology or description as to why this is observed and what irisin could be doing to the osteocyte.

Reviewer #3 (Public Review):

Summary:

Irisin has previously been demonstrated to be a muscle-secreted factor that affects skeletal homeostasis. Through the use of different experimental approaches, such as genetic knockout models, recombinant Irisin treatment, or different cell lines, the role of Irisin on skeletal homeostasis has been revealed to be more complex than previously thought and this warrants further examination of its role. Therefore, the current study sought to rigorously examine the effects of global Irisin knockout (KO) in male and female mouse bone. Authors demonstrated that in calcium-demanding settings, such as lactation or low-calcium diet, female Irisin KO mice lose less bone compared to wild-type (WT) female mice. Interestingly male Irisin KO mice exhibited worse skeletal deterioration compared to WT male mice when fed a low-calcium diet. When examined for transcriptomic profiles of osteocyte-enriched cortical bone, authors found that Irisin KO altered the expression of osteocytic osteolysis genes as well as steroid and fatty acid metabolism genes in males but not in females. These data support the authors' conclusion that Irisin regulates skeletal homeostasis in sex-dependent manner.

Strengths:

The major strength of the study is the rigorous examination of the effects of Irisin deletion in the settings of skeletal maturity and increased calcium demands in female and male mice. Since many of the common musculoskeletal disorders are dependent on sex, examining both sexes in the preclinical setting is crucial. Had the investigators only examined females or males in this study, the conclusions from each sex would have contradicted each other regarding the role of Irisin on bone. Also, the approaches are thorough and comprehensive that assess the functional (mechanical testing), morphological (microCT, BSEM, and histology), and cellular (RNA-seq) properties of bone.

Weaknesses: One of the weaknesses of this study is a lack of detailed mechanistic analysis of why Irisin has a sex-dependent role on skeletal homeostasis. This absence is particularly notable in the osteocyte transcriptomic results where such data could have been used to further probe potential candidate pathways between LC females vs. LC males.

Response: Our future studies will focus on understanding the molecular mechanism behind the sex-dependent effects of irisin. Our RNA seq data shows a significant difference in the lipid, steroid, and fat metabolism pathways between male and female mice, as well as between WT and KO mice. Future studies will focus on these pathways.

Another weakness is authors did not present data that convincingly demonstrate that Irisin secretion is altered in the skeletal muscle between female vs. male WT mice in response to calcium restriction. The supplement skeletal muscle data only present functional and electrophysiolgical outcomes. Since Itgav or Itgb5 were not different in any of the experimental groups, it is assumed that the changes in the level of Irisin is responsible for the phenotypes observed in WT mice. Assessing Irisin expression will further strengthen the conclusion based on observing skeletal changes that occur in Irisin KO male and female mice.

Response: The problem is that the commercial assays for irisin are not dependable, and results can differ widely across and beyond the physiologic range of 1-10 ng/ml. In part this is due to the nature of the polyclonal antibodies used and the resultant cross reactivity with other proteins. It was shown in Islam et al, 2021 (Nature Metabolism) that the commercial ELISAs were completely unreliable in mice and the only reliable method of measuring circulating irisin is mass spectrometry.

Reviewer #1 (Recommendations For The Authors):

Minor comments:

(1) Were there any low calcium diet food intake or body weight alterations between littermates and FDNC5 KO mice?

Response: Yes, and we can now include the body weight data and the food intake data in the supplement. We do not observe any significant difference between the groups.

(2) In Fig 1, ideally the authors would provide the osteocyte lacunar density along with the lacunar area.

Response: We do not observe any difference in osteocyte density in any of the groups. There is not sufficient time within 2 weeks to see a change in osteocyte density because there is no new bone formation.

(3) What is the author's comment on the involvement of irisin on TGF-B signaling since the authors observed peri lacunar remodeling in FDNC5 KO mice? Authors should also include this in the discussion section regarding the Irisin-TGF-B signaling in terms of observed increased matrix-related signals.

Response: Perilacunar modeling is the removal followed by the replacement of the perilacunar and pericanilucular matrix as occurs with lactation (Qing et al 2012). Osteocytic osteolysis is the first half of that process where the matrix is removed. Alliston and colleagues generated transgenic mice with reduced expression of the TGFb Type II receptor in mice by using the Dmp1-Cre (PMID: 32282961). They clearly found a significant difference in bone parameters, the appearance of the osteocyte lacunocanalicular network, and markers of the osteocyte perilacunar remodeling between the sexes, however they did not compare the lacunar remodeling process in males as compared to females. The females were subjected to lactation and were found to be resistant to osteocytic osteolysis. To compare males and females, they would have had to challenge both sexes to a high calcium demanding condition such as low calcium diet as performed in the current study. Their study does suggest that TGF is involved in the osteocytic osteolysis that occurs with lactation. However, as the null males showed an abnormal lacunocanlicular network compared to wildtype males, this does not necessarily indicate a defect in perilacunar remodeling. It is more likely that the defect occurred during bone formation when osteoblasts were differentiating into osteocytes. Therefore, we will reference this paper regarding the role of TGF in osteocytic osteolysis in females with lactation but not in the comparison of males to females. We have examined the normalized expression of TGF1, 2, and 3 in the present study and found no significant differences in TGF1 or 2 in any of the groups, but did find significantly higher expression of TGF3 in females compared to males for WT (fdr < 0.05), LCD WT (fdr < 0.05), and Control KO (p value < 0.01). Perhaps this isoform is playing a major role in osteocytic osteolysis that occurs with lactation.

(4) Did the authors compare the transcriptomic dataset between lactated female WT vs. KO groups? Or were the RNA-seq studies only performed on LCD study samples?

Response: We have examined RNA sequence on the LCD study samples, and not in the lactating females.

Reviewer #2 (Recommendations For The Authors):

Line 401 on page 14 states that the sexes respond differently to calcium deficiency. Lacunar area increases in both sexes, so the response is very similar. What appears to be different between the sexes is the role of FNDC5 in this process.

Response: Female WT mice have higher osteocyte lacunar area at baseline with normal diet compared to WT males. With the low calcium diet, lacunar area increases in both sexes, with female WTs having a greater increase. We agree that what appears to be different between the sexes is the role of FNDC5 when challenged with high calcium demand.

Reviewer #3 (Recommendations For The Authors):

• The authors state in the abstract and discussion that 'We propose Irisin ensures the survival of offspring by targeting the osteocytes...'. However, this appears to be over interpretation of their findings as they have not assessed the number of offspring surviving to weaning or their growth rate between WT and KO breeders.

Response: That was a proposal and we agree that it could be an over interpretation. However we would like to keep this as a speculation that could be tested in future studies.

• Figures 1 and 2 should include cortical Total Area (and maybe Marrow Cavity data from Supp as well). These data will help readers to assess whether the thinning of the cortex is driven by impaired periosteal expansion or accelerated endosteal resorption (or both). Marrow cavity area data seem to suggest increased endosteal resorption (Supp. Table 2), but unclear if periosteal expansion is altered.

Response: The data are included in the supplementary tables. We do not observe any difference in the periosteal area between the groups.

• To further support the author's statement that male KO mice exhibit different material properties of bone compared to WT mice, estimated elastic modulus should be calculated from the stiffness data (see https://doi.org/10.1002/jbmr.2539).

Response: We looked at the elastic modulus and it requires a stress strain curve instead of the force displacement we used in our calculations, therefore we were not able to get the estimated elastic modulus from the raw data we have.

• In Figure 3 there is no legend indicating females or males. Based on the data and results texts it is assumed that red is Female and blue is Male. However, please confirm in the figure legend.

Response: This is now added in the figure legends.

• Transcriptomic data should be deposited to NCBI GEO data repository. Also, please indicate whether cutoff p-value for DEG analysis was adjusted or not.

Response: We have submitted our data to the GEO data repository: GSE242445. Significant genes were defined as genes with p-value less than 0.01 and absolute log2 fold change larger than 1. The p-value is not adjusted. This information is now added.

• The statistical analysis section indicates that a two-way repeated-measure ANOVA was used. However, the data presented in the study are from independent groups, in which case repeated-measure statistical approaches should not be used. Please clarify the statistical tests that were used.

Response: We now use regular ANOVA instead of repeated-measure ANOVA. Repeated-measure ANOVA is used for paired tests. The data remain significant.

In summary, we thank the reviewers for their very useful and thoughtful suggestions for improving our manuscript.

eLife assessment

The study presents valuable findings on sexually dimorphic patterns of osteocytic transcriptomes and low calcium diet-induced osteocytic osteolysis in FNDC5-deficient mice. The authors present solid evidence for sex-specific changes in osteocyte morphology and gene expression under a calcium-demanding setting in this particular strain of mice, although the protective role of FNDC5-deficiency in lactation and low-calcium diet in female mice remains unclear due to lack of mechanistic studies. The study also lacks evidence that irisin, a proteolytically cleaved product of FNDC5, is responsible for the observed phenotypes, as irisin was not directly measured.

Reviewer #1 (Public Review):

In this manuscript, Shimonty and colleagues study the effects of FNDC5/irisin deletion on osteocytes in a sex-specific manner using models of lactation induced bone loss and bone loss due to low calcium diet (LCD). Consistent with the previous findings of Kim et al. (2018), the authors report 'protective' effects of irisin deficiency in lactating female FNDC5-null mice due to reduced osteocytic osteolysis. Interestingly, FNDC5 null mice show distinct changes when placed on LCD, with mutant females showing some protection from hyperparathyroidism-induced bone loss, while mutant males (which have more cortical bone at baseline) show increased LCD-induced bone loss. Furthermore, new insights into irisin's role in osteocytes regarding cellular energetic metabolism were provided by sex and gene-dependent transcriptomic datasets. Strengths of the well-written manuscript include clear description of sex-dependent effects, strong transcriptomic datasets, and focus on cortical bone changes using microCT, histomorphometry, BSEM, and serum analysis. Despite these strengths, important weaknesses are noted (below) which could be addressed to improve the impact of the work for a broad audience.

Major comments:

(1) Overall, the magnitude of the effect size due to FNDC5 deficiency in both male and female mice is rather modest at the level of bone mass. Looking at the data from a qualitative perspective, it is clear that knockout females still lose bone during lactation and on the low calcium diet (LCD). It is difficult to assess the physiologic consequence of the modest quantitative 'protection' seen in FNDC5 mutants since the mutants still show clear and robust effects of lactation and LCD on all parameters measured. Similarly, the magnitude of the 'increased' cortical bone loss in FNDC5 mutant males is also modest, and perhaps could be related to the fact that these mice are starting with slightly more cortical bone. Since the authors do not provide a convincing molecular explanation for why FNDC5 deficiency causes these somewhat subtle changes, I would like to offer a suggestion for the authors to consider (below, point #2) which might de-emphasize the focus of the manuscript on FNDC5. If the authors chose not to follow this suggestion, the manuscript could be strengthened by addressing the consequences of the modest changes observed in WT versus FNDC5 KO mice. I understand that the effects of FNDC5 are more obvious at the level of osteocyte morphology, and it is reasonable to emphasize these findings here.

(2) The bone RNA-seq findings reported in Figures 4-6 are quite interesting. Although Youlten et al previously reported that the osteocyte transcriptome is sex-dependent, the work here certainly advances that notion to a considerable degree, and likely will be of high interest to investigators studying skeletal biology and sexual dimorphism in general. To this end, one direction for the authors to consider might be to refocus their manuscript towards sexually-dimorphic gene expression patterns in osteocytes and the different effects of LCD on male versus female mice. This would allow the authors to better emphasize these major findings, and then to use FNDC5 deficiency as an illustrative example of how sexually-dimorphic osteocytic gene expression patterns might be affected by deletion of an osteocyte-acting endocrine factor. Ideally, the authors would confirm RNA-seq data comparing male versus female mice in osteocytes using in situ hybridization or immunostaining. Of course, this point is only a suggestion for the authors to consider.

(3) It would be appreciated if the authors could provide additional serum parameters (if possible) to clarify incomplete data in both lactation and low-calcium diet models: RANKL/OPG ratio, Ctx, PTHrP, and 1,25-dihydroxyvitamin D levels. I understand that this may not be possible due to lack of available material.

Reviewer #2 (Public Review):

Summary:

The goal of this study was to examine the role of FNDC5 in the response of the murine skeleton to either lactation or a calcium-deficient diet. The authors find that female FNDC5 KO mice are somewhat protected from the bone loss and osteocyte lacunar enlargement caused by either lactation or a calcium-deficient diet. In contrast, male FNDC5 KO mice lose more bone and have a greater enlargement of osteocyte lacunae than their wild type controls. Based on these results, the authors conclude that in males irisin protects bone from calcium deficiency but that in females it promotes calcium removal from bone for lactation.

While some of the conclusions of this study are supported by the results, it is not clear that the modest effects of FNDC5 deletion have an impact on calcium homeostasis or milk production.

Specific comments.

(1) The authors sometimes refer to FNDC5 and other times to irisin when describing causes for a particular outcome. Because irisin was not measured in any of the experiments, the authors should not conclude that lack of irisin is responsible. Along these lines, is there any evidence that either lactation or a calcium-deficient diet increases production of irisin in mice?

(2) The results of the irisin-rescue experiment shown in figure 2G cannot be appropriately interpreted without normal diet controls. In addition, some evidence that the AAV8-irisin virus actually increased irisin levels in the mice would strengthen the conclusion.

(3) There is insufficient evidence to support the idea that the effect of FNDC5 on bone resorption and osteocytic osteolysis is important for the transfer of calcium from bone to milk. Previous studies by others have shown that bone resorption is not required to maintain milk or serum calcium when dietary calcium is sufficient but is critical if dietary calcium is low (Endo. 156:2762-73, 2015). To support the conclusions of the current study, it would be necessary to determine whether FNDC5 is required to maintain calcium levels when lactating mice lack sufficient dietary calcium.

(4) The amount of cortical bone loss due to lactation is very similar in both WT and FNDC5 KO mice. The results of the statistical analysis of the data presented in figure 1B are surprising given the very similar effect size of lactation. The key result from the 2-way ANOVA is whether there is an effect of genotype on the effect size of lactation (genotype-lactation interaction). The interaction terms were not provided. Similar concerns are noted for the results shown in figure 1G and H.

(5) It is not clear what justifies the term 'primed' or 'activated' for resorption. Is there evidence that a certain level of TRAP expression lowers the threshold for osteocytic osteolysis in response to a stimulus?

Reviewer #3 (Public Review):

Summary: Irisin has previously been demonstrated to be a muscle-secreted factor that affects skeletal homeostasis. Through the use of different experimental approaches, such as genetic knockout models, recombinant Irisin treatment, or different cell lines, the role of Irisin on skeletal homeostasis has been revealed to be more complex than previously thought and this warrants further examination of its role. Therefore, the current study sought to rigorously examine the effects of global Irisin knockout (KO) in male and female mouse bone. Authors demonstrated that in calcium-demanding settings, such as lactation or low-calcium diet, female Irisin KO mice lose less bone compared to wildtype (WT) female mice. Interestingly male Irisin KO mice exhibited worse skeletal deterioration compared to WT male mice when fed low-calcium diet. When examined for transcriptomic profiles of osteocyte-enriched cortical bone, authors found that Irisin KO altered the expression of osteocytic osteolysis genes as well as steroid and fatty acid metabolism genes in males but not in females. These data support authors' conclusion that Irisin regulates skeletal homeostasis in a sex-dependent manner.

Strengths:

The major strength of the study is rigorous examination of the effects of Irisin deletion in the settings of skeletal maturity and increased calcium demands in female and male mice. Since many of the common musculoskeletal disorders are dependent on sex, examining both sexes in the preclinical setting is crucial. Had the investigators only examined females or males in this study, the conclusion from each sex would have contradicted each other regarding the role of Irisin on bone. Also, the approaches are thorough and comprehensive that assess the functional (mechanical testing), morphological (microCT, BSEM, and histology), and cellular (RNA-seq) properties of bone. Transcriptomic data deposited to NCBI GEO data repository will be a valuable resource to musculoskeletal researchers who aim to further assess the affects of Irisin on skeleton.

Weaknesses:

One of the weaknesses of this study is a lack of detailed mechanistic analysis of why Irisin has sex-dependent role on skeletal homeostasis. However, the osteocyte transcriptome comparisons between LC females vs. LC males lay a foundation for such future mechanistic studies.

Another weakness is authors did not present data that convincingly demonstrate that Irisin secretion is altered in the skeletal muscle between female vs. male WT mice in response to calcium restriction. The supplement skeletal muscle data only present functional and electrophysiological outcomes. Since Itgav or Itgb5 were not different in any of the experimental groups, it is assumed that the changes in the level of Irisin is responsible for the phenotypes observed in WT mice. Assessing Irisin expression will further strengthen the conclusion based on observing skeletal changes that occur in Irisin KO male and female mice.

Author Response

Reviewer #1 (Public Review):

Response to reviewer 1 comments on “weaknesses”:

“A weakness in the approach is the use of genetic models that do not offer complete deletion of the prolactin receptor from targeted neuronal populations...”

We acknowledge that neither model used provided a complete deletion of the prolactin receptor (Prlr) from the targeted neuronal populations. We suspect that incomplete deletion of targeted genes is not uncommon in these sort of studies, but this remains the best approach to addressing our question, and we believe we have been thorough and transparent in reporting the degree of deletion observed. We thought we had appropriately discussed the implications of the low proportion of Kiss1 cells still expressing Prlr, but will certainly revisit to ensure it is discussed thoroughly. This does not detract, however, from the key conclusion that prolactin action is necessary for full suppression of fertility in lactation in the mouse.

“Results showing no impact of progesterone on LH secretion during lactation are surprising, given the effectiveness of progesterone-containing birth control in lactating women...”

We think that this comment misrepresents what has been done in our study. We did not report a lack of impact of progesterone, as exogenous progesterone was never administered to mice. We did, however, give mifepristone as a progesterone receptor antagonist to determine whether endogenous progesterone contributed to the suppression of kisspeptin neuronal activity. We found that mifepristone, at levels sufficient to terminate pregnancy, had no effect on pulsatile LH secretion in lactating mice. This is consistent with our prior observation that progesterone levels are low in mouse lactation, suggesting that progesterone does not contribute significantly to the suppression of kisspeptin neuronal activity during lactation in the mouse. We agree with the reviewer that if we had given exogenous progesterone, it likely would result in suppression of pulsatile LH secretion (as it does in women). Indeed, in other work, we have found that progesterone administration profoundly suppresses activity of the kisspeptin neurons in mice (https://doi.org/10.1210/en.2019-00193). But this was not the point of the present experiment. We will review how we have described this experiment to ensure that this is absolutely clear.

“While the authors assert their findings may reflect an important role for prolactin in lactational infertility in other mammalian species, that remains to be seen….”

We acknowledge that our study cannot address whether prolactin is necessary for the suppression of lactation in other mammalian species. We hope our data may stimulate a re-examination of this question in other species, however, as some of the prior methodology (such as using pharmacological suppression of prolactin) may have had off target effects that confound interpretation. We thought that this point was discussed appropriately in the manuscript but we will certainly check and make sure this is addressed suitably.

eLife assessment

This study addresses an important long-standing controversy in the field of reproductive biology. Using cutting-edge techniques the authors provide compelling evidence supporting a pivotal role of prolactin as the mediator of lactational infertility. Some methodological, technical, interpretive, stylistic, and typographical aspects of the paper need to be strengthened.

Reviewer #1 (Public Review):

Summary:

In this paper, Hackwell and colleagues performed technically impressive, long-term, GCaMP fiber photometry recordings from Kiss1 neurons in the arcuate nucleus of mice during multiple reproductive states. The data show an immediate suppression of activity of arc Kiss1 neuronal activity during pregnancy that is maintained during lactation. In the absence of any apparent change in suckling stimulus or milk production, mice lacking prolactin receptors in arcuate Kiss1 neurons regained Kiss1 episodic activity and estrous cyclicity faster than control mice, demonstrating that direct prolactin action on Kiss1 neurons is at least partially responsible for suppressing fertility in this species. The effect of loss of prolactin receptors from CamK2a expressing neurons was even greater, indicating either that prolactin sensitivity in Kiss1 neurons of the RP3V contributes to lactational infertility or that other prolactin-sensitive neurons are involved. These data demonstrate the important role of prolactin in suppressing Kiss1 neuron activity and thereby fertility during the lactational period in the mouse.

Strengths:

This is the first study to monitor the activity of the GnRH pulse-generating system across different reproductive states in the same animal. Another strength in the study design is that it isolated the effects of prolactin by maintaining normal lactation and suckling (assessed indirectly using pup growth curves). The study also offers insight into the phenomenon of postpartum ovulation in mice. The results showed a brief reactivation of arcuate Kiss1 activity immediately prior to parturition, attributed to falling progesterone levels at the end of pregnancy. This hypothesis will be of interest to the field and is likely to inspire testing in future studies. With the exceptions mentioned below, the conclusions of the paper are well supported by the data, and the aims of the study were achieved. This paper is likely to raise the standard for technical expectations in the field and spark new interest in the direct impact of prolactin on Kiss1 neurons during lactation in other species.

Weaknesses:

A weakness in the approach is the use of genetic models that do not offer complete deletion of the prolactin receptor from targeted neuronal populations. A substantial proportion of Kiss1 neurons in both models retain the receptor. As a result, it is not clear whether the partial maintenance of cyclicity during lactation in the genetic models is due to incomplete deletion or to the involvement of other factors. This weakness should be more fully discussed in the text. In addition, results showing no impact of progesterone on LH secretion during lactation are surprising, given the effectiveness of progesterone-containing birth control in lactating women. The progesterone-related experiments were not well justified or discussed in the text. While the authors assert their findings may reflect an important role for prolactin in lactational infertility in other mammalian species, that remains to be seen. Hyperprolactinemia is known to suppress GnRH release, but its importance in the suppression of cyclicity during lactation is controversial. Indeed, in several species, the stimulus of suckling is considered to be the main driver of lactational fertility suppression. Data from rats shows that exogenous prolactin was unable to suppress LH release in dams deprived of their pups shortly after birth; both suckling and prolactin were necessary to suppress a post ovariectomy rise in LH levels. The duration of amenorrhea does not correlate with average prolactin levels in humans, and suckling but not prolactin was required to suppress the postpartum rise in LH in the rhesus monkey. The authors should discuss more thoroughly whether the protocol of this or other studies might result in discordant results and whether mice are likely to be an outlier in their mechanism of cycle suppression.

Reviewer #2 (Public Review):

Summary:

The overall goal of Eleni et al. is to determine if the suppression of LH pulses during lactation is mediated by prolactin signaling at kisspeptin neurons. To address this, the authors used GCaMP fiber photometry and serial blood sampling to reveal that in vivo episodic arcuate kisspeptin neuron activity and LH pulses are suppressed throughout pregnancy and lactation. The authors further utilized knockout models to demonstrate that the loss of prolactin receptor signaling at kisspeptin cells prevents the suppression of kisspeptin function and results in early reestablishment of fertility during lactation. The work demonstrates exemplary design and technique, and the outcomes of these experiments are sophistically discussed.

Strengths:

This manuscript demonstrates exemplary skill with powerful techniques and reveals a key role for arcuate kisspeptin neurons in maintaining lactation-induced infertility in mice. In a difficult feat, the authors used fiber photometry to map the activity of arcuate kisspeptin cells into lactation and weaning without disrupting parturition, lactation, or maternal behavior. The authors used a knockout approach to identify if prolactin inhibition of fertility is mediated by direct signaling at arcuate kisspeptin cells. Although the model does not perfectly eliminate prolactin receptor expression in all kisspeptin neurons, results from the achieved knockdown support the conclusion that prolactin signaling at kisspeptin neurons is required to maintain lactational infertility. The methods were advanced and appropriate for the aims, the studies were rigorously conducted, and the conclusions were thoughtfully discussed. Overall, the aims of this study were achieved.

Reviewer #3 (Public Review):

Summary:

Grattan and colleagues were trying to establish the neural mechanism underlying lactational infertility, in particular trying to establish the relative importance of the neurogenic effects of the suckling stimulus versus prolactin per se. They have shown that in the mouse it is rather prolactin and more specifically its action on the hypothalamic arcuate kisspeptin neuronal system, which is the key neural construct underlying gonadotrophin-releasing hormone (GnRH) pulse generation and central to the neuroendocrine control of reproduction, that mediates lactational infertility. The authors have taken a measured tone to emphasise the data pertaining to the mouse without extravagant extrapolation to humans. Nevertheless, the key findings provide a substantial foundation to facilitate interpretation of studies in other species.

Strengths:

The major strength of this study is the use of a combination of cutting-edge technologies, which of course underlie the majority of scientific advances rather than intellectual prowess favoured by the majority of scientists. Their approach avoided the major confounding effects of using pharmacological strategies to suppress prolactin action that has complicated the vast majority of previous studies. The study also provides an elegant and comprehensive contiguous description of GnRH pulse generator frequency across the ovarian cycle, through pregnancy and lactation, and into weaning in individual animals.

Weaknesses:<br /> There are no significant weaknesses.

Author Response

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

Reviewer #1 (Public Review):

The authors present a number of deep-learning models to analyse the dynamics of epithelia. In this way, they want to overcome the time-consuming manual analysis of such data and also remove a potential operator bias. Specifically, they set up models for identifying cell division events and cell division orientation. They apply these tools to the epithelium of the developing Drosophila pupal wing. They confirm a linear decrease of the division density with time and identify a burst of cell division after the healing of a wound that they had induced earlier. These division events happen a characteristic time after and a characteristic distance away from the wound. These characteristic quantities depend on the size of the wound.

Strength:

The methods developed in this work achieve the goals set by the authors and are a very helpful addition to the toolbox of developmental biologists. They could potentially be used on various developing epithelia. The evidence for the impact of wounds on cell division is solid.

Weakness:

Some aspects of the deep-learning models remained unclear, and the authors might want to think about adding details. First of all, for readers not being familiar with deep-learning models, I would like to see more information about ResNet and U-Net, which are at the base of the new deep-learning models developed here. What is the structure of these networks?

We agree with the Reviewer and have included additional information on page 8 of the manuscript, outlining some background information about the architecture of ResNet and U-Net models.

How many parameters do you use?

We apologise for this omission and have now included the number of parameters and layers in each model in the methods section on page 25.

What is the difference between validating and testing the model? Do the corresponding data sets differ fundamentally?

The difference between ‘validating’ and ‘testing’ the model is validating data is used during training to determine whether the model is overfitting. If the model is performing well on the training data but not on the validating data, this a key signal the model is overfitting and changes will need to be made to the network/training method to prevent this. The testing data is used after all the training has been completed and is used to test the performance of the model on fresh data it has not been trained on. We have removed refence to the validating data in the main text to make it simpler and add this explanation to the methods. There is no fundamental (or experimental) difference between each of the labelled data sets; rather, they are collected from different biological samples. We have now included this information in the Methods text on page 24.

How did you assess the quality of the training data classification?

These data were generated and hand-labelled by an expert with many years of experience in identifying cell divisions in imaging data, to give the ground truth for the deep learning model.

Reviewer #1 (Recommendations For The Authors):

You repeatedly use 'new', 'novel' as well as 'surprising' and 'unexpected'. The latter are rather subjective and it is not clear based on what prior knowledge you make these statements. Unless indicated otherwise, it is understood that the results and methods are new, so you can delete these terms.

We have deleted these words, as suggested, for almost all cases.

p.4 "as expected" add a reference or explain why it is expected.

A reference has now been included in this section, as suggested.

p.4 "cell divisions decrease linearly with time" Only later (p.10) it turns out that you think about the density of cell divisions.

This has been changed to "cell division density decreases linearly with time".

p.5 "imagine is largely in one plane" while below "we generated a 3D z-stack" and above "our in vivo 3D image data" (p.4). Although these statements are not strictly contradictory, I still find them confusing. Eventually, you analyse a 2D image, so I would suggest that you refer to your in vivo data as being 2D.

We apologise for the confusion here; the imaging data was initially generated using 3D z-stacks but this 3D data is later converted to a 2D focused image, on which the deep learning analysis is performed. We are now more careful with the language in the text.

p.7 "We have overcome (...) the standard U-Net model" This paragraph remains rather cryptic to me. Maybe you can explain in two sentences what a U-Net is or state its main characteristics. Is it important to state which class you have used at this point? Similarly, what is the exact role of the ResNet model? What are its characteristics?

We have included more details on both the ResNet and U-Net models and how our model incorporates properties from them on Page 8.

p.8 Table 1 Where do I find it? Similarly, I could not find Table 2.

These were originally located in the supplemental information document, but have been moved to the main manuscript.

p.9 "developing tissue in normal homeostatic conditions" Aren't homeostatic and developing contradictory? In one case you maintain a state, in the other, it changes.

We agree with the Reviewer and have removed the word ‘homeostatic’.

p.9 "Develop additional models" I think 'models' refers to deep learning models, not to physical models of epithelial tissue development. Maybe you can clarify this?

Yes, this is correct; we have phrased this better in the text.

p.12 "median error" median difference to the manually acquired data?

Yes, and we have made this clearer in the text, too.

p.12 "we expected to observe a bias of division orientation along this axis" Can you justify the expectation? Elongated cells are not necessarily aligned with the direction of a uniaxially applied stress.

Although this is not always the case, we have now included additional references to previous work from other groups which demonstrated that wing epithelial cells do become elongated along the P/D axis in response to tension.

p.14 "a rather random orientation" Please, quantify.

