Also known as Water head.

Author response:

The following is the authors’ response to the previous reviews

Reviewer #1 (Public review):

Summary:

The authors investigate the neuroprotective effect of reserpine in a retinitis pigmentosa (P23H-1) model, characterized by a mutation in the rhodopsin gene. Their results reveal that female rats show better preservation of both rod and cone photoreceptors following reserpine treatment compared to males.

Strengths:

This study effectively highlights the neuroprotective potential of reserpine and underscores the value of drug repositioning as a strategy for accelerating the development of effective treatments. The findings are significant for their clinical implications, particularly in demonstrating sex-specific differences in therapeutic response.

We sincerely appreciate the reviewer’s comments.

Weaknesses:

The main limitation is the lack of precise identification of the specific pathway through which reserpine prevents photoreceptor death.

We acknowledge that the exact pathway through which reserpine exerts its protective effects on photoreceptors remains undetermined, yet our findings provide critical insights into potential mechanisms. Together with our previous report [PMID: 36975211], the studies being presented here validate proteostasis (including autophagy) and p53 signaling as the key pathways underlying reserpine-mediated survival of photoreceptors in retinal disease models. We also go a step further by showing an influence of the biological sex.

We emphasize that the primary aim of this study was to demonstrate the effectiveness of reserpine in a different retinal degeneration model—specifically, the autosomal dominant RP model—which shares a retinal disease phenotype with the model used for initial screening but involves different genetic and molecular mechanisms of degeneration.

Reviewer #2 (Public review):

Summary:

In the manuscript entitled "Sex-specific attenuation of photoreceptor degeneration by reserpine in a rhodopsin P23H rat model of autosomal dominant retinitis pigmentosa" by Beom Song et al., the authors explore the transcriptomic differences between male and female wild-type (WT) and P23H retinas, highlighting significant gene expression variations and sex-specific trends. The study emphasizes the importance of considering biological sex in understanding inherited retinal degeneration and the impact of drug treatments on mutant retinas.

Strengths:

(1) Relevance to Clinical Challenges: The study addresses a critical limitation in inherited retinal degeneration (IRD) therapies by exploring a gene-agnostic approach. It emphasizes sex-specific responses, which aligns with recent NIH mandates on sex as a biological variable.

(2) Multi-dimensional Methodology: Combining electroretinography (ERG), optical coherence tomography (OCT), histology, and transcriptomics strengthens the study's findings.

(3) Novel Insights: The transcriptomic analysis uncovers sex-specific pathways impacted by reserpine, laying the foundation for personalized approaches to retinal disease therapy.

We are grateful for highlighting the strengths of our work.

Weaknesses:

Dose Optimization

The study uses a fixed dose (40 µM), but no dose-response analysis is provided. Sex-specific differences in efficacy might be influenced by suboptimal dosing, particularly considering potential differences in metabolism or drug distribution.

We acknowledge the limitation of using a fixed dose (40 µM) of reserpine in this study without conducting a comprehensive dose-response analysis. In the primary screens, the EC₅₀ of reserpine was approximately 20 µM. We doubled the concentration for injection to account for the potential loss of reserpine during the in vivo procedures. As we observed the rescue effect of reserpine in mice, we used the same concentration for rats. The fixed-dose approach was chosen to maintain consistency with previous studies evaluating reserpine in retinal degeneration models and to facilitate comparison across studies. Efforts to identify optimal dosing were deprioritized, as the primary goal was different and this information cannot be directly translated to clinical applications.

We also agree that sex-specific differences in efficacy might be influenced by suboptimal dosing, particularly given potential variations in metabolism, drug distribution, and pharmacokinetics between male and female rats. However, recent pharmacokinetic studies on systemically administered reserpine in rats reported no statistically significant covariates, including body weight, age, breed, or sex, affecting pharmacokinetic (PK) or pharmacodynamic (PD) parameters (Alfosea-Cuadrado, G. M., Zarzoso-Foj, J., Adell, A., Valverde-Navarro, A. A., González-Soler, E. M., Mangas-Sanjuán, V., & Blasco-Serra, A. (2024). Population Pharmacokinetic–Pharmacodynamic Analysis of a Reserpine-Induced Myalgia Model in Rats. Pharmaceutics, 16(8), 1101. https://doi.org/10.3390/pharmaceutics16081101). Furthermore, no evidence of sex-specific differences in reserpine pharmacokinetics has been previously identified in available databases (National Center for Biotechnology Information (2025). PubChem Compound Summary for CID 5770, Reserpine. Retrieved January 13, 2025 from https://pubchem.ncbi.nlm.nih.gov/compound/Reserpine). Importantly, the drug in this study was administered intravitreally, where the ocular compartments are relatively isolated from systemic metabolism or excretion. Under these conditions, where absorption, distribution, metabolism, and excretion have minimal impact, we observed sex differences in efficacy using the same dose of drug.

Nonetheless, we agree with the reviewer and plan to pursue dose-response and other studies in future investigations.

Statistical Analysis

In my opinion, there is room for improvement. How were the animals injected? Was the contralateral eye used as control? (no information in the manuscript about it!, line 390 just mentions the volume and concentration of injections). If so, why not use parametric paired analysis? Why use a non-parametric test, as it is the Mann-Whitney U? The Mann-Whitney U test is usually employed for discontinuous count data; is that the case here?<br /> Therefore, please specify whether contralateral eyes or independent groups served as controls. If contralateral controls were used, paired parametric tests (e.g., paired t-tests) would be statistically appropriate. Alternatively, if independent cohorts were used, non-parametric Mann-Whitney U tests may suffice but require clear justification.

We apologize for the lack of clarity. In line 124, we described the injection as “bilateral intravitreal injections of 5 µL of either vehicle or 40 µM reserpine,” and in Figure 1A, we annotated the bilateral injection as DMSO for both eyes and RSP for both eyes. To address this uncertainty, we added the clarification, “with each group receiving bilateral injections of either vehicle or reserpine” (lines 404–405). Since the results are not paired and involve continuous data for which the normality assumption cannot be confidently met or verified, we used the Mann-Whitney U test for statistical analysis.

Sex-Specific Pathways

The authors do identify pathways enriched in female vs. male retinas but fail to explicitly connect these to the changes in phenotype analysed by ERG and OCT. The lack of mechanistic validation weakens the argument.

The study does not explore why female rats respond better to reserpine. Potential factors such as hormonal differences, retinal size, or differential drug uptake are not discussed.

It remains open, whether observed transcriptomic trends (e.g., proteostasis network genes) correlate with sex-specific functional outcomes.

We acknowledge that, while we identified pathways enriched in female versus male retinas, we did not explicitly connect these findings to the functional phenotypes measured by ERG and OCT. Although our transcriptomic data suggest that reserpine differentially influences pathways such as proteostasis and p53 signaling, we did not conduct mechanistic experiments to validate a causal relationship between these pathways and the observed outcomes.

In practice, designing a study to validate the mechanisms of a small molecule modulating multiple pathways presents significant challenges. If the pathways cannot be specifically modulated or if modulation could result in irreversible outcomes, the mechanistic validation becomes difficult to achieve. Drugs demonstrating mutation-agnostic efficacy are often investigated primarily through outcome measures and the analysis of affected pathways rather than through direct mechanistic validation (Leinonen, H., Zhang, J., Occelli, L. M., Seemab, U., Choi, E. H., L P Marinho, L. F., Querubin, J., Kolesnikov, A. V., Galinska, A., Kordecka, K., Hoang, T., Lewandowski, D., Lee, T. T., Einstein, E. E., Einstein, D. E., Dong, Z., Kiser, P. D., Blackshaw, S., Kefalov, V. J., Tabaka, M., … Palczewski, K. (2024). A combination treatment based on drug repurposing demonstrates mutation-agnostic efficacy in pre-clinical retinopathy models. Nature communications, 15(1), 5943. https://doi.org/10.1038/s41467-024-50033-5).

As recommended, we added potential factors that might influence the differential response to reserpine, based on other studies (lines 353–362) highlighting differences in dopamine storage capacity and estrogen independence. We also added a discussion on the possibility of sex-related differences in basal ERG response levels (lines 363–366).

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

The study presents compelling findings on the neuroprotective effects of reserpine in a well-established model of retinitis pigmentosa (P23H-1). The use of ERG, optomotor assays, OCT, immunohistochemistry, and transcriptomic techniques provides a good exploration of the treatment's effects, particularly highlighting the differential response in females. The study underscores the potential of drug repurposing to expedite the availability of therapeutic interventions for patients.

Thanks for your generous comments.

While the manuscript presents an important contribution, I would like to highlight a few points that need clarification or further elaboration to strengthen the work:

(1) Please include the photopic a-wave data in your analysis or provide a justification for its omission. Specifically, it would be valuable to know whether there is an improvement in this parameter under reserpine treatment.

We appreciate the reviewer’s suggestion to include photopic a-wave data in our analysis and acknowledge the importance of this parameter in evaluating cone photoreceptor function. However, we did not analyze the photopic a-wave amplitude in our study because we found the photopic a-wave has low amplitude and high variability, consistent with findings in other studies with P23H-1 rats (Orhan E, Dalkara D, Neuillé M, Lechauve C, Michiels C, et al. (2015) Genotypic and Phenotypic Characterization of P23H Line 1 Rat Model. PLOS ONE 10(5): e0127319. https://doi.org/10.1371/journal.pone.0127319) or even with wild type rats (V.L. Fonteille, J. Racine, S. Joly, A.L. Dorfman, S. Rosolen, P. Lachapelle; Do Rats Generate a Photopic a–Wave? . Invest. Ophthalmol. Vis. Sci. 2005;46(13):2246). We added the description (lines 435-437) explaining why the photopic a-wave was not analyzed. Studies with P23H-1 did not analyze the photopic a-wave, probably for similar reasons.

(2) In Figure 1, it would be helpful to include data from normal control animals to provide a benchmark for retinal degeneration in P23H-1 animals and to better contextualize the effects of reserpine treatment.

Thanks. As suggested, we have included data from normal control animals to Figure 1.

(3) The manuscript states that "Treated female retinas have significantly higher expression of the gene for P62 (SQSTM1), indicating a potential key route for reserpine's activity" (Line 331). Please explain how this difference in expression might translate into a better photoreceptor response in females compared to males.

The difference in P62 (SQSTM1) expression between treated female and male retinas could have important implications for the photoreceptor response. We have identified in our previous study that reserpine increased P62 that mediates proteome balance between ubiquitin-proteasome system (UPS) and autophagy. Together with the role of P62 in the regulation of oxidative stress, P62 might be important for photoreceptor survival and function. Higher expression of P62 in treated females could suggest more efficient cellular maintenance and a better ability to cope with stress, leading to improved photoreceptor survival and function.

(4) Numerous studies have shown that animal models of Parkinson's disease (e.g., those treated with MPTP or rotenone) or retinal tissue from Parkinson's patients exhibit dopaminergic cell death and associated vision loss. Please discuss how these findings relate to your results. Can you hypothesize how dopamine depletion by reserpine may lead to improved photoreceptor responses in your model?

We appreciate the reviewer’s insightful comments. Both MPTP and rotenone act via inhibition of complex I of the respiratory chain, causing cell death and leading to dopamine depletion. In contrast, reserpine acts by inhibiting the vesicular monoamine transporter, depleting catecholamines by preventing their storage and facilitating their metabolism by monoamine oxidase. Although reserpine and other agents can induce animal models of Parkinson's disease, reserpine differs from the others in several aspects: (i) reserpine do not induce neurodegeneration and protein aggregation; (ii) motor performance, monoamine content, and TH staining are partially restored after treatment interruption; and (iii) reserpine lacks specificity regarding dopaminergic neurotransmission (Leão, A. H., Sarmento-Silva, A. J., Santos, J. R., Ribeiro, A. M., & Silva, R. H. (2015). Molecular, Neurochemical, and Behavioral Hallmarks of Reserpine as a Model for Parkinson's Disease: New Perspectives to a Long-Standing Model. Brain pathology (Zurich, Switzerland), 25(4), 377–390. https://doi.org/10.1111/bpa.12253). We have discussed the various effects of catecholamine depletion on retinal diseases (lines 331–337). Both dopamine receptor antagonists and agonists, as well as catecholamine depletion, can exert protective effects on the retina. The reduction in scotopic b-wave amplitude observed at P54, followed by a lack of further progression in degeneration, may support the hypothesis that reduced neuronal activity due to catecholamine depletion could have mitigated damage to retinal neurons.

(5) For readers who may not be familiar with the P23H-1 mutation, it would be beneficial to include a brief description of the timeline and progression of retinal degeneration in this model.

As the progression varies among studies, we have provided our description on observations from the same facility where the animals were housed. The timeline and progression of retinal degeneration are briefly described in the results section (lines 112–115) and Supplementary Figure 1.

(6) Do you have any data on the effects of reserpine treatment in older animals? If available, this could provide additional insight into the potential applicability of reserpine in later stages of disease progression.

Unfortunately, we do not have data from older animals. As described in the results section (lines 116–124), we set the timepoint for interventions before functional impairment peaked, aiming to harness the remaining potential for rescue and promote functional improvement. Our approach focused on developing a gene-agnostic therapy that can delay disease progression and be delivered at an earlier stage than AAV-based therapies, using FDA-approved drugs.

(7) Molecular Basis of Sex Differences: The molecular mechanisms underlying the differential responses in males and females should be elaborated upon. If possible, include a discussion or hypothesis that addresses these sex-specific differences at the molecular level.

We thank the reviewer for highlighting the importance of addressing the molecular basis of sex-specific differences. In our study, we observed distinct transcriptomic responses to reserpine between male and female rats, particularly in molecular pathways related to proteostasis and p53 signaling. While the sex-specific differences in these molecular pathways remain to be fully evaluated, we have added a discussion on sex differences in reserpine responses, incorporating findings from other studies (lines 353–366).

Reviewer #2 (Recommendations for the authors):

(1) There is no mention in the manuscript about the fact that the transgene rats have several copies of rhodopsin and how this can affect these sex differences. Would it be the same in the P23H KO mouse? Or in other models with a single copy of the mutation?

We have described in the Materials and Methods section how they were bred, but we did not specifically mention the allele status in the manuscript. Hemizygous P23H-1 rats used in this study carry a single P23H transgene allele with a transgene copy number of 9, in addition to the normal two wild-type opsin alleles. We added this description to clear the uncertainty (lines 384-387.

(2) This sentence: in abstract lines 26 to 29: "Recently, we identified reserpine as a lead molecule for maintaining rod survival in mouse and human retinal organoids as well as in the rd16 mouse, which phenocopy Leber congenital amaurosis caused by mutations in the cilia-centrosomal gene CEP290 (Chen et al. eLife 2023;12:e83205. DOI: https://doi.org/10.7554/eLife.83205)", to my vew, does not belong to the abstract, maybe in the introduction as stage of art.

Thank you for asking. According to the guidelines for the research advance articles (that follow previously published studies), a reference to the original eLife article should be included in the abstract. As specified in the guidelines, we have updated the citation format to (author, year) for referencing eLife articles (line 29).

(3) Lines 167-170: "Histologic evaluation of the retinas also demonstrated more prominent ONL thinning in the dorsal retina and increased ONL thickness in the dorsal retina measured at 1,000, 1,250, and 1,500 µm distant from the optic nerve head in reserpine-treated group compared with control group (Figure 3C)". I do not understand this sentence. Is it a more prominent thinning or an increased thickness?

We apologize for the confusion caused by this sentence. The histological evaluation showed that ONL thinning was more pronounced in the dorsal retina of control group, which was consistent with OCT findings in Figure 3A. Reserpine treatment increased the ONL thickness in the dorsal retina at specific distances from the optic nerve head (1,000, 1,250, and 1,500 µm). We have revised the sentence for clarity (lines 165-168).

(4) Lines 182-185 and Figure 4B: FL is not the best approach to quantify rhodopsin levels. Since the DAPI staining is overexposed, it is hard to evaluate the staining of RHO in the ONL. From the visible staining in the OS, it is only possible to affirm that the OS are longer in RSP-treated retinas... more is not to be affirmed based on these figures. I suggest using WB.

We acknowledge the reviewer’s concern regarding the use of fluorescence imaging to quantify rhodopsin levels. While our current data highlight structural preservation, such as the length of the outer segments, we agree that drawing conclusions about rhodopsin levels from fluorescence staining is limited. As we do not have samples for WB and fluorescence imaging cannot quantify rhodopsin, we have revised the description (lines 180-184).

(5) Lines 188-190 and Figure 4C: The images in 4C showed an extreme divergence between treated and untreated retina concerning the amount of stained cones, which is not observed at the quantification at 1000µm statistic. Are the images not representative?

We agree with the reviewer that the images in Figure 4C may not adequately represent the quantified data. To address this, we have changed the figure to reflect the quantification results accurately.

(6) Figures 6C-6D and 6G. Why do the authors not use any statistical analysis? Or are the differences not statistically significant? Why do authors use only WT and DMSO controls? What about untreated P23H controls (no DMSO)?

Thanks for checking, and we apologize for the oversight. We have updated figures 5, 6 and S5 to include adjusted p-value in relevant plots. In addition, details of significance threshold are available in supplementary tables. Regarding controls, untreated P23H retinas (without DMSO) were not included in the current analysis, as our experience shows that DMSO injection itself does not cause functional or structural changes. The key data demonstrating the effect of reserpine involve a comparison between the group treated with reserpine and the control group treated with DMSO, as the only difference between these groups is the involvement of the drug.

(7) Validation of findings by testing key genes (e.g., p62/SQSTM1, Nrf2) using qPCR or immunohistochemistry will strengthen the findings.

We appreciate the reviewer’s suggestion to validate key findings using qPCR or immunohistochemistry, as such experiments are crucial for further strengthening our conclusions. While this was not feasible in the current study due to various constraints, we fully recognize their importance and plan to incorporate these in our follow-up studies.

Prueba

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Overall I found the approach taken by the authors to be clear and convincing. It is striking that the conclusions are similar to those obtained in a recent study using a different computational approach (finite state controllers), and lends confidence to the conclusions about the existence of an optimal memory duration. There are a few questions that could be expanded on in future studies:

(1) Spatial encoding requirements

The manuscript contrasts the approach taken here (reinforcement learning in a gridworld) with strategies that involve a "spatial map" such as infotaxis. However, the gridworld navigation algorithm has an implicit allocentric representation, since movement can be in one of four allocentric directions (up, down, left, right), and wind direction is defined in these coordinates. Future studies might ask if an agent can learn the strategy without a known wind direction if it can only go left/right/forward/back/turn (in egocentric coordinates). In discussing possible algorithms, and the features of this one, it might be helpful to distinguish (1) those that rely only on egocentric computations (run and tumble), (2) those that rely on a single direction cue such as wind direction, (3) those that rely on allocentric representations of direction, and (4) those that rely on a full spatial map of the environment.

We agree that the question of what orientation skills are needed to implement an algorithm is interesting. We remark that our agents do not use allocentric directions in the sense of north, east, west and east relative to e.g. fixed landmarks in the environment. Instead, directions are defined relative to the mean wind, which is assumed fixed and known. (In our first answer to reviewers we used “north east south west relative to mean wind”, which may have caused confusion – but in the manuscript we only use upwind downwind and crosswind).

(2) Recovery strategy on losing the plume

The authors explore several recovery strategies upon losing the plume, including backtracking, circling, and learned strategies, finding that a learned strategy is optimal. As insects show a variety of recovery strategies that can depend on the model of locomotion, it would be interesting in the future to explore under which conditions various recovery strategies are optimal and whether they can predict the strategies of real animals in different environments.

Agreed, it will be interesting to study systematically the emergence of distinct recovery strategies and compare to living organisms.

(3) Is there a minimal representation of odor for efficient navigation?

The authors suggest that the number of olfactory states could potentially be reduced to reduce computational cost. They show that reducing the number of olfactory states to 1 dramatically reduces performance. In the future it would be interesting to identify optimal internal representations of odor for navigation and to compare these to those found in real olfactory systems. Does the optimal number of odor and void states depend on the spatial structure of the turbulence as explored in Figure 5?

We agree that minimal odor representations are an intriguing question. While tabular Q learning cannot derive optimal odor representations systematically, one could expand on the approach we have taken here and provide more comparisons. It will be interesting to follow this approach in a future study.

Reviewer #2 (Public review):

Summary:

The authors investigate the problem of olfactory search in turbulent environments using artificial agents trained using tabular Q-learning, a simple and interpretable reinforcement learning (RL) algorithm. The agents are trained solely on odor stimuli, without access to spatial information or prior knowledge about the odor plume's shape. This approach makes the emergent control strategy more biologically plausible for animals navigating exclusively using olfactory signals. The learned strategies show parallels to observed animal behaviors, such as upwind surging and crosswind casting. The approach generalizes well to different environments and effectively handles the intermittency of turbulent odors.

Strengths:

* The use of numerical simulations to generate realistic turbulent fluid dynamics sets this paper apart from studies that rely on idealized or static plumes.

* A key innovation is the introduction of a small set of interpretable olfactory states based on moving averages of odor intensity and sparsity, coupled with an adaptive temporal memory.

* The paper provides a thorough analysis of different recovery strategies when an agent loses the odor trail, offering insights into the trade-offs between various approaches.

* The authors provide a comprehensive performance analysis of their algorithm across a range of environments and recovery strategies, demonstrating the versatility of the approach.

* Finally, the authors list an interesting set of real-world experiments based on their findings, that might invite interest from experimentalists across multiple species.

Weaknesses:

* Using tabular Q-learning is both a strength and a limitation. It's simple and interpretable, making it easier to analyze the learned strategies, but the discrete action space seems somewhat unnatural. In real-world biological systems, actions (like movement) are continuous rather than discrete. Additionally, the ground-frame actions may not map naturally to how animals navigate odor plumes (e.g. insects often navigate based on their own egocentric frame).

We agree with the reviewer, and will look forward to study this problem further to make it suitable for meaningful comparisons with animal behavior.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

The authors have addressed my major concerns and I support publication of this interesting manuscript. A couple of small suggestions:

(1) In discussing performance in different environments (line 328-362) it might be easier to read if you referred to the environments by descriptive names rather than numbers.

Thank you for the suggestion, which we implemented

(2) Line 371: measurements of flow speed depend on antennae in insects. Insects can measure local speed and direct of flow using antennae, e.g. Bell and Kramer, 1979, Suver et al. 2019. Okubo et al. 2020,

Thank you for the references

(3) line 448: "Similarly, an odor detection elicits upwind surges that can last several seconds" maybe "Similarly, an odor detection elicits upwind surges that can outlast the odor by several seconds"?

Thank you for the suggestion

Reviewer #2 (Recommendations for the authors):

I commend the authors for their revisions in response to reviewer feedback.

While I appreciate that the manuscript is now accompanied by code and data, I must note that the accompanying code-repository lacks proper instructions for use and is likely incomplete (e.g. where is the main function one should run to run your simulations? How should one train? How should one recreate the results? Which data files go where?).

For examples of high-quality code-release, please see the documentation for these RL-for-neuroscience code repositories (from previously published papers):

https://github.com/ryzhang1/Inductive_bias

https://github.com/BruntonUWBio/plumetracknets

The accompanying data does provide snapshots from their turbulent plume simulations, which should be valuable for future research.

Thank you for the suggestions for how to improve clarity of the code. The way we designed the repository is to serve both the purpose of developing the code as well as sharing. This is because we are going to build up on this work to proceed further. Nothing is missing in the repository (we know it because it is what we actually use).

We do plan to create a more user-friendly version of the code, hopefully this will be ready in the next few months, but it wont be immediate as we are aiming to also integrate other aspects of the work we are currently doing in the Lab. The Brunton repository is very well organized, thanks for the pointer.

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

Reviewer #1 (Public review):

Overall I found the approach taken by the authors to be clear and convincing. It is striking that the conclusions are similar to those obtained in a recent study using a different computational approach (finite state controllers), and lend confidence to the conclusions about the existence of an optimal memory duration. There are a few points or questions that could be addressed in greater detail in a revision:

(1) Discussion of spatial encoding

The manuscript contrasts the approach taken here (reinforcement learning in a grid world) with strategies that involve a "spatial map" such as infotaxis. The authors note that their algorithm contains "no spatial information." However, I wonder if further degrees of spatial encoding might be delineated to better facilitate comparisons with biological navigation algorithms. For example, the gridworld navigation algorithm seems to have an implicit allocentric representation, since movement can be in one of four allocentric directions (up, down, left, right). I assume this is how the agent learns to move upwind in the absence of an explicit wind direction signal. However, not all biological organisms likely have this allocentric representation. Can the agent learn the strategy without wind direction if it can only go left/right/forward/back/turn (in egocentric coordinates)? In discussing possible algorithms, and the features of this one, it might be helpful to distinguish<br /> (1) those that rely only on egocentric computations (run and tumble),<br /> (2) those that rely on a single direction cue such as wind direction,<br /> (3) those that rely on allocentric representations of direction, and<br /> (4) those that rely on a full spatial map of the environment.

As Referee 1 points out, even if the algorithm does not require a map of space, the agent is still required to tell apart directions relative to the wind direction which is assumed known. Indeed, although in the manuscript we labeled actions allocentrically as “ up down left and right”, the source is always placed in the same location, hence “left” corresponds to upwind; “right” to downwind and “up” and “down” to crosswind right and left. Thus in fact directions are relative to the mean wind, which is therefore assumed known. We have better clarified the spatial encoding required to implement these strategies, and re-labeled the directions as upwind, downwind, crosswind-right and crosswind-left.

In reality, animals cannot measure the mean flow, but rather the local flow speed e.g. with antennas for insects, with whiskers for rodents and with the lateral line for marine organisms. Further work is needed to address how local flow measures enable navigation using Q learning.

(2) Recovery strategy on losing the plume

While the approach to encoding odor dynamics seems highly principled and reaches appealingly intuitive conclusions, the approach to modeling the recovery strategy seems to be more ad hoc. Early in the paper, the recovery strategy is defined to be path integration back to the point at which odor was lost, while later in the paper, the authors explore Brownian motion and a learned recovery based on multiple "void" states. Since the learned strategy works best, why not first consider learned strategies, and explore how lack of odor must be encoded or whether there is an optimal division of void states that leads to the best recovery strategies? Also, although the authors state that the learned recovery strategies resemble casting, only minimal data are shown to support this. A deeper statistical analysis of the learned recovery strategies would facilitate comparison to those observed in biology.

We thank Referee 1 for their remarks and suggestion to give the learned recovery a more prominent role and better characterize it. We agree that what is done in the void state is definitely key to turbulent navigation. In the revised manuscript, we have further substantiated the statistics of the learned recovery by repeating training 20 times and comparing the trajectories in the void (Figure 3 figure supplement 3, new Table 1). We believe however that starting with the heuristic recovery is clearer because it allows to introduce the concept of recovery more clearly. Indeed, the learned “recovery” is so flexible that it ends up mixing recovery (crosswind motion) to aspects of exploitation (surge): we defer a more in-depth analysis that disentangles these two aspects elsewhere. Also, we added a whole new comparison with other biologically inspired recoveries both in the native environment and for generalization (Figure 3 and 5).

(3) Is there a minimal representation of odor for efficient navigation?

The authors suggest (line 280) that the number of olfactory states could potentially be reduced to reduce computational cost. This raises the question of whether there is a maximally efficient representation of odors and blanks sufficient for effective navigation. The authors choose to represent odor by 15 states that allow the agent to discriminate different spatial regimes of the stimulus, and later introduce additional void states that allow the agent to learn a recovery strategy. Can the number of states be reduced or does this lead to loss of performance? Does the optimal number of odor and void states depend on the spatial structure of the turbulence as explored in Figure 5?

We thank the referee for their comment. Q learning defines the olfactory states prior to training and does not allow a systematic optimization of odor representation for the task. We can however compare different definitions of the olfactory states, for example based on the same features but different discretizations. We added a comparison with a drastically reduced number of non-empty olfactory states to just 1, i.e. if the odor is above threshold at any time within the memory, the agent is in the non-void olfactory state, otherwise it is in the void state. This drastic reduction in the number of olfactory states results in less positional information and degrades performance (Figure 5 figure supplement 5).

The number of void states is already minimal: we chose 50 void states because this matches the time agents typically remain in the void (less than 50 void states results in no convergence and more than 50 introduces states that are rarely visited).

One may instead resort to deep Q-learning or to recurrent neural networks, which however do not provide answers as for what are the features or olfactory states that drive behavior (see discussion in manuscript and questions below).

Reviewer #2 (Public review):

Summary:

The authors investigate the problem of olfactory search in turbulent environments using artificial agents trained using tabular Q-learning, a simple and interpretable reinforcement learning (RL) algorithm. The agents are trained solely on odor stimuli, without access to spatial information or prior knowledge about the odor plume's shape. This approach makes the emergent control strategy more biologically plausible for animals navigating exclusively using olfactory signals. The learned strategies show parallels to observed animal behaviors, such as upwind surging and crosswind casting. The approach generalizes well to different environments and effectively handles the intermittency of turbulent odors.

Strengths:

(1) The use of numerical simulations to generate realistic turbulent fluid dynamics sets this paper apart from studies that rely on idealized or static plumes.

(2) A key innovation is the introduction of a small set of interpretable olfactory states based on moving averages of odor intensity and sparsity, coupled with an adaptive temporal memory.

(3) The paper provides a thorough analysis of different recovery strategies when an agent loses the odor trail, offering insights into the trade-offs between various approaches.

(4) The authors provide a comprehensive performance analysis of their algorithm across a range of environments and recovery strategies, demonstrating the versatility of the approach.

(5) Finally, the authors list an interesting set of real-world experiments based on their findings, that might invite interest from experimentalists across multiple species.

Weaknesses:

(1) The inclusion of Brownian motion as a recovery strategy, seems odd since it doesn't closely match natural animal behavior, where circling (e.g. flies) or zigzagging (ants' "sector search") could have been more realistic.

We agree that Brownian motion may not be biologically plausible -- we used it as a simple benchmark. We clarified this point, and re-trained our algorithm with adaptive memory using circling and zigzaging (cast and surge) recoveries. The learned recovery outperforms all heuristic recoveries (Figure 3D, metrics G). Circling ranks second, and achieves these good results by further decreasing the probability of failure and paying slightly in speed. When tested in the non-native environments 2 to 6, the learned recovery performs best in environments 2, 5 and 6 i.e. from long range more relevant to flying insects; whereas circling generalizes best in odor rich environments 3 and 4, representative of closer range and close to the substrate (Figure 5B, metrics G). In the new environments, similar to the native environment, circling favors convergence (Figure 5B, metrics f⁺) over speed (Figure 5B, metrics g⁺ and τ_min/τ), which is particularly deleterious at large distance.

(2) Using tabular Q-learning is both a strength and a limitation. It's simple and interpretable, making it easier to analyze the learned strategies, but the discrete action space seems somewhat unnatural. In real-world biological systems, actions (like movement) are continuous rather than discrete. Additionally, the ground-frame actions may not map naturally to how animals navigate odor plumes (e.g. insects often navigate based on their own egocentric frame).

We agree with the reviewer that animal locomotion does not look like a series of discrete displacements on a checkerboard. However, to overcome this limitation, one has to first focus on a specific system to define actions in a way that best adheres to a species’ motor controls. Moreover, these actions are likely continuous, which makes reinforcement learning notoriously more complex. While we agree that more realistic models are definitely needed for a comparison with real systems, this remains outside the scope of the current work. We have added a remark to clarify this limitation.

(3) The lack of accompanying code is a major drawback since nowadays open access to data and code is becoming a standard in computational research. Given that the turbulent fluid simulation is a key element that differentiates this paper, the absence of simulation and analysis code limits the study's reproducibility.

We have published the code and the datasets at

- code: https://github.com/Akatsuki96/qNav

- datasets: https://zenodo.org/records/14655992

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) Line 59-69: In comparing the results here to other approaches (especially the Verano and Singh papers), it would also be helpful to clarify which of these include an explicit representation of the wind direction. My understanding is that both the Singh and Verano approaches include an explicit representation of wind direction. In Singh wind direction is one of the observations that inputs to the agent, while in Verano, the actions are defined relative to the wind direction. In the current paper, my understanding is that there is no explicitly defined wind direction, but because movement directions are encoded allocentrically, the agent is able to learn the upwind direction from the structure of the plume- is this correct? I think this information would be helpful to spell out and also to address whether an agent without any allocentric direction sense can learn the task.

Thank you for the comment. In our algorithm the directions are defined relative to the mean wind, which is assumed known, as in Verano et al. As far as we understand, Singh et al provide the instantaneous, egocentric wind velocities as part of the input.

(1) Line 105: "several properties of odor stimuli depend on the distance from the source" might cite Boie...Victor 2018, Ackles...Schaefer, 2021, Nag...van Breugel 2024.

Thank you for the suggestions - we have added these references

(2) Line 130: "we first define a finite set of olfactory states" might be helpful to the reader to state what you chose in this paragraph rather than further down.

We have slightly modified the incipit of the paragraph. We first declare we are setting out to craft the olfactory states, then define the challenges, finally we define the olfactory states.

(3) Line 267: "Note that the learned recovery strategy resembles casting behavior observed in flying insects" Might note that insects seem to deploy a range of recovery strategies depending on locomotor mode and environment. For example, flying flies circle and sink when odor is lost in windless environments (Stupski and van Breugel 2024).

Thank you for your comment. We have included the reference and we now added comparisons to results using circling and cast & surge recovery strategies.

(4) Line 289: "from positions beyond the source, the learned strategy is unable to recover the plume as it mostly casts sideways, with little to no downwind action" This is curious as many insects show a downwind bias in the absence of odor that helps them locate the plumes in the first place (e.g. Wolf and Wehner, 2000, Alvarez-Salvado et al. 2018). Is it possible that the agent could learn a downwind bias in the absence of odor if given larger environments or a longer time to learn?

The reviewer is absolutely correct – Downwind motion is not observed in the recovery simply because the agent rarely overshoots the source. Hence overall optimization for that condition is washed out by the statistics. We believe downwind motion will emerge if an agent needs to avoid overshooting the source – we do not have conclusive results yet but are planning to introduce such flexibility in a further work. We added this remark and refs.

(5) Line 377-391: testing these ideas in living systems. Interestingly, Kathman..Nagel 2024 (bioRxiv) shows exactly the property predicted here and in Verano in fruit flies- an odor memory that outlasts the stimulus by a duration of several seconds, appropriate for filling in "blanks." Relatedly, Alvarez-Salvado et al. 2018 showed that fly upwind running reflected a temporal integration of odor information over ~10s, sufficient to avoid responding to blanks as loss of odor.

Indeed, we believe this is the most direct connection between algorithms and experiments. We are excited to discuss with our colleagues and pursue a more direct comparison with animal behavior. We were aware of the references and forgot to cite them, thank you for your careful reading of our work !

Reviewer #2 (Recommendations for the authors):

Suggestions

(1) The paper does not clearly specify which type of animals (e.g., flying insects, terrestrial mammals) the model is meant to approximate or not approximate. The authors should consider clarifying how these simulations are suited to be a general model across varied olfactory navigators. Further, it isn't clear how low/high the intermittency studied in this model is compared to what different animals actually encounter. (Minor: The Figure 4 occupancy circles visualization could be simplified).

Environment 1 represents the lower layers of a moderately turbulent boundary layer. Search occurs on a horizontal plane ~half meter from the ground. The agent is trained at distances of about 10 meters and also tested on longer distances  ~ 17 meters (environment 6), lower heights ~1cm from the ground (environments 3-4), lower Reynolds number (environment 5) and higher threshold of detection (environment 2 and 4). Thus Environments 1,2,5 and 6 are representative of conditions encountered by flying organisms (or pelagic in water), and Environments 3 and 4 of searches near the substrate, potentially involved in terrestrial navigation (benthic in water). Even near the substrate, we use odor dispersed in the fluid, and not odor attached to the substrate (relevant to trail tracking).

Also note that we pick Schmidt number Sc = 1 and this is appropriate for odors in air but not in water. However, we expect a weak dependence on the Schmidt number as the Batchelor and Kolmogorov scales are below the size of the source and we are interested in the large scale statistics Falkovich et al., 2001; Celani et al., 2014; Duplat et al., 2010.

Intermittency contours are shown in Fig 1C, they are highest along the centerline, and decay away from the centerline, so that even within the plume detecting odor is relatively rare. Only a thin region near the centerline has intermittency larger than 66%; the outer and most critical bin of the plume has intermittency under 33%; in the furthest point on the centerline intermittency is <10%. For reference, experimental values in the atmospheric boundary layer report intermittency 25% to 20% at 2 to 15m from the source along the centerline (Murlis and Jones, 1981).

We have more clearly labeled the contours in Fig 1C and added these remarks.

We included these remarks and added a whole table with matching to real conditions within the different environments.

(2) Could some biological examples and references be added to support that backtracking is a biologically plausible mechanism?

Backtracking was observed e.g. in ants displaced in unfamiliar environments (Wystrach et al, P Roy Soc B, 280,  2013), in tsetse flies executing reverse turns uncorrelated to wind, which bring them back towards the location where they last detected odor (Torr, Phys Entom, 13, 1988, Gibson & Brady Phys Entom 10, 1985) and in coackroaches upon loss of contact with the plume (Willis et al, J. Exp. Biol. 211, 2008). It is also used in computational models of olfactory navigation (Park et al, Plos Comput Biol, 12:e1004682, 2016).

(3) Hand-crafted features can be both a strength and a limitation. On the one hand, they offer interpretability, which is crucial when trying to model biological systems. On the other hand, they may limit the generality of the model. A more thorough discussion of this paper's limitations should address this.

(4) The authors mention the possibility of feature engineering or using recurrent neural networks, but a more concrete discussion of these alternatives and their potential advantages/disadvantages would be beneficial. It should be noted that the hand-engineered features in this manuscript are quite similar to what the model of Singh et al suggests emerges in their trained RNNs.

Merged answer to points 3 and 4.

We agree with the reviewer that hand-crafted features are both a strength and a limitation in terms of performance and generality. This was a deliberate choice aimed at stripping the algorithm bare of implicit components, both in terms of features and in terms of memory. Even with these simple features, our model performs well in navigating across different signals, consistent with our previous results showing that these features are a “good” surrogate for positional information.

To search for the most effective temporal features, one may consider a more systematic hand crafting, scaling up our approach. In this case one would first define many features of the odor trace; rank groups of features for their accuracy in regression against distance; train Q learning with the most promising group of features and rank again. Note however that this approach will be cumbersome because multiple factors will have to be systematically varied: the regression algorithm; the discretization of the features and the memory.

Alternatively, to eliminate hand crafting altogether and seek better performance or generalization, one may consider replacing these hand-crafted features and the tabular Q-learning approach with recurrent neural networks or with finite state controllers. On the flip side, neither of these algorithms will directly provide the most effective features or the best memory, because these properties are hidden within the parameters that are optimized for. So extra work is needed to interrogate the algorithms and extract these information. For example, in Singh et al, the principal components of the hidden states in trained agents correlate with head direction, odor concentration and time since last odor encounter. More work is needed to move beyond correlations and establish more systematically what are the features that drive behavior in the RNN.

We have added these points to the discussion.

(5) Minor: the title of the paper doesn't immediately signal its focus on recovery strategies and their interplay with memory in the context of olfactory navigation. Given the many other papers using a similar RL approach, this might help the authors position this paper better.

We agree with the referee and have modified the title to reflect this.

(6) Minor: L 331: "because turbulent odor plumes constantly switch on and off" -- the signal received rather than the plume itself is switching on and off.

Thank you for the suggestion, we implemented it.

@chrisaldrich, @mrcolbyrussell, @steelwagstaff, @remikalir, @heatherstaines, @judell

We've quietly launched at-mentions in the Hypothesis web app. Yours were among the first names that popped into my head; feel free to suggest others (or at-mention them yourself).

Various marketing campaigns are coming, but I thought I'd ping you all here first.

As the saying goes, ignorance is bliss. It is easier to ignore the struggles of life rather than face them head on.

Raicovich's background

Reviewer #1 (Public review):

Summary:

This manuscript investigates the role of the neck linker in coordinating the stepping cycles of the two heads of a kinesin-1 motor. Previous studies in the field showed that kinesin walks by alternating stepping of its heads, referred to as hand-over-hand. In this stepping mechanism, the front head of a kinesin dimer must remain bound until the rear head dissociates from the microtubule, moves forward, and rebinds to the tubulin on the plus-end side of the front head. There is a large body of work done to address this question. These studies all point to the central role of the 14 amino acid extension, a neck-linker, which connects the two heads to a common stalk, in coordination of kinesin motility. In a two-head-bound state, the motor domains (heads) are oriented parallel to the microtubule, but the neck linkers are orienting toward each other, thereby, breaking the symmetry in a homodimeric motor. In addition, the neck linkers are quite short, almost stretching to their near contour length to accommodate the microtubule binding of both heads. Previous studies pointed out that either the opposing orientation or the intramolecular tension of the neck linkers coordinate the stepping cycle.

However, we still do not know which step(s) in the chemo-mechanical cycle is controlled by the neck-linker to keep the two heads out of phase. The front head gating model postulates that ATP binding to the front head is gated until the rear head detaches from the microtubule. The rear head gating model proposes that the neck linker accelerates the detachment of the rear head from the microtubule. In this study, the authors use pre-steady state kinetics and smFRET to address this question. They measured ATP binding and microtubule detachment kinetics of kinesin's catalytic domain with neck linker constraints 1) imposed by disulfide crosslinking of the neck linker in monomeric kinesin in backward (rear head-like) and forward (front head-like) orientations, and 2) using the E236A-WT heterodimer to create a two-head microtubule-bound state with the mutant and WT heads occupying the rear and front positions respectively. They found that neck-linker conformation of the rear head reduces the ATP dissociation rate but has little effect on microtubule affinity. In comparison, the neck-linker conformation of the front head does not change ATP binding to the front head, but it reduces ATP-induced detachment of the front head, suggesting that a step after ATP binding (i.e. ATP hydrolysis or Pi release) is gated in the front head.

Significance:

I believe that this work will make an important contribution to the large body of literature focused on the mechanism of kinesin, which serves as an excellent model system to understand the kinetics and mechanics of a molecular motor. The mechanism proposed by the authors modifies the front-head gating model and is in agreement with recent structural work done on a kinesin dimer bound to a microtubule. Overall, the work is well performed, and the conclusions are well supported by the experimental data.

Reviewer #2 (Public review):

Summary:

In this study, the authors investigate the molecular mechanism behind kinesin-1's coordinated movement along microtubules, with a focus on how ATP binding, hydrolysis, and microtubule attachment/detachment are regulated in the leading and trailing heads. Using pre-steady state kinetics and single-molecule assays, they show that the neck linker's conformation modulates nucleotide affinity and detachment rates in each head differently, establishing an asynchronous chemo-mechanical cycle that prevents simultaneous detachment. Supported by cryo-EM structural data, their findings suggest that strain-induced conformational changes in the nucleotide-binding pockets are crucial for kinesin's hand-over-hand movement, presenting a detailed kinetic model of its stepping mechanism. The manuscript is well-crafted, technically rigorous, and should be of significant interest to cell biology and cytoskeletal motor researchers.

Significance:

All conclusions are well-supported by the provided data. The findings address a critical gap in our understanding of how kinesin's two motor domains coordinate their movements, offering insights into the molecular basis of its stepping mechanism. This work should be of significant interest to the cytoskeletal research community.

Comments on latest version:

The authors have satisfactorily addressed my comments, although I recommend the addition of the following reference:

Lu Rao, Jan O. Wirth, Jessica Matthias, and Arne Gennerich. 2025. A Two-Heads-Bound State Drives KIF1A Superprocessivity. bioRxiv 2025.01.14.632505

This paper provides conclusive evidence that kinesin-1 predominantly adopts a one-head-bound state at limiting ATP concentrations and remains in this state for a significant portion of its enzymatic cycle even at saturating ATP. This limits its processivity compared to KIF1A, which predominantly adopts a two-heads-bound state under saturating ATP conditions. These findings directly support the authors' conclusion that trailing head dissociation is favored over leading head detachment.

Reviewer #3 (Public review):

Kinesin-1 is a dimeric motor protein that transports cargo along microtubules. Its movement relies on the ability of its two catalytic motor domains (heads) to couple microtubule interactions with directional conformational changes and ATP turnover in a coordinated, alternating manner. The kinetics of these processes in each head are tightly regulated (gated) to ensure that at least one motor domain remains bound to the microtubule at all times, preventing detachment.

Niitani et al. investigated the gating mechanism by focusing on the role of the neck linker, a flexible region extending from the motor domain's C-terminus that undergoes conformational changes during stepping. They examined how the neck linker differentially regulates the microtubule affinity and ATP turnover of the front and rear heads. To do this, they designed cross-linkable monomeric motor domains mimicking the conformations of the front and rear heads and employed a combination of pre-steady-state and single-molecule analyses to measure ATP-binding and microtubule-detachment kinetics. Additionally, they studied a kinesin heterodimer with a locked rear head conformation to distinguish the kinetic properties of the front and rear heads within an active dimer.

ATP binding rates were measured using stopped-flow experiments with mant-ATP and nucleotide-free kinesin-microtubule complexes. The results showed that crosslinking the neck linker in the forward-pointing conformation (mimicking the rear head) reduced the ATP dissociation rate, while crosslinking it in the rear-pointing conformation (mimicking the front head) had no significant effect on ATP binding kinetics. ATP dissociation from the rear head was further examined using a kinesin mutant (E236A) that stabilizes the ATP-bound state by significantly slowing ATP hydrolysis.

To assess how neck-linker orientation affects microtubule attachment, the authors monitored turbidity changes after rapidly mixing nucleotide-free, crosslinked kinesin-microtubule complexes with ATP in a stopped-flow apparatus. Their findings demonstrated that the forward-oriented neck linker in the rear head promotes microtubule detachment, whereas the backward-oriented neck linker in the front head reduces detachment rates.

These results indicate that neck-linker conformation governs gating of microtubule affinity and nucleotide binding. Moreover, they show that even partial docking of the neck linker onto the head is sufficient to partially open the gating mechanism. To further investigate the role of neck linker tension, the authors created kinesin dimers with neck linker insertions of varying lengths. Microtubule detachment kinetics and ATPase activity assays revealed that ATP turnover in the rear head is significantly affected by the degree of forward tension applied to its neck linker.

Overall, Niitani et al. build upon previous kinesin gating models by introducing a neck-linker tension-based ATP binding affinity mechanism. Their findings provide a mechanistic basis for recent cryo-EM observations for kinesin-1 and kinesin-3 (KIF14) and distinguish the specific roles of neck linker tension in the front and rear heads in regulating ATP binding, hydrolysis, and microtubule detachment. This study is biochemically rigorous and makes an important contribution, though direct structural validation (e.g., cryo-EM snapshots of crosslinked or mutant kinesins bound to microtubules) would further strengthen their conclusions and clarify the asymmetry in ATP affinity between the front and rear heads.

Author response:

We thank the reviewers for the detailed evaluations and thoughtful comments, which have improved the clarity and readability of this manuscript. We have responded to all reviewer comments and incorporated their suggested changes into the text and figures. We have also included new experimental results suggested by reviewer 2, which further strengthen our main conclusion.

Point-by-point description of the revisions

Reviewer #1:

(1) Introduction, page 3: The statement "Single dimeric kinesin moves processively along microtubules in a hand-over-hand manner by alternately moving the two heads in an 8-nm step toward the plus-end of the microtubule" is inaccurate. The kinesin heads take ~16 nm steps, while the center of mass advances in ~8 nm increments. Please adjust the wording accordingly.

(2) Introduction, page 5: In the sentence "These results are consistent with the closed and open conformations of the nucleotide-binding pocket in the rear and front heads of microtubule-bound kinesin dimers observed in cryo-electron microscopy (cryo-EM) studies," I recommend changing the order to align with the previous sentence. The correct order would be "These results are consistent with the open and closed conformations of the nucleotide-binding pocket in the front and rear heads."

We thank the reviewer for pointing out our misunderstandings. We have corrected these sentences accordingly (lines 45-47 and lines 111-112).

Reviewer #2:

MAJOR CONCERNS

Limitations of this study: The authors need to discuss the limitations of their work. 1) They used a cys-lite kinesins mutant and introduced new surface-exposed cysteines. These mutants have lower kcat values than WT. 2) They used fluorescently labeled ATP molecules, which are hydrolyzed 10 times slower than unlabeled nucleotides. 3) They still observe crosslinking under reducing conditions and partial (but almost complete) crosslinking under oxidized conditions. 4)They assumed that cysteine crosslinked orientation mimics the orientation of the neck-linker in the front and rear conditions. The authors clearly pointed to these issues in the Results section. While these assumptions are also supported by several control experiments, the authors need to acknowledge some of these limitations in the Discussion as well.

We have now reiterated some of the key caveats in the Discussion, and newly described in the Results section those points not mentioned in the original manuscript that do not affect the conclusion. We also added a summary of the limitations and caveats into the first paragraph of the Discussion section (lines 425-431).

(1) We added a sentence in the Results section to describe that the ATP-binding kinetics of the Cys-light mutant remained consistent with previous studies as follows: “First, we demonstrated that k₊₁ and k_-1 of the wild-type head without Cys-modification were unchanged after oxidization (Table 1) and were comparable to those previously reported (Cross, 2004)” (lines 163-166). The reduced kcat values of cysteine pair-added mutants before crosslinking were primarily due to reduced microtubule association rate (data not included in this manuscript). We have added a sentence in the Results section describing the kcat results as follows: “The reduced ATPase activity primarily results from a decreased microtubule association rate (data to be presented elsewhere) with little change in ATP binding or microtubule dissociation rates (Table 1).” (lines 144-146).

(2) Fluorescently-labeled ATP was used to determine the ATP off-rates of the E236A mutant monomer and E236A rear head of the E236A/WT heterodimer. Two caveats in these measurements could lead to underestimating the ATP off-rate: 1) The off rate of Alexa-ATP from the head may be reduced compared to unmodified ATP, as Alexa-ATP driven motility showed a 10-fold reduce velocity. 2) The ATP off-rate of the E236A mutant may differ from that of the rear head in the wild-type dimer, since the E236A mutant likely stabilizes the neck linker-docked state more strongly than in the rear head of the wild-type dimer. These points are crucial for evaluating the results of ATP off-rate and the affinity for ATP, so we have added sentences in the Discussion section as follows: “We note, however, that this K_d of ATP may somewhat underestimate the true value in wild-type kinesin for two reasons: first, the E236A mutation likely stabilizes the neck linker-docked, closed state more than in the rear head of the wild-type dimer (Rice et al., 1999), and second, the Alexa-ATP used to measure the ATP off-rate of E236A head showed ~10-fold smaller velocity compared to unmodified ATP, partly due to a slower ATP off-rate (Figure 2-figure supplement 3).” (lines 449-454).

(3) Under reducing condition, the rear head crosslink contained 30% crosslinked species, while under oxidized condition, the front head crosslink contained 11% un-crosslinked species (Figure 1-figure supplement 1). These heterogeneities likely affect the rate constants of K_-1 for rear head crosslink and K₂ for front head crosslink, as crosslinked and un-crosslinked species showed significantly different rate constants. However, we did not use the rear head crosslink result to determine K_-1, since ATP hydrolysis likely occurred before reversible ATP dissociation. Instead, we used E236A monomer to estimate the K_-1 of the rear head. In addition, the result for K₂ of the front head crosslink was further validated using the E236A/WT heterodimer, which will be described in the next section.

(4) This is an important point, and therefore, we conducted experiments using the E236A/WT heterodimer (including new experimental results of ATP binding kinetics of the front head) and obtained consistent results. To address this point, we have revised the following sentences in the Discussion: “In the front head, backward orientation of the neck linker has little effect on ATP binding and dissociation rates, both when measured for a monomer crosslink (Figure 2A, B) and for the front head of a E236A-WT heterodimer (Figure 4B, C, F).” (lines 432-433); “However, we found that the ATP-induced detachment rates from microtubule (K₂) were similarly reduced for both the front head crosslink (7.0 s^-1; Figure 3A) and the front WT head of the E236A/WT heterodimer (6.3 s^-1; Figures 6D), suggesting that a step subsequent to ATP binding is gated in the front head.” (lines 437-441).

Line 238, the authors wrote that "forward constraint on the neck linker in the rear head does not significantly accelerate the detachment from the microtubule." Can the authors comment on why the read-head-like construct has a low affinity for microtubules even in the absence of ATP (Line 220)? I believe that the low affinity of the head in this conformation is more striking (and potentially more important) than the changes they observe in detachment rates. The authors should also consider that they might not be able to reliably measure the changes in the dissociation rate in single molecule assays of this construct (especially if the release rate of the rear head in the oxidized condition increases a lot higher than that of WT). The kymographs show infrequent and brief events, which raises doubts about how reliably they can measure the release rates under those imaging conditions. Higher motor concentrations and faster imaging rates may address this concern.

The low microtubule affinity of the rear-head-like crosslink stems from an extremely slow ADP release rate upon microtubule binding, not from a fast microtubule-detachment rate. Using stopped-flow measurements of microtubule-binding kinetics (microtubule-stimulated mant-ADP release and microtubule association rates), we found that the rear-head-crosslink resulted in a 2,000-fold decrease in the microtubule-stimulated ADP-release rate. This finding also explains the reduced ATPase of the rear-head-crosslink (Figure 1E). Since this low microtubule-affinity state occurs in the ADP-bound state rather than the ATP-bound state, we hypothesized that the neck-linker docked ADP-bound state cannot effectively bind to microtubules, requiring neck-linker undocking for microtubule binding (Mattson-Hoss et al., Proc. Natl. Acad. Sci., 111, 7000-7005 (2014)). While we acknowledge that understanding slow microtubule binding in the neck linker docked state is important for elucidating the mechanism and regulation of microtubule-binding of the head, this paper focuses specifically on the mechanism and regulation of “microtubule-detachment”. We plan to present these microtubule-binding kinetics data in a separate manuscript currently in preparation.

To explain the low microtubule affinity of the rear-head-crosslink, we added this explanation to the text; “because this constraint on the neck linker dramatically reduces the microtubule-activated ADP release rate (data to be presented elsewhere), creating a weak microtubule binding state” (lines 226-228).

Although the rear head crosslinking construct under oxidative condition showed fewer fluorescent spots per kymographs (images) due to its low microtubule binding rate, we collected more than one hundred spots by recording additional microscope movies (N=140; Figure 3-figure supplement 2B), ensuring sufficient data for statistical analysis.

Figure 2: How do the rates shown in Figure 2A-B compare to the previous kinetics studies in the field? The authors compare the dissociation rate of WT measured in rapid mixing experiments to that of E236A in smFRET assays. It is not clear whether these comparisons can be made reliably using different assays. Can the authors perform rapid mixing of E236A or try to determine the rate for the WT from smFRET trajectories?

The results of ATP on/off rates are comparable to the previous stopped flow measurements of ATP binding to monomeric kinesin-1 on microtubule, which are 2-5 µM^-1s^-1 and ~150 s^-1, respectively (summarized in the review by Cross (2004)). We added a sentence as follows: “First, we demonstrated that K₊₁ and K_-1 of the wild-type head without Cys-modification were unchanged after oxidization (Table 1) and were comparable to those previously reported (Cross, 2004).” (lines 163-166).

As the reviewer pointed out, the rapid mixing and smFRET data cannot be directly compared due to the differences in temporal resolution and fluorescent probe used. In Figure 2E (2F in the revised version), we measured ATP dissociation rate for both WT and E236A using smFRET. Due to the lower temporal resolution, we could not accurately determine ATP binding rate using smFRET. Therefore, to compare the ATP binding rate between WT and E236A heads, we now have added stopped-flow measurements of mant-ATP binding to the E236A monomer, as shown in Fig. 2C and Figure 2-supplement 2, and described in the text (lines 182-185).

Line 396: One of the most significant conclusions of this work is that the backward orientation of the neck linker has little effect on ATP binding to the front head. This is only supported by the results shown in Fig. 2A-B. Can the authors perform/analyze smFRET assays on the E236A/WT heterodimer to directly show whether the ATP binding rate to the WT head is affected or not affected by the orientation of the neck linker of the WT head?

We agree with the reviewer that our finding about ATP binding to the front head is potentially significant in the kinesin field, as it has been widely believed that ATP-binding is suppressed in the front head. In our original manuscript, this conclusion was supported only by the measurement of ATP on-rate of the front-head-crosslink, which may differ from the front head of a dimer in which the backward orientation of the neck linker is maintained by the backward strain. Although the reviewer suggested performing smFRET experiments using E236A/WT heterodimer, smFRET have relatively low temporal resolution (50-100 fps) and cannot accurately measure the frequency of ATP binding, so we used this technique only to determine ATP off rates. In this revised manuscript, we now have added stopped-flow experiments to separately measure the ATP binding to the front and rear heads of the E236A/WT heterodimer. By labeling the rear E236A head with a fluorophore to quench the mant-ATP signal bound to the rear head, we successfully measured mant-ATP binding rate to the front head. We found that the ATP-binding rate to the front head was comparable to that of an unconstrained monomer head, providing direct evidence for our conclusion. The revised version includes Fig. 4 A-C (with Figure 4-supplement 2; Figs. 4 and 5 are swapped in order) showing the kinetics of ATP binding to the front and rear heads of the E236A/WT heterodimer, with corresponding text in the result section (lines 315-324).

MINOR CONCERNS

Lines 31 and 32: I recommend replacing "ATP affinity" with "ATP binding rate" or "the dissociation of ATP" to be more specific. This is because they do not directly measure the affinity (Kd), but instead measure the on or off rates.

Line 41: Replace "cellar" with "cellular".

Line 83: The authors should cite Andreasson et al. here.

We have corrected these sentences accordingly (lines 31, 40, 85).

Lines 83-86: It seems this sentence belongs to the next paragraph. It also needs a citation(s).

This statement lacks experimental evidence and may confuse readers, so we have removed it for clarity.

Line 151: It would be helpful to add a conclusion sentence at the end of this paragraph to explain what these results mean to the reader.

A conclusion sentence of this paragraph has been added: “These results demonstrate that neck linker constraints in both forward and rearward orientations inhibit specific steps in the mechanochemical cycle of the head (lines 151-153)”.

Lines 175-180: I recommend combining and shortening these sentences, as follows, to avoid confusing the reader: "To detect the ATP dissociation event of the rear head, we employed a mutant kinesin with a point mutation of E236A in the switch II loop, which almost abolishes ATPase hydrolysis and traps in the microtubule-bound, neck-linker docked state,"

We have corrected these sentences accordingly (line 179-181).

Line 314: "which was rarely observed ...". This is out of place and confusing as is. I recommend moving this sentence after the sentence that ends in Line 295.

This sentence explains how the dark-field microscopy data was analyzed to determine whether the labeled head was in the leading or trailing position before detaching from the microtubule, but the explanation needs clarification. We removed the phrase “which was rarely observed for E236A-WT heterodimer” and simplified this sentence as follows: “Moreover, these observations allow us to distinguish whether the gold-labeled WT head was in the leading or trailing position just before microtubule detachment; the backward displacement of the detached head indicates that the labeled WT head occupied the leading position prior to detachment (Figure 5-figure supplement 1).” (lines 347-351).

Line 300: Can the authors comment on why E236A/WT has a substantially lower ATPase rate than WT homodimer? Is it possible to determine which step in the catalytic cycle is inhibited?

We demonstrated that the k₂ (microtubule-detachment rate) of the front head matched the ATP turnover rate of the E236A/WT heterodimer (Figure 6 B and E), suggesting that the inhibited step occurs after ATP binding in the front head. In contrast, the rear E236A head showed virtually no ATP hydrolysis activity, since in high-speed dark field microscopy, we observed forward step caused by rear E236A head detachment from microtubule only rarely, approximately once every few seconds (Figure 5-figure supplement 1). We added a sentence in the text as follows: “As described later, the reduced ATPase rate results from suppressed microtubule detachment of the front WT head, while the rear E236A head is virtually unable to detach from microtubules” (lines 311-313).

Line 323: Is the unbound dwell time unchanged?

The unbound dwell time exhibited a weak ATP-dependence, which we described only in Figure 5-supplement 2 (Figure 4-supplement 2 in the old version). We observed three distinct phases in the unbound dwell time based on mobility differences, with ATP dependence appearing only in the third phase. This finding suggests that ATP binding to the microtubule-bound E236A head is sometimes necessary for the detached WT head to rebind to the forward-tubulin binding site, indicating that the microtubule-bound E236A head occasionally releases ATP during the one-head-bound state (without the forward neck linker strain). To describe the ATP-dependence of the unbound dwell time, we added a sentence in the main text as follows: “In contrast, the dwell time of the unbound state of the gold-labeled WT head showed weak ATP dependence (Figure 5-figure supplement 2), indicating that the rear E236A head occasionally releases ATP when the front head detaches from the microtubule and the neck linker of E236A head becomes unconstrainted. This finding further supports the idea that forward neck linker strain plays a crucial role in reducing the reversible ATP release rate.” (lines 372-377).

Line 331: I recommend replacing "ATP-induced detachment" with "nucleotide-induced detachment" for clarity.

We have revised the phrase accordingly (line 371).

Line 344: I recommend replacing "affinity" with "forward strain prevents the release of the nucleotide" or similar to avoid confusion. Forward strain reduces the off-rate of the bound nucleotide, rather than allowing ATP to bind more efficiently to the rear head.

We agree to the reviewer’s comment and have corrected this sentence accordingly (line 338).

Lines 376-385: G7-12 constructs are introduced in Figure 6, but the results in this paragraph are shown in Figure 5. They should be moved to Figure 6 to avoid confusion.

To improve the readability, we have reorganized Figures 4-6, such that all the figure panels related to the neck linker extended mutants are shown in Figure 6; Figure 5D has been moved to Figure 6F.

Line 421: delete "not" before "does not".

We have corrected this typo.

Lines 433-441: Unless I am mistaken, more recent work in the kinesin field showed that backward trajectories of kinesin 1 reported by Carter and Cross are due to slips from the microtubule rather than backward processive runs of the motor.

