eLife Assessment

This paper present an important theoretical exploration of how a flexible protein domain with multiple DNA binding sites may simultaneously provide stability to the DNA-bound state and enables exploration of the DNA strand. The authors present compelling evidence that their findings have implications for the way intrinsically disordered regions (IDR) of transcription factors proteins (TF) can enhance their ability to efficiently find their binding site on the DNA from which they exert control over the transcription of their target gene. The paper concludes with a comparison of model predictions with experimental data which gives further support to the proposed model.

Reviewer #1 (Public review):

Summary:

The authors define the principles that, based on first principles, should be guiding the optimisation of trascription factors with intrinsically disordered regions (IDR). The first part of the study defines the following principles to optimize the binding affinities to the genome in the receiving region that is called the "antenna": (i) reduce the target to IDR-binding distance on the genome, (ii) optimise the distance betwee the DNA binding domain and the binding sites on the IDR to be as close as possible to the distance between their binding sites on the genome; (iii) keep the same number of binding sites and their targets and modulate this number with binding strength, reducing them with increased strenght; (iv) modulate the binding strenght to be above a threshold that depends on the proportion of IDR binding sites in the antenna. The second part defines the scaling of the seach time in function of key parameters such as the volume of the nucleus, and the size of the antenna, derived as a combination of 3D search of the antenna and 1D "octopusing" on the antenna. The third part focuses on validation, where the current results are compared to binding probabilith data from a single experiment, and new experiment are proposed to further validate the model as well as testing designed transcription factors.

Strengths:

The strength of this work is that it provides simple, interpretable and testable theoretical conclusions. This will allow the derived design principles to be understood, evaluated and improved in the future. The theoretical derivations are rigorous. The authors provides a comparison to experiments, and also propose new experiments to be performed in the future, this is a great value in the paper since it will set the stage and inspire new experimental techniques. Further, the field needs inspiration and motivations to develop these techniques, since they are required to benchmark the transcription factors designed with the methods presented in this paper, as well as to develop novel data based or in vivo methods that would greatly benefit the field. As such, this paper is a fundamental contribution to the field.

Weaknesses:

The model assumption that the interaction between the transcription factor and the DNA outside of the antenna region is negligible is probably too strong for many/most transcription factors, particularly in organisms with a longer genome than yeasts. The model presents many first principles to drive the design of transcription factor, but arguably, other principles and mechanisms might also play a role by being beneficial to the search and binding process. Specifically: (i) a role of the IDR in complex formation and cooperativity between multiple trascription factors, (ii) ability of the IDR to do parallel searching based on multiple DNA binding sites spaced by disordered regions, (iii) affinity of the IDR to specific compartmentalisations in the nucleus reducing the search time, etc. The paper would be improved by a discussion over alternative mechanisms.

Reviewer #2 (Public review):

Summary:

This is an interesting theoretical exploration of how a flexible protein domain, which has multiple DNA-binding sites along it, affects the stability of the protein-DNA complex. It proposes a mechanism ("octopusing") for protein doing a random walk while bound to DNA which simultaneously enables exploration of the DNA strand and stability of the bound state.

Strengths:

Stability of the protein-DNA bound state and the ability of the protein to perform 1d diffusion along the DNA are two properties of a transcription factor that are usually seen as being in opposition of each other. The octopusing mechanism is an elegant resolution of the puzzle of how both could be accommodated. This mechanism has interesting biological implications for the functional role of intrinsically disordered domains in transcription factor (TF) proteins. They show theoretically how these domains, if flexible and able to make multiple weak contacts with the DNA, can enhance the ability of the TF to efficiently find their binding site on the DNA from which they exert control over the transcription of their target gene. The paper concludes with a comparison of model predictions with experimental data which gives further support to the proposed model. Overall, this is an interesting and well executed theoretical paper that proposes an interesting idea about the functional role for IDR domains in TFs.

Weaknesses:

IDR domains are assumed flexible which I believe is not always the case. Also, I'm not sure how ubiquitous are the assumed binding sites on the DNA for multiple subdomains along the IDR. These assumptions though seem like interesting points of departure for further experiments.

eLife Assessment

This manuscript applies state-of-the-art techniques to define the cellular composition of the dorsal vagal complex in two rodent species (mice and rats). The result is an important resource that substantially advances our understanding of the dorsal vagal complex's role in the regulation of feeding and metabolism while also highlighting key differences between species. While most of the analyses in the manuscript provide convincing insight into the cellular architecture of the dorsal vagal complex, other aspects are incomplete and could be bolstered by additional evidence.

Reviewer #1 (Public review):

Summary:

This paper uses state-of-the-art techniques to define the cellular composition and its complexity in two rodent species (mice and rats). The study is built on available datasets but extends those in a way that future research will be facilitated. The study will be of high impact for the study of metabolic control.

Strengths:

(1) The study is based on experiments that are combined with two exceptional data sets to provide compelling evidence for the cellular composition of the DVC.

(2) The use of two rodent species is very useful.

Weaknesses:<br /> There is no conceptual weakness, the performance of experiments is state-of-the-art, and the discussion of results is appropriate. One minor point that would further strengthen the data is a more distinct analysis of receptors that are characteristic of the different populations of neuronal and non-neuronal cells; this part could be improved. Currently, it is only briefly mentioned, e.g., line 585ff. See also lines 603ff; it is true that the previous studies lack some information about the neurotransmitter profile of cells, but combining all data sets should result in an analysis of the receptors as well, e.g. in the form of an easy-to-read table.

Reviewer #2 (Public review):

In this manuscript, Hes et al. present a comprehensive multi-species atlas of the dorsal vagal complex (DVC) using single-nucleus RNA sequencing, identifying over 180,000 cells and 123 cell types across five levels of granularity in mice and rats. Intriguingly, the analysis uncovered previously uncharacterized cell populations, including Kcnj3-expressing astrocytes, neurons co-expressing Th and Cck, and a population of leptin receptor-expressing neurons in the rat area postrema, which also express the progenitor marker Pdgfra. These findings suggest species-specific differences in appetite regulation. This study provides a valuable resource for investigating the intricate cellular landscape of the DVC and its role in metabolic control, with potential implications for refining obesity treatments targeting this hindbrain region.

In line with previous work published by the PI, the topic is of clear scientific relevance, and the data presented in this manuscript are both novel and compelling. Additionally, the manuscript is well-structured, and the conclusions are robust and supported by the data. Overall, this study significantly enhances our understanding of the DVC and sheds light on key differences between rats and mice.

I applaud the authors for the depth of their analysis. However, I have a few major concerns, comments, and suggestions that should be addressed.

(1) If I understand the methodology correctly, mice were fasted overnight and then re-fed for 2 hours before being sacrificed (lines 91-92), which occurred 4 hours after the onset of the light phase (line 111). This means that the re-fed animals had access and consequently consumed food when they typically would not. While I completely recognize that every timepoint has its limitations, the strong influence of the circadian rhythm on the DVC gene expression (highlighted by the work published by Lukasz Chrobok), and the fact that timing of food/eating is a potent Zeitgeber, might have an impact on the analysis and should be mentioned as a potential limitation in the discussion (along with citing Dr Chrobok's work). Could this (i.e., eating during a time when the animals are not "primed by their own circadian clock to eat" potentially explain why the meal-related changes in gene expression were relatively small?

(2) In the Materials and Methods section, LiCl is mentioned as one of the treatment conditions; however, very little corresponding data are presented or discussed. Please include these results and elaborate on the rationale for selecting LiCl over other anorectic compounds.

(3) The number of animals used differs significantly between species, which the authors acknowledge as a limitation in the discussion. Since the authors took advantage of previously published mouse data sets (Ludwig and Dowsett data sets), I wonder if the authors could compare/integrate any rat data set currently available in rats as well to partially address the sample size disparity.

(4) Dividing cells in AP vs NTS vs DMX clusters and analyzing potential species differences would significantly enhance the quality of the manuscript, given the partially diverse functions of these regions. This could be done by leveraging existing published datasets that employed spatial transcriptomics or more classical methodologies (e.g., PMID: 39171288, PMID: 39629676, PMID: 38092916). I would be interested to hear the authors' perspective on the feasibility of such an analysis.

(5) Given the manuscript's focus on feeding and metabolism, I believe a more detailed description and comparison of the transcription profile of known receptors, neurotransmitters, and neuropeptides involved in food intake and energy homeostasis between mice and rats would add value. Adding a curated list of key genes related to feeding regulation would be particularly informative.

Reviewer #3 (Public review):

Summary:

This manuscript from Cecilia H et al provides a compelling resource for single nuclei RNA sequencing data with an emphasis on facilitating the integration of future data sets across mouse and rat data sets.

Strengths:

There are also several interesting findings that are highlighted, even though without a functional assay the importance remains unclear. However, the manuscript properly addresses where conclusions are speculative.

As with other snRNA seq datasets the manuscript demonstrates convincingly an increased level of complexity, while other neuronal populations like Cck and Th neurons were reproduced. Several recent findings from other groups are well addressed and put into a new context, e.g., DMV expression of AgRP (and Hcrt) was found to result from non-coding sequences, co-localization of Cck/Th was identified in a small subset. These statements are informative.

The integration of rat data into the mouse data sets is excellent, and the comparison of cellular groups is very detailed, with interesting differences between mouse and rat data.<br /> All data sets are easily accessible and usable on open platforms, this will be an impactful resource for other researchers.

Weaknesses:

The data analysis seems incomplete. The title indicates the integration of mouse and rat data into a unified rodent dataset. But the discrepancy of animal numbers (30 mice vs. 2 rats) does not fit well with that focus.

On the other hand, the mouse group is further separated into different treatments to study genetic changes that are associated with distinct energy states of fed/fasting/refeeding responses. Yet, this aspect is not addressed in depth.

While the authors find transcriptional changes in all neuronal and non-neuronal cell types, which is interesting, the verification of known transcriptional changes (e.g., cFos) is unaddressed. cFos is a common gene upregulated with refeeding that was surprisingly not investigated, even though this should be a strong maker of proper meal-induced neuronal activation in the DMV. This is a missed opportunity either to verify the data set or to highlight important limitations if that had been attempted without success.

Additional considerations:

(1) The focus on transmitter classification is highlighted, but surprisingly, the well-accepted distinction of GABAergic neurons by Slc32a1 was not used, instead, Gad1 and Gad2 were used as GABAergic markers. While this may be proper for the DMV, given numerous findings that Gad1/2 are not proper markers for GABAergic neurons and often co-expressed in glutamatergic populations, this confound should have been addressed to make a case if and why they would be proper markers in the DMV.

(2) Figure S3 for anatomical localization of clusters is excellent, but several of the cluster gene names do not have a good signal in the DMV. Specifically, the mixed neurons that do not seem to have clear marker genes. What top markers (top 10?) were used to identify these anatomical locations? At least some examples should be shown for anatomical areas to support Figure S3.

(3) Page 15, lines 410-411: "...could not find clusters sharing all markers with our neuronal classes...". Are the authors trying to say that the DMV has more diverse neurons than other brain sites? It seems not too unusual that the hypothalamus is different from the brainstem. Maybe this could be stated more clearly, and the importance of this could be clarified.

(4) The finding of GIRK1 astrocytes is interesting, but the emphasis that this means these astrocytes are highly/more excitable is confusing. This was not experimentally addressed and should be put into context that astrocyte activation is very different from neuronal activation. This should be better clarified in the results and discussion.

(5) The Pdgfra IHC as verification is great, but images are not very convincing in distinguishing the 2 (mouse) or 3 (rat) classes of cells. Why not compare Pdgfra and HuC/D co-localization by IHC and snRNAseq data (using the genes for HuC/D) in the mouse and in the rat? That would also clarify how specific HuC/D is for DMV neurons, or if it may also be expressed in non-neuronal populations.

eLife Assessment

This useful study presents computational analyses of over 5,000 predicted extant and ancestral nitrogenase structures. While the data and some analyses are solid, the study remains incomplete in demonstrating that the metrics used for comparing nitrogenase structures are statistically rigorous. The data generated in this study provide a vast resource that can serve as a starting point for functional studies of reconstructed and extant nitrogenases.

Reviewer #1 (Public review):

This was a clearly written manuscript that did an excellent job summarizing complex data. In this manuscript, Cuevas-Zuviría et al. use protein modeling to generate over 5,000 predicted structures of nitrogenase components, encompassing both extant and ancestral forms across different clades. The study highlights that key insertions define the various Nif groups. The authors also examined the structures of three ancestral nitrogenase variants that had been previously identified and experimentally tested. These ancestral forms were shown in earlier studies to exhibit reduced activity in Azotobacter vinelandii, a model diazotroph.

This work provides a useful resource for studying nitrogenase evolution. However, its impact is somewhat limited due to a lack of evidence linking the observed structural differences to functional changes. For example, in the ancestral nitrogenase structures, only a small set of residues (lines 421-431) were identified as potentially affecting interactions between nitrogenase components. Why didn't the authors test whether reverting these residues to their extant counterparts could improve nitrogenase activity of the ancestral variants?

Additionally, the paper feels somewhat disconnected. The predicted nitrogenase structures discussed in the first half of the manuscript were not well integrated with the findings from the ancestral structures. For instance, do the ancestral nitrogenase structures align with the predicted models? This comparison was never explicitly made and could have strengthened the study's conclusions.

Reviewer #2 (Public review):

This work aims to study the evolution of nitrogenanses, understanding how their structure and function adapted to changes in the environment, including oxygen levels and changes in metal availability.

The study predicts > 5000 structures of nitrogenases, corresponding to extant, ancestral, and alternative ancestral sequences. It is observed that structural variations in the nitrogenases correlate with phylogenetic relationships. The amount of data generated in this study represents a massive undertaking that is certain to be a resource for the community. The study also provides strong insight into how structural evolution correlates with environmental and biological phenotypes.

The challenge with this study is that all (or nearly all) of the quantitative analyses presented are based on RMSD calculations, many of which are under 2 angstroms. For all intents and purposes, two structures with RMSD < 2 angstroms could be considered 'structurally identical'. A lot of insight generated is based on minuscule differences in RMSD, for which it is not clear that they are significantly different. The suggestion would be to find a way to evaluate the RMSD metric and determine whether these values, as obtained for structures being compared, are reliable. Some options are provided in earlier studies: PMID: 11514933, PMID: 17218333, PMID: 11420449, PMID: 8289285 (and others).

It could also be valuable to focus more on site-specific RMSDs rather than Global RMSDs. The high conservation in the nitrogenases likely ensures that the global RMSDs will remain low across the family. Focusing on specific regions might reveal interesting differences between clades that are more informative regarding the evolution of structure in tandem with environment/time.

Author response:

Public Reviews:

Reviewer #1 (Public review):

This was a clearly written manuscript that did an excellent job summarizing complex data.

In this manuscript, Cuevas-Zuviría et al. use protein modeling to generate over 5,000 predicted structures of nitrogenase components, encompassing both extant and ancestral forms across different clades. The study highlights that key insertions define the various Nif groups. The authors also examined the structures of three ancestral nitrogenase variants that had been previously identified and experimentally tested. These ancestral forms were shown in earlier studies to exhibit reduced activity in Azotobacter vinelandii, a model diazotroph. This work provides a useful resource for studying nitrogenase evolution.

However, its impact is somewhat limited due to a lack of evidence linking the observed structural differences to functional changes. For example, in the ancestral nitrogenase structures, only a small set of residues (lines 421-431) were identified as potentially affecting interactions between nitrogenase components. Why didn't the authors test whether reverting these residues to their extant counterparts could improve nitrogenase activity of the ancestral variants?

We thank the reviewer for their thoughtful comments. We acknowledge that our current study is primarily focused on a computational exploration of the structural differences in both extant and ancestral nitrogenase variants, which allowed us to generate a comprehensive structural dataset. Although we did not carry out experimental reversion tests in this study, we agree that directly assessing the functional consequences of reverting the specific residues (lines 420 to 429) to their extant counterparts is an important next step to elucidate their functional role. Indeed, these findings provide a valuable foundation for our future work, which is designed to include experimental characterization of these variants and further elucidate the role of critical residues in nitrogenase activity and evolution. We believe that these experiments will offer the direct functional validation that the reviewer has rightly pointed out, and we look forward to reporting on these results in a future study.

Additionally, the paper feels somewhat disconnected. The predicted nitrogenase structures discussed in the first half of the manuscript were not well integrated with the findings from the ancestral structures. For instance, do the ancestral nitrogenase structures align with the predicted models? This comparison was never explicitly made and could have strengthened the study's conclusions.

We thank the reviewer for this suggestion. Our original analysis (previously shown in Figure S9, now Figure S10) included insights into structural align comparisons. In response, we have reorganized the results section (lines 351-355) to explicitly address this comparison.

Reviewer #2 (Public review):

This work aims to study the evolution of nitrogenases, understanding how their structure and function adapted to changes in the environment, including oxygen levels and changes in metal availability. The study predicts > 5000 structures of nitrogenases, corresponding to extant, ancestral, and alternative ancestral sequences. It is observed that structural variations in the nitrogenases correlate with phylogenetic relationships. The amount of data generated in this study represents a massive undertaking that is certain to be a resource for the community. The study also provides strong insight into how structural evolution correlates with environmental and biological phenotypes.

The challenge with this study is that all (or nearly all) of the quantitative analyses presented are based on RMSD calculations, many of which are under 2 angstroms. For all intents and purposes, two structures with RMSD < 2 angstroms could be considered 'structurally identical'. A lot of insight generated is based on minuscule differences in RMSD, for which it is not clear that they are significantly different. The suggestion would be to find a way to evaluate the RMSD metric and determine whether these values, as obtained for structures being compared, are reliable. Some options are provided in earlier studies: PMID: 11514933, PMID: 17218333, PMID: 11420449, PMID: 8289285 (and others). It could also be valuable to focus more on site-specific RMSDs rather than Global RMSDs. The high conservation in the nitrogenases likely ensures that the global RMSDs will remain low across the family. Focusing on specific regions might reveal interesting differences between clades that are more informative regarding the evolution of structure in tandem with environment/time.

We thank the reviewer for their suggestions. We agree that while global RMSD values below 2Å typically indicate high structural similarity, relying solely on these measures can mask subtle yet potentially functionally meaningful differences. Our aim was not to test for overall structural identity but rather to quantify fine-scale variations between highly conserved nitrogenase structures, including extant and ancestral variants. Nevertheless, in light of the reviewer’s suggestions, we have implemented an additional metric ( rmsd₁₀₀) for a more nuanced comparison. The results of our additional analyses (Figure S3) align closely with our original results (Figure 2), supporting our decision to retain the un-normalized results in the main text. As an additional measure, we also computed site-specific RMSDs for the active site’s environments (Figure S6) to further delineate subtle structural variations.

eLife Assessment

Examination of (a)periodic brain activity has gained particular interest in the last few years in the neuroscience fields relating to cognition, disorders, and brain states. Using large EEG/MEG datasets from younger and older adults, the current study provides compelling evidence that age-related differences in aperiodic EEG/MEG signals can be driven by cardiac rather than brain activity. Their findings have important implications for all future research that aims to assess aperiodic neural activity, suggesting control for the influence of cardiac signals is essential.

Reviewer #1 (Public review):

Summary:

The present study addresses whether physiological signals influence aperiodic brain activity with a focus on age-related changes. The authors report age effects on aperiodic cardiac activity derived from ECG in low and high-frequency ranges in roughly 2300 participants from four different sites. Slopes of the ECGs were associated with common heart variability measures, which, according to the authors, shows that ECG, even at higher frequencies, conveys meaningful information. Using temporal response functions on concurrent ECG and M/EEG time series, the authors demonstrate that cardiac activity is instantaneously reflected in neural recordings, even after applying ICA analysis to remove cardiac activity. This was more strongly the case for EEG than MEG data. Finally, spectral parameterization was done in large-scale resting-state MEG and ECG data in individuals between 18 and 88 years, and age effects were tested. A steepening of spectral slopes with age was observed, particularly for ECG and, to a lesser extent, in cleaned MEG data in most frequency ranges and sensors investigated. The authors conclude that commonly observed age effects on neural aperiodic activity can mainly be explained by cardiac activity.

Strengths:

Compared to previous investigations, the authors demonstrate effects of aging on the spectral slope in the currently largest MEG dataset with equal age distribution available. Their efforts of replicating observed effects in another large MEG dataset and considering potential confounding by ocular activity, head movements, or preprocessing methods are commendable and highly valuable to the community. This study also employs a wide range of fitting ranges and two commonly used algorithms for spectral parameterization of neural and cardiac activity, hence providing a comprehensive overview of the impact of methodological choices. The authors discuss their findings in-depth and give recommendations for the separation of physiological and neural sources of aperiodic activity.

Weaknesses:

While the study's aim is well-motivated and analyses rigorously conducted, it remains vague what is reflected in the ECG at higher frequency ranges that contributed to the confounding of the age effects in the neural data. However, the authors address this issue in their discussion.

Reviewer #2 (Public review):

As remains obvious from my previous reviews, I still consider this to be an important paper and that is final and publishable in its current state.

In that previous review, I revealed my identity to help reassure the authors that I was doing my best to remain unbiased because I work in this area and some of the authors' results directly impact my prior research. I was genuinely excited to see the earlier preprint version of this paper when it first appeared. I get a lot of joy out of trying to - collectively, as a field - really understand the nature of our data, and I continue to commend the authors here for pushing at the sources of aperiodic activity!

In their manuscript, Schmidt and colleagues provide a very compelling, convincing, thorough, and measured set of analyses. Previously I recommended that the push even further, and they added the current Figure 5 analysis of event-related changes in the ECG during working memory. In my opinion this result practically warrants a separate paper its own!

The literature analysis is very clever, and expanded upon from any other prior version I've seen.

In my previous review, the broadest, most high-level comment I wanted to make was that authors are correct. We (in my lab) have tried to be measured in our approach to talking about aperiodic analyses - including adopting measuring ECG when possible now - because there are so many sources of aperiodic activity: neural, ECG, respiration, skin conductance, muscle activity, electrode impedances, room noise, electronics noise, etc. The authors discuss this all very clearly, and I commend them on that. We, as a field, should move more toward a model where we can account for all of those sources of noise together. (This was less of an action item, and more of an inclusion of a comment for the record.)

I also very much appreciate the authors' excellent commentary regarding the physiological effects that pharmacological challenges such as propofol and ketamine also have on non-neural (autonomic) functions such as ECG. Previously I also asked them to discuss the possibility that, while their manuscript focuses on aperiodic activity, it is possible that the wealth of literature regarding age-related changes in "oscillatory" activity might be driven partly by age-related changes in neural (or non-neural, ECG-related) changes in aperiodic activity. They have included a nice discussion on this, and I'm excited about the possibilities for cognitive neuroscience as we move more in this direction.

Finally, I previously asked for recommendations on how to proceed. The authors convinced me that we should care about how the ECG might impact our field potential measures, but how do I, as a relative novice, proceed. They now include three strong recommendations at the end of their manuscript that I find to be very helpful.

As was obvious from previous review, I consider this to be an important and impactful cautionary report, that is incredibly well supported by multiple thorough analyses. The authors have done an excellent job responding to all my previous comments and concerns and, in my estimation, those of the previous reviewers as well.

Reviewer #3 (Public review):

Summary:

Schmidt et al., aimed to provide an extremely comprehensive demonstration of the influence cardiac electromagnetic fields have on the relationship between age and the aperiodic slope measured from electroencephalographic (EEG) and magnetoencephalographic (MEG) data.

Strengths:

Schmidt et al., used a multiverse approach to show that the cardiac influence on this relationship is considerable, by testing a wide range of different analysis parameters (including extensive testing of different frequency ranges assessed to determine the aperiodic fit), algorithms (including different artifact reduction approaches and different aperiodic fitting algorithms), and multiple large datasets to provide conclusions that are robust to the vast majority of potential experimental variations.

The study showed that across these different analytical variations, the cardiac contribution to aperiodic activity measured using EEG and MEG is considerable, and likely influences the relationship between aperiodic activity and age to a greater extent than the influence of neural activity.

Their findings have significant implications for all future research that aims to assess aperiodic neural activity, suggesting control for the influence of cardiac fields is essential.

Weaknesses:

The authors have addressed the weaknesses of their study in their manuscript. Most alternative explanations for their results have been explored to ensure their conclusions are robust and are not explained by unexplored confounds. Minor potential weaknesses are:

(1) The number of electrodes used in the EEG analyses was on the lower side, and as such, the results do not confirm that the influence of ECG on the 1/f activity in the EEG is high even for higher density EEG montages where ICA may provide better performance at removing cardiac components (as noted by the authors). Having noted this potential weakness, I doubt the effects of cardiac activity can be completely mitigated with current methods, even in higher-density EEG montages.

(2) Head movements were used as a proxy for muscle activity. However, this may imperfectly address the potential influence of muscle activity on the slope in the EEG activity. As such, remaining muscle artifacts may have affected some of the results, particularly those that included high frequency ranges in the aperiodic estimate. Perhaps if muscle activity were left in the EEG data, it could have disrupted the ability to detect a relationship between age and 1/f slope in a way that didn't disrupt the same relationship in the cardiac data. However, I doubt this would reverse the overall conclusions given the number of converging results, including in lower frequency bands. The authors also note this potential weakness and suggest how future research might address it.

Author response:

The following is the authors’ response to the original reviews

eLife Assessment

Examination of (a)periodic brain activity has gained particular interest in the last few years in the neuroscience fields relating to cognition, disorders, and brain states. Using large EEG/MEG datasets from younger and older adults, the current study provides compelling evidence that age-related differences in aperiodic EEG/MEG signals can be driven by cardiac rather than brain activity. Their findings have important implications for all future research that aims to assess aperiodic neural activity, suggesting control for the influence of cardiac signals is essential.

We want to thank the editors for their assessment of our work and highlighting its importance for the understanding of aperiodic neural activity. Additionally, we want to thank the three present and four former reviewers (at a different journal) whose comments and ideas were critical in shaping this manuscript to its current form. We hope that this paper opens up many more questions that will guide us - as a field - to an improved understanding of how “cortical” and “cardiac” changes in aperiodic activity are linked and want to invite readers to engage with our work through eLife’s comment function.

Public Reviews:

Reviewer #1 (Public review):

Summary:

The present study addresses whether physiological signals influence aperiodic brain activity with a focus on age-related changes. The authors report age effects on aperiodic cardiac activity derived from ECG in low and high-frequency ranges in roughly 2300 participants from four different sites. Slopes of the ECGs were associated with common heart variability measures, which, according to the authors, shows that ECG, even at higher frequencies, conveys meaningful information. Using temporal response functions on concurrent ECG and M/EEG time series, the authors demonstrate that cardiac activity is instantaneously reflected in neural recordings, even after applying ICA analysis to remove cardiac activity. This was more strongly the case for EEG than MEG data. Finally, spectral parameterization was done in large-scale resting-state MEG and ECG data in individuals between 18 and 88 years, and age effects were tested. A steepening of spectral slopes with age was observed particularly for ECG and, to a lesser extent, in cleaned MEG data in most frequency ranges and sensors investigated. The authors conclude that commonly observed age effects on neural aperiodic activity can mainly be explained by cardiac activity.

Strengths:

Compared to previous investigations, the authors demonstrate the effects of aging on the spectral slope in the currently largest MEG dataset with equal age distribution available. Their efforts of replicating observed effects in another large MEG dataset and considering potential confounding by ocular activity, head movements, or preprocessing methods are commendable and valuable to the community. This study also employs a wide range of fitting ranges and two commonly used algorithms for spectral parameterization of neural and cardiac activity, hence providing a comprehensive overview of the impact of methodological choices. Based on their findings, the authors give recommendations for the separation of physiological and neural sources of aperiodic activity.

Weaknesses:

While the aim of the study is well-motivated and analyses rigorously conducted, the overall structure of the manuscript, as it stands now, is partially misleading. Some of the described results are not well-embedded and lack discussion.

We want to thank the reviewer for their comments focussed on improving the overall structure of the manuscript. We agree with their suggestions that some results could be more clearly contextualized and restructured the manuscript accordingly.

Reviewer #2 (Public review):

I previously reviewed this important and timely manuscript at a previous journal where, after two rounds of review, I recommended publication. Because eLife practices an open reviewing format, I will recapitulate some of my previous comments here, for the scientific record.

In that previous review, I revealed my identity to help reassure the authors that I was doing my best to remain unbiased because I work in this area and some of the authors' results directly impact my prior research. I was genuinely excited to see the earlier preprint version of this paper when it first appeared. I get a lot of joy out of trying to - collectively, as a field - really understand the nature of our data, and I continue to commend the authors here for pushing at the sources of aperiodic activity!

In their manuscript, Schmidt and colleagues provide a very compelling, convincing, thorough, and measured set of analyses. Previously I recommended that the push even further, and they added the current Figure 5 analysis of event-related changes in the ECG during working memory. In my opinion this result practically warrants a separate paper its own!

The literature analysis is very clever, and expanded upon from any other prior version I've seen.

In my previous review, the broadest, most high-level comment I wanted to make was that authors are correct. We (in my lab) have tried to be measured in our approach to talking about aperiodic analyses - including adopting measuring ECG when possible now - because there are so many sources of aperiodic activity: neural, ECG, respiration, skin conductance, muscle activity, electrode impedances, room noise, electronics noise, etc. The authors discuss this all very clearly, and I commend them on that. We, as a field, should move more toward a model where we can account for all of those sources of noise together. (This was less of an action item, and more of an inclusion of a comment for the record.)

I also very much appreciate the authors' excellent commentary regarding the physiological effects that pharmacological challenges such as propofol and ketamine also have on non-neural (autonomic) functions such as ECG. Previously I also asked them to discuss the possibility that, while their manuscript focuses on aperiodic activity, it is possible that the wealth of literature regarding age-related changes in "oscillatory" activity might be driven partly by age-related changes in neural (or non-neural, ECG-related) changes in aperiodic activity. They have included a nice discussion on this, and I'm excited about the possibilities for cognitive neuroscience as we move more in this direction.

Finally, I previously asked for recommendations on how to proceed. The authors convinced me that we should care about how the ECG might impact our field potential measures, but how do I, as a relative novice, proceed. They now include three strong recommendations at the end of their manuscript that I find to be very helpful.

As was obvious from previous review, I consider this to be an important and impactful cautionary report, that is incredibly well supported by multiple thorough analyses. The authors have done an excellent job responding to all my previous comments and concerns and, in my estimation, those of the previous reviewers as well.

We want to thank the reviewer for agreeing to review our manuscript again and for recapitulating on their previous comments and the progress the manuscript has made over the course of the last ~2 years. The reviewer's comments have been essential in shaping the manuscript into its current form. Their feedback has made the review process truly feel like a collaborative effort, focused on strengthening the manuscript and refining its conclusions and resulting recommendations.

Reviewer #3 (Public review):

Summary:

Schmidt et al., aimed to provide an extremely comprehensive demonstration of the influence cardiac electromagnetic fields have on the relationship between age and the aperiodic slope measured from electroencephalographic (EEG) and magnetoencephalographic (MEG) data.

Strengths:

Schmidt et al., used a multiverse approach to show that the cardiac influence on this relationship is considerable, by testing a wide range of different analysis parameters (including extensive testing of different frequency ranges assessed to determine the aperiodic fit), algorithms (including different artifact reduction approaches and different aperiodic fitting algorithms), and multiple large datasets to provide conclusions that are robust to the vast majority of potential experimental variations.

The study showed that across these different analytical variations, the cardiac contribution to aperiodic activity measured using EEG and MEG is considerable, and likely influences the relationship between aperiodic activity and age to a greater extent than the influence of neural activity.

Their findings have significant implications for all future research that aims to assess aperiodic neural activity, suggesting control for the influence of cardiac fields is essential.

We want to thank the reviewer for their thorough engagement with our work and the resultant substantive amount of great ideas both mentioned in the section of Weaknesses and Authors Recommendations below. Their suggestions have sparked many ideas in us on how to move forward in better separating peripheral- from neuro-physiological signals that are likely to greatly influence our future attempts to better extract both cardiac and muscle activity from M/EEG recordings. So we want to thank them for their input, time and effort!

Weaknesses:

Figure 4I: The regressions explained here seem to contain a very large number of potential predictors. Based on the way it is currently written, I'm assuming it includes all sensors for both the ECG component and ECG rejected conditions?

I'm not sure about the logic of taking a complete signal, decomposing it with ICA to separate out the ECG and non-ECG signals, then including these latent contributions to the full signal back into the same regression model. It seems that there could be some circularity or redundancy in doing so. Can the authors provide a justification for why this is a valid approach?

After observing significant effects both in the MEG_{ECG component} and MEG_{ECG rejected} conditions in similar frequency bands we wanted to understand whether or not these age-related changes are statistically independent. To test this we added both variables as predictors in a regression model (thereby accounting for the influence of the other in relation to age). The regression models we performed were therefore actually not very complex. They were built using only two predictors, namely the data (in a specific frequency range) averaged over channels on which we noticed significant effects in the ECG rejected and ECG components data respectively (Wilkinson notation: age ~ 1 + ECG rejected + ECG components). This was also described in the results section stating that: “To see if MEG_{ECG rejected} and MEG_{ECG component} explain unique variance in aging at frequency ranges where we noticed shared effects, we averaged the spectral slope across significant channels and calculated a multiple regression model with MEG_{ECG component} and MEG_{ECG rejected} as predictors for age (to statistically control for the effect of MEG_{ECG component}s and MEG_{ECG rejected} on age). This analysis was performed to understand whether the observed shared age-related effects (MEG_{ECG rejected} and MEG_{ECG component}) are in(dependent).”  

We hope this explanation solves the previous misunderstanding.

I'm not sure whether there is good evidence or rationale to support the statement in the discussion that the presence of the ECG signal in reference electrodes makes it more difficult to isolate independent ECG components. The ICA algorithm will still function to detect common voltage shifts from the ECG as statistically independent from other voltage shifts, even if they're spread across all electrodes due to the referencing montage. I would suggest there are other reasons why the ICA might lead to imperfect separation of the ECG component (assumption of the same number of source components as sensors, non-Gaussian assumption, assumption of independence of source activities).

The inclusion of only 32 channels in the EEG data might also have reduced the performance of ICA, increasing the chances of imperfect component separation and the mixing of cardiac artifacts into the neural components, whereas the higher number of sensors in the MEG data would enable better component separation. This could explain the difference between EEG and MEG in the ability to clean the ECG artifact (and perhaps higher-density EEG recordings would not show the same issue).

The reviewer is making a good argument suggesting that our initial assumption that the presence of cardiac activity on the reference electrode influences the performance of the ICA may be wrong. After rereading and rethinking upon the matter we think that the reviewer is correct and that their assumptions for why the ECG signal was not so easily separable from our EEG recordings are more plausible and better grounded in the literature than our initial suggestion. We therefore now highlight their view as a main reason for why the ECG rejection was more challenging in EEG data. However, we also note that understanding the exact reason probably ends up being an empirical question that demands further research stating that:

“Difficulties in removing ECG related components from EEG signals via ICA might be attributable to various reasons such as the number of available sensors or assumptions related to the non-gaussianity of the underlying sources. Further understanding of this matter is highly important given that ICA is the most widely used procedure to separate neural from peripheral physiological sources. ”

In addition to the inability to effectively clean the ECG artifact from EEG data, ICA and other component subtraction methods have also all been shown to distort neural activity in periods that aren't affected by the artifact due to the ubiquitous issue of imperfect component separation (https://doi.org/10.1101/2024.06.06.597688). As such, component subtraction-based (as well as regression-based) removal of the cardiac artifact might also distort the neural contributions to the aperiodic signal, so even methods to adequately address the cardiac artifact might not solve the problem explained in the study. This poses an additional potential confound to the "M/EEG without ECG" conditions.

The reviewer is correct in stating that, if an “artifactual” signal is not always present but appears and disappears (like e.g. eye-blinks) neural activity may be distorted in periods where the “artifactual” signal is absent. However, while this plausibly presents a problem for ocular activity, there is no obvious reason to believe that this applies to cardiac activity. While the ECG signal is non-stationary in nature, it is remarkably more stable than eye-movements in the healthy populations we analyzed (especially at rest). Therefore, the presence of the cardiac “artifact” was consistently present across the entirety of the MEG recordings we visually inspected.

Literature Analysis, Page 23: was there a method applied to address studies that report reducing artifacts in general, but are not specific to a single type of artifact? For example, there are automated methods for cleaning EEG data that use ICLabel (a machine learning algorithm) to delete "artifact" components. Within these studies, the cardiac artifact will not be mentioned specifically, but is included under "artifacts".

The literature analysis was largely performed automatically and solely focussed on ECG related activity as described in the methods section under Literature Analysis, if no ECG related terms were used in the context of artifact rejection a study was flagged as not having removed cardiac activity. This could have been indeed better highlighted by us and we apologize for the oversight on our behalf. We now additionally link to these details stating that:

“However, an analysis of openly accessible M/EEG articles (N_Articles=279; see Methods - Literature Analysis for further details) that investigate aperiodic activity revealed that only 17.1% of EEG studies explicitly mention that cardiac activity was removed and only 16.5% measure ECG (45.9% of MEG studies removed cardiac activity and 31.1% of MEG studies mention that ECG was measured; see Figure 1EF).”

The reviewer makes a fair point that there is some uncertainty here and our results probably present a lower bound of ECG handling in M/EEG research as, when I manually rechecked the studies that were not initially flagged in studies it was often solely mentioned that “artifacts” were rejected. However, this information seemed too ambiguous to assume that cardiac activity was in fact accounted for. However, again this could have been mentioned more clearly in writing and we apologize for this oversight. Now this is included as part of the methods section Literature Analysis stating that:

“All valid word contexts were then manually inspected by scanning the respective word context to ensure that the removal of “artifacts” was related specifically to cardiac and not e.g. ocular activity or the rejection of artifacts in general (without specifying which “artifactual” source was rejected in which case the manuscript was marked as invalid). This means that the results of our literature analysis likely present a lower bound for the rejection of cardiac activity in the M/EEG literature investigating aperiodic activity.”

Statistical inferences, page 23: as far as I can tell, no methods to control for multiple comparisons were implemented. Many of the statistical comparisons were not independent (or even overlapped with similar analyses in the full analysis space to a large extent), so I wouldn't expect strong multiple comparison controls. But addressing this point to some extent would be useful (or clarifying how it has already been addressed if I've missed something).

In the present study we tried to minimize the risk of type 1 errors by several means, such as A) weakly informative priors, B) robust regression models and C) by specifying a region of practical equivalence (ROPE, see Methods Statistical Inference for further Information) to define meaningful effects.

Weakly informative priors can lower the risk of type 1 errors arising from multiple testing by shrinking parameter estimates towards zero (see e.g. Lemoine, 2019). Robust regression models use a Student T distribution to describe the distribution of the data. This distribution features heavier tails, meaning it allocates more probability to extreme values, which in turn minimizes the influence of outliers. The ROPE criterion ensures that only effects exceeding a negligible size are considered meaningful, representing a strict and conservative approach to interpreting our findings (see Kruschke 2018, Cohen, 1988).

Furthermore, and more generally we do not selectively report “significant” effects in the situations in which multiple analyses were conducted on the same family of data (e.g. Figure 2 & 4). Instead we provide joint inference across several plausible analysis options (akin to a specification curve analysis, Simonsohn, Simmons & Nelson 2020) to provide other researchers with an overview of how different analysis choices impact the association between cardiac and neural aperiodic activity.

Lemoine, N. P. (2019). Moving beyond noninformative priors: why and how to choose weakly informative priors in Bayesian analyses. Oikos, 128(7), 912-928.

Simonsohn, U., Simmons, J. P., & Nelson, L. D. (2020). Specification curve analysis. Nature Human Behaviour, 4(11), 1208-1214.

Methods:

Applying ICA components from 1Hz high pass filtered data back to the 0.1Hz filtered data leads to worse artifact cleaning performance, as the contribution of the artifact in the 0.1Hz to 1Hz frequency band is not addressed (see Bailey, N. W., Hill, A. T., Biabani, M., Murphy, O. W., Rogasch, N. C., McQueen, B., ... & Fitzgerald, P. B. (2023). RELAX part 2: A fully automated EEG data cleaning algorithm that is applicable to Event-Related-Potentials. Clinical Neurophysiology, result reported in the supplementary materials). This might explain some of the lower frequency slope results (which include a lower frequency limit <1Hz) in the EEG data - the EEG cleaning method is just not addressing the cardiac artifact in that frequency range (although it certainly wouldn't explain all of the results).

We want to thank the reviewer for suggesting this interesting paper, showing that lower high-pass filters may be preferable to the more commonly used >1Hz high-pass filters for detection of ICA components that largely contain peripheral physiological activity. However, the results presented by Bailey et al. contradict the more commonly reported findings by other researchers that >1Hz high-pass filter is actually preferable (e.g. Winkler et al. 2015; Dimingen, 2020 or Klug & Gramann, 2021) and recommendations in widely used packages for M/EEG analysis (e.g. https://mne.tools/1.8/generated/mne.preprocessing.ICA.html). Yet, the fact that there seems to be a discrepancy suggests that further research is needed to better understand which type of high-pass filtering is preferable in which situation. Furthermore, it is notable that all the findings for high-pass filtering in ICA component detection and removal that we are aware of relate to ocular activity. Given that ocular and cardiac activity have very different temporal and spectral patterns it is probably worth further investigating whether the classic 1Hz high-pass filter is really also the best option for the detection and removal of cardiac activity. However, in our opinion this requires a dedicated investigation on its own..

We therefore highlight this now in our manuscript stating that:

“Additionally, it is worth noting that the effectiveness of an ICA crucially depends on the quality of the extracted components(63,64) and even widely suggested settings e.g. high-pass filtering at 1Hz before fitting an ICA may not be universally applicable (see supplementary material of (64)).

Winkler, S. Debener, K. -R. Müller and M. Tangermann, "On the influence of high-pass filtering on ICA-based artifact reduction in EEG-ERP," 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Milan, Italy, 2015, pp. 4101-4105, doi: 10.1109/EMBC.2015.7319296.

Dimigen, O. (2020). Optimizing the ICA-based removal of ocular EEG artifacts from free viewing experiments. NeuroImage, 207, 116117.

Klug, M., & Gramann, K. (2021). Identifying key factors for improving ICA‐based decomposition of EEG data in mobile and stationary experiments. European Journal of Neuroscience, 54(12), 8406-8420.

It looks like no methods were implemented to address muscle artifacts. These can affect the slope of EEG activity at higher frequencies. Perhaps the Riemannian Potato addressed these artifacts, but I suspect it wouldn't eliminate all muscle activity. As such, I would be concerned that remaining muscle artifacts affected some of the results, particularly those that included high frequency ranges in the aperiodic estimate. Perhaps if muscle activity were left in the EEG data, it could have disrupted the ability to detect a relationship between age and 1/f slope in a way that didn't disrupt the same relationship in the cardiac data (although I suspect it wouldn't reverse the overall conclusions given the number of converging results including in lower frequency bands). Is there a quick validity analysis the authors can implement to confirm muscle artifacts haven't negatively affected their results?

I note that an analysis of head movement in the MEG is provided on page 32, but it would be more robust to show that removing ICA components reflecting muscle doesn't change the results. The results/conclusions of the following study might be useful for objectively detecting probable muscle artifact components: Fitzgibbon, S. P., DeLosAngeles, D., Lewis, T. W., Powers, D. M. W., Grummett, T. S., Whitham, E. M., ... & Pope, K. J. (2016). Automatic determination of EMG-contaminated components and validation of independent component analysis using EEG during pharmacologic paralysis. Clinical neurophysiology, 127(3), 1781-1793.

We thank the reviewer for their suggestion. Muscle activity can indeed be a potential concern, for the estimation of the spectral slope. This is precisely why we used head movements (as also noted by the reviewer) as a proxy for muscle activity. We also agree with the reviewer that this is not a perfect estimate. Additionally, also the riemannian potato would probably only capture epochs that contain transient, but not persistent patterns of muscle activity.

The paper recommended by the reviewer contains a clever approach of using the steepness of the spectral slope (or lack thereof) as an indicator whether or not an independent component (IC) is driven by muscle activity. In order to determine an optimal threshold Fitzgibbon et al. compared paralyzed to temporarily non paralyzed subjects. They determined an expected “EMG-free” threshold for their spectral slope on paralyzed subjects and used this as a benchmark to detect IC’s that were contaminated by muscle activity in non paralyzed subjects.

This is a great idea, but unfortunately would go way beyond what we are able to sensibly estimate with our data for the following reasons. The authors estimated their optimal threshold on paralyzed subjects for EEG data and show that this is a feasible threshold to be applied across different recordings. So for EEG data it might be feasible, at least as a first shot, to use their threshold on our data. However, we are measuring MEG and as alluded to in our discussion section under “Differences in aperiodic activity between magnetic and electric field recordings” the spectral slope differs greatly between MEG and EEG recordings for non-trivial reasons. Furthermore, the spectral slope even seems to also differ across different MEG devices. We noticed this when we initially tried to pool the data recorded in Salzburg with the Cambridge dataset. This means we would need to do a complete validation of this procedure for the MEG data recorded in Cambridge and in Salzburg, which is not feasible considering that we A) don’t have direct access to one of the recording sites and B) would even if we had access face substantial hurdles to get ethical approval for the experiment performed by Fitzgibbon et al..

However, we think the approach brought forward by Fitzgibbon and colleagues is a clever way to remove muscle activity from EEG recordings, whenever EMG was not directly recorded. We therefore suggested in the Discussion section that ideally also EMG should be recorded stating that:

“It is worth noting that, apart from cardiac activity, muscle activity can also be captured in (non-)invasive recordings and may drastically influence measures of the spectral slope(72). To ensure that persistent muscle activity does not bias our results we used changes in head movement velocity as a control analysis (see Supplementary Figure S9). However, it should be noted that this is only a proxy for the presence of persistent muscle activity. Ideally, studies investigating aperiodic activity should also be complemented by measurements of EMG. Whenever such measurements are not available creative approaches that use the steepness of the spectral slope (or the lack thereof) as an indicator to detect whether or not e.g. an independent component is driven by muscle activity are promising(72,73). However, these approaches may require further validation to determine how well myographic aperiodic thresholds are transferable across the wide variety of different M/EEG devices.”

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) As outlined above, I recommend rephrasing the last section of the introduction to briefly summarize/introduce all main analysis steps undertaken in the study and why these were done (for example, it is only mentioned that the Cam-CAN dataset was used to study the impact of cardiac on MEG activity although the author used a variety of different datasets). Similarly, I am missing an overview of all main findings in the context of the study goals in the discussion. I believe clarifying the structure of the paper would not only provide a red thread to the reader but also highlight the efforts/strength of the study as described above.

This is a good call! As suggested by the reviewer we now try to give a clearer overview of what was investigated why. We do that both at the end of the introduction stating that: “Using the publicly available Cam-CAN dataset(28,29), we find that the aperiodic signal measured using M/EEG originates from multiple physiological sources. In particular, significant portions of age-related changes in aperiodic activity –normally attributed to neural processes– can be better explained by cardiac activity. This observation holds across a wide range of processing options and control analyses (see Supplementary S1), and was replicable on a separate MEG dataset. However, the extent to which cardiac activity accounts for age-related changes in aperiodic activity varies with the investigated frequency range and recording site. Importantly, in some frequency ranges and sensor locations, age-related changes in neural aperiodic activity still prevail. But does the influence of cardiac activity on the aperiodic spectrum extend beyond age? In a preliminary analysis, we demonstrate that working memory load modulates the aperiodic spectrum of “pure” ECG recordings. The direction of this working memory effect mirrors previous findings on EEG data(5) suggesting that the impact of cardiac activity goes well beyond aging. In sum, our results highlight the complexity of aperiodic activity while cautioning against interpreting it as solely “neural“ without considering physiological influences.”

and at the beginning of the discussion section:

“Difficulties in removing ECG related components from EEG signals via ICA might be attributable to various reasons such as the number of available sensors or assumptions related to the non-gaussianity of the underlying sources. Further understanding of this matter is highly important given that ICA is the most widely used procedure to separate neural from peripheral physiological sources (see Figure 1EF). Additionally, it is worth noting that the effectiveness of an ICA crucially depends on the quality of the extracted components(63,64) and even widely suggested settings e.g. high-pass filtering at 1Hz before fitting an ICA may not be universally applicable (see supplementary material of (64)). “

(2) I found it interesting that the spectral slopes of ECG activity at higher frequency ranges (> 10 Hz) seem mostly related to HRV measures such as fractal and time domain indices and less so with frequency-domain indices. Do the authors have an explanation for why this is the case? Also, the analysis of the HRV measures and their association with aperiodic ECG activity is not explained in any of the method sections.

We apologize for the oversight in not mentioning the HRV analysis in more detail in our methods section. We added a subsection to the Methods section entitled ECG Processing - Heart rate variability analysis to further describe the HRV analyses.

“ECG Processing - Heart rate variability analysis

Heart rate variability (HRV) was computed using the NeuroKit2 toolbox, a high level tool for the analysis of physiological signals. First, the raw electrocardiogram (ECG) data were preprocessed, by highpass filtering the signal at 0.5Hz using an infinite impulse response (IIR) butterworth filter(order=5) and by smoothing the signal with a moving average kernel with the width of one period of 50Hz to remove the powerline noise (default settings of neurokit.ecg.ecg_clean). Afterwards, QRS complexes were detected based on the steepness of the absolute gradient of the ECG signal. Subsequently, R-Peaks were detected as local maxima in the QRS complexes (default settings of neurokit.ecg.ecg_peaks; see (98) for a validation of the algorithm). From the cleaned R-R intervals, 90 HRV indices were derived, encompassing time-domain, frequency-domain, and non-linear measures. Time-domain indices included standard metrics such as the mean and standard deviation of the normalized R-R intervals , the root mean square of successive differences, and other statistical descriptors of interbeat interval variability. Frequency-domain analyses were performed using power spectral density estimation, yielding for instance low frequency (0.04-0.15Hz) and high frequency (0.15-0.4Hz) power components. Additionally, non-linear dynamics were characterized through measures such as sample entropy, detrended fluctuation analysis and various Poincaré plot descriptors. All these measures were then related to the slopes of the low frequency (0.25 – 20 Hz) and high frequency (10 – 145 Hz) aperiodic spectrum of the raw ECG.”

With regards to association of the ECG’s spectral slopes at high frequencies and frequency domain indices of heart rate variability. Common frequency domain indices of heart rate variability fall in the range of 0.01-.4Hz. Which probably explains why we didn’t notice any association at higher frequency ranges (>10Hz).

This is also stated in the related part of the results section:

“In the higher frequency ranges (10 - 145 Hz) spectral slopes were most consistently related to fractal and time domain indices of heart rate variability, but not so much to frequency-domain indices assessing spectral power in frequency ranges < 0.4 Hz.”

(3) Related to the previous point - what is being reflected in the ECG at higher frequency ranges, with regard to biological mechanisms? Results are being mentioned, but not further discussed. However, this point seems crucial because the age effects across the four datasets differ between low and high-frequency slope limits (Figure 2C).

This is a great question that definitely also requires further attention and investigation in general (see also Tereshchenko & Josephson, 2015). We investigated the change of the slope across frequency ranges that are typically captured in common ECG setups for adults (0.05 - 150Hz, Tereshchenko & Josephson, 2015; Kusayama, Wong, Liu et al. 2020). While most of the physiological significant spectral information of an ECG recording rests between 1-50Hz (Clifford & Azuaje, 2006), meaningful information can be extracted at much higher frequencies. For instance, ventricular late potentials have a broader frequency band (~40-250Hz) that falls straight in our spectral analysis window. However, that’s not all, as further meaningful information can be extracted at even higher frequencies (>100Hz). Yet, the exact physiological mechanisms underlying so-called high-frequency QRS remain unclear (HF-QRS; see Tereshchenko & Josephson, 2015; Qiu et al. 2024 for a review discussing possible mechanisms). Yet, at the same time the HF-QRS seems to be highly informative for the early detection of myocardial ischemia and other cardiac abnormalities that may not yet be evident in the standard frequency range (Schlegel et al. 2004; Qiu et al. 2024). All optimism aside, it is also worth noting that ECG recordings at higher frequencies can capture skeletal muscle activity with an overlapping frequency range up to 400Hz (Kusayama, Wong, Liu et al. 2020). We highlight all of this now when introducing this analysis in the results sections as outstanding research question stating that:

“However, substantially less is known about aperiodic activity above 0.4Hz in the ECG. Yet, common ECG setups for adults capture activity at a broad bandwidth of 0.05 - 150Hz(33,34).

Importantly, a lot of the physiological meaningful spectral information rests between 1-50Hz(35), similarly to M/EEG recordings. Furthermore, meaningful information can be extracted at much higher frequencies. For instance, ventricular late potentials have a broader frequency band (~40-250Hz(35)). However, that’s not all, as further meaningful information can be extracted at even higher frequencies (>100Hz). For instance, the so-called high-frequency QRS seems to be highly informative for the early detection of myocardial ischemia and other cardiac abnormalities that may not yet be evident in the standard frequency range(36,37). Yet, the exact physiological mechanisms underlying the high-frequency QRS remain unclear (see (37) for a review discussing possible mechanisms). ”

Tereshchenko, L. G., & Josephson, M. E. (2015). Frequency content and characteristics of ventricular conduction. Journal of electrocardiology, 48(6), 933-937.

Kusayama, T., Wong, J., Liu, X. et al. Simultaneous noninvasive recording of electrocardiogram and skin sympathetic nerve activity (neuECG). Nat Protoc 15, 1853–1877 (2020). https://doi.org/10.1038/s41596-020-0316-6

Clifford, G. D., & Azuaje, F. (2006). Advanced methods and tools for ECG data analysis (Vol. 10). P. McSharry (Ed.). Boston: Artech house.

Qiu, S., Liu, T., Zhan, Z., Li, X., Liu, X., Xin, X., ... & Xiu, J. (2024). Revisiting the diagnostic and prognostic significance of high-frequency QRS analysis in cardiovascular diseases: a comprehensive review. Postgraduate Medical Journal, qgae064.

Schlegel, T. T., Kulecz, W. B., DePalma, J. L., Feiveson, A. H., Wilson, J. S., Rahman, M. A., & Bungo, M. W. (2004, March). Real-time 12-lead high-frequency QRS electrocardiography for enhanced detection of myocardial ischemia and coronary artery disease. In Mayo Clinic Proceedings (Vol. 79, No. 3, pp. 339-350). Elsevier.

(4) Page 10: At first glance, it is not quite clear what is meant by "processing option" in the text. Please clarify.

Thank you for catching this! Upon re-reading this is indeed a bit oblivious. We now swapped “processing options” with “slope fits” to make it clearer that we are talking about the percentage of effects based on the different slope fits.

(5) The authors mention previous findings on age effects on neural 1/f activity (References Nr 5,8,27,39) that seem contrary to their own findings such as e.g., the mostly steepening of the slopes with age. Also, the authors discuss thoroughly why spectral slopes derived from MEG signals may differ from EEG signals. I encourage the authors to have a closer look at these studies and elaborate a bit more on why these studies differ in their conclusions on the age effects. For example, Tröndle et al. (2022, Ref. 39) investigated neural activity in children and young adults, hence, focused on brain maturation, whereas the CamCAN set only considers the adult lifespan. In a similar vein, others report age effects on 1/f activity in much smaller samples as reported here (e.g., Voytek et al., 2015).

I believe taking these points into account by briefly discussing them, would strengthen the authors' claims and provide a more fine-grained perspective on aging effects on 1/f.

The reviewer is making a very important point. As age-related differences in (neuro-)physiological activity are not necessarily strictly comparable and entirely linear across different age-cohorts (e.g. age-related changes in alpha center frequency). We therefore, added the suggested discussion points to the discussion section.

“Differences in electric and magnetic field recordings aside, aperiodic activity may not change strictly linearly as we are ageing and studies looking at younger age groups (e.g. <22; (44) may capture different aspects of aging (e.g. brain maturation), than those looking at older subjects (>18 years; our sample). A recent report even shows some first evidence of an interesting putatively non-linear relationship with age in the sensorimotor cortex for resting recordings(59)”

(6) The analysis of the working memory paradigm as described in the outlook-section of the discussion comes as a bit of a surprise as it has not been introduced before. If the authors want to convey with this study that, in general, aperiodic neural activity could be influenced by aperiodic cardiac activity, I recommend introducing this analysis and the results earlier in the manuscript than only in the discussion to strengthen their message.

The reviewer is correct. This analysis really comes a bit out of the blue. However, this was also exactly the intention for placing this analysis in the discussion. As the reviewer correctly noted, the aim was to suggest “that, in general, aperiodic neural activity could be influenced by aperiodic cardiac activity”. We placed this outlook directly after the discussion of “(neuro-)physiological origins of aperiodic activity”, where we highlight the potential challenges of interpreting drug induced changes to M/EEG recordings. So the aim was to get the reader to think about whether age is the only feature affected by cardiac activity and then directly present some evidence that this might go beyond age.

However, we have been rethinking this approach based on the reviewers comments and moved that paragraph to the end of the results section accordingly and introduce it already at the end of the introduction stating that:

“But does the influence of cardiac activity on the aperiodic spectrum extend beyond age? In a preliminary analysis, we demonstrate that working memory load modulates the aperiodic spectrum of “pure” ECG recordings. The direction of this working memory effect mirrors previous findings on EEG data(5) suggesting that the impact of cardiac activity goes well beyond aging.”

(7) The font in Figure 2 is a bit hard to read (especially in D). I recommend increasing the font sizes where necessary for better readability.

We agree with the Reviewer and increased the font sizes accordingly.

(8) Text in the discussion: Figure 3B on page 10 => shouldn't it be Figure 4?

Thank you for catching this oversight. We have now corrected this mistake.

(9) In the third section on page 10, the Figure labels seem to be confused. For example, Figure 4 E is supposed to show "steepening effects", which should be Figure 4B I believe.

Please check the figure labels in this section to avoid confusion.

Thank you for catching this oversight. We have now corrected this mistake.

(10) Figure Legend 4 I), please check the figure labels in the text

Thank you for catching this oversight. We have now corrected this mistake.

Reviewer #3 (Recommendations for the authors):

I have a number of suggestions for improving the manuscript, which I have divided by section in the following:

ABSTRACT:

I would suggest re-writing the first sentences to make it easier to read for non-expert readers: "The power of electrophysiologically measured cortical activity decays with an approximately 1/fX function. The slope of this decay (i.e. the spectral exponent, X) is modulated..."

Thank you for the suggestion. We adjusted the sentence as suggested to make it easier for less technical readers to understand that “X” refers to the exponent.

Including the age range that was studied in the abstract could be informative.

Done as suggested.

As an optional recommendation, I think it would increase the impact of the article if the authors note in the abstract that the current most commonly applied cardiac artifact reduction approaches don't resolve the issue for EEG data, likely due to an imperfect ability to separate the cardiac artifact from the neural activity with independent component analysis. This would highlight to the reader that they can't just expect to address these concerns by cleaning their data with typical cleaning methods.

I think it would also be useful to convey in the abstract just how comprehensive the included analyses were (in terms of artifact reduction methods tested, different aperiodic algorithms and frequency ranges, and both MEG and EEG). Doing so would let the reader know just how robust the conclusions are likely to be.

This is a brilliant idea! As suggested we added a sentence highlighting that simply performing an ICA may not be sufficient to separate cardiac contributions to M/EEG recordings and refer to the comprehensiveness of the performed analyses.

INTRODUCTION:

I would suggest re-writing the following sentence for readability: "In the past, aperiodic neural activity, other than periodic neural activity (local peaks that rise above the "power-law" distribution), was often treated as noise and simply removed from the signal"

To something like: "In the past, aperiodic neural activity was often treated as noise and simply removed from the signal e.g. via pre-whitening, so that analyses could focus on periodic neural activity (local peaks that rise above the "power-law" distribution, which are typically thought to reflect neural oscillations).

We are happy to follow that suggestion.

Page 3: please provide the number of articles that were included in the examination of the percentage that remove cardiac activity, and note whether the included articles could be considered a comprehensive or nearly comprehensive list, or just a representative sample.

We stated the exact number of articles in the methods section under Literature Analysis. However, we added it to the Introduction on page 3 as suggested by the reviewer. The selection of articles was done automatically, dependent on a list of pre-specified terms and exclusively focussed on articles that had terms related to aperiodic activity in their title (see Literature Analysis). Therefore, I would personally be hesitant in calling it a comprehensive or nearly comprehensive list of the general M/EEG literature as the analysis of aperiodic activity is still relatively niche compared to the more commonly investigated evoked potentials or oscillations. I think whether or not a reader perceives our analysis as comprehensive should be up to them to decide and does not reflect something I want to impose on them. This is exacerbated by the fact that the analysis of neural aperiodic activity has rapidly gained traction over the last years (see Figure 1D orange) and the literature analysis was performed almost 2 years ago and therefore, in my eyes, only represents a glimpse in the rapidly evolving field related to the analysis of aperiodic activity.

Figure 1E-F: It's not completely clear that the "Cleaning Methods" part of the figure indicates just methods to clean the cardiac artifact (rather than any artifact). It also seems that ~40% of EEG studies do not apply any cleaning methods even from within the studies that do clean the cardiac artifact (if I've read the details correctly). This seems unlikely. Perhaps there should be a bar for "other methods", or "unspecified"? Having said that, I'm quite familiar with the EEG artifact reduction literature, and I would be very surprised if ~40% of studies cleaned the cardiac artifact using a different method to the methods listed in the bar graph, so I'm wondering if I've misunderstood the figure, or whether the data capture is incomplete / inaccurate (even though the conclusion that ICA is the most common method is almost certainly accurate).

The cleaning is indeed only focussed on cardiac activity specifically. This was however also mentioned in the caption of Figure 1: “We were further interested in determining which artifact rejection approaches were most commonly used to remove cardiac activity, such as independent component analysis (ICA(22)), singular value decomposition (SVD(23)), signal space separation (SSS(24)), signal space projections (SSP(25)) and denoising source separation (DSS(26)).” and in the methods section under Literature Analysis. However, we adjusted figure 1EF to make it more obvious that the described cleaning methods were only related to the ECG. Aside from using blind source separation techniques such as ICA a good amount of studies mentioned that they cleaned their data based on visual inspection (which was not further considered). Furthermore, it has to be noted that only studies were marked as having separated cardiac from neural activity, when this was mentioned explicitly.

RESULTS:

Page 6: I would delete the "from a neurophysiological perspective" clause, which makes the sentence more difficult to read and isn't so accurate (frequencies 13-25Hz would probably more commonly be considered mid-range rather than low or high). Additionally, both frequency ranges include 15Hz, but the next sentence states that the ranges were selected to avoid the knee at 15Hz, which seems to be a contradiction. Could the authors explain in more detail how the split addresses the 15Hz knee?

We removed the “from a neurophysiological perspective” clause as suggested. With regards to the “knee” at ~15Hz I would like to defer the reviewer to Supplementary Figure S1. The Knee Frequency varies substantially across subjects so splitting the data at only 1 exact Frequency did not seem appropriate. Additionally, we found only spurious significant age-related variations in Knee Frequency (i.e. only one out of the 4 datasets; not shown).

Furthermore, we wanted to better connect our findings to our MEG results in Figure 4 and also give the readers a holistic overview of how different frequency ranges in the aperiodic ECG would be affected by age. So to fulfill all of these objectives we decided to fit slopes with respective upper/lower bounds around a range of 5Hz above and below the average 15Hz Knee Frequency across datasets.

The later parts of this same paragraph refer to a vast amount of different frequency ranges, but only the "low" and "high" frequency ranges were previously mentioned. Perhaps the explanation could be expanded to note that multiple lower and upper bounds were tested within each of these low and high frequency windows?

This is a good catch we adjusted the sentence as suggested. We now write: “.. slopes were fitted individually to each subject's power spectrum in several lower (0.25 – 20 Hz) and higher (10-145 Hz) frequency ranges.”

The following two sentences seem to contradict each other: "Overall, spectral slopes in lower frequency ranges were more consistently related to heart rate variability indices(> 39.4% percent of all investigated indices)" and: "In the lower frequency range (0.25 - 20Hz), spectral slopes were consistently related to most measures of heart rate variability; i.e. significant effects were detected in all 4 datasets (see Figure 2D)." (39.4% is not "most").

The reviewer is correct in stating that 39.4% is not most. However, the 39.4% is the lowest bound and only refers to 1 dataset. In the other 3 datasets the percentage of effects was above 64% which can be categorized as “most” i.e. above 50%. We agree that this was a bit ambiguous in the sentence so we added the other percentages as well as a reference to Figure 2D to make this point clearer.

Figure 2D: it isn't clear what the percentages in the semi-circles reflect, nor why some semi-circles are more full circles while others are only quarter circles.

The percentages in the semi-circles reflect the amount of effects (marked in red) and null effects (marked in green) per dataset, when viewed as average across the different measures of HRV. Sometimes less effects were found for some frequency ranges resulting in quarters instead of semi circles.

Page 8: I think the authors could make it more clear that one of the conditions they were testing was the ECG component of the EEG data (extracted by ICA then projected back into the scalp space for the temporal response function analysis).

As suggested by the reviewer we adjusted our wording and replaced the arguably a bit ambiguous “... projected back separately” with “... projected back into the sensor space”. We thank the reviewer for this recommendation, as it does indeed make it easier to understand the procedure.

“After pre-processing (see Methods) the data was split in three conditions using an ICA(22). Independent components that were correlated (at r > 0.4; see Methods: MEG/EEG Processing - pre-processing) with the ECG electrode were either not removed from the data (Figure 3ABCD - blue), removed from the data (Figure 2ABCD - orange) or projected back into the sensor space (Figure 3ABCD - green).”

Figure 4A: standardized beta coefficients for the relationship between age and spectral slope could be noted to provide improved clarity (if I'm correct in assuming that is what they reflect).

This was indeed shown in Figure 4A and noted in the color bar as “average beta (standardized)”. We do not specifically highlight this in the text, because the exact coefficients would depend on both on the analyzed frequency range and the selected electrodes.

Figure 4I: The regressions explained at this point seems to contain a very large number of potential predictors, as I'm assuming it includes all sensors for both the ECG component and ECG rejected conditions? (if that is not the case, it could be explained in greater detail). I'm also not sure about the logic of taking a complete signal, decomposing it with ICA to separate out the ECG and non-ECG signals, then including them back into the same regression model. It seems that there could be some circularity or redundancy in doing so. However, I'm not confident that this is an issue, so would appreciate the authors explaining why it this is a valid approach (if that is the case).

After observing significant effects both in the MEG_{ECG component} and MEG_{ECG rejected} conditions in similar frequency bands we wanted to understand whether or not these age-related changes are statistically independent. To test this we added both variables as predictors in a regression model (thereby accounting for the influence of the other in relation to age). The regression models we performed were therefore actually not very complex. They were built using only two predictors, namely the data (in a specific frequency range) averaged over channels on which we noticed significant effects in the ECG rejected and ECG components data respectively (Wilkinson notation: age ~ 1 + ECG rejected + ECG components). This was also described in the results section stating that: “To see if MEG_{ECG rejected} and MEG_{ECG component} explain unique variance in aging at frequency ranges where we noticed shared effects, we averaged the spectral slope across significant channels and calculated a multiple regression model with MEG_{ECG component} and MEG_{ECG rejected} as predictors for age (to statistically control for the effect of MEG_{ECG component}s and MEG_{ECG rejected} on age). This analysis was performed to understand whether the observed shared age-related effects (MEG_{ECG rejected} and MEG_{ECG component}) are in(dependent).”  

We hope this explanation solves the previous misunderstanding.

The explanation of results for relationships between spectral slopes and aging reported in Figure 4 refers to clusters of effects, but the statistical inference methods section doesn't explain how these clusters were determined.

The wording of “cluster” was used to describe a “category” of effects e.g. null effects. We changed the wording from “cluster” to “category” to make this clearer stating now that: “This analysis, which is depicted in Figure 4, shows that over a broad amount of individual fitting ranges and sensors, aging resulted in a steepening of spectral slopes across conditions (see Figure 4E) with “steepening effects” observed in 25% of the processing options in MEG_{ECG not rejected} , 0.5% in MEG_{ECG rejected}, and 60% for MEG_{ECG components}. The second largest category of effects were “null effects” in 13% of the options for MEG_{ECG not rejected} , 30% in MEG_{ECG rejected}, and 7% for MEG_{ECG components}. ”

Page 12: can the authors clarify whether these age related steepenings of the spectral slope in the MEG are when the data include the ECG contribution, or when the data exclude the ECG? (clarifying this seems critical to the message the authors are presenting).

We apologize for not making this clearer. We now write: “This analysis also indicates that a vast majority of observed effects irrespective of condition (ECG components, ECG not rejected, ECG rejected) show a steepening of the spectral slope with age across sensors and frequency ranges.”

Page 13: I think it would be useful to describe how much variance was explained by the MEG-ECG rejected vs MEG-ECG component conditions for a range of these analyses, so the reader also has an understanding of how much aperiodic neural activity might be influenced by age (vs if the effects are really driven mostly by changes in the ECG).

With regards to the explained variance I think that the very important question of how strong age influences changes in aperiodic activity is a topic better suited for a meta analysis. As the effect sizes seems to vary largely depending on the sample e.g. for EEG in the literature results were reported at r=-0.08 (Cesnaite et al. 2023), r=-0.26 (Cellier et al. 2021), r=-0.24/r=-0.28/r=-0.35 (Hill et al. 2022) and r=0.5/r=0.7 (Voytek et al. 2015). I would defer the reader/reviewer to the standardized beta coefficients as a measure of effect size in the current study that is depicted in Figure 4A.

Cellier, D., Riddle, J., Petersen, I., & Hwang, K. (2021). The development of theta and alpha neural oscillations from ages 3 to 24 years. Developmental cognitive neuroscience, 50, 100969.

Cesnaite, E., Steinfath, P., Idaji, M. J., Stephani, T., Kumral, D., Haufe, S., ... & Nikulin, V. V. (2023). Alterations in rhythmic and non‐rhythmic resting‐state EEG activity and their link to cognition in older age. NeuroImage, 268, 119810.

Hill, A. T., Clark, G. M., Bigelow, F. J., Lum, J. A., & Enticott, P. G. (2022). Periodic and aperiodic neural activity displays age-dependent changes across early-to-middle childhood. Developmental Cognitive Neuroscience, 54, 101076.

Voytek, B., Kramer, M. A., Case, J., Lepage, K. Q., Tempesta, Z. R., Knight, R. T., & Gazzaley, A. (2015). Age-related changes in 1/f neural electrophysiological noise. Journal of Neuroscience, 35(38), 13257-13265.

Also, if there are specific M/EEG sensors where the 1/f activity does relate strongly to age, it would be worth noting these, so future research could explore those sensors in more detail.

I think it is difficult to make a clear claim about this for MEG data, as the exact location or type of the sensor may differ across manufacturers. Such a statement could be easier made for source projected data or in case EEG electrodes were available, where the location would be normed eg. according to the 10-20 system.

DISCUSSION:

Page 15: Please change the wording of the following sentence, as the way it is currently worded seems to suggest that the authors of the current manuscript have demonstrated this point (which I think is not the case): "The authors demonstrate that EEG typically integrates activity over larger volumes than MEG, resulting in differently shaped spectra across both recording methods."

Apologies for the oversight! The reviewer is correct we in fact did not show this, but the authors of the cited manuscript. We correct the sentence as suggested stating now that:

“Bénar et al. demonstrate that EEG typically integrates activity over larger volumes than MEG, resulting in differently shaped spectra across both recording methods.”

Page 16: The authors mention the results can be sensitive to the application of SSS to clean the MEG data, but not ICA. I think it would be sensitive to the application of either SSS or ICA?

This is correct and actually also supported by Figure S7, as differences in ICA thresholds affect also the detection of age-related effects. We therefore adjusted the related sentences stating now that:

“ In case of the MEG signal this may include the application of Signal-Space-Separation algorithms (SSS(24,55)), different thresholds for ICA component detection (see Figure S7), high and low pass filtering, choices during spectral density estimation (window length/type etc.), different parametrization algorithms (e.g. IRASA vs FOOOF) and selection of frequency ranges for the aperiodic slope estimation.”

It would be worth clarifying that the linked mastoid re-reference alone has been proposed to cancel out the ECG signal, rather than that a linked-mastoid re-reference improves the performance of the ICA separation (which could be inferred by the explanation as it's currently written).

This is correct and we adjusted the sentence accordingly! Stating now that:

“ Previous work(12,56) has shown that a linked mastoid reference alone was particularly effective in reducing the impact of ECG related activity on aperiodic activity measured using EEG. “

The issue of the number of EEG channels could probably just be noted as a potential limitation, as could the issue of neural activity being mixed into the ECG component (although this does pose a potential confound to the M/EEG without ECG condition, I suspect it wouldn't be critical).

This is indeed a very fair point as a higher amount of electrodes would probably make it easier to better isolate ECG components in the EEG, which may be the reason why the separation did not work so well in our case. However, this is ultimately an empirical question so we highlighted it in the discussion section stating that: “Difficulties in removing ECG related components from EEG signals via ICA might be attributable to various reasons such as the number of available sensors or assumptions related to the non-gaussianity of the underlying sources. Further understanding of this matter is highly important given that ICA is the most widely used procedure to separate neural from peripheral physiological sources. ”

OUTLOOK:

Page 19: Although there has been a recent trend to control for 1/f activity when examining oscillatory power, recent research suggests that this should only be implemented in specific circumstances, otherwise the correction causes more of a confound than the issue does. It might be worth considering this point with regards to the final recommendation in the Outlook section: Brake, N., Duc, F., Rokos, A., Arseneau, F., Shahiri, S., Khadra, A., & Plourde, G. (2024). A neurophysiological basis for aperiodic EEG and the background spectral trend. Nature Communications, 15(1), 1514.

We want to thank the reviewer for recommending this very interesting paper! The authors of said paper present compelling evidence showing that, while peak detection above an aperiodic trend using methods like FOOOF or IRASA is a prerequisite to determine the presence of oscillatory activity, it’s not necessarily straightforward to determine which detrending approach should be applied to determine the actual power of an oscillation. Furthermore, the authors suggest that wrongfully detrending may cause larger errors than not detrending at all. We therefore added a sentence stating that: “However, whether or not periodic activity (after detection) should be detrended using approaches like FOOOF or IRASA still remains disputed, as incorrectly detrending the data may cause larger errors than not detrending at all(75).”

RECOMMENDATIONS:

Page 20: "measure and account for" seems like it's missing a word, can this be re-written so the meaning is more clear?

Done as suggested. The sentence now states: “To better disentangle physiological and neural sources of aperiodic activity, we propose the following steps to (1) measure and (2) account for physiological influences.”

I would re-phrase "doing an ICA" to "reducing cardiac artifacts using ICA" (this wording could be changed in other places also).

I do not like to describe cardiac or ocular activity as artifactual per se. This is also why I used hyphens whenever I mention the word “artifact” in association with the ECG or EOG. However, I do understand that the wording of “doing an ICA” is a bit sloppy. We therefore reworded it accordingly throughout the manuscript to e.g. “separating cardiac from neural sources using an ICA” and “separating physiological from neural sources using an ICA”.

I would additionally note that even if components are identified as unambiguously cardiac, it is still likely that neural activity is mixed in, and so either subtracting or leaving the component will both be an issue (https://doi.org/10.1101/2024.06.06.597688). As such, even perfect identification of whether components are cardiac or not would still mean the issue remains (and this issue is also consistent across a considerable range of component based methods). Furthermore, current methods including wavelet transforms on the ICA component still do not provide good separation of the artifact and neural activity.

This is definitely a fair point and we also highlight this in our recommendations under 3 stating that:

“However, separating physiological from neural sources using an ICA is no guarantee that peripheral physiological activity is fully removed from the cortical signal. Even more sophisticated ICA based methods that e.g. apply wavelet transforms on the ICA components may still not provide a good separation of peripheral physiological and neural activity76,77. This turns the process of deciding whether or not an ICA component is e.g. either reflective of cardiac or neural activity into a challenging problem. For instance, when we only extract cardiac components using relatively high detection thresholds (e.g. r > 0.8), we might end up misclassifying residual cardiac activity as neural. In turn, we can’t always be sure that using lower thresholds won’t result in misinterpreting parts of the neural effects as cardiac. Both ways of analyzing the data can potentially result in misconceptions.”

Castellanos, N. P., & Makarov, V. A. (2006). Recovering EEG brain signals: Artifact suppression with wavelet enhanced independent component analysis. Journal of neuroscience methods, 158(2), 300-312.

Bailey, N. W., Hill, A. T., Godfrey, K., Perera, M. P. N., Rogasch, N. C., Fitzgibbon, B. M., & Fitzgerald, P. B. (2024). EEG is better when cleaning effectively targets artifacts. bioRxiv, 2024-06.

METHODS:

Pre-processing, page 24: I assume the symmetric setting of fastica was used (rather than the deflation setting), but this should be specified.

Indeed the reviewer is correct, we used the standard setting of fastICA implemented in MNE python, which is calling the FastICA implementation in sklearn that is per default using the “parallel” or symmetric algorithm to compute an ICA. We added this information to the text accordingly, stating that:

“For extracting physiological “artifacts” from the data, 50 independent components were calculated using the fastica algorithm(22) (implemented in MNE-Python version 1.2; with the parallel/symmetric setting; note: 50 components were selected for MEG for computational reasons for the analysis of EEG data no threshold was applied).”

Temporal response functions, page 26: can the authors please clarify whether the TRF is computed against the ECG signal for each electrode or sensory independently, or if all electrodes/sensors are included in the analysis concurrently? I'm assuming it was computed for each electrode and sensory separately, since the TRF was computed in both the forward and backwards direction (perhaps the meaning of forwards and backwards could be explained in more detail also - i.e. using the ECG to predict the EEG signal, or using the EEG signal to predict the ECG signal?).

A TRF can also be conceptualized as a multiple regression model over time lags. This means that we used all channels to compute the forward and backward models. In the case of the forward model we predicted the signal of the M/EEG channels in a multivariate regression model using the ECG electrode as predictor. In case of the backward model we predicted the ECG electrode based on the signal of all M/EEG channels. The forward model was used to depict the time window at which the ECG signal was encoded in the M/EEG recording, which appears at 0 time lags indicating volume conduction. The backward model was used to see how much information of the ECG was decodable by taking the information of all channels.

We tried to further clarify this approach in the methods section stating that:

“We calculated the same model in the forward direction (encoding model; i.e. predicting M/EEG data in a multivariate model from the ECG signal) and backward direction (decoding model; i.e. predicting the ECG signal using all M/EEG channels as predictors).”

Page 27: the ECG data was fit using a knee, but it seems the EEG and MEG data was not.

Does this different pose any potential confound to the conclusions drawn? (having said this, Figure S4 suggests perhaps a knee was tested in the M/EEG data, which should perhaps be explained in the text also).

This was indeed tested in a previous review round to ensure that our results are not dependent on the presence/absence of a knee in the data. We therefore added figure S4, but forgot to actually add a description in the text. We are sorry for this oversight and added a paragraph to S1 accordingly:

“Using FOOOF(5), we also investigated the impact of different slope fitting options (fixed vs. knee model fits) on the aperiodic age relationship (see Supplementary Figure S4). The results that we obtained from these analyses using FOOOF offer converging evidence with our main analysis using IRASA.”

Page 32: my understanding of the result reported here is that cleaning with ICA provided better sensitivity to the effects of age on 1/f activity than cleaning with SSS. Is this accurate? I think this could also be reported in the main manuscript, as it will be useful to researchers considering how to clean their M/EEG data prior to analyzing 1/f activity.

The reviewer is correct in stating that we overall detected slightly more “significant” effects, when not additionally cleaning the data using SSS. However, I am a bit wary of recommending omitting the use of SSS maxfilter solely based on this information. It can very well be that the higher quantity of effects (when not employing SSS maxfilter) stems from other physiological sources (e.g. muscle activity) that are correlated with age and removed when applying SSS maxfiltering. I think that just conditioning the decision of whether or not maxfilter is applied based on the amount or size of effects may not be the best idea. Instead I think that the applicability of maxfilter for research questions related to aperiodic activity should be the topic of additional methodological research. We therefore now write in Text S1:

“Considering that we detected less and weaker aperiodic effects when using SSS maxfilter is it advisable to omit maxfilter, when analyzing aperiodic signals? We don’t think that we can make such a judgment based on our current results. This is because it's unclear whether or not the reduction of effects stems from an additional removal of peripheral information (e.g. muscle activity; that may be correlated with aging) or is induced by the SSS maxfiltering procedure itself. As the use of maxfilter in detecting changes of aperiodic activity was not subject of analysis that we are aware of, we suggest that this should be the topic of additional methodological research.”

Page 39, Figure S6 and Figure S8: Perhaps the caption could also briefly explain the difference between maxfilter set to false vs true? I might have missed it, but I didn't gain an understanding of what varying maxfilter would mean.

Figure S6 shows the effect of ageing on the spectral slope averaged across all channels. The maxfilter set to false in AB) means that no maxfiltering using SSS was performed vs. in CD) where the data was additionally processed using the SSS maxfilter algorithm. We now describe this more clearly by writing in the caption:

“Supplementary Figure S6: Age-related changes in aperiodic brain activity are most prominent on explained by cardiac components irrespective of maxfiltering the data using signal space separation (SSS) or not AC) Age was used to predict the spectral slope (fitted at 0.1-145Hz) averaged across sensors at rest in three different conditions (ECG components not rejected [blue], ECG components rejected [orange], ECG components only [green].”

elife Assessment

This useful study examines the neural activity in the motor cortex as a monkey reaches to intercept moving targets, focusing on how tuned single neurons contribute to an interesting overall population geometry. The presented results and analyses are solid, though the investigation of this novel task could be strengthened by clarifying the assumptions behind the single neuron analyses, and further analyses of the neural population activity and its relation to different features of behaviour.

Reviewer #1 (Public review):

Summary:

This study addresses the question of how task-relevant sensory information affects activity in motor cortex. The authors use various approaches to address this question, looking at single units and population activity. They find that there are three subtypes of modulation by sensory information at the single unit level. Population analyses reveal that sensory information affects the neural activity orthogonally to motor output. The authors then compare both single unit and population activity to computational models to investigate how encoding of sensory information at the single-unit level is coordinated in a network. They find that an RNN that displays similar orbital dynamics and sensory modulation to motor cortex also contains nodes that are modulated similarly to the three subtypes identified by the single unit analysis.

Strengths:

The strengths of this study lie in the population analyses and the approach of comparing single-unit encoding to population dynamics. In particular, the analysis in Figure 3 is very elegant and informative about the effect of sensory information on motor cortical activity. The task is also well designed to suit the questions being asked and well controlled.

It is commendable that the authors compare single-unit to population modulation. The addition of the RNN model and perturbations strengthen the conclusion that the subtypes of individual units all contribute to the population dynamics.

Weaknesses:

The main weaknesses of the study lie in the categorization of the single units into PD shift, gain and addition types. The single units exhibit clear mixed selectivity, as the authors highlight. Therefore, the subsequent analyses looking only at the individual classes in the RNN are a little limited. Another weakness of the paper is that the choice of windows for analyses is not properly justified and the dependence of the results on the time windows chosen for single unit analyses is not assessed. This is particularly pertinent because tuning curves are known to rotate during movements (Sergio et al. 2005 Journal of Neurophysiology).

This study uses insights from single-unit analysis to inform mechanistic models of these population dynamics, which is a powerful approach, but is dependent on the validity of the single-cell analysis, which I have expanded on below.

I have clarified some of the areas that would benefit from further analysis below:

Task:

The task is well designed, although it would have benefited from perhaps one more target speed (for each direction). One monkey appears to have experienced one more target speed than the others (seen in Figure 3C). It would have been nice to have this data for all monkeys, although, of course, unfeasible given that the study has been concluded.

Single unit analyses:

The choice of the three categories (PD shift, gain addition) is not completely justified in a satisfactory way. It would be nice to see whether these three main categories are confirmed by unsupervised methods.

The decoder analyses in Figure 2 provide evidence that target speed modulation may change over the trial. Therefore, it is important to see how the window considered for the firing rate in Figure 1 (currently 100ms pre - 100ms post movement onset) affects the results. Whilst it is of course understandable that a window must be chosen and will always be slightly arbitrary, using different windows and comparing the results of two or three different sizes or timed windows would be more convincing that the results are not dependent on this particular window.

RNN:

Mixed selectivity is not analysed in the RNN, which would help to compare the model to the real data where mixed selectivity is common. The CCA and Procrustes analysis are a good start to validate the claim of similarity between RNN and neural dynamics, rather than allowing comparisons to be dominated by geometric similarities that may be features of the task. However, some of the disparity values for the Procrustes analysis are quite high, albeit below that of the shuffle. Maybe a comment about this in the text should be included. There is also an absence of alternate models to compare the perturbation model results to.

Reviewer #2 (Public review):

Summary:

In this manuscript, Zhang et al. examine neural activity in motor cortex as monkeys make reaches in a novel target interception task. Zhang et al. begin by examining the single neuron tuning properties across different moving target conditions, finding several classes of neurons: those that shift their preferred direction, those that change their modulation gain, and those that shift their baseline firing rates. The authors go on to find an interesting, tilted ring structure of the neural population activity, depending on the target speed, and find that 1) the reach direction has consistent positioning around the ring, and 2) the tilt of the ring is highly predictive of the target movement speed. The authors then model the neural activity with a single neuron representational model and a recurrent neural network model, concluding that this population structure requires a mixture of the three types of single neurons described at the beginning of the manuscript.

Strengths:

I find the task the authors present here to be novel and exciting. It slots nicely into an overall trend to break away from a simple reach-to-static-target tasks to better characterize the breadth of how motor cortex generates movements. I also appreciate the movement from single neuron characterization to population activity exploration, which generally serves to anchor the results and make them concrete. Further, the orbital ring structure of population activity is fascinating, and the modeling work at the end serves as a useful baseline control to see how it might arise.

Weaknesses:

While I find the behavioral task presented here to be excitingly novel, I find the presented analyses and results to be far less interesting than they could be. Key to this, I think, is that the authors are examining this task and related neural activity primarily with a single-neuron representational lens. This would be fine as an initial analysis, since the population activity is of course composed of individual neurons, but the field seems to have largely moved towards a more abstract "computation through dynamics" framework that has, in the last several years, provided much more understanding of motor control than the representational framework has. As the manuscript stands now, I'm not entirely sure what interpretation to take away from the representational conclusions the authors made (i.e. the fact that the orbital population geometry arises from a mixture of different tuning types). As such, by the end of the manuscript, I'm not sure I understand any better how motor cortex or its neural geometry might be contributing to the execution of this novel task.

Main Comments:

My main suggestions to the authors revolve around bringing in the computation through a dynamics framework to strengthen their population results. The authors cite the Vyas et al. review paper on the subject, so I believe they are aware of this framework. I have three suggestions for improving or adding to the population results:

(1) Examination of delay period activity: one of the most interesting aspects of the task was the fact that the monkey had a random-length delay period before he could move to intercept the target. Presumably, the monkey had to prepare to intercept at any time between 400 and 800 ms, which means that there may be some interesting preparatory activity dynamics during this period. For example, after 400ms, does the preparatory activity rotate with the target such that once the go cue happens, the correct interception can be executed? There is some analysis of the delay period population activity in the supplement, but it doesn't quite get at the question of how the interception movement is prepared. This is perhaps the most interesting question that can be asked with this experiment, and it's one that I think may be quite novel for the field--it is a shame that it isn't discussed.

(2) Supervised examination of population structure via potent and null spaces: simply examining the first three principal components revealed an orbital structure, with a seemingly conserved motor output space and a dimension orthogonal to it that relates to the visual input. However, the authors don't push this insight any further. One way to do that would be to find the "potent space" of motor cortical activity by regression to the arm movement and examine how the tilted rings look in that space. Presumably, then, the null space should contain information about the target movement. The ring tilt will likely be evident if the authors look at the highest variance neural dimension orthogonal to the potent space (the "null space")--this is akin to PC3 in the current figures, but it would be nice to see what comes out when you look in the data for it.