The division orientations are quantified in Fig. 4F,G; we have now changed our description from ‘random’ to ‘unbiased’.

p.17 "The theories that must be developed will be statistical mechanical (stochastic) in nature" I do not understand. Statistical mechanics refers to systems at thermodynamic equilibrium, stochastic to processes that depend on, well, stochastic input.

We have clarified that we are referring to non-equilibrium statistical mechanics (the study of macroscopic systems far from equilibrium, a rich field of research with many open problems and applications in biology).

Reviewer #2 (Public Review):

In this manuscript, the authors propose a computational method based on deep convolutional neural networks (CNNs) to automatically detect cell divisions in two-dimensional fluorescence microscopy timelapse images. Three deep learning models are proposed to detect the timing of division, predict the division axis, and enhance cell boundary images to segment cells before and after division. Using this computational pipeline, the authors analyze the dynamics of cell divisions in the epithelium of the Drosophila pupal wing and find that a wound first induces a reduction in the frequency of division followed by a synchronised burst of cell divisions about 100 minutes after its induction.

In general, novelty over previous work does not seem particularly important. From a methodological point of view, the models are based on generic architectures of convolutional neural networks, with minimal changes, and on ideas already explored in general. The authors seem to have missed much (most?) of the literature on the specific topic of detecting mitotic events in 2D timelapse images, which has been published in more specialized journals or Proceedings. (TPMAI, CCVPR etc., see references below). Even though the image modality or biological structure may be different (non-fluorescent images sometimes), I don't believe it makes a big difference. How the authors' approach compares to this previously published work is not discussed, which prevents me from objectively assessing the true contribution of this article from a methodological perspective.

On the contrary, some competing works have proposed methods based on newer - and generally more efficient - architectures specifically designed to model temporal sequences (Phan 2018, Kitrungrotsakul 2019, 2021, Mao 2019, Shi 2020). These natural candidates (recurrent networks, long-short-term memory (LSTM) gated recurrent units (GRU), or even more recently transformers), coupled to CNNs are not even mentioned in the manuscript, although they have proved their generic superiority for inference tasks involving time series (Major point 2). Even though the original idea/trick of exploiting the different channels of RGB images to address the temporal aspect might seem smart in the first place - as it reduces the task of changing/testing a new architecture to a minimum - I guess that CNNs trained this way may not generalize very well to videos where the temporal resolution is changed slightly (Major point 1). This could be quite problematic as each new dataset acquired with a different temporal resolution or temperature may require manual relabeling and retraining of the network. In this perspective, recent alternatives (Phan 2018, Gilad 2019) have proposed unsupervised approaches, which could largely reduce the need for manual labeling of datasets.

We thank the reviewer for their constructive comments. Our goal is to develop a cell detection method that has a very high accuracy, which is critical for practical and effective application to biological problems. The algorithms need to be robust enough to cope with the difficult experimental systems we are interested in studying, which involve densely packed epithelial cells within in vivo tissues that are continuously developing, as well as repairing. In response to the above comments of the reviewer, we apologise for not including these important papers from the division detection and deep learning literature, which are now discussed in the Introduction (on page 4).

A key novelty of our approach is the use of multiple fluorescent channels to increase information for the model. As the referee points out, our method benefits from using and adapting existing highly effective architectures. Hence, we have been able to incorporate deeper models than some others have previously used. An additional novelty is using this same model architecture (retrained) to detect cell division orientation. For future practical use by us and other biologists, the models can easily be adapted and retrained to suit experimental conditions, including different multiple fluorescent channels or number of time points. Unsupervised approaches are very appealing due to the potential time saved compared to manual hand labelling of data. However, the accuracy of unsupervised models are currently much lower than that of supervised (as shown in Phan 2018) and most importantly well below the levels needed for practical use analysing inherently variable (and challenging) in vivo experimental data.

Regarding the other convolutional neural networks described in the manuscript:

(1) The one proposed to predict the orientation of mitosis performs a regression task, predicting a probability for the division angle. The architecture, which must be different from a simple Unet, is not detailed anywhere, so the way it was designed is difficult to assess. It is unclear if it also performs mitosis detection, or if it is instead used to infer orientation once the timing and location of the division have been inferred by the previous network.

The neural network used for U-NetOrientation has the same architecture as U-NetCellDivision10 but has been retrained to complete a different task: finding division orientation. Our workflow is as follows: firstly, U-NetCellDivision10 is used to find cell divisions; secondly, U-NetOrientation is applied locally to determine the division orientation. These points have now been clarified in the main text on Page 14.

(2) The one proposed to improve the quality of cell boundary images before segmentation is nothing new, it has now become a classic step in segmentation, see for example Wolny et al. eLife 2020.

We have cited similar segmentation models in our paper and thank the referee for this additional one. We had made an improvement to the segmentation models, using GFP-tagged E-cadherin, a protein localised in a thin layer at the apical boundary of cells. So, while this is primarily a 2D segmentation problem, some additional information is available in the z-axis as the protein is visible in 2-3 separate z-slices. Hence, we supplied this 3-focal plane input to take advantage of the 3D nature of this signal. This approach has been made more explicit in the text (Pages 14, 15) and Figure (Fig. 2D).

As a side note, I found it a bit frustrating to realise that all the analysis was done in 2D while the original images are 3D z-stacks, so a lot of the 3D information had to be compressed and has not been used. A novelty, in my opinion, could have resided in the generalisation to 3D of the deep-learning approaches previously proposed in that context, which are exclusively 2D, in particular, to predict the orientation of the division.

Our experimental system is a relatively flat 2D tissue with the orientation of the cell divisions consistently in the xy-plane. Hence, a 2D analysis is most appropriate for this system. With the successful application of the 2D methods already achieving high accuracy, we envision that extension to 3D would only offer a slight increase in effectiveness as these measurements have little room for improvement. Therefore, we did not extend the method to 3D here. However, of course, this is the next natural step in our research as 3D models would be essential for studying 3D tissues; such 3D models will be computationally more expensive to analyse and more challenging to hand label.

Concerning the biological application of the proposed methods, I found the results interesting, showing the potential of such a method to automatise mitosis quantification for a particular biological question of interest, here wound healing. However, the deep learning methods/applications that are put forward as the central point of the manuscript are not particularly original.

We thank the referee for their constructive comments. Our aim was not only to show the accuracy of our models but also to show how they might be useful to biologists for automated analysis of large datasets, which is a—if not the—bottleneck for many imaging experiments. The ability to process large datasets will improve robustness of results, as well as allow additional hypotheses to be tested. Our study also demonstrated that these models can cope with real in vivo experiments where additional complications such as progressive development, tissue wounding and inflammation must be accounted for.

Major point 1: generalisation potential of the proposed method.

The neural network model proposed for mitosis detection relies on a 2D convolutional neural network (CNN), more specifically on the Unet architecture, which has become widespread for the analysis of biology and medical images. The strategy proposed here exploits the fact that the input of such an architecture is natively composed of several channels (originally 3 to handle the 3 RGB channels, which is actually a holdover from computer vision, since most medical/biological images are gray images with a single channel), to directly feed the network with 3 successive images of a timelapse at a time. This idea is, in itself, interesting because no modification of the original architecture had to be carried out. The latest 10-channel model (U-NetCellDivision10), which includes more channels for better performance, required minimal modification to the original U-Net architecture but also simultaneous imaging of cadherin in addition to histone markers, which may not be a generic solution.

We believe we have provided a general approach for practical use by biologists that can be applied to a range of experimental data, whether that is based on varying numbers of fluorescent channels and/or timepoints. We envisioned that experimental biologists are likely to have several different parameters permissible for measurement based on their specific experimental conditions e.g., different fluorescently labelled proteins (e.g. tubulin) and/or time frames. To accommodate this, we have made it easy and clear in the code on GitHub how these changes can be made. While the model may need some alterations and retraining, the method itself is a generic solution as the same principles apply to very widely used fluorescent imaging techniques.

Since CNN-based methods accept only fixed-size vectors (fixed image size and fixed channel number) as input (and output), the length or time resolution of the extracted sequences should not vary from one experience to another. As such, the method proposed here may lack generalization capabilities, as it would have to be retrained for each experiment with a slightly different temporal resolution. The paper should have compared results with slightly different temporal resolutions to assess its inference robustness toward fluctuations in division speed.

If multiple temporal resolutions are required for a set of experiments, we envision that the model could be trained over a range of these different temporal resolutions. Of course, the temporal resolution, which requires the largest vector would be chosen as the model's fixed number of input channels. Given the depth of the models used and the potential to easily increase this by replacing resnet34 with resnet50 or resnet101 the model would likely be able to cope with this, although we have not specifically tested this. (page 27)

Another approach (not discussed) consists in directly convolving several temporal frames using a 3D CNN (2D+time) instead of a 2D, in order to detect a temporal event. Such an idea shares some similarities with the proposed approach, although in this previous work (Ji et al. TPAMI 2012 and for split detection Nie et al. CCVPR 2016) convolution is performed spatio-temporally, which may present advantages. How does the authors' method compare to such an (also very simple) approach?

We thank the Reviewer for this insightful comment. The text now discusses this (on Pages 8 and 17). Key differences between the models include our incorporation of multiple light channels and the use of much deeper models. We suggest that our method allows for an easy and natural extension to use deeper models for even more demanding tasks e.g. distinguishing between healthy and defective divisions. We also tested our method with ‘difficult conditions’ such as when a wound is present; despite the challenges imposed by the wound (including the discussed reduction in fluorescent intensities near the wound edge), we achieved higher accuracy compared to Nie et al. (accuracy of 78.5% compared to our F1 score of 0.964) using a low-density in vitro system.

Major point 2: innovatory nature of the proposed method.

The authors' idea of exploiting existing channels in the input vector to feed successive frames is interesting, but the natural choice in deep learning for manipulating time series is to use recurrent networks or their newer and more stable variants (LSTM, GRU, attention networks, or transformers). Several papers exploiting such approaches have been proposed for the mitotic division detection task, but they are not mentioned or discussed in this manuscript: Phan et al. 2018, Mao et al. 2019, Kitrungrotaskul et al. 2019, She et al 2020.

An obvious advantage of an LSTM architecture combined with CNN is that it is able to address variable length inputs, therefore time sequences of different lengths, whereas a CNN alone can only be fed with an input of fixed size.

LSTM architectures may produce similar accuracy to the models we employ in our study, however due to the high degree of accuracy we already achieve with our methods, it is hard to see how they would improve the understanding of the biology of wound healing that we have uncovered. Hence, they may provide an alternative way to achieve similar results from analyses of our data. It would also be interesting to see how LTSM architectures would cope with the noisy and difficult wounded data that we have analysed. We agree with the referee that these alternate models could allow an easier inclusion of difference temporal differences in division time (see discussion on Page 20). Nevertheless, we imagine that after selecting a sufficiently large input time/ fluorescent channel input, biologists could likely train our model to cope with a range of division lengths.

Another advantage of some of these approaches is that they rely on unsupervised learning, which can avoid the tedious relabeling of data (Phan et al. 2018, Gilad et al. 2019).

While these are very interesting ideas, we believe these unsupervised methods would struggle under the challenging conditions within ours and others experimental imaging data. The epithelial tissue examined in the present study possesses a particularly high density of cells with overlapping nuclei compared to the other experimental systems these unsupervised methods have been tested on. Another potential problem with these unsupervised methods is the difficulty in distinguishing dynamic debris and immune cells from mitotic cells. Once again despite our experimental data being more complex and difficult, our methods perform better than other methods designed for simpler systems as in Phan et al. 2018 and Gilad et al. 2019; for example, analysis performed on lower density in vitro and unwounded tissues gave best F1 scores for a single video was 0.768 and 0.829 for unsupervised and supervised respectively (Phan et al. 2018). We envision that having an F1 score above 0.9 (and preferably above 0.95), would be crucial for practical use by biologists, hence we believe supervision is currently still required. We expect that retraining our models for use in other experimental contexts will require smaller hand labelled datasets, as they will be able to take advantage of transfer learning (see discussion on Page 4).

References :

We have included these additional references in the revised version of our Manuscript.

Ji, S., Xu, W., Yang, M., & Yu, K. (2012). 3D convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence, 35(1), 221-231. >6000 citations

Nie, W. Z., Li, W. H., Liu, A. A., Hao, T., & Su, Y. T. (2016). 3D convolutional networks-based mitotic event detection in time-lapse phase contrast microscopy image sequences of stem cell populations. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (pp. 55-62).

Phan, H. T. H., Kumar, A., Feng, D., Fulham, M., & Kim, J. (2018). Unsupervised two-path neural network for cell event detection and classification using spatiotemporal patterns. IEEE Transactions on Medical Imaging, 38(6), 1477-1487.

Gilad, T., Reyes, J., Chen, J. Y., Lahav, G., & Riklin Raviv, T. (2019). Fully unsupervised symmetry-based mitosis detection in time-lapse cell microscopy. Bioinformatics, 35(15), 2644-2653.

Mao, Y., Han, L., & Yin, Z. (2019). Cell mitosis event analysis in phase contrast microscopy images using deep learning. Medical image analysis, 57, 32-43.

Kitrungrotsakul, T., Han, X. H., Iwamoto, Y., Takemoto, S., Yokota, H., Ipponjima, S., ... & Chen, Y. W. (2019). A cascade of 2.5 D CNN and bidirectional CLSTM network for mitotic cell detection in 4D microscopy image. IEEE/ACM transactions on computational biology and bioinformatics, 18(2), 396-404.

Shi, J., Xin, Y., Xu, B., Lu, M., & Cong, J. (2020, November). A Deep Framework for Cell Mitosis Detection in Microscopy Images. In 2020 16th International Conference on Computational Intelligence and Security (CIS) (pp. 100-103). IEEE.

Wolny, A., Cerrone, L., Vijayan, A., Tofanelli, R., Barro, A. V., Louveaux, M., ... & Kreshuk, A. (2020). Accurate and versatile 3D segmentation of plant tissues at cellular resolution. Elife, 9, e57613.

eLife assessment

In this potentially useful study, the authors use deep learning models to provide solid evidence that epithelial wounding triggers bursts of cell division at a characteristic distance away from the wound. The usefulness of the methods to the community will depend on documenting their robustness toward variability in temporal resolution and/or mitotic event duration, demonstrating their overall superiority over existing approaches and making the code possible to use by others.

Reviewer #1 (Public Review):

The authors present a number of deep learning models to analyse the dynamics of epithelia. In this way they want to overcome the time-consuming manual analysis of such data and also remove a potential operator bias. Specifically, they set up models for identifying cell division events and cell division orientation. They apply these tools to the epithelium of the developing Drosophila pupal wing. They confirm a linear decrease of the division density with time and identify a burst of cell division after healing of a wound that they had induced earlier. These division events happen a characteristic time after and a characteristic distance away from the wound. These characteristic quantities depend on the size of the wound.

Strengths:

The methods developed in this work achieve the goals set by the authors and are a very helpful addition to the toolbox of developmental biologists. They could potentially be used on various developing epithelia. The evidence for the impact of wounds on cell division is compelling.

The methods presented in this work should prove to be very helpful for quantifying cell proliferation in epithelial tissues.

Reviewer #2 (Public Review):

In this manuscript, the authors propose a computational method based on deep convolutional neural networks (CNNs) to automatically detect cell divisions in two-dimensional fluorescence microscopy timelapse images. Three deep learning models are proposed to detect the timing of division, predict the division axis, and enhance cell boundary images to segment cells before and after division. Using this computational pipeline, the authors analyze the dynamics of cell divisions in the epithelium of the Drosophila pupal wing and find that a wound first induces a reduction in the frequency of division followed by a synchronised burst of cell divisions about 100 minutes after its induction.

Comments on revised version:

Regarding the Reviewer's 1 comment on the architecture details, I have now understood that the precise architecture (number/type of layers, activation functions, pooling operations, skip connections, upsampling choice...) might have remained relatively hidden to the authors themselves, as the U-net is built automatically by the fast.ai library from a given classical choice of encoder architecture (ResNet34 and ResNet101 here) to generate the decoder part and skip connections.

Regarding the Major point 1, I raised the question of the generalisation potential of the method. I do not think, for instance, that the optimal number of frames to use, nor the optimal choice of their time-shift with respect to the division time (t-n, t+m) (not systematically studied here) may be generic hyperparameters that can be directly transferred to another setting. This implies that the method proposed will necessarily require re-labeling, re-training and re-optimizing the hyperparameters which directly influence the network architecture for each new dataset imaged differently. This limits the generalisation of the method to other datasets, and this may be seen as in contrast to other tools developed in the field for other tasks such as cellpose for segmentation, which has proven a true potential for generalisation on various data modalities. I was hoping that the authors would try themselves testing the robustness of their method by re-imaging the same tissue with slightly different acquisition rate for instance, to give more weight to their work.

In this regard, and because the authors claimed to provide clear instructions on how to reuse their method or adapt it to a different context, I delved deeper into the code and, to my surprise, felt that we are far from the coding practice of what a well-documented and accessible tool should be.

To start with, one has to be relatively accustomed with Napari to understand how the plugin must be installed, as the only thing given is a pip install command (that could be typed in any terminal without installing the plugin for Napari, but has to be typed inside the Napari terminal, which is mentioned nowhere). Surprisingly, the plugin was not uploaded on Napari hub, nor on PyPI by the authors, so it is not searchable/findable directly, one has to go to the Github repository and install it manually. In that regard, no description was provided in the copy-pasted templated files associated to the napari hub, so exporting it to the hub would actually leave it undocumented.

Regarding now the python notebooks, one can fairly say that the "clear instructions" that were supposed to enlighten the code are really minimal. Only one notebook "trainingUNetCellDivision10.ipynb" has actually some comments, the other have (almost) none nor title to help the unskilled programmer delving into the script to guess what it should do. I doubt that a biologist who does not have a strong computational background will manage adapting the method to its own dataset (which seems to me unavoidable for the reasons mentioned above).

Finally regarding the data, none is shared publicly along with this manuscript/code, such that if one doesn't have a similar type of dataset - that must be first annotated in a similar manner - one cannot even test the networks/plugin for its own information. A common and necessary practice in the field - and possibly a longer lasting contribution of this work - could have been to provide the complete and annotated dataset that was used to train and test the artificial neural network. The basic reason is that a more performant, or more generalisable deep-learning model may be developed very soon after this one and for its performance to be fairly compared, it requires to be compared on the same dataset. Benchmarking and comparison of methods performance is at the core of computer vision and deep-learning.

Author Response

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

eLife assessment

This important study used Voltage Sensitive Dye Imaging (VSDI) to measure neural activity in the primary visual cortex of monkeys trained to detect an oriented grating target that was presented either alone or against an oriented mask. The authors show convincingly that the initial effect of the mask ran counter to the behavioral effects of the mask, a pattern that reversed in the latter phase of the response. They interpret these results in terms of influences from the receptive field center, and although an alternative view that emphasizes the role of the receptive field surround also seems reasonable, this study stands as an interesting and important contribution to our understanding of mechanisms of visual perception.

Public Reviews:

Reviewer #1 (Public Review):

This is a clear account of some interesting work. The experiments and analyses seem well done and the data are useful. It is nice to see that VSDI results square well with those from prior extracellular recordings.

The authors have done a good job responding to the main points of my previous review. One important question remains, as stated in that review:

"My reading is that this is primarily a study of surround suppression with results that follow pretty directly from what we already know from that literature, and although they engage with some of the literature they do not directly mention surround suppression in the text. Their major effect - what they repeatedly describe as a "paradoxical" result in which the responses initially show a stronger response to matched targets and backgrounds and then reverse - seems to pretty clearly match the expected outcome of a stimulus that initially evokes additional excitation due to increased center contrast followed by slightly delayed surround suppression tuned to the same peak orientation. Their dynamics result seems entirely consistent with previous work, e.g. Henry at al 2020, particularly their Fig. 3 https://elifesciences.org/articles/54264, so it seems like a major oversight to not engage with that work at all, and to explain what exactly is new here."

Their rebuttal of my first review is not convincing -- I still believe that surround influences are important and perhaps predominant in determining the outcome of the experiments. This is particularly clear for the "paradoxical" dynamics that they observe, which seem exactly to reflect the behavior of the surround.

The authors' arguments to the contrary are based on three main points. First, their stimuli cover the center and surround, unlike those of many previous experiments, so they argue that this somehow diminishes the impact of the surround. But the argument is not accompanied by data showing the effects of center stimuli alone or surround stimuli alone. Second, their model -- a normalization model -- does not need surround influences to account for the masking effect. Third, they cite human psychophysical masking results from their collaborators (Sebastian et al 2017), but do not cite an equally convincing demonstration that surround contrast creates potent orientation selective masking when presented alone (Petrov et al 2005, https://doi.org/10.1523/JNEUROSCI.2871-05.2005).

At the end of the day, these issues will be resolved by further experiments, not argumentation. The paper stands as an excellent contribution, but it might be wise for the authors to be less doctrinaire in their interpretations.

We thank the reviewer for their positive comments and constructive criticism. In general, we agree with the reviewer’s comments. Importantly, we do not claim that there is no effect from the surround. What we say in the discussion is:

“Because our targets are added to the background rather than occluding it, it is likely that a significant portion of the behavioral and neural masking effects that we observe come from target-mask interactions at the target location rather than from the effect of the mask in the surround.”

We still stand by this assessment. We also make the point that, at least within the framework of our delayed normalization model, there is no need for the normalization mechanism to extend beyond the center mechanism to account for our results, and even if the normalization mechanism is somewhat larger than the center, the overlap region at the center would still have a large contribution to the modulations. Overall, we agree that these issues will be need to be resolved by future experiments.

For the reasons discussed in our previous reply, we disagree with the reviewers’ statement “…this is primarily a study of surround suppression with results that follow pretty directly from what we already know from that literature”. For similar reasons we disagree with the statement “It is nice to see that VSDI results square well with those from prior extracellular recordings”.

Reviewer #2 (Public Review):

Summary

In this experiment, Voltage Sensitive Dye Imaging (VSDI) was used to measure neural activity in macaque primary visual cortex in monkeys trained to detect an oriented grating target that was presented either alone or against an oriented mask. Monkeys' ability to detect the target (indicated by a saccade to its location) was impaired by the mask, with the greatest impairment observed when the mask was matched in orientation to the target, as is also the case in human observers. VSDI signals were examined to test the hypothesis that the target-evoked response would be maximally suppressed by the mask when it matched the orientation of the target. In each recording session, fixation trials were used to map out the spatial response profile and orientation domains that would then be used to decode the responses on detection trials. VSDI signals were analyzed at two different scales: a coarse scale of the retinotopic response to the target and a finer scale of orientation domains within the stimulus-evoked response. Responses were recorded in three conditions: target alone, mask alone, and target presented with mask. Analyses were focused on the target evoked response in the presence of the mask, defined to be the difference in response evoked by the mask with target (target present) versus the mask alone (target absent). These were computed across five 50 msec bins (total, 250 msec, which was the duration of the mask (target present trials, 50% of trials) / mask + target (target present trials, 50% of trials). Analyses revealed that in an initial (transient) phase the target evoked response increased with similarity between target and mask orientation. As the authors note, this is surprising given that this was the condition where the mask maximally impaired detection of the target in behavior. Target evoked responses in a later ('sustained') phase fell off with orientation similarity, consistent with the behavioral effect. When analyzed at the coarser scale the target evoked response, integrated over the full 250 msec period showed a very modest dependence on mask orientation. The same pattern held when the data were analyzed on the finer orientation domain scale, with the effect of the mask in the transient phase running counter to the perceptual effect of the mask and the sustained response correlating the perceptual effect. The effect of the mask was more pronounced when analyzed at the scale.

Strengths

The work is on the whole very strong. The experiments are thoughtfully designed, the data collection methods are good, and the results are interesting. The separate analyses of data at a coarse scale that aggregates across orientation domains and a more local scale of orientation domains is a strength and it is reassuring that the effects at the more localized scale are more clearly related to behavior, as one would hope and expect. The results are strengthened by modeling work shown in Figure 8, which provides a sensible account of the population dynamics. The analyses of the relationship between VSDI data and behavior are well thought out and the apparent paradox of the anti-correlation between VSDI and behavior in the initial period of response, followed by a positive correlation in the sustained response period is intriguing.

We thank the reviewer for their positive comments.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

None, except perhaps for a more balanced representation of the "surround" possibility in the Discussion. The Petrov et al paper (https://doi.org/10.1523/JNEUROSCI.2871-05.2005) should be considered and cited.

As discussed above, we believe that our discussion of possible contribution from the surround is balanced. While the paper by Petrov et al is interesting, the stimuli used to study the surround effects are quite different (e.g., gap between center and surround, and the sharp edge of the surround inner boundary) so direct comparison with our results is not possible.

Reviewer #2 (Recommendations For The Authors):

The authors have addressed the questions/suggestions I raised in my review.

---

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

We thank the reviewers for their helpful comments and suggestions.

eLife assessment

This is an important contribution that extends earlier single-unit work on orientation-specific center-surround interactions to the domain of population responses measured with Voltage Sensitive Dye (VSD) imaging and the first to relate these interactions to orientation-specific perceptual effects of masking. The authors provide convincing evidence of a pattern of results in which the initial effect of the mask seems to run counter to the behavioral effects of the mask, a pattern that reversed in the latter phase of the response. It seems likely that the physiological effects of masking reported here can be attributed to previously described signals from the receptive field surround.

We thank the reviewers for bringing up the relation of our results to findings from previous orientation-specific center-surround interactions studies. In our final manuscript, we added a paragraph discussing this important issue. Briefly, for multiple reasons, we believe that the orientation-dependent behavioral and neural masking effects that we observe are unlikely to depend on previously described center-surround interactions in V1. First, in human subjects, perceptual similarity masking effects are almost entirely accounted for by target-mask interactions at the target location and are recapitulated when the mask has the same size and location as the target (Sebastian et al 2017). Second, in our computational model, the effect of mask orientation on the dynamics of the response are qualitatively the same if the mask is restricted to the size and location of the target while mask contrast is increased (Fig. 8 – figure supplement 3). Third, in our model, the results are qualitatively the same when the spatial pooling region for the normalization signal is the same as that for the excitation signal (Fig. 8 – figure supplement figure 1). These considerations suggest that center-surround interactions may not be necessary for neural and behavioral similarity masking effects with additive targets.

We would also like to point out some key differences between the stimuli that we use and the ones used in most previous center-surround studies. First, in our experiments, the target and the mask were additive, while in most previous center-surround studies the target occludes the background. Such studies therefore restrict the mask effect to the surround, while in our study we allow target-mask interactions at the center. Second, most center-surround studies have a sharp-edged target/surround, while in our experiments no sharp edges were present. Unpublished results from our lab suggest that such sharp edges have a large impact on V1 population responses. A third key difference is that our stimuli were flashed for a short interval of 250 ms corresponding to a typical duration of a fixation in natural vision, while most previous center-surround studies used either longer-duration drifting stimuli or very short-duration random-order stimuli for reverse-correlation analysis.

In addition, we would like to emphasize that our results go beyond previous studies in two important ways. First, we study the effect of similarity masking in behaving animals and quantitatively compare the effect of similarity masking on behavior and physiology in the same subjects and at the same time. Second, VSD imaging allows us to capture the dynamics of superficial V1 population responses over the entire population of millions of neurons activated by the target at two important spatial scales. Such results therefore complement electrophysiological studies that examine the activity of a very small subset of the active neurons.

Public Reviews:

Reviewer #1 (Public Review):

This is a clear account of some interesting work. The experiments and analyses seem well done and the data are useful. It is nice to see that VSDI results square well with those from prior extracellular recordings. But the work may be less original than the authors propose, and their overall framing strikes me as odd. Some additional clarifications could make the contribution more clear.

Please see our reply above regarding the agreement with previous studies and framing.

My reading is that this is primarily a study of surround suppression with results that follow pretty directly from what we already know from that literature, and although they engage with some of the literature they do not directly mention surround suppression in the text. Their major effect - what they repeatedly describe as a "paradoxical" result in which the responses initially show a stronger response to matched targets and backgrounds and then reverse - seems to pretty clearly match the expected outcome of a stimulus that initially evokes additional excitation due to increased center contrast followed by slightly delayed surround suppression tuned to the same peak orientation. Their dynamics result seems entirely consistent with previous work, e.g. Henry et al 2020, particularly their Fig. 3 https://elifesciences.org/articles/54264, so it seems like a major oversight to not engage with that work at all, and to explain what exactly is new here.

We thank the reviewer for the pointing out this previous work which we now cite in the final version of the manuscript. For the reasons discussed above, while this study is interesting and related to our work, we believe that our results are quite distinct.

• In the discussion (lines 315-316), they state "in order to account for the reduced neural sensitivity with target-background similarity in the second phase of the response, the divisive normalization signal has to be orientation selective." I wonder whether they observed this in their modeling. That is, how robust were the normalization model results to the values of sigma_e and sigma_n? It would be useful to know how critical their various model parameters were for replicating the experimental effects, rather than just showing that a good account is possible.

Thank you for this suggestion. In the final manuscript we include a supplementary figure that shows how the model’s predictions are affected by the orientation tuning and spatial extent of the normalization signal, and by the size and contrast of the mask (Fig. 8 – figure supplement 1-4).

• The majority of their target/background contrast conditions were collected only in one animal. This is a minor limitation for work of this kind, but it might be an issue for some.

We agree that this is a limitation of the current study. These are challenging experiments and we were unable to collect all target/background contrast combinations from both monkeys. However, in the common conditions, the results appear similar in the two animals, and the key results seem to be robust to the contrast combination in the animal in which a wider range of contrast combinations was tested. We added these points to the discussion in the final manuscript.