The slip motion demonstrated by Sudhakar et al. (2021) differs from the backstep motion reported by Carter and Cross (and many other laboratories). Slip motion occurs after kinesin detaches from the microtubule and continues until the bead returns to the trap center. In contrast, backstep motion occurs during processive movement when the trap force either exceeds or approaches the stall force. The kinetics of these motions also differ significantly: slip steps occur with a dwell time of 71 µs and are independent of ATP concentration, while backsteps take ~0.3 s (at 1 mM ATP) and depend on ATP concentration. These differences indicate that slip motion is phenomenologically distinct from backsteps occurring under supra-stall or near-stall force.

Line 474: Replace "suppresses" with "suppressed".

We have corrected this typo.

Figure 4E: I would plot these results with increasing ATP concentration on the x-axis.

We formatted Figure 4E to match Figure 4b from Isojima et al. (Nature Chem. Biol. 2015), to emphasize the difference in ATP dependence of the front and rear head.

Figure 4B: The authors should explain how they distinguish between bound and unbound states in the main text or figure legends. For example, it is not clear how the authors score when the motor rebinds to the microtubule in the first unbinding event shown in Figure 4B (displacement plot).

The method was described in the Materials and Methods section, but we have now described how to distinguish between bound and unbound states in the main text as follows: “Unlike the unbound trailing head of wild-type dimer that showed continuous mobility (Isojima et al., 2016), the unbound WT head of E236A-WT heterodimer exhibited a low-fluctuation state in the middle (Figure 5B, s.d. trace). This low-fluctuation unbound state was distinguishable from the typical microtubule-bound state, having a shorter dwell time of ~5 ms compared to the bound state and positioning backward, closer to the E236A head, relative to the bound state (Figure 5-figure supplement 2).” (lines 351-356).

Reviewer #3:

Minor Issues:

- Line 22, Abstract - The phrase "move in a hand-over-hand manner" could be clearer if phrased as "move in a hand-over-hand fashion" to improve readability.

We changed the word “manner” to “process” (line 23).

- Abstract - Neck linker conformation in the leading head: The sentence "We demonstrate that the neck linker conformation in the leading kinesin head increases microtubule affinity without altering ATP affinity" would benefit from defining this conformation as "backward" for clarity.

- Abstract - Neck linker conformation in the trailing head: The sentence "The neck linker conformation in the trailing kinesin head increases ATP affinity by several thousand-fold compared to the leading head, with minimal impact on microtubule affinity" should also clarify that this conformation is "forward."

We have corrected these sentences accordingly (line 30, 32).

- Abstract - Conformation-specific effects: The authors mention conformation-specific effects in the neck linker structure but do not define the neck linker's conformation or the motor domain's (MD) conformation. Clarifying these conformational changes would improve the explanation of how they promote ATP hydrolysis and dissociation of the trailing head before the leading head detaches from the microtubule, thereby providing a kinetic basis for kinesin's coordinated walking mechanism.

We have revised the last sentence of the abstract accordingly by specifying the neck linker’s conformation as follows: “In combination, these conformation-specific effects of the neck linker favor ATP hydrolysis and dissociation of the rear head prior to microtubule detachment of the front head, thereby providing a kinetic explanation for the coordinated walking mechanism of dimeric kinesin.” (lines 34-37).

- Line 306 - Use of ATP in the E236A-WT heterodimer: In discussing the "ATP-induced detachment rate of the WT head in the E236A-WT heterodimer," the authors should consider justifying their choice of ATP over ADP for inducing microtubule (MT) dissociation. Since ATP typically promotes tighter MT binding and ATP turnover is reduced in forward-positioned WT heads, it may be unclear to some readers why ATP was chosen.

We measured the ATP-induced detachment rate k₂ of the front head of the E236A-WT heterodimer to validate our findings from the front-head-crosslinked monomer experiments, which demonstrated reduced k₂ after oxidation. To clarify this point, we have now included ATP binding kinetics measurements for both front and rear heads of the E236A-WT heterodimer, as suggested by reviewer 2. These additional data demonstrate consistency between the results from the crosslinked monomer and E236A-WT heterodimer experiments.

- Discussion - Backward-oriented neck linker in the front head: The discussion mentions that the backward-oriented neck linker in the front head reduces its ATP-induced detachment rate, suggesting that a step after ATP binding (e.g., isomerization, ATP hydrolysis, or phosphate release) is gated in the front head. However, the authors do not clarify that the backward neck linker orientation would imply the nucleotide pocket should be open or at least not fully closed, thus inhibiting ATP turnover. This is important because, as demonstrated in other studies, full closure of the nucleotide pocket is linked to neck linker docking. This point should be addressed earlier in the discussion.

We have addressed this point by revising this sentence as follows: “These results are consistent with an inability of the front head to fully close its nucleotide pocket to promote ATP hydrolysis and Pi release (Benoit et al., 2023), as will be discussed later.” (lines 441-443)

.

In many voter's eyes Trump was set out as the big bad guy. This was done as a tactic by the democratic party, but sometimes it did more harm than good. Trump was able to gain and influence many of the voters by this as he was treated very unfairly. I find this very interesting that in the pursit of trying to take someone down, they are supported more in some cases. This allowed voters such as Candace to see him in a new light and give their vote to him.

Relating this back to the classroom our students know the rules and expectations, but not all the rules and expectations allow for the student to succeed to the best of their ability. the example that comes, to my head is students must have a calm and safe body. For some students that may mean using a flexible seating choice instead of a normal chair. The rules are being adopted, but also changed slightly to meet the needs of the student.

This statement raises the question of what the Federal Government's enforcement mechanisms is for violations of Title VI. There is a section of the law — §2000d–1 — that spells out how Federal grants, contracts, and loans can be cancelled for non-compliance. A hearing is first required, although it's unclear from the text the nature of the hearing. It appears that the Federal agency gets to decide if compliance is possible. If not, the following happens:

the head of the Federal department or agency shall file with the committees of the House and Senate having legislative jurisdiction over the program or activity involved a full written report of the circumstances and the grounds for such action. No such action shall become effective until thirty days have elapsed after the filing of such report.

The New York Times elaborates on the process in a recent article:

If the government wants to claim an institution has not complied with anti-discrimination law — after exhausting all attempts to obtain voluntary compliance — it must call a hearing and provide evidence, according to federal statute. Then it is tasked with alerting relevant congressional committees that it is about to terminate funds, allowing legislators 30 days to intervene and seek a remedy.

A letter written by a group of constitutional scholars states similarly:

Under Title VI, the government may not cut off funds until it has

• conducted a program-by-program evaluation of the alleged violations;
• provided recipients with notice and “an opportunity for hearing”;
• limited any funding cutoff “to the particular program, or part thereof, in which…noncompliance has been…found”; and
• submitted a report explaining its actions to the relevant committees in Congress at least thirty days before any funds can be stopped.

On the surface, this Executive Order claims to be a move to protect women's rights and safety, but it does not only attack their rights within the work space it also attacks those of the Queer community.

At this point in time the colonies were still "head over heels" for the British because they grew up there and they are getting their funding and food from them. They know what is feels like to win a war from a kingdom that is supporting your travels.

Gaining the ability to empathize with a user's experience, balanced with the bird's eye view of detachment, seems like a wise goal, but difficult to achieve.

This definitely caused for an unfair bias in the section process

Janie doesn’t think it’s funny, it shows that she is different from the others. Her mindset and perspective of the world is at a different level.

add "town" at the end of this. family resort town

In this powerful passage from Bad Indians: A Tribal Memoir, Deborah Miranda describes needing a song that can carry her pain, her memories, and her connection to her culture. She repeats “I need a song” to show how deeply she feels this need. The song she imagines is loud, strong, and emotional. It’s both small and personal, but also big enough to hold all her grief. She uses images from nature, like storms, mountains, and fire, to show how intense these feelings are. At the end, she connects the song to her father, showing that this kind of music and strength is something passed down through family. This passage shows how Miranda uses poetic words to talk about healing, identity, and remembering her roots.

Can be used to explain why the practices are not used as much now.

Quizás incluir head() y tail()

Quizás incluir head() y tail()

I feel like I can picture the stereotypical sleazy advertiser who just wants to make a sale as pictured in older movies. It's interesting how this is no longer the public perception of advertisers, but this ideology was so pervasive that I have some remnant of the sleazy advertiser from a time before egregious advertising was normalized in my head.

Reviewer #1 (Public review):

Summary:

The current study by Xing et al. establishes the methodology (machine vision and gaze pose estimation) and behavioral apparatus for examining social interactions between pairs of marmoset monkeys. Their results enable unrestrained social interactions under more rigorous conditions with detailed quantification of position and gaze. It has been difficult to study social interactions using artificial stimuli, as opposed to genuine interactions between unrestrained animals. This study makes an important contribution for studying social neuroscience within a laboratory setting that will be valuable to the field.

Strengths:

Marmosets are an ideal species for studying primate social interactions due to their prosocial behavior and the ease of group housing within laboratory environments. They also predominantly orient their gaze through head movements during social monitoring. Recent advances in machine vision pose estimation set the stage for estimating 3D gaze position in marmosets but require additional innovation beyond DeepLabCut or equivalent methods. A six-point facial frame is designed to accurately fit marmoset head gaze. A key assumption in the study is that head gaze is a reliable indicator of the marmoset's gaze direction, which will also depend on the eye position. Overall, this assumption has been well supported by recent studies in head-free marmosets. Thus the current work introduces an important methodology for leveraging machine vision to track head gaze and demonstrates its utility for use with interacting marmoset dyads as a first step in that study.

Weaknesses:

One weakness that should be easily addressed is that no data is provided to directly assess how accurate the estimated head gaze is based on calibrations of the animals, for example, when they are looking at discrete locations like faces or video on a monitor. This would be useful to get an upper bound on how accurate the 3D gaze vector is estimated to be, for planned use in other studies. Although the accuracy appears sufficient for the current results, it would be difficult to know if it could be applied in other contexts where more precision might be necessary.

Reviewer #2 (Public review):

Summary:

The manuscript describes novel technique development and experiments to track the social gaze of marmosets. The authors used video tracking of multiple cameras in pairs of marmosets to infer head orientation and gaze and then studied gaze direction as a function of distance between animals, relationships, and social conditions/stimuli.

Strengths:

Overall the work is interesting and well done. It addresses an area of growing interest in animal social behavior, an area that has largely been dominated by research in rodents and other non-primate species. In particular, this work addresses something that is uniquely primate (perhaps not unique, but not studied much in other laboratory model organisms), which is that primates, like humans, look at each other, and this gaze is an important social cue of their interactions. As such, the presented work is an important advance and addition to the literature that will allow more sophisticated quantification of animal behaviors. I am particularly enthusiastic with how the authors approach the cone of uncertainty in gaze, which can be both due to some error in head orientation measurements as well as variable eye position.

Weaknesses:

There are a few technical points in need of clarification, both in terms of the robustness of the gaze estimate, and possible confounds by gaze to non-face targets which may have relevance but are not discussed. These are relatively minor, and more suggestions than anything else.

如果 git add 了一个被 tracked 的文件（即 stage 修改为下一次提交），然后再修改文件，那么 git status 会将文件列出在两个区域：Changes to be committed（上一次 git add 时候那个准确版本）、Changes not staged for commit（刚刚的修改）。

总之，git add 之后的更改，如果想包含进下一次提交，需要 git add again。

I think in boxes and lines, and I think in bodies, but I don't really think in signatures. Probably need to strengthen that

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Referee #2

Evidence, reproducibility and clarity

Schmidt and colleagues are addressing the effects of severe hypoxia on proliferation and differentiation potential of (mouse) cranial neural crest, using a neural crest cell line subjected to hypoxic conditions, assessed by transcriptomics analysis (quantitative reverse transcription PCR, bulk RNA sequencing and bioinformatics). They are reporting a mild effect of cell proliferation and an extensive inhibition of differentiation towards osteoblasts, chondrocytes and smooth muscle cells. They reveal affected biological processes shared between the three fate biasing conditions related to cytoskeleton organization and amino acid metabolism. Lastly, among affected genes upon hypoxic conditions in vitro, they authors identified risk genes linked to non-syndromic (non-genetic) orofacial clefts exclusively downregulated in osteoblasts and smooth muscle cells, namely Fgfr2, Gstt1 and Tbxa2. Similarly, hypoxia-driven downregulation of genes implicated in syndromic orofacial clefts was observed in all three chondrocyte, osteoblast and smooth muscle differentiation scenarios. Lastly, STRING analysis of downregulated genes cross-validated their findings related to affected differentiation.

Major comments:

The conclusions drawn from the experimental data are carefully formulated for the most part. One of the main concerns is that the cells were subjected to extreme hypoxic conditions, while it may be more biologically relevant to include a condition representing more mild hypoxia (e.g. 10%). One of the opening claims regarding severe hypoxia only mildly affecting cell proliferation is not shown clearly, since no mitotic markers have been analyzed (i.e. KI67 or PCNA staining or a simple EdU incorporation assay). Thus, the claim that they assessed cell proliferation is not very convincing, even though cell death was analyzed. Additionally, cellular morphology of the cells could be assessed (brightfield images), since previous studies observed that hypoxia can be an inducive factor in cranial neural crest and driving EMT (Scully et al. 2016; Barriga et al. 2013).

Furthermore, in the RNA seq analysis of chondrogenic fate biased cells the authors draw a conclusion based on the proximity of the samples on the PCA plot, which is not very convincing. More careful analysis of the bulk RNA seq data sets they have generated for key marker genes will be more convincing (for example, a heatmap with selected genes would be a helpful representation). As mentioned above, a straight-forward and not time consuming experiment (given that it was assessed for a maximum of 72 hrs) would be to repeat the culture of NCCs and stain for mitotic markers, and quantify the number of positively stained cells over total cell numbers. Furthermore, it is not that demanding to add an experimental condition of less severe hypoxia in this assay. Without underestimating how time consuming this would be, a major lack of experimental validation of the key genes they identify as important across all conditions may be the limitation of the study (this would be the difference between correlation and a probable underlying mechanism). This can be circumvented by more extensive reference to in situ data sets from mouse or existing data sets of single cell and spatial transcriptomicsA suggested targeted knock-down (for example with siRNA, shRNA or CRISPR) to validate a few of the key genes revealed as important could take a few months, with an estimated cost up to 5,000 euros per targeted gene and replicate. On methods, replicates and statistics: The experimental methods and approach are described efficiently and seem reproducible.All biological and technical replicates are of a minimum of N=3 from independent experiments and statistical tests have been run in all cases.

Minor comments:

One of the key implications of NCCs in palate formation is interaction with orofacial epithelial cells, which the authors also mention. It may be interesting to check if any signaling pathways involved in this crosstalk are affected under hypoxic conditions in their existing data sets of bulk RNA SEQ. This can be done by using available algorithms such as CellChat (Jin et al. 2021; Jin, Plikus, and Nie 2023), which has been reported to work also in bulk RNA seq data analysis (according to GitHub). The authors could mine the literature for existing RNA sequencing data that include osteoblasts, chondrocytes and epithelial cells (Ozekin, O'Rourke, and Bates 2023; Piña et al. 2023).

Additionally, another process that may be affected is EMT (epithelial-to-mesenchymal-transition) and is possible to assess by re-analysis of bulk RNA-seq data while focusing on key genes implicated in this process (i.e. E-cadherin, vimentin, EpCAM, Snail, Twist, PRRX1). Lastly, when the authors report on the significantly up- or down-regulated genes, it may be interesting to categorize them by ligands, receptors, intracellular molecules and transcription factors (and use separate plots to visualize them). While a big focus of the manuscript are down-regulated genes, less emphasis was given in upregulated genes (other than the response to hypoxia gene module).

The authors are referencing extensively and accurately existing studies in the field and the manuscript is exceptionally well-written, with only a few points of limited clarity or increased complexity. Such an example is when the authors refer to OFC risk genes, because it is not clearly stated how the referenced studies reached their conclusions (for example, are they mouse studies, do they involve mutants, are any of these studies based on GWAS on human cohorts). This matter would significantly improve the flow of the text and highlight the importance of the study and their findings. The figures could be redesigned to be more intuitive to interpret. For example, using violin plots and heatmaps, as discussed, and including references or re-analysis/re-use of existing spatial transcriptomics and in situs for marker genes.

In all cases where there is a comparison of gene expression levels, violin plots would be a better representation of up- and down-regulated genes (i.e. selected genes from Fig1K, comparison of gene expression between normoxic and hypoxic NCCs, Fig 2G when analyzing chondrogenesis and the respective analysis for osteoblasts and smooth muscle cells, as well as when comparing the three fate-biasing conditions to identify common genes that are misregulated).

References:

Barriga, Elias H., Patrick H. Maxwell, Ariel E. Reyes, and Roberto Mayor. 2013. "The Hypoxia Factor Hif-1α Controls Neural Crest Chemotaxis and Epithelial to Mesenchymal Transition." The Journal of Cell Biology 201 (5): 759-76. https://doi.org/10.1083/jcb.201212100.

Jin, Suoqin, Christian F. Guerrero-Juarez, Lihua Zhang, Ivan Chang, Raul Ramos, Chen-Hsiang Kuan, Peggy Myung, Maksim V. Plikus, and Qing Nie. 2021. "Inference and Analysis of Cell-Cell Communication Using CellChat." Nature Communications 12 (1). https://doi.org/10.1038/s41467-021-21246-9.

Jin, Suoqin, Maksim V. Plikus, and Qing Nie. 2023. "CellChat for Systematic Analysis of Cell-Cell Communication from Single-Cell and Spatially Resolved Transcriptomics." bioRxiv. https://doi.org/10.1101/2023.11.05.565674.

Ozekin, Yunus H., Rebecca O'Rourke, and Emily Anne Bates. 2023. "Single Cell Sequencing of the Mouse Anterior Palate Reveals Mesenchymal Heterogeneity." Developmental Dynamics : An Official Publication of the American Association of Anatomists 252 (6): 713-27. https://doi.org/10.1002/dvdy.573.

Piña, Jeremie Oliver, Resmi Raju, Daniela M. Roth, Emma Wentworth Winchester, Parna Chattaraj, Fahad Kidwai, Fabio R. Faucz, et al. 2023. "Multimodal Spatiotemporal Transcriptomic Resolution of Embryonic Palate Osteogenesis." Nature Communications 14 (September):5687. https://doi.org/10.1038/s41467-023-41349-9.

Scully, Deirdre, Eleanor Keane, Emily Batt, Priyadarssini Karunakaran, Debra F. Higgins, and Nobue Itasaki. 2016. "Hypoxia Promotes Production of Neural Crest Cells in the Embryonic Head." Development 143 (10): 1742-52. https://doi.org/10.1242/dev.131912.

Significance

Several pieces of evidence have pointed to hypoxia as an environmental factor contributing to congenital orofacial clefts, ranging from studies in mouse to observations in human. The authors are doing an excellent job in putting this information together and the question they are trying to answer is of high importance, given the prevalence of such congenital syndromes. In terms of the methods and model employed, there are some limitations, related to the choice of a mouse cell line over one from human, the severe hypoxia induced (over a more mild), and the conditions of directed differentiation not allowing for simultaneous examination of more complex lineage transitions. The methods as a whole are not that up-to-date, given the single cell and multiplexed transcriptomic advances the last couple of decades, advanced bioinformatics that could be used in combination with in vitro lineage tracing methods.

The audience this work will reach are neural crest experts, developmental biologists, and potentially clinical doctors. The general public outreach of such a paper is also diverse, as more focus and visibility is required for the individuals affected by those syndromes and their families.

Reviewer's expertise: mouse neural crest lineage and multipotency, lineage tracing, single cell transcriptomics, NGS, immunofluorescence, molecular methods (RNA, DNA based). Limited expertise with in vitro studies.

For each list item, take the vehicle model name (e.g. "2025 Mazda CX-30 S" and make it an H3, then put the associated paragraph content below it.

Headings and paragraph content shouldn't be formatted as a list and colons can be removed from the headings

Move this section below the sentence ending with "and boasts a selection of both non-turbo as well as turbocharged gas engine options."

Reviewer #1 (Public Review):

Summary:

Shi and colleagues report the use of modified Cre lines in which the coding region of Cre is disrupted by rox-STOP-rox or lox-STOP-lox sequences to prevent the expression of functional protein in the absence of Dre or Cre activity, respectively. The main purpose of these tools is to enable intersectional or tamoxifen-induced Cre activity with minimal or no leaky activity from the second, Cre-expressing allele. It is a nice study but lacks some functional data required to determine how useful these alleles will be in practice, especially in comparison with the figure line that stimulated their creation.

Strengths:

The new tools can reduce Cre leak in vivo.

Weaknesses:

(1) Activity of R26-loxCre line. As the authors point out, the greatest value of this approach is to accomplish a more complete Cre-mediated gene deletion using CreER transgenes that are combined with low-efficiency floxed alleles using their R26-loxCre line that is similar to the iSure Cre reported by Benedito and colleagues. The data in Figure 5 show strong activity at the Confetti locus, but the design of the newly reported R26-loxCre line lacks a WPRE sequence that was included in the iSure-Cre line to drive very robust protein expression. Thus while the line appears to have minimal leak, as the design would predict, the question of how much of a deletion increase is obtained over simple use of the CreER transgene alone is a key question for use by investigators. This is further addressed in Figure 6 where it is compared with Alb-CreER alone to recombine the Ctnnb1 floxed allele. They demonstrate that recombination frequency is clearly improved, but the western blot in Figure 6E does not look like there was a large amount of remaining b-catenin to remove. These data are certainly promising, but the most valuable experiment for such a new tool would be a head-to-head comparison with iSure (or the latest iSure version from the Benedito lab) using the same CreER and target floxed allele. At the very least a comparision of Cre protein expression between the two lines using identical CreER activators is needed.

(2) In vivo analysis of mCre activities. Why did the authors not use the same driver to compare mCre 1, 4, 7, and 10? The study in Figure 2 uses Alb-roxCre for 1 and 7 and Cdh5-roxCre for 4 and 10, with clearly different levels of activity driven by the two alleles in vivo. Thus whether mCre1 is really better than mCre4 or 10 is not clear.

(3) Technical details are lacking. The authors provide little specific information regarding the precise way that the new alleles were generated, i.e. exactly what nucleotide sites were used and what the sequence of the introduced transgenes is. Such valuable information must be gleaned from schematic diagrams that are insufficient to fully explain the approach.

eLife Assessment

This valuable study investigates how the neural representation of individual finger movements changes during the early period of sequence learning. By combining a new method for extracting features from human magnetoencephalography data and decoding analyses, the authors provide incomplete evidence of an early, swift change in the brain regions correlated with sequence learning, including a set of previously unreported frontal cortical regions. The addition of more control analyses to rule out that head movement artefacts influence the findings, and to further explain the proposal of offline contextualization during short rest periods as the basis for improvement performance would strengthen the manuscript.

Reviewer #1 (Public review):

Summary:

This study addresses the issue of rapid skill learning and whether individual sequence elements (here: finger presses) are differentially represented in human MEG data. The authors use a decoding approach to classify individual finger elements, and accomplish an accuracy of around 94%. A relevant finding is that the neural representations of individual finger elements dynamically change over the course of learning. This would be highly relevant for any attempts to develop better brain machine interfaces - one now can decode individual elements within a sequence with high precision, but these representations are not static but develop over the course of learning.

Strengths:

The work follows a large body of work from the same group on the behavioural and neural foundations of sequence learning. The behavioural task is well established a neatly designed to allow for tracking learning and how individual sequence elements contribute. The inclusion of short offline rest periods between learning epochs has been influential because it has revealed that a lot, if not most of the gains in behaviour (ie speed of finger movements) occur in these so-called micro-offline rest periods.

The authors use a range of new decoding techniques, and exhaustively interrogate their data in different ways, using different decoding approaches. Regardless of the approach, impressively high decoding accuracies are observed, but when using a hybrid approach that combines the MEG data in different ways, the authors observe decoding accuracies of individual sequence elements from the MEG data of up to 94%.

Weaknesses:

A formal analysis and quantification of how head movement may have contributed to the results should be included in the paper or supplemental material. The type of correlated head movements coming from vigorous key presses aren't necessarily visible to the naked eye, and even if arms etc are restricted, this will not preclude shoulder, neck or head movement necessarily; if ICA was conducted, for example, the authors are in the position to show the components that relate to such movement; but eye-balling the data would not seem sufficient. The related issue of eye movements is addressed via classifier analysis. A formal analysis which directly accounts for finger/eye movements in the same analysis as the main result (ie any variance related to these factors) should be presented.

This reviewer recommends inclusion of a formal analysis that the intra-vs inter parcels are indeed completely independent. For example, the authors state that the inter-parcel features reflect "lower spatially resolved whole-brain activity patterns or global brain dynamics". A formal quantitative demonstration that the signals indeed show "complete independence" (as claimed by the authors) and are orthogonal would be helpful

Reviewer #1 (Public review):

Summary:

In the manuscript entitled 'A comparative analysis of planarian regeneration specificity reveals tissue polarity contributions of the axial cWnt signalling gradient.' Cleland et al. study the robustness of regenerating a head or a tail in the proper position in two different planarian species (S. mediterranea and G. sinensis). The authors find that the expression of notum, a Wnt inhibitor that is triggered after any cut, shows different dynamics of expression in both planarian species, being more symmetrical in the species that display a higher number of double-headed or Janus heads (G. sinenesis), which they refer to a less robust regeneration. The authors claim that the reduced robustness of G. sinensis regeneration is partially explained by this anterior-posterior symmetric expression of notum, since in S. mediterranea, which shows a 'robust regeneration' it appears asymmetric. So, the first claim of the manuscript is that the symmetry in notum expression could underlie the poor robustness of regenerating a head/tail in small bipolar regenerating planarian fragments.

Then, they analyse the role of a proposed tail-to-head cWnt signalling gradient during the regeneration of heads and tails in the same planarian species. To do so they develop an antibody that allows the quantification of b-catenin activity along the AP axis, together with a pharmacological approach that reduces the pre-existent cWnt gradient without affecting the wound-induced. Through this strategy the authors can demonstrate the slope of the b-catenin activity, which is a very nice result, and that it changes according to the size of the animal. Furthermore, they are able to demonstrate that by reducing the cWnt signalling in the pre-existent tissue, there is an increase in the number of double-headed regenerates (Janus heads) and that it depends on the body size and on the decreasing steepness of the cWnt gradient. This result relies on G. sinensis species since the drug is not so effective in S. mediterranea. Thus, the authors' second claim is that the slope of the cWnt gradient may contribute to head-tail regeneration specificity in planarians.

To conclude, it is proposed that regeneration of the correct identity in each wound depends on multiple cues acting in parallel and that their species-specificity provides variations in the regenerative capability of the different planarian species.

The study has great potential to have a high impact on the regeneration community, since the opportunity to compare mechanisms between close species provides the framework for understanding the essential mechanism of regeneration.

Strengths:

The project has several strengths. The authors are able to reproduce the Janus heads phenotypes described by Morgan TH by analysing different planarian species. This is of great importance in the planarian field, because with the current model species, S. mediterranea, this could not be reproduced. So, these results demonstrate that small planarian fragments do make errors during regeneration, giving rise to double-headed animals, which supports the well-known hypothesis that it exists an anteroposterior gradient underlying anteroposterior identity during regeneration. However, and importantly, it does not occur in all planarian species. So, there are differences between planarian species in the robustness of regeneration and may be in the mechanisms that drive this regeneration. The finding of different behaviours and gene expressions in different planarian species is very interesting and promising in the field of regeneration.

A second strength of the study is the demonstration of the b-catenin1 slope in planarians and how it changes with the animal size, and also the establishment of a method to decrease it in the pre-existent tissue but not in the wound. This strategy allows us to examine specifically the role of the pre-existent cWnt signal, demonstrating that it does have a role in the decision of making head or tail during regeneration, which was an essential question in the field of planarians and animal regeneration.

Weaknesses:

(1) The finding that notum, which is the main head determinant identified in planarians, has a different dynamic in both planarian species is very suggestive. However, the different dynamics of notum expression during regeneration, which is the basis of the subsequent rationale, is not properly demonstrated, nor is its correlation with the robustness in regenerating a proper head/tail identity. Main concerns regarding this point:

a) The authors observe that 'In regenerating S. mediterranea 2 mm trunk pieces cut from 6 mm animals, notum expression was induced predominantly at anterior-facing wounds as early as 6 h post-amputation (Figure 2A), as previously reported (Petersen and Reddien 2011)'. However, in the graphics in Figures 2B and C, the expression of notum at 6h is shown as symmetric. It definitely does not agree with the in situ, with the text, or with the published data. How was it measured? It should be corrected and explained since it is the basis of the subsequent rationale.

b) Then, when measuring notum in G. sinensis the authors conclude: 'Strikingly and in sharp contrast to S. mediterranea, the number of notum expressing cells was nearly identical between anterior and posterior wounds without any discernible A/P asymmetry at any of the examined time points (Figures 2E-F)'. However, in the in situ results of 12 h regenerating G. sinensis, there is a clear difference in notum expression between anterior and posterior wounds. Is it not representative of the image? Again, how exactly the measurements were performed? Are dots or pixels quantified? It is not explained in the text. This is a crucial result that has to be consistent.

c) A more general weakness of this part of the manuscript is that even if the authors demonstrate that in G. sinensis the expression of notum is symmetrical in contrast to S. mediterranea, this is just an observation of 1 species that has symmetrical notum and regenerates less robustly than 1 species that has asymmetrical expression and regenerates more robustly. If they for instance look at the expression of wnt1, maybe they also see differences between both species that could be linked to their different regeneration properties (related to this, see below the comment on wnt1 expression). That is to say, comparing 1 to 1 species cannot give any cause-effect evidence.<br /> Furthermore, the authors rely on the fact that notum inhibition rescues the wild-type phenotype to conclude that is the symmetric expression of notum that underlies the appearance of Janus heads. This is what can be read in the results: 'Significantly, the rescue of wild-type regenerates by notum(RNAi) suggests that the symmetric G. sinensis notum expression contributes to the formation of double-heads and thus to reduced regeneration specificity'; and in the Summary: We found that the reduced regeneration robustness of G. sinensis was partially explained by wound site-symmetric expression of the head determinant notum, which is highly anterior-specific in S. mediterranea.' However, notum RNAi decreases notum in both wounds, so it does not produce an asymmetric expression (at least this is not shown). So, it does not link the symmetry or asymmetry of notum with the appearance of Janus heads.

d) If the authors want to maintain the claim that the symmetry of notum is one of the reasons that explain the increase in Janus head phenotype in G. sinensis, there are several possibilities to test it. For instance:

i) Analyse notum expression in different planarian species and relate its symmetry or asymmetry with the appearance of Janus heads. If the claim is true, the species that are more robust should show more asymmetric expression of notum. This would sustain strongly the first claim, and would really be a breakthrough in the field of regeneration.

ii) Another possibility is a more in-depth analysis of notum expression in the species of the study. If the authors show that larger fragments show fewer Janus heads, and also that it depends on the anteroposterior level of the fragments, they could try to relate the rate of Janus heads with the degree of asymmetry in notum expression in both wounds. For instance, they could analyze notum expression in bipolar regenerating fragments along the anteroposterior axis in both species; it should be more symmetric in G sinenesis, in all fragments, according to Figure 2 L. Or they could analyze notum expression in bipolar regenerating fragments of different sizes, mainly in 1 or 2 mm fragments of big planarians, since they are the fragments analyzed that form or not the Janus heads. In G sinensis the expression of notum should be more symmetrical than in S. mediterranea in these fragments.

iii) The authors could design an experiment to demonstrate that the symmetry in the expression of notum affects the rate of Janus heads. The experiment that the authors show is the rescue of the Janus heads in G. sinensis after notum RNAi. However, notum RNAi suppresses notum in both wounds, thus not making them asymmetric. Furthermore, the rescue could be explained by the posteriorizing effect that notum RNAi has in planarians, as reported by several authors. A possibility could be to inhibit APC, which increases notum expression in S. mediterranea (Petersen and Reddien 2011). If APC RNAi in G. sinenesis produces an increase in notum in both wounds and the rate of Janus heads is not rescued, then it would support the hypothesis that notum symmetry is the cause of the Janus heads. However, if it produces an increase of notum in an asymmetric manner, then the Janus phenotype should be rescued.

(2) The second weakness of the study is related to the methodology used to support the second claim, that the slope of bcatenin1 activity has a role in the decision of regeneration - a head and a tail in the correct tip. The main concerns relate to the specificity of the anti-bcatenin1 antibody and to the broad effect of C59 in the secretion of all Wnts.

a) Raising an antibody against beta-catenin1 that allows the quantification by western blot is a strength of the study, since beta-catenin1 is the key element of the cWnt pathway, and their levels are directly associated with the activation of the pathway. Since this is one of the tools that support the second claim of the study, a characterization of the antibody and additional tests to prove its specificity are required. The authors show a Western blot in which the band intensity decreases after beta-catenin1 inhibition in both species. Further analysis should be shown:<br /> i) Demonstration that the intensity of the band increases after APC or Axin inhibition.<br /> ii) Does the antibody work in immunohistochemistry? It would provide further evidence of the specificity of a nuclear signal could be demonstrated.<br /> iii) Explanation and discussion of the protocol used to analyse the levels of b-catenin1 activity along the anteroposterior axis is required. It has been reported that beta-catenin1 is highly expressed and required in the brain in planarians, and also in the pharynx, and in the sexual organs (Hill and Petersen 2015, Sureda-Gomez et al 2016). How is it then explained the anterior-to-posterior gradient of expression of beta-catenin1 seen in this study in both species? Has the pharynx been removed before the protein extraction? What about the beta-catenin1 activity demonstrated in the brain? Why is it not reflected in the western blot analysis using the antibody? This point should be clarified.

b) The second tool used in the second part of the manuscript is the drug C59, which inhibits Porcupine, a protein required for palmitoylation and secretion of Wnts. Because Porcupine could be required for the secretion of all Wnts, the phenotype obtained with the drug could be the sum of the inhibition of cWNT signal (wnt1 for instances) and non-canonical WNT (as wnt5). This is in fact the phenotype resulting after the inhibition of Wntless in planarians (Adell et al. 2009), which is also required for the secretion of Wnts. Thus, in the phenotypes resulting from C59 treatment the analysis of the nervous system and posterior/anterior markers is required. Looking at the in vivo phenotype it appears that in fact the drug is affecting both canonical and no canonical pathways since the animal with protrusions in the lateral part (Figure 4B-double head, or Supplementary Figure 3A) is very similar to the one reported after Wntless inhibition. In case the phenotypes observed also show non-canonical Wnt inhibition, this should be clearly shown and discussed.

The above-mentioned weaknesses are the most important concerns about the present manuscript. However, there are other concerns related to a further analysis of the phenotypes and the analysis of additional Wnt elements as wnt1, which are essential to complete the study and are directly discussed with the authors.

Reviewer #2 (Public review):

Summary:

This study identifies a key role for bodywide canonical Wnt gradients in controlling the outcome of regeneration within planarians, likely acting in parallel to injury-induced cues that also use tissue asymmetry to control this process. In S. Mediterranea a central part of this decision process is the asymmetric expression of the Wnt inhibitor notum specifically at injury sites facing in the anterior direction to promote head formation and inhibit tail formation through regulation of canonical Wnt signaling pathways. Leveraging classic studies by T.H. Morgan over a century ago, which found that amputated thin transverse fragments occasionally incorrectly regenerate 2 heads rather than a head and a tail in a species of Girardia planarians, this study identifies a closely related species G. Sinensis which undergoes errors to regeneration specificity under similar challenges. Morgan had proposed that his results might arise from the use of a "gradient of materials" providing axis information across the body axis such that small tissue fragments are too narrow to interpret gradient differences, leading to head/tail polarity defects in regeneration. The authors show very convincingly that this species of planaria undergoes notum expression after injury, but unlike in S. Mediterranea, this occurs symmetrically at the onset of regeneration. Using RNAi, they show notum participates in the regeneration of mispolarized heads (though interestingly apparently not in normal head regeneration unlike in Smeds, at least under these conditions). G. Sinensis planarians, like many organisms, have abundant expression of Wnt genes posteriorly. To test whether this gradient of Wnts may participate in the regeneration distinct from any Wnt signals activated after injury, the authors use chemical inhibition to reduce Wnt signaling prior to injury and then alleviate inhibition following injury by removal of the drug and confirming successful washout of the drug using mass spec. They also raise a new antibody that can detect beta-catenin-1 in this species in order to monitor the body-wide cWnt gradient after these treatments, and correlate this with outcomes on the head/tail regeneration decision. Using this approach, they find that homeostatic inhibition of porcupine (required for Wnt secretion) could dampen the cWnt/beta-catenin gradient and increase the incidence of inappropriate head regeneration at posterior-facing wounds. In addition, they find that the cWnt gradient is less steep in larger animals that also concurrently have a higher incidence of mistakes in regeneration specificity. Together, the paper presents compelling experiments and analysis to support the conclusion that cWnt gradients are an important determinant of head/tail identity determination decisions in G. Sinensis, and thereby proposes a plausible model that the notum asymmetry present in S. Mediterranea could act in parallel to support the higher regeneration robustness observed in that species.

Strengths:

This is a great paper, an instant classic. It addresses an enduring problem that Morgan and others initiated more than a century ago and brings a new synthesis of ideas to clarify an important mechanism. I also like the term "regeneration specificity" which can provide a nice unification and generalization of ideas that other authors have variously described as regeneration patterning or regeneration polarity. The work is a tour de force that creatively builds new tools and observations to leverage a new model of planarian species for unraveling general mechanisms of regeneration decision-making. The experiments are rigorously conducted and I find the overall data to be quite compelling. I have some comments for the authors to consider below for drawing out the interpretation and also clarifying the underlying mechanism.

Comments:

(1) The G. Sinesis species showed accurate head/tail specificity in 2mm thick fragments but was strongly impaired at 1 mm thick. I am assuming that outcomes of pieces greater than 2mm would make similarly robust head/tail choices, implying a rather sharp transition occurring between 1 and 2 mm. In that case, in the gradient model, are there theoretical reasons to predict that polarity outcomes would decline sharply rather than gradually as size thickness decreases? I think the muscle fibers themselves are thought to have lengths on the order of 200 microns, so I wonder what could account for the characteristic length of less than 1mm here? From the lab's prior analysis of beta-cat gradient, is this perhaps the minimal length where a difference in bcat protein levels can be detected? This is not essential to resolve in this draft (in my view), just a very interesting question arising from the present study. Relatedly, it seems that the slope of cWnt at the wound site itself might not be enough information for polarity because at a highly granular level, this should be identical at posterior-facing wounds from trunk fragments versus thin transverse fragments obtained at the same AP position, yet trunk fragments succeed at regeneration specificity whereas thin transverse fragments fail.

(2) The paper nicely shows strong evidence that notum expression is definitely symmetric at the first occurrence of its expression by 6 hours in D. Sinensis, and this is a really important result of the paper. At 12 hours, it does look to me in the FISH experiments that there is more persistence of expression at the anterior-facing wound versus the posterior-facing wounds (Fig 2D), although the methods for quantification in Fig2E/F do not show a difference in expression at the two wound sites at this time point. Could this difference arise from differences in the perdurance or timing of early wound-induced signaling at the two wound sites that was perhaps too subtle to detect in the quantification methods used? Or perhaps these images do not represent the population? On a related note, the quantification method seems to fail to show that in 6h Smeds, notum expression is indeed asymmetric. Probably the issue here is not the data in the FISH images themselves which strongly support the author's interpretations, but rather a deficiency or limitation of the quantification method used, which should be resolved so that the conclusions from the single FISH images can be interpreted robustly. For example, some authors have used a method of counting notum+ cells and I wonder if this could provide better quantitative information here.

(3) Given that the double-headed phenotype is observed from thin transverse fragments, ideally, the symmetry of notum could be established to occur in those types of fragments as well. This experiment would clarify that notum is expressed at posterior-facing wounds in the very same types of fragments that undergo the highest levels of mistakes in regeneration specificity.

(4) Is wnt1 expressed symmetrically at wound sites in this species? It seems there are cases like acoels where wound-induced Wnt activation can occur asymmetrically but through preferential expression of Wnts at posterior-facing wounds, rather than notum. It would be interesting to know although I also think the work the authors already have done in this study itself already constitutes a very comprehensive advance and could be the subject of future work.

(5) I agree that notum is relatively much more strongly expressed at the far posterior region in D. Senesis than in Smeds, but it does seem from the RNAseq data it also has some locally enriched expression at the anterior pole. Because the RNAseq analysis involves scaling expression across the regions for each gene, it is difficult to know if the anterior expression is relatively lower or perhaps even about the same level of expression as the anterior pole expression of this gene in Smeds. Though not essential to make the desired arguments, in situs on notum in the intact animals would be helpful to clarify this. Relatedly it would be fascinating to know whether D. Senesis notum undergoes anterior-pole expression around the 72 hour or similar timepoint as in Smeds.

(6) The assessment of beta-catenin gradients was done through protein extractions from whole tissue fragments. However, it has been shown in other planarian species that beta-catenin can have strong tissue-specific expression in, for example, the pharynx, brain, and reproductive systems. Some supporting evidence or argument should be presented to clarify the interpretation that the graded expression observed by western blotting cannot be fully explained by this kind of tissue-specific expression of beta-catenin rather than representing a true signaling gradient as interpreted by the authors. For example, if this antibody could be used in immunostaining, this could support the beta-catenin signaling gradient. Alternatively, information about the location of the pharynx or any other posterior reproductive tissues in D. Sinensis could be calibrated with respect to the fragment bins used for the gradient--perhaps a portion of the C59-dependent body-wide gradient measured here occurs fully within tail tissue that lacks other regionalized tissue that could be a potential additional source of beta-catenin. Further discussion and interpretation, or additional experiments, should be included to rule out alternative confounding sources of beta-catenin in order to clarify the interpretation of the western blot as representing a beta-catenin signaling gradient.

(7) I find the analysis in Figure 5 to be quite compelling for showing the importance of cWnt/Bcat gradients in contributing to head/tail determination, and I also think that the author's discussion of the limitations of the approach are well articulated and considered. Based on prior literature, it also seems very likely that there is a third redundantly acting component to regeneration specificity, which is the amplification of small differences in cWnt in a directional-dependent manner early in the regeneration process (24-72 hours in Smeds). This would explain why post-amputation with porcupine inhibitor in D. Sinensis caused 100% penetrant defects in regeneration specificity while the pre-treatment paradigm caused a weaker effect (25-40% for larger animals). In Smeds, it is known already that delivery of dsRNAs against beta-catenin-1, wnt1, and notum only after injury caused polarity defects, and thus all three genes certainly have a function relevant for head/tail after injury (Petersen and Reddien 2008, 2009, 2011- please note these experiments were reported in the text of these studies and not in individual figures). This evidence, combined with extensive FISH and complementary RNAi studies in the field, strongly suggests that some combination of the 6-18h injury-induced phase but also very likely the subsequent "pole-specific phase" of wnt1 expression is likely to be important for driving or enacting the tail fate program and is therefore a component of the regeneration specificity mechanism described here.

(8) Prior work has also demonstrated roles for Wnt genes expressed in gradients to participate in regeneration specificity. In particular, inhibition of the wntP-2/wnt11-5 gene, which is expressed in an animal-wide gradient, strongly enhanced the effects of inhibition of wnt1, which is the earliest wound-activated Wnt gene, to cause 100% penetrant posterior head regeneration phenotypes in S. mediterranea (Petersen and Reddien 2009). These observations are complementary to the present study by implicating Wnts expressed in bodywide gradients in the process of regeneration decision-making. Given that this study also showed that wnt1 is necessary for new wntP-2 expression during the wound-induced early phase and that wnt1 activation does not require beta-catenin for its expression, collectively suggest a more complex process involved in gradient detection and the involvement of wound signals likely beyond only autoregulation of the cWnt gradient or notum asymmetry mechanisms. Although this paper is cited already, framing the present study more fully in context with this and other relevant prior work would be helpful to contextualize the advance for the field.

On the contrary-- I think you've hit the nail on the head.

Shanidar 1, Neanderthal remains

Shanidar 1 | The Smithsonian Institution's Human Origins Program:

Through examining his skeletal remains, scientists found evidence that at a young age, Shanidar 1 experienced a crushing blow to his head. The blow damaged the left eye (possibly blinding him) and the brain area controling the right side of the body, leading to a withered right arm and possible paralysis that also crippled his right leg. One of Shanidar 1’s middle foot bones (metatarsal) on his right foot shows a healed fracture, which probably only enhanced his noticeable limp. All of Shanidar 1’s injuries show signs of healing, so none of them resulted in his death. In fact, scientists estimate he lived until 35–45 years of age. He would have been considered old to another Neandertal, and he would probably not have been able to survive without the care of his social group.

Reviewer #1 (Public review):

Summary:

Here, the authors have addressed the recruitment and firing patterns of motor units (MUs) from the long and lateral heads of the triceps in the mouse. They used their newly developed Myomatrix arrays to record from these muscles during treadmill locomotion at different speeds, and they used template-based spike sorting (Kilosort) to extract units. Between MUs from the two heads, the authors observed differences in their firing rates, recruitment probability, phase of activation within the locomotor cycle, and interspike interval patterning. Examining different walking speeds, the authors find increases in both recruitment probability and firing rates as speed increases. The authors also observed differences in the relation between recruitment and the angle of elbow extension between motor units from each head. These differences indicate meaningful variation between motor units within and across motor pools and may reflect the somewhat distinct joint actions of the two heads of triceps.

Strengths:

The extraction of MU spike timing for many individual units is an exciting new method that has great promise for exposing the fine detail in muscle activation and its control by the motor system. In particular, the methods developed by the authors for this purpose seem to be the only way to reliably resolve single MUs in the mouse, as the methods used previously in humans and in monkeys (e.g. Marshall et al. Nature Neuroscience, 2022) do not seem readily adaptable for use in rodents.

The paper provides a number of interesting observations. There are signs of interesting differences in MU activation profiles for individual muscles here, consistent with those shown by Marshall et al. It is also nice to see fine-scale differences in the activation of different muscle heads, which could relate to their partially distinct functions. The mouse offers greater opportunities for understanding the control of these distinct functions, compared to the other organisms in which functional differences between heads have previously been described.

The Discussion is very thorough, providing a very nice recounting of a great deal of relevant previous results.

Weaknesses:

The findings are limited to one pair of muscle heads. While an important initial finding, the lack of confirmation from analysis of other muscles acting at other joints leaves the general relevance of these findings unclear.

While differences between muscle heads with somewhat distinct functions are interesting and relevant to joint control, differences between MUs for individual muscles, like those in Marshall et al., are more striking because they cannot be attributed potentially to differences in each head's function. The present manuscript does show some signs of differences for MUs within individual heads: in Figure 2C, we see what looks like two clusters of motor units within the long head in terms of their recruitment probability. However, a statistical basis for the existence of two distinct subpopulations is not provided, and no subsequent analysis is done to explore the potential for differences among MUs for individual heads.

The statistical foundation for some claims is lacking. In addition, the description of key statistical analysis in the Methods is too brief and very hard to understand. This leaves several claims hard to validate.

Reviewer #2 (Public review):

The present study, led by Thomas and collaborators, aims to describe the firing activity of individual motor units in mice during locomotion. To achieve this, they implanted small arrays of eight electrodes in two heads of the triceps and performed spike sorting using a custom implementation of Kilosort. Simultaneously, they tracked the positions of the shoulder, elbow, and wrist using a single camera and a markerless motion capture algorithm (DeepLabCut). Repeated one-minute recordings were conducted in six mice at five different speeds, ranging from 10 to 27.5 cm·s⁻¹.

From these data, the authors reported that:

(1) a significant portion of the identified motor units was not consistently recruited across strides,<br /> (2) motor units identified from the lateral head of the triceps tended to be recruited later than those from the long head,<br /> (3) the number of spikes per stride and peak firing rates were correlated in both muscles, and<br /> (4) the probability of motor unit recruitment and firing rates increased with walking speed.

The authors conclude that these differences can be attributed to the distinct functions of the muscles and the constraints of the task (i.e., speed).

Strengths:

The combination of novel electrode arrays to record intramuscular electromyographic signals from a larger muscle volume with an advanced spike sorting pipeline capable of identifying populations of motor units.

Weaknesses:

(1) There is a lack of information on the number of identified motor units per muscle and per animal.

(2) All identified motor units are pooled in the analyses, whereas per-animal analyses would have been valuable, as motor units within an individual likely receive common synaptic inputs. Such analyses would fully leverage the potential of identifying populations of motor units.

(3) The current data do not allow for determining which motor units were sampled from each pool. It remains unclear whether the sample is biased toward high-threshold motor units or representative of the full pool.

(4) The behavioural analysis of the animals relies solely on kinematics (2D estimates of elbow angle and stride timing). Without ground reaction forces or shoulder angle data, drawing functional conclusions from the results is challenging.

Major comments:

(1) Spike sorting

The conclusions of the study rely on the accuracy and robustness of the spike sorting algorithm during a highly dynamic task. Although the pipeline was presented in a previous publication (Chung et al., 2023, eLife), a proper validation of the algorithm for identifying motor unit spikes is still lacking. This is particularly important in the present study, as the experimental conditions involve significant dynamic changes. Under such conditions, muscle geometry is altered due to variations in both fibre pennation angles and lengths.

This issue differs from electrode drift, and it is unclear whether the original implementation of Kilosort includes functions to address it. Could the authors provide more details on the various steps of their pipeline, the strategies they employed to ensure consistent tracking of motor unit action potentials despite potential changes in action potential waveforms, and the methods used for manual inspection of the spike sorting algorithm's output?

(2) Yield of the spike sorting pipeline and analyses per animal/muscle

A total of 33 motor units were identified from two heads of the triceps in six mice (17 from the long head and 16 from the lateral head). However, precise information on the yield per muscle per animal is not provided. This information is crucial to support the novelty of the study, as the authors claim in the introduction that their electrode arrays enable the identification of populations of motor units.

Beyond reporting the number of identified motor units, another way to demonstrate the effectiveness of the spike sorting algorithm would be to compare the recorded EMG signals with the residual signal obtained after subtracting the action potentials of the identified motor units, using a signal-to-residual ratio.

Furthermore, motor units identified from the same muscle and the same animal are likely not independent due to common synaptic inputs. This dependence should be accounted for in the statistical analyses when comparing changes in motor unit properties across speeds and between muscles.

(3) Representativeness of the sample of identified motor units

However, to draw such conclusions, the authors should exclusively compare motor units from the same pool and systematically track violations of the recruitment order. Alternatively, they could demonstrate that the motor units that are intermittently active across strides correspond to the smallest motor units, based on the assumption that these units should always be recruited due to their low activation thresholds.

One way to estimate the size of motor units identified within the same muscle would be to compare the amplitude of their action potentials, assuming that all motor units are relatively close to the electrodes (given the selectivity of the recordings) and that motoneurons innervating more muscle fibres generate larger motor unit action potentials.

Currently, the data seem to support the idea that motor units that are alternately recruited across strides have recruitment thresholds close to the level of activation or force produced during slow walking. The fact that recruitment probability monotonically increases with speed suggests that the force required to propel the mouse forward exceeds the recruitment threshold of these "large" motor units. This pattern would primarily reflect spatial recruitment following the size principle rather than flexible motor unit control.

(4) Analysis of recruitment and firing rates

The authors currently report active duration and peak firing rates based on spike trains convolved with a Gaussian kernel. Why not report the peak of the instantaneous firing rates estimated from the inverse of the inter-spike interval? This approach appears to be more aligned with previous studies conducted to describe motor unit behaviour during fast movements (e.g., Desmedt & Godaux, 1977, J Physiol; Van Cutsem et al., 1998, J Physiol; Del Vecchio et al., 2019, J Physiol).

(5) Additional analyses on behaviour

The authors currently analyse motor unit recruitment in relation to elbow angle. It would be valuable to include a similar analysis using the angular velocity observed during each stride, as higher velocity would place each muscle in a less favourable position on the force-velocity relationship for generating the required force. More broadly, comparing stride-by-stride changes in firing rates with changes in elbow angular velocity would further strengthen the final analyses presented in the results section.

Reviewer #1 (Public review):

Summary

In this article, Kawanabe-Kobayashi et al., aim to examine the mechanisms by which stress can modulate pain in mice. They focus on the contribution of noradrenergic neurons (NA) of the locus coeruleus (LC). The authors use acute restraint stress as a stress paradigm and found that following one hour of restraint stress mice display mechanical hypersensitivity. They show that restraint stress causes the activation of LC NA neurons and the release of NA in the spinal cord dorsal horn (SDH). They then examine the spinal mechanisms by which LC→SDH NA produces mechanical hypersensitivity. The authors provide evidence that NA can act on alphaA1Rs expressed by a class of astrocytes defined by the expression of Hes (Hes+). Furthermore, they found that NA, presumably through astrocytic release of ATP following NA action on alphaA1Rs Hes+ astrocytes, can cause an adenosine-mediated inhibition of SDH inhibitory interneurons. They propose that this disinhibition mechanism could explain how restraint stress can cause the mechanical hypersensitivity they measured in their behavioral experiments.

Strengths:

(1) Significance. Stress profoundly influences pain perception; resolving the mechanisms by which stress alters nociception in rodents may explain the well-known phenomenon of stress-induced analgesia and/or facilitate the development of therapies to mitigate the negative consequences of chronic stress on chronic pain.

(2) Novelty. The authors' findings reveal a crucial contribution of Hes+ spinal astrocytes in the modulation of pain thresholds during stress.

(3) Techniques. This study combines multiple approaches to dissect circuit, cellular, and molecular mechanisms including optical recordings of neural and astrocytic Ca2+ activity in behaving mice, intersectional genetic strategies, cell ablation, optogenetics, chemogenetics, CRISPR-based gene knockdown, slice electrophysiology, and behavior.

Weaknesses:

(1) Mouse model of stress. Although chronic stress can increase sensitivity to somatosensory stimuli and contribute to hyperalgesia and anhedonia, particularly in the context of chronic pain states, acute stress is well known to produce analgesia in humans and rodents. The experimental design used by the authors consists of a single one-hour session of restraint stress followed by 30 min to one hour of habituation and measurement of cutaneous mechanical sensitivity with von Frey filaments. This acute stress behavioral paradigm corresponds to the conditions in which the clinical phenomenon of stress-induced analgesia is observed in humans, as well as in animal models. Surprisingly, however, the authors measured that this acute stressor produced hypersensitivity rather than antinociception. This discrepancy is significant and requires further investigation.

(2) Specifically, is the hypersensitivity to mechanical stimulation also observed in response to heat or cold on a hotplate or coldplate?

(3) Using other stress models, such as a forced swim, do the authors also observe acute stress-induced hypersensitivity instead of stress-induced antinociception?

(4) Measurement of stress hormones in blood would provide an objective measure of the stress of the animals.

(5) Results:

a) Optical recordings of Ca2+ activity in behaving rodents are particularly useful to investigate the relationship between Ca2+ dynamics and the behaviors displayed by rodents.

b) The authors report an increase in Ca2+ events in LC NA neurons during restraint stress: Did mice display specific behaviors at the time these Ca2+ events were observed such as movements to escape or orofacial behaviors including head movements or whisking?

c) Additionally, are similar increases in Ca2+ events in LC NA neurons observed during other stressful behavioral paradigms versus non-stressful paradigms?

d) Neuronal ablation to reveal the function of a cell population.

e) The proportion of LC NA neurons and LC→SDH NA neurons expressing DTR-GFP and ablated should be quantified (Figures 1G and J) to validate the methods and permit interpretation of the behavioral data (Figures 1H and K). Importantly, the nocifensive responses and behavior of these mice in other pain assays in the absence of stress (e.g., hotplate) and a few standard assays (open field, rotarod, elevated plus maze) would help determine the consequences of cell ablation on processing of nociceptive information and general behavior.

f) Confirmation of LC NA neuron function with other methods that alter neuronal excitability or neurotransmission instead of destroying the circuit investigated, such as chemogenetics or chemogenetics, would greatly strengthen the findings. Optogenetics is used in Figure 1M, N but excitation of LC→SDH NA neuron terminals is tested instead of inhibition (to mimic ablation), and in naïve mice instead of stressed mice.

g) Alpha1Ars. The authors noted that "Adra1a mRNA is also expressed in INs in the SDH".

h) The authors should comprehensively indicate what other cell types present in the spinal cord and neurons projecting to the spinal cord express alpha1Ars and what is the relative expression level of alpha1Ars in these different cell types.

i) The conditional KO of alpha1Ars specifically in Hes5+ astrocytes and not in other cell types expressing alpha1Ars should be quantified and validated (Figure 2H).

j) Depolarization of SDH inhibitory interneurons by NA (Figure 3). The authors' bath applied NA, which presumably activates all NA receptors present in the preparation.

k) The authors' model (Figure 4H) implies that NA released by LC→SDH NA neurons leads to the inhibition of SDH inhibitory interneurons by NA. In other experiments (Figure 1L, Figure 2A), the authors used optogenetics to promote the release of endogenous NA in SDH by LC→SDH NA neurons. This approach would investigate the function of NA endogenously released by LC NA neurons at presynaptic terminals in the SDH and at physiological concentrations and would test the model more convincingly compared to the bath application of NA.

l) As for other experiments, the proportion of Hes+ astrocytes that express hM3Dq, and the absence of expression in other cells, should be quantified and validated to interpret behavioral data.

m) Showing that the effect of CNO is dose-dependent would strengthen the authors' findings.

n) The proportion of SG neurons for which CNO bath application resulted in a reduction in recorded sIPSCs is not clear.

o) A1Rs. The specific expression of Cas9 and guide RNAs, and the specific KD of A1Rs, in inhibitory interneurons but not in other cell types expressing A1Rs should be quantified and validated.

(6) Methods:

It is unclear how fiber photometry is performed using "optic cannula" during restraint stress while mice are in a 50ml falcon tube (as shown in Figure 1A).

Reviewer #3 (Public review):

Summary:

In this manuscript, the authors apply tissue expansion and tiling light sheet microscopy to study allometric growth and regeneration in planaria. They developed image analysis pipelines to help them quantify different neuronal subtypes and muscles in planaria of different sizes and during regeneration. Among the strengths of this work, the authors provide beautiful images that show the potential of the approaches they are taking and their ability to quantify specific cell types in relatively large numbers of whole animal samples. Many of their findings confirm previous results in the literature, which helps validate the techniques and pipelines they have applied here. Among their new observations, they find that the body wall muscles at the anterior and posterior poles of the worm are organized differently and show that the muscle pattern in the posterior head of beta-catenin RNAi worms resembles the anterior muscle pattern. They also show that glial cell processes appear to be altered in beta-catenin or insulin receptor-1 RNAi worms. Weaknesses include some over-interpretation of the data and lack of consideration or citation of relevant previous literature, as discussed below.

Strengths:

This method of tissue expansion will be useful for researchers interested in studying this experimental animal. The authors provide high-quality images that show the utility of this technique. Their analysis pipeline permits them to quantify cell types in relatively large numbers of whole animal samples.

The authors provide convincing data on changes in total neurons and neuronal sub-types in different-sized planaria. They report differences in body wall muscle pattern between the anterior and posterior poles of the planaria, and that these differences are lost when a posterior head forms in beta-catenin RNAi planaria. They also find that glial cell projections are reduced in insulin receptor-1 RNAi planaria.

Comments on revisions:

The authors have satisfactorily addressed the major concerns of the previous reviewers.

Author response:

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

Reviewer #1(Public review):

comment 1: Lu et al. use their workflow to visualize RNA expression of five enzymes that are each involved in the biosynthetic pathway of different neurotransmitters/modulators, namely chat (cholinergeric), gad (GABAergic), tbh (octopaminergic), th (dopaminergic), and tph (serotonergic). In this way, they generate an anatomical atlas of neurons that produce these molecules. Collectively these markers are referred to as the "neuronpool." They overstate when they write, "The combination of these five types of neurons constitutes a neuron pool that enables the labeling of all neurons throughout the entire body." This statement does not accurately represent the state of our knowledge about the diversity of neurons in S. mediterranea. There are several lines of evidence that support the presence of glutamatergic and glycinergic neurons, including the following. The glutamate receptor agonists NMDA and AMPA both produce seizure-like behaviors in S. mediterranea that are blocked by the application of glutamate receptor antagonists MK-801 and DNQX (which antagonize NMDA and AMPA glutamate receptors, respectively; Rawls et al., 2009). scRNA-Seq data indicates that neurons in S. mediterranea express a vesicular glutamate transporter, a kainite-type glutamate receptor, a glycine receptor, and a glycine transporter (Brunet Avalos and Sprecher, 2021; Wyss et al., 2022). Two AMPA glutamate receptors, GluR1 and GluR2, are known to be expressed in the CNS of another planarian species, D. japonica (Cebria et al., 2002). Likewise, there is abundant evidence for the presence of peptidergic neurons in S. mediterranea (Collins et al., 2010; Fraguas et al., 2012; Ong et al., 2016; Wyss et al., 2022; among others) and in D. japonica (Shimoyama et al., 2016). For these reasons, the authors should not assume that all neurons can be assayed using the five markers that they selected. The situation is made more complex by the fact that many neurons in S. mediterranea appear to produce more than one neurotransmitter/modulator/peptide (Brunet Avalos and Sprecher, 2021; Wyss et al., 2022), which is common among animals (Vaaga et al., 2014; Brunet Avalos and Sprecher, 2021). However the published literature indicates that there are substantial populations of glutamatergic, glycinergic, and peptidergic neurons in S. mediterranea that do not produce other classes of neurotransmission molecule (Brunet Avalos and Sprecher, 2021; Wyss et al., 2022). Thus it seems likely that the neuronpool will miss many neurons that only produce glutamate, glycine or a neuropeptide.