The authors attempt this sort of analysis in the supplement, alongside their dPCA results, but the results seem misinterpreted. The authors do identify one kind of output-potent space using the reach direction components of dPCA, and the reach directions are indeed aligned here. However, they then go on to interpret the target-velocity space as the output-null space, orthogonal to the potent space. There are two problems with this. 1) The target-velocity space is not necessarily orthogonal to the reach-direction space. This is a key aspect of dPCA--while the individual components within a particular marginalization space are orthogonal, the marginalization spaces themselves are not necessarily orthogonal unless they are forced to be (which the authors don't mention doing). 2) Even if the target-velocity space were orthogonal to the reach-direction space, it would not comprise the whole output-null space--such a null space would also include dimensions of neural population activity that have target-velocity/reach-direction interaction, which the authors show is a major component of neural population variance. Incidentally, the dPCA analysis the authors present shows what I would expect from their unsupervised results, but as it is written, the dPCA results are interpreted in a strange or potentially misleading way.

(3) RNN perturbations: as it's currently written, the RNN modeling has promise, but the perturbations performed don't provide me with much insight. I think this is because the authors are trying to use the RNN to interpret the single neuron tuning, but it's unclear to me what was learned from perturbing the connectivity between what seems to me almost arbitrary groups of neurons. It seems to me that a better perturbation might be to move the neural state before the movement onset to see how it changes the output. For example, the authors could move the neural state from one tilted ring to another to see if the virtual hand then reaches a completely different (yet predictable) target. Moreover, if the authors can more clearly characterize the preparatory movement, perhaps perturbations in the delay period would provide even more insight into how the interception might be prepared.

Reviewer #3 (Public review):

Summary:

This experimental study investigates the influence of sensory information on neural population activity in M1 during a delayed reaching task. In the experiment, monkeys are trained to perform a delayed interception reach task, in which the goal is to intercept a potentially moving target.

This paradigm allows the authors to investigate how, given a fixed reach end point (which is assumed to correspond to a fixed motor output), the sensory information regarding the target motion is encoded in neural activity.

At the level of single neurons, the authors find that target motion modulates the activity is three main ways: gain modulation (scaling of the neural activity depending on the target direction), shift (shift of the preferred direction of neurons tuned to reach direction), or addition (offset to the neural activity).

At the level of the neural population, target motion information was largely encoded along the 3rd PC of the neural activity, leading to a tilt of the manifold along which reach direction was encoded that was proportional to target speed. The tilt of the neural manifold was found to be largely driven by the variation of activity of the population of gain modulated neurons.

Finally, the authors study the behaviour of an RNN trained to generate the correct hand velocity given the sensory input and reach direction. The RNN units are found to similarly exhibit mixed selectivity to the sensory information, and the geometry of the « neural population » resembles that observed in the monkeys.

Overall, the experiment is well set up to address the question of how sensory information that is directly relevant to the behaviour but does not lead to a direct change in behavioural output modulates motor cortical activity.<br /> The finding that sensory information modulates the neural activity in M1 during motor preparation and execution is non trivial, given that this modulation of the activity must occur in the nullspace of the movement.<br /> The authors provide analyses at both the single neuron and the population level, leading to a relatively complete characterization of the effect of the target motion on neural activity.<br /> Additionally, they start exploring the link between the population geometry and the mixed selectivity of the single neurons in their RNN model. While they could be extended in future work, the analyses of the RNN provide a good starting point to address how exactly the task setup and constraints on the network shape the single neuron selectivity and the population geometry.

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public Review):

Summary:

This study addresses the question of how task-relevant sensory information affects activity in the motor cortex. The authors use various approaches to address this question, looking at single units and population activity. They find that there are three subtypes of modulation by sensory information at the single unit level. Population analyses reveal that sensory information affects the neural activity orthogonally to motor output. The authors then compare both single unit and population activity to computational models to investigate how encoding of sensory information at the single unit level is coordinated in a network. They find that an RNN that displays similar orbital dynamics and sensory modulation to the motor cortex also contains nodes that are modulated similarly to the three subtypes identified by the single unit analysis.

Strengths:

The strengths of this study lie in the population analyses and the approach of comparing single-unit encoding to population dynamics. In particular, the analysis in Figure 3 is very elegant and informative about the effect of sensory information on motor cortical activity.

The task is also well designed to suit the questions being asked and well controlled.

We appreciate these kind comments.

It is commendable that the authors compare single units to population modulation. The addition of the RNN model and perturbations strengthen the conclusion that the subtypes of individual units all contribute to the population dynamics. However, the subtypes (PD shift, gain, and addition) are not sufficiently justified. The authors also do not address that single units exhibit mixed modulation, but RNN units are not treated as such.

We’re sorry that we didn’t provide sufficient grounds to introduce the subtypes. We have updated this in the revised manuscript, in Lines 102-104 as:

“We determined these modulations on the basis of the classical cosine tuning model (Georgopoulos et al., 1982) and several previous studies (Bremner and Andersen, 2012; Pesaran et al., 2010; Sergio et al., 2005).”

In our study, we applied the subtype analysis as a criterion to identify the modulation in neuron populations, rather than sorting neurons into exclusively different cell types.

Weaknesses:

The main weaknesses of the study lie in the categorization of the single units into PD shift, gain, and addition types. The single units exhibit clear mixed selectivity, as the authors highlight. Therefore, the subsequent analyses looking only at the individual classes in the RNN are a little limited. Another weakness of the paper is that the choice of windows for analyses is not properly justified and the dependence of the results on the time windows chosen for single-unit analyses is not assessed. This is particularly pertinent because tuning curves are known to rotate during movements (Sergio et al. 2005 Journal of Neurophysiology).

In our study, the mixed selectivity or specifically the target-motion modulation on reach- direction tuning is a significant feature of the single neurons. We categorized the neurons into three subclasses, not intending to claim their absolute cell types, but meaning to distinguish target-motion modulation patterns. To further characterize these three patterns, we also investigated their interaction by perturbing connection weights in RNN.

Yes, it’s important to consider the role of rotating tuning curves in neural dynamics during interception. In our case, we observed population neural state with sliding windows, and we focused on the period around movement onset (MO) due to the unexpected ring-like structure and the highest decoding accuracy of transferred decoders (Figure S7C). Then, the single-unit analyses were implemented.

This paper shows sensory information can affect motor cortical activity whilst not affecting motor output. However, it is not the first to do so and fails to cite other papers that have investigated sensory modulation of the motor cortex (Stavinksy et al. 2017 Neuron, Pruszynski et al. 2011 Nature, Omrani et al. 2016 eLife). These studies should be mentioned in the Introduction to capture better the context around the present study. It would also be beneficial to add a discussion of how the results compare to the findings from these other works.

Thanks for the reminder. We’ve introduced these relevant researches in the updated manuscript in Lines 422-426 as:

“To further clarify, the discussing target-motion effect is different from the sensory modulation in action selection (Cisek and Kalaska, 2005), motor planning (Pesaran et al., 2006), visual replay and somatosensory feedback (Pruszynski et al., 2011; Stavisky et al., 2017; Suway and Schwartz, 2019; Tkach et al., 2007), because it occurred around movement onset and in predictive control trial-by-trial.”

This study also uses insights from single-unit analysis to inform mechanistic models of these population dynamics, which is a powerful approach, but is dependent on the validity of the single-cell analysis, which I have expanded on below.

I have clarified some of the areas that would benefit from further analysis below:

(1) Task:

The task is well designed, although it would have benefited from perhaps one more target speed (for each direction). One monkey appears to have experienced one more target speed than the others (seen in Figure 3C). It would have been nice to have this data for all monkeys.

A great suggestion; however, it is hardly feasible as the Utah arrays have already been removed.

(2) Single unit analyses:

In some analyses, the effects of target speed look more driven by target movement direction (e.g. Figures 1D and E). To confirm target speed is the main modulator, it would be good to compare how much more variance is explained by models including speed rather than just direction. More target speeds may have been helpful here too.

A nice suggestion. The fitting goodness of the simple model (only movement direction) is much worse than the complex models (including target speed). We’ve updated the results in the revised manuscript in Lines 119-122, as “We found that the adjusted R2 of a full model (0.55 ± 0.24, mean ± sd.) can be higher than that of the PD shift (0.47 ± 0.24), gain (0.46 ± 0.22), additive (0.41 ± 0.26), and simple models (only reach direction, 0.34 ± 0.25) for three monkeys (1162 neurons, ranksum test, one-tailed, p<0.01, Figure S5).”

The choice of the three categories (PD shift, gain addition) is not completely justified in a satisfactory way. It would be nice to see whether these three main categories are confirmed by unsupervised methods.

A good point. It is a pity that we haven’t found an appropriate unsupervised method.

The decoder analyses in Figure 2 provide evidence that target speed modulation may change over the trial. Therefore, it is important to see how the window considered for the firing rate in Figure 1 (currently 100ms pre - 100ms post movement onset) affects the results.

Thanks for the suggestion and close reading. Because the movement onset (MO) is the key time point of this study, we colored this time period in Figure 1 to highlight the perimovement neuronal activity.

(3) Decoder:

One feature of the task is that the reach endpoints tile the entire perimeter of the target circle (Figure 1B). However, this feature is not exploited for much of the single-unit analyses. This is most notable in Figure 2, where the use of a SVM limits the decoding to discrete values (the endpoints are divided into 8 categories). Using continuous decoding of hand kinematics would be more appropriate for this task.

This is a very reasonable suggestion. In the revised manuscript, we’ve updated the continuous decoding results with support vector regression (SVR) in Figure S7A and in Lines 170-173 as:

“These results were stable on the data of the other two monkeys and the pseudopopulation of all three monkeys (Figure S6) and reconfirmed by the continuous decoding results with support vector regressions (Figure S7A), suggesting that target motion information existed in M1 throughout almost the entire trial.”

(4) RNN:

Mixed selectivity is not analysed in the RNN, which would help to compare the model to the real data where mixed selectivity is common. Furthermore, it would be informative to compare the neural data to the RNN activity using canonical correlation or Procrustes analyses. These would help validate the claim of similarity between RNN and neural dynamics, rather than allowing comparisons to be dominated by geometric similarities that may be features of the task. There is also an absence of alternate models to compare the perturbation model results to.

Thank you for these helpful suggestions. We have performed decoding analysis on RNN units and updated in Figure S12A and Lines 333-334 as: “First, from the decoding result, target motion information existed in nodes’ population dynamics shortly after TO (Figure S12A).”

We also have included the results of canonical correlation analysis and Procrustes analysis in Table S2 and Lines 340-342 as: “We then performed canonical component analysis (CCA) and Procrustes analysis (Table S2; see Methods), the results also indicated the similarity between network dynamics and neural dynamics.”

Reviewer #2 (Public Review):

Summary:

In this manuscript, Zhang et al. examine neural activity in the motor cortex as monkeys make reaches in a novel target interception task. Zhang et al. begin by examining the single neuron tuning properties across different moving target conditions, finding several classes of neurons: those that shift their preferred direction, those that change their modulation gain, and those that shift their baseline firing rates. The authors go on to find an interesting, tilted ring structure of the neural population activity, depending on the target speed, and find that (1) the reach direction has consistent positioning around the ring, and (2) the tilt of the ring is highly predictive of the target movement speed. The authors then model the neural activity with a single neuron representational model and a recurrent neural network model, concluding that this population structure requires a mixture of the three types of single neurons described at the beginning of the manuscript.

Strengths:

I find the task the authors present here to be novel and exciting. It slots nicely into an overall trend to break away from a simple reach-to-static-target task to better characterize the breadth of how the motor cortex generates movements. I also appreciate the movement from single neuron characterization to population activity exploration, which generally serves to anchor the results and make them concrete. Further, the orbital ring structure of population activity is fascinating, and the modeling work at the end serves as a useful baseline control to see how it might arise.

Thank you for your recognition of our work.

Weaknesses:

While I find the behavioral task presented here to be excitingly novel, I find the presented analyses and results to be far less interesting than they could be. Key to this, I think, is that the authors are examining this task and related neural activity primarily with a singleneuron representational lens. This would be fine as an initial analysis since the population activity is of course composed of individual neurons, but the field seems to have largely moved towards a more abstract "computation through dynamics" framework that has, in the last several years, provided much more understanding of motor control than the representational framework has. As the manuscript stands now, I'm not entirely sure what interpretation to take away from the representational conclusions the authors made (i.e. the fact that the orbital population geometry arises from a mixture of different tuning types). As such, by the end of the manuscript, I'm not sure I understand any better how the motor cortex or its neural geometry might be contributing to the execution of this novel task.

This paper shows the sensory modulation on motor tuning in single units and neural population during motor execution period. It’s a pity that the findings were constrained in certain time windows. We are still working on this task, please look forward to our following work.

Main Comments:

My main suggestions to the authors revolve around bringing in the computation through a dynamics framework to strengthen their population results. The authors cite the Vyas et al. review paper on the subject, so I believe they are aware of this framework. I have three suggestions for improving or adding to the population results:

(1) Examination of delay period activity: one of the most interesting aspects of the task was the fact that the monkey had a random-length delay period before he could move to intercept the target. Presumably, the monkey had to prepare to intercept at any time between 400 and 800 ms, which means that there may be some interesting preparatory activity dynamics during this period. For example, after 400ms, does the preparatory activity rotate with the target such that once the go cue happens, the correct interception can be executed? There is some analysis of the delay period population activity in the supplement, but it doesn't quite get at the question of how the interception movement is prepared. This is perhaps the most interesting question that can be asked with this experiment, and it's one that I think may be quite novel for the field--it is a shame that it isn't discussed.

It’s a great idea! We are on the way, and it seems promising.

(2) Supervised examination of population structure via potent and null spaces: simply examining the first three principal components revealed an orbital structure, with a seemingly conserved motor output space and a dimension orthogonal to it that relates to the visual input. However, the authors don't push this insight any further. One way to do that would be to find the "potent space" of motor cortical activity by regression to the arm movement and examine how the tilted rings look in that space (this is actually fairly easy to see in the reach direction components of the dPCA plot in the supplement--the rings will be highly aligned in this space). Presumably, then, the null space should contain information about the target movement. dPCA shows that there's not a single dimension that clearly delineates target speed, but the ring tilt is likely evident if the authors look at the highest variance neural dimension orthogonal to the potent space (the "null space")-this is akin to PC3 in the current figures, but it would be nice to see what comes out when you look in the data for it.

Thank you for this nice suggestion. While it was feasible to identify potent subspaces encoding reach direction and null spaces for target-velocity modulation, as suggested by the reviewer, the challenge remained that unsupervised methods were insufficient to isolate a pure target-velocity subspace from numerous possible candidates due to the small variance of target-velocity information. Although dPCA components can be used to construct orthogonal subspaces for individual task variables, we found that the targetvelocity information remained highly entangled with reach-direction representation. More details can be found in Figure S8C and its caption as below:

“We used dPCA components with different features to construct three subspaces (same data in A, reach-direction space #3, #4, #5; target-velocity space #10, #15, #17; interaction space #6, #11, #12), and we projected trial-averaged data into these orthogonal subspaces using different colormaps. This approach allowed us to obtain a “potent subspace” coding reach direction and a “null space” for target velocity. The results showed that the reach-direction subspace effectively represented the reach direction. However, while the target-velocity subspace encoded the target velocity information, it still contained reach-direction clusters within each target-velocity condition, corroborating the results of the addition model in the main text (Figure 4). The interaction subspace revealed that multiple reach-direction rings were nested within each other, similar to the findings from the gain model (Figure 3 & 4). The interaction subspace also captured more variance than target-velocity subspace, consistent with our PCA results, suggesting the target-velocity modulation primarily coexists with reach-direction coding. Furthermore, we explored alternative methods to verify whether orthogonal subspaces could effectively separate the reach direction and target velocity. We could easily identify the reach-direction subspace, but its orthogonal subspace was relatively large, and the target-velocity information exhibited only small variance, making it difficult to isolate a subspace that purely encodes target velocity.”

(3) RNN perturbations: as it's currently written, the RNN modeling has promise, but the perturbations performed don't provide me with much insight. I think this is because the authors are trying to use the RNN to interpret the single neuron tuning, but it's unclear to me what was learned from perturbing the connectivity between what seems to me almost arbitrary groups of neurons (especially considering that 43% of nodes were unclassifiable). It seems to me that a better perturbation might be to move the neural state before the movement onset to see how it changes the output. For example, the authors could move the neural state from one tilted ring to another to see if the virtual hand then reaches a completely different (yet predictable) target. Moreover, if the authors can more clearly characterize the preparatory movement, perhaps perturbations in the delay period would provide even more insight into how the interception might be prepared.

We are sorry that we did not clarify the definition of “none” type, which can be misleading. The 43% unclassifiable nodes include those inactive ones; when only activate (taskrelated) nodes included, the ratio of unclassifiable nodes would be much lower. We recomputed the ratios with only activated units and have updated Table 1. By perturbing the connectivity, we intended to explore the interaction between different modulations.

Thank you for the great advice. We considered moving neural states from one ring to another without changing the directional cluster. However, we found that this perturbation design might not be fully developed: since the top two PCs are highly correlated with movement direction, such a move—similar to exchanging two states within the same cluster but under different target-motion conditions—would presumably not affect the behavior.

Reviewer #3 (Public Review):

Summary:

This experimental study investigates the influence of sensory information on neural population activity in M1 during a delayed reaching task. In the experiment, monkeys are trained to perform a delayed interception reach task, in which the goal is to intercept a potentially moving target.

This paradigm allows the authors to investigate how, given a fixed reach endpoint (which is assumed to correspond to a fixed motor output), the sensory information regarding the target motion is encoded in neural activity.

At the level of single neurons, the authors found that target motion modulates the activity in three main ways: gain modulation (scaling of the neural activity depending on the target direction), shift (shift of the preferred direction of neurons tuned to reach direction), or addition (offset to the neural activity).

At the level of the neural population, target motion information was largely encoded along the 3rd PC of the neural activity, leading to a tilt of the manifold along which reach direction was encoded that was proportional to the target speed. The tilt of the neural manifold was found to be largely driven by the variation of activity of the population of gain-modulated neurons.

Finally, the authors studied the behaviour of an RNN trained to generate the correct hand velocity given the sensory input and reach direction. The RNN units were found to similarly exhibit mixed selectivity to the sensory information, and the geometry of the “ neural population” resembled that observed in the monkeys.

Strengths:

- The experiment is well set up to address the question of how sensory information that is directly relevant to the behaviour but does not lead to a direct change in behavioural output modulates motor cortical activity.

- The finding that sensory information modulates the neural activity in M1 during motor preparation and execution is non trivial, given that this modulation of the activity must occur in the nullspace of the movement.

- The paper gives a complete picture of the effect of the target motion on neural activity, by including analyses at the single neuron level as well as at the population level. Additionally, the authors link those two levels of representation by highlighting how gain modulation contributes to shaping the population representation.

Thank you for your recognition.

Weaknesses:

- One of the main premises of the paper is the fact that the motor output for a given reach point is preserved across different target motions. However, as the authors briefly mention in the conclusion, they did not record muscle activity during the task, but only hand velocity, making it impossible to directly verify how preserved muscle patterns were across movements. While the authors highlight that they did not see any difference in their results when resampling the data to control for similar hand velocities across conditions, this seems like an important potential caveat of the paper whose implications should be discussed further or highlighted earlier in the paper.

Thanks for the suggestion. We’ve highlighted the resampling results as an important control in the revised manuscript in Figure S11 and Lines 257-260 as:

“To eliminate hand-speed effect, we resampled trials to construct a new dataset with similar distributions of hand speed in each target-motion condition and found similar orbital neural geometry. Moreover, the target-motion gain model provided a better explanation compared to the hand-speed gain model (Figure S11).”

- The main takeaway of the RNN analysis is not fully clear. The authors find that an RNN trained given a sensory input representing a moving target displays modulation to target motion that resembles what is seen in real data. This is interesting, but the authors do not dissect why this representation arises, and how robust it is to various task design choices. For instance, it appears that the network should be able to solve the task using only the motion intention input, which contains the reach endpoint information. If the target motion input is not used for the task, it is not obvious why the RNN units would be modulated by this input (especially as this modulation must lie in the nullspace of the movement hand velocity if the velocity depends only on the reach endpoint). It would thus be important to see alternative models compared to true neural activity, in addition to the model currently included in the paper. Besides, for the model in the paper, it would therefore be interesting to study further how the details of the network setup (eg initial spectral radius of the connectivity, weight regularization, or using only the target position input) affect the modulation by the motion input, as well as the trained population geometry and the relative ratios of modulated cells after training.

Great suggestions. In the revised manuscript, we’ve added the results of three alternative modes in Table S4 and Lines 355-365 as below:

“We also tested three alternative network models: (1) only receives motor intention and a GO-signal; (2) only receives target location and a GO-signal; (3) initialized with sparse connection (sparsity=0.1); the unmentioned settings and training strategies were as the same as those for original models (Table S4; see Methods). The results showed that the three modulations could emerge in these models as well, but with obviously distinctive distributions. In (1), the ring-like structure became overlapped rings parallel to the PC1PC2 plane or barrel-like structure instead; in (2), the target-motion related tilting tendency of the neural states remained, but the projection of the neural states on the PC1-PC2 plane was distorted and the reach-direction clusters dispersed. These implies that both motor intention and target location seem to be needed for the proposed ring-like structure. The initialization of connection weights of the hidden layer can influence the network’s performance and neural state structure, even so, the ring-like structure”

- Additionally, it is unclear what insights are gained from the perturbations to the network connectivity the authors perform, as it is generally expected that modulating the connectivity will degrade task performance and the geometry of the responses. If the authors wish the make claims about the role of the subpopulations, it could be interesting to test whether similar connectivity patterns develop in networks that are not initialized with an all-to-all random connectivity or to use ablation experiments to investigate whether the presence of multiple types of modulations confers any sort of robustness to the network.

Thank you for these great suggestions. By perturbations, we intended to explore the contribution of interaction between certain subpopulations. We’ve included the ablation experiments in the updated manuscript in Table S3 and Lines 344-346 as below: “The ablation experiments showed that losing any kind of modulation nodes would largely deteriorate the performance, and those nodes merely with PD-shift modulation could mostly impact the neural state structure (Table S3).”

- The results suggest that the observed changes in motor cortical activity with target velocity result from M1 activity receiving an input that encodes the velocity information. This also appears to be the assumption in the RNN model. However, even though the input shown to the animal during preparation is indeed a continuously moving target, it appears that the only relevant quantity to the actual movement is the final endpoint of the reach. While this would have to be a function of the target velocity, one could imagine that the computation of where the monkeys should reach might be performed upstream of the motor cortex, in which case the actual target velocity would become irrelevant to the final motor output. This makes the results of the paper very interesting, but it would be nice if the authors could discuss further when one might expect to see modulation by sensory information that does not directly affect motor output in M1, and where those inputs may come from. It may also be interesting to discuss how the findings relate to previous work that has found behaviourally irrelevant information is being filtered out from M1 (for instance, Russo et al, Neuron 2020 found that in monkeys performing a cycling task, context can be decoded from SMA but not from M1, and Wang et al, Nature Communications 2019 found that perceptual information could not be decoded from PMd)?

How and where sensory information modulating M1 are very interesting and open questions. In the revised manuscript, we discuss these in Lines 435-446, as below: “It would be interesting to explore whether other motor areas also allow sensory modulation during flexible interception. The functional differences between M1 and other areas lead to uncertain speculations. Although M1 has pre-movement activity, it is more related to task variables and motor outputs. Recently, a cycling task sets a good example that the supplementary motor area (SMA) encodes context information and the entire movement (Russo et al., 2020), while M1 preferably relates to cycling velocity (Saxena et al., 2022). The dorsal premotor area (PMd) has been reported to capture potential action selection and task probability, while M1 not (Cisek and Kalaska, 2005; Glaser et al., 2018; Wang et al., 2019). If the neural dynamics of other frontal motor areas are revealed, we might be able to tell whether the orbital neural geometry of mixed selectivity is unique in M1, or it is just inherited from upstream areas like PMd. Either outcome would provide us some insights into understanding the interaction between M1 and other frontal motor areas in motor planning.”

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

At times the writing was a little hard to parse. It could benefit from being fleshed out a bit to link sentences together better.

There are a few grammatical errors, such as:

"These results support strong and similar roles of gain and additive nodes, but what is even more important is that the three modulations interact each other, so the PD-shift nodes should not be neglected."

should be

"These results support strong and similar roles of gain and additive nodes, but what is even more important is that the three modulations interact WITH each other, so the PDshift nodes should not be neglected."

The discussion could also be more extensive to benefit non-experts in the field.

Thank you. We have proofread and polished the updated manuscript.

Reviewer #2 (Recommendations For The Authors):

Other comments:

- The authors mention mixed selectivity a few times, but Table 1 doesn't have a column for mixed selective neurons--this seems like an important oversight. Likewise, it would be good to see an example of a "mixed" neuron.

- The structure of the writing in the results section often talked about the supplementary results before the main results - this seems backwards. If the supplementary results are important enough to come before the main figures, then they should not be supplementary. Otherwise, if the results are truly supplementary, they should come after the main results are discussed.

- Line 305: Authors say "most" RNN units could be classified, and this is technically true, but only barely, according to Table 1. It might be good to put the actual percentage here in the text.

- Figure 5a: typo ("Motion intention" rather than "Motor")

- I couldn't find any mention of code or data availability in the manuscript.

- There were a number of lines that didn't make much sense to me and should probably be rewritten or expanded on:

- Lines 167-168: "These results qualitatively imply the interaction as that target speeds..." - Lines 178-179: "However, these neural trajectories were not yet the ideal description, because they were shaped mostly by time."

- Lines 187-188: "...suggesting that target motion affects M1 neural dynamics via a topologically invariant transformation."

- Lines 224-226: "Note that here we performed an linear transformation on all resulting neural state points to make the ellipse of the static condition orthogonal to the z-axis for better visualization." Does this mean that the z-axis is not PC 3 anymore?

- Lines 272-274: "These simulations suggest that the existence of PD-shift and additive modulation would not disrupt the neural geometry that is primarily driven by gain modulation; rather it is possible that these three modulations support each other in a mixed population."

Thank you for these detailed suggestions. By “mixed selectivity”, we mean the joint tuning of both target-motion and movement. In this case, the target-motion modulated neurons (regardless of the modulation type) are of mixed selectivity. The term “motor intention” refers to Mazzoni et al., 1996, Journal of Neurophysiology. We also revised the manuscript for better readership.

We have updated the data and code availability in Data availability as below:

“The example experimental datasets and relevant analysis code have been deposited in Mendeley Data at https://data.mendeley.com/datasets/8gngr6tphf. The RNN relevant code and example model datasets are available at https://github.com/yunchenyc/RNN_ringlike_structure.“

Reviewer #3 (Recommendations For The Authors):

Minor typos:

Line 153: “there were”

Line 301: “network was trained to generate”

Line 318: “interact with each other”

Suggested reformulations :

Line 310 : “tilting angles followed a pattern similar to that seen in the data” Line 187 : the claim of a “topologically invariant transformation” seems strong as the analysis is quite qualitative.

Suggested changes to the paper (aside from those mentioned in the main review): It could be nice to show behaviour in a main figure panel early on in the paper. This could help with the task description (as it would directly show how the trials are separated based on endpoint) and could allow for discussing the potential caveats of the assumption that behaviour is preserved.

Thank you. We have corrected these typos and writing problems. As the similar task design has been reported, we finally decided not to provide extra figures or videos. Still, we thank this nice suggestion.

eLife Assessment

This comprehensive study presents important findings that delineate how specific dopaminergic neurons (DANs) instruct aversive learning in Drosophila larvae exposed to high salt through an integration of behavioral experiments, imaging, and connectomic analysis. The work reveals how a numerically minimal circuit achieves remarkable functional complexity, with redundancies and synergies within the DL1 cluster that challenge our understanding of how few neurons generate learning behaviors. By establishing a framework for sensory-driven learning pathways, the study makes a compelling and substantial contribution to understanding associative conditioning while demonstrating conservation of learning mechanisms across Drosophila developmental stages.

Reviewer #1 (Public review):

Summary:

In this paper Weber et al. investigate the role of 4 dopaminergic neurons of the Drosophila larva in mediating the association between an aversive high-salt stimulus and a neutral odor. The 4 DANs belong to the DL1 cluster and innervate non-overlapping compartments of the mushroom body, distinct from those involved in appetitive associative learning. Using specific driver lines for individual neurons, the authors show that activation of the DAN-g1 is sufficient to mimic an aversive memory and it is also necessary to form a high-salt memory of full strength, although optogenetic silencing of this neuron has only a partial phenotype. The authors use calcium imaging to show that the DAN-g1 is not the only DAN responding to salt. DAN-c1 and d1 also respond to salt, but they seem to play no role for the associative memory. DAN-f1, which does not respond to salt, is able to lead to the formation of a memory (if optogenetically activated), but it is not necessary for the salt-odor memory formation in normal conditions. However, when silenced together with DAN-g1, it enhances the memory deficit of DAN-g1. Overall, this work brings evidence of a complex interaction between DL1 DANs in both the encoding of salt signals and their teaching role in associative learning, with none of them being individually necessary and sufficient for both functions.

Strengths:

Overall, the manuscript contributes interesting results that are useful to understand the organization and function of the dopaminergic system. The behavioral role of the specific DANs is accessed using specific driver lines which allow to test their function individually and in pairs. Moreover, the authors perform calcium imaging to test whether DANs are activated by salt, a prerequisite for inducing a negative association to it. Proper genetic controls are carried across the manuscript.

Weaknesses:

The authors use two different approaches to silence dopaminergic neurons: optogenetics and induction of apoptosis. The results are not always consistent, but the authors discuss these differences appropriately. In general, the optogenetic approach is more appropriate as developmental compensations are not of major interest for the question investigated.

The physiological data would suggest the role of a certain subset of DANs in salt-odor association, but a different partially overlapping set is necessary in behavioral assays (with a partial phenotype). No manipulation completely abolishes the salt-odor association, leaving important open questions on the identity of the neural circuits involved in this behavior.

The EM data analysis reveals a non-trivial organization of sensory inputs into DANs, but it is difficult to extrapolate a link to the functional data presented in the paper.

Reviewer #2 (Public review):

Summary:

In this work the authors show that dopaminergic neurons (DANs) from the DL1 cluster in Drosophila larvae are required for the formation of aversive memories. DL1 DANs complement pPAM cluster neurons which are required for the formation of attractive memories. This shows the compartmentalized network organization of how an insect learning center (the mushroom body) encodes memory by integrating olfactory stimuli with aversive or attractive teaching signals. Interestingly, the authors found that the 4 main dopaminergic DL1 neurons act partially redundant, and that single cell ablation did not result in aversive memory defects. However, ablation or silencing of a specific DL1 subset (DAN-f1,g1) resulted in reduced salt aversion learning, which was specific to salt but no other aversive teaching stimuli tested. Importantly, activation of these DANs using an optogenetic approach was also sufficient to induce aversive learning in the presence of high salt. Together with the functional imaging of salt and fructose responses of the individual DANs and the implemented connectome analysis of sensory (and other) inputs to DL1/pPAM DANs this represents a very comprehensive study linking the structural, functional and behavioral role of DL1 DANs. This provides fundamental insight into the function of a simple yet efficiently organized learning center which displays highly conserved features of integrating teaching signals with other sensory cues via dopaminergic signaling.

Strengths:

This is a very careful, precise and meticulous study identifying the main larval DANs involved in aversive learning using high salt as a teaching signal. This is highly interesting because it allows to define the cellular substrates and pathways of aversive learning down to the single cell level in a system without much redundancy. It therefore sets the basis to conduct even more sophisticated experiments and together with the neat connectome analysis opens the possibility to unravel different sensory processing pathways within the DL1 cluster and integration with the higher order circuit elements (Kenyon cells and MBONs). The authors' claims are well substantiated by the data and balanced, putting their data in the appropriate context. The authors also implemented neat pathway analyses using the larval connectome data to its full advantage, thus providing network pathways that contribute towards explaining the obtained results.

Weaknesses:

Previous comments were fully addressed by the authors.

Reviewer #3 (Public review):

The study of Weber et al. provides a thorough investigation of the roles of four individual dopamine neurons for aversive associative learning in the Drosophila larva. They focus on the neurons of the DL-1 cluster which already have been shown to signal aversive teaching signals. But the authors go beyond the previous publications and test whether each of these dopamine neurons responds to salt or sugar, is necessary for learning about salt, bitter, or sugar, and is sufficient to induce a memory when optogenetically activated. In addition, previously published connectomic data is used to analyze the synaptic input to each of these dopamine neurons. The authors conclude that the aversive teaching signal induced by salt is distributed across the four DL-1 dopamine neurons, with two of them, DAN-f1 and DAN-g1, being particularly important. Overall, the experiments are well designed and performed, support the authors' conclusions, and deepen our understanding of the dopaminergic punishment system.

Strengths:

(1) This study provides, at least to my knowledge, the first in vivo imaging of larval dopamine neurons in response to tastants. Although the selection of tastants is limited, the results close an important gap in our understanding of the function of these neurons.<br /> (2) The authors performed a large number of experiments to probe for the necessity of each individual dopamine neuron, as well as combinations of neurons, for associative learning. This includes two different training regimen (1 or 3 trials), three different tastants (salt, quinine and fructose) and two different effectors, one ablating the neuron, the other one acutely silencing it. This thorough work is highly commendable, and the results prove that it was worth it. The authors find that only one neuron, DAN-g1, is partially necessary for salt learning when acutely silenced, whereas a combination of two neurons, DAN-f1 and DAN-g1, are necessary for salt learning when either being ablated or silenced.<br /> (3) In addition, the authors probe whether any of the DL-1 neurons is sufficient for inducing an aversive memory. They found this to be the case for two of the neurons, largely confirming previous results obtained by a different learning paradigm, parameters and effector.<br /> (4) This study also takes into account connectomic data to analyze the sensory input that each of the dopamine neurons receives. This analysis provides a welcome addition to previous studies and helps to gain a more complete understanding. The authors find large differences in inputs that each neuron receives, and little overlap in input that the dopamine neurons of the "aversive" DL-1 cluster and the "appetitive" pPAM cluster seem to receive.<br /> (5) Finally, the authors try to link all the gathered information in order to describe an updated working model of how aversive teaching signals are carried by dopamine neurons to the larva's memory center. This includes important comparisons both between two different aversive stimuli (salt and nociception) and between the larval and adult stages.

Author response:

The following is the authors’ response to the original reviews

Public reviews:

Reviewer #1 (Public Review):

Summary:

In this paper, Weber et al. investigate the role of 4 dopaminergic neurons of the Drosophila larva in mediating the association between an aversive high-salt stimulus and a neutral odor. The 4 DANs belong to the DL1 cluster and innervate non-overlapping compartments of the mushroom body, distinct from those involved in appetitive associative learning. Using specific driver lines, they show that activation of the DAN-g1 is sufficient to mimic an aversive memory and it is also necessary to form a high-salt memory of full strength, although optogenetic silencing of this neuron only partially affects the performance index. The authors use calcium imaging to show that the DAN-g1 is not the only one that responds to salt. DAN-c1 and d1 also respond to salt, but they seem to play no role in the assays tested. DAN-f1, which does not respond to salt, is able to lead to the formation of memory (if optogenetically activated), but it is not necessary for the salt-odor memory formation in normal conditions. However, silencing of DAN-f1 together with DAN-g1, enhances the memory deficit of DAN-g1.

Strengths:

The paper therefore reveals that also in the Drosophila larva as in the adult, rewards and punishments are processed by exclusive sets of DANs and that a complex interaction between a subset of DANs mediates salt-odor association.

Overall, the manuscript contributes valuable results that are useful for understanding the organization and function of the dopaminergic system. The behavioral role of the specific DANs is accessed using specific driver lines which allow for testing of their function individually and in pairs. Moreover, the authors perform calcium imaging to test whether DANs are activated by salt, a prerequisite for inducing a negative association with it. Proper genetic controls are carried across the manuscript.

Weaknesses:

The authors use two different approaches to silence dopaminergic neurons: optogenetics and induction of apoptosis. The results are not always consistent, and the authors could improve the presentation and interpretation of the data. Specifically, optogenetics seems a better approach than apoptosis, which can affect the overall development of the system, but apoptosis experiments are used to set the grounds of the paper.

The physiological data would suggest the role of a certain subset of DANs in salt-odor association, but a different partially overlapping set seems to be necessary. This should be better discussed and integrated into the author's conclusion. The EM data analysis reveals a non-trivial organization of sensory inputs into DANs and it is hard to extrapolate a link to the functional data presented in the paper.

We would like to thank reviewer 1 for the positive evaluation of our work and for the critical suggestions for improvement. In the new version of the manuscript, we have centralized the optogenetic results and moved some of the ablation experiments to the Supplement. We also discuss in detail the experimental differences in the results. In addition, we have softened our interpretation of the specificity of memory for salt. As a result, we now emphasize more the general role of DANs for aversive learning in the larva. These changes are now also summarized and explained more simply and clearly in the Discussion, along with a revised discussion of the EM data.

Reviewer #2 (Public Review):

Summary:

In this work, the authors show that dopaminergic neurons (DANs) from the DL1 cluster in Drosophila larvae are required for the formation of aversive memories. DL1 DANs complement pPAM cluster neurons which are required for the formation of attractive memories. This shows the compartmentalized network organization of how an insect learning center (the mushroom body) encodes memory by integrating olfactory stimuli with aversive or attractive teaching signals. Interestingly, the authors found that the 4 main dopaminergic DL1 neurons act redundantly, and that single-cell ablation did not result in aversive memory defects. However, ablation or silencing of a specific DL1 subset (DAN-f1,g1) resulted in reduced salt aversion learning, which was specific to salt but no other aversive teaching stimuli were tested. Importantly, activation of these DANs using an optogenetic approach was also sufficient to induce aversive learning in the presence of high salt. Together with the functional imaging of salt and fructose responses of the individual DANs and the implemented connectome analysis of sensory (and other) inputs to DL1/pPAM DANs, this represents a very comprehensive study linking the structural, functional, and behavioral role of DL1 DANs. This provides fundamental insight into the function of a simple yet efficiently organized learning center which displays highly conserved features of integrating teaching signals with other sensory cues via dopaminergic signaling.

Strengths:

This is a very careful, precise, and meticulous study identifying the main larval DANs involved in aversive learning using high salt as a teaching signal. This is highly interesting because it allows us to define the cellular substrates and pathways of aversive learning down to the single-cell level in a system without much redundancy. It therefore sets the basis to conduct even more sophisticated experiments and together with the neat connectome analysis opens the possibility of unraveling different sensory processing pathways within the DL1 cluster and integration with the higher-order circuit elements (Kenyon cells and MBONs). The authors' claims are well substantiated by the data and clearly discussed in the appropriate context. The authors also implement neat pathway analyses using the larval connectome data to its full advantage, thus providing network pathways that contribute towards explaining the obtained results.

Weaknesses:

While there is certainly room for further analysis in the future, the study is very complete as it stands. Suggestions for clarification are minor in nature.

We would like to thank reviewer 2 for the positive evaluation of our work. In fact, follow-up work is already underway to further analyze the role of the individual DL1 DANs. We have addressed the constructive and detailed suggestions for improvement in our point-by-point responses in the “Recommendations for the authors” section.

Reviewer #3 (Public Review):

The study of Weber et al. provides a thorough investigation of the roles of four individual dopamine neurons for aversive associative learning in the Drosophila larva. They focus on the neurons of the DL-1 cluster which already have been shown to signal aversive teaching signals. However, the authors go far beyond the previous publications and test whether each of these dopamine neurons responds to salt or sugar, is necessary for learning about salt, bitter, or sugar, and is sufficient to induce a memory when optogenetically activated. In addition, previously published connectomic data is used to analyze the synaptic input to each of these dopamine neurons. The authors conclude that the aversive teaching signal induced by salt is distributed across the four DL-1 dopamine neurons, with two of them, DAN-f1 and DAN-g1, being particularly important. Overall, the experiments are well designed and performed, support the authors' conclusions, and deepen our understanding of the dopaminergic punishment system.

Strengths:

(1) This study provides, at least to my knowledge, the first in vivo imaging of larval dopamine neurons in response to tastants. Although the selection of tastants is limited, the results close an important gap in our understanding of the function of these neurons.

(2) The authors performed a large number of experiments to probe for the necessity of each individual dopamine neuron, as well as combinations of neurons, for associative learning. This includes two different training regimens (1 or 3 trials), three different tastants (salt, quinine, and fructose) and two different effectors, one ablating the neuron, the other one acutely silencing it. This thorough work is highly commendable, and the results prove that it was worth it. The authors find that only one neuron, DAN-g1, is partially necessary for salt learning when acutely silenced, whereas a combination of two neurons, DAN-f1 and DAN-g1, are necessary for salt learning when either being ablated or silenced.

(3) In addition, the authors probe whether any of the DL-1 neurons is sufficient for inducing an aversive memory. They found this to be the case for three of the neurons, largely confirming previous results obtained by a different learning paradigm, parameters, and effector.

(4) This study also takes into account connectomic data to analyze the sensory input that each of the dopamine neurons receives. This analysis provides a welcome addition to previous studies and helps to gain a more complete understanding. The authors find large differences in inputs that each neuron receives, and little overlap in input that the dopamine neurons of the "aversive" DL-1 cluster and the "appetitive" pPAM cluster seem to receive.

(5) Finally, the authors try to link all the gathered information in order to describe an updated working model of how aversive teaching signals are carried by dopamine neurons to the larva's memory center. This includes important comparisons both between two different aversive stimuli (salt and nociception) and between the larval and adult stages.

Weaknesses:

(1) The authors repeatedly claim that they found/proved salt-specific memories. I think this is problematic to some extent.

(1a) With respect to the necessity of the DL-1 neurons for aversive memories, the authors' notion of salt-specificity relies on a significant reduction in salt memory after ablating DAN-f1 and g1, and the lack of such a reduction in quinine memory. However, Fig. 5K shows a quite suspicious trend of an impaired quinine memory which might have been significant with a higher sample size. I therefore think it is not fully clear yet whether DAN-f1 and DAN-g1 are really specifically necessary for salt learning, and the conclusions should be phrased carefully.

(1b) With respect to the results of the optogenetic activation of DL-1 neurons, the authors conclude that specific salt memories were established because the aversive memories were observed in the presence of salt. However, this does not prove that the established memory is specific to salt - it could be an unspecific aversive memory that potentially could be observed in the presence of any other aversive stimuli. In the case of DAN-f1, the authors show that the neuron does not even get activated by salt, but is inhibited by sugar. Why should activation of such a neuron establish a specific salt memory? At the current state, the authors clearly showed that optogenetic activation of the neurons does induce aversive memories - the "content" of those memories, however, remains unknown.

(2) In many figures (e.g. figures 4, 5, 6, supplementary figures S2, S3, S5), the same behavioural data of the effector control is plotted in several sub-figures. Were these experiments done in parallel? If not, the data should not be presented together with results not gathered in parallel. If yes, this should be clearly stated in the figure legends.

We would also like to thank reviewer 3 for his positive assessment of our work. As already mentioned by reviewer 1, we understand the criticism that the salt specificity for which the individual DANs are coded is not fully always supported by the results of the work. We have therefore rewritten the relevant passages, which are also cited by the reviewer. We have also included the second point of criticism and incorporated it into our manuscript. As the control groups were always measured in parallel with the experimental animals, we can also present the data together in a sub-figure. We clearly state this now in the revised figure legends.

Summary of recommendations to authors:

Overall, the study is commendable for its systematic approach and solid methodology. Several weaknesses were identified, prompting the need for careful revisions of the manuscript:

We thank the reviewers for the careful revision of our manuscript. In the subsequent sections, we aim to address their concerns as thoroughly as possible. A comprehensive one-to-one listing can be found below.

(1) The authors should reconsider their assertion of uncovering a salt-specific memory, as the evidence does not conclusively demonstrate the exclusive necessity of DAN-f1 and DAN-g1 for salt learning. In particular, the optogenetic activation of DAN-f1 leads to plasticity but this might not be salt-specific. The precise nature of the memory content remains elusive, warranting a nuanced rephrasing of the conclusions.

We only partially agree – optogenetic activation of DANs does not really allow to comment on its salt-specificity, true. However, we used high-salt concentrations during test. Over the years, the Gerber lab nicely demonstrated in several papers that larvae recall an aversive odor-salt memory only if salt is present during test (Gerber and Hendel, 2006; Niewalda et al 2008; Schleyer et al. 2011; Schleyer et al. 2015). The used US has to be present during test. Even at the same concentration other aversive stimuli (e.g. bitter quinine) are not able to allow the larvae to recall this particular type of memory. So, if the optogenetic activation of DAN-f1 establishes a memory that can be recalled on salt, we argue that it has to encode aspects of the salt information. On the other hand, only for DAN-g1 we see the necessity for salt learning. And – although (based on the current literature) very unlikely, we cannot fully exclude that the activation of DAN-f1 establishes a yet unknown type of memory that can be also recalled on a salt plate. Therefore, we partially agree and accordingly have rephrased the entire manuscript to avoid an over-interpretation of our data. Throughout the manuscript we avoid now to use the term salt-specific memory but rather describe the type of memory as aversive memory.

(2) A thorough examination or discussion about the potential influence of blue light aversion on behavioral observations is necessary to ensure a balanced interpretation of the findings.

To address this point every single behavioral experiment that uses optogenetic blue light activation runs with appropriate and mandatory controls. For blue light activation experiments, two genetic controls are used that either get the same blue light treatment (effector control, w1118>UAS-ChR2XXL) or no blue light treatment (dark control, XY-split-Gal4>UAS-ChR2XXL). For blue light inactivation experiments one group is added that has exactly the same genotype but did not receive food containing retinal. These experiments show that blue light exposure itself does not induce an aversive nor positive memory and blue light exposure does not impair the establishment of odor-high salt memory. In addition, we used the latest established transgenes available. ChR2^XXL is very sensitive to blue light. Only 220 lux (60 µW/cm^²) were necessary to obtain stable results. In our hands – short term exposure for up to 5 minutes with such low intensities does not induce a blue light aversion. Following the advice of the reviewer, we also address this concern by adding several sentences into the related results and methods sections.

(3) The authors should address the limitations associated with the use of rpr/hid for neuronal ablations, such as the effects of potential developmental compensation.

We agree with this concern. It is well possible that the ablation experiments induce compensatory effects during larval development. Such an effect may be the reason for differences in phenotypes when comparing hid,rpr ablation with optogenetic inhibition. This is now part of the discussion. In addition, we evaluated if the ablation worked in our experiments. So far controls were missing that show that the expression of hid,rpr really leads to the ablation of DANs. We now added these experiments and clearly show anatomically that the DANs are ablated (related to figure 4-figure supplement 6).

(4) While the connectome analysis offers valuable insights into the observed functions of specific DANs in relation to their extrinsic (sensory) and intrinsic (state) inputs, integrating this data more cohesively within the manuscript through careful rewriting would enhance the coherence of the study.

We understand this concern. Therefore, the new version of our manuscript is now intensifying the inclusion of the EM data in our interpretation of the results. Throughout the entire manuscript we have now rewritten the related parts. We have also completely revised the corresponding section in the results chapter.

(5) More generally, the authors are encouraged to discuss internal discrepancies in the results of their functional manipulation experiments.

Thank you for this suggestion. We do of course understand that we have not given the different results enough space in the discussion. We have now changed this and have been happy to comprehensively address the concern. 

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Here are some suggestions for clarification and improvement of the manuscript:

(1) The authors should discuss why the silencing experiment with TH-GAL4 (Fig. 1) does not abolish memory formation (I assume that the PI should go to zero). Does it mean that other non-TH neurons are involved in salt-odor memory formation? Are there other lines that completely abolish this type of learning?

Thank you very much for highlighting this crucial point. Indeed, the functional intervention does not completely eliminate the memory. There could be several reasons, or a combination thereof, for this outcome. For instance, it's plausible that the UAS-GtACR2 effector doesn't entirely suppress the activity of dopaminergic neurons. Additionally, the memory may comprise different types, not all of which are linked to dopamine function. It's also noteworthy that TH-Gal4 doesn't encompass all dopaminergic neurons – even a neuron from the DL1 cluster is absent (as previously reported in Selcho et al., 2009). Considering we're utilizing high salt concentrations in this experiment, it's conceivable that non gustatory-driven memories are formed based solely on the systemic effects of salt (e.g., increased osmotic pressure). These possibilities are now acknowledged in the text.

(2) The Rpr experiments in Fig. 4 do not lead to any phenotype and there is a general assumption that the system compensates during development. However, there is no demonstration that Rpr worked or that development compensated for that. What do we learn from these data? Would it make sense to move it to supplement to make the story more compact? In addition: the conclusion at L 236 "DL1.... Are not individually necessary" is later disproved by optogenetic silencing. Similarly, optogenetic silencing of f1+g1 is affecting 1X and 3X learning, but not when using Rpr. Moreover, Rpr wdid not give any phenotype in other data in the supplementary material. I'm not sure how valid these results are.

We acknowledge this concern and have actively deliberated various options for restructuring the presented ablation data. Ultimately, we reached a consensus that relocating Figure 4 to the supplement is warranted. Furthermore, corresponding adjustments have been made in the text. This decision amplifies the significance of the optogenetic results. In addition, we also addressed the other part of the concern. We examined the efficacy of hid and rpr in our experiments. Indeed, we successfully ablated specific DANs, as illustrated in the new anatomical data presented in Figure 4- figure supplement 6, which strengthens the interpretation of the hid,rpr experiments.

(3) In most figures that show data for 1X and 3X training, there is no difference between these two conditions (I would suggest moving one set as a supplement). When a difference appears (Fig.5A-D) the implications are not discussed properly. Is it known that some circuits are necessary for the 1X but not for the 3X protocol? Is that a reasonable finding? I would expect the opposite, but I might lack of knowledge here. However, the optogenetic silencing of the same neurons in Figure 7 shows the same phenotype for 1X and 3X. Again, the validity of the Rpr experiments seems debatable.

Different training protocols lead to different memory phases (STM and STM+ARM). We have shown that in the past in Widmann et al. 2016. Therefore, we are convinced that it makes sense to keep both data sets in the main manuscript. However, we agree that this was not properly introduced and discussed and therefore made the respective changes in the manuscript.

(4) In Figure 3, it is unclear what the responses were tested against. Since they are so small and noisy there would be a need for a control. Moreover, in some cases, it looks like the DF/F is normalized to the wrong value: e.g. in DAN-c1 100mM, the activity in 0-10s is always above zero, and in pPAM with fructose is always below zero. This might not have any consequence on the results but should be adjusted.

Thank you very much for your criticism, which we greatly appreciate. We have carefully re-examined the data and found that there was a mistake for the normalization of the values. We made the necessary adjustments to the evaluation, as per your suggestions. The updated figures, figure legends, and results have been incorporated into the new version of the manuscript. As noted by the reviewer, these corrections have not altered the interpretation of the data or the primary responses of the various DANs.

(5) In the abstract: "Optogenetic activation of DAN-f1 and DAN-g1 alone suffices to substitute for salt punishment... Each DAN encodes a different aspect of salt punishment". These sentences might be misleading and an overstatement: only DAN-g1 shows a clear role, while the function of the other DANs in the context of salt-odor learning remains obscure.

We have refined the respective part of the abstract accordingly. Consequently, we have reworded the related section, aiming to avoid any exaggeration.

(6) The physiology is done in L1 larvae but behavior is tested in L3 larvae. There could be a change in this time that could explain the salt responses in c1 and d1 but no role in salt-odor learning?

While we cannot dismiss the possibility of a developmental change from L1 to L3, a comparison of the anatomical data of the DL1 DANs from electron microscopy (EM) and light microscopy (LM) data indicates that their overall morphology remains consistent. However, it's important to note that this observation does not analyse the physiological aspects of these cells. Consequently, we have incorporated this concern into the discussion of the revised version of the manuscript.

(7) The introduction needs some editing starting at L 129, as it ends with a discussion of a previously published EM data analysis. I would rather suggest stating which questions are addressed in this paper and which methods will be used and perhaps a hint on the results obtained.

We understand the concern. We have added a concise paragraph to the conclusion of the introduction, highlighting the biological question, technical details, and a short hint on the acquired findings.

(8) It is clear to me that the presentation of salt during the test is necessary for recall, however in L 166 I don't understand the explanation: how is the memory used in a beneficial way in the test? The salt is present everywhere and the odor cue is actually useless to escape it.

Extensive research, exemplified by studies such as Schleyer et al. (2015) published in Elife, clearly demonstrates that the recall of odor-high salt memory occurs exclusively when tested on a high salt plate. Even when tested on a bitter quinine plate, the aversive memory is not recalled. This phenomenon is attributed to the triggering of motivation to recall the memory by the omnipresent abundance of the unconditioned stimulus (US) during the test, which in our case is high salt. Furthermore, the concentration of the stimulus plays a crucial role (Schleyer et al. 2011). The odor cue indicates where the situation could potentially be improved; however, if high salt is absent, this motivational drive diminishes as there is no memory present to enhance the already favorable situation. Additionally, the motivation to evade the omnipresent and unpleasant high salt stimulus persists throughout the entire 5-minute test period.

(9) L288: the fact that f1 shows a phenotype in this experiment does not mean that it encodes a salt signal, indeed it does not respond to salt. It perhaps induces a plasticity that can be recalled by salt, but not necessarily linked to salt. The synergy between f1 and g1 in the salt assay was postulated based on exp with Rpr, but the validity of these experiments is dubious. I'm not sure there is sufficient evidence from Figures 6 and 7 to support a synergistic action between f1 and g1.

It is true that DAN-f1 alone is not necessary for mediating a high salt teaching signal based on ablation, optogenetic inhibition and even physiology. However, optogenetic activation alone shows a memory tested on a salt plate. Given the logic explained above that is accepted by several publications, we would like to keep the statement. Especially as the joined activation with DAN-g1 gives rise to significant higher or lower values after joined optogenetic activation or inactivation (Figure 5E and F, Figure 6E and F in the new version). Nevertheless, we have modified the sentence. In the text we describe these effects now as “these results may suggest that DAN-f1 and DAN-g1 encode aspects of the natural aversive high salt teaching signal under the conditions that we tested”. We think that this is an appropriate and three-fold restricted statement. Therefore, we would like to keep it in this restricted version. However, we are happy to reconsider this if the reviewer thinks it is critical. 

(10) I find the EM analysis hard to read. First of all, because of the two different graphical representations used in Fig. 8, wouldn't one be sufficient to make the point? Secondly, I could not grasp a take-home-message: what do we learn from the EM data? Do they explain any of the results? It seems to me that they don't provide an explanation of why some DL1 neurons respond to salt and others don't.

We understand that the EM analysis is hard to read and have now carefully rewritten this part of the manuscript. See also general concern 4 above. The main take home message is not to explain why some DL1 neurons respond to salt and other do not. This cannot be resolved due to the missing information on the salt perceiving receptor cells. Unfortunately, we miss the peripheral nervous system in the EM - the first layer of salt information processing. However, our analysis shows clearly that the 4 DANs have their own identity based on their connectivity. None of them is the same – but to a certain extent similarities exist. This nicely reflects the physiological and behavioral results. We have now clarified that in the result to ease the understanding for the readership. In addition, we also clearly state that we don’t address the point why some DL1 neurons respond to salt and why others don’t respond.

(11) Do the manipulations (activation and silencing) affect odor preference in the presence of salt? Did the authors test that the two odors do not drive different behaviors on the salty plate? Or did they only test the odor preference on plain agarose? Can we exclude a role for the DAN in driving multisensory-driven innate behavior?

Innate odor preferences are not changed by the presence of salt or even other tastants (this work but see also Schleyer et al 2015, Figure 3, Elife). Even the naïve choice between two odors is the same if tested in the presence of different tastants (Schleyer et al 2015, Figure 3, Elife). This shows – at least for the tested stimuli and conditions – that are similar to the ones that we use – that there is no multisensory-driven innate odor-taste behavior. Therefore – at least to our knowledge - experiments as the ones suggested by the reviewer were never done in larval odor-taste learning studies. Therefore, we suggest that DAN activation has no effect on innate larval behavior. However, we are happy to reconsider this if the reviewer thinks it is critical. 

(12) L 280: the authors generalize the conclusion to all DL1-DANs, but it does not apply to c1 and d1.

Thanks for this comment. We deleted that sentence as suggested and thus do not anymore generalize the conclusion to all DL-DANs.

(13) L345: I do not see the described differences in Fig. 8F, presynaptic sites of both types seem to appear in rather broad regions: could the author try to clarify this?

We understand that the anatomical description of the data is often hard to read. Especially to readers that are not used to these kind of figures. We have therefore modified the text to ease the understanding and clarify the difference in the labeled brain regions for the broad readership.

(14) L373: the conclusion on c1 is unsupported by data: this neuron responds to both salt and fructose (Figure 3 ) while the conclusion is purely based on EM data analysis.

The sentence is not a conclusion but a speculation and we also list the cell's response to positive and negative gustatory stimuli. Therefore, we do not understand exactly what the reviewer means here. However, we have tried to address the criticism and have revised the sentences.

(15) L385: the data on d1 seem to be inconsistent with Eschbach 2020, but the authors do not discuss if this is due to the differential vs absolute training, or perhaps the presence of the US during the test (which does not seem to be there in Eschbach, 2020) - is the training protocol really responsible for this inconsistency? For f1 the data seem to be consistent across these studies. The authors should clarify how the exp in Fig 6 differs from Eschbach, 2020 and how one could interpret the differences.

True. This concern is correct. We now discuss the difference in more detail. Eschbach et al. used Cs-Crimson as a genetic tool, a one odor paradigm with 3 training cycles, and no gustatory cues in their approach. These differences are now discussed in the new version of the manuscript.

(16) L460-475 A long part of this paragraph discusses the similarities between c1 and d1 and corresponding PPL1 neurons in the adult fly. However, c1 and d1 do not really show any phenotype in this paper, I'm not sure what we learn from this discussion and how much this paper can contribute to it. I would have wished for a discussion of how one could possibly reconcile the observed inconsistencies.

Based on the comments of the different reviewers several paragraphs in the discussion were modified. We agree that the part on the larval-adult comparison is quite long. Thus we have shortened it as suggested by the reviewer.

Minor corrections:

L28 "resultant association" maybe resulting instead.

L55 "animals derive benefit": remove derive.

L78 "composing 12,000 neurons": composed of.

L79 what is stable in a "stable behavioral assay"?

L104: 2 times cluste.

L122: "DL1 DANs are involved" in what?

Fig. 1 please check subpanels labels, D repeats.

L 362: "But how do individual neurons contribute to the teaching signal of the complete cluster?" I don't understand the question.

L364 I did not hear before about the "labeled line hypothesis" in this context - could the author clarify?

L368: edit "combinatorically".

L390: "current suppression" maybe acute suppression.

L 400 I'm not sure what is meant by "judicious functional configuration" and "redundancy". The functions of these cells are not redundant, and no straightforward prediction of their function can be done from their physiological response to salt.

Thanks a lot for your in detail review of our manuscript. We welcome your well-taken concerns and have made the requested changes for all points that you have raised.

Reviewer #2 (Recommendations For The Authors):

(1) In Figure 1 the reconstruction of pPAM and DL1 DANs shows the compartmentalized innervation of the larval MB. However, the images are a bit low in color contrast to appreciate the innervation well. In particular in panel B, it is hard to identify the innervated MB body structure. A schematic model of the larval MB and DAN innervation domains like in Fig. 2A would help to clarify the innervation pattern to the non-specialist.

We understand this concern and have changed figure 1 as suggested by the reviewer. A schematic model of the MB and DANs is now presented already in figure 1 as well as the according supplemental figure.

(2) Blue light itself can be aversive for larvae and thus interfere with the aversive learning paradigm. Does the given Illuminance (220 lux) used in these experiments affect the behavior and learning outcome?

Yes, in former times high intensities of blue light were necessary to trigger the first generation optogenetic tools. The high intensity blue light itself was able to establish an aversive memory (e.g. Rohwedder et al. 2016). Usage of the second generation optogenetic tools allowed us to strongly reduce the applied light intensity. Now we use 220 lux (equal to 60 µW/cm²). Please note that all Gal4 and UAS controls in the manuscript are nonsignificant different from zero. The mild blue light stimulation therefore does not serve as a teaching signal and has neither an aversive nor an appetitive effect. Furthermore, we use this mild light intensity for several other behavioral paradigms (locomotion, feeding, naïve preferences) and have never seen an effect on the behavior.

(3) Fig.2: Except for MB054B-Gal4 only the MB expression pattern is shown for other lines. Is there any additional expression in other cells of the brain? In the legend in line 761, the reporter does not show endogenous expression, rather it is a fluorescent reporter signal labeling the mushroom body.

The lines were initially identified by a screen on larval MB neurons done together with Jim Truman, Marta Zlatic and Bertram Gerber. Here full brain scans were always analyzed. These images can be seen in Eschbach et al. 2020, extended figure 1. Neither in their evaluation nor in our anatomical evaluation (using a different protocol) additional expression in brain cells was detectable. We also modified the figure legend as suggested.

(4) Fig.3: Precise n numbers per experiment should be stated in the figure legend.

True, we now present n numbers per experiment whenever necessary.

(5) Fig.4: Have the authors confirmed complete ablation of the targeted neuron using rpr/hid? Ablations can be highly incomplete depending on the onset and strength of Gal4 expression, leaving some functionality intact. While the ablation experiments are largely in line with the acute silencing of single DANs during high salt learning performed later on (Fig.7), there is potentially an interesting aspect of developmental compensation hidden in this data. Not a major point, but potentially interesting to check.

We agree with this criticism. We have not tested if the expression of hid,rpr in DL1 DANs does really ablate them. Therefore we did an additional experiment to show that. The new data is now present as a supplemental figure (Figure 4- figure supplement 6). The result shows that expression of hid,rpr ablates also DL1 DANs similar to earlier experiments where we used the same effectors to ablate serotoniergic neurons (Huser et al., 2012, figure 5).

(6) The performance index in Fig. 4 and 5 sometimes seems lower and the variability is higher than in some of the other experiments shown. Is this due to the high intrinsic variability of these particular experiments, or the background effects of the rpr/hid or splitGal4 lines?

The general variability of these experiments is within the expected and known borders. In these kind of experiments there is always some variation due to several external factors (e.g. experimental time over the year). Therefore it is always important to measure controls and experimental animals at the same time. Of course that’s what we did and we only compare directly results of individual datasets. But not between different datasets. This is further hampered given that the experiments of Figure 4 (now Figure 4- figure supplement 1) and Figure 5 (now Figure 4) differ in several parameters from other learning experiments presented later in the text. Optogenetic activation uses blue light stimulation instead of “real world” high salt. Most often direct activation of specific DANs in the brain is more stable than the external high salt stimulation. Also optogenetic inactivation uses blue light stimulation and also retinal supplemented food. Both factors can affect the measurement. We thus want to argue that it is for each experiment most often the particular parameters that affect the variability of the results rather than background effects of the rpr/hid and split-Gal4 lines.

(7) Fig.7: This is a neat experiment showing the effects of acute silencing of individual DL1 DANs. As silencing DAN-f1/g1 does not result in complete suppression of aversive learning, it would be highly interesting to test (or speculate about) additive or modulatory effects by the other DANs. Dan-c-1/d-1 also responds to high salt but does not show function on its own in these assays. I am aware that this is currently genetically not feasible. It would however be a nice future experiment.

True, we were intensively screening for DL1 cluster specific driver lines that cover all 4 DL1 neurons or other combinations than the ones we tested. Unfortunately, we did not succeed in identifying them. Nevertheless, we will further screen new genetic resources (e.g. Meissner et al., 2024, bioRxiv) to expand our approach in future experiments. Please also see our comment on concern 1 of reviewer 1 for further technical limitations and biological questions that can also potentially explain the absence of complete suppression of high salt learning and memory. Some of these limitations are now also mentioned and discussed in the new version of the manuscript.

(8) The discussion is excellent. I would just amend that it is likely that larval DAN-c1, which has high interconnectivity within the larval CNS, is likely integrating state-dependent network changes, similar to the role of some DANs in innate and state-dependent preference behavior. This might contribute to modulating learned behavior depending on the present (acute) and previous environmental conditions.

Thanks a lot for bringing this up. We rewrote this part and added a discussion on recent work on DAN-c1 function in larvae as well as results on DAN function in innate and state-dependent preference behavior.

(9) Citation in line 1115 missing access information: "Schnitzer M, Huang C, Luo J, Je Woo S, Roitman L, et al. 2023. Dopamine signals integrate innate and learned valences to regulate memory dynamics. Research Square".

Unfortunately this escaped our notice. The paper is now published in Nature: Huang, C., Luo, J., Woo, S.J. et al. Dopamine-mediated interactions between short- and long-term memory dynamics. Nature 634, 1141–1149 (2024). https://doi.org/10.1038/s41586-024-07819-w. We have now changed the citation. The new citation includes the missing access information.

Reviewer #3 (Recommendations For The Authors):

Regarding my issue about salt specificity in the public review, I want to make clear that I do not suggest additional experiments, but to be very careful in phrasing the conclusions, in particular whenever referring to the experiments with optogenetic activation. This includes presenting these experiments as "(salt) substitution" experiments - inferring that the optogenetic activation would substitute for a natural salt punishment. As important and interesting as the experiments are, they simply do not allow such an interpretation at this point.

Results, line 140ff: When presenting the results regarding TH-Gal4 crossed to ChR2-XXL, please cite Schroll et al. 2006 who demonstrated the same results for the first time.

Thanks for mentioning this. We now cite Schroll et al. 2006 here in the text of the manuscript.

Figure 3: The subfigure labels (ABC) are missing.

Unfortunately this escaped our notice. Thanks a lot – we have now corrected this mistake.

Figure 5: For I and L, it reads "salt replaced with fru", but the sketch on the left shows salt in the test. I assume that fructose was not actually present in the test, and therefore the figure can be misleading. I suggest separate sketches. Also, I and L are not mentioned in the figure legend.

True, this is rather confusing. Based on the well taken concern we have changed the figure by adding a new and correct scheme for sugar reward learning that does not symbolize fructose during test.

Figure S1: The experimental sketches for E,F and G,H seem to be mixed up.

We thank the reviewer for bringing this up. In the new version we corrected this mistake.

Figure S5: There are three sub-figures labelled with B. Please correct.

Again, thanks a lot. We made the suggested correction in Figure S5.

Discussion, line 353ff: this and the following sentences can be read as if the authors have discovered the DL-1 neurons as aversive teaching mediators in this study. However, Eschbach et al. 2020 already demonstrated very similar results regarding the optogenetic activation of single DL-1 DANs. I suggest to rephrase and cite Eschbach et al. 2020 at this point.

That is correct. Our focus was on the gustatory pathway. The original discovery was made by Eschbach et al. We have now corrected this in the discussion and clarified our contribution. It was never our intention to hide this work, as the laboratory was also involved. Nevertheless, this is an annoying omission on our side.

Line 385-387: this sentence is only correct with respect to Eschbach et al. 2020. Weiglein et al. 2021 used ChR2-XXL as an effector, but another training regimen.

We understand this criticism. Therefore, we changed the sentence as suggested by the reviewer. See also our response on concern 15 of reviewer 1.

Line 389ff: I do not understand this sentence. What is meant by persistent and current suppression of activity? If this refers to the behavioural experiments, it is misleading as in the hid, reaper experiments neurons are ablated and not suppressed in activity.

We made the requested changes in the text. It is true that the ablation of a neuron throughout larval life is different from constantly blocking the output of a persisting neuron.

Methods, line 615 ff: the performance index is said to be calculated as the difference between the two preferences, but the equation shows the average of the preferences.

Thanks a lot. We are sorry for the confusion. We have carefully rewritten this part of the methods section to avoid any misunderstanding.

When discussing the organization of the DL1 cluster, on several occasions I have the impression the authors use the terms "redundant" and "combinatorial" synonymously. I suggest to be more careful here. Redundancy implies that each DAN in principle can "do the job", whereas combinatorial coding implies that only a combination of DANs together can "do the job". If "the job" is establishing an aversive salt memory, the authors' results point to redundancy: no experimental manipulation totally abolished salt learning, implying that the non-manipulated neurons in each experiment sufficed to establish a memory; and several DANs, when individually activated, can establish an aversive memory, implying that each of them indeed can "do the job".

Based on this concern we have rewritten the discussion as suggested to be more precise when talking about redundancy or combinatorial coding of the aversive teaching signal. Basically, we have removed all the combinatorial terms and replaced them by the term “redundancy”.

The authors mix parametric and non-parametric statistical tests across the experiments dependent on whether the distribution of the data is normal or not. It would help readers if the authors would clearly state for which data which tests were used.

We understand the criticism and now have added an additional supplemental file that includes all the information on the statistical tests applied and the distribution of the data.

eLife Assessment

This work presents a valuable approach based on a complex systems theoretical framework to characterize diet-host-microbe interactions and develop targeted bacteriotherapies through a three-phase workflow. Despite the partial support of the description and experimental setup of the 'complex systems theoretical approach,' the collected data are solid and advance our understanding of oxalate bacterial metabolism in microbial communities. This study will interest researchers working on gut microbiomes and the possible modulation of host-microbial interactions.

Reviewer #2 (Public review):

Summary:

Using the well-studied oxalate-microbiome-host system, the authors propose a novel conceptual and experimental framework for developing targeted bacteriotherapies using a three-phase pre-clinical workflow. The third phase is based on a 'complex system theoretical approach' in which multi-omics technologies are combined in independent in vivo and in vitro models to successfully identify the most pertinent variables that influence specific phenotypes in diet-host-microbe systems. The innovation relies on the third phase since phase I and phase II are the dominant approaches everyone in the microbiome field uses.

Strengths:

The authors used a multidisciplinary approach which included i] fecal transplant of two distinct microbial communities into Swiss-Webster mice (SWM) to characterize the host response (hepatic response-transcriptomics) and microbial activity (untargeted metabolomics of the stool samples) to different oxalate concentrations; 2] longitudinal analysis of the N. albigulia gut microbiome composition in response to varying concentrations of oxalate by shotgun metagenomics, with deep bioinformatic analyses of the genomes assembled; and 3] development of synthetic microbial communities around oxalate metabolisms and evaluation of these communities' activity into oxalate degradation in vivo.

Weaknesses:

This study presents a valuable finding on the oxalate-microbiome-host system using a multitude of approaches. Although the multidisciplinary approach allows for a unique perspective on the system and more robust conclusions, it is challenging for any authors to present all the data clearly and systematically in a conclusive way-especially when introducing unfamiliar concepts such as a complex systems theoretical approach.

Author response:

The following is the authors’ response to the original reviews

Reviewer #1 (Public review):

Summary:

This study experimentally examined diet-microbe-host interactions through a complex systems framework, centered on dietary oxalate. Multiple, independent molecular, animal, and in vitro experimental models were introduced into this research. The authors found that microbiome composition influenced multiple oxalate-microbe-host interfaces. Oxalobacter formigenes were only effective against a poor oxalate-degrading microbiota background and give critical new insights into why clinical intervention trials with this species exhibit variable outcomes. Data suggest that, while heterogeneity in the microbiome impacts multiple diet-host-microbe interfaces, metabolic redundancy among diverse microorganisms in specific diet-microbe axes is a critical variable that may impact the efficacy of bacteriotherapies, which can help guide patient and probiotic selection criteria in probiotic clinical trials.

Thank you. The main message of this research, is that through complex modelling, we believe we have identified the critical variable (metabolic redundancy) that is responsible for the efficacy of probiotics designed to reduce oxalate levels, thus allowing for improved patient selection in clinical trials. We also believe that this process and the critical features identified can be translated to other critical microbial functions such as short chain fatty acid synthesis, secondary bile acid synthesis, and others.

Strengths:

The paper has made significant progress in both the depth and breadth of scientific research by systematically comparing multiple experimental methods across multiple dimensions. Particularly through in-depth analysis from the enzymatic perspective, it has not only successfully identified several key strains and redundant genes, which is of great significance for understanding the functions of enzymes, the characteristics of strains, and the mechanisms of genes in microbial communities, but also provided a valuable reference for subsequent experimental design and theoretical research.

More importantly, the establishment of a novel research approach to probiotics and gut microbiota in this paper represents a major contribution to the current research field. The proposal of this new approach not only breaks through the limitations of traditional research but also offers new perspectives and strategies for the screening, optimization of probiotics, and the regulation of gut microbiota balance. This holds potential significant value for improving human health and the prevention and treatment of related diseases.

Thank you for the comments. We believe that the approach taken here, which contrasts with conventional reductionist techniques, will be critical for translating gut microbiome research into actionable therapeutic approaches.

Weaknesses:

While the study has excellently examined the overall changes in microbial community structure and the functions of individual bacteria, it lacks a focused investigation on the metabolic cross-feeding relationships between oxalate-degrading bacteria and related microorganisms, failing to provide a foundational microbial community or model for future research. Although this paper conducts a detailed study on oxalate metabolism, it would be beneficial to visually present the enrichment of different microbial community structures in metabolic pathways using graphical models.

Thank you for this critique.  In the current study, we broadly examined the response of the gut microbiota to dietary oxalate. Based on initial shotgun metagenomic results, we focused in on specific taxa and metabolic functions.  Through metagenomic and multiple culture-based studies, we quickly honed in on redundancy in oxalate-degrading function as a key feature for oxalate homeostasis. We believe that the defined microbial community we used for microbial transplants (particularly the taxonomic cohort) provides a strong, minimal community to explore oxalate homeostasis further. In fact, we are using this consortium in multiple follow-up studies to fully understand the cross-feeding that may occur among these microorganisms, as you suggest.  We note that figure 3 shows the change of species and metabolic pathways with oxalate exposure.   

Furthermore, the authors have done a commendable job in studying the roles of key bacteria. If the interactions and effects of upstream and downstream metabolically related bacteria could be integrated, it would provide readers with even more meaningful information. By illustrating how these bacteria interact within the metabolic network, readers can gain a deeper understanding of the complex ecological and functional relationships within microbial communities. Such an integrated approach would not only enhance the scientific value of the study but also facilitate future research in this area.

Thank you. We note that based on the collective data obtained in this study, that redundancy in the oxalate degradation is the critical feature that maintains oxalate homeostasis. However, we are interested potential metabolic interactions between microbes in our defined community and are currently investigating these interactions through extensive investigations.   

Reviewer #2 (Public review):

Summary:

Using the well-studied oxalate-microbiome-host system, the authors propose a novel conceptual and experimental framework for developing targeted bacteriotherapies using a three-phase pre-clinical workflow. The third phase is based on a 'complex system theoretical approach' in which multi-omics technologies are combined in independent in vivo and in vitro models to successfully identify the most pertinent variables that influence specific phenotypes in diet-host-microbe systems. The innovation relies on the third phase since phase I and phase II are the dominant approaches everyone in the microbiome field uses.

Thank you. As you note, the proposed phases I and II are the predominant approaches used. In fact, many clinical trials have been conducted to try and reduce urine oxalate in patients, based solely on mechanistic studies with Oxalobacter formigenes.  As noted in our manuscript, only 43% of those studies results in the intended outcome, necessitating the approach we took in the current study. Our results suggest that the reason for the high rate of failure, despite well established mechanisms, is due to insufficient patient selection that focused only on the presence or absence of O. formigenes, which is a species that exhibits very low prevalence and abundance in the human gut microbiota, normally.

Strengths:

The authors used a multidisciplinary approach which included:

(1) fecal transplant of two distinct microbial communities into Swiss-Webster mice (SWM) to characterize the host response (hepatic response-transcriptomics) and microbial activity (untargeted metabolomics of the stool samples) to different oxalate concentrations;

(2) longitudinal analysis of the N. albigulia gut microbiome composition in response to varying concentrations of oxalate by shotgun metagenomics, with deep bioinformatic analyses of the genomes assembled; and

(3) development of synthetic microbial communities around oxalate metabolisms and evaluation of these communities' activity in oxalate degradation in vivo.

Thank you for these comments.  In the complex modelling approach, we focused on complete microbiota from host species known to have high and low capacities for oxalate tolerance, combined with targeting specific metabolic functions vs. specific taxa that may include unknown functions important for oxalate metabolism.  Further, we examined the influence of our target communities on oxalate metabolism through multiple in vitro and in vivo studies.

Weaknesses:

However, I have concerns about the frame the authors tried to provide for a 'complex system theoretical approach' and how the data are interpreted within this frame. Several of the conclusions the authors provide do not seem to have sufficient data to support them.

Thank you.  We have tried to address these concerns by adding an exhaustive figure that broadly represents our complex modelling approach that includes potential complex system-based hypotheses, how they were tested, and the host-microbiome-oxalate interactions found in our study.

Recommendations for the authors:  

Reviewer #2 (Recommendations for the authors):

Major Concerns

(1) The authors argue about the importance of bringing 'Complex System Theory' to the microbiome field systematically and consistently. However, the authors fail to introduce this theory throughout the entire manuscript. For example, the authors tried to describe key elements and their nomenclature, such as nodes and fractal layers, in the first part of the result section. But the description is wordy and not precise. It would be more useful if the authors connected the model description with a visual representation, such as a figure. Unfortunately, these elements are not emphasizing and carried across the results section and are not mentioned in the discussion section.

We have now added a figure (Figure 7) that details this process extensively and ties each of our findings to the complex system model and nomenclature.  We have also reiterated how our results fit in the complex system model in the discussion.

In addition, there is no straightforward approach to integrating multi-omics datasets to identify the variables that are determinants of the system. For example, Figure 1 focuses on the impact of the host, hepatic activity, to oxalate exposure on fecal transplants into Swiss Webster mice; Figure 2 focuses on the effects of oxalate exposure on stool metabolic activity, not only microbial metabolic activity, on fecal transplants into Swiss Webster mice; and Figure 3 focuses on microbiome responses to different oxalate concentration in Neotoma albigula. There is no "model" to really integrate the host, the microbiome activity, and the microbiome composition information. And, unfortunately, the data generated between experiments cannot directly integrate; see major concern # 2.

Thank you.  We have made more clear the experimental approach and how it applied to understanding the critical factors that maintain oxalate homeostasis.  Specifically, Figure 1 established that the effect of oxalate on the host was dependent on the microbiota, rather than host genetics.  Figure 2 established the effect of oxalate on the gut microbiota was again dependent on the whole gut microbiota and that these oxalate-microbe effects also influenced oxalate-host effects through a direct multi-omic data integration.  Once we established that the oxalate effects on host and microbiota were dependent on the whole microbiota composition, Figure 3 then sought to figure out how oxalate impacted the gut microbiota, using our model of high oxalate tolerance (N. albigula). With the finding in Figure 3 that there were multiple genes attributed to the degradation of oxalate, or acetogenic, methanogenic, and sulfate reducing pathways, Figure 4 and relevant supplemental figures sought to quantify the redundancy of these pathways.  After establishing a very high degree of redundancy, we sought to use a culturomic approach to determine what environmental factors impacted oxalate metabolism and to evaluate oxalate metabolism using our defined, hypothesized communities of microorganisms.  Finally, figure 6 sought to validate our metagenomic, metabolomic, and culturomic results from multiple animal and in vitro models using targeted microbial transplants in mice.  While we did have some direct multi-omic data integration (Figures 2 and 3), the process employed here sought to systematically determine which factors were most important for the oxalate-microbiota-host relationship, and then to use those results to design the subsequent experiments.  We have added this description to the discussion, which helps to contextualize the complex system modelling approach we took here.

Finally, the authors did not provide a novel variable that successfully influences oxalate degradation in the oxalate-microbiome-host system. The authors argue that "both resource availability and community composition impact oxalate metabolism," which we currently inferred by the failure of the clinical tries and do not provide a clear intervention strategy to develop functional bacteriotherapy. The identification of composition as an important variable that was predictable without any multi-omics approach was highlighted by the development of synthetic microbial communities. Synthetic microbial communities are critical to characterizing complex microbiomes. Still, the authors did not explain how this strategy can be used in their theoretical framework (that is their goal), and these communities are not well introduced across the manuscript; see major concern # 4.

As stated, it is clear from the failed clinical trials that we do not fully understand what microbial features dictate oxalate homeostasis.  We have specifically identified, through fecal transplant studies, that microbial composition is critical for oxalate homeostasis and that diverse oxalate-degrading bacteria exist.  However, ours is the first study that explicitly shows that it is this diversity that controls oxalate homeostasis.  This is specifically ascertained through the targeted microbial transplants in mice whereby O. formigenes was given alone or with different combinations of other microorganisms.  In other words, we were able to replicate both successful and failed studies by manipulating which specific species were introduced into animals.  This is unprecedented in the literature.

(2) The authors provide several conclusions that are not completely supported by the data available. For example:

(a) Lines 236-239: "Within the framework of complex systems, results show microbe-host cooperation whereby oxalate effectively processed within the SW-NALB gut microbiota reduced overall liver activity, indicative of a beneficial impact." - The authors did not provide data related to oxalate levels of oxalate processing for this dataset.

While we did not specifically quantify oxalate degradation for this specific study, as cited in the text when describing this Swiss-Webster, Neotoma albigula system, we have previously published multiple animal studies explicitly showing that the N. albigula animals were highly effective oxalate degraders, which is transferable to Swiss-Webster mice through fecal transplants. Since the gut microbiota’s impact on oxalate has been welll established through experiments by our group, the purpose of these specific experiments were to look the other way and examine the effect of oxalate on the gut microbiota of these two animal models.  In the referenced text, we again cited our studies showing that the SW-NALB system effectively degrades oxalate.

(b) Lines 239-243: "Data also suggest that both the gut microbiota and the immune system are involved in oxalate remediation (redundancy), such that if oxalate cannot be neutralized in the gut microbiota or liver, then the molecule will be processed through host immune response mechanisms (fractality), in this case indicated through an overall increase in hepatic activity and specifically in mitochondrial activity." - The authors did not provide any evidence related to the immune system and oxalate metabolism.

We corrected that statement as follows: “…in this case indicated through an overall increase in inflammatory cytokines with oxalate exposure combined with an ineffective oxalate-degrading microbiota (Figures S6a,b; S9a,b).”  In other words, if the liver and gut microbiota can’t eliminate a toxin, then the immune system must deal with it through inflammatory pathways.  Oxalate is a well established, pro-inflammatory compound.  Our data show that this is dependent on the gut microbiota.

(c) Lines 250-252: "Following the diet trial, colon stool was collected post-necropsy and processed for untargeted metabolomics, which is a measure of total microbial metabolic output." - Although most metabolites in stool samples are indeed microbial, there are also host metabolites. So, it is not technically correct to relate the metabolomic analysis of stool samples to only microbial metabolic analysis. In addition, the authors discussed compounds such as alkaloids and cholesterol as microbial metabolites, which these compounds are more related to the diet and host correspondingly.

We have corrected this to state: “total metabolites present in stool from the diet, microbial activity, and host activity”

(d) Lines 270-273. "Specifically, the SW-NALB mice exhibit hallmarks of homeostatic feedback with oxalate exposure to maintain a consistent metabolic output, defined by the relatively small, net negative, microbial metabolite-hepatic gene network compared to the large, net positive, network of SW-SW mice." - How do the authors define oxalate homeostasis? In addition, do the authors imply feedback between the liver and the microbiome in which the microbiome responds to a liver response related to oxalate levels? Or could the observation in Figure 1 be explained just by microbial consumption of oxalate that would reduce the impact of oxalate that arrives at the liver?

Oxalate homeostasis is defined in that sentence: “relatively small, net negative, microbial metabolite-hepatic gene network compared to the large, net positive, network of SW-SW mice” – in other words, for SW-NALB mice, oxalate did not produce a considerable change to either microbial or hepatic metabolic activity.  We did not really test the liver impact on gut microbiota and can’t speak to that.  We believe, based on Figure 2 data, that it is not just the degradation of oxalate that explains the lack of change in hepatic activity in SW-NALB mice, rather that the oxalate-induced shift in the gut microbiota metabolic activity broadly altered hepatic activity, as inferred from Figure 2 c.  We made this more clear in the results: “suggests that the oxalate-induced change in microbial metabolism is responsible for the change in hepatic activity”.

(e) Lines 297-301: "The oxalate-dependent metagenomic divergence of the NALB gut microbiota (Figure 3), combined with the lack of change in the microbial metabolomic profile with oxalate exposure (Figure 2), suggest that oxalate stimulates taxonomically diverse, but metabolically redundant microorganisms, in support of maintaining homeostasis." - The authors cannot conclude anything related between taxonomic changes and microbial activity since the taxonomic data presented is for microbial enrichment in N. albigulia, and the "microbial activity data" is from the fecal transplantation experiment in SWM. These are two completely different systems with two completely different experimental designs.

We have shown very similar results in that oxalate induces the taxonomic divergence for the NALB gut microbiota, in multiple previous studies.  The experiment in which a minimal, positive increase in microbial metabolites, was saw with oxalate was based on the SW-NALB model whereby Swiss-Webster mice have an NALB microbiota.  We show throughout the manuscript, that the impact of oxalate is very microbiota dependent and supports our claim.  However, the claim is hypothesis generating – that metabolic redundancy is important for oxalate homeostasis.  We modified our statement to make all of this more clear.   

Related to microbial composition, the authors did not show data validating the efficiency of the fecal transplantations (allograft or xenograft) in the SWM after antibiotic treatment. They also did not show evidence of microbial composition dynamics in response to oxalate exposure.

Again, the efficacy of fecal transplants, used in the way they were here, has been shown in multiple past studies of our group.  In past studies, we have extensively characterized the microbiota from fecal transplants and which taxa were associated with oxalate levels.  Therefore, that topic was not the focus of the current study, instead focusing on the oxalate impact on gut microbiota activity.  Our past studies, referenced multiple times through the current manuscript, were used in large part to help determine which microbes to include in our taxonomic cohort, as described in the manuscript.

(f) Lines 301-303: "Given that data came from the same hosts sampled longitudinally, these data also reflect a microbiota that is adaptive to oxalate exposure, which is another important characteristic of complex systems." - In their dataset, what is the evidence that the microbiota of N. albigulia is adapted to oxalate exposure? Is the increase in genomes with pathways related to oxalate metabolism related to an increase of oxalate in the diet? If so, does the microbiota exposure with a higher oxalate concentration decrease the systemic level of oxalate? In neither of the experiments related to Figures 1 to 3, the authors showed a correlation of systemic oxalate levels with microbial composition, hepatic host response, or stool metabolism.

Figure 3 explicitly shows the longitudinal impact of increasing levels of oxalate showing an increase in oxalate degrading genes (Figure 3d). The specific samples selected for analysis here come from a previous study in which we explicitly quantified changes to the gut microbiota composition and both stool and urine oxalate for every time point listed in figure 3a.  This information is explicitly stated in the methods coupled with the fact that “neither fecal nor urinary oxalate levels increased significantly.”  Again, the effect of the gut microbiota on oxalate in these model systems have been extensively studied by our group and provide the foundation for the current study to look at the effect of oxalate on the gut microbiota and host.

Considering my last two points, the authors do not present substantial evidence to support their hypothesis that oxalate stimulates taxonomically diverse, metabolically redundant communities.

As stated above, that oxalate stimulates taxonomically diverse taxa was ascertained through multiple past studies, as well as the current study (Figure 3e).  The metabolically redundant part is ascertained both through untargeted metabolomics (Figure 2a,b) and shotgun metagenomics (Figure 3c,d).  Further evidence for the metabolic redundancy with oxalate comes from our culturomic approach, which showed that 14.58% of isolates could grow on oxalate as a carbon and energy source, in addition to the high proportion of isolates that could grow on other carbon and energy sources, at least much more than can be ascribed to a single species  (Figure 5c).  We made this more clear in the discussion.