• The authors point out (line 193-195) that "Because the first phase of the response is shorter than the second phase, when V1 response is integrated over both phases, the overall response is positively correlated with the behavioral masking effect." I wonder if this could be explored a bit more at the behavioral level - i.e. does the "similarity masking" they are trying to explain show sensitivity to presentation time?

We agree that testing the effect of stimulus duration on similarity masking is interesting, but unfortunately, it is beyond the scope of the current study. We would also like to point out that the duration of the presentation was selected to match the typical time of fixation during natural behaviors, so much shorter or much longer stimulus durations would be less relevant for natural vision.

• From Fig. 3 it looks like the imaging ROI may include some opercular V2. If so, it's plausible that something about the retinotopic or columnar windowing they used in analysis may remove V2 signals, but they don't comment. Maybe they could tell us how they ensured they only included V1?

We thank the reviewer for this comment. As part of our experiments, we extract a detailed retinotopic map for each chamber, so we were able to ensure that the area used for the decoding analysis lays entirely within V1. We now incorporate this information in the final manuscript (Fig. 3 – figure supplement 1).

• In the discussion (lines 278-283) they say "The positive correlation between the neural and behavioral masking effects occurred earlier and was more robust at the columnar scale than at the retinotopic scale, suggesting that behavioral performance in our task is dominated by columnar scale signals in the second phase of the response. To the best of our knowledge, this is the first demonstration of such decoupling between V1 responses at the retinotopic and columnar scales, and the first demonstration that columnar scale signals are a better predictor of behavioral performance in a detection task." I am having trouble finding where exactly they demonstrate this in the results. Is this just by comparison of Figs. 4E,K and 5E,K? I may just be missing something here, but the argument needs to be made more clearly since much of their claim to originality rests on it.

We thank the reviewer for this comment. In the final manuscript we are more explicit when we discuss this point and refer to the relevant panels in Figs. 4, 5 and their figure supplements. To substantiate this key claim, we also report the timing of the transition between the two phases in all temporal correlation panels and report the neural-behavioral correlation for the integration period.

Reviewer #2 (Public Review):

Summary

In this experiment, Voltage Sensitive Dye Imaging (VSDI) was used to measure neural activity in macaque primary visual cortex in monkeys trained to detect an oriented grating target that was presented either alone or against an oriented mask. Monkeys' ability to detect the target (indicated by a saccade to its location) was impaired by the mask, with the greatest impairment observed when the mask was matched in orientation to the target, as is also the case in human observers. VSDI signals were examined to test the hypothesis that the target-evoked response would be maximally suppressed by the mask when it matched the orientation of the target. In each recording session, fixation trials were used to map out the spatial response profile and orientation domains that would then be used to decode the responses on detection trials. VSDI signals were analyzed at two different scales: a coarse scale of the retinotopic response to the target and a finer scale of orientation domains within the stimulus-evoked response. Responses were recorded in three conditions: target alone, mask alone, and target presented with mask. Analyses were focused on the target evoked response in the presence of the mask, defined to be the difference in response evoked by the mask with target (target present) versus the mask alone (target absent). These were computed across five 50 msec bins (total, 250 msec, which was the duration of the mask (target present trials, 50% of trials) / mask + target (target present trials, 50% of trials). Analyses revealed that in an initial (transient) phase the target evoked response increased with similarity between target and mask orientation. As the authors note, this is surprising given that this was the condition where the mask maximally impaired detection of the target in behavior. Target evoked responses in a later ('sustained') phase fell off with orientation similarity, consistent with the behavioral effect. When analyzed at the coarser scale the target evoked response, integrated over the full 250 msec period showed a very modest dependence on mask orientation. The same pattern held when the data were analyzed on the finer orientation domain scale, with the effect of the mask in the transient phase running counter to the perceptual effect of the mask and the sustained response correlating the perceptual effect. The effect of the mask was more pronounced when analyzed at the scale.

Strengths

The work is on the whole very strong. The experiments are thoughtfully designed, the data collection methods are good, and the results are interesting. The separate analyses of data at a coarse scale that aggregates across orientation domains and a more local scale of orientation domains is a strength and it is reassuring that the effects at the more localized scale are more clearly related to behavior, as one would hope and expect. The results are strengthened by modeling work shown in Figure 8, which provides a sensible account of the population dynamics. The analyses of the relationship between VSDI data and behavior are well thought out and the apparent paradox of the anti-correlation between VSDI and behavior in the initial period of response, followed by a positive correlation in the sustained response period is intriguing.

Points to Consider / Possible Improvements

The biphasic nature of the relationship between neural and behavioral modulation by the mask and the surprising finding that the two are anticorrelated in the initial phase are left as a mystery. The paper would be more impactful if this mystery could be resolved.

We thank the reviewer for the positive comments. In our view, while our results are surprising, there may not be a remaining mystery that needs to be resolved. As our model shows, the biphasic nature of V1’s response can be explained by a delayed orientation-tuned gain control. Our results are consistent with the hypothesis that perception is based on columnar-scale V1 signals that are integrated over an approximately 200 ms long period that incorporates both the early and the late phase of the response, since such decoded V1 signals are positively correlated with the behavioral similarity masking effect (Fig. 5D, J; Fig. 5 – figure supplement 1). We now explain this more clearly in the discussion of our final manuscript.

The finding is based on analyses of the correlation between behavior and neural responses. This appears in the main body of the manuscript and is detailed in Figures S1 and S2, which show the correlation over time between behavior and target response for the retinotopic and columnar scale.

One possible way of thinking of this transition from anti- to positive correlation with behavior is that it might reflect the dynamics of a competitive interaction between mask and target, with the initial phase reflecting predominantly the mask response, with the target emerging, on some trials, in the latter phase. On trials when the mask response is stronger, the probability of the target emerging in the latter phase, and triggering a hit, might be lower, potentially explaining the anticorrelation in the initial phase. The sustained response may be a mixture of trials on which the target response is or is not strong enough to overcome the effect of the mask sufficiently to trigger target detection.

It would, I think, be worth examining this by testing whether target dynamics may vary, depending on whether the monkey detected the target (hit trials) or failed to detect the target (miss trials). Unless I missed it I do not think this analysis was done. Consistent with this possibility, the authors do note (lines 226-229) that "The trajectories in the target plus mask conditions are more complex. For example, when mask orientation is at +/- 45 deg to the target, the population response is initially dominated by the mask, but then in mid-flight, the population response changes direction and turns toward the direction of the target orientation." This suggests (to this reviewer, at least) that the emergence of a positive correlation between behavioral and neural effects in the latter phase of the response could reflect either a perceptual decision that the target is present or perhaps deployment of attention to the location of the target.

It may be that this transition reflected detection, in which it might be more likely on hit trials than miss trials. Given the SNR it would presumably be difficult to do this analysis on a trial-by-trial basis, but the hit and miss trials (which make each make up about 1/2 of all trials) could be averaged separately to see if the mid-flight transition is more prominent on hit trials. If this is so for the +/- 45 degree case it would be good to see the same analysis for other combinations of target and mask. It would also be interesting to separate correct reject trials from false alarms, to determine whether the mid-flight transition tends to occur on false alarm trials.

If these analyses do not reveal the predicted pattern, they might still merit a supplemental figure, for the sake of completeness.

We thank the reviewer for suggesting this interesting possibility. The original analysis in the manuscript was based on both correct and incorrect trials, raising the possibility that our results reflect some contribution from decision- and/or attention-related signals rather than from low-level nonlinear encoding mechanisms in V1 that we postulate in our model (Fig. 8). To explore this possibility, we re-examined our results while excluding error trials. We found that our key results from Figs 4 and 5 – namely that there is an early transient phase in which the neural and behavioral similarity effects are anti-correlated, and a later sustained phase in which they are positively correlated – hold even for the subset of correct trials, reducing the possibility that decision/attention-related signals play a major role in explaning our results. We now include the results of this analysis as a supplementary figure in the final manuscript (Fig. 4 – figure supplement 2). While there may be some interesting differences in the response dynamics between correct and incorrect trials, the current study was not designed to address this question and the large number of conditions and small number of repeats that it necessitated make this data set suboptimal for examining these phenomena.

References

Sebastian S, Abrams J, Geisler WS. 2017. Constrained sampling experiments reveal principles of detection in natural scenes. Proc Natl Acad Sci U S A 114: E5731-e40

Reviewer #1 (Public Review):

This is a clear account of some interesting work. The experiments and analyses seem well done and the data are useful. It is nice to see that VSDI results square well with those from prior extracellular recordings.

The authors have done a good job responding to the main points of my previous review. One important question remains, as stated in that review:

"My reading is that this is primarily a study of surround suppression with results that follow pretty directly from what we already know from that literature, and although they engage with some of the literature they do not directly mention surround suppression in the text. Their major effect - what they repeatedly describe as a "paradoxical" result in which the responses initially show a stronger response to matched targets and backgrounds and then reverse - seems to pretty clearly match the expected outcome of a stimulus that initially evokes additional excitation due to increased center contrast followed by slightly delayed surround suppression tuned to the same peak orientation. Their dynamics result seems entirely consistent with previous work, e.g. Henry at al 2020, particularly their Fig. 3 https://elifesciences.org/articles/54264, so it seems like a major oversight to not engage with that work at all, and to explain what exactly is new here."

Their rebuttal of my first review is not convincing -- I still believe that surround influences are important and perhaps predominant in determining the outcome of the experiments. This is particularly clear for the "paradoxical" dynamics that they observe, which seem exactly to reflect the behavior of the surround.

The authors' arguments to the contrary are based on three main points. First, their stimuli cover the center and surround, unlike those of many previous experiments, so they argue that this somehow diminishes the impact of the surround. But the argument is not accompanied by data showing the effects of center stimuli alone or surround stimuli alone. Second, their model -- a normalization model -- does not need surround influences to account for the masking effect. Third, they cite human psychophysical masking results from their collaborators (Sebastian et al 2017), but do not cite an equally convincing demonstration that surround contrast creates potent orientation selective masking when presented alone (Petrov et al 2005, https://doi.org/10.1523/JNEUROSCI.2871-05.2005).

At the end of the day, these issues will be resolved by further experiments, not argumentation. The paper stands as an excellent contribution, but it might be wise for the authors to be less doctrinaire in their interpretations.

Reviewer #2 (Public Review):

Summary

In this experiment, Voltage Sensitive Dye Imaging (VSDI) was used to measure neural activity in macaque primary visual cortex in monkeys trained to detect an oriented grating target that was presented either alone or against an oriented mask. Monkeys' ability to detect the target (indicated by a saccade to its location) was impaired by the mask, with the greatest impairment observed when the mask was matched in orientation to the target, as is also the case in human observers. VSDI signals were examined to test the hypothesis that the target-evoked response would be maximally suppressed by the mask when it matched the orientation of the target. In each recording session, fixation trials were used to map out the spatial response profile and orientation domains that would then be used to decode the responses on detection trials. VSDI signals were analyzed at two different scales: a coarse scale of the retinotopic response to the target and a finer scale of orientation domains within the stimulus-evoked response. Responses were recorded in three conditions: target alone, mask alone, and target presented with mask. Analyses were focused on the target evoked response in the presence of the mask, defined to be the difference in response evoked by the mask with target (target present) versus the mask alone (target absent). These were computed across five 50 msec bins (total, 250 msec, which was the duration of the mask (target present trials, 50% of trials) / mask + target (target present trials, 50% of trials). Analyses revealed that in an initial (transient) phase the target evoked response increased with similarity between target and mask orientation. As the authors note, this is surprising given that this was the condition where the mask maximally impaired detection of the target in behavior. Target evoked responses in a later ('sustained') phase fell off with orientation similarity, consistent with the behavioral effect. When analyzed at the coarser scale the target evoked response, integrated over the full 250 msec period showed a very modest dependence on mask orientation. The same pattern held when the data were analyzed on the finer orientation domain scale, with the effect of the mask in the transient phase running counter to the perceptual effect of the mask and the sustained response correlating the perceptual effect. The effect of the mask was more pronounced when analyzed at the scale.

Strengths

The work is on the whole very strong. The experiments are thoughtfully designed, the data collection methods are good, and the results are interesting. The separate analyses of data at a coarse scale that aggregates across orientation domains and a more local scale of orientation domains is a strength and it is reassuring that the effects at the more localized scale are more clearly related to behavior, as one would hope and expect. The results are strengthened by modeling work shown in Figure 8, which provides a sensible account of the population dynamics. The analyses of the relationship between VSDI data and behavior are well thought out and the apparent paradox of the anti-correlation between VSDI and behavior in the initial period of response, followed by a positive correlation in the sustained response period is intriguing.

eLife assessment

This study provides valuable new insights as to how two evolutionary conserved motifs in CD4 contribute to the CD4-mediated enhancement of TCR signaling independently of the CD4-LCK interaction. The data at hand are convincing, even if confined to a cell line model and not substantiated in vivo and with little new mechanistic insight provided regarding the domains of CD4 shown to have significant roles in the signaling process. Without the data from primary cells it is difficult to make statements about the quantitative contribution of LCK-dependent and independent functions of CD4 in TCR signaling.

Author Response

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

Reviewer #1 (Public Review):

Summary:

This study by Lee et al. is a direct follow-up on their previous study that described an evoluBonary conservancy among placental mammals of two moBfs (a transmembrane moBf and a juxtamembrane palmitoylaBon site) in CD4, an anBgen co-receptor, and showed their relevance for T-cell anBgen signaling. In this study, they describe the contribuBon of these two moBfs to the CD4-mediated anBgen signaling in the absence of CD4-LCK binding. Their approach was the comparison of anBgen-induced proximal TCR signaling and distal IL-2 producBon in 58-/- T-cell hybridoma expressing exogenous truncated version of CD4 (without the interacBon with LCK), called T1 with T1 version with the mutaBons in either or both of the conserved moBfs. They show that the T1 CD4 can support signaling to the extend similar to WT CD4, but the mutaBon of the conserved moBfs substanBally reduced the signaling. The authors conclude that the role of these moBfs is independent of the LCK-binding.

Strengths:

The authors convincingly show that T1 CD4, lacking the interacBon with LCK supports the TCR signaling and also that the two studied moBfs have a significant contribuBon to it.

Weaknesses:

The study has several weaknesses.

(1) The whole study is based on a single experimental system, geneBcally modified 58-/- hybridoma. It is unclear at this moment, how the molecular moBfs studied here contribute to the signaling in a real T cell. The evoluBonary conservancy suggests that these moBfs are important for T cell biology. However, the LCK-binding moBf is conserved as well (perhaps even more) and it plays a very minor role in their model. Without verifying their results in primary cells, the quanBtaBve, but even qualitaBve, importance of these moBfs for T-cell signaling and biology is unclear. Although the authors discuss this issue in the Discussion, it should be noted in all important parts of the manuscript, where conclusions are made (abstract, end of introducBon, perhaps also in the Btle) that the results are coming from the hybridoma cells.

We appreciate the Reviewer’s thoughWul comments and suggesBon. We now state in the abstract and introducBon that wet-lab experiments were performed with T cell hybridomas. We have also beXer highlighted work from Killeen and LiXman (PMID: 8355789) wherein they showed that C-terminally truncated CD4, which lacked the moBfs that mediate CD4-Lck interacBons, can drive CD4+ T cell development, proliferaBon, and T-helper funcBon because we now provide mechanisBc data to help explain those in vivo results. Also, as noted by the reviewer, we discuss how the sum of our data provides jusBficaBon for the investment in and use of mouse models to interrogate how the funcBonally important residues/moBfs idenBfied and studied here influence T cell biology.

We will take the opportunity to reiterate here that, while the study is based on a well characterized, albeit single, wet-lab experimental system, the whole study is based on two lines of invesBgaBon. The other approach was a systems biology computaBonal approach that analyzes data from real-world experiments in a variety of jawed vertebrate species over evoluBon. Specifically, we used a computaBonal reconstrucBon of the evoluBonary history of CD4 by performing mulBple analyses of CD4 from 99 jawed vertebrates spanning ~435 million years of evoluBon. This analysis allowed us to idenBfy residues, and networks of evoluBonarily coupled residues, that are predicted to be funcBonally important in vivo. Like other systems biology approaches, this allowed us to look at the larger picture by evaluaBng data points that have emerged from constant tesBng and adjustments of CD4 funcBon in vivo through selecBon on an evoluBonary Bmescale in more jawed vertebrate species, and under more real-world condiBons, than can be tested in the laboratory. Our structure-funcBon analysis provided a second, wet-lab reducBonist experimental system to cross-validate that the residues idenBfied by our evoluBonary analysis are funcBonally significant. This experimental validaBon is criBcal and elevates the relevance of our studies above ad hoc observaBons. Our work also provides mechanisBc insights for why the residues studied here are funcBonally significant (i.e., key determinants of pMHCII-specific signaling iniBaBon). In short, using both systems allowed us to cross-validate the funcBonal significance of the residues within the GGXXG and (C/F)CV+C moBfs studied here by two independent methods.

(2) Many of the experiments lack the negaBve control. I believe that two types of negaBve controls should be included in all experiments. First, hybridoma cells without CD4 (or with CD4 mutant unable to bind MHCII). Second, no pepBde control, i.e., acBvaBon of the hybridoma cells with the APC not loaded with the cognate pepBde. These controls are required to disBnguish the basal levels of phoshorylaBon and CD4-independent anBgen-induced phosphorylaBon to quanBfy, what is the contribuBon of the parBcular moBfs to the CD4-mediated support. Although these controls are included in some of the experiments, they are missing in other ones. The binding mutant appears in some FC results as a horizontal bar (without any error bar/variability), showing that CD4 does not give a huge advantage in these readouts. Why don't the authors show no pepBde controls here as well? Why the primary FC data (histograms) are not shown? Why neither of these two controls is shown for the % of responders plots? Although the IL-2 producBon is a very robust and convincing readout, the phosphoflow is much less sensiBve. It seems that the signaling is elevated only marginally. Without the menBoned controls and showing the raw data, the precise interpretaBon is not possible.

These comments, and those in point #3, concern our flow cytometry-based analysis of early intracellular signaling events where we asked: how do the moBfs under invesBgaBon impact phosphorylaBon of CD3z, ZAP-70, and PLCg1 in response to agonist pMHCII? Thank you for poinBng out areas of confusion regarding these analyses. We will try to clarify here and have worked to clarify the text.

Our approach was to mutate consBtuent residues within the moBfs that our evoluBonary analysis predicted to be funcBonally significant, compare the performance of the mutants to that of controls bearing WT moBfs, and then infer the funcBon of the moBfs based on the differenBal phenotype of the mutants relaBve to their controls. In most cases, the C-terminally truncated CD4-T1 mutant served as the appropriate CD4 control backbone against which to evaluate the phenotypes of the GGXXG and (C/F)CV+C moBf mutants. This is a convenBonal structure-funcBon strategy.

All experiments included APCs expressing null pMHCII (Hb:I-Ek) as negaBve controls. These were a necessary component of the data analysis, explained further below, which involved background subtracBon of the signal from control or mutant T cell hybridomas bound to these negaBve control APCs from those bound to the agonist pMHCII (MCC:I-Ek). Doing so allowed us to establish a true signal over background for calculaBng percent responders and signaling intensity. These negaBve controls served the same purpose of APCs expressing I-Ek not loaded with cognate pepBde requested by the reviewer. It is important to note that we previously published that TCR-CD3-pMHCII interacBons reciprocally increase CD4-pMHCII dwell Bme, and vice versa, such that dwell Bmes of the 5c.c7 TCR and CD4 to the null Hb:I-Ek are both basal in this system relaBve to antagonist, weak agonist, and agonist pMHCII (PMID 29386113). A recent study using different techniques also concluded that TCR-CD3 and CD4 cooperaBvely enhance signaling to pMHCII (PMID 36396644). The use of the null pMHCII, Hb:I-Ek, in each experiment thus serves as a well-characterized negaBve control for both TCR and CD4 engagement in this experimental system with regards to assembly of the TCR-CD3 and CD4 around pMHCII to drive signaling. In our view, it is the most important negaBve control for interpreBng our results, and it is present in each experiment. In Fig 1B and related supplemental figures we compare the Cterminally truncated CD4-T1 mutant to the full-length WT CD4 to evaluate the contribuBons of the intracellular domains to early signaling events. We found no significant differences for pCD3z, pZAP-70, and pPLCg1 levels demonstraBng that, in our system, CD4 WT and T1 are staBsBcally indisBnguishable.

In Fig 1C we asked: what is the contribuBon of CD4-pMHCII interacBons made by CD4 T1, which lacks the intracellular domain, using our CD4 T1Dbind mutant. Fig 2C and Table 3 show that pCD3z levels for T1Dbind were ~54% of T1, meaning that CD4 binding to pMHCII roughly doubles pCD3z levels (even without the intracellular domain). We also showed that the percent of responders were not different between the CD4 T1 and T1Dbind mutant in Fig 2C. The impact on ZAP-70 and PLCg1 are shown in Figure 2—figure supplement 4. These differences, including the magnitude of the decrease, were observed reproducibly (p<0.001) in three independently generated sets of lines. We believe that this analysis saBsfies the request by the reviewer for an analysis of the contribuBons of CD4 binding to pMHCII. We did not include this as a negaBve control in experiments evaluaBng the contribuBons of the GGXXG and (C/F)CV+C moBfs to CD4 T1 signaling because the quesBon being asked in those experiments was how do the moBfs impact signaling in the absence of the intracellular domain (i.e., within the CD4 T1 backbone, making CD4 T1 the proper comparator for the quesBon we were asking). We showed the average normalized intensity for the T1Dbind mutant, relaBve to T1, for this lower bound of signaling mediated by TCR-CD3-only as a doXed line in those figures to provide a reference point for the readers to evaluate and put into perspecBve how the mutants we generated impacted the overall contribuBon of CD4 to these early signaling events. The T1Dbind mutants were not always measured in the same experiment at the same Bme with other mutants, because the cell lines used were not always made at the same Bme, so we did not think it appropriate to graph the results together.

We do not know how to interpret the comment “Although the IL-2 producBon is a very robust and convincing readout, the phosphoflow is much less sensiBve. It seems that the signaling is elevated only marginally.” We will offer our perspecBve that we do not know how to equate the sensiBvity of the phos-flow to the IL-2. Because the IL-2 is a signaling output, it results from signaling amplificaBon from the membrane to the nucleus. If CD3z phosphorylaBon is the iniBaBng event for a signaling cascade that leads to IL-2 gene transcripBon and transducBon, as is widely believed, our data strongly suggests that the ~2-fold difference in pCD3z levels between CD4 T1 and T1Dbind (Fig 2C/Table 3 data) contributes to the difference between no IL-2 output for T1Dbind and IL-2 output by T1 in this experimental system. Because CD4 WT and T1 have significantly different levels of IL-2 output, but show no significant differences in pCD3z, pZAP-70, or pPLCg1 levels, there are likely to be other differences we did not measure via other pathways that intersect at the nucleus. At many levels, biology works on gradients such that small differences can Bp a system in one direcBon or another. The kineBc discriminaBon model (PMID 8643643), which is thought to be a reasonable descripBon of the relaBonship between pMHC engagement and signaling outcomes, suggests that very small differences in molecular interacBons at the earliest stages of a response can lead to big differences in signaling outcome. We therefore have no basis at this juncture to think that ~2-fold differences in pCD3z levels could not account for bigger differences in signaling output such as IL-2.

(3) The processing of the data is not clear. Some of the figures seem to be overprocessed. For instance, I am not sure what "Normalized % responders of pCD3zeta" means (e.g., Fig. 1C and elsewhere)? Why do not the authors show the actual % of pCD3zeta+ cells including the gaBng strategy? Why do the authors subtract the two histograms in Fig. 2- Fig.S3? It is very unusual.

We did develop and implement a novel strategy for measuring the impact of our mutaBons on CD3z, ZAP-70, and PLCg1 phosphorylaBon. This was explained in more detail in our prior study. The instrucBons to authors indicated that we should not repeat methods in the current manuscript. However, we will go through the approach here, and address why we did not show primary FC histograms for all experiments from above. First, we think that a brief explanaBon as to what moBvated us to develop our approach will add to a beXer understanding:

(1) For experimental and staBsBcal rigor, our goal was to perform both experimental and biological replicates by measuring and comparing the average of at least three independently generated sets of paired WT/T1 control Vs. mutant cells lines generated at different Bmes to determine the staBsBcal significance of the difference, if any, between averages of the control and mutant lines.

(2) Our quesBons necessitated that we measure signals generated naturally by the cooperaBve engagement of cognate pMHCII by TCR-CD3 and CD4 on APCs, rather than through aCD3/aCD4 crosslinking.

(3) We chose to use flow cytometry rather than bulk cell analysis by Western Bloung to analyze signaling occurring in cells that were engaged to the agonist APC in order to avoid diluBon of that signal by cells that are not engaged to APCs and not signaling. 4. For each experiment, we wanted to subtract background signals from cells bound to APCs expressing a null pMHCII (Hb:I-Ek) from signals generated by cells bound to APCs expressing agonist pMHCII (MCC:I-Ek). Doing so allowed us to idenBfy cells that are signaling (responders) to agonist over null pMHCII. The goal here was to quanBtate the level of signaling in an objecBve manner with a method that can be applied to all samples uniformly rather than seung a flow cytometry gate on posiBve cells (e.g. pCD3z) because gaBng is subjecBve and can vary from experiment to experiment. To put that another way, as detailed below, we used our subtracBon method to idenBfy signaling responders rather than seung a signaling gate on the posiBve populaBon.

Regarding gaBng schemes, controls, and data processing:

Figure 2—figure supplement 3 of the current study and Figure 6—figure supplement 1 of our prior study are designed to walk the reader through our experimental design, gaBng, data processing and thinking. Here we will provide a detailed explanaBon to complement the figure legend as well as the methods provided in our prior manuscript (see pt #4 below).

We will refer to Figure 2—figure supplement 3 here:

Panel A. The dot plots show our approach to idenBfying 5c.c7+ CD4+ 58a-b- T cell hybridomas (yaxis, GFP posiBve) coupled to M12 cells (x-axis, TagIt Violet) expressing the null pMHCII Hb:I-Ek (lev) or agonist pMHCII MCC:I-Ek (right). The gaBng shows the frequency of GFP+ T cell hybridomas that are bound to TagIt violet posiBve APCs (i.e., cell couples). The histogram on the right then shows the staining intensity for pCD3z on the x-axis for the 10,000 coupled events collected wherein the APCs express the null pMHCII (filled cyan) or the agonist pMHCII (black line).

Panel B. The data presented here is the same as in Panel A, but for CD4 T1 cells.

Panel C. The data presented here walks through how we idenBfy 5c.c7+ CD4+ 58a-b- T cell hybridomas responding (i.e., signaling) to agonist pMHCII, as well as the mean signaling intensity of the responding populaBon, in a gaBng-independent manner aver background subtracBon. For the lev graph, we exported the data for the histograms shown in Panel A from FlowJo 10 sovware and ploXed them here using Prism 9 as smoothed lines (500 nearest neighbors). The cyan line is therefore a replicate of the flow cytometry histogram shown in Panel A for pCD3z intensity from 5c.c7+ CD4+ 58a-b- T cell hybridomas coupled to M12 cells expressing the null pMHCII (Hb:I-Ek), while the black histogram is a replicate of the pCD3z intensity for 5c.c7+ CD4+ 58a-b- T cell hybridomas coupled to M12 cells expressing the agonist pMHCII (MCC:I-Ek). Next, to idenBfy the responding populaBon in a gaBng-independent manner, we used Excel to subtract the pCD3z intensity for the null pMHCII (cyan) negaBve control populaBon on a bin-by-bin bases from the pCD3z intensity for the agonist pMHCII (black) responding populaBon. We then transferred the background subtracted values to Prism 9 for smoothing and ploung (grey line: MCC:I-Ek minus Hb:I-Ek). The middle graph shows the same data processing for the data from Panel B for the CD4 T1 cells. Please note that the background subtracted grey line has negaBve values and posiBve values. The negaBve values represent intensity bins where signaling in response to agonist pMHCII leads to fewer cells per bin than in the null pMHCII populaBon that is not signaling, while the posiBve values represent bins of intensity where signaling cells outnumber non-signaling cells. The right graph in this panel shows the populaBons aver background subtracBon for intensity bins that had more cells with pCD3z signal in the agonist pMHCII populaBon than the null pMHCII populaBon (grey = WT full length CD4 and blue = T1). In short, the right graph shows idenBficaBon of those cells that are signaling in response to agonist pMHCII. This approach miBgated the need for subjecBve gaBng in FlowJo to idenBfy signaling cells (i.e., pCD3z posiBve) and allowed for background subtracBon which could not be done in FlowJo. We used this approach for all analyses of pCD3z, pZAP-70, and pPLCg1 in this study.

The number of cells in these background-subtracted populaBons were divided by 10,000 (the number of events collected and analyzed) to calculate the percent of responding 5c.c7+ CD4+ 58a-b- T cell hybridomas, while the mean fluorescent intensity for the cells within these populaBon represent the signaling intensity.