In response to your comments, we agree that our initial statement regarding the "neuron pool" overstated the extent of neuronal coverage provided by the five selected markers. We have revised the sentence as “The combination of these five types of neurons constitutes a neuron pool that enables the labeling of most of the neurons throughout the entire body, including the eyes, brain, and pharynx”.

Furthermore, we chose the five neurotransmitter systems (cholinergic, GABAergic, octopaminergic, dopaminergic, and serotonergic) based on their well-characterized roles in planarian neurobiology and the availability of reliable markers. However, we acknowledge the limitations of this approach and recognize that it does not encompass all neuron types, particularly those involved in glutamatergic, glycinergic, and peptidergic signaling, which have been documented in S. mediterranea. We have also added the content about other neuron types in our revised results section “Additionally, the neuron system of S. mediterranea is complex which characterized by considerable diversity among glutamatergic, glycinergic, and peptidergic neurons in planarians and many neurons in S. mediterranea express more than one neurotransmitter or neuropeptide, which adds further complexity to the system. We used five markers for a proof of concept illustration. By employing Fluorescence in Situ Hybridization (FISH), we successfully visualized a variety of planarian neurons, including cholinergic (chat⁺), serotonergic (tph⁺), octopaminergic (tbh⁺), GABAergic (gad⁺), and dopaminergic (th⁺) neurons based on their well-characterized roles in planarian neurobiology and the availability of reliable markers. (Figure S2A, Supplemental video 2) (Currie et al., 2016). The combination of these five types of neurons constitutes a neuron pool that enables the labeling of most of the neurons throughout the entire body, including the eyes, brain, and pharynx (Figure 1B).”

comment 2: The authors use their technique to image the neural network of the CNS using antibodies raised vs. Arrestin, Synaptotagmin, and phospho-Ser/Thr. They document examples of both contralateral and ipsilateral projections from the eyes to the brain in the optic chiasma (Figure 1C-F). These data all seem to be drawn from a single animal in which there appears to be a greater than normal number of nerve fiber defasciculatations. It isn't clear how well their technique works for fibers that remain within a nerve tract or the brain. The markers used to image neural networks are broadly expressed, and it's possible that most nerve fibers are too densely packed (even after expansion) to allow for image segmentation. The authors also show a close association between estrella-positive glial cells and nerve fibers in the optic chiasma.

Thank you for your detailed feedback. While we did not perform segmentation of all neuron fibers, we were able to segment more isolated fibers that were not densely packed within the neural tracts. We use 120 nm resolution to segment neurons along the three axes. Our data show the presence of both contralateral and ipsilateral projections of visual neurons. Although Figure 1C-F shows data from one planarian, we imaged three independent specimens to confirm the consistency of these observations. In the revised manuscript, we have included a discussion on the limitations of TLSM in reconstructing neural networks. In the discussion part, we added “It should be noted that the current resolution for our segmentation may be limited when resolving fibers within densely packed regions of the nerve tracts”.

comment 3: The authors count all cell types, neuron pool neurons, and neurons of each class assayed. They find that the cell number to body volume ratio remains stable during homeostasis (Figure S3C), and that the brain volume steadily increases with increasing body volume (Figure S3E). They also observe that the proportion of neurons to total body cells is higher in worms 2-6 mm in length than in worms 7-9 mm in length (Figure 2D, S3F). They find that the rate at which four classes of neurons (GABAergic, octopaminergic, dopaminergic, serotonergic) increase relative to the total body cell number is constant (Figure S3G-J). They write: "Since the pattern of cholinergic neurons is the major cell population in the brain, these results suggest that the above observation of the non-linear dynamics between neurons and cell numbers is likely from the cholinergic neurons." This conclusion should not be reached without first directly counting the number of cholinergic neurons and total body cells. Given that glutamatergic, glycinergic, and peptidergic neurons were not counted, it also remains possible that the non-linear dynamics are due (in part or in whole) to one or more of these populations.

We have revised the statement into “These results suggest that the above observation of the non-linear dynamics between neuron and total cell number is not likely from the octopaminergic, GABAergic, dopaminergic, and serotonergic neurons. Since our neuron pool may not include glutamatergic, glycinergic, and peptidergic neurons, the non-linear dynamics may be from cholinergic neurons or other neurons not included in our staining.”

Reviewer #2 (Public review):

Weaknesses:

(1) The proprietary nature of the microscope, protected by a patent, limits the technical details provided, making the method hard to reproduce in other labs.

Thank you for your comment. We understand the importance of reproducibility and transparency in scientific research. We would like to point out that the detailed design and technical specifications of the TLSM are publicly available in our published work: Chen et al., Cell Reports, 2020. Additionally, the protocol for C-MAP, including the specific experimental steps, is comprehensively described in the methods section of this paper. We believe that these resources should provide sufficient information for other labs to replicate the method.

(2) The resolution of the analyses is mostly limited to the cellular level, which does not fully leverage the advantages of expansion microscopy. Previous applications of expansion microscopy have revealed finer nanostructures in the planarian nervous system (see Fan et al. Methods in Cell Biology 2021; Wang et al. eLife 2021). It is unclear whether the current protocol can achieve a comparable resolution.

Thank you for raising this important point. The strength of our C-MAP protocol lies in its fluorescence-protective nature and user convenience. Notably, the sample can be expanded up to 4.5-fold linearly without the need for heating or proteinase digestion, which helps preserve fluorescence signals. In addition, the entire expansion process can be completed within 48 hours. While our current analysis focused on cellular-level structures, our method can achieve comparable or better resolution and we will add this information in the revised manuscript as “It is important to point out that the strength of our C-MAP protocol lies in its fluorescence-protective nature and user convenience. Notably, the sample can be expanded up to 4.5-fold linearly without the need for heating or proteinase digestion, which helps preserve fluorescence signals. In addition, the entire expansion process can be completed within 48 hours. Based on our research requirement, two spatial resolutions were adopted to image expanded planarians, 2×2×5 μm³ and 0.5×0.5×1.6 μm³. The resolution can be further improved to 500 nm and 120 nm, respectively.”

(3) The data largely corroborate past observations, while the novel claims are insufficiently substantiated.

A few major issues with the claims:

Line 303-304: While 6G10 is a widely used antibody to label muscle fibers in the planarian, it doesn't uniformly mark all muscle types (Scimone at al. Nature 2017). For a more complete view of muscle fibers, it is important to use a combination of antibodies targeting different fiber types or a generic marker such as phalloidin. This raises fundamental concerns about all the conclusions drawn from Figures 4 and 6 about differences between various muscle types. Additionally, the authors should cite the original paper that developed the 6G10 antibody (Ross et al. BMC Developmental Biology 2015).

We appreciate the reviewer’s insightful comments and acknowledge that 6G10 does not uniformly label all muscle fiber types. We agree that this limitation should be recognized in the interpretation of our results. We have revised the manuscript to explicitly state the limitations of using 6G10 alone for muscle fiber labeling and highlight the need for additional markers. We have included the following statement in the Results section: “It is noted that previous studies reported that 6G10 does not label all body wall muscles equivalently with the limitation of predominantly labeling circular and diagonal fibers (Scimone et al., 2017; Ross et al., 2015). Our observation may be limited by this preference”. We would also clarify that the primary objective of our study was to demonstrate the application of our 3D tissue reconstruction method in addressing traditional research questions. Nonetheless, we agree that expanding the labeling strategy in future studies would allow for a more thorough investigation of muscle fiber diversity. Relevant citations have been properly revised and updated.

(4) Lines 371-379: The claim that DV muscles regenerate into longitudinal fibers lacks evidence. Furthermore, previous studies have shown that TFs specifying different muscle types (DV, circular, longitudinal, and intestinal) both during regeneration and homeostasis are completely different (Scimone et al., Nature 2017 and Scimone et al., Current Biology 2018). Single-cell RNAseq data further establishes the existence of divergent muscle progenitors giving rise to different muscle fibers. These observations directly contradict the authors' claim, which is only based on images of fixed samples at a coarse time resolution.

Thank you for your valuable feedback. Our intent was not to suggest that DV muscles regenerate into longitudinal fibers. Our observations focused on the wound site, where DV muscle fibers appear to reconnect, and longitudinal fibers, along with other muscle types, gradually regenerate to restore the structure of the injured area. We have revised the our statement as:“During the regeneration process, DV muscle fibers reconnect at the wound site, with longitudinal fibers and other muscle types gradually restoring the structure at the anterior tip and later integrating with circular and diagonal fibers through small DV fiber branches (Figure S5O1-O3).”

(5) Line 423: The manuscript lacks evidence to claim glia guide muscle fiber branching.

We agree with your concerns that our statement may be overestimated. We have removed this statement from the revised version. Instead, we focused on describing our observations of the connections between glial cells and muscle fibers. We have revised the section as follows: “Considering the interaction between glial and muscle cells, the localization of estrella⁺ glia and muscle fibers is further investigated. By dual-staining of anti-Phospho (Ser/Thr) and 6G10 in inr-1 RNAi and β-catenin-1 RNAi planarians, we found that the morphologies of neurons are normal, and they have close contact with muscle fibers (Figure 6D, E). However, by dual staining of estrella and 6G10, we found that the structure of glial cells is star-shaped in egfp RNAi planarian, however, glial cells in inr-1 RNAi and β-catenin-1 RNAi planarians have shorter cytoplasmic projections, and their sizes are smaller, lacking the major projection onto the muscles (Figure 6D, E, Figure S6E-K). Especially, in the posterior head of β-catenin-1 RNAi planarians, the glial cell has few axons and can hardly connect with muscle fibers (Figure 6E). These results indicated that proper neuronal guidance and muscle fiber distribution could potentially contribute to facilitating accurate glial-to-muscle projections.

(6) Lines 432/478: The conclusion about neuronal and muscle guidance on glial projections is similarly speculative, lacking functional evidence. It is possible that the morphological defects of estrella+ cells after bcat1 RNAi are caused by Wnt signaling directly acting on estrella+ cells independent of muscles or neurons.

We understand that this approach is insufficient and we have revised the this section as follows: “Further investigation is required to distinguish the cell-autonomous and non-autonomous effects of inr-1 RNAi and β-catenin-1 RNAi on muscle and glial cells.”

(7) Finally, several technical issues make the results difficult to interpret. For example, in line 125, cell boundaries appear to be determined using nucleus images; in line 136, the current resolution seems insufficient to reliably trace neural connections, at least based on the images presented.

We use two setups for imaging cells and neuron projections. For cellular resolution imaging, we utilized a 1× air objective with a numerical aperture (NA) of 0.25 and a working distance of 60 mm (OLYMPUS MV PLAPO). The voxel size used was 0.8×0.8×2.5 μm³. This configuration resulted in a resolution of 2×2×5 μm³ and a spatial resolution of 0.5×0.5×1.25 μm³ with 4.5× isotropic expansion. Alternatively, for sub-cellular imaging, we employed a 10×0.6 SV MP water immersion objective with 0.8 NA and a working distance of 8 mm (OLYMPUS). The voxel size used in this configuration was 0.26×0.26×0.8 μm³. As a result of this configuration, we achieved a resolution of 0.5×0.5×1.6 μm³ and a spatial resolution of 0.12×0.12×0.4 μm³ with a 4.5× isotropic expansion. The higher resolution achieved with sub-cellular imaging allows us to observe finer structures and trace neural connections.

Regarding your question about cell boundaries, we have revised the manuscript to specify that the boundaries we identified are those of each nucleus.

Reviewer #3 (Public review):

Weaknesses:

(1) The work would have been strengthened by a more careful consideration of previous literature. Many papers directly relevant to this work were not cited. Such omissions do the authors a disservice because in some cases, they fail to consider relevant information that impacts the choice of reagents they have used or the conclusions they are drawing.

For example, when describing the antibody they use to label muscles (monoclonal 6G10), they do not cite the paper that generated this reagent (Ross et al PMCID: PMC4307677), and instead, one of the papers they do cite (Cebria 2016) that does not mention this antibody. Ross et al reported that 6G10 does not label all body wall muscles equivalently, but rather "predominantly labels circular and diagonal fibers" (which is apparent in Figure S5A-D of the manuscript being reviewed here). For this reason, the authors of the paper showing different body wall muscle populations play different roles in body patterning (Scimone et al 2017, PMCID: PMC6263039, also not cited in this paper) used this monoclonal in combination with a polyclonal antibody to label all body wall muscle types. Because their "pan-muscle" reagent does not label all muscle types equivalently, it calls into question their quantification of the different body wall muscle populations throughout the manuscript. It does not help matters that their initial description of the body wall muscle types fails to mention the layer of thin (inner) longitudinal muscles between the circular and diagonal muscles (Cebria 2016 and citations therein).

Ipsilateral and contralateral projections of the visual axons were beautifully shown by dye-tracing experiments (Okamoto et al 2005, PMID: 15930826). This paper should be cited when the authors report that they are corroborating the existence of ipsilateral and contralateral projections.

Thank you for your feedback. We have incorporated these citations and clarifications into the revised manuscript. We acknowledge the limitations of this approach and have added a statement for this limitation in the revised manuscript “It is noted that previous studies reported that 6G10 does not label all body wall muscles equivalently with the limitation of predominantly labeling circular and diagonal fibers (Scimone et al., 2017; Ross et al., 2015). Our observation may be limited by this preference.”

(2) The proportional decrease of neurons with growth in S. mediterranea was shown by counting different cell types in macerated planarians (Baguna and Romero, 1981; https://link.springer.com/article/10.1007/BF00026179) and earlier histological observations cited there. These results have also been validated by single-cell sequencing (Emili et al, bioRxiv 2023, https://www.biorxiv.org/content/10.1101/2023.11.01.565140v). Allometric growth of the planaria tail (the tail is proportionately longer in large vs small planaria) can explain this decrease in animal size. The authors never really discuss allometric growth in a way that would help readers unfamiliar with the system understand this.

Thank you for your feedback. We have incorporated these citations and clarifications into the revised manuscript “These findings provide evidence to support the previous prediction and consistency between different planarian species (Baguñà et al., 1981; Emili et al.,2023). Because the tail is proportionately longer in large than in small planarians, the allometric growth of the planarians can be one possibility for this decrease along with the increase in animal size. The phenomenon may also suggest the existence of a threshold in the increase of planarian neuron numbers, which may ultimately contribute to some physiological changes, such as planarian fission.”

(3) In some cases, the authors draw stronger conclusions than their results warrant. The authors claim that they are showing glial-muscle interactions, however, they do not provide any images of triple-stained samples labeling muscle, neurons, and glia, so it is impossible for the reader to judge whether the glial cells are interacting directly with body wall muscles or instead with the well-described submuscular nerve plexus. Their conclusion that neurons are unaffected by beta-cat or inr-1 RNAi based on anti-phospho-Ser/Thr staining (Fig. 6E) is unconvincing. They claim that during regeneration "DV muscles initially regenerate into longitudinal fibers at the anterior tip" (line 373). They provide no evidence for such switching of muscle cell types, so it is unclear why they say this.

We acknowledge that some of our conclusions were overclaimed given the current data, and we appreciate the opportunity to clarify and refine these claims in the revised manuscript. Due the technique reason, we have not achieved the triple-staining to address this concern. We hope to make a progress in our future studies. Regarding the statement that "DV muscles initially regenerate into longitudinal fibers at the anterior tip" (line 373), as addressed in our previous response, this statement was unclear. Our intent was not to imply that DV muscles switch into longitudinal fibers. Instead, we observed that muscle fibers reconnect at the wound site, with longitudinal fibers and other muscle types gradually restoring the structure. We have revised this section: “During the regeneration process, DV muscle fibers reconnect at the wound site, with longitudinal fibers and other muscle types gradually restoring the structure at the anterior tip and later integrating with circular and diagonal fibers through small DV fiber branches (Figure S5O1-O3).”

(4) The authors show how their automated workflow compares to manual counts using PI-stained specimens (Figure S1T). I may have missed it, but I do not recall seeing a similar ground truth comparison for their muscle fiber counting workflow. I mention this because the segmented image of the posterior muscles in Figure 4I seems to be missing the vast majority of circular fibers visible to the naked eye in the original image.

Thank you for raising this important point. We have included a ground truth comparison of our automated muscle fiber segmentation with the original image in the revised Figure S6. The original Figure S6 has been changed as Figure S7. Regarding the observation of missing circular fibers in Figure 4I, we agree that the segmentation appears to have missed a significant number of circular fibers in this particular image. This may have been due to limitations in the current parameters of the segmentation algorithm, especially in distinguishing fibers in regions of varying intensity or overlap.

(5) It is unclear why the abstract says, "We found the rate of neuron cell proliferation tends to lag..." (line 25). The authors did not measure proliferation in this work and neurons do not proliferate in planaria.

Thank you for pointing out this mistake. What we intended to convey was the increase in neuron number during homeostasis. We have revised the abstract “We found that the increase in neuron cell number tends to lag behind the rapid expansion of somatic cells during the later phase of homeostasis.”

(6) It is unclear what readers are to make of the measurements of brain lobe angles. Why is this a useful measurement and what does it tell us?

The measurement of brain lobe angles is intended to provide a quantitative assessment of the growth and morphological changes of the planarian brain during regeneration. Additionally, the relevance of brain lobe angles has been explored in previous studies, such as Arnold et al., Nature, 2016, further supporting its use as a meaningful parameter.

(7) The authors repeatedly say that this work lets them investigate planarians at the single-cell level, but they don't really make the case that they are seeing things that haven't already been described at the single-cell level using standard confocal microscopy.

Thank you for your comment. We agree that single-cell level imaging has been previously achieved in planarians using conventional confocal microscopy. However, our goal was to extend the application of expansion microscopy by combining C-MAP with tiling light sheet microscopy (TLSM), which allows for faster and high-resolution 3D imaging of whole-mount planarians. We have added in the discussion section: “This combination offers several key advantages over standard techniques. For example, it enables high-throughput imaging across entire organisms with a level of detail and speed that is not easily achieved using confocal methods. This approach allows us to investigate the planarian nervous system at multiple developmental and regenerative stages in a more comprehensive manner, capturing large-scale structures while preserving fine cellular details. The ability to rapidly image whole planarians in 3D with this resolution provides a more efficient workflow for studying complex biological processes.”

Author response:

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

Reviewer #1:

We thank the Reviewer for being very supportive of the work and acknowledging how important it is to understand allosteric modulation in the spike and the potential of this knowledge to contribute to the design of novel therapeutic strategies (for example, disrupting or altering the allosteric networks within the spike can be a novel strategy for drug development against COVID-19). We address their comments below: 

(1) The Reviewer states that although the strategy used to extract the responses has been "previously validated", the complexity of the interactions investigated requires "a robust statistical analysis, which is not shown quantitatively". 

As the Reviewer points out, the D-NEMD approach has been previously validated in various protein systems ranging from soluble enzymes to integral membrane proteins, including the spike (e.g. [Kamsri et al. (2024) Biochem; Beer et al. (2024) Chem Sci; Oliveira et al. (2023) J Mol Cell Biol; Chan et al. (2023) JACS Au; Castelli et al. (2023) JACS; Castelli et al. (2023) Protein Sci; Oliveira et al. (2022) Comput Struct Biotechnol J; Gupta et al. (2022) Nat Comm; Oliveira et al. (2021) JACS; Galdadas et al. (2021) eLife; Abreu et al. (2019) Proteins; Oliveira et al. (2019) JACS; Oliveira et al. (2019) Structure]. The Kubo-Onsager relation is used to extract the evolution of the protein's response to a perturbation by comparing the equilibrium and nonequilibrium trajectories at equivalent points in time. The calculated responses at individual times are then averaged over all the repeats (210 repeats in the current work), and the standard error of the mean (SEM) is used to assess the significance of the average response. The SEM indicates how much the calculated mean deviates from the true population mean. Calculating the SEM allows us to determine how accurate the measured response is as an estimate of the population response and assess the convergence of our calculations. The evolution of the average C_α displacement and corresponding SEM values for each individual monomer can be visualised in detail in Figures S7-S9. We have added a new sentence to the Materials and Methods section in the Supporting Information, explicitly stating how the convergence and statistical significance of the responses were assessed.

(2) The Reviewer considers that the evidence presented in the paper "is compelling" but suggests performing a sequence analysis to facilitate the understanding of the results by the scientific community. 

We thank the Reviewer for their excellent suggestion to perform a sequence analysis of the FA site region and its allosteric connections. Indeed, this analysis (Figure S24) clearly shows that several of the mutations, deletions and insertions in the Alpha, Beta, Gamma, Delta, and Omicron variants are located either in or near the regions of the protein shown to respond to the removal of linoleate from the FA site. These sequence changes affect the protein's responses, and are responsible for the differences in allosteric behaviour observed between variants, as described previously for the non-glycosylated spike [Oliveira et al. (2023) J Mol Cell Biol]. Furthermore, some variants, such as Beta, Gamma, and Omicron, contain residue substitutions at the FA site. For example, the lysine in position 417 in the ancestral spike is mutated to asparagine in Beta and Omicron and threonine in the Gamma variant. Another example is arginine 408 in the original protein, which has been replaced by asparagine in several Omicron sub-variants. 

To summarise, the sequence analysis (Figure S24) supports our initial 3D analysis (Figure S25), indicating that many of the changes observed in the variants of concern are indeed in or close to the allosteric networks involving the FA site. We have now included the sequence analysis results in the current paper and added a new figure to Supporting Information showing the sequence alignments between the ancestral spike and different variants (Figure S24). 

(3) The Reviewer also has "minor considerations": first, they point to a discrepancy in the presentation of residue values S325 in the plots of Chains A, B, and C of Figure S3; second, they ask why several regions, such as RBM and Furin Site in figures S6, S7, and S8 show significant changes.

To answer both points raised by the Reviewer, we need to start by explaining that the spike typically features 22 N-glycosylation and at least two O-glycans sites per monomer. These sites have been found to be heterogeneously populated in different experimental studies (e.g. [Watanabe et al. (2020) Science; Shajahan et al. (2020) Glycobiology; Zhang et al. (2021) Mol Cell Proteomics]). Given this, the spike model used as the starting point for this work reflects this heterogeneity, with asymmetric site-specific glycosylation profiles derived from the glycoanalytic data reported by Watanable et al. for N-glycans [Watanabe et al. (2020) Science] and Shajahan et al. for O-glycans [Shajahan et al. (2020) Glycobiology]. This means that the glycan occupancy and composition for each site differ between the three monomers. For example, while monomer A contains the two O-glycans sites (linked to T323 and S325, respectively) fully occupied, monomers B and C only contain the T323 O-glycan. A detailed description of the glycosylation of the spike model is given in the supporting information of [Casalino et al. (2020) ACS Cent Sci].

Regarding the Reviewer's first minor point, the discrepancy in behaviour observed in Figure S3 for S325 is related to the fact that this glycosylation site is only occupied in monomer A, with no glycans present in this site in monomers B and C. 

Regarding the second point, the differences observed in the responses between the three monomers in Figures S7-S9 are probably due to asymmetries in the protein dynamics introduced by the different glycosylation patterns in the monomers. 

We have now added a new paragraph to the materials and methods section in the Supporting Information describing the asymmetric site-specific glycosylation profiles of the monomers.

(4) Due to the complexity of the allosteric interactions observed, the Reviewer suggests including in the paper a "diagram showing the flow of allosteric interactions" or a "vector showing how the perturbation done in the FA Active site takes contact with other relevant regions". 

This is an excellent suggestion to facilitate the visualisation of the allosteric networks. We have added a new figure to Supporting Information highlighting the allosteric pathways identified from the DNEMD simulations and the direction of the propagation of the structural changes (Figure S26).

Reviewer #2:

We thank the Reviewer for their time in evaluating our manuscript and providing suggestions for improving it and ideas for further work. We are happy that the Reviewer found this to be a "nice paper" with the calculations "well done" and interesting results. We address their comments below: 

(1) The Reviewer suggests improving the paper by adding a more detailed explanation of the DNEMD simulations approach, a method that, although proposed decades ago, is still generally unfamiliar to the community. They also asked for "information on the convergence of the observables".

As stated by the Reviewer, a dynamical approach to nonequilibrium molecular dynamics (D-NEMD) was first proposed in the seventies by Ciccotti et al. [Ciccotti et al. (1975) Phys Rev Lett; Ciccotti et al. (1979) J Stat Phys]. This approach combines MD simulations in equilibrium and nonequilibrium conditions. The rationale for the D-NEMD approach is simple and can be described as follows: if an external perturbation (e.g. binding/unbinding of a ligand) is added to a simulation sampling an equilibrium state and, by doing so, a parallel nonequilibrium simulation is started, the structural response of the protein to the perturbation can be directly measured by comparing the equilibrium and nonequilibrium trajectories at equivalent points in time by using the Kubo-Onsager relation as long as enough sapling is gathered (for more details, please see the reviews [Balega et al. (2024) Mol Phys; Oliveira et al. (2021) Eur Phys J B; Ciccotti et al. (2016) Mol Simul]). This approach, although conceptually simple, is very powerful as it allows for computing the evolution of the dynamic response of the protein to the external perturbation, while assessing the convergence and statistical significance of that response. This approach also has the advantage that the convergence and significance of the response can be easily evaluated, and the associated errors can be computed and made as small as desirable by increasing the number of nonequilibrium trajectories. Determining the statistical errors associated with the responses (through, e.g., the determination of the standard error of the mean, SEM) is essential to test if the sampling gathered is sufficient. In this paper, the SEM was calculated for each average C_α displacement value at times 0.1, 1 and 10 ns after the removal of linoleate, LA (see Figures S7-S9). The SEM indicates how accurate the measured response is as an estimate of the population response and allows us to assess the convergence of the results. 

Generally, multiple (tens to hundreds) D-NEMD simulations are needed to achieve statistically significant results for biomolecular systems (for examples, see [Balega et al. (2024) Mol Phys; Oliveira et al. (2021) Eur Phys J B]). As such, the length of the D-NEMD simulations (typically 5 to 10 ns) reflects the balance between the computational resources available and the number of replicates needed to achieve statistically significant responses from the system. Following the Reviewer's suggestion, we have now added a brief description of the D-NEMD approach to the main manuscript and expanded the D-NEMD section in the Supporting Information with a more detailed description of the method, including adding a new figure showing a schematic representation of the D-NEMD approach (Figure S5) as well as explicitly stating the settings used in these simulations and how the statistical significance of the responses was assessed. 

(2) The Reviewer suggests comparing the D-NEMD results with "more traditional analysis, such as correlation analysis, or community network analysis". 

We agree with the Reviewer that this is an important comparison, which can provide a broader, more articulate and coherent picture of spike allostery and have, therefore, performed additional analysis. The dynamic cross-correlation analysis suggested by the Reviewer is a valuable tool for identifying the regions in the protein influenced by the FA site in equilibrium conditions. However, such an approach is not straightforwardly applicable to D-NEMD simulations, as these simulations are not in equilibrium. Nevertheless, as suggested by the Reviewer, we have determined the cross-correlation matrices for both the equilibrium and D-NEMD simulations (Figure S22), similar to those in our previous work [Galdadas et al. (2021) eLife] and [Oliveira et al. (2022) J Mol Cell Biol]. The analysis of these matrices can provide information about possible allosteric networks. In Figure S22, the cyan and blue regions represent moderate and high negative correlations between C_α atoms, while orange and red regions correspond to moderate and high positive correlations. Negative correlations indicate residues moving in opposite directions (moving toward or away from each other). In contrast, positive values imply that the residues are moving in similar directions. We also note that, with collaborators, we have compared D-NEMD and other nonequilibrium and equilibrium MD analysis methods for allostery [Castelli et al.  (2023) JACS].

The cross-correlation maps depicted in Figure S22 show moderate to high positive correlations between the FA sites and two of the three RBDs in the protein. This happens because each FA site sits at the interface between two neighbouring RBDs. Low to moderate negative and mildly positive correlated motions can also be observed between the FA site and the NTDs and fusion peptide surrounding regions, respectively. To facilitate the visualisation of the above-described motions, we have also mapped the statistical correlations for R408 and K417 (two FA site residues able to directly form salt-bridge interactions with the carboxylate head group of LA) on the protein's three-dimensional structure (Figure S23). Figure S23 highlights the patterns of movement described above and allows us to identify the regions whose motions are coupled to the FA site.

Interestingly, some segments forming the signal propagation pathways, such as R454-K458 in all three monomers, and C525-K537 in monomers B and C, can also be identified from the cross-correlation matrices, showing moderate to high correlations with the FA site (Figures S22-S23). The crosscorrelation maps computed from the equilibrium trajectories (with FA sites occupied with LA) show a slight increase in the dynamic correlations, mainly for the RBDs, compared to the maps obtained from the nonequilibrium trajectories (Figure S22). This indicates that the presence of LA in the FA strengthens the connections between the FA site and other parts of the protein. 

We have updated the manuscript to include the cross-correlation analysis, with two new figures added to Supporting Information: one depicting the cross-correlation maps for the D-NEMD and equilibrium simulations (Figure S22), and the other showing the statistical correlations for R408 and K417 (Figure S23). 

(3) The Reviewer considers the observed connection between the fatty acid site and the heme/biliverdin site "interesting" and suggests "exploring the impact of ligand removal on this secondary site on the protein".

Similarly to the Reviewer, we find the connection between the FA and the heme/biliverdin site fascinating and worthy of further investigation. The observed connection between these two sites shows the complexity of the allosteric effects in the spike. It would be interesting and informative to perform new equilibrium simulations of the heme/biliverdin spike complex and a new set of D-NEMD simulations in which this site is perturbed (e.g. through the removal of the heme group) to map the networks connecting this allosteric site to other functionally important regions of the spike, including the FA site and potentially other allosteric sites. These new simulations would allow us to assess the reversibility of the connection between the FA and heme/biliverdin sites and enhance our understanding of allosteric modulation in the spike and the role of the heme/biliverdin site in this process. However, due to the large size of the system and the associated computational demands, such simulations are not possible within the timeframe of the revision of this paper. These simulations would take many months to complete using our HPC resources. We also note that an experimental structure of the spike containing both heme and linoleate is not available. Further simulation analysis of the communication pathways involving the heme/biliverdin site is an excellent idea for future work.

(4) The Reviewer "liked the mapping of existing mutations on the communication pathway" and suggested a more detailed study focusing on the effect of the mutations. 

We fully agree with the Reviewer and consider that a detailed study focusing on the effect of the mutations, insertions, and deletions in the different glycosylated variants of concern (including new emerging ones) would be of great interest. Our previous work using D-NEMD on the non-glycosylated ancestral, Alpha, Delta, Delta plus and Omicron BA.1 spikes revealed significant differences in the allosteric responses to LA removal, with the changes in the variants affecting both the amplitude of the structural responses and the rates at which these rearrangements propagate within the protein [Oliveira et al. (2023) J Mol Cell Biol]. 

Using the D-NEMD approach to systematically investigate the impact of each individual mutation and their contribution to the overall allosteric response of the glycosylated variants (similar to what we have done previously for the D614G mutation in the non-glycosylated protein [Oliveira et al. (2021) Comput Struct Biotechnol J]) would provide insights into the functional modulation of the spike. However, as noted above in point 3, spike simulations are highly computationally expensive, both in terms of processing and data storage requirements, because of the large size of the protein and the need for equilibrium and D-NEMD simulations. This makes the suggested mutational study unfeasible within the timeframe of the current revisions. It is, however, an excellent idea for future research.

Reviewer #3:

We thank the Reviewer for carefully reading and critically reviewing this work and recognising that the findings reported are "based on an impressive amount of sampling" and "meticulous" analysis. We address their comments below: 

(1) The Reviewer considers that this work "does not clearly show any new findings" as it shows that the glycans do not significantly impact the internal networks in the protein.

We respectfully disagree with the Reviewer. This work identifies new allosteric effects in the spike, specifically, the connection of the FA site with the heme binding site. The equilibrium simulations alone provide the first analysis of the effects of linoleate binding in the fully glycosylated spike. The finding that glycosylation does not significantly affect the allosteric pathways in the spike is in itself an important finding. Previous D-NEMD simulations investigated only the non-glycosylated spike ([Oliveira et al. (2021) Comput Struct Biotechnol J; Oliveira et al. (2022) J Mol Cell Biol] ) leading to questions of whether the allosteric effects pathways were changed by glycosylation; our results here show that the main conclusions are reinforced, but glycosylation does have some effect on networks, and also on the speed of the dynamical response. To the best of our knowledge, our work represents the first investigation to analyse the impact of glycosylation on the allosteric networks in the spike. We show that even though the presence of glycans in the exterior of the spike does not significantly alter the internal communication pathways in the protein, in some cases (for example, the glycans linked to N234, T373 and S375), they create direct connections between different regions, which may facilitate the propagation of the structural changes. 

(2) The Reviewer suggests adding a "clear and concise description" of the D-NEMD approach to the manuscript.

We appreciate that the use of the D-NEMD method to study biomolecular systems is relatively new, and so may be unfamiliar. As explained above in our response to Reviewer 2 (point 1), a brief description of the D-NEMD approach was now included in the main manuscript. A detailed description of the method was also added to Supporting Information, including a new figure representing the rationale for the approach (Figure S5). The interested reader is directed to previous applications and reviews for more details of the method (e.g. [Balega et al. (2024) Mol Phys; Oliveira et al. (2021) Eur Phys J B; Ciccotti et al. (2016) Mol Simul; Kamsri et al. (2024) Biochem; Beer et al. (2024) Chem Sci; Oliveira et al. (2023) J Mol Cell Biol; Chan et al. (2023) JACS Au; Castelli et al. (2023) JACS; Castelli et al. (2023) Protein Sci; Oliveira et al. (2022) Comput Struct Biotechnol J; Gupta et al. (2022) Nat Comm; Oliveira et al. (2021) JACS; Galdadas et al. (2021) eLife; Abreu et al. (2019) Proteins; Oliveira et al. (2019) JACS; Oliveira et al. (2019) Structure]). 

(3) The Reviewer invites us to "discuss the robustness of the findings with respect to forcefield choices".

The Reviewer raises an important but rather complex question, and one which can, of course, be posed for any molecular dynamics simulation study. The short answer is that we have chosen state-of-the-art forcefields, which have been shown to give results for the spike that are in good agreement with experiments; glycosylated spike simulations are rather computationally expensive, and constructing the models also requires significant human time and effort. Thus, while in principle interesting, it is not practical to repeat the current simulations with different forcefields. However, as detailed below, comparison of our simulations of the glycosylated and non-glycosylated [Oliveira et al. (2022) Comput Struct Biotechnol J] spike using different forcefields indicates that our conclusions are robust and are not dependent on the choice of forcefield. 

Comparing the performance and accuracy of different force fields is not straightforward, as the results depend on the system of interest, properties simulated and sampling. In this work, the CHARMM36m all-atom additive force field was used to describe the protein and glycans. CHARMM36m is a widely used force field that has previously been validated for the simulations of biological systems [Huang et al. (2013) J Comput Chem; Guvench et al. (2009) J Chem Theory Comput], including proteins, lipids and glycans, with many of studies adopting it in the literature. Additionally, the glycosylated models of the spike used in this work have also been successfully applied and tested before (e.g. [Dommer et al. (2023) Int J High Perform Comput Appl; Sztain et al. (2021) Nat Chem; Casalino et al. (2021) Int J High Perform Comput Appl; Casalino et al. (2020) ACS Cent Sci]), with their dynamics shown to correlate well with experimental data.   

It is also worth pointing out that, despite differences in the amplitude of the responses, the allosteric networks identified using the D-NEMD approach for the non-glycosylated [Oliveira et al. (2022) Comput Struct Biotechnol J] and glycosylated spikes are generally similar (Figure S13). While the responses for the non-glycosylated protein were extracted from simulations using the AMBER99SBILDN forcefield [Oliveira et al. (2022) Comput Struct Biotechnol J], those reported in this work were obtained from trajectories using the CHARMM36m forcefield. The similarity between the responses for the two systems (which were simulated using different forcefields) is a good indication that our findings are forcefield independent. 

(4) The Reviewer suggests comparing our findings with "alternative methods of analysing allostery". 

As stated above in our response to Reviewer 2 point 2, we consider the suggested comparison an excellent idea. We have therefore performed a dynamic cross-correlation analysis to identify the regions in the protein coupled to the FA site in both equilibrium and nonequilibrium conditions (see Figures S22-S23). Overall, this analysis shows that the FA site motions are strongly coupled to the RBDs and moderately to weakly connected to the NTDs and fusion peptide surrounding regions (please see a detailed description of the results of the correlation analysis in our response to Reviewer 2 point 2). The cross-correlation analysis performed was added to the manuscript, and two new figures were included in the Supporting Information (Figures S22-S23): the first, showing the cross-correlation maps for the D-NEMD and equilibrium simulations; the second, showing the statistical correlations for R408 and K417 (two residues forming the FA site and that can directly interact with the carboxylate head group of LA). 

We agree that comparing different allosteric analysis methods is interesting, informative and important. As noted above, we have compared D-NEMD and other nonequilibrium and equilibrium MD analysis methods for allostery in the well-characterised K-Ras system [Castelli et al.  (2023) JACS].

Reviewer #1 (Public review):

In this manuscript, Sterrett et al. assess whether and how the olfactory system may integrate odor-driven activity with contextual, egocentric variables such as instantaneous location in space and active odor sampling. To address this, they co-record respiration and the spiking activity of principal output neurons of the mouse olfactory bulb (OB), while mice explore a small arena in the absence of any explicit reward or task structure. The authors find that mice exploring the arena breathe in bouts, switching between discrete states of particular breathing rates that persist over varying time scales (seconds to minutes). This state-like activity is also apparent in the OB population activity. Zooming into the activity of individual OB neurons, the authors show that OB activity in this setting is primarily modulated by respiration. In general, while the response times of individual neurons remain tightly locked to the inhalation onset, the overall response amplitude is modulated by the instantaneous sniff frequency. The authors further suggest that a subset of OB neurons appear to show place-selectivity, in a manner that is not explained simply by respiratory or olfactory variables.

Overall this work addresses an important question regarding the basic temporal structuring of odor sampling behavior and activity patterns in the mouse OB. A good understanding of these features is essential to further investigate how stimulus and/or task-driven activity may add on top of this already ongoing modulation. The authors do a commendable job of analyzing the behavior and neuronal activity using a variety of analysis methods. However, in its current form, the results presented are high-level summary figures that are largely comparative (role of parameter A vs B) and hard to assess quantitatively (how well does a given parameter/model explain the responses to begin with). This makes it hard to build a clear model of the underlying mechanisms and to evaluate alternative hypotheses. These concerns can largely be addressed by some additional analyses and by presenting more intermediate-stage output of their existing analyses. In addition, the authors report that a small fraction of OB neurons show spatially selective firing patterns, akin to those observed in the Hippocampus. While this is a very exciting possibility, in my opinion, the data and analysis presented currently are not sufficient to conclude this and additional experiments would be required to test this rigorously.

Major concerns:

A) Regarding the claim about Spatial selectivity in OB neuron responses:

i) From the data presented, it is very hard to assess whether a simple modulation of sniff rate, selectively in some parts of the arena can explain apparent spatial selectivity. The authors attempt to address this concern with Figure 8 - Figure Supplement 1, but the presented combinatorial color maps are hard to interpret. A simpler format would be to show the sniff-aligned raster of the given unit in question along with a heatmap (location distribution) of the actual sniff rates in the arena (not the behavioral states).

If the authors allow the mice to explore the arena over large periods, such that the sniff rates are relatively uniform in space, are the place fields still apparent? A complementary control is to compare responses in the 'place field' with other parts in the arena with comparable sniff rate distributions.

ii) The analysis shown in Figure 8 suggests that sniff parameters are the main predictors of individual neuron responses. The authors point out that there is however a small, but significant fraction of cells that are better predicted by place than by the sniff parameters. It would be useful to provide more raw data to get a better sense of what distinguishes these cells from the rest. Are spatially selective cells typically less sniff-aligned on average? Do they tend to be less or more frequency-modulated?

iii) The authors compare the decoding performance of OB and hippocampal neurons. While it appears space can indeed be decoded from OB neurons, it would be useful to know how the performance scales with the number of neurons and number of traversals in the arena in the two brain regions. Further, the authors should provide some analysis of the robustness of these apparent 'place fields' within a session.

iv) The floor rotation control is underwhelming. First, the arena is quite small and one would generally expect this to impact much more so the 'place fields' that are biased towards the corners than in the center. Second, olfactory cues on the walls may be as important - why did the authors not rotate the entire arena?

Considering the possibility that floor rotation rules out trivial olfactory explanations, what would happen if the authors rotated the entire arena? If these are truly place fields, then one should expect that while they are robust to floor rotation, they should reformat if the distal cues change. Without these additional analyses, I find it hard to conclude the presence of spatial selectivity in the OB.

Moderate concerns:

B) Regarding the lack of state-like structure during head-fixation:

While it is clear that overall sniff rates are lower and that mice do not typically sniff at peak rates during head-fixation, it is unclear if the transitions in breathing rhythm are necessarily less structured, and further whether this can be attributed to head-fixation alone. For example, if the mice are head-fixed but in a floating-platform arena or VR that is non-static - the sniffing distributions may change dramatically.

i) The breathing patterns shown in Figure 1E, in particular during the second head-fixation phase do not appear fundamentally different from the freely moving stretch (20-30 minute window). If one subsamples the free-moving data to match overall sniff distributions, will the long-timescale autocorrelation still be more apparent in freely moving stretches than in the head-fixation periods?

ii) Are the mice on a running wheel? How does the overall distribution of sniff rates and temporal structure change if the mice are head-fixed but simply allowed to run?

Minor concerns:

C) Regarding the parsing of breathing and movement into 3 distinct behavioral states:<br /> The authors show breathing patterns of freely exploring mice are temporally structured with extended bouts of sniffing at select rates. They use a HMM model to show that this structure can be captured by a 3 state-model wherein each state can be thought of as a joint distribution of movement and sniff rate. While the approach is interesting and the data are well presented, I have some minor concerns regarding the exact interpretation.

i) While the relationship between movement and sniffing is indeed non-trivial, it is unclear if the statelike partitioning requires the incorporation of the movement variable at all in the HMM model. The state-like patterns are also apparent if one focuses exclusively on the instantaneous sniff rate while ignoring movement velocities (Figure 1 - Figure Supplement 1) or the inferred HMM states (Figure 1E). Have the authors tried modeling the breathing activity alone using an HMM with each state just being a biased distribution of sniff rates, from which the instantaneous sniff rate is drawn? Will the authors' conclusions be fundamentally different from such a model?

ii) While it is clear that there are at least 2 distinct states a) resting (mice are generally uninterested and sniff at 2-3 Hz) and b) exploration (mice are interested in their local environment and sniff rapidly). It is hard to assess whether there is indeed a third distinct and behaviorally interpretable state that the authors call grooming or are there simply intervening periods where it is unclear what's driving the variability in sniff rates - change in movement speed, moderate curiosity, boredom, etc. From the movement velocities shown in the supplement (Figure 1 - Figure Supplement 1), it appears that the movement speed during this 'grooming' state is significantly higher than at rest. It is not obvious why a mouse should move around more while grooming. It would help if the authors provide supporting data, perhaps from behavioral pose analysis to better justify the classification of this state as grooming or alternatively choose a different name to avoid confusion.

iii) Insufficient analysis of state transition matrices: The authors do not show the transition matrices for individual sessions and/or mice. This limits what one can learn about the behavior from the 3 state modeling of breathing states. Do individual mice have stereotypical transition patterns across sessions? How well does the model perform: can one predict the expected sniff rate in one part of the session from knowing sniff patterns in another part of the session?

D) Regarding the dependence of individual neuron responses on sniff and movement parameters:

i) Could the authors report the relative proportions of sniff frequency insensitive vs. frequency sensitive neurons in their data?

ii) Could some of the striking frequency modulation the authors show in Figure 3A result from the fact that mice selectively sniffed at high or low rates in different parts of the arena? While it is unlikely that all of the modulation the authors see results from the location/presence of trace odors in different parts of the arena, it would be informative to perform the same analysis on the data recorded during head-fixation where its external environment is less variable.

iii) Comparison of SnF latency profiles between head-fixed and freely moving conditions:<br /> The SnF latency profiles of a given OB neuron appear strikingly similar during head-fixed and freely moving conditions. It would be useful if the authors could explicitly quantify this.

iv) Comparison of SnF frequency profiles between head-fixed and freely moving conditions: The authors comment that SnF frequency profiles are different across the head-fixed versus freely moving conditions and that they do not observe the 3 distinct clusters present in the freely moving state in their head-fixed data. If true, this is an interesting observation. Together with the observation of relatively similar SnF latency profiles in both head-fixed and freely moving conditions, this implies that sniff frequency dependence is selectively enhanced during free-moving behavior perhaps through a top-down signal.

However, this is hard to conclude from the current data as the overall distribution of sniff rates is very different in the two conditions, with a clear underrepresentation of high-frequency sniffs in the head-fixed periods. To enable a fair comparison, the authors should undersample the sniffs in the freely moving period and compare sniff fields constructed from frequency-matched distributions.

v) The authors suggest that the 2 types of SnF latency profiles may putatively map onto tufted and mitral cells. While this is an interesting possibility, it would be nice to support the claim with auxiliary analysis of other features such as recording depth, baseline firing rates, spike shapes, etc that indicate that these are indeed two different cell types.

Reviewer #2 (Public review):

In this study, the authors investigate the structure of breathing rhythms in freely moving mice during exploratory behaviour in the absence of explicit cues or tasks. Additionally, they link behavioural states, derived from sniffing frequency and speed movement data, to the neural activity recorded in the olfactory bulb (OB). To further characterize OB neuronal responses, the authors introduce the concept of "sniff fields" which consider the joint distribution of sniff frequency and the latency from inhalation. Lastly, they explore how OB neurons encode spatial information, and they compare this finding with previously known spatially encoding cells in the hippocampus.

The authors successfully establish that breathing in freely moving mice is structured even in the absence of explicit olfactory cues. By simultaneously recording sniffing and movement data, they find that this structure is associated with movement in a non-linear manner and can be modelled using a Hidden Markov Model (HMM). Interestingly, they demonstrate that neuronal activity in the OB tracks this behavioural structure by showing that HMM states can effectively cluster the neural data. Additionally, they describe OB activity using sniff fields, advancing our understanding of how individual neurons encode sniffing properties such as frequency and phase. Furthermore, they report unprecedented findings showing that some OB neurons encode place independently of the sniffing field contribution. Overall, the authors provide valuable insights regarding the contribution of different behavioural variables to OB activity.

However, some of the conclusions presented by the authors are not fully supported by the data provided. Quantitative analysis and statistical tests are missing from the description of the breathing structure. Regarding spatial encoding, the authors claim in the abstract that "at the population level, a mouse's location can be decoded from olfactory bulb with similar accuracy to hippocampus". However, they show that place was significantly decoded in only 18/31 sessions from OB activity, and in 12/13 sessions from hippocampal activity. No further comparison of decoding accuracy between OB and HPC is provided. Moreover, it is unclear whether place contributes independently of movement, which was previously shown in this study to influence neuronal activity.

Additionally, there is a lack of methodological detail regarding the experimental procedures, which could affect the interpretation of the data. Specifically, information is missing on aspects such as head-fixed conditions, the number of mice used per experiment, and the number of sessions per mouse.

Studying mice behaviour in more naturalistic conditions, without explicit tasks, is a very interesting approach that provides new insights into the structure of sniffing and its neuronal representation. The fact that some OB neurons encode spatial information is highly relevant beyond the field of olfaction, even though this information was not as accessible as in the hippocampus. I believe the manuscript would benefit from a revision to ensure the text aligns more closely with the data presented in the figures.

Author response:

We thank the editor and reviewers for recognizing the value of studying neural dynamics and behavior in naturalistic, task-free conditions and the importance of linking olfactory bulb activity to movement and place.  We appreciate the suggestions for analyses and edits to further quantify these relationships and clarify our interpretation.

The primary sticking point regards our result that olfactory bulb neurons are selective for place:

“analysis supporting the potentially exciting result on the encoding of place is currently incomplete”

In this paper, we report evidence for spatial selectivity in the olfactory bulb, make relative comparisons with canonical “place cells” in the hippocampus, and control for alternative hypotheses such as odor- or behavior-driven sources, to motivate future experiments which can more precisely identify the mechanistic basis of these responses. Throughout the reviews, our result on the correlation of OB activity with place is not questioned, but rather whether we can better determine how much behavior or odor explain this result. Regarding the concern about behavior, we are confident that the spatial non-uniformities of breathing rhythms do not explain OB spatial selectivity based on the analyses included in the paper. We thank the reviewers for suggestions of additional analyses with which we can further test this claim and will incorporate several, as we will detail below.

Regarding the points about odor, indeed we do not claim that we have entirely ruled out odors as an explanation of place selectivity in the bulb. Rather, our claim is that our analyses show that scent marks on the floor, the most obvious olfactory place cue, cannot fully explain place selectivity.  We acknowledge that our experiments do not exclude the possibility that other odors in the environment may also contribute. Odors are invisible and difficult to measure, and the odor sensitivity of rodents vastly outstrips that of any device known to humanity. Indeed, no study of which we are aware can fully rule out odor as a cue to the animal’s internal model of place. However, encoding of place, even if explained by odor, is still encoding of place. We will clarify our interpretation of the data, and we thank the reviewers for proposing ideas for further analysis, some of which we are implementing. However, experiments such as effects of distal cues on spatially selective olfactory bulb neurons are beyond the scope of this paper.

We will further test whether neurons in the olfactory bulb are spatially selective by reporting additional statistical analyses including:

- More completely quantifying the spatial distribution of sniffing patterns (visualized in Figure 8 - Sup 1) by plotting sniff-frequency distributions across locations in the arena.

- Demonstrating independent contribution of place over speed in GLMs

- Characterizing the temporal stability of spatially selective cells across a session (1st half vs second half)

- reporting mean decoding errors for olfactory bulb and hippocampal decoders (visualized in Fig 7C)

We will add to the analyses of behavioral state models by:

- Comparing the performance of hidden Markov models fit to breathing frequency alone with those fit to breathing frequency and movement speed

- Quantifying individual differences in state-transition matrices

Further, we address the question around the use of “grooming” as a descriptor of the intermediate sniff frequency state. We used the term ‘grooming’ based on extensive video observation. During this state, ‘Speed’ is significantly non-zero because we defined speed as the movement of the head keypoint which moves substantially during grooming. We will make this point more explicit in the figures and text, and we will provide additional video documentation of these and the other behavioral states.

Lastly, we will further discuss the fact stated in the first paragraph of the Results section that mice are placed in “head-fixation on a stationary platform” and thus inhibited from running. While different breathing states than those observed in our stationary platform may occur during head-fixation with a treadmill, we believe the differences between head-fixed running and free moving running are beyond the scope of this paper. Nevertheless, it’s an important point that we will more explicitly discuss in our revision.

We appreciate these constructive comments and hope these additional analyses and textual edits will help clarify our interpretations and motivate future experiments to further test and refine them.

It is so important to be able to be persuasive in the work place. This can allow so many opportunities to open up for you. I think it is so important to come in with as much information as possible from both angles in order to know potential draw backs that will be brought up. It is also extremely important to be able to keep a level-head in stating your ideas while not letting emotions get the better of your argument.

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• We thank the reviewers for their useful suggestions regarding how to improve our manuscript.
• Reviewer 3 declared that s/he did not find and evaluate the provided Supplementary Materials. As a result, many of her/his criticisms seem invalid: the requested data, validations etc. were already there in the Supplementary Figures and Tables.
• To avoid confusion, we renamed the transgene that is commonly used as a readout for STAT-activated transcription from 10xStat92E-GFP to 10xStat92E DNA binding site-GFP (please see comments by Reviewer 2 that show how easily one can think that Stat92E protein levels go up because of the misleading name of this transgene).
• One co-author, Martin Csordós was among the authors by mistake. Although first considered, his contribution was not included in either the original or the current manuscript version, so we removed his name from the revised version with his permission.
• We prefer to use colour coding for Sections 2., 3. and 4. in our responses to Reviewer comments rather than splitting the responses to queries in separate sections, because many of our answers contain a mixture of planned experiments (labeled as bold), already available data (labeled as underlined), and *explanations why we think that no additional analyses are necessary* (between asterisks). Data already provided in the original submission but missed by Reviewers has white background in our responses. Reviewer comments

Reviewer 1

Major comments:

R1/1. ”Figure 6E seems to indicate that a subset of Su(var)2-10/PIAS isoforms may bind to ATG8 (directly or indirectly). This leads to the straightforward prediction that this subset should be differentially affected by the selective autophagy at the center of the manuscript. That could be tested to strengthen that point. “

Response:

The Atg8a-binding subset of Su(var)2-10/PIAS isoforms could indeed be differentially affected by selective autophagy__. To test this, we will analyze in vivo Su(var)2-10 isoform abundance on western blots with an anti- Su(var)2-10 antibody in __Atg8aΔ12and ____Atg8aK48A/Y49A (Atg8aLDS) mutants.

Minor comments:

R1/2. “ in Fig S1B,C the colocalization between GFP reporters for STAT92E and AP-1 activity and glia marker does not seem convincing, indicating other cell types may be expressing them as well.”

*Response: *

*The overlap between glia labelling and STAT92E and AP-1 transcriptional readout reporter expression is indeed not complete. First of all, epithelial cells in the wing display both STAT92E and AP-1 activity even in uninjured conditions when glial expression of these reporters is not yet observed. Transcriptional reporter activity outside of the wing nerve was previously indicated in figures with arrowheads, now the epithelium is labeled and the regions containing nerve glia are outlined everywhere. *

The fiber-like reporter expression after injury in the wing nerve could correspond to either glia or axons1–3. Glia in the wing nerve have a filament-like appearance resembling axons in confocal images, even glial nuclei are flat/elongated1. Importantly, STAT92E enhancer-driven GFP also labels the nucleus in expressing cells, as opposed to glially driven mtdTomato that is membrane-tethered (and thus excluded from the nucleus: see Fig. S1B, C). Of note, TRE-GFP and Stat-GFP are not expressed in neurons because the cell bodies and nuclei of wing vein neurons are never GFP-positive, see Fig. 2C, Figs. S1, S4 in Neukomm et al.1 and Figure 1 for Reviewers. We also explain this better now in the revised manuscript (please see the legend of Fig. S1).

Nonetheless, we plan to analyze colocalization of mtdTomato-labeled neurons and TRE-GFP and Stat-GFP around the neuronal cell bodies to unequivocally show their different identities. Additionally, we will include transverse confocal sections of the genotypes in Fig. S1B, C that may better illustrate the colocalization.

Fig. 1 for Reviewers. Neuronal (nSyb+) and Stat92E-GFP+ cell morphology in the L1 vein at the anterior wing margin around the neuronal cell bodies which occupy a stereotypical position at the sensilla1. The location and shape of neuronal nuclei (left panel) are different from Stat-GFP+ cell nuclei (right panel, please see also Fig. S1B, C) based on the circumferential GFP signal. Therefore, cells expressing TRE-GFP and Stat-GFP in injured wing nerves are glia and not neurons.

R1/3. “p.7 Instead of "Su(var)2-10 is mainly nuclear due to its transcriptional repressor and chromatin organizer functions" It may be better to say" .. .consistent with its transcriptional repressor and chromatin organizer functions"”

Response:

We have modified the manuscript accordingly.

R1/4. “It is not clear whether the differences in Su(var)2-10/PIAS accumulation between Atg16 and Atg101 RNAi indicate functional differences of blocking autophagy at different stages or simply differences in RNAi efficiency (Atg16) versus the Atg101 mutant.”

Response:

We have added glial Atg1 (the catalytic subunit of the autophagy initiation complex that also includes Atg101) knockdown experiments that show the same lack of Su(var)2-10 accumulation in uninjured conditions as seen in the Atg101 null mutant (please see Fig. S6C). Please note that Atg16-Atg5-Atg12 dependent conjugation of LC3/Atg8a is involved in various vesicle trafficking pathways in addition to autophagy4–6, alterations of which may perturb baseline Su(var)2-10 levels in uninjured animals.

Significance:

R1/5. “STAT92E-dependent glial upregulation of vir-1, but not Draper, is shown, but consequences for glial functions in nerve injury are not tested.”

Response:

We will test antimicrobial peptide (AMP) expression in glia after nerve injury and whether this is affected by STAT92E and vir-1. Certain AMPs such as Attacin C are known to be regulated by both the Stat and NF-____κΒpathways7, and AMPs can be generally upregulated in response to brain injury8,9. This could serve pathogen clearance functions after defence lines such as the epithelium and blood-brain barrier are compromised. In addition, we will test the recruitment of glial processes into the antennal lobe after olfactory nerve injury in animals with glial STAT92E or vir-1 deficiency. Glial invasion is an adaptive response to axon injury and a first step towards debris clearance10.

R1/6. “experiments indicate a role for Su(var)2-10/PIAS SUMOylation activity in tis autophagic degradation, but it is not clear whether the critical substrata Su(var)2-10/PIAS itself or another protein.”

“binding of Su(var)2-10/PIAS to ATG8 is indicated, but no in vitro experiment performed to test whether this is direct and perhaps SUMOylation dependent.”

Response:

*We aimed to answer this question by using a point mutant form of Su(var)2-10: CTD2, which is unable to properly autoSUMOylate itself11, see Fig. 6D. CTD2 mutant Su(var)2-10 levels increased in S2 cells transfected with the mutant construct relative to the wild-type, similar to lysosome inhibition affecting the wild-type protein level but not the mutant variant. Importantly, wild-type Su(var)2-10 is present in CTD2 mutant Su(var)2-10-transfected cells, which can still SUMOylate other Su(var)2-10 targets. It is thus the intrinsic SUMOylation defect of the CTD2 mutant that results in its impaired degradation. It is firmly established that increased Su(var)2-10/PIAS levels repress STAT92E activity12, mammalian example: Liu et al., 199813, pointing to Su(var)2-10 as the critical substrate for autophagy during STAT92E derepression.*

We will further address this point and investigate if Su(var)2-10 directly binds to Atg8a by in vitro SUMOylation of GST-Su(var)2-10 and subsequent GST pulldown assay with HA-Atg8a. In vitro SUMOylation reaction with purified GST-Su(var)2-10 and negative controls are available via in-house collaboration11. We will incubate the resulting proteins and non-SUMOylated counterparts with in vitro transcribed /translated HA-Atg8a, and interactions will be tested by anti-HA western blotting with quantitative fluorescent LICOR Odyssey CLX detection.

Reviewer 2

Major comments:

R2/1. “The working hypothesis is that upon injury, Su(var)2-10 is degraded by autophagy and, as a consequence, Stat92E induces vir-1 expression.

Could the authors clarify why do Stat92E levels increase upon injury? Does Stat92E stability increase upon ATG mediated Su(var)2-10 degradation? Or does it expression/nuclear translocation change?“

Response:

We did not state that Stat92E levels increase during injury - we only used the 10xStat92E DNA binding site-GFP reporter (we have renamed it as such in our revised manuscript to avoid confusion) that is commonly referred to as 10xStat92E-GFP in the literature14, as a readout for Stat92E-dependent transcription.

To address these questions, we will use an endogenous promoter-driven STAT92E::GFP::FLAG protein-protein fusion transgene (https://flybase.org/reports/FBti0147707.htm) to test if STAT92E stability/expression or translocation is altered during injury or upon disruption of selective autophagy. We have already tested this reporter and it is detected in the wing nerve nuclei after injury (Figure 2 for Reviewers, panel A).

As the Atg8aLDS mutation specifically impairs selective autophagy, we will use this mutant and wild-type controls to assess STAT92E::GFP::FLAG abundance on western blots from fly lysates with anti-GFP antibody. To assess STAT92E::GFP::FLAG nuclear translocation as well as stability/expression, we will use independently Atg8aLDS and Su(var)2-10 RNAi in glia to perturb STAT92E -dependent transactivation and visualize glia cell membrane by membrane-tethered tdTomato, glial nuclei by DAPI/anti-Repo and STAT92E with the STAT92E::GFP::FLAG fusion transgene in dissected brains. We can also evaluate STAT92E nuclear translocation with the same genotypes in the injured wing nerve glia. Of note, studies in mammals failed to identify an obvious effect of PIAS1 on STAT1 abundance13, please see Figure 2B from this paper as Figure 2 for Reviewers, panel B. Rather, PIAS family proteins bind tyrosine-phosporylated STAT dimers and impair their DNA binding thereby their transcriptional activation function15.

A.

Proc. Natl. Acad. Sci. USA Vol. 95, pp. 10626–10631

https://doi.org/10.1073/pnas.95.18.10626.

Fig. 2 for Reviewers.

1. Stat92E::GFP::FLAG expression and nuclear appearance in the wing nerve before and after injury
2. Increasing PIAS1 (Su(var)2-10 ortholog) levels does not affect STAT1 abundance in mammalian cells R2/2. “Also, since Su(var) levels increase upon ATG RNAi, independently of injury, do ATG levels increase upon injury? It does not seem to be the case from Fig 6D, but then, if the ATG levels do not increase, how to explain the injury mediated effects of Su(var)2-10? “

Response:

*We have not seen an effect of injury on the rate of autophagic degradation (flux) using the common flux reporter GFP-mCherry -Atg8a in glia after injury (shown in Fig. S2D – not 6D). Also, levels of the typical autophagic cargo p62/Ref(2)P and core autophagy proteins such as Atg12, Atg5, Atg16 do not change after nervous system injury16suggesting no change in general autophagic turnover. *

*An increase in general autophagy would be one option to promote degradation of a given cargo. Just as for the ubiquitin-proteasome system, in selective autophagy the labelling of the cargo/substrate for degradation is a regulated process. Dynamic ubiquitylation of a cargo often promotes its autophagic degradation17. We hypothesize that SUMO may fulfil a similar role in labelling cargo for elimination and this may be promoted by injury in the case of Su(var)2-10, which warrants future studies. *

R2/3. “Su(var)2-10 levels in control and injured wings are different between ATG18RNAi and ATG101 mutant (Fig 5). Could the authors explain the rational for using two ATG mutants? and the meaning of this difference? Also, why comparing data using the RNAi approach and a mutation?”