(g) Lines 330-335. "Additionally, the broad diversity of species that contain oxalate-related genes suggests that the distribution of metabolic genes is somewhat independent of the distribution of microbial species, which suggests that microbial genes exist in an autonomous fractal layer, to some degree. This hypothesis is supported by studies which show a high degree of horizontal gene transfer within the gut microbiota as a means of adaptation." - This conclusion is highly speculative, especially since the author did not do any analysis to directly evaluate a relationship between the oxalate metabolic pathways and the microbial species where these pathways are present.

Figure 3c,d,e explicitly shows the metabolic pathways and species enriched by oxalate exposure.  Figure 4d, generated using the same data from Figure 3, explicitly shows the taxa that harbor oxalate-degrading genes.   

(h) Lines 364-366. "Collectively, data show that both resource availability and community composition impacts oxalate metabolism, which helps to define the adaptive nature of the NALB gut microbiota." - The authors indeed showed evidence that community composition impacts oxalate metabolism. However, the authors did not show any evidence to directly evaluate the resource availability to impact oxalate metabolism.

This is explicitly shown through in vitro community-based and single species assays varying multiple different carbon and energy sources to quantify changes to oxalate degradation (chosen based on shotgun metagenomic results; Figure 5a,b).

(3) Lines 321-325. "Acetogenic genes were also present in 97.18% of genomes, dominated by acetate kinase and formate-tetrahydrofolate ligase (Figure S3A323C). Methanogenic genes were present in 100% of genomes, dominated by phosphoserine phosphatase, atpdependent 6-phosphofructokinase, and phosphate acetyltransferase (Figure S4A-C)." - The authors spent much time analyzing the adjacent pathways related to oxalate and oxalaterelated products of oxalate metabolism. However, my understanding is that the genes used to analyze these pathways (formate metabolism, acetogenesis, methanogenesis), such as the ones named above, are not unique/specific for those pathways but participate in other "housekeeping" pathways. What is the relevance of these analyses when those genes are not unique/specific to the function/pathways that the authors describe? If I infer correctly, these bioinformatic analyses aim to evaluate the hypothesis of whether oxalate metabolism could be a social/cooperation metabolism and whether other species could participate in the metabolism of oxalate subproducts. However, these analyses did not explicitly evaluate this hypothesis.

The reviewer is correct in that we aimed to evaluate the potential that oxalate metabolism could benefit from metabolic cooperation.  The specific genes chosen for this analysis were those explicitly listed in the target metabolic pathways in KEGG, as described.  However, while the analyses do show the strong potential that the CO2 and formate produced from oxalate degradation could be used in these other pathways, as intended, the genes can be used in other metabolic pathways.  We did, however, explicitly test the hypothesis that formate, produced from oxalate degradation, could be utilized by the gut microbiota.  While the targeted transplants with the taxonomic cohort did not clearly show the use of formate in this way, those from the metabolic cohort did (Figures 6d and S8d).  This question is still in ongoing investigations in our group.  

We have made it more clear that our genome analyses provide the potential for metabolic redundancy rather than definitive proof for metabolic redundancy, which was evaluated more extensively in other experiments from this study.

(a) Lines 481-484. "Collectively, data offer strong support for the hypothesis that metabolic redundancy among diverse taxa, is the primary driver of oxalate homeostasis, rather than metabolic cooperation in which the by-products of oxalate degradation are used in downstream pathways such as acetogenesis, methanogenesis, and sulfate reduction." - Although the authors recognize that their data about the metabolic cooperation hypothesis is inconclusive, they never tested the hypothesis related to metabolic cooperation, as mentioned above. This is highly speculative.

As stated above, the targeted microbial transplants to animals and in vitro studies (Figure 5e,f) did explicitly test the cooperation hypothesis, but it the results did not support it and instead pointed much more strongly to metabolic redundancy.    

(4) Lines 355-359. "Cohorts, defined in the STAR methods, were used to delineate hypotheses that either carbon and energy substrates are sufficient to explain known effects of the oxalate-degrading microbial network or that additional aspects of taxa commonly stimulated by dietary oxalate are required to explain past results (taxa defined through previous meta-analysis of studies)." - The definition of the metabolic cohorts and the taxonomic cohorts should not be hidden in the material and methods section. It should be explicit and clearly explained in the main text. Related, the table presented in Figure 5D is exceptionally confusing and does not help to understand and differentiate between the metabolic and the taxonomic cohorts. The authors need to explicitly identify the synthetic communities used in each cohort and each group by their members and their characteristics in supplementary tables.

In the sentences before those referenced, we state: “Culturomic data recapitulates molecular data to show a considerable amount of redundancy surrounding oxalate metabolism (Fig. 5C). Isolates generated from this assay were used for subsequent study (metabolic cohort; Figure 5D). Additionally, a second cohort was defined and commercially purchased based both on known metabolic functions and the proportion of studies that saw an increase in their taxonomic population with oxalate consumption (Fig. 5D; taxonomic cohort). Where possible, isolates from human sources were obtained.”  Figure 5d explicitly shows the specific species used in each cohort along with the groups they were in for transplant studies, the explicit metabolic pathways we were targeting, along with the % of studies that these species were associated with oxalate metabolism.  All of this information is both in the main text of the results and in the figure legends.  It is not hidden in the methods, but the methods do reiterate what was also placed in the results.   

In Figures 5 and 6, the authors used the following groups with the corresponding nomenclature: 'Group 1, No_bact; Group 2, Ox; Group 3, Ox_form; Group 4, All; Group 5, No_ox'. Although the information related to these groups is present in the material and method section in lines 1139-1143, the authors also need to explicitly explain the groups and their nomenclature in the main text.

Since this information is explicitly and succinctly given in the referenced figures, I believe that adding the same information in the text would be too redundant.

Related to the development of the synthetic communities. How did the authors prepare the synthetic communities or 'cohort' for the in vitro experiments? 

We added more information for the preparation of microbes and execution of the in vitro assays, as needed.  

Also, it is unclear in the material and method section how the metabolic profile of each isolated was evaluated (Figure 5C). Related to the bacteria isolated from the culturomic assays, including Figure 5C and metabolic cohort, the authors indeed reported the isolation methodology in lines 1262-1275. However, there is no information about the sequencing of these isolates. The authors should present these isolates as a list (supplementary table) with their names, taxonomy, metabolic profile, and Genome ID if these genomes were submitted to NCBI.

We added additional information for how metabolic cohort isolates were chosen and how they were taxonomically identified.  The taxonomy and substrate utilization of isolates are in Figure 5D.  We did not sequence the genomes of metabolic cohort bacteria.  However, the ATCC isolates, which comprise the taxonomic cohort, are publicly available.

The author presented the 248 metagenomics assembles in Figure S1 in a circular chart in context with other genomes. However, the metagenomic assembles should be presented in a table form, with their name, taxonomy, coverage, completeness, and Genome ID, if these genomes were submitted to NCBI.

The information for the genomes submitted to the NCBI is provided in the data availability statement.  However, we added a table (Table S9) that includes the requested information.   

(5) Lines 371-3374: "To delineate hypotheses of metabolic redundancy or cooperation for mitigating the negative effects of oxalate on the gut microbiota and host, two independent diet trials were conducted with analogous microbial communities derived from the metabolic and taxonomic cohorts". 

Lines 494-496: "we and others have found that oxalate can differentially exhibit positive or negative effects on microbial growth and metabolism dependent on the species and environment present" - What is the evidence that oxalate has a negative effect on the gut microbiota? The authors clearly showed the negative effect of oxalate on the host. Although there are reports in the literature of oxalate consumers with a negative effect on the microbiome, such as Lactobacilli and Bifidobacteria, there is no evidence in this manuscript about a negative effect of oxalate on the microbiome, and there is not an experimental design to evaluate it.

These data are presented in Figure 2A and B.  As stated, oxalate led to a net reduction in total microbial metabolites produced of 34 metabolites, with a significant shift in overall metabolome, indicative of metabolic inhibition.  This is in comparison to the net gain of 9 metabolites, with no significant shift overall,  in the mice with the NALB microbiota.  The positive and negative effects of oxalate on the whole gut microbiota here are bolstered by previous studies on the effect of oxalate on pure cultures as discussed and cited on line 623624.

(6) Related to the last section, it is hard to really compare the results of the taxonomic cohort versus the metabolic cohort when the data of one cohort is in the main figure and the other in a supplementary figure. In addition, all the comparisons between the two cohorts seem to be qualitative. For any comparisons, the authors need to do a statistical comparison between the groups of the two cohorts.

The comparison of the two sets of data are indeed qualitative.  This is because these mouse models were run in separate experiments to test separate hypotheses (whether utilization of specific substrates is enough to improve oxalate metabolism or if specific taxa previously responsive to dietary oxalate was better, which is stated in the manuscript).  Given that these experimental models were tested separately, it would not be statistically valid to do a direct statistical comparison, even though the experimental procedures were the same and the only difference were the transplanted bacteria.  The separation of the experiments into a main and supplemental figure was done out of necessity given the very large amount of data and many experimental mouse models that were run in this study overall.   

Minor Comments.

(1) The authors should define 'antinutrients'. This term is not a familiar concept and could create confusion.

This is defined in line 104 “molecules produced in plants to deter herbivory, disrupt homeostasis by targeting the function of the microbiome, host, or both”

(2) The authors should explicitly describe the N. albigulia, aka White-throated woodrat system, as early as possible in the result section.

We added some statements about the Swiss webster and N. albigula gut microbiota as poor and effective oxalate degraders in the second section of the results.

(3) SW-SW mice exhibited an oxalate-dependent alteration of 219 hepatic genes, with a net increase in activity. In comparison, the SW-NALB mice exhibited an oxalate-dependent alteration of 21 genes with a net decrease in activity. However, the visual representation of the PCoA in Figure 1B showed that the most different samples are the SW-NALB 0% and 1.5%. Could you please explain this difference?

In Figure 1b, the SW-NALB data are represented by the blue and black data points, which directly overlap with each other.  The SW-SW data are the orange and purple data points, which exhibit very little overlap.  

(4) Is Table S7 the same as Table S6? If not, there is a missing supplementary table.

These tables are different.  We ensured that both are present.

(5) How did the authors test bacterial growth in in vivo studies (Figure 5B)?

We added a statement to the culturomic section of the methods – we used media with or without oxalate and quantified colony-forming units.

(6) A section of 16S rRNA metagenomics in the material and method section is not used across the main manuscript.

These data are presented in figures S7 and S10, as stated in the results.  We added statements in the results to clarify that these figures show the 16S sequencing data.

(7) Lines 506-511: "Collectively, data from the current and previous studies on the effect of oxalate exposure on the gut microbiota support the hypothesis that the gut microbiota serves as an adaptive organ in which specific, metabolically redundant microbes respond to and eliminate dietary components, for the benefit of themselves, but which can residually protect or harm host health depending on the dietary molecules and gut microbiota composition." - What is the benefit to bacteria in eliminating oxalate? This is highly speculative to this system.

The benefit to bacteria is stated earlier in that paragraph – “In the current (Figs. 2B, 5B) and previous studies(33,34,64,65), we and others have found that oxalate can differentially exhibit positive or negative effects on microbial growth and metabolism dependent on the species and environment present.”

(8) Lines 504 -506: "Importantly, the near-universal presence of formate metabolism genes suggest that formate may be an even greater source of ecological pressure (Figures S2-S5)."

- Formate is primarily produced by fermentative anaerobic bacteria, such as Bacteroides, Clostridia, and certain species of Escherichia coli, since formate would be present in anaerobic communities independently of oxalate. How is formate an even greater source of ecological pressure?

We added a statement about the toxicity of formate to both bacteria and mammalian hosts.

eLife Assessment

This intracranial EEG study presents important and convincing neural evidence supporting the high spatial specificity (receptive field) of visually driven alpha-band oscillation in human brains and its potential role in exogenous cuing attention. The work challenges the predominant view about the role of alpha-band oscillation in visual attention and advocates that stimulus-driven alpha suppression is precisely tuned and might contribute to exogenous spatial attention.

Reviewer #1 (Public Review):

In this study, the authors build upon previous research that utilized non-invasive EEG and MEG by analyzing intracranial human ECoG data with high spatial resolution. They employed a receptive field mapping task to infer the retinotopic organization of the human visual system. The results present compelling evidence that the spatial distribution of human alpha oscillations is highly specific and functionally relevant, as it provides information about the position of a stimulus within the visual field.

Using state-of-the-art modeling approaches, the authors not only strengthen the existing evidence for the spatial specificity of the human dominant rhythm but also provide new quantification of its functional utility, specifically in terms of the size of the receptive field relative to the one estimated based on broad band activity.

Reviewer #2 (Public Review):

Summary:

In this work, Yuasa et al. aimed to study the spatial resolution of modulations in alpha frequency oscillations (~10Hz) within the human occipital lobe. Specifically, the authors examined the receptive field (RF) tuning properties of alpha oscillations, using retinotopic mapping and invasive electroencephalogram (iEEG) recordings. The authors employ established approaches for population RF mapping, together with a careful approach to isolating and dissociating overlapping, but distinct, activities in the frequency domain. Whereby, the authors dissociate genuine changes in alpha oscillation amplitude from other superimposed changes occurring over a broadband range of the power spectrum. Together, the authors used this approach to test how spatially tuned estimated RFs were when based on alpha range activity, vs. broadband activities (focused on 70-180Hz). Consistent with a large body of work, the authors report clear evidence of spatially precise RFs based on changes in alpha range activity. However, the size of these RFs were far larger than those reliably estimated using broadband range activity at the same recording site. Overall, the work reflects a rigorous approach to a previously examined question, for which improved characterization leads to improved consistency in findings and some advance of prior work.

Strengths:

Overall, the authors take a careful and well-motivated approach to data analyses. The authors successfully test a clear question with a rigorous approach and provide strong supportive findings. Firstly, well-established methods are used for modeling population RFs. Secondly, the authors employ contemporary methods for dissociating unique changes in alpha power from superimposed and concomitant broadband frequency range changes. This is an important confound in estimating changes in alpha power not employed in prior studies. The authors show this approach produces more consistent and robust findings than standard band-filtering approaches. As noted below, this approach may also account for more subtle differences when compared to prior work studying similar effects.

Original Weaknesses:

- Theoretical framing: The authors frame their study as testing between two alternative views on the organization, and putative functions, of occipital alpha oscillations: i) alpha oscillation amplitude reflects broad shifts in arousal state, with large spatial coherence and uniformity across cortex; ii) alpha oscillation amplitude reflects more specific perceptual processes and can be modulated at local spatial scales. However, in the introduction this framing seems mostly focused on comparing some of the first observations of alpha with more contemporary observations. Therefore, I read their introduction to more reflect the progress in studying alpha oscillations from Berger's initial observations to the present. I am not aware of a modern alternative in the literature that posits alpha to lack spatially specific modulations. I also note this framing isn't particularly returned to in the discussion. A second important variable here is the spatial scale of measurement. It follows that EEG based studies will capture changes in alpha activity up to the limits of spatial resolution of the method (i.e. limited in ability to map RFs). This methodological distinction isn't as clearly mentioned in the introduction, but is part of the author's motivation. Finally, as noted below, there are several studies in the literature specifically addressing the authors question, but they are not discussed in the introduction.

- Prior studies: There are important findings in the literature preceding the author's work that are not sufficiently highlighted or cited. In general terms, the spatio-temporal properties of the EEG/iEEG spectrum are well known (i.e. that changes in high frequency activity are more focal than changes in lower frequencies). Therefore, the observations of spatially larger RFs for alpha activities is highly predicted. Specifically, prior work has examined the impact of using different frequency ranges to estimate RF properties, for example ECoG studies in the macaque by Takura et al. NeuroImage (2016) [PubMed: 26363347], as well as prior ECoG work by the author's team of collaborators (Harvey et al., NeuroImage (2013) [PubMed: 23085107]), as well as more recent findings from other groups (Luo et al., (2022) BioRxiv: https://doi.org/10.1101/2022.08.28.505627). Also, a related literature exists for invasively examining RF mapping in the time-voltage domain, which provides some insight into the author's findings (as this signal will be dominated by low-frequency effects). The authors should provide a more modern framing of our current understanding of the spatial organization of the EEG/iEEG spectrum, including prior studies examining these properties within the context of visual cortex and RF mapping. Finally, I do note that the author's approach to these questions do reflect an important test of prior findings, via an improved approach to RF characterization and iEEG frequency isolation, which suggests some important differences with prior work.

- Statistical testing: The authors employ many important controls in their processing of data. However, for many results there is only a qualitative description or summary metric. It appears very little statistical testing was performed to establish reported differences. Related to this point, the iEEG data is highly nested, with multiple electrodes (observations) coming from each subject, how was this nesting addressed to avoid bias?

[Editors' note: the authors have addressed the original concerns.]

Reviewer #3 (Public Review):

Summary:

This study tackles the important subject of sensory driven suppression of alpha oscillations using a unique intracranial dataset in human patients. Using a model-based approach to separate changes in alpha oscillations from broadband power changes, the authors try to demonstrate that alpha suppression is spatially tuned, with similar center location as high broadband power changes, but much larger receptive field. They also point to interesting differences between low-order (V1-V3) and higher-order (dorsolateral) visual cortex. While I find some of the methodology convincing, I also find significant parts of the data analysis, statistics and their presentation incomplete. Thus, I find that some of the main claims are not sufficiently supported. If these aspects could be improved upon, this study could potentially serve as an important contribution to the literature with implications for invasive and non-invasive electrophysiological studies in humans.

Strengths:

The study utilizes a unique dataset (ECOG & high-density ECOG) to elucidate an important phenomenon of visually driven alpha suppression. The central question is important and the general approach is sound. The manuscript is clearly written and the methods are generally described transparently (and with reference to the corresponding code used to generate them). The model-based approach for separating alpha from broadband power changes is especially convincing and well-motivated. The link to exogenous attention behavioral findings (figure 8) is also very interesting. Overall, the main claims are potentially important, but they need to be further substantiated (see weaknesses).

Original Weaknesses:

I have three major concerns:

(1) Low N / no single subject results/statistics: The crucial results of Figure 4,5 hang on 53 electrodes from four patients (Table 2). Almost half of these electrodes (25/53) are from a single subject. Data and statistical analysis seem to just pool all electrodes, as if these were statistically independent, and without taking into account subject-specific variability. The mean effect per each patient was not described in text or presented in figures. Therefore, it is impossible to know if the results could be skewed by a single unrepresentative patient. This is crucial for readers to be able to assess the robustness of the results. N of subjects should also be explicitly specified next to each result.

(2) Separation between V1-V3 and dorsolateral electrodes: Out of 53 electrodes, 27 were doubly assigned as both V1-V3 and dorsolateral (Table 2, Figures 4,5). That means that out of 35 V1-V3 electrodes, 27 might actually be dorsolateral. This problem is exasperated by the low N. for example all the 20 electrodes in patient 8 assigned as V1-V3 might as well be dorsolateral. This double assignment didn't make sense to me and I wasn't convinced by the authors' reasoning. I think it needlessly inflates the N for comparing the two groups and casts doubts on the robustness of these analyses.

(3) Alpha pRFs are larger than broadband pRFs: first, as broadband pRF models were on average better fit to the data than alpha pRF models (dark bars in Supp Fig 3. Top row), I wonder if this could entirely explain the larger Alpha pRF (i.e. worse fits lead to larger pRFs). There was no anlaysis to rule out this possibility. Second, examining closely the entire 2.4 section there wasn't any formal statistical test to back up any of the claims (not a single p-value is mentioned). It is crucial in my opinion to support each of the main claims of the paper with formal statistical testing.

[Editors' note: the authors have addressed the original concerns.]

Author response:

The following is the authors’ response to the original reviews

Reviewer #1 (Public Review):

Summary

In this study, the authors build upon previous research that utilized non-invasive EEG and MEG by analyzing intracranial human ECoG data with high spatial resolution. They employed a receptive field mapping task to infer the retinotopic organization of the human visual system. The results present compelling evidence that the spatial distribution of human alpha oscillations is highly specific and functionally relevant, as it provides information about the position of a stimulus within the visual field.

Using state-of-the-art modeling approaches, the authors not only strengthen the existing evidence for the spatial specificity of the human dominant rhythm but also provide new quantification of its functional utility, specifically in terms of the size of the receptive field relative to the one estimated based on broad band activity.

We thank the reviewer for their positive summary.

Weakness 1.1

The present manuscript currently omits the complementary view that the retinotopic map of the visual system might be related to eye movement control. Previous research in non-human primates using microelectrode stimulation has clearly shown that neuronal circuits in the visual system possess motor properties (e.g. Schiller and Styker 1972, Schiller and Tehovnik 2001). More recent work utilizing Utah arrays, receptive field mapping, and electrical stimulation further supports this perspective, demonstrating that the retinotopic map functions as a motor map. In other words, neurons within a specific area responding to a particular stimulus location also trigger eye movements towards that location when electrically stimulated (e.g. Chen et al. 2020).

Similarly, recent studies in humans have established a link between the retinotopic variation of human alpha oscillations and eye movements (e.g., Quax et al. 2019, Popov et al. 2021, Celli et al. 2022, Liu et al. 2023, Popov et al. 2023). Therefore, it would be valuable to discuss and acknowledge this complementary perspective on the functional relevance of the presented evidence in the discussion section.

The reviewer notes that we do not discuss the oculomotor system and alpha oscillations. We agree that the literature relating eye movements and alpha oscillations are relevant.

At the Reviewer’s suggestion, we added a paragraph on this topic to the first section of the Discussion (section 3.1, “Other studies have proposed … “).

Reviewer #2 (Public Review):

Summary:

In this work, Yuasa et al. aimed to study the spatial resolution of modulations in alpha frequency oscillations (~10Hz) within the human occipital lobe. Specifically, the authors examined the receptive field (RF) tuning properties of alpha oscillations, using retinotopic mapping and invasive electroencephalogram (iEEG) recordings. The authors employ established approaches for population RF mapping, together with a careful approach to isolating and dissociating overlapping, but distinct, activities in the frequency domain. Whereby, the authors dissociate genuine changes in alpha oscillation amplitude from other superimposed changes occurring over a broadband range of the power spectrum. Together, the authors used this approach to test how spatially tuned estimated RFs were when based on alpha range activity, vs. broadband activities (focused on 70-180Hz). Consistent with a large body of work, the authors report clear evidence of spatially precise RFs based on changes in alpha range activity. However, the size of these RFs were far larger than those reliably estimated using broadband range activity at the same recording site. Overall, the work reflects a rigorous approach to a previously examined question, for which improved characterization leads to improved consistency in findings and some advance of prior work.

We thank the reviewer for the summary.

Strengths:

Overall, the authors take a careful and well-motivated approach to data analyses. The authors successfully test a clear question with a rigorous approach and provide strong supportive findings. Firstly, well-established methods are used for modeling population RFs. Secondly, the authors employ contemporary methods for dissociating unique changes in alpha power from superimposed and concomitant broadband frequency range changes. This is an important confound in estimating changes in alpha power not employed in prior studies. The authors show this approach produces more consistent and robust findings than standard band-filtering approaches. As noted below, this approach may also account for more subtle differences when compared to prior work studying similar effects.

We thank the reviewer for the positive comments.

Weaknesses:

Weakness 2.1 Theoretical framing:

The authors frame their study as testing between two alternative views on the organization, and putative functions, of occipital alpha oscillations: i) alpha oscillation amplitude reflects broad shifts in arousal state, with large spatial coherence and uniformity across cortex; ii) alpha oscillation amplitude reflects more specific perceptual processes and can be modulated at local spatial scales. However, in the introduction this framing seems mostly focused on comparing some of the first observations of alpha with more contemporary observations. Therefore, I read their introduction to more reflect the progress in studying alpha oscillations from Berger's initial observations to the present. I am not aware of a modern alternative in the literature that posits alpha to lack spatially specific modulations. I also note this framing isn't particularly returned to in the discussion.

This was helpful feedback. We have rewritten nearly the entire Introduction to frame the study differently. The emphasis is now on the fact that several intracranial studies of spatial tuning of alpha (in both human and macaque) tend to show increases in alpha due to visual stimulation, in contrast to a century of MEG/EEG studies, from Berger to the present, showing decreases. We believe that the discrepancy is due to an interaction between measurement type and brain signals. Specifically, intracranial measurements sum decreases in alpha oscillations and increases in broadband power on the same trials, and both signals can be large. In contrast, extracranial measures are less sensitive to the broadband signals and mostly just measure the alpha oscillation. Our study reconciles this discrepancy by removing the baseline broadband power increases, thereby isolating the alpha oscillation, and showing that with iEEG spatial analyses, the alpha oscillation decreases with visual stimulation, consistent with EEG and MEG results.

Weakness 2.2 A second important variable here is the spatial scale of measurement.

It follows that EEG based studies will capture changes in alpha activity up to the limits of spatial resolution of the method (i.e. limited in ability to map RFs). This methodological distinction isn't as clearly mentioned in the introduction, but is part of the author's motivation. Finally, as noted below, there are several studies in the literature specifically addressing the authors question, but they are not discussed in the introduction.

The new Introduction now explicitly contrasts EEG/MEG with intracranial studies and refers to the studies below.

Weakness 2.3 Prior studies:

There are important findings in the literature preceding the author's work that are not sufficiently highlighted or cited. In general terms, the spatio-temporal properties of the EEG/iEEG spectrum are well known (i.e. that changes in high frequency activity are more focal than changes in lower frequencies). Therefore, the observations of spatially larger RFs for alpha activities is highly predicted. Specifically, prior work has examined the impact of using different frequency ranges to estimate RF properties, for example ECoG studies in the macaque by Takura et al. NeuroImage (2016) [PubMed: 26363347], as well as prior ECoG work by the author's team of collaborators (Harvey et al., NeuroImage (2013) [PubMed: 23085107]), as well as more recent findings from other groups (Luo et al., (2022) BioRxiv: https://doi.org/10.1101/2022.08.28.505627). Also, a related literature exists for invasively examining RF mapping in the time-voltage domain, which provides some insight into the author's findings (as this signal will be dominated by low-frequency effects). The authors should provide a more modern framing of our current understanding of the spatial organization of the EEG/iEEG spectrum, including prior studies examining these properties within the context of visual cortex and RF mapping. Finally, I do note that the author's approach to these questions do reflect an important test of prior findings, via an improved approach to RF characterization and iEEG frequency isolation, which suggests some important differences with prior work.

Thank you for these references and suggestions. Some of the references were already included, and the others have been added.

There is one issue where we disagree with the Reviewer, namely that “the observations of spatially larger RFs for alpha activities is highly predicted”. We agree that alpha oscillations and other low frequency rhythms tend to be less focal than high frequency responses, but there are also low frequency non-rhythmic signals, and these can be spatially focal. We show this by demonstrating that pRFs solved using low frequency responses outside the alpha band (both below and above the alpha frequency) are small, similar to high frequency broadband pRFs, but differing from the large pRFs associated with alpha oscillations. Hence we believe the degree to which signals are focal is more related to the degree of rhythmicity than to the temporal frequency per se. While some of these results were already in the supplement, we now address the issue more directly in the main text in a new section called, “2.5 The difference in pRF size is not due to a difference in temporal frequency.”

We incorporated additional references into the Introduction, added a new section on low frequency broadband responses to the Results (section 2.5), and expanded the Discussion (section 3.2) to address these new references.

Weakness 2.4 Statistical testing:

The authors employ many important controls in their processing of data. However, for many results there is only a qualitative description or summary metric. It appears very little statistical testing was performed to establish reported differences. Related to this point, the iEEG data is highly nested, with multiple electrodes (observations) coming from each subject, how was this nesting addressed to avoid bias?

We reviewed the primary claims made in the manuscript and for each claim, we specify the supporting analyses and, where appropriate, how we address the issue of nesting. Although some of these analyses were already in the manuscript, many of them are new, including all of the analyses concerning nesting. We believe that putting this information in one place will be useful to the reader, and we now include this text as a new section in supplement, Graphical and statistical support for primary claims.

Reviewer #2 (Recommendations For The Authors):

Recommendation 2.1:

Data presentation: In several places, the authors discuss important features of cortical responses as measured with iEEG that need to be carefully considered. This is totally appropriate and a strength of the author's work, however, I feel the reader would benefit from more depiction of the time-domain responses, to help better understand the authors frequency domain approach. For example, Figure 1 would benefit from showing some form of voltage trace (ERP) and spectrogram, not just the power spectra. In addition, part (a) of Figure 1 could convey some basic information about the timing of the experimental paradigm.

We changed panel A of Figure 1 to include the timing of the experimental paradigm, and we added panels C and D to show the electrode time series before and after regression out of the ERP.

Recommendation 2.2

Update introduction to include references to prior EEG/iEEG work on spatial distribution across frequency spectrum, and importantly, prior work mapping RFs with different frequencies.

We have addressed this issue and re-written our introduction. Please refer to our response in Public Review for further details.

Recommendation 2.3

Figure 3 has several panels and should be labeled to make it easier to follow.The dashed line in lower power spectra isn't defined in a legend and is missing from the upper panel - please clarify.

We updated Figure 3 and reordered the panels to clarify how we computed the summary metrics in broadband and alpha for each stimulus location (i.e., the “ratio” values plotted in panel B). We also simplified the plot of the alpha power spectrum. It now shows a dashed line representing a baseline-corrected response to the mapping stimulus, which is defined in the legend and explained in the caption.

Recommendation 2.4

Power spectra are always shown without error shading, but they are mean estimates.

We added error shading to Figures 1, 2 and 3.

Recommendation 2.5

The authors deal with voltage transients in response to visual stimulation, by subtracting out the trail averaged mean (commonly performed). However, the efficacy of this approach depends on signal quality and so some form of depiction for this processing step is needed.

We added a depiction of the processing steps for regressing out the averaged responses in Figure 1 in an example electrode (panels C and D). We also show in the supplement the effect of regressing out the ERP on all the electrode pRFs. We have added Supplementary Figure 1-2.

Recommendation 2.6

I have a similar request for the authors latency correction of their data, where they identified a timing error and re-aligned the data without ground truth. Again, this is appropriate, but some depiction of the success of this correction is very critical for confirming the integrity of the data.

We now report more detail on the latency correction, and also point out that any small error in the estimate would not affect our conclusions (4.6 ECoG data analysis | Data epoching). The correction was important for a prior paper on temporal dynamics (Groen et al, 2022), which used data from the same participants and estimated the latency of responses. In this paper, our analyses are in the spectral domain (and discard phase), so small temporal shifts are not critical. We now also link to the public code associated with that paper, which implemented the adjustment and quantified the uncertainty in the latency adjustment.

More details on latency adjustment provided in section 4.6.

Recommendation 2.7

In many places the authors report their data shows a 'summary' value, please clarify if this means averaging or summation over a range.

For both broadband and alpha, we derive one summary value (a scalar) for trial for each stimulus. For broadband, the summary metric is the ratio of power during a given trial and power during blanks, where power in a trial is the geometric mean of the power at each frequency within the defined band). This is equation 3 in the methods, which is now referred to the first time that summary metrics are mentioned in the results.  For alpha, the summary metric is the height of the Gaussian from our model-based approach. This is in equations 1 and 2, and is also now referred to the first time summary metrics are mentioned in the results.

We added explanation of the summary metrics in the figure captions and results where they are first used, and also referred to the equations in the methods where they are defined.

Recommendation 2.8

The authors conclude: "we have discovered that spectral power changes in the alpha range reflect both suppression of alpha oscillations and elevation of broadband power." It might not have been the intention, but 'discovered' seems overstated.

We agree and changed this sentence.

Recommendation 2.9

Supp Fig 9 is a great effort by the authors to convey their findings to the reader, it should be a main figure.

We are glad you found Supplementary Figure 9 valuable. We moved this figure to the main text.

Reviewer #3 (Public Review):

Summary:

This study tackles the important subject of sensory driven suppression of alpha oscillations using a unique intracranial dataset in human patients. Using a model-based approach to separate changes in alpha oscillations from broadband power changes, the authors try to demonstrate that alpha suppression is spatially tuned, with similar center location as high broadband power changes, but much larger receptive field. They also point to interesting differences between low-order (V1-V3) and higher-order (dorsolateral) visual cortex. While I find some of the methodology convincing, I also find significant parts of the data analysis, statistics and their presentation incomplete. Thus, I find that some of the main claims are not sufficiently supported. If these aspects could be improved upon, this study could potentially serve as an important contribution to the literature with implications for invasive and non-invasive electrophysiological studies in humans.

We thank the reviewer for the summary.

Strengths:

The study utilizes a unique dataset (ECOG & high-density ECOG) to elucidate an important phenomenon of visually driven alpha suppression. The central question is important and the general approach is sound. The manuscript is clearly written and the methods are generally described transparently (and with reference to the corresponding code used to generate them). The model-based approach for separating alpha from broadband power changes is especially convincing and well-motivated. The link to exogenous attention behavioral findings (figure 8) is also very interesting. Overall, the main claims are potentially important, but they need to be further substantiated (see weaknesses).

We thank the reviewer for the positive comments.

Weaknesses:

I have three major concerns:

Weakness 3.1. Low N / no single subject results/statistics:

The crucial results of Figure 4,5 hang on 53 electrodes from four patients (Table 2). Almost half of these electrodes (25/53) are from a single subject. Data and statistical analysis seem to just pool all electrodes, as if these were statistically independent, and without taking into account subject-specific variability. The mean effect per each patient was not described in text or presented in figures. Therefore, it is impossible to know if the results could be skewed by a single unrepresentative patient. This is crucial for readers to be able to assess the robustness of the results. N of subjects should also be explicitly specified next to each result.

We have added substantial changes to deal with subject specific effects, including new results and new figures.

• Figure 4 now shows variance explained by the alpha pRF broken down by each participant for electrodes in V1 to V3. We also now show a similar figure for dorsolateral electrodes in Supplementary Figure 4-2.

• Figure 5, which shows results from individual electrodes in V1 to V3, now includes color coding of electrodes by participant to make it clear how the electrodes group with participant. Similarly, for dorsolateral electrodes, we show electrodes grouped by participant in Supplementary Figure 5-1. Same for Supplementary Figure 6-2.

• Supplementary Figure 7-2 now shows the benefits of our model-based approach for estimating alpha broken down by individual participants.

• We also now include a new section in the supplement that summarizes for every major claim, what the supporting data are and how we addressed the issue of nesting electrodes by participant, section Graphical and statistical support for primary claims.

Weakness 3.2. Separation between V1-V3 and dorsolateral electrodes:

Out of 53 electrodes, 27 were doubly assigned as both V1-V3 and dorsolateral (Table 2, Figures 4,5). That means that out of 35 V1-V3 electrodes, 27 might actually be dorsolateral. This problem is exasperated by the low N. for example all the 20 electrodes in patient 8 assigned as V1-V3 might as well be dorsolateral. This double assignment didn't make sense to me and I wasn't convinced by the authors' reasoning. I think it needlessly inflates the N for comparing the two groups and casts doubts on the robustness of these analyses.

Electrode assignment was probabilistic to reflect uncertainty in the mapping between location and retinotopic map. The probabilistic assignment is handled in two ways.

(1) For visualizing results of single electrodes, we simply go with the maximum probability, so no electrode is visualized for both groups of data. For example, Figure 5a (V1-V3) and supplementary Figure 5-1a (dorsolateral electrodes) have no electrodes in common: no electrode is in both plots.

(2) For quantitative summaries, we sample the electrodes probabilistically (for example Figures 4, 5c). So, if for example, an electrode has a 20% chance of being in V1 to V3, and 30% chance of being in dorsolateral maps, and a 50% chance of being in neither, the data from that electrode is used in only 20% of V1-V3 calculations and 30% of dorsolateral calculations. In 50% of calculations, it is not used at all. This process ensures that an electrode with uncertain assignment makes no more contribution to the results than an electrode with certain assignment. An electrode with a low probability of being in, say, V1-V3, makes little contribution to any reported results about V1-V3. This procedure is essentially a weighted mean, which the reviewer suggests in the recommendations. Thus, we believe there is not a problem of “double counting”.

The alternative would have been to use maximum probability for all calculations. However, we think that doing so would be misleading, since it would not take into account uncertainty of assignment, and would thus overstate differences in results between the maps.

We now clarify in the Results that for probabilistic calculations, the contribution of an electrode is limited by the likelihood of assignment (Section 2.3). We also now explain in the methods why we think probabilistic sampling is important.

Weakness 3.3. Alpha pRFs are larger than broadband pRFs:

First, as broadband pRF models were on average better fit to the data than alpha pRF models (dark bars in Supp Fig 3. Top row), I wonder if this could entirely explain the larger Alpha pRF (i.e. worse fits lead to larger pRFs). There was no anlaysis to rule out this possibility.

We addressed this question in a new paragraph in Discussion section 3.1 (“What is the function of the large alpha pRFs?”, paragraph beginning… “Another possible interpretation is that the poorer model fit in the alpha pRF is due to lower signal-to-noise”). This paragraph both refers to prior work on the relationship between noise and pRF size and to our own control analyses (Supplementary Figure 5-2).

Weakness 3.4 Statistics

Second, examining closely the entire 2.4 section there wasn't any formal statistical test to back up any of the claims (not a single p-value is mentioned). It is crucial in my opinion to support each of the main claims of the paper with formal statistical testing.

We agree that it is important for the reader to be able to link specific results and analyses to specific claims. We are not convinced that null hypothesis statistical testing is always the best approach. This is a topic of active debate in the scientific community.

We added a new section that concisely states each major claim and explicitly annotates the supporting evidence. (Section 4.7). Please also refer to our responses to Reviewer #2 regarding statistical testing (Reviewer weakness 2.4 “Statistical testing”)

Weakness 3.5 Summary

While I judge these issues as crucial, I can also appreciate the considerable effort and thoughtfulness that went into this study. I think that addressing these concerns will substantially raise the confidence of the readership in the study's findings, which are potentially important and interesting.

We again thank the reviewer for the positive comments.

Reviewer #3 (Recommendations For The Authors):

Suggestions for how to address the three major concerns:

Suggestion 3.1.

I am very well aware that it's very hard to have n=30 in a visual cortex ECOG study. That's fine. Best practice would be to have a linear mixed effects model with patients as a random effect. However, for some figures with just 3-4 patients (Figure 4,5) the sample size might be too small even for that. At the very minimum, I would expect to show in figures/describe in text all results per patient (perhaps one can do statistics within each patient, and show for each patient that the effect is significant). Even in primate studies with just two subjects it is expected to show that the results replicate for subject A and B. It is necessary to show that your results don't depend on a single unrepresentative subject. And if they do, at least be transparent about it.

We have addressed this thoroughly. Please see response to Weakness 3.1 (“Low N / no single subject results/statistics”).

Suggestion 3.2.

I just don't get it. I would simply assign an electrode to V1-V3 or dorsolateral cortex based on which area has the highest probability. It doesn't make sense to me that an electrode that has 60% of being in dorsolateral cortex and only 10% to be in V1-V3 would be assigned as both V1-V3 and dorsolateral. Also, what's the rationale to include such electrode in the analysis for let's say V1-V3 (we have weak evidence to believe it's there)? I would either assign electrodes based on the highest probability, or alternatively do a weighted mean based on the probability of each electrode belonging to each region group (e.g. electrode with 40% to be in V1-V3, will get twice the weight as an electrode who has 20% to be in V1-V3) but this is more complicated.

We have addressed this issue. Please refer to our response in Public Review (“Weakness 3.2 Separation between V1-V3 and dorsolateral”) for details.

Suggestion 3.3.

First, to exclude the possibility that alpha pRF are larger simply because they have a worse fit to the neural data, I would show if there is a correlation between the goodnessof-fit and pRF size (for alpha and broadband signals, separately). No [negative] correlation between goodness-of-fit and pRF size would be a good sign. I would also compare alpha & broadband receptive field size when controlling for the goodness-of-fit (selecting electrodes with similar goodness-of-fit for both signals). If the results replicate this way it would be convincing.

Second, there are no statistical tests in section 2.4, possibly also in others. Even if you employ bootstrap / Monte-Carlo resampling methods you can extract a p-value.

We have addressed this issue. Please refer to our response in Public Review Point 3.3 (“Alpha pRFs are larger than broadband pRFs”) for further details.

Suggestion 3.4.

Also, I don't understand the resampling procedure described in lines 652-660: "17.7 electrodes were assigned to V1-V3, 23.2 to dorsolateral, and 53 to either " - but 17.7 + 23.2 doesn't add up to 53. It also seems as if you assign visual areas differently in this resampling procedure than in the real data - "and randomly assigned each electrode to a visual area according to the Wang full probability distributions". If you assign in your actual data 27 electrodes to both visual areas, the same should be done in the resampling procedure (I would expect exactly 35 V1-V3 and 45 dorsolateral electrodes in every resampling, just the pRFs will be shuffled across electrodes).

We apologize for the confusion.

We fixed the sentence above, clarified the caption to Table 2, and also explained the overall strategy of probabilistic resampling better. See response to Public Review point 3.2 for details.

Suggestion 3.5.

These are rather technical comments but I believe they are crucial points to address in order to support your claims. I genuinely think your results are potentially interesting and important but these issues need to be first addressed in a revision. I also think your study may carry implications beyond just the visual domain, as alpha suppression is observed for different sensory modalities and cortical regions. Might be useful to discuss this in the discussion section.

Agree. We added a paragraph on this point to the Discussion (very end of 3.2).

eLife Assessment

This fundamental study examines whether synaptic cell adhesion molecules neuroligin 1-3 resident on astrocytes, rather than neurons, exert effect on synaptic structure and function. With compelling evidence, the authors report that deletion of neuroligins 1-3 specifically in astrocytes does not alter synapse formation or astrocyte morphology in the hippocampus or visual cortex. This study highlights the specific role of neuronal neuroligins rather than their astrocytic counterparts in synaptogenesis.

Reviewer #1 (Public review):

Astrocytes are known to express neuroligins 1-3. Within neurons, these cell adhesion molecules perform important roles in synapse formation and function. Within astrocytes, a significant role for neuroligin 2 in determining excitatory synapse formation and astrocyte morphology was shown in 2017. However, there has been no assessment of what happens to synapses or astrocyte morphology when all three major forms of neuroligins within astrocytes (isoforms 1-3) are deleted using a well characterized, astrocyte specific, and inducible cre line. By using such selective mouse genetic methods, the authors here show that astrocytic neuroligin 1-3 expression in astrocytes is not consequential for synapse function or for astrocyte morphology. They reach these conclusions with careful experiments employing quantitative western blot analyses, imaging and electrophysiology. They also characterize the specificity of the cre line they used. Overall, this is a very clear and strong paper that is supported by rigorous experiments. The discussion considers the findings carefully in relation to past work. This paper is of high importance, because it now raises the fundamental question of exactly what neuroligins 1-3 are actually doing in astrocytes. In addition, it enriches our understanding of the mechanisms by which astrocytes participate in synapse formation and function. The paper is very clear, well written and well illustrated with raw and average data.

Comments on revisions:

My previous comments have been addressed. I have no additional points to make and congratulate the authors.

Reviewer #2 (Public review):

In the present manuscript, Golf et al. investigate the consequences of astrocyte-specific deletion of Neuroligin (Nlgn) family cell adhesion proteins on synapse structure and function in the brain. Decades of prior research had shown that Neuroligins mediate their effects at synapses through their role in the postsynaptic compartment of neurons and their transsynaptic interaction with presynaptic Neurexins. More recently, it was proposed for the first time that Neuroligins expressed by astrocytes can also bind to presynaptic Neurexins to regulate synaptogenesis (Stogsdill et al. 2017, Nature). However, several aspects of the model proposed by Stogsdill et al. on astrocytic Neuroligin function conflict with prior evidence on the role of Neuroligins at synapses, prompting Golf et al. to further investigate astrocytic Neuroligin function in the current study. Using postnatal conditional deletion of Nlgn1-3 specifically from astrocytes in mice, Golf et al. show that virtually no changes in the expression of synaptic proteins or in the properties of synaptic transmission at either excitatory or inhibitory synapses are observed. Moreover, no alterations in the morphology of astrocytes themselves were found. To further extend this finding, the authors additionally analyzed human neurons co-cultured with mouse glia lacking expression of Nlgn1-4. No difference in excitatory synaptic transmission was observed between neurons cultured in the present of wildtype vs. Nlgn1-4 conditional knockout glia. The authors conclude that while Neuroligins are indeed expressed in astrocytes and are hence likely to play some role there, this role does not include any direct consequences on synaptic structure and function, in direct contrast to the model proposed by Stogsdill et al.

Overall, this is a strong study that addresses a fundamental and highly relevant question in the field of synaptic neuroscience. Neuroligins are not only key regulators of synaptic function, they have also been linked to numerous psychiatric and neurodevelopmental disorders, highlighting the need to precisely define their mechanisms of action. The authors take a wide range of approaches to convincingly demonstrate that under their experimental conditions, Nlgn1-3 are efficiently deleted from astrocytes in vivo, and that this deletion does not lead to major alterations in the levels of synaptic proteins or in synaptic transmission at excitatory or inhibitory synapses, or in the morphology of astrocytes. While the co-culture experiments are somewhat more difficult to interpret due to lack of a control for the effect of wildtype mouse astrocytes on human neurons, they are also consistent with the notion that deletion of Nlgn1-4 from astrocytes has no consequences for the function of excitatory synapses. Together, the data from this study provide compelling and important evidence that, whatever the role of astrocytic Neuroligins may be, they do not contribute substantially to synapse formation or function under the conditions investigated.

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public Review):

Astrocytes are known to express neuroligins 1-3. Within neurons, these cell adhesion molecules perform important roles in synapse formation and function. Within astrocytes, a significant role for neuroligin 2 in determining excitatory synapse formation and astrocyte morphology was shown in 2017. However, there has been no assessment of what happens to synapses or astrocyte morphology when all three major forms of neuroligins within astrocytes (isoforms 1-3) are deleted using a well characterized, astrocyte specific, and inducible cre line. By using such selective mouse genetic methods, the authors here show that astrocytic neuroligin 1-3 expression in astrocytes is not consequential for synapse function or for astrocyte morphology. They reach these conclusions with careful experiments employing quantitative western blot analyses, imaging and electrophysiology. They also characterize the specificity of the cre line they used. Overall, this is a very clear and strong paper that is supported by rigorous experiments. The discussion considers the findings carefully in relation to past work. This paper is of high importance, because it now raises the fundamental question of exactly what neuroligins 1-3 are actually doing in astrocytes. In addition, it enriches our understanding of the mechanisms by which astrocytes participate in synapse formation and function. The paper is very clear, well written and well illustrated with raw and average data.

We thank the reviewer for the balanced and informative summary.

Reviewer #2 (Public Review):

In the present manuscript, Golf et al. investigate the consequences of astrocyte-specific deletion of Neuroligin family cell adhesion proteins on synapse structure and function in the brain. Decades of prior research had shown that Neuroligins mediate their effects at synapses through their role in the postsynaptic compartment of neurons and their transsynaptic interaction with presynaptic Neurexins. More recently, it was proposed for the first time that Neuroligins expressed by astrocytes can also bind to presynaptic Neurexins to regulate synaptogenesis (Stogsdill et al. 2017, Nature). However, several aspects of the model proposed by Stogsdill et al. on astrocytic Neuroligin function conflict with prior evidence on the role of Neuroligins at synapses, prompting Golf et al. to further investigate astrocytic Neuroligin function in the current study. Using postnatal conditional deletion of Neuroligins 1, 2 and 3 specifically from astrocytes, Golf et al. show that virtually no changes in the expression of synaptic proteins or in the properties of synaptic transmission at either excitatory or inhibitory synapses are observed. Moreover, no alterations in the morphology of astrocytes themselves were found. The authors conclude that while Neuroligins are indeed expressed in astrocytes and are hence likely to play some role there, this role does not include any direct consequences on synaptic structure and function, in direct contrast to the model proposed by Stogsdill et al.

Overall, this is a strong study that addresses an important and highly relevant question in the field of synaptic neuroscience. Neuroligins are not only key regulators of synaptic function, they have also been linked to numerous psychiatric and neurodevelopmental disorders, highlighting the need to precisely define their mechanisms of action. The authors take a wide range of approaches to convincingly demonstrate that under their experimental conditions, no alterations in the levels of synaptic proteins or in synaptic transmission at excitatory or inhibitory synapses, or in the morphology of astrocytes, are observed.

We are also grateful for this reviewer’s constructive comments.

One caveat to this study is that the authors do not directly provide evidence that their Tamoxifen-inducible conditional deletion paradigm does indeed result in efficient deletion of all three Neuroligins from astrocytes. Using a Cre-dependent tdTomato reporter line, they show that tdTomato expression is efficiently induced by the current paradigm, and they refer to a prior study showing efficient deletion of Neuroligins from neurons using the same conditional Nlgn1-3 mouse lines but a different Cre driver strategy. However, neither of these approaches directly provide evidence that all three Neuroligins are indeed deleted from astrocytes in the current study. In contrast, Stogsdill et al. employed FACS and qPCR to directly quantify the loss of Nlgn2 mRNA from astrocytes. This leaves the current Golf et al. study somewhat vulnerable to the criticism, however unlikely, that their lack of synaptic effects may be a consequence of incomplete Neuroligin deletion, rather than a true lack of effect of astrocytic Neuroligins.

The concern is valid. In the original submission of this paper, we did not establish that the Cre recombinase we used actually deleted neuroligins in astrocytes. We have now addressed this issue in the revised paper with new experiments as described below.

However, the reviewer’s impression that the Stogsdill et al. paper confirmed full deletion of Nlgn2 is a misunderstanding of the data in that paper. The reviewer is correct that Stogsdill et al. performed FACS to test the efficacy of the GLAST-Cre mediated deletion of Nlgn2-flox mice, followed by qRT-PCR comparing heterozygous with homozygous mutant mice. With their approach, no wild-type control could be used, as these would lack reporter expression. However, this experiment does NOT allow conclusions about the degree of recombination, both overall recombination (i.e. recombination in all astrocytes regardless of TdT+) and recombination in TdT+ astrocytes because it doesn’t quantify recombination. To quantify the degree of recombination, the paper would have had to perform genomic PCR measurements.  

The problem with the data on the degree of recombination in the Stogsdill et al. (2017) paper, as we understand them, is two-fold.

First, the GLAST-Cre line only targets ~40-70% of astrocytes, at least as evidenced by highly sensitive Cre-reporter mice in a variety of studies using this Cre line. The 40-70% variation is likely due to differences in the reporter mice and the tamoxifen injection schedule used. In comparison, we are targeting most astrocytes using the Aldh1l1-CreERT2 mice. Moreover, GLAST-Cre mice exhibit neuronal off-targeting, consistent with at least some of the remaining Nlgn2 qRT-PCR signal in the FACS-sorted cells. As we describe next, this signal also likely comes from astrocytes where recombination was incomplete This is the reason why we, like everyone else, are now using the Aldh1l1-Cre line that has been shown to be more efficient both in terms of the overall targeting of astrocytes (i.e. nearly complete) and the level of recombination observed in reporter(+) astrocytes.

Second, Stogsdill et al. detected a significant decrease in the Nlgn2 qRT-PCR signal in the FACS-sorted homozygous Nlgn2 KO cells compared to the heterozygous Nlgn2 KO cells but the Nlgn2 qRT-PCR signal was still quite large. The data is presented as normalized to the HET condition. As a result, we don’t know the true level of gene deletion (i.e. compared to TdT- astrocytes). For example, based on the Stogsdill et al. data the HET manipulation could have induced only a 20% reduction in Nlgn2 mRNA levels in TdT(+) astrocytes, in which case the KO would have produced a 40% reduction in Nlgn2 mRNA in TdT(+) astrocytes. Moreover, it is possible based on our own experience with the GLAST-Cre line, that the reporter may also not turn on in some astrocytes where other alleles have been independently recombined – just as some astrocytes that are Td(+) would still be wild-type or heterozygous for Nlgn2. Thus, it is impossible to calculate the actual percentage of recombination from these data, even in TdT(+) cells, absent of PCR of genomic DNA from isolated cells. Alternatively, comparison of mRNA levels using primers sensitive to floxed sequences in wild-type controls versus cKO mice would have also yielded a much better idea of the recombination efficiency.

In summary, it is unclear whether the Nlgn2 deletion in the Stogsdill et al. paper was substantial or marginal – it is simply impossible to tell.

Reviewer #3 (Public Review):

This study investigates the roles of astrocytes in the regulation of synapse development and astrocyte morphology using conditional KO mice carrying mutations of three neuroligins1-3 in astrocytes with the deletion starting at two different time points (P1 and P10/11). The authors use morphological, electrophysiological, and cell-biological approaches and find that there are no differences in synapse formation and astrocyte cytoarchitecture in the mutant hippocampus and visual cortex. These results differ from the previous results (Stogsdill et al., 2017), although the authors make several discussion points on how the differences could have been induced. This study provides important information on how astrocytes and neurons interact with each other to coordinate neural development and function. The experiments were well-designed, and the data are of high quality.

We also thank this reviewer for helpful comments!

Recommendations for the authors:

This project was meant to rigorously test the intriguing overall question whether neuroligins, which are abundantly expressed in astrocytes, regulate synapse formation as astrocytic synapse organizers. The goal of the paper was NOT to confirm or dispute the conclusion by Stogsdill et al. (Nature 2017) that Nlgn2 expressed in astrocytes is essential for excitatory synapse formation and that astrocytic Nlgn1-3 are required for proper astrocyte morphogenesis. Instead, the project was meant to address the much broader question whether the abundant expression of any neuroligin, not just Nlgn2, in astrocytes is essential for neuronal excitatory or inhibitory synapse formation and/or for the astrocyte cytoarchitecture. We felt that this was an important question independent of the Stogsdill et al. paper. We analyzed in our experiments young adult mice, a timepoint that was chosen deliberately to avoid the possibility of observing a possible developmental delay rather than a fundamental function that extends beyond development.

We do recognize that the conclusion by Stogsdill et al. (2017) that Nlgn2 expression in astrocytes is essential for excitatory synapse formation was very exciting to the field but contradicted a large literature demonstrating that Nlgn2 protein is exclusively localized to inhibitory synapses and absent from excitatory synapses (to name just a few papers, see Graf et al., Cell 2004; Varoqueaux et al., Eur. J. Cell Biol. 2004; Patrizi et al., PNAS 2008;  Hoon et al., J. Neurosci. 2009). In addition, the conclusion of Stogsdill et al. that astrocytic Nlgn2 specifically drove excitatory synapse formation was at odds with previous findings documenting that the constitutive deletion of Nlgn2 in all cells, including astrocytes, has no effect on excitatory synapse numbers (again, to name a few papers, see Varoqueaux et al., Neuron 2006; Blundell et al., Genes Brain Behav. 2008; Poulopoulos et al., Neuron 2009; Gibson et al., J. Neurosci. 2009). These contradictions conferred further urgency to our project, but please note that this project was primarily driven by our curiosity about the function of astrocytic neuroligins, not by a fruitless desire to test the validity of one particular Nature paper.

The general goal of our paper notwithstanding, few papers from our lab have received as much attention and as many negative comments on social media as this paper when it was published as a preprint. Because we take these criticisms seriously, we have over the last year performed extensive additional experiments to ensure that our findings are well founded. We feel that, on balance, our data are incompatible with the notion that astrocytic neuroligins play a fundamental role in excitatory synapse formation but are consistent with other prior findings obtained with neuroligin KO mice. In the new data we added to the paper, we not only characterized the Cre-mediated deletion of neuroligins in depth, but also employed an independent second system -human neurons cultured on mouse glia- to further validate our conclusions as described below. Although we believe that our results are incompatible with the notion that astrocytic neuroligins fundamentally regulate excitatory or inhibitory synapse formation, we also conclude with regret that we still don’t know what astrocytic neuroligins actually do. Thus, the function of astrocytic neuroligins, as there surely must be one, remains a mystery.

Finally, there are many possible explanations for the discrepancies between our conclusions and those of Stogsdill et al. as described in our paper. Most of these explanations are technical and may explain why not only our, but also the results of many other previous studies from multiple labs, are inconsistent with the conclusions by Stogsdill et al. (2017), as discussed in detail in the revised paper.

Reviewer #1 (Recommendations For The Authors):

The paper is very clear and well written. I have only one comment and that is to increase the sizes of Figs 2, 4 and 6 so that the imaging panels can be seen more clearly. Also, although I know the n numbers are provided in the figure legends, the authors may help the reader by providing them in the results when key data and findings are reported.

We agree and have followed the reviewer’s suggestions as best as we could.

Reviewer #2 (Recommendations For The Authors):

(1) Given the strength and importance of the claims that the authors make, I would highly recommend adding some quantitative evidence regarding the efficacy of deletion in astrocytes, e.g. using the same strategy as in Stogsdill et al. As unlikely as it may be that Neuroligin deletion is in fact incomplete, this possibility cannot be excluded unless directly measured. To avoid future discussions on this subject, it seems that the onus is on the authors to provide this information.

We concur that this is an important point and have devoted a year-long effort to address it. Note, however, that the strategy employed by Stogsdill et al. does not actually allow conclusions about their recombination efficiency. As described above, it only allows the conclusion that some recombination took place. The Stogsdill et al. Nature paper (2017) is a bit confusing on this point. This approach is thus not appropriate to address the question raised by the reviewer.

We have performed two experiments to address the issue raised by the reviewer.

First, we used a viral (i.e. AAV2/5) approach to express Rpl22 with a triple HA-tag, also known as Ribotag, which allows us to purify ribosome-bound mRNA from targeted cells for downstream gene expression analysis. The novel construct is driven by the GfaABC1D promoter and includes two additional features which make it particularly useful. First, upstream of Ribotag is a membrane-targeted, Lck-mVenus followed by a self-cleaving P2A sequence. This allows easy visualization of targeted astrocytes. Second, we have incorporated a cassette of four copies of six miRNA targeting sequences (4x6T) for mIR-124 as was recently published (Gleichman et al., 2023) to eliminate off-target expression in neurons. Based on qPCR analysis, the updated construct allowed >95% de-enrichment of neuronal mRNA and slightly improved observed recombination rates (~10% per gene) relative to an earlier version without 4x6T. Mice that were injected with tamoxifen at P1, similar to other experiments in the paper, were then stereotactically injected at ~P35-40 within the dorsal hippocampus with AAV2/5-GfaABC1D-Lck-mVenus-P2A-Rpl22-HA-4x6T. Approximately 3 weeks later, acute slices were prepared, visualized for fluorescence, and both CA1 and nearby cortex that was partially targeted were isolated for downstream ribosome affinity purification with HA antibodies. Total RNA was saved as input. qPCR was performed using assays that are sensitive to the exons that are floxed in the Nlgn123 cKO mice, so that our quantifications are not confounded by potential differences in non-sense mediated decay. Our control data reveals a striking enrichment of an astrocyte marker gene (e.g. aquaporin-4) and de-enrichment of genes for other cell types. In the CA1, we observed robust loss of Nlgn3 (~96%), Nlgn2 (~86%), and Nlgn1 (65%) gene expression. Similarly, in the cortex, we observed a similarly robust loss of Nlgn3 (93%), Nlgn2 (83%), and Nlgn1 (72%) expression. Given that our targeting of astrocytes based on Ai14 Cre-reporter mice was ~90-99%, these reductions are striking and definitive. The existence of some residual transcript reflects the presence of a small population of astrocytes heterozygous for Nlgn2 and Nlgn3. In contrast, Nlgn1 appears more difficult to recombine and it is likely that some astrocytes are either heterozygous or homozygous knockout cells. Although it is thus possible that Nlgn1 could provide some compensation in our experiments, it is worth noting that Stogsdill et al. found that only Nlgn2 and Nlgn3 knockdown with shRNAs resulted in impaired astrocyte morphology by P21. Moreover, they found that Nlgn2 cKO in astrocytes with PALE of a Cre-containing pDNA impaired astrocyte morphology in a gene-dosage dependent manner and suppressed excitatory synapse formation at P21. Thus, our inability to delete all of Nlgn1 doesn’t readily explain contradictions between our findings and theirs.

Second, in an independent approach we have cultured glia from mouse quadruple conditional Nlgn1234 KO mice and infected the glia with lentiviruses expressing inactive (DCre, control) or active Cre-recombinase. We confirmed complete recombination by PCR. We then cultured human neurons forming excitatory synapses on the glia expressing or lacking neuroligins and measured the frequency and amplitude of mEPSCs as a proxy for synapse numbers and synaptic function. As shown in the new Figure 9, we detected no significant changes in mEPSCs, demonstrating in this independent system that the glial neuroligins do not detectably influence excitatory synapse formation.

(2) Along the same lines, the authors should be careful not to overstate their findings in this direction. For example, the figure caption for Figure 2 reads 'Nlgn1-3 are efficiently and selectively deleted in astrocytes by crossing triple Nlgn1-3 conditional KO mice with Adh1l1-CreERT2 driver mice and inducing Cre-activity with tamoxifen early during postnatal development'. This is not technically correct and should be modified to reflect that the authors are not in fact assessing deletion of Nlgn1-3, but only expression of a tdTomato reporter.

We agree – this is essentially the same criticism as comment #1.

(3) In general, the animal numbers used for the experiments are rather low. With an n = 4 for most experiments, only large abnormalities would be detected anyway, while smaller alterations would not reach statistical significance due to the inherent biological and technical variance. For the most part, this is not a concern, since there really is no difference between WTs and Nlgn1-3 cKOs. However, trends are observed in some cases, and it is conceivable that these would become significant changes with larger n's, e.g. Figure 3H (Vglut2); Figure 4E (VGlut2 S.P., D.G.); Figure 6D (Vglut2). Increasing the numbers to n = 6 here would greatly strengthen the claims that no differences are observed.

We concur that small differences would not have been detected in our experiments but feel that given the very large phenotypes of the neuroligin deletions in neurons and of the phenotypes reported by Stogsdill et al. (2017), which also did not employ a large number of animals, a very small phenotype in astrocytes would not have been very informative.

Minor points:

(1) Please state the exact genetic background for the mouse lines used.

Our lab generally uses hybrid CD1/Bl6 mice to avoid artifacts produced by inbred genetic mutations in so-called ‘pure’ lines, especially Bl6 mice. This standard protocol was followed in the present study. Thus, the mice are on a mixed CD1/Bl6 hybrid background.

Reviewer #3 (Recommendations For The Authors):

(1) Figure 4 demonstrates that neuroligin 1-3 deletions restricted to astrocytes do not affect the number of excitatory and inhibitory synapses in layer IV of the primary visual cortex. This conclusion could be further strengthened if the authors could provide electrophysiological evidence such as mE/IPSCs.

We agree but have chosen a different avenue to further test our conclusions because slice electrophysiological experiments are time-consuming, labor intensive, and difficult to quantitate, especially in cortex.

Specifically, we have co-cultured human neurons with astrocytes that either contain or lack neuroligins (new Fig. 9). With this experimental design, we have total control over ALL neuroligins in astrocytes. Electrophysiological recordings then demonstrated that the complete deletion of all glial neuroligins has no effect on mEPSC frequencies and amplitudes. Although clearly much more needs to be done, the new results confirm in an independent system that glial neuroligins have no effect on synapse formation in the neurons, even though neurons depend on astrocytes for synaptogenic factors as Ben Barres brilliantly showed a decade ago. However, it is important to note that dissociated glia in culture, while synaptogenic, are reactive and may not faithfully recapitulate all roles of astrocytes in synaptogenesis.

(2) It would help readers if the images showing the punctate double marker stainings of excitatory/inhibitory synapses are presented in merged colors (i.e., yellow colors for red and green puncta colors).

We have tried to improve the visualization of the rather voluminous studies we performed and illustrate in the figures as best as we could.

(3) The resolutions of the images in the figures are not good, although I guess it is because the images are for review processes.

We apologize and would like to assure the reviewer that we are supplying high-resolution images to the journal.

(4) Typos in lines 82 and 274.

We have corrected these errors.

eLife Assessment

This important work combines theory and experiment to demonstrate convincingly how humans make decisions about sequences of pairs of correlated observations. The proposed model for evidence integration in correlated environments will be of use for the study of decision-making.

Reviewer #1 (Public review):

Summary:

The behavioral strategies underlying decisions based on perceptual evidence are often studied in the lab with stimuli whose elements provide independent pieces of decision-related evidence that can thus be equally weighted to form a decision. In more natural scenarios, in contrast, the information provided by these pieces is often correlated, which impacts how they should be weighted. Tardiff, Kang & Gold set out to study decisions based on correlated evidence and compare observed behavior of human decision makers to normative decision strategies. To do so, they presented participants with visual sequences of pairs of localized cues whose location was either uncorrelated, or positively or negatively correlated, and whose mean location across a sequence determined the correct choice. Importantly, they adjusted this mean location such that, when correctly weighted, each pair of cues was equally informative, irrespective of how correlated it was. Thus, if participants follow the normative decision strategy, their choices and reaction times should not be impacted by these correlations. While Tardiff and colleagues found no impact of correlations on choices, they did find them to impact reaction times, suggesting that participants deviated from the normative decision strategy. To assess the degree of this deviation, Tardiff et al. adjusted drift diffusion models (DDMs) for decision-making to process correlated decision evidence. These fits, and a comparison of different model variants revealed that participants considered correlations when weighing evidence, but did so with a slight underestimation of magnitude of this correlation. This finding made Tardiff et al. conclude that participants followed a close-to normative decision strategy that adequately took into account correlated evidence.

Strength:

The authors adjust a previously used experimental design to include correlated evidence in a simple, yet powerful way. The way it does so is easy to understand and intuitive, such that participants don't need extensive training to perform the task. Limited training makes it more likely that the observed behavior is natural and reflective of every-day decision-making. Furthermore, the design allowed the authors to make the amount of decision-related evidence equal across different correlation magnitudes, which makes it easy to assess whether participants correctly take account of these correlations when weighing evidence: if they do, their behavior should not be impacted by the correlation magnitude.

The relative simplicity with which correlated evidence is introduced also allowed the authors to fall back to the well-established DDM for perceptual decisions, that has few parameters, is known to implement the normative decision strategy in certain circumstances, and enjoys a great deal of empirical support. The authors show how correlations ought to impact these parameters, and which changes in parameters one would expect to see if participants mis-estimate these correlations or ignore them altogether (i.e., estimate correlations to be zero). This allowed them to assess the degree to which participants took into account correlations on the full continuum from perfect evidence weighting to complete ignorance. More specifically, the authors showed that a consistent mis-estimation of the correlation magnitude would not impact the fraction of correct choices (as they observe), but only the reaction times. With this, they could show that participants in fact performed rational evidence weighting if one assumed that they slightly underestimated the correlation magnitude.

Weaknesses:

While the authors convincingly demonstrate that the observed decision-making behavior seems to stem from a slight underestimation of the correlation magnitudes, their experimental paradigm did not allow them to determine the origin of this bias. Through additional analyses they rule out various possibilities, like the impact of a Bayesian prior on estimated correlations. Nonetheless, the authors provide no normative explanation of the observed bias.

A further minor weakness is that the authors only focus on a single normative aspect of the observed behavior, namely on whether participants optimally accumulate decision-related evidence across time. Another question is whether participants tune their decision boundaries to maximize reward rates or some other overall performance measures. While the authors discuss that the chosen diffusion models (DDMs) have the potential of also implementing normative decisions in the latter sense, the authors' analysis does not address this question in the context of their task.

Reviewer #2 (Public review):

This study by Tardiff, Kang & Gold seeks to i) develop a normative account of how observers should adapt their decision-making across environments with different levels of correlation between successive pairs of observations, and ii) assess whether human decisions in such environments are consistent with this normative model. The authors first demonstrate that, in the range of environments under consideration here, an observer with full knowledge of the generative statistics should take both the magnitude and sign of the underlying correlation into account when assigning weight in their decisions to new observations: stronger negative correlations should translate into stronger weighting (due to the greater information furnished by an anticorrelated generative source), while stronger positive correlations should translate into weaker weighting (due to the greater redundancy of information provided by a positively correlated generative source). The authors then report an empirical study in which human participants performed a perceptual decision-making task requiring accumulation of information provided by pairs of perceptual samples, under different levels of pairwise correlation. They describe a nuanced pattern of results with effects of correlation being largely restricted to response times and not choice accuracy, which could be captured through fits of their normative model (in this implementation, an extension of the well-known drift diffusion model) to the participants' behaviour while allowing for mis-estimation of the underlying correlations. An intriguing result is that the observed pattern of behavioural effects is best explained by a model in which observers marginally underestimated the level of correlation between the generative sources, and that this bias affects behaviour through effects on stimulus encoding that then shape how the evidence furnished by each stimulus sample is weighted in decision formation.

As the authors point out in their very well-written paper, appropriate weighting of information gathered in correlated environments has important consequences for real-world decision-making. Yet, while this function has been well studied for 'high-level' (e.g. economic) decisions, how we account for correlations when making simple perceptual decisions on well-controlled behavioural tasks has not been investigated. As such, this study addresses an important and timely question that will be of broad interest to psychologists and neuroscientists. The computational approach to arrive at normative principles for evidence weighting across environments with different levels of correlation is elegant, makes strong connections with prior work in different decision-making contexts, and should serve as a valuable reference point for future studies in this domain. The empirical study is well designed and executed, and the modelling approach applied to these data showcases an impressively deep understanding of relationships between different parameters of the drift diffusion model and its novel application to this setting. Another strength of the study is that it is preregistered.

In my view, any major weaknesses of the study have been well addressed by the authors during review. An outstanding question that arises from the current work and remains unanswered here is around the (normative?) origin of the correlation underestimates, and the present work lays a strong foundation from which to pursue this question in the future.

Author response:

The following is the authors’ response to the original reviews

We thank the reviewers for their thoughtful feedback. We have made substantial revisions to the manuscript to address each of their comments, as we detail below. We want to highlight one major change in particular that addresses a concern raised by both reviewers: the role of the drift rate in our models. Motivated by their astute comments, we went back through our models and realized that we had made a particular assumption that deserved more scrutiny. We previously assumed that the process of encoding the observations made correct use of the objective, generative correlation, but then the process of calculating the weight of evidence used a mis-scaled, subjective version of the correlation. These assumptions led us to scale the drift rate in the model by a term that quantified how the standard deviation of the observation distribution was affected by the objective correlation (encoding), but to scale the bound height by the subjective estimate of the correlation (evidence weighing). However, we realized that encoding may also depend on the subjective correlation experienced by the participant. We have now tested several alternative models and found that the best-fitting model assumes that a single, subjective estimate of the correlation governs both encoding and evidence weighing. An important consequence of updating our models in this way is that we can now account for the behavioral data without needing the additional correlation-dependent drift terms (which, as reviewer #2 pointed out, were difficult to explain).

We also note that we changed the title slightly, replacing “weighting” with “weighing” for consistency with our usage throughout the manuscript.

Please see below for more details about this important point and our responses to the reviewers’ specific concerns. 

Public Reviews:

Reviewer #1 (Public review):

Summary:

The behavioral strategies underlying decisions based on perceptual evidence are often studied in the lab with stimuli whose elements provide independent pieces of decision-related evidence that can thus be equally weighted to form a decision. In more natural scenarios, in contrast, the information provided by these pieces is often correlated, which impacts how they should be weighted. Tardiff, Kang & Gold set out to study decisions based on correlated evidence and compare the observed behavior of human decision-makers to normative decision strategies. To do so, they presented participants with visual sequences of pairs of localized cues whose location was either uncorrelated, or positively or negatively correlated, and whose mean location across a sequence determined the correct choice. Importantly, they adjusted this mean location such that, when correctly weighted, each pair of cues was equally informative, irrespective of how correlated it was. Thus, if participants follow the normative decision strategy, their choices and reaction times should not be impacted by these correlations. While Tardiff and colleagues found no impact of correlations on choices, they did find them to impact reaction times, suggesting that participants deviated from the normative decision strategy. To assess the degree of this deviation, Tardiff et al. adjusted drift-diffusion models (DDMs) for decision-making to process correlated decision evidence. Fitting these models to the behavior of individual participants revealed that participants considered correlations when weighing evidence, but did so with a slight underestimation of the magnitude of this correlation. This finding made Tardiff et al. conclude that participants followed a close-to-normative decision strategy that adequately took into account correlated evidence.

Strengths:

The authors adjust a previously used experimental design to include correlated evidence in a simple, yet powerful way. The way it does so is easy to understand and intuitive, such that participants don't need extensive training to perform the task. Limited training makes it more likely that the observed behavior is natural and reflective of everyday decision-making. Furthermore, the design allowed the authors to make the amount of decision-related evidence equal across different correlation magnitudes, which makes it easy to assess whether participants correctly take account of these correlations when weighing evidence: if they do, their behavior should not be impacted by the correlation magnitude.

The relative simplicity with which correlated evidence is introduced also allowed the authors to fall back to the well-established DDM for perceptual decisions, which has few parameters, is known to implement the normative decision strategy in certain circumstances, and enjoys a great deal of empirical support. The authors show how correlations ought to impact these parameters, and which changes in parameters one would expect to see if participants misestimate these correlations or ignore them altogether (i.e., estimate correlations to be zero). This allowed them to assess the degree to which participants took into account correlations on the full continuum from perfect evidence weighting to complete ignorance. With this, they could show that participants in fact performed rational evidence weighting if one assumed that they slightly underestimated the correlation magnitude.

Weaknesses:

The experiment varies the correlation magnitude across trials such that participants need to estimate this magnitude within individual trials. This has several consequences:

(1) Given that correlation magnitudes are estimated from limited data, the (subjective) estimates might be biased towards their average. This implies that, while the amount of evidence provided by each 'sample' is objectively independent of the correlation magnitude, it might subjectively depend on the correlation magnitude. As a result, the normative strategy might differ across correlation magnitudes, unlike what is suggested in the paper. In fact, it might be the case that the observed correlation magnitude underestimates corresponds to the normative strategy.

We thank the reviewer for raising this interesting point, which we now address directly with new analyses including model fits (pp. 15–24). These analyses show that the participants were computing correlation-dependent weights of evidence from observation distributions that reflected suboptimal misestimates of correlation magnitudes. This strategy is normative in the sense that it is the best that they can do, given the encoding suboptimality. However, as we note in the manuscript, we do not know the source of the encoding suboptimality (pp. 23–24). We thus do not know if there might be a strategy they could have used to make the encoding more optimal.

(2) The authors link the normative decision strategy to putting a bound on the log-likelihood ratio (logLR), as implemented by the two decision boundaries in DDMs. However, as the authors also highlight in their discussion, the 'particle location' in DDMs ceases to correspond to the logLR as soon as the strength of evidence varies across trials and isn't known by the decision maker before the start of each trial. In fact, in the used experiment, the strength of evidence is modulated in two ways:

(i) by the (uncorrected) distance of the cue location mean from the decision boundary (what the authors call the evidence strength) and

(ii) by the correlation magnitude. Both vary pseudo-randomly across trials, and are unknown to the decision-maker at the start of each trial. As previous work has shown (e.g. Kiani & Shadlen (2009), Drugowitsch et al. (2012)), the normative strategy then requires averaging over different evidence strength magnitudes while forming one's belief. This averaging causes the 'particle location' to deviate from the logLR. This deviation makes it unclear if the DDM used in the paper indeed implements the normative strategy, or is even a good approximation to it.

We appreciate this subtle, but important, point. We now clarify that the DDM we use includes degrees of freedom that are consistent with normative decision processes that rely on the imperfect knowledge that participants have about the generative process on each trial, specifically: 1) a single drift-rate parameter that is fit to data across different values of the mean of the generative distribution, which is based on the standard assumption for these kinds of task conditions in which stimulus strength is varied randomly from trial-to-trial and thus prevents the use of exact logLR (which would require stimulus strength-specific scale factors; Gold and Shadlen, 2001); 2) the use of a collapsing bound, which in certain cases (including our task) is thought to support a stimulus strength-dependent calibration of the decision variable to optimize decisions (Drugowitsch et al, 2012); and 3) free parameters (one per correlation) to account for subjective estimates of the correlation, which affected the encoding of the observations that are otherwise weighed in a normative manner in the best-fitting model.

Also, to clarify our terminology, we define the objective evidence strength as the expected logLR in a given condition, which for our task is dependent on both the distance of the mean from the decision boundary and the correlation (p. 7). 

Given that participants observe 5 evidence samples per second and on average require multiple seconds to form their decisions, it might be that they are able to form a fairly precise estimate of the correlation magnitude within individual trials. However, whether this is indeed the case is not clear from the paper.

These points are now addressed directly in Results (pp. 23–24) and Figure 7 supplemental figures 1–3. Specifically, we show that, as the reviewer correctly surmised above, empirical correlations computed on each trial tended to be biased towards zero (Fig 7–figure supplement 1). However, two other analyses were not consistent with the idea that participants’ decisions were based on trial-by-trial estimates of the empirical correlations: 1) those with the shortest RTs did not have the most-biased estimates (Fig 7–figure supplement 2), and 2) there was no systematic relationship between objective and subjective fit correlations across participants (Fig 7–figure supplement 3).

Furthermore, the authors capture any underestimation of the correlation magnitude by an adjustment to the DDM bound parameter. They justify this adjustment by asking how this bound parameter needs to be set to achieve correlation-independent psychometric curves (as observed in their experiments) even if participants use a 'wrong' correlation magnitude to process the provided evidence. Curiously, however, the drift rate, which is the second critical DDM parameter, is not adjusted in the same way. If participants use the 'wrong' correlation magnitude, then wouldn't this lead to a mis-weighting of the evidence that would also impact the drift rate? The current model does not account for this, such that the provided estimates of the mis-estimated correlation magnitudes might be biased.

We appreciate this valuable comment, and we agree that we previously neglected the potential impact of correlation misestimates on evidence strength. As we now clarify, the correlation enters these models in two ways: 1) via its effect on how the observations are encoded, which involves scaling both the drift and the bound; and 2) via its effect on evidence weighing, which involves scaling only the bound (pp. 15–18). We previously assumed that only the second form of scaling might involve a subjective (mis-)estimate of the correlation. We now examine several models that also include the possibility of either or both forms using subjective correlation estimates. We show that a model that assumes that the same subjective estimate drives both encoding and weighing (the “full-rho-hat” model) best accounts for the data. This model provides better fits (after accounting for differences in numbers of parameters) than models with: 1) no correlation-dependent adjustments (“base” model), 2) separate drift parameters for each correlation condition (“drift” model), 3) optimal (correlation-dependent) encoding but suboptimal weighing (“bound-rho-hat” model, which was our previous formulation), 4) suboptimal encoding and weighing (“scaled-rho-hat” model), and 5) optimal encoding but suboptimal weighing and separate correlation-dependent adjustments to the drift rate (“boundrho-hat plus drift” model). We have substantially revised Figures 5–7 and the associated text to address these points.

Lastly, the paper makes it hard to assess how much better the participants' choices would be if they used the correct correlation magnitudes rather than underestimates thereof. This is important to know, as it only makes sense to strictly follow the normative strategy if it comes with a significant performance gain.

We now include new analyses in Fig. 7 that demonstrate how much participants' choices and RT deviate from: 1) an ideal observer using the objective correlations, and 2) an observer who failed to adjust for the fit subjective correlation when weighing the evidence (i.e., using the subjective correlation for encoding but a correlation of zero for weighing). We now indicate that participants’ performance was quite close to that predicted by the ideal observer (using the true, objective correlation) for many conditions. Thus, we agree that they might not have had the impetus to optimize the decision process further, assuming it were possible under these task conditions.

Reviewer #2 (Public review):

Summary:

This study by Tardiff, Kang & Gold seeks to: i) develop a normative account of how observers should adapt their decision-making across environments with different levels of correlation between successive pairs of observations, and ii) assess whether human decisions in such environments are consistent with this normative model.

The authors first demonstrate that, in the range of environments under consideration here, an observer with full knowledge of the generative statistics should take both the magnitude and sign of the underlying correlation into account when assigning weight in their decisions to new observations: stronger negative correlations should translate into stronger weighting (due to the greater information furnished by an anticorrelated generative source), while stronger positive correlations should translate into weaker weighting (due to the greater redundancy of information provided by a positively correlated generative source). The authors then report an empirical study in which human participants performed a perceptual decision-making task requiring accumulation of information provided by pairs of perceptual samples, under different levels of pairwise correlation. They describe a nuanced pattern of results with effects of correlation being largely restricted to response times and not choice accuracy, which could partly be captured through fits of their normative model (in this implementation, an extension of the well-known drift-diffusion model) to the participants' behaviour while allowing for misestimation of the underlying correlations.

Strengths:

As the authors point out in their very well-written paper, appropriate weighting of information gathered in correlated environments has important consequences for real-world decisionmaking. Yet, while this function has been well studied for 'high-level' (e.g. economic) decisions, how we account for correlations when making simple perceptual decisions on well-controlled behavioural tasks has not been investigated. As such, this study addresses an important and timely question that will be of broad interest to psychologists and neuroscientists. The computational approach to arrive at normative principles for evidence weighting across environments with different levels of correlation is very elegant, makes strong connections with prior work in different decision-making contexts, and should serve as a valuable reference point for future studies in this domain. The empirical study is well designed and executed, and the modelling approach applied to these data showcases a deep understanding of relationships between different parameters of the drift-diffusion model and its application to this setting. Another strength of the study is that it is preregistered.

Weaknesses:

In my view, the major weaknesses of the study center on the narrow focus and subsequent interpretation of the modelling applied to the empirical data. I elaborate on each below:

Modelling interpretation: the authors' preference for fitting and interpreting the observed behavioural effects primarily in terms of raising or lowering the decision bound is not well motivated and will potentially be confusing for readers, for several reasons. First, the entire study is conceived, in the Introduction and first part of the Results at least, as an investigation of appropriate adjustments of evidence weighting in the face of varying correlations. The authors do describe how changes in the scaling of the evidence in the drift-diffusion model are mathematically equivalent to changes in the decision bound - but this comes amidst a lengthy treatment of the interaction between different parameters of the model and aspects of the current task which I must admit to finding challenging to follow, and the motivation behind shifting the focus to bound adjustments remained quite opaque. 

We appreciate this valuable feedback. We have revised the text in several places to make these important points more clearly. For example, in the Introduction we now clarify that “The weight of evidence is computed as a scaled version of each observation (the scaling can be applied to the observations or to the bound, which are mathematically equivalent; Green and Swets, 1966) to form the logLR” (p. 3). We also provide more details and intuition in the Results section for how and why we implemented the DDM the way we did. In particular, we now emphasize that the correlation enters these models in two ways: 1) via its effect on encoding the observations, which scales both the drift and the bound; and 2) via its effect on evidence weighing, which scales only the bound (pp. 15–18).

Second, and more seriously, bound adjustments of the form modelled here do not seem to be a viable candidate for producing behavioural effects of varying correlations on this task. As the authors state toward the end of the Introduction, the decision bound is typically conceived of as being "predefined" - that is, set before a trial begins, at a level that should strike an appropriate balance between producing fast and accurate decisions. There is an abundance of evidence now that bounds can change over the course of a trial - but typically these changes are considered to be consistently applied in response to learned, predictable constraints imposed by a particular task (e.g. response deadlines, varying evidence strengths). In the present case, however, the critical consideration is that the correlation conditions were randomly interleaved across trials and were not signaled to participants in advance of each trial - and as such, what correlation the participant would encounter on an upcoming trial could not be predicted. It is unclear, then, how participants are meant to have implemented the bound adjustments prescribed by the model fits. At best, participants needed to form estimates of the correlation strength/direction (only possible by observing several pairs of samples in sequence) as each trial unfolded, and they might have dynamically adjusted their bounds (e.g. collapsing at a different rate across correlation conditions) in the process. But this is very different from the modelling approach that was taken. In general, then, I view the emphasis on bound adjustment as the candidate mechanism for producing the observed behavioural effects to be unjustified (see also next point).

We again appreciate this valuable feedback and have made a number of revisions to try to clarify these points. In addition to addressing the equivalence of scaling the evidence and the bound in the Introduction, we have added the following section to Results (Results, p.18):

“Note that scaling the bound in these formulations follows conventions of the DDM, as detailed above, to facilitate interpretation of the parameters. These formulations also raise an apparent contradiction: the “predefined” bound is scaled by subjective estimates of the correlation, but the correlation was randomized from trial to trial and thus could not be known in advance. However, scaling the bound in these ways is mathematically equivalent to using a fixed bound on each trial and scaling the observations to approximate logLR (see Methods). This equivalence implies that in the brain, effectively scaling a “predefined” bound could occur when assigning a weight of evidence to the observations as they are presented.”

We also note in Methods (pp. 40–41):

“In the DDM, this scaling of the evidence is equivalent to assuming that the decision variable accumulates momentary evidence of the form (x1 + x2) and then dividing the bound height by the appropriate scale factor. An alternative approach would be to scale both the signal and noise components of the DDM by the scale factor. However, scaling the bound is both simpler and maintains the conventional interpretation of the DDM parameters in which the bound reflects the decision-related components of the evidence accumulation process, and the drift rate represents sensory-related components.”

We believe we provide strong evidence that participants adjust their evidence weighing to account for the correlations (see response below), but we remain agnostic as to how exactly this weighing is implemented in the brain.

Modelling focus: Related to the previous point, it is stated that participants' choice and RT patterns across correlation conditions were qualitatively consistent with bound adjustments (p.20), but evidence for this claim is limited. Bound adjustments imply effects on both accuracy and RTs, but the data here show either only effects on RTs, or RT effects mixed with accuracy trends that are in the opposite direction to what would be expected from bound adjustment (i.e. slower RT with a trend toward diminished accuracy in the strong negative correlation condition; Figure 3b). Allowing both drift rate and bound to vary with correlation conditions allowed the model to provide a better account of the data in the strong correlation conditions - but from what I can tell this is not consistent with the authors' preregistered hypotheses, and they rely on a posthoc explanation that is necessarily speculative and cannot presently be tested (that the diminished drift rates for higher negative correlations are due to imperfect mapping between subjective evidence strength and the experimenter-controlled adjustment to objective evidence strengths to account for effects of correlations). In my opinion, there are other candidate explanations for the observed effects that could be tested but lie outside of the relatively narrow focus of the current modelling efforts. Both explanations arise from aspects of the task, which are not mutually exclusive. The first is that an interesting aspect of this task, which contrasts with most common 'univariate' perceptual decision-making tasks, is that participants need to integrate two pieces of information at a time, which may or may not require an additional computational step (e.g. averaging of two spatial locations before adding a single quantum of evidence to the building decision variable). There is abundant evidence that such intermediate computations on the evidence can give rise to certain forms of bias in the way that evidence is accumulated (e.g. 'selective integration' as outlined in Usher et al., 2019, Current Directions in Psychological Science; Luyckx et al., 2020, Cerebral Cortex) which may affect RTs and/or accuracy on the current task. The second candidate explanation is that participants in the current study were only given 200 ms to process and accumulate each pair of evidence samples, which may create a processing bottleneck causing certain pairs or individual samples to be missed (and which, assuming fixed decision bounds, would presumably selectively affect RT and not accuracy). If I were to speculate, I would say that both factors could be exacerbated in the negative correlation conditions, where pairs of samples will on average be more 'conflicting' (i.e. further apart) and, speculatively, more challenging to process in the limited time available here to participants. Such possibilities could be tested through, for example, an interrogation paradigm version of the current task which would allow the impact of individual pairs of evidence samples to be more straightforwardly assessed; and by assessing the impact of varying inter-sample intervals on the behavioural effects reported presently.

We thank the reviewer for this thoughtful and valuable feedback. We have thoroughly updated the modeling section to include new analysis and clearer descriptions and interpretations of our findings (including Figs. 5–7 and additional references to the Usher, Luyckx, and other studies that identified decision suboptimalities). The comment about “an additional computational step” in converting the observations to evidence was particularly useful, in that it made us realize that we were making what we now consider to be a faulty assumption in our version of the DDM. Specifically, we assumed that subjective misestimates of the correlation affected how observations were converted to evidence (logLR) to form the decision (implemented as a scaling of the bound height), but we neglected to consider how suboptimalities in encoding the observations could also lead to misestimates of the correlation. We have retained the previous best-fitting models in the text, for comparison (the “bound-rho-hat” and “bound-rho-hat + drift” models). In addition, we now include a “full-rho-hat” model that assumes that misestimates of rho affect both the encoding of the observations, which affects the drift rate and bound height, and the weighing of the evidence, which affects only the bound height. This was the best-fitting model for most participants (after accounting for different numbers of parameters associated with the different models we tested). Note that the full-rho-hat model predicts the lack of correlation-dependent choice effects and the substantial correlation-dependent RT effects that we observed, without requiring any additional adjustments to the drift rate (as we resorted to previously).