Panel D. The graph on the lev shows the mean fluorescence intensity (MFI) ± SEM for the posiBve signaling populaBon from the right graph of panel C. We see in this example comparing a WT and T1 cell line, generated at the same Bme from the same parental 58a-b- T cell hybridoma populaBon, that the T1 MFI is significantly greater than the WT. These intensity values represent one of the paired intensity values used in the main Fig 2B (Lev graph), where we show the paired MFI analysis of responding populaBons from 5 independently generated sets of cell lines. Please note that these single MFI values are directly derived from the flow cytometry histograms aver background subtracBon. Figure 2B, and similar figures, therefore equate to a disBllaBon of all of the histograms for the populaBons tested in a manner that we consider easier to digest than either overlaying all histograms or showing mulBple panels individually. It also conserves more space. This is why we only showed representaBve flow cytometry histograms, rather than all histograms.

The graph on the right shows the % responders for the posiBve signaling populaBon from the right graph of panel C. Specifically, the total number of cells that were determined to be signaling in response to agonist pMHCII was divided by 10,000 (the number of coupled cells collected by flow cytometry) to determine the percent responders. These values represent one of five sets of values used to determine the average normalized percent responders (all normalized to WT). There was no significant difference between these two populaBons in terms of percent responders.

Regarding graphing normalized values for the mean MFI for signaling intensity or the percent responders: in our first manuscript, we presented the individual MFI intensity values for matched pairs of cells as well as the actual percent responders per group. The feedback we received from colleagues on this presentaBon was that it was confusing, distracBng, and otherwise hard to digest. It was suggested to us by mulBple individuals that the normalized values would be preferable because it is easier and faster to understand. Upon reflecBon, we agreed with this feedback because the normalized presentaBon with staBsBcs allows for the two key relevant quesBons to be quickly evaluated: 1. Are the mutants different than the control? 2. By how much? We have lev the raw intensity values and well as the normalized intensity values in the version of record. Given the Reviewer’s comments, we have now graphed the average % responders instead of normalized values in the figures, and lev the normalized values in Table 3.

(4) The manuscript lacks Materials and Methods. It only refers to the previous paper, which is very unusual. Although most of the methods are the same, they sBll should be menBoned here. Moreover, some of the mutants presented here were not generated in the previous study, as far as I understand. Perhaps the authors plan to include Materials and Methods during the revision...

Because we submiXed this as a Research Advances arBcle we followed the journal instrucBons to reference the Materials and Methods in our prior publicaBon, upon which this work builds, as the methods used are the same. They are detailed in that study. We have now included a copy of the Materials and Methods for the eLife staff to determine how best to link with this manuscript. We have also included the gene sequences for the novel constructs used in this study. Thank you for poinBng out the omission.

(5) Membrane rafts are a very controversial topic. I recommend the authors stick to the more consensual term "detergent resistant microdomains" in all cases/occurances.

We agree this is a controversial topic with a variety of viewpoints. Because we are not experts in the field of membrane composition, we turned to the literature to inform our view of how best to refer to these membrane subdomains. In our reading, we found a 2006 meeting report from a Keystone symposium on lipid rafts and cell function authored by Linda Pike (PMID 16645198). At this meeting, a central focus was reaching a consensus on how best to refer to these domains. The consensus term agreed upon by this group was “membrane rafts”. Specifically, we will quote from this report published in the Journal of Lipid Research, ‘Together, the discussions permitted the generation of a definition for “lipid rafts” in an ad hoc session on the final day of the meeting. All participants were invited to contribute to this effort, and the work product reflects the consensus of this broad-based group…… First and foremost, the term “lipid raft” was discarded in favor of the term “membrane raft.”’ We chose to use the term “membrane raft” based on this consensus opinion.

(6) Last, but not least, the mechanistic explanation (beyond the independence of LCK binding) of the role of these motifs is very unclear at the moment.

We agree with this comment. One goal in making these results, and those in our prior study, available to the field at large is to provide evidence in support of our view that the dominant paradigm that is thought to explain the earliest events in T cell signaling needs re-evaluating. How T cell signaling is initiated in response to pMHCII is clearly more complex than is currently thought. However, out data is inconsistent with the dominant paradigm in which CD4 recruits Lck to TCR-CD3 to phosphorylate ITAMs to initiate signaling.

Reviewer #2 (Public Review):

Summary:

The paper by Kuhn and colleagues follows upon a 2022 eLife paper in which they identified residues in CD4 constrained by evolutionary purifying selection in placental mammals and then performed functional analyses of these conserved sequences. They showed that sequences distinct from the CXC "clamp" involved in recruitment of Lck have critical roles in TCR signaling, and these include a glycine-rich motif in the transmembrane (TM) domain and the cyscontaining juxtamembrane (JM) motif that undergoes palmitoylation, both of which promote TCR signaling, and a cytoplasmic domain helical motif, also involved in Lck binding, that constrains signaling. Mutations in the transmembrane and juxtamembrane sequences led to reduced proximal signaling and IL-2 production in a hybridoma's response to antigen presentation, despite retention of abundant CD4 association with Lck in the detergent-soluble membrane fraction, presumably mislocalized outside of lipid rafts and distal to the TCR. A major conclusion of that study was that CD4 sequences required for Lck association, including the CXC "clasp" motif, are not as consequential for CD4 co-receptor function in TCR signaling as the conserved TM and JM motifs. However, the experiments did not determine whether the functions of the TM and JM motifs are dependent on the Lck-binding properties of CD4 - the mutations in those motifs could result in free Lck redistributing to associate with CD4 in signaling-incompetent membrane domains or could function independently of CD4-Lck association. The current study addresses this specific question.

Using the same model system as in the earlier eLife paper (the entire methods section is a citation to the earlier paper), the authors show that truncation of the Lck-binding intracellular domain resulted in a moderate reduction in IL-2 response, as previously shown, but there was no apparent effect on proximal phosphorylation events (CD3z, Lck, ZAP70, PLCg1). They then evaluated a series of TM and JM motif mutations in the context of the truncated Lck-nonbinding molecule, and showed that these had substantially impaired co-receptor function in the IL-2 assay and reduced proximal signaling. The proximal signaling could be observed at high ligand density even with a MHC non-binding mutation in CD4, although there was still impaired IL-2 production. This result additionally illustrates that phosphorylation of the proximal signaling molecules is not sufficient to activate IL-2 expression in the context of antigen presentation.

Strengths:

The strength of the paper is the further clear demonstration that the classical model of CD4 coreceptor function (MHCII-binding CD4 bringing Lck to the TCR complex, for phosphorylation of the CD3 chain ITAMs and of the ZAP70 kinase) is not sufficient to explain TCR activation. The data, combined with the earlier eLife paper, further implicate the gly-rich TM sequence and the palmitoylation targets in the JM region as having critical roles in productive co-receptordependent TCR activation.

Weaknesses:

The major weakness of the paper is the lack of mechanistic insight into how the TM and JM motifs function. The new results are largely incremental in light of the earlier paper from this group as well as other literature, cited by the authors, that implicates "free" Lck, not associated with co-receptors, as having the major role in TCR activation. It is clear that the two motifs are important for CD4 function at low pMHCII ligand density. The proposal that they modulate interactions of TCR complex with cholesterol or other membrane lipids is an interesting one, and it would be worth further exploring by employing approaches that alter membrane lipid composition. The JM sequence presumably dictates localization within the membrane, by way of palmitoylation, which may be critical to regulate avidity of the TCR:CD4 complex for pMHCII or TCR complex allosteric effects that influence the activation threshold. Experiments that explore the basis of the mutant phenotype could substantially enhance the impact of this study.

We appreciate these thoughtful comments and suggestions. We will restate what we wrote in our preliminary response to the reviews to explain the scope of the current study:

To address comments about the limited scope of this study and referencing of the Methods secBon to our prior study, we would like to note that we submiXed the current study via the Research Advance mechanism. Our goal was to build upon the conclusions of our 2022 eLife publicaBon (PMID: 35861317) and address an unresolved quesBon from that study (as nicely summarized by Reviewer #2). In the current manuscript we present data from reducBonist experiments that were designed specifically for this purpose and, as noted by the reviewers, we provide answers to the quesBon being asked. We think that the Research Advance mechanism is an ideal opportunity to make these results available to the field given the stated purpose of such arBcles (for reference: “A Research Advance might use a new technique or a different experimental design to generate results that build upon the conclusions of the original research by, for example, providing new mechanis=c insights or extend the pathway under inves=ga=on…”). Now that we have provided evidence that CD4 does not recruit Lck to phosphorylate TCR-CD3 ITAMs in our system, nor do the GGXXG and (C/F)CV+C motifs play a role in enabling CD4 to regulate Lck proximity to TCR-CD3, we agree that it is important to form and test alternative hypotheses for how TCR-CD3 signaling is initiated.

Reviewer #1 (Public Review):

Summary:

This study by Lee et al. is a direct follow-up on their previous study that described an evolutionary conservancy among placental mammals of two motifs (a transmembrane motif and a juxtamembrane palmitoylation site) in CD4, an antigen co-receptor, and showed their relevance for T-cell antigen signaling. In this study, they describe the contribution of these two motifs to the CD4-mediated antigen signaling in the absence of CD4-LCK binding. Their approach was the comparison of antigen-induced proximal TCR signaling and distal IL-2 production in 58-/- T-cell hybridoma expressing exogenous truncated version of CD4 (without the interaction with LCK), called T1 and T1 version with the mutations in either or both of the conserved motifs. They show that the T1 CD4 can support signaling to extend similar to WT CD4, but the mutation of the conserved motifs substantially reduced the signaling. The authors conclude that the role of these motifs is independent of the LCK-binding.

Strengths:

The authors convincingly show that CD4 is capable of contributing to TCR signaling in a manner independent of LCK, but dependent on the two studied motifs in CD4.

Weaknesses:

(1) Experiments in primary T cells are required to estimate the relative contribution of LCK-dependent and LCK-independent mechanisms of CD4 signaling.

(2) The mechanistic explanation (beyond the independence of LCK binding) of the role of these motifs is unclear at the moment.

Reviewer #2 (Public Review):

Summary:

The paper by Kuhn and colleagues follows upon a 2022 eLife paper in which they identified residues in CD4 constrained by evolutionary purifying selection in placental mammals, and then performed functional analyses of these conserved sequences. They showed that sequences distinct from the CXC "clamp" involved in recruitment of Lck have critical roles in TCR signaling, and these include a glycine-rich motif in the transmembrane (TM) domain and the cys-containing juxtamembrane (JM) motif that undergoes palmitylation, both of which promote TCR signaling, and a cytoplasmic domain helical motif, also involved in Lck binding, that constrains signaling. Mutations in the transmembrane and juxtamembrane sequences led to reduced proximal signaling and IL-2 production in a hybridoma's response to antigen presentation, despite retention of abundant CD4 association with Lck in the detergent-soluble membrane fraction, presumably mislocalized outside of lipid rafts and distal to the TCR. A major conclusion of that study was that CD4 sequences required for Lck association, including the CXC "clasp" motif, are not as consequential for CD4 co-receptor function in TCR signaling as the conserved TM and JM motifs. However, the experiments did not determine whether the functions of the TM and JM motifs are dependent on the Lck-binding properties of CD4 - the mutations in those motifs could result in free Lck redistributing to associate with CD4 in signaling-incompetent membrane domains or could function independently of CD4-Lck association. The current study addresses this specific question.

Using the same model system as in the earlier eLife paper (the entire methods section is a citation to the earlier paper), the authors show that truncation of the Lck-binding intracellular domain resulted in a moderate reduction in IL-2 response, as previously shown, but there was no apparent effect on proximal phosphorylation events (CD3z, Lck, ZAP70, PLCg1). They then evaluated a series of TM and JM motif mutations in the context of the truncated Lck-nonbinding molecule and showed that these had substantially impaired co-receptor function in the IL-2 assay and reduced proximal signaling. The proximal signaling could be observed at high ligand density even with a MHC non-binding mutation in CD4, although there was still impaired IL-2 production. This result additionally illustrates that phosphorylation of the proximal signaling molecules is not sufficient to activate IL-2 expression in the context of antigen presentation.

Strengths:

The strength of the paper is the further clear demonstration that the classical model of CD4 co-receptor function (MHCII-binding CD4 bringing Lck to the TCR complex, for phosphorylation of the CD3 chain ITAMs and of the ZAP70 kinase) is not sufficient to explain TCR activation. The data, combined with the earlier eLife paper, further implicate the gly-rich TM sequence and the palmitylation targets in the JM region as having critical roles in productive co-receptor-dependent TCR activation.

Weaknesses:

The major weakness of the paper is the lack of mechanistic insight into how the TM and JM motifs function. The new results are largely incremental in light of the earlier paper from this group as well as other literature, cited by the authors, that implicates "free" Lck, not associated with co-receptors, as having the major role in TCR activation. It is clear that the two motifs are important for CD4 function at low pMHCII ligand density. The proposal that they modulate interactions of TCR complex with cholesterol or other membrane lipids is an interesting one, and it would be worth further exploring by employing approaches that alter membrane lipid composition. The JM sequence presumably dictates localization within the membrane, by way of palmitylation, which may be critical to regulate avidity of the TCR:CD4 complex for pMHCII or TCR complex allosteric effects that influence the activation threshold. Experiments that explore the basis of the mutant phenotype could substantially enhance the impact of this study.

Additional comments:

- Is the "IL-2 sensitivity" measurement for the T1-TP (3C) meaningful (Table 3)? It is showing only a moderate reduction compared to T1 control, while TP (2C) or just the 3C palmitylation mutations essentially eliminate response.

- It is unclear how the pairs of control and mutant cells connected by lines in the figures are related. They are presumably cells from distinct biological experiments, with technical replicates for each, but are they paired because they were derived at the same time with different constructs? This should be explained in this paper, not in a reference.

Author Response

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

We would like to express our sincere appreciation for the invaluable comments provided by the reviewers and their constructive suggestions to enhance the quality of our manuscript. In response to their 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 noteworthy 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 reviewer comments, addressing each point in a meticulous and specific manner. We extend our sincere gratitude to the reviewers for their diligent examination of our manuscript and for offering insightful recommendations.

In our latest revision, we have taken great care to respond to every reviewer's comment, ensuring that we clarify the manuscript and provide robust evidence where required. The primary focus of these revisions was to provide additional context regarding the cooperative role between PR-Set-7 and PARP-1 in the repression of metabolic genes, accompanied by a thorough description of the current state of the field. Substantial modifications and new analyses, presented in the supplemental figures, have been included to comprehensively address this concern.

Another concern raised was regarding the interaction between PARP-1 and mono-methylated active histone marks, which was not adequately described in the previous version of our manuscript. In this revised version, we have updated our Fig. 1 and Supplemental Fig. S1 and introduced Supplemental Fig. S2 to properly demonstrate that PARP-1 binds to all mono-methylated active histone marks tested. Furthermore, we extensively revised the Discussion section of our manuscript to discuss the implications of this discovery and how it fits into the broader context of PARP-1 research.

Addressing another reviewer's concern about the potential indirect regulation of transcription by PARP1 and PR-SET7, we revised the discussion section and incorporated findings from our recent study. These findings clearly demonstrate PARP1's binding to the loci of misregulated genes, suggesting a direct involvement in their regulation.

Furthermore, we have improved the description of the reagents and Drosophila lines used in this study to provide a more comprehensive understanding for readers. Finally, we conducted a comprehensive revision of the entire manuscript to rectify the identified typos and grammatical errors.

Enclosed, you will find a detailed, point-by-point response to each of the reviewer's comments, showcasing our commitment to addressing their concerns with precision.

We firmly believe that our revisions successfully resolve all the concerns raised by the reviewers, and we are confident that this improved version of our manuscript contributes significantly to the scientific discourse.

Reviewer #1:

The study investigates the role of PARP-1 in transcriptional regulation. Biochemical and ChIP-seq analyses demonstrate specific binding of PARP-1 to active histone marks, particularly H4K20me, in polytene chromosomes of Drosophila third instar larvae. Under heat stress conditions, PARP-1's dynamic repositioning from the Hsp70 promoter to its gene body is observed, facilitating gene activation. PARP-1, in conjunction with PR-Set7, plays a crucial role in the activation of Hsp70 and a subset of heat shock genes, coinciding with an increase in H4K20me1 levels at these gene loci. This study proposes that H4K20me1 is a key facilitator of PARP-1 binding and gene regulation. However, there are several critical concerns that are yet to be addressed. The experimental validation and demonstration of results in the main manuscript are scant. Recent developments in the area are omitted, as an important publication hasn't been discussed anywhere in the work (PMID: 36434141). The proposed mechanism operates quite selectively, and any extrapolations require intensive scientific evidence.

Major Comments:

(1) PARP1 hypomorphic mutant validation data must be provided at RNA levels as the authors have mentioned about its global reduction in RNA levels.

We sincerely appreciate Reviewer 1 for their meticulous review of our manuscript and for providing valuable insights. In response to the raised concern, we would like to highlight that the validation data for the PARP1 hypomorphic mutant at the RNA level has been previously documented in our study (PMID: 20371698), where we found that PARP1 RNA level was deeply impacted in parp1C03256. To enhance clarity, we have made corresponding modifications to the Materials and Methods section to explicitly articulate this aspect: parp-1C03256 significantly lowers the level of PARP-1 RNA and protein level (14) but also significantly diminishes the level of pADPr (11).

We hope these revisions effectively address the reviewer's suggestion and contribute to a more comprehensive understanding of our findings.

(2) The authors should provide immunoblot data for global Poly (ADP) ribosylation levels in PARP1 hypomorphic mutant condition as compared to the control. They must also provide the complete details of the mouse anti-pADPr antibody used in their immunoblot in Figure 5B.

We extend our gratitude to Reviewer 1 for drawing attention to aspects requiring further clarification. In response to the inquiry about global Poly (ADP) ribosylation levels in the PARP1 hypomorphic mutant condition, we want to emphasize that our study extensively reported on the diminished levels of pADPr in comparison to the wildtype, as documented in our previous work (PMID: 21444826). To address this, we have incorporated pertinent details in the Materials and Methods section, providing a comprehensive account of our findings. parp-1C03256 significantly lowers the level of PARP-1 RNA and protein level (14) but also significantly diminishes the level of pADPr (11).

Furthermore, in addressing the request for complete details of the mouse anti-pADPr antibody (10H) used in Figure 5B, we have taken steps to enhance transparency. The Materials and Methods section has been revised to incorporate more comprehensive information about the antibody, ensuring a clearer understanding of our experimental procedures. anti-pADPr (Mouse monoclonal, 1:500, 10H - sc-56198, Santa Cruz).

We appreciate the reviewer's diligence in ensuring the robustness of our methodology, and we believe these modifications strengthen the overall quality and transparency of our study.

(3) PR-Set7 mutant validation results should be provided in the main manuscript, as done by the authors using qRT-PCR. Also, immunoblot data for the PR-set7 null condition should be supplemented in the main manuscript as the authors have already mentioned their anti-PR-Set7 (Rabbit, 1:1000, Novus Biologicals, 44710002) antibody in the materials and methods section.

We appreciate Reviewer 1's thorough examination of our manuscript and their constructive feedback. The pr-set7 null mutant has been rigorously characterized in a study conducted by Dr. Ruth Steward's laboratory (PMID: 15681608). Additionally, we employed our PR-SET7 antibody to validate the mutant, and the corresponding data can be found in Supplemental Figure 3. To enhance clarity, we have made necessary modifications to both the results and Materials and Methods sections, providing explicit details on the validation process. Result section: To validate our hypothesis, we initially confirmed that the pr-set720 mutant not only eliminated PR-SET7 RNA and protein but also abrogated H4K20me1 modification (Supplemental Fig.S3).

Material and methods section: The pr-set720 null mutant was validated in (15) and we confirmed that this mutant abolishes PR-SET7 RNA and protein level but also leads to the absence of H4K20me1 (Supplemental Fig. S3).

We believe these revisions address the reviewer's concerns and contribute to a more comprehensive presentation of our study.

(4) The authors have probably missed out on a very important recent report (PMID: 36434141), suggesting the antagonistic nature of the PARP1 and PR-SET7 association. In light of these important observations, the authors must check for the levels of PR-SET7 in PARP1 hypomorphic conditions.

We appreciate the insightful comment from Reviewer 1, drawing our attention to the recent study by Estève et al. (PMID: 36434141) highlighting the potential antagonistic relationship between PARP1 and PR-SET7. To address this important point, we have carefully examined the levels of PR-SET7 in PARP1 hypomorphic conditions.

In response to this concern, we have added two new supplemental figures, Supplemental Fig. S4 and S5, which specifically address the impact of PARP1 deficiency on PR-Set7 expression. These figures clearly demonstrate that there were no significant changes observed in PR-SET7 RNA (Fig. S4) or protein levels (Fig. S5) in the absence of Parp1. This finding supports the conclusion that Parp1 is not directly involved in the regulation of PR-SET7 in Drosophila.

Furthermore, 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.

Additionally, we have included information about the anti-H3 antibody used in Supplemental Fig. S4 in the Materials and Methods section: anti-H3 (Rabbit polyclonal, 1/1000, FL-136 sc-10809 Santa Cruz).

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.

(5) Also, the results of the aforementioned study should be adequately discussed in the present study along with its implications in the same.

We appreciate Reviewer 1's valuable suggestion to discuss the implications of the study by Estève et al. (PMID: 36434141) within the context of our own findings. Estève et al. reported a potential antagonistic relationship between PARP1 and PR-SET7, showing that a decrease in PARP1 proteins leads to an increase in PR-SET7 protein levels. In our investigation, however, we did not observe significant changes in PR-SET7 RNA and protein levels in the parp1C03256 mutant, as demonstrated in the newly added Supplemental Fig. S3 and S4.

We acknowledge the discrepancy between our results and those of Estève et al., and we propose that this difference may be due to distinct experimental approach: Estève et al.'s study focused on mammalian cell populations and in vitro experiments, whereas our investigation employed Drosophila third-instar larvae as the whole organism model. It is plausible that regulatory mechanisms governing PR-SET7 differ between mammals and Drosophila. Another possibility is that PARP-1 may cooperate with PR-SET7 in the context of Drosophila development but could exhibit antagonistic roles against PR-SET7 in specific cell lines and under certain biological or developmental conditions.

In the Discussion section, we have incorporated this information, stating: A recent study demonstrated that in human cells overexpressing PARP-1, PR-SET7/SET8 is degraded (33). This implies that the absence of PARP-1 might lead to increased levels of PR-SET7. However, in our study involving parp-1 mutant in Drosophila third-instar larvae, we observed a slightly different 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 these modifications provide a comprehensive discussion of the observed discrepancies and enhance the overall interpretation of our findings. We hope that these clarifications satisfactorily address the concerns raised by Reviewer 1.

(6) Gene transcriptional activation requires open chromatin and RNA polymerase II binding to the promoter. Since, differentially expressed genes in both PR-Set7 null and PARP1 hypomorph mutants, co-enriched with PARP-1 and H4K20me1 were mainly upregulated, the authors should provide RNA polymerase II occupancy data of these genes via RNA-Pol II ChIP-seq to further attest their claims.

We appreciate the insightful comment from Reviewer 1 regarding the necessity for RNA-polymerase II (PolII) occupancy data to further support our claims on gene transcriptional activation. To address this concern, we conducted an analysis of PolII occupancy around genes co-enriched with PARP-1 and H4K20me1 that are upregulated in both pr-set720 and parp-1C03256 mutants during the third instar larvae stage. The results of this analysis have been included in the newly added supplemental Fig. S5.

Our findings reveal that these upregulated genes exhibit higher PolII occupancy compared to other genes, both at their promoter regions and gene bodies, suggesting heightened activity during third instar larval stage in wild type animals (Supplemental Fig. S6). To further validate these results, we cross-referenced publicly available RNA-seq data at the same developmental stage, confirming that, on average, these upregulated genes display a 40% higher expression compared to other genes (supplemental Fig. S6B).

Moreover, we would like to highlight the consistency of our current findings with our previous study (PMID: 38012002), where we reported the critical involvement of PARP-1 in tempering the expression of active metabolic genes at the end of the third instar larvae. The current data, suggesting a role for PR-SET7 in this regulatory process, adds another layer to our understanding of the nuanced control exerted by PARP-1 on the expression of active metabolic genes during this critical developmental transition.

In light of these results, we have modified the Results section to emphasize these findings: Intriguingly, under wild-type conditions, these genes displayed expression levels approximately 40% higher than the average and demonstrated increased RNA-Polymerase II occupancy both at their promoter regions and gene bodies compared to other genes (supplemental Fig.S6), indicating their high activity in wild type context.

Additionally, we have incorporated this information into the Discussion section to underscore the cooperative role of PARP-1 and PR-SET7 in repressing the expression of active metabolic genes: Notably, genes co-enriched with PARP-1 and H4K20me1, and are upregulated in both parp-1C03256 and pr-set720 mutants, are predominantly metabolic genes exhibiting high expression levels under wild-type conditions and a high occupancy of polymerase II both at their promoter region and gene body (Supplemental Fig. S6). In our previous study, we discovered that PARP-1 plays a crucial role in repressing highly active metabolic genes during the development of Drosophila by binding directly to their loci (34). Also, PARP-1 is required for maintaining optimum glucose and ATP levels at the third-instar larval stage (34). During Drosophila development, repression of metabolic genes is crucial for larval to pupal transition (35, 36). This repression is linked to the reduced energy requirements as the organism prepares for its sedentary pupal stage (35, 37). Notably, we observed that PARP-1 shows a high affinity for binding to the gene bodies of these metabolic genes (34).

Our data indicates that in both parp-1 and pr-set7 mutant animals, there was a preferential repression of metabolic genes at sites where PARP-1 and H4K20me1 are co-bound (Fig.3E), while these metabolic genes are highly active during third-instar larval stage (Supplemental Fig.S6). Thus, we propose that the presence of H4K20me1 may be essential for the binding of PARP-1 at these gene bodies, contributing to their repression. Importantly, this mechanism of gene repression has broader developmental implications. As earlier stated, mutant animals lacking functional PARP-1 and PR-SET7 undergo developmental arrest during larval to pupal transition. This arrest could be directly linked to the disruption of the normal metabolic gene repression during development. Without the repressive action of PARP-1 and PR-SET7, key metabolic processes might remain unchecked, leading to metabolic imbalances that are incompatible with the normal progression to the pupal stage.

Finaly, we have updated the Materials and Methods section to include information about the RNA-seq and PolII ChIP-seq datasets used: GSE15292 (RNA-polymerase II). In addition, we used the Developmental time-course RNA-seq dataset (54), SRP001065.

We believe that these modifications comprehensively address Reviewer 1's concern and provide a more robust foundation for our claims regarding the role of PARP-1 and PR-SET7 in the transcriptional regulation of co-enriched genes during the critical developmental transition.

(7) As discussed in Figure 4, the authors found transcriptional activation of group B genes even after a significant reduction of H3K20me1 in their gene body after heat shock. Given the dynamic equilibrium shift in epigenetic marks that regulate gene expression and their locus-specific transcriptional regulation, the authors should further look for the enrichment of other epigenetic marks and even H4K20me1 specific demethylases such as PHF8 (PMID: 20622854), and their cross-talk with PARP1 to further bridge the missing links of this tale. This will add more depth to this work.

We appreciate the thoughtful input provided by Reviewer 1 and acknowledge the importance of exploring additional epigenetic marks and potential cross-talk association with PARP1 to enhance the depth of our study. Our investigation has primarily focused on the interplay between PR-SET7/H4K20me1 and PARP-1, as evidenced by the colocalization and robust binding affinity observed between PARP-1 and H4K20me1 (Fig 1C, 2B, and 3A). This interaction is particularly noteworthy in the context of regulating specific heat shock genes, as highlighted in Figure 4A. While we recognize the potential significance of examining a broader spectrum of epigenetic marks and considering the involvement of specific demethylases, such as PHF8 (PMID: 20622854), in this regulatory network, our research strategy is intentionally tailored to leverage the unique characteristics of the PR-SET7/H4K20me1 and PARP-1 interplay in Drosophila. A key consideration is the technical advantage afforded by the fact that PR-SET7 is the exclusive methylase responsible for H4K20 in Drosophila (PMID: 15681608), allowing for specific depletion of H4K20me1 without the confounding influence of other methyltransferases.

This specificity is pivotal, especially given the similar developmental arrest patterns observed in both PR-SET7 and PARP-1 mutants. Such parallel phenotypes provide a distinct opportunity to delve deeply into the intricacies of their interaction during organismal development and in response to heat stress. Additionally, the identity of the demethylase for H4K20me1 in Drosophila remains unknown, further underscoring the rationale for our focused approach.

While we acknowledge the broader implications of exploring additional epigenetic marks, we believe that our deliberate focus on the PR-SET7/H4K20me1 and PARP-1 pathway provides a unique and valuable perspective on the regulation of gene expression in Drosophila. We hope that this clarification addresses the concerns raised by Reviewer 1 and conveys the rationale behind our chosen research strategy.

Reviewer #2:

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 histone array experiment in Fig1 strongly suggests that PARP binds to all mono-methylated histone residues (including H3K27, which is not discussed). Phosphorylation of nearby residues sometimes blocks this binding (S10 and T11 modifications block binding to K9me1, and S28P blocks binding to K27me1). However, H3S3P did not block H3K4me1, which may be worth highlighting. The H3K9me2/3 "blocking effect" is not nearly as strong as some of these other modifications, yet the authors chose to focus on it. Rather than focusing on subtle effects and the possibility that PARP "reads" a "histone code," the authors should consider focusing on the simple but dramatic observation that PARP binds pretty much all mono-methylated histone residues. This result is interesting because nucleosome mono-methylation is normally found on nucleosomes with high turnover rates (Chory et al. Mol Cell 2019)- which mostly occurs at promoters and highly transcribed genes. The author's binding experiments could help to partially explain this correlation because PARP could both bind mono-methylated nucleosomes and then further promote their turnover and lower methylation state.