Response:

This issue was also raised in R1/4 and we refer the Reviewer/Editor to that section for our new Atg1 knockdown data and explanations.

*There is a consensus in the autophagy community that mutants for multiple Atg genes should always be used to ensure that it is indeed canonical autophagy that is affected (because Atg proteins can have non-autophagic roles, as is the case for Atg16 in regulation of phagosome maturation - LAP). *

R2/4. “Fig 6 What is the relevance of the Atg8, Sumo and Su(var)2-10 colocalization at puncta, since there is a lot of colocalization outside the puncta and also lots of Su(var)2-10 or Atg8 labeling that does not colocalize? “

Response:

*Su(var)2-10 orthologs PIAS1-4 localize to the nuclear matrix and certain foci in the chromatin and may play roles in heterochromatin formation, DNA repair, and repression of transposable elements in addition to transcriptional repression18–20. SUMO-modified proteins accumulate in response to PIAS activity in phase-separated foci also referred to as SUMO glue21. We show colocalization of Atg8a with similar Su(var)2-10 and SUMO double positive structures in foci. *

*We do not expect a full overlap between Su(var)2-10 and Atg8a labeling for a number of reasons. First, Su(var)2-10 has many different roles that may not be regulated by autophagy. Second, Atg8a+ autophagosomes in the cytoplasm deliver not only indidivual proteins such as Su(var)2-10 for degradation but also many other cellular components. Third, nuclear Atg8a is implicated in the removal of the Sequoia transcriptional repressor from autophagy genes that is unlikely to involve Su(var)2-1022. Now we include these points in the Discussion section.*

R2/5. “The statement made in the first sentence of the discussion is very strong: 'we have uncovered an activation mechanism for Stat92E', without sufficient supporting evidence.”

Response:

We have rephrased this section as follows:

Here we have uncovered the autophagy-dependent clearance of a direct repressor of the Stat92E transcription factor. This, synergistically with injury-induced Stat92E phosphorylation, may ensure proper Stat92E-dependent responses in glia after nerve injury to promote glial reactivity.

R2/6. “Could the authors validate (some) expression data by in situ hybridization experiments?”

Response:

*Our gene expression data were derived from wing nerve imaging or wing tissue. Unfortunately, in situ hybridization is not feasible in this organ because probes do not penetrate the thick chitin-based cuticule and wax cover of the wing (and the same is true for wing immunostaining).* We do provide independent evidence for vir-1 upregulation in the wing after injury via quantitative PCR (qPCR) in Fig. S5C. To corroborate reporter-based data, we will also analyze drpr in qPCR using wing material after injury at the same time points.

R2/7. “Could the authors validate the RNAi lines molecularly (or refer to published data on these lines?”

Response:

*Almost all RNAi lines have already been validated by qPCR, western blot, or immunostaining in Szabo et al., 202316 and other publications23–25. The only exception is Su(var)2-10JF03384 and we show that it is indistinguishable from the validated Su(var)2-10HMS00750 RNAi line (which causes 95% transcript reduction): it also strongly derepresses STAT activity. These reagents have also been widely used in the community (e.g. https://flybase.org/reports/FBal0242556.htm, https://flybase.org/reports/FBal0233496.htm).*

R2/8. „Clarifying the role of Su(var)2-10 on Stat92E would benefit to the presented work. Does Atg8-Su(var)2-10 binding affect Stat92E accumulation, expression, translocation to the nucleus? Some of these experiments could be obtained in S2 cell transfection assays, if too complex in vivo.”

Response:

As explained in R2/1, we will use an endogenous promoter-driven STAT92E::GFP::FLAG protein-protein fusion transgene to test if STAT92E stability/expression or translocation is altered upon disruption of selectiveautophagy (in Atg8aLDS mutant flies).

R2/9. „Also, what happens to the axons in the mutant conditions described in the manuscript? This would higher the impact of the work, but would require in vivo work with fly stocks containing several transgenes.”

Response:

We have already published in our previous paper, Szabo et al., 202316 that the mutants used in the current study display normal axon morphology__. There are only two mutants that we did not test in that paper: Atg8aLDS and our new Atg8anull and we will examine these remaining two during the revision, __but we already published in the above paper that axons appear normal in Atg8aΔ4, a widely used Atg8a mutant allele.

R2/10. „It has been published that Draper is involved in the response to injury in the adult wing nerve. See for example Neukomm et al (2014). The authors should discuss how this fits with their hypothesis and data. In this respect, Fig S4B, which should support the hypothesis, should be improved. It is rather hard to interpret it.”

Response:

Fig. S3 (draper protein trap-Gal4 driven GFP-RFP reporter expression) and S4B (intronic STAT92E binding site of the draper gene driven GFP-RFP reporter expression) show similar results: drpr is already expressed in wing nerve glia before injury, which is in line with Draper’s crucial role in the injury response because Draper-mediated glial signaling triggers glial reactivity. This has been added to the Discussion.

Minor comments:

R2/11. „Rubicon is also a negative regulator of autophagy (doi:10.1038/s41598-023-44203-6). in (Fig2 B, D) we have a higher GFP intensity in both uninjured and injured, and the difference between Injured/uninjured is less significant compared to control. It is possible that Rubicon KD causes more autophagy leading to a higher activation of Stat92E even in control. I wouldn't take the results as a proof of canonical autophagy implication and not LC3-associated phagocytosis”

Response:

Loss of Rubicon could indeed potentially remove more Su(var)2-10 via increased autophagy, leading to higher Stat92E activity. However, there is no statistically significant difference between injured and uninjured controls and injured and uninjured Rubicon knockdown, respectively, in Fig2 B, D (p=0.6975 and >0.9999 for each comparison). We are puzzled by the statement that the reviewer „wouldn't take the results as a proof of canonical autophagy implication and not LC3-associated phagocytosis”. We analyzed Rubicon as a factor critical for LAP and its deficiency does not prevent Stat transcriptional activity following injury unlike the loss of Atg8a, Atg16, Atg13 and Atg5. We will further support this result with a mutant of Atg16 with part of the WD40 domain deleted, because this region is critical for LAP but not for autophagy.16,26,27

R2/12. „The rationale for using both repoGal4 and repoGS is unclear. If, as mentioned, the goal is to avoid developmental defects, repoGS should be consistently used. Especially I don't understand how both were utilized to knock down the same genes, such as Atg16”

Response:

*We had to use repoGS (a drug-inducible Gal4 active in glia) because knocking down Su(var)2-10 with repoGal4 resulted in no viable adult progeny. Su(var)2-10 is an essential gene as opposed to most autophagy genes and its absence results in embryonic lethality24. Thus all Su(var)2-10 silencing experiments were done with repoGS. Similarly, Stat92E is involved in various developmental processes and its loss is embryonic lethal. repoGal4 was used for genes generally not having an adverse effect when absent during development16 in the first two figures. In Fig. 4D, we silenced Atg16 by repoGS because it is one of the controls for testing a genetic epistasis between Su(var)2-10 and Atg16. Please note that we see exactly the same phenotype in case of Atg16 knockdown when using either Gal4 version.* This has been explained in the revised methods section.

R2/13. „In the third paragraph of the introduction, I am confused whether Stat92E regulates drpr of the reverse”

Response:

Upon antennal injury, Drpr receptor binding to phagocytic cargo initiates a positive feedback loop in glial cells to promote its own transcription28. Drpr receptor in the plasma membrane regulates Stat92E and AP-1 activity via signal transduction. Stat92E and AP-1, in turn, increases drpr transcription10,28–30 that will result in more plasma membrane Drpr protein expression. We have explained this more clearly in the revised Introduction.

R2/14. „I cannot find the evidence for vir-1 being expressed in glia and target of Gcm in the refences that have been cited.”

Response:

We apologize for not explaining this better: vir-1 is called CG5453 in Freeman et al., 200331. It is listed in Table 1 as a Gcm target since there is no detectable CG5453 expression in a Gcm null mutant, please see below. We have updated the manuscript with this gene name.

.....

.....

Part of Table 1 from Freeman et al., 200331.

R2/15. „The presence of a Stat92E binding site on the vir-1 promoter has already bene described in the paper from Imler and collaborators, Nature immunology 2005. Actually, if this site is present in their transgenic line, it would help the authors strengthen the argument that Stat92E has a direct role on vir1 (for which they make a very strong statement in the discussion, with no direct evidence).”

Response:

*The evidence that Stat92E may have a direct role in vir-1 transcription in glia comes exactly from the same reporter transgene described by Imler and collaborators in the mentioned paper32. We received this transgenic line from the Imler group and monitored its expression after injury upon depletion of Stat92E (Fig. 3B). It thus contains the studied Stat binding site. This was referenced in the Methods and in all relevant sections of the main text, and we now explicitly state this in the revised text.*

R2/16. „In the Fig S2D, I do not see a lot of GFP+ (Glia) cells. I see more Atg8a in injured 3 dpi regardless of colocalization with glia”

Response:

Fig S2D uses one of the standard assays for autophagic turnover, which we now explain in more detail in the Results section. Basically, the dual tagged GFP::mCherry::Atg8a transgene is expressed in glia, and GFP is quenched in lysosomes after delivery by autophagy while mCherry remains fluorescent. So, in addition to double positive dots (autophagosomes), there are mCherry dots lacking GFP (autolysosomes) if autophagy is functional. All of these dots are in glia but the cell boudaries are not visible.

The images shown are single optical slices. The number of mCherry+ puncta are around 7-8 per field in both uninjured and injured (3 dpi) conditions, but puncta brightness is always variable. Since most mCherry+ puncta were rather bright in the original 3 dpi image, we changed it to a more representative image.

R2/17. „The quantification of the signals is made in a specific region of the wing, I guess throughout the nerve thickness. This could be represented more carefully in a schematic and It would also help defining colocalization in the first figure, by using a transverse section.”

Response:

The quantification method is described in Materials and Methods and we have added that quantification was done on single optical slices. The imaged region is depicted in Fig. S1A, where we indicated the rectangular region used in Fiji for image quantification. We will add transverse sections of wings as suggested.

R2/18. „A number of ATG genes are considered in the manuscript, but the rational for using them is not always clear. Showing a schematic would help clarify this. „

Response:

We have added a table showing the different steps of autophagy where the studied Atg genes/proteins function (now Supplementary Table 1). We also added whether the gene is considered specific for autophagy or can play a role in another process, e.g. LAP. We studied different autophagy genes in line with the assumption that disabling distinct autophagic complexes should produce the same phenotype if this process is indeed autophagy (and not LC3-associated phagocytosis for example).

R2/19. „Fig 7 is not cited and its legend is very short.”

Response:

We have now cited Fig 7 and expanded its legend.

R2/20. „Clarify the color coding in Fig S1E”

Response:

We added that red is injured, black is uninjured.

R2/21. „What is the tandem tagged autophagic fly reporter in fig S2D?”

Response:

This is one of the most common tools to study autophagy, please see the updated explanation above at your first question regarding Fig. S2D.

R2/22. „Add a schematic on the vir-1 isoforms.”

Response:

We have added a a schematic showing the vir-1 isoforms in Fig. S5B.

R2/23. „Fig S6B and Fig 5 relate on the levels of Su(var)2-10 upon Atg16 RNAi, but the scale is not the same, why?”

Response:

*The scales are different because these two images measure different things. Fig. 5 indeed displays quantification of Su(var)2-10 levels in brain glia. However, Fig S6B shows quantification of Stat92E-induced GFP reporter levels (as a proxy of Stat92E transcriptional activity) in the wing nerve upon Atg16 knockdown. *

Reviewer 3

R3/1. „The claim that the negative regulator of Stat92E signaling is removed by selective autophagy, involving selective autophagy receptors different from/in addition to Ref(2)P/p62 is not convincingly shown. This claim probably needs to be softened.”

Response:

*We have rephrased this sentence as follows: *

„These data suggest that selective autophagy is involved in Stat92E-dependent transcriptional activation in glia.”

R3/2. „The reporter that was used (10xSTAT92E-eGFP) is not a dynamic reporter of STAT92E activity. It accumulates in glia and is highly stable. The appropriate reporter to look at dynamic changes would be 10XSTAT92E-dGFP, which has a degradable (unstable) GFP that is required to see dynamic changes even in the CNS. All of the claims about STAT92E regulation use this reporter, so they are questionable.”

Response:

10XSTAT92E-dGFP featuring destabilized GFP could be a more appropriate tool for monitoring dynamic changes in transcription when short term- e.g. few hours - changes are investigated. However, we did not see any expression of 10XSTAT92E-dGFP (we tried 2 different transgenic insertions) in the wing nerve, please see Figure 3 for Reviewers. In the brain, dGFP expression with this reporter is also several times lower than stable GFP, please compare Fig. 4A and B in Doherty et al28.

The use of 10xSTAT92E-eGFP to follow dynamic expression changes is justified by many lines of evidence. First, there is no 10xSTAT92E-EGFP expression in uninjured wing nerves (Fig. S1D,E). Injury induces EGFP expression in the wing nerve with a sustained activation from 1 to 3 dpi (days post injury), and the EGFP expression returns to the baseline by 5 dpi (Fig. S1D, E). Second, the initial Stat-dependent upregulation of drpr and the 10XSTAT92E-dGFP signal in the brain both occur in the first 24 hours after injury and are sustained for 72 hours28 similar to our results with 10xSTAT92E-EGFP ((Fig. S1D,E). These results indicate that the dynamics of 10xSTAT92E-EGFP expression allows monitoring changes in Stat-dependent transcription occurring over days.

Figure 3 for Reviewers. Lack of 10XSTAT92E-dGFP signal in the wing nerve from two independent insertions of the same transgene at the indicated time points after wing injury.

R3/3. „The claim that glial drpr is not upregulated by wing injury and drpr accumulation is not apparently a prerequisite for efficient debris processing within the wing is weak. First, they did not stain for Draper using antibodies, rather they used expression constructs. Dee7 is a promoter that was found to be injury activated in the CNS (were they able to replicate that result? I did not receive the supplemental data), but it might not be the crucial regulator in the periphery. The MIMIC line that was converted is better, but might not represent the full spectrum of regulatory events at the draper locus. Finally, they never actually test for endogenous RNA changes, or use the antibody on westerns. Their lack of evidence is not as compelling as it could be.”

Response:

The__ original Supplemental Material already provides answers for this and subsequent questions of Reviewer 3__. We deposited the Supplemental Material to bioRxiv at the time of the first Review Commons submission and it was/is available at https://www.biorxiv.org/content/10.1101/2024.08.28.610109v2.supplementary-material.

Figs. S3 and S4 show in the wing and the brain (using two different drpr reporters for its transcriptional regulation) that drpr expression does not change much in the wing after nerve injury, as opposed to the brain.

*We did indeed replicate that dee7-Gal4 expression is induced in the brain after antennal injury using UAS- TransTimer (Fig. S4A). In contrast, wing cell nuclei already show expression of both fluorescent proteins in uninjured conditions, and RFP+ nucleus numbers do no change after wing injury (Fig. S4B, C). drpr-Gal4 was generated by conversion of a MiMIC gene trap element into a Gal4 that traps all transcripts. drprMI07659 is in an intron that is common in all drpr isoforms so it should capture the regulation of all transcript isoforms. *

We will further analyze drpr expression via independent methods during the revision: qPCR amplification of a common region of drpr transcripts, and western blot with anti-Drpr antibody to compare injured and uninjured wing material. Of note, we see no upregulation of drpr 2 days after wing injury in our (unpublished) RNAseq results either.

*Unfortunately, immunostaining of the adult wing is not feasible because antibodies do not penetrate the thick chitin-based cuticle and wax cover of the wing.*

R3/4. „The authors claim autophagy contributes to glial reactive states in part by acting on JAK-STAT pathway via regulation of Stat92E. They did not investigate other potential STAT92E targets. Does Atg16 knockdown alter STAT92E expression? Apparently Vir1 is still upregulated in the absence of Atg16 following injury, but they don’t show STAT92E changes.”

Response:

We did investigate other potential STAT92E targets besides vir-1. This is referred to in the text as „*immunity-related gene reporters” and it again can be found in the Supplemental Material (____Supplementary Table 2). None of these genes showed glia-specific upregulation following injury. *

We will investigate STAT92E expression with the STAT92E::GFP::FLAG protein-protein fusion transgene after disrupting autophagy as also suggested by Reviewer 2. Please see our detailed answer to the first comment of Reviewer 2.

*We do not agree with the comment that „Vir1 is still upregulated in the absence of Atg16 following injury” because Fig. 3F,G show that lack of Atg16 abolishes the upregulation of the vir-1 reporter: the change from uninjured to injured becomes statistically not significant and the mean GFP intensities are practically identical. *

R3/5. „The authors claim Su(var)2-10 is an autophagic cargo. They should better characterize Su(var)2-10 degradation and its regulation, and image quality needs to be improved (better images, merged examples, and clearer indication of what they are highlighting. There are many arrows in figures that I don't know what they are pointing to. Much of the labeling in Fig 1 (and others) looks like axons. Could TRE-GFP be turned on in neurons? How did they discriminate?”

Response:

As also explained to Reviewer 1’s last comment, we will carry out experiments to address whether SUMOylated Su(var)2-10 binds Atg8a, which can provide evidence for a direct SUMO-dependent autophagic elimination of Su(var)2-10. Please see our detailed response there.

We will further improve image quality for brain images and we already incorporated new images in Fig. S6. *Merged images were missing only in Fig 5, which we have included in the current version. Arrows and arrowheads were used as described in Figure legends, but instead of those, we now clearly label the epithelium and we outlined the region of wing nerve glia in all images. *

Please see our response to the first minor comment of Reviewer 1 regarding the expression of reporters in wing tissues.

R3/6. „The authors claim interaction of Su(var)2-10 with Atg8a in the nucleus and cytoplasm can trigger autophagic breakdown, involving Su(var)2-10 SUMOylation. The paper would benefit from showing direct SUMOylation of Su(var)2-10 after injury. Is there any way to examine this in vivo?”

Response:

We will test direct SUMOylation of Su(var)2-10 using a recently described method by Andreev et al., 202233. FLAG-GFP-Smt3 (SUMO)____ is expressed under SUMO transcriptional regulation and we will immunoprecipitate FLAG-GFP-SUMO and GFP alone as negative control with GFPTrap beads from lysates of heads subjected to traumatic brain injury that results in glial reactivity16____, and also from uninjured head lysates. We will use anti-____Su(var)2-10 ____western blotting to visualize SUMOylated Su(var)2-10 and whether its levels are modulated by brain injury.

R3/7. „The authors state in discussion "we find that draper is highly expressed in wing nerve glia already in uninjured conditions and it is not further induced by wing transection - indicating high phagocytic capacity in wing glia ... axon debris clearance takes substantially longer in the wing nerve than in antennal lobe glomeruli, thus draper levels may not readily predict actual phagocytic activity in glia". However, they never actually assess this in their experiments. All the conclusions about Draper are made from promoter fusions of integrated reporters, which are imperfect. This conclusion cannot be made.”

Response:

As described in our response to R3/3, we will further test drpr expression changes after wing injury using two independent methods: qPCR and western blot .

We deleted this part from the Discussion that were criticized by the reviewer because these are not important for the main message of our manuscript.

R3/8. „Both STAT92E and Jun are activated by a stress response. Could this be a stress response to disrupting autophagy that is somehow enhance by injury?”

Response:

*Stress responses are indeed relayed by AP-1 and Stat signaling, and impaired autophagy could be a source of stress. We would like to emphasize, though, that the main finding of our manuscript is that disrupting autophagy suppresses Stat-dependent transcription. Autophagy inhibition does not increase Stat signaling in uninjured wing nerves and while control flies upregulate Stat activity upon injury, autophagy-deficient animals fail to do so (Fig. 1). Thus, Stat signaling is not activated by loss of autophagy – it is activated by injury (that is the stress) and Stat activation requires autophagy in this setting.*

R3/9. „Minor:

I don't think that "glially" is a word.”

Response:

Online dictionaries such as Wiktionary list glially as a word, and many scientific articles use it: https://doi.org/10.1016/j.conb.2022.102653, https://doi.org/10.1016/j.yexcr.2013.08.016,https://doi.org/10.1016/j.jpain.2006.04.001*, to give some examples. *

We nonetheless refrain from using it in the updated text.

References

1. Neukomm, L.J., Burdett, T.C., Gonzalez, M.A., Züchner, S., and Freeman, M.R. (2014). Rapid in vivo forward genetic approach for identifying axon death genes in Drosophila. Proc National Acad Sci 111, 9965–9970. https://doi.org/10.1073/pnas.1406230111.
2. Giangrande, A., Murray, M.A., and Palka, J. (1993). Development and organization of glial cells in the peripheral nervous system of Drosophila melanogaster. Development 117, 895–904. https://doi.org/10.1242/dev.117.3.895.
3. Stork, T., Engelen, D., Krudewig, A., Silies, M., Bainton, R.J., and Klämbt, C. (2008). Organization and Function of the Blood–Brain Barrier in Drosophila. J. Neurosci. 28, 587–597. https://doi.org/10.1523/jneurosci.4367-07.2008.
4. Figueras-Novoa, C., Timimi, L., Marcassa, E., Ulferts, R., and Beale, R. (2024). Conjugation of ATG8s to single membranes at a glance. J. Cell Sci. 137, jcs261031. https://doi.org/10.1242/jcs.261031.
5. Galluzzi, L., and Green, D.R. (2019). Autophagy-Independent Functions of the Autophagy Machinery. Cell 177, 1682–1699. https://doi.org/10.1016/j.cell.2019.05.026.
6. Nieto-Torres, J.L., Leidal, A.M., Debnath, J., and Hansen, M. (2021). Beyond Autophagy: The Expanding Roles of ATG8 Proteins. Trends Biochem Sci 46, 673–686. https://doi.org/10.1016/j.tibs.2021.01.004.
7. Huang, Z., Kingsolver, M.B., Avadhanula, V., and Hardy, R.W. (2013). An Antiviral Role for Antimicrobial Peptides during the Arthropod Response to Alphavirus Replication. J. Virol. 87, 4272–4280. https://doi.org/10.1128/jvi.03360-12.
8. Purice, M.D., Ray, A., Münzel, E.J., Pope, B.J., Park, D.J., Speese, S.D., and Logan, M.A. (2017). A novel Drosophila injury model reveals severed axons are cleared through a Draper/MMP-1 signaling cascade. Elife 6, e23611. https://doi.org/10.7554/elife.23611.
9. Alphen, B. van, Stewart, S., Iwanaszko, M., Xu, F., Li, K., Rozenfeld, S., Ramakrishnan, A., Itoh, T.Q., Sisobhan, S., Qin, Z., et al. (2022). Glial immune-related pathways mediate effects of closed head traumatic brain injury on behavior and lethality in Drosophila. Plos Biol 20, e3001456. https://doi.org/10.1371/journal.pbio.3001456.
10. MacDonald, J.M., Beach, M.G., Porpiglia, E., Sheehan, A.E., Watts, R.J., and Freeman, M.R. (2006). The Drosophila Cell Corpse Engulfment Receptor Draper Mediates Glial Clearance of Severed Axons. Neuron 50, 869–881. https://doi.org/10.1016/j.neuron.2006.04.028.
11. Bence, M., Jankovics, F., Kristó, I., Gyetvai, Á., Vértessy, B.G., and Erdélyi, M. (2024). Direct interaction of Su(var)2‐10 via the SIM‐binding site of the Piwi protein is required for transposon silencing in Drosophila melanogaster. FEBS J. 291, 1759–1779. https://doi.org/10.1111/febs.17073.
12. Betz, A., Lampen, N., Martinek, S., Young, M.W., and Darnell, J.E. (2001). A Drosophila PIAS homologue negatively regulates stat92E. Proc. Natl. Acad. Sci. 98, 9563–9568. https://doi.org/10.1073/pnas.171302098.
13. Liu, B., Liao, J., Rao, X., Kushner, S.A., Chung, C.D., Chang, D.D., and Shuai, K. (1998). Inhibition of Stat1-mediated gene activation by PIAS1. Proc. Natl. Acad. Sci. 95, 10626–10631. https://doi.org/10.1073/pnas.95.18.10626.
14. Bach, E.A., Ekas, L.A., Ayala-Camargo, A., Flaherty, M.S., Lee, H., Perrimon, N., and Baeg, G.-H. (2007). GFP reporters detect the activation of the Drosophila JAK/STAT pathway in vivo. Gene Expr Patterns 7, 323–331. https://doi.org/10.1016/j.modgep.2006.08.003.
15. Hu, X., li, J., Fu, M., Zhao, X., and Wang, W. (2021). The JAK/STAT signaling pathway: from bench to clinic. Signal Transduct. Target. Ther. 6, 402. https://doi.org/10.1038/s41392-021-00791-1.
16. Szabó, Á., Vincze, V., Chhatre, A.S., Jipa, A., Bognár, S., Varga, K.E., Banik, P., Harmatos-Ürmösi, A., Neukomm, L.J., and Juhász, G. (2023). LC3-associated phagocytosis promotes glial degradation of axon debris after injury in Drosophila models. Nat. Commun. 14, 3077. https://doi.org/10.1038/s41467-023-38755-4.
17. Goodall, E.A., Kraus, F., and Harper, J.W. (2022). Mechanisms underlying ubiquitin-driven selective mitochondrial and bacterial autophagy. Mol. Cell 82, 1501–1513. https://doi.org/10.1016/j.molcel.2022.03.012.
18. Zhang, T., Yang, H., Zhou, Z., Bai, Y., Wang, J., and Wang, W. (2022). Crosstalk between SUMOylation and ubiquitylation controls DNA end resection by maintaining MRE11 homeostasis on chromatin. Nat. Commun. 13, 5133. https://doi.org/10.1038/s41467-022-32920-x.
19. Chen, Z., Zhang, Y., Guan, Q., Zhang, H., Luo, J., Li, J., Wei, W., Xu, X., Liao, L., Wong, J., et al. (2021). Linking nuclear matrix–localized PIAS1 to chromatin SUMOylation via direct binding of histones H3 and H2A.Z. J. Biol. Chem. 297, 101200. https://doi.org/10.1016/j.jbc.2021.101200.
20. Brown, J.R., Conn, K.L., Wasson, P., Charman, M., Tong, L., Grant, K., McFarlane, S., and Boutell, C. (2016). SUMO Ligase Protein Inhibitor of Activated STAT1 (PIAS1) Is a Constituent Promyelocytic Leukemia Nuclear Body Protein That Contributes to the Intrinsic Antiviral Immune Response to Herpes Simplex Virus 1. J. Virol. 90, 5939–5952. https://doi.org/10.1128/jvi.00426-16.
21. Gutierrez-Morton, E., and Wang, Y. (2024). The role of SUMOylation in biomolecular condensate dynamics and protein localization. Cell Insight 3, 100199. https://doi.org/10.1016/j.cellin.2024.100199.
22. Jacomin, A.-C., Petridi, S., Monaco, M.D., Bhujabal, Z., Jain, A., Mulakkal, N.C., Palara, A., Powell, E.L., Chung, B., Zampronio, C., et al. (2020). Regulation of Expression of Autophagy Genes by Atg8a-Interacting Partners Sequoia, YL-1, and Sir2 in Drosophila. Cell Reports 31, 107695. https://doi.org/10.1016/j.celrep.2020.107695.
23. Maimon, I., Popliker, M., and Gilboa, L. (2014). Without children is required for Stat-mediated zfh1 transcription and for germline stem cell differentiation. Development 141, 2602–2610. https://doi.org/10.1242/dev.109611.
24. Ninova, M., Chen, Y.-C.A., Godneeva, B., Rogers, A.K., Luo, Y., Tóth, K.F., and Aravin, A.A. (2020). Su(var)2-10 and the SUMO Pathway Link piRNA-Guided Target Recognition to Chromatin Silencing. Mol. Cell 77, 556-570.e6. https://doi.org/10.1016/j.molcel.2019.11.012.
25. Pircs, K., Nagy, P., Varga, A., Venkei, Z., Erdi, B., Hegedus, K., and Juhasz, G. (2012). Advantages and Limitations of Different p62-Based Assays for Estimating Autophagic Activity in Drosophila. PLoS ONE 7, e44214. https://doi.org/10.1371/journal.pone.0044214.
26. Fletcher, K., Ulferts, R., Jacquin, E., Veith, T., Gammoh, N., Arasteh, J.M., Mayer, U., Carding, S.R., Wileman, T., Beale, R., et al. (2018). The WD40 domain of ATG16L1 is required for its non‐canonical role in lipidation of LC3 at single membranes. EMBO J 37, e97840. https://doi.org/10.15252/embj.201797840.
27. Rai, S., Arasteh, M., Jefferson, M., Pearson, T., Wang, Y., Zhang, W., Bicsak, B., Divekar, D., Powell, P.P., Nauman, R., et al. (2018). The ATG5-binding and coiled coil domains of ATG16L1 maintain autophagy and tissue homeostasis in mice independently of the WD domain required for LC3-associated phagocytosis. Autophagy 15, 1–14. https://doi.org/10.1080/15548627.2018.1534507.
28. Doherty, J., Sheehan, A.E., Bradshaw, R., Fox, A.N., Lu, T.-Y., and Freeman, M.R. (2014). PI3K Signaling and Stat92E Converge to Modulate Glial Responsiveness to Axonal Injury. PLoS Biol 12, e1001985. https://doi.org/10.1371/journal.pbio.1001985.
29. Logan, M.A., Hackett, R., Doherty, J., Sheehan, A., Speese, S.D., and Freeman, M.R. (2012). Negative regulation of glial engulfment activity by Draper terminates glial responses to axon injury. Nat. Neurosci. 15, 722–730. https://doi.org/10.1038/nn.3066.
30. MacDonald, J.M., Doherty, J., Hackett, R., and Freeman, M.R. (2013). The c-Jun kinase signaling cascade promotes glial engulfment activity through activation of draper and phagocytic function. Cell Death Differ 20, 1140–1148. https://doi.org/10.1038/cdd.2013.30.
31. Freeman, M.R., Delrow, J., Kim, J., Johnson, E., and Doe, C.Q. (2003). Unwrapping Glial Biology Gcm Target Genes Regulating Glial Development, Diversification, and Function. Neuron 38, 567–580. https://doi.org/10.1016/s0896-6273(03)00289-7.
32. Dostert, C., Jouanguy, E., Irving, P., Troxler, L., Galiana-Arnoux, D., Hetru, C., Hoffmann, J.A., and Imler, J.-L. (2005). The Jak-STAT signaling pathway is required but not sufficient for the antiviral response of drosophila. Nat. Immunol. 6, 946–953. https://doi.org/10.1038/ni1237.
33. Andreev, V.I., Yu, C., Wang, J., Schnabl, J., Tirian, L., Gehre, M., Handler, D., Duchek, P., Novatchkova, M., Baumgartner, L., et al. (2022). Panoramix SUMOylation on chromatin connects the piRNA pathway to the cellular heterochromatin machinery. Nat. Struct. Mol. Biol. 29, 130–142. https://doi.org/10.1038/s41594-022-00721-x.

Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

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Reply to the reviewers

Reviewer # 1: The study is well-executed, and the claims are supported by appropriate experiments. As introduced by the authors in their introduction, ubiquitin-dependent endocytosis of AA transporters has been previously shown in S. cerevisiae and TXNIP has previously been identified as a regulator of glucose uptake by promoting endocytosis of GLUT1 and GLUT4. Here, the authors identify the molecular mechanism by which TXNIP promotes the endocytosis, and degradation of amino acid transporters (SLC7A5-SLC7A3) through its interaction with HECT-type ubiquitin ligases. This is an advance in the field and will be of interest for researchers in the fields of quiescence, metabolism and cell biology. Experiments are well designed and important controls have been performed. Overall, the claims and the conclusions are supported by the data.* *

Response: We thank the reviewer for the thorough evaluation of our manuscript and for the insightful, constructive comments. Reviewer 1 had five minor comments, and we have addressed them all.

Minor comment 1: The authors should indicate how often western blot experiments were repeated with similar results. Ideally band quantification (as in Fig. 2b) for the most relevant proteins should be provided for all shown Western blots.* *

Response: Each Western Blot (WB) experiment has been performed at least 3 times and each WB result for SLC7A5 is complemented by immunofluorescence and/or additionally by FACS analysis, across the manuscript.

In the partially revised version of the manuscript, we already__ incorporated WB quantifications of SLC7A5 protein levels__ for Figures 1c, f, h, Figure 3b, Figure 4b, and Figure 5a, c in Supplementary Figure 1b, Supplementary Figure 2c, f, Supplementary Figure 4a, e, and in Supplementary Figure 5a, c, respectively.

Minor comment 2: For confocal images no n number of experiments/analyzed cells is stated. Often only 2-3 cells are shown in these images. In some figures, conclusions from these confocal images are additionally supported by cell surface FACS.

Response: Each immunofluorescence experiment has been performed at least 3 times.

Minor ____comment 3: For panels with missing cell surface FACS quantifications, the authors should consider using the existing imaging data to perform quantifications of the membrane signal. In this way the reader can get the right impression of the reproducibility of the phenotype described.* *

Response: Each immunofluorescence experiment has been performed at least 3 times. In the partially revised version of the manuscript, line-scan quantification of immunofluorescence (IF) of SLC3A2 at the plasma membrane (PM) is now provided for immunofluorescence experiments in Figure 1e, g, Figure 3c, e in Supplementary Figure 2b, e, Supplementary Figure 4b, c, and for SLC2A1 in Supplementary Figure 3i, were FACS data was missing. In addition, WB experiments complement the results of each IF experiment.

Minor comment 4: I appreciate that the authors have also investigated SLC2A1 endocytosis in their experimental setup. Interestingly, they found that TXNIP mediated downregulation of SLC7A5-SLC3A2 was not linked to TXNIP mediated SLC2A1 endocytosis. Since the role of TXNIP in glucose metabolism has been studied in more detail in the past, it would be interesting if the authors could further comment on the differences/similarities in the molecular mechanism of glucose and AA transporter downregulation in the discussion.* *

Response: Thank you for bringing up this point. We now have added the following paragraph to the discussion to speculate about the differences/similarities in the molecular mechanism of glucose and AA transporter downregulation in the discussion:

‘Moreover, in RPE1 cells entering quiescence, GLUT1/4 was not downregulated. Hence, it seems that TXNIP can discriminate, in a context dependent manner, between targeting SLC7A5-SLC3A2 or GLUT1/4 for endocytosis. Since AKT mediated phosphorylation invariably appeared to inactivate TXNIP, and dephosphorylation re-activated it, additional mechanism must confer TXNIP selectivity towards SLC7A5-SLC3A2 or GLUT1/4. We consider it likely, that the exposure of sorting motifs in cytosolic tails of SLC7A5 or GLUT1/4 could regulate the binding of activated TXNIP and thus controls selective endocytosis to adapt nutrient uptake. The exposure of these sorting motifs could be dependent on the metabolic context / state of the cell. Indeed, yeast a-arrestins can detect n- or c-terminal acidic sorting motifs in amino acid transporters, respectively, that are alternatively exposed in response to amino acid excess or starvation (Ivashov et al., 2020a) (Guiney et al, 2016). Inspection of the SLC7A5 sequence indicates a possible n-terminal acidic sorting motif (17EEKEEAREK25). Two lysine residues (K19, K25) in this sequence have been found to be ubiquitinated in an earlier study upon protein kinase C (PKC) activation and mTORC1 inhibition (Barthelemy & Andre, 2019; Rosario et al, 2016).’

Minor ____comment 5: I would recommend a colour blind-friendly colour palette for the confocal images* *

Response: Thank you for pointing this out – we have changed the color palette accordingly.

Reviewer # 2: This study establishes TXNIP as a regulator of LAT1 endocytosis and metabolic homeostasis in quiescence. The integration of KO models and a TXNIP-deficient patient strengthens the findings, though clinical characterization remains underdeveloped relative to the mechanism reported, and biochemical interactions require endogenous validation. The work expands our understanding of TXNIP beyond association studies, positioning it as a key player in nutrient sensing and metabolic regulation. Addressing the concerns will enhance its relevance across fields - particularly metabolism, cell biology, and disease research. Overall, this is a very interesting study indeed. The use of TXNIP knockout models and a loss-of-function patient variant strengthens the conclusion that TXNIP is required for LAT1 degradation. The functional consequences of TXNIP deficiency (elevated intracellular aa, sustained mTORC1 activation, and accelerated quiescence exit) are well-supported by the data. The major concerns are as follows:

Response: We thank the reviewer for the thorough evaluation of our manuscript and for the insightful, constructive comments. Reviewer 2 had three major concerns and one minor comment.

Major concern 1. The identification of a biallelic TXNIP loss-of-function variant in a patient with metabolic disease and neurological dysfunction is highly significant. However, it is problematic that the manuscript effectively presents a case report but does not explicitly frame it as such, and the clinical details are very superficial (lack of pedigree, genetics, structured disease timeline, differential diagnosis, any histology/scans/photography and broader metabolic profiling - please see best practices for case reports). Although whole-exome sequencing identified the TXNIP variant, it remains unclear whether other genetic or metabolic contributors were systematically excluded. At first glance, the clinical discovery strengthens the physiological significance of the cell biology. However, a discrepancy remains between the clear neurological presentation of the patient (intellectual disability, autism and epilepsy) and the fibroblast-based TXNIP-LAT1 mechanism described in the study. Furthermore, the metabolic phenotype described in this manuscript is significantly more severe than that reported in a previous Swedish study of TXNIP deficiency in humans, where the clinical presentation was milder. This discrepancy suggests that different TXNIP mutations may lead to a spectrum of clinical outcomes, which is highly novel (i.e. metabolic and neurological in terms of loss of function, and carcinogenesis with respect to association studies, reviewed in PMID: 37794178). Of course, this could be influenced by mutation type, genetic background, compensatory mechanisms or environmental factors - it is noteworthy that the previous siblings had mitochondrial dysfunction, and this remains unknown in the present individual. Addressing this variability and discussing potential reasons for the pronounced phenotype observed in this patient would strengthen the manuscript overall. It is noteworthy that LAT1 is highly expressed in brain endothelial cells, which can also adopt a quiescent state (PMID: 33627876), and the authors should expand beyond the single sentence in their discussion. In the absence of the above details, the title and conclusions of Figure 3 and in the discussion greatly overstate causality, implying a direct relationship between TXNIP loss and metabolic dysfunction, despite data from only one patient. his may indeed be the case, but the claims should be carefully revised to reflect an association rather than definitive causation until additional patients are identified. Additionally, while it is assumed that the authors have obtained ethical approval and informed consent, this needs to be explicitly stated for transparency, with dedicated details in the methods sections. Addressing these issues will improve the rigor and mechanistic coherence of the study - otherwise it is quite disjointed.

Response: We have addressed many these valid concerns and provide a detailed description of the patient in the partially revised manuscript (please see below).

‘The patient is a boy, born in 2014 as the first child of healthy, consanguineous parents of Turkish origin. During pregnancy, the mother was diagnosed with polyhydramnios. At 38 + 6 weeks of gestation, the baby was in a breech position, leading to a cesarean section. At birth, he weighed 3880 g (P90), measured 55 cm in length, and had a head circumference of 38 cm.

On the seventh day of life, he exhibited floppiness, recurrent hypoglycaemia, and lactic acidosis, prompting his transfer from the birth hospital to a tertiary care centre. During the first three days there, his lowest recorded blood glucose level was 30 mg/dl, lactate levels were approximately 6.5 mmol/l, and pH was 7.11. Subsequently, he developed hypertriglyceridemia, with triglyceride levels reaching 364 mg/dl. Initially stable, he began experiencing elevated pCO2 levels (up to 70 mmHg due to bradypnea) and metabolic acidosis on day 10. A glucose infusion (10 mg/kg/min) stabilized his glucose and lactate concentrations, though lactate remained elevated at around 3-4 mmol/l. Regardless, his muscular hypotonia persisted. On day 12, a skin punch biopsy for a fibroblast culture was performed.

By day 20, glucose and lactate levels had stabilized with regular feeding, allowing his transfer back to a peripheral hospital. During infancy, his blood glucose concentrations were within standard range (Supplementary Table 1), but the boy experienced recurrent hypoglycaemia in response to metabolic stress, e.g., infections. He exhibited psychomotor developmental delays and, from 18 months of age, experienced increasing epileptic seizures (up to 3-4 per month), which were managed with levetiracetam, topiramate, and lamotrigine. Currently, he remains metabolically stable but presents with significant developmental delay, muscular hypotonia, and autistic features.

Whole-exome sequencing from peripheral blood of the patient detected a homozygous single nucleotide insertion c.642_643insT in exon 5 of 8 of the TXNIP gene. This variant was not recorded in the population genetic variant database gnomAD that lists TXNIP as likely haplosufficient (pLI = 0, LOEUF = 0,709: https://gnomad.broadinstitute.org accessed Sept. 10, 2024). No other (likely) pathogenic variant in any other gene, with known function in metabolism was identified as explanation of the clinical features in the child. Potential pathogenic variants in genes required for mitochondrial functions were also not detected, although they were initially expected to cause the phenotype of the boy.

The TXNIP variant c.642_643insT caused a frameshift and a premature stop codon after 59 AA (denoted p.Ile215TyrfsTer59), likely causing nonsense-mediated decay (NMD) or the synthesis of a severely truncated TXNIP protein (Figure 3a). Both parents are healthy heterozygous carriers for the TXNIP variant. Serendipitously, this TXNIP variant was similar to the gene-edited version in the RPE1 TXNIPKO cells (p.I215TfsX11).

The patient showed consistent metabolic alterations compatible with an AA transporter deficiency. Blood plasma concentrations of several large neutral amino acids (LNAAs, including L, I, V) were elevated throughout the years 2014 – 2022 (Supplementary Table 1). The increased molar ratio of the LNAAs (L, I, V) to aromatic AAs (F, Y), resulted in an elevated Fischer’s ratio (FR, 2014: FR = 4.46; 2016: FR = 5.38, 2018: FR = 5.90; 2021; FR= 6.98; 2022: FR = 4.23; FR reference range = 2.10 - 4). The methionine levels are not dramatically altered (Supplementary Table 1).’

We also provide the following ethical statement:

__‘Ethical statement __

All patients’ data were extracted from the medical routine records. Written informed consent for molecular genetic studies and publication of data was obtained from the legal guardians of the patient. This approach was approved by the ethics committee of the Medical University of Innsbruck (UN4501-MUI). The study was conducted in accordance with the principles of the Declaration of Helsinki.’

During the revision, we will additionally address how the other known TXNIP variant (TXNIP p.Gln58His; p.Gly59*; PMID: 30755400) affects nutrient transporter endocytosis. This TXNIP variant will be expressed in TXNIPKO RPE1 cells to analyze its effect on quiescence induced SLC7A5 downregulation. The results of this experiment will allow comparing directly the effect of both known TXNIP variants (p.Gln58His; p.Gly59* and p.Ile215TyrfsTer59) on SLC7A5 downregulation in an identical genetic background. In addition, we will compare how both TXNIP variants affect mitochondrial function (using Seahorse technology).

Major concern 2. The authors report that TXNIP interacts with HECT E3 ligases to regulate substrate degradation, yet this conclusion is drawn from overexpression-based immunoprecipitation studies, which do not confirm interaction under endogenous conditions. Without direct evidence of TXNIP-HECT E3 binding at native expression levels, this mechanistic link remains unresolved. Given that the authors have already generated antibody-validated TXNIP KO models, endogenous validation should be feasible if the interactions are not super-transient.

Response: While the manuscript was under review, we have improved the stringency of our TXNIP-HECT type ubiquitin ligase interaction experiments and developed additional biochemical experiments that strengthen our original conclusions. In the course of these experiments, we found that the interaction of TXNIP with NEDD4, WWP2 and HECW1/2 (but not with WWP1 or ITCH) were particularly dependent on the PPxY331 motif.

During the revision, we will conduct additional experiments to substantiate these findings and to narrow down the list possible ubiquitin ligases that are required for the downregulation of SLC7A5. In particular, we will test if endogenous TXNIP co-immunoprecipitates (in a PPxY motif dependent manner) NEDD4, HECW1/2 or another HECT type ubiquitin ligase.

Furthermore, we will include a newly developed ‘Bead-Immobilized Prey Assay (BIPA)’, were protein-protein interactions can be analyzed by microscopy in a fast in straight forward manner. In the BIPA, ALFA-TXNIP (or mutant variants) are first captured on ALFA-beads (Bead immobilized). These TXNIP beads are then incubated with cell lysates from HEK293 expressing GFP-tagged HECT type ubiquitin ligases (Prey). The binding of the GFP-tagged ubiquitin ligases to the TXNIP beads is analyzed by fluorescence microscopy and quantified (Figure 1b, a BIPA with YFP-NEDD4). This efficient assay will also be conducted with NEDD4, WWP1, WWP2, HECW2, and ITCH to analyze how they bind to TXNIP, TXNIP-PPxY331 and the PPxY double mutant.

Together we are confident that our experiments establish that TXNIP must interact with a specific subset of HECT type ubiquitin ligase (our prime candidate are NEDD4 and HECW1/2) to trigger SLC7A5-SLC3A2 ubiquitination, endocytosis and lysosomal degradation.

Major concern 3. What are the temporal dynamics of TXNIP-associated degradation, and is this process distinct from endosomal microautophagy (as reported in PMID: 30018090)? The authors present convincing, high-quality FACS-based data supporting TXNIP-mediated turnover. If this pathway is mechanistically separate from endosomal microautophagy, it suggests a hierarchy of degradation pathways leading to quiescence. Live cell imaging studies that define the temporal dynamics of this process using the tools the authors have created may reveal the relationship between these processes and refine the broader implications of TXNIP in homeostatic adaptation.

Response: Thank you for this interesting suggestion.

During the revision, we will first investigate a potential temporal correlation of endosomal micro-autophagy of p62/SQSTM1, NBR1, TAX1BP1, NDP52, and NCOA4 (PMID: 30018090) and the downregulation of SLC7A5 as cells enter quiescence. For these experiments, we will follow the turn-over of the above-mentioned autophagy adaptors and compare it to the turnover of SLC7A5, using either WB analysis, or microscopy or both.

Next, we will test if SLC7A5-SLC3A2 endocytosis and lysosomal degradation is required to initiate endosomal micro-autophagy of p62/SQSTM1, NBR1, TAX1BP1, NDP52, and NCOA4 in TXNIPKO cells.

Together, these experiments will address if the endosomal micro-autophagy and TXNIP mediated downregulation of SLC7A5 are mechanistically linked during entry into quiescence.

Minor comment 1. In the discussion, the authors might briefly speculate on the implications of any functional redundancy that might exist between other arrestins.

We will provide this information in the fully revised version of the manuscript.

silence nothing dark

objet

Determinism: Idea that all events, including human behavior are all due to prior causes and can be shaped by external factors such as genetics, enviorment, and evoultion. This here contrast to he belief of free will, which is still a debatable topic in neuroscience.

Materialaism/Physicalism: Belief that everything, consisting of the mind is a phsycial rpocess all controlled by the brain. This is on contrast with mind-body dualism which rathe rbelieves that the mind is entirely separate from the brain. --> That being said through study from brain damage, the evideince supports the materialist view.

The brain is the key to controlling behavior and mental processees, which in turn it is a primary focus of biopsychologists. They do this through practices of fMRI, PET, aand CET scans.

Author response:

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

Reviewer #1 (Public review):

This paper examines the role of MLCK (myosin light chain kinase) and MLCP (myosin light chain phosphatase) in axon regeneration. Using loss-of-function approaches based on small molecule inhibitors and siRNA knockdown, the authors explore axon regeneration in cell culture and in animal models from central and peripheral nervous systems. Their evidence shows that MLCK activity facilitates axon extension/regeneration, while MLCP prevents it.

Major concerns:

(1) In the title, authors indicate that the observed effects from loss-of-function of MLCK/MLCP take place via F-actin redistribution in the growth cone. However, there are no experiments showing a causal effect between changes in axon growth mediated by MLCK/MLCP and F-actin redistribution.

Thank you for your comments. We revised the title of our manuscript to “MLCK/MLCP regulates mammalian axon regeneration and redistributes the growth cone F-actin”. (line 3)

(2) The author combines MLCK inhibitors with Bleb (Figure 6), trying to verify if both pairs of inhibitors act on the same target/pathway. MLCK may regulate axon growth independent of NMII activity. However, this has very important implications for the understanding not only on how NMII works and affects axon extension, but also in trying to understand what MLCP is doing. One wonders if MLCP actions, which are opposite of MLCK, also independent of NMII activity? The authors, in the discussion section, try to find an explanation for this finding, but I consider it fails since the whole rationale of the manuscript is still around how MLCK and MLCP affect NMII phosphorylation.

Thank you for your comments. Although both MLCK and MLCP regulate the activity of NMII, it has been reported that they also govern domain-specific spatial control of actin-based motility in the growth cone. Specifically, MLCK activity is essential for arc translocation and retrograde flow within the P domain, while MLCP appears to specifically modulate arc movement and associated myosin II contractility in the T zone and C domain (Ref). Therefore, it is proposed that the regulatory mechanisms of MLCK and MLCP are highly complex during the process of axon growth. 

[Ref]：Xiao-Feng Zhang, Andrew W Schaefer, Dylan T Burnette, Vincent T Schoonderwoert, Paul Forscher. Rho-dependent contractile responses in the neuronal growth cone are independent of classical peripheral retrograde actin flow. Neuron. 2003 Dec 4;40(5):931-44.

What follows is a discussion of the merits and limitations of different claims of the manuscript in light of the evidence presented.

(1) Using western blot and immunohistochemical analyses, authors first show that MLCK expression is increased in DRG sensory neurons following peripheral axotomy, concomitant to an increase in MLC phosphorylation, suggesting a causal effect (Figure 1). The authors claim that it is common that axon growth-promoting genes are upregulated. It would have been interesting at this point to study in this scenario the regulation of MLCP.

We thank Reviewer for the positive comment on our manuscript.

(2) Using DRG cultures and sciatic nerve crush in the context of MLCK inhibition (ML-7) and down-regulation, authors conclude that MLCK activity is required for mammalian peripheral axon regeneration both in vitro and in vivo (Figure 2). In parallel, the authors show that these treatments affect as expected the phosphorylation levels of MLC.

The in vitro evidence is of standard methods and convincing. However, here, as well as in all other experiments using siRNAs, no Control siRNAs were used. Authors do show that the target protein is downregulated, and they can follow transfected cells with GFP. Still, it should be noted that the standard control for these experiments has not been done.

Thank you for your comments. We utilized scrambled siRNA as a control. I sincerely apologize for the oversight in the manuscript; although we mentioned that scrambled siRNA was used as a control in the figure legends, we failed to clearly articulate this important information in the methods section. We have revised the manuscript accordingly. (line 87, line 549, line, line 562, line 568).

(3) The authors then examined the role of the phosphatase MLCP in axon growth during regeneration. The authors first use a known MLCP blocker, phorbol 12,13-dibutyrate (PDBu), to show that is able to increase the levels of p-MLC, with a concomitant increase in the extent of axon regrowth of DRG neurons, both in permissive as well as non-permissive substrates. The authors repeat the experiments using the knockdown of MYPT1, a key component of the MLC-phosphatase, and again can observe a growth-promoting effect (Figure 3).

The authors further show evidence for the growth-enhancing effect in vivo, in nerve crush experiments. The evidence in vivo deserves more evidence and experimental details (see comment 2). A key weakness of the data was mentioned previously: no control siARN was used.

Thank you for your comments. As mentioned above, we used scramble siRNA as control in vivo experiment as well.

(4) In the next set of experiments (presented in Figure 4) authors extend the previous observations in primary cultures from the CNS. For that, they use cortical and hippocampal cultures, and pharmacological and genetic loss-of-function using the above-mentioned strategies. The expected results were obtained in both CNS neurons: inhibition or knockdown of the kinase decreases axon growth, whereas inhibition or knockdown of the phosphatase increases growth. A main weakness in this set is that drugs were used from the beginning of the experiment, and hence, they would also affect axon specification. As pointed in Materials and Method (lines 143-145) authors counted as "axons" neurites longer than twice the diameter of the cell soma, and hence would not affect the variable measured. In any case, to be sure one is only affecting axon extension in these cells, the drugs should have been used after axon specification and maturation, which occurs at least after 5 DIV.

Thank you for your comments. We acknowledge that the early administration of drugs can lead to unintended effects on neuronal polarization and axon formation. However, in line with our previous publication, we focused exclusively on measuring the longest length of the axon. To quantify axon length, we selected neurons exhibiting an axonal process exceeding twice the diameter of their cell body and measured the longest axon from 100 neurons for each condition (Ref 1, Ref 2). Consequently, we believe that drug administration at the onset of cell culture influences axon formation; however, it does not significantly affect the drug's impact on axon length.

[Ref 1]: Chang-Mei Liu, Rui-Ying Wang, Saijilafu, Zhong-Xian Jiao, Bo-Yin Zhang, Feng-Quan Zhou. MicroRNA-138 and SIRT1 form a mutual negative feedback loop to regulate mammalian axon regeneration. Genes Dev. 2013 Jul 1;27(13):1473-83.

[Ref 2]: Eun-Mi Hur, Saijilafu, Byoung Dae Lee, Seong-Jin Kim, Wen-Lin Xu, Feng-Quan Zhou. GSK3 controls axon growth via CLASP-mediated regulation of growth cone microtubules. Genes Dev. 2011 Sep 15;25(18):1968-81.

(5) In Figure 7, the authors a local cytoskeletal action of the drug, but the evidence provided does not differentiate between a localized action of the drugs and a localized cell activity.

We appreciate the reviewer’s insightful comments and have revised our title to “MLCK/MLCP Regulates mammalian axon regeneration and redistributes growth cone F-actin.” Furthermore, we have made corresponding revisions to the manuscript (line31, line 73).

References:

(1) Eun-Mi Hur 1, In Hong Yang, Deok-Ho Kim, Justin Byun, Saijilafu, Wen-Lin Xu, Philip R Nicovich, Raymond Cheong, Andre Levchenko, Nitish Thakor, Feng-Quan Zhou. 2011. Engineering neuronal growth cones to promote axon regeneration over inhibitory molecules. Proc Natl Acad Sci U S A. 2011 Mar 22;108(12):5057-62. doi: 10.1073/pnas.1011258108.

(2) Garrido-Casado M, Asensio-Juárez G, Talayero VC, Vicente-Manzanares M. 2024. Engines of change: Nonmuscle myosin II in mechanobiology. Curr Opin Cell Biol. 2024 Apr;87:102344. doi: 10.1016/j.ceb.2024.102344.

(3) Karen A Newell-Litwa 1, Rick Horwitz 2, Marcelo L Lamers. 2015. Non-muscle myosin II in disease: mechanisms and therapeutic opportunities. Dis Model Mech. 2015 Dec;8(12):1495-515. doi: 10.1242/dmm.022103.

Reviewer #2 (Public review):

Summary:

Saijilafu et al. demonstrate that MLCK/MLCP proteins promote axonal regeneration in both the central nervous system (CNS) and peripheral nervous system (PNS) using primary cultures of adult DRG neurons, hippocampal and cortical neurons, as well as in vivo experiments involving sciatic nerve injury, spinal cord injury, and optic nerve crush. The authors show that axon regrowth is possible across different contexts through genetic and pharmacological manipulation of these proteins. Additionally, they propose that MLCK/MLCP may regulate F-actin reorganization in the growth cone, which is significant as it suggests a novel strategy for promoting axonal regeneration.

Strengths:

This manuscript presents a wide range of experimental models to address its hypothesis and biological question. Notably, the use of multiple in vivo models significantly enhances the overall validity of the study.

We thank Reviewer for the positive comment on our manuscript.

Weaknesses:

- The authors previously published that blocking myosin II activity stimulates axonal growth and that MLCK activates myosin II. The present work shows that inhibiting MLCK blocks axonal regeneration while blocking MLCP (the protein that dephosphorylates myosin II) produces the opposite effect. Although this contradiction is discussed, no new evidence has been added to the manuscript to clarify this mechanism or address the remaining questions. Critical unresolved questions include: what happens to myosin II expression when both MLCK and MLCP are inhibited? If MLCK/MLCP are acting through an independent mechanism, what would that mechanism be?

- In the discussion, the authors mention the existence of two myosin II isoforms with opposing effects on axonal growth. Still, there is no evidence in the manuscript to support this point.

- It is also unclear how MLCK/MLCP acts on the actin cytoskeleton. The authors suggest that proteins such as ADF/cofilin, Arp 2/3, Eps8, Profilin, Myosin II, and Myosin V could regulate changes in F-actin dynamics. However, this study provides no experimental evidence to determine which proteins may be involved in the mechanism.

Thank you for your comments. Axon growth is an exceptionally intricate process, facilitated by the coordinated regulation of gene expression in the soma, axonal transport along the shaft, and the assembly of cytoskeletal elements and membrane proteins at the growth cone. In this paper, our results primarily demonstrate that MLCK/MLCP plays a crucial role in regulating mammalian axon regeneration and redistributing F-actin within the growth cone; however, we did not investigate which specific proteins act downstream of MLCK/MLCP during axon regeneration.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

- A title more suitable for the evidence shown can be: MLCK/MLCP regulates mammalian axon regeneration and redistributes the growth cone F-actin.

Thank you for your comments. We revised the title of our manuscript to“MLCK/MLCP regulates mammalian axon regeneration and redistributes the growth cone F-actin” (line 3).

-In figure 3, It would be useful to indicate in the figure legend, that the red arrow is pointing to a suture that was performed during surgery to mark clearly the injury site.

Thank you for your comments. We revised Figure 3 legend that indicates the red arrow is pointing to a suture that was performed during surgery to mark clearly the injury site (line 571-572).

- The following is a concern raised in the previous round, and that the response by the authors was so complete and accurate that I consider it would be useful to include it in the discussion section.

Thank you for your comments. We included those contents in the discussion section of our revised manuscript (line 348-354, line 355-359).

The author combines MLCK inhibitors with Bleb (Figure 6), trying to verify if both pairs of inhibitors act on the same target/pathway. The rationale is wrong for at least two reasons.

a- Because both lines of evidence point to contrasting actions of NMII on axon growth, one approach could never "rescue" the other.

Reply by authors in R1：If MLCK regulates axon growth through the activation of Myosin, the inhibitory effect of ML-7 (an MLCK inhibitor) on axon growth might be influenced by Bleb, a NMII inhibitor. However, our findings reveal that the combination of Bleb and ML-7 does not alter the rate of axon outgrowth compared to ML-7 alone. This suggests that the roles of ML-7 and Bleb in axon growth are independent. It means MLCK may regulate axon growth independent of NMII activity.

b- Because the approaches target different steps on NMII activation, one could never "prevent" or rescue the other. For example, for Bleb to provide a phenotype, it should find any p-MLC, because it is only that form of MLC that is capable of inhibiting its ATPase site. In light of this, it is not surprising that Bleb is unable to exert any action in a situation where there is no p-MLC (ML-7, which by inhibiting the kinase drives the levels of p-MLC to zero, Figure 4A). Hence, the results are not possible to validate in the current general interpretation of the authors. (See 'major concern').

Reply by authors in R1: The reported mechanism of blebbistatin is not through competition with the ATP binding site of myosin. Instead, it selectively binds to the ATPase intermediate state associated with ADP and inorganic phosphate, which decelerates the phosphate release. Importantly, blebbistatin does not impede myosin's interaction with actin or the ATP-triggered disassociation of actomyosin. It rather inhibits the myosin head when it forms a product complex with a reduced affinity for actin. This indicates that blebbistatin functions by stabilizing a particular myosin intermediate state that is independent of the phosphorylation status of myosin light chain (MLC).

[Ref] Kovács M, Tóth J et al. Mechanism of blebbistatin inhibition of myosin II. J Biol Chem. 2004 Aug 20;279(34):35557-63.

Reviewer #3 (Public review):

Summary:

This study examines the roles of Rab10 and Rab4 proteins in structural long-term potentiation (sLTP) and AMPA receptor (AMPAR) trafficking in hippocampal dendritic spines using various different methods and organotypic slice cultures as the biological model.<br /> The paper shows that Rab10 inactivation enhances AMPAR insertion and dendritic spine head volume increase during sLTP, while Rab4 supports the initial stages of these processes. The key contribution of this study is identifying Rab10 inactivation as a previously unknown facilitator of AMPAR insertion and spine growth, acting as a brake on sLTP when active. Rab4 and Rab10 seems to be playing opposing roles, suggesting a somewhat coordinated mechanism that precisely controls synaptic potentiation, with Rab4 facilitating early changes and Rab10 restricting the extent and timing of synaptic strengthening.

Strengths:

The study combines multiple techniques such as FRET/FLIM imaging, pharmacology, genetic manipulations and electrophysiology to dissect the roles of Rab10 and Rab4 in sLTP. The authors developed highly sensitive FRET/FLIM-based sensors to monitor Rab protein activity in single dendritic spines. This allowed them to study the spatiotemporal dynamics of Rab10 and Rab4 activity during glutamate uncaging induced sLTP. They also developed various controls to ensure the specificity of their observations. For example, they used a false acceptor sensor to verify the specificity of the Rab10 sensor response.

This study reveals previously unknown roles for Rab10 and Rab4 in synaptic plasticity, showing their opposing functions in regulating AMPAR trafficking and spine structural plasticity during LTP.

Weaknesses:

In the first round of revision I raised these points:

(1) In sLTP, the initial volume of stimulated spines is an important determinant of induced plasticity. To address changes in initial volume and those induced by uncaging, the authors present Extended Data Figure 2. In my view, the methods of fitting, sample selection, or both may pose significant limitations for interpreting the overall results. While the initial spine size distribution for Rab10 experiments spans ~0.1-0.4 fL (with an unusually large single spine at the upper end), Rab4 spine distribution spans a broader range of ~0.1-0.9 fL. If the authors applied initial size-matched data selection or used polynomial rather than linear fitting, panels a, b, e, f, and g might display a different pattern. In that case, clustering analysis based on initial size may be necessary to enable a fair comparison between groups-not only for this figure but also for main Figures 2 and 3.