In summary, we believe that we now have a much more parsimonious account of our data, in terms of a model in which subjective estimates of the correlation are alone able to account for our patterns of choice and RT data. We fully agree that more work is needed to better understand the source of these misestimates but also think those questions are outside the scope of the present study.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

A few minor comments:

(1) Evidence can be correlated in multiple ways. It could be correlated within individual pieces of evidence in a sequence, or across elements in that sequence (e.g., across time). This distinction is important, as it determines how evidence ought to be accumulated across time. In particular, if evidence is correlated across time, simply summing it up might be the wrong thing to do. Thus, it would be beneficial to make this distinction in the Introduction, and to mention that this paper is only concerned with the first type of correlation.

We now clarify this point in the Introduction (p. 5–6).

(2) It is unclear without reading the Methods how the blue dashed line in Figure 4c is generated. To my understanding, it is a prediction of the naive DDM model. Is this correct?

We now specify the models used to make the predictions shown in Fig. 4c (which now includes an additional model that uses unscaled observations as evidence).

(3) In Methods, given the importance of the distribution of x1 + x2, it would be useful to write it out explicitly, e.g., x1 + x2 ~ N(2 mu_g, ..), specifying its mean and its variance.

Excellent suggestion, added to p. 38.

(4) From Methods and the caption of Figure 6 - Supplement 1 it becomes clear that the fitted DDM features a bound that collapses over time. I think that this should also be mentioned in the main text, as it is a not-too-unimportant feature of the model.

Excellent suggestion, added to p. 15, with reference to Fig. 6-supplement 1 on p. 20.

(5) The functional form of the bound is 2 (B - tb t). To my understanding, the effective B changes as a function of the correlation magnitude. Does tb as well? If not, wouldn't it be better if it does, to ensure that 2 (B - tb t) = 0 independent of the correlation magnitude?

In our initial modeling, we also considered whether the correlation-dependent adjustment, which is a function of both correlation sign and magnitude, should be applied to the initial bound or to the instantaneous bound (i.e., after collapse, affecting tb as well). In a pilot analysis of data from 22 participants in the 0.6 correlation-magnitude group, we found that this choice had a negligible effect on the goodness-of-fit (deltaAIC = -0.9, protected exceedance probability = 0.63, in favor of the instantaneous bound scaling). We therefore used the instantaneous bound version in the analyses reported in the manuscript but doubt this choice was critical based on these results. We have clarified our implementation of the bound in Methods (p. 43–44).

Reviewer #2 (Recommendations for the authors):

In addition to the points raised above, I have some minor suggestions/open questions that arose from my reading of the manuscript:

(1) Are the predictions outlined in the paper specific to cases where the two sources are symmetric around zero? If distributions are allowed to be asymmetric then one can imagine cases (i.e. when distribution means are sufficiently offset from one another) where positive correlations can increase evidence strength and negative correlations decrease evidence strength. There's absolutely still value and much elegance in what the authors are showing with this work, but if my intuition is correct, it should ideally be acknowledged that the predictions are restricted to a specific set of generative circumstances.

We agree that there are a lot of ways to manipulate correlations and their effect on the weight of evidence. At the end of the Discussion, we emphasize that our results apply to this particular form of correlation (p. 32).

(2) Isn't Figure 4C misleading in the sense that it collapses across the asymmetry in the effect of negative vs positive correlations on RT, which is clearly there in the data and which simply adjusting the correlation-dependent scale factor will not reproduce?

We agree that this analysis does not address any asymmetries in suboptimal estimates of positive versus negative correlations. We believe that those effects are much better addressed using the model fitting, which we present later in the Results section. We have now simplified the analyses in Fig. 4c, reporting the difference in RT between positive and negative correlation conditions instead of a linear regression.

(3) I found the transition on p.17 of the Results section from the scaling of drift rate by correlation to scaling of bound height to be quite abrupt and unclear. I suspect that many readers coming from a typical DDM modelling background will be operating under the assumption that drift rate and bound height are independent, and I think more could be done here to explain why scaling one parameter by correlation in the present case is in fact directly equivalent to scaling the other.

Thank you for the very useful feedback, we have substantially revised this text to make these points more clearly.

(4) P.3, typo: Alan *Turing*

That’s embarrassing. Fixed.

(5) P.27, typo: "participants adopt a *fixed* bound"

Fixed.

eLife Assessment

This study focuses on the role of a T-cell-specific receptor, ctla-4, in a new zebrafish model of IBD-like phenotype. Although implicated in IBD diseases, the function of ctla-4 has been hard to study in mice as the KO is lethal. Ctla-4 mutant zebrafish exhibited significant intestinal inflammation and dysbiosis, mirroring the pathology of inflammatory bowel disease (IBD) in mammals, providing a new valuable model to the field of IBD research. This is an key study with convincing evidence, comprehensive transcriptomic analysis, histological examinations, and functional assays all supporting the findings.

Reviewer #1 (Public review):

"Unraveling the Role of Ctla-4 in Intestinal Immune Homeostasis: Insights from a novel Zebrafish Model of Inflammatory Bowel Disease" generates a 14bp deletion/early stop codon mutation that is viable in a zebrafish homolog of ctla-4. This mutant exhibits an IBD-like phenotype, including decreased intestinal length, abnormal intestinal folds, decreased goblet cells, abnormal cell junctions between epithelial cells, increased inflammation, and alterations in microbial diversity. Bulk and single-cell RNA-seq show upregulation of immune and inflammatory response genes in this mutant (especially in neutrophils, B cells, and macrophages) and downregulation of genes involved in adhesion and tight junctions in mutant enterocytes. The work suggests that the makeup of immune cells within the intestine is altered in these mutants, potentially due to changes in lymphocyte proliferation. Introduction of recombinant soluble Ctla-4-Ig to mutant zebrafish rescued body weight, histological phenotypes, and gene expression of several pro-inflammatory genes, suggesting a potential future therapeutic route.

Strengths:

- Generation of a useful new mutant in zebrafish ctla-4<br /> - The demonstration of an IBD-like phenotype in this mutant is extremely comprehensive.<br /> - Demonstrated gene expression differences provide mechanistic insight into how this mutation leads to IBD-like symptoms.<br /> - Demonstration of rescue with a soluble protein suggests exciting future therapeutic potential<br /> - The manuscript is mostly well organized and well written.

Initial Weaknesses were addressed during review.

Reviewer #2 (Public review):

Summary:

The authors aimed to elucidate the role of Ctla-4 in maintaining intestinal immune homeostasis by using a novel Ctla-4-deficient zebrafish model. This study addresses the challenge of linking CTLA-4 to inflammatory bowel disease (IBD) due to the early lethality of CTLA-4 knockout mice. Four lines of evidence were shown to show that Ctla-4-deficient zebrafish exhibited hallmarks of IBD in mammals: 1) impaired epithelial integrity and infiltration of inflammatory cells; 2) enrichment of inflammation-related pathways and the imbalance between pro- and anti-inflammatory cytokines; 3) abnormal composition of immune cell populations; and 4) reduced diversity and altered microbiota composition. By employing various molecular and cellular analyses, the authors established ctla-4-deficient zebrafish as a convincing model of human IBD.

Strengths:

The characterization of the mutant phenotype is very thorough, from anatomical to histological and molecular levels. The finding effectively established ctla-4 mutants as a novel zebrafish model for investigating human IBD. Evidence from the histopathological and transcriptome analysis was very strong and supports a severe interruption of immune system homeostasis in the zebrafish intestine. Additional characterization using sCtla-4-Ig further probed the molecular mechanism of the inflammatory response, and provided a potential treatment plan for targeting Ctla-4 in IBD models.

Weaknesses:

To probe the molecular mechanism of Ctla-4, the authors used a spectrum of antibodies that target Ctla-4 or its receptors. The phenotype assayed was lymphocyte proliferation, while it was the composition rather than number of immune cells that was observed to be different in the scRNASeq assay. Although sCtla-4 has an effect of alleviating the IBD-like phenotypes, I found this explanation a bit oversimplified.

Comments on revised version:

The authors have sufficiently addressed all my concerns and I don't have further suggestions.

Reviewer #3 (Public review):

Summary:

Current study on the mutant zebrafish for IBD modeling is worth trying. The author provided lots of evidence, including histopathological observation, gut microflora, as well as intestinal tissue or mucosa cells' transcriptomic data. The multi-omic study has demonstrated the enteritis pathology at multi levels in zebrafish model.

Strengths:

The important immune checkpoint of Treg cells were knockout in zebrafish, and the enteritis were found then. It could be a substitution of mouse knockout model to investigate the molecular mechanism of gut disease.

Weaknesses:

(1) In Fig. 2I, as to the purple glycogen signals stained by PAS was ignored for the quantitative statistics. The purple stained area could be calculated by ImageJ.<br /> (2) Those characters in Fig. 3G are too small to recognize. It is suggested to adjusted this picture or just put it in the supplementation, with bigger size.<br /> (3) The tissue seems damaged for IgG ctrl in Fig. 8B. It is suggested to find another slice to present here.<br /> (4) Line 667 & 743: "16S rRNA sequencing" should be "16S rRNA gene sequencing". Please check this point throughout the text.

Author response:

The following is the authors’ response to the original reviews

Reviewer #1:

The manuscript suggests the zebrafish homolog of ctla-4 and generates a new mutant in it. However, the locus that is mutated is confusingly annotated as both CD28 (current main annotation in ZFIN) and CTLA-4/CD152 (one publication from 2020), see: https://zfin.org/ZDB-GENE-070912-128. Both human CTLA-4 and CD28 align with relatively similar scores to this gene. There seem to be other orthologs of these receptors in the zebrafish genome, including CD28-like (https://zfin.org/ZDB-GENE-070912-309) which neighbors the gene annotated as CD28 (exhibiting similar synteny as human CD28 and CTLA-4). It would be helpful to provide more information to distinguish between this family of genes and to further strengthen the evidence that this mutant is in ctla-4, not cd28. Also, is one of these genes in the zebrafish genome (e.g. cd28l) potentially a second homolog of CTLA-4? Is this why this mutant is viable in zebrafish and not mammals? Some suggestions:

(a) A more extensive sequence alignment that considers both CTLA-4 and CD28, potentially identifying the best homolog of each human gene, especially taking into account any regions that are known to produce the functional differences between these receptors in mammals and effectively assigns identities to the two genes annotated as "cd28" and "cd28l" as well as the gene "si:dkey-1H24.6" that your CD28 ORF primers seem to bind to in zebrafish.

In response to the reviewer's insightful suggestions, we have conducted more extensive sequence alignment and phylogenetic analyses that consider both CTLA-4, CD28, and CD28-like molecules, taking into account key regions crucial for the functionalities and functional differences between these molecules across various species, including mammals and zebrafish.

Identification of zebrafish Ctla-4: We identified zebrafish Ctla-4 as a homolog of mammalian CTLA-4 based on key conserved structural and functional characteristics. Structurally, the Ctla-4 gene shares similar exon organization compared to mammalian CTLA-4. Ctla-4 is a type I transmembrane protein with typical immunoglobulin superfamily features. Multiple amino acid sequence alignments revealed that Ctla-4 contains a ¹¹³LFPPPY¹¹⁸ motif and a ¹²³GNGT¹²⁶ motif in the ectodomain, and a tyrosine-based ²⁰⁶YVKF²⁰⁹ motif in the distal C-terminal region. These motifs closely resemble MYPPPY, GNGT, and YVKM motifs in mammalian CTLA-4s, which are essential for binding to CD80/CD86 ligands and molecular internalization and signaling inhibition. Despite only 23.7% sequence identity to human CTLA-4, zebrafish Ctla-4 exhibits a similar tertiary structure with a two-layer β-sandwich architecture in its extracellular IgV-like domain. Four cysteine residues responsible for the formation of two pairs of disulfide bonds (Cys²⁰-Cys⁹¹/Cys⁴⁶-Cys⁶⁵ in zebrafish and Cys²¹-Cys⁹²/Cys⁴⁸-Cys⁶⁶ in humans) that connect the two-layer β-sandwich are conserved. Additionally, a separate cysteine residue (Cys¹²⁰ in zebrafish and Cys¹²⁰ in humans) involved in dimerization is also present, and Western blot analysis under reducing and non-reducing conditions confirmed Ctla-4’s dimerization. Phylogenetically, Ctla-4 clusters with other known CTLA-4 homologs from different species with high bootstrap probability, while zebrafish Cd28 groups separately with other CD28s. Functionally, Ctla-4 is predominantly expressed on CD4⁺ T and CD8⁺ T cells in zebrafish. It plays a pivotal inhibitory role in T cell activation by competing with CD28 for binding to CD80/86, as validated through a series of both in vitro and in vivo assays, including microscale thermophoresis assays which demonstrated that Ctla-4 exhibits a significantly higher affinity for Cd80/86 than Cd28 (KD = 0.50 ± 0.25 μM vs. KD = 2.64 ± 0.45 μM). These findings confirm Ctla-4 as an immune checkpoint molecule, reinforcing its identification within the CTLA-4 family.

Comparison between zebrafish Cd28 and "Cd28l": Zebrafish Cd28 contains an extracellular SYPPPF motif and an intracellular FYIQ motif. The extracellular SYPPPF motif is essential for binding to Cd80/CD86, while the intracellular FYIQ motif likely mediates kinase recruitment and co-stimulatory signaling. In contrast, the "Cd28l" molecule lacks the SYPPPF motif, which is critical for Cd80/CD86 binding, and exhibits strong similarity in its C-terminal 79 amino acids to Ctla-4 rather than Cd28. Consequently, "Cd28l" resembles an atypical Ctla-4-like molecule but fails to exhibit Cd80/CD86 binding activity.

We have incorporated the relevant analysis results into the main text of the revised manuscript and updated Supplementary Figure 1. Additionally, we provide key supplementary analyses here for the reviewer's convenience.  

Author response image 1.

Illustrates the alignment of Ctla-4 (XP_005167576.1) and Ctla-4-like (XP_005167567.1, previously referred to as "Cd28l") in zebrafish, generated using ClustalX and Jalview. Conserved and partially conserved amino acid residues are highlighted in color gradients ranging from carnation to red, respectively. The B7-binding motif is encircled with a red square.

(b) Clearer description in the main text of such an analysis to better establish that the mutated gene is a homolog of ctla-4, NOT cd28.

We appreciate the reviewer's advice. Additional confirmation of zebrafish Ctla-4 is detailed in lines 119-126 of the revised manuscript.

(c) Are there mammalian anti-ctla-4 and/or anti-cd28 antibodies that are expected to bind to these zebrafish proteins? If so, looking to see whether staining is lost (or western blotting is lost) in your mutants could be additionally informative. (Our understanding is that your mouse anti-Ctla-4 antibody is raised against recombinant protein generated from this same locus, and so is an elegant demonstration that your mutant eliminates the production of the protein, but unfortunately does not contribute additional information to help establish its homology to mammalian proteins).

This suggestion holds significant value. However, a major challenge in fish immunology research is the limited availability of antibodies suitable for use in fish species; antibodies developed for mammals are generally not applicable. We attempted to use human and mouse anti-CTLA-4 and anti-CD28 antibodies to identify Ctla-4 and Cd28 in zebrafish, but the results were inconclusive, with no expected signals. This outcome likely arises from the low sequence identity between human/mouse CTLA-4 and CD28 and their zebrafish homologs (ranging from 21.3% to 23.7% for CTLA-4 and 21.2% to 24.0% for CD28). Therefore, developing specific antibodies against zebrafish Ctla-4 is essential for advancing this research.

The methods section is generally insufficient and doesn't describe many of the experiments performed in this manuscript. Some examples:

(a) No description of antibodies used for staining or Western blots (Figure1C, 1D, 1F).

(b) No description of immunofluorescence protocol (Figure 1D, 1F).

(c) No description of Western blot protocol (Figure 1C, 2C).

(d) No description of electron microscopy approach (Figure 2K).

(e) No description of the approach for determining microbial diversity (Entirety of Figure 6).

(f) No description of PHA/CFSE/Flow experiments (Figure 7A-E).

(g) No description of AlphaFold approach (Figures 7F-G).

(h) No description of co-IP approach (Figure 7H).

(i) No description of MST assay or experiment (Figure 7I).

(j) No description of purification of recombinant proteins, generation of anti-Ctla-4 antibody, or molecular interaction assays (Figures S2 and S6).

We apologize for this oversight. The methods section was inadvertently incomplete due to an error during the file upload process at submission. This issue has been addressed in the revised manuscript. We appreciate your understanding.

Figure 5 suggests that there are more Th2 cells 1, Th2 cells 2, and NKT cells in ctla-4 mutants through scRNA-seq. However, as the cell numbers for these are low in both genotypes, there is only a single replicate for each genotype scRNA-seq experiment, and dissociation stress can skew cell-type proportions, this finding would be much more convincing if another method that does not depend on dissociation was used to verify these results. Furthermore, while Th2 cells 2 are almost absent in WT scRNA-seq, KEGG analysis suggests that a major contributor to their clustering may be ribosomal genes (Fig. 5I). Since no batch correction was described in the methods, it would be beneficial to verify the presence of this cluster in ctla-4 mutants and WT animals through other means, such as in situ hybridization or transgenic lines.   

We are grateful for the insightful comments provided by the reviewer. Given that research on T cell subpopulations in fish is still in its nascent stages, the availability of specific marker antibodies and relevant transgenic strains remains limited. Our single-cell RNA sequencing (scRNA-seq) analysis revealed that a distinct Th2 subset 2 was predominantly observed in Ctla-4 mutants but was rare in wild-type zebrafish, it suggests that this subset may primarily arise under pathological conditions associated with Ctla-4 mutation. Due to the near absence of Th2 subset 2 in wild-type samples, KEGG enrichment analysis was performed exclusively on this subset from Ctla-4-deficient intestines. The ribosome pathway was significantly enriched, suggesting that these cells may be activated to fulfill their effector functions. However, confirming the presence of Th2 subset 2 using in situ hybridization or transgenic zebrafish lines is currently challenging due to the lack of lineage-specific markers for detailed classification of Th2 cell subsets and the preliminary nature of scRNA-seq predictions.

To address the reviewers' suggestion to confirm compositional changes in Th2 and NKT cells using dissociation-independent methods, we quantified mRNA levels of Th2 (il4, il13, and gata3) and NKT (nkl.2, nkl.4, and prf1.1) cell marker genes via RT-qPCR in intestines from wild-type and mutant zebrafish. As shown in Figure S7B and S7C, these markers were significantly upregulated in Ctla-4-deficient intestines compared to wild-type controls. This indicates an overall increase in Th2 and NKT cell activity in mutant zebrafish, aligning with our scRNA-seq analysis and supports the validity of our initial findings.

Before analyzing the scRNA-seq data, we performed batch correction using the Harmony algorithm via cloud-based Cumulus v1.0 on the aggregated gene-count matrices. This methodological detail has been included in the “Materials and Methods” section of the revised manuscript. Moreover, the RT-qPCR results are presented in Supplementary Figures S7B and S7C.

Quality control (e.g., no. of UMIs, no. of genes, etc.) metrics of the scRNAseq experiments should be presented in the supplementary information for each sample to help support that observed differential expression is not merely an outcome of different sequencing depths of the two samples.

As illustrated in Fig. S5, the quality control data have been supplemented to include the effective cell number of the sample, along with pre- and post-filtering metrics such as nFeature_RNA, nCount_RNA and mitochondrial percentage (percent.mito). Furthermore, scatter plots comparing the basic information of the sample cells before and after filtering are provided.

Some references to prior research lack citations. Examples:

(a)"Given that Ctla-4 is primarily expressed on T cells (Figure 1E-F), and its absence has been shown to result in intestinal immune dysregulation, indicating a crucial role of this molecule as a conserved immune checkpoint in T cell inhibition."

The references were incorporated into line 71 of the revised manuscript.

(b) Line 83: Cite evidence/review for the high degree of conservation in adaptive immunity.

The references were incorporated into line 93 of the revised manuscript.

(c) Lines 100-102: Cite the evidence that MYPPPY is a CD80/86 binding motif.

The references were incorporated into line 117 of the revised manuscript.

The text associated with Figure 8 (Lines 280-289) does not clearly state that rescue experiments are being done in mutant zebrafish.

We have provided a clear explanation of the rescue experiments conducted in Ctla-4-deficient zebrafish. This revision has been incorporated into line 319.

Line 102: Is there evidence from other animals that LFPPPY can function as a binding site for CD80/CD86? Does CD28 also have this same motif?

The extracellular domains of CTLA-4 and CD28, which bind to CD80/CD86, are largely conserved across various species. This conservation is exemplified by a central PPP core motif, although the flanking amino acids exhibit slight variations. In mammals, both CTLA-4 and CD28 feature the conserved MYPPPY motif. By contrast, in teleost fish, such as rainbow trout, CTLA-4 contains an LYPPPY motif, while CD28 has an MYPPPI motif (Ref. 1). Grass carp CTLA-4 displays an LFPPPY motif, whereas its CD28 variant bears an IYPPPF motif. Yeast two-hybrid assays confirm that these motifs facilitate interactions between grass carp CTLA-4 and CD28 with CD80/CD86 (Ref. 2). Similarly, zebrafish Ctla-4 contains the LFPPPY motif observed in grass carp, while Cd28 exhibits a closely related SYPPPF motif.

References:

(1) Bernard, D et al. (2006) Costimulatory Receptors in a Teleost Fish: Typical CD28, Elusive CTLA-4. J Immunol. 176: 4191-4200.

(2) Lu T Z et al. (2022) Molecular and Functional Analyses of the Primordial Costimulatory Molecule CD80/86 and Its Receptors CD28 and CD152 (CTLA-4) in a Teleost Fish. Frontiers in Immunology. 13:885005.

Line 110-111: Suggest adding citation of these previously published scRNAseq data to the main text in addition to the current description in the Figure legend.

The reference has been added in line 129 in the main text.

Figure 3B: It would be helpful to label a few of the top differentially expressed genes in Panel B?

The top differentially expressed genes have been labeled in Figure 3B.

Figure 3G: It's unclear how this analysis was conducted, what this figure is supposed to demonstrate, and in its current form it is illegible.

Figure 3G displays a protein-protein interaction network constructed from differentially expressed genes. The densely connected nodes, representing physical interactions among proteins, provide valuable insights for basic scientific inquiry and biological or biomedical applications. As proteins are crucial to diverse biological functions, their interactions illuminate the molecular and cellular mechanisms that govern both healthy and diseased states in organisms. Consequently, these networks facilitate the understanding of pathogenic and physiological processes involved in disease onset and progression.

To construct this network, we first utilized the STRING database (https://string-db.org) to generate an initial network diagram using the differentially expressed genes. This diagram was subsequently imported into Cytoscape (version 3.9.1) for visualization and further analysis. Node size and color intensity reflect the density of interactions, indicating the relative importance of each protein. Figure 3G illustrates that IL1β was a central cytokine hub in the disease process of intestinal inflammation in Ctla-4-deficient zebrafish.

Expression scale labeling:

(a) Most gene expression scales are not clearly labeled: do they represent mean expression or scaled expression? Has the expression been log-transformed, and if so, which log (natural log? Log10? Log2?). See: Figure 3E, 3I, 4D, 4E, 5B, 5G, 5H, 6I.

The gene expression scales are detailed in the figure legends. Specifically, Figures 3E, 3I, and 6I present heatmaps depicting row-scaled expression levels for the corresponding genes. In contrast, Figures 4D and 4E display heatmaps illustrating the mean expression of these genes. Additionally, the dot plots in Figures 5B, 5G, and 5H visualize the mean expression levels of the respective genes.

(b) For some plots, diverging color schemes (i.e. with white/yellow in the middle) are used for non-diverging scales and would be better represented with a sequential color scale. See: 4D, 4E, and potentially others (not fully clear because of the previous point).

The color schemes in Figures 4D and 4E have been updated to a sequential color scale. The gene expression data depicted in these figures represent mean expression values and have not undergone log transformation. This information has been incorporated into the figure legend for clarity.

Lines 186-187: Though it is merely suggested, apoptotic gene expression can be upregulated as part of the dissociation process for single-cell RNAseq. This would be much stronger if supported by a staining, such as anti-Caspase 3.

Following the reviewer's insightful recommendations, we conducted a TUNEL assay to evaluate apoptosis in the posterior intestinal epithelial cells of both wild-type and Ctla-4-deficient zebrafish. As expected, our results demonstrate a significant increase in epithelial cell apoptosis in Ctla-4-deficient zebrafish compared with wild-type fish. The corresponding data are presented in Figure S6D and have been incorporated into the manuscript. Detailed protocols for the TUNEL assay have also been included in the Materials and Methods section.

Author response image 2.

Illustrates the quantification of TUNEL-positive cells per 1 × 10⁴ μm^2/⁻ in the posterior intestines of both wild-type (WT) and ctla-4^⁻/⁻ zebrafish (n = 5). The data demonstrate a comparative analysis of apoptotic cell density between the two genotypes.

Lines 248-251: This manuscript demonstrates gut inflammation and also changes in microbial diversity, but I don't think it demonstrates an association between them, which would require an experiment that for instance rescues one of these changes and shows that it ameliorates the other change, despite still being a ctla-4 mutant.

We appreciate the valuable comments from the reviewer. Recently, the relationship between inflammatory bowel disease (IBD) and gut microbial diversity has garnered considerable attention, with several key findings emerging from human IBD studies. For instance, patients with IBD (including ulcerative colitis and Crohn's disease) exhibit reduced microbial diversity, which is correlated with disease severity. This decrease in microbial richness is thought to stem from the loss of normal anaerobic bacteria, such as Bacteroides, Eubacterium, and Lactobacillus (Refs. 1-6). Research using mouse models has shown that inflammation increases oxygen and nitrate levels within the intestinal lumen, along with elevated host-derived electron acceptors, thereby promoting anaerobic respiration and overgrowth of Enterobacteriaceae (Ref 7). Consistent with these findings, our study observed a significant enrichment of Enterobacteriaceae in the inflamed intestines of Ctla-4-deficient zebrafish, which supporting the observations in mice. Despite this progress, the zebrafish model for intestinal inflammation remains under development, with limitations in available techniques for manipulating intestinal inflammation and reconstructing gut microbiota. These challenges hinder investigations into the association between intestinal inflammation and changes in microbial diversity. We plan to address these issues through ongoing technological advancements and further research. We thank the reviewer for their understanding.

References:

(1) Ott S J, Musfeldt M, Wenderoth D F, Hampe J, Brant O, Fölsch U R et al. (2004) Reduction in diversity of the colonic mucosa associated bacterial microflora in patients with active inflammatory bowel disease. Gut 53:685-693.

(2) Manichanh C, Rigottier-Gois L, Bonnaud E, Gloux K, Pelletier E, Frangeul L et al. (2006) Reduced diversity of faecal microbiota in Crohn's disease revealed by a metagenomic approach. Gut 55:205-211.

(3) Qin J J, Li R Q, Raes J, Arumugam M, Burgdorf K S, Manichanh C et al. (2010) A human gut microbial gene catalogue established by metagenomic sequencing. Nature 464:59-U70.

(4) Sha S M, Xu B, Wang X, Zhang Y G, Wang H H, Kong X Y et al. (2013) The biodiversity and composition of the dominant fecal microbiota in patients with inflammatory bowel disease. Diagn Micr Infec Dis 75:245-251.

(5) Ray K. (2015) IBD. Gut microbiota in IBD goes viral. Nat Rev Gastroenterol Hepatol 12:122.

(6) Papa E, Docktor M, Smillie C, Weber S, Preheim S P, Gevers D et al. (2012) Non-Invasive Mapping of the Gastrointestinal Microbiota Identifies Children with Inflammatory Bowel Disease. Plos One 7: e39242-39254.

(7) Hughes E R, Winter M G, Duerkop B A, Spiga L, de Carvalho T F, Zhu W H et al. (2017) Microbial Respiration and Formate Oxidation as Metabolic Signatures of Inflammation-Associated Dysbiosis. Cell Host Microbe 21:208-219.

Lines 270-272 say that interaction between Cd28/ctla-4 and Cd80/86 was demonstrated through bioinformatics, flow-cytometry, and Co-IP. Does this need to reference Fig S6D for the flow data? Figures 7F-G are very hard to read or comprehend as they are very small. Figure 7H is the most compelling evidence of this interaction and might stand out better if emphasized with a sentence referencing it on its own in the manuscript. 

In this study, we utilized an integrated approach combining bioinformatics prediction, flow cytometry, and co-immunoprecipitation (Co-IP) to comprehensively investigate and validate the interactions between Cd28/Ctla-4 and Cd80/86. Flow cytometry analysis, as depicted in Supplementary Figure 6D (revised as Supplementary Figure 8F), demonstrated the surface expression of Cd80/86 on HEK293T cells and quantified their interactions with Cd28 and Ctla-4. These experiments not only validated the interactions between Cd80/86 and Cd28/Ctla-4 but also revealed a dose-dependent relationship, providing robust supplementary evidence for the molecular interactions under investigation. Furthermore, in Figure 7F-G, the axis font sizes were enlarged to improve readability. Additionally, in response to reviewers' feedback, we have emphasized Figure 7H, which presents the most compelling evidence for molecular interactions, by including a standalone sentence in the text to enhance its prominence.

For Figure 7A-E, for non-immunologists, it is unclear what experiment was performed here - it would be helpful to add a 1-sentence summary of the assay to the main text or figure legend.

We apologize for this oversight. Figures 7A–E illustrate the functional assessment of the inhibitory role of Ctla-4 in Cd80/86 and Cd28-mediated T cell activation. A detailed description of the methodologies associated with Figures 7A–E is provided in the ‘Materials and Methods’ section of the revised manuscript.

For Figure 7F-G, it is extremely hard to read the heat map legends and the X and Y-axis. Also, what the heatmaps show and how that fits the overall narrative can be elaborated significantly.

We regret this oversight. To enhance clarity, we have increased the font size of the heatmap legends and the X and Y-axes, as shown in the following figure. Additionally, a detailed analysis of these figures is provided in lines 299–306 of the main text.

In general, the main text that accompanies Figure 7 should be expanded to more clearly describe these experiments/analyses and their results.

We have conducted a detailed analysis of the experiments and results presented in Figure 7. This analysis is described in lines 278-314.

Reviewer #2:

The scRNASeq assay is missing some basic characterization: how many WT and mutant fish were assayed in the experiment? how many WT and mutant cells were subject to sequencing? Before going to the immune cell types, are intestinal cell types comparable between the two conditions? Are there specific regions in the tSNE plot in Figure 4A abundant of WT or ctla-4 mutant cells?

In the experiment, we analyzed 30 wild-type and 30 mutant zebrafish for scRNA-seq, with an initial dataset comprising 8,047 cells in the wild-type group and 8,321 cells in the mutant group. Sample preparation details are provided on lines 620-652. Due to the relatively high expression of mitochondrial genes in intestinal tissue, quality control filtering yielded 3,263 cells in the wild-type group and 4,276 cells in the mutant group. Given that the intestinal tissues were dissociated using identical protocols, the resulting cell types are comparable between the two conditions. Both the wild-type and Ctla-4-deficient groups contained enterocytes, enteroendocrine cells, smooth muscle cells, neutrophils, macrophages, B cells, and a cluster of T/NK/ILC-like cells. Notably, no distinct regions were enriched for either condition in the tSNE plot (Figure 4A).

The cell proliferation experiment using PHA stimulation assay demonstrated the role of Ctla-4 in cell proliferation, while the transcriptomic evidence points towards activation rather than an overall expansion of T-cell numbers. This should be discussed towards a more comprehensive model of how subtypes of cells can be differentially proliferating in the disease model.

In the PHA-stimulated T cell proliferation assay, we aimed to investigate the regulatory roles of Ctla-4, Cd28, and Cd80/86 in T cell activation, focusing on validating Ctla-4's inhibitory function as an immune checkpoint. While our study examined general regulatory mechanisms, it did not specifically address the distinct roles of Ctla-4 in different T cell subsets. We appreciate the reviewer's suggestion to develop a more comprehensive model that elucidates differential T cell activation across various subsets in disease models. However, due to the nascent stage of research on fish T cell subsets and limitations in lineage-specific antibodies and transgenic strains, such investigations are currently challenging. We plan to pursue these studies in the future. Despite these constraints, our single-cell RNA sequencing data revealed an increased proportion of Th2 subset cells in Ctla-4-deficient zebrafish, as evidenced by elevated expression levels of Th2 markers (Il4, Il13, and Gata3) via RT-qPCR (see Figures S7B). Notably, recent studies in mouse models have shown that naïve T cells from CTLA-4-deficient mice tend to differentiate into Th2 cells post-proliferation, with activated Th2 cells secreting higher levels of cytokines like IL-4, IL-5, and IL-13, thereby exerting their effector functions (Refs. 1-2). Consequently, our findings align with observations in mice, suggesting conserved CTLA-4 functions across species. We have expanded the "Discussion" section to clarify these points.

References:

(1) Bour-Jordan H, Grogan J L, Tang Q Z, Auger J A, Locksley R M, Bluestone J A et al. (2003) CTLA-4 regulates the requirement for cytokine-induced signals in T_H2 lineage commitment. Nature Immunology 4: 182-188.

(2) Khattri Roli, Auger, Julie A, Griffin Matthew D, Sharpe Arlene H, Bluestone Jeffrey A et al. (1999) Lymphoproliferative Disorder in CTLA-4 Knockout Mice Is Characterized by CD28-Regulated Activation of Th2 Responses. The Journal of Immunology 162:5784-5791.

It would be nice if the authors could also demonstrate whether other tissues in the zebrafish have an inflammation response, to show whether the model is specific to IBD.

In addition to intestinal tissues, we also performed histological analysis on the liver of Ctla-4-deficient zebrafish. The results showed that Ctla-4 deficiency led to mild edema in a few hepatocytes, and lymphocyte infiltration was not significant. Compared to the liver, we consider intestinal inflammation to be more pronounced.

Some minor comments on terminology

(a) "multiomics" usually refers to omics experiments with different modalities (e.g. transcriptomics, proteomics, metabolomics etc), while the current paper only has transcriptomics assays. I wouldn't call it "multiomics" analysis.

We appreciate the reviewer's attention to this issue. The "multi-omics" has been revised to "transcriptomics".

(b) In several parts of the figure legend the author mentioned "tSNE nonlinear clustering" (Figures 4A and 5A). tSNE is an embedding method rather than a clustering method.

The "tSNE nonlinear clustering" has been revised to "tSNE embedding”.

(c) Figure 1E is a UMAP rather than tSNE.

The "tSNE" has been revised to "UMAP" in the figure legend in line 1043.

Reviewer #3: 

Line 28: The link is not directly reflected in this sentence describing CTLA-4 knockout mice.

We appreciate the reviewer for bringing this issue to our attention. We have expanded our description of CTLA-4 knockout mice on lines 77-84.

Line 80-83: There is a lack of details about the CTLA-4-deficient mice. The factor that Th2 response could be induced has been revealed in mouse model. See the reference entitled "CTLA-4 regulates the requirement for cytokine-induced signals in TH2 lineage commitment" published in Nature Immunology.

We thank the reviewer for providing valuable references. We have added descriptions detailing the differentiation of T cells into Th2 cells in CTLA-4-deficient mice on lines 78–81, and the relevant references have been cited in the revised manuscript.

To better introduce the CTLA-4 immunobiology, the paper entitled "Current Understanding of Cytotoxic T Lymphocyte Antigen-4 (CTLA-4) Signaling in T-Cell Biology and Disease Therapy" published in Molecules and Cells should be referred.

We have provided additional details on CTLA-4 immunology (lines 75-84) and have included the relevant reference in the revised manuscript.

In current results, there are many sentences that should be moved to the discussion, such as lines 123-124, lines 152-153, lines 199-200, and lines 206-207. So, the result sections just describe the results, and the discussions should be put together in the discussion.

We have relocated these sentences to the 'Discussion' section and refined the writing.

In the discussion, the zebrafish enteritis model, such as DSS/TNBS and SBMIE models, should also be compared with the current CTLA-4 knockout model. Also, the comparison between the current fish IBD model and the previous mouse model should also be included, to enlighten the usage of CTLA-4 knockout zebrafish IBD model.

We compared the phenotypes of our current Ctla-4-knockout zebrafish IBD model with other models, including DSS-induced IBD models in zebrafish and mice, as well as TNBS- and SBM-induced IBD models in zebrafish. The details are included in the "Discussion" section (lines 353-365).

As to the writing, the structure of the discussion is poor. The paragraphs are very long and hard to follow. Many findings from current results were not yet discussed. I just can't find any discussion about the alteration of intestinal microbiota.

In response to the reviewers' constructive feedback, we have revised and enhanced the discussion section. Furthermore, we have integrated the most recent research findings relevant to this study into the discussion to improve its relevance and comprehensiveness.

In the discussion, the aerobic-related bacteria in 16s rRNA sequencing results should be focused on echoing the histopathological findings, such as the emptier gut of CTLA-4 knockout zebrafish.

As mentioned above, the discussion section has been revised and expanded to provide a better understanding of the potential interplay among intestinal inflammatory pathology, gut microbiota alterations, and immune cell dysregulation in Ctla-4-deficient zebrafish. Furthermore, promising avenues for future research that warrant further investigation were also discussed.

In the current method, there are no descriptions for many used methods, which already generated results, such as WB, MLR, MST, Co-IP, AlphaFold2 prediction, and how to make currently used anti-zfCTLA4 antibody. Also, there is a lack of description of the method of the husbandry of knockout zebrafish line.

We regret these flaws. The methods section was inadvertently incomplete due to an error during the file upload process at submission. This issue has been rectified in the revised manuscript. Additionally, Ctla-4-deficient zebrafish were reared under the same conditions as wild-type zebrafish, and the rearing methods are now described in the "Generation of Ctla-4-deficient zebrafish" section of the Materials and Methods.

Line 360: the experimental zebrafish with different ages could be a risk for unstable intestinal health. See the reference entitled "The immunoregulatory role of fish-specific type II SOCS via inhibiting metaflammation in the gut-liver axis" published in Water Biology and Security. The age-related differences in zebrafish could be observed in the gut.

We appreciate the reviewers' reminders. The Ctla-4 mutant zebrafish used in our experiments were 4 months old, while the wild-type zebrafish ranged from 4 to 6 months old. These experimental fish were relatively young and uniformly distributed in age. During our study, we examined the morphological structures of the intestines in zebrafish aged 4 to 6 months and observed no significant abnormalities. These findings align with previous research indicating no significant difference in intestinal health between 3-month-old and 6-month-old wild-type zebrafish (Ref. 1). Consequently, we conclude that there is no notable aging-related change in the intestines of zebrafish aged 4 to 6 months. This reduces the risk associated with age-related variables in our study. We have added an explanation stating that the Ctla-4 mutant zebrafish used in the experiments were 4 months old (Line 449) in the revised manuscript.

Reference

(1) Shan Junwei, Wang Guangxin, Li Heng, Zhao Xuyang et al. (2023) The immunoregulatory role of fish-specific type II SOCS via inhibiting metaflammation in the gut-liver axis. Water Biology and Security 2: 100131-100144.

Section "Generation of Ctla-4-deficient zebrafish": There is a lack of description of PCR condition for the genotyping.

The target DNA sequence was amplified at 94 °C for 4 min, followed by 35 cycles at 94°C for 30 s, 58°C for 30 s and 72°C for 30 s, culminating in a final extension at 72 °C for 10 min. The polymerase chain reaction (PCR) conditions are described in lines 458-460.

How old of the used mutant fish? There should be a section "sampling" to provide the sampling details.

The "Sampling" information has been incorporated into the "Materials and Methods" section of the revised manuscript. Wild-type and Ctla-4-deficient zebrafish of varying months were housed in separate tanks, each labeled with its corresponding birth date. Experiments utilized Ctla-4-deficient zebrafish aged 4 months and wild-type zebrafish aged between 4 to 6 months.

Line 378-380: The index for the histopathological analysis should be detailed, rather than just provide a reference. I don't think these indexes are good enough to specifically describe the pathological changes of intestinal villi and mucosa. It is suggested to improve with detailed parameters. As described in the paper entitled "Pathology of Gastric Intestinal Metaplasia: Clinical Implications" published in Am J Gastroenterol., histochemical, normal gastric mucins are pH neutral, and they stain magenta with periodic acid-Schiff (PAS). In an inflamed gut, acid mucins replace the original gastric mucins and are stained blue with Alcian blue (AB). So, to reveal the pathological changes of goblet cells and involved mucin components, AB staining should be added. Also, for the number of goblet cells in the inflammatory intestine, combining PAS and AB staining is the best way to reveal all the goblet cells. In Figure 2, there were very few goblet cells. The infiltration of lymphocytes and the empty intestinal lumen could be observed. Thus, the ratio between the length of intestinal villi and the intestinal ring radius should calculated.

In response to the reviewers’ valuable suggestions, we have augmented the manuscript by providing additional parameters related to the pathological changes observed in the Ctlta-4-deficient zebrafish intestines, including the mucin component changes identified through PAS and AB-PAS staining, the variations in the number of goblet cells evaluated by AB-PAS staining, and the ratio of intestinal villi length to the intestinal ring radius, as illustrated in the following figures. These new findings are detailed in the "Materials and Methods" (lines 563-566) and "Results" (lines 143-146) sections, along with Supplementary Figure S3 of the revised manuscript.

Section "Quantitative real-time PCR": What's the machine used for qPCR? How about the qPCR validation of RNA seq data? I did not see any related description of data and methods for qPCR validation. In addition, beta-actin is not a stable internal reference gene, to analyze inflammation and immune-related gene expression. See the reference entitled "Actin, a reliable marker of internal control?" published in Clin Chim Acta. Other stable housekeeping genes, such as EF1alpha and 18s, could be better internal references.

RT-qPCR experiments were conducted using a PCR thermocycler device (CFX Connect Real-Time PCR Detection System with Precision Melt Analysis^TM Software, Bio-Rad, Cat. No. 1855200EM1). This information has been incorporated into lines 608-610 of the "Materials and Methods" section. In these experiments, key gene sequences of interest, including il13, mpx, and il1β, were extracted from RNA-seq data for RT-qPCR validation. To ensure accurate normalization, potential internal controls were evaluated, and β-actin was identified as a suitable candidate due to its consistent expression levels in the intestines of both wild-type and Ctla-4-deficient zebrafish. The use of β-actin as an internal control is further supported by its application in recent studies on intestinal inflammation (Refs 1–2).

References:

(1) Tang Duozhuang, Zeng Ting, Wang Yiting, Cui Hui et al. (2020) Dietary restriction increases protective gut bacteria to rescue lethal methotrexate-induced intestinal toxicity. Gut Microbes 12: 1714401-1714422.

(2) Malik Ankit, Sharma Deepika et al. (2023) Epithelial IFNγ signaling and compartmentalized antigen presentation orchestrate gut immunity. Nature 623: 1044-1052.

How to generate sCtla-4-Ig, Cd28-Ig and Cd80/86? No method could be found.

We apologize for the omission of these methods. The detailed protocols have now been added to the "Materials and Methods" section of the revised manuscript (lines 464-481).

Figure 5: As reviewed in the paper entitled "Teleost T and NK cell immunity" published in Fish and Shellfsh Immunology, two types of NK cell homologues have been described in fish: non-specific cytotoxic cells and NK-like cells. There is no NKT cell identified in the teleost yet. Therefore, "NKT-like" could be better to describe this cell type.

We refer to "NKT" cells as "NKT-like" cells, as suggested.

For the supplementary data of scRNA-seq, there lacks the details of expression level.

The expression levels of the corresponding genes are provided in Supplemental Table 4.

Supplemental Table 1: There are no accession numbers of amplified genes.

The accession numbers of the amplified genes are included in Supplemental Table 1.

The English needs further editing.

We have made efforts to enhance the English to meet the reviewers' expectations.

Line 32: The tense should be the past.

This tense error has been corrected.

Line 363-365: The letter of this approval should be provided as an attachment.

The approval document is provided as an attachment.

Line 376: How to distinguish the different intestinal parts? Were they judged as the first third, second third, and last third parts of the whole intestine?

The differences among the three segments of zebrafish intestine are apparent. The intestinal tube narrows progressively from the anterior to the mid-intestine and then to the posterior intestine. Moreover, the boundaries between the intestinal segments are well-defined, facilitating the isolation of each segment.

Line 404: Which version of Cytoscape was used?

The version of Cytoscape used in this study is 3.9.1. Information about the Cytoscape version is provided on line 603.

The product information of both percoll and cell strainer should be provided.

The information regarding Percoll and cell strainers has been added on lines 626 and 628, respectively.

Line 814: Here should be a full name to tell what is MST.

The acronym MST stands for "Microscale Thermophoresis", a technique that has been referenced on lines 1157-1158.

eLife Assessment

This translational study presents a direct cross-species comparison (between mice, rats, and humans) of choice behavior in the same perceptual decision-making task. The study is rare in opening a window on the evolution of decision-making, and the results will be important for many disciplines including behavioral sciences, psychology, neuroscience, and psychiatry. While the strength of the evidence presented is solid, the manuscript would benefit from additional information and analyses to strengthen and clarify its main conclusions.

Reviewer #1 (Public review):

This work presents data from three species (mice, rats, and humans) performing an evidence accumulation task, that has been designed to be as similar as possible between species (and is based on a solid foundation of previous work on decision-making). The tasks are well-designed, and the analyses are solid and clearly presented - showing that there are differences in the overall parameters of the decision-making process between the species. This is valuable to neuroscientists who aim to translate behavioral and neuroscientific findings from rodents to humans and offers a word of caution for the field in readily claiming that behavioral strategies and computations are representative of all mammals. The dataset would be of great interest to the community and may be a source of further modelling of across-species behavior, but unfortunately, neither data or code are currently shared.

A few other questions remain, that make the conclusions of the paper a bit hard to assess:

(1) The main weakness is that the authors claim that all species rely on evidence accumulation as a strategy, but this is not tested against other models (see e.g. Stine et al. https://elifesciences.org/articles/55365): the fact that the DDM fits rather well does not mean that this is the strategy that each species was carrying out.

(2) In all main analyses, it is unclear what the effect is of the generative flash rate and how this has been calibrated between species. Only in Figure 6C do we see basic psychometric functions, but these should presumably also feature as a crucial variable dominating the accuracy and RTs (chronometric functions) across species. The very easy trials are useful to constrain the basic sensorimotor differences that may account for RT variability, e.g. perhaps the small body of mice requires them to move a relatively longer distance to trigger the response.

(3) The GLM-HMM results (that mice are not engaged in all trials) are very important, but they imply that mouse DDM fits may well be more similar to rats and humans if done only on engaged trials. Could it be that the main species differences are driven by different engagement state occupations?

(4) It would be very helpful if the authors could present a comprehensive overview (perhaps a table) of the factors that may be relevant for explaining the observed species differences. This may include contextual/experimental variables (age range (adolescent humans vs. mice/rats, see https://www.jax.org/news-and-insights/jax-blog/2017/november/when-are-mice-considered-old; reward source, etc) and also outcomes (e.g. training time required to learn the task, # trials per session and in total).

Reviewer #2 (Public review):

Summary:

Chakravarty et al. propose a 'synchronized framework' for studying perceptual decision-making (DM) across species -namely humans, rats, and mice. Although all species shared hallmarks of evidence accumulation, the results highlighted species-specific differences. Humans were the slowest and most accurate, rats optimized the speed-accuracy tradeoff to maximize the reward rate and mice were the fastest but least accurate. In addition, while humans were better fit by a classic DDM with fixed bounds, rodents were better fit by a DDM with collapsing bounds. While comparing behavioral strategies in evidence accumulation tasks across species is an important and timely question, some of the presented differences across species lack a clear interpretation and could be simply caused by differences in the task design. There is important information and analyses missing about the DDM and the other models used, which lowers the confidence and enthusiasm about the results.

Strengths:

The comparison of behavior across species, including humans and commonly used laboratory species like rats and mice, is a fundamental step in neuroscience to establish more informed links between animal experiments and human cognition. In this work, Chakravarty et al. analyze and model the behavior of three species during the same evidence accumulation task. They draw conclusions about the different strategies used in each case.

Weaknesses:

Novelty:<br /> While quite relevant, some parts of the work presented are more novel than others. That EA drives choice behavior and these choices can be described with a DDM have been shown before (see e.g. (Kane et al. 2023; Brunton et al. in 2013; Pinto et al 2018)). The novelty here mostly lies in the comparison of three species in the same task and in fitting the same exact model (close quantitative comparison of behavioral strategies). However, some of the differences lack a clear interpretation. For instance, the values of some of the DDM fitted parameters between the three species are not ordered "as expected" (e.g. non-decision time or DDM BIC). Other comparison results completely lack an explanation (e.g. rats' RT are near optimal while humans and mice are not). The aspect that I found most novel and exciting is the application of HMMs to each of the species. However, this part comes at the end of the paper and has been done without sufficient depth. There is almost no explanation for the results. I would suggest the authors bring up this part and move back to other aspects which are, in my opinion, less novel or interpretable (e.g. results around the optimality of RT).

Task design:<br /> Since there is no fixation, the response time (RT) reflects both the evidence integration time plus the motor time (stimuli are played until a response is given). This design makes it hard to compare RTs between species. While humans just had to press a button, rodents had to move their whole bodies from a central port to a side port. When comparing rats and mice, their difference in size relative to port distance could explain different RTs. This could for example explain the large difference in non-decision time (ndt) in Figure 3F between mice and rats. Are the measurements of the rat and the mouse boxes comparable? The authors should explain this difference more openly and discuss its implications when interpreting the results. The Methods should also provide information about the distance between ports for each species. I also strongly recommend including a few videos of rats and mice performing the task to have a sense of the movements involved in the task in each species.

(1) DDM

Goodness of fit:<br /> The authors conclude that the three species use an accumulation of evidence strategy because they can fit a DDM. However, there is little information about the goodness of these fits. They only show the RT distributions for one example subject (too small to distinguish whether the fit of the histograms is good or not). We suggest they make a figure showing in more detail the match of the RT distributions across subjects (e.g. they can compare RT quartiles for data and model for the entire group of subjects). Then they provide BIC which is a measure that depends on the number of trials. Were the number of trials matched across subjects/species? Could the authors provide a measure independent of the number of trials (e.g. cross-validated log-likelihood per trial)? Moreover, is this BIC computed only on the RTs, mouse responses, or both?

Overparameterization:<br /> The authors chose to include as DDM parameters the variability of the initial offset, the variability in non-decision time, and the variability of the drift rate. Having so many parameters with just one stimulus condition (80:20 ratio of flashes) may lead to unidentifiability problems as recognized previously (e.g. see M. Jones (2021) here osf.io/preprints/psyarxiv/gja3u). Their parameter recovery Supplementary Figure 3 shows that at least two of these variability parameters can not be recovered. I also couldn't find the values of these parameters for the fitted DDM. So I was wondering the extent to which adding these parameters improves the fits and is overall necessary.

Tachometric curves:<br /> The authors show increasing tachometric curves (i.e. Accuracy vs RT) and use this finding as proof of accumulation. They fit these curves using a GAAM with little justification or detail (in fact the GAAM seems to over-fit the data a bit). The authors do not say, however, that the other model used, i.e. the DDM, may not reproduce these increasing tachometric curves because "in its basic form", the DDM gives flat tachometric curves. Does the DDM fitted to the individual RT and choice data capture the monotonic increase observed in the tachometric curves?

Correct vs Error trials:<br /> In a similar line, the authors do not test the fitted DDM separately in correct vs error trials, which is a classical distinction that most DDMs can't capture. It would be good to know if: (1) the RT in the data of correct vs error responses are similar (quantified in panel Figure 2B because in 2E it is not clear) and (2) the same trend between correct and error RTs are observed in the fitted DDMs.

Urgency model:<br /> It is not clear how the urgency model used works. The authors cite Ditterich (2006), but in that paper, the urgency signal was applied to a race model with two decision variables: the urgency signal "accelerated" both DVs equally and sped up the race without favoring one DV versus the other. In a one-dimensional DDM, it is not clear where the urgency is applied. We assume it is applied in the direction of the stimulus, but then it is unclear how the urgency knows about the stimulus, which is what the DDM is trying to estimate in the first place. The authors should explain this model in greater detail and try to resolve this question.

Despite finding differences between species, the analyses seem mostly exploratory instead of hypothesis-driven. There is little justification for why differences in some DDM parameters across species would be expected.

(2) GLM and HMM

The GLM fits show nicely that humans, rats, and mice weigh differently the total provided evidence (Figures 6C-D). This may be because the internal noise in the accumulation of evidence is higher but also it could simply be because animals do not weigh the evidence that is presented when they are already moving towards the side ports. A parsimonious alternative to the "more noisy" species is simply that they only consider the first part of the stimulus. Extending the GLM to capture the differential weighting of each sequential sample (what is called the Psychophysical kernel, PK) should be straightforward and would provide a more fair comparison between species (i.e. perhaps the slope of the psychometric curves is not that different, once evidence is weighted in each species with its corresponding PK.

Choice Bias:<br /> Panel 3G (DDM starting point) shows that both rats and mice are slightly but systematically biased to the Left (x0 < 0.5). Panel 6D "Bias" seems to be showing the absolute value of the GLM bias parameter. It would be nice to (i) show the signed GLM bias parameter. (ii) Compare that the biases computed in the DDM and GLM are comparable across species and subjects; it looks like from the GLM they are comparable in magnitude across species whereas the in DDM they weren't (mice had a much bigger |x0| in the DDM), (iii) explain (or at least comment) on why animals show a systematic bias to one side.

Reviewer #3 (Public review):

Summary:

This study directly compares decision-making strategies between three species, humans, rats, and mice. Based on a new and common behavioral task that is largely shared across species, specific features of evidence accumulation could be quantified and compared between species. The authors argue their work provides a framework to study decision-making across species, which can be studied by the same decision models. The authors report specific features of decision-making strategies, such as humans having a larger decision threshold leading to more accurate responses, and rodents deciding under time pressure.

Strengths:

The behavioral task is set up in similar, comparable ways across species, allowing for employing the same decision models and directly comparing specific features of decision behavior. This approach is compelling since it is otherwise challenging to compare behavior between species. Data analysis is solid and does not only quantify features of classic drift-diffusion models, but also additional commonly applied behavior models or features such as win-stay/lose-shift strategies, reward-maximization behavior, and slow, latent changes in behavior strategies. This approach reveals some interesting species differences, which are a starting point to investigate species-specific decision strategies more deeply and could inform a broad set of past and future behavior studies commonly used in cognitive and neuroscience.

Weaknesses:

(1) The choice of the stimulus difficulty is unclear, as choosing a single, specific evidence strength (80:20) could limit model fitting performance and interpretation of psychometric curves. This could also limit conclusions about species differences since the perceptual sensitivity seems quite different between species. Thus, the 80:20 lies at different uncertainty levels for the different species, which are known to influence behavioral strategies. This might be addressed by exploiting the distribution of actually delivered flashes, but it remained unclear to me to what degree this is the case. Previous perceptual discrimination studies typically sample multiple evidence levels to differentiate the source of variability in choice behavior.

(2) The authors argue that their task is novel and that their task provides a framework to investigate perceptual decision-making. However, very similar, and potentially more powerful, perceptual decision-making tasks (e.g., using several evidence strength levels) have been used in humans, non-human primates, rats, mice, and other species. In some instances, analogous behavioral tasks, including studies using the same sensory stimulus, have been used across multiple species. While these may have been published in different papers, they have been conducted in some instances by the same lab and using the same analyses. Further, much of this work is not referenced here. This limits the impact of this work.

(3) The employed drift-diffusion model has many parameters, which are not discussed in detail. Results in Supplementary Figures 3-5 are not explained or discussed, including the interpretation that model recovery tests fail to recover some of the parameters (eg, Figures S3E, G). This makes the interpretation of such models more difficult.

(4) The results regarding potential reward-maximization strategies are compelling and connect perceptual and normative decision models. The results are however limited by the different inter-trial intervals and trial initiation times between species, which are shown in Figure S6. It's unclear to me how to interpret, for example, how the long trial initiation times in rats relate to a putative reward-maximizing strategy. This compares to the very low trial initiation times (ie, very 'efficient') of humans, even though they are 'too accurate' in terms of their sampling time. Reward-maximizing strategies seem difficult with such different trial times and in the absence of experimental manipulation.

eLife Assessment

In this important study, the authors use computational modeling to explore how rapid learning can be reconciled with the accumulation of stable memories in the olfactory bulb, where adult neurogenesis is prominent. They focus on the "flexibility-stability dilemma" and how it is resolved through local mechanisms within the olfactory bulb. These compelling results present a coherent picture of a neurogenesis-dependent learning process that aligns with diverse experimental observations and may serve as a foundation for further experimental and computational studies.

Reviewer #1 (Public review):

Summary:

Sakelaris and Riecke used computational modeling to explore how neurogenesis and sequential integration of new neurons into a network support memory formation and maintenance. They focus on the integration of granule cells in the olfactory bulb, a brain area where adult neurogenesis is prominent. Experimental results published in recent years provide an excellent basis to address the question at hand by biologically constrained models. The study extends previous computational models and provides a coherent picture of how multiple processes may act in concert to enable rapid learning, high stability of memories, and high memory capacity. This computational model generates experimentally testable predictions and is likely to be valuable to understand the roles of neurogenesis and related phenomena in memory. One of the key findings is that important features of the memory system depend on transient properties of adult-born granule cells such as enhanced excitability and apoptosis during specific phases of the development of individual neurons. The model can explain many experimental observations and suggests specific functions for different processes (e.g., importance of apoptosis for continual learning). While this model is obviously a massive simplification of the biological system, it conceptualizes diverse experimental observations into a coherent picture, it generates testable predictions for experiments, and it will likely inspire further modeling and experimental studies. Nonetheless, there are issues that the authors should address.

Strengths:

(1) The model can explain diverse experimental observations.

(2) The model directly represents the biological network.

Weaknesses:

As with many other models of biological networks, this model contains major simplifications.

Reviewer #2 (Public review):

Summary:

This is an excellent paper that demonstrates Computational Modeling at its best. The authors propose a mechanism to provide flexibility to learn new information while preserving stability in neural networks by combining structural plasticity and synaptic plasticity.

Strengths:

An intriguing idea, that is well embedded in experimental data.

The problem posed is real, the model uses data to be designed and implemented yet adds to the data novel and useful insight. The project proposes a parsimonious explanation for why neurogenesis can be better than classical plasticity and how stability versus flexibility can be solved with this approach.

Weaknesses:

No weaknesses were identified by this reviewer.

Reviewer #3 (Public review):

The manuscript is focused on local bulbar mechanisms to solve the flexibility-stability dilemma in contrast to long-range interactions documented in other systems (hippocampus-cortex). The network performance is assessed in a perceptual learning task: the network is presented with alternating, similar artificial stimuli (defined as enrichment) and the authors assess its ability to discriminate between these stimuli by comparing the mitral cell representations quantified by Fisher discriminant analysis. The authors use enhancement in discriminability between stimuli as a function of the degree of specificity of connectivity in the network to quantify the formation of an odor-specific network structure which as such has memory - they quantify memory as the specificity of that connectivity.

The focus on neurogenesis, excitability, and synaptic connectivity of abGCs is topical, and the authors systematically built their model, clearly stating their assumptions and setting up the questions and answers. In my opinion, the combination of latent dendritic representations, excitability, and apoptosis in an age-dependent manner is interesting and as the authors point out leads to experimentally testable hypotheses. I have however several concerns with the novelty of the work, the lack of referencing of previous work on granule cells-mitral cell interactions more generally, and the biological plausibility of the model that, in my opinion, should be further addressed to better contextualize the model.

(1) The authors find that a network with age-dependent synaptic plasticity outperforms one with constant age-independent plasticity and that having more GC per se is not sufficient to explain this effect. In addition, having an initial higher excitability of GCs leads to increased performance. To what degree the increased excitability of abGCs is conceptually necessarily independent of them having higher synaptic plasticity rates / fast synapses?

(2) The authors do not mention previous theoretical work on the specificity of mitral to granule cell interactions from several groups (Koulakov & Rinberg - Neuron, 2011; Gilra & Bhalla, PLoSOne, 2015; Grabska-Bawinska...Mainen, Pouget, Latham, Nat. Neurosci. 2017; Tootoonian, Schaefer, Latham, PLoS Comput. Biol., 2022), nor work on the relevance of top-down feedback from the olfactory cortex on the abGC during odor discrimination tasks (Wu & Komiyama, Sci. Adv. 2020), or of top-down regulation from the olfactory cortex on regulating the activity of the mitral/tufted cells in task engaged mice (Lindeman et al., PLoS Comput. Biol., 2024), or in naïve mice that encounter odorants (in the absence of specific context; Boyd, et al., Cell Rep, 2015; Otazu et al., Neuron 2015, Chae et al., Neuron, 2022). In particular, the presence of rich top-down control of granule cell activity (including of abGCs) puts into question the plausibility of one of the opening statements of the authors with respect to relying solely on local circuit mechanisms to solve the flexibility-stability dilemma. I think the discussion of this work is important in order to put into context the idea of specific interactions between the abGCs and the mitral cells.

(3) To what the degree of specific connectivity reflects a specific stimulus configuration, and is a good proxy for determining the stimulus discriminability and memory capacity in terms of temporal activity patterns (difference in latency/phase with respect to the respiration cycle, etc.) which may account to a substantial fraction of ability to discriminate between stimuli? The authors mention in the discussion that this is, indeed, an upper bound and specific connectivity is necessary for different temporal activity patterns, but a further expansion on this topic would help in understanding the limitations of the model.

(4) Reward or reward prediction error signals are not considered in the model. They however are ubiquitous in nature and likely to be encountered and shape the connectivity and activity patterns of the abGC-mitral cell network. Including a discussion of how the model may be adjusted to incorporate reward/error signals would strengthen the manuscript.

Specific Comments

(1) Lines 84-86; 507-509; Eq(3): Sensory input is defined by a basal parameter of MCs spontaneous activity (Sspontaneus) and the odor stimuli input (Siodor) but is not clear from the main text or methods how sensory inputs (glomerular patterns) were modeled.

(2) Lines 118-122: The used perceptual learning task explanation is done only in the context of the discriminability of similar artificial stimuli using the Fisher discriminant and "Memory" metric. A detailed description of the logic of the perceptual learning task methods and objective, taking into account Comment 1, would help to better understand the model.

(3) Rapid re-learning of forgotten odor pair is enabled by sensory-dependent dendritic elaboration of neurons that initially encoded the odors and the observed re-learning would occur even if neurogenesis was blocked following the first enrichment and even though the initial learning did require neurogenesis. When this would ever occur in nature? The re-learning of an odor period? Why is this highlighted in the study?

eLife Assessment

This study presents a useful reassessment of the potential role of dendritic cell-derived IL-27 p28 cytokine in the functional maturation of CD4+CD8- thymocytes, and CD4+ recent thymic emigrants. The evidence supporting the claims of the authors is solid and serves to reaffirm what has been previously described, with the overall advance in understanding the mechanism(s) responsible for the intrathymic functional programming of CD4+ T cells being limited.

Reviewer #1 (Public Review):

Summary:

Zhang et al. demonstrate that CD4+ single positive (SP) thymocytes, CD4+ recent thymic emigrants (RTE), and CD4+ T naive (Tn) cells from Cd11c-p28-flox mice, which lack IL-27p28 selectively in Cd11c+ cells, exhibit a hyper-Th1 phenotype instead of the expected hyper Th2 phenotype. Using IL-27R-deficient mice, the authors confirm that this hyper-Th1 phenotype is due to IL-27 signaling via IL-27R, rather than the effects of monomeric IL-27p28. They also crossed Cd11c-p28-flox mice with autoimmune-prone Aire-deficient mice and showed that both T cell responses and tissue pathology are enhanced, suggesting that SP, RTE, and Tn cells from Cd11c-p28-flox mice are poised to become Th1 cells in response to self-antigens. Regarding mechanism, the authors demonstrate that SP, RTE, and Tn cells from Cd11c-p28-flox mice have reduced DNA methylation at the IFN-g and Tbx21 loci, indicating 'de-repression', along with enhanced histone tri-methylation at H3K4, indicating a 'permissive' transcriptional state. They also find evidence for enhanced STAT1 activity, which is relevant given the well-established role of STAT1 in promoting Th1 responses, and surprising given IL-27 is a potent STAT1 activator. This latter finding suggests that the Th1-inhibiting property of thymic IL-27 may not be due to direct effects on the T cells themselves.

Strengths:

Overall the data presented are high quality and the manuscript is well-reasoned and composed. The basic finding - that thymic IL-27 production limits the Th1 potential of SP, RTE, and Tn cells - is both unexpected and well described.

Weaknesses from the original round of review:

A credible mechanistic explanation, cellular or molecular, is lacking. The authors convincingly affirm the hyper-Th1 phenotype at epigenetic level but it remains unclear whether the observed changes reflect the capacity of IL-27 to directly elicit epigenetic remodeling in developing thymocytes or knock-on effects from other cell types which, in turn, elicit the epigenetic changes (presumably via cytokines). The authors propose that increased STAT1 activity is a driving force for the epigenetic changes and resultant hyper-Th1 phenotype. That conclusion is logical given the data at hand but the alternative hypothesis - that the hyper-STAT1 response is just a downstream consequence of the hyper-Th1 phenotype - remains equally likely. Thus, while the discovery of a new anti-inflammatory function for IL-27 within the thymus is compelling, further mechanistic studies are needed to advance the finding beyond phenomenology.

Reviewer #2 (Public Review):

Summary:

Naïve CD4 T cells in CD11c-Cre p28-floxed mice express highly elevated levels of proinflammatory IFNg and the transcription factor T-bet. This phenotype turned out to be imposed by thymic dendritic cells (DCs) during CD4SP T cell development in the thymus [PMID: 23175475]. The current study affirms these observations, first, by developmentally mapping the IFNg dysregulation to newly generated thymic CD4SP cells [PMID: 23175475], second, by demonstrating increased STAT1 activation being associated with increased T-bet expression in CD11c-Cre p28-floxed CD4 T cells [PMID: 36109504], and lastly, by confirming IL-27 as the key cytokine in this process [PMID: 27469302]. The authors further demonstrate that such dysregulated cytokine expression is specific to the Th1 cytokine IFNg, without affecting the expression of the Th2 cytokine IL-4, thus proposing a role for thymic DC-derived p28 in shaping the cytokine response of newly generated CD4 helper T cells. Mechanistically, CD4SP cells of CD11c-Cre p28-floxed mice were found to display epigenetic changes in the Ifng and Tbx21 gene loci that were consistent with increased transcriptional activities of IFNg and T-bet mRNA expression. Moreover, in autoimmune Aire-deficiency settings, CD11c-Cre p28-floxed CD4 T cells still expressed significantly increased amounts of IFNg, exacerbating the autoimmune response and disease severity. Based on these results, the investigators propose a model where thymic DC-derived IL-27 is necessary to suppress IFNg expression by CD4SP cells and thus would impose a Th2-skewed predisposition of newly generated CD4 T cells in the thymus, potentially relevant in autoimmunity.

Strengths:

Experiments are well-designed and executed. The conclusions are convincing and supported by the experimental results.

Weaknesses from the original round of review:

The premise of the current study is confusing as it tries to use the CD11c-p28 floxed mouse model to explain the Th2-prone immune profile of newly generated CD4SP thymocytes. Instead, it would be more helpful to (1) give full credit to the original study which already described the proinflammatory IFNg+ phenotype of CD4 T cells in CD11c-p28 floxed mice to be mediated by thymic dendritic cells [PMID: 23175475], and then, (2) build on that to explain that this study is aimed to understand the molecular basis of the original finding.

In its essence, this study mostly rediscovers and reaffirms previously reported findings, but with different tools. While the mapping of epigenetic changes in the IFNg and T-bet gene loci and the STAT1 gene signature in CD4SP cells are interesting, these are expected results, and they only reaffirm what would be assumed from the literature. Thus, there is only incremental gain in new insights and information on the role of DC-derived IL-27 in driving the Th1 phenotype of CD4SP cells in CD11c-p28 floxed mice.

Altogether, the major issues of this study remain unresolved:

(1) It is still unclear why the p28-deficiency in thymic dendritic cells would result in increased STAT1 activation in CD4SP cells. Based on their in vitro experiments with blocking anti-IFNg antibodies, the authors conclude that it is unlikely that the constitutive activation of STAT1 would be a secondary effect due to autocrine IFNg production by CD4SP cells. However, this possibility should be further tested with in vivo models, such as Ifng-deficient CD11c-p28 floxed mice. Alternatively, is this an indirect effect by other IFNg producers in the thymus, such as iNKT cells? It is necessary to explain what drives the STAT1 activation in CD11c-p28 floxed CD4SP cells in the first place.

(2) It is also unclear whether CD4SP cells are the direct targets of IL-27 p28. The cell-intrinsic effects of IL-27 p28 signaling in CD4SP cells should be assessed and demonstrated, ideally by CD4SP-specific deletion of IL-27Ra, or by establishing bone marrow chimeras of IL-27Ra germline KO mice.

[Editors' note: The resubmitted paper was minimally revised, and many of the initial concerns remain unresolved.]

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public Review):

Summary:

Zhang et al. demonstrate that CD4⁺ single positive (SP) thymocytes, CD4⁺ recent thymic emigrants (RTE), and CD4⁺ T naive (Tn) cells from Cd11c-p28-flox mice, which lack IL-27p28 selectively in Cd11c+ cells, exhibit a hyper-Th1 phenotype instead of the expected hyper Th2 phenotype. Using IL-27R-deficient mice, the authors confirm that this hyper-Th1 phenotype is due to IL-27 signaling via IL-27R, rather than the effects of monomeric IL-27p28. They also crossed Cd11c-p28-flox mice with autoimmune-prone Aire-deficient mice and showed that both T cell responses and tissue pathology are enhanced, suggesting that SP, RTE, and Tn cells from Cd11c-p28-flox mice are poised to become Th1 cells in response to self-antigens. Regarding mechanism, the authors demonstrate that SP, RTE, and Tn cells from Cd11c-p28-flox mice have reduced DNA methylation at the IFN-g and Tbx21 loci, indicating 'de-repression', along with enhanced histone tri-methylation at H3K4, indicating a 'permissive' transcriptional state. They also find evidence for enhanced STAT1 activity, which is relevant given the well-established role of STAT1 in promoting Th1 responses, and surprising given IL-27 is a potent STAT1 activator. This latter finding suggests that the Th1-inhibiting property of thymic IL-27 may not be due to direct effects on the T cells themselves.

Strengths:

Overall the data presented are high quality and the manuscript is well-reasoned and composed. The basic finding - that thymic IL-27 production limits the Th1 potential of SP, RTE, and Tn cells - is both unexpected and well described.

Weaknesses:

A credible mechanistic explanation, cellular or molecular, is lacking. The authors convincingly affirm the hyper-Th1 phenotype at epigenetic level but it remains unclear whether the observed changes reflect the capacity of IL-27 to directly elicit epigenetic remodeling in developing thymocytes or knock-on effects from other cell types which, in turn, elicit the epigenetic changes (presumably via cytokines). The authors propose that increased STAT1 activity is a driving force for the epigenetic changes and resultant hyper-Th1 phenotype. That conclusion is logical given the data at hand but the alternative hypothesis - that the hyper-STAT1 response is just a downstream consequence of the hyper-Th1 phenotype - remains equally likely. Thus, while the discovery of a new anti-inflammatory function for IL-27 within the thymus is compelling, further mechanistic studies are needed to advance the finding beyond phenomenology.

Thank you for your insightful comments and suggestions. We appreciate your feedback and have carefully considered the concerns raised regarding the mechanistic explanation of our findings. To address the issue of whether developing thymocytes are the direct targets of IL-27, we plan to conduct further studies using Cd4-IL-27ra knockout mice or mixed bone marrow chimeras consisting of wildtype and IL-27ra knockout cells. This approach will help us determine if IL-27 directly induces epigenetic remodeling in thymocytes or if the observed effects are secondary to influences from other cell types.

Regarding the potential autocrine loop contributing to STAT1 hyperactivation, we have performed preliminary experiments by adding IFN-γ antibody to CD4⁺ T cell cultures and observed no significant impact on STAT1 phosphorylation. If necessary, we will further investigate this possibility in vivo using Cd4-Ifng and CD11c-p28 double knockout mice.

The detailed mechanisms underlying STAT1 hyperactivation remain to be elucidated. Recent studies have shown that IL-27p28 can act as an antagonist of gp130-mediated signaling. Structural analyses have also demonstrated that IL-27p28 interacts with EBI3 and the two receptor subunits IL-27Rα and gp130. Given these findings and the similar phenotypes observed in p28 and IL-27ra deficient mice, we speculate that the deficiency of either p28 or IL-27ra may increase the availability of gp130 for signaling by other cytokines. We will focus our future research on gp130-related cytokines to identify potential candidates that could lead to enhanced STAT1 activation in the absence of p28. Alternatively, the release of EBI3 in p28-deficient conditions may promote its interaction with other cytokine subunits. IL-35, which is composed of EBI3 and p35, is of particular interest given the involvement of IL-27Rα in its signaling pathway.

To narrow down the candidate cytokines, we reanalyzed single-cell RNA sequencing data from CD11c-cre p28^f/f and wild-type thymocytes (Signal Transduct Target Ther. 2022, DOI: 10.1038/s41392-022-01147-z). Our analysis revealed that thymic dendritic cells (DCs) were categorized into two distinct clusters, with both Il12a (p35, which forms IL-35 with EBI3) and Clcf1 (CLCF1) being upregulated in CD11c-cre p28^f/f mice. In CD4 single-positive (SP) thymocytes, the expression levels of gp130 and IL-12Rβ2 (the receptor for IL-35) were comparable between knockout and wild-type mice. However, the mRNA levels of Lifr and Cntfr were low in CD4 SP thymocytes.

Author response image 1.

Single-cell RNA sequencing data from CD11c-cre p28^f/f (KO) and wild-type thymocytes (Signal Transduct Target Ther. 2022, DOI: 10.1038/s41392-022-01147-z).

We have planned to assess the protein levels of IL-35 and CLCF1 in dendritic cells, as well as their respective receptors, to evaluate their effects on STAT1 phosphorylation in CD4⁺ thymocytes from both wild-type and p28-deficient mice. Unfortunately, we have encountered challenges with the mouse breeding and anticipate that it will take approximately six months to obtain the appropriate genotype necessary to complete these experiments.

Reviewer #2 (Public Review):

Summary:

Naïve CD4 T cells in CD11c-Cre p28-floxed mice express highly elevated levels of proinflammatory IFNg and the transcription factor T-bet. This phenotype turned out to be imposed by thymic dendritic cells (DCs) during CD4SP T cell development in the thymus [PMID: 23175475]. The current study affirms these observations, first, by developmentally mapping the IFNg dysregulation to newly generated thymic CD4SP cells [PMID: 23175475], second, by demonstrating increased STAT1 activation being associated with increased T-bet expression in CD11c-Cre p28-floxed CD4 T cells [PMID: 36109504], and lastly, by confirming IL-27 as the key cytokine in this process [PMID: 27469302]. The authors further demonstrate that such dysregulated cytokine expression is specific to the Th1 cytokine IFNg, without affecting the expression of the Th2 cytokine IL-4, thus proposing a role for thymic DC-derived p28 in shaping the cytokine response of newly generated CD4 helper T cells. Mechanistically, CD4SP cells of CD11c-Cre p28-floxed mice were found to display epigenetic changes in the Ifng and Tbx21 gene loci that were consistent with increased transcriptional activities of IFNg and T-bet mRNA expression. Moreover, in autoimmune Aire-deficiency settings, CD11c-Cre p28-floxed CD4 T cells still expressed significantly increased amounts of IFNg, exacerbating the autoimmune response and disease severity. Based on these results, the investigators propose a model where thymic DC-derived IL-27 is necessary to suppress IFNg expression by CD4SP cells and thus would impose a Th2-skewed predisposition of newly generated CD4 T cells in the thymus, potentially relevant in autoimmunity.

Strengths:

Experiments are well-designed and executed. The conclusions are convincing and supported by the experimental results.

Weaknesses:

The premise of the current study is confusing as it tries to use the CD11c-p28 floxed mouse model to explain the Th2-prone immune profile of newly generated CD4SP thymocytes. Instead, it would be more helpful to (1) give full credit to the original study which already described the proinflammatory IFNg+ phenotype of CD4 T cells in CD11c-p28 floxed mice to be mediated by thymic dendritic cells [PMID: 23175475], and then, (2) build on that to explain that this study is aimed to understand the molecular basis of the original finding.

In its essence, this study mostly rediscovers and reaffirms previously reported findings, but with different tools. While the mapping of epigenetic changes in the IFNg and T-bet gene loci and the STAT1 gene signature in CD4SP cells are interesting, these are expected results, and they only reaffirm what would be assumed from the literature. Thus, there is only incremental gain in new insights and information on the role of DC-derived IL-27 in driving the Th1 phenotype of CD4SP cells in CD11c-p28 floxed mice.

Thank you for your valuable comments and suggestions. We appreciate your input and have carefully reviewed the concerns raised regarding the premise and novelty of our study.

Indeed, the current study is built upon the foundational work of Zhang et al. (PMID: 23175475), which first described the proinflammatory IFN-γ⁺ phenotype of CD4 T cells in CD11c-p28 floxed mice mediated by thymic dendritic cells. We have cited this study multiple times in our manuscript to acknowledge its significance. Our goal was to expand on this original finding by exploring the functional bias of newly generated CD4⁺ T cells, elucidating the mechanisms underlying the hyper-Th1 phenotype in the absence of thymic DC-derived IL-27, and exploring its relevance in pathogenesis of autoimmunity.

Our study revisits this phenomenon with a focus on the molecular and epigenetic changes that drive the Th1 bias in CD4SP cells. We demonstrated that the deletion of p28 in thymic dendritic cells leads to an unexpected hyperactivation of STAT1, which is associated with epigenetic modifications that favor Th1 differentiation. These findings provide a deeper understanding of the molecular basis behind the original observation of the Th1-skewed phenotype in CD11c-p28 floxed mice.

However, as you pointed out, there is still a gap in understanding the precise link between p28 deficiency and STAT1 activation. We acknowledge that our study primarily reaffirms previously reported findings with different tools and approaches. While the mapping of epigenetic changes in the IFN-γ and T-bet gene loci and the STAT1 gene signature in CD4SP cells are interesting, they are indeed expected results based on the existing literature. This limits the novelty and incremental gain in new insights provided by our study.

To address this gap and enhance the novelty of our findings, we plan to conduct further investigations to elucidate the detailed mechanisms connecting p28 deficiency to STAT1 hyperactivation. We will explore potential compensatory pathways or alternative signaling mechanisms that may contribute to the observed epigenetic changes and Th1 bias. Additionally, we will consider the broader impact of IL-27 deficiency on the thymic environment and its downstream effects on CD4⁺ T cell differentiation.

We appreciate your feedback and will work to strengthen the mechanistic underpinnings of our study. We believe that these additional efforts will provide a more comprehensive understanding of the role of DC-derived IL-27 in shaping the Th1 phenotype of CD4SP cells and contribute meaningful insights to the field.

Altogether, the major issues of this study remain unresolved:

(1) It is still unclear why the p28-deficiency in thymic dendritic cells would result in increased STAT1 activation in CD4SP cells. Based on their in vitro experiments with blocking anti-IFNg antibodies, the authors conclude that it is unlikely that the constitutive activation of STAT1 would be a secondary effect due to autocrine IFNg production by CD4SP cells. However, this possibility should be further tested with in vivo models, such as Ifng-deficient CD11c-p28 floxed mice. Alternatively, is this an indirect effect by other IFNg producers in the thymus, such as iNKT cells? It is necessary to explain what drives the STAT1 activation in CD11c-p28 floxed CD4SP cells in the first place.

Thank you for your insightful suggestions. We appreciate your feedback and are committed to addressing the critical questions raised regarding the mechanisms underlying STAT1 activation in CD4SP cells in the context of p28 deficiency in thymic dendritic cells.

To further investigate the potential autocrine loop for IFN-γ production, we will conduct in vivo studies using Cd4-Ifng and CD11c-p28 double knockout mice. This model will allow us to directly test whether IFN-γ produced by CD4SP cells themselves contributes to the observed STAT1 activation. Additionally, this approach will help exclude the possibility of indirect effects from other IFN-γ-producing cells in the thymus, such as invariant natural killer T (iNKT) cells, as suggested by the reviewer.

As you correctly pointed out, a key unanswered question is what drives the initial STAT1 activation in CD4SP cells of CD11c-p28 floxed mice. Our current hypothesis is that p28 deficiency enhances the responsiveness of developing thymocytes to STAT1-activating cytokines. This hypothesis is supported by several lines of evidence:

(1) Functional Antagonism: Recent studies have shown that IL-27p28 can act as an antagonist of gp130-mediated signaling. This suggests that in the absence of p28, the inhibitory effect of IL-27p28 on downstream signaling may be lost, leading to increased sensitivity to other cytokines that activate STAT1.

(2) Structural Insights: Structural studies have demonstrated that IL-27p28 is centrally positioned within the complex formed with EBI3 and the two receptor subunits IL-27Rα and gp130. This positioning implies that p28 deficiency could disrupt the balance of cytokine signaling pathways involving these components.

(3)  Phenotypic Similarity: We have observed a similar hyper-Th1 phenotype in mice lacking either p28 or IL-27ra. This similarity suggests that the absence of p28 may lead to increased availability of gp130 for signaling by other cytokines, thereby enhancing STAT1 activation.

Based on these considerations, we hypothesize that the deficiency of p28 results in a greater availability of gp130 to transduce signals from other cytokines, ultimately leading to enhanced STAT1 activation in CD4SP cells. To identify the specific cytokine(s) responsible for this effect, we will focus on gp130-related cytokines, as outlined in our response to Reviewer 1. This will involve reanalysis of single-cell RNA sequencing data and further experimental validation to pinpoint the candidate cytokines driving the observed STAT1 hyperactivation.

We are confident that these additional studies will provide a clearer understanding of the mechanisms linking p28 deficiency in thymic dendritic cells to increased STAT1 activation in CD4SP cells. We appreciate your guidance and look forward to sharing our findings.

(2) It is also unclear whether CD4SP cells are the direct targets of IL-27 p28. The cell-intrinsic effects of IL-27 p28 signaling in CD4SP cells should be assessed and demonstrated, ideally by CD4SP-specific deletion of IL-27Ra, or by establishing bone marrow chimeras of IL-27Ra germline KO mice.

Thanks for the suggestions. Further studies will be performed to test whether developing thymocytes are the direct targets of IL-27 using Cd4-IL-27ra knockout mice or mixed bone marrow chimeras of wildtype and IL-27ra knockout cells. Unfortunately, we have encountered challenges with the mouse breeding and anticipate that it will take approximately six months to obtain the appropriate genotype necessary to complete these experiments.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

(1) Is the hyper-STAT1 response seen in T cells from Cd11c-p28-flox mice due to increased availability and/or increased responsiveness to STAT1 activating cytokines? Studies, where SP, RTE, and Tn cells are pulsed ex vivo with IL-27 and/or other STAT1-activating cytokines, would address the latter (with STAT1 phosphorylation as the major readout). Given the ability of IL-27 to activate STAT3, this pathway should also be addressed. It would be of interest if STAT1 signaling is selectively impaired, as suggested by the work of Twohig et al. (doi: 10.1038/s41590-019-0350-0.)(which should be cited and discussed).

Thank you for your insightful suggestions. We appreciate your input and are committed to addressing the critical questions raised regarding the mechanisms underlying the hyper-activation of STAT1 in T cells from Cd11c-p28-flox mice.

The detailed mechanisms driving the hyper-activation of STAT1 remain to be fully elucidated. Recent studies have shown that IL-27p28 can act as an antagonist of gp130-mediated signaling. Structural analyses have also demonstrated that IL-27p28 interacts with EBI3 and the two receptor subunits IL-27Rα and gp130. Considering these findings and the similar phenotypes observed in p28 and IL-27ra deficient mice, we speculate that the deficiency of either p28 or IL-27ra may increase the availability of gp130 for signaling by other cytokines. This could potentially enhance the responsiveness of developing thymocytes to STAT1-activating cytokines. We will focus our future research on gp130-related cytokines to identify the candidate(s) responsible for the enhanced STAT1 activation in the absence of p28. Alternatively, the release of EBI3 in the absence of p28 may facilitate its coupling with other cytokine subunits. IL-35, which is composed of EBI3 and p35, is of particular interest given the involvement of IL-27Rα in its signaling pathway.

To narrow down the candidate cytokines, we reanalyzed single-cell RNA sequencing data from CD11c-cre p28^f/f and wild-type thymocytes (Signal Transduct Target Ther. 2022, DOI: 10.1038/s41392-022-01147-z). Our analysis revealed that thymic dendritic cells (DCs) were categorized into two distinct clusters, with both Il12a (p35, which forms IL-35 with EBI3) and Clcf1 (CLCF1) being upregulated in CD11c-cre p28^f/f mice. In CD4 single-positive (SP) thymocytes, the expression levels of gp130 and IL-12Rβ2 (the receptor for IL-35) were comparable between knockout and wild-type mice. However, the mRNA levels of Lifr and Cntfr were low in CD4 SP thymocytes.

Single-cell RNA sequencing data from CD11c-cre p28^f/f (KO) and wild-type thymocytes (Signal Transduct Target Ther. 2022, DOI: 10.1038/s41392-022-01147-z).

We have planned to assess the protein levels of IL-35 and CLCF1 in dendritic cells, as well as their respective receptors, to evaluate their effects on STAT1 phosphorylation in CD4⁺ thymocytes from both wild-type and p28-deficient mice. Unfortunately, we have encountered challenges with the mouse crosses and anticipate that it will take approximately six months to obtain the appropriate genotype necessary to complete these experiments.

As you correctly noted, the ability of IL-27 to activate STAT3 signaling is an important consideration. We have carefully examined this pathway in our current study, and our results indicate that neither total nor phosphorylated STAT3 and STAT4 were found to be altered with IL-27p28 ablation (Figure 5B). This suggests that the impact is indeed specific to the STAT1 axis. We will also consider the possibility of selective impairment of STAT1 signaling, as suggested by the work of Twohig et al. (doi: 10.1038/s41590-019-0350-0), which we will cite and discuss in our revised manuscript.

We appreciate your guidance and will work diligently to address these questions in our future studies. We look forward to sharing our findings and contributing to a deeper understanding of the role of IL-27 in the regulation of STAT1 activation in T cells.

(2) It may be that the hyper-Th1 phenotype is not due to cell-intrinsic differences in STAT1 signaling (see Major Point 1) but rather, hyper-responsiveness to TCR + Co-stimulation (as provided in the re-stim assays used throughout). This issue is particularly relevant for the ChIP studies where the author notes that, "...we chose to treat the cells with anti-CD3 and anti-CD28 for 3 days prior to the assay". Why not treat these cells ex vivo with STAT1-activating cytokines instead of anti-CD3/CD28? The current methodology makes it impossible to distinguish between enhanced TCR/CD28 and cytokine signaling, and ultimately does not address SP, RTE, and Tn cells (since they are now activated, blasts.).

Thank you for raising this important point. We appreciate your feedback and fully recognize the limitations of our current methodology, which uses anti-CD3/CD28 stimulation for ChIP experiments. This approach indeed complicates the distinction between enhanced TCR/CD28 signaling and cytokine-mediated STAT1 activation, particularly in the context of SP, RTE, and Tn cells, which become activated blasts under these conditions.