We appreciate the comprehensive review and valuable insights provided. In response to the comments, we have made substantial revisions to address the concerns and enhance the clarity of our findings. In Figure 1B, C, D, F, and G, we have expanded our data presentation to demonstrate PARP-1's binding affinity for H3K27me1. This addition is now incorporated into the revised results section. Additionally, we have updated Supplemental Fig.S1 and introduced new supplemental data (Supplemental Fig.S2) to illustrate the inhibition of PARP-1 binding by H3S10P, H3S28P, and H3T11P. The comprehensive exploration of PARP-1's interaction with mono-methylated histones, as suggested by the reviewer, is now more robustly documented in our revised figures and supplementary materials.

Our Discussion section has been refined to articulate more clearly how PARP-1 may be selectively recruited to active chromatin domains through its interaction with mono-methylated histone marks. We have proposed a model where PARP-1 actively participates in the turnover process, contributing to the maintenance of an active chromatin environment. This proposed mechanism involves 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. The enzymatic action of Poly(ADP)-ribose glycohydrolase (PARG) subsequently cleaves pADPr, allowing for 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 Discussion section is modified a followed: Finaly, highly transcribed genes have been reported to present a high turnover of mono-methylated modifications, maintaining a state of low methylation (50). Then, our findings suggest that PARP-1 might actively participate in the turnover process to uphold an active chromatin environment. The proposed mechanism unfolds as follows: 1) PARP-1 selectively binds to mono-methylated active histone marks associated with highly transcribed genes. 2) Upon activation, PARP-1 undergoes automodification and is subsequently released from chromatin, facilitating the reassembly of nucleosomes carrying the mono-methylated marks. 3) The enzymatic action of Poly(ADP)-ribose glycohydrolase (PARG) cleaves pADPr, allowing for the restoration of PARP-1's binding affinity to mono-methylated active histone marks. This proposed hypothesis aligns cohesively with existing research conducted across various model organisms, including mice, Drosophila, and Humans (7, 23, 29, 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. Further experimental validation is warranted to elucidate the precise details of this proposed mechanism and its implications in the broader context of chromatin dynamics and transcriptional control.

We hope that these revisions address the reviewer's concerns and contribute to the overall strength and clarity of our manuscript.

(2) 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 the metabolic genes that they mention. The authors should consider discussing this possibility.

We hope that these revisions address the reviewer's concerns and contribute to the overall strength and clarity of our manuscript.

We extend our gratitude to Reviewer 2 for their thoughtful consideration of our manuscript and the insightful suggestion. In response to the raised concern regarding the conclusion on Line 251, where we proposed that "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," we acknowledge the alternative possibility suggested by the reviewer. It is plausible that many of the observed gene expression changes are indirect consequences of altered development induced in parp-1 or pr-set7 mutants. For example, PARP-1 could activates a transcription factor that represses the mentioned metabolic genes.

To address this concern, we have revisited our data and incorporated relevant findings from one of our recent studies that utilized a ChIP-seq approach. The results from this study suggest a direct binding of PARP-1 to the loci of metabolic genes, providing support for the notion that PARP-1 may indeed directly regulate their expression (PMID: 37347109). We have updated the Discussion section to reflect this information, aiming to provide a more comprehensive perspective on the potential mechanisms underlying the observed gene expression changes: In our previous study, we discovered that PARP-1 plays a crucial role in repressing highly active metabolic genes during the development of Drosophila by binding directly to their loci (34). Also, PARP-1 is required for maintaining optimum glucose and ATP levels at the third-instar larval stage (34). During Drosophila development, repression of metabolic genes is crucial for larval to pupal transition (35, 36). This repression is linked to the reduced energy requirements as the organism prepares for its sedentary pupal stage (35, 37). Notably, we observed that PARP-1 shows a high affinity for binding to the gene bodies of these metabolic genes (34).

We believe these modifications contribute to a more informed interpretation of our findings.

(3) The section on the 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 thank Reviewer 2 for their valuable comment on our manuscript. We acknowledge your hypothesis suggesting that PARP-1 may induce H3/H4 dissociation from group B genes, potentially leading to a reduction in H4K20me1. However, our findings support a different interpretation.

Our data indicate that while H4K20me1 is present under normal conditions at group B genes, its reduction following heat shock does not appear to hinder PARP-1's role in transcriptional activation (Fig 4A, C and E). We propose that the observed decrease in H4K20me1 might reflect a regulatory shift in chromatin structure that is conducive to transcriptional activation during heat shock, facilitated by PARP-1 independently of sustained H4K20me1 levels at group B genes. Additionally, the literature suggests a dual role for H4K20me1 in gene regulation, from facilitating transcriptional elongation in certain contexts to acting as a repressor in others.

Unlike in group A genes which had low enrichment of H4K20me1 before heat shock (Fig 4B and D), the high enrichment of H4K20me1 in group B genes (Fig 4C and E) could imply a repressive role for this mark prior to heat stress. Thus, in the context of group B genes, it's conceivable that the removal of H4K20me1 might be necessary for their activation during heat stress. Thus, PR-SET7 may possess functions beyond its role as a histone methylase, which are crucial for activating group B genes under heat stress conditions. These functions could include methylation of non-histone substrates and non-catalytic activities.

Furthermore, our analysis of gene expression in pr-set720 and parp-1C03256 mutants indicates that while PARP-1 and H4K20me1 interaction may have overlapping roles in gene regulation, they also possess distinct functions in the modulation of gene expression (Fig 3E). Thus, we propose that the relationship between PR-SET7 and PARP-1 in transcriptional regulation involves a complex regulatory mechanism that extends beyond the presence of H4K20me1.

We modified the discussion section to address this point: 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, 25, 38, 39, 41-45). 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 (46, 47). 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. Understanding the intricate relationship between these molecular players is crucial for elucidating the complexities of gene expression modulation under heat stress conditions.

We hope that this modification will adequately address Reviewer 2 concerns and enhance the clarity of our conclusions.

Reviewer #1 (Recommendations For The Authors):

(1) Please check the entire manuscript for grammatical errors and typos. PR-set7 has been wrongly written as PR-ste7 in quite a few places in the manuscript. Poly (ADP)-ribosylation has been written as poly(ADP-ribosyl)ation in the last result heading. There are more such errors. Please rectify them.

We express our sincere appreciation to Reviewer 1 for their meticulous review of our manuscript, and we acknowledge the importance of ensuring grammatical accuracy and clarity. We have taken your feedback seriously and conducted a comprehensive revision of the entire manuscript to rectify the identified typos and grammatical errors. We hope that these revisions contribute to an improved overall presentation of our research, and we appreciate the reviewer's diligence in ensuring the accuracy of the manuscript.

(2) The authors can also look up publicly available mammalian ChIP-seq data for H4K20me1 and PARP1, in order to further ossify their findings and increase the breadth of their work.

We appreciate the suggestion from Reviewer 1 and have taken steps to further validate and broaden the scope of our findings. Specifically, we compared the distribution of PARP1 and H4K20me1 in Human K562 cells. The results of this analysis revealed a correlation in their distribution, supporting the idea that the observed correlation between PARP-1 and H4K20me1 is not limited to fruit flies. We have incorporated these findings into the Results section and added a new Supplemental Fig. S6 to visually highlight this correlation: Finally, to extend the generalizability of our observations beyond Drosophila, we compared the distribution of PARP1 and H4K20me1 in Human K562 cells. Strikingly, we observed a correlation in their distribution, suggesting that the interplay between PARP-1 and H4K20me1 is not limited to fruit flies (Supplemental Fig. S6).

We believe that this modification addresses Reviewer 1's suggestion by providing additional evidence that supports the broader relevance of our findings beyond the Drosophila model system.

(3) Please discuss in greater detail how the PARP1-H4K20me1 axis orchestrates the repression program (metabolic pathways in this case) with proper references.

We appreciate Reviewer 1's continued engagement with our manuscript and have adjusted the discussion section to provide a more detailed insight into how the PARP1-H4K20me1 axis orchestrates the repression program, particularly focusing on metabolic pathways. The modified discussion section now reads: In our previous study, we discovered that PARP-1 plays a crucial role in repressing highly active metabolic genes during the development of Drosophila by binding directly to their loci (34). Also, PARP-1 is required for maintaining optimum glucose and ATP levels at the third-instar larval stage (34). During Drosophila development, repression of metabolic genes is crucial for larval to pupal transition (35, 36). This repression is linked to the reduced energy requirements as the organism prepares for its sedentary pupal stage (35, 37). Notably, we observed that PARP-1 shows a high affinity for binding to the gene bodies of these metabolic genes (34). Our data indicates that in both parp-1 and pr-set7 mutant animals, there was a preferential repression of metabolic genes at sites where PARP-1 and H4K20me1 are co-bound (Fig.3E), while these metabolic genes are highly active during third-instar larval stage (Supplemental Fig.S6). Thus, we propose that the presence of H4K20me1 may be essential for the binding of PARP-1 at these gene bodies, contributing to their repression. Importantly, this mechanism of gene repression has broader developmental implications. As earlier stated, mutant animals lacking functional PARP-1 and PR-SET7 undergo developmental arrest during larval to pupal transition. This arrest could be directly linked to the disruption of the normal metabolic gene repression during development. Without the repressive action of PARP-1 and PR-SET7, key metabolic processes might remain unchecked, leading to metabolic imbalances that are incompatible with the normal progression to the pupal stage.

We hope these modifications provide a more comprehensive discussion on how the PARP1-H4K20me1 axis influences the repression program, particularly within metabolic pathways, and how this mechanism contributes to the broader context of Drosophila development.

eLife assessment

This valuable study presents convincing evidence for an association between PARP-1 and H4K20me1 in transcriptional regulation, supported by biochemical and ChIP-seq analyses. The work contributes significantly to our understanding of how Parp1 associates with target genes to regulate their expression.

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.

(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.

eLife assessment

This solid study addresses the unresolved question of why many thousands of small-effect loci contribute more to the heritability of a trait than the large-effect lead variants. The authors explore resource competition within the transcriptional machinery as one possible explanation with a simple theoretical model, concluding that the effects of resource competition would be too small to explain the heritability effects. The topic and approximation of the problem are important and offer an intuitive way to think about polygenic variation, but there are concerns on the derivation of the equations with respect to dropping vs. including certain terms that deal inherently with small numbers.

Author Response

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

eLife assessment

This important study addresses the fundamentally unresolved question of why many thousands of small-effect loci contribute more to the heritability of a trait than the large-effect lead variants. The authors explore resource competition within the transcriptional machinery as one possible explanation with a simple theoretical model, concluding that the effects of resource competition would be too small to explain the heritability effects. The topic and approximation of the problem are very timely and offer an intuitive way to think about polygenic variation, but the analysis of the simple model appears to be incomplete, leaving the main claims only partially supported.

We thank eLife for recognizing the importance of our work. We hope the revised manuscript addresses the reviewers’ reservations.

Public Reviews:

Reviewer #1 (Public Review):

This study explores whether the extreme polygenicity of common traits can be explained in part by competition among genes for limiting molecular resources (such as RNA polymerases) involved in gene regulation. The authors hypothesise that such competition would cause the expression levels of all genes that utilise the same molecular resource to be correlated and could thus, in principle, partly explain weak trans-regulatory effects and the observation of highly polygenic architectures of gene expression. They study this hypothesis under a very simple model where the same molecule binds to regulatory elements of a large number m of genes, and conclude that this gives rise to trans-regulatory effects that scale as 1/m, and which may thus be negligible for large m.

We thank the reviewer for their thorough and thoughtful review of our manuscript.

The main limitation of this study lies in the details of the mathematical analysis, which does not adequately account for various small effects, whose magnitude scales inversely with the number m of genes that compete for the limiting molecular resource. In particular, the fraction of "free" molecule (which is unbound to any of the genes) also scales as 1/m, but is not accounted for in the analysis, making it difficult to assess whether the quantitative conclusions are indeed correct.

It is explicitly accounted for in the supplement.

Second, the questions raised in this study are better analysed in the framework of a sensitivity or perturbation analysis, i.e., by asking how changes in expression level or binding affinity at one gene (rather than the total expression level or total binding affinity) affect expression level at other genes. In the context of complex traits, where an increase in gene expression can either increase or decrease the trait, we believe the most important quantity of interest is variation in expression and, therefore, trait variation. Nevertheless, our results do show that the relative change in expression due to competition is also small.

Thus, while the qualitative conclusion that resource competition in itself is unlikely to mediate trans-regulatory effects and explain highly polygenic architectures of gene expression traits probably holds, the mathematical reasoning used to arrive at this conclusion requires more care.

In my opinion, the potential impact of this kind of analysis rests at least partly on the plausibility of the initial hypothesis- namely whether most molecular resources involved in gene regulation are indeed "limiting resources". This is not obvious, and may require a careful assessment of existing evidence, e..g., what is the concentration of bound vs. unbound molecular species (such as RNA polymerases) in various cell types?

We intentionally looked at the most extreme case of extreme resource limitation, and we conclude that since extreme resource limitation is a small effect, the same would be true of weak resource limitation, when unbound molecules play an important role. We put more emphasis on this point in our revised text.

Reviewer #1 (Recommendations For The Authors):

While the main conclusion that resource competition in itself is unlikely to mediate trans effects and explain high levels of polygenicity may well be correct, I am not convinced that the mathematical reasoning presented in support of this conclusion is entirely correct. I will attempt to outline my concerns mainly in the context of section 2, since the arguments in sections 3 and 4 build upon this.

(a) The key assumption underlying the approximations in equations 3, 4, and 5 is that there is very little free polymerase, in other words /_0 is a small quantity. However, the second and third terms that emerge in equation 7 are also small quantities and (as far as I can see) of the same order as /_0. Thus, one cannot simply use equation 4 or 5 as a starting point to derive eq. 7 and should instead use the exact x_i = (g_i [G])/ (1+g_tot [G]), in order to make sure that all (and not just some) terms that are similar in order of magnitude are accounted for in the analysis.

The concentration of free polymerase is marked as [P], and we explicitly assume (just before eq. 2) that [P]<<[P]0 with [P]0 being the overall concentration of polymerase. This is a conservative assumption – we consider extreme resource competition with little free polymerase and since we since only a small effect in this extreme scenario we assume it would be a small effect also for less extreme scenarios. We put more emphasis on this point in our revised text.

More concretely, the difference between the exact x_i = (g_i [G])/ (1+g_tot [G]) and the approximate x_i = (g_i / g_tot) is precisely 1/m (for large m) in the example considered line 246 onwards. Thus, I suspect that the conclusion that Var[x_i] = (1-1/m)Var[g_i] in that example is just an artefact of starting with eqs. 4 and 5. As a sanity check, it may be useful to actually simulate resource competition explicitly (maybe using a deterministic simulation) under the explicit model [PG_i] = g_i [G] and _0 = + Sum[[PG]_i , i=1,m] without making any further approximations to see if perturbations in g_i actually produce Order [1/m] effects in the variance of x_i for the example considered line 246 onwards (this would require simulating with a few different m and plotting Var[x_i] vs. m for example).

The exact equation the reviewer is alluding to describes a scenario of non-extreme resource competition. If g_tot [G]>>1, i.e. if most polymerase is bound to a gene then x_i is equal to g_i/g_tot and this is the scenario we are considering of extreme competition. If g_tot [G]<<1, then x_i=g_i [G] and competition has no effect. While the intermediate case is interesting, we see no reason for the effects to be larger than in the extreme competition case. We have added the results of simulations in the supplement to validate our arguments.

Lines 231-239: Because of the concerns highlighted above and questions about the validity of equation 7, I am not convinced that the interpretations given here and also in section 4 are correct.

(b) Lines 219-230 (including equations 6 and 7): I think to address the question of whether genetic changes in cis-regulatory elements for a given gene have an effect on other genes (under this model of resource competition), it is better to spell out the argument in terms of Var[ dx_i ] rather than Var[x_i], where dx_i is the change in expression level at gene i due to changes at all m genes, dg_i is the change in gene activity due to (genetic) changes in the relevant regulatory elements associated with gene i etc. Var[ dx_i ] can then be expressed as a sum of Var[dg_i], Var[dg_tot] and Cov[d g_i, dg_tot]. However, I suspect that to do this correctly, one should not start with the approximate x_i=g_i/g_tot : see previous comment.

The variance of the deviation from the mean is mathematically identical to the overall variance, Var[ dx_i ]= Var[ x_i ]. Our analysis is therefore equivalent to the suggested analysis.

Somewhere in all of this, there is also an implicit assumption that E[dg_i] is zero, i.e, mutations are as likely to increase as to decrease binding affinities so that one needs to only consider Var[dx_i] and not E[dx_i]; this assumption should be spelled out.

Our results concern the variation around trait means and therefore we have not included a possible mean effect of mutation, which would not affect the results but just shift the mean.

Some minor comments (mostly related to the introduction and general context):

• I think it would be worth connecting more with the literature on molecular competition and gene regulation (see e.g., How Molecular Competition Influences Fluxes in Gene Expression Networks, De Vos et al, Plos One 2011). Even though this literature does not frame questions in terms of "polygenicity of traits", these analyses address the same basic questions: to what extent do perturbations in gene expression at one gene affect other genes, or to what extent is there crosstalk between different genes or pathways?

We have expanded our introduction to refer to De Vos et al, as well as a few other papers we have recently become aware of. (e.g., Jie Lin & Ariel Amir Nature Communications volume 9, Article number: 4496 (2018))

• Lines 88-89: "supports the network component of the model" is a vague phrase that does not convey much. It would be useful to clarify and make this more precise.

We have clarified this phrasing in the text.

• Lines 113-114: In the context of "selective constraint", it may also be worth discussing previous work by one of the authors: "A population genetic interpretation of GWAS findings for human quantitative traits". What implications would stabilizing selection on multiple traits (as opposed to simple purifying selection) have for the distribution of variances across trait loci and the extent to which trait architectures appear to be polygenic?

While most definitely of great interest to some of the authors, the distribution of variance across loci does not affect our results.

References: Barton and Etheridge 2018 in line 54 is not the correct reference; it should be Barton et al 2017 (paper with Amandine Veber). Fisher 1919 in line 52 is actually Fisher 1918. The formatting of references in the next paragraph (and in various other places in the paper) is also a bit unusual, with some authors referred to by their full names and others only by their last. I believe that it may be useful to crosscheck references throughout the paper.

We have crosschecked the references in the paper.

Line 164: Some word appears to be missing here. Maybe bound -> bound to ?

Fixed

Reviewer #2 (Public Review):

The question the authors pose is very simple and yet very important. Does the fact that many genes compete for Pol II to be transcribed explain why so many trans-eQTL contribute to the heritability of complex traits? That is, if a gene uses up a proportion of Pol II, does that in turn affect the transcriptional output of other genes relevant or even irrelevant for the trait in a way that their effect will be captured in a genome-wide association study? If yes, then the large number of genetic effects associated with variation in complex traits can be explained but such trans-propagating has effects on the transcriptional output of many genes.

This is a very timely question given that we still don't understand how, mechanistically, so many genes can be involved in complex traits variation. Their approach to this question is very simple and it is framed in classic enzyme-substrate equations. The authors show that the trans-propagating effect is too small to explain the ~70% of heritability of complex traits that are associated with trans-effects. Their conclusion relies on the comparison of the order of magnitude of a) the quantifiable transcriptional effects due to Pol II competition, and b) the observed percentage of variance explained by trans effects (data coming from Liu et al 2019, from the same lab).

The results shown in this manuscript rule out that competition for limited resources in the cell (not restricted to Pol II, but applicable to any other cellular resource like ribosomes, etc) could explain the heritability of complex traits.

We thanked the Reviewer for his resounding support of our paper!

Reviewer #2 (Recommendations For The Authors):

The authors rely on simulated data, and although the conclusions hold in a biologically-realistic scenario given the big difference in effect sizes, I wonder if the authors could provide data from the literature (if available) that give the reader a point of reference for the steady state of cells in terms of free/occupied Pol II molecules and/or free/occupied transcription binding sites. This information won't change the conclusion of the manuscript, but it will put it in the context of real biological data.

We have scoured the literature, but have not found readily available data with which to validate our results (beyond that which is already referenced).

Reviewer #3 (Public Review):

Human complex traits including common diseases are highly polygenic (influenced by thousands of loci). This observation is in need of an explanation. The authors of this manuscript propose a model that competition for a single global resource (such as RNA polymerase II) may lead to a highly polygenic architecture of traits. Following an analytical examination, the authors reject their hypothesis. This work is of clear interest to the field. It remains to be seen if the model covers the variety of possible competition models.

We thank the Reviewer for his assessment, support and comments.

Reviewer #3 (Recommendations For The Authors):

This manuscript provides a straightforward and elegant quantitative argument that the competition for the RNA polymerase is not a significant source of trans-eQTLs and, more generally, of genetic variance of complex polygenic phenotypes. This is an unusual manuscript because the authors propose a hypothesis that they confidently reject based on a calculation. This negative result is intuitive. Still, the manuscript is of interest. Progress in understanding the highly polygenic architecture of complex traits is welcome, and the resource competition hypothesis is quite natural. I have three specific comments/concerns listed below.

(1) The manuscripts states that V(x_i)=V(g_i/g_tot). Unless I am missing something, this seems to result from a very strong implicit assumption that all genetic variance is due to variation in the binding of RNA polymerase, while x_i_max is a constant. I would expect that x_i_max may also be genetically variable due to many effects unrelated to the Pol II binding (e.g. transcription rate, bursting, presence of R-loops etc.). I guess that the assumption made by the authors is conservative.

Indeed. We made conservative assumptions throughout, aiming to consider the most extreme scenario in which resource competition may affect trait variation. Our logic being that if even under the most extreme scenario resource competition is a small effect then it is a small effect in all scenarios. We put more emphasis on this point in our revised text.

(2) The manuscript focuses on the competition for RNA polymerase but suggests that the lesson learned is highly generalizable. However, it is an example of a single global limiting resource resulting in first-order kinetics. What happens in a realistic scenario of competition for multiple resources associated with transcription and with downstream processes (free ribonucleotides, spliceosome, polyadenylation machinery, ribosome, post-translational modifications)? It is possible that in most cases a single resource is a limiting factor, but an investigation (or even a brief discussion) of this question would support the claim that the results are generalizable.

We expect competition for multiple resource to result in similarly weak effects. Since there is not a great number of such resources, we do not expect it to change our qualitative result. We added language to that effect in the main text.

(3) Alternatively, what happens in a scenario of competition for multiple local resources shared by a few genes (co-factors, substrates, chaperones, micro-RNAs, post-translational modification factors such as kinases, degradation factors, scaffolding proteins)? In this case, each gene would compete for resources with a few other genes increasing polygenicity without a global competition with all other genes. Intuitively, a large set of such local competitions may lead to a highly polygenic architecture.

This is indeed a scenario in which competition may be a large effect which we mention in our discussion. “the conclusions may differ in contexts where a very small number of genes compete for a highly limited resource, such as access to a particular molecular transporter”

Reviewer #1 (Public Review):

This study explores whether the extreme polygenicity of common traits (the fact that variation in such traits is explained by a very large number of genetic variants) could be explained in part by competition among genes for limiting molecular resources involved in gene regulation, which would cause the expression of most genes to be correlated. While the hypothesis is interesting, I still have some concerns about the analysis and interpretation.

As the authors say in their rebuttal, assuming extreme resource limitation, i.e., going from equation 2 to 5 essentially assumes assuming that 1/(gtot [G] ) <<1 and that terms that are order [ 1/(gtot [G] ) ] can neglected. However, then the authors derive so-called resource competition terms that are order (1/m) where m is the number of genes, so that gtot is proportional to m. My main criticism (which I am not sure was addressed) is thus: can we reliably derive small order (1/m) effects while neglecting order [ 1/(gtot [G] ) ] terms, when both are presumably similar in order of magnitude? Is this mathematically sound?

I do not think the supplement that the authors have added actually gets to this. For example, section 7.1 just gives the textbook derivation of Michelis-Menten kinetics, and does not address my earlier criticism that the terms neglected in going from eq. 16 to eq. 17 (or from eq. 2 to 3) may be similar in magnitude to the terms being derived and interpreted in eqs. 6 and 7.<br /> Similarly, it is unclear from section 7.2 how the authors are doing the simulations. Are these true Michelis-Menten simulations involving equation 2? If yes, then what is the value of [G] and [P_0] in the simulations? If these are not true Michelis-Menten simulations, but instead something that already uses equation 5, then this still does not address my earlier criticism.

Reviewer #2 (Public Review):

The question the authors pose is very simple, and yet very important. Does the fact that many genes compete for Pol II to be transcribed explain why so many trans-eQTL contribute to the heritability of complex traits? That is, if a gene uses up a proportion of Pol II, does that in turn affect the transcriptional output of other genes relevant or even irrelevant for the trait in a way that their effect will be captured in a genome-wide association study? If yes, then the large number of genetic effects associated with variation in complex traits can be explained but such trans-propagating effects on transcriptional output of many genes.

This is a very timely question given that we still don't understand how, mechanistically, so many genes can be involved in complex traits variation. Their approach to this question is very simple and it is framed in classic enzyme-substrate equations. The authors show that the trans-propagating effect is too small to explain the ~70% of heritability of complex traits that is associated with trans-effects. Their conclusion relies on the comparison of the order of magnitude of a) the quantifiable transcriptional effects due to Pol II competition, and b) the observed percentage of variance explained by trans effects (data coming from Liu et al 2019, from the same lab).

The results shown in this manuscript rule out that competition for limiting resources in the cell (not restricted to Pol II, but applicable to any other cellular resource like ribosomes, etc) could explain heritability of complex traits.

Reviewer #3 (Public Review):

Human complex traits including common diseases are highly polygenic (influenced by thousands of loci). This observation is in need of an explanation. The authors of this manuscript propose a model that a competition for a single global resource (such as RNA polymerase II) may lead to a highly polygenic architecture of traits. Following an analytical examination the authors reject their hypothesis. This work is of clear interest to the field. It remains to be seen if the model covers the variety of possible competition models.

eLife assessment

This important study examines the ancestral function of Hippo pathway kinases in contractility and cell density in the ameboid organism Capsaspora owczarzaki, a unicellular animal that is a close relative of multicellular animals. There is convincing evidence for Hippo kinases regulating contractility and cell density but not proliferation in C. owczarzaki. The work complements previous work on the Hippo effector Yorkie homolog in this species, although the unavailability of extensive genetic tools in this species precludes informative epistasis experiments. The work would be of interest to evolutionary and developmental biologists.

Reviewer #3 (Public Review):

Summary:

The authors aimed to study the activation of gliogenesis and the role of newborn astrocytes in a post-ischemic scenario. Combining immunofluorescence, BrdU-tracing, and genetic cellular labelling, they tracked the migration of newborn astrocytes (expressing Thbs4) and found that Thbs4-positive astrocytes modulate the extracellular matrix at the lesion border by synthesis but also degradation of hyaluronan. Their results point to a relevant function of SVZ newborn astrocytes in the modulation of the glial scar after brain ischemia. This work's major strength is the fact that it is tackling the function of SVZ newborn astrocytes, whose role is undisclosed so far.

Strengths:

The article is innovative, of good quality, and clearly written, with properly described Materials and Methods, data analysis, and presentation. In general, the methods are designed properly to answer the main question of the authors, being a major strength. Interpretation of the data is also in general well done, with results supporting the main conclusions of this article.

Weaknesses:

However, there are some points of this article that still need clarification to further improve this work.

- As a first general comment, is it possible that the increase in Thbs4-positive astrocytes can also happen locally close to the glia scar, through the proliferation of local astrocytes or even from local astrocytes at the SVZ? As it was shown in published articles most of the newborn astrocytes in the adult brain actually derive from proliferating astrocytes, and a smaller percentage is derived from NSCs. How can the authors rule out a contribution of local astrocytes to the increase of Thbs4-positive astrocytes? The authors also observed that only about one-third of the astrocytes in the glial scar derived from the SVZ.

- It is known that the local, GFAP-reactive astrocytes at the scar can form the required ECM. The authors propose a role of Thbs4-positive astrocytes in the modulation, and perhaps maintenance, of the ECM at the scar, thus participating in scar formation likewise. So, this means that the function of newborn astrocytes is only to help the local astrocytes in the scar formation and thus contribute to tissue regeneration. Why do we need specifically the Thbs4-positive astrocytes migrating from the SVZ to help the local astrocytes? Can you discuss this further?

- The authors observed that the number of BrdU- and DCX-positive cells decreased 15 dpi in all OB layers (Fig. S5). They further suggest that ischemia-induced a change in the neuroblasts ectopic migratory pathway, depriving the OB layers of the SVZ newborn neurons. Are the authors suggesting that these BrdU/DCX-positive cells now migrate also to the ischemic scar, or do they die? In fact, they see an increase in caspase-3 positive cells in the SVZ after ischemia, but they do not analyse which type of cells are dying. Alternatively, is there a change in the fate of the cells, and astrogliogenesis is increased at the expense of neurogenesis? The authors should understand which cells are Cleaved-caspase-3 positive at the SVZ and clarify if there is a change in cell fate. Also please clarify what happens to the BrdU/DCX-positive cells that are born at the SVZ but do not migrate properly to the OB layers.