- The authors responded to this point as follows: For sensor uncaging experiments, we usually uncaged glutamate at large mushroom spines because we need to have a good signal-to-noise ratio. We just happen to choose these spines with different initial sizes for Rab4 sensor and Rab10 sensor uncaging experiments.

Even if they happen to choose these spine sizes, it is possible to compare only those that match in size. This does not require any additional experiments. Because of this, I do not find this response satisfactory.

(2) Another limitation is the absence of in vivo validation, as the experiments were performed in organotypic hippocampal slices, which may not fully replicate the complexity of synaptic plasticity in an intact brain, where excitatory and inhibitory processes occur concurrently. High concentrations of MNI-glutamate (4 mM in this study) are known to block GABAergic responses due to its antagonistic effect on GABA-A receptors, thereby precluding the study of inhibitory network activity or connectivity, which is already known to be altered in organotypic slice cultures.

- I found the Authors following response reasonable and useful:

We appreciate the reviewer's comments and would like to clarify that we have conducted experiments in acute slices for LTP using conditional Rab10 knockout (Fig. 4k, 4l), and we obtained similar results. Additionally, we have recently published findings on the behavioral deficits observed in heterozygous Rab10 knockout mice (PubMed 37156612). These studies further support our conclusions and provide additional context for our findings.

Author response:

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

Reviewer #1 (Public review):

Summary:

Wang et al. created a series of specific FLIM-FRET sensors to measure the activity of different Rab proteins in small cellular compartments. They apply the new sensors to monitor Rab activity in dendritic spines during induction of LTP. They find sustained (30 min) inactivation of Rab10 and transient (5 min) activation of Rab4 after glutamate uncaging in zero Mg. NMDAR function and CaMKII activation are required for these effects. Knockdown of Rab4 reduced spine volume change while knockdown of Rab10 boosted it and enhanced functional LTP (in KO mice). To test Rab effects on AMPA receptor exocytosis, the authors performed FRAP of fluorescently labeled GluA1 subunits in the plasma membrane. Within 2-3 min, new AMPARs appear on the surface via exocytosis. This process is accelerated by Rab10 knock-down and slowed by Rab4 knock-down. The authors conclude that CaMKII promotes AMPAR exocytosis by i) activating Rab4, the exocytosis driver and ii) inhibiting Rab10, possibly involved in AMPAR degradation.

Strengths:

The work is a technical tour de force, adding fundamental insights to our understanding of the crucial functions of different Rab proteins in promoting/preventing synaptic plasticity. The complexity of compartmentalized Ras signaling is poorly understood and this study makes substantial inroads. The new sensors are thoroughly characterized, seem to work very well, and will be quite useful for the neuroscience community and beyond (e.g. cancer research). The use of FLIM for read-out is compelling for precise activity measurements in rapidly expanding compartments (i.e., spines during LTP).

Thank you for the evaluation.

Weaknesses:

The interpretation of the FRAP experiments (Figure 5, Ext. Data Figure 13) is not straightforward as spine volume and surface area greatly expand during uncaging. I appreciate the correction for the added spine membrane shown in Extended Data Figure 14i, but shouldn't this be a correction factor (multiplication) derived from the volume increase instead of a subtraction?

We thank the reviewer for this question. The fluorescence change should reflect a subtraction of surface area, as SEP-GluA1 is only fluorescent on the cell surface, unlike cytosolic mCherry, whose fluorescence intensity is proportional to spine volume. Therefore, the overall fluorescence change (ΔF) should be the addition of the contribution from AMPAR trafficking (ΔF_t) and the change in surface area (ΔS) multiplied by the remaining SEP-GluA1 fluorescence per unit area (f):

ΔF = ΔF_t + fΔS

Since fluorescence immediately after photobleaching (before AMPAR trafficking happens), F_o, is given by fS (S is the surface area of the spine):

ΔF/F_o = ΔF_t/ F_o + fΔS / fS

\= ΔF_t/fS + ΔS/S

Assuming that the surface area change (ΔS/S) is the volume change (ΔV/V) to the power of 2/3, the contribution of the AMPAR trafficking can be calculated as:

ΔF_t/F = ΔF/F – (Δ<sup>V/V)^2/3

This is the reason that we subtracted the contribution of the spine surface area. We have discussed this in the updated method section.

Also, experiments were not conducted or analyzed blind, risking bias in the selection/exclusion of experiments for analysis. This reduces my confidence in the results.

We acknowledge the reviewer's concern regarding the lack of blinding in our experiments. However, it is challenging to conduct blinded experiments for certain types of studies, such as sensor screening for a protein family, where we do not have expected results or a specific hypothesis prior to the experiments. In these cases, our primary readout is whether the sensor indicates any activity change upon stimulation.

To address this concern, after identifying that Rab10 is inactivated during structural LTP (sLTP) and is likely important for inhibiting spine structural LTP, we performed blinded electrophysiology experiments and obtained similar results (deletion of Rab10 from Camk2a-positive neurons leads to enhanced LTP; Fig. 4k, 4l).

Reviewer #2 (Public review):

Summary:

Wang et al. developed a set of optical sensors to monitor Rab protein activity. Their investigation into Rab activity in dendritic spines during structural long-term plasticity (sLTP) revealed sustained Rab10 inactivation (>30min) and transient Rab4 activation (~5 min). Through pharmacological and genetic manipulation to constitutively activate or inhibit Rab proteins, they found that Rab10 negatively regulates sLTP and AMPA receptor insertion, while Rab4 positively influences sLTP but only in the transient phase. The optical sensors provide new tools for studying Rab activity in cells and neurobiology. However, a full understanding of the timing of Rab activity will require a detailed characterization of sensor kinetics.

Strengths:

(1) Introduction of a series of novel sensors that can address numerous questions in Rab biology.

(2) Multiple methods to manipulate Rab proteins to reveal the roles of Rab10 and rab4 in LTP.

(3) Discovery of Rab4 activation and Rab10 inhibition with different kinetics during sLTP, correlating with their functional roles in the transient (Rab4) and both transient and sustained (Rab10) phases of sLTP.

Thank you for the positive evaluation.

Weaknesses:

(1) Lack of characterization of sensor kinetics, making it difficult to determine if the observed Rab kinetics during sLTP were due to sensor behavior or actual Rab activity.

We estimated that the kinetics of the sensors for Rab4 and Rab10 are within a few minutes. For Rab4, we observed rapid increase and decrease of the activation in response to glutamate uncaging. Thus, this would be the upper limit of the ON/OFF time constants of Rab4. For Rab10, we observed a rapid dissociation of the sensor in response to sLTP induction within ~1 min. This means that the donor and acceptor molecules are quickly dissociated during the process. Thus, the off kinetics of the sensor is within the range of minute. Meanwhile, we have the on-kinetics from Rab10 activation (donor/accepter association) in response to NMDA application and again this is within a few minutes. Given these rapid sensor kinetics in neurons, our observation of the sustained inactivation of Rab10 should reflect the true behavior of Rab10, rather than just the sensor’s response.

We revised our manuscript discussion session as follows:

“Understanding the kinetics of Rab4 and Rab10 sensors is essential for interpreting their actual activity during sLTP. The Rab4 sensor exhibits a rapid rise and fall in activation (Fig. 3), indicating ON/OFF times of less than a few minutes. In contrast, the Rab10 sensor rapidly dissociates during sLTP induction (Fig. 2), with OFF kinetics occurring within one minute and fast ON kinetics in response to NMDA (Fig. 1j). Given these rapid kinetics, the observed sustained inactivation of Rab10 likely reflects its true behavior rather than sensor dynamics.”

(2) It is crucial to assess whether the overexpression of Rab proteins as reporters, affects Rab activity and cellular structure and physiology (e.g. spine number and size).

While we did not measure the effects of Rab sensor overexpression on Rab activity or cellular structure and physiology, we showed that sLTP is similar in neurons expressing sensors. This suggests that the overexpression of Rab sensors does not significantly disrupt signaling required for sLTP.

(3) The paper does not explain the apparently different results between NMDA receptor activation and glutamate uncaging. NMDA receptor activation increased Rab10 activity, while glutamate uncaging decreased it. NMDA receptor activation resulted in sustained Rab4 activation, whereas glutamate uncaging caused only brief activation of about 5 minutes. A potential explanation, ideally supported by data, is needed.

It is a long-standing question in the field why simple NMDA receptor activation by bath application of NMDA does not induce LTP, but instead induce LTD. Rab proteins are regulated by many GEFs and GAPs and identifying different mechanisms requires completely different techniques, such as molecular screening. While our manuscript provides some insights into this question by showing that they provide opposing signals for Rab10, we believe that identifying exact mechanisms would be out of the scope of this manuscript.

(4) There is a discrepancy between spine phenotype and sLTP potential with Rab10 perturbation. Rab10 perturbation affected spine density but not size, suggesting a role in spinogenesis rather than sLTP. However, glutamate uncaging affected sLTP, and spinogenesis was not examined. Explaining the discrepancy between spine size and sLTP potential is necessary. Exploring spinogenesis with glutamate uncaging would strengthen these results. Additionally, Figure 4j shows no change in synaptic transmission with Rab10 knockout, despite an increase in spine density. An explanation, ideally supported by data, is needed for the unchanged fEPSP slope despite an increase in spine density.

We thank the reviewer for raising these important questions. In our findings, shRNA-mediated knockdown of Rab10 did not alter spine size but did increase spine density in the basal state (Extended Data Fig. 11i). This suggests that Rab10 may restrict spinogenesis without affecting spine size. Conversely, sLTP induction via glutamate uncaging is an activity-dependent process that may involve different molecular mechanisms. The signal interplay between spinogenesis and sLTP and how the exact roles of Rab signaling in different modalities of plasticity would remain elusive for the future study.

The lack of change in synaptic transmission with Rab10 knockout, despite the increase in spine density from Rab10 shRNA knockdown, may be due to different preparation and developmental stages: spine density measurements were conducted with shRNA knockdown in organotypic slices (sliced at P6-8, DIV 9-13), while electrophysiological recordings were performed in knockout mice in acute slices from adult animals (P30-60).

(5) Spine volume was imaged using acceptor fluorophores (mCherry, or mCherry/Venus) at 920nm, where the two-photon cross-section of mCherry is minimal. 920nm was also used to excite the donor fluorophore, hence the spine volume measurement based on total red channel fluorescence is the sum of minimal mCherry fluorescence from direct 920nm excitation, bleed-through from the green channel, and FRET. This confounded measurement requires correction and clarification.

We assumed that the most of fluorescence is from direct excitation of mCherry at 920 nm. The contribution from the bleed-through from mEGFP-Rab (~3%) and from FRET changes (~20%) may influence the volume measurements. However, since we observed similar fluorescence changes in the green and red channels, these factors would have only a minor impact on our results (Extended Data Fig. 6a, 6d). Also, please note that the volume change in neurons expressing sensors is just to check if the volume change is normal, and not a major point of this manuscript.  We clarified this in the method section as:

“For the sensor experiments, we used mCherry as a volume indicator. We acknowledge that contributions from bleed-through from mEGFP-Rab (approximately 3%) and FRET changes (around 20%) could affect the volume measurements. However, since we observed similar fluorescence changes in both the green and red channels, we believe these factors have a minimal impact on our results (Extended Data Fig. 6a, 6d).”

Reviewer #3 (Public review):

Summary:

This study examines the roles of Rab10 and Rab4 proteins in structural long-term potentiation (sLTP) and AMPA receptor (AMPAR) trafficking in hippocampal dendritic spines using various different methods and organotypic slice cultures as the biological model.

The paper shows that Rab10 inactivation enhances AMPAR insertion and dendritic spine head volume increase during sLTP, while Rab4 supports the initial stages of these processes. The key contribution of this study is identifying Rab10 inactivation as a previously unknown facilitator of AMPAR insertion and spine growth, acting as a brake on sLTP when active. Rab4 and Rab10 seem to be playing opposing roles, suggesting a somewhat coordinated mechanism that precisely controls synaptic potentiation, with Rab4 facilitating early changes and Rab10 restricting the extent and timing of synaptic strengthening.

Strengths:

The study combines multiple techniques such as FRET/FLIM imaging, pharmacology, genetic manipulations, and electrophysiology to dissect the roles of Rab10 and Rab4 in sLTP. The authors developed highly sensitive FRET/FLIM-based sensors to monitor Rab protein activity in single dendritic spines. This allowed them to study the spatiotemporal dynamics of Rab10 and Rab4 activity during glutamate uncaging-induced sLTP. They also developed various controls to ensure the specificity of their observations. For example, they used a false acceptor sensor to verify the specificity of the Rab10 sensor response.

This study reveals previously unknown roles for Rab10 and Rab4 in synaptic plasticity, showing their opposing functions in regulating AMPAR trafficking and spine structural plasticity during LTP.

Thank you for the positive evaluation.

Weaknesses:

In sLTP, the initial volume of stimulated spines is an important determinant of induced plasticity. To address changes in initial volume and those induced by uncaging, the authors present Extended Data Figure 2. In my view, the methods of fitting, sample selection, or both may pose significant limitations for interpreting the overall results. While the initial spine size distribution for Rab10 experiments spans ~0.1-0.4 fL (with an unusually large single spine at the upper end), Rab4 spine distribution spans a broader range of ~0.1-0.9 fL. If the authors applied initial size-matched data selection or used polynomials rather than linear fitting, panels a, b, e, f, and g might display a different pattern. In that case, clustering analysis based on initial size may be necessary to enable a fair comparison between groups not only for this figure but also for main Figures 2 and 3.

We thank the reviewer for these questions. For sensor uncaging experiments, we usually uncaged glutamate at large mushroom spines because we need to have a good signal-to-noise ratio. We just happen to choose these spines with different initial sizes for Rab4 sensor and Rab10 sensor uncaging experiments.

Another limitation is the absence of in vivo validation, as the experiments were performed in organotypic hippocampal slices, which may not fully replicate the complexity of synaptic plasticity in an intact brain, where excitatory and inhibitory processes occur concurrently. High concentrations of MNI-glutamate (4 mM in this study) are known to block GABAergic responses due to its antagonistic effect on GABA-A receptors, thereby precluding the study of inhibitory network activity or connectivity [1], which is already known to be altered in organotypic slice cultures.

(1) https://www.frontiersin.org/journals/neural-circuits/articles/10.3389/neuro.04.002.2009/full

We appreciate the reviewer's comments and would like to clarify that we have conducted experiments in acute slices for LTP using conditional Rab10 knockout (Fig. 4k, 4l), and we obtained similar results. Additionally, we have recently published findings on the behavioral deficits observed in heterozygous Rab10 knockout mice (PubMed 37156612). These studies further support our conclusions and provide additional context for our findings.

Recommendations for the authors:

From the Senior/Reviewing Editor:

I apologize that this took longer than intended. As you will see from the reviews there was some disagreement on several points. There was some disagreement among reviewers as to the strength of the evidence with some characterizing it as "compelling," "convincing," or "solid" while others felt the characterization of the sensors was "incomplete" and that this could have affected some of the conclusions. After extensive discussion, reviewers agreed that there was a valid concern that the conclusion that Rab10 activation is sustained could reflect a feature of the sensor. If Rab10/RBD dissociation rate were very low, and the affinity of binding were very high, this could lead to an incorrect estimate of the sustained binding due to sensor kinetics, not Rab10 activation. It was noted that this has been seen in other sensors previously (e.g. first generation PKA activity sensors), which the developers altered in later generations to increase reversibility and off kinetics of the sensor.

There was also discussion of how this might be addressed and we would be interested in your comments on this issue. It was suggested that it might be helpful to revise Figure 2b to show binding fraction dynamics separately for each spine (to determine whether any actually return to baseline). Subsequently, clustering of these binding dynamics into two groups could be summarized in a version of Fig. 2e for each cluster. Differences in spine volume dynamics between these clusters would provide a measure of how strongly Rab10 binding correlates with spine volume. If they never go back to baseline, some extra experiments with longer post-plasticity induction (150mins instead of 35), might show if any reversible Rab10 binding exists post-LTP induction.

An alternative suggestion was to measure the time course in the presence of a GAP or GEF, which should alter the kinetics.

Thanks for the comments. It is important that the inactivation is observed as the dissociation of the donor and acceptor of the sensor.  Thus, the fact that the sensor rapidly decreases in response to uncaging means that they have rapid off kinetics. In addition, we provide evidence of a rapid increase of Rab10 in response to NMDA application, suggesting that kinetics is also rapid. We added discussion about this in the revised manuscript as:

“Understanding the kinetics of Rab4 and Rab10 sensors is essential for interpreting their actual activity during sLTP. The Rab4 sensor exhibits a rapid rise and fall in activation (Fig. 3), indicating ON/OFF times of just a few minutes. In contrast, the Rab10 sensor rapidly dissociates during sLTP induction (Fig. 2), with OFF kinetics occurring within one minute and fast ON kinetics in response to NMDA (Fig. 1j). Given these rapid kinetics, the observed sustained inactivation of Rab10 likely reflects its true behavior rather than sensor dynamics.”

There was also further discussion of the nature of the "spine volume" signal, given the fact that the two-photon cross-section of mCherry is minimal at 920nm. It was suggested that this could be due to direct acceptor excitation rather than FRET, but there was agreement that further clarity on this issue would be valuable.

We assumed that the most of fluorescence is from direct excitation of mCherry at 920 nm. The contribution from the bleed-through from mEGFP-Rab (~3%) and from FRET changes (~20%) may influence the volume measurements. However, since we observed similar fluorescence changes in the green and red channels, these factors would have only a minor impact on our results (Extended Data Fig. 6a, 6d). Also, please note that the volume change in neurons expressing sensors is just to check if the volume change is normal, and not a major point of this manuscript.  We clarified this in the method section as:

“For the sensor experiments, we used mCherry as a volume indicator. We acknowledge that contributions from bleed-through from mEGFP-Rab (approximately 3%) and FRET changes (around 20%) could affect the volume measurements. However, since we observed similar fluorescence changes in both the green and red channels, we believe these factors have a minimal impact on our results (Extended Data Fig. 6a, 6d).”

The equations in the methods section differ from other papers by the same lab (e.g. Laviv et al, Neuron 2020, Tu et al. Sci Adv. 2023, Jain et al. Nature 2024). Please clarify which equations are correct.

Thanks for pointing this out. In fact, some of the equations in this manuscript were wrong, and we have corrected them in the method session.

Reviewer #1 (Recommendations for the authors):

The effects of Rab knockdown affect both spine volume expansion and AMPAR recovery in a very similar fashion. To explain this tight coupling, the authors suggest that the availability of membrane could be a limiting factor for spine enlargement. However, some Rabs are known to affect actin dynamics, which could also explain the dual effects on AMPAR exocytosis and spine enlargement. It is not easy to come up with an experiment to differentiate between these alternative explanations, as blocking actin polymerization would likely affect exocytosis, too. The authors should consider/discuss the possibility that all of the observed Ras effects result from altered actin dynamics and that the lipid bilayer is sufficiently fluid to form a minimal surface around the expanding cytoskeleton.

Thanks for the suggestions. We included the discussion about the potential impact on the actin cytoskeleton by Rab10.

Typos: heterougenous, compartmantalization, chemaical, ballistically/biolistically (chose one).

Thanks for pointing out these typos. We have corrected them in the revised manuscript.

Reviewer #2 (Recommendations for the authors):

(1) Venus shows pH sensitivity, which can be significant at synapses due to pH changes. Characterizing the pH sensitivity of the sensors is essential.

Thanks for the suggestions. We did not measure pH dependence, but the PKa of these fluorophores has already been published. PKa for EGFP and Venus are both 6.0, and it is unlikely that it influenced our measurements.

(2) Presenting individual data points within all bar graphs (e.g. Fig. 2c, 2d) would enhance data transparency.

Thanks for the suggestions. We now provide individual data points in the revised main figures.

(3) In Figure 1f: Rab5 GAP expression increased the binding fraction against expectations. In addition, clarifying the color scheme in Figure 1 is needed. Are GAPs supposed to be blue/green, and GEFs red/orange? Figure 1f seems to contradict this color scheme.

Thanks for the suggestions. We clarified these issues.

(4) Quantification of the point spread function of the uncaging laser, response/settle time of the scan mirror during uncaging, and reason for changes in neighboring spines in many example images (e.g. Figure 2a, especially at 240 s; Figure 4a) would be important.

The laser is controlled by Pockels cells, which changes the laser intensity with microsecond resolution. The laser is parked for milliseconds during uncaging, much longer than the settling time of the mirror (~0.1 milliseconds). The point spread function of the uncaging laser is limited by the diffraction (~0.5 um). The uncaging spot size is mostly limited by the diffusion of uncaged glutamate, but our calcium imaging and CaMKII imaging show that the signaling is induced mostly in the stimulated spines (Lee et al., 2009; Chang et al., 2017, 2019).

(5) Please include traces for "false" sensors in stimulated spines in Figures 2b, 2e, 3b, and 3e.

The traces for the false sensors have been presented in Extended Data Fig. 3 and Extended Data Fig. 8.

(6) The traces in Figure 4k (fEPSP slope in response to theta burst stimulation, where there is a decrease in fEPSP slope followed by a gradual increase) differ from prior publications (e.g. PMID: 1359925, 3967730, 19144965, 20016099). An investigation and explanation for these differences are necessary.

We appreciate the reviewer’s comments. We performed the experiments blindly and did not try to find a condition providing control data similar to previous publications. The variations in fEPSP responses compared to prior publications may be attributed to several factors, including differences in experimental conditions such as the genetic background of the animals used, the specific protocols for theta burst stimulation, and variations in the preparation of the hippocampal slices.

(7) The title and text state that Rab10 inactivation promotes AMPAR insertion. It is unclear if this is a direct effect on AMPAR insertion or an indirect effect through membrane remodeling. Providing data to distinguish these possibilities or adjusting the title/text to reflect alternative interpretations would be beneficial.  

We appreciate the reviewer's feedback. To clarify, we have revised our terminology to use "AMPAR trafficking" instead of "AMPAR insertion", as it includes both insertion and other mechanisms of AMPAR movement within the cell.

(8) Please provide an explanation for the initial Rab10 inactivation observed in Figure 1j upon NMDA application.

The application of NMDA in Fig. 1j is similar to the commonly used chemical LTD induction protocol. We used this broad stimulation approach to test whether our sensors could report Rab activity changes in neurons upon strong stimulation. However, it is an entirely different stimulation approach from the sLTP induction protocol, thus resulting in different sensor activity changes.  We describe the phenomenon in the revised manuscript, but we believe that detailed analyses of Rab10 activation in response to NMDA application are beyond the scope of this manuscript.

(9) Please explain why the study focuses on Rab4 and Rab10 instead of other Rab proteins.

During our initial screening of sensors for various Rab proteins, we observed significant activity changes in the sensors for Rab4 and Rab10 upon sLTP induction. This suggested their potential relevance in synaptic processes, leading us to focus on understanding their specific roles in structural long-term potentiation.

Reviewer #3 (Recommendations for the authors):

(1) Although it might seem trivial, the definition of adjacent spine has not been made in the text. It would be nice to have it in the Methods section.

We included it in the Methods section as follows:

"The adjacent spine refers to the first or second spine located next to the stimulated spine, typically positioned opposite the stimulated spine. Additionally, the size of the adjacent spine must be sufficiently large for imaging."

(2) The transfection method has been mentioned as "ballistic" and "biolistic" transfection. You might want to use only one term. Additionally, you can add the equipment used (Bio-rad?) and pressure (psi) in the Methods section.

We use “biolistic” throughout the manuscript now. We also added the equipment and conditions used.

Reviewer #1 (Public review):

SNeuronal activity spatiotemporal fine-tuning of cerebral blood flow balances metabolic demands of changing neuronal activity with blood supply. Several 'feed-forward' mechanisms have been described that contribute to activity-dependent vasodilation as well as vasoconstriction leading to a reduction in perfusion. Involved messengers are ionic (K+), gaseous (NO), peptides (e.g., NPY, VIP) and other messengers (PGE2, GABA, glutamate, norepinephrine) that target endothelial cells, smooth muscle cells, or pericytes. Contributions of the respective signaling pathways likely vary across brain regions or even within specific brain regions (e.g., across cortex) and are likely influenced by the brain's physiological state (resting, active, sleeping) or pathological departures from normal physiology.

The manuscript "Elevated pyramidal cell firing orchestrates arteriolar vasoconstriction through COX-2-derived prostaglandin E2 signaling" by B. Le Gac, et al. investigates mechanisms leading to activity-dependent arteriole constriction. Here, mainly working in brain slices from mice expressing channelrhodopsin 2 (ChR2) in all excitatory neurons (Emx1-Cre; Ai32 mice), the authors show that strong optogenetic stimulation of cortical pyramidal neurons is leading to constriction that is mediated through the cyclooxygenase-2 / prostaglandin E2 / EP1 and EP3 receptor pathway with contribution of NPY-releasing interneurons and astrocytes releasing 20-HETE. Specifically, using patch clamp, the authors show that 10-s optogenetic stimulation at 10 and 20 Hz leads to vasoconstriction (Figure 1), in line with a stimulation frequency-dependent increase in somatic calcium (Figure 2). The vascular effects were abolished in presence in TTX and significantly reduced in presence of glutamate receptor antagonists (Figure 3). The authors further show with RT-PCR on RNA isolated from patched cells that ~50% of analyzed cells express COX-1 or -2 and other enzymes required to produce PGE2 or PGF2a (Figure 4). Further, blockade of COX-1 and -2 (indomethacin), or COX-2 (NS-398) abolishes constriction. In animals with chronic cranial window that were anesthetized with ketamine and medetomidine, 10-s long optogenetic stimulation at 10 Hz leads to considerable constriction, which is reduced in presence of indomethacin. Blockade of EP1 and EP3 receptors leads to significant reduction of the constriction in slices (Figure 5). Finally, the authors show that blockade of 20-HETE synthesis caused moderate and NPY Y1 receptor blockade a complete reduction of constriction.

The mechanistic analysis of neurovascular coupling mechanisms as exemplified here will guide further in-vivo studies and has important implications for human neuroimaging in health and disease. Most of the data in this manuscript uses brain slices as experimental model which contrasts with neurovascular imaging studies performed in awake (headfixed) animals. However, the slice preparation allows for patch clamp as well as easy drug application and removal. Further, the authors discuss their results in view of differences between brain slices and in vivo observations experiments, including the absence of vascular tone as well as blood perfusion required for metabolite (e.g., PGE2) removal, and the presence of network effects in the intact brain. The manuscript and figures present the data clearly; regarding the presented mechanism, the data supports the authors conclusions. Some of the data was generated in vivo in head-fixed animals under anesthesia; in this regard, the authors should revise introduction and discussion to include the important distinction between studies performed in slices, or in acute or chronic in-vivo preparations under anesthesia (reduced network activity and reduced or blockade of neuromodulation, or in awake animals (virtually undisturbed network and neuromodulatory activity). Further, while discussed to some extent, the authors could improve their manuscript by more clearly stating if they expect the described mechanism to contribute to CBF regulation under 'resting state conditions' (i.e., in absence of any stimulus), during short or sustained (e.g., visual, tactile) stimulation, or if this mechanism is mainly relevant under pathological conditions; especially in context of the optogenetic stimulation paradigm being used (10-s long stimulation of many pyramidal neurons at moderate-high frequencies) and the fact that constriction leading to undersupply in response to strongly increased neuronal activity seems counterintuitive?

The authors have addressed all comments, and I appreciate their insightful discussion and revision of the manuscript.

Reviewer #1 (Public review):

Summary:

The study addresses how faces and bodies are integrated in two STS face areas revealed by fMRI in the primate brain. It builds upon recordings and analysis of the responses of large populations of neurons to three sets of images, that vary face and body positions. These sets allowed the authors to thoroughly investigate invariance to position on the screen (MC HC), to pose (P1 P2), to rotation (0 45 90 135 180 225 270 315), to inversion, to possible and impossible postures (all vs straight), to the presentation of head and body together or in isolation. By analyzing neuronal responses, they found that different neurons showed preferences for body orientation, head orientation, or the interaction between the two. By using a linear support vector machine classifier, they show that the neuronal population can decode head-body angle presented across orientations, in the anterior aSTS patch (but not middle mSTS patch), except for mirror orientation.

Strengths:

These results extend prior work on the role of Anterior STS fundus face area in face-body integration and its invariance to mirror symmetry, with a rigorous set of stimuli revealing the workings of these neuronal populations in processing individuals as a whole, in an important series of carefully designed conditions.

Minor issues and questions that could be addressed by the authors:

(1) Methods. While monkeys certainly infer/recognize that individual pictures refer to the same pose with varying orientations based on prior studies (Wang et al.), I am wondering whether in this study monkeys saw a full rotation of each of the monkey poses as a video before seeing the individual pictures of the different orientations, during recordings.

(2) Experiment 1. The authors mention that neurons are preselected as face-selective, body-selective, or both-selective. Do the Monkey Sum Index and ANOVA main effects change per Neuron type?

(3) I might have missed this information, but the correlation between P1 and P2 seems to not be tested although they carry similar behavioral relevance in terms of where attention is allocated and where the body is facing for each given head-body orientation.

(4) Is the invariance for position HC-MC larger in aSTS neurons compared to mSTS neurons, as could be expected from their larger receptive fields?

(5) L492 "The body-inversion effect likely results from greater exposure to upright than inverted bodies during development". Monkeys display more hanging upside-down behavior than humans, however, does the head appear more tilted in these natural configurations?

(6) Methods in Experiment 1. SVM. How many neurons are sufficient to decode the orientation?

(7) Figure 3D 3E. Could the authors please indicate for each of these neurons whether they show a main effect of face, body, or interaction, as well as their median corrected correlation to get a flavor of these numbers for these examples?

(8) Methods and Figure 1A. It could be informative to precise whether the recordings are carried in the lateral part of the STS or in the fundus of the STS both for aSTS and mSTS for comparison to other studies that are using these distinctions (AF, AL, MF, ML).

Wang, G., Obama, S., Yamashita, W. et al. Prior experience of rotation is not required for recognizing objects seen from different angles. Nat Neurosci 8, 1768-1775 (2005). https://doi-org.insb.bib.cnrs.fr/10.1038/nn1600

Reviewer #2 (Public review):

Summary:

This paper investigates the neuronal encoding of the relationship between head and body orientations in the brain. Specifically, the authors focus on the angular relationship between the head and body by employing virtual avatars. Neuronal responses were recorded electrophysiologically from two fMRI-defined areas in the superior temporal sulcus and analyzed using decoding methods. They found that: (1) anterior STS neurons encode head-body angle configurations; (2) these neurons distinguish aligned and opposite head-body configurations effectively, whereas mirror-symmetric configurations are more difficult to differentiate; and (3) an upside-down inversion diminishes the encoding of head-body angles. These findings advance our understanding of how visual perception of individuals is mediated, providing a fundamental clue as to how the primate brain processes the relationship between head and body - a process that is crucial for social communication.

Strengths:

The paper is clearly written, and the experimental design is thoughtfully constructed and detailed. The use of electrophysiological recordings from fMRI-defined areas elucidated the mechanism of head-body angle encoding at the level of local neuronal populations. Multiple experiments, control conditions, and detailed analyses thoroughly examined various factors that could affect the decoding results. The decoding methods effectively and consistently revealed the encoding of head-body angles in the anterior STS neurons. Consequently, this study offers valuable insights into the neuronal mechanisms underlying our capacity to integrate head and body cues for social cognition-a topic that is likely to captivate readers in this field.

Weaknesses:

I did not identify any major weaknesses in this paper; I only have a few minor comments and suggestions to enhance clarity and further strengthen the manuscript, as detailed in the Private Recommendations section.

Reviewer #3 (Public review):

Summary:

Zafirova et al. investigated the interaction of head and body orientation in the macaque superior temporal sulcus (STS). Combining fMRI and electrophysiology, they recorded responses of visual neurons to a monkey avatar with varying head and body orientations. They found that STS neurons integrate head and body information in a nonlinear way, showing selectivity for specific combinations of head-body orientations. Head-body configuration angles can be reliably decoded, particularly for neurons in the anterior STS. Furthermore, body inversion resulted in reduced decoding of head-body configuration angles. Compared to previous work that examined face or body alone, this study demonstrates how head and body information are integrated to compute a socially meaningful signal.

Strengths:

This work presents an elegant design of visual stimuli, with a monkey avatar of varying head and body orientations, making the analysis and interpretation straightforward. Together with several control experiments, the authors systematically investigated different aspects of head-body integration in the macaque STS. The results and analyses of the paper are mostly convincing.

Weaknesses:

(1) Using ANOVA, the authors demonstrate the existence of nonlinear interactions between head and body orientations. While this is a conventional way of identifying nonlinear interactions, it does not specify the exact type of the interaction. Although the computation of the head-body configuration angle requires some nonlinearity, it's unclear whether these interactions actually contribute. Figure 3 shows some example neurons, but a more detailed analysis is needed to reveal the diversity of the interactions. One suggestion would be to examine the relationship between the presence of an interaction and the neural encoding of the configuration angle.

(2) Figure 4 of the paper shows a better decoding of the configuration angle in the anterior STS than in the middle STS. This is an interesting result, suggesting a transformation in the neural representation between these two areas. However, some control analyses are needed to further elucidate the nature of this transformation. For example, what about the decoding of head and body orientations - dose absolute orientation information decrease along the hierarchy, accompanying the increase in configuration information?

(3) While this work has characterized the neural integration of head and body information in detail, it's unclear how the neural representation relates to the animal's perception. Behavioural experiments using the same set of stimuli could help address this question, but I agree that these additional experiments may be beyond the scope of the current paper. I think the authors should at least discuss the potential outcomes of such experiments, which can be tested in future studies.

method and madness by [[Alan Jacobs]]

via In which I describe my writing “methods." by [[Alan Jacobs]]

reply:

@ayjay Thanks for sharing this. My method is often very much like yours. Lots of internal distillation, slowly over time. I remember hearing a story that Mozart wrote music "like a cow pees" (in one giant and immediate flood and then done). I feel like large works of writing, composing, etc. springing, as if fully formed from the head of Zeus is more common than is acknowledged. Cory Doctorow hints at a similar sort of method in his own work in The Memex Method. I'm also reminded of bits of what neuroscientist Barbara Oakley calls "diffuse thinking" or a more internalized version of Michael Ondaatje's "thinkering" described in The English Patient.

I can't get over the fact that this photo looks like someone photoshopped Hart's head on someone else hahaha

Author response:

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

General Response to Public Reviews

We thank the three reviewers for their positive evaluation of our work, which presents the first molecular characterization of type-II NB lineages in an insect outside the fly Drosophila. They seem convinced of our finding of an additional type-II NB and increased proliferation during embryogenesis in the red flour beetle. The reviewers expressed hesitations on our interpretation that the observed quantitative differences of embryonic lineages can directly be linked to the embryonic development of the central complex in Tribolium. While we still believe that a connection of both observations is a valid and likely hypothesis, we acknowledge that due the lack of functional experiments and lineage tracing a causal link has not directly been shown. We have therefore changed the manuscript to an even more careful wording that on one hand describes the correlation between increased embryonic proliferation with the earlier development of the Cx but on the other hand also stresses the need for additional functional and lineage tracing experiments to test this hypothesis. We have also strengthened the discussion on alternative explanations of the increased lineage size and emphasize the less disputed elements like presence and conservation of type-II NB lineages. 

While our manuscript could in conclusion not directly show that the reason of the heterochronic shift lies in the progenitor behaviour, we still provide a first approach to answering the question of the developmental basis of this shift and testable hypotheses directly emerge from our work. We agree with reviewer#1 that functional work is best suited to test our hypothesis and we are planning to do so. However, we believe that the presented work is already rich in novel data and significantly advances our understanding on the conservation and divergence of type-II NBs in insects. We would also like to stress that most transgenic tools for which genome-wide collections exist for Drosophila have to be created for Tribolium and doing so can be quite time consuming. Conducting RNAi experiments is certainly possible in Tribolium but observing phenotypes in this defined cellular context will need laborious optimization. We have for example tried knocking down Tc-fez/erm but could not see any embryonic phenotype which might be due to an escaper effect in which only mildly affected or wild type-like embryos survive while the others die in early embryogenesis. Due to pleiotropic functions of the involved genes a cell-specific knockdown might be necessary and we are working towards establishing a system to do that in the red flour beetle. For the stated reasons, we see our work as an important basis to inspire future functional studies that build up on the framework that we introduced. 

In response to these common points, we have made the following changes to the manuscript

-        The title has been changed from ‘being associated’ to ‘correlate’

-        The conclusions part of the abstract has been changed

-        We deleted the statement ‘…thus providing the material for the early central complex formation…’

-        Rephrased to saying that the two observations just correlate

-        The part of the discussion ‘Divergent timing of type-II NB activity and heterochronic development of the central complex’ has been extensively rewritten and now discusses several alternative explanations that were suggested by the reviewers. It also stresses the need for further functional work and lineage tracing (line 859-862 (608-611)).

In addition, we have made numerous changes to the manuscript to account for more specific comments of the reviewers and to the recommendations for the authors.

Our responses to the individual comments can be found in the following. 

Public Reviews: 

Reviewer #1 (Public Review): 

Summary: 

Insects inhabit diverse environments and have neuroanatomical structures appropriate to each habitat. Although the molecular mechanism of insect neural development has been mainly studied in Drosophila, the beetle, Tribolium castaneum has been introduced as another model to understand the differences and similarities in the process of insect neural development. In this manuscript, the authors focused on the origin of the central complex. In Drosophila, type II neuroblasts have been known as the origin of the central complex. Then, the authors tried to identify those cells in the beetle brain. They established a Tribolium fez enhancer trap line to visualize putative type II neuroblasts and successfully identified 9 of those cells. In addition, they also examined expression patterns of several genes that are known to be expressed in the type II neuroblasts or their lineage in Drosophila. They concluded that the putative type II neuroblasts they identified were type II neuroblasts because those cells showed characteristics of type II neuroblasts in terms of genetic codes, cell diameter, and cell lineage. 

Strengths: 

The authors established a useful enhancer trap line to visualize type II neuroblasts in Tribolium embryos. Using this tool, they have identified that there are 9 type II neuroblasts in the brain hemisphere during embryonic development. Since the enhancer trap line also visualized the lineage of those cells, the authors found that the lineage size of the type II neuroblasts in the beetle is larger than that in the fly. They also showed that several genetic markers are also expressed in the type II neuroblasts and their lineages as observed in Drosophila. 

Weaknesses: 

I recommend the authors reconstruct the manuscript because several parts of the present version are not logical. For example, the author should first examine the expression of dpn, a well-known marker of neuroblast. Without examining the expression of at least one neuroblast marker, no one can say confidently that it is a neuroblast. The purpose of this study is to understand what makes neuroanatomical differences between insects which is appropriate to their habitats. To obtain clues to the question, I think, functional analyses are necessary as well as descriptive analyses. 

The expression of an exclusive type-II neuroblast marker would indeed have been the most convincing evidence. However, asense is absent from type-II NBs and deadpan is not specific enough as it is expressed in many other cells of the developing protocerebrum. The gene pointed, although also expressed elsewhere, emerged as the the most specific marker. Therefore, we start with pointed and fez/erm to describe the first appearance and developmental progression of the cells and then add further evidence that these cells are indeed type-II neuroblasts. Further evidence is provided in the following chapters.  We have discussed the need for functional work in the general response. 

Reviewer #2 (Public Review): 

The authors address the question of differences in the development of the central complex (Cx), a brain structure mainly controlling spatial orientation and locomotion in insects, which can be traced back to the neuroblast lineages that produce the Cx structure. The lineages are called type-II neuroblast (NB) lineages and are assumed to be conserved in insects. While Tribolium castaneum produces a functional larval Cx that only consists of one part of the adult Cx structure, the fan-shaped body, in Drosophila melanogaster a non-functional neuropile primordium is formed by neurons produced by the embryonic type-II NBs which then enter a dormant state and continue development in late larval and pupal stages. 

The authors present a meticulous study demonstrating that type-II neuroblast (NB) lineages are indeed present in the developing brain of Tribolium castaneum. In contrast to type-I NB lineages, type-II NBs produce additional intermediate progenitors. The authors generate a fluorescent enhancer trap line called fez/earmuff which prominently labels the mushroom bodies but also the intermediate progenitors (INPs) of the type-II NB lineages. This is convincingly demonstrated by high-resolution images that show cellular staining next to large pointed labelled cells, a marker for type-II NBs in Drosophila melanogaster. Using these and other markers (e.g. deadpan, asense), the authors show that the cell type composition and embryonic development of the type-II NB lineages are similar to their counterparts in Drosophila melanogaster. Furthermore, the expression of the Drosophila type-II NB lineage markers six3 and six4 in subsets of the Tribolium type-II NB lineages (anterior 1-4 and 1-6 type-II NB lineages) and the expression of the Cx marker skh in the distal part of most of the lineages provide further evidence that the identified NB lineages are equivalent to the Drosophila lineages that establish the central complex. However, in contrast to Drosophila, there are 9 instead of 8 embryonic type-II NB lineages per brain hemisphere and the lineages contain more progenitor cells compared to the Drosophila lineages. The authors argue that the higher number of dividing progenitor cells supports the earlier development of a functional Cx in Tribolium. 

While the manuscript clearly shows that type-II NB lineages similar to Drosophila exist in Tribolium, it does not considerably advance our understanding of the heterochronic development of the Cx in these insects. First of all, the contribution of these lineages to a functional larval Cx is not clear. For example, how do the described type-II NB lineages relate to the DM1-4 lineages that produce the columnar neurons of the Cx? What is the evidence that the embryonically produced type-II NB lineage neurons contribute to a functional larval Cx? The formation of functional circuits could rely on larval neurons (like in Drosophila) which would make a comparison of embryonic lineages less informative with respect to understanding the underlying variations of the developmental processes. Furthermore, the higher number of progenitors (and consequently neurons) in Tribolium could simply reflect the demand for a higher number of cells required to build the fan-shaped body compared to Drosophila. In addition, the larger lineages in Tribolium, including the higher number of INPs could be due to a greater number of NBs within the individual clusters, rather than a higher rate of proliferation of individual neuroblasts, as suggested. What is the evidence that there is only one NB per cluster? The presented schemes (Fig. 7/12) and description of the marker gene expression and classification of progenitor cells are inconsistent but indicate that NBs and immature INPs cannot be consistently distinguished. 

We thank this reviewer for pointing out the inconsistency in our classification of cells within the lineages as one central part of our manuscript. These were due to a confusion in the used terms (young vs. immature). We have corrected this mistake and have changed the naming of the INP subtypes to immature-I and immature-II. We are confident that based on the analysed markers, type-II NBs and immature INPs can actually be distinguished with confidence.

We agree that a functional link of increased proliferation to heterochronic CX development is not shown although we consider it to be likely. As stated in the general response we have changed the manuscript to saying that the two observations (higher number of progenitors and larger lineages/more INPs) correlate but that a causal link can only be hypothesized for the time being. At the same time, we have strengthened the discussion on alternative explanations.

We would like to remain with our statement of an increased number of embryonic progeny of Tribolium type-II NBs. We counted the total number of progenitor cells emerging from the anterior median cluster and divided this by the number of type II NBs in that cluster. Hence, the shown increased number of cells represents an average per NB but is not influenced by the increased number of NBs. On the same line, we have never seen indication for the presence of additional NBs within any cluster while one type-II NB is what we regularly found. Hence, we are confident that we know the number of respective NBs. The fact that the fly data included also neurons and was counted at a later stage indicates that the observed differences are actually minimum estimates.

We have discussed that based on the position and comparison to the grasshopper we believe that Tribolium type-II NB 1-4 contribute to the x, y, z and w tracts. To confirm this, lineage tracing experiments would be necessary, for which tools remain to be developed. 

We agree that the role of larvally born neurons and the fate of Tribolium neuroblasts through the transition from embryo to larva and pupa need to be further studied.

Available data suggests that the adult fan shaped body in Tribolium does not hugely differ in size from the Drosophila counterpart, although no data in terms of cell number is available. In the larva, however, no fan shaped body or protocerebral bridge can be distinguished in flies while in beetle larvae, these structures are clearly developed. Hence, we think that it is more likely that differences observed in the embryo reflect differences in the larval central complex. We discuss the need for further investigation of larval stages.

The main difference between Tribolium and Drosophila Cx development with regards to the larval functionality might be that Drosophila type-II NB lineage-derived neurons undergo quiescence at the end of embryogenesis so that the development of the Cx is halted, while a developmental arrest does not occur in Tribolium. However, this needs to be confirmed (as the authors rightly observe). 

Indeed, there is evidence that cells contributing to the CX go into quiescence in flies – hence, this certainly is one of the mechanisms. However, based on our data we would suggest that in addition, the balance of embryonic versus larval proliferation of type-II lineages is different between the two insects: The increased embryonic proliferation and development leads to a functional larval CX in beetles while in flies, postembryonic proliferation may be increased in order to catch up.

Reviewer #3 (Public Review):

Summary: 

In this paper, Rethemeier et al capitalize on their previous observation that the beetle central complex develops heterochronically compared to the fly and try to identify the developmental origin of this difference. For this reason, they use a fez enhancer trap line that they generated to study the neuronal stem cells (INPs) that give rise to the central complex. Using this line and staining against Drosophila type-II neuroblast markers, they elegantly dissect the number of developmental progression of the beetle type II neuroblasts. They show that the NBs, INPs, and GMCs have a conserved marker progression by comparing to Drosophila marker genes, although the expression of some of the lineage markers (otd, six3, and six4) is slightly different. Finally, they show that the beetle type II neuroblast lineages are likely longer than the equivalent ones in Drosophila and argue that this might be the underlying reason for the observed heterochrony. 

Strengths: 

- A very interesting study system that compares a conserved structure that, however, develops in a heterochronic manner. 

- Identification of a conserved molecular signature of type-II neuroblasts between beetles and flies. At the same time, identification of transcription factors expression differences in the neuroblasts, as well as identification of an extra neuroblast. 

- Nice detailed experiments to describe the expression of conserved and divergent marker genes, including some lineaging looking into the co-expression of progenitor (fez) and neuronal (skh) markers. 

Weaknesses: 

- Comparing between different species is difficult as one doesn't know what the equivalent developmental stages are. How do the authors know when to compare the sizes of the lineages between Drosophila and Tribolium? Moreover, the fact that the authors recover more INPs and GMCs could also mean that the progenitors divide more slowly and, therefore, there is an accumulation of progenitors who have not undergone their programmed number of divisions. 

We understand the difficulty of comparing stages between species, but we feel that our analysis is on the save side. At stages comparable with respect to overall embryonic development (retracting or retracted germband), the fly numbers are clearly smaller. To account for potential heterochronic shifts in NB activity, we have selected the stages to compare based on the criteria given: In Drosophila the number of INPs goes down after stage 16, meaning that they reach a peak at the selected stages. In Tribolium the chosen stages also reflect the phase when lineage size is larger than in all previous stages. Therefore, we believe that the conclusion that Tribolium has larger lineages and more INPs is well founded. Lineage size in Tribolium might further increase just before hatching (stage 15) but we were for technical reasons not able to look at this. As lineage size goes down in the last stage of Drosophila embryogenesis the number of INPs goes down and type-II NB enter quiescence, we think it is highly unlikely that the ratio between Tribolium and Drosophila INPs reverses at this stage, but a study of the behaviour of type-II NB in Tribolium and whether there is a stage of quiescence is still needed.

- The main conclusion that the earlier central complex development in beetles is due to the enhanced activity of the neuroblasts is very handwavy and is not the only possible conclusion from their data. 

As discussed in the general response we have made several changes to the manuscript to account for this criticism and discuss alternative explanations for the observations.

- The argument for conserved patterns of gene expression between Tribolium and Drosophila type-II NBs, INPs, and GMCs is a bit circular, as the authors use Drosophila markers to identify the Tribolium cells. 

We tested the hypothesis that in Tribolium there are type-II NBs with a molecular signature similar to flies. Our results are in line with that hypothesis. If pointed had not clearly marked cells with NB-morphology or fez/erm had not marked dividing cells adjacent to these NBs, we would have concluded that no such cells/lineages exist in the Tribolium embryo, or that central complex producing lineages exist but express different markers. Therefore, we regard this a valid scientific approach and hence find this argument not problematic.  

An appraisal of whether the authors achieved their aims, and whether the results support their conclusions: Based on the above, I believe that the authors, despite advancing significantly, fall short of identifying the reasons for the divergent timing of central complex development between beetle and fly. 

We agree that based on the available data, we cannot firmly make that link and we have changed the text accordingly.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors): 

In addition to these descriptive analyses, functional analyses can be included. RNAi is highly effective in this beetle. 

We agree that functional analyses of some of the studied genes and possible effects of gene knockdowns on the studied cell lineages and on central complex development could be highly informative. However, when studying specific cell types or organs these experiments are less straight forward than it may seem as knockdowns often lead to pleiotropic effects, sterility or lethality. All the genes involved are expressed in additional cells and may have essential functions there. Given the systemic RNAi of Tribolium, it is challenging to unequivocally assign phenotypes to one of the cell groups. Overcoming these challenges is often possible but needs extensive optimization. Our study, though descriptive is already rich in data and is the first description of NB-II lineages in Tribolium central complex development. We see it as a basis for future studies on central complex development that will include functional experiments.

(1) Introduction 

For these reasons the beetle... 

Could you explain the differences in the habitats between Tribolium and Drosophila? or What is the biggest difference between these two species at the ecological aspect? 

We have added a short characterisation of the main differences.

The insect central complex is an anterior... 

The author should explain why they focus on the structure. 

Added

It is however not known how these temporal... 

If the authors want to get the answer to the question, they need to conduct functional analyses. 

While we agree with the importance of functional work (see above) we believe that detailed descriptions under the inclusion of molecular markers as presented here is very informative by itself for understanding developmental processes and sets the foundation for the analysis of mutant/RNAi- phenotypes in future studies.

CX - Central complex? 

We have opted to not use this abbreviation anymore for clarity.

“because intermediate cycling progenitors have also been...” 

Is the sentence correct? 

We have included ‘INPs’ in the sentence to make clear what the comparison refers to and added a comma

“However, molecular characterization of such lineage in another...” 

The authors should explain why molecular characterization is necessary. 

We have done so

(2) Results 

a) Figure 8. Could you delineate the skh/eGFP expression region? 

We have added brackets to figure 1 panel A to indicate the extent of skh and other gene expressions within the lineages.

b) This section should be reorganized for better logical flow. 

There certainly are different ways to organize this part and we have considered different structures of the results part. We eventually subjectively concluded that the chosen one is the best fit for our data (also see comment below on dpn-expression).

c) For the tables. The authors should mention what statistical analysis they have conducted. 

The tables themselves are just listing the raw numbers. They are the basis for the graph in figure 9. Statistical tests (t-test) are mentioned in the legend of that figure and now also in the Methods sections.

“We also found that the large Tc-pnt...” 

The authors could examine the mitotic index using an anti-pH3 antibody. 

We have used the anti-pH3 antibody to detect mitoses (figure 3C, table 1 and 3) but as data on mitoses based on this antibody is only a snapshot it would require a lot of image data to reliably determine an index in this specific cells. While mitotic activity over time possibly combined with live imaging might be very interesting in this system also with regards to the timing of development, for this basic study we are satisfied with the statement that the type-II NB are indeed dividing at these stages.

“Based on their position by the end of embryogenesis...” 

How can the authors conclude that they are neuroblasts without examining the expression of NB markers? 

Type-II NB do not express asense as the key marker for type I neuroblasts. To corroborate our argument that the cells are neuroblasts we have used several criteria:

- We have used the same markers that are used in Drosophila to label type-II NBs (pnt, dpn, six4). We are not aware of any other marker that would be more specific.

- We have shown that these cells are larger and have larger nuclei than neighbouring cells and they are dividing

- We have shown that these cells through their INP lineages give rise to central complex neuropile

We believe that these features taken together leave little doubt that the described cells are indeed neuroblasts. 

“We found that the cells they had assigned as...” 

How did the authors distinguish that they are really neuroblasts? 

We see the difficulty that we first describe the position and development of these cells (e.g. fig 3) and then add further evidence (cell size, additional marker dpn) that these are neuroblasts (also see above). However, without previous knowledge on position (and on pnt expression as the most specific marker) the type-II NB could not have been distinguished from other NBs based on cell size or expression of other markers.

“Conserved patterns of gene expression...” 

This must be the first (especially dpn). 

Dpn is not specific to type-II NB because it is also expressed in type-1 NBs, mature INPs and possibly other neural cells. It is therefore impossible to identify type-II NBs based on this gene alone. We therefore first used the most specific marker, pnt, in addition to adjacent fez expression to identify candidates for type-II lineages. Then we mapped expression of further genes on these lineages to support the interpretation (and show homology to the Drosophila lineages). Although of course the structure of a paper does not necessarily have to reflect the sequence in which experiments were done we would find putting dpn expression first misleading as it would not be clear why exactly a certain part of the expression should belong to type-II NB. Also, our pnt-fez expression data shows the position of the NB-II in the context of the whole head lobe whereas the other gene expressions are higher magnifications focussing on details. We therefore believe that the structure we chose best fits our data and the other reviewers seemed to find it acceptable as well.  

“As type-II NBs contribute to central...” 

Before the sentence, the author could explain differences in the central complex structure between Tribolium and Drosophila in terms of cell number and tissue size. 

We have added references on the comparisons of tissue sizes, but unfortunately there is no Tribolium data that can be directly compared to available Drosophila resources in terms of cell number.  

“We conclude that the embryonic development of...” 

How did the authors conclude? They must explain their logic. 

Actually, before this sentence, I only found the description of the comparison between Tribolium NBs and Drosophila once. 

We agree that this conclusion is not fully evident from the presented data. We have therefore changed this part to stating that there is a correlation with the earlier central complex development described in Tribolium. See also response to the general reviewer comments.

“Hence, we wondered...” 

The authors need to do a functional assessment of the genes they mentioned. 

We agree that the goals originally stated at the beginning of this paragraph can only be achieved with functional experiments. We have therefore rephrased this part.

(3) Discussion

“A beetle enhancer trap line...” 

This part should be moved elsewhere (it does not seem to be a discussion) 

In accordance with this comment and reviewer#2’s similar comment we have removed this section. We have added a statement on the importance of testing the expression of an enhancer trap line to the results part and an added the use of CRISPR-Cas9 for line generation to the introduction. 

“We have identified a total...” 

The authors emphasized that they discovered 9 type II NBs. The authors should clarify how important this it

We have added some discussion on the importance of this finding.

Dpn is a neural marker - Is this correct? 

According to Bier et al 1992 (now added as reference) dpn is a pan-neural marker. Reviewer#2 also recommended calling dpn a neural marker.

“Previous work described a heterochronic...” - reference? 

Reference have been added

“By contrast, we show that Tribolium...” 

What about the number of neurons in the central complex in Tribolium and Drosophila? 

Does the lineage size of type II NBs reflect the number? 

Unfortunately, we do not have numbers for that.  

Reviewer #2 (Recommendations For The Authors): 

I recommend using page and line numbers to make reviewing and revising less timeconsuming. 

We apologize for this oversight. We include a line numbering system into our resubmission.

(1) Abstract 

"These neural stem cells are believed to be conserved among insects, but their molecular characteristics and their role in brain development in other insect neurogenetics models, such as the beetle Tribolium castaneum have so far not been studied." 

I recommend explaining the importance of studying Tribolium with regard to the evolution of brain centres rather than just stating that data are lacking. 

We have now emphasized the importance of Tribolium as model for the evolution of brain centres.

"Intriguingly, we found 9 type-II neuroblast lineages in the Tribolium embryo while Drosophila produces only 8 per brain hemisphere." 

It should be made clear that the 9 lineages also refer to brain hemispheres. 

We have added this information

(2) Introduction 

I would remove the first paragraph of the introduction; the use of Tribolium as model representative for insects is too general. The authors should focus on the specific question, i.e. the introduction should start with paragraph 2. 

While we can relate to the preference for short and concise writing, we feel that giving some background on Tribolium might be important as we expect that many of our readers might be primarily Drosophila researchers. Keeping this paragraph also seems in line with a recommendation of reviewer#1 to add some additional information on Tribolium ecology.  

"Several NBs of the anterior-most part of the neuroectoderm contribute to the CX and compared…”

The abbreviation has not been introduced. 

For clarity we have now opted to not use this abbreviation but to always spell out central complex.

"Several NBs of the anterior-most part of the neuroectoderm contribute to the CX and compared to the ventral ganglia produced by the trunk segments, it is of distinctively greater complexity..." 

Puzzling statement. Why would you compare a brain center with ventral ganglia? I recommend removing this. 

We have changed this statement to just emphasizing the complexity of the brain structure.

"The dramatically increased number of neural cells that are produced by individual type-II lineages, and the fact that one lineage can produce different types of neurons..."  In my opinion, this statement is too vague and unprofessional in style. Instead of "dramatically increased" use numbers. 

We have removed ‘dramatically increased’ and now give a numeric example.

"The dramatically increased number of neural cells that are produced by individual type-II lineages, and the fact that one lineage can produce different types of neurons, leads to the generation of increased neural complexity within the anterior insect brain when compared to the ventral nerve cord.." 

I assume that this statement relates to the comparison of type I and II nb lineages. However, type I NB lineages also produce different types of neurons due to GMC temporal identity, and neuronal hemi-lineage identity. 

We have rephrased and tried to make clear that the second part of the statement is not specific to type-II NB only. In line with the comment above we have also removed the reference to the ventral nerve cord.

"In addition, in Drosophila brain tumours have been induced from type-II NBs lineages [34], opening up the possibility of modelling tumorigenesis in an invertebrate brain, thus making these lineages one of the most intriguing stem cell models in invertebrates [35,36]." 

This statement is misplaced here; it should be mentioned at the start (if at all). 

We have moved this statement up.

"However, molecular characterisation of such lineages in another insect but the fly and a thorough comparison of type-II NBs lineages and their sub-cell-types between fly and beetle are still lacking" 

The background information should include what is known about type-II NB lineages in Tribolium, including marker gene expression, e.g. Farnworth et al. 

We refer to He et al 2019, Farnworth et al 2020 and Garcia-Perez 2021. All these publications speculate about a contribution of type-II NBs to Tribolium central complex development but do not show evidence of it. As we emphasize throughout the manuscript, the present work is the first description of type-II NB in Tribolium. 

"The ETS-transcription factor pointed (pnt) marks type-II NBs [40,41], which do not express the type-I NB marker asense (ase) but the pro-neural gene deadpan (dpn)"  Deadpan is considered a pan-neural gene. To avoid confusion, I would remove "proneural" throughout.

We have done so throughout the manuscript.

"We further found that, like the type-II NBs itself, the youngest Tc-pnt-positive but fezmm-eGFP-negative INPs neither express Tc-ase (Fig. 5D, pink arrowheads)."  What is the evidence that these are the youngest pnt positive cells? Position? This needs to be explained. 

We have clarified that ‘youngest pnt-positive cells’ refers to the position of these cells close to the type-II NB.

"Therefore these neural markers can be used for a classification of type II NBs (Tc-pnt+, Tcase-), young INPs (Tc-pnt+, Tc-fez/erm-, Tc-ase-), immature INPs (Tc-pnt+, Tcfez/erm+, Tcase+), mature INPs (Tc-dpn+, Tc-ase+, Tc-fez/erm+, Tc-pros+), and GMCs (Tc-ase+, Tcfez/ erm+, Tc-pros+, Tc-dpn). This classification is summarized in Fig. 7 A-B." 

This is not the best classification and not in line with the schemes in Figure 7 - the young INPs are also immature. What is the difference? It needs to be explained what "mature" means (dividing?). 

Thank you for pointing this out. We have corrected the error in this part that confused the two original groups (young and immature). To take the immaturity of both types of INPs into account we have then also changed our naming of INP subtypes into immature-I and immature-II and throughout the manuscript). Figure 7 and figure 12 were also changed accordingly. While our classification if primarily based on gene expression the available data indicates that both types of immature INPs are not dividing, whereas mature INPs are. We have added a statement on that to this part.

"In beetles a single-unit functional central complex develops during embryogenesis while in flies the structure is postembryonic." 

This statement is vague - the authors need to explain what is meant by "single-unit". The phrase "The structure is postembryonic" also needs more explanation. The Drosophila CX neuroblasts lineages originate in the embryo and the neurons form a commissural tract that becomes incorporated into the fan-shaped body of the Cx. 

We have explained single-unit central complex and have improved our summary of known differences in central complex development between fly and beetle.

"To assess the size of the embryonic type-II NBs lineages in beetles we counted the Tc- fez/erm positive (fez-mm-eGFP) cells (INPs and GMCs) associated with a Tc-pntexpressing type-II NBs of the anterior medial group (type-II NBs lineages 1-7).  It is not clear what is meant by "with a Tc-pnt-expressing type-II NBs". Is this a typo?" 

We have removed this bit.

(3) Discussion 

I would remove the first paragraph "A beetle enhancer trap lines reflects Tc-fez/earmuff expression". This is a repetition of the methods rather than a discussion. 

This part has been removed also in line with reviewer#1’s comment.

(4) Figures 

Figure 2 

To which developing structure do the strongly labelled areas in Figure 2D correspond? 

We believe that these areas from the protocerebrum including central complex, mushroom bodies and optic lobe. We have added this to the text and to the figure legend.

Figure 7 

What do A and B represent? Different stages? 

A and B show the same lineage but map the expression of different additional markers for clarity. We have added an explanation of this. 

The classification contradicts the description in the section "Conserved patterns of gene expression mark Tribolium type-II NBs, different stages of INPs and GMCs" (last sentence) where young INPs are first in the sequence and described as pnt+, erm-, ase- and immature INPs as pnt+ erm+ and ase+. 

We have corrected this mistake and changed the names of the subtypes into immatureI and immature-II (see above).

