Reviewer #1 (Public review):

Summary:

Migration of the primordial germ cells (PGCs) in mice is asynchronous, such that leading and lagging populations of migrating PGCs emerge. Prior studies found that interactions between the cells the PGCs encounter along their migration routes regulates their proliferation. In this study, the authors used single cell RNAseq to investigate PGC heterogeneity and to characterize their niches during their migration along the AP axis. Unlike prior scRNAseq studies of mammalian PGCs, the authors conducted a time course covering 3 distinct stages of PGC migration (pre, mid, and post migration) and isolated PGCs from defined somite positions along the AP axis. In doing so, this allowed the authors to uncover differences in gene expression between leading and lagging PGCs and their niches and to investigate how their transcript profiles change over time. Among the pathways with the biggest differences were regulators of actin polymerization and epigenetic programming factors and Nodal response genes. In addition, the authors report changes in somatic niches, specifically greater non-canonical WNT in posterior PGCs compared to anterior PGCs. This relationship between the hindgut epithelium and migrating PGCs was also detected in reanalysis of a previously published dataset of human PGCs. Using whole mount immunofluorescence, the authors confirmed elevated Nodal signaling based on detection of the LEFTY antagonists and targets of Nodal during late stage PGC migration. Taken together, the authors have assembled a temporal and spatial atlas of mouse PGCs and their niches. This resource and the data herein provide support for the model that interactions of migrating mouse PGCs with their niches influences their proliferation, cytoskeletal regulation, epigenetic state and pluripotent state.

Overall, the findings provide new insights into heterogeneity among leading and lagging PGC populations and their niches along the AP axis, as well as comparisons between mouse and human migrating PGCs. The data are clearly presented, and the text is clear and well-written. This atlas resource will be valuable to reproductive and developmental biologists as a tool for generating hypotheses and for comparisons of PGCs across species.

Strengths:

(1) High quality atlas of individual PGCs prior to, during and post migration and their niches at defined positions along the AP axis.<br /> (2) Comparisons to available datasets, including human embryos, provide insight into potentially conserved relationships among PGCs and the identified pathways and gene expression changes.<br /> (3) Detailed picture of PGC heterogeneity.<br /> (4) Valuable resource for the field.<br /> (5) Some validation of Nodal results and further support for models in the literature based on less comprehensive expression analysis.

Weaknesses:

(1) No indication of which sex(es) were used for the mouse data and whether or not sex-related differences exist or can excluded at the stages examined. This should be clarified.

Reviewer #2 (Public review):

Summary:

This work addresses the question of how 'leading' and 'lagging' PGCs differ, molecularly, during their migration to the mouse genital ridges/gonads during fetal life (E9.5, E10.5, E11.5), and how this is regulated by different somatic environments encountered during the process of migration. E9.5 and E10.5 cells differed in expression of genes involved in canonical WNT signaling and focal adhesions. Differences in cell adhesion, actin cytoskeletal dynamics were identified between leading and lagging cells, at E9.5, before migration into the gonads. At E10.5, when some PGCs have reached the genital ridges, differences in Nodal signaling response genes and reprogramming factors were identified. This last point was verified by whole mount IF for proteins downstream of Nodal signaling, Lefty1/2. At E11.5, there was upregulation of genes associated with chromatin remodeling and oxidative phosphorylation. Some aspects of the findings were also found to be likely true in human development, established via analysis of a dataset previously published by others.

Strengths:

The work is strong in that a large number of PGCs were isolated and sequenced, along with associated somatic cells. The authors dealt with problem of very small number of migrating mouse PGCs by pooling cells from embryos (after ascertaining age matching using somite counting). 'Leading' and 'lagging' populations were separated by anterior and posterior embryo halves and the well-established Oct4-deltaPE-eGFP reporter mouse line was used.

Weaknesses:

The work seems to have been carefully done, but I do not feel the manuscript is very accessible, and I do not consider it well written. The novel findings are not easy to find. The addition of at least one figure to show the locations of putative signaling etc. would be welcome.

(1) The initial discussion of CellRank analysis (under 'Transcriptomic shifts over developmental time...' heading) is somewhat confusing - e.g. If CellRank's 'pseudotime analysis' produces a result that seems surprising (some E9.5 cells remain in a terminal state with other E9.5 cells) and 'realtime analysis' produces something that makes more sense, is there any point including the pseudotime analysis (since you have cells from known timepoints)? Perhaps the 'batch effects' possible explanation (in Discussion) should be introduced here. Do we learn anything novel from this CellRank analysis? The 'genetic drivers' identified seem to be genes already known to be key to cell transitions during this period of development.