To address these concerns and provide more precise insights into the mechanisms underlying the hyper-Th1 phenotype, we are revising our experimental strategy. Specifically, we are shifting our focus to directly investigate the role of STAT1-activating cytokines in the absence of p28. Based on our previous analysis and re-evaluation of single-cell RNA sequencing data, we have identified IL-35 and CLCF1 as the most promising candidate cytokines.

We are now planning to perform ChIP experiments using these cytokines directly, rather than relying on TCR + co-stimulation. This approach will allow us to more accurately evaluate the impact of these cytokines on STAT1 signaling in CD4⁺ T cells. By treating cells ex vivo with IL-35 and CLCF1, we aim to elucidate whether the observed hyper-Th1 phenotype is driven by enhanced responsiveness to these cytokines, independent of TCR/CD28 signaling.

We regret to inform you that we have encountered unforeseen challenges with mouse crosses, which have delayed our progress. As a result, we anticipate a delay of approximately six months to obtain the appropriate genotypes necessary to complete these experiments. We understand the importance of these revisions and are committed to overcoming these challenges to provide a more robust and accurate analysis.

(3) Studies involving STAT1-deficient mice are necessary (ideally with STAT1 deficiency restricted to the T cell compartment). At a minimum, it must be confirmed that these phenocopy Cd11c-p28-flox mice in terms of SP, RTE, and Tn cells (and their Th1-like character). If a similar hyper-Th1 phenotype is not seen, then the attendant hyper STAT1 response can only be viewed as a red herring.

Thank you for raising this important consideration. We acknowledge the significance of addressing the role of STAT1 specifically within the T cell compartment to validate the mechanisms underlying the hyper-Th1 phenotype observed in Cd11c-p28-flox mice.

We agree that studies involving STAT1-deficient mice, particularly with STAT1 deficiency restricted to the T cell compartment, are essential to confirm whether the hyper-Th1 phenotype is directly driven by STAT1 hyperactivation in T cells. Ideally, such studies would help determine if STAT1 deficiency in T cells phenocopies the Cd11c-p28-flox mice, particularly in terms of the SP, RTE, and Tn cells and their Th1-like characteristics.

Unfortunately, we currently face challenges in obtaining and breeding the appropriate STAT1 conditional knockout mice with T cell-specific deletion. This has limited our ability to conduct these experiments in a timely manner. However, we recognize the importance of these studies and are actively working to secure the necessary resources and models to address this critical question.

We understand that without these experiments, any conclusions drawn about the role of STAT1 hyperactivation in driving the hyper-Th1 phenotype must be considered with caution. If a similar hyper-Th1 phenotype is not observed in STAT1-deficient T cells, then the hyper-STAT1 response may indeed be a secondary or compensatory effect rather than a primary driver.

We are committed to pursuing these studies and will prioritize them in our future work. We will keep you informed of our progress and will update the manuscript with the results of these experiments once completed. We appreciate your patience and understanding as we work to address this important aspect of our research.

(4) The authors mine their RNA-seq data using a STAT1 geneset sourced from studies involving IL-21 as the upstream stimulus. Why was this geneset was chosen? It is true that IL-21 can activate STAT1 but STAT3 is typically viewed as its principal signaling pathway. There are many more appropriate genesets, especially from studies where T cells are cultured with traditional STAT1 stimuli (e.g. IL-27 in Hirahara et al., Immunity 2015 or interferons in Iwata et al., Immunity 2017)doi: 10.1016/j.immuni.2015.04.014, 10.1016/j.immuni.2017.05.005).

Thank you for your insightful comments. We appreciate your attention to the choice of the STAT1 gene set in our RNA-seq analysis.

Initially, we selected the STAT1 gene set from a study involving IL-21 stimulation (GSE63204) because IL-21 is known to activate STAT1, despite STAT3 being its principal signaling pathway. However, we acknowledge that this choice may not have been optimal given the context of our study, which focuses on the role of IL-27 and its impact on STAT1 signaling in T cells.

We agree that gene sets derived from studies using more canonical STAT1 stimuli, such as IL-27 or interferons, would be more relevant for our analysis. In response to your suggestion, we have revised our approach and adopted a gene set from GSE65621, which compares STAT1-/- and wild-type CD4 T cells following IL-27 stimulation. This gene set is more aligned with the focus of our study and provides a more appropriate reference for identifying STAT1-activated genes.

Our re-analysis revealed that 270 genes (FPKM > 1, log2FC > 2) were downregulated in STAT1-/- cells compared to wild-type cells, which we defined as STAT1-activated genes. Notably, approximately 50% of the upregulated differentially expressed genes (55 out of 137) in our dataset fell into the category of STAT1-activated genes, while none were classified as STAT1-suppressed genes (Figure 4B). Furthermore, Gene Set Enrichment Analysis (GSEA) demonstrated significant enrichment of STAT1-activated genes in the transcriptome of CD4 SP thymocytes from the knockout mice (NES = 1.67, nominal p-value = 10^-16, Figure 4D).

These findings support our conclusion that IL-27p28 deficiency leads to enhanced STAT1 activity in CD4 SP thymocytes. We believe that using a more relevant gene set has strengthened our analysis and provided clearer insights into the molecular mechanisms underlying the observed phenotype.

We have cited the relevant studies (Hirahara et al., Immunity 2015; Iwata et al., Immunity 2017) to provide context for our revised analysis and to acknowledge the importance of canonical STAT1 stimuli in T cell signaling. We appreciate your guidance and are confident that these revisions have improved the robustness and relevance of our findings.

(5) Given the ability of IL-27 to activate STAT1 in T cells, it is surprising that SP, RTE, and Tn cells from Cd11c-p28-flox mice exhibit more STAT1 signaling than WT controls. If not IL-27, then what is the stimulus for this STAT1 activity? The authors rule out autocrine IFN-g production in vitro (not in vivo) but provide no further insight.

Thank you for raising this important question. We appreciate your interest in understanding the source of enhanced STAT1 signaling in SP, RTE, and Tn cells from Cd11c-p28-flox mice, especially given the role of IL-27 in activating STAT1 in T cells. As previously discussed, we have identified IL-35 and CLCF1 as the most likely candidate cytokines driving the observed STAT1 activity in the absence of p28. These cytokines are of particular interest due to their potential to activate STAT1 and their relevance in the context of our study.

To address the question of what drives the enhanced STAT1 signaling, we are planning to perform ChIP experiments using these cytokines directly. This approach will allow us to evaluate their impact on STAT1 signaling more precisely, without relying on TCR + co-stimulation. By treating cells ex vivo with IL-35 and CLCF1, we aim to determine whether these cytokines are responsible for the increased STAT1 activity observed in Cd11c-p28-flox mice.

We acknowledge that ruling out autocrine IFN-γ production in vitro, as we have done, does not fully address the potential role of IFN-γ in vivo. Therefore, we are also considering additional in vivo experiments to further investigate this possibility. These studies will help us determine whether other sources of IFN-γ or other cytokines contribute to the observed STAT1 hyperactivation. Unfortunately, due to unforeseen challenges with mouse crosses, we anticipate a delay of approximately six months to obtain the appropriate genotypes necessary for these experiments. We are actively working to resolve these challenges and will update the manuscript with the results of these experiments upon completion.

(6) The RNAseq data affirms that SP, RTE, and Tn cells from Cd11c-p28-flox mice exhibit more STAT1 signaling than WT controls. However, this does little to explain the attendant hyper-Th1 phenotype. Is there evidence that epigenetic machinery is deregulated (to account for changes in DNA. histone methylation)? Were IFN-g and Tbet among these few observed DEG? If so, then this should be highlighted. If not, then the authors must address why not. Are there clues as to why STAT1 signing is exaggerated? Also, the hyper-STAT1 effect should be better described using more rigorous STAT1- and interferon-signature genesets (see the work of Virginia Pascual, Anne O'Garra).

Thank you for your valuable feedback and suggestions. We appreciate your interest in understanding the mechanisms underlying the hyper-Th1 phenotype observed in Cd11c-p28-flox mice. Below, we address each of your points in detail:

(1) Epigenetic Regulation:

We have conducted a thorough analysis of the global levels of key histone modifications, including H3K4me3, H3K9me3, and H3K27me3, as well as the mRNA expression of the enzymes responsible for catalyzing these marks. Our results indicate that there are no significant differences in these histone modifications or the expression of the associated enzymes between Cd11c-p28^f/f and wildtype mice (Figure 3-figure supplement 1A-C). This suggests that the enhanced STAT1 signaling is not a consequence of broad epigenetic deregulation. Instead, we hypothesize that the observed changes may be driven by more specific molecular mechanisms, such as cytokine signaling pathways.

(2) IFN-γ and Tbx21 Expression:

Regarding the expression of Th1-associated genes, our analysis revealed a modest induction of ifng and tbx21 (encoding T-bet) in the CD4SP population following TCR stimulation. However, the baseline expression levels of these genes were quite low in freshly isolated CD4SP cells. Specifically, ifng was undetectable, and tbx21 had an FPKM of 0.29 in wildtype mice compared to 1.05 in Cd11c-p28^f/f mice. While these findings indicate some upregulation of Th1-associated genes, the overall expression levels remain relatively low, suggesting that additional factors may contribute to the hyper-Th1 phenotype.

(3) STAT1 Signature Genesets:

We have revised our analysis to incorporate more rigorous STAT1 and interferon-signature genesets, as suggested. We have adopted gene sets from well-established studies, including those by Virginia Pascual and Anne O'Garra, to provide a more comprehensive and accurate assessment of STAT1 signaling. This approach has enhanced our ability to identify and characterize the genes involved in the STAT1 pathway, providing clearer insights into the exaggerated STAT1 signaling observed in our model.

We appreciate your guidance and are committed to refining our analysis to provide a more detailed understanding of the mechanisms driving the hyper-Th1 phenotype in Cd11c-p28-flox mice. We will continue to explore the potential roles of cytokines such as IL-35 and CLCF1, as well as other factors that may contribute to the observed changes in STAT1 signaling and Th1 differentiation. We look forward to sharing our updated findings and further discussing these mechanisms in our revised manuscript.

(7) Is the hyper-Th1 phenotype of SP, RTE, and Tn cells from Cd11c-p28-flox mice unique to the CD4 compartment? Are developing CD8⁺ cells similarly prone to increased STAT1 signaling and IFN-g production?

Thank you for raising this important point. Our data indeed suggests that the hyper-Th1 phenotype observed in SP, RTE, and Tn cells from Cd11c-p28^f/f mice is unique to the CD4⁺ T cell compartment. Specifically, we found that while CD4⁺ SP cells from Cd11c-p28^f/f mice exhibited a significant upregulation in IL-27 receptor expression (both IL27Ra and gp130) compared to wild-type (WT) mice, CD8⁺ SP cells from the same genotype showed markedly lower expression of these receptor subunits (Figure 1C in Sci Rep. 2016 Jul 29:6:30448. DOI: 10.1038/srep30448). This finding is further supported by our observation that the phosphorylation levels of STAT1, STAT3, and STAT4, downstream targets of IL-27 signaling, were comparable between CD8 SP cells from Cd11c-p28^f/f and WT mice (Author response image 1). Additionally, we observed no significant difference in IFN-γ and granzyme B production between naïve CD8 T cells isolated from the lymph nodes of the two genotypes (Author response image 1). Taken together, these results suggest that the enhanced Th1 differentiation and IFN-γ production seen in the CD4⁺ T cell population from Cd11c-p28^f/f mice is not recapitulated in the CD8⁺ T cell lineage.

Author response image 2.

(A) Intracellular staining was performed with freshly isolated thymocytes from Cd11c-p28^f/f mice and WT littermates mice using antibodies against phosphorylated STAT1 (Y701), STAT3 (Y705), and STAT4 (Y693). The mean fluorescence intensity (MFI) for CD8 SP from three independent experiments (mean ± SD, n=3). (B) CD8⁺ naive T cells were cultured under Th0 conditions for 3 days. The frequency of IFN-γ-, and granzyme B-producing CD8⁺ T cells were determined analyzed by intracellular staining. Representative dot plots (left) and quantification (right, mean ± SD, n=6).

Minor points and questions

(1) Line 84 - Villarino et al. and Pflanz et al. are mis-referenced. Neither involves Trypanosome studies. The former is on Toxoplasma infection and, thus, should be properly referenced in the following sentence.

Thank you for pointing out this error. You are correct that the references to Villarino et al. and Pflanz et al. were misapplied in the context of Trypanosome studies. Villarino et al. focuses on Toxoplasma infection, and we appreciate your guidance to ensure accurate citation. We will correct this in the manuscript and properly cite the studies in their appropriate contexts. Thank you for your vigilance in maintaining the accuracy of our references.

(2) T－bet protein should also be measured by cytometry

We sincerely thank the reviewer for the valuable suggestion regarding the measurement of T-bet protein levels. In response to this comment, we have performed additional experiments to quantify T-bet protein expression using flow cytometry. The results of these analyses have been incorporated into the revised manuscript as Figure 1F.

Reviewer #2 (Recommendations For The Authors):

(1) When new mouse strains are generated in this study, there is no comment on whether there are any changes in the frequency or cell number of CD4 T cells. For instance, in Aire-deficient CD11c-p28 floxed mice, it should be noted whether CD4SP, naïve CD4, and CD4 RTE are all the same in frequency and number compared to their littermate controls. Also, is there any effect on the generation of these thymocytes?

We sincerely thank the reviewer for raising this important point regarding the potential changes in the frequency and cell numbers of CD4⁺ T cells in the newly generated mouse strains. In response to the reviewer’s question, we would like to clarify the following:

(1) Impact of Aire deficiency on CD4⁺ T Cells:

As previously reported by us and others (Aging Dis. 2019, doi: 10.14336/AD.2018.0608; Science. 2002, doi: 10.1126/science.1075958), Aire deficiency does not significantly alter the overall number or frequency of CD4 single-positive (CD4SP) thymocytes, recent thymic emigrants (RTEs), or naïve CD4⁺ T cells. However, it profoundly affects their composition and functional properties, leading to the escape of autoreactive T cells and subsequent autoimmune manifestations.

(2) Observations in Cd11c-p28^f/fAire^-/- mice:

In our study, we observed that the number and frequency of CD4⁺ T cells in the spleen and lymph nodes were comparable among Cd11c-p28^f/f, Aire^-/-, and Cd11c-p28^f/fAire^-/- mice, and WT controls. This suggests that the genetic modifications did not significantly impact the overall development or peripheral maintenance of CD4⁺ T cells.

Author response image 3.

(3) Challenges in assessing RTEs in double knockout mice:

To accurately assess RTEs in the double knockout mice, it would be necessary to cross these mice with Rag-GFP reporter mice, which specifically label RTEs. However, breeding the appropriate mouse strain for this analysis would require additional time and resources, which were beyond the scope of the current study.

(2) There are a couple of typos throughout the manuscript. For example, line 91: IL-27Rα or line 313: phenotype.

We apologize for the typographical errors. We have carefully reviewed the entire manuscript and corrected all identified mistakes, including those on line 91 (IL-27Rα) and line 305 (phenotype).

(4) The authors should show each data point on their bar graphs.

Thank you for the suggestion. We have presented each data point on their bar graphs in the revised manuscript.

(4) It should be noted from which organs the RTE and the naïve T cells were harvested.

Thank you for the constructive suggestion. We isolated CD4⁺ RTEs and mature naive CD4⁺ T cells by sorting GFP⁺CD4⁺CD8^-CD^-NK1.1^- cells (RTEs) and GFP^-CD4⁺CD8^-CD^-CD44^lo cells (naive T cells) from lymph nodes. This detail has been added to the manuscript on line 475.

eLife Assessment

This study presents valuable findings related to seasonal brain size plasticity in the Eurasian common shrew (Sorex araneus), which is an excellent model system for these studies. The evidence supporting the authors' claims is convincing. The work will be of interest to biologists working on neuroscience, plasticity, and evolution.

Reviewer #1 (Public review):

Summary:

In this paper, Thomas et al. set out to study seasonal brain gene expression changes in the Eurasian common shrew. This mammalian species is unusual in that it does not hibernate or migrate but instead stays active all winter while shrinking and then regrowing its brain and other organs. The authors previously examined gene expression changes in two brain regions and the liver. Here, they added data from the hypothalamus, a brain region involved in the regulation of metabolism and homeostasis. The specific goals were to identify genes and gene groups that change expression with the seasons and to identify genes with unusual expression compared to other mammalian species. The reason for this second goal is that genes that change with the season could be due to plastic gene regulation, where the organism simply reacts to environmental change using processes available to all mammals. Such changes are not necessarily indicative of adaptation in the shrew. However, if the same genes are also expression outliers compared to other species that do not show this overwintering strategy, it is more likely that they reflect adaptive changes that contribute to the shrew's unique traits.

The authors succeeded in implementing their experimental design and identified significant genes in each of their specific goals. There was an overlap between these gene lists. The authors provide extensive discussion of the genes they found.

The scope of this paper is quite narrow, as it adds gene expression data for only one additional tissue compared to the authors' previous work in a 2023 preprint. The two papers even use the same animals, which had been collected for that earlier work. As a consequence, the current paper is limited in the results it can present. This is somewhat compensated by an expansive interpretation of the results in the discussion section, but I felt that much of this was too speculative. More importantly, there are several limitations to the design, making it hard to draw stronger conclusions from the data. The main contribution of this work lies in the generated data and the formulation of hypotheses to be tested by future work.

Strengths:

The unique biological model system under study is fascinating. The data were collected in a technically sound manner, and the analyses were done well. The paper is overall very clear, well-written, and easy to follow. It does a thorough job of exploring patterns and enrichments in the various gene sets that are identified.

I specifically applaud the authors for doing a functional follow-up experiment on one of the differentially expressed genes (BCL2L1), even if the results did not support the hypothesis. It is important to report experiments like this and it is terrific to see it done here.

Comments on revised version:

This updated version of the paper is improved compared to its initial version. As such, the strengths remain the same as before, with a fascinating model system and an interesting research question. The earlier weaknesses related to overinterpretation of the data have been largely fixed by shortening the paper and adding appropriate caveats throughout. The paper now also includes a significance test for its overlap between gene lists. While this turned out to be negative (i.e., there is not more overlap between lists than expected by chance), reporting this result transparently has strengthened the paper.

Reviewer #2 (Public review):

Summary:

Shrews go through winter by shrinking their brain and most organs, then regrow them in the spring. The gene expression changes underlying this unusual brain size plasticity were unknown. Here, the authors looked for potential adaptations underlying this trait by looking at differential expression in the hypothalamus. They found enrichments for DE in genes related to the blood brain barrier and calcium signaling, as well as used comparative data to look at gene expression differences that are unique in shrews. This study leverages a fascinating organismal trait to understand plasticity and what might be driving it at the level of gene expression. This manuscript also lays the groundwork for further developing this interesting system.

Strengths:

One strength is that the authors used OU models to look for adaptation in gene expression. The authors also added cell culture work to bolster their findings.

Comments on revised version:

I think that the authors have made a strong revision. No other comments.

Reviewer #3 (Public review):

Summary:

In their study, the authors combine seasonal and comparative transcriptomics to identify candidate genes with plastic, canalized, or lineage-specific (i.e., divergent) expression patterns associated with an unusual overwintering phenomenon (Dehnel's phenomenon - seasonal size plasticity) in the Eurasian shrew. Their focus is on the shrinkage and regrowth of the hypothalamus, a brain region that undergoes significant seasonal size changes in shrews and plays a key role in regulating metabolic homeostasis. Through comparative transcriptomic analysis, they identify genes showing derived (lineage-specific), plastic (seasonally regulated), and canalized (both lineage-specific and plastic) expression patterns. The authors hypothesize that genes involved in pathways such as the blood-brain barrier, metabolic state sensing, and ion-dependent signaling will be enriched among those with notable transcriptomic patterns. They complement their transcriptomic findings with a cell culture-based functional assessment of a candidate gene believed to reduce apoptosis.

Strengths:

The study's rationale and its integration of seasonal and comparative transcriptomics are well-articulated and represent an advancement in the field. The transcriptome, known for its dynamic and plastic nature, is also influenced by evolutionary history. The authors effectively demonstrate how multiple signals-evolutionary, constitutive, and plastic-can be extracted, quantified, and interpreted. The chosen phenotype and study system are particularly compelling, as it not only exemplifies an extreme case of Dehnel's phenotype, but the metabolic requirements of the shrew suggest that genes regulating metabolic homeostasis are under strong selection.

Weaknesses:

The results of the expression patterns are quite compelling and a number of interesting downstream hypotheses are outlined; however, the interpretation of the role of each gene and pathway identified is speculative which dampens the overall impact of the work. That said, I commend the authors on functionally testing one of the differentially expressed genes. I also commend the inclusion of that negative result.

Author response:

The following is the authors’ response to the original reviews

eLife Assessment

This study presents valuable findings related to seasonal brain size plasticity in the Eurasian common shrew (Sorex araneus), which is an excellent model system for these studies. The evidence supporting the authors' claims is convincing. However, the authors should be careful when applying the term adaptive to the gene expression changes they observe; it would be challenging to demonstrate the differential fitness effects of these gene expression changes. The work will be of interest to biologists working on neuroscience, plasticity, and evolution.

We appreciate the reviewers’ suggestions and comments. For the phylogenetic ANOVA we used (EVE), which tests for a separate RNA expression optimum specific to the shrew lineage consistent with expectations for adaptive evolution of gene expression. But, as you noted, while this analysis highlights many candidate genes evolving in a manner consistent with positive selection, further functional validation is required to confirm if and how these genes contribute to Dehnel’s phenomenon. In the discussion, we now emphasize that inferred adaptive expression of these genes is putative and outline that future studies are needed to test the function of proposed adaptations. For example, cell line validations of BCL2L1 on apoptosis is a case study that tests the function of a putatively adaptive change in gene expression, and it illuminates this limitation. We also have refined our discussion to focus more on pathway-level analyses rather than on individual genes, and have addressed other issues presented, including clarity of methods and using sex as a covariate in our analyses.

Public Reviews:

Reviewer #1 (Public review):

Summary:

In this paper, Thomas et al. set out to study seasonal brain gene expression changes in the Eurasian common shrew. This mammalian species is unusual in that it does not hibernate or migrate but instead stays active all winter while shrinking and then regrowing its brain and other organs. The authors previously examined gene expression changes in two brain regions and the liver. Here, they added data from the hypothalamus, a brain region involved in the regulation of metabolism and homeostasis. The specific goals were to identify genes and gene groups that change expression with the seasons and to identify genes with unusual expression compared to other mammalian species. The reason for this second goal is that genes that change with the season could be due to plastic gene regulation, where the organism simply reacts to environmental change using processes available to all mammals. Such changes are not necessarily indicative of adaptation in the shrew. However, if the same genes are also expression outliers compared to other species that do not show this overwintering strategy, it is more likely that they reflect adaptive changes that contribute to the shrew's unique traits.

The authors succeeded in implementing their experimental design and identified significant genes in each of their specific goals. There was an overlap between these gene lists. The authors provide extensive discussion of the genes they found.

The scope of this paper is quite narrow, as it adds gene expression data for only one additional tissue compared to the authors' previous work in a 2023 preprint. The two papers even use the same animals, which had been collected for that earlier work. As a consequence, the current paper is limited in the results it can present. This is somewhat compensated by an expansive interpretation of the results in the discussion section, but I felt that much of this was too speculative. More importantly, there are several limitations to the design, making it hard to draw stronger conclusions from the data. The main contribution of this work lies in the generated data and the formulation of hypotheses to be tested by future work.

Thank you for your interest in our manuscript and for your insights. We addressed your comments below: we now highlight the limitations of our study design in the discussion and emphasize that, while a second optimum of gene expression in shrews is consistent with adaptive evolution, we recognize that not all sources of variation in gene expression can be fully accounted for. We highlight the putative nature of these results in our revisions, especially in our new limitations section (lines 541-555).

Strengths:

The unique biological model system under study is fascinating. The data were collected in a technically sound manner, and the analyses were done well. The paper is overall very clear, well-written, and easy to follow. It does a thorough job of exploring patterns and enrichments in the various gene sets that are identified.

I specifically applaud the authors for doing a functional follow-up experiment on one of the differentially expressed genes (BCL2L1), even if the results did not support the hypothesis. It is important to report experiments like this and it is terrific to see it done here.

We are glad to hear that you found our manuscript fascinating and clearly written. While we hoped to see an effect of BCL2L1 on apoptosis as proposed, we agree that reporting null results is valuable when validating evolutionary inferences.

Weaknesses:

While the paper successfully identifies differentially expressed seasonal genes, the real question is (as explained by the authors) whether these are evolved adaptations in the shrews or whether they reflect plastic changes that also exist in other species. This question was the motivation for the inter-species analyses in the paper, but in my view, these cannot rigorously address this question. Presumably, the data from the other species were not collected in comparable environments as those experienced by the shrews studied here. Instead, they likely (it is not specified, and might not be knowable for the public data) reflect baseline gene expression. To see why this is problematic, consider this analogy: if we were to compare gene expression in the immune system of an individual undergoing an acute infection to other, uninfected individuals, we would see many, strong expression differences. However, it would not be appropriate to claim that the infected individual has unique features - the relevant physiological changes are simply not triggered in the other individuals. The same applies here: it is hard to draw conclusions from seasonal expression data in the shrews to non-seasonal data in the other species, as shrew outlier genes might still reflect physiological changes that weren't active in the other species.

There is no solution for this design flaw given the public data available to the authors except for creating matched data in the other species, which is of course not feasible. The authors should acknowledge and discuss this shortcoming in the paper.

Thank you for taking the time to provide such insightful feedback. As you noted, whiles shrews experience seasonal size changes, their environments may differ from the other species used in this experiment, leading to increased or decreased expression of certain genes and reducing our ability accurately detect selection across the phylogeny. Although we sought to control for as many sources of variation as possible, such as using only post-pubescent, wild, or non-domesticated individuals when feasible, we recognize that not all sources of variation can be fully accounted for within a practical experiment. We agree that these sources of variation can introduce both false positives and negatives into our results, and we have now highlighted this limitation within our discussion (lines 538-552).

Related to the point above: in the section "Evolutionary Divergence in Expression" it is not clear which of the shrew samples were used. Was it all of them, or only those from winter, fall, etc? One might expect different results depending on this. E.g., there could be fewer genes with inferred adaptive change when using only summer samples. The authors should specify which samples were included in these analyses, and, if all samples were used, conduct a robustness analysis to see which of their detected genes survive the exclusion of certain time points.

Thank you for this attention to detail. We used spring adults for this analysis. This decision was made as only used post pubescent individuals for all species in the analysis, and this was the only season where adult shrews were going through Dehnel’s phenomenon. We have now clarified this in both the methods and results (line 247 and line 667)

In the same section, were there also genes with lower shrew expression? None are mentioned in the text, so did the authors not test for this direction, or did they test and there were no significant hits?

We did test for decreased shrew expression compared to the rest of the species, but there were no significant genes with significant decreases. We hypothesize that there are two potential reasons for this results; 1) If a gene were to be selected for decreased expression, selection for constitutive expression of the gene across all species may be weak, and thus found in other lineages as well, or 2) decreased or no expression may relax selection on the coding regions, and thus these genes are not pulled out as we identify 1:1 orthologs. This is consistent with results provided from the original methods manuscript. Thank you for pointing out that we did not discuss this information in the text, and we now include it in our results (lines 250-251).

The Discussion is too long and detailed, given that it can ultimately only speculate about what the various expression changes might mean. Many of the specific points made (e.g. about the blood-brain-barrier being more permissive to sensing metabolic state, about cross-organ communication, the paragraphs on single, specific genes) are a stretch based on the available data. Illustrating this point, the one follow-up experiment the authors did (on BCL2L1) did not give the expected result. I really applaud the authors for having done this experiment, which goes beyond typical studies in this space. At the same time, its result highlights the dangers of reading too much into differential expression analyses.

We agree with your point, while our extensive discussion is useful for testing future hypotheses, ultimately some of the discussion may be too speculative for our readers. To amend this, we have reduced some portions of our discussion and focused more on pathways than individual genes, including removing mechanisms related to HRH2, FAM57B, GPR3, and GABAergic neurons. We hope that this highlights to the reader the speculative nature of many of our results.

There is no test of whether the five genes observed in both analyses (seasonal change and inter-species) exceed the number expected by chance. When two gene sets are drawn at random, some overlap is expected randomly. The expected overlap can be computed by repeated draws of pairs of random sets of the same size as seen in real data and by noting the overlap between the random pairs. If this random distribution often includes sets of five genes, this weakens the conclusions that can be drawn from the genes observed in the real data.

Thank you for highlighting this approach, it is greatly needed. After running this test, we found that observed overlapping genes were more than the expected overlap, yet not significant. We now show this in our methods (lines 277-278) and results (lines 719-720).

Reviewer #2 (Public review):

Summary:

Shrews go through winter by shrinking their brain and most organs, then regrow them in the spring. The gene expression changes underlying this unusual brain size plasticity were unknown. Here, the authors looked for potential adaptations underlying this trait by looking at differential expression in the hypothalamus. They found enrichments for DE in genes related to the blood-brain barrier and calcium signaling, as well as used comparative data to look at gene expression differences that are unique in shrews. This study leverages a fascinating organismal trait to understand plasticity and what might be driving it at the level of gene expression. This manuscript also lays the groundwork for further developing this interesting system.

We are glad you found our manuscript interesting and thank and thank you for your feedback. We hope that we have addressed all of your concerns as described below.

Strengths:

One strength is that the authors used OU models to look for adaptation in gene expression. The authors also added cell culture work to bolster their findings.

Weaknesses:

I think that there should be a bit more of an introduction to Dehnel's phenomenon, given how much it is used throughout.

Thank you for this insight. With a lengthy introduction and discussion, we agree that the importance of Dehnel’s phenomenon may have been overshadowed. We have shortened both sections and emphasized the background on Dehnel’s phenomenon in the first two paragraphs of the introduction, allowing this extraordinary seasonal size plasticity to stand out.

Reviewer #3 (Public review):

Summary:

In their study, the authors combine developmental and comparative transcriptomics to identify candidate genes with plastic, canalized, or lineage-specific (i.e., divergent) expression patterns associated with an unusual overwintering phenomenon (Dehnel's phenomenon - seasonal size plasticity) in the Eurasian shrew. Their focus is on the shrinkage and regrowth of the hypothalamus, a brain region that undergoes significant seasonal size changes in shrews and plays a key role in regulating metabolic homeostasis. Through combined transcriptomic analysis, they identify genes showing derived (lineage-specific), plastic (seasonally regulated), and canalized (both lineage-specific and plastic) expression patterns. The authors hypothesize that genes involved in pathways such as the blood-brain barrier, metabolic state sensing, and ion-dependent signaling will be enriched among those with notable transcriptomic patterns. They complement their transcriptomic findings with a cell culture-based functional assessment of a candidate gene believed to reduce apoptosis.

Strengths:

The study's rationale and its integration of developmental and comparative transcriptomics are well-articulated and represent an advancement in the field. The transcriptome, known for its dynamic and plastic nature, is also influenced by evolutionary history. The authors effectively demonstrate how multiple signals-evolutionary, constitutive, and plastic-can be extracted, quantified, and interpreted. The chosen phenotype and study system are particularly compelling, as it not only exemplifies an extreme case of Dehnel's phenotype, but the metabolic requirements of the shrew suggest that genes regulating metabolic homeostasis are under strong selection.

Weaknesses:

(1) In a number of places (described in detail below), the motivation for the experimental, analytical, or visualization approach is unclear and may obscure or prevent discoveries.

Thank you for finding our research and manuscript compelling, as well as the valuable feedback that will drastically improve our manuscript. We hope that we have alleviated your concerns below by following your instructions below.

(2) Temporal Expression - Figure 1 and Supplemental Figure 2 and associated text:

- It is unclear whether quantitative criteria were used to distinguish "developmental shift" clusters from "season shift" clusters. A visual inspection of Supplemental Figure 2 suggests that some clusters (e.g., clusters 2, 8, and to a lesser extent 12) show seasonal variation, not just developmental differences between stages 1 and 2. While clustering helps to visualize expression patterns, it may not be the most appropriate filter in this case, particularly since all "season shift" clusters are later combined in KEGG pathway and GO analyses (Figure 1B).

- The authors do not indicate whether they perform cluster-specific GO or KEGG pathway enrichment analyses. The current analysis picks up relevant pathways for hypothalamic control of homeostasis, which is a useful validation, but this approach might not fully address the study's key hypotheses.

Thank you for this valuable feedback. We did not want to include clusters we deemed to be related to development, as this should not be attributed to changes associated with Dehnel’s phenomenon. We did this through qualitative, visual inspection, which we realize can differ between parties (i.e., clusters 2, 8, and 12 appeared to be seasonal). Qualitatively, we were looking for extreme divergence between Stage 1 and Stage 5 individuals, as expression was related to season and not development, then the average of these stages within cluster should be relatively similar. We have now quantified this as large differences in z-score (abs(summer juvenile-summer adult)>1.25) without meaningful interseason variations determined by a second local maximum (abs(autumn-winter)<0.5 and abs(winter-summer)<0.5)), and added it both our methods (lines 699-702) and results (line 192).

Regarding the combination of clusters for pathway enrichment compared to individual pathways, we agree that combining clusters may be more informative for overall homeostasis, compared to individual clusters which may inform us on processes directly related to Dehnel’s phenomenon. Initially, we were tentative to conduct this analysis, as clusters contain small gene sets, reducing the ability to detect pathway enrichments. We have now included this analysis, which is reported in our methods (lines 703-704), results (lines 203-204)., and new supplemental table.

(3) Differential expression between shrinkage (stage 2) and regrowth (stage 4) and cell culture targets

- The rationale for selecting BCL2L1 for cell culture experiments should be clarified. While it is part of the apoptosis pathway, several other apoptosis-related genes were identified in the differential gene expression (DGE) analysis, some showing stronger differential expression or shrew-specific branch shifts. Why was BCL2L1 prioritized over these other candidates?

We agree that our rationale for validating BCL2L1 function in neural cell lines was not clearly explained in the manuscript. We selected BCL2L1 because it is the furthest downstream gene in the apoptotic pathway, thus making it the most directly involved gene in programmed cell death, whereas upstream genes could influence additional genes or alternative processes. We have clarified this choice in the revised methods section (lines 748-750).

- The authors mention maintaining (or at least attempting to maintain) a 1:1 sex ratio for the comparative analysis, but it is unclear if this was also done for the S. araneus analysis. If not, why? If so, was sex included as a covariate (e.g., a random effect) in the differential expression analysis? Sex-specific expression elevates with group variation and could impact the discovery of differentially expressed genes.

Regarding the use of sex as a covariate, we acknowledge the concerns raised. In our evolutionary analyses, we maintained a balanced sex ratio within species when possible. EVE models handle the effect of sex on gene expression as intraspecific variation. In shrews, however, we used males exclusively, as females were only found among juvenile individuals. Including those juvenile females would have introduced age effects, with perhaps a larger effect on our results. For the seasonal data, we have now included sex as a covariate in differential expression analyses. However, our design is imbalanced in relation to sex, which we have now discussed in our methods (lines 713-714) and discussion limitations (lines 544-548).

(4) Discussion: The term "adaptive" is used frequently and liberally throughout the discussion. The interpretation of seasonal changes in gene expression as indicators of adaptive evolution should be done cautiously as such changes do not necessarily imply causal or adaptive associations.

Thank you for this insight. We have reviewed our discussion and clarified that adaptations are putative (i.e. lines 146, 285, and 332), and highlighted this in our limitations section.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) I would recommend always spelling out "Dehnel's phenomenon" or even replacing this term (after crediting the DP term) with the more informative "seasonal size plasticity". Every time I saw "DP", I had to remind myself what this referred to. If the authors choose not to do so, please use the acronym consistently (e.g. line 186 has it spelled out).

We have replaced the acronym DP with either the full term or the more informative “seasonal size plasticity” throughout the text.

(2) Line 202: "DEG" has not been defined. Simply add to the line before.

Thank you for this attention to detail. We have added this to the line above (210).

(3) Please add a reference for the "AnAge" tool that was used to determine if samples were pubescent.

Thank you for identifying this oversight. We have now cited the proper paper in line 634.

(4) In the BCL2L1 section in the results, add a callout to Figure 2D.

We have now added a callout to Figure 2D within the results (line 234).

Reviewer #2 (Recommendations for the authors):

(1) Line 122: is associated? These adaptations?

Thank you for identifying that we were missing the words “associated with” here. We have fixed this in the revision.

(2) The first paragraph of the Results should be moved to the methods, except maybe the number of orthologs.

Thank you for this insight. We have removed this portion from the results section.

(3) Why a Bonferroni correction on line 188? That seems too strict.

We agree the Bonferroni correction is strict. Results when using other less strict methods for controlling false discovery rate are also not significant after correction. These corrections can be found within the data, however, we only report on the Bonferroni correction.

(4) Line 427: "is a novel candidate gene for several neurological disorders" needs some references. I see them a couple of sentences later, but that's quite a sentence with no references at the end.

We have added the proper citations for this sentence (line 524).

Reviewer #3 (Recommendations for the authors):

(1) Temporal Expression - Figure 1 and Supplemental Figure 2 and associated text Line176-193:

- The authors report the total number of genes meeting inclusion criteria (>0.5-fold change between any two stages and 2 samples >10 normalized reads), but it would be more informative to also provide the number of genes within each temporal cluster. This would offer a clearer understanding of how gene expression patterns are distributed over time.

Unfortunately, this information is difficult to depict on our figure and would use too much space in the text. We have thus added a description of the range of genes in a new supplemental table depicting this information.

- It is unclear whether quantitative criteria were used to distinguish "developmental shift" clusters from "season shift" clusters. A visual inspection of Supplemental Figure 2 suggests that some clusters (e.g., clusters 2, 8, and to a lesser extent 12) show seasonal variation, not just developmental differences between stages 1 and 2. While clustering helps to visualize expression patterns, it may not be the most appropriate filter in this case, particularly since all "season shift" clusters are later combined in KEGG pathway and GO analyses (Fig. 1B). Using a differential gene expression criterion might be more suitable. For example, do excluded genes show significant log-fold differences between late-stage comparisons?

As previously mentioned, we have now quantified seasonal shifts as large differences in z-score (abs(summer juveniles-summer adults)>1.25) without meaningful interseason variations determined by a second local maximum (abs(autumn-winter)<0.5 and abs(winter-summer)<0.5)), and added it to our methods (lines 699-702).  We then follow this up with differential expression analyses as described in Figure 2.

- Did the authors perform cluster-specific GO or KEGG pathway enrichment analyses instead of focusing on the combined set of genes across the season shift clusters? While I understand that the small number of genes in each cluster may be limiting, if pathways emerge from cluster-specific analysis, they could provide more detailed insights into the functional significance of these temporal expression patterns. The current analysis picks up relevant pathways for hypothalamic control of homeostasis, which is a useful validation, but this approach might not fully address the study's key hypotheses. Additionally, no corrections for multiple hypothesis testing were applied, as noted in the results. A more refined gene set (e.g., using differential expression criteria, described above) could be more appropriate for these analyses.

We have now included cluster-specific KEGG enrichments as previously described.

(2) Differential expression between shrinkage (stage 2) and regrowth (stage 4) and cell culture targets - Figure 2 and lines195-227:

- The rationale for selecting BCL2L1 for cell culture experiments should be clarified. While it is part of the apoptosis pathway, several other apoptosis-related genes were identified in the differential gene expression (DGE) analysis, some showing stronger differential expression or shrew-specific branch shifts. Why was BCL2L1 prioritized over these other candidates?

We have now included the reasoning for further validation of BCL2L1 as described above.

- The relevance of the "higher degree" differentially expressed genes needs more explanation. Although this group of genes is highlighted in the results, they are not featured in any subsequent analyses, leaving their importance unclear.

Thank you for this insight. We have removed this from the methods as it is not relevant to subsequent analyses or conclusions.

- The authors mention maintaining (or at least attempting to maintain) a 1:1 sex ratio for the comparative analysis (Line 525), but it is unclear if this was also done for the S. araneus analysis. If so, was sex included as a covariate (e.g., a random effect) in the differential expression analysis?

We have now incorporated information on sex as described above.

(3) Discussion:

The term "adaptive" is used frequently and liberally throughout the discussion, but the authors should be cautious in interpreting seasonal changes in gene expression as indicators of adaptive evolution. Such changes do not necessarily imply causal or adaptive associations, and this distinction should be clearly stated when discussing the results.

Thank you for this feedback and we agree with your conclusion, while a second expression optimum in the shrew lineage is indicative of adaptive expression, we cannot fully determine whether these are caused by genetic or environmental factors, despite careful attention to experimental design. We have highlighted this as a limitation in the discussion.

(4) Minor Editorial Comment:

Line 105: "... maintenance of an energy budgets..." delete "an"

We have removed this grammatical error.

eLife Assessment

The manuscript by de La Forest Divonne et al. offers an important and detailed exploration of the immune cells in the oyster Crassostrea gigas, by correlating distinct hemocyte morphotypes with specific single-cell transcriptional profiles. The evidence supporting the conclusion is convincing, deriving from the comprehensive dataset that not only captures unicellular diversity but also associates these cells with distinct immune roles, making it an invaluable resource for the broader research community.

Reviewer #1 (Public review):

Summary

In this manuscript, De La Forest Divonne et al. build a repertory of hemocytes from adult Pacific oysters combining scRNAseq data with cytologic and biochemical analyses. Three categories of hemocytes were described previously in this species (i.e. blast, hyalinocyte and granulocytes). Based on scRNAseq data, the authors identified 7 hemocyte clusters presenting distinct transcriptional signatures. Using Kegg pathway enrichment and RBGOA, the authors determined the main molecular features of the clusters. In parallel, using cytologic markers, the authors classified 7 populations of hemocytes (i.e. ML, H, BBL, ABL, SGC, BGC, and VC) presenting distinct sizes, nucleus sizes, acidophilic/basophilic, presence of pseudopods, cytoplasm/nucleus ratio and presence of granules. Then, the authors compared the phenotypic features with potential transcriptional signatures seen in the scRNAseq. The hemocytes were separated in a density gradient to enrich for specific subpopulations. The cell composition of each cell fraction was determined using cytologic markers and the cell fractions were analysed by quantitative PCR targeting major cluster markers (two per cluster). With this approach, the authors could assign cluster 7 to VC, cluster 2 to H, and cluster 3 to SGC. The other clusters did not show a clear association with this experimental approach. Using phagocytic assays, ROS, and copper monitoring, the authors showed that ML and SGC are phagocytic, ML produces ROS, and SGC and BGC accumulate copper. Then with the density gradient/qPCR approach, the authors identified the populations expressing anti-microbial peptides (ABL, BBL, and H). At last, the authors used Monocle to predict differentiation trajectories for each subgroup of hemocytes using cluster 4 as the progenitor subpopulation.

The manuscript provides a comprehensive characterisation of the diversity of circulating immune cells found in Pacific oysters.

Strengths

The combination of scRNAseq, cytologic markers and gradient based hemocyte sorting offers an integrative view of the immune cell diversity.<br /> Hemocytes represent a very plastic cell population that has key roles in homeostatic and challenged conditions. Grasping the molecular features of these cells at the single-cell level will help understand their biology.<br /> This type of study may help elucidate the diversification of immune cells in comparative studies and evolutionary immunology.

Weaknesses

Several figures show inconsistency leading to erroneous conclusions and some conclusions are poorly supported. Moreover, the manuscript remains highly descriptive with limited comparison with the available literature.

Comments on revisions:

The authors replied to most comments.

Author response:

The following is the authors’ response to the previous reviews

Reviewer #1 (Recommendations for the authors):

Major comments

(1) Line 201: The threshold of 0.25 was maintained to select enriched genes, which minimize the value of the GO term enrichment analyses. It may notably explain why the term phagosome is enriched in cluster 7, while experimental data indicate that cluster 7 is not phagocytic. In addition, the authors mentioned in the 1st response to reviewer that they would include DotPlot to illustrate the specificity of the genes corresponding to the main GO terms. This should notably include the ribosomal genes found enriched in cluster 4, which constitute the basis used by the authors to call cluster 4 the progenitor cluster.

We appreciate the reviewer’s concern regarding our chosen log2FC threshold (0.25) for GO term enrichment. To assess the robustness of our approach, we tested more stringent thresholds (e.g., 0.5) and verified that our overall interpretations remain consistent. However, we acknowledge that certain GO terms, such as phagosome, may appear in clusters that are not primarily phagocytic. This is likely due to the fact that genes involved in vesicle trafficking, endo-lysosomal compartments and intracellular degradation processes overlap with those classically associated with phagocytosis.

Therefore, the KEGG-based enrichment of phagosome in cluster 7 does not necessarily imply active phagocytosis but could instead reflect these alternative vesicular processes. As we show, cluster 7 correspond to vesicular cells, and as seen in cytology we named these cells after their very high content of vesicular structures. As functional annotation based solely on transcriptomic data can sometimes lead to overinterpretations, we emphasize the importance of biological validation, which we have partially addressed through functional assays in this study.

Regarding the specificity of ribosomal gene expression in cluster 4, we analyzed the distribution of ribosomal genes expressed across all clusters, as shown in Supplementary Figure S1-J. This analysis demonstrates that cluster 4 is specifically enriched in ribosome-related genes, reinforcing its characterization as a transcriptionally active population. Given that ribosomal gene expression is a key feature often associated with proliferative or metabolically active cells, these findings support our initial interpretation that cluster 4 may represent an undifferentiated or progenitor-like population.

We acknowledge the reviewer’s suggestion to include a DotPlot to further illustrate the specificity of these genes in cluster 4. However, we believe that Supplementary Figure S1-J already effectively demonstrates this enrichment by presenting the percentage of ribosomal genes per cluster. A DotPlot representation would primarily convey the same information in a different format, but without providing additional insight into the specificity of ribosomal gene expression within cluster 4.

(2) The lineage analysis is highly speculative and based on weak evidences. Initiating the hemocyte lineage to C4 is based on rRNA expression levels. C6 would constitute a better candidate, notably with the expression of PU-1, ELF2 and GATA3 that regulate progenitors differentiation in mammals (doi: 10.3389/fimmu.2019.00228, doi:10.1128/microbiolspec.mchd-0024-2, doi: 10.1098/rsob.180152) while C4 do not display any specific transcription factors (Figure 7I). In addition, the representation and interpretation of the transcriptome dynamics in the different lineages are erroneous. There are major inconsistencies between the data shown in the heatmaps Fig7C-H, Fig S10 and the dotplot in Fig7I. For example, Gata3 (G31054) and CgTFEB (G30997) illustrate the inconsistency. Fig S10C show GATA3 going down from cluster 4 to cluster 6 while Fig 7I show an increase level of expression in 6 compared to 4. CgTFEB (G30997) decrease from C4 to VC in Fig 7F while it increases according to Fig 7I. At last, Figure 7D: the umap show transition from C4 to C5 while the heatmap mention C4 to C6 (I believe there is a mix up with Figure 7E.

We sincerely apologize for the inconsistencies noted between the different panels of Figure 7. These discrepancies resulted from using an incorrect matrix dataset during the initial representation. To address this issue, we have fully reprocessed the data and now provide a corrected and improved depiction of gene expression dynamics along the pseudotime trajectory. We are grateful to the reviewer for having help us to correct theses mistakes.

In the revised version, we offer a comprehensive and consistent representation of expression level variations for key genes identified by the Monocle3 algorithm. Supplementary Figure S10 now presents the average expression variation of these significant genes as a function of pseudotime. Based on this dataset, we carefully selected representative genes to construct panels C to H of Figure 7, ensuring coherence across all figures. These updated panels show both average expression levels and the percentage of expressing cells along the pseudotime trajectory, providing a clearer interpretation of transcriptomic dynamics.

We appreciate the reviewer’s helpful feedback regarding our lineage analysis and the suggestion that cluster 6 might be a more appropriate progenitor based on the expression of mammalian-like transcription factors such as PU-1, ELF2, and GATA3. Below, we clarify our rationale for choosing cluster 4 as the root of the pseudotime and discuss the functional implications of the identified transcription factors.

We can hypothesize that clusters 4, 5, or 6 could each potentially represent early progenitor-like states, as these three clusters are transcriptionally close (Lines 539-541). These clusters have not yet been conclusively identified in terms of classical hemocyte morphology, and they appear to arise from ABL- or BBL-type cells. Our decision to root the pseudotime at cluster 4 was motivated by its strong expression of core transcription and translation genes, suggesting a particular stage of translation activity that was not observed for cluster 5 or cluster 6. Cluster 5 and 6 may correspond to a similar population of cells, most probably Blast-Like cells at different stages of cell cycle or differentiation engagement.

Although cluster 6 expresses PU-1, ELF2, and GATA3, which are known regulators of haematopoietic progenitor differentiation in vertebrates, it is essential to highlight that structural homology does not necessarily imply functional equivalence. Moreover, the expression of PU-1, ELF2, and GATA3 does not strictly characterize a population as “undifferentiated” or progenitor-like. Studies such as those by Buenrostro et al. (Cell, 2018) have demonstrated that these transcription factors can remain active in or reemerge during more lineage-committed stages. For instance, PU-1 is essential for myeloid and B-cell differentiation, GATA3 is involved in T-lymphocyte lineage commitment (though transiently expressed in early progenitors), and ELF2 participates in lineage-specific pathways. Thus, their presence does not imply a primitive state but rather highlights their broader functional roles in guiding and refining lineage decisions. Functional annotation of these transcription factors in invertebrate systems remains speculative, particularly as morphological or molecular markers specific to these early hemocyte lineages are not yet fully established. Further functional assays (e.g., knockdown/overexpression or lineage tracing using cells (ABL and BBL) from clusters 4, 5 and 6) will be necessary to determine which hemocyte population harbor progenitor properties and differentiation potential.

To further address the reviewer’s concern, we performed complementary pseudotime analyses by initiating Monocle 3 trajectories from clusters 4, 5, and 6 individually, as well as collectively (4/5/6). These analyses (see attached figure) confirm that the overall differentiation topology remains unchanged regardless of the selected root, consistently revealing two main pathways: one leading to hyalinocytes and the other to the granular lineage (ML, SGC, and VC). This consistency strongly suggests that clusters 4, 5, and 6 represent related pools of progenitor-like cells. Therefore, choosing cluster 4 based on its transcription/translation readiness does not alter the inferred branching architecture of hemocyte differentiation.

We appreciate the reviewer’s suggestions, which have helped us improve our manuscript and clarify our rationale.

Author response image 1.

Representation of the trajectories obtained from Monocle3 analysis using different pseudotime origins, showing that changing the rooting did not alter the overall differentiation topology. (A) Pathways identified with cluster 4, (B) cluster 5, (C) cluster 6, and (D) cluster 4/5/6 origins.

(3) Concerning the AMP expression analysis in Figure 6: the qPCR data show that Cg-BPI and Cg-Defh are expressed broadly in all fractions including 6 and 7, which is in conflict with the statement Line 473 indicating that SGC (fractions 6 and 7) is not expressing AMP. In addition, this analysis should be combined with the expression profile of all AMP in the scRNAseq data (list available in 10.1016/j.fsi.2015.02.040).

We thank the reviewer for highlighting this point. We acknowledge that the qPCR data show expression of Cg-BPI and Cg-Defh across all fractions, including fractions 6 and 7 corresponding to SGC. However, our conclusion that SGCs do not express antimicrobial peptides (AMPs) was based on a correlation analysis rather than direct detection of AMPs in granular cells. Specifically, the qPCR experiments were designed to measure AMP expression levels in fractionated hemocyte populations relative to a control sample of whole hemolymph. We then performed a correlation analysis between AMP expression levels and the proportion of each hemocyte type in the fractions. This approach allowed us to infer a lower expression of AMP in granular cells, as reflected in the heatmap presented in Figure 6.

Regarding the suggestion to integrate AMP expression profiles from scRNA-seq data, we wrote that the limited sequencing depth of our scRNA-seq analysis was insufficient to accurately detect AMP expression (Ligne 472-473 → “However, due to the limited sequencing depth, the scRNA-seq analysis was not sensitive enough to reveal AMP expression.”.  Additionally, many of the known AMPs of Crassostrea gigas are not annotated in the genome, further complicating their identification within the scRNA-seq dataset. As a result, we were unable to perform the requested integration of AMP expression profiles from scRNA-seq data.

(4) The transcription factor expression analysis is descriptive and the interpretation too partial. These data should be compared with other systems. Most transcription factors show functional conservation, notably in the inflammatory pathways, which can provide valuable information to understand the function of the clusters 5 and 6 for which limited data are available.

We appreciate the reviewer’s suggestion to compare the identified transcription factors with other systems. However, since we did not perform a detailed phylogenetic analysis of the transcription factors identified in our dataset, we refrain from making assumptions about their functional conservation across species. Our analysis aims to provide a descriptive overview of transcription factor expression patterns in hemocyte clusters, which serves as a foundation for future functional studies. While transcription factor profiles may provide insights into the potential roles of clusters 5 and 6, assigning precise functions based solely on bioinformatic predictions remains speculative. Further experimental validation, including functional assays and evolutionary analyses, would be necessary to confirm the roles of these transcription factors, which is beyond the scope of the present study.

Minor comments

Line 212-213: the text should be reformulated. In the result part, it is more important to mention that the reannotation is based on conserved proteins functions than to mention the tool Orson.

We have reworded this section to emphasize that the updated annotation is function-based, using Orson primarily as the bioinformatics tool for improved GO annotation. We now place the emphasis on the conserved protein functions underlying the reannotation. Lines 212-215 : “Using the Orson pipeline (see Materials and Methods), these files were used to extract and process the longest CDSs for GO-term annotation, and we then reannotated each predicted protein by sequence homology, assigning putative functions and improving downstream GO-term analyses.”

Figure 2: I would recommend homogenizing the two Dotplot representation with the same color gradient and representing the gene numbers in both case.

We appreciate the reviewer’s suggestion to improve the clarity and consistency of Figure 2. In response, we have homogenized the color gradients across the two DotPlot representations and have included gene numbers in both cases to ensure a more uniform and informative visualization.

Table 2: pct1 and pct2 should be presented individually like in table 1

We now present these columns separately (pct1, pct2) as in Table 1, so readers can compare the fraction of expressing cells in each cluster more transparently.

Line 403-414: how many cells were quantified for the phagocytic experiments ?

We have added the exact number of cells that were counted to determine phagocytic indices and the number of technical/biological replicates. Line 411, the text was modified : “Macrophage-like cells and small granule cells showed a phagocytic activity of 49 % and 55 %, respectively, and a phagocytosis index of 3.5 and 5.2 particles per cell respectively (Fig. 5B and Supp. Fig. 7B), as confirmed in 3 independent experiments examining a total of 2,807 cells.”

Line 458: for copper staining, how many cells and how many replicates were done for the quantification ?

We have specified the number of hemocytes and number of independent replicates used when quantifying rhodanine-stained (copper-accumulating) cells. Line 458 the following text was added : “and a total of 1,562 cells were examined across three independent experiments.”

Line 461: what are the authors referring to when mentioning the link between copper homeostasis and scRNAseq?

Single-cell RNA sequencing (scRNA-seq) analysis revealed an upregulation of several copper transport– related genes, including G4790 (a copper transporter) with a 2.7 log2FC and a pct ratio of 42, as well as the divalent cation transporters G5864 (zinc transporter ZIP10) and G4920 (zinc transporter 8), specifically in cluster 3 cells identified as small granule cells. These findings reinforce a potential role for this cluster in metal homeostasis.

We modified lines 462-467 as : “ These results provide functional evidence that small granule cells (SGCs) are specialized in metal homeostasis in addition to phagocytosis, as suggested by the scRNA-seq data identifying cluster 3. Specifically, single-cell RNA sequencing revealed an upregulation of copper transport– related genes, including G4790 (a copper transporter) with a 2.7 log2FC and a pct ratio of 42, reinforcing the role of SGCs in copper homeostasis (see Supp. File S1).”

Line 611: it would be nice to display the enrichment of the phagocytic receptor in cluster 3 (dotplot or feature plot) to illustrate the comment.

We appreciate the reviewer’s insightful suggestion regarding a more comprehensive analysis of phagocytic receptors. While a full inventory is beyond the scope of this study, we acknowledge the value of such an approach and hope that our findings will serve as a foundation for future investigations in this direction.

Although we have highlighted certain phagocytic receptors (e.g., a scavenger receptor domain-containing gene) in our scRNA-seq dataset, it is beyond the scope of the current study to inventory all phagocytosisrelated receptors in the C. gigas genome, which itself would be a substantial undertaking. Moreover, singlecell RNA sequencing captures only about 15–20% of each cell’s mRNA, so we inherently lose a significant portion of the transcriptome, further limiting our ability to pinpoint all relevant phagocytic receptor genes. Adding more figures to cover every candidate receptor would risk overloading this paper, thus we focus on the most prominent examples. A promising approach for more exhaustive analysis would involve efficiently isolating granulocytes (e.g., via Percoll gradient) and performing targeted RNA-seq on this cell population to thoroughly explore genes involved in phagocytosis.

Line 640-644: the authors mentioned that ML may be able to perform ETosis based on the oxidative burst.

This hypothesis requires further evidences. Are other markers of ETosis expressed in this cell type?

We agree that additional experimental evidence (e.g., detection of histone citrullination, extracellular DNA networks) is necessary to confirm ETosis in molluscan immune cells. We present ML-mediated ETosis only as a speculative possibility based on oxidative burst capacity as it was shown in different pieces of work that ETosis is inhibited by NADPH inhibitors (Poirier et al. 2014). Nevertheless, the expression of histones in the macrophage-like cluster (cluster 1) reinforces this possibility, as histone modifications play a key role in chromatin decondensation during ETosis.

Reviewer #2 (Recommendations for the authors):

Figure 1: In Figure 1B, the cell clusters are named 1 to 7, whereas in Figure 1C they are displayed as clusters 0 to 6. There is a mismatch between the identification of the clusters.

We thank the reviewer for identifying this inconsistency. The cluster numbering has been corrected to ensure consistency between Figures 1B and 1C.

Figure 2B: the font size could be increased for greater clarity.

We thank the reviewer for this suggestion. The font size in Figure 2B has been increased to improve clarity and readability.

Line 221: "Figures 2B, C and D" appears to refer to Figure S2 rather than the main Figure 2.

The text has been corrected to properly reference the figure.

Line 754: "Anopheles gambiae" should be italicised

We thank the reviewer for pointing this out. "Anopheles gambiae" has been italicized accordingly.

Bibliography

Integrated Single-Cell Analysis Maps the Continuous Regulatory Landscape of Human Hematopoietic

Differentiation. Buenrostro, Jason D. et al. Cell, Volume 173, Issue 6, 1535 - 1548.e16

Antimicrobial Histones and DNA Traps in Invertebrate Immunity

Poirier, Aurore C. et al. Journal of Biological Chemistry, Volume 289, Issue 36, 24821 - 24831

eLife Assessment

This important study shows how the relative importance of inter-species interactions in microbiomes can be inferred from empirical species abundance data. The methods based on statistical physics of disordered systems are convincing and rigorous, and allow for distinguishing healthy and non-healthy human gut microbiomes via differences in their inter-species interaction patterns. This work should be of broad interest to researchers in microbial ecology and theoretical biophysics.

Reviewer #1 (Public review):

Summary:

In this manuscript, the authors develop a novel method to infer ecologically-informative parameters across healthy and diseased states of the gut microbiota, although the method is generalizable to other datasets for species abundances. The authors leverage techniques from theoretical physics of disordered systems to infer different parameters - mean and standard deviation for the strength of bacterial interspecies interactions, a bacterial immigration rate, and the strength of demographic noise - that describe the statistics of microbiota samples from two groups-one for healthy subjects and another one for subjects with chronic inflammation syndromes. To do this, the authors simulate communities with a modified version of the Generalized Lotka-Volterra model and randomly-generated interactions, and then use a moment-matching algorithm to find sets of parameters that better reproduce the data for species abundances. They find that these parameters are different for the healthy and diseased microbiota groups. The results suggest, for example, that bacterial interaction strengths, relative to noise and immigration, are more dominant for microbiota dynamics in diseased states than in healthy states.

We think that this manuscript brings an important contribution that will be of interest in the areas of statistical physics, (microbiota) ecology, and (biological) data science. The evidence of their results is solid and the work improves the state-of-the-art in terms of methods. There are a few weaknesses that, in our opinion, the authors could address to further improve the work.

Strengths:

(1) Using a fairly generic ecological model, the method can identify the change in the relative importance of different ecological forces (distribution of interspecies interactions, demographic noise, and immigration) in different sample groups. The authors focus on the case of the human gut microbiota, showing that the data are consistent with a higher influence of species interactions (relative to demographic noise and immigration) in a disease microbiota state than in healthy ones.

(2) The method is novel, original, and it improves the state-of-the-art methodology for the inference of ecologically relevant parameters. The analysis provides solid evidence for the conclusions.

Weaknesses:

In the way it is written, this work might be mostly read by physicists. We believe that, with some rewriting, the authors could better highlight the ecological implications of the results and make the method more accessible to a broader audience.

Reviewer #2 (Public review):

Summary:

This valuable work aims to infer, from microbiome data, microbial species interaction patterns associated with healthy and unhealthy human gut microbiomes. Using solid techniques from statistical physics, the authors propose that healthy and unhealthy microbiome interaction patterns substantially differ. Unhealthy microbiomes are closer to instability and single-strain dominance; whereas healthy microbiomes showcase near-neutral dynamics, mostly driven by demographic noise and immigration.

Strengths:

A well-written article, relatively easy to follow and transparent despite the high degree of technicality of the underlying theory. The authors provide a powerful inferring procedure, which bypasses the issue of having only compositional data.

Weaknesses:

(1) This sentence in the introduction seems key to me: "Focusing on single species properties as species abundance distribution (SAD), fail to characterise altered states of microbiome." Yet it is not explained what is meant by 'fail', and thus what the proposed approach 'solves'.

(2) Lack of validation, following arbitrary modelling choices made (symmetry of interactions, weak-interaction limit, uniform carrying capacity).<br /> Inconsistent interpretation of instability. Here, instability is associated with the transition to the marginal phase, which becomes chaotic when interaction symmetry is broken. But as the authors acknowledge, the weak interaction limit does not reproduce fat-tailed abundance distributions found in data. On the other hand, strong interaction regimes, where chaos prevails, tend to do so (Mallmin et al, PNAS 2024). Thus, the nature of the instability towards which unhealthy microbiomes approach is unclear.

(3) Three technical points about the methodology and interpretation.<br /> a) How can order parameters h and q0 can be inferred, if in the compositional data they are fixed by definition?<br /> b) How is it possible that weaker interaction variance is associated with approach to instability, when the opposite is usually true?<br /> c) Having an idea of what the empirical data compares to the theoretical fits would be valuable.

Implications:

As the authors say, this is a proof of concept. They point at limits and ways to go forward, in particular pointing at ways in which species abundance distributions could be better reproduced by the predicted dynamical models. One implication that is missing, in my opinion, is the interpretability of the results, and what this work achieves that was missing from other approaches (see weaknesses section above): what do we learn from the fact that changes in microbial interactions characterise healthy from unhealthy microbiota? For instance, what does this mean for medical research?

Reviewer #3 (Public review):

Summary:

I found the manuscript to be well-written. I have a few questions regarding the model, though the bulk of my comments are requests to provide definitions and additional clarity. There are concepts and approaches used in this manuscript that are clear boons for understanding the ecology of microbiomes but are rarely considered by researchers approaching the manuscript from a traditional biology background. The authors have clearly considered this in their writing of S1 and S2, so addressing these comments should be straightforward. The methods section is particularly informative and well-written, with sufficient explanations of each step of the derivation that should be informative to researchers in the microbial life sciences who are not well-versed with physics-inspired approaches to ecology dynamics.

Strengths:

The modeling efforts of this study primarily rely on a disordered form of the generalized Lotka-Volterra (gLV) model. This model can be appropriate for investigating certain systems, and the authors are clear about when and how more mechanistic models (i.e., consumer-resource) can lead to gLV. Phenomenological models such as this have been found to be highly useful for investigating the ecology of microbiomes, so this modeling choice seems justified, and the limitations are laid out.

Weaknesses:

The authors use metagenomic data of diseased and healthy patients that were first processed in Pasqualini et al. (2024). The use of metagenomic data leads me to a question regarding the role of sampling effort (i.e., read counts) in shaping model parameters such as $h$. This parameter is equal to the average of 1/# species across samples because the data are compositional in nature. My understanding is that $h$ was calculated using total abundances (i.e., read counts). The number of observed species is strongly influenced by sampling effort, so it would be useful if the number of reads were plotted against the number of species for healthy and diseased subjects.

However, the role of sampling effort can depend on the type of data, and my instinct about the role that sampling effort plays in species detection is primarily based on 16S data. The dependency between these two variables may be less severe for the authors' metagenomic pipeline. This potential discrepancy raises a broader issue regarding the investigation of microbial macroecological patterns and the inference of ecological parameters. Often microbial macroecology researchers rely on 16S rRNA amplicon data because that type of data is abundant and comparatively low-cost. Some in microbiology and bioinformatics are increasingly pushing researchers to choose metagenomics over 16S. Sometimes this choice is valid (discovery of new MAGs, investigate allele frequency changes within species, etc.), sometimes it is driven by the false equivalence "more data = better". The outcome, though, is that we have a body of more-or-less established microbial macroecological patterns which rest on 16S data and are now slowly incorporating results from metagenomics. To my knowledge, there has not been a systematic evaluation of the macroecological patterns that do and do not vary by one's choice in 16S vs. metagenomics. Several of the authors in this manuscript have previously compared the MAD shape for 16S and metagenomic datasets in Pasqualini et al., but moving forward, a more comprehensive study seems necessary (2024).

References

Pasqualini, Jacopo, et al. "Emergent ecological patterns and modelling of gut microbiomes in health and in disease." PLOS Computational Biology 20.9 (2024): e1012482.

Author response:

Reviewer #1:

Strengths:

(1) Using a fairly generic ecological model, the method can identify the change in the relative importance of different ecological forces (distribution of interspecies interactions, demographic noise, and immigration) in different sample groups. The authors focus on the case of the human gut microbiota, showing that the data are consistent with a higher influence of species interactions (relative to demographic noise and immigration) in a disease microbiota state than in healthy ones. (2) The method is novel, original, and it improves the state-of-the-art methodology for the inference of ecologically relevant parameters. The analysis provides solid evidence for the conclusions. 

Weaknesses:

In the way it is written, this work might be mostly read by physicists. We believe that, with some rewriting, the authors could better highlight the ecological implications of the results and make the method more accessible to a broader audience.

We thank the reviewer for their positive and constructive feedback. We particularly appreciate the recognition of the novelty and robustness of our method, as well as the insight that it sheds light on the shifting ecological forces between healthy and diseased microbiomes. In response to the concern about the manuscript’s accessibility, we aim to revise key sections – including the Introduction, Results, and Discussion – to more clearly articulate the ecological relevance of our theoretical findings. We would like to emphasize that our approach offers a novel perspective for analyzing individual species' abundances, as well as for understanding interaction patterns and stability at the community level. By placing our results within a broader context accessible to readers from diverse backgrounds, we aim for the revised version to appeal to a wider audience, including ecologists and microbiome scientists, while preserving the rigor of our underlying statistical physics framework.

Reviewer #2:

Strengths:

A well-written article, relatively easy to follow and transparent despite the high degree of technicality of the underlying theory. The authors provide a powerful inferring procedure, which bypasses the issue of having only compositional data. 

Weaknesses:

(1) This sentence in the introduction seems key to me: "Focusing on single species properties as species abundance distribution (SAD), it fails to characterise altered states of microbiome." Yet it is not explained what is meant by 'fail', and thus what the proposed approach 'solves'. (2) Lack of validation, following arbitrary modelling choices made (symmetry of interactions, weak-interaction limit, uniform carrying capacity). Inconsistent interpretation of instability. Here, instability is associated with the transition to the marginal phase, which becomes chaotic when interaction symmetry is broken. But as the authors acknowledge, the weak interaction limit does not reproduce fat-tailed abundance distributions found in data. On the other hand, strong interaction regimes, where chaos prevails, tend to do so (Mallmin et al, PNAS 2024). Thus, the nature of the instability towards which unhealthy microbiomes approach is unclear. (3) Three technical points about the methodology and interpretation. a) How can order parameters ℎ and 𝑞0 can be inferred, if in the compositional data they are fixed by definition? b) How is it possible that weaker interaction variance is associated with an approach to instability, when the opposite is usually true? c) Having an idea of what the empirical data compares to the theoretical fits would be valuable. Implications: As the authors say, this is a proof of concept. They point at limits and ways to go forward, in particular pointing at ways in which species abundance distributions could be better reproduced by the predicted dynamical models. One implication that is missing, in my opinion, is the interpretability of the results, and what this work achieves that was missing from other approaches (see weaknesses section above): what do we learn from the fact that changes in microbial interactions characterise healthy from unhealthy microbiota? For instance, what does this mean for medical research?

We greatly appreciate the reviewer’s thoughtful analysis highlighting both the strengths and areas of ambiguity in our work.

(1) To clarify the sentence on the limitations of species abundance distributions (SADs), we aim to explain in the revised version that while SADs summarize the relative abundance of individual species, they fail to capture the species-species correlations that we have shown (Seppi et al., Biomolecules 2023) to be more susceptible to the healthy state of the host. Our method thus focused on the interaction statistics among species, providing insights into underlying dynamics and stability of the microbiomes and their differences between healthy and unhealthy hosts.

(2) Regarding model assumptions, we acknowledge that the weak interaction regime and symmetry hypotheses simplify the analysis and may not capture all empirical richness, such as fat-tailed distributions of species abundance. However, we interpret instability not as a path to chaos per se, but as a transition toward a multi-attractor phase, where each microbiome reaches a different fixed point. This is consistent with prior empirical findings invoking the “Anna Karenina principle”, where healthy microbiomes resemble one another, but disease states tend to deviate from this picture (see Pasqualini et al., PLOS Comp. Bio. 2024). We consider our framework as a starting point and agree that further extensions incorporating strong interaction regimes (as suggested by Mallmin et al., PNAS 2024) or relaxing other model assumptions could reveal even richer dynamical patterns. The computational pipeline we present can be, in fact, easily generalizable to include different population dynamics models.

On the technical questions: (a) While compositional data constrain relative abundances, we can still estimate diversity-dependent parameters (h and q0) using alpha-diversity statistics across samples, which show meaningful variation; (b) The counter-intuitive instability that the reviewer pointed out arises from the interplay between demographic stochasticity and quenched disorder. It is the combined contribution of these two factors in phase space – not either one alone – that drives the transition. For clarity, see Figure 1 in Altieri et al., Phys. Rev. Lett. 2021; (c) We plan to include plots that compare empirical data to theoretical model fits. This will help visualize how well the model captures observed microbial community properties demographic noise (𝑇), healthy communities are more stable (i.e., distantσ from the and how even with larger species interaction heterogeneity (σ) and larger critical line), as measured, by the replicon eigenvalue. Finally, regarding interpretability and implications: by showing that ecological interaction networks – not just species identities – differ between healthy and unhealthy states, our work suggests a conceptual shift. This could inform medical strategies aimed at restoring community-level stability rather than targeting individual microbes. In the revised Discussion section, we will elaborate on this point to better highlight its practical implications and outline potential directions for future research.

Reviewer #3:

Strengths:

The modeling efforts of this study primarily rely on a disordered form of the generalized Lotka-Volterra (gLV) model. This model can be appropriate for investigating certain systems, and the authors are clear about when and how more mechanistic models (i.e., consumer-resource) can lead to gLV. Phenomenological models such as this have been found to be highly useful for investigating the ecology of microbiomes, so this modeling choice seems justified, and the limitations are laid out. 

Weaknesses:

The authors use metagenomic data of diseased and healthy patients that were first processed in Pasqualini et al. (2024). The use of metagenomic data leads me to a question regarding the role of sampling effort (i.e., read counts) in shaping model parameters such as h. This parameter is equal to the average of 1/# species across samples because the data are compositional in nature. My understanding is that it was calculated using total abundances (i.e., read counts). The number of observed species is strongly influenced by sampling effort, so it would be useful if the number of reads were plotted against the number of species for healthy and diseased subjects. However, the role of sampling effort can depend on the type of data, and my instinct about the role that sampling effort plays in species detection is primarily based on 16S data. The dependency between these two variables may be less severe for the authors' metagenomic pipeline. This potential discrepancy raises a broader issue regarding the investigation of microbial macroecological patterns and the inference of ecological parameters. Often microbial macroecology researchers rely on 16S rRNA amplicon data because that type of data is abundant and comparatively low-cost. Some in microbiology and bioinformatics are increasingly pushing researchers to choose metagenomics over 16S. Sometimes this choice is valid (discovery of new MAGs, investigate allele frequency changes within species, etc.), sometimes it is driven by the false equivalence "more data = better". The outcome, though, is that we have a body of more-or-less established microbial macroecological patterns which rest on 16S data and are now slowly incorporating results from metagenomics. To my knowledge, there has not been a systematic evaluation of the macroecological patterns that do and do not vary by one's choice in 16S vs. metagenomics. Several of the authors in this manuscript have previously compared the MAD shape for 16S and metagenomic datasets in Pasqualini et al., but moving forward, a more comprehensive study seems necessary.

We thank the reviewer for this insightful and nuanced comment, which particularly highlights the broader methodological context of our data sources. Indeed, metagenomic sequencing introduces different biases with respect to 16S data. First, we would like to emphasize that we estimated the order parameters from the data by using relative abundances. Second, while the concern regarding the influence of sequencing depth and species diversity on the estimation of the order parameters is valid, we refer to a previous publication by some of the authors (Pasqualini et al., 2024; see Figure 4, panels g and h). There, we pointed out that the observed outcome is weakly influenced by sequencing depth in our dataset, while the main impact on the order parameters estimate comes from the species diversity of the two groups. In the same publication, we showed that other well-known patterns (species abundance distribution, mean abundance distribution) are also observed. Also, to mitigate the effect of the number of samples and sequencing depth, we estimated the order parameters by a bootstrap procedure (90% of samples for healthy and diseased groups, 5000 resamples), which resulted in the error bars in Figure 2.

We also fully agree with the broader call for a systematic comparison of macroecological patterns derived from 16S and metagenomic data. While some of us have already begun exploring this direction (e.g., Pasqualini et al., 2024), the reviewer’s comment highlights its significance and motivates us to pursue a more comprehensive, integrative analysis across data types. While we found qualitative agreement of these patterns with previous publications (e.g., Grilli, Nature Comm. 2020), we will acknowledge this as an important future direction in the Discussion section.

References

(1) Seppi, M., Pasqualini, J., Facchin, S., Savarino, E.V. and Suweis, S., 2023. Emergent functional organization of gut microbiomes in health and diseases. Biomolecules, 14(1), p.5.

(2) Pasqualini, J., Facchin, S., Rinaldo, A., Maritan, A., Savarino, E. and Suweis, S., 2024. Emergent ecological patterns and modelling of gut microbiomes in health and in disease. PLOS Computational Biology, 20(9), p.e1012482.

(3) Mallmin, E., Traulsen, A. and De Monte, S., 2024. Chaotic turnover of rare and abundant species in a strongly interacting model community. Proceedings of the National Academy of Sciences, 121(11), p.e2312822121.

(4) Altieri, A., Roy, F., Cammarota, C., & Biroli, G. (2021). Properties of equilibria and glassy phases of the random Lotka-Volterra model with demographic noise. Physical Review Letters, 126(25), 258301.

(5) Grilli, J. (2020). Macroecological laws describe variation and diversity in microbial communities. Nature communications, 11(1), 4743.

eLife Assessment

This study makes an important contribution to the molecular mechanisms of neural circuit formation. The data convincingly show that the transcription factor Sp1 regulates ephrin-mediated axon guidance in the spinal cord. Although the authors show that Sp1 and its co-activators p300 and CBP are required to induce ephrin expression, additional discussion and/or experiments are needed to support the claims that Sp1 regulates cis-binding of Epha receptors, or that Sp1 controls ephrin expression in relevant motor neuron populations. The study will be of broad interest to developmental neurobiologists.

Reviewer #1 (Public review):

The manuscript by Liao et al investigates the mechanisms that induce ephrin expression in spinal cord lateral motor column (LMC) neurons to facilitate axon guidance into the dorsal and ventral limb. The authors show that Sp1 and its co-activators p300 and CBP are required to induce ephrin expression to modulate the responsiveness of motor neurons to external ephrin cues. The study is well done and convincingly demonstrates the role of Sp1 in motor neuron axon guidance.

Further discussion and clarification of some results would further improve the study.

(1) The mechanism that the authors propose (Figure 7) and is also supported by their data is that Sp1 induces ephrinA5 in LMCm and ephrinB2 in LMCl to attenuate inappropriate responses to external ephrins in the limb. Therefore, deletion of Sp1 should result in mistargeting of LMCl and LMCm axons, as shown in the mouse data, but no overt changes in the number of axons in the ventral and dorsal limb. From the mouse backfills, it seems that an equal number of LMCm/LMCl project into the wrong side of the limb. However, the chick data show an increase of axons projecting into the ventral limb in the Sp1 knockout. Is this also true in the mouse? The authors state that medial and lateral LMC neurons differ in their reliance on Sp1 function but that is not supported by the mouse backfill data (27% vs 32% motor neurons mistargeted). Also, the model presented in Figure 7 does not explain how Sp1 overexpression leads to axon guidance defects.

(2) The authors do not directly show changes in ephrin expression in motor neurons, either in chick or mouse, after Sp1 knockout, which is the basis of their model. The experiment in Figure 4G seems to be Sp1 overexpression rather than knockdown (as mentioned in the results) and NSC-34 cells may not be relevant to motor neurons in vivo. NSC-34 experiments are also not described in the methods.

(3) There is no information about how the RNA-sequencing experiment was done (which neurons were isolated, how, at what age, how many replicates, etc) so it is hard to interpret the resulting data.

(4) It is unclear why the authors chose to use a Syn1-cre driver rather than a motor neuron restricted cre driver. Since this is a broad neuronal cre driver, the behavioral defects shown in Figure 7 may not be solely due to Sp1 deletion in motor neurons. Are there other relevant neuronal populations that express Sp1 that are targeted by this cre-mediated deletion?

Reviewer #2 (Public review):

Summary:

This study shows that transcription factor Sp1 is required for correct ventral vs. dorsal targeting of limb-innervating LMC motor neurons using mouse and chick as model systems. In a wild-type embryo, lateral LMC axons specifically target dorsal muscles while medial LMC axons target ventral muscles. The authors convincingly show that this specificity is lost when Sp1 is knocked down or knocked out - axons of both lateral and medial LMC motor neurons project to both dorsal and ventral muscles in mutant conditions. The authors then conduct RNA-seq and ChIP experiments to show that Sp1 loss of function disrupts Ephrin-Epha receptor signaling pathway genes. These molecules are known to provide attractive or repulsive cues to guide LMC axons to their targets. The authors show that attraction/repulsion properties of medial and lateral LMC axons to specific Ephrin/Epha molecules are in fact disrupted in Sp1 mutants using ex vivo explant studies. Finally, the authors show that behaviors like coordinated movement and grip strength are also affected in Sp1 mutant mice. This study convincingly shows that Sp1 is important for correct circuit wiring of LMC neurons, and moves the field forward by elucidating a new level of transcriptional regulation required in this process. However, the claims made by the authors that the mode of Sp1-mediated regulation is through cis-attenuation of Epha activity is not well supported. These and additional strengths and weaknesses in approach and in data interpretation are discussed below.

Strengths:

(1) The study convincingly shows that wildtype levels of Sp1 are necessary for LMC axon targeting specificity. The combination of the following approaches is a strength:<br /> a) Both loss of function and gain of function experiments are performed for Sp1 and show complementary effects on the axon targeting phenotype.<br /> b) Retrograde labeling of LMC neurons from dorsal and ventral muscles shows that Sp1 mutants clearly lose the specificity of LMC axon targeting.<br /> c) The authors also use explant experiments to show that both loss of Sp1 and gain of Sp1 show clear changes in attraction and repulsion to specific ephrin and epha receptor molecules.<br /> d) The Sp1 loss and gain of function experiments are well controlled to show that the changes in axon wiring observed are not due to cell death, cell fate switches, or due to unequal numbers of medial and lateral LMC neurons being labeled in the experiments.

(2) It is also convincing that Sp1 requires cofactors p300 and CBP for its function. In the absence of these cofactors, the gain of function phenotypes of Sp1 are subdued.

Weaknesses:

(1) The robustness of RNAseq and ChIP experiments is difficult to judge as methods are not described. For example, it is unclear if RNAseq is performed on purified motor neurons or on whole spinal cords. This is an important consideration as Sp1 is a broadly expressed protein.

(2) The authors state that expression of Ephrin A5 and Ephrin B2 is reduced based on RNAseq data, however, it is not shown that this reduction occurs specifically in LMC neurons.

(3) The authors show Sp1 ChIP peaks at Ephrin B2 promoter, but nothing is mentioned about peaks at Eprin A5 or other types of signaling molecules like Sema7a, which are also differentially expressed in Sp1 mutants. There is also no mention of the correlation between changes in gene expression seen in RNAseq data and the binding profile of Sp1 seen in ChIP data, which could help establish the robustness of these datasets.

(4) The authors conclude that Sp1 functions by activating Ephrin A5 in medial LMC and Ephrin B2 in lateral LMC. The argument, as I understand it, is that this activation leads to cis attenuation of their respective Epha receptors and therefore targeting the correct muscle. Though none of the data presented go against this hypothesis, this hypothesis is also not fully supported. Specifically:<br /> a) It would be important to know that modulation of Sp1 expression leads to changes in EphrinA5 and B2 in LMC lateral/medial neurons.<br /> b) It would also be important to show that none of the other changes caused by Sp1 are responsible for axon mistargeting by performing rescue experiments with Ephrin A5 and Ephrin B2.<br /> c) To make the most convincing case, experiments showing increased or decreased cis-binding of Ephrin molecules with Epha receptors would be necessary. This study would still be compelling without this last experiment, but the language in the abstract would need to be modulated.

(5) All behavior experiments are done in a pan-neuronal knockout of Sp1. As Sp1 is broadly expressed in neurons, a statement describing whether and why the authors think the phenotypes arise from Sp1's function in LMC motor neurons would be helpful. Experimentally, rescue experiments in which Sp1 is restored in LMC neurons or motor neurons would also make this claim more convincing.

Reviewer #3 (Public review):

Summary:

This is a compelling study on the role of Sp1 in motor axon trajectory selection, demonstrating that Sp1 is both necessary and sufficient for correct axon guidance in the limb. Sp1 regulates ephrin ligand expression to fine-tune Eph/ephrin signaling in the lateral motor column (LMC) neurons.

Strengths:

The study integrates multiple approaches. These include in ovo electroporation in chick embryos, conditional knockout mouse models, transcriptomic analyses, and functional assays such as stripe assays and behavioral testing-to provide robust evidence for Sp1's role in axon guidance mechanisms. The manuscript is well-written and scientifically rigorous, and the findings are of broad interest to the developmental neuroscience community.

Weaknesses:

Some aspects of the manuscript could be improved to enhance clarity, ensure logical flow, and strengthen the impact of the findings.

Author response:

Reviewer 1:

(1) Clarification of axon mistargeting patterns and model interpretation

We will clarify the apparent discrepancy between chick and mouse axon mistargeting data. Specifically, we will expand the explanation in the main text and Figure 7 legend and/or revise the model in Figure 7 to better reflect observed phenotypes and clarify how Sp1 overexpression contributes to mistargeting.

(2) Evidence for Sp1-dependent ephrin expression

We agree that demonstrating ephrin expression changes in motor neurons is essential. We will: • Conduct in situ hybridization and/or immunostaining for ephrins in control and Sp1 mutant spinal cords from both chick and mouse embryos.

Clarify and expand the methodological details of the NSC-34 cell experiments shown in Figure 4G.

(3) RNA-seq experiment details

We will revise the Methods section to provide additional experimental details.

(4) Use of Syn1-cre

We acknowledge concerns about the broad expression of Syn1-cre. To address this:

We will clarify our rationale for using Syn1-cre and describe its expression pattern in the spinal cord.

We are evaluating the feasibility of additional experiments using a motor neuron-specific Cre driver to confirm cell-type specificity.

We will include a new paragraph in the Discussion addressing potential contributions from other neuronal populations.

Reviewer 2:

(1) & (2) Clarification and localization of RNA-seq data

We will expand the Methods section to provide greater detail on the RNA-seq approach. In addition, we will validate ephrin downregulation in LMC neurons using in situ hybridization and/or immunostaining.

(3) Integration of ChIP and RNA-seq data We will:

Report additional ChIP peaks for ephrinA5 and other differentially expressed genes such as Sema7a.

Add a summary figure that integrates ChIP and RNA-seq results to strengthen the link between Sp1 binding and transcriptional regulation.

(4) Clarification of the cis-attenuation model

We recognize that our data do not yet directly demonstrate Sp1’s role in cis-attenuation. To address this:

We will revise the abstract and main text to frame Sp1's role in cis-attenuation as a hypothesis. • We are exploring the feasibility of ephrinA5 and B2 rescue experiments in Sp1-deficient embryos to test specificity.

(5) Behavioral phenotypes and cell-type specificity

We will clarify that behavioral phenotypes may result from combined effects across neuron populations due to Syn1-cre expression. To address this:

We are planning rescue experiments with Sp1 expression in chick embryos to test for rescue of axon misrouting.

We will include a new paragraph in the Discussion to highlight this limitation and discuss alternative interpretations.

Reviewer 3:

We appreciate your positive evaluation and support for the rigor of our study.

In response to your suggestions:

We are revising the manuscript to improve clarity and flow, particularly the transitions between datasets.

We will update Figure 7 and the associated text to more clearly convey the working model and avoid overinterpretation.

We thank all reviewers for their constructive feedback and are committed to addressing each point thoroughly. All revisions will be clearly marked in the resubmitted manuscript.

eLife Assessment

This important study uses the delay line axon model in the chick brainstem auditory circuit to examine the interactions between oligodendrocytes and axons in the formation of internodal distances. This is a significant and actively studied topic, and the authors have used this preparation to support the hypothesis that regional heterogeneity in oligodendrocytes underlies the observed variation in internodal length. In a solid series of experiments, the authors have used enhanced tetanus neurotoxin light chains, a genetically encoded silencing tool, to inhibit vesicular release from axons and support the hypothesis that regional heterogeneity among oligodendrocytes may underlie the biased nodal spacing pattern in the sound localization circuit.

Reviewer #1 (Public review):

Summary:

The manuscript by Egawa and colleagues investigates differences in nodal spacing in an avian auditory brain stem circuit. The results are clearly presented and data are of very high quality. The authors make two main conclusions:

(1) Node spacing, i.e. internodal length, is intrinsically specified by the oligodendrocytes in the region they are found in, rather than axonal properties (branching or diameter).

(2) Activity is necessary (we don't know what kind of signaling) for normal numbers of oligodendrocytes and therefore the extent of myelination.

These are interesting observations, albeit phenomenon. I have only a few criticisms that should be addressed:

(1) The use of the term 'distribution' when describing the location of nodes is confusing. I think the authors mean rather than the patterns of nodal distribution, the pattern of nodal spacing. They have investigated spacing along the axon. I encourage the authors to substitute node spacing or internodal length for node distribution.

(2) In Seidl et al. (J Neurosci 2010) it was reported that axon diameter and internodal length (nodal spacing) were different for regions of the circuit. Can the authors help me better understand the difference between the Seidl results and those presented here?

(3) The authors looked only in very young animals - are the results reported here applicable only to development, or does additional refinement take place with aging?

(4) The fact that internodal length is specified by the oligodendrocyte suggests that activity may not modify the location of nodes of Ranvier - although again, the authors have only looked during early development. This is quite different than this reviewer's original thoughts - that activity altered internodal length and axon diameter. Thus, the results here argue against node plasticity. The authors may choose to highlight this point or argue for or against it based on results in adult birds?:

Significance:

This paper may argue against node plasticity as a mechanism for tuning of neural circuits. Myelin plasticity is a very hot topic right now and node plasticity reflects myelin plasticity. this seems to be a circuit where perhaps plasticity is NOT occurring. That would be interesting to test directly. One limitation is that this is limited to development.