"We conclude that the evolutionary ancient six3 territory gives rise to the neuropile of the z, y, x and w tracts." 

Please clarify if six3 is also expressed in the corresponding grasshopper NB lineages or if your conclusion is based on the comparison of Drosophila and Tribolium and you assume that this is the ancestral condition. 

Six3 expression has not been studied in grasshoppers. Owing to the highly conserved nature of an anterior median six3 domain in arthropods and bilaterian animals in general, we would expect it to be expressed anterior-medially in grasshoppers as well. In Drosophila the gene is expressed in the anterior-medial embryonic region where the type-II NBs are expected to develop, but to our knowledge it has not been specifically studied which type-II NB lineages are located within this domain. We have clarified in our text that we do not claim that the origin of anterior-medial type-II NB 1-4 and the X,Y, Z and W lineages from the six3 territory is highly conserved but only the territory itself. As far as we know our work is the first to analyse the relationship of type-II lineages and the conserved head patterning genes six3 and otd. We have added some clarification of this into this part of the discussion.

(5) Methods 

The methods section should include the methods for cell counting, as well as cell and nuclei size measurements including statistics (e.g. how many embryos, how many NB lineages). The comparison of the Tribolium NB lineage cell numbers to published Drosophila data should include a brief description of the method used in Drosophila (in addition to the method used here in Tribolium) so that the reader can understand how the data compare. 

We have added a separate section on this to the Methods part which also includes the criteria used in Drosophila. We have also included some more information to the results part on the inclusion of neurons in the Drosophila counts that may only be partially included in our numbers. This does however not change the results in terms of larger numbers of progenitor cells in Tribolium.

(6) Typos and minor errors 

Abstract 

“However, little is known on the developmental processes that create this diversity” 

Change to ... little is known about

Changed.

NBs lineages 

Change to NB lineages throughout. 

We have used text search to find and replace all position where this was used erroneously,

Results 

"Schematic drawing of expression different markers in type-II NB lineages.." 

Schematic drawing of expression of different markers 

Corrected

Discussion 

"However, the type-II NB 7, which is we assigned to the anterior medial group but which..." 

.... which we assigned.... 

corrected

"......might be the one that does not have a homologue in the fly embryo The identification of more..."  Full stop missing. 

Added.

"Adult like x, y, and w tracts as well as protocerebral bridge are...." 

Change to "The adult like x, y, and w tracts as well as the protocerebral bridge are.... 

This part has been removed with the rewriting of this paragraph.  

Reviewer #3 (Recommendations For The Authors): 

(1) Suggestions for improved or additional experiments, data, or analyses: 

a) The analysis of nuclear size is wrong. The authors compare the largest cell of a cluster of cells with a number of random cells from the same brain. It is obvious that the largest cell of a cluster will be larger than the average cell of the same brain. A better control would be to compare the largest cell of the pnt+ cluster with the largest cell of a random sample of cells, although this also comes with biases. Personally, I have no doubt that the authors are looking at neuroblasts, based on the markers they are using, so I would recommend completely eliminating Figure 4.

We agree that we produced a somewhat biased and expected result when we select the largest cell of a cluster for size comparison. However, we found it important to show based on a larger sample that these cells are also statistically larger than the average cell of a brain, which we think our assessment shows. We do not claim that type-II NBs are the largest cells of a brain, or that they are larger than type-I NBs, therefore in a random sample there might be cells that are equally big (see also distribution of the control sample shown in figure 4, and we have added a note on this to the text). We are happy to hear that this reviewer has no doubts we are looking at neural stem cells. However, reviewer#1 did express some hesitations and therefore we think it is important to keep the information on cell size as part of our argument that we are indeed looking at type-II NBs (gene expression, cell size, dividing, part of a neural lineage).

b) The comparison of NB, INP, and GMC numbers between Drosophila and Trbolium (section "The Tribolium embryonic lineages of type-II NBs are larger and contain more mature INPs than those of Drosophila") compares an experiment that the authors did with published data. I would suggest that the authors repeat the Drosophila stainings and compare themselves to avoid cases of batch effects, inconsistent counting, etc.

None of the authors is a Drosophila expert or has any experience at working with this model and reassessing the lineage size would require a number of combinatorial staining. Therefore, we feel that using the published data produced by experts and which also includes repeat experiments is for us the more reliable approach.

c) In Figure 10, there are some otd+ GFP+ cells laterally. What are these? 

We believe that these cells contribute to the eye anlagen. We have added this information to the legend.

(2) Minor corrections to the text and figures: 

a) There are some typos in the text: e.g. "pattering" in the abstract. 

We have carefully checked the text for typos and hope that we have found everything.

b) The referencing of figures in the text is inconsistent (eg "Figure 5 panel A" vs "Figure 5D" on page 12). 

We have checked throughout the manuscript and made sure to always refer to a panel correctly.

c) In Figure 3C, the white staining (anti-PH3) is not indicated in the Figure. 

The label has been added in the figure.

d) Moreover, in Figure 3, green is not very visible in the images. 

We have improved the colour intensity where possible.

e) In the figures, it might be better to outline the cells with color-coded dashed circles instead of using arrows. 

We think that this would obscure some details of the stainings and create a rather artificial representation. We also feel that doing this consistently in all our images is an amount of work not justified by the degree of expected improvement to the figures

NOTE: We are submitting a revised version of the supplementary material which only contains two minor changes: a headline was added to Table S4 (Antibodies and staining reagents) and a typo was corrected in line one of table S5 (TC to Tc).

Reviewer #3 (Public review):

Summary:

This study employs an optogenetics approach aimed at activating oncogene (KRASG12V) expression in a single somatic cell, with a focus on following the progression of activated cell to examine tumourigenesis probabilities under altered tissue environments. The research explores the role of stemness factors (VENTX/NANOG/OCT4) in facilitating oncogenic RAS (KRASG12V)-driven malignant transformations. Although the evidence provided is incomplete, the authors propose an important mechanism whereby reactivation of re-programming factors correlates with the increased likelihood of a mutant cell undergoing malignant transformation.

Strengths:

· Innovative Use of Optogenetics: The application of optogenetics for precise activation of KRAS in a single cell is valuable to the field of cancer biology, offering an opportunity to uncover insight into cellular responses to oncogenic mutations.<br /> · Important Observations: The findings concerning stemness factors' role in promoting oncogenic transformation are important, contributing data to the field of cancer biology.

Weaknesses:

Lack of Methodological Clarity: The manuscript lacks detailed descriptions of methodologies, making it difficult to fully evaluate the experimental design and reproducibility, rendering incomplete evidence to support the conclusion. Improving methodological transparency and data presentation will crucially strengthen the paper's contributions to understanding the complex processes of tumorigenesis.

Sub-optimal Data Presentation and Quality:<br /> The resolution of images through-out the manuscript are too low. Images presented in Figure 2 and Figure 4 are of very low resolution. It is very hard to distinguish individual cells and in which tissue they might reside.<br /> Lack of quantitative data and control condition data obtained from images of higher magnification limits the ability to robustly support the conclusions.

Here are some details:<br /> · Tissue specificity of the cells express KRASG12V oncogene: In this study, the ubiquitin promoter was used to drive oncogenic KRASG12V expression. Despite this, the authors claim to activate KRAS in a single brain cell based on their localized photo-activation strategy. However, upon reviewing the methods section, the description was provided that 'Localized uncaging was performed by illumination for 7 minutes on a Nikon Ti microscope equipped with a light source peaking at 405 nm, Figure 1. The size of the uncaging region was controlled by an iris that defines a circular illumination with a diameter of approximately 80 μm.' It is surprising that an epi-fluorescent microscope with an illumination diameter of around 80μm can induce activation in a single brain cell beneath skin tissue. Additionally, given that the half-life for mTFP maturation is around 60 minutes, it is likely that more cells from a variety of different lineages could be activated, but the fluorescence would not be visible until more than 1-hour post-illumination. Authors might want to provide more evidence to support their claim on the single cell KRAS activation.<br /> · Stability of cCYC: The manuscript does not provide information on the half-life and stability of cCYC. Understanding these properties is crucial for evaluating the system's reliability and the likelihood of leakiness, which could significantly influence the study's outcomes.<br /> · Metastatic Dissemination claim: Typically, metastatic cancer cells migrate to and proliferate within specific niches that are conducive to outgrowth, such as the caudal hematopoietic tissue (CHT) or liver. In Figure 3 A, an image showing the presence of mTFP expressing cells in both the head and tail regions of the larva, with additional positive dots located at the fin fold. This is interpreted as "metastasis" by the authors. However, the absence of supportive cellular compartment within the fin-fold tissue makes the presence of mTFP-positive metastatic cells there particularly puzzling. This distribution raises concerns about the spatial specificity of the optogenetic activation protocol.<br /> The unexpected locations of these signals suggest potential ectopic activation of the KRAS oncogene, which could be occurring alongside or instead of targeted activation. This issue is critical as it could affect the interpretation of whether the observed mTFP signal expansion over time is due to actual cell proliferation and infiltration, or merely a result of ectopic RAS transgene activation.<br /> · Image Resolution Concerns: The cells depicted in Figure 3C β, which appear to be near the surface of the yolk sac and not within the digestive system as suggested in the MS, underscore the necessity for higher-resolution imaging. Without clearer images, it is challenging to ascertain the exact locations and states of these cells, thus complicating the assessment of experimental results.<br /> · The cell transplantation experiment is lacking protocol details: The manuscript does not adequately describe the experimental protocols used for cell transplantation, particularly concerning the origin and selection of cells used for injection into individual larvae. This omission makes it difficult to evaluate the reliability and reproducibility of the results. Such as the source of transplanted cells:<br /> • If the cells are derived from hyperplastic growths in larvae where RAS and VX (presumably VENTX) were locally activated, the manuscript fails to mention any use of fluorescence-activated cell sorting (FACS) to enrich mTFP-positive cells. Such a method would be crucial for ensuring the specificity of the cells being studied and the validity of the results.<br /> • If the cells are obtained from whole larvae with induced RAS + VX expression, it is notable and somewhat surprising that the larvae survived up to six days post-induction (6dpi) before cells were harvested for transplantation. This survival rate and the subsequent ability to obtain single cell suspensions raise questions about the heterogeneity of the RAS + VX expressing cells that transplanted.<br /> · Unclear Experimental Conditions in Figure S3B: The images in Figure S3B lack crucial details about the experimental conditions. It is not specified whether the activation of KRAS was targeted to specific cells or involved whole-body exposure. This information is essential for interpreting the scope and implications of the results accurately.<br /> · Contrasting Data in Figure S3C compared to literatures: The graph in Figure S3C indicates that KRAS or KRAS + DEX induction did not result in any form of hyperplastic growth. This observation starkly contrasts with previous literature where oncogenic KRAS expression in zebrafish led to significant hyper-proliferation and abnormal growth, as evidenced by studies such as those published in and Neoplasia (2018), DOI: 10.1016/j.neo.2018.10.002; Molecular Cancer (2015), DOI: 10.1186/s12943-015-0288-2; Disease Models & Mechanisms (2014) DOI: 10.1242/dmm.007831. The lack of expected hyperplasia raises questions about the experimental setup or the specific conditions under which KRAS was expressed. The authors should provide detailed descriptions of the conditions under which the experiments were conducted in Figure S3B and clarifying the reasons for the discrepancies observed in Figure S3C are crucial. The authors should discuss potential reasons for the deviation from previous reports.<br /> Further comments:<br /> Throughout the study, KRAS-activated cell expansion and metastasis are two key phenotypes discussed that Ventx is promoting. However, the authors did not perform any experiments to directly show that KRAS+ cells proliferate only in Ventx-activated conditions. The authors also did not show any morphological features or time-lapse videos demonstrating that KRAS+ cells are motile, even though zebrafish is an excellent model for in vivo live imaging. This seems to be a missed opportunity for providing convincing evidence to support the authors' conclusions.<br /> There were minimal experimental details provided for the qPCR data presented in the supplementary figures S5 and S6, therefore, it is hard to evaluate results obtained.

eLife Assessment

This important study presents a method to visualize the location of the cell types discovered through single-cell RNA sequencing. The data allowed the authors to build spatial tissue atlases of the fly head and body, and to identify the location of previously unknown cell types. The data are convincing and appropriate, and the authors validate the methodology in line with the current state-of-the-art.

Reviewer #1 (Public review):

Summary:

In this manuscript, Janssens et al. addressed the challenge of mapping the location of transcriptionally unique cell types identified by single nuclei sequencing (snRNA-seq) data available through the Fly Cell Atlas. They identified 100 transcripts for head samples and 50 transcripts for fly body samples allowing identification of every unique cell type discovered through the Fly Cell Atlas. To map all of these cell types, the authors divided the fly body into head and body samples and used the Molecular Cartography (Resolve Biosciences) method to visualize these transcripts. This approach allowed them to build spatial tissue atlases of the fly head and body, to identify the location of previously unknown cell types and the subcellular localization of different transcripts. By combining snRNA-seq data from the Fly Cell Atlas with their spatially resolved transcriptomics (SRT) data, they demonstrated an automated cell type annotation strategy to identify uncharacterized clusters and infer their location in the fly body. This manuscript constitutes a proof-of-principle study to map the location of the cells identified by ever-growing single-cell transcriptomics datasets generated by others.

Strengths:

The authors used the Molecular Cartography (Resolve Biosciences) method to visualize 100 transcripts for head samples and 50 transcripts for fly body samples in high resolution. This method achieves high resolution by multiplexing a large number of transcript visualization steps and allows the authors to map the location of unique cell types identified by the Fly Cell Atlas.

Weaknesses:

Combining single-nuclei sequencing (snRNA-seq) data with spatially resolved transcriptomics (SRT) data is challenging, and the methods used by the authors in this study cannot reliably distinguish between cells, especially in brain regions where the processes of different neurons are clustered, such as neuropils. This means that a grid that the authors mark as a unique cell may actually be composed of processes from multiple cells.

Comments on revisions:

I believe the authors have improved the manuscript by addressing all the concerns and incorporating the suggestions raised by the reviewers. I have no further concerns or suggestions.

More good examples of terrible product.

So gas prices will end up being higher, and so will everything else. Hopefully Trump will realize the stupidity of his actions sooner than later and reverse the tariffs he has imposed.

This work has been peer reviewed in GigaScience (https://doi.org/10.1093/gigascience/giaf008), which carries out open, named peer-review. These reviews are published under a CC-BY 4.0 license and were as follows:

**Reviewer 1: Georgios Fotakis **

1) General Comments In this manuscript, the authors present TransHLA, a hybrid transformer model that integrates a transformer-based language model with a deep Convolutional Neural Network (CNN) module. The transformer encoder module leverages a pre-trained large language model (Evolutionary Scale Modeling - ESM2) to extract global features using a multi-head attention mechanism. The feature extraction is further enhanced by two consecutive CNN modules, maximizing the mutual information between query features (sequences) and their label predictions (epitope/non-epitope) through a modified Transductive Information Maximization (TIM) loss function. TransHLA is designed to collectively consider all HLA sites across all alleles and is the first neoantigen prediction tool of its kind, since it does not require HLA alleles as input. The authors also present benchmark study results, showcasing the increased predictive accuracy of TransHLA and its potential as a valuable pre-screening tool.

The computational method presented in this manuscript demonstrates a strong scientific foundation and shows promise for future refinement and extension, suggesting significant potential for meaningful research output. However, there are some conceptual and technical concerns that need to be addressed.

2) Specific comments for revision a) Major Manuscript: i) Introduction - The authors distinguish between two categories of models: those that need only epitopes as input and those that require both epitopes and HLA alleles as inputs. However, the basis for this classification is unclear. For instance, MHCNuggets and DeepSeqPanII, cited as examples of the first category, actually require both an allele and an epitope to predict neoantigens. This is supported by the algorithms' manuals and the supplementary material provided by the authors, where they specify the need for HLA alleles to execute the commands.

• The authors state: "Considering that TransHLA is the first epitope prediction software that does not impose restrictions on HLA alleles" This needs clarification, as all available "pan-allele" models do not impose restrictions on HLA alleles (the models are trained on nearly all sequenced HLAs). Perhaps the authors meant that TransHLA does not require HLA alleles as input?

ii) Results - The reason for conducting two separate benchmarks (case study and validation) with different HLA binding affinity predictors is unclear. For instance, it is not explained why netMHCpan/netMHCpanII were not included in the first benchmark and only used in the validation part.

• It would be very informative if the authors were able to include other widely used HLA binding affinity predictors in their benchmarks, such as mixMHCpred and mixMHCpred2.

• The authors state: "the details information of alleles used in each tool can be found in the Supplementary File" However, no information about the alleles used in this study is provided (or at least it was not made available to me at the time of reviewing this version of the manuscript).

• The "protein structural flexibility" should be briefly explained and properly cited (Vihinen et al., 1994, Proteins, 19(2), 141-149).

iii) Conclusion and Discussion - The authors claim that TransHLA alleviates "the restrictive requirement of knowing the specific HLA alleles." However, this is not typically a restriction, as serological typing of HLA is routinely performed in clinics, and samples usually come with relevant metadata. Additionally, HLA typing can be easily performed with RNAseq and/or WES data, the same data usually required to produce the putative epitopes initially, with high accuracy (e.g., OptiType can reach 93.5% [CI95: 91.8-95.1%] accuracy for HLA class I). Therefore, this information is generally readily available for processing. While the authors effectively demonstrate the accuracy of TransHLA, they fail to clarify the context in which this computational tool could be utilized.

• To the best of my knowledge, in the research field of personalized medicine, neoantigen vaccines are typically produced at the patient level, taking the patients' HLA alleles into consideration. Binding affinity, by definition, can quantitatively differentiate between strong (low IC50) and weak (high IC50) binders. Thus, binding affinity predictions are a pivotal step for neoantigen prioritization. Given that the authors suggest TransHLA as an "alternative for filtering potential epitopes", how would TransHLA perform in such situations? To enhance clarity, the authors should elaborate on a scenario where TransHLA would be a superior choice compared to high-performing HLA binding affinity predictors in this context.

• The authors mention in the introduction that TransHLA can be used to "expedite the precise screening of peptides". Additionally, in their GitHub repository it is stated that TransHLA "can serve as a preliminary screening for the currently popular tools that are specific for HLA-epitope binding affinity", which is quite accurate. They might consider incorporating this concept into their concluding remarks as well.

Implementation: - Since neoantigen prediction is typically carried out using computational pipelines, it would be very helpful if the authors could provide instructions for end-users to install the software and its dependencies in isolated (contained) computational environments. To enhance clarity, I am attaching the files I used to create these environments via Conda (transhla_env.yaml), Singularity (TransHLA.def), and Docker (Dockerfile).

• Following the previous point, the authors should consider providing a CLI (similar to the "train.py" and "inference.py" scripts in their GitHub repository) to enhance the software's usability in computational pipelines. As an example, I am attaching the script I used to test the software (TransHLA.py).

b) Minor - It would enhance the clarity (especially for readers who are not familiar with artificial intelligence) if the authors would briefly explain each technical term and then use the abbreviations. For example, "Evolutionary Scale Modeling (ESM2)" and so on.

• Additionally, the manuscript and its supplementary material contain several grammatical and spelling errors that need to be rectified.

Even as a researcher, I find myself believing that the numbers are more concrete and therefore trustworthy compared to qualitative data. However, the numbers derive from lived experiences and therefore cannot be seen without the attachment of the experience of the person they refer to.

The middle part of the brain is connected to a total of twelve cranial nerves, which regulates motor and sensory processes in the head and neck. Some handle both roles.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

This study introduces a novel therapeutic strategy for patients with high-risk HER2-positive breast cancer and demonstrates that the incorporation of pyrotinib into adjuvant trastuzumab therapy can improve invasive disease-free survival.

Strengths:

The study features robust logic and high-quality data. Data from 141 patients across 23 centers were analyzed, thereby effectively mitigating regional biases and endowing the research findings with high applicability.

Weaknesses:

(1) Introduction and Discussion: Update the literature regarding the efficacy of pyrotinib combined with trastuzumab in treating HER2-positive advanced breast cancer.

Thank you for this helpful suggestion. The literature regarding the efficacy of pyrotinib combined with trastuzumab in treating HER2-positive advanced breast cancer referenced in our manuscript was the PHILA study, but we mistakenly cited its corrections (reference 14). We revised this reference as suggested.

Changes in the text: Page 6, line 347-353.

(2) Did all the data have a normal distribution? Expand the description of statistical analysis.

As the sample size increases, the sampling distribution of the mean follows a normal distribution even when the underlying distribution of the original variable is non-normal, allowing the use of a normal distribution to calculate their confidence interval. We believe it is unnecessary to specifically describe whether the data followed a normal distribution in this study. Therefore, we did not revise the statistical section.

(3) The novelty and innovative potential of your manuscript compared to the published literature should be described in more detail in the abstract and discussion section.

Thank you for your suggestion. The word count for abstracts recommended by eLife is around 250 words. Therefore, we did not compare the present study with published literature in detail in the abstract, as this might exceed the recommended word limit. We revised the discussion section to provide a more detailed comparison between published literature and our study, and to analyze the novelty of our findings accordingly.

Changes in the text: Page 11, line 177-180.

(4) Figure legend should provide a bit more detail about what readers should focus on.

Thank you for this suggestion. We did not revise the figure legend of Figure 1, as it provides a common description. For the figure legend of Figure 2, we added the method used to estimate the invasive disease-free survival curve. For the figure legend of Figure 3, we added more details regarding methods and numbers of patients in different subgroups.

Changes in the text: Page 7, line 463-472.

(5) P-values should be clarified for the analysis.

Thank you for this comment. All subgroup analyses were post-hoc and lacked predefined hypotheses. Kaplan-Meier curves were used to present the subgroup results with the aim of performing descriptive statistics rather than inferential statistics. Therefore, we did not calculate their p-values.

(6) The order (A, B, and C) in Figure 3 should be labeled in the upper left corner of the Figure.

Thanks for this comment. We revised Figure 3 accordingly.

Changes in the text: Figure 3.

Reviewer #2 (Public review):

In this manuscript, Cao et al. evaluated the efficacy and safety of 12 months pyrotinib after trastuzumab-based adjuvant therapy in patients with high-risk, HER2-positive early or locally advanced breast cancer. Notably, the 2-year iDFS rate reached 94.59% (95% CI: 88.97-97.38) in all patients, and 94.90% (95% CI: 86.97-98.06) in patients who completed 1-year treatment of pyrotinib. This is an interesting and uplifting results, given that in ExteNET study, the 2-year iDFS rate was 93.9% (95% CI 92·4-95·2) in the 1-year neratinib group, and the 5-year iDFS survival was 90.2%, and 1-year treatment of neratinib in ExteNET study did not translate into OS benefit after 8-year follow-up. In this case, readers will be eagerly anticipating the long-term follow-up results of the current PERSIST study, as well as the results of the phase III clinical trial (NCT03980054).

I have the following comments:

(1) The introduction of the differences between pyrotinib and neratinib in terms of mechanism, efficacy, resistance, etc. is supposed to be included in the text so that authors could better highlight the clinical significance of the current trial.

Thanks for this comment.

In terms of mechanism, pyrotinib and neratinib are both irreversible pan-HER tyrosine kinase inhibitors that target HER1, HER2 and HER4 by covalently binding to ATP binding sites. Overall, the similarities between them far outweigh the differences. This is the reason why we referenced the ExteNET study, which used neratinib as extended adjuvant therapy, for the sample size calculation.

Regarding efficacy, currently, no head-to-head studies comparing efficacy of pyrotinib and neratinib have been reported, and the comparison of the efficacy between them using historical data from different studies have inevitable bias due to differences in treatment regimens, study populations, assessment criteria, etc.

Regarding resistance, only a few studies with small sample size and case reports have investigated their mechanisms of resistance, and the underlying mechanisms have not been fully understood.

Collectively, we believe that the similarities in the mechanisms of these two drugs far outweigh their differences, and their efficacy and resistance cannot be reasonably compared. Moreover, the sample size calculation was conducted based on the premise that the two drugs are similar. After careful consideration, we believe that overanalyzing the differences between neratinib and pyrotinib would shift the focus of this manuscript. Therefore, we did not discuss their differences in the article.

(2) Please make sure that a total of 141 patients were enrolled in the study, 38 patients had a treatment duration of less than or equal to 6 months, and a total of 92 and 31 patients completed 1-year and 6-month treatment of extended adjuvant pyrotinib, respectively, which means 7 patients had a treatment duration of fewer than 6 months.

Thank you for raising this relevant question. There were 141 patients enrolled in the study and received study treatment, and a total of 92 and 31 patients completed 1-year and 6-month treatment of extended adjuvant pyrotinib. Of the remaining 18 patients, 16 patients had a treatment duration of fewer than 6 months, and 2 patients had a treatment duration longer than 6 months but less than 1 year.

(3) The previous surgery history should be provided, and how many patients received lumpectomy, and mastectomy.

Thank you for your suggestion. All patients in the present study underwent breast cancer surgery. Unfortunately, we did not collect data on the specific types of surgeries performed.

Recommendations for the authors:

Reviewing Editor:

I have carefully reviewed the content and findings of your study, and while I recognize the potential impact of your research, there are several critical aspects that need to be addressed to fully appreciate the contribution of your work.

Significance of Findings:

Your study provides valuable insights into the efficacy and safety of pyrotinib as an extended adjuvant therapy following trastuzumab-based treatment in patients with high-risk HER2-positive breast cancer. The 2-year invasive disease-free survival (iDFS) rate of 94.59% is notably high and suggests that pyrotinib could be a promising option for patients who have completed trastuzumab therapy. This is particularly significant given the unmet need for effective therapies that can extend disease-free survival in this patient population.

Strength of Evidence:

The strength of the evidence presented is supported by the multicenter phase II trial design, which included a substantial number of patients across 23 centers in China. The rigorous methodology, including the use of the Kaplan-Meier method for estimating iDFS and the application of the Brookmeyer-Crowley method for confidence intervals, adds to the credibility of your findings. However, the single-arm study design without a control group limits the ability to draw definitive conclusions about the comparative effectiveness of pyrotinib.

In conclusion, your study presents intriguing findings that contribute to the field of breast cancer therapy. However, the current evidence, while suggestive of pyrotinib's potential, requires further validation in controlled trials to confirm its efficacy and optimal use in clinical practice. I encourage you to address the issues raised and consider resubmitting a revised version of your work.

Thank you for your comments. We acknowledge the limitation of our single-arm study design without a control group and agree that it restricts definitive conclusions about the comparative effectiveness of pyrotinib. This limitation was noted in our manuscript. Furthermore, we have revised our manuscript in response to the issues raised by the reviewers.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

In this valuable study, the authors found that the macrolide drug rapamycin, which is an important pharmacological tool in the clinic and the research lab, is less specific than previously thought. They provide solid functional evidence that rapamycin activates TRPM8 and develop an NMR method to measure the specific binding of a ligand to a membrane protein.

Strengths:

The authors use a variety of complementary experimental techniques in several different systems, and their results support the conclusions drawn.

Weaknesses:

Controls are not shown in all cases, and a lack of unity across the figures makes the flow of the paper disjointed. The proposed location of the rapamycin binding pocket within the membrane means that molecular docking approaches designed for soluble proteins alone do not provide solid evidence for a rapamycin binding pocket location in TRPM8, but the authors are appropriately careful in stating that the model is consistent with their functional experiments.

Impact:

This work provides still more evidence for the polymodality of TRP channels, reminding both TRP channel researchers and those who use rapamycin in other contexts that the adjective "specific" is only meaningful in the context of what else has been explicitly tested.

Reviewer #2 (Public Review):

Summary:

Tóth and Bazeli et al. find rapamycin activates heterologously-expressed TRPM8 and dissociated sensory neurons in a TRPM8-dependent way with Ca2+-imaging. With electrophysiology and STTD-NMR, they confirmed the activation is through direct interaction with TRPM8. Using mutants and computational modeling, the authored localized the binding site to the groove between S4 and S5, different than the binding pocket of cooling agents such as menthol. The hydroxyl group on carbon 40 within the cyclohexane ring in rapamycin is indispensable for activation, while other rapalogs with its replacement, such as everolimus, still bind but cannot activate TRPM8. Overall, the findings provide new insights into TRPM8 functions and may indicate previously unknown physiological effects or therapeutic mechanisms of rapamycin.

Strengths:

The authors spent extensive effort on demonstrating that the interaction between TRPM8 and rapamycin is direct. The evidence is solid. In probing the binding site and the structural-function relationship, the authors combined computational simulation and functional experiments. It is very impressive to see that "within" a rapamycin molecule, the portion shared with everolimus is for "binding", while the hydroxyl group in the cyclohexane ring is for activation. Such detailed dissection represents a successful trial in the computational biology-facilitated, functional experiment-validated study of TRP channel structuralactivity relationship. The research draws the attention of scientists, including those outside the TRP channel field, to previously neglected effects of rapamycin, and therefore the manuscript deserves broad readership.

Weaknesses:

The significance of the research could be improved by showing or discussing whether a similar binding pocket is present in other TRP channels, and hence rapalogs might bind to or activate these TRP channels. Additionally, while the finding on TRPM8 is novel, it is worthwhile to perform more comprehensive pharmacological characterization, including single-channel recording and a few more mutant studies to offer further insight into the mechanism of rapamycin binding to S4~S5 pocket driving channel opening. It is also necessary to know if rapalogs have independent or synergistic effects on top of other activators, including cooling agents and lower temperature, and their dependence on regulators such as PIP2.

Additional discussion that might be helpful:

The authors did confirm that rapamycin does not activate TRPV1, TRPA1 and TRPM3. But other TRP channels, particularly other structurally similar TRPM channels, should be discussed or tested. Alignment of the amino acid sequences or structures at the predicted binding pocket might predict some possible outcomes. In particular, rapamycin is known to activate TRPML1 in a PI(3,5)P2-dependent manner, which should be highlighted in comparison among TRP channels (PMID: 35131932, 31112550).

Reviewer #3 (Public Review):

Summary:

Rapamycin is a macrolide of immunologic therapeutic importance, proposed as a ligand of mTOR. It is also employed as in essays to probe protein-protein interactions.

The authors serendipitously found that the drug rapamycin and some related compounds, potently activate the cationic channel TRPM8, which is the main mediator of cold sensation in mammals. The authors show that rapamycin might bind to a novel binding site that is different from the binding site for menthol, the prototypical activator of TRPM8. These solid results are important to a wide audience since rapamycin is a widely used drug and is also employed in essays to probe protein-protein interactions, which could be affected by potential specific interactions of rapamycin with other membrane proteins, as illustrated herein.

Strengths:

The authors employ several experimental approaches to convincingly show that rapamycin activates directly the TRPM8 cation channel and not an accessory protein or the surrounding membrane. In general, the electrophysiological, mutational and fluorescence imaging experiments are adequately carried out and cautiously interpreted, presenting a clear picture of the direct interaction with TRPM8. In particular, the authors convincingly show that the interactions of rapamycin with TRPM8 are distinct from interactions of menthol with the same ion channel.

Weaknesses:

The main weakness of the manuscript is the NMR method employed to show that rapamycin binds to TRPM8. The authors developed and deployed a novel signal processing approach based on subtraction of several independent NMR spectra to show that rapamycin binds to the TRPM8 protein and not to the surrounding membrane or other proteins. While interesting and potentially useful, the method is not well developed (several positive controls are missing) and is not presented in a clear manner, such that the quality of data can be assessed and the reliability and pertinence of the subtraction procedure evaluated.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Major points

(1) Given the novelty of the STTD NMR approach, please provide more details and data for the reader.

• I would like to see all of the collected spectra so that readers can see and judge the effect sizes for themselves, perhaps as an additional supplementary figure.

We agree with the reviewer that the data transparency of the NMR measurements should be improved. We changed panel C of Figure 2 in the main text and provided all the STD and the computed STDD and STTD spectra recorded on one set of experiments. We carried out additional experimental replicas on new samples and addressed the variability of cell samples by rescaling the STD effects based on reference ¹H measurements. We provided supplementary spectra of the reference experiments without saturation (Figure S5) and the obtained STTD spectra from the three parallel NMR sessions (Figure S6).

• I appreciate the labels for STDD-1, STDD-2, and STTD on the lower two spectra of Figure 2C. Is the top spectrum from STD-1 or is it prior to saturation? In Figure 2C, what do the x1 and x2 notations on the right-hand side of the spectra indicate?

We showed the top spectrum as an overview and a demonstration of the spectral complexity of the samples. ¹H experiments were run before the STD measurements to assess the sample quality and stability. The demonstrated spectrum on sample 1 (TRPM8 with rapamycin in HEK cells) was recorded with more transients than the corresponding STDs, thus it is only visually comparable with the difference spectra after scaling (2x). Figure 2 was changed and all the spectra were replaced as mentioned before. All the recorded ¹H-experiments without saturation including the one removed are now available in the supplementary information (Figure S5).

• The STTD NMR results with WT TRPM8 are consistent with rapamycin binding directly to the channel. Testing whether rapamycin binding observed with STTD NMR is disrupted by one of the most compelling mutations (D796A, D802A, G805A, or Q861A) would be a further test of this direct interaction.

We thank the reviewer for the suggestion and agree that testing the most compelling mutants would be a promising next step. These mutations were generated in plasmid vectors and only transiently transfected into HEK cells. For NMR analysis we would need a high amount of cells stably overexpressing the mutant channels which were not available for experimentation.

• Given that this is not a methods paper, it is probably outside the scope to further validate the STTD NMR measurements by performing parallel ITC, SPR, MST, or radiolabeled ligand experiments. Nevertheless, I would be excited to see such a comparison since STTD NMR appears to have promise as an experimental technique for assessing ligand binding to membrane proteins that does not require large amounts of purified protein or radioactive isotopes.

We agree with the reviewer that additional independent biophysical measurements on the interactions are necessary to further validate the STTD methodology. This paper is a preliminary demonstration of the STTD concept and our group is currently working on the challenges of on-cell NMR (e.g., sample and spectral complexity) and the standardization of the proposed workflow.     

(2) Please clarify the methods used to model of rapamycin binding. Docking can be imprecise in TRP channels, even with a sophisticated docking scheme (Hughes et al., 2019, doi: https://doi.org/10.7554/eLife.49572.001).  

Thank you for mentioning this point and providing the reference. We have further clarified our methods and included the reference in our discussion, indicating the limitations of our approach.

• As a positive control, does the docking strategy accurately predict binding of known compounds (menthol, icilin, etc.) to TRPM8 consistent with cryo-EM structures?  

Yes, the binding site for menthol, based on a similar docking strategy as for rapamycin, is also presented, and matches with predictions from other publications. This is now clarified in the revised manuscript.

• Why was homology modeling to the human sequence used with the mouse structure but not the avian structure?  

At this onset of the project, only the avian structure was available, and it was used in the primary docking. Later, to get more precise docking relevant for human TRPM8 pharmacology, we did revert to the then available structure of the mouse ortholog.  

• How many rapamycin structural clusters were built, and how many structures were there in each cluster? How many were used? "most populated" is unspecific.  

Thank you for your comment. We have added the following highlighted information to the methods section to address your comment:

“Representative conformations of rapamycin were identified by clustering of the 1000-membered pools, having the macrocycle backbone atoms compared with 1.0 Å RMSD cut-off. Middle structures of the ten most populated clusters, accounting for more than 90% of the total conformational ensemble generated by simulated annealing, were used for further docking studies. To refine initial docking results and to identify plausible binding sites, the above selected rapamycin structures were docked again, following the same protocol as above, except for the grid spacing which was set to 0.375 Å in the second pass. The resultant rapamycin-TRPM8 complexes were, again, clustered and ranked according to the corresponding binding free energies. Selected binding poses were subjected to further refinement. The three most populated and plausible binding poses were further refined by a third pass of docking, where amino acid side chains of TRPM8, identified in the previous pass to be in close contact with rapamycin (< 4 Å), were kept flexible. Grid volumes were reduced to these putative binding sites including all flexible amino acid side chains (21.0-26.2 Å x 26.2-31.5 Å x 24.8-29.2 Å).”

However, it is important to clarify that the clusters are not built and their number is not specified by the user. The number of clusters found depends on how similar the structures are in the structural ensemble analyzed by clustering. A high number of clusters indicates a diverse, whereas a low number suggests a uniform structural ensemble. Furthermore, it is arbitrarily controlled by the similarity cutoff specified by the user. If the cutoff is selected well, then the number of structures is different in each cluster. There are some highly populated clusters and a few which only have one structure. The selection of how many cluster representatives are used is usually based on the decision of whether or not the sum of the population of selected clusters sufficiently covers the mapped conformational space.

• Additionally, the rapamycin poses were generated using a continuum solvent model that is unlikely to replicate the conditions existing in the lipid bilayer or in a lipid-exposed binding pocket as is predicted here. It is therefore possible that the rapamycin poses chosen for docking do not represent the physiological rapamycin binding pose, hampering the ability of the docking algorithm to find an appropriate docking pocket.  

• Furthermore, accurately docking that may bind to membrane-exposed pockets is a challenging problem, particularly because many scoring algorithms, including those employed by Autodock, do not distinguish between solvent-exposed and membrane-exposed faces of the protein. This affects the predicted binding energies.  

We appreciate the reviewer's insightful comments. We add a note in discussion part, mentioning these important limitations.  

• In Figure 4, it appears that the proposed rapamycin binding pocket is located at the interface between two subunits, but only one is shown. Is there any contact with residues in the neighboring subunit? Based on Figure S4, I assume not, but am unsure.

Based on the estimated distances, we do not think that there are any relevant interactions with residues from neighboring subunits. This is now indicated in the results section.

• Consider uploading the rapamycin-docked model to a public repository such as Zenodo for readers to examine and manipulate themselves  

As suggested, the model will be uploaded in a public repository. A link to the file on Zenodo is now included.

(3) Please discuss the spatial location of the proposed rapamycin binding pocket relative to the vanilloid binding pocket in TRPV1.

• The mutagenesis indicates that D745, D802, G805, and Q861 are most important for rapamycin sensitivity in TRPM8. Interestingly, the proposed rapamycin binding pocket appears to overlap spatially with the vanilloid binding pocket in TRPV1. Consistent with this, Q861 aligns with E570 in TRPV1, which is a critical residue for resiniferatoxin sensitivity. Indeed, similar to Q861's modeled proximity to the cyclohexyl ring, the hydroxyl group of the vanillyl moity of capsaicin (4DY in 7LR0, for example) is in proximity to E750 in TRPV1. Additionally, searching PubChem by structural similarity suggests that vanillyl head group of the TRP channel modulators capsaicin and eugenol are similar structurally to the trans-2Methoxycyclohexan-1-ol ring. Without overlaying the two structures myself, it is difficult to say more than that, but I encourage the authors to comment on any similarities and differences they observe.

• If the proposed rapamycin pocket is indeed similar to the location of the vanilloid binding site, the authors may wish to discuss other TRPM channel structures that show ligands and lipids bound to this pocket because this provides evidence that this pocket influences TRPM channel function. For example, how does the proposed rapamycin binding pocket compare to TRPM8 bound to agonist AITC (PDBID 8e4l), TRPM5 bound to inhibitor NDNA (7mbv), and TRPM2 bound to phosphatidylcholine (6co7)?

• Other TRP channel structures with ligands or lipids modeled in this region include TRPV1 bound to resiniferatoxin, capsaicin, or phosphatidylinositol (7l2j, 7l24, 7l2s, 7l2t, 7l2u, 7lp9, 7lpc, 7lqy, 7mz6, 7mz9, 7mza); TRPV3 bound to phosphatidylcholine (7mij, 7mik, 7mim, 7min, 7ugg); TRPV5 bound to econazole (6b5v) or ZINC9155 (6pbf); TRPV6 bound to piperazine (7d2k, 7k4b, 7k4c, 7k4d, 7k4e, 7k4f) or cholesterol hemisuccinate (7s8c); TRPC6 bound to BTDM (7dxf) or phosphatidylcholine (6uza); and TRP1 bound to PIP2 (6pw5).

We thank the reviewer for these valuable insights. We have included some additional discussion highlighting the similarities between the proposed rapamycin binding site and some of the other ligandchannel interactions in the TRP superfamily, in particular the well-known vanilloid binding site in TRPV1. However, to keep the discussion focused, we have not fully discussed all the indicated interactions, to best serve the clarity and scope of the manuscript.  

(4) I would like to see negative control calcium imaging and electrophysiology data with untransfected HEK cells to confirm that the observed activation is mediated by TRPM8 to parallel the TRPM8 KO sensory neuron experiments.  

This important information is now included in the revised manuscript (Figure S2).

(5) The DM-nitrophen Ca uncaging experiments are an interesting method to test Ca sensitivity of rapamycin, but the results make these experiments more complex to interpret. Ca has been shown to be an obligate cofactor for icilin sensitivity in TRPM8 under conditions where both the internal and external Ca concentrations are tightly controlled (Kuhn et al., 2009, doi: https://doi.org/10.1074/jbc.M806651200), which is necessary because TRPM8 allows Ca permeation through the pore when open. The large icilin-evoked currents in Figure 5A and 5B indicate that the effective intracellular calcium concentration is not zero prior to calcium uncaging, which may be high enough to mask any Ca-dependence of rapamycin that occurs at low Ca concentrations. Given this ambiguity, the inside-out patch clamp configuration would provide more control over the internal and external Ca concentration than is achieved in the Ca uncaging experiments. Because the authors have already demonstrated their ability to perform such experiments (Figure 2 panel B), it would be nice to see tests of Ca dependence using inside-out patch clamp.

As was already shown in Figure 2, Rapamycin activates TRPM8 in inside-out patches, and these experiments were performed using calcium-free cytosolic and extracellular solutions. Note that earlier studies have already shown that icilin activates outward TRPM8 currents in the full absence of calcium: see e.g. Janssens et al. eLife, 2016. Chuang et al. 2004. In the case of Icilin, increased calcium further potentiates the current, which is more prominent for the inward current.

In the Ca uncaging experiments, considering the Kd of DM-nitrophen of 5 nM, we expect that the intracellular calcium concentration before the UV flash would be approximately 15 nM. Taken together, both the inside-out experiments and the flash uncaging experiments confirm that rapamycin responses are not directly regulated by intracellular calcium, contrary to icilin.

(6) Sequence conservation within TRPM channels could be used in combination with the binding pocket model and mutagenesis to predict rapamycin selectivity for TRPM8 over other TRPMs. For example, some important residues, specifically G805 and Q861, are not conserved in TRPM3, which agrees with the lack of rapamycin sensitivity observed in TRPM3 (Figure S1). Further sequence comparison would provide testable hypotheses for future exploration of rapamycin sensitivity in other TRPMs that could validate the proposed binding pocket.

Thank you for the suggestion. We now indicate in the discussion that only some of the key residues are conserved and make suggestions for future studies.  

(7) Please unify the color scheme across the figures to improve clarity.

• The authors frequently use the colors blue, red, and green to represent menthol and rapamycin in the figures, but they are inconsistent in which one represents menthol and which represents rapamycin. It would be clearer for the audience if, for example, rapamycin is always represented with red and menthol is always represented with blue.  

Thank you for pointing this out. We have made the coloring schemes more uniform.

• In Figure 1, panel E, the coloring for Menthol and Pregnenolone Sulfate changes between the TRPM8+/+ and TRPM8-/- panels.  

Thank you for pointing this out. We have updated the coloring schemes to ensure consistency between the TRPM8+/+ and TRPM8-/- panels.

• Figure 3 B and E, perhaps color the plot background as a 3-color gradient (blue to white to red) rather than yellow and aqua. Center the white at the WT ratio, keeping the dashed line, with diverging gradients to, for example, blue for mutations that selectively affect menthol sensitivity and red for rapamycin.

Thank you for the suggestion – we have changed the figure accordingly.  

• Figure 4 panels A and B use the same color (green) to show two different things (menthol molecule and mutated residues that affect rapamycin sensitivity). It would be clearer for readers to change these colors to agree with a unified color scheme such that, for example, the menthol molecule is colored blue and the rapamycin-neighboring residues are colored red.

Thank you for the suggestion. We have updated the figure to use a unified color scheme, with the menthol molecule now colored green and the rapamycin-neighboring residues colored cyan, to enhance clarity for readers.

• I recommend adding a figure or panel that shows side chains for all mutations, colored by menthol/rapamycin selectivity, as indicated by the functional data in Figure 3B and 3E. This will highlight spatial patterns of the selective residues that are discussed in the text.

Thank you for your suggestion, we added all the side residues in Figure S10.

Minor points

(1) It would be nice to have one more concentration data point in the middle of the dose response curve shown in Figure 1 panel B. The response is not saturating at the top or foot of the curve in Figure 1 panel D, precluding a confident fit to a two-state Boltzmann function.

Instead of adding a single data point to this figure, we performed independent measurements on a plate reader system, comparing concentration responses at room temperature and 37 degrees. These data are now included as Figure S1.   

(2) The cartoon in Figure 2 panel B should be made more accurate. For example, only the transmembrane helices should be depicted embedded in the membrane, not the whole protein including the intracellular domain. Because the experiment was performed with cells, change the orientation of TRPM8 in the cartoon to show the intracellular domain of the protein facing away from the extracellular side of the membrane where the rapamycin is applied.

Thank you for this comment. We have corrected the cartoon accordingly

(3) Perhaps put the yellow circles under or around the carbon atoms to which the identified hydrogen atoms belong in Figure 2 panel E and Figure 4 panel C. I found it difficult to visualize and compare the STTD NMR results with the predicted binding pocket.

Thank you for the feedback. We have added yellow circles around the carbon atoms corresponding to the identified hydrogen atoms in Figure S9.  

(4) Regarding the sentence on p. 12 beginning "In agreement with this notion..."

• Include icilin, Cooling Agent-10, and WS-3 as other cooling agents whose sensitivity has been modulated by mutation of Y745

• Cryosim-3 responses were not tested in either of the two papers cited; please add citation to Yin et al., 2022, doi: https://doi.org/10.1126/science.add1268 .

• Other relevant papers include:

– Malkia et al., 2009, doi: https://doi.org/10.1186/1744-8069-5-62 which includes molecular docking showing the hydroxyl group of menthol interacting with Y745

– Beccari et al., 2017, doi: https://doi.org/10.1038/s41598-017-11194-0 Figure 5 shows disruption of icilin and Cooling Agent-10 sensitivity by Y745A

– Palchevskyi et al., 2023, doi: https://doi.org/10.1038/s42003-023-05425-6 Figure 3 shows disruption of icilin, cooling agent-10, WS-3, and menthol sensitivity by Y745A o Plaza-Cayon et al., 2022, https://doi.org/10.1002%2Fmed.21920 Review of TRPM8 mutations

• typo: Y754H should be Y745H

Thank you for these suggestions. We have added the above references to the text and corrected the typo.

(5) The authors use the competitive action of everolimus on rapamycin activation as evidence that the different macrolides are binding to the same binding pocket. In addition, prior work showed that Y745H and N799A mutations (which render TRPM8 insensitive to menthol and icilin, respectively) do not affect TRPM8 sensitivity to the structurally-related compound tacrolimus (Arcas et al., 2019). This is consistent with the docking and mutagenesis results presented here.

Thank you for this valuable suggestion. We discuss these data in the revised version.

(6) Rapamycin sensitivity has also been observed in TRPML1 (Zhang et al. 2019, doi: https://doi.org/10.1371/journal.pbio.3000252).

We added a short reference to this interesting finding in the discussion.

(7) The whole-cell currents are very large in several of the electrophysiology experiments (for example Figure 3 panel D and Figure S1), which could lead to artifacts of voltage errors as well as ion accumulation/depletion. However, because this paper is not relying on reversal potential measurements or trying to quantify V1/2, these errors are unlikely to affect the qualitative conclusions drawn.

This is a fair point, but indeed unlikely to affect our main conclusions. Note that we compensated between 70 and 90% of the series resistance, so we don’t expect voltage errors exceeding ~10 mV.

(8) Ligand sensitivity is frequently species-dependent in TRP channels, so it is interesting that multiple species were used here and that both human and mouse isoforms exhibit rapamycin sensitivity. It should be emphasized that human TRPM8 was used in the calcium imaging and electrophysiology experiments, as well as some docking models, while the mouse isoform was used in the sensory neuron experiments and a mutated avian isoform was used for some docking models.

This information is available in the Methods and we believe it is clear for the readers.

(9) Perhaps discuss the unclear mechanism of G805A action in icilin (but not menthol, cold, or praziquantel) sensitivity because it is not in direct contact with the ligand. For example, Yin et al., 2019 propose flexibility allowing Ca binding site and larger binding site for icilin.

Yin et al. (2019) suggests that the G805A mutation impacts icilin sensitivity by influencing the flexibility of the binding site and possibly affecting calcium binding. In our study, we found that G805A significantly reduces rapamycin sensitivity, likely due to its direct role in the rapamycin binding pocket rather than affecting calcium binding. This is now briefly mentioned in the results section.

(10) The Figure S1 legend indicates that n=5 for all panels, so please show normalized population IV curves rather than individual examples. Additionally, it would be interesting to see what happens when each agonist is co-applied with rapamycin. Does rapamycin potentiate or inhibit agonist activation in these channels and/or TRPM8?

We believe that normalized population IVs are not ideal for representing whole-cell currents, considering the substantial variation in current densities. We therefore prefer to show example traces in Figure S3 of the revised version but include mean values of current densities for all tested cells in the text.

While the effects of co-application of rapamycin with activating ligands could be of interest, we consider this somewhat outside the scope of the present manuscript. The combination of HEK293 cell experiments, along with results obtained in WT and TRPM8-deficient mice does, in our opinion, sufficiently describe the selectivity of rapamycin towards TRPM8 compared to other sensory TRP channels.

(11) Figure S1 panel A does not contain units for Rapamycin or AITC concentrations.

Thank you for pointing this out. The units were added to the figure.  

(12) It would be nice if the authors characterized the different mutations as predicted to contribute to site 1 (D796, H845, Q861, based on Figure S4), site 2 (D796, M801, F847, and R851), and/or site 3 (F847, V849, and R851).

The indicated mutants were all tested, as shown in Figure 3.

(13) The numbering scheme in Figure S4 does not appear to match the residue numbers in the rest of the paper for certain residues (HIS-844 rather than H845, PHE-846 rather than F847, VAL-848 rather than V849, ARG-850 rather than R851, and GLN-860 rather than Q861), and labels are often overlapping and difficult to see. I also find the transparent spheres very difficult to distinguish from the transparent background, which makes it difficult to appreciate the STTD NMR data overlay.

We apologize for the confusing numbering scheme. The lower numbers refer to the initial docking that was done using the avian TRPM8 ortholog. We have made a newer, clearer version of Figure S4 and inserted as Figure S9.  

(14) Please superpose the Ligplots in Figure S5 panels E and F as described in the LigPlus manual (https://www.ebi.ac.uk/thornton-srv/software/LigPlus/manual/manual.html) to facilitate easier comparison.

Thank you for the suggestion. We followed the suggestion to superpose the Ligplots as described but found that the result was visually cluttered and difficult to interpret. To avoid confusion, we instead decided to remove panels E and F from Figure S5, as we believe that the visualization in panels A-D is clear and informative.

(15) Some n values are missing in figure legends.

We checked all legends, and added n numbers were missing.

(16) There is an inconsistent specification of error bars as SEM in the figure legends, though it is specified in methods.

A question for my own edification: Here, you have looked at ligand interactions with the protein by saturating the protein resonances and observing transfer to the ligand. Would it be possible to instead saturate lipid or solute resonances and observe transfer to a ligand? I am curious whether this would be one way to measure equilibrium partitioning of ligand into a membrane and/or determine the effective concentration of a ligand in the membrane. Additionally, could one determine whether the compound is fully partitioned into the center of the membrane or just sitting on the surface?

The reviewer highlights an interesting aspect. The widely used WaterLOGSY NMR experiment (doi: 10.1023/a:1013302231549) saturates water molecules then the magnetization is transferred to the ligand of interest. Characteristic changes in ligand resonances are observed in the case of a binding event with proteins. On the other hand, the selective saturation of lipids is -while theoretically possible –technically challenging mainly because of the inherent low signal-dispersion of lipids and peak overlapping with ligand resonances. Additionally, lipid systems are more dynamic compared to proteins and ligand-lipid interactions could be weaker and less specific, significantly affecting the sensitivity of STD experiments.

Reviewer #2 (Recommendations For The Authors):

Major:

• Is it feasible to test rapamycin on TRPM8 with single-channel recording? This will allow us to better probe the mechanism of rapamycin activation and compare it with menthol, with parameters of singlechannel conductance and maximal open probability.

In our experience, it is very difficult to obtain single-channel recordings from TRPM8. The channel expresses at high densities, typically leading to patches contain multiple channels, making a proper analysis of mean open and closed times very difficult. Therefore, we have decided not to include such measurements in the manuscript.

• The authors classified rapamycin as a type I agonist, the type that stabilizes the open conformation, same as menthol but more prominent. Does that indicate that rapamycin work synergistically (rather than independently) with menthol, because co-application of them can allow them to add to each other in stabilizing the open conformation? I wonder if the authors agree that this could be tested with experiments as in Figure S3, by showing a much more prolonged deactivation with co-application of menthol and rapamycin than applying each alone.

Thank you for the insightful suggestion. We conducted co-application experiments, and our results show that the deactivation time is indeed significantly prolonged when both compounds are applied together compared to each alone. In fact, very little deactivation is seen when both compounds are co-applied, which made it virtually impossible to perform reliable fits to the deactivation time course for the Menthol+Rapamycin condition. Instead, we have now included summary results showing the percentage of deactivation after 100 ms. We included these findings in FigureS8.  

• It could be tested whether rapamycin activation of TRPM8 requires or overrides the requirement of PIP2 with inside-out patch by briefly exposing the patch to poly-lysine to sequester PIP2.

This is certainly a good suggestion for further follow-up studies. However, we considered that examination of the (potential) interaction between ligands and PIP2 was outside the scope of the current manuscript.

• Figure 1C suggests that the authors test rapamycin when there is a relatively high baseline TRPM8 activation (prior to rapamycin) activation. This raises the possibility that rapamycin is more a potentiator than an activator. I wonder if the following two experiments could address it: (1) perfuse rapamycin while holding at different membrane potentials, wash-off rapamycin in the solution and quickly (in a few seconds) test the activated current magnitude (before rapamycin dissociation), to compare whether a more depolarized membrane potential (high baseline open probability) allows rapamycin to potentiate more. (2) Perform the experiment at a higher temperature (low baseline open probability) and test whether rapamycin EC50 shifts to the right.

Thank you for the thoughtful suggestion. Overall, we are not really in favor of making a distinction between a potentiator and an activator since it is not really feasible to create a situation where TRPM8 activity is zero. As suggested, we performed the dose response experiment at a higher temperature (37 °C) and observed that rapamycin’s EC₅₀ shifts to the right FigureS2. This is similar to what has been observed for menthol on TRPM8 and for many other ligands on other temperature-sensitive TRP channels.

Minor:

(1) The author should report hill coefficient together with EC50 when showing dose-responses.

We have added Hill coefficients for all the fits.

(2) In Figure 1 (E, F), it might be clearer to use Venn-diagram to show whether there is overlapping among rapamycin-, menthol-, and cinnamaldehyde-responsive neurons. According to the authors' explanation, we can predict that rapamycin-insensitive, menthol-sensitive neurons should predominantly be cinnamaldehyde-responsive.

Thank you for your suggestion. In these experiments, we applied several agonists and the combination of them would result in a visually crowded Venn diagram difficult to interpret. However, we agree, with the reviewer’s suggestion, and discuss the percentage of the cinnamaldehyde+ neurons in the rapa- menthol+ population in Trpm8^-/- neurons.

(3) In Figure 3(C), since F847 does not respond to either menthol or rapamycin, it should be excluded from (B). Otherwise it is misleading.

Thank you for pointing this out. To clarify, we have included a calcium imaging trace for the F847 mutant, demonstrating a clear response to rapamycin in FigureS9. This additional data highlights that F847 does respond to rapamycin, albeit with a more modest response amplitude. This is now also clarified in the results section.  

(4) The word "potency" in pharmacology usually refers to a smaller EC50 number in dose-dependent experiments. In "Effect of rapamycin analogs on TRPM8" session, the authors use "potency" to refer to response to a single-dose experiment of different compounds. The experiment does not measure potency.

Thank you for pointing out this mistake. We have corrected the text and replaced “potency” with “efficacy”.

(5)  "2-methoxyl-" is misspelled in the text body.

We have corrected the typo.

(6) It will be nice to include "vehicle" in Figure 6B, or alternatively normalize all individual traces to vehicle. In Figure 6C and D, everolimus has almost no effect with compared to vehicle, and should not be shown as if it had ~8% in Figure 6B.

We have added the vehicle values to Figure 6B from the same experiments.

Reviewer #3 (Recommendations For The Authors):

(1) The NMR method presented here as novel and employed to identify a proposed molecule bound to a membrane protein (TRPM8 in this case) is not well explained and presented. Since several spectra need to be subtracted, the authors should present the raw data and the results of the subtractions step by step. Also, it seems that the height of the peaks in each spectra will be highly variable and thus a reliable criterion employed to scale spectra before subtraction. None of these problems are discussed of described.

The reviewer is right, that the data transparency should be improved and due to the high molecular complexity of the samples the size of the STD effects should be carefully scaled. We carried out additional experimental replicas on new samples and addressed the inherent sample/peak height variability by rescaling the STD effects based on reference ¹H measurements. We provided supplementary spectra of the reference experiments without saturation (Figure S5) and the computed STTD spectra from three parallel NMR sessions (Figure S6). We changed panel C of Figure 2 in the main text and provided all the STD and the computed STDD and STTD spectra recorded on one set of NMR experiments. We added the following paragraph to the main text: “To address the effect of the inherent variability of cellular samples on peak heights, STD effects were normalized based on the comparison of independent ¹H experiments (Figure S5). Three STTD replicates were computed, unambiguously confirming direct binding to TRPM8 in two datasets (Figure S6 A,B)”.

Importantly since this signal subtraction method is proposed as a new development, control experiments employing well-established pairs of ligand and membrane protein receptor should be performed to demonstrate the reliability of the method.

We agree with the reviewer, that the STTD experiment as a new development needs further validation, however, this paper is a preliminary demonstration of a new strategy building on the well-established STD and STDD NMR methodologies. Our group is actively engaged in studying additional biological samples to enhance our understanding of the applicability of STTD NMR. These efforts also aim to address challenges such as sample and spectral complexity by refining and standardizing the proposed workflow.

(2) The tail currents shown in supplementary figure 3 are clearly not monoexponential. The fit to a single exponential can be seen to be inadequate and thus the comparison of kinetics of control, rapamycin and menthol is incorrect. At least two exponentials should be fitted and their values compared.

We agree that the decay in the (combined) presence of agonists deviates from a simple monoexponential behavior. While we agree that fitting with two (or more) exponentials would provide a better fit, this also comes with greater variations/uncertainties in the fit parameters. This is particularly the case when inactivation is very slow and incomplete, or when the difference between slow and fast exponential time constants is <5, as seen with rapamycin and rapamycin +menthol. Therefore, we decided to provide monoexponential time constants as a proxy to describe the clear slowing down of activation and deactivation time courses in the presence of Type I agonists.   

Also related to this aspect, recordings of TRPM8 currents can not be leak subtracted with a p/n protocol, thus a large fraction of the initial tail current must be the capacitive transient. There is no indication in the methods of how was this dealt with for the fitting of tail currents.

As explained in the methods, capacitive transients and series resistance were maximally compensated. Therefore, we do not agree that a large fraction of the initial tail current must be capacitive. This can also be clearly seen in experiment such as Figure 1C, where the inward tail current is fully abolished in the presence of a TRPM8 antagonist. Likewise, very small and rapidly inactivating tail currents can be seen during voltage steps under control conditions (e.g. Figure S7  and S8 in the revised version).  

(3) The docking procedure employed, as the authors show, is not appropriate for membrane proteins since it does not include a lipid membrane. It is not clear in the methods section if the MD minimization described applies only to the rapamycin molecule or to rapamycin bound to TRPM8.  

It is also not clear if the important residue Q861 (and other residues that are identified as interacting with rapamycin) were identified from dockings or proposed based on other evidence.

(4) Identifying amino acid residues that diminish the response to a ligand, does not uniquely imply that they form a binding site or even interact with said ligand. It is entirely possible that they can be involved in the allosteric networks involved in the activating conformational change. This caveat should be clearly posited by the authors when discussing their results.

In our study, we identified several residues that significantly reduce the response to rapamycin when mutated, while retaining robust responses to menthol, which indicates that these mutations do not affect crucial conformational changes leading to channel gating. While our cumulative data suggest that these residues may be involved in direct interaction with rapamycin, we recognize the alternative possibility that they allosterically affect rapamycin-induced channel gating. This is now clearly stated in the first paragraph of the discussion.

Author response:

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

Reviewer 1 (Public Review):

• While the title is fair with respect to the data shown, in the summary and the rest of the paper, the comparison between anesthetized and awake conditions is systematically stated, while more caution should be used.

First, isoflurane is one of the (many) anesthetics commonly used in pre-clinical research, and its effect on the brain vasculature cannot be generalized to all the anesthetics. Indeed, other anesthesia approaches do not produce evident vasodilation; see ketamine + medetomidine mixtures. Second, the imaged awake state is head-fixed and body-constrained in mice. A condition that can generate substantial stress in the animals. In this study, there is no evaluation of the stress level of the mice. In addition, the awake imaging sessions were performed a few minutes after the mouse woke up from isoflurane induction, which is necessary to inject the MB bolus. It is known that the vasodilator effects of isoflurane last a long time after its withdrawal. This aspect would have influenced the results, eventually underestimating the difference with respect to the awake state.

These limitations should be clearly described in the Discussion.