(2) In Discussion - with respect to Y-chromosome correlation, it is not clear why this analysis would be done at E10.5, when E11.5 data is available (because some testis-specific effect might be more apparent at the later stage).

(3) Figure 2A - it seems surprising that there are two clusters of E9.5 anterior cells

(4) Figure 5F - there does seem to be more LEFTY1/2 staining in the anterior region, but also more germ cells as highlighted by GFP

Reviewer #3 (Public review):

Summary:

The migration of primordial germ cells (PGCs) to the developing gonad is a poorly understood, yet essential step in reproductive development. Here, the authors examine whether there are differences in leading and lagging migratory PGCs using single-cell RNA sequencing of mouse embryos. Cleverly, the authors dissected embryonic trunks along the anterior-to-posterior axis prior to scRNAseq in order to distinguish leading and lagging migratory PGCs. After batch corrections, their analyses revealed several known and novel differences in gene expression within and around leading and lagging PGCs, intercellular signaling networks, as well as number of genes upregulated upon gonad colonization. The authors then compared their datasets with publicly available human datasets to identify common biological themes. Altogether, this rigorous study reveals several differences between leading and lagging migratory PGCs, hints at signatures for different fates among the population of migratory PGCs, and provides new potential markers for post-migratory PGCs in both humans and mice. While many of the interesting hypotheses that arise from this work are not extensively tested, these data provide a rich platform for future investigations.

Strengths:

-The authors have successfully navigated significant technical challenges to obtain a substantial number of mouse migratory primordial germ cells for robust transcriptomic analysis. Here the authors were able to collect quality data on ~13,000 PGCs and ~7,800 surrounding somatic cells, which is ten times more PGCs than previous studies.

- The decision to physically separate leading and lagging primordial germ cells was clever and well-validated based on expected anterior-to-posterior transcriptional signatures.

- Within the PGCs and surrounding tissues, the authors found many gene expression dynamics they would expect to see both along the PGC migratory path as well as across developmental time, increasing confidence in the new differentially expressed genes they found.

- The comparison of their mouse-based migratory PGC datasets with existing human migratory PGC datasets is appreciated.

- The quality control, ambient RNA contamination elimination, batch correction, cell identification and analysis of scRNAseq data were thorough and well-done such that the new hypotheses and markers found through this study are dependable.

- The subsetting of cells in their trajectory analysis is appreciated, further strengthening their cell terminal state predictions.

Weaknesses:

- Although it is useful to compare their mouse-based dataset with human datasets, the authors used two different analysis pipelines for each dataset. While this may have been due to the small number of cells in the human dataset as mentioned, it does make it difficult to compare them.

- There were few validation experiments within this study. For one such experiment, whether there is a difference in pSMAD2/3 along the AP axis is unclear and not quantified as was nicely done for Lefty1/2.

eLife Assessment

This useful manuscript provides a newly curated database (termed AACDB) of antibody-antigens structural information, alongside annotations that are either taken and from the PDB, or added de-novo. Sequences, structures, and annotations can be easily downloaded from the AACDB website, speeding up the development of structure-based algorithms and analysis pipelines to characterize antibody-antigen interactions. The methodology presented for this data curation is solid, although some key annotations are missing which would greatly improve the database.

Reviewer #1 (Public review):

This manuscript introduces a useful curation pipeline of antibody-antigen structures downloaded from the PDB database. The antibody-antigen structures are presented in a new database called AACDB, alongside annotations that were either corrected from those present in the PDB database or added de-novo with a solid methodology. Sequences, structures, and annotations can be very easily downloaded from the AACDB website, speeding up the development of structure-based algorithms and analysis pipelines to characterize antibody-antigen interactions. However, AACDB is missing some key annotations that would greatly enhance its usefulness.

Here are detailed comments regarding the three strengths above:

(1) I think potentially the most significant contribution of this database is the manual data curation to fix errors present in the PDB entries, by cross-referencing with the literature. However, as a reviewer, validating the extent and the impact of these corrections is hard, since the authors only provided a few anecdotal examples in their manuscript.

I have personally verified some of the examples presented by the authors and found that SAbDab appears to fix the mistakes related to the misidentification of antibody chains, but not other annotations.

(a) "the species of the antibody in 7WRL was incorrectly labeled as "SARS coronavirus B012" in both PDB and SabDab" → I have verified the mistake and fix, and that SAbDab does not fix is, just uses the pdb annotation.<br /> (b) "1NSN, the resolution should be 2.9 , but it was incorrectly labeled as 2.8" → I have verified the mistake and fix, and that saabdab does not fix it, just uses the PDB annotation.<br /> (c) "mislabeling of antibody chains as other proteins (e.g. in 3KS0, the light chain of B2B4 antibody was misnamed as heme domain of flavocytochrome b2)" → SAbDab fixes this as well in this case.<br /> (d) "misidentification of heavy chains as light chains (e.g. both two chains of antibody were labeled as light chain in 5EBW)" → SAbDab fixes this as well in this case.