- The authors showed decreased Nestin protein levels at 15 dpi by western blot and immunostaining shows a decrease already at 7div (Figure 2). These results mean that there is at least a transient depletion of NSCs due to the promotion of astrogliogenesis. However, the authors show that at 30dpi there is an increase of slow proliferating NSCs (Figure 3). Does this mean, that there is a reestablishment of the SVZ cytogenic process? How does it happen, more specifically, how NSCs number is promoted at 30dpi? Please explain how are the NSCs modulated throughout time after ischemia induction and its impact on the cytogenic process.

- The authors performed a classification of Thbs4-positive cells in the SVZ according to their morphology. This should be confirmed with markers expressed by each of the cell subtypes.

- In Figure S6, the authors quantified HABP spots inside Thbs4-positive astrocytes. Please show a higher magnification picture to show how this quantification was done.

eLife assessment

The authors analyze the role of newborn astrocytes in the modulation of glial scar formation in a middle carotid artery occlusion (MCAO) hypoxia lesion model. The findings are valuable because they have implications for understanding the molecular and cellular processes contributing to brain repair in response to ischemia. The results are currently incomplete, in the absence of data showing: (i) cell-target specificity of molecular markers for newborn astrocytes; (ii) consistent phenomena across different rodent species.

Reviewer #1 (Public Review):

Summary:

The authors show that SVZ-derived astrocytes respond to a middle carotid artery occlusion (MCAO) hypoxia lesion by secreting and modulating hyaluronan at the edge of the lesion (penumbra) and that hyaluronan is a chemoattractant to SVZ astrocytes. They use lineage tracing of SVZ cells to determine their origin. They also find that SVZ-derived astrocytes express Thbs-4 but astrocytes at the MCAO-induced scar do not. Also, they demonstrate that decreased HA in the SVZ is correlated with gliogenesis. While much of the paper is descriptive/correlative they do overexpress Hyaluronan synthase 2 via viral vectors and show this is sufficient to recruit astrocytes to the injury. Interestingly, astrocytes preferred to migrate to the MCAO than to the region of overexpressed HAS2.

Strengths:

The field has largely ignored the gliogenic response of the SVZ, especially with regard to astrocytic function. These cells and especially newborn cells may provide support for regeneration. Emigrated cells from the SVZ have been shown to be neuroprotective via creating pro-survival environments, but their expression and deposition of beneficial extracellular matrix molecules are poorly understood. Therefore, this study is timely and important. The paper is very well written and the flow of results is logical.

Weaknesses:

The main problem is that they do not show that Hyaluronan is necessary for SVZ astrogenesis and or migration to MCAO lesions. Such loss of function studies have been carried out by studies they cite (e.g. Girard et al., 2014 and Benner et al., 2013). Similar approaches seem to be necessary in this work.

Major points:

(1) How good of a marker for newborn astrocytes is Thbs4? Did you co-label with B cell markers like EGFr? Is the Thbs4 gene expressed in B cells? Do scRNAseq papers show it is expressed in B cells? Are they B1 or B2 cells?

(2) It is curious that there was no increase in Type C cells after MCAO - do the authors propose a direct NSC-astrocyte differentiation?

(3) The paper would be strengthened with orthogonal views of z projections to show co-localization.

(4) It is not clear why the dorsal SVZ is analysed and focused on in Figure 4. This region emanates from the developmental pallium (cerebral cortex anlagen). It generates some excitatory neurons early postnatally and is thought to have differential signalling such as Wnt (Raineteau group).

(5) Several of the images show the lesion and penumbra as being quite close to the SVZ. Did any of the lesions contact the SVZ? If so, I would strongly recommend excluding them from the analysis as such contact is known to hyperactivate the SVZ.

(6) The authors switch to a rat in vitro analysis towards the end of the study. This needs to be better justified. How similar are the molecules involved between mouse and rat?

(7) Similar comment for overexpression of naked mole rat HA.

Reviewer #2 (Public Review):

Summary:

In their manuscript, Ardaya et al have addressed the impact of ischemia-induced gliogenesis from the adult SVZ and their effect on the remodeling of the extracellular matrix (ECM) in the glial scar. They use Thbs4, a marker previously identified to be expressed in astrocytes of the SVZ, to understand its role in ischemia-induced gliogenesis. First, the authors show that Thbs4 is expressed in the SVZ and that its expression levels increase upon ischemia. Next, they claim that ischemia induces the generation of newborn astrocyte from SVZ neural stem cells (NSCs), which migrate toward the ischemic regions to accumulate at the glial scar. Thbs4-expressing astrocytes are recruited to the lesion by Hyaluronan where they modulate ECM homeostasis.

Strengths:

The findings of these studies are in principle interesting and the experiments are in principle good.

Weaknesses:

The manuscript suffers from an evident lack of clarity and precision in regard to their findings and their interpretation.

eLife assessment:

This valuable work substantially advances our understanding of the organization of neural dynamics relative to behavioral outputs within recurrent neural networks, with implications for biological neural networks. Evidence supporting the conclusions is convincing, with rigorous analyses of neural variance and robustness to noise. The work will be of broad interest to neuroscientists studying computation through dynamics.

Reviewer #1 (Public Review):

Summary:

In this work, the authors utilize recurrent neural networks (RNNs) to explore the question of when and how neural dynamics and the network's output are related from a geometrical point of view. The authors found that RNNs operate between two extremes: an 'aligned' regime in which the weights and the largest PCs are strongly correlated and an 'oblique' regime where the output weights and the largest PCs are poorly correlated. Large output weights led to oblique dynamics, and small output weights to aligned dynamics. This feature impacts whether networks are robust to perturbation along output directions. Results were linked to experimental data by showing that these different regimes can be identified in neural recordings from several experiments.

Strengths:

A diverse set of relevant tasks.

A well-chosen similarity measure.

Exploration of various hyperparameter settings.

Weaknesses:

One of the major connections found BCI data with neural variance aligned to the outputs. Maybe I was confused about something, but doesn't this have to be the case based on the design of the experiment? The outputs of the BCI are chosen to align with the largest principal components of the data.

Proposed experiments may have already been done (new neural activity patterns emerge with long-term learning, Oby et al. 2019). My understanding of these results is that activity moved to be aligned as the manifold changed, but more analyses could be done to more fully understand the relationship between those experiments and this work.

Analysis of networks was thorough, but connections to neural data were weak. I am thoroughly convinced of the reported effect of large or small output weights in networks. I also think this framing could aid in future studies of interactions between brain regions.

This is an interesting framing to consider the relationship between upstream activity and downstream outputs. As more labs record from several brain regions simultaneously, this work will provide an important theoretical framework for thinking about the relative geometries of neural representations between brain regions.

It will be interesting to compare the relationship between geometries of representations and neural dynamics across connected different brain areas that are closer to the periphery vs. more central.

It is exciting to think about the versatility of the oblique regime for shared representations and network dynamics across different computations.

The versatility of the oblique regime could lead to differences between subjects in neural data.

Reviewer #2 (Public Review):

Summary:

This paper tackles the problem of understanding when the dynamics of neural population activity do and do not align with some target output, such as an arm movement. The authors develop a theoretical framework based on RNNs showing that an alignment of neural dynamics to output can be simply controlled by the magnitude of the read-out weight vector while the RNN is being trained. Small magnitude vectors result in aligned dynamics, where low-dimensional neural activity recapitulates the target; large magnitude vectors result in "oblique" dynamics, where encoding is spread across many dimensions. The paper further explores how the aligned and oblique regimes differ, in particular, that the oblique regime allows degenerate solutions for the same target output.

Strengths:

- A really interesting new idea that different dynamics of neural circuits can arise simply from the initial magnitude of the output weight vector: once written out (Eq 3) it becomes obvious, which I take as the mark of a genuinely insightful idea.

- The offered framework potentially unifies a collection of separate experimental results and ideas, largely from studies of the motor cortex in primates: the idea that much of the ongoing dynamics do not encode movement parameters; the existence of the "null space" of preparatory activity; and that ongoing dynamics of the motor cortex can rotate in the same direction even when the arm movement is rotating in opposite directions.

- The main text is well written, with a wide-ranging set of key results synthesised and illustrated well and concisely.

- The study shows that the occurrence of the aligned and oblique regimes generalises across a range of simulated behavioural tasks.

- A deep analytical investigation of when the regimes occur and how they evolve over training.

- The study shows where the oblique regime may be advantageous: allows multiple solutions to the same problem; and differs in sensitivity to perturbation and noise.

- An insightful corollary result that noise in training is needed to obtain the oblique regime.

- Tests whether the aligned and oblique regimes can be seen in neural recordings from primate cortex in a range of motor control tasks.

Weaknesses:

- The magnitude of the output weights is initially discussed as being fixed, and as far as I can tell all analytical results (sections 4.6-4.9) also assume this. But in all trained models that make up the bulk of the results (Figures 3-6) all three weight vectors/matrices (input, recurrent, and output) are trained by gradient descent. It would be good to see an explanation or results offered in the main text as to why the training always ends up in the same mapping (small->aligned; large->oblique) when it could, for example, optimise the output weights instead, which is the usual target (e.g. Sussillo & Abbott 2009 Neuron).

- It is unclear what it means for neural activity to be "aligned" for target outputs that are not continuous time-series, such as the 1D or 2D oscillations used to illustrate most points here. Two of the modelled tasks have binary outputs; one has a 3-element binary vector.

- It is unclear what criteria are used to assign the analysed neural data to the oblique or aligned regimes of dynamics.

eLife assessment

This work presents a valuable data-driven method to extract the "true" synaptic plasticity rule (or learning rule) operating in a neural circuit from empirical measurements of neural activity. The approach aims to train a generative adversarial network (GAN) to match neural activity measurements in terms of statistics, learning them from the data, rather than being pre-determined by the experimenter. The main conclusion is that the extracted learning rules are not unique, but rather degenerate, meaning that multiple plasticity rules can produce the same neural activity. Although the paper presents a thorough investigation using one learning rule as a case study (the Oja rule), the evidence that the results can be inferred beyond the specific numerical experiments presented in the paper is incomplete.

Reviewer #1 (Public Review):

Summary:

The pioneering work of Eve Marder on central pattern generators in the stomatogastric ganglion (STG) has made a strong case for redundancy as a biological mechanism for ensuring functional robustness, where multiple configurations of biophysical parameters are equivalent in terms of their ability to generate desired patterns of periodic circuit activity. In parallel, normative theories of synaptic plasticity have argued for functional equivalences between learning objectives and corresponding plasticity rules in implementing simple unsupervised learning (see Brito & Gerstner 2016, although similar arguments have been made long before e.g. in Aapo Hyvarinen's ICA book). This manuscript argues that similar notions of redundancy need to be taken into account in the study of synaptic plasticity rules in the brain, more specifically in the context of data-driven approaches to extract the "true" synaptic plasticity rule operating in a neural circuit from neural activity recordings. Concretely, the modeling approach takes a set of empirical measurements of the evolution of neural activity and trains a flexibly parametrized model to match that in statistical terms. Instead of being predefined by the experimenter, the features that determine this match are themselves extracted from data using a generative adversarial network framework (GAN). They show that the flexible models manage to reproduce the neural activity to a reasonable degree (though not perfectly), but lead to very different synaptic trajectories.

Strengths:

The idea of learning rule redundancy is a good one, and the use of GANs for the learning rule estimation is a good complement to other data-driven approaches to extract synaptic plasticity ruled from neural data.

Weaknesses:

(1) Numerics provide only partial support to the statements describing the results.

(2) Even if believing the results, I don't necessarily agree with the interpretation. First: unlike the Marder example where there is complementary evidence to argue that the parameter variations actually reflect across animal biophysical variations, here the statements are really about uncertainty that the experimenter has about what is going on in a circuit observed through a certain measurement lens. Second, while taking into account this uncertainty when using the outcomes of this analysis for subsequent scientific goals is certainly sensible, the biggest punchline for me is that simply observing neural activity in a simple and very restricted context does not provide enough information about the underlying learning mechanism, especially when the hypothesis space is very large (as is the case for the MLP). So it seems more useful to use this framework to think about how to enrich the experimental design/ learning paradigms/ or the measurements themselves to make the set of hypotheses more discriminable (in the spirit of the work by Jacob Portes et al, 2022 for instance). Conversely, one should perhaps think about other ways in which to use other forms of experimental data to reasonably constrain the hypothesis space in the first place.

Reviewer #2 (Public Review):

Summary:

This paper poses the interesting and important question of whether plasticity rules are mathematically degenerate, which would mean that multiple plasticity rules can give rise to the same changes in neural activity. They claim that the answer is "yes," which would have major implications for many researchers studying the biological mechanisms of learning and memory. Unfortunately, I found the evidence for the claim to be weak and confusing, and I don't think that readers can currently infer much beyond the results of the specific numerical experiments reported in the paper.

Strengths:

I love the premise of the paper. I agree with the authors that neuroscientists often under-emphasize the range of possible models that are consistent with empirical findings and/or theoretical demands. I like their proposal that the field is shifting its thinking towards characterizing the space of plasticity rules. I do not doubt the accuracy of most reported numerical results, just their meaning and interpretation. I therefore think that readers can safely use most of the the numerical results to revise their thinking about plasticity mechanisms and draw their own conclusions.

Weaknesses:

Unfortunately, I found many aspects of the paper to be problematic. As a result, I did not find the overarching conclusions drawn by the authors to be convincing.

First, the authors aren't consistent in how they mathematically define and conceptually interpret the "degeneracy" of plasticity mechanisms. In practice, they say that two plasticity mechanisms are "degenerate" if they can't build a neural network to distinguish between a set of neural trajectories generated by them. Their interpretation extrapolates far beyond this, and they seem to conclude that such plasticity rules are in principle indistinguishable. I think that this conclusion is wrong. Plasticity rules are simply mathematical functions that specify how the magnitude of a synaptic weight changes due to other factors, here presynaptic activity (x), postsynaptic activity (y), and the current value of the weight (w). Centuries-old mathematics proves that very broad classes of functions can be parameterized in a variety of non-degenerate ways (e.g., by their Taylor series or Fourier series). It seems unlikely to me that biology has developed plasticity rules that fall outside this broad class. Moreover, the paper's numerical results are all for Oja's plasticity rule, which is a third-order polynomial function of x, y, and w. That polynomial functions cannot be represented by any other Taylor series is a textbook result from calculus. One might wonder if this unique parameterization is somehow lost when many synapses combine to produce neural activity, but the neuron model used in this work is linear, so the function that specifies how the postsynaptic activity changes is simply a fourth-order polynomial in 3N+1 variables (i.e., the presynaptic activities of N neurons prior to the plasticity event, the weights of N synapses prior to the plasticity event, the postsynaptic activity prior to the plasticity event, the presynaptic activities of N neurons after the plasticity event). The same fundamental results from calculus apply to the weight trajectories and the activity trajectories, and a non-degenerate plasticity rule could in principle be inferred from either. What the authors instead show is that their simulated datasets, chosen parameterizations for the plasticity rule, and fitting procedures fail to reveal a non-degenerate representation of the plasticity rule. To what extent this failure is due to the nature of the simulated datasets (e.g., their limited size), the chosen parameterization (e.g., an overparameterized multi-layer perceptron), and their fitting procedure (e.g., their generative adversarial network framework) is unclear. I suspect that all three aspects contribute.

Second, I am concerned by the authors' decision to use a generative adversarial network (GAN) to fit the plasticity rule. Practically speaking, the quality of the fits shown in the figures seems unimpressive to me, and I am left wondering if the authors could have gotten better fits with other fitting routines. For example, other authors fit plasticity rules through gradient descent learning, and these authors claimed to accurately recover Oja's rule and other plasticity rules (Mehta et al., "Model-based inference of synaptic plasticity rules," bioRxiv, 2023). Whether this difference is one of author interpretation or method accuracy is not currently clear. The authors do include some panels in Figure 3A and Figure 8 that explore more standard gradient descent learning, but their networks don't seem to be well-trained. Theoretically speaking, Eqn. (7) in Section 4.4 indicates that the authors only try to match p(\vec y) between the data and generator network, rather than p(\vec x, \vec y). If this equation is an accurate representation of the authors' method, then the claimed "degeneracy" of the learning rule may simply mean that many different joint distributions for \vec x and \vec y can produce the same marginal distribution for \vec y. This is true, but then the "degeneracy" reported in the paper is due to hidden presynaptic variables. I don't think that most readers would expect that learning rules could be inferred by measuring postsynaptic activity alone.

Third, it's important for readers to note that the 2-dimensional dynamical systems representations shown in figures like Figures 2E are incomplete. Learning rules are N-dimensional nonlinear dynamical systems. The learning rule of any individual synapse depends only on the current presynaptic activity, the current postsynaptic activity, and the current weight magnitude, and slices through this function are shown in figures like Figure 2D. However, the postsynaptic activity is itself a dynamical variable that depends on all N synaptic weights. It's therefore unclear how one is supposed to interpret figures like Figure 2E, because the change in y is not a function of y and any single w. My best guess is that figures like Figure 2E are generated for the case of a single presynaptic neuron, but the degeneracies observed in this reduced system need not match those found when fitting the larger network.

Reviewer #3 (Public Review):

Summary:

The authors show that a GAN can learn to reproduce the distribution of outputs of a neuron endowed with Oja's plasticity rule throughout its learning process by learning a plasticity rule. The GAN does not, however, learn Oja's rule. Indeed, the plasticity dynamics it infers can differ dramatically from the true dynamics. The authors propose this approach as a way to uncover families of putative plasticity rules consistent with observed activity patterns in biological systems.

Oja's rule was a great choice for the comparison because it makes explicit, I think, the limitations of this approach. As is well known, Oja's rule allows a (linear) neuron to learn the first principal component of its inputs; the synaptic weights converge to the first eigenvector of the input covariance. After this learning process, the response of a neuron to a particular input sample measures the weighted angle between that input and that principal component.

The other, meta-learned plasticity rules that the authors' GAN uncovers notably do not learn the same computation as Oja's rule (Figure 2). This is, to me, the central finding of the paper and fleshed out nicely. It seems to me that this may be because the objective of the GAN is only to reproduce the marginal output statistics of the neuron. It is, if I understand correctly, blind to the input samples, the inputs' marginal statistics, and to correlations between the input and output. I wonder if a GAN that also had some knowledge of the correlation between input and outputs might be more successful at learning the underlying true dynamics.

The focus on reproducing output statistics has some similarity to some types of experiments (e.g., in vivo recordings) but also seems willfully blind to other aspects of these experiments. In my experience, experimentalists are well aware that the circuits they record receive external inputs. Those inputs are often recorded (perhaps in separate experiments or studies). The point being that I'm not sure that this is an entirely fair comparison to the field.

Finally, the plasticity models studied by theoreticians are not only constructed by intuition and hand-tuning. They also draw, often heavily, on biological data and principles. Oja's rule, for example, is simply the combination of Hebbian learning with a homeostatic constraint on the total synaptic weight amplitude (under the choice of a Euclidean norm).

To me, this study very nicely exposes the caveats and risks associated with a blind machine-learning approach to model specification in biology and highlights the need for understanding underlying biological mechanisms and principles. I agree with the authors that heterogeneity and degeneracy in plasticity rules deserve much more attention in the field.

eLife assessment

This paper presents useful results that extend our understanding of how the visual cortex encodes temporal structure, providing new information about sequence representations in superficial layers of the visual cortex. The evidence for prediction errors is solid, however, support for other claims regarding sparsification and simplification of activity following training is incomplete.

Reviewer #1 (Public Review):

Summary:

Knudstrup et al. use two-photon calcium imaging to measure neural responses in the mouse primary visual cortex (V1) in response to image sequences. The authors presented mice with many repetitions of the same four-image sequence (ABCD) for four days. Then on the fifth day, they presented unexpected stimulus orderings where one stimulus was either omitted (ABBD) or substituted (ACBD). After analyzing trial-averaged responses of neurons pooled across multiple mice, they observed that stimulus omission (ABBD) caused a small, but significant, strengthening of neural responses but observed no significant change in the response to stimulus substitution (ACBD). Next, they performed population analyses of this dataset. They showed that there were changes in the correlation structure of activity and that many features of sequence ordering could be reliably decoded. This second set of analyses is interesting and exhibited larger effect sizes than the first results about predictive coding. However, concerns about the design of the experiment temper my enthusiasm.

Strengths:

(1) The topic of predictive coding in the visual cortex is exciting, and this task builds on previous important work by the senior author (Gavornik and Bear 2014) where unexpectedly shuffling sequence order caused changes in LFPs recorded from the visual cortex.

(2) Deconvolved calcium responses were used appropriately here to look at the timing of the neural responses.

(3) Neural decoding results showing that the context of the stimuli could be reliably decoded from trial-averaged responses were interesting. However I have concerns about how the data was formatted for performing these analyses.

Weaknesses:

(1) All analyses were performed on trial-averaged neural responses that were pooled across mice. Owing to differences between subjects in behavior, experimental preparation quality, and biological variability, it seems important to perform at least some analyses on individual analyses to assess how behavioral training might differently affect each animal.

(2) The correlation analyses presented in Figure 3 (labeled the second Figure 2 in the text) should be conducted on a single-animal basis. Studying population codes constructed by pooling across mice, particularly when there is no behavioral readout to assess whether learning has had similar effects on all animals, appears inappropriate to me. If the results in Figure 3 hold up on single animals, I think that is definitely an interesting result.

(3) On Day 0 and Day 5, the reordered stimuli are presented in trial blocks where each image sequence is shown 100 times. Why wasn't the trial ordering randomized as was done in previous studies (e.g. Gavornik and Bear 2014)? Given this lack of reordering, did neurons show reduced predictive responses because the unexpected sequence was shown so many times in quick succession? This might change the results seen in Figure 2, as well as the decoder results where there is a neural encoding of sequence order (Figure 4). It would be interesting if the Figure 4 decoder stopped working when the higher-order block structure of the task was disrupted.

(4) A primary advantage of using two-photon calcium imaging over other techniques like extracellular electrophysiology is that the same neurons can be tracked over many days. This is a standard approach that can be accomplished by using many software packages-including Suite2P (Pachitariu et al. 2017), which is what the authors already used for the rest of their data preprocessing. The authors of this paper did not appear to do this. Instead, it appears that different neurons were imaged on Day 0 (baseline) and Day 5 (test). This is a significant weakness of the current dataset.

Reviewer #2 (Public Review):

Knudstrup et al set out to probe prediction errors in the mouse visual cortex. They use a variant of an oddball paradigm and test how repeated passive exposure to a specific sequence of visual stimuli affects oddball responses in layer 2/3 neurons. Unfortunately, there are problems with the experimental design which make it difficult to interpret the results in light of the question the authors want to address. The conceptual framing, choice of block design structure, and not tracking the same cells over days, are just some of the reasons that make this work difficult to interpret. Specific comments are as follows:

(1) There appears to be some confusion regarding the conceptual framing of predictive coding. Assuming the mouse learns to expect the sequence ABCD, then ABBD does not probe just for negative prediction errors, and ACBD is not just for positive prediction errors. With ABBD, there is a combination of a negative prediction error for the missing C in the 3rd position, and a positive prediction error for B in the 3rd. Likewise, with ACBD, there is a negative prediction error for the missing B at 2nd and missing C at 3rd, and a positive prediction error for the C in 2nd and B in 3rd. Thus, the authors' experimental design does not have the power to isolate either negative or positive prediction errors. Moreover, looking at the raw data in Figure 2C, this does not look like an "omission" response to C, but more like a stronger response to a longer B. The pitch of the paper as investigating prediction error responses is probably not warranted - we see no way to align the authors' results with this interpretation.

(2) Related to the interpretation of the findings, just because something can be described as a prediction error does not mean it is computed in (or even is relevant to) the visual cortex. To the best of our knowledge, it is still unclear where in the visual stream the responses described here are computed. It is possible that this type of computation happens before the signals reach the visual cortex, similar to mechanisms predicting moving stimuli already in the retina (https://pubmed.ncbi.nlm.nih.gov/10192333/). This would also be consistent with the authors' finding (in previous work) that single-cell recordings in V1 exhibit weaker sequence violation responses than the author's earlier work using LFP recordings.

(3) Recording from the same neurons over the course of this paradigm is well within the technical standards of the field, and there is no reason not to do this. Given that the authors chose to record from different neurons, it is difficult to distinguish representational drift from drift in the population of neurons recorded.

(4) The block paradigm to test for prediction errors appears ill-chosen. Why not interleave oddball stimuli randomly in a sequence of normal stimuli? The concern is related to the question of how many repetitions it takes to learn a sequence. Can the mice not learn ACBD over 100x repetitions? The authors should definitely look at early vs. late responses in the oddball block. Also, the first few presentations after the block transition might be potentially interesting. The authors' analysis in the paper already strongly suggests that the mice learn rather rapidly. The authors conclude: "we expected ABCD would be more-or-less indistinguishable from ABBD and ACBD since A occurs first in each sequence and always preceded by a long (800 ms) gray period. This was not the case. Most often, the decoder correctly identified which sequence stimulus A came from." This would suggest that whatever learning/drift could happen within one block did indeed happen and responses to different sequences are harder to interpret.

(5) Throughout the manuscript, many of the claims are not statistically tested, and where they are the tests do not appear to be hierarchical (https://pubmed.ncbi.nlm.nih.gov/24671065/), even though the data are likely nested.

(6) The manuscript would greatly benefit from thorough proofreading (not just in regard to figure references).

(7) With a sequence of stimuli that are 250ms in length each, the use of GCaMP6s appears like a very poor choice.

(8) The data shown are unnecessarily selective. E.g. it would probably be interesting to see how the average population response evolves with days. The relevant question for most prediction error interpretations would be whether there are subpopulations of neurons that selectively respond to any of the oddballs. E.g. while the authors state they "did" not identify a separate population of omission-responsive neurons, they provide no evidence for this. However, it is unclear whether the block structure of the experiments allows the authors to analyze this.

Reviewer #3 (Public Review):

Summary:

This work provides insights into predictive coding models of visual cortex processing. These models predict that visual cortex neurons will show elevated responses when there are unexpected changes to learned sequential stimulus patterns. This model is currently controversial, with recent publications providing conflicting evidence. In this work, the authors test two types of unexpected pattern variations in layer 2/3 of the mouse visual cortex. They show that pattern omission evokes elevated responses, in favor of a predictive coding model, but find no evidence for prediction errors with substituted patterns, which conflicts with both prior results in L4, and with the expectations of a predictive coding model. They also report that with sequence training, responses sparsify and decorrelate, but surprisingly find no changes in the ability of an ideal observer to decode stimulus identity or timing.

These results are an important contribution to the understanding of how temporal sequences and expectations are encoded in the primary visual cortex. However, there are several methodological concerns with the study, and some of the authors' interpretations and conclusions are unsupported by data.

Major concerns:

(1) Experimental design using a block structure. The use of a block structure on test days (0 and 5) in which sequences were presented in 100 repetition blocks leads to several potential confounds. First, there is the potential for plasticity within blocks, which could alter the responses and induce learned expectations. The ability of the authors to clearly distinguish blocks 1 and 2 on Day 0 with a decoder suggests this change over time may be meaningful.

Repeating the experiments with fully interleaved sequences on test days would alleviate this concern. With the existing data, the authors should compare responses from the first trials in a block to the last trials in a block.

This block design likely also accounts for the ability of a decoder to readily distinguish stimulus A in ABCD from A in ABBD. As all ABCD sequences were run in a contiguous block separate from ABBD, the recent history of experience is different for A stimuli in ABCD versus ABBD. Running fully interleaved sequences would also address this point, and would also potentially mitigate the impact of drift over blocks (discussed below).

(2) The computation of prediction error differs significantly for omission as opposed to substitutions, in meaningful ways the authors do not address. For omission errors, PE compares the responses of B1 and B2 within ABBD blocks. These responses are measured from the same trial, within tens of milliseconds of each other. In contrast, substitution PE is computed by comparing C in ABCD to C in ACBD. As noted above, the block structure means that these C responses were recorded in different blocks, when the state of the brain could be different. This may account for the authors' detection of prediction error for omission but not substitution. To address this, the authors should calculate PE for omission using B responses from ABCD.

(3) The behavior of responses to B and C within the trained sequence ABCD differs considerably, yet is not addressed. Responses to B in ABCD potentiate from d0-> d5, yet responses to C in the same sequence go down. This suggests there may be some difference in either the representation of B vs C or position 2 vs 3 in the sequence that may also be contributing to the appearance of prediction errors in ABBD but not ACBD. The authors do not appear to consider this point, which could potentially impact their results. Presenting different stimuli for A,B,C,D across mice would help (in the current paper B is 75 deg and C is 165 deg in all cases). Additionally, other omissions or substitutions at different sequence positions should be tested (eg ABCC or ABDC).

(4) The authors' interpretation of their PCA results is flawed. The authors write "Experience simplifies activity in principal component space". This is untrue based on their data. The variance explained by the first set of PCs does not change with training, indicating that the data is not residing in a lower dimensional ("simpler") space. Instead, the authors show that the first 5 PCs better align with their a priori expectations of the stimulus structure, but that does not mean these PCs necessarily represent more information about the stimulus (and the fact that the authors fail to see an improvement in decoding performance argues against this case). Addressing such a question would be highly interesting, but is lacking in the current manuscript. Without such analysis, referring to the PCs after training as "highly discretized" and "untangled" are largely meaningless descriptions that lack analytical support.

(5) The authors report that activity sparsifies, yet provide only the fraction of stimulus-selective cells. Given that cell detection was automated in a manner that takes into account neural activity (using Suite2p), it is difficult to interpret these results as presented. If the authors wish to claim sparsification, they need to provide evidence that the total number of ROIs drawn on each day (the denominator for sparseness in their calculation) is unbiased. Including more (or less) ROIs can dramatically change the calculated sparseness.