Looking at Figure 2e, it takes more than 5' to reach the 5 Millions MB count useful for good imaging. However, the MB count per pixel drops to a few % at that time. This information tells me that (i) repeated measurements are feasible but with limited brain coverage since a single 'wake up' is needed to acquire a single brain section and (ii) this approach cannot fit the requirements of functional ULM that requires to merge the responses to multiple stimuli to get a complete functional image. Of course, a chronic i.v. catheter would fix the issue, but this configuration is not trivial to test in the experimental setup proposed by the authors, hindering the extension of the approach to fULM.

Thank you for highlighting these limitations, as they address aspects that were not fully considered during the experimental design and manuscript writing. In response, we have added the following paragraphs to the discussion section, addressing these limitations of our study:

(Line 310) “Although isoflurane is widely used in ultrasound imaging because it provides long-lasting and stable anesthetic effects, it is important to note that the vasodilation observed with isoflurane is not representative of all anesthetics. Some anesthesia protocols, such as ketamine combined with medetomidine, do not produce significant vasodilation and are therefore preferred in experiments where vascular stability is essential, such as functional ultrasound imaging(47). Therefore, in future studies, it would be valuable to design more rigorous control experiments with larger sample sizes to systematically compare the effects of isoflurane anesthesia, awake states, and other anesthetics that do not induce vasodilation on cerebral blood flow.

Our proposed method enabled repeatable longitudinal brain imaging over a three-week period, addressing a key limitation of conventional ULM imaging and offering potential for various preclinical applications. However, there are still some limitations in this study. 

One of the limitations is the lack of objective measures to assess the effectiveness of head-fix habituation in reducing anxiety. This may introduce variability in stress levels among mice. Recent studies suggest that tracking physiological parameters such as heart rate, respiratory rate, and corticosterone levels during habituation can confirm that mice reach a low stress state prior to imaging(48). This approach would be highly beneficial for future awake imaging studies. Furthermore, alternative head-fixation setups, such as air-floated balls or treadmills, which allow the free movement of limbs, have been shown to reduce anxiety and facilitate natural behaviors during imaging(30). Adopting these approaches in future studies could enhance the reliability of awake imaging data by minimizing stress-related confounds.

Another limitation of this study is the potential residual vasodilatory effect of isoflurane anesthesia on awake imaging sessions. The awake imaging sessions were conducted shortly after the mice had emerged from isoflurane anesthesia, required for the MB bolus injections. The lasting vasodilatory effects of isoflurane may have influenced vascular responses, potentially contributing to an underestimation of differences in vascular dynamics between anesthetized and awake state. Future applications of awake ULM in functional imaging using an indwelling jugular vein catheter presents a promising alternative to enable more accurate functional imaging in awake animals, addressing current limitations associated with anesthesia-induced vascular effects.”

• Statistics are often poor or not properly described. 

The legend and the text referring to Figure 2 do not report any indication of the number of animals analyzed. I assume it is only one, which makes the findings strongly dependent on the imaging quality of THAT mouse in THAT experiment. Three mice have been displayed in Figure 3, as reported in the text, but it is not clear whether it is a mouse for each shown brain section. Figure 5 reports quantitative data on blood vessels in awake VS isoflurane states but: no indication about the number of tested mice is provided, nor the number of measured blood vessels per type and if statistics have been done on mice or with a multivariate method.

Also, a T-test is inappropriate when the goal is to compare different brain regions and blood vessel types.

Similar issues partially apply to Figure 6, too.

Thank you for bringing this to our attention. 

We acknowledge that the statistical analyses were not clearly explained in the original version. In the revised manuscript, we have ensured that the statistical methods are clearly described. 

(Fig.4 caption) “b,c, Comparisons of vessel diameter (b) and flow velocity (c) for the selected arterial and venous segments. Statistical analysis was conducted using t-test at each measurement point along the segments.”

(Fig.6 caption) “b,c, Comparisons of vessel diameter (b) and flow velocity (c) for the selected arterial and venous segments. Statistical analysis was conducted using the two one-sided test (TOST) procedure, which evaluates the null hypothesis that the difference between the two weeks is larger than three times the standard deviation of one week.”

Additionally, we corrected an error in the previous comparison of the violin plots on flow velocities, where a t-test was incorrectly applied; this has now been removed.

We acknowledge that the original version did not clearly indicate the numbers of animals in the statistical analysis. In the revised manuscript, we have added Supplementary Figure 1 to specify the mice used, and we have labeled each mouse accordingly in the figures or captions. In the revised Figures 4 and 6, we have ensured that each quantitative analysis figure or its caption clearly indicate the specific mice.

For original Figures 1 and 2, these are presented as case studies to illustrate the methodology. Since the anesthesia time required for tail vein injection for each animal varies slightly, it is challenging to have the consistent time taken for each mouse to recover from anesthesia across all mice. For instance, in Figure 1, the mouse took nearly 500 seconds to recover from anesthesia, but this duration is not consistent across all animals, which is a limitation of the bolus injection technique. We have noted this point in the discussion (discussion on the limitation of bolus injection), and we have also clarified in the results section and figure captions that these figures represent a case study of a single mouse rather than a standardized recovery time for all animals.

We further clarified this point in the end of the Figure 2 caption:

(Fig.2 caption) “This figure presents a case study based on the same mouse shown in Fig 1. The x-axis for d-f begins at 500 seconds because, at this point, the mouse’s pupil size stabilized, indicating it had recovered to an awake state. Consequently, ULM images were accumulated starting from this time. It is important to note that not every mouse requires 500 seconds to fully awaken; the time to reach a stable awake state varies across individual mice.” We added the following statement before introducing Figure 1e:

(Line 93) “Due to differences in tail vein injection timing and anesthesia depth, the time required for each mouse to fully awaken varied. Although it was not feasible to get pupil size stabilized just after 500 seconds for each animal, ULM reconstruction only used the data that acquired after the animal reached full pupillary dilation, to ensure that ULM accurately captures the cerebrovascular characteristics in the awake state.”

We added the following statement before introducing Figure 2d:

(Line 139) “To further verify that the proposed MB bolus injection method can help to achieve ULM image saturation shortly after mice awaken from anesthesia, an analysis on the change in MB concentration over time was conducted once pupil size had stabilized (T = 500s).”

For Figures 3, 4, and 5 (in the revised version, Figures 4 and 5 have been combined into a single Figure 4), the data represents results from three individual mice, with each coronal plane corresponding to a different mouse. In the revised version, we have added labels to indicate the specific mouse in each image to improve clarity. We also recognize that some analyses in the original submission (original Figure 5) may have lacked sufficient statistical power due to the small sample size. Therefore, in the revised version, we have focused only on findings that were consistently observed across the three mice to ensure robust conclusions.

Reviewer 1 (Recommendations For the Authors):

• If the study's main goal is to compare awake vs anesthetized ULM, the authors should test at least another anesthetic with no evident vasodilator effect.

Thank you for this valuable suggestion. We would like to clarify that the primary aim of our study is not to comprehensively compare the effects of anesthesia versus the awake state, as a rigorous comparison would indeed require a more controlled experimental design, including additional anesthetics, a larger cohort of mice, and broader controls to ensure sufficient statistical power. We also add the following statement in the Discussion to clarify this point:

(Line 314) “Therefore, in future studies, it would be valuable to design more rigorous control experiments with larger sample sizes to systematically compare the effects of isoflurane anesthesia, awake states, and other anesthetics that do not induce vasodilation on cerebral blood flow.”

We acknowledge that the initial organization of Figures 3–5 placed excessive emphasis on comparisons between the awake and anesthetized states, but without yielding consistently significant findings. Meanwhile, our longitudinal observations in original Figure 6 were underrepresented, despite their potential importance.

In the revised version, we shifted our focus toward the main goal of awake longitudinal imaging. By consolidating the previous Figures 4 and 5 into the new Figure 4, we emphasize conclusions that are both more consistent and broadly applicable, avoiding areas that may lack sufficient rigor or consensus. Additionally, we expanded the quantitative analysis related to longitudinal imaging, highlighting its role as the ultimate objective of this study. The awake vs. anesthetized ULM comparison was intended to demonstrate the value of awake imaging and introduce the importance of awake longitudinal imaging. In the revised text, we have reframed this comparison to emphasize the specific response to isoflurane rather than a general response to anesthesia. For example, in Figures 3 and 4, we have replaced the original term "Anesthetized" with "Isoflurane". We have also added a discussion noting that isoflurane may induces more vasodilation than other anesthetic agents.

(Line 310) “Although isoflurane is widely used in ultrasound imaging because it provides long-lasting and stable anesthetic effects, it is important to note that the vasodilation observed with isoflurane is not representative of all anesthetics. Some anesthesia protocols, such as ketamine combined with medetomidine, do not produce significant vasodilation and are therefore preferred in experiments where vascular stability is essential, such as functional ultrasound imaging(47).”

• The claims made about the proposed experimental protocol to be suitable for the "long-term" (line 255) are not supported by the data and should be modified according to the presented evidence.

Thank you for your valuable feedback. We agree that our current three-week experimental results do not yet fulfill the requirements for extended longitudinal imaging that may span several months. We have revised the relevant text accordingly. For instance, the phrase “Our proposed method enabled long-term, repeatable longitudinal brain imaging” has been modified to “Our proposed method enabled repeatable longitudinal brain imaging over a threeweek period.” (Similar changes also in Line 67, Line 318, and Line 337) Additionally, we have added the following paragraph in the discussion section to indicate that extending the monitoring period to several months is a meaningful direction for future exploration:

(Line 337) “In our longitudinal study, consistent imaging results were obtained over a three-week period, demonstrating the feasibility of awake ULM imaging for this duration. However, for certain research applications, a monitoring period of several months would be valuable. Extending the duration of longitudinal awake ULM imaging to enable such long-term studies is a potential direction for future development.”

Recommendations for improving the writing and presentation:

• Reporting the number of mice and blood vessels and statistics for each quantitative figure.

Thank you for highlighting this issue. We acknowledge that the quantitative figures in the previous version lacked clarity in specifying the number of mice, vessels, and associated statistics. In the revised version, we have ensured that each quantitative figure or its caption clearly indicate the specific mice, vessels, and statistical methods used. To further minimize any potential confusion, we have also added Supplementary Figure 1 to clearly label and reference each individual mouse included in the study.

Minor corrections to the text and figures.

• Line 22: "vascularity reduction from anesthesia" is not clear, nor it is a codified property of brain vasculature. Explain or rephrase.

Thank you for your comment. We apologize for any confusion caused by the phrase “vascularity reduction from anesthesia” in the abstract. We agree that this phrasing was unclear without context. To improve clarity, we have revised this statement in the abstract to make it more straightforward and easier to understand. 

(Line 24) “Vasodilation induced by isoflurane was observed by ULM. Upon recovery to the awake state, reductions in vessel density and flow velocity were observed across different brain regions.” 

Additionally, we have added a section in the Methods titled Quantitative Analysis of ULM Images to provide a clear definition of vascularity. This section outlines how vascularity is quantified in our study, ensuring that our terminology is well-defined. 

The following sentence shows the definition of vascularity:

(Line 547) “Vascularity was defined as the proportion of the pixel count occupied by blood vessels within each ROI, obtained by binarizing the ULM vessel density maps and calculating the percentage of the pixels with MB signal.”

We have also added an instant definition when it was firstly used in Results part:

(Line 161) “When comparing vessel density maps, ULM images that are acquired in the awake state demonstrate a global reduction of vascularity, which refers to percentage of pixels that occupied by blood vessels.”

• Line 76: putting the mice in a tube is also intended "To further reduce animal anxiety and minimize tissue motion" I agree with tissue motion, not with animal anxiety, which, indeed, I expect to be higher than if it could, for example, run on a ball or a treadmill.

Thank you for pointing this out. We acknowledge the limitations of our setup regarding reducing animal anxiety. We have replaced the original phrase “to further reduce animal anxiety and minimize tissue motion” with “to further minimize tissue motion.” (Line 78) Additionally, we have added the following paragraph in Discussion section to address the limitations of our setup in reducing anxiety.

(Line 321) “One of the limitations is the lack of objective measures to assess the effectiveness of head-fix habituation in reducing anxiety. This may introduce variability in stress levels among mice. Recent studies suggest that tracking physiological parameters such as heart rate, respiratory rate, and corticosterone levels during habituation can confirm that mice reach a low stress state prior to imaging(48). This approach would be highly beneficial for future awake imaging studies. Furthermore, alternative head-fixation setups, such as air-floated balls or treadmills, which allow the free movement of limbs, have been shown to reduce anxiety and facilitate natural behaviors during imaging(30). Adopting these approaches in future studies could enhance the reliability of awake imaging data by minimizing stress-related confounds.”

• Line 79: PMP has been used by Sieu et al., Nat Methods, 2015; it should be acknowledged.

Thank you for highlighting this. We have now included the reference to Sieu et al. Nat Methods, 2015 to appropriately acknowledge their use of PMP. (Line 81)

• Figure: is there a reason why the plots start at 500 sec? What happened before that time?

Thank you for your question regarding the starting time in the plots. Figures 1 and 2 are case studies using a single mouse to demonstrate the feasibility of our method. The “zero” timepoint was defined as the moment when anesthesia was stopped, and the microbubble injection began. However, the mouse does not fully recover immediately after anesthesia is stopped. As shown in Figure 1e, there is a period of approximately 500 seconds during which the pupil gradually dilates, indicating recovery. Only after this period does the mouse reach a relatively stable physiological state suitable for ULM imaging, which is why the plots in Figure 2 begin at T = 500 seconds.

We recognize that this was not sufficiently explained in the main text and figure captions. In the revised manuscript, we have clarified this timing rationale in both the results section and the figure captions. We added the following sentence to the result section to introduce Fig.2d:

(Line 139) “To further verify that the proposed MB bolus injection method can help to achieve ULM image saturation shortly after mice awaken from anesthesia, an analysis on the change in MB concentration over time was conducted once pupil size had stabilized (T = 500s).”

We also added the following statement to note that this recover time varies across individual mice:

(Line 154, Fig.2 caption) “This figure presents a case study based on the same mouse shown in Fig 1. The x-axis for d-f begins at 500 seconds because, at this point, the mouse’s pupil size stabilized, indicating it had recovered to an awake state. Consequently, ULM images were accumulated starting from this time. It is important to note that not every mouse requires 500 seconds to fully awaken; the time to reach a stable awake state varies across individual mice.”

Reviewer 2 (Public Review):

• The only major comment (calling for further work) I would like to make is the relative weakness of the manuscript regarding longitudinal imaging (mostly Figure 6), compared to the exhaustive review of the effect of isoflurane on the vasculature (3 rats, 3 imaging planes, quantification on a large number of vessels, in 9 different brain regions). The 6 cortical vessels evaluated in Figure 6 feel really disappointing. As longitudinal imaging is supposed to be the salient element of this manuscript (first word appearing in the title), it should be as good and trustworthy as the first part of the paper. Figure 6c. is of major importance, and should be supported by a more extensive vessel analysis, including various brain areas, and validated on several animals to validate the robustness of longitudinal positioning with several instances of the surgical procedure. Figure 6d estimates the reliability of flow measurements on 3 vessels only. Therefore I recommend showing something similar to what is done in Figures 4 and 5: 3 animals, and more extensive quantification in different brain regions.

We thank the reviewer for pointing out this issue. We acknowledge that the first version of the manuscript lacked in-depth quantitative analysis in the section on the longitudinal study, which should have been a focal point. It also did not provide a sufficient number of animals to demonstrate the reproducibility of the technique. In this revised version, we have included results from more animals and conducted a more comprehensive quantitative analysis, with the corresponding text updated accordingly. Specifically, we combined the previous Figures 4 and 5 into the current Figure 4 (corresponding revised text from Line 169 to Line 207). The revised Figures 5 and 6

compare the results of the longitudinal study, presenting data from three mice (corresponding revised text from

Line 224 to Line 258). Detailed information about the mice used has been added to Supplementary Figure 1, and Supplementary Figure 4 further provides a detailed display of the results for the three mice in longitudinal study. We hope that these adjustments will provide a more thorough validation of the longitudinal imaging.

Reviewer 2 (Recommendations For The Authors):

Minor comments:

• The statistical analyses are not always explained: could they be stated briefly in the legends of each figure, or gathered in a statistical methods section with details for each figure? Be sure to use the appropriate test (e.g. student t-test is used in Fig 5 k whereas normality of distribution is not guaranteed.)

Thank you for pointing this out. We acknowledge that the statistical analyses were not clearly explained in the original version. In the revised manuscript, we have ensured that the statistical methods are clearly described. 

(Fig.4 caption) “b,c, Comparisons of vessel diameter (b) and flow velocity (c) for the selected arterial and venous segments. Statistical analysis was conducted using t-test at each measurement point along the segments.”

(Fig.6 caption) “b,c, Comparisons of vessel diameter (b) and flow velocity (c) for the selected arterial and venous segments. Statistical analysis was conducted using the two one-sided test (TOST) procedure, which evaluates the null hypothesis that the difference between the two weeks is larger than three times the standard deviation of one week.”

Additionally, we corrected an error in the previous comparison of the violin plots on flow velocities, where a t-test was incorrectly applied; this has now been removed.

• The authors use early in the manuscript the term vascularity, e.g. in "vascularity reduction", it is not exactly clear what they mean by vascularity, and would require a proper definition at that moment. If I am correct, a quantification of that "vascularity reduction" (page 5 line 132), is then done in Figures 5 d e f and j.

Thank you for highlighting this issue. We acknowledge that our initial use of the term “vascularity” may have been unclear and potentially confusing. In the revised manuscript, we have included a clear definition of “vascularity” in the Methods section under Quantitative Analysis of ULM Images (Line 534). 

The following sentence shows the definition of vascularity:

(Line 547) “Vascularity was defined as the proportion of the pixel count occupied by blood vessels within each ROI, obtained by binarizing the ULM vessel density maps and calculating the percentage of the pixels with MB signal.”

We have also added an instant definition when it was firstly used in Results part:

(Line 161) “When comparing vessel density maps, ULM images that are acquired in the awake state demonstrate a global reduction of vascularity, which refers to percentage of pixels that occupied by blood vessels.”

• There is very little motion in the images presented, except for the awake "Bregma -4.2 mm" (Figure 3, directional maps), especially in the area including colliculi and mesencephalon, while the cortical vessels do not move. Can you comment on that?

Thank you for highlighting this important aspect of motion in awake animal imaging. Motion correction is indeed a critical factor in such studies. In the original version of our discussion, we briefly addressed this issue (from Line 342 to Line 346), but we agree that a more detailed discussion is needed.

To minimize motion artifacts, we conducted habituation to acclimate the animals to the head-fixation setup, which helps reduce anxiety during imaging. With thorough head-fixed habituation, the imaging quality is generally well-preserved. We also applied correlation-based motion correction techniques based on ULM images, which can partially correct for overall brain motion, as stated in the previous version. However, this ULM-images-based correction is limited to addressing only rigid motion.

In the revised discussion, we have expanded on the limitations of our current motion correction approach and referenced recent work about more advanced motion correction methods:

(Line 346) “While rigid motion correction is often effective in anesthetized animals, awake animal imaging presents greater challenges due to the more prominent non-rigid motion, particularly in deeper brain regions. This is evidenced in Supplementary Fig. 1 (Mouse 7), where cortical vessels remain relatively stable, but regions around the colliculi and mesencephalon exhibit more noticeable motion artifacts, indicating that displacement is more pronounced in deeper areas. To address these deeper, non-rigid motions, recent studies suggest estimating nonrigid transformations from unfiltered tissue signals before applying corrections to ULM vascular images(16,50). Such advanced motion correction strategies may be more effective for awake ULM imaging, which experiences higher motion variability. The development of more robust and effective motion correction techniques will be crucial to reduce motion artifacts in future awake ULM applications.”

• Figure 1f maybe flip the color bar to have an upward up and downward down.

Thank you for your suggestion. This display method indeed makes the images more intuitive. In the revised manuscript, all directional flow color bars have been flipped to ensure that upward flow is displayed as ‘up’ and downward flow as ‘down.’

• Figure 2b the figure is a bit confusing in what is displayed between dashed lines, solid lines, dots... maybe it would be easier to read with

- bigger dots and dashed lines in color for each of the 4 series

- and so in the legend, thin solid lines in the corresponding color for the fit, but no solid line in the legend (to distinguish data/fit)

- no lines for FWHM as they are not very visible, and the FWHM values are not mentioned for these examples.

Thank you for your detailed suggestions. We agree that the original Fig. 2b appeared messy and confusing. Based on this feedback and other comments, we decided to replace the FWHM-based vessel diameter measurement with a more stable binarization-based approach. In the revised version, we selected a specific segment of each vessel and measured the diameter by calculating the distance from the vessel’s centerline to both side after binarization. Each point on the centerline of this segment provides a diameter measurement, which can be further used to calculate the mean and standard error. This updated method is more stable and reproducible, providing reliable measurements even for vessels that are not fully saturated. It also facilitates comparison across more vessels, helping to further demonstrate the generalizability of our saturation standard. We believe these adjustments make the revised Fig. 2b clearer and more readable.

• Page 7, lines 144-147. This passage is not really clear when linking going up or down and going from the stem to the branches that it is specific to Figure 4a (and therefore to this particular location).

Thank you for your insightful comments on our vessel classification method. We recognize the limitations of the previous approach and, in order to enhance the rigor of the study, we have opted not to continue using this method in the revised manuscript. We have removed all content related to vessel classification based on branchin and branch-out criteria. This includes the original Classification of Cerebral Vessels section in the Methods, the relevant descriptions in the Results section under “ULM reveals detailed cerebral vascular changes from anesthetized to awake for the full depth of the brain”, limitation of this classification method in Discussion section, as well as related content in the original Figures 4 and 5.

In the revised analysis, for the comparison between arteries and veins, we focus solely on penetrating vessels in the cortex. For these vessels, it is generally accepted that downward-flowing vessels are arterioles, while upwardflowing vessels are venules. Accordingly, in the revised Figures 4 and 6, we analyze arterioles and venules exclusively in the cortex, without relying on the previous classification method that could be considered controversial.

• Page 11 line 222 "higher vascular density" seems unprecise.

Thank you for pointing this out. We have revised the sentence to more precisely convey our observations regarding changes in vascular diameter and vascularity within the ROI. We present these findings as evidence of the vasodilation effect under isoflurane, in alignment with existing research. The revised statement is as follows:

(Line 275) “Statistical analysis from Fig. 4 shows that certain vessels exhibit a larger diameter under isoflurane anesthesia, and the vascularity, calculated as the percentage of vascular area within selected brain region ROIs, is also higher in the anesthetized state. These findings suggest a vasodilation effect induced by isoflurane, consistent with existing research(20,40,41,43,44).

• Discussion: page 12, lines 257-267: it is not exactly clear how 3D imaging will help for the differentiation of veins/arteries. However, some methods have already been proposed to discriminate between arteries and veins using pulsatility (Bourquin et al., 2022) or 3D positioning when vessels are overlapped (Renaudin et al., 2023). The latter can also help estimate the out-of-plane positioning during longitudinal imaging.

Bourquin, C., Poree, J., Lesage, F., Provost, J., 2022. In Vivo Pulsatility Measurement of Cerebral Microcirculation in Rodents Using Dynamic Ultrasound Localization Microscopy. IEEE Trans. Med. Imaging 41, 782-792. https://doi.org/10.1109/TMI.2021.3123912

Renaudin, N., Pezet, S., Ialy-Radio, N., Demene, C., Tanter, M., 2023. Backscattering amplitude in ultrasound localization microscopy. Sci. Rep. 13, 11477. https://doi.org/10.1038/s41598-023-38531-w

Thank you for pointing this out. We have revised the relevant paragraph in the discussion to clarify the potential advantages of advances in ULM imaging methods, such as those based on pulsatility (as described by Bourquin et al., 2022) or backscattering amplitude (as demonstrated by Renaudin et al., 2023). These established methods could be helpful for longitudinal imaging. Below is the revised text in the discussion section:

(Line 370) “Advances in ULM imaging methods can benefit longitudinal awake imaging. For instance, dynamic ULM can differentiate between arteries and veins by leveraging pulsatility features(51). 3D ULM, with volumetric imaging array(52,53), enables the reconstruction of whole-brain vascular network, providing a more comprehensive understanding of vessel branching patterns. Meanwhile, 3D ULM also helps to mitigate the challenge of aligning the identical coronal plane for longitudinal imaging, a process that requires precise manual alignment in 2D ULM to ensure consistency. Additionally, this alignment issue can also be alleviated in 2D imaging using backscattering amplitude method, which may assist in estimating out-of-plane positioning during longitudinal imaging(54).”

Reviewer 3 (Public Review):

• It is unclear whether multiple animals were used in the statistical analysis.

Thank you for bringing this to our attention. We acknowledge that the original version did not clearly indicate the use of animals in the statistical analysis. In the revised manuscript, we have added Supplementary Figure 1 to specify the mice used, and we have labeled each mouse accordingly in the figures or captions. In the revised Figures 4 and 6, we have ensured that each quantitative analysis figure or its caption clearly indicate the specific mice.

• Generalizations are sometimes drawn from what seems to be the analysis of a single vessel.

Thank you for pointing this out. To enhance the generalizability of our conclusions, we have expanded our analysis beyond single vessels in several parts of the study. For instance, in Figure 2, we analyzed three vessels at different depths within the same brain region of a single mouse, and we have included additional results in the Supplementary Figure 2 to further support these findings. Additionally, we have revised the language in the manuscript to ensure that conclusions are appropriately qualified and avoid overgeneralization.

In Figures 4 and 6, we extended the analysis from single vessels to larger region-of-interest (ROI) analyses across entire brain regions. Unlike single-vessel measurements, which are susceptible to bias based on specific measurement locations, ROI-based analyses are less influenced by the operator and provide more objective, generalizable insights.

• The description of the statistical analysis is mostly qualitative.

We recognize that some aspects of the original statistical analysis (Figures 4 and 5 in the previous version) lacked rigor and description is more qualitative. The revised version of statistical analysis (Figure 4 and Figure 6) presents our findings from multiple dimensions, ranging from individual vessels to individual cortical ROI of arteries and veins, and ultimately to broader brain regions. For instance, as illustrated in the revised Figure 4f, the average cortical arterial flow speed decreases by approximately 20% from anesthesia to wakefulness, while venous flow speed decreases by an average of 40%, with the reduction in venous flow speed being significantly greater than that of arterial flow. We believe that this kind of description offers more quantitative analysis.

For more examples, please refer to the Results section where Figure 4 (Line 169 to Line 207) and Figure 6 (Line 224 to Line 258) are described. These sections have been extensively rewritten to emphasize quantitative interpretation of the data. Each part of the analysis now focuses more heavily on quantitative analyses that consistently show similar trends across all animals.

• Some terms used are insufficiently defined.

• Additional limitations should be included in the discussion.

• Some technical details are lacking. 

Thank you for highlighting these issues. In response, we have made several improvements in the revised manuscript to address these issues. We have clarified terms such as “vascularity” (Line 547) and “saturation point” (Line 112) to ensure precision and prevent ambiguity. We have expanded the discussion (Line 310 to Line 377) to include limitations such as motion correction challenges and advances in ULM imaging methods, including dynamic ULM and backscattering amplitude techniques. We have added further details on interleaved sampling (Line 494 to Line 497), ULM tracking (Line 517 to Line 529), and quantitative analysis (Line 535 to Line 551) in the Methods section to provide a clearer understanding of our approach. 

Please refer to our other responses for more specific adjustments.

• Without information about whether the results obtained come from multiple animals, it is difficult to conclude that the authors generally achieved their aim. They do achieve it in a single animal. The results that are shown are interesting and could have an impact on the ULM community and beyond. In particular, the experimental setup they used along with the high reproducibility they report could become very important for the use of ULM in larger animal cohorts.

We thank the reviewer for recognizing the impact of our work. We also acknowledge that there were some issues—specifically, we did not provide sufficient proof of reproducibility. In the revised version, we have included additional animal experiment results to ensure that the conclusions were not drawn from a single animal but are generally representative of our aim. (See supplementary figure 1 for detailed use of the animals) 

Reviewer 3 (Recommendations For The Authors):

• The manuscript would be more convincing by removing some of the superlatives used in the text. For instance, shouldn't "super-resolution ultrasound localization microscopy" simply be "ultrasound localization microscopy"? Expressions such as "first study", "essential", and "invaluable", etc could be replaced by more factual terms. The word "significant" is also used sometimes with statistics to back it up and sometimes without.

Thank you for highlighting this issue. We have removed the superlatives throughout the manuscript to make the language more precise. For instance, we have simplified “super-resolution ultrasound localization microscopy” to “ultrasound localization microscopy” throughout the main text and removed expressions such as “first study” and “invaluable”. We also reviewed all uses of “essential” and “significant,” replacing “essential” with more modest alternatives where it does not indicate a strict requirement. Similarly, where “significant” does not refer to statistical significance, we have used other terms to avoid any ambiguity.

• The section "Microbubble count serves as a quantitative metric for awake ULM image reconstruction" had several issues that I think should be addressed. Mainly, the authors make the case that after detecting 5 million microbubbles, there is no clear gain in detecting more. The argument is not very convincing as we know many vessels will not have had a microbubble circulate in them within that timeframe, which will be especially true in smaller vessels. While the analysis in Figure 2 shows nicely that the diameter estimate for vessels in the 20-30 um range is stable at 5 million microbubbles, it is not necessarily the case for smaller vessels. A better approach here might be to select, e.g., a total of 5 million detected microbubbles for practical reasons and then to determine which vessel parameters estimation (e.g., diameter, flow velocity) remain stable. In addition:

a. Terms such as 'complete ULM reconstruction', 'no obvious change', 'ULM image saturation' are not well defined within the manuscript.

Thank you for pointing out these issues and for offering a more rigorous approach. We completely agree with your suggestion. While our analysis demonstrated stable diameter estimates for vessels with diameter around 20 µm at 5 million microbubbles, this does not necessarily ensure stability for smaller vessels. Therefore, the choice of 5 million microbubbles was primarily for practical reasons. In the revised version, we have provided a more objective description and clarification of this limitation. We also recognize that terms such as “complete ULM reconstruction,” “no obvious change,” and “ULM image saturation” were not well defined and may have caused confusion, reducing the rigor of this manuscript. Based on your feedback, we have clearly defined “ULM image saturation” within the context of our study, removed absolute and ambiguous terms like “complete ULM reconstruction” and “no obvious change”. We revised the entire section accordingly:

(Line 109) “To facilitate equitable comparison of brain perfusion at different states, a practical saturation point enabling stable quantification of most vessels needs to be established. Our observations indicated that when the cumulative MB count reached 5 million, ULM images achieved a relatively stable state. Accordingly, in this study, the saturation point was defined as a cumulative MB count of 5 million. There are also possible alternatives for ULM image normalization. For example, different ULM images can be normalized to have the same saturation rate. However, the proposed method of using the same number of cumulative MB count for normalization enables the analysis of blood flow distribution across different brain regions from a probabilistic perspective. The following analysis substantiates this criterion.

Fig. 2a compares ULM directional vessel density maps and flow speed maps generated with 1, 3, 5, and 6 million MBs, using the same animal as shown in Fig. 1. To quantitatively confirm saturation, multiple vessel segments were selected for further analysis. Fig. 2b presents the measured vessel diameter for a specific segment at various MB counts. After binarizing the ULM map, the vessel diameter was measured by calculating the distance from the vessel centerline to the edge. Each point along the centerline of the segment provided a diameter measurement, enabling calculation of the mean and standard error. At low MB counts, vessels appeared incompletely filled, leading to inaccurate estimation of vessel diameter due to incomplete profiles. For example, at 1–2 million MBs, the binarized ULM map displayed a width of only one or two pixels along the segment. As a result, the measurements always yielded the same diameter values (two pixels, ~10um) with a consistently low standard error of the mean across the entire segment. With increased MB counts, the measured vessel diameter gradually rose, ultimately reaching saturation. The plots in Fig. 2b show that vessel diameter stabilized at 5 million MB count. Additionally, Fig. 2c illustrates the changes in flow velocity measured at different cumulative MB counts. The violin plots display the distribution of flow speed estimates for all valid centerline pixels within the selected segment. At low MB counts (1–3 million), flow velocity estimates fluctuated, but they stabilized as the MB count increased (4–6 million MBs). At 5 million MBs, flow velocity estimates were nearly identical to those at 6 million MBs, corroborating previous findings that vessel velocity measurements stabilize as MB count grows(39). To assess the generalizability of the 5 million MB saturation condition, vessel segments from three different mice across various brain regions were examined. The results, shown in Supplementary Fig. 2, confirm that this saturation criterion applies broadly. Although the 5 million MB threshold may not ensure absolute saturation for all vessels, it is generally effective for vessels larger than 15 μm. This MB count threshold was therefore adopted as a practical criterion.” 

b. The choice of 10 consecutive tracking frames is arbitrary and should be described as such unless a quantitative optimization study was conducted. Was there a gap-filling parameter? What was the maximum linking distance and what is its impact on velocity estimation?

Thank you for your comment. We acknowledge that the choice of 10 consecutive tracking frames was based on our common practice rather than a specific quantitative optimization. Additionally, with the uTrack algorithm, we set both the gap-filling parameter and maximum linking distance to 10 pixels. Setting these parameters too high could potentially overestimate velocity. These details have now been added to the Methods section for clarity:

(Line 517) “The choice of 10 consecutive frames (10 ms) was based on established practice but can be adjusted as needed. For the uTrack algorithm, two additional key parameters were specified: the maximum linking distance and the gap-filling distance, both set to 10 pixels (~50 microns). This configuration means that only bubble centroids within 10 pixels of each other across consecutive frames are considered part of the same bubble trajectory. Additionally, when the start and end points of two tracks fall within this threshold, the gap-filling parameter merges them into a single, continuous track. It is important to select these parameters carefully, as overly large values could lead to an overestimation of flow velocity. By setting the maximum linking distance to 10 pixels, we effectively limited the measurable velocity to 50 mm/s, under the assumption that no bubble would exceed a 50-micron displacement within the 1 ms interval between frames. After determining bubble tracks with the specified parameters for uTrack algorithm, accumulating the MB tracks resulted in the flow intensity map. Considering the velocity distribution across the mouse brain, this 50 mm/s limit ensures that the vast majority of blood flow is captured accurately.”

c. 'The plots (Figure 2b) clearly indicate that the vessel diameter stabilized beyond 5 million MB count.' This is true for one vessel. To generalize that claim, the analysis should be performed quantitatively on a larger sample of vessels in various areas of the brain, across multiple animals.

Thank you for pointing out this limitation. We agree that conclusions drawn from a single vessel cannot be generalized across all regions. Following your suggestion, we have added Supplementary Figure 2, where we analyzed multiple vessels from different brain regions across three mice. This expanded analysis further confirms that a 5 million MB count is sufficient to stabilize vessel diameter measurements across various samples.

(Line 133) “To assess the generalizability of the 5 million MB saturation condition, vessel segments from three different mice across various brain regions were examined. The results, shown in Supplementary Fig. 2, confirm that this saturation criterion applies broadly. Although the 5 million MB threshold may not ensure absolute saturation for all vessels, it is generally effective for vessels larger than 15 μm. This MB count threshold was therefore adopted as a practical criterion.” 

• "Statistical analysis validates the increase in blood flow induced by anesthesia" is a very interesting section but even though a quantitative analysis was conducted in Figure 5, the language used remains mostly qualitative. I think this section should include quantitative conclusions from the statistical analysis to increase the impact of this work.

Thank you for your valuable feedback. We recognize that some aspects of the original quantitative analysis (Figures 4 and 5 in the previous version) lacked rigor, such as the classification of arteries, veins, and capillaries, and that the data presented in each row of Figure 5 represented only one mouse per coronal section, limiting the generalizability of statistical conclusions.

In response to the reviewers’ feedback, the revised version incorporates a new approach by merging the previous Figure 4 and Figure 5 into a single, consolidated figure (now Figure 4). This updated figure aims to present our findings from multiple dimensions, ranging from individual vessels to individual cortical ROI of arteries and veins, and ultimately to broader brain regions. We have focused on quantitative analyses that consistently show similar trends across all animals. For instance, as illustrated in the revised Figure 4f, the average cortical arterial flow speed decreases by approximately 20% from anesthesia to wakefulness, while venous flow speed decreases by an average of 40%, with the reduction in venous flow speed being significantly greater than that of arterial flow. We believe that this approach offers more insightful analysis and enhances the overall impact of the study.

For more examples, please refer to the revised Results section where Figure 4 are described (from Line 169 to Line 212). These sections have been extensively rewritten to emphasize quantitative interpretation of the data. Each part of the analysis now focuses more heavily on quantitative analyses that consistently show similar trends across all animals.

• In the methods, it is claimed that 6 healthy female C57 mice were used in the study, but it is hard to tell whether more than one animal is shown in the figures. It is also unclear whether the statistics were performed within or across animals. Since one of the major strengths of the manuscript is that it shows the feasibility of performing reproducible measurements using ULM, most figures should be repeated for each individual animal and provided in supplementary data and statistics should be performed across animals.

Thank you for bringing this to our attention. We acknowledge that the original version did not clearly indicate the use of individual animals. In the revised manuscript, we have added Supplementary Figure 1 to specify the mice used, and we have labeled each mouse accordingly in the figures or captions. Additionally, we included statistics across animals in the revised Figures 4 and 6, and detailed data for each individual mouse are now provided in Supplementary Figures 3 and 4.

• The effect of aliasing should be discussed given that 1) a high-frequency probe is used along with a correspondingly relatively low frame rate (1000 fps) and 2) Doppler filtering is used to separate upward from downward-moving microbubbles. There will be microbubbles that circulate faster than the Nyquist limit, which will thus appear as moving in the opposite direction in the Doppler spectrum. It would be important to double-check that the effect is not too important and to report this as a limitation in the discussion.

Thank you for highlighting this important point. Aliasing is indeed a relevant issue to consider, especially for higher flow velocities in large vessels. We have added a discussion on this limitation in the revised manuscript:

(Line 359) “Based on the maximum linking distance and gap closing parameters outlined in the Methods section, blood flow with velocities below 50 mm/s can be detected. However, the use of a directional filter to estimate flow direction may introduce aliasing. MBs moving at higher velocities may be subject to incorrect flow direction estimation due to aliasing effects. Given that the compounded frame rate is 1000 Hz, with an ultrasound center frequency of 20 MHz and a sound speed of 1540 m/s, the relationship between Doppler frequency and the axial blood flow velocity(12) indicates that aliasing will not occur for axial flow velocities below 19.25 mm/s. In all flow velocity maps presented in this study, the range is limited to a maximum of 15 mm/s, remaining below the critical threshold for aliasing. Additionally, all vessels analyzed in the violin plots for arteriovenous flow comparisons fall within this range. While cortical arterioles and venules generally exhibit moderate flow speeds, aliasing remains a factor to consider when combining directional filtering with velocity analysis.”

• The method used to classify vessels may be incorrect and may not be needed. I would recommend the authors not use it and describe the vessels as vessels that branch in or out, etc. Applying an arbitrary threshold of 2 to detect capillaries is also not very convincing. I understand that the authors might decide to maintain this nomenclature, in which case I would recommend clearly explaining it at the beginning of the manuscript along with some of the caveats that are already reported in the discussion.

Thank you for your comments on our vessel classification method. We recognize the limitations of the previous approach and, in order to enhance the rigor of the study, we have opted not to continue using this method in the revised manuscript.

In the revised analysis regarding artery and vein, we focus solely on penetrating vessels in the cortex. For these vessels, it is generally accepted that downward-flowing vessels are arterioles, while upward-flowing vessels are venules. Accordingly, in the revised Figures 4 and 6, we analyze arterioles and venules exclusively in the cortex, without relying on the previous classification method that could be considered controversial.

Additionally, we agree that classifying vessels with values below 2 as capillaries was not a robust approach. Thus, we have removed all related analyses from the revised manuscript.

Minor comments:

• Line 16: "resolves capillary-scale ..."; it is not clear that the resolution that is achieved in this work is at the capillary scale.

Thank you for your valuable feedback. We understand that “capillary-scale” may overstate the achieved resolution in our work. To clarify, we have revised the sentence as follows:

(Line 18) “Ultrasound localization microscopy (ULM) is an emerging imaging modality that resolves microvasculature in deep tissues with high spatial resolution.” 

This adjustment more accurately reflects the resolution capabilities of ULM as used in our study.

• Line 22: 'vascularity' is not well defined in the manuscript. Consider defining or using another term.

Thank you for pointing out the need for clarification on vascularity. We acknowledge that our initial use of the term “vascularity” may have been unclear and potentially confusing. In the revised manuscript, we have included a clear definition of “vascularity” in the Methods section under Quantitative Analysis of ULM Images (Line 534). 

The following sentence shows the definition of vascularity:

(Line 547) “Vascularity was defined as the proportion of the pixel count occupied by blood vessels within each ROI, obtained by binarizing the ULM vessel density maps and calculating the percentage of the pixels with MB signal.”

We have also added an instant definition when it was firstly used in Results part:

(Line 161) “When comparing vessel density maps, ULM images that are acquired in the awake state demonstrate a global reduction of vascularity, which refers to percentage of pixels that occupied by blood vessels.”

• Line 30: I'm not convinced the first two sentences are useful.

Thank you for pointing out this issue. The opening sentence of the article lacked focus and was too broad. We have rewritten the sentence as follows:

(Line 34) “Sensitive imaging of correlates of activity in the awake brain is fundamental for advancing our understanding of neural function and neurological diseases.”

• Line 37: 'micron-scale capillaries': this expression is unclear. Capillaries are typically micron-scaled, so it gives the impression that ULM can image ULM at the one-micron scale, which is not the case.

Thank you for your helpful comment. We agree that “micron-scale capillaries” could be misleading, as it might imply a resolution at the single-micron level. To clarify, we have revised the sentence as follows:

(Line 40) “ULM is uniquely capable of imaging microvasculature situated in deep tissue (e.g., at a depth of several centimeters).”

This revised wording more accurately describes ULM’s capability without implying single-micron level resolution.

• Line 74: I don't think motion-free imaging is possible in the context of awake animals. Consider 'limiting motion' instead.

Thank you for pointing out the potential issue with the term “motion-free”. We agree that achieving entirely motion-free imaging is challenging, especially in the context of awake animals. In response to your suggestion, we have revised the sentence to better reflect this limitation:

(Line 76) “To achieve consistent ULM brain imaging while allowing limited movement in awake animals, a headfixed imaging platform with a chronic cranial window was used in this study.”

This revised wording more accurately conveys our approach to minimizing motion without implying that motion is completely eliminated.

• Line 134:'clearly reveals decreased vessel diameter' How was that demonstrated?

• Line 153: 'significant' according to which statistical test?

• Line 167: 'slight increase', by how much, is it significant?

• Line 183: 'smaller vessels' the center of the distribution is not at 10mm/s, and velocity is not necessarily correlated with diameter.

• Line 184: 'more large vessels', see above. What is a large vessel, and how was this measured?

• Line 205: 'significantly lower', according to which statistical test?

We acknowledge that the original version did not properly use the terms of statistical analysis. In the revised manuscript, we have deleted the related points, and rewritten the statistical analysis part to ensure the terms are used correctly. Please refer to the revised part of “ULM reveals an increase in blood flow induced by isoflurane anesthesia” (From Line 169 to Line 209). In the revised Figures 4 and 6, we have also ensured that each quantitative analysis figure or its caption is clearly explained.

•    Line 398: the interleaved sampling scheme should be described in more detail.

Thank you for pointing out this issue. The previous version did not clearly explain the details of interleaved sampling. We have now added the following paragraph to the Ultrasound imaging sequence section in Methods:

(Line 494) “Interleaved sampling is employed to capture high-frequency echoes more effectively. With the system’s sampling rate limited to 62.5 MHz, the upper limit of the center frequency of the transducer passband is 15.625 MHz. To mitigate aliasing, two transmissions are sent per angle, staggered in time. This approach effectively doubles the sampling rate, ensuring more accurate image reconstruction.”

• Figure 1: Which mouse is it? Are these results consistent across all animals?

• Figure 2: Which mouse is it? Are these results consistent across all animals?

• Figure 3: Which mouse is it? Are these results consistent across all animals?

• Figure 4: Which mouse is it? Are these results consistent across all animals?

• Figure 5: Is it a single mouse or multiple mice? Are these results consistent across all animals?

We acknowledge that the original version did not clearly indicate the numbers of animals in the statistical analysis. In the revised manuscript, we have added Supplementary Figure 1 to specify the mice used, and we have labeled each mouse accordingly in the figures or captions. In the revised Figures 4 and 6, we have ensured that each quantitative analysis figure or its caption clearly indicate the specific mice.

For original Figures 1 and 2, these are presented as case studies to illustrate the methodology. Since the anesthesia time required for tail vein injection for each animal varies slightly, it is challenging to have the consistent time taken for each mouse to recover from anesthesia across all mice. For instance, in Figure 1, the mouse took nearly 500 seconds to recover from anesthesia, but this duration is not consistent across all animals, which is a limitation of the bolus injection technique. We have noted this point in the discussion (discussion on the limitation of bolus injection), and we have also clarified in the results section and figure captions that these figures represent a case study of a single mouse rather than a standardized recovery time for all animals.

We further clarified this point in the end of the Figure 2 caption:

(Fig.2 caption) “This figure presents a case study based on the same mouse shown in Fig 1. The x-axis for d-f begins at 500 seconds because, at this point, the mouse’s pupil size stabilized, indicating it had recovered to an awake state. Consequently, ULM images were accumulated starting from this time. It is important to note that not every mouse requires 500 seconds to fully awaken; the time to reach a stable awake state varies across individual mice.” We added the following statement before introducing Figure 1e:

(Line 93) “Due to differences in tail vein injection timing and anesthesia depth, the time required for each mouse to fully awaken varied. Although it was not feasible to get pupil size stabilized just after 500 seconds for each animal, ULM reconstruction only used the data that acquired after the animal reached full pupillary dilation, to ensure that ULM accurately captures the cerebrovascular characteristics in the awake state.”

We added the following statement before introducing Figure 2d:

(Line 139) “To further verify that the proposed MB bolus injection method can help to achieve ULM image saturation shortly after mice awaken from anesthesia, an analysis on the change in MB concentration over time was conducted once pupil size had stabilized (T = 500s).”

For Figures 3, 4, and 5 (in the revised version, Figures 4 and 5 have been combined into a single Figure 4), the data represents results from three individual mice, with each coronal plane corresponding to a different mouse. In the revised version, we have added labels to indicate the specific mouse in each image to improve clarity. We also recognize that some analyses in the original submission (original Figure 5) may have lacked sufficient statistical power due to the small sample size. Therefore, in the revised version, we have focused only on findings that were consistently observed across the three mice to ensure robust conclusions.

Minor corrections and typos from all reviewers:

We would like to sincerely thank the reviewers for their careful reading of our manuscript. We appreciate the time and effort taken to point out the minor typographical errors. We have carefully addressed and corrected all the identified typos, as listed below:

From Reviewer #1:

• Line 316: "insensate": correct, please.

(Line 409) “After confirming that the mouse was anesthetized, the head of the animal was fixed in the stereotaxic frame.”

From Reviewer #3:

• Line 15: Super-resolution ultrasound localization microscopy -- consider removing super-resolution as it gives the impression that it is different from standard ULM.

(Line 18) “Ultrasound localization microscopy (ULM) is an emerging imaging modality that resolves microvasculature in deep tissues with high spatial resolution.”

• Line 39: typo: activities should be activity.

(Line 41) “ULM can also be combined with the principles of functional ultrasound (fUS) to image whole-brain neural activity at a microscopic scale.”

• Line 47: typo: over under.

(Line 50) “Therefore, in neuroscience research, brain imaging in the awake state is often preferred over imaging under anesthesia.”

Once again, we are grateful for the reviewers’ thorough review and valuable input, which have helped us improve the clarity and precision of the manuscript.

Reviewer #2 (Public review):

The authors developed a strategy to image inferior olive somata via viral GCaMP6s expression, an implanted GRIN lens, and a one-photon head-mounted microscope, providing the first in vivo somatic recordings from these neurons. The main new findings relate to the activation of the nucleoolivary pathway, specifically that: this manipulation does not produce a spiking rebound in the IO; it exerts a larger effect on spontaneous IO spiking than stimulus (airpuff)-evoked spiking. In addition, several findings previously demonstrated in vivo in Purkinje cell complex spikes or inferior olivary axons are confirmed here in olivary somata: differences in event sizes from single cells versus co-activated cells; reduced coactivation when activating the NO pathway; more coactivation within a single zebrin compartment.

The study presents some interesting findings, and for the most part, the analyses are appropriate. My two principal critiques are that the study does not acknowledge major technical limitations and their impact on the claims; and the study does not accurately represent prior work with respect to the current findings.

Several significant technical limitations necessarily impact the veracity of several of the claims:

(1) The authors use GCaMP6s, which has a tau_1/2 of >1 s for a normal spike, and probably closer to 2 s (10.1038/nature12354) for the unique and long type of olivary spikes that give rise to axonal bursts (10.1016/j.neuron.2009.03.023). Indeed, the authors demonstrate as much (Fig. 2B1). This affects at least several claims:

a. The authors report spontaneous spike rates of 0.1 Hz. They attribute this to anesthesia, yet other studies under anesthesia recording Purkinje complex spikes via either imaging or electrophysiology report spike rates as high as 1.5 Hz (10.1523/JNEUROSCI.2525-10.2011). This discrepancy is not acknowledged and a plausible explanation is not given. Citations are not provided that demonstrate such low anesthetized spike rates, nor are citations provided for the claim that spike rates drop increasingly with increasing levels of anesthesia when compared to awake resting conditions. More likely, this discrepancy reflects spikes that are missed due to a combination of the indicator kinetics and low imaging sensitivity (see (2)), neither of which are presented as possible plausible alternative explanations.

b. Many claims are made throughout about co-activation ("clustering"), but with the GCaMP6s rise time to peak (0.5 s), there is little technical possibility to resolve co-activation. This limitation is not acknowledged as a caveat and the implications for the claims are not engaged with in the text.

c. The study reports an ultralong "refractory period" (L422-etc) in the IO, but this again must be tempered by the possibility that spikes are simply being missed due to very slow indicator kinetics and limited sensitivity. Indeed, the headline numeric estimate of 1.5 s (L445) is suspiciously close to the underlying indicator kinetic limitation of ~1-2 s.

(2) The study uses endoscopic one-photon miniaturized microscope imaging. Realistically, this is expected to permit an axial point spread function (z-PSF) on the order of ~40um, which must substantially reduce resolution and sensitivity. This means that if there *is* local coactivation, the data in this study will very likely have individual ROIs that integrate signals from multiple neighboring cells. The study reports relationships between event magnitude and clustering, etc; but a fluorescence signal that contains photons contributed by multiple neighboring neurons will be larger than a single neuron, regardless of the underlying physiology - the text does not acknowledge this possibility or limitation.

Second, the text makes several claims for the first multicellular in vivo olivary recordings. (L11; L324, etc). I am aware of at least two studies that have recorded populations of single olivary axons using two-photon Ca2+ imaging up to 6 years ago (10.1016/j.neuron.2019.03.010; 10.7554/eLife.61593). This technique is not acknowledged or discussed, and one of these studies is not cited. No argument is presented for why axonal imaging should not "count" as multicellular in vivo olivary recording: axonal Ca2+ reflects somatic spiking.

Author response:

Reviewer #1 (Public review):

Summary:

This manuscript by Guo and Uusisaari describes a series of experiments that employ a novel approach to address long-standing questions on the inferior olive in general and the role of the nucleoolivary projection specifically. For the first time, they optimized the ventral approach to the inferior olive to facilitate imaging in this area that is notoriously difficult to reach. Using this approach, they are able to compare activity in two olivary regions, the PO and DAO, during different types of stimulation. They demonstrate the difference between the two regions, linked to Aldoc-identities of downstream Purkinje cells, and that there is co-activation resulting in larger events when they are clustered. Periocular stimulation also drives larger events, related to co-activation. Using optogenetic stimulation they activate the nucleoolivary (N-O) tract and observe a wide range of responses, from excitation to inhibition. Zooming in on inhibition they test the assumption that N-O activation can be responsible for suppression of sensoryevoked events. Instead, they suggest that the N-O input can function to suppress background activity while preserving the sensory-driven responses.

Strengths:

This is an important study, tackling the long-standing issue of the impossibility to do imaging in the inferior olive and using that novel method to address the most relevant questions. The experiments are technically very challenging, the results are presented clearly and the analysis is quite rigorous. There is quite a lot of room for interpretation, see weaknesses, but the authors make an effort to cover many options.

Weaknesses:

The heavy anesthesia that is required during the experiment could severely impact the findings. Because of the anesthesia, the firing rate of IO neurons is found to be 0.1 Hz, significantly lower than the 1 Hz found in non-anesthetized mice. This is mentioned and discussed, but what the consequences could be cannot be understated and should be addressed more. Although the methods and results are described in sufficient detail, there are a few points that, when addressed, would improve the manuscript.

We sincerely thank the reviewer for their encouraging comments and recognition of our study’s significance. We fully acknowledge the confounding effects of the deep anesthesia used in our experiments, which was necessary to ensure the animals’ welfare while establishing this technically demanding methodology. We elaborate on these effects below and will further clarify them in the revised manuscript.

Ultimately, the full resolution of this issue will require recordings in awake animals, as we consider our approach an advancement from acute slice preparations but not yet a complete representation of in vivo IO function. However, key findings from our study—such as amplitude modulation with co-activation and the potential role of IO refractoriness in complex spike generation—could be further explored in existing cerebellar cortical recordings from awake, behaving animals. We hope our work will motivate re-examination of such datasets to assess whether these mechanisms contribute to overall cerebellar function.

Reviewer #1 (Recommendations for the authors):

On page 10 the authors indicate that 2084 events were included for DAO and 1176 for PO. Is that the total number of events? What was the average and the range per neuron and the average recording duration?

Thank you for pointing out lack of clarity. The sentence should say "in total, 2084 and 1176 detected events from DAO and PO were included in the study". We will add the averages and ranges of events detected per neuron in different categories, as well as the durations of the recordings (ranging from 120s to 270s) to the tables.

On page 10 it is also stated that: "events in PO reached larger values than those in DAO even though the average values did not differ". Please clarify that statement. Which parameter + p-value in the table indicates this difference?

Apologies for omission. Currently the observation is only visible in the longer tail to the right in the PO data in Figure 2B2. We will add the range of values (3.0-75.2 vs 3.1-39.6 for PO and DAO amplitudes, respectively) in text and the tables in the revision.

Abbreviating airpuff to AP is confusing, I would suggest not abbreviating it.

Understood. We will change AP to airpuff in the text. In figure labels, at least in some panels, the abbreviation will be necessary due to space constraints.

What type of pulse was used to drive ChrimsonR? Could it be that the pulse caused a rebound-like phenomenon with the pulse duration that drove the excitation?

As described on line 229 and in the Methods, we used 5-second trains of 5-ms LED light pulses. Importantly, these stimulation parameters were informed by our extensive in vitro examination of various stimulation patterns (Lefler et al., 2014), which consistently produced stable postsynaptic responses without inducing depolarization or rebound effects. Additionally, Loyola et al. (2024) reported no evidence of rebound activity in IO cells following optogenetic activation of N-O axons in the absence of direct neuronal depolarization. We will incorporate these considerations into the discussion, while also acknowledging that unequivocal confirmation of “direct” rebound excitation would require intracellular recordings, such as patch clamp experiments.

The authors indicate that the excitatory activity was indistinguishable in shape from other calcium activity, but can anything be said about the timing (the scale bar in Figure 4A2 has no value, is it the same 2s pulse)?

Apologies for oversight in labeling the scale bar in Figure 4A2 (it is 2s). While we deliberately refrain from making strong claims regarding the origin of the NO-evoked spikes, their timing can be examined in more detail in Figure 4 - Supplement 1, panels C and D. We will make sure this is clearly stated in the revised text.

Did the authors check for accidental sparse transfection with ChrimsonR of olivary neurons in the post-mortem analysis?

Good point! However, we have never seen this AAV9-based viral construct to drive trans-synaptic expression in the IO, nor is this version of AAV known to have the capacity for transsynaptic expression in general.

No sign of retrograde labeling (via the CF collaterals in the cerebellar nuclei) was seen either. Notably, the hSyn promoter used to drive ChrimsonR expression is extremely ineffective in the IO. Thus, we doubt that such accidental labeling could underlie the excitatory events seen upon N-O stimulation. We will add these mentions with relevant references to the discussion of the revised manuscript.

On page 18 the authors state that: "The lower SS rate was attributed to intrinsic factors of PNs, while the reduced frequency of CSs was speculated to result from increased inhibition of the IO via the nucleo-olivary (N-O) pathway targeting the same microzone." I think I understand what you mean to say, but this is a bit confusing.

Agreed. We will rephrase this sentence to clarify that a lower SS rate in a given microzone may lead to increased activation of inhibitory N-O axons that target the region of IO that sends CF to the same microzone.

Is airpuff stimulation not more likely to activate PO dan DAO because of the related modalities (more face vs. more trunk/limbs?), and thereby also more likely to drive event co-activation (as it is stated in the abstract).

We agree that the specific innervation patterns of different IO regions likely explain the discrepancy between previous reports of airpuff-evoked complex spikes in cerebellar cortical regions targeted by DAO and the absence of airpuff responses in the particular region of DAO accessible via our surgical approach. As in the present dataset virtually no airpuff-evoked events were seen in DAO regions, we are unable to directly compare airpuff-evoked event co-activation between PO and DAO. The higher co-activation for PO was observed for "spontaneous" activity.

The Discussion addresses the question of why N-O pathway activation does not remove the airpuff response.

Given the potentially profound effect, I would propose to expand the discussion on the role of aneasthesia, including longer refractory periods but also potential disruption of normal network interactions (even though individually the stimulations work). Briefly indicating what is known about alpha-chloralose would help interpret the results as well.

We fully agree that the anesthetic state introduces confounding factors that must be considered when interpreting our results. We will expand the discussion to address how anesthesia, particularly alphachloralose as well as tissue cooling, may contribute to prolonged refractory periods and potential disruptions in normal network interactions. However, we recognize that certain aspects cannot be fully resolved without recordings in awake animals. For this reason, we characterize our preparation as an "upgraded" in vitro approach rather than a fully representative in vivo model.

Please clearly indicate that the age range of P35-45 is for the moment of virus injection and specify the age range for the imaging experiment.

Apologies for the oversight. We will indicate these age ranges in the results (as they are currently only specified in Methods). The P35-45 range refers to moment of virus injection.

The methods indicate that a low-pass filter of 1Hz was used. I am sure this helps with smoothing, but does it not remove a lot of potentially interesting information. How would a higher low-pass filter affect the analysis and results?

We acknowledge that applying a 1 Hz low-pass filter inevitably removes high-frequency components, including potential IO oscillations and fine details such as spike "doublets." However, given the temporal resolution constraints of our recording approach, we prioritized capturing robust, interpretable events over attempting to extract finer features that might be obscured by both the indicator kinetics and imaging speed.

While a higher cut-off frequency could, in principle, allow more precise measurement of rise times and peak timings, it would also amplify high-frequency noise, complicating automated event detection and reducing confidence in distinguishing genuine neural signals from artifacts. Given these trade-offs, we opted for a conservative filtering approach to ensure stable event detection. Future work, particularly with faster imaging rates and improved sensors (GCaMP8s) will be used to explore the finer temporal structure of IO activity. We will deliberate on these matters more extensively in the revised discussion.

Reviewer #2 (Public review):

The authors developed a strategy to image inferior olive somata via viral GCaMP6s expression, an implanted GRIN lens, and a one-photon head-mounted microscope, providing the first in vivo somatic recordings from these neurons. The main new findings relate to the activation of the nucleoolivary pathway, specifically that: this manipulation does not produce a spiking rebound in the IO; it exerts a larger effect on spontaneous IO spiking than stimulus (airpuff)-evoked spiking. In addition, several findings previously demonstrated in vivo in Purkinje cell complex spikes or inferior olivary axons are confirmed here in olivary somata: differences in event sizes from single cells versus co-activated cells; reduced coactivation when activating the NO pathway; more coactivation within a single zebrin compartment.

The study presents some interesting findings, and for the most part, the analyses are appropriate. My two principal critiques are that the study does not acknowledge major technical limitations and their impact on the claims; and the study does not accurately represent prior work with respect to the current findings.

We thank the reviewer for recognising the value of the findings in our "reduced" in vivo preparation, and apologize for omissions in the work that led to critique. We will elaborate on these matters below and prepare a revised manuscript.

The authors use GCaMP6s, which has a tau1/2 of >1 s for a normal spike, and probably closer to 2 s (10.1038/nature12354) for the unique and long type of olivary spikes that give rise to axonal bursts (10.1016/j.neuron.2009.03.023). Indeed, the authors demonstrate as much (Fig. 2B1). This affects at least several claims:

a. The authors report spontaneous spike rates of 0.1 Hz. They attribute this to anesthesia, yet other studies under anesthesia recording Purkinje complex spikes via either imaging or electrophysiology report spike rates as high as 1.5 Hz (10.1523/JNEUROSCI.2525-10.2011). This discrepancy is not acknowledged and a plausible explanation is not given. Citations are not provided that demonstrate such low anesthetized spike rates, nor are citations provided for the claim that spike rates drop increasingly with increasing levels of anesthesia when compared to awake resting conditions.

We fully acknowledge that anesthesia is a major confounding factor in our study. Given the unusually invasive nature of our surgical preparation, we prioritized deep anesthesia to ensure the animals’ welfare. This, along with potential cooling effects from tissue removal and GRIN lens contact, likely contributed to the observed suppression of IO activity.

We recognize that reported complex spike rates under anesthesia vary considerably across studies, and we will expand our discussion to provide a more comprehensive comparison with prior literature. Notably, different anesthetic protocols, levels of anesthesia, and recording methodologies can lead to widely different estimates of firing rates. While we cannot resolve this issue without recordings in awake animals, we will clarify that our observed rates likely reflect both the effects of anesthesia and specific methodological constraints. We will also incorporate additional references to studies examining cerebellar activity under different anesthetic conditions.