Reviewer #2 (Public review):

Summary:

Egawa et al describe the developmental timeline of the assembly of nodes of Ranvier in the chick brainstem auditory circuit. In this unique system, the spacing between nodes varies significantly in different regions of the same axon from early stages, which the authors suggest is critical for accurate sound localization. Egawa et al set out to determine which factors regulate this differential node spacing. They do this by using immunohistological analyses to test the correlation of node spacing with morphological properties of the axons, and properties of oligodendrocytes, glial cells that wrap axons with the myelin sheaths that flank the nodes of Ranvier. They find that axonal structure does not vary significantly, but that oligodendrocyte density and morphology varies in the different regions traversed by these axons, which suggests this is a key determinant of the region-specific differences in node density and myelin sheath length. They also find that differential oligodendrocyte density is partly determined by secreted neuronal signals, as (presumed) blockage of vesicle fusion with tetanus toxin reduced oligodendrocyte density in the region where it is normally higher. Based on these findings, the authors propose that oligodendrocyte morphology, myelin sheath length, and consequently nodal distribution are primarily determined by intrinsic oligodendrocyte properties rather than neuronal factors such as activity.

Major comments:

(1) It is essential that the authors validate the efficiency of TeNT to prove that vesicular release is indeed inhibited, to be able to make any claims about the effect of vesicular release on oligodendrogenesis/myelination.

(2) Related to 1, can the authors clarify if their TeNT expression system results in the whole tract being silenced? It appears from Fig. 6 that their approach leads to sparse expression of TeNT in individual neurons, which enables them to measure myelination parameters. Can the authors discuss how silencing a single axon can lead to a regional effect in oligodendrocyte number?

(3) The authors need to fully revise their statistical analyses throughout and supply additional information that is needed to assess if their analyses are adequate:<br /> (3.1) the authors use a variety of statistical tests and it is not always obvious why they chose a particular test. For example, in Fig. 2G they chose a Kruskal-Wallis test instead of a two-way ANOVA or Mann-Whitney U test, which are much more common in the field. What is the rationale for the test choice?<br /> (3.2) in some cases, the choice of test appears wholly inappropriate. For example, in Fig. 3H-K, an unpaired t-test is inappropriate if the two regions were analysed in the same samples. In Fig. 5, was a t-test used for comparisons between multiple groups in the same dataset? If so, an ANOVA may be more appropriate.<br /> (3.3) in some cases, the authors do not mention which test was used (Fig 3: E-G no test indicated, despite asterisks; G/L/M - which regression test that was used? What does r indicate?)<br /> (3.4) more concerningly, throughout the results, data may have been pseudo-replicated. t-tests and ANOVAs assume that each observation in a dataset is independent of the other observations. In figures 1-4 and 6 there is a very large "n" number, but the authors do not indicate what this corresponds to. This leaves it open to interpretation, and the large values suggest that the number of nodes, internodal segments, or cells may have been used. These are not independent experimental units, and should be averaged per independent biological replicate - i.e. per animal (N).<br /> (3.5) related to the pseudo-replication issue, can the authors include individual datapoints in graphs for full transparency, per biological replicates, in addition or in alternative to bar-graphs (e.g. Fig. 5 and 6).

(4) The main finding of the study is that the density of nodes differs between two regions of the chicken auditory circuit, probably due to morphological differences in the respective oligodendrocytes. Can the authors discuss if this finding is likely to be specific to the bird auditory circuit?

(5) Provided the authors amend their statistical analyses, and assuming significant differences remain as shown, the study shows a correlation (but not causation) between node spacing and oligodendrocyte density, but the authors did not manipulate oligodendrocyte density per se (i.e. cell-autonomously). Therefore, the authors should either include such experiments, or revise some of their phrasing to soften their claims and conclusions. For example, the word "determine" in the title could be replaced by "correlate with" for a more accurate representation of the work. Similar sentences throughout the main text should be amended.

(6) The authors fail to introduce, or discuss, very pertinent prior studies, in particular to contextualize their findings with:<br /> (6.1) known neuron-autonomous modes of node formation prior to myelination, e.g. Zonta et al (PMID 18573915); Vagionitis et al (PMID 35172135); Freeman et al (PMID 25561543)<br /> (6.2) known effects of vesicular fusion directly on myelinating capacity and oligodendrogenesis, e.g. Mensch et al (PMID 25849985)<br /> (6.3) known correlation of myelin length and thickness with axonal diameter, e.g. Murray & Blakemore (PMID 7012280); Ibrahim et al (PMID 8583214); Hildebrand et al (PMID 8441812).<br /> (6.4) regional heterogeneity in the oligodendrocyte transcriptome (page 9, studies summarized in PMID 36313617)

Significance:

In our view the study tackles a fundamental question likely to be of interest to a specialized audience of cellular neuroscientists. This descriptive study is suggestive that in the studied system, oligodendrocyte density determines the spacing between nodes of Ranvier, but further manipulations of oligodendrocyte density per se are needed to test this convincingly.

Reviewer #3 (Public review):

Summary:

The authors have investigated the myelination pattern along the axons of chick avian cochlear nucleus. It has already been shown that there are regional differences in the internodal length of axons in the nucleus magnocellularis. In the tract region across the midline, internodes are longer than in the nucleus laminaris region. Here the authors suggest that the difference in internodal length is attributed to heterogeneity of oligodendrocytes. In the tract region oligodendrocytes would contribute longer myelin internodes, while oligodendrocytes in the nucleus laminaris region would synthesize shorter myelin internodes. Not only length of myelin internodes differs, but also along the same axon unmyelinated areas between two internodes may vary. This is an interesting contribution since all these differences contribute to differential conduction velocity regulating ipsilateral and contralateral innervation of coincidence detector neurons. However, the demonstration falls rather short of being convincing.

Major comments:

(1) The authors neglect the possibility that nodal cluster may be formed prior to myelin deposition. They have investigated stages E12 (no nodal clusters) and E15 (nodal cluster plus MAG+ myelin). Fig. 1D is of dubious quality. It would be important to investigate stages between E12 and E15 to observe the formation of pre-nodes, i.e., clustering of nodal components prior to myelin deposition.

(2) The claim that axonal diameter is constant along the axonal length need to be demonstrated at the EM level. This would also allow to measure possible regional differences in the thickness of the myelin sheath and number of myelin wraps.

(3) The observation that internodal length differs is explain by heterogeneity of sources of oligodendrocyte is not convincing. Oligodendrocytes a priori from the same origin remyelinate shorter internode after a demyelination event.

Significance:

The authors suggest that the difference in internodal length is attributed to heterogeneity of oligodendrocytes. In the tract region oligodendrocytes would contribute longer myelin internodes, while oligodendrocytes in the nucleus laminaris region would synthesize shorter myelin internodes. Not only length of myelin internodes differs, but also along the same axon unmyelinated areas between two internodes may vary. This is an interesting contribution since all these differences contribute to differential conduction velocity regulating ipsilateral and contralateral innervation of coincidence detector neurons.

Author response:

General Statements

We sincerely appreciate the constructive comments from the reviewers, which have significantly enhanced the clarity and rigor of our manuscript. Most of their suggestions have already been incorporated into the revised version. Additionally, we are conducting an additional experiment to further substantiate our conclusions, and preliminary data seem to support our findings.

As pointed out by Reviewer #1, the regulation of neural circuit function by oligodendrocytes is currently a highly significant and actively studied topic. Our study demonstrates that regional heterogeneity in oligodendrocytes underlies the microsecond-level computational processes in the sound localization circuit. We believe this work represents a substantial contribution to the field.

Description of the planned revisions

• Evaluation of node formation along axons sparsely expressing eTeNT (related to Reviewer #2: comment 1)

Based on the approximately 90% expression efficiency of A3V-eTeNT in NM neurons, we interpreted that vesicular release from NM axons was largely inhibited in the NL region, leading to the suppression of oligodendrogenesis and the subsequent emergence of unmyelinated segments. However, the effects of eTeNT on myelination are likely diverse, and a possibility remains that eTeNT directly disrupted axon-oligodendrocyte interactions, preventing oligodendrocytes from myelinating the axons expressing eTeNT.

To test this possibility, we have initiated an additional experiment to evaluate formation of nodes along axons, while expressing eTeNT sparsely by electroporation. Preliminary results indicated that unmyelinated segments did not increase, supporting our original conclusion. After completion of the experiment, we will include the findings as a Supplementary Figure associated with Figure 6, which will provide a clearer understanding of how eTeNT influences myelination.

Description of the revisions that have already been incorporated in the transferred manuscript

• Revised terminology from "nodal distribution" to "nodal spacing" throughout the manuscript. (Reviewer #1: comment 1)

• Emphasized that our analyses were focused on the main trunk of NM axons (Reviewer #1: comment 2) We explicitly stated throughout the manuscript that we analyzed the main trunk of NM axons and made it clear that our findings do not contradict those by Seidl et al. (J Neurosci 2010), showing the similar axon diameter between midline and ventral NL regions (page 7, line 7).

• Added an explanation on the maturation of sound localization circuit (Reviewer #1: comment 3) We explained that chickens have high ability of sound localization at hatch, emphasizing that the sound localization circuit is almost fully developed by E21 (page 4, line 12).

• Emphasized the diverse effects of neuronal activity on oligodendrocytes (page 10, line 18) (Reviewer #1: comment 4)

• Added details on the efficiency of A3V-eTeNT expression in NM neurons to the Results section (page 8, line 5) (Reviewer #2: comment 1)  

• Made it clear in Figure Legend for Figure 6D that the analysis was conducted under the condition, where most of the axons were labeled by A3V-eTeNT (page 31, line 9) (Reviewer #2: comment 2)

• Clarified the rationale for statistical test selection (Reviewer #2: comment 3.1)

• Reanalyzed all statistical data with appropriate methods using R (Reviewer #2: comment 3.2)

• Clearly indicated which statistical tests were used in each figure (Reviewer #2: comment 3.3)

• Clarified what n represents and N used in each experiment (Reviewer #2: comment 3.4)

• Added individual data points to bar graphs in Figure  5 and 6 (Reviewer #2: comment 3.5)

• Emphasized the importance of comparing the ITD circuit with that of rodents (page 11, line 32) (Reviewer #2: comment 4) 

• Softened the expressions related to "determine" (Reviewer #2: comment 5)

Our study demonstrates that regional differences in the intrinsic properties of oligodendrocytes are the prominent determinant of nodal spacing patterns. However, we acknowledge that this does not establish a direct causation. Accordingly, relevant expressions have been revised throughout the manuscript.

• Added references (Reviewer #2: comment 6)

• Corrected units in Figure 1G (Reviewer #2: comment 7)

• Added discussion about the involvement of pre-nodal clusters in the regional differences in nodal spacing (page 9, line 35) (Reviewer #3: comment 1).

Related to this issue, we have added new data to Figure 6I.

• Discussed the possibility that the developmental origin and/or the pericellular microenvironment of OPCs contributed to the regional heterogeneity of oligodendrocytes (page 9, line 21) (Reviewer #3: comment 3).

• Added references used in the response to reviewers into the main text.

• Corrected the data error in Figure 6G, H

• Corrected the dataset in Figure 3E

We limited the data in Figure 3E–G to those measuring both myelin length and diameter simultaneously.

Description of analyses that authors prefer not to carry out

• Analysis in adult chickens (Reviewer #1: comment 3,4)

The chick brainstem auditory circuit is nearly fully developed by E21, and we have also demonstrated that nodal spacing increases by approximately 20% while maintaining regional differences up to P9. Therefore, our study covers the period from pre-myelination to postfunctional maturation, and we think that the necessity of analyzing aged animals is small.

• Functional evaluation of the efficiency of eTeNT suppression (Reviewer #2: comment 1)

It is technically challenging to quantitatively assess the inhibition of vesicular release by eTeNT in NM axons given that multiple synapses from different NM axons converge onto postsynaptic neurons. In addition, previous studies have already validated the efficacy of this construct in multiple species. Therefore, we will not evaluate electrophysiologically the extent of vesicular release inhibition by eTeNT in this study. Instead, we have provided clear evidence that A3V-eTeNT is expressed efficiently and leads to notable phenotypic changes, such as the inhibition of oligodendrogenesis. (page 8, line 5).

• Replacing figures with data averaged per animal (Reviewer #2: comment 3.4)

Our study focuses on the distribution of morphological characteristics at the single-cell level rather than solely on group means. Averaging measurements per animal could obscure this cellular heterogeneity and potentially misrepresent our findings. Given that data distributions in our plots show clear distinctions, we believe that averaging per biological replicate is not essential in this case. If requested, we will be happy to provide the outputs of PlotsOfDifferences as supplementary source data files, similar to those used in eLife publications, for each figure.

• Additional experiments to manipulate oligodendrocyte density (Reviewer #2: comment 5)

We have already demonstrated that A3V-eTeNT reduces oligodendrocyte density in the NL region, and some of the arguments in our study are based on this result. Therefore, we think that further experiments are not necessary.

• Verification of the presence of pre-nodal clusters (Reviewer #3: comment 1)

We investigated the presence of pre-nodal clusters on NM axons, but we could not identify them in the immunohistochemistry of AnkG. As the occurrence of pre-nodal clusters varies depending on neuronal type, we consider that pre-nodal clusters are not prominent in the NM axons and that further experimental validation would not be necessary. Instead, we have added a discussion on the possibility that pre-nodal clusters contribute to regional differences in nodal spacing along NM axons (page 9, line 35).

• Axon diameter measurements using EM (Reviewer #3: comment 2)

This experiment was already done by Seidl et al. (2010), and hence, we do not think it necessary to repeat it. We believe that the relative differences in axon diameter between the regions could be adequately assessed using the optical approach with membrane-targeted GFP.

eLife Assessment

This study offers valuable insights into the role of miR-283 in ventral-lateral neurons (LNvs) and its impact on senescence, cardiac function, and aging in the Drosophila melanogaster model. However, the evidence supporting some of the conclusions remains incomplete, and further mechanistic studies are needed to clarify how miR-283 affects normal aging and influences exercise adaptations. Nonetheless, the work can be of interest to cell biologists studying miRNA biology, aging, and age-related diseases.

Reviewer #1 (Public review):

In this study, Li et al et al. investigated the role of miR-283 in regulating cardiac aging and its potential contribution to age-related bradyarrhythmia. Using Drosophila as a model, the authors demonstrated that systemic overexpression or knockdown of miR-283 induced age-associated bradycardia. Notably, the study found that miR-283 knockdown in ventral-lateral neurons (LNvs), rather than in the heart, was sufficient to induce bradyarrhythmia, an effect the authors linked to the upregulation of miR-283 expression in both the brain and heart. The study also explored the beneficial impact of exercise on cardiac aging, showing that endurance training mitigated bradyarrhythmia, correlating with reduced miR-283 accumulation in the brain and myocardium.

The conclusions of this paper are mostly well supported by data; however, some concerns arise from the unexpected finding that bradyarrhythmia was triggered by miR-283 knockdown in LNvs rather than in the heart, suggesting a non-cell-autonomous mechanism. A more precise mechanistic explanation linking miR-283 dysregulation in LNvs to cardiac dysfunction would strengthen the study's conclusions. While the authors propose cwo as a potential target of miR-283, no functional experiments were conducted to confirm its role in mediating miR-283's effects. Additionally, it remains unclear whether reduced miR-283 levels in LNvs lead to accelerated aging rather than a cardiac-specific effect. Likewise, the potential influence of miR-283 on the circadian clock and its broader impact on aging warrant further investigation.

Major Comments:

(1) A significant concern arises from the unexpected outcome observed in miR-283 knockdown in LNvs, which suggests a non-cell-autonomous mechanism. Elucidating the mechanisms by which miR-283 deficiency leads to the observed phenotypes would provide a more comprehensive understanding of the study's implications.

(2) The authors propose cwo as a potential target of miR-283; however, no functional experiments were conducted to confirm its role in mediating miR-283's effects. Similarly, direct evidence demonstrating that cwo is a bona fide target of miR-283 in LNvs should be provided.

(3) It remains unclear whether miR-283 knockdown in LNvs results in accelerated aging rather than a cardiac-specific effect. This hypothesis is supported by observations that pdf>miR-283SP animals exhibit systemic premature senescence (elevated SA-β-gal activity in both the heart and brain), cardiac dysfunction, impaired climbing ability, and reduced lifespan.

(4) The finding that reduced miR-283 levels in LNvs lead to accelerated aging raises an important, yet unexplored, question: does miR-283 influence the circadian clock, thereby broadly affecting aging?

Two aspects of this question should be addressed:<br /> (a) Is the circadian rhythm disrupted in miR-283 knockdown experiments?<br /> (b) Do circadian rhythm defects impact aging?

(5) The authors state that miR-283 knockdown in LNvs led to bradyarrhythmia, which was mainly caused by miR-283 upregulation in the whole brain and heart. However, it is unclear which experiments support this conclusion. Could the authors clarify this point?

(6) Given that miR-283 expression varies with age, could the upregulation of miR-283 in both the brain and heart be a consequence of accelerated aging rather than a specific effect of miR-283 knockdown in LNvs?

(7) While the beneficial effects of exercise on cardiac function appear clear, the claim that this effect is mediated through miR-283 function in LNvs seems premature. The data suggest that exercise-induced improvement occurs in both wild-type and miR-283-SP animals, raising the possibility that exercise acts through a miR-283-independent mechanism.

Reviewer #2 (Public review):

Summary:

The manuscript presents findings that indicate a role in controlling Drosophila heart rate for a conserved miRNA (miR-238 in flies). Further, the manuscript localizes the relevant tissue for the function of this miRNA to a subset of neurons that are heavily involved in circadian regulation, thus presenting an interesting mechanistic link between the circadian system and heart rate. Either ubiquitous knockout or ubiquitous overexpression negatively impacts several aspects of heart performance, with a pronounced effect on heart rate. Interestingly, knockdowns in the heart itself are innocuous, but knockdown in LNvS neurons recapitulates the effect on heart rate. Authors use bioinformatics to identify the clockwork orange (cwo) gene as a potential target and validate that cwo expression is reduced when miR-238 is knocked down in LNvS neurons in vivo and also validate that cwo is regulated by miR-238 in cell culture luciferase assays. Exercise shows a modest ability to restore normal cwo expression and a trend toward an effect on survival, but shows a much stronger rescue of the heart rate phenotype.

Strengths:

Evidence is strong for the effect of miR-238 in pdf-positive neurons on the control of heart rate and for cwo as a downstream effector of miR-238.

Work to identify specific targets of miR-283 is well-done and successfully identified a key downstream regulator in cwo.

The potential mechanism using miR-238 to link circadian neurons to heart rate regulation is novel and exciting.

Weaknesses:

The evidence that this is related to normal aging is rather weak, and the effect of exercise on the observed parameters is small and not necessarily working through the miR-238/cwo mechanism.

The authors seem to be conflating two hypotheses in their interpretations. Is miR-283 working through circadian mechanisms or age-related mechanisms? While it is true that aging tends to reduce heart rate, I don't think that means that any intervention that reduces heart rate is causing "senescence". Similarly, reduced survival in miR-283 knockdown flies does not prove that miR-283 promotes healthy aging per se, just that miR-283 is required for health regardless of age.

Survival reduction is quite modest which does not necessarily support the idea that the bradycardia is causing major health issues or premature senescence for the flies. The interpretation of the longevity experiments throughout the manuscript seems overstated.

The study would benefit greatly from a direct test of the author's proposed pathway for exercise to improve bradycardia.

The statement in the discussion "inducing endurance exercise of anti gravity climbing in flies with miR-283 knockdown in LNvs can improve bradyarrhythmic features by decreasing brain miR-283 expression" is not fully supported by data in the paper. There is an association there, but it cannot be said to be the full cause (or even required) without doing more experiments

The summary figure includes both data-supported mechanistic relationships and mechanisms that are inferred or assumed.

eLife Assessment

This study presents useful findings on the role of AFD thermosensory neurons in locomotory behaviours. The study appears solid with respect to parsing out the non-thermosensory role of AFD and also brings to light the role of AFD and AIB (linked through electrical synapses) in tactile-dependent locomotory modulation.

Reviewer #1 (Public review):

Summary:

In this manuscript, Rosero and Bai examined how the well-known thermosensory neuron in C. elegans, AFD, regulates context-dependent locomotory behavior based on the tactile experience. Here they show that AFD uses discrete cGMP signalling molecules and independent of its dendritic sensory endings regulates this locomotory behavior. The authors also show here that AFD's connection to one of the hub interneurons, AIB, through gap junction/electrical synapses, is necessary and sufficient for the regulation of this context-dependent locomotion modulation.

Strengths:

This is an interesting paper showcasing how a sensory neuron in C. elegans can employ a distinct set of molecular strategies and different physical parts to regulate a completely distinct set of behaviors, which were not been shown to be regulated by AFD before. The experiments were well performed and the results are clear. However, there are some questions about the mechanism of this regulation. This reviewer thinks that the authors should address these concerns before the final published version of this manuscript.

Weaknesses:

(1) The authors argued about the role of prior exposure to different physical contexts which might be responsible for the difference in their locomotory behaviour. However, the worms in the binary chamber (with both non-uniformly sized and spaced pillars) experienced both sets of pillars for one hour prior to the assay and they were also free to move between two sets of environments during the assay. So, this is not completely a switch between two different types of tactile barriers (or not completely restricted to prior experience), but rather a difference between experiencing a more complex environment vs a simple uniform environment. They should rephrase their findings. To strictly argue about the prior experience, the authors need to somehow restrict the worms from entering the uniform assay zone during the 1hr training period.

(2) The authors here argued that the sensory endings of AFD are not required for this novel role of AFD in context-dependent locomotion modulation. However, gcy-18 has been shown to be exclusively localized to the ciliated sensory endings of AFD and even misexpression of GCY-18 in other sensory neurons also leads to localizations in sensory endings (Nguyen et. al., 2014 and Takeishi et. al., 2016). They should check whether gcy-18 or tax-2 gets mislocalized in kcc-3 or tax-1 mutants.

(3) MEC-10 was shown to be required for physical space preference through its action in FLP and not the TRNs (PMID: 28349862). Since FLP is involved in harsh touch sensation while TRNs are involved in gentle touch sensation, which are the neuron types responsible for tactile sensation in the assay arena? Does mec-10 rescue in TRNs rescue the phenotype in the current paper?

(4) The authors mention that the most direct link between TRNs and AFD is through AIB, but as far as I understand, there are no reports to suggest synapses between TRNs and AIB. However, FLP and AIB are connected through both chemical and electrical synapses, which would make more sense as per their mec-10 data. (the authors mentioned about the FLP-AIB-AFD circuit in their discussion but talked about TRNs as the sensory modality). mec-10 rescue experiment in TRNs would clarify this ambiguity.

(5) Do inx-7 or inx-10 rescue in AFD and AIB using cell-specific promoters rescue the behaviour?

(6) How Guanylyl cyclase gcy-18 function is related to the electrical synapse activity between AFD and AIB? Is AFD downstream or upstream of AIB in this context?

Reviewer #2 (Public review):

Summary:

The goal of the study was to uncover the mechanisms mediating tactile-context-dependent locomotion modulation in C. elegans, which represents an interesting model of behavioral plasticity. Starting from a candidate genetic screen focusing on guanylate cyclase (GCY) mutants, the authors identified the AFD-specific gcy-18 gene as essential for tactile-context-dependent locomotion modulation. AFD is primarily characterized as a thermo-sensory neuron. However, key thermosensory transduction genes and the sensory ending structure of AFD were shown here to be dispensable for tactile-context locomotion modulation. AFD actuates tactile-context locomotion modulation via the cell-autonomous actions of GCY-18 and the CNG-3 cyclic nucleotide-gated channel, and via AFD's connection with AIB interneurons through electrical synapses. This represents a potentially relevant synaptic connection linking AFD to the mechanosensory-behavior circuit.

Strengths:

(1) The fact that AFD mediates tactile-context locomotion modulation is new, rather surprising, and interesting.

(2) The authors have combined a very clever microfluidic-based behavioral assay with a large set of genetic manipulations to dissect the molecular and cellular pathways involved. Rescue experiments with single-copy transgenes are very convincing.

(3) The study is very clearly written, and figures are nicely illustrated with diagrams that effectively convey the authors' interpretation.

Weaknesses:

(1) Whereas GCY-18 in AFD and the AFD-AIB synaptic connection clearly play a role in tactile-context locomotion modulation, whether and how they actually modulate the mechanosensory circuit and/or locomotion circuit remains unclear. The possibility of non-synaptic communication linking mechanosensory neurons and AFD (in either direction) was not explored. Thus, in the end, we have not learned much about what GCY-18 and the AFD-AIB module are doing to actuate tactile context-dependent locomotion modulation.

(2) The authors only focused on speed readout, and we don't know if the many behavioral parameters that are modulated by tactile context are also under the control of AFD-mediated modulation.

(3) The AFD-AIB gap junction reconstruction experiment was conducted in an innexin double mutant background, in which the whole nervous system's functioning might be severely impaired, and its results should be interpreted with this limitation in mind.

Reviewer #3 (Public review):

Summary:

Rosero and Bai report an unconventional role of AFD neurons in mediating tactile-dependent locomotion modulation, independent of their well-established thermosensory function. They partially elucidate the signaling mechanisms underlying this AFD-dependent behavioral modulation. The regulation does not require the sensory dendritic endings of AFD but rather the AFD neurons themselves. This process involves a distinct set of cGMP signaling proteins and CNG channel subunits separate from those involved in thermosensation or thermotaxis. Furthermore, the authors demonstrate that AIB interneurons connect AFD to mechanosensory circuits through electrical synapses. They conclude that, beyond its primary function in thermosensation, AFD contributes to context-dependent neuroplasticity and behavioral modulation via broader circuit connectivity.

While the discovery of multifunctionality in AFD is not entirely unexpected, given the limited number of neurons in C. elegans (302 in total), the molecular and cellular mechanisms underlying this AFD-dependent behavioral modulation, as revealed in this study, provide valuable insights into the field.

Strengths:

(1) The authors uncover a novel role of AFD neurons in mediating tactile-dependent locomotion modulation, distinct from their well-established thermosensory function.

(2) They provide partial insights into the signaling mechanisms underlying this AFD-dependent behavioral modulation.

(3) The neural behavior assays utilizing two types of microfluidic chambers (uniform and binary chambers) are innovative and well-designed.

(4) By comparing AFD's role in locomotion modulation to its thermosensory function throughout the study, the authors present strong evidence supporting these as two independent functions of AFD.

(5) The finding that AFD contributes to context-dependent behavioral modulation is significant, further reinforcing the growing evidence that individual neurons can serve multiple functions through broader circuit connectivity.

Weaknesses:

(1) Limited Behavioral Assays: The study relies solely on neural behavior assays conducted using two types of microfluidic chambers (uniform and binary chambers) to assess context-dependent locomotion modulation. No additional behavioral assays were performed. To strengthen the conclusions, the authors should validate their findings using an independent method, at the very least by testing AFD-ablated animals and gcy-18 mutants with a second behavioral approach.

(2) Clarity in Behavioral Assay Methodology: The methodology for conducting the behavioral assays is unclear. It appears that worms were free to move between the exploration and assay zones, with no control over the duration each worm spent in either zone. This lack of regulation may introduce variability in tactile experience across individuals, potentially affecting the reproducibility and quantitativeness of the method. The authors should clarify whether and how they accounted for this variability.

(3) Potential Developmental and Behavioral Confounds in Mutant Analysis: Several neuronal mutant strains were used in this study, yet the effects of these mutations on development and general behavior (e.g., movement ability) were not discussed. Although young adult worms were used for behavioral assays, were they at similar biological ages? To rule out confounding factors, locomotion assays assessing movement ability should be conducted (see reference PMID 25561524).

(4) Definition and Baseline Measurements for Locomotion Categories: The finding that tax-4 and kcc-3 contribute to basal locomotion but not to context-dependent locomotion modulation is intriguing. The authors argue that distinct mechanisms regulate these two processes; however, the study does not clearly define the concepts of "basal locomotion" and "context-dependent locomotion," nor does it provide baseline measurements. A clear definition and baseline data are needed to support this conclusion.

eLife Assessment

This important study provides a description of how single-neuron firing rates in the human medial temporal lobe and frontal cortex are modulated by theta-burst stimulation of the basolateral amydala. The results are supported by solid evidence obtained from a rigorous task design and analysis of an incredibly rare dataset. The results may help guide future studies incorporating amygdala stimulation to improve patient health. Additional analyses could have been performed, and additional experimental details included, to address open questions related to mechanistic effects of the stimulation protocol on single unit properties and memory-related behavior.

Reviewer #1 (Public review):

Summary:

In this manuscript, Campbell et al. assess how intracranial theta-burst stimulation (TBS) applied to the basolateral amygdala in 23 epilepsy patients affects neuronal spiking in the medial temporal lobe and prefrontal cortex during a visual recognition memory task.

Strengths:

This is an incredibly rare dataset; collecting single-unit spiking data from behaving humans during active intracranial stimulation is a Herculaean task, with immense potential for translational studies of how stimulation may be applied to modulate biological mechanisms of memory. The authors utilize careful, high-quality methodology throughout (e.g. task design, spike recording and sorting, statistical analysis), providing high confidence in the validity of their findings.

Weaknesses:

(1) This is an exploratory study that doesn't explore quite enough. Critically, the authors make a point of mentioning that neuronal firing properties vary across cell types, but only use baseline firing rate as a proxy metric for cell type. This leaves several important explorations on the table, not limited to the following:<br /> a) Do waveform shape features, which can also be informative of cell type, predict the effect of stimulation?<br /> b) Is the autocorrelation of spike timing, which can be informative about temporal dynamics, altered by stimulation? This is especially interesting if theta-burst stimulation either entrains theta-rhythmic spiking or is more modulatory of endogenously theta-modulated units.<br /> c) The authors reference the relevance of spike-field synchrony (30-55 Hz) in animal work, but ignore it here. Does spike-field synchrony (comparing the image presentation to post-stimulation) change in this frequency range? This does not seem beyond the scope of investigation here.<br /> d) How does multi-unit activity respond to stimulation? At this somewhat low count of neurons (total n=156 included) it would be valuable to provide input on multi-unit responses to stimulation as well.<br /> e) Several intracranial studies have implicated proximity to white matter in determining the effects of stimulation on LFPs; do the authors see an effect of white matter proximity here?

(2) It is a little confusing to interpret stimulation-induced modulation of neuronal spiking in the absence of stimulation-induced change in behavior. How do the authors findings tell us anything about the neural mechanisms of stimulation-modulated memory if memory isn't altered? In line with point #1, I would suggest a deeper dive into behavior (e.g. reaction time? Or focus on individual sessions that do change in Figure 4A?) to make a stronger statement connecting the neural results to behavioral relevance.

(3) It is not clear to me why the assessment of firing rates after image onset and after stim offset is limited to one second - this choice should be more theoretically justified, particularly for regions that spike as sparsely as these.

(4) This work coincides with another example of human intracranial stimulation investigating the effect on firing rates (doi: https://doi.org/10.1101/2024.11.28.625915). Given how incredibly rare this type of work is, I think the authors should discuss how their work converges with this work (or doesn't).

(5) What information does the pseudo-population analysis add? It's not totally clear to me.

Reviewer #2 (Public review):

Summary:

This study presents a valuable characterization of the effects of intracranial theta-burst stimulation of the basolateral amygdala on single units spiking activity in several areas in the human brain, associated with memory processing. It is written clearly and concisely, allowing readers to fully understand the analysis used.

The authors used a visual recognition memory task previously employed by their group to characterize the effects of basolateral amygdala stimulation upon memory consolidation (Inman et al, 2018). This current report is an interesting analysis to complement the results reported in the 2018 paper.

Strengths:

Rare combination of human neurophysiology and behavior -<br /> The type of experiment performed in the manuscript, which contains both neurophysiological data, behavior, and a deep brain stimulation intervention (DBS), is incredibly rare, takes many years to accomplish with tight collaboration between clinical and research teams. Our understanding of spiking dynamics of human neurons is very limited, and this report is an important piece in the puzzle that allows DBS to be used in future interventions that will benefit patients' health.

Multiple brain areas included -<br /> It's important to note that the report analyzes brain areas with which the Amygdala has extensive connections (Fig. 1A) - Hippocampus, OFC, Amygdala, ACC. It seems that neurons in all these areas were modulated by the stimulation, except the ACC, in which firing rates were so low, that only a handful of neurons were included in the analysis. This is an important demonstration that low amplitude stimulation (even when reduced to 0.5mA) can travel far and wide across the human brain.

The experiment is cleverly designed to tease apart responses due to visual stimuli (image presentation) and electrical stimulation. Authors suggest that the units modulated by stimulation are largely distinct from those responsive to image offset during trials without stimulation. The subpopulation that responds strongly also tends to have a higher baseline of firing rate. It's important to add that the chosen modulation index is more likely to be significant in neurons with higher firing rates.

Weaknesses:

Readers can benefit from understanding with more details the locations chosen for stimulation - in light of previous studies that found differences between effects based on proximity to white matter (For example - PMID 32446925, Mohan et al, Brain Stimul. 2020 and PMID 33279717 Mankin et al Brain Stimul. 2021).

eLife Assessment

This valuable paper provides refined gene expression datasets for 52 neuron classes in C. elegans using a new method that takes advantage of the complementary strengths of bulk sequencing of flow-sorted cells and single-cell sequencing. In general, support for the paper's findings is convincing. However, more rigorous consideration of some of the method's statistical assumptions and validation of the predicted gene sets would improve the work.

Reviewer #1 (Public review):

This is an interesting manuscript aimed at improving the transcriptome characterization of 52 C. elegans neuron classes. Previous single-cell RNA seq studies already uncovered transcriptomes for these, but the data are incomplete, with a bias against genes with lower expression levels. Here, the authors use cell-specific reporter combinations to FACS purify neurons and bulk RNA sequencing to obtain better sequencing depth. This reveals more rare transcripts, as well as non-coding RNAs, pseudogenes, etc. The authors develop computational approaches to combine the bulk and scRNA transcriptome results to obtain more definitive gene lists for the neurons examined.

To ultimately understand features of any cell, from morphology to function, an understanding of the full complement of the genes it expresses is a pre-requisite. This paper gets us a step closer to this goal, assembling a current "definitive list" of genes for a large proportion of C. elegans neurons. The computational approaches used to generate the list are based on reasonable assumptions, the data appear to have been treated appropriately statistically, and the conclusions are generally warranted. I have a few issues that the authors may choose to address:

(1) As part of getting rid of cross-contamination in the bulk data, the authors model the scRNA data, extrapolate it to the bulk data and subtract out "contaminant" cell types. One wonders, however, given that low expressed genes are not represented in the scRNA data, whether the assignment of a gene to one or another cell type can really be made definitive. Indeed, it's possible that a gene is expressed at low levels in one cell, and high levels in another, and would therefore be considered a contaminant. The result would be to throw out genes that actually are expressed in a given cell type. The definitive list would therefore be a conservative estimate, and not necessarily the correct estimate.

(2) It would be quite useful to have tested some genes with lower expression levels using in vivo gene-fusion reporters to assess whether the expression assignments hold up as predicted. i.e. provide another avenue of experimentation, non-computational, to confirm that the decontamination algorithm works.

(3) In many cases, each cell class would be composed of at least 2 if not more neurons. Is it possible that differences between members of a single class would be missed by applying the cleanup algorithms? Such transcripts would be represented only in a fraction of the cells isolated by scRNAseq, and might then be considered not real.

(4) I didn't quite catch whether the precise staging of animals was matched between the bulk and scRNAseq datasets. Importantly, there are many genes whose expression is highly stage-specific or age-specific so even slight temporal differences might yield different sets of gene expression.

(5) To what extent does FACS sorting affect gene expression? Can the authors provide some controls?

Reviewer #2 (Public review):

Summary:

This study from the CenGEN consortium addresses several limitations of single-cell RNA (scRNA) and bulk RNA sequencing in C. elegans with a focus on cells in the nervous system. scRNA datasets can give very specific expression profiles, but detecting rare and non-polyA transcripts is difficult. In contrast, bulk RNA sequencing on isolated cells can be sequenced to high depth to identify rare and non-polyA transcripts but frequently suffers from RNA contamination from other cell types. In this study, the authors generate a comprehensive set of bulk RNA datasets from 53 individual neurons isolated by fluorescence-activated cell sorting (FACS). The authors combine these datasets with a previously published scRNA dataset (Taylor et al., 2021) to develop a novel method, called LittleBites, to estimate and subtract contamination from the bulk RNA data. The authors validate the method by comparing detected transcripts against gold-standard datasets on neuron-specific and non-neuronal transcripts. The authors generate an "integrated" list of protein-coding expression profiles for the 53 neuron sub-types, with fewer but higher confidence genes compared to expression profiles based only on scRNA. Also, the authors identify putative novel pan-neuronal and cell-type specific non-coding RNAs based on the bulk RNA data. LittleBites should be generally useful for extracting higher confidence data from bulk RNA-seq data in organisms where extensive scRNA datasets are available. The additional confidence in neuron-specific expression and non-coding RNA expands the already great utility of the neuronal expression reference atlas generated by the CenGEN consortium.

Strengths:

The study generates and analyzes a very comprehensive set of bulk RNA datasets from individual fluorescently tagged transgenic strains. These datasets are technically challenging to generate and significantly expand our knowledge of gene expression, particularly in cells that were poorly represented in the initial scRNA-seq datasets. Additionally, all transgenic strains are made available as a resource from the Caenorhabditis Elegans Genetics Center (CGC).

The study uses the authors' extensive experience with neuronal expression to benchmark their method for reducing contamination utilizing a set of gold-standard validated neuronal and non-neuronal genes. These gold-standard genes will be helpful for benchmarking any C. elegans gene expression study.

Weaknesses:

The bulk RNA-seq data collected by the authors has high levels of contamination and, in some cases, is based on very few cells. The methodology to remove contamination partly makes up for this shortcoming, but the high background levels of contaminating RNA in the FACS-isolated neurons limit the confidence in cell-specific transcripts.

The study does not experimentally validate any of the refined gene expression predictions, which was one of the main strengths of the initial CenGEN publication (Taylor et al, 2021). No validation experiments (e.g., fluorescence reporters or single molecule FISH) were performed for protein-coding or non-coding genes, which makes it difficult for the reader to assess how much gene predictions are improved, other than for the gold standard set, which may have specific characteristics (e.g., bias toward high expression as they were primarily identified in fluorescence reporter experiments).

The study notes that bulk RNA-seq data, in contrast to scRNA-seq data, can be used to identify which isoforms are expressed in a given cell. However, no analysis or genome browser tracks were supplied in the study to take advantage of this important information. For the community, isoform-specific expression could guide the design of cell-specific expression constructs or for predictive modeling of gene expression based on machine learning.

Reviewer #3 (Public review):

The manuscript by Barrett et al. "Integrating bulk and single cell RNA-seq refines transcriptomic profiles of individual C. elegans neurons" presents a comprehensive approach to integrating bulk RNA-seq and single-cell RNA-seq (scRNA-seq) data to refine transcriptomic profiles of individual C. elegans neurons. The study addresses the limitations of scRNA-seq, such as the under-detection of lowly expressed and non-polyadenylated transcripts, by leveraging the sensitivity of bulk RNA-seq. The authors deploy a computational method, LittleBites, to remove non-neuronal contamination in bulk RNA-seq, that aims to enhance specificity while preserving the sensitivity advantage of bulk sequencing. Using this approach, the authors identify lowly expressed genes and non-coding RNAs (ncRNAs), many of which were previously undetected in scRNA-seq data.

Overall, the study provides high-resolution gene expression data for 53 neuron classes, covering a wide range of functional modalities and neurotransmitter usage. The integrated dataset and computational tools are made publicly available, enabling community-driven testing of the robustness and reproducibility of the study. Nevertheless, while the study represents a relevant contribution to the field, certain aspects of the work require further refinement to ensure the robustness and rigor necessary for peer-reviewed publication. Below, I outline the areas where improvements are needed to strengthen the overall impact and reliability of the findings.

(1) The study relies on thresholding to determine whether a gene is expressed or not. While this is a common practice, the choice of threshold is not thoroughly justified. In particular, the choice of two uniform cutoffs across protein-encoding RNAs and of one distinct threshold for non-coding RNAs is somewhat arbitrary and has several limitations. This reviewer recommends the authors attempt to use adaptive threshold-methods that define gene expression thresholds on a per-gene basis. Some of these methods include GiniClust2, Brennecke's variance modeling, HVG in Seurat, BASiCS, and/or MAST Hurdle model for dropout correction.

(2) Most importantly, the study lacks independent experimental validation (e.g., qPCR, smFISH, or in situ hybridization) to confirm the expression of newly detected lowly expressed genes and non-coding RNAs. This is particularly important for validating novel neuronal non-coding RNAs, which are primarily inferred from computational approaches.

(3) The novel biology is somewhat limited. One potential area of exploration would be to look at cell-type specific alternative splicing events.

(4) The integration method disproportionately benefits neuron types with limited representation in scRNA-seq, meaning well-sampled neuron types may not show significant improvement. The authors should quantify the impact of this bias on the final dataset.

(5) The authors employ a logit transformation to model single-cell proportions into count space, but they need to clarify its assumptions and potential pitfalls (e.g., how it handles rare cell types).

(6) The LittleBites approach is highly dependent on the accuracy of existing single-cell references. If the scRNA-seq dataset is incomplete or contains classification biases, this could propagate errors into the bulk RNA-seq data. The authors may want to discuss potential limitations and sensitivity to errors in the single-cell dataset, and it is critical to define minimum quality parameters (e.g. via modeling) for the scRNAseq dataset used as reference.

(7) Also very important, the LittleBites method could benefit from a more intuitive explanation and schematic to improve accessibility for non-computational readers. A supplementary step-by-step breakdown of the subtraction process would be useful.

(8) In the same vein, the ROC curves and AUROC comparisons should have clearer annotations to make results more interpretable for readers unfamiliar with these metrics.

(9) Finally, after the correlation-based decontamination of the 4,440 'unexpressed' genes, how many were ultimately discarded as non-neuronal?<br /> a) Among these non-neuronal genes, how many were actually known neuronal genes or components of neuronal pathways (e.g., genes involved in serotonin synthesis, synaptic function, or axon guidance)?<br /> b) Conversely, among the "unexpressed" genes classified as neuronal, how many were likely not neuron-specific (e.g., housekeeping genes) or even clearly non-neuronal (e.g., myosin or other muscle-specific markers)?

(10) To increase transparency and allow readers to probe false positives and false negatives, I suggest the inclusion of:<br /> a) The full list of all 4,440 'unexpressed' genes and their classification at each refinement step. In that list flag the subsets of genes potentially misclassified, including:<br /> - Neuronal genes wrongly discarded as non-neuronal.<br /> - Non-neuronal genes wrongly retained as neuronal.<br /> b) Add a certainty or likelihood ranking that quantifies confidence in each classification decision, helping readers validate neuronal vs. non-neuronal RNA assignments.<br /> This addition would enhance transparency, reproducibility, and community engagement, ensuring that key neuronal genes are not erroneously discarded while minimizing false positives from contaminant-derived transcripts.

eLife Assessment

This study presents valuable findings on the role of specific dopamine neurons for aversive learning and modulation of innate behavior in Drosophila larvae. The authors present solid evidence backed up by detailed behavioral quantification and rigorous testing. Their data confirms previous findings and will be of interest to the learning and memory community.

Reviewer #1 (Public review):

Summary:

The authors investigate the role of different specific dopaminergic neurons in the mushroom body of Drosophila larvae for learning and innate behavior. All the tested neurons are thought to be involved in punishment learning. The authors discover that artificial activation of single DANs in training leads to safety learning, but not punishment learning. Furthermore, activation of single DANs can lead to changes in locomotion behavior, which can affect light preference. The authors provide a deeper understanding of the functional diversity of single dopamine neurons; however, it is unclear how translatable these findings are to learning experiments with real punishment stimuli.

Strengths:

The authors attempt to disentangle what kind of memories are formed with the activation of different dopamine neurons - safety learning, and punishment learning, will the US be required to test for recall or not? They do indeed find differences and the results will be of interest to the learning and memory community.

Interestingly, optogenetic activation of a single DAN during training leads to safety memory, but not punishment memory. Furthermore, DAN activation also affects innate locomotion, and the authors can show that optogenetic activation of different DANs affects locomotion differently.

Weaknesses:<br /> All experiments in the manuscript use optogenetic activation of DANs, thus it is not clear what kind of memories are formed. Several stimuli can be used as punishment, such as electric shock, salt, bitter, and light - it is not clear what kind of memory the authors investigate here. The findings could be discussed in the context of what DANs respond to. Furthermore, studies in adults and larvae showed that most DANs can code for both valences - etc., aversive DANs can be activated by punishment, and inhibited by reward. Thus, safety learning might be a result of a decrease in activity in DANs during odor presentation. The authors also do not discuss possible feedback loops from MBONs to DANs across compartments. Could such connections allow for safety learning in larvae?

The authors show that artificial activation with different light intensities can form different memories and that increasing the light intensity sometimes leads to no memories. Also, using different optogenetic tools reveals different results. This again raises the question of how applicable the results will be for learning with real stimuli. Is there a natural stimulus that only induces safety learning, but no punishment learning?<br /> The authors provide a detailed behavioral analysis of locomotion behavior; however, the detailed analysis seems unnecessary for that dataset. Modulation of speed and bending rate has been described before with simpler methods (specifically for MBONs). The revealed locomotion phenotypes probably affect larval locomotion during memory recall with light activation, thus the authors should show that larvae are potentially able to move during light-on memory tests.

Reviewer #2 (Public review):

Summary:

This study provides valuable context for ongoing research on the role of dopamine in memory and locomotion. DANs have been a fascinating area of study due to their complexity, and this work dissects specific DANs, exploring their roles in different memory-related behaviors while offering some explanations. The discussions provided by the authors effectively situates the study in the broader field of learning, memory, DAN circuitry and behavioral computation in insect brains. The study achieves what it sets out to and it does so unequivocally. The experiments were elegantly designed, leaving little room for doubt in the study's claims. However, the study lacks context regarding the molecular pathways underlying these results. While it strengthens current knowledge by providing robust evidence, it does little to explore the molecular mechanisms behind these effects.

Strengths:

(1) Experiment design is one of the strengths of this study. The experiments are thorough and cover the length and breadth of the core findings of the study. Although a lot of work has already been done in studying the role of dopamine in memory and locomotion, the dissection of the functions of distinct DANs in larvae has been done meticulously with well-structured experiments.<br /> (2) This study fits quite nicely into the puzzle of memory, especially in the context of Dopamine. Previous studies in *Drosophila* adults have shown the opposing roles of DANs in locomotion depending on the context of DAN activation. This study drives that point home for larvae, providing conclusive evidence in that regard.<br /> (3) The use of clear figures and simple language is one of the strengths of this paper. The figures are comprehensive, complete and manage to narrate the story by themselves. The flow of information is smooth. The simple and effective language used maintains scientific rigor while remaining accessible to those new to the field. A pleasant read.

Weaknesses:<br /> (1) The authors have done a great job at structuring the figures. But some main figures would benefit from including the controls instead of placing them in supplementary.<br /> (2) The paper would benefit from a deeper discussion regarding molecular mechanisms underlying their results. It would be interesting to see what the authors think about different Dopamine receptors and how they relate to the findings of this paper.<br /> (3) Throughout the paper, the authors have been clear and comprehensive, but in some cases, further explanation of their choices were missing. For example, the choice to compare bending and tail velocity over other parameters within the same clusters is unclear.

Reviewer #3 (Public review):

Summary

Across species, dopamine release carries out seemingly diverse functions, like reinforcing memories and regulating locomotion and flight. However, whether distinct dopaminergic neurons (DANs) are allocated for each function is not clear. In this study, Toshima et al. have used the numerically simple organization of the Drosophila larval brain to answer this question. They use optogenetic activation to systematically stimulate a small set of DANs, individually and collectively, and study the effect on diverse functions such as memory formation, retrieval, and locomotion. They find that singly or collectively, DL1 DANs can induce punishment and/or safety memory formation and retrieval. DANs can even gate the expression of memory. Finally, the same DANs also modulate locomotion in the larvae. The authors speculate that dopaminergic neurons in other species may also share such overlapping functions. Their findings are nicely summarised in Figure 9.

Strengths

The study comprehensively activates the neurons in the DL1 cluster in a systematic manner. Individual and collective stimulation of the Dl1 DANs has been conducted to assess the induction and gating of aversive punishment memory, safety memory, and acute locomotion.

Specific adult Drosophila DANs are known to induce dual behaviors and functions. The same MP1/y1pedc DANs are recognized for gating appetitive memory expression and representing aversive teaching signals downstream of sensory stimuli such as electric shocks, bitter tastes, and heat. Neurons in the PPL1 cluster regulate adult flight and food-seeking behavior. The authors deserve credit for conducting an organized examination of dopaminergic neuron functions in larvae, which makes their findings more comparable and facilitates the proposal of a holistic model.

They have provided substantial evidence for their findings and frequently presented replicated behavioral data sets. They have been transparent about results that were difficult to explain. Additionally, they have provided an impressive body of supporting data to strengthen their main findings.

Weaknesses

The larvae exhibit directed locomotory action to express punishment or safety memory. If the larvae did not move, we would not be able to assess memory function. Hence, functional activation of DANs could result in one action, which seems like two different functions of memory expression and locomotion. It can also be argued that activation of DANs represents a teaching signal to the KCs, and then eventually, downstream of the MBONs, it results in locomotion modulation. Hence, the seeming functional diversity could be a function of different downstream neuronal pathways and not molecular context-dependent diversity inside dopaminergic neurons. The authors should address this possibility or point out the fallacy in the above argument.

The finding that activation of TH-GAL4 conveys aversive valence and R58E02-GAL4 conveys appetitive valence seems redundant (Figure 6). I understand they say this in the context of locomotion. However, they may not have mentioned similar findings in adults. In adults, artificial activation of DANs covered by the same GAL4 lines acts as aversive and appetitive teaching signals for memory formation. These references should be cited appropriately in the results and discussion if not currently included.

The evidence for the role of dopamine (Figure 7) can be bolstered by using other available RNAi lines against TH. A valium20 vector-based shRNA line is recommended. The current evidence is based mainly on non-specific pharmacological intervention with 3IY.

eLife Assessment

This fundamental work significantly enhances our understanding of how structural variants influence human phenotypes. The conclusion is convincingly supported by rigorous analyses of long-read sequencing data. If the raw data are made publicly available, these high-quality datasets and findings will further advance our knowledge of genetic variation in the human population.

Reviewer #1 (Public review):

Summary:

The authors sequenced 888 individuals from the 1000 Genomes Project using the Oxford Nanopore long-read sequencing method to achieve highly sensitive, genome-wide detection of structural variants (SVs) at the population level. They conducted solid benchmarking of SV calling and systematically characterized the identified SVs. While short-read sequencing methods, including those used in the 1000 Genomes Project, have been widely applied, they exhibit high accuracy in detecting single nucleotide variants (SNVs) and small insertions and deletions but have limited sensitivity for SV detection. This study significantly enhances SV detection capabilities, establishing it as a valuable resource for human genetic research. Furthermore, the authors constructed an SV imputation panel using the generated data and imputed SVs in 488,130 individuals from the UK Biobank. They then conducted a proof-of-principle genome-wide association study (GWAS) analysis based on the imputed SVs and selected traits within the UK Biobank. Their findings demonstrate that incorporating SV-GWAS analysis provides additional insights beyond conventional GWAS frameworks focusing on SNVs, particularly in improving fine mapping.

Strengths:

The authors constructed a high-sensitivity reference panel of genome-wide SVs at the population level, addressing a critical gap in the field of human genetics. This resource is expected to significantly advance research in human genetics. They demonstrated the imputation of SVs in individuals from the UK Biobank using this panel and conducted a proof-of-concept SV-based GWAS. Their findings highlight a novel and effective strategy for integrating SVs into GWAS, which will facilitate the analysis of human genetic data from the UK Biobank and other datasets. Their conclusions are supported by comprehensive analyses.

Weaknesses:

(1) Although the authors employ state-of-the-art analytical approaches for the identification of SVs, the overall accuracy remains suboptimal, as indicated by an F1 score of 74.0%, particularly in tandem repeat regions. To enhance accuracy, it would be beneficial to explore alternative SV detection methods or develop novel approaches. Given the value of the reference panel and the fact that improved SV accuracy would lead to more precise SV imputation and GWAS results, investing effort in methodological refinement is highly encouraged.

(2) From the Methods section, it appears that the authors employed Beagle for both the "leave-one-out" imputation and the UK Biobank imputation. It would be better to explicitly clarify this in the Results section and provide a detailed description of the corresponding procedures and parameters in the Methods section for both analyses, as this represents a key aspect of the study. Additionally, Beagle is not specifically designed for SV imputation, the imputation quality of SVs is generally lower than that of SNVs. Exploring strategies to improve SV imputation, such as developing a novel method with reference panel data, may enhance performance. It is also important to assess how this reduced imputation quality may influence GWAS results. For instance, it would be useful to examine whether associated SVs exhibit higher imputation quality and whether SVs with lower quality are less likely to achieve significant association signals. In addition, the lower imputation quality observed for INV, DUP, and BND variants (Figure 3) may be due to their greater lengths (Figure 2). It is better to investigate the relationship between SV length and imputation quality.

(3) All examples presented in the manuscript focus on SVs that overlap with genes. It may also be valuable to investigate SVs that do not overlap with genes but intersect with enhancer regions. SVs can contribute to disease by altering regulatory elements, such as enhancers, which play a crucial role in gene expression. Including such analyses would further demonstrate the utility of SV-GWAS and provide deeper insights into the functional impact of SVs.

(4) The data availability link currently provides only a VCF file ("sniffles2_joint_sv_calls.vcf.gz") containing the identified SVs. It would be beneficial for the authors to make all raw sequencing data (FASTQ files) and key processed datasets (such as alignment results and merged SV and SNV files) available. Providing these resources would enable other researchers to develop improved SV detection and imputation methods or conduct further genetic analyses. Furthermore, establishing a dedicated website for data access, along with a genome browser for SV visualization, could significantly enhance the impact and accessibility of the study. Additionally, all code, particularly the SV imputation pipeline accompanied by a detailed tutorial, should be deposited in a public repository such as GitHub. This would support researchers in imputing SVs and conducting SV-GWAS on their own datasets.

Reviewer #2 (Public review):

Summary:

The authors aimed to develop a novel and efficient method for SV detection, utilizing data from the 1000 Genomes Project (1KGP) for modeling and calibration. This method was subsequently validated using UK population data and applied to identify structural variants associated with specific disease phenotypes.

Strengths:

Third-generation single-molecule sequencing data offers several advantages over traditional high-throughput sequencing methods, particularly due to its long-read lengths, which provide valuable insights into significant forms of genomic variation. The authors have developed an efficient method for detecting structural variations and optimizing the utilization of genomic data. We hope that this method will continue to be refined, enabling researchers to more effectively leverage long-read data, high-throughput data, or even a synergistic combination of both.

Weaknesses:

Although this research contributes to our ability to more effectively utilize long-length and high-throughput data, there are some key issues that need to be addressed in terms of analyzing the specific results as well as writing the article.

Reviewer #3 (Public review):

Summary:

This study successfully identified genetic loci associated with various traits by generating large-scale long-read sequencing data from a diverse set of samples. This study is significant because it not only produces large-scale long-read genome sequencing data but also demonstrates its application in actual genetics research. Given its potential utility in various fields, this study is expected to make a valuable contribution to the academic community and to this journal. However, there are several critical aspects that could be improved. Below are specific comments for consideration.

Strengths:

Producing high-quality, large-scale variant datasets and imputation datasets

Weaknesses:

(1) Data availability

Currently, it appears that only the Genomic Lens SV Panel is available on the webpage described in the Data Availability section. It is unclear whether the authors intend to release the raw sequencing data. Since the study utilized samples from the 1000 Genomes Project, there should be no restriction on making the data publicly accessible. Given this, would the authors consider making the raw sequencing reads publicly available? If so, NCBI SRA or EBI ENA would be the most appropriate repositories for data deposition. I strongly encourage the authors to consider public data release.

Additionally, accessing the Genomic Lens SV Panel data does not seem straightforward. The manuscript should provide a more detailed description of how researchers can access and utilize these data. In my opinion, the best approach would be to upload the variant data (VCF files) to a public database such as the European Variation Archive (EVA) hosted by EBI.

I strongly request that the authors publicly deposit the variant data. At a minimum:

a) The joint genotype data for all 888 samples from the 1000 Genomes Project must be publicly available.<br /> b) For the UK Biobank samples, at least allele frequency data should be disclosed.

Since eLife has a well-established data-sharing policy, compliance with these guidelines is essential for publication in this journal.

(2) Long-read sequencing data quality

While the manuscript presents N50 read length and mean or median read base quality for each sample in a table, it would be highly beneficial to visualize these data in figures as well. A violin plot or similar visualization summarizing these distributions would significantly improve data presentation.

Notably, the base quality of ONT long-read sequencing data appears lower than expected. This may be attributed to the use of pore version 9.4.1, but the unexpectedly low base quality still warrants attention. It would be helpful to include a small figure within Figure 2 to illustrate this point. A visual representation of read length distribution and base quality distribution would strengthen the manuscript.

(3) Variant detection precision, recall, and F1 score

This study focuses on insertions and deletions (indels) {greater than or equal to}50 bp, but it remains unclear how well variants <50 bp are detected. I am particularly interested in the precision, recall, and F1 score for variants between 5-49 bp.

While ONT base quality is relatively low, single-base variants are challenging to analyze, but variants {greater than or equal to}5 bp should still be detectable as their read accuracy is still approximately 90%, making analysis feasible. Given that Sniffles supports the detection of variants as small as 1 bp, I strongly encourage the authors to conduct an additional analysis.

A simple two-category classification (e.g., 5-49 bp and {greater than or equal to}50 bp) should suffice. Additionally, a comparative analysis with HiFi and short-read sequencing data would be highly valuable. If possible, I strongly recommend that all detected variants {greater than or equal to}5 bp be made publicly available as VCF files.

(4) Assembly-based methods

Given the low read accuracy and low sequencing depth in this dataset, it is understandable that genome assembly is challenging. However, the latest high-quality human genome datasets-such as those produced by the Human Pangenome Reference Consortium (HPRC)-demonstrate that assembly-based approaches provide significant advantages, particularly for resolving complex and long structural variants.

Since HPRC data also utilize 1000 Genomes Project samples, it would be highly informative to compare the accuracy of ONT sequencing in this study with HPRC's assembly-based genome data. The recent publication on 47 HPRC samples provides a valuable reference for such a comparison. Given its relevance, the authors should consider providing a comparative analysis with HPRC data.

References:

(1) A draft human pangenome reference<br /> https://www.nature.com/articles/s41586-023-05896-x

(2) The Human Pangenome Project: a global resource to map genomic diversity<br /> https://www.nature.com/articles/s41586-022-04601-8

(3) A pangenome reference of 36 Chinese populations<br /> https://www.nature.com/articles/s41586-023-06173-7

(4) Long-read sequencing of 3,622 Icelanders provides insight into the role of structural variants in human diseases and other traits<br /> https://www.nature.com/articles/s41588-021-00865-4

(5) Increased mutation and gene conversion within human segmental duplications<br /> https://www.nature.com/articles/s41586-023-05895-y

(6) Structural polymorphism and diversity of human segmental duplications<br /> https://www.nature.com/articles/s41588-024-02051-8

(7) Highly accurate Korean draft genomes reveal structural variation highlighting human telomere evolution<br /> https://academic.oup.com/nar/article/53/1/gkae1294/7945385

eLife Assessment

Pannexin (Panx) channels are a family of poorly understood large-pore channels that mediate the release of substrates like ATP from cells, yet the physiological stimuli that activate these channels remain poorly understood. The study by Henze et al. describes an elegant approach wherein activity-guided fractionation of mouse liver led to the discovery that lysophospholipids (LPCs) activate Panx1 and Panx2 channels expressed in cells or reconstituted into liposomes. The authors provide compelling evidence that LPC-mediated activation of Panx1 is involved in joint pain and that Panx1 channels are required for the established effects of LPC on inflammasome activation in monocytes, suggesting that Panx channels play a role in inflammatory pathways. Overall, this important study reports a previously unanticipated mechanism wherein LPCs directly activate Panx channels. The work will be of interest to scientists investigating phospholipids, Panx channels, purinergic signalling and inflammation.

[Editors' note: this paper was reviewed and curated by Biophysics Colab]

Joint Public Review:

Pannexin (Panx) hemichannels are a family of heptameric membrane proteins that form pores in the plasma membrane through which ions and relatively large organic molecules can permeate. ATP release through Panx channels during the process of apoptosis is one established biological role of these proteins in the immune system, but they are widely expressed in many cells throughout the body, including the nervous system, and likely play many interesting and important roles that are yet to be defined. Although several structures have now been solved of different Panx subtypes from different species, their biophysical mechanisms remain poorly understood, including what physiological signals control their activation. Electrophysiological measurements of ionic currents flowing in response to Panx channel activation have shown that some subtypes can be activated by strong membrane depolarization or caspase cleavage of the C-terminus. Here, Henze and colleagues set out to identify endogenous activators of Panx channels, focusing on the Panx1 and Panx2 subtypes, by fractionating mouse liver extracts and screening for activation of Panx channels expressed in mammalian cells using whole-cell patch clamp recordings. The authors present a comprehensive examination with robust methodologies and supporting data that demonstrate that lysophospholipids (LPCs) directly Panx-1 and 2 channels. These methodologies include channel mutagenesis, electrophysiology, ATP release and fluorescence assays, and molecular modelling. Mouse liver extracts were initially used to identify LPC activators, but the authors go on to individually evaluate many different types of LPCs to determine those that are more specific for Panx channel activation. Importantly, the enzymes that endogenously regulate the production of these LPCs were also assessed along with other by-products that were shown not to promote pannexin channel activation. In addition, the authors used synovial fluid from canine patients, which is enriched in LPCs, to highlight the importance of the findings in pathology. Overall, we think this is likely to be an important study because it provides strong evidence that LPCs can function as activators of Panx1 and Panx2 channels, linking two established mediators of inflammatory responses and opening an entirely new area for exploring the biological roles of Panx channels. This study provides an excellent foundation for future studies and importantly provides clinical relevance.

[Editors' note: this paper has been through two rounds of review and revisions, available here: https://sciety.org/articles/activity/10.1101/2023.10.23.563601]

Author response:

(This author response relates to the first round of peer review by Biophysics Colab. Reviews and responses to both rounds of review are available here: https://sciety.org/articles/activity/10.1101/2023.10.23.563601.)

General Assessment:

Pannexin (Panx) hemichannels are a family of heptameric membrane proteins that form pores in the plasma membrane through which ions and relatively large organic molecules can permeate. ATP release through Panx channels during the process of apoptosis is one established biological role of these proteins in the immune system, but they are widely expressed in many cells throughout the body, including the nervous system, and likely play many interesting and important roles that are yet to be defined. Although several structures have now been solved of different Panx subtypes from different species, their biophysical mechanisms remain poorly understood, including what physiological signals control their activation. Electrophysiological measurements of ionic currents flowing in response to Panx channel activation have shown that some subtypes can be activated by strong membrane depolarization or caspase cleavage of the C-terminus. Here, Henze and colleagues set out to identify endogenous activators of Panx channels, focusing on the Panx1 and Panx2 subtypes, by fractionating mouse liver extracts and screening for activation of Panx channels expressed in mammalian cells using whole-cell patch clamp recordings. The authors present a comprehensive examination with robust methodologies and supporting data that demonstrate that lysophospholipids (LPCs) directly Panx-1 and 2 channels. These methodologies include channel mutagenesis, electrophysiology, ATP release and fluorescence assays, molecular modelling, and cryogenic electron microscopy (cryo-EM). Mouse liver extracts were initially used to identify LPC activators, but the authors go on to individually evaluate many different types of LPCs to determine those that are more specific for Panx channel activation. Importantly, the enzymes that endogenously regulate the production of these LPCs were also assessed along with other by-products that were shown not to promote pannexin channel activation. In addition, the authors used synovial fluid from canine patients, which is enriched in LPCs, to highlight the importance of the findings in pathology. Overall, we think this is likely to be a landmark study because it provides strong evidence that LPCs can function as activators of Panx1 and Panx2 channels, linking two established mediators of inflammatory responses and opening an entirely new area for exploring the biological roles of Panx channels. Although the mechanism of LPC activation of Panx channels remains unresolved, this study provides an excellent foundation for future studies and importantly provides clinical relevance.

We thank the reviewers for their time and effort in reviewing our manuscript. Based on their valuable comments and suggestions, we have made substantial revisions. The updated manuscript now includes two new experiments supporting that lysophospholipid-triggered channel activation promotes the release of signaling molecules critical for immune response and demonstrates that this novel class of agonist activates the inflammasome in human macrophages through endogenously expressed Panx1. To better highlight the significance of our findings, we have excluded the cryo-EM panel from this manuscript. We believe these changes address the main concerns raised by the reviewers and enhance the overall clarity and impact of our findings. Below, we provide a point-by-point response to each of the reviewers’ comments.

Recommendations:

(1) The authors present a tremendous amount of data using different approaches, cells and assays along with a written presentation that is quite abbreviated, which may make comprehension challenging for some readers. We would encourage the authors to expand the written presentation to more fully describe the experiments that were done and how the data were analysed so that the 2 key conclusions can be more fully appreciated by readers. A lot of data is also presented in supplemental figures that could be brought into the main figures and more thoroughly presented and discussed.

We appreciate and agree with the reviewers’ observation. Our initial manuscript may have been challenging to follow due to our use of both wild-type and GS-tagged versions of Panx1 from human and frog origins, combined with different fluorescence techniques across cell types. In this revision, we used only human wild-type Panx1 expressed in HEK293S GnTI- cells, except for activity-guided fractionation experiments, where we used GS-tagged Panx1 expressed in HEK293 cells (Fig. 1). For functional reconstitution studies, we employed YO-PRO-1 uptake assays, as optimizing the Venus-based assay was challenging. We have clarified these exceptions in the main text. We think these adjustments simplify the narrative and ensure an appropriate balance between main and supplemental figures.

(2) It would also be useful to present data on the ion selectivity of Panx channels activated by LPC. How does this compare to data obtained when the channel is activated by depolarization? If the two stimuli activate related open states then the ion selectivity may be quite similar, but perhaps not if the two stimuli activate different open states. The authors earlier work in eLife shows interesting shifts in reversal potentials (Vrev) when substituting external chloride with gluconate but not when substituting external sodium with N-methyl-D-glucamine, and these changed with mutations within the external pore of Panx channels. Related measurements comparing channels activated by LPC with membrane depolarization would be valuable for assessing whether similar or distinct open states are activated by LPC and voltage. It would be ideal to make Vrev measurements using a fixed step depolarization to open the channel and then various steps to more negative voltages to measure tail currents in pinpointing Vrev (a so called instantaneous IV).

We fully agree with the reviewer on the importance of ion selectivity experiments. However, comparing the properties of LPC-activated channels with those activated by membrane depolarization presented technical challenges, as LPC appears to stimulate Panx1 in synergy with voltage. Prolonged LPC exposure destabilizes patches, complicating G-V curve acquisition and kinetic analyses. While such experiments could provide mechanistic insights, we think they are beyond the scope of current study.

(3) Data is presented for expression of Panx channels in different cell types (HEK vs HEKS GnTI-) and different constructs (Panx1 vs Panx1-GS vs other engineered constructs). The authors have tried to be clear about what was done in each experiment, but it can be challenging for the reader to keep everything straight. The labelling in Fig 1E helps a lot, and we encourage the authors to use that approach systematically throughout. It would also help to clearly identify the cell type and channel construct whenever showing traces, like those in Fig 1D. Doing this systematically throughout all the figures would also make it clear where a control is missing. For example, if labelling for the type of cell was included in Fig 1D it would be immediately clear that a GnTI- vector alone control for WT Panx1 is missing as the vector control shown is for HEK cells and formally that is only a control for Panx2 and 3. Can the authors explain why PLC activates Panx1 overexpressed in HEK293 GnTl- cells but not in HEK293 cells? Is this purely a function of expression levels? If so, it would be good to provide that supporting information.

As mentioned above, we believe our revised version is more straightforward to digest. We have improved labeling and provided explanations where necessary to clarify the manuscript. While Panx1 expression levels are indeed higher in GnTI- than in HEK293 cells, we are uncertain whether the absence of detectable currents in HEK293 cells is solely due to expression levels. Some post-translational modifications that inhibit Panx1, such as lysine acetylation, may also impact activity. Future studies are needed to explore these mechanisms further.

(4) The mVenus quenching experiments are somewhat confusing in the way data are presented. In Fig 2B the y axis is labelled fluorescence (%) but when the channel is closed at time = 0 the value of fluorescence is 0 rather than 100 %, and as the channel opens when LPC is added the values grow towards 100 instead of towards 0 as iodide permeates and quenches. It would be helpful if these types of data could be presented more intuitively. Also, how was the initial rate calculated that is plotted in Fig 2C? It would be helpful to show how this is done in a figure panel somewhere. Why was the initial rate expressed as a percent maximum, what is the maximum and why are the values so low? Why is the effect of CBX so weak in these quenching experiments with Panx1 compared to other assays? This assay is used in a lot of experiments so anything that could be done to bolster confidence is what it reports on would be valuable to readers. Bringing in as many control experiments that have been done, including any that are already published, would be helpful.

We modified the Y-axis in Figure 2 to “Quench (%)” for clarity. The data reflects fluorescence reduction over time, starting from LPC addition, normalized to the maximal decrease observed after Triton-X100 addition (3 minutes), enabling consistent quenching value comparisons. Although the quenching value appears small, normalization against complete cell solubilization provides reproducible comparisons. We do not fully understand why CBX effects vary in Venus quenching experiments, but we speculate that its steroid-like pentacyclic structure may influence the lysophospholipid agonistic effects. As noted in prior studies (DOI: 10.1085/jgp.201511505; DOI: 10.7554/eLife.54670), CBX likely acts as an allosteric modulator rather than a simple pore blocker, potentially contributing to these variations.

(5) Could provide more information to help rationalize how Yo-Pro-1, which has a charge of +2, can permeate what are thought to be anion favouring Panx channels? We appreciate that the biophysical properties of Panx channel remain mysterious, but it would help to hear how a bit more about the authors thinking. It might also help to cite other papers that have measured Yo-Pro-1 uptake through Panx channels. Was the Strep-tagged construct of Panx1 expressed in GnTI- cells and shown to be functional using electrophysiology?

Our recent study suggest that the electrostatic landscape along the permeation pathway may influence its ion selectivity (DOI: 10.1101/2024.06.13.598903). However, we have not yet fully elucidated how Panx1 permeates both anions and cations. Based on our findings, ion selectivity may vary with activation stimulus intensity and duration. Cation permeation through Panx1 is often demonstrated with YO-PRO-1, which measures uptake over minutes, unlike electrophysiological measurements conducted over milliseconds to seconds. We referenced two representative studies employing YO-PRO-1 to assess Panx1 activity. Whole-cell current measurements from a similar construct with an intracellular loop insertion indicate that our STREP-tagged construct likely retains functional capacity.

(6) In Fig 5 panel C, data is presented as the ratio of LPC induced current at -60 mV to that measured at +110 mV in the absence of LPC. What is the rationale for analysing the data this way? It would be helpful to also plot the two values separately for all of the constructs presented so the reader can see whether any of the mutants disproportionately alter LPC induced current relative to depolarization activated current. Also, for all currents shown in the figures, the authors should include a dashed coloured line at zero current, both for the LPC activated currents and the voltage steps.

We used the ratio of LPC-induced current to the current measured at +110 mV to determine whether any of the mutants disproportionately affect LPC-induced current relative to depolarization-activated current. Since the mutants that did not respond to LPC also exhibited smaller voltage-stimulated currents than those that did respond, we reasoned that using this ratio would better capture the information the reviewer is suggesting to gauge. Showing the zero current level may be helpful if the goal was to compare basal currents, which in our experience vary significantly from patch to patch. However, since we are comparing LPC- and voltage-induced currents within the same patch, we believe that including basal current measurements would not add useful information to our study.

Given that new experiments included to further highlight the significance of the discovery of Panx1 agonists, we opted to separate structure-based mechanistic studies from this manuscript and removed this experiment along with the docking and cryo-EM studies.

(7) The fragmented NTD density shown in Fig S8 panel A may resemble either lipid density or the average density of both NTD and lipid. For example, Class7 and Class8 in Fig.S8 panel D displayed split densities, which may resemble a phosphate head group and two tails of lipid. A protomer mask may not be the ideal approach to separate different classes of NTD because as shown in Fig S8 panel D, most high-resolution features are located on TM1-4, suggesting that the classification was focused on TM1-4. A more suitable approach would involve using a smaller mask including NTD, TM1, and the neighbouring TM2 region to separate different NTD classes.

We agree with the reviewer and attempted 3D classification using multiple smaller masks including the suggested region. However, the maps remained poorly defined, and we were unable to confidently assign the NTD.

(8) The authors don’t discuss whether the LPC-bound structures display changes in the external part of the pore, which is the anion-selective filter and the narrower part of the pore. If there are no conformational changes there, then the present structures cannot explain permeability to large molecules like ATP. In this context, a plot for the pore dimension will be helpful to see differences along the pore between their different structures. It would also be clearer if the authors overlaid maps of protomers to illustrate differences at the NTD and the "selectivity filter."

Both maps show that the narrowest constriction, formed by W74, has a diameter of approximately 9 Å. Previous steered molecular dynamics simulations suggest that ATP can permeate through such a constriction, implying an ion selection mechanism distinct from a simple steric barrier.

(9) The time between the addition of LPC to the nanodisc-reconstituted protein and grid preparation is not mentioned. Dynamic diffusion of LPC could result in equal probabilities for the bound and unbound forms. This raises the possibility of finding the Primed state in the LPC-bound state as well. Additionally, can the authors rationalize how LPC might reach the pore region when the channel is in the closed state before the application of LPC?

We appreciate the reviewer’s insight. We incubated LPC and nanodisc-reconstituted protein for 30 minutes, speculating that LPC approaches the pore similarly to other lipids in prior structures. In separate studies, we are optimizing conditions to capture more defined conformations.

(10) In the cryo-EM map of the “resting” state (EMDB-21150), a part of the density was interpreted as NTD flipped to the intracellular side. This density, however, is poorly defined, and not connected to the S1 helix, raising concerns about whether this density corresponds to the NTD as seen in the “resting” state structure (PDB-ID: 6VD7). In addition, some residues in the C-terminus (after K333 in frog PANX1) are missing from the atomic model. Some of these residues are predicted by AlphaFold2 to form a short alpha helix and are shown to form a short alpha helix in some published PANX1 structures. Interestingly, in both the AF2 model and 6WBF, this short alpha helix is located approximately in the weak density that the authors suggest represents the “flipped” NTD. We encourage the authors to be cautious in interpreting this part as the “flipped” NTD without further validation or justification.

We agree that the density corresponding the extended NTD into the cytoplasm is relatively weak. In our recent study, we compared two Panx1 structures with or without the mentioned C-terminal helix and found evidence suggesting the likelihood of NTD extension (DOI: 10.1101/2024.06.13.598903). Nevertheless, to prevent potential confusion, we have removed the cryo-EM panel from this manuscript.

(11) Since the authors did not observe densities of bound PLC in the cryo-EM map, it is important to acknowledge in the text the inherent limitations of using docking and mutagenesis methods to locate where PLC binds.

Thank you for the suggestion. We have removed this section to avoid potential confusion.

Optional suggestions:

(1) The authors used MeOH to extract mouse liver for reversed-phase chromatography. Was the study designed to focus on hydrophobic compounds that likely bind to the TMD? Panx1 has both ECD and ICD with substantial sizes that could interact with water soluble compounds? Also, the use of whole-cell recordings to screen fractions would not likely identify polar compounds that interact with the cytoplasmic part of the TMD? It would be useful for the authors to comment on these aspects of their screen and provide their rationale for fractionating liver rather than other tissues.

We have added a rationale in line 90, stating: “The soluble fractions were excluded from this study, as the most polar fraction induced strong channel activities in the absence of exogenously expressed pannexins.” Additionally, we have included a figure to support this rationale (Fig. S1A).

(2) The authors show that LPCs reversibly increase inward currents at a holding voltage of -60 mV (not always specified in legends) in cells expressing Panx1 and 2, and then show families of currents activated by depolarizing voltage steps in the absence of LPC without asking what happens when you depolarize the membrane after LPC activation? If LPCs can be applied for long enough without disrupting recordings, it would be valuable to obtain both I-V relations and G-V relations before and after LPC activation of Panx channels. Does LPC disproportionately increase current at some voltages compared to others? Is the outward rectification reduced by LPC? Does Vrev remain unchanged (see point above)? Its hard to predict what would be observed, but almost any outcome from these experiments would suggest additional experiments to explore the extent to which the open states activated by LPC and depolarization are similar or distinct.

Unfortunately, in our hands, the prolonged application of lysolipids at concentrations necessary to achieve significant currents tends to destabilize the patch. This makes it challenging to obtain G-V curves or perform the previously mentioned kinetic analyses. We believe this destabilization may be due to lysolipids’ surfactant-like qualities, which can disrupt the giga seal. Additionally, prolonged exposure seems to cause channel desensitization, which could be another confounding factor.

(3) From the results presented, the authors cannot rule out that mutagenesis-induced insensitivity of Panx channels to LPCs results from allosteric perturbations in the channels rather than direct binding/gating by LPCs. In Fig 5 panel A-C, the authors introduced double mutants on TM1 and TM2 to interfere with LPC binding, however, the double mutants may also disrupt the interaction network formed within NTD, TM1, and TM2. This disruption could potentially rearrange the conformation of NTD, favouring the resting closed state. Three double Asn mutants, which abolished LPC induced current, also exhibited lower currents through voltage activation in Fig 5S, raising the possibility the mutant channels fail to activate in response to LPC due to an increased energy barrier. One way to gain further insight would be to mutate residues in NTD that interact with those substituted by the three double Asn mutants and to measuring currents from both voltage activation and LPC activation. Such results might help to elucidate whether the three double Asn mutants interfere with LPC binding. It would also be important to show that the voltage-activated currents in Fig. S5 are sensitive to CBX?

Thank you for the comment, with which we agree. Our initial intention was to use the mutagenesis studies to experimentally support the docking study. Due to uncertainties associated with the presented cryo-EM maps, we have decided to remove this study from the current manuscript. We will consider the proposed experiments in a future study.

(4) Could the authors elaborate on how LPC opens Panx1 by altering the conformation of the NTDs in an uncoordinated manner, going from “primed” state to the “active” state. In the “primed” state, the NTDs seem to be ordered by forming interactions with the TMD, thus resulting in the largest (possible?) pore size around the NTDs. In contrast, in the “active” state, the authors suggest that the NTDs are fragmented as a result of uncoordinated rearrangement, which conceivably will lead to a reduction in pore size around NTDs (isn’t it?). It is therefore not intuitive to understand why a conformation with a smaller pore size represents an “active” state.

We believe the uncoordinated arrangement of NTDs is dynamic, allowing for potential variations in pore size during the activated conformation. Alternatively, NTD movement may be coupled with conformational changes in TM1 and the extracellular domain, which in turn could alter the electrostatic properties of the permeation pathway. We believe a functional study exploring this mechanism would be more appropriately presented as a separate study.

(5) Can the authors provide a positive control for these negative results presented in Fig S1B and C?

The positive results are presented in Fig. 1D and E.

(6) Raw images in Fig S6 and Fig S7 should contain units of measurement.

Thank you for pointing this out.

(7) It may be beneficial to show the superposition between primed state and activated state in both protomer and overall structure. In addition, superposition between primed state and PDB 7F8J.

We attempted to superimpose the cryo-EM maps; however, visually highlighting the differences in figure format proved challenging. Higher-resolution maps would allow for model building, which would more effectively convey these distinctions.

(8) Including particles number in each class in Fig S8 panel C and D would help in evaluating the quality of classification.

Noted.

(9) A table for cryo-EM statistics should be included.

Thanks, noted.

(10) n values are often provided as a range within legends but it would be better to provide individual values for each dataset. In many figures you can see most of the data points, which is great, but it would be easy to add n values to the plots themselves, perhaps in parentheses above the data points.

While we agree that transparency is essential, adding n-values to each graph would make some figures less clear and potentially harder to interpret in this case. We believe that the dot plots, n-value range, and statistical analysis provide adequate support for our claims.

(11) The way caspase activation of Panx channels is presented in the introduction could be viewed as dismissive or inflammatory for those who have studied that mechanism. We think the caspase activation literature is quite convincing and there is no need to be dismissive when pointing out that there are good reasons to believe that other mechanisms of activation likely exist. We encourage you to revise the introduction accordingly.

Thank you for this comment. Although we intended to support the caspase activation mechanism in our introduction, we understand that the reviewer’s interpretation indicates a need for clarification. We hope the revised introduction removes any perception of dismissiveness.

(12) Why is the patient data in Fig 4F normalized differently than everything else? Once the above issues with mVenus quenching data are clarified, it would be good to be systematic and use the same approach here.

For Fig. 4F, we used a distinct normalization method to account for substantial day-to-day variation in experiments involving body fluids. Notably, we did not apply this normalization to other experimental panels due to their considerably lower day-to-day variation.

(13) What was the rational for using the structure from ref 35 in the docking task?

The docking task utilized the human orthologue with a flipped-up NTD. We believe that this flipped-up conformation is likely the active form that responds to lysolipids. As our functional experiments primarily use the human orthologue for biological relevance, this structure choice is consistent. Our docking data shows that LPC does not dock at this site when using a construct with the downward-flipped NTD.

(14) Perhaps better to refer to double Asn ‘substitutions’ rather than as ‘mutations’ because that makes one think they are Asn in the wt protein.

Done.

(15) From Fig S1, we gather that Panx2 is much larger than Panx1 and 3. If that is the case, its worth noting that to readers somewhere.

We have added the molecular weight of each subtype in the figure legend.

(16) Please provide holding voltages and zero current levels in all figures presenting currents.

We provided holding voltages. However, the zero current levels vary among the examples presented, making direct comparisons difficult. Since we are comparing currents with and without LPC, we believe that indicating zero current levels is unnecessary for this study.

(17) While the authors successfully establish lysophospholipid-gating of Panx1 and Panx2, Panx3 appears unaffected. It may be advisable to be more specific in the title of the article.

We are uncertain whether Panx3 is unaffected by lysophospholipids, as we have not observed activation of this subtype under any tested conditions.

eLife Assessment

This descriptive study used multiparameter spectral flow cytometry and clustering analysis of a subset of CD4 T cells, termed circulating T follicular helper (cTfh), responding to Plasmodium falciparum antigens, PfSEA -1A and PfGARP. The results from this comprehensive study provide valuable information regarding differences in cTfh response profiles between children and adults living in malaria-endemic Kenya and thus offer a potential usefulness towards improving choices of antigen candidates for malaria vaccines. However, the analysis and interpretation of antigen-specific CD4 cTfh responses remain incomplete.

Reviewer #1 (Public review):

Summary:

This study aims to understand the malaria antigen-specific cTfh profile of children and adults living in malaria holoendemic area. PBMC samples from children and adults were unstimulated or stimulated with PfSEA-1A or PfGARP in vitro for 6h and analysed by a cTfh-focused panel. Unsupervised clustering and analysis on cTfh was performed. The main conclusions are: A) the children cohort has a more diverse (cTfh1/2/17) recall responses compared to adults (mainly cTfh17) and, B) Pf-GARP stimulates better cTfh17 responses in adults, thus a promising vaccine candidate.

Strengths:

This study is, in general, well-designed and with excellent data analysis. The use of unsupervised clustering is a nice attempt to understand the heterogeneity of cTfh cells.

Weaknesses:

The authors have provided additional data in Supplementary Figures 14-16. However, I remain concerned about whether cTfh cells are truly responding to antigen stimulation. In Supplementary Figure 15A-F, the IFNg responses appear as expected, SEB elicits the strongest response, as it stimulates bulk T cells, and the staining is promising, showing a clear distinction between IFNg+ and IFNg- populations. However, in Supplementary Figure 15I-N, the IL-21 secretion assay is concerning. The FACS plots make it difficult to distinguish IL-21+ from IL-21- cells, raising concerns about the validity of this analysis. Additionally, in panel J, the responses to PfSEA-1A or PfGARP appear even greater than those to SEB stimulation. In PBMCs, only a small percentage of T cells should be specific to a particular antigen. How can the positive control (SEB) produce a weaker response than stimulation with a specific antigen? This suggests that the IL-21 secretion assay may not have worked, making the authors' interpretation unreliable.

I also have similar concerns about the IL-4 secretion in Sup Figure 16. First, the FACS plot shows that appear double-positive for IL-21 and IL-4, so it suggests the staining may be due to autofluorescence rather than true cytokine signals. Also in B-C the responses of SEB stimulation is generally weaker than stimulated by one antigen, further questioning the reliability of the IL-4 assay. In summary, I am not convinced that the in vitro antigen stimulation assay worked as intended. Consequently, the manuscript's claims regarding PfSEA-1A- and PfGARP-specific cTfh responses are not sufficiently supported by the presented data.

Reviewer #3 (Public review):

Summary:

The goal of this study was to carry out an in-depth granular and unbiased phenotyping of peripheral blood circulating Tfh specific to two malaria vaccine candidates, PfSEA-1A and PfGARP, and correlate these with age (children vs adults) and protection from malaria (antibody titers against Plasmodium antigens.) Authors further attempted to identify any specific differences of the Tfh responses to these two distinct malaria antigens.

Strengths:

The authors had access to peripheral blood samples from children and adults living in a malaria-endemic region of Kenya. The authors studied these samples using in vitro restimulation in the presence of specific malaria antigens. Authors generated a very rich data set from these valuable samples using cutting-edge spectral flow cytometry and a 21-plex panel that included a variety of surface markers, cytokines and transcription factors.

Update following first revision (R1) of the manuscript:

The authors have made a great effort to comprehensively address comments raised by the reviewers. In particular, clearly showing expression of ICOS and Bcl6 on CXCR5+ cells greatly strengthens the case for defining these cells as Tfh-like circulatory lymphocytes (cTfh).

Weaknesses:

Update following first revision (R1) of the manuscript:

Unfortunately, my main concern remains. As it stands, the study is not really on antigen-specific T cells, but rather on the overall CD4 T cell compartment plus or minus antigenic stimulation. Although authors used an in vitro restimulation strategy with malaria antigens, they do not focus on cells de-novo expressing activation markers as a result of restimulation, neither they use tetramers to detect antigen-specific T cells. Moreover, their data shows that the number of CXCR5+ CD4 T cells de-novo expressing activation markers and/or cytokines as a result of their in vitro restimulation is negligible, even when using a prototypic superantigen (SEB).

Thus, no antigen-specific CXCR5+ CD4 T cells could be analysed with the data that the authors provide in this manuscript.

Reviewer #4 (Public review):

Summary:

This manuscript is a descriptive study of circulating T follicular helper (cTfh) responses to PfSEA -1A or PfGARP (targets of new antimalaria vaccine candidates) in PBMCs from a convenience sample of children (7 yrs of age) and adults living in a malaria holo endemic Kenya using multiparameter flow cytometry and clustering analysis. This cell type promotes B cell production of long-lived antimalarial antibodies to provide protection against malaria. They find that children had a wider cTFH cytokine and TF profile cellular response in comparison to adults who responded to both antigens but had a narrower response profile.

Strengths:

Carefully done study, very detailed, nice summary model at the end of the paper. The revision provides requested clarification on a number of issues, including CD40L expression which was not differentially expressed between groups. They add additional data into the supplemental files, including IL4 and IL21 data by presenting the cytoplots.