The authors mention sparsification as contributing to coding efficiency but do not test this. Training a decoder on variously sized subsets of their data on days 0 and 5 would test whether redundant information is being eliminated in the network over training.

(6) The authors claim their results show representational drift, but this isn't supported in the data. Rather they show that there is some information in the structure of activity that allows a decoder to learn block ID. But this does not show whether the actual stimulus representations change, and could instead reflect an unrelated artifact that changes over time (responsivity, alertness, bleaching, etc). To actually assess representational drift, the authors should directly compare representations across blocks (one could train a decoder on block 1 and test on blocks 2-5). In the absence of this or other tests of representational drift over blocks, the authors should remove the statement that "These findings suggest that there is a measurable amount of representational drift".

(7) The authors allude to "temporal echoes" in a subheading. This term is never defined, or substantiated with analysis, and should be removed.

Author Response

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

eLife assessment

This study presents a useful inventory of immune signatures that are correlated with cancer treatment-related pneumonitis. The data were collected and analysed using solid and validated methodology and can be used as a starting point for further functional studies.

We sincerely thank the editor for their encouraging comments regarding our study. As rightly pointed out, this study indeed serves as a pivotal starting point for subsequent functional studies.

Reviewer #2 (Recommendations For The Authors):

I greatly appreciate the authors diligence in addressing all the suggested points. The paper now presents significantly stronger evidence to support the findings.

I do have one final question: Could you clarify how the correlation presented in Supplementary Figure 3 was calculated? Is it a Pearson correlation of CTCAE grade directly to marker expression? Additionally, could you explain how the significance was determined? The authors mention a significant correlation for CCR7, but the heatmap displays similarly high values for CD7 and CD57. Finally, I'm curious about the absence of CD16 in the heatmap.

Thank you for your insightful query. To clarify, the correlation shown in Supplementary Figure 3 was indeed calculated using the Pearson correlation coefficient. This involved correlating the CTCAE grade directly with the mean expression levels of each marker. The computations were conducted using GraphPad Prism version 9. Regarding the statistical significance, we defined a threshold of P < 0.05 as significant. Specifically, the P-values for CCR7, CD7, and CD57 were found to be 0.009, 0.035, and 0.039, respectively. Hence, while CCR7 showed a significant correlation, CD7 and CD57 also exhibited relatively high values, as correctly observed. We have added CD7 and CD57 along with CCR7 in the discussion section, though not to mention much for better focusing on CD16.

CD16 was initially omitted from Supplementary Figure 3 to prevent redundancy and preserve data clarity. Nonetheless, in light of your query, we have included CD16 in the correlation matrix to provide a comprehensive view of its association with other markers.

We hope this adequately addresses your question and further clarifies our findings.

Reviewer #3 (Recommendations For The Authors):

General suggestions for presentation in the future:

It is essential to concretely define the numbers presented in all figures and plots. For example, in Figure 6 (I), what does it mean by "percentage representation of FCGR3A (CD16)"? Percentage of what? How did you calculate that? It is also important to show more statistics in general, for example, in dot plots like Figure 6 (H), where are the means and p-values? Little things like that completely change the impact of the figures. For the narrative of this paper, it is OK, but in the future, fine-tuning the presentation would massively improve the impact of the work which the contents deserve.

Thank you for your insightful feedback. Addressing your concerns, I have revised Figure 6H and Figure 6I to provide a more precise and informative presentation of our data. In Figure 6H, the violin plots illustrate the expression intensity of FCGR3A (CD16) on CD4+ and CD8+ T cells. Each dot represents an individual cell within the BALF from both healthy controls (HC) and COVID-19 patients. This data was derived from the single-cell RNA-seq dataset GSE145926. To enhance clarity and statistical robustness, I have now included p-values directly in Figure 6H. Additionally, for a more comprehensive understanding, the means ± standard deviation (SD) have been incorporated into the main text of the manuscript.

Regarding Figure 6I, it depicts the proportion of FCGR3A (CD16)-positive cells within the CD4+ and CD8+ T cell populations in BALF from HC and COVID-19 patients. The threshold for FCGR3A expression was set at 0.5. Upon further review and in response to your feedback, I realized an error in the calculation of the proportion of FCGR3A-positive cells among CD4+ and CD8+ T cells. Initially, the proportion of FCGR3A-positive CD4+ T cells was calculated in relation to the entire CD4+ T cell population, without differentiation between the groups. This has now been corrected, and the adjusted figures are presented in Figure 6I.

I am grateful for the opportunity to refine these figures, as your suggestions have not only helped to correct the error but have also significantly enhanced the impact and clarity of our work. Your guidance has been instrumental in improving the overall quality and presentation of our research, ensuring that the findings are communicated effectively and accurately.

Reviewer #3 (Public Review):

The authors collected BALF samples from lung cancer patients newly diagnosed with PCP, DI-ILD or ICI-ILD. CyTOF was performed on these samples, using two different panels (T-cell and B-cell/myeloid cell panels). Results were collected, cleaned-up, manually gated and pre-processed prior to visualisation with manifold learning approaches t-SNE (in the form of viSNE) or UMAP, and analysed by CITRUS (hierarchical clustering followed by feature selection and regression) for population identification - all using Cytobank implementation - in an attempt to identify possible biomarkers for these disease states. By comparing cell abundances from CITRUS results and qualitative inspection of a small number of marker expressions, the authors claimed to have identified an expansion of CD16+ T-cell population in PCP cases and an increase in CD57+ CD8+ T-cells, FCRL5+ B-cells and CCR2+ CCR5+ CD14+ monocytes in ICI-ILD cases.

By the authors' own admission, there is an absence of healthy donor samples and, perhaps as a result of retrospective experimental design and practical clinical reasons, also an absence of pre-treatment samples. The entire analysis effectively compares three yet-established disease states with no common baseline - what really constitutes a "biomarker" in such cases? These are very limited comparisons among three, and only these three, states.

By including a new scRNA-Seq analysis using a publicly available dataset, the authors addressed this fundamental problem. Though a more thorough and numerical analysis would be appreciated for a deeper and more impactful analysis, this is adequate for the intended objectives of the study.

Author Response

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

eLife assessment

This study presents valuable findings on diabetogenic risk from colorectal cancer (CRC) treatment. The authors claim that postoperative screening for type 2 diabetes should be prioritized in CRC survivors with overweight/obesity, irrespective of the oncological treatment received. The evidence supporting the claims is solid but requires confirmation in different populations. These results have theoretical or practical implications and will be of interest to endocrinologists, oncologists, general practitioners, gastrointestinal surgeons, and policymakers working on CRC and diabetes.

Author response: We thank you for taking the time to provide constructive feedback on our manuscript and for the useful suggestions. We have provided a point-by-point response to each of the reviewers’ comments with clearly marked changes to the manuscript.

Public reviews

Reviewer #1 (Public Review):

Summary:

In this study, the authors set out to determine whether colorectal cancer surgery site (right, left, rectal) and chemotherapy impact the subsequent risk of developing T2DM in the Danish national health register.

Strengths:

• The research question is conceptually interesting

• The Danish national health register is a comprehensive health database

• The data analysis was thorough and appropriate

• The findings are interesting, and a little surprising that there was no impact of chemotherapy on the development of T2DM

Weaknesses:

This is not a weakness as such, but in the discussion, I would consider adding some brief comment on the international generalizability of the findings - e.g. demographic make up of the Danish population health register and background rates of DM and obesity in this population with CRC compared to countries on other continents.

Author response: We agree that this information would be valuable. It has now been added in the Discussion section.

Changes in manuscript: "In Denmark, the overall T2D prevalence is 6.9%25, lower than the global average in 2021 (10.5%) and also falls below the estimate of high-income countries (11.1%).26 Similarly, the obesity rate of 20% aligns with other Scandinavian countries and is below that of most high-income nations.27” (Page 8, line 256-258)

A little more information would be helpful regarding how T2DM was diagnosed in the registry.

Author response: We have now added a more thorough explanation of how T2D was diagnosed in the Methods section.

Changes in manuscript: “Diabetes is defined as the second occurrence of any event across three types of inclusion events: 1) Diabetes diagnosed during hospitalisation 2) diabetes-specific services received at podiatrist 3) purchases of glucose lowering. Thus, if a patient developed transient T2D during chemotherapy treatment, it will only be an inclusion event if they purchase glucose lowering drugs. Individuals were classified as having T1D if they had received prescriptions for insulin combined with a diagnosis of type 1 from a medical hospital department. Otherwise, diabetes was classified as type 2.22” (Page 5, line 154-160)

If someone did develop transient hyperglycemia requiring DM medications during chemotherapy, would the investigators have been able to identify these people?

Author response: Yes, we have added a sentence in the Methods section.

Changes in manuscript: “Thus, if a patient developed transient T2D during chemotherapy treatment, it will only be an inclusion event if they purchase glucose lowering drugs.” (Page 5, line 156-158)

Would they have been classified as T2DM based on filling a prescription for DM meds for a period of time? Also, did the authors have information regarding time to development of T2DM after surgery?

Author response: Yes, if they have 2 (or more) prescriptions of oral glucose lowering drugs. Yes, we have information regarding time to development of T2DM after surgery and found no difference between the groups.

Changes in manuscript: Information on mean time to develop T2D post-surgery has now been added to Table 2.

In the adjusted Models, the authors did not adjust for cancer stage, even though cancer stage appears to be very different between the chemo and no chemo groups. It would be interesting to know if it affects the results if the model adjusted for cancer stage

Author response: We agree that adjustment for cancer stage would be a valuable information and we have performed the analysis and added a sentence in the Result section.

Changes in manuscript: An adjusted analysis of cancer stage now appears in the Supplementary table 1.

“Moreover, adjusting for cancer stage did not affect the results (Supplementary table 1).” (Page 7, line 219-220)

It would be worthwhile to report if mortality rates were different between the groups during follow up, and if the authors investigated whether perhaps differences in mortality rates led to specific groups living longer, and therefore having more time to develop DM

Author response: This situation is accounted for in the analysis by using Cox-regression analysis. This method accounts for the potential competing effect of mortality.

Changes in manuscript: None.

Overall, the authors achieved their aims, and the conclusions are supported by their results as reported.

The results are unlikely to significantly change patient treatment or T2DM screening in this population. With some additional information, as described above, the results would be of interest to the community.

Reviewer #2 (Public Review):

Summary:

The study showed the impact of cancer treatment on new onset of diabetes among patients with colorectal cancer using the national database. Findings reported that individuals with rectal cancer without chemotherapy were less likely to develop diabetes but among other groups, treatment didn't show any impact on the development of diabetes. BMI still played a significant role in developing diabetes regardless of treatment types.

Strengths:

One of the strengths of this study is innovative findings about the prognosis of colorectal cancer treatment stratified by treatment types. Especially, as it examined the impact of treatment on the risk of new chronic disease after diagnosis, it became significant evidence that suggests practical insights in developing a proper monitoring system for patients with colorectal cancer and their outcomes after treatment and diagnosis. It is imperative for providers to guide patients and caregivers to prevent adverse outcomes like new onset of chronic disease based on BMI and types of treatment. The next strength is the national database. As the study used the national database, the generalizability is validated.

Weaknesses:

Even though the study attempted to examine the impact of each treatment option, the dosage of chemotherapy and the types of chemotherapy were not able to be examined due to the data source.

Author response: No unfortunately not. We agree that this would have been valuable information. This is stated in the original manuscript as a limitation. Please refer to page 10 line 305-306.

Changes in manuscript: None.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Minor things:

There are minor inconsistencies in the methods and results regarding BMI. In the methods, the authors state that BMI <18.5 and >/=40 were excluded, but these groups are included in Table 2.

Author response: This has been corrected

Changes in manuscript: BMI groups <18.5 and >/=40 are now excluded in Table 2. (Page 18)

Line 204, I believe should be BMI 18.5-24.9, not 20-24.9.

Author response: This has been corrected

Changes in manuscript: “For each group (type of surgery ± chemotherapy), the HR for developing T2D depending on BMI subgroups was calculated by using Cox regression analysis adjusted for age, sex, year of surgery, and ASA score using normal weight (BMI:18.5-24.9) as the reference group.” (Page 6, line 184-186)

Rather than showing the BMI mean in Table 1, it would be interesting to see the BMI breakdown by category.

Author response: Yes, we agree. This analysis has now been added to Table 1

Changes in manuscript: Please refer to Table 1

Re line 215, I would consider rewriting to remove the multiple negatives -e.g. Radiation therapy in rectal resected had did not impact the incidence rate of T2D in the Rectal-No-Chemo group or Rectal-Chemo group

Author response: This has been corrected. Please refer to the Result section.

Changes in manuscript: “Radiation therapy in the rectal resected groups had no impact on the incidence rate of T2D (Table 2); and the unadjusted/adjusted HR of developing T2D was non-significant when comparing Rectal-No-Radiation patients with Rectal-Radiation patients (Table 3).” (Page 7, 223-225)

Consider changing some of the "didn't"s in the discussion to "did not"

Author response: This has been corrected.

Changes in manuscript: Revised and corrected throughout the discussion.

Reviewer #2 (Recommendations For The Authors):

Some points need to be clarified and improved.

In the method, patients with Type 1 Diabetes were excluded in the baseline but some patients were diagnosed with Type 1 diabetes after treatment and they were included in your analysis. It is interesting to identify Type 1 Diabetes after the treatment as an outcome, do you think that this diagnosis is caused by the treatment? And incidence rate or other HRs did not seem to include Type 1 Diabetes as stated in the methods. Did you exclude every Type 1 diabetes? If not, It needs to give further explanation about this outcome since the mechanism of Type 1 Diabetes and Type 2 Diabetes is different.

Author response: This matter has now been clarified in the Methods section.

Changes in manuscript: “Additionally, individuals diagnosed with Type 1 diabetes (T1D) either before or after surgery were excluded, along with those diagnosed with T2D preoperatively or within the first 2 weeks postoperatively, as the last group probably represents patients with preoperatively unknown pre-existing prediabetes or diabetes.22” (Page 4, line: 125-128)

Despite limited existing findings, some studies actually reported the incidence rates of Type 2 Diabetes among patients with CRC (Singh S, Earle CC, Bae SJ, et al. Incidence of Diabetes in Colorectal Cancer Survivors. J Natl Cancer Inst. 2016;108(6):djv402. Published 2016 Feb 2. doi:10.1093/jnci/djv402; Khan NF, Mant D, Carpenter L, Forman D, Rose PW. Long-term health outcomes in a British cohort of breast, colorectal and prostate cancer survivors: a database study. Br J Cancer. 2011;105 Suppl 1(Suppl 1):S29-S37. doi:10.1038/bjc.2011.420; Jo A, Scarton L, O'Neal LJ, et al. New onset of type 2 diabetes as a complication after cancer diagnosis: A systematic review. Cancer Med. 2021;10(2):439-446. doi:10.1002/cam4.3666) whereas your study examined the impact of the different types of treatments.

Author response: Our findings of T2D rate among CRC patients are now commented on in discussion section, and the abovementioned studies are included as references.

Changes in manuscript: “This national cohort study demonstrated an IR of developing T2D after CRC surgery similar to previous studies.5,11” (Page 8, line 237-238)

To strengthen the presentation, some places should be revised.

• Line 216: it says that Table 1 showed no impact of radiation therapy on the incidence rate of T2D. However, either the interpretation or the table number seems wrong. Table 1 does not have this information. Correct this statement.

• Line 239: There are typo and incomplete sentence. Check the sentence and correct the sentence.

• Line 257-261: It may be a systematic issue to separate these two paragraphs. But two paragraphs seem related so make them one paragraph.

Author response: These suggested changes have been made. Regarding line 216 the paragraph has been adjusted to the following:

Changes in manuscript: “Radiation therapy in the rectal resected groups had no impact on the incidence rate of T2D (Table 2); and the unadjusted/adjusted HR of developing T2D was non-significant when comparing Rectal-No-Radiation patients with Rectal-Radiation patients (Table 3).” (Page 7, 223-225)

Reference

(1) Araghi M, Soerjomataram I, Jenkins M, et al. Global trends in colorectal cancer mortality: projections to the year 2035. Int J Cancer. 2019;144(12):2992-3000. doi:10.1002/ijc.32055

(2) Arnold M, Sierra MS, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global patterns and trends in colorectal cancer incidence and mortality. Gut. 2017;66(4):683-691. doi:10.1136/gutjnl-2015-310912

(3) González N, Prieto I, del Puerto-Nevado L, et al. 2017 Update on the Relationship between Diabetes and Colorectal Cancer: Epidemiology, Potential Molecular Mechanisms and Therapeutic Implications. Vol 8.; 2017. www.impactjournals.com/oncotarget

(4) Mills KT, Bellows CF, Hoffman AE, Kelly TN, Gagliardi G. Diabetes mellitus and colorectal cancer prognosis: A meta-analysis. Dis Colon Rectum. 2013;56(11):1304-1319. doi:10.1097/DCR.0b013e3182a479f9

(5) Singh S, Earle CC, Bae SJ, et al. Incidence of Diabetes in Colorectal Cancer Survivors. J Natl Cancer Inst. 2016;108(6). doi:10.1093/jnci/djv402

(6) Xiao Y, Wang H, Tang Y, et al. Increased risk of diabetes in cancer survivors: a pooled analysis of 13 population-based cohort studies. ESMO Open. 2021;6(4). doi:10.1016/j.esmoop.2021.100218

(7) Colorectal D, Nordcan 2019. 5-Year Age-Standardised Relative Survival (%), Males and Females. Accessed September 12, 2022. “https://nordcan.iarc.fr/en/dataviz/survival?cancers=520&set_scale=0&sexes=1_2&populations=208”" has been copied into your clipboard

(8) Nano J, Dhana K, Asllanaj E, et al. Trajectories of BMI Before Diagnosis of Type 2 Diabetes: The Rotterdam Study. Obesity. 2020;28(6):1149-1156. doi:10.1002/oby.22802

(9) Maddatu J, Anderson-Baucum E, Evans-Molina C. Smoking and the risk of type 2 diabetes. Translational Research. 2017;184:101-107. doi:10.1016/j.trsl.2017.02.004

(10) Lega IC, Lipscombe LL. Review: Diabetes, Obesity, and Cancer-Pathophysiology and Clinical Implications. Endocr Rev. 2020;41(1). doi:10.1210/endrev/bnz014 (11) Jo A, Scarton L, O’Neal LTJ, et al. New onset of type 2 diabetes as a complication after cancer diagnosis: A systematic review. Cancer Med. 2021;10(2):439-446. doi:10.1002/cam4.3666

(12) Feng JP, Yuan XL, Li M, et al. Secondary diabetes associated with 5-fluorouracil-based chemotherapy regimens in non-diabetic patients with colorectal cancer: Results from a single-centre cohort study. Colorectal Disease. 2013;15(1):27-33. doi:10.1111/j.1463-1318.2012.03097.x

(13) Lee EK, Koo B, Hwangbo Y, et al. Incidence and disease course of new-onset diabetes mellitus in breast and colorectal cancer patients undergoing chemotherapy: A prospective multicenter cohort study. Diabetes Res Clin Pract. 2021;174. doi:10.1016/j.diabres.2021.108751

Author Response

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

Reviewer 1

Summary:

In the present study, authors found the ternary complex formed by NCAN, TNC, and HA as an important factor facilitating the multipolar to bipolar transition in the intermediate zone (IZ) of the developing cortex. NCAM binds HA via the N-terminal Link modules, meanwhile, TNC cross-links NCAN through the CDL domain at the C-terminal. The expression and right localization of these three factors facilitate the multipolar-bipolar transition necessary for immature neurons to migrate radially. TNC and NCAM are also involved in neuronal morphology. The authors used a wide range of techniques to study the interaction between these three molecules in the developing cortex. In addition, single and double KO mice for NCAN and TNC were analyzed to decipher the role of these molecules in neuronal migration and morphology.

Strengths:

The study of the formation of the cerebral cortex is crucial to understanding the pathophysiology of many neurodevelopmental disorders associated with malformation of the cerebral cortex. In this study, the authors showed, for the first time, that the ternary complex formed by NCAN, TNC, and HA promotes neuronal migration. The results regarding the interaction between the three factors forming the ternary complex are convincing.

We appreciate the reviewers' positive assessment of our research.

Weaknesses:

However, regarding the in vivo experiments, the authors should consider some points for the interpretation of the results:

• The authors did not use the proper controls in their experiments. For embryonic analysis, such as cortical migration, neuronal morphology, and protein distribution (Fig. 6, 7, and 9), mutant mice should be compared with control littermates, since differences in the results could be due to differences in embryonic stages. For example, in Fig. 6 the dKO is more developed than the WT embryo.

It was challenging to compare double knockout mice with control littermates. When crossing Ncan and Tcn double heterozygous mice, the probability of obtaining double knockout mice is 1/16. Given an average litter size of around 8, acquiring a substantial number of double knockout mice would necessitate an impractical number of breeding pairs. Consequently, we were constrained to use non-littermate control mice. To address potential differences in developmental stages, we analyzed 19-20 embryos obtained from five individuals in each group, demonstrating that the observed differences between the two groups are more substantial than the inherent variability within each group.

• The authors claim that NCAM and TNC are involved in neuronal migration from experiments using single KO embryos. This is a strong statement considering the mild results, with no significant difference in the case of TNC KO embryos, and once again, using embryos from different litters.

We agree with the reviewer's comment that a single deletion of TNC has a minimal impact on neuronal migration. We have revised the Results section to reflect the mild nature of the TNC KO phenotype more accurately.

Page 8, line 225: "In NCAN KO mice, a significantly lower percentage of labeled cells resided in the upper layer (Bin2), and more cells remained in the lower layer (Bin5) than in WT mice (Figure 7a). In contrast, the impact of a single deletion of TNC on neuronal cell migration was minimal. Although TNC KO mice exhibited a tendency to have a higher proportion of labeled cells in the lower layer (Bin4) than in WT mice, this did not reach statistical significance (Figure 7a). The delay in neuronal migration observed in the single KO mice was milder when compared to that observed in DKO mice (Figure 6a-c), suggesting that simultaneous deletion of both NCAN and TNC is necessary for a more pronounced impairment in neuronal cell migration."

• The measurement of immunofluorescence intensity is not the right method to compare the relative amount of protein between control and mutant embryos unless there is a right normalization.

We agree that measuring immunofluorescence intensity alone is insufficient for comparing the relative amount of protein. In Figure 8, we have employed Western blotting to compare the protein levels, revealing an approximately 50% reduction in NCAN and TNC following hyaluronidase digestion. In Figures 7b and 7c, we demonstrated alterations in the localization patterns of TNC and NCAN in Ncan KO and Tnc KO mice; however, we did not mention their quantity.

• Page 7, line 206. "No significant abnormalities were observed in the laminar structure in 4-week-old DKO mice". The authors should be more careful with this statement since they did not check the lamination of the adult cortex. I would recommend staining, control and mutant mice, with markers of different cortical populations, such as Cux1, Ctip2, Tbr1, to asses this point.

In response to the suggestion, we have conducted additional experiments to provide a more detailed examination of the laminar structure in the cerebral cortex. The results have been incorporated into the revised manuscript as follows:

Page 7, line 209: "To investigate the laminar organization of the postnatal cerebral cortex, we analyzed the distribution of NeuN-positive postmitotic neurons in DKO mice at 2 weeks of age. No notable abnormalities were observed in the laminar structure of DKO mice (Figure 6-figure supplement 3a, b). Additionally, the laminar distribution of Ctip2-positive deep layer neurons showed no significant differences between WT and DKO mice (Figure 6-figure supplement 3a, c)."

• The authors do not explain how they measured the intensity of TNC around the transfected Turbo-RFP-positive neurons.

We added the following description to the Materials and Methods:

Page 18, line 608: "Images were captured in the IZ region containing Turbo-RFP-positive neurons using a 100X magnification objective lens with 3.0X optical zoom on an AX R confocal microscope (Nikon). A total of 10 optical sections were acquired with a step size of 190 nm. Z-projection views were generated, and the staining intensity of TNC around Turbo-RFP-positive neurons was measured in a 59 × 59 µm area using ImageJ FIJI."

• The loading control of the western blots should be always included.

In Figure 6-figure supplement 1, we have incorporated western blot data using a GAPDH antibody as a loading control. We have added an explanation in the figure legend of Figure 3c, stating that we analyzed the same samples as those used in Figure 1e.

• For Fig. 3e, I think values are represented relative to E18 instead to P2.

Thank you for pointing that out. As suggested, we have corrected the representation in Fig. 3e to be relative to E18 instead of P2.

• I would recommend authors use the standard nomenclature for the embryonic stages. The detection of the vaginal plug is considered as E0.5 and therefore, half a day should be added to embryonic stages (E14.5...).

We have revised our manuscript to designate the detection of the vaginal plug as E0.5, and subsequently, we have adjusted all embryonic stages by adding half a day, such as E14.5.

• Fig 10K: I do not see the differences in the number of neurites in the graph.

We have modified the presentation from a box-and-whisker plot to a bar graph to enhance the visibility of differences in the average number of neurites.

• Line 37: Not all of the cerebral cortex is structured in 6 layers but the neocortex.

We have changed 'cerebral cortex' to 'cerebral neocortex.'

Reviewer 2

Summary:

ECM components are prominent constituents of the pericellular environment of CNS cells and form complex and dynamic interactomes in the pericellular spaces. Based on bioinformatic analysis, more than 300 genes have been attributed to the so-called matrisome, many of which are detectable in the CNS. Yet, not much is known about their functions while increasing evidence suggests important contributions to developmental processes, neural plasticity, and inhibition of regeneration in the CNS. In this respect, the present work offers new insights and adds interesting aspects to the facets of ECM contributions to neural development. This is even more relevant in view of the fact that neurocan has recently been identified as a potential risk gene for neuropsychiatric diseases. Because ECM components occur in the interstitial space and are linked in interactomes their study is very difficult. A strength of the manuscript is that the authors used several approaches to shed light on ECM function, including proteome studies, the generation of knockout mouse lines, and the analysis of in vivo labeled neural progenitors. This multi-perspective approach permitted to reveal hitherto unknown properties of the ECM and highlighted its importance for the overall organization of the CNS.

Strengths:

Systematic analysis of the ternary complex between neurons, TNC, and hyaluronic acid; establishment of KO mouse lines to study the function of the complex, use of in utero electroporation to investigate the impact on neuronal migration;

We appreciate the reviewers' insightful comments.

Weaknesses:

The analysis is focused on neuronal progenitors, however, the potential impact of the molecules of interest, in particular, their removal on differentiation and /or survival of neural stem/progenitor cells is not addressed. The potential receptors involved are not considered. It also seems that rather the passage to the outer areas of the forming cortex is compromised, which is not the same as the migration process. The movement of the cells is not included in the analysis.

In this study, we demonstrated that the ternary complex of NCAN, TNC, and HA is predominantly localized in the subplate/intermediate zone. This region lacks neural stem/progenitor cells but serves as the initiation site for the radial migration of postmitotic neurons. Consequently, our study focused on the role of the ternary complex in neuronal migration and polarity formation. We acknowledge that we did not investigate in-depth the potential effects of ECM perturbation on the differentiation and survival of neural stem/progenitor cells. However, as highlighted by the reviewer, it is important to explore the effects on neural stem/progenitor cells. To address this concern, we analyzed Pax6-positive radial glial cells and Tbr2-positive intermediate progenitor cells in the ventricular zone of wild-type and Ncan/Tnc double knockout (DKO) mice. Immunohistochemical analysis revealed no significant differences between WT and DKO mice (Figure 6-figure supplement 4a). Furthermore, the morphology of nestin-positive radial fibers exhibited no distinguishable variations between WT and DKO mice (Figure 6-figure supplement 4b, c).

(1) In the description of the culture of cortical neurons the authors mentioned the use of 5% horse serum as a medium constituent. HS is a potent stimulus for astrocyte differentiation and astrocytes in vitro release neurocan. Therefore, the detection of neurocan in the supernatant of the cultures as shown in Figure 1h might as well reflect release by cultivated astrocytes.

As pointed out by the reviewer, Figure 1h did not conclusively demonstrate that neurons are the sole source of NCAN production. Indeed, in situ hybridization analysis revealed the widespread distribution of Ncan mRNA throughout the cerebral cortex (Figure 2a). This result suggests that the production of NCAN involves not only neurons but also other cell populations, including radial glial cells and astrocytes. While we acknowledge the potential contribution of other cell types to NCAN production, Ncan expression by neurons during radial migration is a crucial aspect of our findings (Figure 1i, j). We have revised the manuscript as follows:

Page 5, line 111: "This result suggested the secretion of NCAN by developing neurons; however, we cannot rule out the involvement of coexisting glial cells in the culture system. To investigate the expression of Ncan mRNA during radial migration in vivo, we labeled radial glial cells in the VZ with GFP through in utero electroporation at E14.5 (Figure 1i, Figure 1-figure supplement 1)."

(2) It is known that neurocan in vivo is expressed by neurons, but may be upregulated in astrocytes after lesion, or in vitro, where the cells become reactive.

We have incorporated the following description into the discussion:

Page 11, line 359: "Previous studies have reported an upregulation of NCAN and TNC in reactive astrocytes, indicating the potential formation of the ternary complex of NCAN, TNC, and HA in the adult brain in response to injury (Deller et al., 1997; Haas et al., 1999)."