More likely, this discrepancy reflects spikes that are missed due to a combination of the indicator kinetics and low imaging sensitivity (see (2)), neither of which are presented as possible plausible alternative explanations.

We acknowledge that the combination of slow indicator kinetics and limited optical power in our miniature microscope setup constrains the temporal resolution of our recordings. However, we are confident that we can reliably detect events occurring at intervals of 1 second or longer. This confidence is based on data from another preparation using the same viral vector and optical system, where we observed spike rates an order of magnitude higher.

That said, we do not make claims regarding the presence or absence of somatic events occurring at very short intervals (e.g., 100-ms "doublets," as described by Titley et al., 2019), as these would likely fall below our temporal resolution. We will clarify this limitation in the revised manuscript to ensure that the constraints of our approach are fully acknowledged.

While GCaMP6s is not as sensitive as more recent variants (Zhang et al., 2023, PMID 36922596), our previous work (Dorgans et al., 2022) demonstrated that its dynamic range and sensitivity are sufficient to detect both spikes and subthreshold activity in vitro. Although the experimental conditions differ in the current miniscope experiments, we took measures to optimize signal quality, including excluding recordings with a low signal-to-noise ratio (see Methods). This need for high signal fidelity also informed our decision to limit the sampling rate to 20 fps. In future work, we plan to adopt newer GCaMP variants that were not available at the start of this project, which should further improve sensitivity and temporal resolution.

Many claims are made throughout about co-activation ("clustering"), but with the GCaMP6s rise time to peak (0.5 s), there is little technical possibility to resolve co-activation. This limitation is not acknowledged as a caveat and the implications for the claims are not engaged with in the text.

As noted in the manuscript (L492-), "interpreting fluorescence signals relative to underlying voltage changes is challenging, particularly in IO neurons with unusual calcium dynamics." We acknowledge that the slow rise time of GCaMP6s ( 0.5 s) limits our ability to precisely resolve the timing of co-activation at very short intervals. However, given the relatively slow timescales of IO event clustering and the inherent synchrony in olivary network dynamics, we believe that the observed co-activation patterns remain meaningful, even if finer temporal details cannot be fully resolved.

To ensure clarity, we will expand this section to explicitly acknowledge the temporal resolution limitations of our approach and discuss their implications for interpreting co-activation. While the precise timing of individual spikes within a cluster may not be resolvable, the observed increase in event magnitude with coarse co-activation suggests that clustering effects remain functionally relevant even when exact spike synchrony is not detectable at millisecond resolution.

This finding is consistent with the idea that co-activation enhances calcium influx, leading to larger amplitude events — a relationship that does not require perfect temporal resolution to be observed. The fact that this effect persists across a broad range of clustering windows (as shown in Figure 2 Supplement 2) further supports its robustness. While we cannot make strong claims about precise spike timing within these clusters nor about the mechanism underlying enhanced calcium signal, our results demonstrate that co-activation may influence IO activity in a quantifiable way. We will clarify these points in the revised manuscript to ensure that our findings are appropriately framed given the temporal constraints of our imaging approach.

The study reports an ultralong "refractory period" (L422-etc) in the IO, but this again must be tempered by the possibility that spikes are simply being missed due to very slow indicator kinetics and limited sensitivity. Indeed, the headline numeric estimate of 1.5 s (L445) is suspiciously close to the underlying indicator kinetic limitation of 1-2 s.

Our findings suggest a potential refractory period limiting the frequency of events in the inferior olive under our recording conditions. This interpretation is supported by the observed inter-event interval distribution, the inability of N-O stimulation to suppress airpuff-evoked events, and lower bounds reported in earlier literature on complex spike intervals recorded in awake animals under various behavioral contexts. Taking into account the likely cooling of tissue, a refractory period of 1.5s is not unreasonable. Of course, we recognize that the slow decay kinetics of GCaMP6s may cause overlapping fluorescence signals, potentially obscuring closely spaced events. This is in line with data presented in the Chen et al 2013 manuscript describing GCaMp6s (PMID: 36922596; Figure 3b showing events detected with intervals less than 500 ms).

The consideration of refractoriness only arose late in the project while we were investigating the explanations for lack of inhibition of airpuff-evoked spikes. Future experiments, particularly in awake animals, will be instrumental in validating this interpretation. To ensure that the refractory period is understood as one possible mechanism rather than a definitive explanation, we will rephrase the discussion to clarify that while our data are compatible with a refractory period, they do not establish it conclusively.

The study uses endoscopic one-photon miniaturized microscope imaging. Realistically, this is expected to permit an axial point spread function (z-PSF) on the order of 40um, which must substantially reduce resolution and sensitivity. This means that if there *is* local coactivation, the data in this study will very likely have individual ROIs that integrate signals from multiple neighboring cells. The study reports relationships between event magnitude and clustering, etc; but a fluorescence signal that contains photons contributed by multiple neighboring neurons will be larger than a single neuron, regardless of the underlying physiology - the text does not acknowledge this possibility or limitation.

We acknowledge that the use of one-photon endoscopic imaging imposes limitations on axial resolution, potentially leading to signal contributions from neighboring neurons. To mitigate this, we applied CNMFe processing, which allows for the deconvolution of overlapping signals and the differentiation of multiple neuronal sources within shared pixels. However, as the reviewer points out, if two neurons are perfectly overlapping in space, they may be treated as a single unit.

To clarify this limitation, we will expand the discussion to explicitly acknowledge the impact of one-photon imaging on signal separation and to emphasize that, while CNMFe helps resolve some overlaps, perfect separation is not always possible. As already noted in the manuscript (L495-), "the absence of optical sectioning in the whole-field imaging method can lead to confounding artifacts in densely labeled structures such as the IO’s tortuous neuropil." We will further elaborate on how this factor was considered in our analysis and interpretation.

Second, the text makes several claims for the first multicellular in vivo olivary recordings. (L11; L324, etc).

I am aware of at least two studies that have recorded populations of single olivary axons using two-photon Ca2+ imaging up to 6 years ago (10.1016/j.neuron.2019.03.010; 10.7554/eLife.61593). This technique is not acknowledged or discussed, and one of these studies is not cited. No argument is presented for why axonal imaging should not "count" as multicellular in vivo olivary recording: axonal Ca2+ reflects somatic spiking.

We appreciate the reviewer’s point and acknowledge the important prior work using two-photon imaging to record olivary axonal activity in the cerebellar cortex. However, while axonal calcium signals do reflect somatic spiking, these recordings inherently lack information about the local network interactions within the inferior olive itself.

A key motivation for our study was to observe neuronal activity within the IO at the level of its gap-junctioncoupled local circuits, rather than at the level of its divergent axonal outputs. The fan-like spread of climbing fibers across rostrocaudal microzones in the cerebellar cortex makes them relatively easy to record in vivo, but it also means that individual imaging fields contain axons from neurons that may be distributed across different IO microdomains. As a result, while previous work has provided valuable insight into olivary output patterns, it has not allowed for the examination of coordinated somatic activity within localized IO neuron clusters.

With apologies, we recognize that this distinction was not sufficiently emphasized in our introduction. We will clarify this key point and ensure that the important climbing fiber imaging studies are properly cited and contextualized in the revised manuscript.

Reviewer #2 (Recommendations for the authors):

The authors state: "we found no reports that examined coactivation levels between Z+ and Z- microzones in cerebellar complex spike recordings" (L359). Multiple papers (that are not cited) using AldolaceC-tdTomato mice with two photon Purkinje dendritic calcium imaging showed synchronization (at similar levels) within but not across z+/z- bands. (2015 10.1523/JNEUROSCI.2170-14.2015, 2023 https://doi.org/10.7554/eLife.86340).

We apologize for the misleading phrasing. We will rephrase this statement to: "While complex spike coactivation within individual zebrin zones has been extensively studied (references), we found no reports directly comparing the levels of intra-zone co-activation between Z+ and Z microzones."

Additionally, we will ensure that the relevant studies demonstrating synchronization within zebrin zones, as well as (lack of) interactions between neighboring zones, are properly cited and discussed in the revised manuscript.

The figures could use more proofreading, and several decisions should be reconsidered:

Normalizing the amplitude to maximum is not a good strategy, as it can overemphasize noise or extremely small-magnitude signals, and should instead follow standard convention and present in fixed units (3A2, 4B2, and even 2C).

As noted earlier, we have excluded recordings and cells with high noise or a low signal-to-noise ratio for event amplitudes, ensuring that such data do not influence the color-coded panels. Importantly, all quantitative analyses and traces presented in the manuscript are normalized to baseline noise level, not to maximal amplitude, ensuring that noise or low-magnitude signals do not skew the analysis.

The decision to use max-amplitude normalization in color-coded panels was made specifically to aid visualization of temporal structure across recordings. This approach allows for clearer comparisons without the distraction of inter-cell variability in absolute signal strength. However, we recognize the potential for confusion and will revise the Results text to explicitly clarify that the color-coded visualizations use a different scaling method than the quantitative analyses.

x axes with no units: Figures 2B2, 2E1, 3B2, 3C2, 5B2, 5C2, 5D2.

No colorbar units: 5A3 (and should be shown in real not normalized units).

No y axis units: 5D1.

No x axis label or units: 5E1.

5E3 says "stim/baseline" for the y-axis units and then the first-panel title says "absolute frequencies" meaning it’s *not* normalized and needs a separate (accurate) y-axis with units.

Illegibly tiny fonts: 2E1, 3E1, etc.

We will correct all these in the revised manuscript. Thank you for careful reading.

Author response:

Reviewer #1:

Summary:

In this study, the authors propose a "unifying method to evaluate inter-areal interactions in different types of neuronal recordings, timescales, and species". The method consists of computing the variance explained by a linear decoder that attempts to predict individual neural responses (firing rates) in one area based on neural responses in another area.

The authors apply the method to previously published calcium imaging data from layer 4 and layers 2/3 of 4 mice over 7 days, and simultaneously recorded Utah array spiking data from areas V1 and V4 of 1 monkey over 5 days of recording. They report distributions over "variance explained" numbers for several combinations: from mouse V1 L4 to mouse V1 L2/3, from L2/3 to L4, from monkey V1 to monkey V4, and from V4 to V1. For their monkey data, they also report the corresponding results for different temporal shifts. Overall, they find the expected results: responses in each of the two neural populations are predictive of responses in the other, more so when the stimulus is not controlled than when it is, and with sometimes different results for different stimulus classes (e.g., gratings vs. natural images).

Strengths:

(1) Use of existing data.

(2) Addresses an interesting question.

Unfortunately, the method falls short of the state of the art: both generalized linear models (GLMs), which have been used in similar contexts for at least 20 years (see the many papers, both theoretical and applied to neural population data, by e.g. Simoncelli, Paninsky, Pillow, Schwartz, and many colleagues dating back to 2004), and the extension of Granger causality to point processes (e.g. Kim et al. PLoS CB 2011). Both approaches are substantially superior to what is proposed in the manuscript, since they enforce non-negativity for spike rates (the importance of which can be seen in Figure 2AB), and do not require unnecessary coarse-graining of the data by binning spikes (the 200 ms time bins are very long compared to the time scale on which communication between closely connected neuronal populations within an area, or between related areas, takes place).

We thank the reviewer for this suggestion. Our goal was to use a simple and unified linear ridge regression framework that can be applied to both calcium imaging (mouse) and MUAe (monkey) data.

We will perform a GLM-based analysis enforcing non-negativity as suggested, including in the GLM any additional available variables that may contribute to the neuronal responses.

We also would like to note that:

● Macaque data: Our MUAe data are binned at 25 ms, not 200 ms. We used the envelope

of multi-unit activity as reported in the original study [1]. We did not perform spike sorting on these data and therefore, strictly speaking, this is not a point process and methods developed for point processes are not directly applicable.

● Mouse data: The Stringer et al. dataset [2,3] uses two-photon calcium imaging sampled at 2.5 or 3 Hz. Additionally, responses were computed by averaging two frames per stimulus (yielding an effective bin size of 666 ms or 800 ms), dictated by acquisition constraints. We will emphasize the low temporal resolution of these signals as a limitation in the discussion section, but we cannot improve the temporal resolution with our analyses. These signals are not point processes either (although there is a correlation between two-photon calcium signals and spike rates).

Regardless of these considerations, the reviewer’s points are well taken, and we will conduct additional analyses as described above.

In terms of analysis results, the work in the manuscript presents some expected and some less expected results. However, because the monkey data are based on only one monkey (misleadingly, the manuscript consistently uses the plural ‘monkeys’), none of the results specific to that monkey, nor the comparison of that one monkey to mice, are supported by robust data.

We will add data from at least two more monkeys, as suggested by the reviewer:

● First, we will include a second monkey from the same dataset [1]. The reason this monkey was not included in the original submission is that the dataset for this second monkey consisted of much less data than the original. For example, for the lights-off condition, the number of V4 channels with signal-to-noise ratio greater than 2 (recommended electrodes to use by dataset authors) is 9-12 in this second monkey, compared to 68-74 in the first monkey [1]. However, we will still add results for this second monkey.

● Additionally, we will include data from a new monkey by collaborating with the Ponce lab who will collect new data for this study.

One of the main results for mice (bimodality of explained variance values, mentioned in the abstract) does not appear to be quantified or supported by a statistical test.

We appreciate this point. We will conduct statistical tests to quantify the degree of bimodality and clarify these findings in the results.

Moreover, the two data sets differ in too many aspects to allow for any conclusions about whether the comparisons reflect differences in species (mouse vs. monkey), anatomy (L2/3-L4 vs. V1-V4), or recording technique (calcium imaging vs. extracellular spiking).

We agree that the methodological and anatomical differences between the mouse and monkey datasets make any direct cross-species comparisons hard to interpret. We explicitly discuss this point in the Discussion section. We will add a section within the Discussion entitled “Limitations of this study”. We will further emphasize that our goal is not to attempt a direct quantitative comparison across species. We will further emphasize that the two experiments differ in terms of: (i) differences in recording modalities (calcium vs. electrophysiology) and associated differences in temporal resolution, neuronal types, and SNR, (ii) cortical targets (layers vs. areas), (iii) sample size, (iv) stimuli, (v) task conditions. In the revised manuscript, we will further highlight that our primary aim is to investigate inter-areal interactions within each species rather than to draw comparisons across species.

Reviewer #2:

Summary:

In this work, the authors investigated the extent of shared variability in cortical population activity in the visual cortex in mice and macaques under conditions of spontaneous activity and visual stimulation. They argue that by studying the average response to repeated presentations of sensory stimuli, investigators are discounting the contribution of variable population responses that can have a significant impact at the single trial level. They hypothesized that, because these fluctuations are to some degree shared across cortical populations depending on the sources of these fluctuations and the relative connectivity between cortical populations within a network, one should be able to predict the response in one cortical population given the response of another cortical population on a single trial, and the degree of predictability should vary with factors such as retinotopic overlap, visual stimulation, and the directionality of canonical cortical circuits.

To test this, the authors analyzed previously collected and publicly available datasets. These include calcium imaging of the primary visual cortex in mice and electrophysiology recordings in V1 and V4 of macaques under different conditions of visual stimulation. The strength of this data is that it includes simultaneous recordings of hundreds of neurons across cortical layers or areas. However, the weaknesses of calcium dynamics (which has lower temporal resolution and misses some non-linear dynamics in cortical activity) and multi-unit envelope activity (which reflects fluctuations in population activity rather than the variance in individual unit spike trains), underestimate the variability of individual neurons. The authors deploy a regression model that is appropriate for addressing their hypothesis, and their analytic approach appears rigorous and well-controlled.

We agree that both calcium imaging and multi-unit envelope recordings have inherent limitations in capturing the variability of individual neuron spiking. Among other factors, the slower temporal resolution of calcium signals can blur fast spiking events, and multi-unit envelopes can mask single-unit heterogeneity. In the Discussion, we will explicitly mention these modality-specific caveats and note that our approach is meant to capture shared variability at the population level rather than the fine temporal structure of individual neurons and individual spikes.

From their analysis, they found that there was significant predictability of activity between layer II/III and layer IV responses in mice and V1 and V4 activity in macaques, although the specific degree of predictability varied somewhat with the condition of the comparison with some minor differences between the datasets. The authors deployed a variety of analytic controls and explored a variety of comparisons that are both appropriate and convincing that there is a significant degree of predictability in population responses at the single trial level consistent with their hypothesis. This demonstrates that a significant fraction of cortical responses to stimuli is not due solely to the feedforward response to sensory input, and if we are to understand the computations that take place in the cortex, we must also understand how sensory responses interact with other sources of activity in cortical networks. However, the source of these predictive signals and their impact on function is only explored in a limited fashion, largely due to limitations in the datasets. Overall, this work highlights that, beyond the traditionally studied average evoked responses considered in systems neuroscience, there is a significant contribution of shared variability in cortical populations that may contextualize sensory representations depending on a host of factors that may be independent of the sensory signals being studied.

We will include a section within the Discussion to emphasize the limitations in the datasets used in this study. We also agree and appreciate the reviewer’s description and will borrow some of the reviewer’s terminology to provide context in the Discussion section.

The different recording modalities and comparisons (within vs. across cortical areas) limit the interpretability of the inter-species comparisons.

We agree that the methodological and anatomical differences between the mouse and monkey datasets make any direct cross-species comparisons hard to interpret. We explicitly discuss this point in the Discussion section. We will add a section within the Discussion entitled “Limitations of this study”. We will further emphasize that our goal is not to attempt a direct quantitative comparison across species. We will further emphasize that the two experiments differ in terms of: (i) differences in recording modalities (calcium vs. electrophysiology) and associated differences in temporal resolution, neuronal types, and SNR, (ii) cortical targets (layers vs. areas), (iii) sample size, (iv) stimuli, (v) task conditions. In the revised manuscript, we will further highlight that our primary aim is to investigate inter-areal interactions within each species rather than to draw comparisons across species.

Strengths:

This work considers a variety of conditions that may influence the relative predictability between cortical populations, including receptive field overlap, latency that may reflect feed-forward or feedback delays, and stimulus type and sensory condition. Their analytic approach is well-designed and statistically rigorous. They acknowledge the limitations of the data and do not over-interpret their findings.

Weaknesses:

The different recording modalities and comparisons (within vs. across cortical areas) limit the interpretability of the inter-species comparisons.The mechanistic contribution of known sources or correlates of shared variability (eye movements, pupil fluctuations, locomotion, whisking behaviors) were not considered, and these could be driving or a reflection of much of the predictability observed and explain differences in spontaneous and visual activity predictions.

We also appreciate this important point. We agree that multiple behavioral factors may significantly contribute to shared variability. In our analyses of the mouse data, we addressed non-visual influences by projecting out “non-visual ongoing neuronal activity” (as shown in Figure 6C, following the approach in Stringer et al. 2019). Additionally, we will further evaluate the contribution of behavioral measures available in the open dataset—such as running speed, whisking, pupil area, and “eigenface” components– to predictivity of neuronal responses.

For the macaque data, the head-fixed and eye-fixation conditions help minimize some of these other potential behavioral contributions. Moreover, we have performed comparisons of eyes-open versus eyes-closed conditions (see Figure 5D). We will also analyze pupil size specifically for the lights-off condition. We do not have access to any other behavioral data from monkeys.

Previous work has explored correlations in activity between areas on various timescales, but this work only considered a narrow scope of timescales.

We appreciate this suggestion. We will perform additional analyses to evaluate predictivity at different temporal scales, as suggested.

The observation that there is some degree of predictability is not surprising, and it is unclear whether changes in observed predictability with analysis conditions are informative of a particular mechanism or just due to differences in the variance of activity under those conditions. Some of these issues could be addressed with further analysis, but some may be due to limitations in the experimental scope of the datasets and would require new experiments to resolve.

Our initial analyses in Fig.6A examined the effect of variance in activity and predictability in mice. As the reviewer intuited, there is a correlation between variance and predictability, at least when presenting a stimulus. Importantly, however, this is not the case when predicting activity in the absence of any stimulus. In the macaque, we cannot compute the variance across stimuli in the checkerboard case (single stimulus), but we will compute it for the conditions of the 4 moving bars. In addition, inspired by the reviewer’s question, we will perform an analysis where we further normalize the variance in activity.

We would like to note that our key contribution is not to merely show that some degree of predictability is possible (which we agree is not surprising) but rather: (i) to use a simple approach to quantify this predictability, (ii) to assess directional differences in predictability, (iii) to evaluate how this predictability depends on neuronal properties and receptive field overlap, (iv) how it depends on the stimuli, and, importantly, (v) to compare predictability during visual stimulation versus absence of visual input.

We agree with the limitations in the datasets. We will include a section within the Discussion to emphasize these limitations.

Reviewer #3:

Neural activity in the visual cortex has primarily been studied in terms of responses to external visual stimuli. While the noisiness of inputs to a visual area is known to also influence visual responses, the contribution of this noisy component to overall visual responses has not been well characterized.

In this study, the authors reanalyze two previously published datasets - a Ca++ imaging study from mouse V1 and a large-scale electrophysiological study from monkey V1-V4. Using regression models, they examine how neural activity in one layer (in mice) or one cortical area (in monkeys) predicts activity in another layer or area. Their main finding is that significant predictions are possible even in the absence of visual input, highlighting the influence of non-stimulus-related downstream activity on neural responses. These findings can inform future modeling work of neural responses in the visual cortex to account for such non-visual influences.

A major weakness of the study is that the analysis includes data from only a single monkey. This makes it hard to interpret the data as the results could be due to experimental conditions specific to this monkey, such as the relative placement of electrode arrays in V1 and V4.

We will add data from at least two more monkeys, as suggested by the reviewer:

● First, we will include a second monkey from the same dataset [1]. The reason this monkey was not included in the original submission is that the dataset for this second monkey consisted of much less data than the original. For example, for the lights-off condition, the number of V4 channels with signal-to-noise ratio greater than 2 (recommended electrodes to use by dataset authors) is 9-12 in this second monkey, compared to 68-74 in the first monkey [1]. However, we will still add results for this second monkey.

● Additionally, we will include data from a new monkey by collaborating with the Ponce lab who will collect new data for this study.

The authors perform a thorough analysis comparing regression-based predictions for a wide variety of combinations of stimulus conditions and directions of influence. However, the comparison of stimulus types (Figure 4) raises a potential concern. It is not clear if the differences reported reflect an actual change in predictive influence across the two conditions or if they stem from fundamental differences in the responses of the predictor population, which could in turn affect the ability to measure predictive relationships. The authors do control for some potential confounds such as the number of neurons and self-consistency of the predictor population. However, the predictability seems to closely track the responsiveness of neurons to a particular stimulus. For instance, in the monkey data, the V1 neuronal population will likely be more responsive to checkerboards than to single bars. Moreover, neurons that don't have the bars in their RFs may remain largely silent. Could the difference in predictability be just due to this? Controlling for overall neuronal responsiveness across the two conditions would make this comparison more interpretable.

This is also a valid concern. As the reviewer noted, we controlled for the number of neurons and degree of self-consistency (Fig. 3A, 3C), and this was always done within their respective stimulus type.

As the reviewer intuits, in Fig. 6A in mice, we show that predictability correlates with neuronal responsiveness. This observation only held during the stimulus condition and not during the gray screen condition. We also showed correlations with self-consistency metrics as a proxy for responsiveness in Fig. 6A and 6C. However, we will directly assess the impact of responsiveness in two ways: (i) by correlating predictability directly with neuronal responsiveness and (ii) by following the same subsampling approach in Fig. 3 to normalize the degree of responsiveness and recompute the predictability metrics.

REFERENCES

(1) Chen, X., Morales-Gregorio, A., Sprenger, J., Kleinjohann, A., Sridhar, S., van Albada, S.J., Grün, S., and Roelfsema, P.R. (2022). 1024-channel electrophysiological recordings in macaque V1 and V4 during resting state. Sci Data 9, 77. https://doi.org/10.1038/s41597-022-01180-1.

(2) Stringer, C., Pachitariu, M., Steinmetz, N., Carandini, M., and Harris, K.D. (2019). High-dimensional geometry of population responses in visual cortex. Nature 571, 361–365. https://doi.org/10.1038/s41586-019-1346-5.

(3) Stringer, C., Pachitariu, M., Carandini, M., and Harris, K. (2018). Recordings of 10,000 neurons in visual cortex in response to 2,800 natural images. (Janelia Research Campus). https://doi.org/10.25378/janelia.6845348.v4 https://doi.org/10.25378/janelia.6845348.v4.

Again, generalizing his culture

I can relate to this, many families that we serve at capic head start have been expressing their anxiousness about coming and bring their children. They are frightened that ICE will be outside of the building looking for them because they are undocumented. I feel sad about how this change has made people feel and live their lives. Half of the classroom isn't attending the class because their parents are scared from deportation.

I like the description and the details here. reminds me of the song "Jolene", same vibe I think

Very descriptive! It describes a well played with dal and it shows' it and it is very thought provoking.

Author response:

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

Public Reviews:

Reviewer 1:

(1) Some conclusions are not completely supported by the present data, and at times the manuscript is disjoint and hard to follow. While the work has some interesting observations, additional experiments and controls are warranted to support the claims of the manuscript.

Thank you for the comments. We revised some of the claims and conclusions to be more objective and result-supportive.

(2) While the authors present compelling data that is relevant to the development of anti-bacterial vaccinations, the data does not completely match their assertions and there are places where some further investigation would further the impact of their interesting study.

We do not fully agree with the reviewer's comments. We have demonstrated that changes in CPS levels during infection are associated with pathogenesis, which will guide future studies on the underlying mechanisms. A significant amount of effort is required for studying mechanisms, which is beyond the scope of this research. We concur with the reviewer that assertions should be made cautiously until further studies are conducted. We have revised these assertions to align with the data and to avoid extrapolating the results (pages 7, lines 126, 133-136; page 11, lines 216-218; page 13, line 264; and page 18, lines 378-383).

(3) The difference in the pathogenesis of a log phase vs. stationary phage intranasal infection would be interesting. Especially because the bacteria is a part of the natural microbial community of swine tonsils, it is curious if the change in growth phase and therefore CPS levels may be a causative reason for pathogenic invasion in some pigs.

S. suis is a part of the natural microbial community of swine tonsils but not mouse NALT. It is interesting to know if CPS levels are low in pig tonsils since CPS is hydrophilic and not conducive to bacterial adhesion. In the study, mice were i.n. infected with a high dose of the bacteria, which could increase opportunities for dissemination (acidic acid may not be a contributor since with or without it is similar). S. suis getting into other body compartments from pig tonsils might be triggered by other conditions, such as viral coinfection, nasal cavity inflammation, cold weather, and decreased immunity.

Experiments with pig blood and phagocytes have shown that genes involved in the synthesis of CPS are upregulated in pig blood. In contrast, these genes are downregulated [1]. In addition, the absence of CPS correlated with increased hydrophobicity and phagocytosis, proposing that S. suis undergoes CPS phase variation and could play a role in the different steps of S. suis infection [2]. We showed direct evidence of encapsulation modulation associated with S. suis pathogenesis in mice. A pig infection model is required to confirm these findings.

(4) The authors should consider taking the bacteria from NALT/CSF and blood and compare the lag times bacteria from different organs take to enter a log growth phase to show whether the difference in CPS is because S. suis in each location is in a different growth phase. If log phase bacteria were intranasally delivered, would it adapt a stationary phase life strategy? How long would that take? 

What causes CPS regulation in vivo is not known. CPS changes in different culture stages, indicating that stress, such as nutrition levels, is one of the signals triggering CPS regulation. The microenvironment in the body compartments is far more complex than in vitro, in which host cells, immune factors and others may affect CPS regulation, individually or collectively. The reviewer’ question is important but the suggested experiment is impracticable since bacterial numbers taken from organs are few, and culturing the bacteria in vitro would obliterate the in vivo status.  

(5) Authors should be cautious about claims about S. suis downregulating CPS in the NALT for increased invasion and upregulating CPS to survive phagocytosis in blood. While it is true that the data shows that there are different levels of CPS in these locations, the regulation and mechanism of the recorded and observed cell wall difference are not investigated past the correlation to the growth phase.

We lower the tone and change the claim as “suggest a correlation between lower CPS in the NALT and a greater capacity for cellular association, whereas elevated CPS levels in the blood are linked to improved resistance against bactericidal activity. However, the mechanisms behind these associations remain unknown.” (page 7, lines 133-136).

(6) The mouse model used in this manuscript is useful but cannot reproduce the nasal environment of the natural pig host. It is not clear if the NALTs of pigs and mice have similar microbial communities and how this may affect the pathogenesis of S. Suis in the mouse. Because the authors show a higher infection rate in the mouse with acetic acid, they may want to consider investigating what the mouse NALT microenvironment is naturally doing to exclude more bacterial invasion. Is it simply a host mismatch or is there something about the microbiome or steady-state immune system in the nose of mice that is different from pigs?

It is a very interesting comment. The mice are SPF level. The microenvironment in SPF mouse NALT should be significantly different from conventional pig tonsils. Although NALT in mice resembles pig tonsils in function, many factors may contribute to the sensitivity to S. suis colonization in the pig nasal cavity, such as the microbiome and local steady-state immune system. More complex microbiota in tonsils could be one of the factors. Analyzing what makes S. suis inclined towards colonization in pig tonsils by SPF and conventional pigs are an ideal experiment to answer the question. 

(7) Have some concerns regarding the images shown for neuroinvasion because I think the authors mistake several compartments of the mouse nasal cavity as well as the olfactory bulb. These issues are critical because neuroinvasion is one of the major conclusions of this work.

Thank you for your comments. The olfactory epithelium (OE) is located directly underneath the olfactory bulb in the olfactory mucosa area and lines approximately half of the nasal cavities of the nasal cavity. The remaining surface of the nasal cavity is lined by respiratory epithelium, which lacks neurons. The olfactory receptor neuron in OE is stained green in the images by β-tubulin III, a neuron-specific marker. The respiratory epithelium is colorless due to the absence of nerve cells. Similarly, the green color stained by β-tubulin III identifies the olfactory bulb. The accuracy of the anatomic compartments of the mouse nasal cavity has been checked and confirmed by referring to related literature [3, 4].

References

(1) Wu Z, Wu C, Shao J, Zhu Z, Wang W, Zhang W, Tang M, Pei N, Fan H, Li J, Yao H, Gu H, Xu X, Lu C. The Streptococcus suis transcriptional landscape reveals adaptation mechanisms in pig blood and cerebrospinal fluid. RNA. 2014 Jun;20(6):882-98.

(2) Charland N, Harel J, Kobisch M, Lacasse S, Gottschalk M. Streptococcus suis serotype 2 mutants deficient in capsular expression. Microbiology (Reading). 1998 Feb;144 ( Pt 2):325-332.

(3) Pägelow D, Chhatbar C, Beineke A, Liu X, Nerlich A, van Vorst K, Rohde M, Kalinke U, Förster R, Halle S, Valentin-Weigand P, Hornef MW, Fulde M. The olfactory epithelium as a port of entry in neonatal neurolisteriosis. Nat Commun. 2018;9(1):4269.

(4) Sjölinder H, Jonsson AB. Olfactory nerve--a novel invasion route of Neisseria meningitidis to reach the meninges. PLoS One. 2010 Nov 18;5(11):e14034.

Reviewer 2:

(1) However, there are serious concerns about data collection and interpretation that require further data to provide an accurate conclusion. Some of these concerns are highlighted below:

Both reviewers were concerned about some of the interpretations of the results. We modified the interpretations in related lines throughout the manuscript (Please see the related responses to Reviewer 1).

(2) In figure 2, the authors conclude that high levels of CPS confer resistance to phagocytic killing in blood exposed S. suis. However, it seems equally likely that this is resistance against complement mediated killing. It would be important to compare S. suis killing in animals depleted of complement components (C3 and C5-9).

We thank the reviewer for the comment. The experiment should be Bactericidal Assay instead of anti-phagocytosis killing. CPS is a main inhibitor of C3b deposition [1]. It interferes with complement-mediated and receptor-mediated phagocytosis; and direct killing. Data in Figure 2C is expressed as “% of bacterial survival in whole blood” for clarity (page 8, Fig. 2C and page 23, lines 489-490).

(3) Intranasal administration non-CPS antisera provides a nice contrast to intravenous administration, especially in light of the recently identified "blood-olfactory barrier". Can the authors provide any insight into how long and where this antibody would be located after intranasal administration? Would this be antibody mediated cellular resistance, or something akin to simple antibody "neutralization"

Anti-V5 may not stay long locally following intranasal administration. Efficient reduction of S. suis colonization in NALT supports that anti-V5 could recognize and neutralize the bacteria in NALT quickly, thereby reducing further dissemination in the body. Antibody-mediated phagocytosis may not play a major role because neutrophils are mainly present in the blood but not in the tissues.  

(4) The micrographs in Figure 7 depict anatomy from the respiratory mucosa. While there is no histochemical identification of neurons, the tissues labeled OE are almost certainly not olfactory and in fact respiratory. However, more troubling is that in figures 7A,a,b,e, and f, the lateral nasal organ has been labeled as the olfactory bulb. This undermines the conclusion of CNS invasion, and also draws into question other experiments in which the brain and CSF are measured.

We understand the significance of your concerns and appreciate your careful review of Figure 7. The olfactory epithelium (OE) is situated directly beneath the olfactory bulb in the olfactory mucosa area and covers about half of the nasal cavity. This positioning allows information transduction between the olfactory and the olfactory epithelium. The remaining surface of the nasal cavity is lined with respiratory epithelium, which does not contain neurons and primarily serves as a protective barrier. In contrast, the olfactory epithelium consists of basal cells, sustentacular cells, and olfactory receptor neurons. The olfactory receptor neurons are specifically stained green in the images using β-tubulin III, a marker that is unique to neurons. The respiratory epithelium appears colorless due to the lack of nerve cells. Similarly, the green staining with β-tubulin III also highlights the olfactory bulb. The anatomical structures indicated in the images are consistent with those described in the literature [2, 3], confirming that the anatomy of the nasal cavity has been accurately identified.

(5) Micrographs of brain tissue in 7B are taken from distal parts of the brain, whereas if olfactory neuroinvasion were occurring, the bacteria would be expected to arrive in the olfactory bulb. It's also difficult to understand how an inflammatory process would be developed to this point in the brain -even if we were looking at the appropriate region of the brain -within an hour of inoculation (is there a control for acetic acid induced brain inflammation?). Some explanations about the speed of the immune responses recorded are warranted.

Thank you for highlighting this issue. Cerebrospinal fluid (CSF) flows into the subarachnoid space surrounding the spinal cord and the brain. There are direct connections from this subarachnoid space to lymphatic vessels that wrap around the olfactory nerves as they cross the cribriform plate towards the nasal submucosa. This connection allows for the drainage of CSF into the nasal submucosal lymphatics in mice [4, 5]. Bacteria may utilize this CSF outflow channel in the opposite direction, which explains the development of brain inflammation in the distal areas of brain tissue adjacent to the subarachnoid space. We have included additional relevant information in the revised manuscript (page 16, lines 323-325).

(6) The detected presence of S. suis in the CSF 0.5hr following intranasal inoculation is difficult to understand from an anatomical perspective. This is especially true when the amount of S. suis is nearly the same as that found within the NALT. Even motile pathogens would need far longer than 0.5hr to get into the brain, so it's exceedingly difficult to understand how this could occur so extensively in under an hour. The authors are quantifying CSF as anything that comes out of the brain after mincing. Firstly, this should more accurately be referred to as "brain", not CSF. Secondly, is it possible that the lateral nasal organ -which is mistakenly identified as olfactory bulb in figure 7- is being included in the CNS processing? This would explain the equivalent amounts of S. suis in NALT and "CSF".

The high dose of inoculation used in the experiment may explain the rapid presence of S. suis in the CSF. Mice exhibit low sensitivity to S. suis infection, and the range for the effective intranasal infectious dose is quite narrow. Higher doses lead to the quick death of the mice, while lower doses do not initiate an infection at all. The dose used in this study is empirical and is intended to facilitate the observation of the progression of S. suis infection in mice.

The NALT tissue and CSF samples are collected separately. After obtaining the NALT tissue, the nasal portion was carefully separated from the rest of the head along the line of the eyeballs. The brain tissue was then extracted from the remaining part of the head to collect the CSF, and it was lacerated to expose the subarachnoid space without being minced. This procedure aims to preserve the integrity of the brain tissue as much as possible. Further details about the CSF collection process can be found in the Materials and Methods section (page 24, lines 508-512).

(7) To support their conclusions about neuroinvasion along the olfactory route and /CSF titer the authors should provide more compelling images to support this conclusion: sections stained for neurons and S. suis, images of the actual olfactory bulb (neurons, glomerular structure etc).

Thank you. We respectfully disagree with the reviewer. We stained neurons using a neuron-specific marker to identify the anatomical structures of the olfactory bulb and olfactory epithelium (in green). We used an S. suis-specific antibody to highlight the bacteria present in these areas (in orange and red). The images, along with the bacteria found in the cerebrospinal fluid (CSF) and the brain inflammation observed early in the infection, strongly support our conclusion regarding brain invasion through the olfactory pathway. Please see the response to question 4 for further clarification.

References

(1) Seitz M, Beineke A, Singpiel A, Willenborg J, Dutow P, Goethe R, Valentin-Weigand P, Klos A, Baums CG. Role of capsule and suilysin in mucosal infection of complement-deficient mice with Streptococcus suis. Infect Immun. 2014 Jun;82(6):2460-71.

(2) Sjölinder H, Jonsson AB. Olfactory nerve--a novel invasion route of Neisseria meningitidis to reach the meninges. PLoS One. 2010 Nov 18;5(11):e14034.

(3) Pägelow D, Chhatbar C, Beineke A, Liu X, Nerlich A, van Vorst K, Rohde M, Kalinke U, Förster R, Halle S, Valentin-Weigand P, Hornef MW, Fulde M. The olfactory epithelium as a port of entry in neonatal neurolisteriosis. Nat Commun. 2018;9(1):4269.

(4) Yoon JH, Jin H, Kim HJ, Hong SP, Yang MJ, Ahn JH, Kim YC, Seo J, Lee Y, McDonald DM, Davis MJ, Koh GY. Nasopharyngeal lymphatic plexus is a hub for cerebrospinal fluid drainage. Nature. 2024 Jan;625(7996):768-777.

(5) Spera I, Cousin N, Ries M, Kedracka A, Castillo A, Aleandri S, Vladymyrov M, Mapunda JA, Engelhardt B, Luciani P, Detmar M, Proulx ST. Open pathways for cerebrospinal fluid outflow at the cribriform plate along the olfactory nerves. EBioMedicine. 2023 May;91:104558.

Response to Recommendations for the authors:

Reviewer 1:

Minor concerns for the manuscript:

(1) In the introduction, please consider giving a little more background about the bacteria itself and how it causes pathogenesis.

We appreciate your suggestion. We have included additional background on the virulent factors and the pathogenesis of the bacteria in the introduction to enhance understanding of the results (page 4, lines 63-69).

(2) Figure 2C would be more correct to say percent survival as the CFUs before and after are what are being compared and not if the bacteria is being phagocytosed or not. Flow cytometry of the leukocytes and a fluorescent S. Suis would show phagocytosis. Unless that experiment is performed, the authors cannot claim that there is a resistance to phagocytosis.

Thank you for your feedback. We agree with the reviewer that the experiment should be Bactericidal Assay rather than anti-phagocytosis killing. CPS interferes with complement-mediated phagocytosis and direct killing, and receptor-mediated phagocytosis. To enhance clarity, the data in Fig. 2C has been presented as “% of bacterial survival in whole blood” (page 8).  

(3) There are two different legends present for Figure 1. Please resolve.

We apologize for the oversight. The redundant figure legend has been removed (page 6).

(4) There are places such as in lines 194-195, that there are assertions and interpretations about the data that are not directly drawn from the data. These hypotheses are valuable, but please move them to the discussion.

Thank you for your suggestion. The hypothesis has been moved to the Discussion section (page 19, lines 402 - 405).

(5) In Figure 4B, higher resolution images would strengthen the ability of non-microbiologists to see the differences in CPS levels in the cell wall.

We achieved the highest resolution possible for clearer distinctions in CPS levels. To enhance the visualization of the different CPS levels in the images, we revised the description of the CPS changes in Figure 4B within the results section (page 11, lines 208-213).

(6) In Figure 5 there is no D. Further, the schematics throughout would be easier to parse with the text if the challenge occurred at time 0. Consider revising them for clarity.

Thank you for highlighting the error. We have removed "i.v + i.n (Fig. 5)" from Figure 5A and made adjustments to the schematic illustrations in Figures 5 and 6 as recommended by the reviewer (page 14).

(7) What is the control for the serum? The findings for figures 5 and 6 would be much stronger if a non- S. Suis isotype control serum was also infused.

We used a naive serum as a control to avoid interference from a non-S. suis isotype control that targets other surface molecules of S. suis serotypes.

(8) Figure 6 legend does not include the anti-CPS treatment.

Thank you. We have added anti-CPS serum in the legend (page 15, line 249).

(9) Figure 7 legend does not include the time point for panel 7A.

Thank you. The time point is shown on Fig.7A (page 17).

(10) Figure 7 should show OB micrographs or entire brain including the OB.

The neuron-specific marker, β-tubulin III, identifies the neuro cells in the olfactory bulb (OB) as shown in Fig. 7A. Unfortunately, we were unable to provide an image of the entire brain that includes the OB due to limitations in our section preparation. We apologize for the mislabeled structure in Fig. 7A, which may have caused confusion. We have corrected the labeling for consistency (see page 15, lines 257-260). Additionally, we included a drawing of the sagittal plane of the rodent's nose, depicting the compartments of the OB, olfactory epithelium (OE), nasal cavity (NC), and brain. This illustration, presented in Fig. 7B on page 17, aims to clarify the structural and functional connections between the nasopharynx and the CNS.

(11) Some conclusions may be better drawn if figures were to be consolidated. As noted above, the data at times feels disjointed and the importance is more difficult for readers to follow because data are presented further apart. Particularly figures 5 and 6 which are similar with different time points and controls of antisera administrative routes; placing these figures together would be an example of increasing continuity throughout the paper.

Thank you for the valuable suggestion. Figures 5 and 6, along with their related descriptions in the results section, have been combined for better cohesiveness (pages 14-15).

Reviewer #2:

To support their conclusions about neuroinvasion along the olfactory route and /CSF titer the authors should provide more compelling images to support this conclusion: sections stained for neurons and S. suis, images of the actual olfactory bulb (neurons, glomerular structure etc).

Please refer to our responses to Reviewer 1's Question 7, Reviewer 2's Questions 4 and 7 in the public reviews, and Reviewer 1's Question 10 in the authors' recommendations.

Author response:

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

Reviewer #1 (Public review):

The authors have strengthened their conclusions by providing additional information about the specificity of their antibodies, but at the same time the authors have revealed concerning information about the source of their antibodies.

It appears that many of the antibodies used in this study have been discontinued because the supplier company was involved in a scandal of animal cruelty and all their goats and rabbits Ab products were sacrificed. The authors acknowledge that this is unfortunate but they also claim that the issue is out of their hands.

The authors' statement is false; the authors ought to not use these antibodies, just as the providing company chose to discontinue them, as those antibodies are tied to animal cruelty. The issue that the authors feel OK with using them is of concern. In short, please remove any results from unethical antibodies.

Removal of such results also best serves science. That is, any of their results using the discontinued antibodies means that the authors' results are non-reproducible and we should be striving to publish good, reproducible science.

For the antibodies that do not have unethical origins the authors claim that their antibodies have been appropriately validated, by "testing in positive control tissue and/or Western blot or in situ hybridization". This is good but needs to be expanded upon. It is a strong selling point that the Abs are validated and I want to see additional information in their Supplementary Table 2 stating for each Ab specifically:

(1) What +ve control tissue was used in the validation of each Ab and which species that +ve control came from. Likewise, if competition assays to confirm validity was used, please also specify.

(2) Which assay was the Ab validated for (WB, IHC, ELISA, all etc)

(3) For Antibodies that were validated for, or using WBs please let the reader know if there were additional bands showing.

(4) Include references to the literature that supports these validations. That is, please make it easy for the reader to appreciate the hard work that went into the validation of the Antibodies.

Finally, for the Abs, when the authors write that "All antibodies used have been validated by testing in positive control tissue and/or Western blot or in situ hybridization" I fail to understand what in situ hybridisation means in this context. I am under the impression that in situ hybridisation is some nucleic acid -hybridising-to-organ or tissue slice. Not polypeptide binding.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

Remove results that have been obtained by unethically-sourced antibody reagents.

Strengthen the readers' confidence about the appropriateness & validity of your antibodies.

First, we want to stress that reviewer 1 has raised his critique related to the used of antibodies from Santa Cruz biotechnology not only through the journal. The head of our department and two others were contacted by reviewer 1 directly without going through the journal or informing/approaching the corresponding or first author. It is our opinion that this debate and critique should be handled through the journal and editorial office and not with people without actual involvement in the project.

It is correct that we have purchased antibodies from Santa Cruz Biotechnologies both mouse, rabbit and goat antibodies as stated in the correspondence with the reviewer.

As stated in our previous rebuttal – the goat antibodies from Santa Cruz were discontinued due to inadequate treatment of goats after settling with the authorities in 2016.

https://www.nature.com/articles/nature.2016.19411

https://www.science.org/content/blog-post/trouble-santa-cruz-biotechnology

We have used 11 mouse, rabbit or goat antibodies from Santa Cruz biotechnologies in the manuscript as listed in supplementary table 2 of the manuscript and all of them have been carefully validated in other control tissues supported by ISH and/or WB and many of them already used in several publications by our group (https://pubmed.ncbi.nlm.nih.gov/34612843/, https://pubmed.ncbi.nlm.nih.gov/33893301/, https://pubmed.ncbi.nlm.nih.gov/32931047/, https://pubmed.ncbi.nlm.nih.gov/32729975/, https://pubmed.ncbi.nlm.nih.gov/30965119/, https://pubmed.ncbi.nlm.nih.gov/29029242/, https://pubmed.ncbi.nlm.nih.gov/23850520/, https://pubmed.ncbi.nlm.nih.gov/23097629/, https://pubmed.ncbi.nlm.nih.gov/22404291/, https://pubmed.ncbi.nlm.nih.gov/20362668/, https://pubmed.ncbi.nlm.nih.gov/20172873/,  and other research groups. All antibodies used in this manuscript were purchased before the whole world was aware of mistreatment of goats that was evident several years later.

We do not support animal cruelty in anyway but the purchase of antibodies from Santa Cruz biotechnologies were conducted long before mistreatment was reported. Moreover, antibodies from Santa Cruz biotechnologies are being used in thousands of publications annually. The company has been punished for their misconduct, and subsequently granted permission to produce antibodies from the relevant authorities again.

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

Despite the study being a collation of important results likely to have an overall positive effect on the field, methodological weaknesses and suboptimal use of statistics make it difficult to give confidence to the study's message.

Strengths:

Relevant human and mouse models approached with in vivo and in vitro techniques.

Weaknesses:

The methodology, statistics, reagents, analyses, and manuscripts' language all lack rigour.

(1) The authors used statistics to generate P-values and Rsquare values to evaluate the strength of their findings.

However, it is unclear how stats were used and/or whether stats were used correctly. For instance, the authors write: "Gaussian distribution of all numerical variables was evaluated by QQ plots". But why? For statistical tests that fall under the umbrella of General Linear Models (line ANOVA, t-tests, and correlations (Pearson's)), there are several assumptions that ought to be checked, including typically:

(a) Gaussian distribution of residuals.

(b) Homoskedasticity of the residuals.

(c) Independence of Y, but that's assumed to be valid due to experimental design.

So what is the point of evaluating the Gaussian distribution of the data themselves? It is not necessary. In this reviewer's opinion, it is irrelevant, not a good use of statistics, and we ought to be leading by example here.

Additionally, it is not clear whether the homoscedasticity of the residuals was checked. Many of the data appear to have particularly heteroskedastic residuals. In many respects, homoscedasticity matters more than the normal distribution of the residuals. In Graphpad analyses if ANOVA is used but equal variances are assumed (when variances among groups are unequal then standard deviations assigned in each group will be wrong and thus incorrect p values are being calculated.

Based on the incomplete and/or wrong statistical analyses it is difficult to evaluate the study in greater depth.

We agree with the reviewer that we should lead by example and improve clarity on the use of the different statistical tests and their application. In response to the reviewer’s suggestion, we have extended the statistical section, focusing on the analyses used. Additionally, we have specified the statistical test used in the figure legends for each figure. Additionally, we did check for Gaussian distribution and homoskedasticity of residuals before conducting a general linear model test, and this has now been specified in the revised manuscript. In case the assumptions were not met, we have specified which non-parametric test we used. If the assumptions were not met, we specified which non-parametric test was used.

While on the subject of stats, it is worth mentioning this misuse of statistics in Figure 3D, where the authors added the Slc34a1 transcript levels from controls in the correlation analyses, thereby driving the intercept down. Without the Control data there does not appear to be a correlation between the Slc34a1 levels and tumor size.

We agree with the reviewer that a correlation analysis is inappropriate here and have removed this part of the figure.

There is more. The authors make statements (e.g. in the figure levels as: "Correlations indicated by R2.". What does that mean? In a simple correlation, the P value is used to evaluate the strength of the slope being different from zero. The authors also give R2 values for the correlations but they do not provide R2 values for the other stats (like ANOVAs). Why not?

We agree with the reviewer and have replaced the R2 values with the Pearson correlation coefficient in combination with the P value.

(2) The authors used antibodies for immunos and WBs. I checked those antibodies online and it was concerning:

(a) Many are discontinued.

Many of the antibodies we have used were from the major antibody provider Santa Cruz Biotechnology (SCBT). SCBT was involved in a scandal of animal cruelty and all their goats and rabbits were sacrificed, which explains why several antibodies were discontinued, while the mice antibodies were allowed to continue. This is unfortunate but out of our hands.

(b) Many are not validated.

We agree with the reviewer that antibody validation is essential. All antibodies used in this manuscript have been validated. The minimal validation has been to evaluate cellular expression in positive control tissue for instance bone, kidney, or mamma. Moreover, many of the antibodies have been used and validated in previous publications (doi: 10.1593/neo.121164, doi:10.1096/fj.202000061RR, doi: 10.1093/cvr/cvv187) including knockout models. Moreover, many antibodies but not all have been validated by western blot or in situ hybridization. We have included the following in the Materials and Methods section: “All antibodies used have been validated by testing in positive control tissue and/or Western blot or in situ hybridization”.

(c) Many performed poorly in the Immunos, e.g. FGF23, FGFR1, and Kotho are not really convincing. PO5F1 (gene: OCT4) is the one that looks convincing as it is expressed at the correct cell types.

We fail to understand the criticism raised by the reviewer regarding the specificity of these specific antibodies. We believe the FGF23 and Klotho antibodies are performing exceptionally well, and FGFR1 is abundantly expressed in many cell types in the testis. As illustrated in Figure 2E, the expression of Klotho, FGF23, and FGFR1 is very clear, specific, and convincing. FGF23 is not expressed in normal testis – which is in accordance with no RNA present there either. However, it is abundantly expressed in GCNIS where RNA is present. On the other hand, Klotho is abundantly expressed in germ cells from normal testis but not expressed in GCNIS.

(d) Others like NPT2A (product of gene SLC34A1) are equally unconvincing. Shouldn't the immuno show them to be in the plasma membrane?

If there is some brown staining, this does not mean the antibodies are working. If your antibodies are not validated then you ought to omit the immunos from the manuscript.

We acknowledge your concerns regarding the NPT2A, NPT2B, and NPT2C staining. While the NPT2A antibody is performing well, we understand your reservations about the other antibodies. It's worth noting that NPT2A is not expressed in normal testis (no RNA either) but is expressed in GCNIS where the RNA is also present. Although it is typically present in the plasma membrane, cytoplasmic expression can be acceptable as membrane availability is crucial for regulating NPT2A function, particularly in the kidney where FGF23 controls membrane availability. We are currently involved in a comprehensive study exploring these phosphate transporters in the organs lining the male reproductive tract. In functional animal models, we have observed very specific staining with this NPT2A antibody following exposed to high phosphate or FGF23. Additionally, we are conducting Western Blot analyses with this antibody, which reinforces our belief that the antibody has a specific binding.

Reviewer #2 (Public Review):

Summary:

This study set out to examine microlithiasis associated with an increased risk of testicular germ cell tumors (TGCT). This reviewer considers this to be an excellent study. It raises questions regarding exactly how aberrant Sertoli cell function could induce osteogenic-like differentiation of germ cells but then all research should raise more questions than it answers.

Strengths:

Data showing the link between a disruption in testicular mineral (phosphate)homeostasis, FGF23 expression, and Sertoli cell dysfunction, are compelling.

Weaknesses:

Not sure I see any weaknesses here, as this study advances this area of inquiry and ends with a hypothesis for future testing.

We thank the reviewer for the acknowledgment and highlighting that this is an important message that addresses several ways to develop testicular microlithiasis, which indicates that it is not only due to malignant disease but also frequent in benign conditions.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

I applaud the authors' approach to nomenclature for rodent and human genes and proteins (italicised for genes, all caps for humans, capitalised only for rodents, etc), but the authors frequently got it wrong when referring to genes or proteins. A couple of examples include:

(1) SLC34A1 (italics) refers to gene (correct use by the authors) but then again the authors use e.g. SLC34A1 (not italics) to refer to the protein product of SLC34A1(italics) gene. In fact, the protein product of the SLC34A1 (italics) gene is called NPT2A (non-italics).

(2) OCT4 (italics) refers to gene (correct use by the authors) but then again the authors use e.g. OCT4 (not italics) to refer to the protein product of OCT4 (italics)gene. In fact, the protein product of the OCT4 gene (italics) gene is called PO5F1(non-italics).

The problem with their incorrect and inconsistent nomenclature is widespread in the manuscript making further evaluation difficult.

Please consult a reliable protein-based database like Uniprot to derive the correct protein names for the genes. You got NANOG correct though.

We thank the reviewer for addressing this important point. We have corrected the nomenclature throughout the manuscript as suggested.

(3) The authors use the word "may" too many times. Also often in conjunction with words like "indicates", and "suggests". Examples of phrases that reflect that the authors lack confidence in their own results, conclusions, and understanding of the literature are:

"...which could indicate that the bone-specific RUNX2 isoform may also be expressed... "

"...which indicates that the mature bone may have been..."

Are we shielding ourselves from being wrong in the future because "may" also means "may not"? It is far more engaging to read statements that have a bit more tooth to them, and some assertion too. How about turning the above statements around, to :

"...which shows that the bone-specific RUNX2 isoform is also expressed... "

"...which reveals that the mature bone were..."

...then revisit ambiguous language ("may", "might" "possibly", "could", "indicate" etc.) throughout the manuscript?

It's OK to make a statement and be found wrong in the future. Being wrong is integral to Science.

Thank you for addressing this. We agree with the reviewer that it is fair to be more direct and have revised many of these vague phrases throughout the manuscript.

(4) The authors use the word "transporter" which in itself is confusing. For instance, is SLC34A1 an importer or an exporter of phosphate? Or both? Do SLC34As move phosphate in or out of the cells or cellular compartments? "Transporter" sounds too vague a word.

We understand that it might be easier for the reader with the term "importer". However, we should use the specific nomenclature or "wording" that applies to these transporters. The exact terminology is a co-transporter or sodium-dependent phosphate cotransporter as reported here (doi: 10.1152/physrev.00008.2019). Thus, we will use the terms “co-transporter” and “transporter” throughout the revised manuscript.

Reviewer #1 (Public review):

This study uses structural and functional approaches to investigate the regulation of the Na/Ca exchanger NCX1 by an activator, PIP2, and an inhibitor, SEA0400.

State-of-the-art methods are employed, and the data are of high quality and presented very clearly. The manuscript combines two rather different studies (one on PIP2; and one on SEA0400) neither of which is explored in the depth one might have hoped to form robust conclusions and significantly extend knowledge in the field.

The novel aspect of this work is the study of PIP2. Unfortunately, technical limitations precluded structural data on binding of the native PIP2, so an unnatural short-chained analog, di-C8 PIP2, was used instead. This raises the question of whether these two molecules, which have similar but very distinctly different profiles of activation, actually share the same binding pocket and mode of action. In an effort to address this, the authors mutate key residues predicted to be important in forming the binding site for the phosphorylated head group of PIP2. However, none of these mutations prevent PIP2 activation. The only ones that have a significant effect also influence the Na-dependent inactivation process independently of PIP2, thus casting doubt on their role in PIP2 binding, and thus identification of the PIP2 binding site. A more extensive mutagenic study, based on the di-C8 PIP2 binding site, would have given more depth to this work and might have been more revealing mechanistically.

The SEA0400 aspect of the work does not integrate particularly well with the rest of the manuscript. This study confirms the previously reported structure and binding site for SEA0400 but provides no further information. While interesting speculation is presented regarding the connection between SEA0400 inhibition and Na-dependent inactivation, further experiments to test this idea are not included here.

Reviewer #2 (Public review):

The study by Xue et al. reports the structural basis for the regulation of the human cardiac sodium-calcium exchanger, NCX1, by the endogenous activator PIP2 and the small molecule inhibitor SEA400. This well-written study contextualizes the new data within the existing literature on NCX1 and the broader NCX family. This work builds upon the authors' previous study (Xue et al., 2023), which presented the cryo-EM structures of human cardiac NCX1 in both inactivated and activated states. The 2023 study highlighted key structural differences between the active and inactive states and proposed a mechanism where the activity of NCX1 is regulated by the interactions between the ion-transporting transmembrane domain and the cytosolic regulatory domain. Specifically, in the inward-facing state and at low cytosolic calcium levels, the transmembrane (TM) and cytosolic domains form a stable interaction that results in the inactivation of the exchanger. In contrast, calcium binding to the cytosolic domain at high cytosolic calcium levels disrupts the interaction with the TM domain, leading to active ion exchange.

In the current study, the authors present two mechanisms explaining how both PIP2 stimulates NCX1 activity by destabilizing the protein's inactive state (i.e., by disrupting the interaction between the TM domain and the cytosolic domain) and how SEA400 stabilizes this interaction, thereby acting as a specific inhibitor of the system.

The first part of the results section addresses the effect of PIP2 and PIP2 diC8 on NCX1 activity. This is pertinent as the authors use the diC8 version of this lipid (which has a shorter acyl chain) in their subsequent cryo-EM structure due to the instability of native PIP2. I am not an electrophysiology expert; however, my main comment would be to ask whether there is sufficient data here to characterise fully the differences between PIP2 and PIP2 diC8 on NCX1 function. It appears from the text that this study is the first to report these differences, so perhaps this data needs to be more robust. The spread of the data points in Figure 1B is possibly a little unconvincing given that only six measurements were taken. Why is there one outlier in Figure 1A? Were these results taken using the same batch of oocytes? Are these technical or biological replicates? Is the convention to use statistical significance for these types of experiments?

I am also somewhat skeptical about the modelling of the PIP2 diC8 molecule. The authors state, "The density of the IP3 head group from the bound PIP2 diC8 is well-defined in the EM map. The acyl chains, however, are flexible and could not be resolved in the structure (Fig. S2)."

However, the density appears rather ambiguous to me, and the ligand does not fit well within the density. Specifically, there is a large extension in the volume near the phosphate at the 5' position, with no corresponding volume near the 4' phosphate. Additionally, there is no bifurcation of the volume near the lipid tails. I attempted to model cholesterol hemisuccinate (PDB: Y01) into this density, and it fits reasonably well - at least as well as PIP2 diC8. I am also concerned that if this site is specific for PIP2, then why are there no specific interactions with the lipid phosphates? How can the authors explain the difference between PIP2 and PIP2 diC8 if the acyl chains don't make any direct interactions with the TM domain? In short, the structures do not explain the functional differences presented in Figure 1.

The side chain densities for Arg167 and Arg220 are also quite weak. While there is some density for the side chain of Lys164, it is also very weak. I would expect that if this site were truly specific for PIP2, it should exhibit greater structural rigidity - otherwise, how is this specific?

Given this observation, have the authors considered using other PIP2 variants to determine if the specificity lies with PI4,5P2 as opposed to PI3,5P2 or PI3,4P2? A lack of specificity may explain the observed poor density.

I also noticed many lipid-like densities in the maps for this complex. Is it possible that the authors overlooked something? For instance, there is a cholesterol-like density near Val51, as well as something intriguing near Trp763, where I could model PIP2 diC8 (though this leads to a clash with Trp763). I wonder if the authors are working with mixed populations in their dataset. The accompanying description of the structural changes is well-written (assuming it is accurate).

I would recommend that the authors update the figures associated with this section, as they are currently somewhat difficult to interpret without prior knowledge of NCX architecture. My suggestions include:

- Including the density for the PIP2 diC8 in Figure 2A.

- Adding membrane boundaries (cytosolic vs. extracellular) in Figure 2B.

- Labeling the cytosolic domains in Figure 2B.

- Adding hydrogen bond distances in Figure 2A.

- Detailing the domain movements in Figure 2B (what is the significance of the grey vs. blue structures?).

The section on the mechanism of SEA400-induced inactivation is strong. The maps are of better quality than those for the PIP2 diC8 complex, and the ligand fits well. However, I noticed a density peak below F02 on SEA400 that lies within the hydrogen bonding distance of Asp825. Is this a water molecule? If so, is this significant?

Furthermore, there are many unmodeled regions that are likely cholesterol hemisuccinate or detergent molecules, which may warrant further investigation.

The authors introduce SEA400 as a selective inhibitor of NCX1; however, there is little to no comparison between the binding sites of the different NCX proteins. This section could be expanded. Perhaps Fig. 4C could include sequence conservation data.

Additionally, is the fenestration in the membrane physiological, or is it merely a hole forced open by the binding of SEA400? I was unclear as to whether the authors were suggesting a physiological role for this feature, similar to those observed in sodium channels.