Weaknesses:

To know the significance of these cTfh cells for long-term protection of malaria requires functional and transfer experiments in animal models which is outside the scope of this work.

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public Review):

Summary:

This study aims to understand the malaria antigen-specific cTfh profile of children and adults living in a malaria holoendemic area. PBMC samples from children and adults were unstimulated or stimulated with PfSEA-1A or PfGARP in vitro for 6h and analysed by a cTfh-focused panel. Unsupervised clustering and analysis on cTfh were performed.

The main conclusions are:

(1) the cohort of children has more diverse (cTfh1/2/17) recall responses compared to the cohort of adults (mainly cTfh17) and

(2) Pf-GARP stimulates better cTfh17 responses in adults, thus a promising vaccine candidate.

Strengths:

This study is in general well-designed and with excellent data analysis. The use of unsupervised clustering is a nice attempt to understand the heterogeneity of cTfh cells. Figure 9 is a beautiful summary of the findings.

Weaknesses:

(1) Most of my concerns are related to using PfSEA-1A and PfGARP to analyse cTfh in vitro stimulation response. In vitro, stimulation on cTfh cells has been frequently used (e.g. Dan et al, PMID: 27342848), usually by antigen stimulation for 9h and analysed CD69/CD40L expression, or 18h and CD25/OX40. However, the authors use a different strategy that has not been validated to analyse in vitro stimulated cTfh. Also, they excluded CD25+ cells which might be activated cTfh. I am concerned about whether the conclusions based on these results are reliable.

It has been shown that cTfh cells can hardly produce cytokines by Dan et al. However, in this paper, the authors report the significant secretion of IL-4 and IFNg on some cTfh clusters after 6h stimulation. If the stimulation is antigen-specific through TCR, why cTfh1 cells upregulate IL-4 but not IFNg in Figure 6? I believe including the representative FACS plots of IL-4, IFNg, IL21 staining, and using %positive rather than MFI can make the conclusion more convincing. Similarly, the author should validate whether TCR stimulation under their system for 6h can induce robust BCL6/cMAF expression in cTfh cells. Moreover, there is no CD40L expression. Does this mean TCR stimulation mediated BCl6/cMAF upregulation and cytokine secretion precede CD40L expression?

In summary, I am particularly concerned about the method used to analyse PfSEA-1A and PfGARP-specific cTfh responses because it lacks proper validation. I am unsure if the conclusions related to PfSEA-1A/PfGARP-specific responses are reliable.

An unfortunate reality of these types of complex immunologic studies is that it takes time to optimize a multiparameter flow cytometry panel, run this number of samples, and then conduct the analysis (not to mention the time it takes for a manuscript to be accepted for peer-review). An unexpected delay, frankly, was the COVID-19 pandemic when non-essential research lab activities were put on hold. We designed our panel in 2019 and referred to the “T Follicular Helper Cells” Methods and Protocols book from Springer 2015. Obviously the field of human immunology took a huge leap forward during the pandemic as we sought to characterize components of protective immunity, and as a result there are several new markers we will choose for future studies of Tfh subsets. We agree with the reviewer that cytokine expression kinetics differ depending on the in vitro stimulation conditions. Due to small blood volumes obtained from healthy children, we were limited in the number of timepoints we could test. However, since we were most interested in IL21 expression, we found 6 hrs to be the best in combination with the other markers of interest during our optimization experiments. We did find IFNg expression from non-Tfh cells, therefore we believe our stimulation conditions worked.

Dan et al used stimulated tonsils cells to assess the CXCR5^posPD1^posCD45RA^neg Tfh and CXCR5^neg CD45RA^neg non-Tfh whereas in our study, we evaluated CXCR5^posPD1^posCD45RA^neg Tfh from PBMCs. Dan et al PBMCs’ work used EBV/CMV or other pathogen product stimuli and only gated on CD25^posOX40^pos cells which are not the cells we are assessing in our study. This might explain in part the differences in cytokine kinetics, as we evaluated CD25^neg PBMCs only. However, we agree that more recent studies focused on CXCR5^posPD1^pos cells included more Activation-induced marker (AIM) markers, which are missing in our study, inducing a lack of depth in our analysis.

Percentage of positive cells and MFI are complementary data. Indeed, the percentage of positive cells only indicates which cells express the marker of interest without giving a quantitative value of this expression. MFI indicates how much the marker of interest is expressed by cells which is important as it can indicate degree of activation or exhaustion per cell. Meta-cluster analysis is not ideal to assess the percentage of positivity whereas it does provide essential information regarding the intensity of expression. We added supplemental figures 14 (Bcl6 and cMAF), 15 (INFg and IL21) and 16 (IL4 and IL21) where percentage of positive cells were manually gated directly from the total CXCR5^posCD4^posCD45RA^negCD25^neg TfH based on the FMO or negative control, and we overlaid the positive cells on the UMAP of all the CXCR5^posCD4^posCD45RA^negCD25^neg meta-clusters. Results from the manual gating are consistent with the results we show using clustering. However, it helps to better visualize that antigen-specific IL21 expression was statistically significant in children whereas the high background observed for adults did not reveal higher expression after stimulation, perhaps suggesting an upper threshold of cytokine expression (supplemental figure 15). The following sentence has been added in the methods at the end of the “OMIQ analysis” section: “ However, the percentage of positive IFN𝛾, IL-4, IL-21, Bcl6, or cMAF using manual gating can be found in Supplemental Figures 14, 15, and 16 along with the overlay of the gated positive cells on the CD4^posCXCR5^posCD25^neg UMAP and the cytoplots of the gated positive cells for each meta-cluster (Supplemental Figures 14, 15, and 16).”

Indeed cMAF can be induced by TCR signaling, ICOS and IL6 (Imbratta et. al, 2020). However, in our study populations, ICOS was expressed (see Author response image 1, panel A) in absence of any stimulation suggesting that CXCR5^posCD4^posCD25^negCD45RA^neg cells were already capable of expressing cMAF. Indeed, after gating Bcl6 and cMAF positive cells based on their FMOs (Author response image 1, panel B and C, respectively), we overlaid positive cells on the CXCR5^posCD4^posCD25^negCD45RA^neg cells UMAP and we can see that most of our cells already express cMAF alone (Author response image 1, panel D), co-express cMAF and Bcl6 (Author response image 1, panel E), confirming that they are TfH cells, whereas very few cells only expressed Bcl6 alone (Author response image 1, panel F). Because we knew that cT_FH already expresses Bcl6 and cMAF, we focused our analysis on the intensity of their expression to assess if our vaccine candidates were inducing more expression of these transcription factors.

Author response image 1.

(2) The section between lines 246-269 is confusing. Line 249, comparing the abundance after antigen stimulation is improper because 6h stimulation (under Golgi stop) should not induce cell division. I think the major conclusions are contained in Figure 5e, that (A) antigen stimulation will not alter cell number in each cluster and (B) children have more MC03, 06 and fewer MC02, etc.). The authors should consider removing statements between lines 255-259 because the trends are the same regardless of stimulations.

We agree, there is no cell division after 6h and that different meta clusters did not proliferate after this short of in vitro stimulation. The use of the word ‘abundance’ in the context of cluster analysis is in reference to comparing the contribution of events by each group to the concatenated data. After the meta clusters are defined and then deconvoluted by study group, certain meta clusters could be more abundant in one group compared to another - meaning they contributed more events to a particular metacluster.

Dimensionality reduction is more nuanced than manual gating and reveals a continuum of marker expression between the cell subsets, as there is no hard “straight line” threshold, as observed when using in 2D gating. Because of this, differences are revealed in marker expression levels after stimulation making them shift from one cluster to another - thereby changing their abundance.

To clarify how this type of analysis is interpreted, we have modified lines 255-259 as follows:

“In contrast, the quiescent PfSEA-1A- and PfGARP-specific cT_FH2-like cluster (MC02) was significantly more abundant in adults compared to children (Figure 5c and 5d, pf<0.05). Interestingly, following PfGARP stimulation, the activated cT_FH1/17-like subset (MC09) became more abundant in children compared to adults (Figure 5d, pf<0.05 with a False Discovery Rate=0.08), but no additional subsets shifted phenotype after PfSEA-1A stimulation (Figure 5c).”

Reviewer #2 (Public Review):

Summary:

Forconi et al explore the heterogeneity of circulating Tfh cell responses in children and adults from malaria-endemic Kenya, and further compare such differences following stimulation with two malaria antigens. In particular, the authors also raised an important consideration for the study of Tfh cells in general, which is the hidden diversity that may exist within the current 'standard' gating strategies for these cells. The utility of multiparametric flow cytometry as well as unbiased clustering analysis provides a potentially potent methodology for exploring this hidden depth. However, the current state of analysis presented does not aid the understanding of this heterogeneity. This main goal of the study could hopefully be achieved by putting all the parameters used in one context, before dissecting such differences into their specific clinical contexts.

Strengths:

Understanding the full heterogeneity of Tfh cells in the context of infection is an important topic of interest to the community. The study included clinical groupings such as age group differences and differences in response to different malaria antigens to further highlight context-dependent heterogeneity, which offers new knowledge to the field. However, improvements in data analyses and presentation strategies should be made in order to fully utilize the potential of this study.

Weaknesses:

In general, most studies using multiparameter analysis coupled with an unbiased grouping/clustering approach aim to describe differences between all the parameters used for defining groupings, prior to exploring differences between these groupings in specific contexts. However, the authors have opted to separate these into sections using "subset chemokine markers", "surface activation markers" and then "cytokine responses", yet nuances within all three of these major groups were taken into account when defining the various Tfh identities. Thus, it would make sense to show how all of these parameters are associated with one another within one specific context to first logically establish to the readers how can we better define Tfh heterogeneity. When presented this way, some of the identities such as those that are less clear such as "MC03/MC04/ MC05/ MC08" may even be better revealed. once established, all of these clusters can then be subsequently explored in further detail to understand cluster-specific differences in children vs adults, and in the various stimulation conditions. Since the authors also showed that many of the activation markers were not significantly altered post-stimulation thus there is no real obstacle for merging the entire dataset for the first part of this study which is to define Tfh heterogeneity in an unbiased manner regardless of age groups or stimulation conditions. Other studies using similar approaches such as Mathew et al 2020 (doi: 10.1126/science.abc8) or Orecchioni et al 2017 (doi: 10.1038/s41467-017-01015-3) can be referred to for more effective data presentation strategies.

Accordingly, the expression of cytokines and transcription factors can only be reliably detected following stimulation. However, the underlying background responses need to be taken into account for understanding "true" positive signals. The only raw data for this was shown in the form of a heatmap where no proper ordering was given to ensure that readers can easily interpret the expression of these markers following stimulation relative to no stimulation. Thus, it is difficult to reliably interpret any real differences reported without this. Finally, the authors report differences in either cluster abundance or cluster-specific cytokine/ transcription factor expression in Tfh cell subsets when comparing children vs adults, and between the two malaria antigens. The comparisons of cytokine/transcription factor between groups will be more clearly highlighted by appropriately combining groupings rather than keeping them separate as in Figures 6 and 7.

Thank you for sharing these references. Similar to SPADE clustering and ViSNE dimensionality algorithms used in Orecchioni et al, we used all the extracellular markers from our panel in our FlowSOM algorithm with consensus meta-clustering which includes both the chemokine receptors and activation markers even though they are presented separately in our manuscript across the figure 3 and 4. This was explained in the methods section (lines 573 - 587). We then chose the UMAP algorithm as visual dimensionality reduction of the meta-clusters generated by FlowSOM-consensus meta-clustering as explained under the “OMIQ analysis” subpart of our methods (lines 588- 604). Therefore, we believe we have conducted the analysis as this reviewer suggests even if we chose to show the figures that were informative to our story. The heatmap of the results brings the possibility to see which combination of markers respond or not to the different conditions and between groups, all the raw data are present from the supplemental figures 10 to 13 showing, using bar plots, the differences expressed in the heatmaps. We believe it strengthens our interpretation of the results.

Regarding the transcription factor and cytokine background, we added supplemental figures 14, 15 and 16 where we used manual gating to select Bcl6, cMAF, IFNg, IL21 or IL4 positive cells directly from total CXCR5^posCD4^posCD45RA^negCD25^neg TfH cells based on the FMO or negative control, and we overlaid the positive cells on the UMAP of all the CXCR5^posCD4^posCD45RA^negCD25^neg meta-clusters. Moreover, all the dot plots (with their statistics) used for the heatmap figure 6 and 7 can be found in the supplemental figures 10, 11, 12 and 13. These supplemental figures address the concerns above by showing the difference of signals between unstimulated and stimulated conditions.

Reviewer #3 (Public Review):

Summary:

The goal of this study was to carry out an in-depth granular and unbiased phenotyping of peripheral blood circulating Tfh specific to two malaria vaccine candidates, PfSEA-1A and PfGARP, and correlate these with age (children vs adults) and protection from malaria (antibody titers against Plasmodium antigens.). The authors further attempted to identify any specific differences in the Tfh responses to these two distinct malaria antigens.

Strengths:

The authors had access to peripheral blood samples from children and adults living in a malaria-endemic region of Kenya. The authors studied these samples using in vitro restimulation in the presence of specific malaria antigens. The authors generated a very rich data set from these valuable samples using cutting-edge spectral flow cytometry and a 21-plex panel that included a variety of surface markers, cytokines, and transcription factors.

Weaknesses:

- Quantifying antigen-specific T cells by flow cytometry requires the use of either 1- tetramers or 2- in vitro restimulation with specific antigens followed by identification of TCR-activated cells based on de-novo expression of activation markers (e.g. intracellular cytokine staining and/or surface marker staining). Although authors use an in vitro restimulation strategy, they do not focus their study on cells de-novo expressing activation markers as a result of restimulation; therefore, their study is not really on antigen-specific cTfh. Moreover, the authors report no changes in the expression of activation markers commonly used to identify antigen-specific T cells upon in vitro restimulation (including IFNg and CD40L); therefore, it is not clear if their in vitro restimulation with malaria antigens actually worked.

We understand the reviewer’s point of view and apologies for any confusion. IFNg was expressed but not statistically different between groups. Indeed, looking at the CD8 T cells and using manual gating, we were able to show that IFNg was increased but not statistically significant upon stimulation from CD4^posCXCR5^pos cells (supplemental figure 15, panel C), confirming our primary observation using clustering analysis. These results showed that our malaria antigen induced IFNg response in some participants, but not all of them, revealing heterogeneity in this response among individuals within the same group.

Regarding CD40L, in the supplemental figure 7, we can see that some of our meta-clusters expressed more CD40L upon stimulation, but again without leading to statistical differences between groups. Combined with the increased expression of other cytokines and transcription factors, we showed that our stimulation did indeed work. However, because of the high variation within groups, there were no statistical differences across our groups. Because CD40L is not the only marker showing specific T cell activation, and not all T cells respond using this marker alone, a more comprehensive multimarker AIM panel might have highlighted differences between groups. We recognized the limitations of our study and believe that future study will benefit from more activation markers commonly used to identify antigone-specific T cells such as CD69, OX40, 4-1BB (AIM panel), among other markers.

- CXCR5+CD4+ memory T cells have been shown to present multi-potency and plasticity, capable of differentiating to non-Tfh subsets upon re-challenge. Although authors included in their flow panel a good number of markers commonly used in combination to identify Tfh (CXCR5, PD-1, ICOS, Bcl-6, IL-21), they only used one single marker (CXCR5) as their basis to define Tfh, thus providing a weak definition for Tfh cells and follow up downstream analysis.

Sorry for the confusion, even though the subsampled on the CD4^posCXCR5^pos CD25^neg cells to run our FlowSOM, we showed the different levels of expression across meta-clusters (figure 4 panels A and B) of PD1 (Tfh being PD1 positive cells) and ICOS (indicating the activation stage of the Tfh, “T Follicular Helper Cells” Methods and Protocols book from Springer 2015). We also included an overlay of the manually gated double positive Bcl6-cMAF cells on the CXCR5^posCD45RA^negCD25^neg CD4 T cell UMAP plot to show that most of them express Bcl6 (supplemental figure 14). Interestingly, the manually gated IL21 positive cells were less abundant, particularly for children (supplemental figure 15). Because we were not able to include all the markers that are now used to define Tfh cells, we referred to our cell subsets as “TFH-like”. This is an acknowledged limitation of our study. Due to the limited blood volume obtained from children and cost of running multiplex flow cytometry assays, our results showing antigen-specific heterogeneity of Tfh subset will have to be validated in future studies that include these additional defining markers.

- Previous works have used FACS-sorting and in vitro assays for cytokine production and B cell help to study the functional capacity of different cTfh subsets in blood from Plasmodium-infected individuals. In this study, authors do not carry out any such assays to isolate and evaluate the functional capacity of the different Tfh subsets identified. Thus, all the suggestions for the role that these different cTfh subsets may have in vivo in the context of malaria remain highly hypothetical.

Unfortunately, low blood volumes obtained from children prevented us from running in vitro functional assays and the study design did not allow us to correlate them with protection. However, since the function of identified Tfh subsets from malaria-exposed individuals has been evaluated using Pf lysates in other studies, we referenced them when interpreting the differences we reported in Tfh subset recognition between malaria antigens. If either of these antigens move forward into vaccine trials, then evaluating their function would be important.

- The authors have not included malaria unexposed control groups in their study, and experimental groups are relatively small (n=13).

This study design did not include the recruitment of malaria naive negative controls as its goal was to assess malaria antigen-specific responses comparing the quality and abundance between malaria-exposed children to adults to these potential new vaccine targets PfSEA-1A and PfGARP. We did however test 3 malaria-naive adults and found no non-specific activation after stimulation with these two malaria antigens. Since this was done as part of our assay optimization, we did not feel the need to show these negative findings.

And even with our small sample size, we demonstrated significant age-associated differences in malaria antigen-specific responses from cT_FH-like subsets.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Minor points are:

(1) Line 88, cTfh cells are not only from GC-Tfh, they have GC-independent origin (He et al, PMID: 24138884).

The following sentence was added line 88 “Interestingly, cT_FH cells can also come from peripheral cT_FH precursor CCR7^lowPD1^highCXCR5^pos cells; thus, they also have a GC-independent origin (He, Cell, 2013 PMID: 24138884).

(2) I believe all participants were free of blood-stage infection upon enrolment. But can authors clearly state this information between lines 151-159?

We mentioned in the methods, line 495-496 “Participants were eligible if they were healthy and not experiencing any symptoms of malaria at the time venous blood was collected”. However, using qPCR we found 5 children with malaria blood stage. As shown in Author response image 2, comparing malaria free to blood-stage children, no differences were observed without any stimulation. However, MC03 is more abundant upon malaria antigen stimulation in the blood-stage group whereas MC04 is more abundant in the malaria free group upon PfGARP stimulation only confirming that our stimulation worked.

Author response image 2.

Reviewer #3 (Recommendations For The Authors):

(1) The strategy for gating on antigen-specific cTfh cells needs to be revised. The correct approach would be to gate on those cells that respond by de-novo expression of activation markers upon antigen restimulation (also termed activation-induced markers. e.g. CD69, CD40L, CXCL13 and IL-21, Niessl 2020; CD69, CD40L, CD137 and OX40, Lemieux 2023; CD137 and OX40, Grifoni 2020). As it stands, the study is not really on antigen-specific T cells, but rather on the overall CD4 T cell compartment plus or minus antigenic stimulation.

We recognized the limitation in our flow panel design which prevents us from performing this gating. We originally based our panel design on the “T follicular helper cells methods and protocols” book (Springer 2015) which used CD45RA, CD25, CXCR5, CCR6, CXCR3, CCR7, ICOS and PD1 to define cT_FH. We had already optimized our 21-color panel, purchased reagents and started to run our experiments by the time these publications modified how to define TFH cells Niessl, Lemieux and Grifoni’s publication. Indeed we optimized and performed our assay from November 2019 to March 2020, finishing to run the samples during the first quarantine. Because of the urgent needs of research on SARS-CoV-2 that we were involved with from this time and moving forward, the analysis of our TFH work got highly postponed. Moreover, 2020 is also the year where many TFH papers came out with better ways to define cT_FH and responses to antigen stimulations. In our future studies, our panel will include AIM.

(2) It is not clear if the antigenic stimulation actually worked. Does the proportion of IFNg+ or IL-4+ or IL-21+ or CD40L+ or CD25+ CD4 or CD8 T cells increase following in vitro antigen restimulation?

Yes, using manual gating, we are able to show an increase of IL4 (supplemental figure 16 panel B and C), and IL21 (supplemental figure 15 panel J and K) production in both children and adults. However, we did not observe significant production of IFNg (supplemental figure 15, panel C) and changes in CD40L expression (supplemental figure 7) after malaria antigen stimulation, however, our positive control SEB worked. So, yes our stimulation assay worked but these 2 malaria antigens did not significantly induce these cytokines. This could be that they are too low to detect in every participant since they are single antigens and not whole parasite lysates, as other studies have used. It could also be that these antigens don’t stimulate CD40L or IFNg in all our participants. We brought up this limitation as follow in the discussion, line 473: “Although the heterogeneity in the response of CD40L and IFNγ suggests that our tested malaria antigens did not induce significant differences in the expression of these markers in all our participants, our panel did not include other activated induced markers, such as OX40, 4-1BB, and CD69”.

(3) It is not clear what is the proportion of cTfh over the total CD4 T cell compartment among the different groups. Does this vary among different groups? It would be valuable to display this as an old-fashioned combination of contour plots with outliers for illustrating flow cytometry and bar graphs for the cumulative data.

The proportion of CD3^posCD4^posCD25^negCXCR5^pos cTfh cells did not differ within the total number of CD4 T cells between groups (figure 2).

(4) The gating strategy could be refined and become more robust if adding additional markers in combination with CXCR5 for identifying cTfh (e.g. CXCR5+Bcl6+).

Thank you for this suggestion. An overlay of Bcl6 expression can be found in supplemental figure 14 where we confirm that our CXCR5+ cT_FH-like subsets express cMAF and Bcl6.

(5) The protocols for intracellular and intranuclear staining seem to be incomplete in Materials and Methods. In particular, cell permeabilization strategies seem to be missing.

Our apologies for this oversight, we added the following sentences in the methods line 545: “Cells were fixed and permeabilized for 45 mins using the transcription factor buffer set (BD Pharmingen) followed by a wash with the perm-wash buffer. Intracellular staining was performed at 4 °C for 45 more mins followed by two washes using the kit’s perm-wash buffer”.

(6) In Materials and Methods, the authors mention they have used fluorescence minus one control to set their gating strategy. It would be valuable to show these, either on the main body or as part of supplementary figures.

We added the cytoplots of the FMOs and/or negative controls as appropriate in the supplemental figures 14 (cMAF and Bcl6), 15 (IFNg and IL21) and 16 (IL4 and IL21).

(7) Line 194 and Figure 3, it is not clear the criteria that the authors used for down-sampling events before FlowSOM analysis. Was this random? Was this done with unstimulated or stimulated samples?

We chose to down-sample on CD3posCD4^posCD25^negCD45RA^neg and CXCR5^pos cells prior to our FlowSOM to allow more cluster analysis to focus only on the differences among those cells. The down-sampling used 1,000 CD3posCD4^posCD25^neg CD45RA^negCXCR5^pos cells from each fcs file (unstimulated and stimulated samples). If the fcs file had more than 1,000 CXCR5^pos cells, the down-sampling was done randomly by the OMIQ platform algorithm to select only 1,000 CXCR5^pos cells within this specific fcs file. The latest sentence was added to the methods line 593.

(8) Lanes 201, 202, As it stands, the take of the authors on the role of different cTfh subsets during infection remains highly speculative. Are these differences in cTfh phenotypes actually reflected in their in vitro capacity to provide B cell help (e.g. as in the Obeng-Adjei 2015 paper) or to produce IL-21, express co-stimulatory molecules, or any other characteristic that would allow them to better infer their functional roles during infection? Any additional in vitro analysis of the functional capacity of isolated cTfh subsets identified in this research would greatly increase its value.

We agree with the reviewer that this sentence is speculative, and we rephrase it as follow: “First, we found different CXCR5 expression levels between meta-clusters (Figure 3b); CXCR5 is essential for cT_FH cells to migrate to the lymph nodes and interact with B-cells”. We would have liked to perform in vitro functional assays. However, as explained above, we did not have sufficient cells collected from children to do so.

(9) It is not clear why authors omitted IL-17 and did not use IFNg and IL-4 to refine their definition of Th1, Th2 and Th17 cTfh.

We would have liked to include IL-17, however we were constrained by only having access to a 4 lasers cytometer at the time we ran our assay. In light of needing to prioritize markers, when we were designing our flow panel, cTfh1 were shown to be preferentially activated during episodes of acute febrile malaria children (Obeng-Adjei). Therefore, we chose to focus on IFNg and IL4 to differentiate Tfh1 from Tfh2, in addition to other markers as surrogate of functional potential. We did not use IFNg and IL4 to refine our definition of Tfh1, Tfh2 and Tfh17 as recent publications have shown that IL4 is not only expressed in Tfh2 but also in the other Tfh subsets, at lower intensity (Gowthaman among others). Therefore IFNg and IL4 by themselves were not sufficient to properly define the different Tfh subsets. In future studies, we plan to include transcription factor profiles (T-bet, BATF, GATA3) to further refine definitions of Tfh subsets.

(10) Lines, 226, 228, based on the combination of markers that the MC03 subset expresses, it is tempting to think that this is the only "truly" committed Tfh subset from the entire analysis. Please, discuss.

If the reviewer is referring to changes in marker expression levels that indicate they have not reached a level of differentiation that would make them reliable (ie “true) Tfh cells, we agree that this is an important question now that we have technology that can measure and analyse so many phenotypic markers at once. This brings forward the need for the scientific method - to replicate study findings to determine whether they are consistent given the same study design and experimental conditions.

(11) Lines 243 244, Again, is this reflected in functional capacity?

The study described in this manuscript did not include functional assays. However, this did not change the key finding that different malaria antigens behaved differently, demonstrating heterogeneity in Tfh recognition of malaria antigens. Regarding CD40L expression, we did not observe differences between groups, however some individuals had an increase of their CD40L (supplemental figure 7). It is possible that some individuals had responded through other activated induced markers (CD69, ICOS, OX40, 4-1BB among others) and that our stimulation condition was not long enough to assess CD40L expression upon malaria antigen stimulation. This limitation has been addressed by editing the line 243-244 as follows: “we were unable to find statistical differences in the CD40L expression between groups as only few individuals responded through it (supplemental figure 7).”

(12) Lines 243, 244, Are these cTfh subsets exclusively detected in malaria-exposed individuals? This is confounded by the lack of a malaria unexposed control group in this study, which would have been highly valuable.

We agree with the reviewer that having non-naive children would have been valuable as a negative control group. However, this study was conducted in Kenya where all children are suspected to have had at least one malaria infection. We also did not have ethical approval or the means to enroll children in the USA who would not have been exposed to malaria as a negative control group. Since we were also evaluating differences by age group, comparing US adults would not have helped to address this point. Therefore, this remains an open question that might be addressed by another study recruiting children in non-malaria endemic areas.

(13) Line 267, as the authors have not gated on T cells de-novo expressing activation markers in response to antigen restimulation, how do they know these are indeed antigen-specific cTfh?

Omiq analysis accounts for marker expression levels in the resting cells (unstimulated well) for each individual compared to each experimental/stimulated well. The algorithm computationally determines whether that expression level changed without an arbitrary positive threshold, keeping the expression levels as a continuous variable, not dichotomous - which is the power of unbiased cluster analyses. Therefore, we know that these cells are antigen-specific based on the statistical difference in intensity expression between the resting cells and the stimulated ones. Nevertheless, manual gating to show “de-novo” responding cells, produced the same results as assessing the MFI of each meta-cluster (supplemental figures 14, 15 and 16).

(14) Lines, 292-295, it is very surprising that Tfh cells would not produce IL-21 upon restimulation. Have the authors observed upregulation of IL-21 following SEB restimulation?

Yes, we observed IL21 positive cells upon SEB stimulation (supplemental figure 15, panel J and K). However we found unexpectedly high background levels of IL21, specifically within the adult group (supplemental figure 15, panel K and M) making it challenging to find antigen-specific increases above background. Interestingly, an increase in IL21 using manual gating was observed upon PfSEA-1A or PfGARP stimulation in children (supplemental figure 15, panel J and L).

(15) In Figures 3 and 4, it is not clear if there are any significant differences in expression of different markers between different cTfh subsets and/or different conditions. Moreover, the lack of differences in response to antigen stimulation seems to suggest that it did not work adequately.

We intentionally chose 6-hours stimulation to better assess changes in cytokines which we did. However, because it is a short stimulation, we did not expect dramatic changes in the extracellular markers presented in the figure 3 and 4. A longer stimulation, such as 24h, will highlight properly these changes.

(16) Figure 5b would benefit from bar graphs.

Please find below the bar-graphs for the highlighted meta-clusters in figure 5b. We did not include these bar-graphs to our figure 5 as they do not bring new information. They repeat the information already presented through the EdgeR plot.

Author response image 3.

(17) Figures 6 and 7 would greatly benefit from showing individual examples of old-fashioned contour with outliers flow plots to illustrate the different cTfh subsets identified in the study.

The different cT_FH subsets can be found with a contour plot with outliers in the supplemental figure 4.

(18) Figures 3,4, 6, and 7, the authors exclusively focused on the study of MFI to measure the expression of cytokine and transcription factors among different groups/stimulations. Have the authors observed any differences in the percentage or absolute counts of cytokine+ and/or TF+ between different subsets of cTfh and/or different conditions?

Yes. We added the supplemental figures 14 (transcription factors) and 15/16 (cytokines) where cytokines and transcription factors were assessed using manual gating. We found that total CD4^posCXCR5^pos IL4 was significantly increased upon stimulation in both adults and children while IFNg was not. However, we found significantly higher IFNg on total CD8^pos cells showing that the stimulation worked, but the total CD4^posCXCR5^pos did not express IFNg. Finally, we observed a trend of higher IL21^posCD4^posCXCR5^pos in adults, not significant due to high background whereas IL21 was significantly increased upon stimulation in children. Regarding cMAF and Bcl6, both transcription factors were significantly increased upon stimulation within children only.

(19) Figure 8, the definition for high and low PfGARP antibody titers seems rather arbitrary. Are these associations still significant when attempting a regular correlation analysis between Ab values (i.e. Net MFI) and different cTfh subsets?

Yes, the definition for high and low PfGARP antibody levels is arbitrary but when looking at the antibody data (figure 1b), it was naturally bimodal. Therefore as a sub-analysis, we assess the association between PfGARP antibodies levels and cT_FH subsets, see Author response image 4. We checked the correlation between the abundance of the meta-clusters and the level of IgG anti-PfGARP and anti-PfSEA after PfGARP and PfSEA stimulation. We also checked the correlation between the MFI expression of Bcl6 and cMAF after stimulation (PfGARP or PfSEA-1A minus the unstimulated) by the meta-clusters and the level of IgG anti-PfGARP and anti-PfSEA. However, we believe that because of our small sample size, our results are not robust enough and that we risk over-interpreting the data. Therefore, we choose not to include this analysis in the manuscript.

Author response image 4.

(20) The comprehensive 21-plex panel that authors used in this study could generate insights on additional immune cells beyond cTfh (e.g. additional CD4 T cell subsets, CD8 T cells, CD19 B cells). It is not clear why the authors limited their analysis to cTfh only.

The primary goal of the study was to assess the cT_FH response to malaria vaccine candidates. However, we were able to assess the IFNg expression for CD8 T cells upon stimulation using the manual gating as indicated in the supplemental figure 15. Without additional markers to more clearly define other CD4 T cell or B cell subsets, we do not believe this dataset would go deep enough into characterizing antigen-specific responses to malaria antigens that would yield new insight.

(21) Minor point, the punctuation should be revised throughout the manuscript.

Punctuation was revised throughout the manuscript by our departmental scientific writer Dr. Trombly, as per reviewer request.

eLife Assessment

Understanding bacterial growth mechanisms potentially uncover novel drug targets which are crucial for maintaining cellular viability, particularly for bacterial pathogens. In this important study, Kapoor et al, investigate the role of Wag31 in lipid and peptidoglycan biosynthesis in mycobacteria. A detailed analysis of Wag31 domain architecture revealed a role in membrane tethering. More specifically, the N-terminal and C-terminal domains appeared to have distinct functional roles. The data presented are solid and support the conclusion made. This study will be of broad interest to microbiologists and molecular biologists.

Reviewer #1 (Public review):

This is a comprehensive study that sheds light on how Wag31 functions and localises in mycobacterial cells. A clear link to interactions with CL is shown using a combination of microscopy in combination with fusion fluorescent constructs, and lipid specific dyes. Furthermore, studies using mutant versions of Wag31 shed light on the functionalities of each domain in the protein. My concerns/suggestions for the manuscript are minor:

(1) Ln 130. A better clarification/discussion is required here. It is clear that both depletion and overexpression have an effect on levels of various lipids, but subsequent descriptions show that they affect different classes of lipids.<br /> (2) The pulldown assays results are interesting, but the links are tentative.<br /> (3) The authors may perhaps like to rephrase claims of effects lipid homeostasis, as my understanding is that lipid localisation rather than catabolism/breakdown is affected.

In response to the above reviews the authors have made the required changes in the revised manuscript.

Reviewer #2 (Public review):

Summary:

Kapoor et. al. investigated the role of the mycobacterial protein Wag31 in lipid and peptidoglycan synthesis and sought to delineate the role of the N- and C- terminal domains of Wag31. They demonstrated that modulating Wag31 levels influences lipid homeostasis in M. smegmatis and cardiolipin (CL) localisation in cells. Wag31 was found to preferentially bind CL-containing liposomes, and deleting the N-terminus of the protein significantly decreased this interaction. Novel interactions between Wag31 and proteins involved in lipid metabolism and cell wall synthesis were identified, suggesting that Wag31 recruits proteins to the intracellular membrane domain by direct interaction.

Strengths:

(1) The importance of Wag31 in maintaining lipid homeostasis is supported by several lines of evidence.<br /> (2) The interaction between Wag31 and cardiolipin, and the role of the N-terminus in this interaction was convincingly demonstrated.

Weakness:

(1) Interactome analysis with truncated versions of the proteins could not be performed in M. smegmatis due to protein instability.

Reviewer #3 (Public review):

Summary:

This manuscript describes the characterization of mycobacterial cytoskeleton protein Wag31, examining its role in orchestrating protein-lipid and protein-protein interactions essential for mycobacterial survival. The most significant finding is that Wag31, which directs polar elongation and maintains the intracellular membrane domain, was revealed to have membrane tethering capabilities.

Strengths:

The authors provided a detailed analysis of Wag31 domain architecture, revealing distinct functional roles: the N-terminal domain facilitates lipid binding and membrane tethering, while the C-terminal domain mediates protein-protein interactions. Overall, this study offers a robust and new understanding of Wag31 function.

Weaknesses:

The authors did not address some of the comments. The following concerns should be addressed.

• As far as I can tell, authors did not address my prior comments on Line 270, which is Line 280 in the revised manuscript: the N-terminal region is important for lipid homeostasis, but the statement in Line 270, "the maintenance of lipid homeostasis by Wag31 is a consequence of its tethering activity" requires additional proof. Please indicate the page and line numbers in the revised manuscript so that I can identify the specific changes the authors made.

• Since this pull-down assay was conducted by mixing E. coli lysate expressing Wag31 and Msm lysate expression Wag31 interactors like MurG, it is possible that the interactions are not direct. Authors acknowledge that this is a valid point, and indicated that they "will describe this caveat in the revised manuscript". I have difficulty finding where this revision was made. Please indicate the page and line numbers.

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public review):

This a comprehensive study that sheds light on how Wag31 functions and localises in mycobacterial cells. A clear link to interactions with CL is shown using a combination of microscopy in combination with fusion fluorescent constructs, and lipid specific dyes. Furthermore, studies using mutant versions of Wag31 shed light on the functionalities of each domain in the protein. My concerns/suggestions for the manuscript are minor:

(1) Ln 130. A better clarification/discussion is required here. It is clear that both depletion and overexpression have an effect on levels of various lipids, but subsequent descriptions show that they affect different classes of lipids.

We thank the reviewer for the comment. We have added a better clarification on this in the discussion of revised manuscript. The lipid classes that get impacted by the depletion of Wag31 vs overexpression are different. Wag31 is an adaptor protein that interacts with proteins of the ACCase complex (Meniche et al., 2014; Xu et al., 2014) that synthesize fatty acid precursors and regulate their activity (Habibi Arejan et al., 2022).

The varied response on lipid homeostasis could be attributed to a change in the stoichiometry of these interactions of Wag31. While Wag31 depletion would prevent such interactions from occurring and might affect lipid synthesis that directly depends on Wag31-protein partner interactions, its overexpression would lead to promiscuous interactions and a change in the stoichiometry of native interactions that would ultimately modulate lipid synthesis pathways.

(2) The pulldown assays results are interesting, but links are tentative.

We thank the reviewer for the comment. The interactome of Wag31 was identified through the immunoprecipitation of FLAG-Wag31 complemented at an integrative locus in Wag31 mutant background to avoid overexpression artifacts. We used Msm::gfp expressing an integrative copy (at L5 locus) of FLAG-GFP as a control to subtract non-specific interactions. The experiment was performed in biological triplicates, and interactors that appeared in all replicates but not in the control were selected for further analysis. Although we identified more than 100 interactors of Wag31, we analyzed only the top 25 hits, with a PSM cut-off 18 and unique peptides5. Additionally, two of Wag31's established interactors, AccD5 and Rne, were among the top five hits, thus validating our data.

As mentioned in line 139 of the previous version of the manuscript, we agree that the interactions can either be direct or through a third partner. The fact that we obtained known interactors of Wag31 makes us believe these interactions are genuine. Moreover, for validation, we performed pulldown experiments by mixing E. coli lysates expressing His-Wag31 full-length or truncated protein with M. smegmatis lysates expressing FLAG-tagged interacting proteins. The wash conditions used were quite stringent for these pull-down assays—the wash buffer contained 1% Triton X100 that eliminates all non-specific and indirect interactions. However, we agree that we cannot conclusively state that the interactions are direct without purifying the proteins and performing the experiment. As mentioned above, this caveat was stated in the previous version of the manuscript.

(3) The authors may perhaps like to rephrase claims of effects lipid homeostasis, as my understanding is that lipid localisation rather than catabolism/breakdown is affected.

We thank the reviewer for the comment. In this manuscript, we are trying to convey that Wag31 is a spatiotemporal regulator of lipid metabolism. It is a peripheral protein that is hooked to the membrane via Cardiolipin and forms a scaffold at the poles, which helps localize several enzymes involved in lipid metabolism.

Homeostasis is the process by which an organism maintains a steady-state of balance and stability in response to changes. Depletion of Wag31 not only results in delocalisation of lipids in intracellular lipid inclusions but also leads to changes in the levels of various lipid classes. Advancement in the field of spatial biology underscores the importance of native localization of various biological molecules crucial for maintaining a steady-cell of the cell. Hence, we have used the word “homeostasis” to describe both the changes observed in lipid metabolism.

Reviewer #2 (Public review):

Summary:

Kapoor et. al. investigated the role of the mycobacterial protein Wag31 in lipid and peptidoglycan synthesis and sought to delineate the role of the N- and C- terminal domains of Wag31. They demonstrated that modulating Wag31 levels influences lipid homeostasis in M. smegmatis and cardiolipin (CL) localisation in cells. Wag31 was found to preferentially bind CL-containing liposomes, and deleting the N-terminus of the protein significantly decreased this interaction. Novel interactions between Wag31 and proteins involved in lipid metabolism and cell wall synthesis were identified, suggesting that Wag31 recruits proteins to the intracellular membrane domain by direct interaction.

Strengths:

(1) The importance of Wag31 in maintaining lipid homeostasis is supported by several lines of evidence. (2) The interaction between Wag31 and cardiolipin, and the role of the N-terminus in this interaction was convincingly demonstrated.

Weaknesses:

(1) MS experiments provide some evidence for novel protein-protein interactions. However, the pulldown experiments lack a valid negative control.

We thank the reviewer for the comment. We have included two non-interactors of Wag31 i.e. MmpL4 and MmpS5 which were not identified in our interactome database as negative controls in the experiment. As shown in Figure S3, we performed His pull-down experiments with both of them independently twice, each time with a positive control (known interactor of Wag31 (Msm2092)). Fig. S3b revised shows E. coli lysate expressing His-Wag31 which was incubated with Msm lysates expressing either FLAG tagged-MmpL4 or -MmpS5 or Msm2092 (revised Fig. S3c). The mixed lysates were pulled down with Cobalt beads that bind to the His-tagged protein and analysed using Western blot analysis by probing with anti-FLAG antibody (revised Fig. S3d.). The data presented confirms that the interactions validated through the pull down assay were indeed specific.

(2) The role of the N-terminus in the protein-protein interaction has not been ruled out.

We thank the reviewer for the comment. Wag31_Msm is a 272 amino acids long protein. The Nterminal of Wag31, which houses the DivIVA-domain, comprises the first 60 amino acids. Previously, we attempted to express the N-terminal (60 aa long) and the C-terminal (212 aa long) truncated proteins in various mycobacterial shuttle vectors to perform MS/MS experiments. Despite numerous efforts, neither expressed with the N/C-terminal FLAG tag or no tag in episomal or integrative vectors due to instability of the protein. Eventually, we successfully expressed the C-terminal Wag31 with an N and Cterminal hexa-His tag. However, this expression was not sufficient or stable enough for us to perform Ni²⁺-affinity pull-down experiments for mass spectrometry. N-terminal of Wag31 could not be expressed in M. smegmatis even with N and C-terminal Hexa-His tags.

To rule out the role of the N-terminal in mediating protein-protein interactions, we cloned the N-terminal of Wag31 that comprises the DivIVA-domain in pET28b vector (Fig. 7a revised). Subsequently, the truncated protein, hereafter called  Wag31_∆C  flanked by 6X His tags at both the termini was expressed in E. coli and mixed with Msm lysates expressing interactors of Wag31 (Fig. 7b-c revised). Earlier experiments with Wag31<sub>∆1-60</sub or Wag31_∆N (in the revised manuscript) were performed with MurG, SepIVA, Msm2092 and AccA3 (Fig. 7e-g). Thus, we used the same set of interactors to test our hypothesis. Briefly, His-  Wag31_∆C  was mixed with Msm lysates expressing either FLAG-MurG, -SepIVA, -Msm2092 or -AccA3 and pull down experiments were performed as described previously. FLAGMmpS5, a non-interactor of Wag31 was used as a negative control. As shown in Fig. 7d revised, His-Wag31 could bind to all the four interactors whereas His- Wag31_∆C  couldn’t, strengthening the conclusion that interactions of Wag31 with other proteins are mediated by its Cterminal. However, we can’t ignore the possibility of other interactors binding to the N-terminal of Wag31. Unfortunately, due to poor expression/instability of  Wag31_∆C  in mycobacterial shuttle vectors, we are unable to perform a global interactome analysis of  Wag31_∆C

Reviewer #3 (Public review):

Summary:

This manuscript describes the characterization of mycobacterial cytoskeleton protein Wag31, examining its role in orchestrating protein-lipid and protein-protein interactions essential for mycobacterial survival. The most significant finding is that Wag31, which directs polar elongation and maintains the intracellular membrane domain, was revealed to have membrane tethering capabilities.

Strengths:

The authors provided a detailed analysis of Wag31 domain architecture, revealing distinct functional roles: the N-terminal domain facilitates lipid binding and membrane tethering, while the C-terminal domain mediates protein-protein interactions. Overall, this study offers a robust and new understanding of Wag31 function.

Weaknesses:

The following major concerns should be addressed.

• Authors use 10-N-Nonyl-acridine orange (NAO) as a marker for cardiolipin localization. However, given that NAO is known to bind to various anionic phospholipids, how do the authors know that what they are seeing is specifically visualizing cardiolipin and not a different anionic phospholipid? For example, phosphatidylinositol is another abundant anionic phospholipid in mycobacterial plasma membrane.

We thank the reviewer for the comment. Despite its promiscuous binding to other anionic phospholipids, 10-N-Nonyl-acridine orange is widely used to stain Cardiolipin and determine its localisation in bacterial cells and mitochondria of eukaryotes (Garcia Fernandez et al., 2004; Mileykovskaya & Dowhan, 2000; Renner & Weibel, 2011). This is because it has a stronger affinity for Cardiolipin than other anionic phospholipids with the affinity constant being 2 × 10⁶ M−¹ for Cardiolipin association and 7 × 10⁴ M−¹ for that of phosphatidylserine and phosphatidylinositol association (Petit et al., 1992). Additionally, there is not yet another stain available for detecting Cardiolipin. Our proteinlipid binding assays suggest that Wag31 preferentially binds to Cardiolipin over other anionic phospholipids (Fig. 4b), hence it is likely that the majority of redistribution of NAO fluorescence that we observe might be contributed by Cardiolipin mislocalization due to altered Wag31 levels, with smaller degree of NAO redistribution intensity coming indirectly from other anionic phospholipids displaced from the membrane due to the loss of membrane integrity and cell shape changes due to Wag31.

• Authors' data show that the N-terminal region of Wag31 is important for membrane tethering. The authors' data also show that the N-terminal region is important for sustaining mycobacterial morphology. However, the authors' statement in Line 256 "These results highlight the importance of tethering for sustaining mycobacterial morphology and survival" requires additional proof. It remains possible that the N-terminal region has another unknown activity, and this yet-unknown activity rather than the membrane tethering activity drives the morphological maintenance. Similarly, the N-terminal region is important for lipid homeostasis, but the statement in Line 270, "the maintenance of lipid homeostasis by Wag31 is a consequence of its tethering activity" requires additional proof. The authors should tone down these overstatements or provide additional data to support their claims.

We agree with the reviewer that there exists a possibility for another function of the N-terminal that may contribute to sustaining mycobacterial physiology and survival. We would revise our statements in the paper to reflect the data. Results shown suggest that the tethering activity of the Nterminal region may contribute to mycobacterial morphology and survival. However, additional functions of this region can’t be ruled out. Similarly, the maintenance of lipid homeostasis by Wag31 may be associated with its tethering activity, although other mechanisms could also contribute to this process.

• Authors suggest that Wag31 acts as a scaffold for the IMD (Fig. 8). However, Meniche et. al. has shown that MurG as well as GlfT2, two well-characterized IMD proteins, do not colocalize with Wag31 (DivIVA) (https://doi.org/10.1073/pnas.1402158111). IMD proteins are always slightly subpolar while Wag31 is located to the tip of the cell. Therefore, the authors' biochemical data cannot be easily reconciled with microscopic observations in the literature. This raises a question regarding the validity of protein-protein interaction shown in Figure 7. Since this pull-down assay was conducted by mixing E. coli lysate expressing Wag31 and Msm lysate expression Wag31 interactors like MurG, it is possible that the interactions are not direct. Authors should interpret their data more cautiously. If authors cannot provide additional data and sufficient justifications, they should avoid proposing a confusing model like Figure 8 that contradicts published observations.

In the literature, MurG and GlfT2 have been shown to have polar localisation (Freeman et al., 2023; Hayashi et al., 2016; Kado et al., 2023) and two groups have shown slightly sub-polar localisation of MurG (García-Heredia et al., 2021; Meniche et al., 2014). Additionally, (Freeman et al., 2023) showed SepIVA to be a spatio-temporal regulator of MurG. MS/MS analysis of Wag31 immunoprecipitation data yielded both MurG and SepIVA to be interactors of Wag31 (Fig. 3). Given Wag31 also displays polar localisation, it is likely that it associates with the polar MurG. However, since a sub-polar localisation of MurG has also been reported, it is possible that they do not interact directly and another protein mediates their interaction. Based on the above, we will modify the model proposed in Fig. 8.

We agree that for validation of interaction, we performed pulldown experiments by mixing E. coli lysates expressing His-Wag31 full-length or truncated protein with M. smegmatis lysates expressing FLAG-tagged interacting proteins. The wash conditions used were quite stringent for these pull-down assays—the wash buffer contained 1% Triton X100 that eliminates all non-specific and indirect interactions. However, we agree that we cannot conclusively state that the interactions are direct without purifying the proteins and performing the experiment. We will describe this caveat in the revised manuscript and propose a model that reflects the results we obtained.

References:

Freeman, A. H., Tembiwa, K., Brenner, J. R., Chase, M. R., Fortune, S. M., Morita, Y. S., & Boutte, C. C. (2023). Arginine methylation sites on SepIVA help balance elongation and septation in Mycobacterium smegmatis. Mol Microbiol, 119(2), 208-223. https://doi.org/10.1111/mmi.15006

Garcia Fernandez, M. I., Ceccarelli, D., & Muscatello, U. (2004). Use of the fluorescent dye 10-N-nonyl acridine orange in quantitative and location assays of cardiolipin: a study on different experimental models. Anal Biochem, 328(2), 174-180. https://doi.org/10.1016/j.ab.2004.01.020

García-Heredia, A., Kado, T., Sein, C. E., Puffal, J., Osman, S. H., Judd, J., Gray, T. A., Morita, Y. S., & Siegrist, M. S. (2021). Membrane-partitioned cell wall synthesis in mycobacteria. eLife, 10. https://doi.org/10.7554/eLife.60263

Habibi Arejan, N., Ensinck, D., Diacovich, L., Patel, P. B., Quintanilla, S. Y., Emami Saleh, A., Gramajo, H., & Boutte, C. C. (2022). Polar protein Wag31 both activates and inhibits cell wall metabolism at the poles and septum. Front Microbiol, 13, 1085918. https://doi.org/10.3389/fmicb.2022.1085918

Hayashi, J. M., Luo, C. Y., Mayfield, J. A., Hsu, T., Fukuda, T., Walfield, A. L., Giffen, S. R., Leszyk, J. D., Baer, C. E., Bennion, O. T., Madduri, A., Shaffer, S. A., Aldridge, B. B., Sassetti, C. M., Sandler, S. J., Kinoshita, T., Moody, D. B., & Morita, Y. S. (2016). Spatially distinct and metabolically active membrane domain in mycobacteria. Proc Natl Acad Sci U S A, 113(19), 5400-5405. https://doi.org/10.1073/pnas.1525165113

Kado, T., Akbary, Z., Motooka, D., Sparks, I. L., Melzer, E. S., Nakamura, S., Rojas, E. R., Morita, Y. S., & Siegrist, M. S. (2023). A cell wall synthase accelerates plasma membrane partitioning in mycobacteria. eLife, 12, e81924. https://doi.org/10.7554/eLife.81924

Meniche, X., Otten, R., Siegrist, M. S., Baer, C. E., Murphy, K. C., Bertozzi, C. R., & Sassetti, C. M. (2014). Subpolar addition of new cell wall is directed by DivIVA in mycobacteria. Proc Natl Acad Sci U S A, 111(31), E32433251. https://doi.org/10.1073/pnas.1402158111

Mileykovskaya, E., & Dowhan, W. (2000). Visualization of phospholipid domains in Escherichia coli by using the cardiolipin-specific fluorescent dye 10-N-nonyl acridine orange. J Bacteriol, 182(4), 1172-1175. https://doi.org/10.1128/JB.182.4.1172-1175.2000

Petit, J. M., Maftah, A., Ratinaud, M. H., & Julien, R. (1992). 10N-nonyl acridine orange interacts with cardiolipin and allows the quantification of this phospholipid in isolated mitochondria. Eur J Biochem, 209(1), 267273. https://doi.org/10.1111/j.1432-1033.1992.tb17285.x

Renner, L. D., & Weibel, D. B. (2011). Cardiolipin microdomains localize to negatively curved regions of Escherichia coli membranes. Proc Natl Acad Sci U S A, 108(15), 6264-6269. https://doi.org/10.1073/pnas.1015757108

Schägger, H. (2006). Tricine-SDS-PAGE. Nat Protoc, 1(1), 16-22. https://doi.org/10.1038/nprot.2006.4

Xu, W. X., Zhang, L., Mai, J. T., Peng, R. C., Yang, E. Z., Peng, C., & Wang, H. H. (2014). The Wag31 protein interacts with AccA3 and coordinates cell wall lipid permeability and lipophilic drug resistance in Mycobacterium smegmatis. Biochem Biophys Res Commun, 448(3), 255-260. https://doi.org/10.1016/j.bbrc.2014.04.116

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) Ln 130. A better clarification/discussion is required here. It is clear that both depletion and overexpression have an effect in levels of various lipids, but subsequent descriptions show that they affect different classes of lipids.

We thank the reviewer for the comment. We have included a clarification for this in the discussion section.

(2) The pulldown assays results are interesting, but the links are tentative.

We thank the reviewer for the comment. The interactome of Wag31 was identified through the immunoprecipitation of Flag-tagged Wag31 complemented at an integrative locus in Wag31 mutant background to avoid overexpression artifacts. We used Msm::gfp expressing an integrative copy (at L5 locus) of FLAG-GFP as a control to subtract non-specific interactions. The experiment was performed in biological triplicates, and interactors that appeared in all replicates were selected for further analysis. Although we identified more than 100 interactors of Wag31, we analyzed only the top 25 hits, with a PSM cut-off 18 and unique peptides5. Additionally, two of Wag31's established interactors, AccD5 and Rne, were among the top five hits, thus validating our data.

Though we agree that the interactions can either be direct or through a third partner, the fact that we obtained known interactors of Wag31 makes us believe these interactions are genuine. Moreover, for validation, we performed pulldown experiments by mixing E. coli lysates expressing HisWag31 full-length or truncated protein with M. smegmatis lysates expressing FLAG-tagged interacting proteins. The wash conditions used were quite stringent for these pull-down assays—the wash buffer contained 1% Triton X100 that eliminates all non-specific and indirect interactions. However, we agree that we cannot conclusively state that the interactions are direct without purifying the proteins and performing the experiment. We will describe this caveat in the revised manuscript.

(3) The authors may perhaps like to rephrase claims of effects lipid homeostasis, as my understanding is that lipid localisation rather than catabolism/breakdown is affected.

We thank the reviewer for the comment. In this manuscript, we are trying to convey that Wag31 is a spatiotemporal regulator of lipid metabolism. It is a peripheral protein that is hooked to the membrane via Cardiolipin and forms a scaffold at the poles, which helps localize several enzymes involved in lipid metabolism.

Homeostasis is the process by which an organism maintains a steady-state of balance and stability in response to changes. Depletion of Wag31 not only results in delocalisation of lipids in intracellular lipid inclusions but also leads to changes in the levels of various lipid classes. Advancement in the field of spatial biology underscores the importance of native localization of various biological molecules crucial for maintaining a steady-cell of the cell. Hence, we have used the word “homeostasis” to describe both the changes observed in lipid metabolism.

Reviewer #2 (Recommendations for the authors):

I recommend the following experiments to strengthen the data presented:

(1) Include a non-interacting FLAG-tagged protein as a negative control in the pull-down experiment to strengthen this data.

We thank the reviewer for the comment. As suggested, we have included non-interacting FLAGtagged proteins as negative controls in the pulldown experiment. We chose MmpL4 and MmpS5 which were not found in the Wag31 interactome data. We performed pull-down experiments with both of them and included an interactor of Wag31 i.e. Msm2092 as a positive control. Fig. S3b revised shows E. coli lysate expressing His-Wag31 which was incubated with Msm lysates expressing either FLAG taggedMmpL4 or -MmpS5 or -Msm2092 (Fig. S3c revised). The mixed lysates were pulled down with Cobalt beads that bind to the His-tagged protein and analysed using Western blot analysis by probing with anti-FLAG antibody. The pull down experiments were performed independently twice, every time with Msm2092 as the positive control (Fig. S3d. revised).

(2) Perform the pull-down experiments using only the Wag31 N-terminus to rule out any role that it may have in the protein-protein interactions.

We thank the reviewer for the comment. To rule out the possibility of N-terminal of Wag31 in mediating protein-protein interactions, we cloned the N-terminal of Wag31 that comprises the DivIVAdomain in pET28b vector (Fig. 7a revised). Subsequently, the truncated protein, hereafter called Wag31_∆C flanked by 6X His tags at both the termini was expressed in E. coli and subsequently mixed with Msm lysates expressing interactors of Wag31 (Fig. 7b-c revised). Earlier experiments with Wag31_∆1-60 or Wag31_∆N  were performed with MurG, SepIVA, Msm2092 and AccA3 (Fig. 7 previous) so we used the same set of interactors to test our hypothesis. Briefly, His-Wag31_∆Cwas mixed with Msm lysates expressing either FLAG-MurG, -SepIVA, -Msm2092 or -AccA3 and pull down experiments were performed as described previously. FLAG-MmpS5, a non-interactor of Wag31 was used as a negative control. As shown in Fig. 7d revised, His-Wag31 could bind to all the four interactors whereas His-Wag31_∆C couldn’t, strengthening the conclusion that interactions of Wag31 with other proteins are mediated by its C-terminal. However, we can’t ignore the possibility of other proteins binding to the Nterminal of Wag31. Unfortunately, due to poor expression/instability of Wag31_∆C in mycobacterial shuttle vectors, we couldn’t perform a global interactome analysis of Wag31_∆C.

Minor comments:

- Please check the legend of Fig. 1g, it appears to be labelled incorrectly.

We have checked it. It is correct. From Fig. 1g we are trying to reflect on the percentages of cells of the three strains i.e. Msm+ATc, Δwag31-ATc, and Δwag31+ATc displaying rod, round or bulged morphology.

- For MS/MS analysis, a GFP control is mentioned but it is not indicated how this was incorporated in the data analysis. This information should be added.

We have incorporated that in the revised methodology.

- The information presented in Fig. 3a, e and f could be combined in one table.

We appreciate the idea of the reviewer but we prefer a pictorial representation of the data. It allows readers to consume the information in parts, make quicker comparisons and understand trends easily.

- Fig. 4c Wag31K20A appears smaller in size than the wild-type protein - why is this the case? Is this not a single amino acid substitution?

Though K20A is a single amino acid substitution, it alters the mobility of Wag31 on SDS-PAGE gel. The sequence analysis of the plasmid expressing Wag31_K20A doesn’t show additional mutations other than the desired K20A. The change in mobility could be due to a change in the conformation of Wag31_K20A or its ability to bind to SDS or both that modify its mobility under the influence of electric field.

- Please clarify what is contained in the first panel of fig 4e. compared to what is in the second panel.

The first panel represents CL-Dil-Liposomes before incubation with Wag31-GFP and the second panel shows CL-Dil-Liposomes after incubation with Wag31-GFP. The third panel shows the mixture as observed in the green channel to investigate the localisation of Wag31-GFP in the liposome-protein mix. Fourth panel shows the merged of second and third.

- The data in Fig 6d suggests higher levels of CL in the ∆wag31 compared to wild-type - how do the authors reconcile this with the MS data in Fig. 2g showing lower CL levels?

Fig. 6d represents the distribution of CL localisation in the tested strains of mycobacteria whereas Fig. 2g shows the absolute levels of CL in various strains. We attribute greater confidence on the lipidomics data which suggests down regulation of CL species. The NAO staining and microscopy is merely for studying localization of the CL along the cell, and cannot be used to reliably quantify or equate it to CL levels. The staining using a probe such as NAO is dependent on factors such as hydrophobicity and permeability of the cell wall, which we expect to be severely altered in a Wag31 mutant. Therefore, the increased staining of NAO seen in Wag31 mutant could just be reflective of the increased uptake of the dye rather than absolute levels of CL. The specificity of staining and localization however can be expected to be unaltered.

Reviewer #3 (Recommendations for the authors):

Following are suggestions for improving the writing and presentation.

• Figure 1, the meaning of the yellow arrows present in f and h should be mentioned in the figure legend.

We have incorporated that in the revised legend. In Fig.1f, the yellow arrowhead represents the bulged pole morphology whereas in Fig. 1h, it indicates intracellular lipid inclusions.

• Figure 7 legend refers to panels g, h, and i. However, Figure 7 only has panels a-c. The legend lacks a description of panel c.

We have corrected the typos and the legend.

• Figure S1, F2-R2 and F3-R3 expected sizes should be stated in the legend of the figure.

We have updated the legends.

• Figure S5, is this the same figure as 5e? If so, there is no need for this figure.

We have removed Fig. S5.

• Methods need to be written more carefully with enough details. I listed some of the concerns below.

Detailed methodology was previously provided in the supplementary material and now we have moved it to the materials and methods in the revised manuscript.

• Line 392, provide more details on western blotting. What is the secondary antibody? What image documentation system was used?

We have updated the methodology.

• Line 400, while the methods may be the same as the reference 64, authors should still provide key details such as the way samples were fixed and processed for SEM and TEM.

We have provided a detailed description of the same in methodology in the revised version.

• Line 437, how do authors calculate the concentration of liposome to be 10 µM? Do they possibly mean the concentration of phospholipids used to make the liposomes?

Yes, this is the concentration of total lipids used to make liposomes. 1 μM of Wag31 or its mutants were mixed with 100 nm extruded liposomes containing 10 μm total lipid in separate Eppendorf tubes.

• Supplemental Line 9, "turns of" should read "turns off".

We have edited this.

• Supplemental Line 13, define LHS and RHS.

LHS or left hand sequence and RHS or right hand sequence refers to the upstream and downstream flanking regions of the gene of interest.

• Supplemental Line 20, indicate the manufacturer of the microscope and type of the objective lens.

We have added these details now.

• Supplemental Line 31, define MeOH, or use a chemical formula like chloroform.

MeOH is methanol. We have provided a chemical formula in the revised version.

• Supplemental Line 53, indicate the concentration of trypsin.

We have included that in the revised version.

• Supplemental Line 72, g is not a unit. "30,000 g" should be "30,000x g".

We have revised this in the manuscript.

• Supplemental Line 114, provide more details on western blotting. What is the manufacturer of antiFLAG antibody? What is the secondary antibody? How was the antibody binding visualized? What image documentation system was used?

We have provided these details in the revised version.

eLife Assessment

This important study reports a reanalysis of one experiment of a previously-published report to characterize the dynamics of neural population codes during visual working memory in the presence of distracting information. This paper presents solid evidence that working memory representations are dynamic and distinct from sensory representations of intervening distractions. This research will be of interest to cognitive neuroscientists working on the neural bases of visual perception and memory.

Reviewer #1 (Public review):

Summary:

In this study, the authors re-analyzed a public dataset (Rademaker et al, 2019, Nature Neuroscience) which includes fMRI and behavioral data recorded while participants held an oriented grating in visual working memory (WM) and performed a delayed recall task at the end of an extended delay period. In that experiment, participants were pre-cued on each trial as to whether there would be a distracting visual stimulus presented during the delay period (filtered noise or randomly-oriented grating). In this manuscript, the authors focused on identifying whether the neural code in retinotopic cortex for remembered orientation was 'stable' over the delay period, such that the format of the code remained the same, or whether the code was dynamic, such that information was present, but encoded in an alternative format. They identify some timepoints - especially towards the beginning/end of the delay - where the multivariate activation pattern fails to generalize to other timepoints, and interpret this as evidence for a dynamic code. Additionally, the authors compare the representational format of remembered orientation in the presence vs absence of a distracting stimulus, averaged over the delay period. This analysis suggested a 'rotation' of the representational subspace between distracting orientations and remembered orientations, which may help preserve simultaneous representations of both remembered and viewed stimuli. Intriguingly, this rotation was a bit smaller for Expt 2, in which the orientation distractor had a greater behavioral impact on the participants' behavioral working memory recall performance, suggesting that more separation between subspaces is critical for preserving intact working memory representations.

Strengths:

(1) Direct comparisons of coding subspaces/manifolds between timepoints, task conditions, and experiments is an innovative and useful approach for understanding how neural representations are transformed to support cognition

(2) Re-use of existing dataset substantially goes beyond the authors' previous findings by comparing geometry of representational spaces between conditions and timepoints, and by looking explicitly for dynamic neural representations

(3) Simulations testing whether dynamic codes can be explained purely by changes in data SNR are an important contribution, as this rules out a category of explanations for the dynamic coding results observed

Weaknesses:

(1) Primary evidence for 'dynamic coding', especially in early visual cortex, appears to be related to the transition between encoding/maintenance and maintenance/recall, but the delay period representations seem overall stable, consistent with some previous findings. However, given the simulation results, the general result that representations may change in their format appears solid, though the contribution of different trial phases remains important for considering the overall result.

(2) Converting a continuous decoding metric (angular error) to "% decoding accuracy" serves to obfuscate the units of the actual results. Decoding precision (e.g., sd of decoding error histogram) would be more interpretable and better related to both the previous study and behavioral measures of WM performance.

Reviewer #2 (Public review):

Summary:

In this work, Degutis and colleagues addressed an interesting issue related to the concurrent coding of sensory percepts and visual working memory contents in visual cortices. They used generalization analyses to test whether working memory representations change over time, diverge from sensory percepts, and vary across distraction conditions. Temporal generalization analysis demonstrated that off-diagonal decoding accuracies were lower than on-diagonal decoding accuracies, regardless of the presence of intervening distractions, implying that working memory representations can change over time. They further showed that the coding space for working memory contents showed subtle but statistically significant changes over time, potentially explaining the impaired off-diagonal decoding performance. The neural coding of sensory distractions instead remained largely stable. Generalization analyses between target and distractor codes showed overlaps but were not identical. Cross-condition decodings had lower accuracies compared to within-condition decodings. Finally, within-condition decoding revealed more reliable working memory representations in the condition with intervening random noises compared to cross-condition decoding using a trained classifier on data from the no-distraction condition, indicating a change in the VWM format between the noise distractor and no-distractor trials.

Strengths:

This paper demonstrates a clever use of generalization analysis to show changes in the neural codes of working memory contents across time and distraction conditions. It provides some insights into the differences between representations of working memory and sensory percepts, and how they can potentially coexist in overlapping brain regions.

Comments on revisions:

I appreciate the authors' efforts in addressing my previous concerns. The inclusion of additional analyses and data has strengthened the paper. I have no further concerns.

Author response:

The following is the authors’ response to the original reviews

Reviewer #1:

Weaknesses:

(1) Only Experiment 1 of Rademaker et al (2019) is reanalyzed. The previous study included another experiment (Expt 2) using different types of distractors which did result in distractor-related costs to neural and behavioral measures of working memory. The Rademaker et al (2019) study uses these two results to conclude that neural WM representations are protected from distraction when distraction does not impact behavior, but conditions that do impact behavior also impact neural WM representations. Considering this previous result is critical for relating the present manuscript's results to the previous findings, it seems necessary to address Experiment 2's data in the present work

We thank the reviewer for the proposal to analyze Experiment 2 where subjects completed the same type of visual working memory task, but instead had either a flashing orientation distractor or a naturalistic (gazebo or face) distractor present during two-thirds of the trials. As the reviewer points out, unlike Experiment 1, these two conditions in Experiment 2 had a behavioral impact on recall accuracy, when compared to the blank delay. We have now run the temporal cross-decoding analysis, temporally-stable neural subspace analysis, and condition cross-decoding analysis in Experiment 2. The results from the stable subspace analysis are present in Figure 3, while the results from the temporal cross-decoding analysis and condition cross-decoding analysis are present in the Supplementary Data.

First, we are unable to draw strong conclusions from the temporal cross-decoding analysis, as the decoding accuracies across time in Experiment 2 are much lower compared to Experiment 1. In some ROIs of the naturalistic distractor condition we see that some diagonal elements are not part of the above-chance decoding cluster, making it difficult to draw any conclusions regarding dynamic clusters. We do see some dynamic coding in the naturalistic condition in V3 where the off-diagonals do not show above-chance decoding. Since the temporal cross-decoding provides low accuracies, we do not examine the dynamics of neural subspaces across time.

We do, however, run the stable subspace analysis on the flashing orientation distractor condition. Just like in Experiment 1, we examine temporally stable target and distractor subspaces. When projecting the distractor onto the working memory target subspace, we see a higher overlap between the two as compared to Experiment 1. A similar pattern is seen also when projecting the target onto the distractor subspace. We still see an above-chance principal angle between the target and distractor; however, this angle is qualitatively smaller compared to Experiment 1. This shows that the degree of separation between the two neural subspaces is impacted by behavioral performance during recall.

(2) Primary evidence for 'dynamic coding', especially in the early visual cortex, appears to be related to the transition between encoding/maintenance and maintenance/recall, but the delay period representations seem overall stable, consistent with previous findings

We agree with the reviewer that we primarily see dynamic coding between the encoding/maintenance and at the end of the maintenance periods, implying the WM representations are stable in most ROIs. The only place where we argue that we might see more dynamic coding during the delay itself is in V1 during the noise distractor trials in Experiment 1.

(3) Dynamicism index used in Figure 1f quantifies the proportion of off-diagonal cells with significant differences in decoding performance from the diagonal cell. It's unclear why the proportion of time points is the best metric, rather than something like a change in decoding accuracy. This is addressed in the subsequent analysis considering coding subspaces, but the utility of the Figure 1f analysis remains weakly justified.

We agree that other metrics can also provide a summary of dynamics; here, the dynamicism index just acts as a summary visualizing the dynamic elements. It offers an intuitive way to visualize peaks and troughs of the dynamic code across the extent of the trial.

(4) There is no report of how much total variance is explained by the two PCs defining the subspaces of interest in each condition, and timepoint. It could be the case that the first two principal components in one condition (e.g., sensory distractor) explain less variance than the first two principal components of another condition.

We thank the reviewer for this comment. We have now included the percent variance explained for the two PCs in both the temporally-stable target and distractor subspace and the dynamic subspace analysis. The percent-explained is comparable across analyses; the first PC ranges from 43-50% and the second ranges from 28-37%. The PCs within each analysis (dynamic no-distractor, orientation and noise distractor; temporally-stable target and distractor) are even closer in range (Figure 2c and 3d).

(5) Converting a continuous decoding metric (angular error) to "% decoding accuracy" serves to obfuscate the units of the actual results. Decoding precision (e.g., sd of decoding error histogram) would be more interpretable and better related to both the previous study and behavioral measures of WM performance.

We thank the reviewer for the comments. FCA is a linear function of the angular error that uses the following equation:

We think that the FCA does not obfuscate the results, but instead provides an intuitive scale where 0% accuracy corresponds to a 180° error, 50% to a 90° error and so on. This also makes it easy to reverse-calculate the absolute error if need be. Our lab has previously used this method in other neuroimaging papers with continuous variables (Barbieri et al. 2023, Weber et al. 2024).

We do, however, agree that “% decoding accuracy” does not provide an accurate reflection of the metric used. We have thus now changed “% decoding accuracy” to “Accuracy (% FCA)”.

(6) This report does not make use of behavioral performance data in the Rademaker et al (2019) dataset.

We have now analyzed Experiment 2 which, as previously mentioned by the reviewer and unlike Experiment 1, showed a decrease in recall accuracy during the two distractor conditions. We address the results from Experiment 2 in a previous response (please see Weaknesses 1).

We do not, however, relate single subject behavioral performance to neural measurements, as we do not think there is enough power to do so with a small number of subjects in both Experiment 1 and 2. 

(7) Given there were observed differences between individual retinotopic ROIs in the temporal cross-decoding analyses shown in Figure 1, the lack of data presented for the subspace analyses for the corresponding individual ROIs is a weakness

We have now included an additional supplementary figure that shows individual plots of each ROI for the temporally stable subspace analysis for both Experiment 1 and Experiment 2 (Supplementary Figure 5). 

Reviewer #1 (Recommendations For The Authors):

(1) Is there any relationship between stable/dynamic coding properties and aspects of behavioral performance? This seems like a major missed opportunity to better understand the behavioral relevance or importance of the proposed dynamic and orthogonal coding schemes. For example, is it the case that participants who have more orthogonal coding subspaces between orientation distractor and remembered orientation show less of a behavioral consequence to distracting orientations? Less induced bias? I know these differences weren't significant at the group level in the original study, but maybe individual variability in the metrics of this study can explain differences in performance between participants in the reported dataset

As mentioned in the previous response, we do not run individual correlations between dynamic or orthogonal coding metrics and behavioral performance, because of the small number of subjects in both experiments. We believe that for a brain-behavior correlation between average behavioral error of subjects and an average brain measure, we would need a larger sample size.  

(2) The voxel selection procedure differs from the original study. The authors should add additional detail about the number of voxels included in their analyses, and how this number of voxels compares to that used in the original study.

We have now added a figure summarizing the number of voxels selected across participants. We do select fewer voxels compared to Rademaker et al. 2019 (see their Supplementary Tables 9 and 10 and our Supplementary Figure 8). For example we have ~500 voxels on average in V1 in Experiment 1, while the original study had ~1000. As mentioned in the methods, we aimed to select voxels that reliably responded to both the perception localizer conditions and the working memory trials.

(3) Lines 428-436 specify details about how data is rescaled prior to decoding. The procedure seems to estimate rescaling factors according to some aspect of the training data, and then apply this rescaling to the training and testing data. Is there a possibility of leakage here? That is - do aspects of the training data impact aspects of the testing data, and could a decoder pick up on such leakage to change decoding? It seems this is performed for each training/testing timepoint pair, and so the temporal unfolding of results may depend on this analysis choice.

Thank you for the suggestion. To prevent data leakage, the mean and standard deviation are computed exclusively from the training set. These scaling parameters are then applied to the test set, ensuring that no information from the test set influences the training process. This transformation simply adjusts the test set to the same scale as the training data, without exposing the model to unseen test data during training.

(4) Figure 1d, V1: it looks like the 'dynamics' are a bit non-symmetric - perhaps the authors could comment on this detail of the results? Why would we expect there would be a dynamic cluster on one side of the diagonal, but not the other? Given that this region, condition is the primary evidence for a dynamic code that's not related to the beginning/end of delay (see other comments), figuring this out is of particular importance.

We thank the reviewer for this question. We think that this is just due to small numerical differences in the upper and lower triangles of the matrix, rather than a neuroscientifically interesting effect. However, this is only a speculative observation.

(5) I think it's important to address the issue I raised in "weaknesses" about variance explained by the top N principal components in each condition. What are we supposed to learn from data projected into subspaces fit to different conditions if the subspaces themselves are differently useful?

Thank you, this has now been addressed in a previous comment (please see Weakness 4). 

Reviewer #2:

Weaknesses:

(1) An alternative interpretation of the temporal dynamic pattern is that working memory representations become less reliable over time. As shown by the authors in Figure 1c and Figure 4a, the on-diagonal decoding accuracy generally decreased over time. This implies that the signal-to-noise ratio was decreasing over time. Classifiers trained with data of relatively higher SNR and lower SNR may rely on different features, leading to poor generalization performance. This issue should be addressed in the paper.

We thank the reviewer for raising this issue and we have now run three simulations that aim to address whether a changing SNR across time might create dynamic clusters. 

In the first simulation we created a dataset of 200 voxels that have a sine or cosine response function to orientations between 1° to 180°, the same orientations as the remembered target. A circular shift is applied to each voxel to vary preferred (or maximal) responses of each simulated voxel. We then assess the decoding performance under different SNR conditions during training and testing. For each of the seven iterations we selected 108 responses (out of 180) to train on and 108 to test on. To increase variability the selected trials differed in each iteration. Random white noise was applied to the data and thus the SNR was independently scaled according to the specified levels for train and test data. We then use the same pSVR decoder as in the temporal cross decoding analysis to train and test. 

The second and third simulations more directly address whether increased noise levels  would induce the decoder to rely on different features of the no-distractor and noise distractor data. We use empirical data from the primary visual cortex (V1; where dynamic coding was seen in the noise distractor trials) under the no-distractor and noise distractor conditions for the second and third simulations, respectively. Data from time points 5.6–8.8 seconds after stimulus onset are averaged across five TRs. As in the first simulation, SNR is systematically manipulated by adding white noise. Additionally, to see whether the initial decrease in SNR and subsequent increase would result in dynamic coding clusters, we initially increased and subsequently decreased the amplitude of added noise. The same pSVR decoder was used to train and test on the data with different levels of added noise.

We see an absence of dynamic elements in the SNR cross-decoding matrices, as the decoding accuracy primarily depends on the training data rather than test data. This results in some off-diagonal values in the decoding matrix that are higher, rather than smaller, than corresponding on-diagonal elements.

We have now added a Methods section explaining the simulations in more detail and Supplementary Figure 9 showing the SNR cross-decoding matrices. 

(2) The paper tests against a strong version of stable coding, where neural spaces representing WM contents must remain identical over time. In this version, any changes in the neural space will be evidence of dynamic coding. As the paper acknowledges, there is already ample evidence arguing against this possibility. However, the evidence provided here (dynamic coding cluster, angle between coding spaces) is not as strong as what prior studies have shown for meaningful transformations in neural coding. For instance, the principal angle between coding spaces over time was smaller than 8 degrees, and around 7 degrees between sensory distractors and WM contents. This suggests that the coding space for WM was largely overlapping across time and with that for sensory distractors. Therefore, the major conclusion that working memory contents are dynamically coded is not well-supported by the presented results.

We thank the reviewer for this comment. The principal angles we calculate are above-baseline, meaning that we subtract the within-subspace principal angles from the between-subspace principal angles and take the average. Thus a 7 degree difference does not imply that there are only 7 degrees separating e.g. the sensory distractor from the target; it just indicates that the separation is 7 degrees above chance. 

(3) Relatedly, the main conclusions, such as "VWM code in several visual regions did not generalize well between different time points" and "VWM and feature-matching sensory distractors are encoded in separable coding spaces" are somewhat subjective given that cross-condition generalization analyses consistently showed above chance-level performance. These results could be interpreted as evidence of stable coding. The authors should use more objective descriptions, such as 'temporal generalization decoding showed reduced decoding accuracy in off-diagonals compared to on-diagonals.

Thank you, we agree that our previous claims might have been too strong. We have now toned down our statements in the Abstract and use “did not fully generalize” and “VWM and feature-matching sensory distractors are encoded in coding spaces that do not fully overlap.”

Reviewer #2 (Recommendations For The Authors):

Weakness 1 can potentially be addressed with data simulations that fix the signal pattern, vary the noise pattern, and perform the same temporal generalization analysis to test whether changes in SNR can lead to seemingly dynamic coding formats.

Thank you for the great suggestion. We have now run the suggested simulations. Please see above (response to Weakness 1).

There are mismatches in the statistical symbols shown in Figure 4 and Supplementary Table 2. It seems that there was a swap between the symbols for the noise between-condition and noise within-condition.

Thank you, this has now been fixed.

eLife Assessment

This study describes an improved adaptive sampling approach, multiple-walker Supervised Molecular Dynamics (mwSuMD), and its application to G protein-coupled receptors (GPCRs), which are the most abundant membrane proteins and key targets for drug discovery. The manuscript provides solid evidence that the mwSuMD approach can assist in the sampling of complex binding processes, leading to useful findings for GPCR activity, including resolution of interactions not seen experimentally. The method has the potential to have broad applicability in structural biology and pharmacology.

Reviewer #1 (Public review):

Summary:

The authors investigate ligand and protein-binding processes in GPCRs (including dimerization) by the multiple walker supervised molecular dynamics method. The paper is interesting and it is very well written.

Strengths:

The authors' method is a powerful tool to gain insight on the structural basis for the pharmacology of G protein-coupled receptors.

Reviewer #2 (Public review):

The study by Deganutti and co-workers is a methodological report on an adaptive sampling approach, multiple walker supervised molecular dynamics (mwSuMD), which represents an improved version of the previous SuMD.<br /> Case-studies concern complex conformational transitions in a number of G protein Coupled Receptors (GPCRs) involving long time-scale motions such as binding-unbinding and collective motions of domains or portions. GPCRs are specialized GEFs (guanine nucleotide exchange factors) of heterotrimeric Gα proteins of the Ras GTPase superfamily. They constitute the largest superfamily of membrane proteins and are of central biomedical relevance as privileged targets of currently marketed drugs.<br /> MwSuMD was exploited to address:

a) binding and unbinding of the arginine-vasopressin (AVP) cyclic peptide agonist to the V2 vasopressin receptor (V2R);<br /> b) molecular recognition of the β2-adrenergic receptor (β2-AR) and heterotrimeric GDP-bound Gs protein;<br /> c) molecular recognition of the A1-adenosine receptor (A1R) and palmotoylated and geranylgeranylated membrane-anchored heterotrimeric GDP-bound Gi protein;<br /> d) the whole process of GDP release from membrane-anchored heterotrimeric Gs following interaction with the glucagon-like peptide 1 receptor (GLP1R), converted to the active state following interaction with the orthosteric non-peptide agonist danuglipron.

The revised version has improved clarity and rigor compared to the original also thanks to the reduction in the number of complex case studies treated superficially.<br /> The mwSuMD method is solid and valuable, has wide applicability and is compatible with the most world-widely used MD engines. It may be of interest to the computational structural biology community.<br /> The huge amount of high-resolution data on GPCRs makes those systems suitable, although challenging, for method validation and development.<br /> While the approach is less energy-biased than other enhanced sampling methods, knowledge, at the atomic detail, of binding sites/interfaces and conformational states is needed to define the supervised metrics, the higher the resolution of such metrics is the more accurate the outcome is expected to be. Definition of the metrics is a user- and system-dependent process.

Reviewer #3 (Public review):

Summary:

In the present work Deganutti et al. report a structural study on GPCR functional dynamics using a computational approach called supervised molecular dynamics.

Strengths:

The study has the potential to provide novel insight into GPCR functionality. An example is the interaction between D344 and R385 identified during the Gs coupling by GLP-1R. However, validation of the findings, even computationally through for instance in silico mutagenesis study, is advisable.

Weaknesses:

No significant advance of the existing structural data on GPCR and GPCR/G protein coupling is provided. Most of the results are reproductions of the previously reported structures.

Author response:

The following is the authors’ response to the previous reviews

Public Reviews:

Reviewer #1 (Public review):

Summary:

The authors investigate ligand and protein-binding processes in GPCRs (including dimerization) by the multiple walker supervised molecular dynamics method. The paper is interesting and it is very well written.

Strengths:

The authors' method is a powerful tool to gain insight on the structural basis for the pharmacology of G protein-coupled receptors.

We thank the Reviewer for the positive comment on the manuscript and the proposed methods.

Reviewer #2 (Public review):

The study by Deganutti and co-workers is a methodological report on an adaptive sampling approach, multiple walker supervised molecular dynamics (mwSuMD), which represents an improved version of the previous SuMD.

Case-studies concern complex conformational transitions in a number of G protein Coupled Receptors (GPCRs) involving long time-scale motions such as binding-unbinding and collective motions of domains or portions. GPCRs are specialized GEFs (guanine nucleotide exchange factors) of heterotrimeric Gα proteins of the Ras GTPase superfamily. They constitute the largest superfamily of membrane proteins and are of central biomedical relevance as privileged targets of currently marketed drugs.

MwSuMD was exploited to address:

a) binding and unbinding of the arginine-vasopressin (AVP) cyclic peptide agonist to the V2 vasopressin receptor (V2R);

b) molecular recognition of the β2-adrenergic receptor (β2-AR) and heterotrimeric GDPbound Gs protein;

c) molecular recognition of the A1-adenosine receptor (A1R) and palmotoylated and geranylgeranylated membrane-anchored heterotrimeric GDP-bound Gi protein;

d) the whole process of GDP release from membrane-anchored heterotrimeric Gs following interaction with the glucagon-like peptide 1 receptor (GLP1R), converted to the active state following interaction with the orthosteric non-peptide agonist danuglipron.

The revised version has improved clarity and rigor compared to the original also thanks to the reduction in the number of complex case studies treated superficially.

The mwSuMD method is solid and valuable, has wide applicability and is compatible with the most world-widely used MD engines. It may be of interest to the computational structural biology community.

The huge amount of high-resolution data on GPCRs makes those systems suitable, although challenging, for method validation and development.

While the approach is less energy-biased than other enhanced sampling methods, knowledge, at the atomic detail, of binding sites/interfaces and conformational states is needed to define the supervised metrics, the higher the resolution of such metrics is the more accurate the outcome is expected to be. Definition of the metrics is a user- and system-dependent process.

We thank the Reviewer for the positive comment on the revised manuscript and mwSuMD. We agree that the choice of supervised metrics is user- and systemdependent. We aim to improve this aspect in the future with the aid of interpretable machine learning.

Reviewer #3 (Public review):

Summary:

In the present work Deganutti et al. report a structural study on GPCR functional dynamics using a computational approach called supervised molecular dynamics.

Strengths:

The study has potential to provide novel insight into GPCR functionality. Example is the interaction between D344 and R385 identified during the Gs coupling by GLP-1R. However, validation of the findings, even computationally through for instance in silico mutagenesis study, is advisable.

Weaknesses:

No significant advance of the existing structural data on GPCR and GPCR/G protein coupling is provided. Most of the results are reproductions of the previously reported structures.

The method focus of our study (mwSuMD) is an enhancement of the supervised molecular dynamics that allows supervising two metrics at the same time and uses a score, rather than a tabù-like algorithm, for handing the simulation. Further changes are the seeding of parallel short replicas (walkers) rather than a series of short simulations, and the software implementation on different MD engines (e.g. Acemd, OpenMM, NAMD, Gromacs).

We agree with the Reviewer that experimental validation of the findings would be advisable, in line with any computational prediction. We are positive that future studies from our group employing mwSuMD will inform mutagenesis and BRET-based experiments.

Reviewer #2 (Recommendations for the authors):

As for GLP1R, I remain convinced that the 7LCI would have been better as a reference for all simulations than 7LCJ, also because 7LCI holds a slightly more complete ECD.

We agree that 7LCJ would have been a better starting point than 7LCI for simulations because it presents the stalk region, contrary to 7LCJ. However, we do not think it might have influenced the output because the stalk is the most flexible segment of GLP1R, and any initial conformation is usually not retained during MD simulations.

Please, correct everywhere the definition of the 6LN2 structure of GPL1R as a ligand-free or apo, because that structure is indeed bound to a negative allosteric modulator docked on the cytosolic end of helix-6

We thank the reviewer for this precision. The text has been modified accordingly.

As for the beta2-AR, the "full-length" AlphaFold model downloaded from the GPCRdb is not an intermediate active state because it is very similar to the receptor in the 3SN6 complex with Gs. Please, eliminate the inappropriate and speculative adjective "intermediate".

We have changed “intermediate” to “not fully active”, which is less speculative since full activation can be achieved only in the presence of the G protein.

Incidentally, in that model, the C-tail, eliminated by the authors, is completely wrong and occupies the G protein binding site. It is not clear to me the reason why the authors preferred to used an AlphaFold model as an input of simulations rather than a high resolution structural model, e.g. 4LDO. Perhaps, the reason is that all ICL regions, including ICL3, were modeled by AlphaFold even if with low confidence. I disagree with that choice.

We understand the reviewer’s point of view. Should we have simulated an “equilibrium” receptor-ligand complex, we would have made the same choice. However, the conformational changes occurring during a G protein binding are so consistent that the starting conformation of the receptor becomes almost irrelevant as long as a sensate structure is used.  

Reviewer #3 (Recommendations for the authors):

The revised version of the manuscript is more concise, focusing only on two systems. However, the authors have responded superficially to the reviewers' comments, merely deleting sections of text, making minor corrections, or adding small additions to the text. In particular, the authors have not addressed the main critical points raised by both Reviewer 2 and Reviewer 3. 

For example, the RMSD values for the binding of PF06882961 to GLP-1R remain high, raising doubts about the predictive capabilities of the method, at least for this type of system.

What is the RMSD of the ligand relative to the experimental pose obtained in the simulations? This value must be included in the text.

We have added this piece of information about PF06882961 RMSD in the text, which on page 6 now reads “We simulated the binding of PF06882961, reaching an RMSD to its bound conformation in 7LCJ of 3.79 +- 0.83 Å (computed on the second half of the merged trajectory, superimposing on GLP-1R Ca atoms of TMD residues 150 to 390), using multistep supervision on different system metrics (Figure 2) to model the structural hallmark of GLP-1R activation (Video S5, Video S6).”

Similarly, the activation mechanism of GLP-1R is only partially simulated.