(3) Do NCAN KO neurons show an increase in neurite growth on the TNC substrates? The response on POL was changed (Fig. 10h-k), but the ECM substrates were not tested with the KO neurons.

The impact of ECM substrates on NCAN KO neurons has not been investigated, and this remains an avenue for further exploration in our ongoing research. Future studies aim to elucidate the NCAN-TNC connection by identifying TNC cell surface receptors and unraveling the subsequent intracellular signaling pathways.

(4) Do the authors have an explanation for why the ternary complex is concentrated in the SP/IZ zone?

In the mature brain, hyaluronan acts as a scaffold that facilitates the accumulation of ECM components, including proteoglycans and tenascins around neurons. Therefore, it is conceivable that the ECM components bind to hyaluronan in the embryonic brain, resulting in its accumulation in the subplate/intermediate zone. In support of this hypothesis, enzymatic digestion of hyaluronan in the subplate/intermediate zone led to the disappearance of TNC and NCAN accumulation (Figure 8a-c). This result may account for the disparity observed, where Tnc mRNA is expressed in the ventricular zone while the TNC protein localizes to the subplate/intermediate zone.

(5) Are hyaluronic acid synthesizing complexes (HAS) concentrated in the SP/IZ?

According to the reviewer's comment, we have investigated the localization of Has2 and Has3 mRNA using in situ hybridization. However, due to the relatively low expression levels of these enzymes, we encountered challenges in obtaining clear signals (Author response image 1). Further research is needed to understand the mechanisms behind the localization of hyaluronan in the intermediate zone.

Author response image 1.

In situ hybridization analysis of Has2 and 3 mRNA on the E16.5 cerebral cortex. Upper images show results of in situ hybridization using antisense against Has2 and 3. Lower images are in situ hybridization using sense probes as negative controls.

(6) CSPGs as well as TNC are part of the neural stem/progenitors cell niche environment. Does the removal of either of the ECM compounds affect the proliferation, differentiation, and/or survival of NSPCs, or their progeny?

)7) This question relates to the fact that the migration process itself is not visualized in the present study, rather its outcome - the quantitative distribution of labeled neurons in the different bins of the analysis. This could also derive from modified cell numbers.

As pointed out by the reviewer, previous studies have shown the role of CSPGs and TNC as components of the neural stem/progenitor cell niche (see reviews by (Faissner et al., 2017; Faissner and Reinhard, 2015). However, as mentioned in Response #2, based on our analyses, we did not observe a reduction in neural stem/progenitor cells in NCAN/TNC double-knockout mice. While we cannot precisely explain this discrepancy, it is worth noting that many past studies evaluated the activities of the ECM molecules in in vitro systems such as neurospheres. The observed differences may stem from variations in experimental systems.

(8) What is the role of the ECM in the SP/IZ area? Do the cells need the ECM to advance, the reduction would then leave the neuronal progenitors in the VZ area? This somehow contrasts with interpretations that the ECM acts as an obstacle for neurite growth or cell migration, or as a kind of barrier.

The role of the ECM is multifaceted, with certain ECM molecules known to inhibit neurite outgrowth while others facilitate it. Additionally, the effects of ECM can vary depending on the cell type. It is established that after migrating neurons adhere to radial fibers, they utilize these fibers as a scaffold to migrate toward the cortical surface. However, in the subplate/intermediate zone, migrating neurons have not yet adhered to radial fibers. This study provides evidence that multipolar neurons undergo morphological changes into bipolar cells with the assistance of the NCAN, TNC, and HA complex. Subsequently, this facilitates their movement along radial fibers.

(9) A direct visualization of the movement of neural progenitors in the tissue as has been for example performed by the Kriegstein laboratory might help resolve some of these issues.

As suggested by the reviewer, utilizing live imaging techniques to directly observe the movement of neural progenitors within the tissue is indeed a powerful tool. We recognize the significance of addressing these points in future research.

Author Response

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

We would like to thank the reviewer for the constructive comments. We have revised the papers to address the concerns. In summary, here is what we included in the revised version.

• Statistical analysis using biological replicate datasets for WT and K40R doublet microtubule.

• Addition figures for statistical analysis and MIP decorations in MEC17-KO and K40R.

• Revised texts and figures to reflect the new changes, cite proper references and fix small errors throughout the text.

Reviewer #1 (Public Review):

Summary:

The study "Effect of alpha-tubulin acetylation on the doublet microtubule structure" by S. Yang et al employs a multi-disciplinary approach, including cryo-electron microscopy (cryo-EM), molecular dynamics, and mass spectrometry, to investigate the impact of α-tubulin acetylation at the lysine 40 residue (αK40) on the structure and stability of doublet microtubules in cilia. The work reveals that αK40 acetylation exerts a small-scale, but significant, effect by influencing the lateral rotational angle of the microtubules, thereby affecting their stability. Additionally, the study provided an explanation of the relationship between αK40 acetylation and phosphorylation within cilia, despite that the details still remain elusive. Overall, these findings contribute to our understanding of how post-translational modifications can influence the structure, composition, stability, and functional properties of important cellular components like cilia.

Strengths:

(1) Multi-Disciplinary Approach: The study employs a robust combination of cryo-electron microscopy (cryo-EM), molecular dynamics, and mass spectrometry, providing a comprehensive analysis of the subject matter.

(2) Significant Findings: The paper successfully demonstrates the impact of αK40 acetylation on the lateral rotational angles between protofilaments (inter-PF angles) of doublet microtubules in cilia, thereby affecting their stability. This adds valuable insights into the role of post-translational modifications in cellular components.

(3) Exploration of Acetylation-Phosphorylation Relationship: The study also delves into the relationship between αK40 acetylation and phosphorylation within cilia, contributing to a broader understanding of post-translational modifications.

(4) High-quality data: The authors are cryo-EM experts in the field and the data quality presented in the manuscript is excellent.

(5) Depth of analysis: The authors analyzed the effects of αK40 acetylation in excellent depth which significantly improved our understanding of this system.

Thank you for highlighting the strength of our paper.

Weaknesses:

I have no major concerns about this paper, but would recommend that a few minor issues be addressed.

(1) Lack of Statistical Details: The review points out that the paper could benefit from providing more statistical details, such as the number of particles and maps used for analysis, randomization methods, and dataset splitting for statistical analyses.

To address this, we analyzed the true biological replicate datasets (different cultures, cryo-EM vitrification and data collection) from WT and K40R. Since the MEC17-KO was collected as only one dataset, we decided to not divide the MEC-17 using randomization since the division does not lead to independent sets, which tends to yield identical results in the case of cryo-EM. The biological replicates help us to see how consistent is our structure data for interpretation. The information about the replicate dataset is now included in Table 1. The description of the analysis is highlighted in the manuscript and included in the Materials & Methods and Fig. S4.

In summary, the biological replicate between the WT data indicates that the inter-PF rotation angles are significantly consistent between two biological replicates. On the other hand, there are variations in the inter-PF angles between two replicates of K40R data in the B-tubule (Fig. S4B).

Overall, when pooling the data together ( 6 + 6 measurement points for WT dataset 1 & 2 and 6 + 6 measurement points for K40R dataset 1 & 2 and 6 measurement points for MEC17-KO) (Fig. S4), our analysis yields the same statistical significance as the average of all datasets (6 measurement points of the total averages for WT, K40R and MEC17-KO) (Fig. 3).

In addition, the variation in inter-PF rotation angles between certain PF pairs within the K40R replicates (B7B8 and B9B10) is similar to the variation to MEC17-KO. This suggests that the deacetylation induces variation in inter-PF angles while the inter-PF angles are maintained consistently in WT.

(2) Questionable Conclusion Regarding MIPs: The reviewer suggests caution in the paper's conclusion that "Acetylation of αK40 does not affect tubulin and MIPs." The reviewer recommends that this conclusion be more specific or supported by additional evidence to exclude all other possibilities.

We now revised the text to make sure we do not overclaim that “Acetylation of αK40 does not affect tubulin and MIPs.” We now describe more specifically as “Lack acetylation of αK40 does not significantly affect tubulin and MIP interactions”. Also the text was edited to make the statement more specific.

(3) Need for Additional Visual Data: The reviewer recommends that an enlarged local density map along with fitted PDB models be provided in a supplementary figure, such as Figure 4.

We now include the density maps and fitted PDB models in Fig. 4 and Fig. S5. We also include more snapshots of the MIP in K40R and MEC17-KO in Figure S3.

Overall, the paper is strong in its scientific approach and findings but could benefit from additional statistical rigor and clarification of certain conclusions.

Page 11, Line 226: "cluster consists of only ~ acetylated", lacks the percentage. Please correct this.

We corrected it.

Author Response

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The manuscript describes the crystal structures of Streptococcus pneumoniae NOXs. Crystals were obtained for the wild-type and mutant dehydrogenase domain, as well as for the full-length protein comprising the membrane domain. The manuscript further carefully studies the enzyme's kinetics and substrate-specificity properties. Streptococcus pneumoniae NOX is a non-regulated enzyme, and therefore, its structure should provide a view of the NOX active conformation. The structural and biochemical data are discussed on this ground.

Strengths:

This is very solid work. The protein chemistry and biochemical analysis are well executed and carefully described. Similarly, the crystallography must be appreciated given the difficulty of obtaining good enzyme preparations and the flexibility of the protein. Even if solved at medium resolution, the crystal structure of the full-length protein conveys relevant information. The manuscript nicely shows that the domain rotations are unlikely to be the main mechanistic element of NOX regulation. It rather appears that the NADPH-binding conformation is pivotal to enzyme activation. The paper extensively refers to the previous literature and analyses the structures comprehensively with a comparison to previously reported structures of eukaryotic and prokaryotic NOXs.

We thank the referee for these very nice comments about our work.

Weaknesses:

The manuscript is not always very clear with regard to the analysis of NADPH binding. The last section describes a "crevice" featured by the NADPH-binding sites in NOXs. It remains unclear whether this element corresponds to the different conformations of the protein C-terminal residues or more extensive structural differences. This point must be clarified.

We agree with the referee that our terminology was not very clear. Responding to your comment helped us to improve our explanation: we have changed the text to emphasize the differences we observe in the distances between the FAD binding groove and the entire NADPH binding groove, which includes conserved NADPH-contacting motifs as well as the critical aromatic.

A second less convincing point concerns the nature of the electron acceptor. The manuscript states that this NOX might not physiologically act as a ROS producer. A question then immediately arises: Is this protein an iron reductase?

Can the authors better discuss or provide more data about this point?

The referee has a legitimate point, which was also our first idea. In the initial work on SpNOX, where we discovered bacterial NOX enzymes (see Hajjar et al 2017 in mBio), we evaluated its possible role as an iron reductase. There we showed that SpNOX can reduce CytC directly; however, while some reduction of Fe3+-NTA complex (used classically in ferric reductase activity assay) occurred, this reduction was inhibitable by SOD and occurred indirectly by the superoxide produced, so therefore not a true iron reductase activity. This represents a mixed situation of direct and indirect reduction of an iron-containing acceptor that appears to preclude physiological iron reductase activity since it appears that the protein component of CytC allows it to interact with SpNOX. As these questions had been already addressed in a previous paper, we did not add anything here and we prefer to underline this possibility of another acceptor and to leave this question open for future works.

Reviewer #2 (Public Review):

The authors describe the structure of the S. pneumoniae Nox protein (SpNOX). This is a first. The relevance of it to the structure and function of eukaryotic Noxes is discussed in depth.

Strengths and Weaknesses

One of the strengths of this work is the effort put into preparing a pure and functionally active SpNOX preparation. The protein was expressed in E. coli and the purification and optimization of its thermostability and activity are described in detail, involving salt concentration, glycerol concentration, and pH.

This reviewer was surprised by the fact that the purification protocol in the eLife paper differs from those in the mBio and Biophys. J. papers by the absence of the detergent lauryl maltose neopentyl glycol (LMNG). LMNG is only present in the activity assay at a low concentration (0.003%; molar data should be given; by my calculation, this corresponds to 30 μM).

We regret this misunderstanding: our description was not clear enough. As the referee points out, in previous papers we purified the full length SpNOX with the detergent LMNG. In the current paper, we described only the protocol for SpNOX DH domain variant, a soluble cytoplasmic domain. We have now modified the text to clarify the difference between the purification of fulllength SpNOX variants, which were performed with detergent as cited in Vermot et al 2020, and the purification of DH domains, which are soluble and thus did not require detergent in the purification.

In light of the presence of lipids in cryo-EM-solved structures of DUOX and NOX2, it is surprising that the authors did not use reconstitution of the purified SpNOX in phospholipid (nanodisk?). The issue is made more complicated by the statement on p. 18 of "structures solved in detergent like ours" when no use of detergent in the solubilization and purification of SpNOX is mentioned in the Methods section (p. 21-22).

As stated above, detergent used to purify the full-length version of SpNOX. We did in fact perform some preliminary tests of reconstitution in nanodiscs. Different trials of negative staining studies showed heterogeneous size of SpNOX in nanodiscs and the initial images were not promising. Furthermore, in parallel, we had positive results in crystallography relatively quickly with protein in detergent. We thus focused on refining the crystals, which was a fairly long and mobilizing task; we decided to allocate time and resources to the promising avenue and did not further pursue nanodiscs.

We did not go in theCryo-EM direction because the small size of the protein was initially believed to be a significant barrier to successful Cryo-EM. Perhaps we could have pursued this avenue: while our manuscript here was submitted to eLife, another group deposited a preprint in BioRxiv using CryoEM to solve the structure of SpNOX (see comment below). This structure was solved in detergent so even in this CryEM structure there is no information on the potential roles of lipids as asked by the referee.

In this revised version, we have added a comment, in the last paragraph, in reference to the additional data available today thanks to the other structures generated by this other group (Murphy's group).

Can the authors provide information on whether E. coli BL21 is sufficiently equipped for the heme synthesis required for the expression of the TM domain of SpNOX. Was supplementation with δaminolevulinic acid used

The production of His-SpNox in E.coli C41(DE3) is without any δ-aminolevulinic acid supplementation. Supplementation was tested but no change was observed regarding the heme content (UV/Visible spectra) so we settled on the purification described by Vermot et al 2020. Initially, for the mBio paper (Haajar et al 2017), we performed heme titrations which gave stoichiometry between 1.35 to 1.5 heme/protein, indicating 2 hemes (these data were not shown). In the end in this work we observed two hemes in the crystal structure, thus confirming that E.coli, at least for this protein, did not need supplementation with δ-aminolevulinic acid .

The 3 papers on SpNOX present more than convincing evidence that SpNOX is a legitimate Nox that can serve as a legitimate model for eukaryotic Noxes (cyanide resistance, inhibition by DPI, absolute FAD dependence, and NADPH/NADH as the donor or electrons to FAD). It is also understood that the physiological role of SpNOX in S. pneumoniae is unknown and that the fact that it can reduce molecular oxygen may be an experimental situation that does not occur in vivo.

I am, however, linguistically confused by the statement that "SpNOX requires "supplemental" FAD". Noxes have FAD bound non-covalently and this is the reason that, starting from the key finding of Babior on NOX2 back in 1977 to the present, FAD has to be added to in vitro systems to compensate for the loss of FAD in the course of the purification of the enzyme from natural sources or expression in a bacterial host. I wonder whether this makes FAD more of a cosubstrate than a prosthetic group unless what the authors intend to state is that SpNOX is not a genuine flavoprotein.

We believe there is some confusion between SpNOX – the full length transmembran protein -- and SpNOXDH -- the cytosolic domain only. The sentence pinpointed by the referee was in fact “The strict requirement of FAD addition for SpNOXDH activity suggests that the flavin behaves as a cosubstrate”. This statement was about the isolated cytosolic domain that does not contain the TM part of the protein.

We agree that in WT NOX enzymes (including SpNOX) FAD is held within the enzyme structure and thus can be considered, by definition, as a prosthetic group. This is supported by the nanomolar affinity for FAD of SpNOX. We did not intend to say that NOX and SpNOX are not genuine flavoproteins.

On the other hand, when isolated, the affinity of DH domain for flavins drops to the µM level. This µM level of affinity does not allow stable maintenance of the flavin in the active site as illustrated by the spectra of Figure 3. This is instead the typical affinity of a substrate or a co-substrate (similar to that of substrate NADPH) that can be exchangeable and diffuse in and out of the active site. The DH domain recognizes and reduces flavins but, as a consequence of its lower affinity, will release to its environment free reduced flavins. Thus the isolated DH behaves as a flavin reductase that uses flavin as substrate. Such enzymes have already been well described (and some of them are of the FNR family). Such enzymes, using flavin as substrate, typically have affinity for flavin in the µM range and share with the SpNOX DH binding properties centered on the isoalloxazine ring only.

We understand that, in the text, to switch from the SpNOX to the SpNOX DH and for FAD from a prosthetic group to a diffusible co-substrate can be confusing. So, to make it clearer, we modified the following sentences and added references to “some flavin reductases characterization” that could provide support for the reader.

“The strict requirement of FAD addition for SpNOXDH activity and its µM level of affinity suggests that the flavin behaves as a co-substrate rather than a prosthetic group. As an isolated domain, SpNOXDH may work as a flavin reductase enzyme (Gaudu et al, 1994; Fieschi et al 1995; Nivière et al 1996), ..”

We hope that it will help.

I am also puzzled by the statement that SpNOX "does not require the addition of Cyt c to sustain superoxide production". Researchers with a Cartesian background should differentiate between cause and effect. Cyt c serves merely as an electron acceptor from superoxide made by SpNOX but superoxide production and NADPH oxidation occur independently of the presence of added Cyt c.

Thanks to the referee for pointing out this poor wording. We agree and have amended the text to clarify what we originally meant. It is now:

“SpNOXDH requires supplemental FAD to sustain both superoxide production, which can be observed in the presence of Cyt c (Figure 2A), and NADPH oxidation, which can be observed in the absence of Cyt c (Figure 2B).”

The ability of the DH domain of SpNOX (SpNOXDH) to produce superoxide is surprising to this reviewer.The result is based on the inhibition of Cyt c reduction by added superoxide dismutase (SOD) by 40%. In all eukaryotic Noxes superoxide is produced by the one-electron reduction of molecular oxygen by electrons originating from the distal heme, having passed from reduced FAD via two hemes. The proposal that superoxide is generated by direct transfer of electrons from FAD to oxygen deserves a more in-depth discussion and relies too heavily on the inhibitory effect of SOD. A control experiment with inactivated SOD should have been done (SOD is notoriously heat resistant and inactivation might require autoclaving).

The initial reports of a NOX DH-domain-only construct (that of human Nox4) producing superoxide are cited in the text. Moreover, natural flavin reductases are known to produce superoxide due to the release of free reduced flavin in the medium.

As explain above, FAD in full length SpNox is a relay for the electrons from NADPH to heme and is internal to the protein and thus devoted to this specific task.

In the case of SpNOX DH, its flavin reductase behavior leads to the release in the medium of free reduced flavin as a nonspecific diffusible electron carrier. It has been already demonstrated that such free reduced flavin can efficiently reduce soluble O2 and be a source of superoxide.

This has been particularly well documented in (Gaudu et al, 1994. J.Biol.Chem). We have added this reference to the text (see the modified sentence in a reply, 2 comments above).

Furthermore, we want to point to the referee that the link between flavin and superoxide production here is not only based on the inhibition by SOD. When we added the flavin inhibitor DPI we observed no more superoxide production from the DH domain (Figure 2C). This supports the role of free-reduced flavin in both the production of superoxide and also part of direct cyt C reduction as observed.

An unasked and unanswered question is that, since under aerobic conditions, both direct Cyt c reduction (60%) and superoxide production (40%) occur, what are the electron paths responsible for the two phenomena occurring simultaneously?

We thank the referee for dedication to a clear understanding of the mechanism used by the SpNOXDH construct. It pushes us to develop a clear description of the mechanism at work here for the readers. Please find below a proposal mechanism describing the electron transfer from NAD(P)H to free flavin that can, as diffusible species, then reduce non-specifically either the O2 or the Cyt.C encountered.

Author response image 1.

However, it is important to remember that this is not physiological, and rather the result of using a DH domain isolated from the TM of SpNOX. Nonetheless, it shows that the DH domain is fully functional for NAD(P)H as well as the hydride transfer.

This reviewer had difficulty in following the argument that the fact that the kcat of SpNOX and SpNOXDH are similar supports the thesis that the rate of enzyme activation is dependent on hydride transfer from nicotinamide to FAD.

We have amended the text to clarify this point. If the reaction rate is not affected by the presence or absence of the hemes in the TM domain, this inevitably implies that the rate is NOT limited by the electron transfer to the heme, and ultimately to O2, from the FAD, and thus the hydride transfer step that oxidizes the FAD must be the rate limiting step.

The section dealing with mutating F397 is a key part of the paper. There is a proper reference to the work of the Karplus group on plant FNRs (Deng et al). However, later work, addressing comparison with NOX2, should be cited (Kean et al., FEBS J., 284, 3302-3319, 2017). Also, work from the Dinauer group on the minimal effect of mutating or deleting the C-terminal F570 in NOX2 on superoxide production should be cited (Zhen et al., J. Biol. Chem. 273, 6575-6581, 1998).

We thank the reviewer for pointing out our unintended omission of these important works; we have amended the text and added the citations.

It is not clear why mutating F397 to W (both residues having aromatic side chains) would stabilize FAD binding.

In a few words, trp’s double ring can establish larger and stronger vanderWaals contact with the isoalloxazine ring than the phe sidechain. Our discussion regarding this point is extensive in the structural section where we compare the structures with F and W in this position. At this time we do not think it is necessary to add anything to the text.

Also, what is meant by "locking the two subdomains of the DH domain"? What subdomains are meant?

The two subdomains are the NADPH-binding domain and the FAD-binding domain, which we define on p 11 (“SpNOXDH presents a typical fold of the FNR superfamily of reductase domain containing two sub-domains, the FAD-binding domain (FBD) and an NADPH-binding domain (NBD) “) and which are labeled in Fig. 4. By “locking” we meant to convey immobilizing them into a specific conformation; we have amended the text to clarify this point.

Methodological details on crystallization (p. 11) should be delegated to the Methodology section. How many readers are aware that SAD means "Single Wavelength Anomalous Diffraction" or know what is the role of sodium bromide?

We have amended the text to emphasize the intended point, which is the different origins of the two DH structures: the de novo structure was possible through co crystallization with bromide, and the molecular replacement structure used the de novo structure as a model.

The data on the structure of SpNOX are supportive of a model of Nox activation that is "dissident" relative to the models offered for DUOX and NOX2 activation. These latter models suggested that the movement of the DH domain versus the TM domain was related to conversion from the resting to the activated state. The findings reported in this paper show that, unexpectedly, the domain orientation in SpNOX (constitutively active!) is much closer to that of resting NOX2. One of the criteria associated with the activated state in Noxes was the reduction of the distance between FAD and the proximal heme. The authors report that, paradoxically, this distance is larger in the constitutively active SpNOX (9.2 Å) than that in resting state NOX2 (7.6 Å) and the distance in Ca2+-activated DUOX is even larger (10.2 Å).

A point made by the authors is the questioning of the paradigm that activation of Noxes requires DH domain motion.

Instead, the authors introduce the term "tensing", within the DH domain, from a "relaxed" to a more rigid conformation. I believe that this proposal requires a somewhat clearer elaboration

It is clear that the distance between the FAD and NADPH shown in the Duox and Nox2 structures is too large for the chemical reaction of hydride transfer. Wu et al used the terms ‘tense’ and ‘relaxed’ to describe conformations of the DH domain corresponding to ‘short distance’ and ‘longer distance’, respectively, between the two ligand binding sites. We quoted this terminology and have amended the text to clarify that we envision a motion of the NBD relative to the FBD, as distinct from a larger motion of the whole DH domain relative to the TM domain.

The statement on p. 18, in connection to the phospholipid environment of Noxes, that the structure of SpNOX was "solved in detergent" is puzzling since the method of SpNOX preparation and purification does not mention the use of a detergent. As mentioned before, this absence of detergent in the present report was surprising because LMNG was used in the methods described in the mBio and Biophys. J. papers. The only mention of LMNG in the present paper was as an addition at a concentration of 0.003% in the activity assay buffers.

Please see our response to similar points above. Detergent was present for the solubilization of the full-length SpNOX.

The Conclusions section contains a proposal for the mechanism of conversion of NOX2 from the resting to the activated state. The inclusion of this discussion is welcome but the structural information on the constitutively active SpNOX can, unfortunately, contribute little to solving this important problem. The work of the Lambeth group, back in 1999 (cited as Nisimoto et al.), on the role of p67-phox in regulating hydride transfer from NADPH to FAD in NOX2 may indeed turn out to have been prophetic. However, only solving the structure of the assembled NOX2 complex will provide the much-awaited answer. The heterodimerization of NOX2 with p22-phox, the regulation of NOX2 by four cytosolic components, and the still present uncertainty about whether p67-phox is indeed the final distal component that converts NOX2 to the activated state make this a formidable task.

The work of the Fieschi group on SpNOX is important and relevant but the absence of external regulation, the absence of p22-phox, and the uncertainty about the target molecule make it a rather questionable model for eukaryotic Noxes. The information on the role of the C-terminal Phe is of special value although its extension to the mechanism of eukaryotic Nox activation proved, so far, to be elusive.

We really thank the referee for the positive comments on our work and the deep interest shown by this careful evaluation.

We understand the arguments of the referee regarding the relevance of our work here to eukaryotic NOX, but we do not share the reservations expressed. While human NOXes need interactions with other proteins or have EF-hand or other domains that control them, SpNOX corresponds exactly to the minimal core common to any NOX isoform. In fact, because SpNOX has only this conserved core, it is unique in that it can work as a constitutively active NOX without protein-protein interactions or regulatory domains. Thus the fundamentals of electron transfer mechanisms of NOX enzyme are present in SpNOX.

There might be some differences in the internal organization from isoform to isoform (as regarding the relative DH domain vs TM domain orientation) but considering the similarity between NOX2 and SpNOX topology we are rather confident that the SpNOX structure will turn out to be a reasonable model of the activated NOX2 structure. History will tell.

In any case, this work on SpNOX allowed us to highlight hydride transfer as the limiting step and also to highlight some structural differences that could be at the source of the regulation in eukaryotic NOX. In itself, we think this is a significant contribution to the field.

We warmly thank both referees for their constructive remarks and their help in the improvement of this manuscript.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

• The manuscript states that the flavin "behaves" like a co-substrate and thereby reports on the Km for the flavins. I feel that this terminology might be confusing. The flavin is unchanged after the reaction, and what matters is the enzyme's affinity for the flavin and the flavin concentration needed to saturate the enzyme (to have it in the fully holo form).

See above -- answering many questions from referee2, we have extensively commented on that point (substrate, cofactor, affinity, etc..) and made some adjustments in the text to clarify. We hope it is now satisfactory.

• I could not find the methodological description of the experiments performed to measure the Km for the flavins, and the legend of Figure S4 does not help in this regard. I think that the data (left panels of S4) should be interpreted as binding curves with associated Kd values.

We have changed the text to clarify the method used to measure Km for flavins.

• A related point is that the manuscript refers to Km as an "affinity". This is inappropriate and should be avoided, as the Km is not the Kd.

We agree with the referee that the Km is not the Kd. However, under the appropriate conditions, to which our experiments conform, Km is accepted as a relevant approximation of affinity (Srinisivan, FEBS Journal, v 289 pp 6086-6098 2022). We have added a sentence to clarify this point and cite this reference in the text.

• The environment around the putative oxygen site should be shown. The text indicates that "the residues characteristic of the O2 reducing center in eukaryotic FRD domains of NOX and DUOX enzymes are not conserved in SpNOX." How does the site look? This point relates to the more general comment above on the oxidizing substrate used by this bacterial NOX.

This is a really interesting point that contains many potential biological developments for future studies of this prokaryotic family of NOX enzymes. While we were submitting this work to eLife for evaluation, another group (Murphy's lab) filed a pre-publication in BioRXiv, in which they also solved the structure of SpNOX but this time by CryoEM with an unexpected level of resolution for such a small protein (their paper is not yet published but probably under peer review somewhere). In their work, they made a special effort to identify the O2 reducing center (bacterial NOX sequences alignment, mutation studies, …) They were not able to localize such a site with accuracy. There is also other complementary data between their work and ours. So, we will add a paragraph at the end of the discussion to comment on this parallel work and to emphasize on the complementarity of their studies and what it brings to the final understanding of this enzyme.

• The section "A Close-up View of NOX's NAD(P)H Binding Domains vs the FNR Gold Standard" should be clarified.

I found it difficult to understand. Is the different conformation of Phe397 creating the crevice? Could NADPH be modeled in NOX2 and DUOX in the same conformation observed in FNR and modeled in the bacterial NOX? Or would there be clashes, implying the necessity of larger conformational changes to bring the nicotinamide closer to the FAD?

Please see responses above on this point; we have amended the text to clarify. In a few words, we propose that activation in the eukaryotic enzymes would entail NBD subdomain (containing NADPH site) towards the FBD subdomain (containing FAD) through an internal motion within the DH domain. Doing so, they would approach the DH domain topology of SpNOX, which models an active state.

Reviewer #2 (Recommendations For The Authors):

On p. 6, second line, it should be (Figure 1C and 1D). Space is missing between C and "and".

On p. 9, in Figure 3, the labeling A and B are missing. Also, the legend of part B does not correspond to the actual graph colors. Thus, the tracing of F397W is red and not grey as indicated in the legend.

Corrected. Thank you