Furthermore, it is not particularly meaningful to justify the high RMSD values of the SuMD simulations for the binding of Gs to GLP-1R by comparing them with those reported under unbiased MD conditions. "Replica 2, in particular, well reproduced the cryo-EM GLP-1R complex as suggested by RMSDs to 7LCI of 7.59{plus minus}1.58Å, 12.15{plus minus}2.13Å, and 13.73{plus minus}2.24Å for Gα, Gβ, and Gγ respectively. Such values are not far from the RMSDs measured in our previous simulations of GLP-1R in complex with Gs and GLP-149 (Gα = 6.18 {plus minus} 2.40 Å; Gβ = 7.22 {plus minus} 3.12 Å; Gγ = 9.30 {plus minus} 3.65 Å), which indicates overall higher flexibility of Gβ and Gγ compared to Gα, which acts as a sort of fulcrum bound to GLP-1R."

Without delving into the accuracy of the various calculations, the authors should acknowledge that comparing protein structures with such high RMSD values has no meaningful significance in terms of convergence toward the same three-dimensional structure.

The text has been edited to accommodate the reviewer’s suggestion and still give the readers the measure of the high flexibility of Gs bound to GLP-1R. It now reads “Such values do not support convergence with the static experimental structure but are not far from the RMSDs measured in our previous simulations of GLP-1R in complex with G_s and GLP-1 (G_α = 6.18 ± 2.40 Å; G_b = 7.22 ± 3.12 Å; G_g = 9.30 ± 3.65 Å), which indicates overall higher flexibility of G_b and G_g compared to G_α, which acts as a sort of fulcrum bound to GLP-1R.”

Have the authors simulated the binding of the Gs protein using the experimentally active structure of GLP-1R in complex with the ligand PF06882961 (PDB ID 7LCJ)? Such a simulation would be useful to assess the quality of the binding simulation of Gs to the GLP1R/PF06882961 complex obtained from the previous SuMD.

We considered performing the Gs binding simulation to the active structure of GLP-1R.

However, the GLP-1R (and other class B receptors) fully active state, as reported in 7LCJ, depends on the presence of the Gs and can be reached only upon effector coupling. Since it is unlikely that the unbound receptor is already in the fully active state, we reasoned that considering it as a starting point for Gs binding simulations would have been an artifact.

An example of the insufficient depth of the authors' replies can be seen in their response: "We note that among the suggested references, only Mafi et al report about a simulated G protein (in a pre-formed complex) and none of the work sampled TM6 rotation without input of energy."

This statement is inaccurate. For instance, D'Amore et al. (Chem 2024, doi: 10.1016/j.chempr.2024.08.004) simulated Gs coupling to A2A as well as TM6 rotation, as did Maria-Solano and Choi (eLife 2023, doi: 10.7554/eLife.90773.1). The former employed path collective variables metadynamics, which is not cited in the introduction or the discussion, despite its relevance to the methodologies mentioned.

Respectfully, our previous reply is correct, as all of the mentioned articles used enhanced (energy-biased) approaches, so the claim “none of the work sampled TM6 rotation without input of energy” stands. The reference to D’Amore et al. (published after the previous round of reviews of this manuscript) has been added to the introduction; we thank the reviewer for pointing it out. 

Additionally, SuMD employs a tabu algorithm that applies geometric supervision to the simulation, serving as an alternative approach to enhancing sampling compared to the "input of energy" techniques as called by the authors. A fair discussion should clearly acknowledge this aspect of the SuMD methodology.

We have now specified in the Methods that a tabù-like algorithm is part of SuMD, which, despite being the parent technique of mwSuMD, is not the focus of the present work. We provide extended references for readers interested in SuMD. mwSuMD, on the other hand, does not use a tabù-like algorithm but rather a continuative approach based on a score to select the best walker for each batch, as described in the Methods.

eLife Assessment

In Plasmodium male gametocytes, rapid nuclear division occurs with an intact nuclear envelope, requiring precise coordination between nuclear and cytoplasmic events to ensure proper packaging of each nucleus into a developing gamete. This valuable study characterizes two proteins involved in the formation of Plasmodium berghei male gametes. By integrating live-cell imaging, ultrastructural expansion microscopy, and proteomics, this study convincingly identifies SUN1 and its interaction partner ALLAN as crucial nuclear envelope components in male gametogenesis. A role for SUN1 in membrane dynamics and lipid metabolism is less well supported. The results are of interest for general cell biologists working on unusual mitosis pathways.

[Editors' note: this paper was reviewed by Review Commons.]

Reviewer #1 (Public review):

Summary:

Activated male Plasmodium gametocytes undergo very rapid nuclear division, while keeping the nuclear envelope intact. There is interest in how events inside the nucleus are co-ordinated with events in the parasite cytoplasm, to ensure that each nucleus is packaged into a nascent male gamete.

This manuscript by Zeeshan et al describes the organisation of a nuclear membrane bridging protein, SUN1, during nuclear division. SUN1 is expected from studies in other organisms to be a component of a bridging complex (LINC) that connects the inner nuclear membrane to the outer nuclear membrane, and from there to the cytoplasmic microtubule-organising centres, the centrosome and the basal body.

The authors show that knockout of the SUN1 in gametocytes leads to severe disruption of the mitotic spindle and failure of the basal bodies to segregate. The authors show convincingly that functional SUN1 is required for male gamete formation and subsequent oocyst development.

The authors identified several SUN1-interacting proteins, thus providing information about the nuclear membrane bridging machinery.

Strengths:

The authors have used state of the art imaging, genetic manipulation and immunoprecipitation approaches.

Weaknesses:

Technical limitations of some of the methods used make it difficult to interpret some of the micrographs.

From studies in other organisms, a protein called KASH is a critical component the bridging complex (LINC). That is, KASH links SUN1 to the outer nuclear membrane. The authors undertook a gene sequence analysis that reveals that Plasmodium lacks a KASH homologue. Thus, further work is needed to identify the functional equivalent of KASH, to understand bridging machinery in Plasmodium.

Comments on revised version:

The authors have addressed the comments and suggestions that I provided as part of a Review Commons assessment.

Reviewer #2 (Public review):

Zeeshan et al. investigate the function of the protein SUN1, a proposed nuclear envelope protein linking nuclear and cytoplasmic cytoskeleton, during the rapid male gametogenesis of the rodent malaria parasite Plasmodium berghei. They reveal that SUN1 localises to the nuclear envelope (NE) in male and female gametes and show that the male NE has unexpectedly high dynamics during the rapid process of gametogenesis. Using expansion microscopy, the authors find that SUN1 is enriched at the neck of the bipartite MTOC that links the intranuclear spindle to the basal bodies of the cytoplasmic axonemes. Upon deletion of SUN1, the basal bodies of the eight axonemes fail to segregate, no spindle is formed, and emerging gametes are anucleated, leading to a complete block in transmission. By interactomics the authors identify a divergent allantoicase-like protein, ALLAN, as a main interaction partner of SUN1 and further show that ALLAN deletion largely phenocopies the effect of SUN1.

Overall, the authors use an extensive array of fluorescence and electron microscopy techniques as well as interactomics to convincingly demonstrate that SUN1 and ALLAN play a role in maintaining the structural integrity of the bipartite MTOC during the rapid rounds of endomitosis in male gametogenesis.

Two suggestions for improvement of the work remain:

(1) Lipidomic analysis of WT and SUN1-knockout gametocytes before and after activation resulted in only minor changes in some lipid species. Without statistical analysis, it remains unclear if these changes are statistically significant and not rather due to expected biological variability. While the authors clearly toned down their conclusions in the revised manuscript, some phrasings in the results and the discussion still suggest that gametocyte activation and/or SUN1-knockout affects lipid composition. Similarly, some phrases suggest that SUN1 is responsible for the observed loops and folds in the NE and that SUN1 KO affects the NE dynamics. Currently, I do not think that the data supports these statements.

(2) It is interesting to note that ALLAN has a much more specific localisation to basal bodies than SUN1, which is located to the entire nuclear envelope. Knock out of ALLAN also exhibits a milder (but still striking) phenotype than knockout of SUN1. These observations suggest that SUN1 has additional roles in male gametogenesis besides its interaction with ALLAN, which could be discussed a bit more.

This study uses extensive microscopy and genetics to characterise an unusual SUN1-ALLAN complex, thus providing new insights into the molecular events during Plasmodium male gametogenesis, especially how the intranuclear events (spindle formation and mitosis) are linked to the cytoplasmic separation of the axonemes. The characterisation of the mutants reveals an interesting phenotype, showing that SUN1 and ALLAN are localised to and maintain the neck region of the bipartite MTOC. The authors here confirm and expand the previous knowledge about SUN1 in P. berghei, adding more detail to its localisation and dynamics, and further characterise the interaction partner ALLAN. Given the evolutionary divergence of Plasmodium, these results are interesting not only for parasitologists, but also for more general cell biologists.

Author response:

Reviewer #1 (Evidence, reproducibility and clarity):

Minor comments:

In the results section (lines 498-499), the authors describe free kinetochores in many cells without associated spindle microtubules. However, some nuclei appear to have kinetochores, as presented in Figure 6. Could the authors clarify how this conclusion was derived using transmission electron microscopy (TEM) without serial sectioning, as this is not explicitly mentioned in the materials and methods?

We observed free kinetochores in the ALLAN-KO parasites with no associated spindle microtubules (see Fig. 6Gh), while kinetochores are attached to spindle microtubules in WT-GFP cells (see Fig. 6Gc). To provide further evidence we analysed additional images and found that ALLAN-KO cells have free kinetochores in the centre of nucleus, unattached to spindle microtubules. We provide some more images clearly showing free kinetochores in these cells (new supplementary Fig. S11).

However, in the ALLAN mutant, this difference is not absolute: in a search of over 50 cells, one example of a cell with a “normal” nuclear spindle and attached kinetochores was observed.

The use of serial sectioning has limitations for examining small structures like kinetochores in whole cells. The limitations of the various techniques (for example, SBF-SEM vs tomography) are highlighted in our previous study (Hair et al 2022; PMID: 38092766), and we consider that examining a population of randomly sectioned cells provides a better understanding of the overall incidence of specific features.

Discussion Section:

Could the authors expand on why SUN1 and ALLAN are not required during asexual replication, even though they play essential roles during male gametogenesis?

We observed no phenotype in asexual blood stage parasites associated with the sun1 and allan gene deletions. Several other Plasmodium berghei gene knockout parasites with a phenotype in sexual stages, for example CDPK4 (PMID: 15137943), SRPK (PMID: 20951971), PPKL (PMID: 23028336) and kinesin-5 (PMID: 33154955) have no phenotype in blood stages, so perhaps this is not surprising. One explanation may be the substantial differences in the mode of cell division between these two stages. Asexual blood stages produce new progeny (merozoites) over 24 hours with closed mitosis and asynchronous karyokinesis during schizogony, while male gametogenesis is a rapid process, completed within 15 min to produce eight flagellated gametes. During male gametogenesis the nuclear envelope must expand to accommodate the increased DNA content (from 1N to 8N) before cytokinesis. Furthermore, male gametogenesis is the only stage of the life cycle to make flagella, and axonemes must be assembled in the cytoplasm to produce the flagellated motile male gametes at the end of the process. Thus, these two stages of parasite development have some very different and specific features.

Lines 611-613 states: "These loops serve as structural hubs for spindle assembly and kinetochore attachment at the nuclear MTOC, separating nuclear and cytoplasmic compartments." Could the authors elaborate on the evidence supporting this statement?

We observed the loops/folds in the nuclear envelope (NE) as revealed by SUN1-GFP and 3D TEM images during male gametogenesis. These folds/loops occur mainly in the vicinity of the nuclear MTOC where the spindles are assembled (as visualised by EB1 fluorescence) and attached to kinetochores (as visualised by NDC80 fluorescence). These loops/folds may form due to the contraction of the spindle pole back to the nuclear periphery, inducing distortion of the NE. Since there is no physical segregation of chromosomes during the three rounds of mitosis (DNA increasing from 1N to 8N), we suggest that these folds provide additional space for spindle and kinetochore dynamics within an intact NE to maintain separation from the cytoplasm (as shown by location of kinesin-8B).

In lines 621-622, the authors suggest that ALLAN may have a broader role in NE remodelling across the parasite's lifecycle. Could they reflect on or remind readers of the finding that ALLAN is not essential during the asexual stage?

ALLAN-GFP is expressed throughout the parasite life cycle but as the reviewer points out, a functional role is more pronounced during male gametogenesis. This does not mean that it has no role at other stages of the life cycle even if there is no obvious phenotype following deletion of the gene during the asexual blood stage. The fact that ALLAN is not essential during the asexual blood stage is noted in lines 628-29.

Reviewer #2 (Evidence, reproducibility and clarity):

Introduction

Line 63: The authors stat: "NE is integral to mitosis, supporting spindle formation, kinetochore attachment, and chromosome segregation..". Seemingly at odds, they also say (Line 69) that 'open' "mitosis is "characterized by complete NE disassembly".

The authors could explain better the ideas presented in their quoted review from Dey and Baum, which points out that truly 'open' and 'closed' topologies may not exist and that even in 'open' mitosis, remnants of the NE may help support the mitotic spindle.

We have modified the sentence in which we discuss current opinions about ‘open’ and ‘closed’ mitosis. It is believed that there is no complete disassembly of the NE during open mitosis and no completely intact NE during closed mitosis, respectively. In fact, the NE plays a critical role in the different modes of mitosis during MTOC organisation and spindle dynamics. Please see the modified lines 64-71.

Results

Fig 7 is the final figure; but would be more useful upfront.

We have provided a new introductory figure (Fig 1) showing a schematic of conventional /canonical LINC complexes and evidence of SUN protein functions in model eukaryotes and compare them to what is known in apicomplexans.

Fig 1D. The authors generated a C-terminal GFP-tagged SUN1 transfectants and used ultrastructure expansion microscopy (U-ExM) and structured illumination microscopy (SIM) to examine SUN1-GFP in male gametocytes post-activation. The immuno-labelling of SUN1-GFP in these fixed cells appears very different to the live cell images of SUN1-GFP. The labelling profile comprises distinct punctate structures (particularly in the U-ExM images), suggesting that paraformaldehyde fixation process, followed by the addition of the primary and secondary antibodies has caused coalescing of the SUN1-GFP signal into particular regions within the NE.

We agree with the reviewer. Fixation with paraformaldehyde (PFA) results in a coalescence of the SUN1-GFP signal. We have also tried methanol fixation (see new Fig. S2), but a similar problem was encountered.

Given these fixation issues, the suggestion that the SUN1-GFP signal is concentrated at the BB/ nuclear MTOC and "enriched near spindle poles" needs further support.

These statements seem at odd with the data for live cell imaging where the SUN1-GFP seems evenly distributed around the nuclear periphery. Can the observation be quantitated by calculating the percentage of BB/ nuclear MTOC structures with associated SUN1-GFP puncta? If not, I am not convinced these data help understand the molecular events.

We agree with the reviewer that whilst the live cell imaging showed an even distribution of SUN1-GFP signal, after fixation with either PFA or methanol, then SUN1-GFP puncta are observed in addition to the peripheral location around the stained DNA (Hoechst) (See Fig. S2; puncta are indicated by arrows). These SUN1-GFP labelled puncta were observed at the junction of the nuclear MTOC and the basal body (Fig. 2F). Quantification of the distribution showed that these SUN1-GFP puncta are associated with nuclear MTOC in more than 90 % of cells (18 cells examined). Live cell imaging of the dual labelled parasites; SUN1xkinesin-8B (Fig. 2H) and SUN1x EB1 (Fig. 2I) provides further support for the association of SUN1-GFP puncta with BB (kinesin-8B) /nuclear MTOC (EB1).

The authors then generated dual transfectants and examined the relative locations of different markers in live cells. These data are more informative.

The authors state; " ..SUN1-GFP marked the NE with strong signals located near the nuclear MTOCs situated between the BB tetrads". The nuclear MTOCs are not labelled in this experiment. The SUN1-GFP signal between the kinesin-8B puncta is evident as small puncta on regions of NE distortion. I would prefer to not describe this signal as "strong". The signal is stronger in other regions of the NE.

We have modified the sentence on line 213 to accommodate this suggestion.

Line 219. The authors state; "..SUN1-GFP is partially colocalized with spindle poles as indicated by EB1,.. it shows no overlap with kinetochores (NDC80)." The authors should provide an analysis of the level of overlap at a pixel by pixel level to support this statement.

We now provide the overlap at a pixel-by-pixel level for representative images, and we have quantified more cells (n>30), as documented in the new Fig. S4A. We have also modified the sentence on line 219 to reflect these additions.

The SUN1 construct is C-terminally GFP-tagged. By analogy with human SUN1, the C-terminal SUN domain is expected to be in the NE lumen. That is in a different compartment to EB1, which is located in the nuclear lumen (on the spindle). Thus, the overlap of signal is expected to be minimal.

We agree with the reviewer that the overlap between EB1 and Sun1 signals is expected to be minimal. We have quantified the data and included it in Supplementary Fig. S4A.

Similarly, given that EB1 and NDC80 are known to occupy overlapping locations on the spindle, it seems unlikely that SUN1 can overlap with one and not the other.

We agree with the reviewer’s analysis that EB1 and NDC80 occupy overlapping locations on the spindle, although the length of NDC80 is less at the ends of spindles (see Author response image 1A) as shown in our previous study where we compared the locations of two spindle proteins, ARK2 and EB1, with that of NDC80 (Zeeshan et al, 2022; PMID: 37704606). In the present study we observed that Sun1-GFP partially overlaps with EB1 at the ends of the spindle, but not with NDC80. Please see Author response image 1B.

Author response image 1.

I note on Line 609, the authors state "Our study demonstrates that SUN1 is primarily localized to the nuclear side of the NE.." As per Fig 7D, and as discussed above, the bulk of the protein, including the SUN1 domain, is located in the space between the INM and the ONM.

We appreciate the reviewer’s correction; we have now modified the sentence to indicate that the protein is largely localized in the space between the INM and the ONM on line 617.

Interestingly, as the authors point out, nuclear membrane loops are evident around EB1 and NDC80 focal regions. The data suggests that the contraction of the spindle pole back to the nuclear periphery induces distortion of the NE.

We agree with the reviewer’s suggestion that the data indicate that contraction of spindle poles back to the nuclear periphery may induce distortion of the NE.

The author should discuss further the overlap of findings of this study with that from a recent manuscript (https://doi.org/10.1016/j.cels.2024.10.008). That Sayers et al. study identified a complex of SUN1 and ALLC1 as essential for male fertility in P. berghei. Sayers et al. also provide evidence that this complex particulate in the linkage of the MTOC to the NE and is needed for correct mitotic spindle formation during male gametogenesis.

We thank the reviewer for this suggestion. The study by Sayers et al, (2024) was published while our manuscript was under preparation. It was interesting to see that these complementary studies have similar findings about the role of SUN1 and the novel complex of SUN1-ALLAN. Our study contains a more detailed, in-depth analysis both by Expansion and TEM of SUN1. We include additional studies on the role of ALLAN.  We discuss the overlap in the findings of the two studies in lines 590-605.

While the work is interesting, the conclusions may need to be tempered. The authors suggestion that in the absence of KASH-domain proteins, the SUN1-ALLAN complex forms a non-canonical LINC complex (that is, a connection across the NE), that "achieves precise nuclear and cytoskeletal coordination".

We have toned down the wording of this conclusion in lines 665-677.

In other organisms, KASH interacts with the C-terminal domain on SUN1, which as mentioned above is located between the INM and ONM. By contrast, ALLAN interacts with the N-terminal domain of SUN1, which is located in the nuclear lumen. The SUN1-ALLAN interaction is clearly of interest, and ALLAN might replace some of the roles of lamins. However, the protein that functionally replaces KASH (i.e. links SUN1 to the ONM) remains unidentified.

We agree with reviewer, and future studies will need to focus on identifying the KASH replacement that links SUN1 to the ONM.

It may also be premature to suggest that the SUN1-ALLAN complex is promising target for blocking malaria transmission. How would it be targeted?

We have deleted the sentence that raised this suggestion.

While the above datasets are interesting and internally consistent, there are two other aspects of the manuscript that need further development before they can usefully contribute to the molecular story.

The authors undertook a transcriptomic analysis of Δsun1 and WT gametocytes, at 8 and 30 min post-activation, revealing moderate changes (~2-fold change) in different genes. GO-based analysis suggested up-regulation of genes involved in lipid metabolism. Given the modest changes, it may not be correct to conclude that "lipid metabolism and microtubule function may be critical functions for gametogenesis that can be perturbed by sun1 deletion." These changes may simply be a consequence of the stalled male gametocyte development.

Following the reviewer’s suggestion we have moved these data to the supplementary information (Fig. S5D-I) and toned down their discussion in the results and discussion sections.

The authors have then undertaken a detailed lipid analysis of the Δsun1 and WT gametocytes, before and after activation. Substantial changes in lipid metabolites might not be expected in such a short period of time. And indeed, the changes appear minimal. Similarly, there are only minor changes in a few lipid sub-classes between Δsun1 and WT gametocytes. In my opinion, the data are not sufficient to support the authors conclusion that "SUN1 plays a crucial role, linking lipid metabolism to NE remodelling and gamete formation."

In agreement with the reviewer’s comments we have moved  these data to supplementary information (Fig. S6) and substantially toned down the conclusions based on these findings.

Reviewer #3 (Evidence, reproducibility and clarity):

Major comments:

My main concern with this manuscript is that the authors do conclude not only that SUN1 is important for spindle formation and basal body segregation, but also that it influences for lipid metabolism and NE dynamics. I don't think the data supports this conclusion, for several reasons listed below. I would suggest to remove this claim from the manuscript or at least tone it down unless more supporting data are provided, in particular showing any change in NE dynamics in the SUN1-KO. Instead I would recommend to focus on the more interesting role of SUN1-ALLAN in bipartite MTOC organisation, which likely explains all observed phenotypes (including those in later stages of the parasite life cycle). In addition, some aspects of the knockout phenotype should be quantified to a bit deeper level.

In more detail:

- The lipidomics analysis is clearly the weakest point of the manuscript: The authors state that there are significant changes in some lipid populations between WT and sun1-KO, and between activated and non-activated cells, yet no statistical analysis is shown and the error bars are quite high compared to only minor changes in the means. For some discussed lipids, the result text does not match the graphs, e.g. PA, where the increase upon activation is more pronounced in the SUN1-KO vs WT (contrary to the text), or MAG, which is reduced in the SUN1-KO vs WT (contrary to the text). I don't see the discussed changes in arachidonic acid levels and myristic acid levels in the data either. Even if the authors find after analysis some statistically significant differences between some groups, they should carefully discuss the biological significance of these differences. As it is, I do not think the presented data warrants the conclusion that deletion of SUN1 changes lipid homeostasis, but rather shows that overall lipid homeostasis is not majorly affected by gametogenesis or SUN1 deletion. As a minor comment, if you decide to keep the lipidomics analysis in the manuscript, please state how many replicates were done.

As detailed above we have moved the lipidomics data to supplementary information (Fig. S6) and substantially toned down the discussion of these data in the results and discussion sections.

- I can't quite follow the logic why the authors performed transcriptomic analysis of the SUN1 and how they chose their time points. Their data up to this point indicate that SUN1 has a structural or coordinating role in the bipartite MTOC during male gametogenesis. Based on that it is rather unlikely that SUN1 KO directly leads to transcriptional changes within the 8 min of exflagellation. Isn't it more likely that transcriptional differences are purely a downstream effect of incomplete/failed gametogenesis? This is particularly true for the comparison at 30 min, which compares a mixture of exflagellated/emerged gametes and zygotes in WT to a mixture of aberrant, arrested gametes in the knockout, which will likely not give any meaningful insight. The by far most significant GO-term is then also nuclear-transcribed mRNA catabolic process, which is likely not related at all to SUN1 function (and the authors do not even comment on this in the main text). I would therefore suggest removing the 30 min data set from this manuscript. As a minor point, I would suggest highlighting some of the top de-regulated gene IDs in the volcano plots and stating their function. Also, please state how you prepared the cells for the transcriptomes and in how many replicates this was done.

As suggested by the reviewer we have removed the 30 min post activation data from the manuscript. We have also moved the rest of the transcriptomics data to supplementary information (Fig. S5) and toned down the presentation of this aspect of the work in the results and discussion sections.

- Live-cell imaging of SUN1-GFP does nicely visualise the NE during gametogenesis, showing a highly dynamic NE forming loops and folds, which is very exciting to see. It would be beneficial to also show a video from the life-cell imaging.

We have now added videos to the manuscript as suggested by the reviewer. Please see the supplementary Videos S1 and S2.

In their discussion, the authors state multiple times that NE dynamics are changed upon SUN1 KO. Yet, they do not provide data supporting this claim, i.e. that the extended loops and folds found in the nuclear envelope during gametogenesis are affected in any way by the knockout of SUN1 or ALLAN. What happens to the NE in absence of SUN1? Are there less loops and folds? In absence of a reliable NE marker this may not be entirely easy to address, but at least some SBF-SEM images of the sun1-KO gametocytes could provide insight.

It was difficult to provide SBF-SEM images as that work is beyond the scope of this manuscript. We will consider this approach in our future work. We re-examined many of our TEM images of SUN1-KO and ALLAN-KO parasites and did find some micrographs showing aberrant nuclear membrane folding (<5%) (Please see Author response image 2). However, we also observed similar structures in some of the WT-GFP samples (<5%), so we do not think this is a strong phenotype of the SUN1 or ALLAN mutants.

Author response image 2.

 

- I think the exciting part of the manuscript is the cell biological role of SUN1 on male gametogenesis, which could be carved out a bit more by a more detailed phenotyping. Specifically it would be good to quantify

(1) If DNA replication to an octoploid state still occurs in SUN1-KO and ALLAN-KO,

DNA replication is not affected in the SUN1-KO and ALLAN-KO mutants: DNA content increases to 8N (data added in Fig. 3J and Fig. S10F).

(2) The proportion of anucleated gametes in WT and the KO lines

We have added these data in Fig. 3K and Fig. S10G

(3) A quantification of the BB clustering phenotype (in which proportion of cells do the authors see this phenotype). This could be addressed by simple fixed immunofluorescence images of the respective WT/KO lines at various time points after activation (or possibly by reanalysis of the already obtained images) and would really improve the manuscript.

We have reanalysed the BB clustering phenotype and added the quantitative data in Fig. 4E and Fig. S7.

Especially the claim that emerged SUN1-KO gametes lack a nucleus is currently only based on single slices of few TEM cells and would benefit from a more thorough quantification in both SUN1- and ALLAN-Kos

We have examined many microgametes (100+ sections). In WT parasites a small proportion of gametes can appear to lack a nucleus if it does not extend all the way to the apical and basal ends (Hair et al. 2022). However, the proportion of microgametes that appear to lack a nucleus (no nucleus seen in any section) was much higher in the SUN1 mutant. In contrast, this difference was not as clear cut in the ALLAN mutant with a small proportion of intact (with axoneme and nucleus) microgametes being observed.

We have done additional analysis of male gametes, looking for the presence of the nucleus by live cell imaging after DNA staining with Hoechst. These data are added in Fig. 3K (for Sun1-KO) and Fig. S10G (for Allan-KO).

- The TEM suggests that in the SUN1-KO, kinetochores are free in the nucleus. Are all kinetochores free or do some still associate to a (minor/incorrectly formed) spindle? The authors could address this by tagging NDC80 in the KO lines.

Our observation and quantification of the data indicated that 100% of kinetochores were attached to spindle microtubules and that 0% were unattached kinetochores in the WT parasites. However, the exact opposite was found for the SUN1 mutant with 100% unattached kinetochores and 0% attached. The result was not quite as clear cut in the ALLAN mutant, with 98% unattached and 2% attached. An important observation was the lack of separation of the nuclear poles and any spindle formation. Spindle formation was never or very rarely observed in the mutants.

- Finally, I think it is curious that in contrast to SUN1, ALLAN seems to be less important, with some KO parasite completing the life cycle. Maybe a more detailed phenotyping as above gives some more hints to where the phenotypic difference between the two proteins lies. I would assume some ALLAN-KO cells can still segregate the basal body. Can the authors speculate/discuss in more detail why these two proteins seems to have slightly different phenotypes?

We agree with the reviewer. Overall, the ALLAN-KO has a less prominent phenotype than that of the Sun1-KO. The main difference is that in the ALLAN-KO mutant some basal body segregation can occur, leading to the production of some fertile microgametocytes, and ookinetes, and oocyst formation (Fig. 8). Approximately 5% of oocysts sporulated to release infective sporozoites that could infect mice in bite back experiments and complete the life cycle. In contrast the Sun1-KO mutant made no healthy oocysts, or infective sporozoites, and could not complete the life cycle in bite back experiments. We have analysed the phenotype in detail and provide quantitative data for gametocyte stages by EM and ExM in Figs. 4 and S8 (SUN1) and Figs. 7 and S11 (ALLAN). We have also performed detailed analysis of oocyst and sporozoite stages and included the data in Fig. 3 (SUN1) and S10 (ALLAN).

Based on the location, and functional and interactome data, we think that SUN1 plays a central role in coordinating nucleoplasm and cytoplasmic events as a key component of the nuclear membrane lumen, whereas ALLAN is located in the nucleoplasm. Deleting the SUN1 gene may disrupt the connection between INM and ONM whereas the deletion of ALLAN may affect only the INM.

Some additional points where the data is not entirely sound yet or could be improved:

- Localisation of SUN1: There seems to be a discrepancy between SUN1-GFP location as observed by live cell microscopy, and by Expansion Microscopy (ExM), similar for ALLAN-GFP. By live-cell microscopy, the SUN1 localisation is much more evenly distributed around the NE, while the localisation in ExM is much more punctuated, and e.g. in Figure 1E seems to be within the nucleus. Do the authors have an explanation for this? Also, in Fig. 1D there are two GFP foci at the cell periphery (bottom left of the image), which I would think are not SUN1-Foci, as they seem to be outside of the cell. Is the antibody specific? Was there a negative control done for the antibody (WT cells stained with GFP antibodies after ExM)?

High resolution SIM and expansion microscopy showed that the SUN1-GFP molecules coalesce to form puncta, in contrast to the more uniform distribution observed by live cell imaging. This apparent difference may be due to a better resolution that could not be achieved by live cell imaging. We agree with the reviewer that the two green foci are outside of the cell. As a negative control we have used WT-ANKA cells (which contain no GFP) and the anti-GFP antibody, which gave no signal. This confirms the specificity of the antibody (please see the new Fig. S3). 

- The authors argue that SIM gave unexpected results due to PFA fixation leading to collapse of the NE loops. However, they also fix their ExM cells and their EM cells with PFA and do not observe a collapse, at least from what I see in the two presented images and in the 3D reconstruction. Is there something else different in the sample preparation?

There was no difference in the fixation process for samples examined by SIM and ExM, but we used an anti-GFP antibody in ExM to visualise the SUN1-GFP, while in SIM the images of GFP signal were collected directly after fixation.  We used both PFA and methanol as fixative, and both methods showed a coalescing of the SUN1-GFP signal (please see the new Fig. S2 and S3).

Can the authors trace their NE in ExM according to the NHS-Ester signal?

We could trace the NE in the ExM by the NHS-ester signal and observed that the SUN1-GFP signal was largely coincident with the NE (Please see the new Fig. S3B).

- Fig 2D: It would be good to not just show images of oocysts but actually quantify their size from images. Also, have the authors determined the sporozoite numbers in SUN1-KO?

We have measured oocyst size (data added in new Fig. 3) and added the sporozoite quantification data in Fig. 3D.

- Line 481-483: the authors state that oocyst size is reduced in ALLAN-KO but do not show the data. Please quantify oocyst size or at least show representative images. Also the drastic decrease in sporozoite numbers (Fig. 6D, E) is not mentioned in the text. Please add reference to Fig S7D when talking about the bite back data.

We have added the oocyst size data in Fig. S10. We mention the changes in sporozoite numbers (now  shown in Fig. 7D, E), and refer to  the bite back data shown in current Fig. 7E.

- Fig S1C, 6C: Both WB images are stitched, but this is not clearly indicated e.g. by leaving a small gap between the lanes. Also please show a loading control along with the western blots. Also there seems to be a (unspecific?) band in the control, running at the same height as Allan-GFP WB. What exactly is the control?

We have provided the original blot showing the bands of ALLAN-GFP and SUN1-GFP. As a positive control, we used an RNA associated protein (RAP-GFP) that is highly expressed in Plasmodium and regularly used in our lab for this purpose.

- Regarding the crossing experiment: The authors conclude from this cross that SUN1 is only needed in males, yet for this conclusion they would need to also show that a cross with a female line does not rescue the phenotype. The authors should repeat the cross with a male-deficient line to really test if the phenotype is an exclusively male phenotype. In addition, line 270-272 states that no oocysts/sporozoites were detected in sun1-ko and nek4-ko parasites. However, the figure 2E shows only oocysts, not sporozoites, and shows also that sun1-ko does form oocysts, albeit dead ones.

We have now performed the experiment of crossing the Sun1-KO parasite line with a male deficient line (Hap2-KO) and added the data in Fig. 3I. We have added images showing sporozoites in oocysts.

- In Fig S1 the authors show that they also generated a SUN1-mCherry line, yet they do not use it in any of the presented experiments (unless I missed it). Would it be beneficial to cross the SUN1-mCherry line with the Allan1-GFP line to test colocalisation (possibly also by expansion microscopy)?

We did generate a SUN1-mCherry line, with the intent to cross ALLAN-GFP and SUN1-mCherry lines and observe the co-location of the proteins. Despite multiple attempts this cross was unsuccessful. This may have been due to their close proximity such that the addition of both GFP and mCherry was difficult to facilitate a proper protein-protein interaction between either of the proteins.

- Line 498: "In a significant proportion of cells" - What was the proportion of cells, and what does significant mean in this context?

Approximately 67% of cells showed the clumping of BBs. We have now added the numbers in Figs. 6H and S11I.

- The authors should discuss a bit more how their work relates to the work of Sayers et al. 2024, which also identified the SUN1-ALLAN complex. The paper is cited, but only very briefly commented on.

We have extended this discussion now in lines 590-605.

Suggestions how to improve the writing and data presentation.

- General presentation of microscopy images: Considering that large parts of the manuscript are based on microscopy data, their presentation could be improved. Single-channel microscopy images would benefit from being depicted in gray scale instead of color, which would make it easier to see the structures and intensities (especially for blue channels).

Whilst we agree with the reviewer, sometimes it is difficult to see the features in the merged images. Therefore, we would like to request to be allowed to retain the colours, which can be easily followed in both individual and merged images.

Also, it would be good to harmonize in which panels arrows are shown (e.g. Fig 1G, where some white arrows are in the SUN1-GFP panel, while others are in the merge panel, but they presumably indicate the same thing.). At the same time, Fig 1H doesn't have any with arrows, even though the figure legend states so.

We apologise for this lack of consistency, and we have now added arrows wherever they are missing to harmonise in the presentations.

Fig 3A and S4 show the same experiment but are coloured in different colours (NHS-Eester in green vs grey scale).

- Are the scale bars of all expansion microscopy images adjusted for the expansion factor?

Yes, the scale bars are adjusted accordingly.

- The figure legends would benefit from streamlining, as they have very different style between figures (eg Fig. 6 which has a concise figure legend vs microscopy figures where figure legends are very long and describe not only the figure but the results)

The figure legends have been streamlined, with removal of the description of results.

- Line 155-156: The text makes it sound like the expression only happens after activation. is that the case? Are these images activated or non-activated gametocytes?

They are expressed before activation, but the signal intensifies after activation. Images from before and after activation of gametocytes have been added in Fig. S1F.

- Line 267: Reference to the original nek4-KO paper missing

This reference is now included.

- Line 301: The reference to Figure 2J seems to be a bit arbitrarily placed. Also, this schematic of lipid metabolism is never discussed in relation to the transcriptomic or lipidomic data.

We have moved these data to supplementary information and modified the text.

- Line 347-349 states that gametes emerged, but the referenced figure shows activated gametocytes before exflagellation.

We have corrected the text to the start of exflagellation.

- Line 588: Spelling mistake in SUN1-domain

Corrected.

- Line 726/731: i missing in anti-GFP

Corrected.

- Line 787-789: statement of scale bar and number of cells imaged is not at the right position in the figure legend.

Moved to right place

- Line 779, 783: "shades of green" should be just "green". Same goes for line 986, 989 with "shades of grey"

Changed.

- Line 974, 976: please correct to WT-GFP and dsun1

Corrected.

- Line 1041, 1044: WT-GFP instead of WTGFP.

Corrected to WT-GFP.

- Fig 1B, D, E, Fig S1G, H: What are the time points of imaging?

We have added the time points to the images in these figures.

- Fig 1D/Line 727: the scale of the scale bar on the inset is missing.

We have added the scale bar.

- Fig 3 E-G and 6H-J: Please indicate total number of cells/images analysed per quantification, either in the graphs themselves or in the figure legend.

We indicate now the number of cells analysed in individual figures and also in Fig. S5C and S8C, respectively.

- Fig 5B: What is NP

Nuclear Pole (NP), also known as the nuclear/acentriolar MTOC (Zeeshan et al 2022; PMID: 35550346).

- Fig S1B/D: The legend states that there is an arrow indicating the band, but there is none.

We have added the arrow.

- Fig S2C: Is the scale bar really the same for the zygote and the ookinete?

We have checked this and used the same for both zygote and ookinete.

- Fig S3C, S7C: which stages was qRT-PCR done on?

Gametocytes activated for 8 min.

- Fig. S3D, S7D: According to the figure legend, three independent experiments were performed. How many mice were used per experiment? It would be good to depict the individual data points instead of the bar graph. For S7D, 3 data points are depicted (one in WT, two in allan-KO), what do they mean?

The bite back experiment was performed using 15-20 mosquitoes infected with WT-GFP and gene knockout lines to feed on one naïve mouse each, in three different experiments. We have now included the data points in the bar diagrams.

- Fig S3: Panel letters E and G are missing

We have updated the lettering in current Fig. S5

- Fig 3D: Please indicate what those boxes are. I presume that these are the insets show in b, e and j, but it is never mentioned. J is not even larger than i. Also, f is quite cropped, it would be good to see the large-scale image it comes from to see where in the nucleus these kinetochores are placed. Were there unbound kinetochores found in WT?

We mention the boxes in the figure legends. It is rare to find unbound kinetochores in WT parasite. We provide large scale and zoomed-in images of free kinetochores in Fig. S8.

- Fig S4: Insets are not mentioned in the figure legend. Please add scale bar to zoom-ins

We now describe the insets in the figure legends and have added scale bars to the zoomed-in images.

- Fig S5A, B: Please indicate which inset belongs to which sub-panel. Where does Ac stem from?

We have now included the full image showing the inset (new Fig. S8).

- Fig S5C and S8C: Change "DNA" to "Nucleus".

We have changed “DNA” to “Nucleus”. Now they are Fig. S8K and S11I.

Reviewer #3 (Significance):

Yet, the statement that SUN1 is also important for lipid homoeostasis and NE dynamics is currently not backed up by sufficient data. I believe that the manuscript would benefit from removing the less convincing transcriptomic and lipidomic datasets and rather focus on more deeply characterising the cell biology of the knockouts. This way, the results would be interesting not only for parasitologists, but also for more general cell biologists.

We have moved the lipidomics and transcriptomics data to supplementary information and toned down the emphasis on these data to make the manuscript more focused on the cell biology and analysis of the genetic KO data.

eLife Assessment

In this valuable study, the authors used rats to determine the receptor for a food-related perception that has been characterized in humans. The data are solid in terms of methods and analysis: the data show that this stimulus (ornithine) has some additive effects in terms of increasing preference and taste response in rats when it is mixed with other more common taste stimuli. Therefore, the combinations of experiments generally support (but do not conclusively prove) the hypothesis that the "kokumi" taste effect elicited by this stimulus in humans may be mediated by the specific receptor examined in the study.

Reviewer #1 (Public review):

Summary:

This paper contains what could be described as a "classic" approach towards evaluating a novel taste stimuli in an animal model, including standard behavioral tests (some with nerve transections), taste nerve physiology, and immunocytochemistry of taste cells of the tongue. The stimulus being tested is ornithine, from a class of stimuli called "kokumi" (in terms of human taste); these kokumi stimuli appear to enhance other canonical tastes, increasing what are essentially hedonic attributes of other stimuli. The mechanism for ornithine detection is thought to be GPRC6A receptors expressed in taste cells. The authors showed evidence for this in an earlier paper with mice; this paper evaluates ornithine taste in a rat model, and comes to a similar conclusion, albeit with some small differences between the two rodent species.

Strengths:

The data show effects of ornithine on taste/intake in laboratory rats: In two-bottle and briefer intake tests, adding ornithine results in higher intake of most, but all not all stimuli tested. Bilateral chorda tympani (CT) nerve cuts or the addition of GPRC6A antagonists decreased or eliminated these effects. Ornithine also evoked responses by itself in the CT nerve, but mainly at higher concentrations; at lower concentrations it potentiated the response to monosodium glutamate. Finally, immunocytochemistry of taste cell expression indicated that GPRC6A was expressed predominantly in the anterior tongue, and co-localized (to a small extent) with only IP3R3, indicative of expression in a subset of type II taste receptor cells.

Weaknesses:

As the authors are aware, it is difficult to assess a complex human taste with complex attributes, such as kokumi, in an animal model. In these experiments they attempt to uncover mechanistic insights about how ornithine potentiates other stimuli by using a variety of established experimental approaches in rats. They partially succeed by finding evidence that GPRC6A may mediate effects of ornithine when it is used at lower concentrations. In the revisions they have scaled back their interpretations accordingly. A supplementary experiment measuring certain aspects of the effects of ornithine added to Miso soup in human subjects is included for the express purpose of establishing that the kokumi sensation of a complex solution is enhanced by ornithine. This (supplementary) experiment was conducted with a small sample size, and though perhaps useful, these preliminary results do not align particularly well with the animal experiments. It would be helpful to further explore human taste of ornithine in a larger and better-controlled study.

Reviewer #2 (Public review):

Summary:

The authors used rats to determine the receptor for a food-related perception (kokumi) that has been characterized in humans. They employ a combination of behavioral, electrophysiological, and immunohistochemical results to support their conclusion that ornithine-mediated kokumi effects are mediated by the GPRC6A receptor. They complemented the rat data with some human psychophysical data. I find the results intriguing, but believe that the authors overinterpret their data.

Strengths:

The authors provide compelling evidence that ornithine enhances the palatability of several chemical stimuli (i.e., IMP, MSG, MPG, Intralipos, sucrose, NaCl, quinine). Ornithine also increases CT nerve responses to MSG. Additionally, the authors provide evidence that the effects of ornithine are mediated by GPRC6A, a G-protein-coupled receptor family C group 6 subtype A, and that this receptor is expressed primarily in fungiform taste buds. Taken together, these results indicate that ornithine enhances the palatability of multiple taste stimuli in rats, and that the enhancement is mediated, at least in part, within fungiform taste buds. This finding could stand on its own. The question of whether ornithine produces these effects by eliciting kokumi-like perceptions (see below) should be presented as speculation in the Discussion section.

Weaknesses:

I am still unconvinced that the measurements in rats reflect the "kokumi" taste percept described in humans. The authors conducted long-term preference tests, 10-min avidity tests and whole chorda tympani (CT) nerve recordings. None of these procedures specifically model features of "kokumi" perception in humans, which (according to the authors) include increasing "intensity of whole complex tastes (rich flavor with complex tastes), mouthfulness (spread of taste and flavor throughout the oral cavity), and persistence of taste (lingering flavor)." While it may be possible to develop behavioral assays in rats (or mice) that effectively model kokumi taste perception in humans, the authors have not made any effort to do so. As a result, I do not think that the rat data provide support for the main conclusion of the study--that "ornithine is a kokumi substance and GPRC6A is a novel kokumi receptor."

Why are the authors hypothesizing that the primary impacts of ornithine are on the peripheral taste system? While the CT recordings provide support for peripheral taste enhancement, they do not rule out the possibility of additional central enhancement. Indeed, based on the definition of human kokumi described above, it is likely that the effects of kokumi stimuli in humans are mediated at least in part by the central flavor system.

The authors include (in the supplemental data section) a pilot study that examined the impact of ornithine on variety of subjective measures of flavor perception in humans. The presence of this pilot study within the larger rat study does not really make sense. If the human studies are so important, as the authors state, then why did the authors relegate them to the supplemental data section? Usually one places background and negative findings in this section of a paper. Accordingly, I recommend that the human data be published in a separate article.

Reviewer #3 (Public review):

Summary:

In this study the authors set out to investigate whether GPRC6A mediates kokumi taste initiated by the amino acid L-ornithine. They used Wistar rats, a standard laboratory strain, as the primary model and also performed an informative taste test in humans, in which miso soup was supplemented with various concentrations of L-ornithine. The findings are valuable and overall the evidence is solid. L-Ornithine should be considered to be a useful test substance in future studies of kokumi taste and the class C G protein coupled receptor known as GPRC6A (C6A) along with its homolog, the calcium-sensing receptor (CaSR) should be considered candidate mediators of kokumi taste. The researchers confirmed in rats their previous work on Ornithine and C6A in mice (Mizuta et al Nutrients 2021).

Strengths:

The overall experimental design is solid based on two bottle preference tests in rats. After determining the optimal concentration for L-Ornithine (1 mM) in the presence of MSG, it was added to various tastants including: inosine 5'-monophosphate; monosodium glutamate (MSG); mono-potassium glutamate (MPG); intralipos (a soybean oil emulsion); sucrose; sodium chloride (NaCl; salt); citric acid (sour) and quinine hydrochloride (bitter). Robust effects of ornithine were observed in the cases of IMP, MSG, MPG and sucrose; and little or no effects were observed in the cases of sodium chloride, citric acid; quinine HCl. The researchers then focused on the preference for Ornithine-containing MSG solutions. Inclusion of the C6A inhibitors Calindol (0.3 mM but not 0.06 mM) or the gallate derivative EGCG (0.1 mM but not 0.03 mM) eliminated the preference for solutions that contained Ornithine in addition to MSG. The researchers next performed transections of the chord tympani nerves (with sham operation controls) in anesthetized rats to identify a role of the chorda tympani branches of the facial nerves (cranial nerve VII) in the preference for Ornithine-containing MSG solutions. This finding implicates the anterior half-two thirds of the tongue in ornithine-induced kokumi taste. They then used electrical recordings from intact chorda tympani nerves in anesthetized rats to demonstrate that ornithine enhanced MSG-induced responses following the application of tastants to the anterior surface of the tongue. They went on to show that this enhanced response was insensitive to amiloride, selected to inhibit 'salt tastant' responses mediated by the epithelial Na+ channel, but eliminated by Calindol. Finally they performed immunohistochemistry on sections of rat tongue demonstrating C6A positive spindle-shaped cells in fungiform papillae that partially overlapped in its distribution with the IP3 type-3 receptor, used as a marker of Type-II cells, but not with (i) gustducin, the G protein partner of Tas1 receptors (T1Rs), used as a marker of a subset of type-II cells; or (ii) 5-HT (serotonin) and Synaptosome-associated protein 25 kDa (SNAP-25) used as markers of Type-III cells.

At least two other receptors in addition to C6A might mediate taste responses to ornithine: (i) the CaSR, which binds and responds to multiple L-amino acids (Conigrave et al, PNAS 2000), and which has been previously reported to mediate kokumi taste (Ohsu et al., JBC 2010) as well as responses to Ornithine (Shin et al., Cell Signaling 2020); and (ii) T1R1/T1R3 heterodimers which also respond to L-amino acids and exhibit enhanced responses to IMP (Nelson et al., Nature 2001). These alternatives are appropriately discussed and, taken together, the experimental results favor the authors' interpretation that C6A mediates the Ornithine responses. The authors provide preliminary data in Suppl. 3 for the possibility of co-expression of C6A with the CaSR.

In the Discussion, the authors consider the potential effects of kokumi substances on the threshold concentrations of key tastants such as glutamate, arguing that extension of taste distribution to additional areas of the mouth (previously referred to as 'mouthfulness') and persistence of taste/flavor responses (previously referred to as 'continuity') could arise from a reduction in the threshold concentrations of umami and other substances that evoke taste responses. This concept may help to design future experiments.

Weaknesses:

The authors point out that animal models pose some difficulties of interpretation in studies of taste and raise the possibility in the Discussion that umami substances may enhance the taste response to ornithine (Line 271, Page 9).

The status of one of the compounds used as an inhibitor of C6A, the gallate derivative EGCG, as a potential inhibitor of the CaSR or T1R1/T1R3 is unknown. It would have been helpful to show that a specific inhibitor of the CaSR failed to block the ornithine response.

It would have been helpful to include a positive control kokumi substance in the two bottle preference experiment (e.g., one of the known gamma glutamyl peptides such as gamma-glu-Val-Gly or glutathione), to compare the relative potencies of the control kokumi compound and Ornithine, and to compare the sensitivities of the two responses to C6A and CaSR inhibitors.

Author response:

The following is the authors’ response to the previous reviews

Public Reviews:

Reviewer #1 (Public review):

Summary:

This paper contains what could be described as a "classic" approach towards evaluating a novel taste stimuli in an animal model, including standard behavioral tests (some with nerve transections), taste nerve physiology, and immunocytochemistry of taste cells of the tongue. The stimulus being tested is ornithine, from a class of stimuli called "kokumi" (in terms of human taste); these kokumi stimuli appear to enhance other canonical tastes, increasing what are essentially hedonic attributes of other stimuli. The mechanism for ornithine detection is thought to be GPRC6A receptors expressed in taste cells. The authors showed evidence for this in an earlier paper with mice; this paper evaluates ornithine taste in a rat model, and comes to a similar conclusion, albeit with some small differences between the two rodent species.

Strengths:

The data show effects of ornithine on taste/intake in laboratory rats: In two-bottle and briefer intake tests, adding ornithine results in higher intake of most, but all not all stimuli tested. Bilateral chorda tympani (CT) nerve cuts or the addition of GPRC6A antagonists decreased or eliminated these effects. Ornithine also evoked responses by itself in the CT nerve, but mainly at higher concentrations; at lower concentrations it potentiated the response to monosodium glutamate. Finally, immunocytochemistry of taste cell expression indicated that GPRC6A was expressed predominantly in the anterior tongue, and co-localized (to a small extent) with only IP3R3, indicative of expression in a subset of type II taste receptor cells.

Weaknesses:

As the authors are aware, it is difficult to assess a complex human taste with complex attributes, such as kokumi, in an animal model. In these experiments they attempt to uncover mechanistic insights about how ornithine potentiates other stimuli by using a variety of established experimental approaches in rats. They partially succeed by finding evidence that GPRC6A may mediate effects of ornithine when it is used at lower concentrations. In the revision they have scaled back their interpretations accordingly. A supplementary experiment measuring certain aspects of the effects of ornithine added to Miso soup in human subjects is included for the express purpose of establishing that the kokumi sensation of a complex solution is enhanced by ornithine; however, they do not use any such complex solutions in the rat studies. Moreover, the sample size of the human experiment is (still) small - it really doesn't belong in the same manuscript with the rat studies.

Despite the reviewer’s suggestion, we would like to include the human sensory experiment. Our rationale is that we must first demonstrate that the kokumi of miso soup is enhanced by the addition of ornithine, which is then followed by basic animal experiments to investigate the underlying mechanisms of kokumi in humans.

We did not present the additive effects of ornithine on miso soup in the present rat study because our previous companion paper (Fig. 1B in Mizuta et al., 2021, Ref. #26) already confirmed that miso soup supplemented with 3 mM L-ornithine (but not D-ornithine) was statistically significantly (P < 0.001) preferred to plain miso soup by mice.

Furthermore, we believe that our sample size (n = 22) is comparable to those employed in other studies. For example, the representative kokumi studies by Ohsu et al. (Ref. #9), Ueda et al. (Ref. #10), Shibata et al. (Ref. #20), Dunkel et al. (Ref. #37), and Yang et al. (Ref. #44) used sample sizes of 20, 19, 17, 9, and 15, respectively.

Reviewer #2 (Public review):

Summary:

The authors used rats to determine the receptor for a food-related perception (kokumi) that has been characterized in humans. They employ a combination of behavioral, electrophysiological, and immunohistochemical results to support their conclusion that ornithine-mediated kokumi effects are mediated by the GPRC6A receptor. They complemented the rat data with some human psychophysical data. I find the results intriguing, but believe that the authors overinterpret their data.

Strengths:

The authors provide compelling evidence that ornithine enhances the palatability of several chemical stimuli (i.e., IMP, MSG, MPG, Intralipos, sucrose, NaCl, quinine). Ornithine also increases CT nerve responses to MSG. Additionally, the authors provide evidence that the effects of ornithine are mediated by GPRC6A, a G-protein-coupled receptor family C group 6 subtype A, and that this receptor is expressed primarily in fungiform taste buds. Taken together, these results indicate that ornithine enhances the palatability of multiple taste stimuli in rats and that the enhancement is mediated, at least in part, within fungiform taste buds. This is an important finding that could stand on its own. The question of whether ornithine produces these effects by eliciting kokumi-like perceptions (see below) should be presented as speculation in the Discussion section.

Weaknesses:

I am still unconvinced that the measurements in rats reflect the "kokumi" taste percept described in humans. The authors conducted long-term preference tests, 10-min avidity tests and whole chorda tympani (CT) nerve recordings. None of these procedures specifically model features of "kokumi" perception in humans, which (according to the authors) include increasing "intensity of whole complex tastes (rich flavor with complex tastes), mouthfulness (spread of taste and flavor throughout the oral cavity), and persistence of taste (lingering flavor)." While it may be possible to develop behavioral assays in rats (or mice) that effectively model kokumi taste perception in humans, the authors have not made any effort to do so. As a result, I do not think that the rat data provide support for the main conclusion of the study--that "ornithine is a kokumi substance and GPRC6A is a novel kokumi receptor."

Kokumi can be assessed in humans, as demonstrated by the enhanced kokumi perception observed when miso soup is supplemented with ornithine (Fig. S1). Currently, we do not have a method to measure the same kokumi perception in animals. However, in the two-bottle preference test, our previous companion paper (Fig. 1B in Mizuta et al. 2021, Ref. #26) confirmed that miso soup supplemented with 3 mM L-ornithine (but not D-ornithine) was statistically significantly (P < 0.001) preferred over plain miso soup by mice.

Of the three attributes of kokumi perception in humans, the “intensity of whole complex tastes (rich flavor with complex tastes)” was partly demonstrated in the present rat study. In contrast, “mouthfulness (the spread of taste and flavor throughout the oral cavity)” could not be directly detected in animals and had to be inferred in the Discussion. “Persistence of taste (lingering flavor)” was evident at least in the chorda tympani responses; however, because the tongue was rinsed 30 seconds after the onset of stimulation, the duration of the response was not fully recorded.

It is well accepted in sensory physiology that the stronger the stimulus, the larger the tonic response—and consequently, the longer it takes for the response to return to baseline. For example, Kawasaki et al. (2016, Ref. #45) clearly showed that the duration of sensation increased proportionally with the concentration of MSG, lactic acid, and NaCl in human sensory tests. The essence of this explanation has been incorporated into the Discussion (p. 12).

Why are the authors hypothesizing that the primary impacts of ornithine are on the peripheral taste system? While the CT recordings provide support for peripheral taste enhancement, they do not rule out the possibility of additional central enhancement. Indeed, based on the definition of human kokumi described above, it is likely that the effects of kokumi stimuli in humans are mediated at least in part by the central flavor system.

We agree with the reviewer’s comment. Our CT recordings indicate that the effects of kokumi stimuli on taste enhancement occur primarily at the peripheral taste organs. The resulting sensory signals are then transmitted to the brain, where they are processed by the central gustatory and flavor systems, ultimately giving rise to kokumi attributes. This central involvement in kokumi perception is discussed on page 12. Although kokumi substances exert their effects at low concentrations—levels at which the substance itself (e.g., ornithine) does not become more favorable or (in the case of γ-Glu-Val-Gly) exhibits no distinct taste—we cannot rule out the possibility that even faint taste signals from these substances are transmitted to the brain and interact with other taste modalities.

The authors include (in the supplemental data section) a pilot study that examined the impact of ornithine on variety of subjective measures of flavor perception in humans. The presence of this pilot study within the larger rat study does not really mice sense. While I agree with the authors that there is value in conducting parallel tests in both humans and rodents, I think that this can only be done effectively when the measurements in both species are the same. For this reason, I recommend that the human data be published in a separate article.

Despite the reviewer’s suggestion, we intend to include the human sensory experiment. Our rationale is that we must first demonstrate that the kokumi of miso soup is enhanced by the addition of ornithine, and then follow up with basic animal experiments to investigate the potential underlying mechanisms of kokumi in humans.

In our previous companion paper (Fig. 1B in Mizuta et al., 2021, Ref. #26), we confirmed with statistical significance (P < 0.001) that mice preferred miso soup supplemented with 3 mM L-ornithine (but not D-ornithine) over plain miso soup. However, as explained in our response to Reviewer #2’s first concern (in the Public review), it is difficult to measure two of the three kokumi attributes—aside from the “intensity of whole complex tastes (rich flavor with complex tastes)”—in animal models.

The authors indicated on several occasions (e.g., see Abstract) that ornithine produced "synergistic" effects on the CT nerve response to chemical stimuli. "Synergy" is used to describe a situation where two stimuli produce an effect that is greater than the sum of the response to each stimulus alone (i.e., 2 + 2 = 5). As far as I can tell, the CT recordings in Fig. 3 do not reflect a synergism.

We appreciate your comments regarding the definition of synergy. In Fig. 5 (not Fig. 3), please note the difference in the scaling of the ordinate between Fig. 5D (ornithine responses) and Fig. 5E (MSG responses). When both responses are presented on the same scale, it becomes evident that the response to 1 mM ornithine is negligibly small compared to the MSG response, which clearly indicates that the response to the mixture of MSG and 1 mM ornithine exceeds the sum of the individual responses to MSG and 1 mM ornithine. Therefore, we have described the effect as “synergistic” rather than “additive.” The same observation applies to the mice experiments in our previous companion paper (Fig. 8 in Mizuta et al. 2021, Ref. #26), where synergistic effects are similarly demonstrated by graphical representation. We have also added the following sentence to the legend of Fig. 5:

“Note the different scaling of the ordinate in (D) and (E).”

Reviewer #3 (Public review):

Summary:

In this study the authors set out to investigate whether GPRC6A mediates kokumi taste initiated by the amino acid L-ornithine. They used Wistar rats, a standard laboratory strain, as the primary model and also performed an informative taste test in humans, in which miso soup was supplemented with various concentrations of L-ornithine. The findings are valuable and overall the evidence is solid. L-Ornithine should be considered to be a useful test substance in future studies of kokumi taste and the class C G protein coupled receptor known as GPRC6A (C6A) along with its homolog, the calcium-sensing receptor (CaSR) should be considered candidate mediators of kokumi taste. The researchers confirmed in rats their previous work on Ornithine and C6A in mice (Mizuta et al Nutrients 2021).

Strengths:

The overall experimental design is solid based on two bottle preference tests in rats. After determining the optimal concentration for L-Ornithine (1 mM) in the presence of MSG, it was added to various tastants including: inosine 5'-monophosphate; monosodium glutamate (MSG); mono-potassium glutamate (MPG); intralipos (a soybean oil emulsion); sucrose; sodium chloride (NaCl; salt); citric acid (sour) and quinine hydrochloride (bitter). Robust effects of ornithine were observed in the cases of IMP, MSG, MPG and sucrose; and little or no effects were observed in the cases of sodium chloride, citric acid; quinine HCl. The researchers then focused on the preference for Ornithine-containing MSG solutions. Inclusion of the C6A inhibitors Calindol (0.3 mM but not 0.06 mM) or the gallate derivative EGCG (0.1 mM but not 0.03 mM) eliminated the preference for solutions that contained Ornithine in addition to MSG. The researchers next performed transections of the chord tympani nerves (with sham operation controls) in anesthetized rats to identify a role of the chorda tympani branches of the facial nerves (cranial nerve VII) in the preference for Ornithine-containing MSG solutions. This finding implicates the anterior half-two thirds of the tongue in ornithine-induced kokumi taste. They then used electrical recordings from intact chorda tympani nerves in anesthetized rats to demonstrate that ornithine enhanced MSG-induced responses following the application of tastants to the anterior surface of the tongue. They went on to show that this enhanced response was insensitive to amiloride, selected to inhibit 'salt tastant' responses mediated by the epithelial Na+ channel, but eliminated by Calindol. Finally they performed immunohistochemistry on sections of rat tongue demonstrating C6A positive spindle-shaped cells in fungiform papillae that partially overlapped in its distribution with the IP3 type-3 receptor, used as a marker of Type-II cells, but not with (i) gustducin, the G protein partner of Tas1 receptors (T1Rs), used as a marker of a subset of type-II cells; or (ii) 5-HT (serotonin) and Synaptosome-associated protein 25 kDa (SNAP-25) used as markers of Type-III cells.

At least two other receptors in addition to C6A might mediate taste responses to ornithine: (i) the CaSR, which binds and responds to multiple L-amino acids (Conigrave et al, PNAS 2000), and which has been previously reported to mediate kokumi taste (Ohsu et al., JBC 2010) as well as responses to Ornithine (Shin et al., Cell Signaling 2020); and (ii) T1R1/T1R3 heterodimers which also respond to L-amino acids and exhibit enhanced responses to IMP (Nelson et al., Nature 2001). These alternatives are appropriately discussed and, taken together, the experimental results favor the authors' interpretation that C6A mediates the Ornithine responses. The authors provide preliminary data in Suppl. 3 for the possibility of co-expression of C6A with the CaSR.

Weaknesses:

The authors point out that animal models pose some difficulties of interpretation in studies of taste and raise the possibility in the Discussion that umami substances may enhance the taste response to ornithine (Line 271, Page 9).

Ornithine and umami substances interact to produce synergistic effects in both directions—ornithine enhances responses to umami substances, and vice versa. These effects may depend on the concentrations used, as described in the Discussion (pp. 9–10). Further studies are required to clarify the precise nature of this interaction.

One issue that is not addressed, and could be usefully addressed in the Discussion, relates to the potential effects of kokumi substances on the threshold concentrations of key tastants such as glutamate. Thus, an extension of taste distribution to additional areas of the mouth (previously referred to as 'mouthfulness') and persistence of taste/flavor responses (previously referred to as 'continuity') could arise from a reduction in the threshold concentrations of umami and other substances that evoke taste responses.

Thank you for this important suggestion. If ornithine reduces the threshold concentrations of tastants—including glutamate—and enhances their suprathreshold responses, then adding ornithine may activate additional taste cells. This effect could explain kokumi attributes such as an “extension of taste distribution” and possibly the “persistence of responses.” As shown in Fig. 2, the lowest concentrations used for each taste stimulus are near or below the thresholds, which indicates that threshold concentrations are reduced—especially for MSG and MPG. We have incorporated this possibility into the Discussion as follows (p.12):

“Kokumi substances may reduce the threshold concentrations as well as they increase the suprathreshold responses of tastants. Once the threshold concentrations are lowered, additional taste cells in the oral cavity become activated, and this information is transmitted to the brain. As a result, the brain perceives this input as coming from a wider area of the mouth.”

The status of one of the compounds used as an inhibitor of C6A, the gallate derivative EGCG, as a potential inhibitor of the CaSR or T1R1/T1R3 is unknown. It would have been helpful to show that a specific inhibitor of the CaSR failed to block the ornithine response.

Thank you for this important comment. We attempted to identify a specific inhibitor of CaSR. Although we considered using NPS-2143—a commonly used CaSR inhibitor—it is known to also inhibit GPRC6A. We agree that using a specific CaSR inhibitor would be beneficial and plan to pursue this in future studies.

It would have been helpful to include a positive control kokumi substance in the two bottle preference experiment (e.g., one of the known gamma glutamyl peptides such as gamma-glu-Val-Gly or glutathione), to compare the relative potencies of the control kokumi compound and Ornithine, and to compare the sensitivities of the two responses to C6A and CaSR inhibitors.

We agree with this comment. In retrospect, it may have been advantageous to directly compare the potencies of CaSR and GPRC6A agonists in enhancing taste preferences—and to evaluate the sensitivity of these preferences to CaSR and GPRC6A antagonists. However, we did not include γ-Glu-Val-Gly in the present study because we have already reported its supplementation effects on the ingestion of basic taste solutions in rats using the same methodology in a separate paper (Yamamoto and Mizuta, 2022, Ref. #25). The results from both studies are compared in the Discussion (p. 11).

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

Major:

I am not convinced by the Author's arguments for including the human data. I appreciate their efforts in adding a few (5) subjects and improving the description, but it still feels like it is shoehorned into this paper, and would be better published as a different manuscript.

This human study is short, but it is complete rather than preliminary. The rationale for us to include the human data as supplementary information is shown in responses to the reviewer’s Public review.

Minor concerns:

Page 3 paragraph 1: Suggest "contributing to palatability".

Thank you for this suggestion. We have rewritten the text as follows:

“…, the brain further processes these sensations to evoke emotional responses, contributing to palatability or unpleasantness”.

Page 4 paragraph 2: The text still assumes that "kokumi" is a meaningful descriptor for what rodents experience. Re-wording the following sentence like this could help:

"Neuroscientific studies in mice and rats provide evidence that gluthione and y-Glu-Val-Gly activate CaSRs, and modify behavioral responses to other tastants in a way that may correspond to kokumi taste as experienced by humans. However, to our..."

Or something similar.

Thank you for this suggestion. We have rewritten the sentence according to your suggestion as follows:

"Neuroscientific studies (23,25,30) in mice and rats provide evidence that glutathione and y-Glu-Val-Gly activate CaSRs, and modify behavioral responses to other tastants in a way that may correspond to kokumi as experienced by humans”.

Page 7 paragraph 1 - put the concentrations of Calindol and EGCG used (in the physiology exps) in the text.

We have added the concentrations: “300 µM calindol and 100 µM EGCG”.

Reviewer #2 (Recommendations for the authors):

I have included all of my recommendations in the public review section.

Reviewer #3 (Recommendations for the authors):

Although the definitions of 'thickness', 'mouthfulness' and 'continuity' have been revised very helpfully in the Introduction, 'mouthfulness' reappears at other points in the MS e.g., Page 4, Results, Line 3; Page 9, Line 3. It is best replaced by the new definition in these other locations too.

We wish to clarify that our revised text stated, “…to clarify that kokumi attributes are inherently gustatory, in the present study we use the terms ‘intensity of whole complex tastes (rich flavor with complex tastes)’ instead of ‘thickness,’ ‘mouthfulness (spread of taste and flavor throughout the oral cavity)’ instead of ‘continuity,’ and ‘persistence of taste (lingering flavor)’ instead of ‘continuity.’” The term “mouthfulness” was retained in our text, though we provided a more specific explanation. In the re-revised version, we have added “(spread of taste in the oral cavity)” immediately after “mouthfulness.”

I doubt that many scientific readers will be familliar with the term 'intragemmal nerve fibres' (Page 8, Line 4). It is used appropriately but it would be helpful to briefly define/explain it.

We have added an explanation as follows:

“… intragemmal nerve fibers, which are nerve processes that extend directly into the structure of the taste bud to transmit taste signals from taste cells to the brain.”

I previously pointed out the overlap between the CaSR's amino acid (AA) and gamma-glutamyl-peptide binding site. I was surprised by the authors' response which appeared to miss the point being made. It was based on the impacts of selected mutations in the receptor's Venus FlyTrap domain (Broadhead JBC 2011) on the responses to AAs and glutathione analogs. The significantly more active analog, S-methylglutathione is of additional interest because, like glutathione itself, it is present in mammalian body fluids. My apologies to the authors for not more carefully explaining this point.

Thank you for this comment. Both CaSR and GPRC6A are recognized as broad-spectrum amino acid sensors; however, their agonist profiles differ. Aromatic amino acids preferentially activate CaSR, whereas basic amino acids tend to activate GPRC6A. For instance, among basic amino acids, ornithine is a potent and specific activator of GPRC6A, while γ-Glu-Val-Gly in addition to amino acids is a high-potency activator of CaSR. It remains unclear how effectively ornithine activates CaSR and whether γ-glutamyl peptides also activate GPRC6A. These questions should be addressed in future studies.

eLife Assessment

This valuable study uses consensus-independent component analysis to highlight transcriptional components (TC) in high-grade serous ovarian cancers (HGSOC). The study presents a convincing preliminary finding by identifying a TC linked to synaptic signaling that is associated with shorter overall survival in HGSOC patients, highlighting the potential role of neuronal interactions in the tumour microenvironment. This finding is corroborated by comparing spatially resolved transcriptomics in a small-scale study; a weakness is it being descriptive, non-mechanistic, and requires experimental validation.

Reviewer #1 (Public review):

Summary:

This manuscript explores the transcriptional landscape of high-grade serous ovarian cancer (HGSOC) using consensus-independent component analysis (c-ICA) to identify transcriptional components (TCs) associated with patient outcomes. The study analyzes 678 HGSOC transcriptomes, supplemented with 447 transcriptomes from other ovarian cancer types and noncancerous tissues. By identifying 374 TCs, the authors aim to uncover subtle transcriptional patterns that could serve as novel drug targets. Notably, a transcriptional component linked to synaptic signaling was associated with shorter overall survival (OS) in patients, suggesting a potential role for neuronal interactions in the tumor microenvironment. Given notable weaknesses like lack of validation cohort or validation using other platforms (other than the 11 samples with ST), the data is considered highly descriptive and preliminary.

The study reveals significant findings by identifying a transcriptional component (TC121) associated with synaptic signaling, which is linked to shorter survival in patients with high-grade serous ovarian cancer, highlighting the potential role of neurons in the tumor microenvironment. However, the evidence could be strengthened by experimental validation to confirm the functional roles of key genes within TC121 and further exploration of its spatial aspects, including deeper analysis of neuronal and synaptic and other neuronal gene expression.

Strengths:

Innovative Methodology:<br /> The use of c-ICA to dissect bulk transcriptomes into independent components is a novel approach that allows for the identification of subtle transcriptional patterns that may be overshadowed in traditional analyses.

Comprehensive Data Integration:<br /> The study integrates a large dataset from multiple public repositories, enhancing the robustness of the findings. The inclusion of spatially resolved transcriptomes adds a valuable dimension to the analysis.

Clinical Relevance:<br /> The identification of a synaptic signaling-related TC associated with poor prognosis highlights a potential new avenue for therapeutic intervention, emphasizing the role of the tumor microenvironment in cancer progression.

Weaknesses:

Mechanistic Insights:<br /> While the study identifies TCs associated with survival, it provides limited mechanistic insights into how these components influence cancer progression. Further experimental validation is necessary to elucidate the underlying biological processes.

Generalizability:<br /> The findings are primarily based on transcriptomic data from HGSOC. It remains unclear how these results apply to other subtypes of ovarian cancer or different cancer types.

Innovative Methodology:<br /> Requires more validation using different platforms (IHC) to validate the performance of this bulk derived data. Also, the lack of control on data quality is a concern.

Clinical Application:<br /> Although the study suggests potential drug targets, the translation of these findings into clinical practice is not addressed. Probably given lack of some QA/QC procedures it'll be hard to translate these results. Future studies should focus on validating these targets in clinical settings.

Reviewer #2 (Public review):

Summary:

Consensus-independent component analysis and closely related methods have previously been used to reveal components of transcriptomic data which are not captured by principal component or gene-gene coexpression analyses.

Here, the authors asked whether applying consensus-independent component analysis (c-ICA) to published high-grade serous ovarian cancer (HGSOC) microarray-based transcriptomes would reveal subtle transcriptional patterns which are not captured by existing molecular omics classifications of HGSOC.

Statistical associations of these (hitherto masked) transcriptional components with prognostic outcomes in HGSOC would lead to additional insights into underlying mechanisms and, coupled with corroborating evidence from spatial transcriptomics, are proposed for further investigation.

This approach is complementary to existing transcriptomics classifications of HGSOC.

The authors have previously applied the same approach in colorectal carcinoma (for example, Knapen et al. (2024) Commun. Med).

Strengths:

Overall, this study describes a solid data-driven description of c-ICA-derived transcriptional components that the authors identified in HGSOC microarray transcriptomics data, supported by detailed methods and supplementary documentation.

The biological interpretation of transcriptional components is convincing based on (data-driven) permutation analysis and a suite of analyses of association with copy-number, gene sets, and prognostic outcomes.<br /> The resulting annotated transcriptional components have been made available in a searchable online format.

For the highlighted transcriptional component which has been annotated as related to synaptic signalling, the detection of the transcriptional component among 11 published spatial transcriptomics samples from ovarian cancers is compelling and supports the need for further mechanistic follow-up.

Further comments:

This revised version includes a suite of comparisons between the c-ICA-derived components and existing published transcriptomic/genomic-based classifications of ovarian cancers. Newly described components will require experimental validation, as acknowledged by the authors.

Here, the authors primarily interpret the c-ICA transcriptional components as a deconvolution of bulk transcriptomics due to the presence of cells from tumour cells and the tumour microenvironment.<br /> In this revised version, the authors additionally investigate their TC scores in single cells from a published HGSOC single-cell RNAseq dataset, highlighting examples of TC scores within and between cell types.

c-ICA is not explicitly a deconvolution method with respect to cell types: the transcriptional components do not necessarily correspond to distinct cell types, and may reflect differential dysregulation within a cell type. This application of c-ICA for the purpose of data-driven deconvolution of cell populations is distinct from other deconvolution methods which explicitly use a prior cell signature matrix.

Author response:

The following is the authors’ response to the original reviews

eLife Assessment

This valuable study uses consensus-independent component analysis to highlight transcriptional components (TC) in high-grade serous ovarian cancers (HGSOC). The study presents a convincing preliminary finding by identifying a TC linked to synaptic signaling that is associated with shorter overall survival in HGSOC patients, highlighting the potential role of neuronal interactions in the tumour microenvironment. This finding is corroborated by comparing spatially resolved transcriptomics in a small-scale study; a weakness is in being descriptive, non-mechanistic, and requiring experimental validation.”

We sincerely thank the editors for their valuable and constructive feedback. We are grateful for the recognition of our findings and the importance of identifying transcriptional components in high-grade serous ovarian cancers.

We acknowledge the editors’ observation regarding the descriptive nature of our study and its limited mechanistic depth. We agree that additional experimental validation would further strengthen our conclusions. We are planning and executing the experiments for a future study to provide mechanistic insights into the associations found in this study. In addition, recent reviews focused on the emerging field of cancer neuroscience emphasize the early stages the field is in, specifically in terms of a mechanistic understanding of the contributions of tumor-infiltrating nerves in tumor initiation and progression (Amit et al., 2024; Hwang et al., 2024). Nonetheless, we wish to emphasize that emerging mechanistic preclinical studies have demonstrated the influence of tumour-infiltrating nerves on disease progression (Allen et al., 2018; Balood et al., 2022; Darragh et al., 2024; Globig et al., 2023; Jin et al., 2022; Restaino et al., 2023; Zahalka et al., 2017). Several of these studies include contributions from our co-authors and feature in vitro and in vivo research on head and neck squamous cell carcinoma as well as high-grade serous ovarian carcinoma samples. This study further strengthens the preclinical work by showing in patient data, the potential relevance of neuronal signaling on disease outcome.

For instance, Restiano et al. (2023) demonstrated that substance P, released from tumour-infiltrating nociceptors, potentiates MAP kinase signaling in cancer cells, thereby driving disease progression. Crucially, this effect was shown to be reversible in vivo by blocking the substance P receptor (Restaino et al., 2023). These findings offer compelling evidence of the role of tumour innervation in cancer biology.

Our current study in tumor samples of patients with high-grade serous ovarian cancer identifies a transcriptional component that is enriched for genes for which the protein is located in the synapse. We believe that the previously published mechanistic insights support our findings and suggest that this transcriptional component could serve as a valuable screening tool to identify innervated tumours based on bulk transcriptomes. Clinically, this information is highly relevant, as patients with innervated tumours may benefit from alternate therapeutic strategies targeting these innervations.

Reviewer #1 (Public review)

This manuscript explores the transcriptional landscape of high-grade serous ovarian cancer (HGSOC) using consensus-independent component analysis (c-ICA) to identify transcriptional components (TCs) associated with patient outcomes. The study analyzes 678 HGSOC transcriptomes, supplemented with 447 transcriptomes from other ovarian cancer types and noncancerous tissues. By identifying 374 TCs, the authors aim to uncover subtle transcriptional patterns that could serve as novel drug targets. Notably, a transcriptional component linked to synaptic signaling was associated with shorter overall survival (OS) in patients, suggesting a potential role for neuronal interactions in the tumour microenvironment. Given notable weaknesses like lack of validation cohort or validation using another platform (other than the 11 samples with ST), the data is considered highly descriptive and preliminary.

Strengths:

(1) Innovative Methodology:

The use of c-ICA to dissect bulk transcriptomes into independent components is a novel approach that allows for the identification of subtle transcriptional patterns that may be overshadowed in traditional analyses.

We thank the reviewer for recognizing the strengths and novelty of our study. We appreciate the positive feedback on using consensus-independent component analysis (c-ICA) to decompose bulk transcriptomes, which allowed us to detect subtle transcriptional signals often overlooked in traditional analyses.

(2) Comprehensive Data Integration:

The study integrates a large dataset from multiple public repositories, enhancing the robustness of the findings. The inclusion of spatially resolved transcriptomes adds a valuable dimension to the analysis.

We thank the reviewer for recognizing the robustness of our study through comprehensive data integration. We appreciate the acknowledgment of our efforts to leverage a large, multi-source dataset, as well as the additional insights gained from spatially resolved transcriptomes. We consider this integrative approach enhances the depth of our analysis and contributes to a more nuanced understanding of the tumour microenvironment.

(3) Clinical Relevance:

The identification of a synaptic signaling-related TC associated with poor prognosis highlights a potential new avenue for therapeutic intervention, emphasizing the role of the tumour microenvironment in cancer progression.

We appreciate the recognition of the clinical implications of our findings. The identification of a synaptic signaling-related transcriptional component associated with poor prognosis underscores the potential for novel therapeutic targets within the tumour microenvironment. We agree that this insight could open new avenues for intervention and further highlights the role of neuronal interactions in cancer progression.

Weaknesses:

(1) Mechanistic Insights:

While the study identifies TCs associated with survival, it provides limited mechanistic insights into how these components influence cancer progression. Further experimental validation is necessary to elucidate the underlying biological processes.

We acknowledge the point regarding the limited mechanistic insights provided in our study. We agree that further experimental validation would significantly enhance our understanding of how the biological processes captured by these transcriptional components influence cancer progression. We are planning and executing the experiments for  a future study to provide mechanistic insights into the associations found in this study.

Our analyses were performed on publicly available bulk and spatial resolved expression profiles. To investigate the mechanistic insights in future studies, we plan to integrate spatial transcriptomic data with immunohistochemical analysis of the same tumour samples to validate our findings. Additionally, we have initiated efforts to set up in vitro co-cultures of neurons and ovarian cancer cells. These co-cultures will enable us to investigate how synaptic signaling impacts ovarian cancer cell behavior.

(2) Generalizability:

The findings are primarily based on transcriptomic data from HGSOC. It remains unclear how these results apply to other subtypes of ovarian cancer or different cancer types.

To respond to this remark, we utilized survival data from Bolton et al. (2022) and TCGA to investigate associations between TC activity scores and overall survival of patients with ovarian clear cell carcinoma, the second most common subtype of epithelial ovarian cancer, and  other cancer types respectively. However, we acknowledge the limitations of TCGA survival data, as highlighted in the referenced article (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8726696/). Additionally, as shown in Figure 5, we provided evidence of TC121 activity across various cancer types, suggesting broader relevance. For the results of the analyses mentioned above, please refer to our response to remark 1.3 of the recommendation section (page 4).

(3) Innovative Methodology:

Requires more validation using different platforms (IHC) to validate the performance of this bulk-derived data. Also, the lack of control over data quality is a concern.

We acknowledge the value of validating our results with alternative platforms such as IHC. We are planning and executing the experiments for a future study to provide mechanistic insights into the associations found in this study.

We implemented regarding data quality control, the following measures to ensure the reliability of our analysis:

Bulk Transcriptional Profiles: To assess data quality, we conducted principal component analysis (PCA) on the sample Pearson product-moment correlation matrix. The first principal component (PCqc), which explains approximately 80-90% of the variance, was used to distinguish technical variability from biological signals (Bhattacharya et al., 2020). Samples with a correlation coefficient below 0.8 relative to PCqc were identified as outliers and excluded. Additionally, MD5 hash values were generated for each CEL file to identify and remove duplicate samples. Expression values were standardized to a mean of zero and a variance of one for each gene to minimize probeset- or gene-specific variability across datasets (GEO, CCLE, GDSC, and TCGA).

Spatial Transcriptional Profiles: PCA was also applied to spatial transcriptomic data for quality control. Only samples with consistent loading factor signs for the first principal component across all individual spot profiles were retained. Samples failing this criterion were excluded from further analyses.

(4) Clinical Application:

Although the study suggests potential drug targets, the translation of these findings into clinical practice is not addressed. Probably given the lack of some QA/QC procedures it'll be hard to translate these results. Future studies should focus on validating these targets in clinical settings.”

Regarding clinical applications, we acknowledge the importance of further exploring strategies targeting synaptic signaling and neurotransmitter release in the tumour microenvironment (TME). As partially discussed in the first version of the manuscript, drugs such as ifenprodil and lamotrigine—commonly used to treat neuronal disorders—can block glutamate release, thereby inhibiting subsequent synaptic signaling. Additionally, the vesicular monoamine transporter (VMAT) inhibitor reserpine blocks the formation of synaptic vesicles (Reid et al., 2013; Williams et al., 2001). Previous in vitro studies with HGSOC cell lines demonstrated that ifenprodil significantly reduced cancer cell proliferation, while reserpine triggered apoptosis in cancer cells (North et al., 2015; Ramamoorthy et al., 2019). The findings highlight the potential of such approaches to disrupt synaptic neurotransmission in the TME.

To address potential translation of our findings into clinical practice more comprehensively, we have included additional details in the manuscript:

Section discussion, page 16, lines 338-341:

“This interaction can be targeted with pan-TRK inhibitors such as entrectinib and larotrectinib. Both drugs are showing promising results in multiple phase II trials, including ovarian cancer and breast cancer patients. Furthermore, a TRKB-specific inhibitor was developed (ANA-12), but has not been subjected to any clinical trials in cancer so far (Ardini et al., 2016; Burris et al., 2015; Drilon et al., 2018, 2017).”

On page 17, lines 361-374:

“Strategies to disrupt neuronal signaling and neurotransmitter release in neurons target key elements of excitatory neurotransmission, such as calcium flux and vesicle formation. Drugs like ifenprodil and lamotrigine, commonly used to treat neuronal disorders, block glutamate release and subsequent neuronal signaling. Additionally, the vesicular monoamine transporter (VMAT) inhibitor reserpine prevents synaptic vesicle formation (Reid et al., 2013; Williams, 2001). In vitro studies with HGSOC cell lines have demonstrated that ifenprodil significantly inhibits tumour proliferation, while reserpine induces apoptosis in cancer cells (North et al., 2015; Ramamoorthy et al., 2019). These approaches hold promise for inhibiting neuronal signaling and interactions in the TME.”

Reviewer #2 (Public review):

Summary:

Consensus-independent component analysis and closely related methods have previously been used to reveal components of transcriptomic data that are not captured by principal component or gene-gene coexpression analyses.

Here, the authors asked whether applying consensus-independent component analysis (c-ICA) to published high-grade serous ovarian cancer (HGSOC) microarray-based transcriptomes would reveal subtle transcriptional patterns that are not captured by existing molecular omics classifications of HGSOC.

Statistical associations of these (hitherto masked) transcriptional components with prognostic outcomes in HGSOC could lead to additional insights into underlying mechanisms and, coupled with corroborating evidence from spatial transcriptomics, are proposed for further investigation.

This approach is complementary to existing transcriptomics classifications of HGSOC.

The authors have previously applied the same approach in colorectal carcinoma (Knapen et al. (2024) Commun. Med).

Strengths:

(1) Overall, this study describes a solid data-driven description of c-ICA-derived transcriptional components that the authors identified in HGSOC microarray transcriptomics data, supported by detailed methods and supplementary documentation.

We thank the reviewer for acknowledging the strength of our data-driven approach and the use of consensus-independent component analysis (c-ICA) to identify transcriptional components within HGSOC microarray data. We aimed to provide comprehensive methodological detail and supplementary documentation to support the reproducibility and robustness of our findings. We believe this approach allows for the identification of subtle transcriptional signals that might have been overlooked by traditional analysis methods.

(2) The biological interpretation of transcriptional components is convincing based on (data-driven) permutation analysis and a suite of analyses of association with copy-number, gene sets, and prognostic outcomes.

We appreciate the positive feedback on the biological interpretation of our transcriptional components. We are pleased that our approach, which includes data-driven permutation testing and analyses of associations with copy-number alterations, gene sets, and prognostic outcomes, was found to be convincing. These analyses were integral to enhancing our findings’ robustness and biological relevance.

(3) The resulting annotated transcriptional components have been made available in a searchable online format.

Thank you for this important positive remark.

(4) For the highlighted transcriptional component which has been annotated as related to synaptic signalling, the detection of the transcriptional component among 11 published spatial transcriptomics samples from ovarian cancers appears to support this preliminary finding and requires further mechanistic follow-up.

Thank you for acknowledging the accessibility of our annotated transcriptional components. We prioritized making these data available in a searchable online format to facilitate further research and enable the community to explore and validate our findings.

Weaknesses:

(1) This study has not explicitly compared the c-ICA transcriptional components to the existing reported transcriptional landscape and classifications for ovarian cancers (e.g. Smith et al Nat Comms 2023; TCGA Nature 2011; Engqvist et al Sci Rep 2020) which would enable a further assessment of the additional contribution of c-ICA - whether the cICA approach captured entirely complementary components, or whether some components are correlated with the existing reported ovarian transcriptomic classifications.

We acknowledge the reviewer’s insightful suggestion to compare our c-ICA-derived transcriptional components with previously reported ovarian cancer classifications, such as those from Smith et al. (2023), TCGA (2011), and Engqvist et al. (2020). To address this, we incorporated analyses comparing the activity scores of our transcriptional components with these published landscapes and classifications, particularly focusing on any associations with overall survival. Additionally, we evaluated correlations between gene signatures from a subset of these studies and our identified TCs, enhancing our understanding of the unique contributions of the c-ICA approach. Please refer to our response to remark 10 for the results of these analyses.

(2) Here, the authors primarily interpret the c-ICA transcriptional components as a deconvolution of bulk transcriptomics due to the presence of cells from tumour cells and the tumour microenvironment.

However, c-ICA is not explicitly a deconvolution method with respect to cell types: the transcriptional components do not necessarily correspond to distinct cell types, and may reflect differential dysregulation within a cell type. This application of c-ICA for the purpose of data-driven deconvolution of cell populations is distinct from other deconvolution methods that explicitly use a prior cell signature matrix.”

We acknowledge that c-ICA, unlike traditional deconvolution methods, is not specifically designed for cell-type deconvolution and does not rely on a predefined cell signature matrix. While we explored the transcriptional components in the context of tumour and microenvironmental interactions, we agree that these components may not correspond directly to distinct cell types but rather reflect complex patterns of dysregulation, potentially within individual cell populations.

Our goal with c-ICA was to uncover hidden transcriptional patterns possibly influenced by cellular heterogeneity. However, we recognize these patterns may also arise from regulatory processes within a single cell type. To investigate further, we used single-cell transcriptional data (~60,000 cell-types annotated profiles from GSE158722) and projected our transcriptional components onto these profiles to obtain activity scores, allowing us to assess each TC’s behavior across diverse cellular contexts after removing the first principal component to minimize background effects. Please refer to our response to remark 2.2 in the recommendations to the authors (page 14) for the results of this analysis.

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