I personally believe the authors should make public the corrections made, and describe the procedures - if systematic - to identify and correct the mistakes. For example, what was the exact procedure (e.g. where were sequences found, how were the sequences aligned, etc.) to find mutations? Was the procedure run on every entry?

(2) I believe the splitting of the pdb files is a valuable contribution as it standardizes the distribution of antibody-antigen complexes. Indeed, there is great heterogeneity in how many copies of the same structure are present in the structure uploaded to the PDB, generating potential artifacts for machine learning applications to pick up on. That being said, I have two thoughts both for the authors and the broader community. First, in the case of multiple antibodies binding to different epitopes on the same antigen, one should not ignore the potentially stabilizing effect that the binding of one antibody has on the complex, thereby enabling the binding of the second antibody. In general, I urge the community to think about what is the most appropriate spatial context to consider when modeling the stability of interactions from crystal structure data. Second, and in a similar vein, some antigens occur naturally as homomultimers - e.g. influenza hemagglutinin is a homotrimer. Therefore, to analyze the stability of a full-antigen-antibody structure, I believe it would be necessary to consider the full homo-trimer, whereas, in the current curation of AACDB with the proposed data splitting, only the monomers are present.

(3) I think the annotation of interface residues is a useful addition to structural datasets, but their current presentation is lacking on several fronts.

I think the manuscript is lacking in justification about the numbers used as cutoffs (1A^2 for change in SASA and 5A for maximum distance for contact) The authors just cite other papers applying these two types of cutoffs, but the underlying physico-chemical reasons are not explicit even in these papers. I think that, if the authors want AACDB to be used globally for benchmarks, they should provide direct sources of explanations of the cutoffs used, or provide multiple cutoffs. Indeed, different cutoffs are often used (e.g. ATOM3D uses 6A instead of 5A to determine contact between a protein and a small molecule https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/hash/c45147dee729311ef5b5c3003946c48f-Abstract-round1.html)

I think the authors should provide a figure with statistics pertaining to the interface atoms. I think showing any distribution differences between interface atoms determined according to either strategy (number of atoms, correlation between change in SASA and distance...) would be fundamental to understanding the two strategies. I think other statistics would constitute an enhancement as well (e.g. proportion of heavy vs. light chain residues).

Some obvious limitations of AACDB in its current form include:

AACDB only contains entries with protein-based antigens of at most 50 amino acids in length. This excludes non-protein-based antigens, such as carbohydrate- and nucleotide-based, as well as short peptide antigens.

AACDB does not include annotations of binding affinity, which are present in SAbDab and have been proven useful both for characterizing drivers of antibody-antigen interactions (cite https://www.sciencedirect.com/science/article/pii/S0969212624004362?via%3Dihub) and for benchmarking antigen-specific antibody-design algorithms (cite https://www.biorxiv.org/content/10.1101/2023.12.10.570461v1)).

In conclusion, I believe AACDB has the potential to be a more standardized and error-light database for antibody-antigen complex structures. It is, however, hard to evaluate the extent to which errors have been corrected since the authors do not provide a list of the errors or a step-by-step procedure for fixing the errors. Unfortunately, AACDB is currently missing binding affinity annotations, which hinders its usefulness.

Reviewer #2 (Public review):

Summary:

Antibodies, thanks to their high binding affinity and specificity to cognate protein targets, are increasingly used as research and therapeutic tools. In this work, Zhou et al. have created, curated, and made publicly available a new database of antibody-antigen complexes to support research in the field of antibody modelling, development, and engineering.

Strengths:

The authors have performed a manual curation of antibody-antigen complexes from the Protein Data Bank, rectifying annotation errors; they have added two methods to estimate paratope-epitope interfaces; they have produced a web interface that is capable of both effective visualisation and of summarising the key useful information in one page. The database is also cross-linked to other databases that contain information relevant to antibody developability and therapeutic applications.

Weaknesses:

The database does not import all the experimental information from PDB and contains only complexes with large protein targets.

Author response:

We sincerely appreciate the insightful feedback and constructive suggestions provided by the reviewers. We thank reviewers for their valuable support in improving our manuscript.

In response to the public reviews raised by reviewers, we plan to make the following revisions:

(1) Most metadata have been rectified through collaborative review of original literature sources rather than automated processes. We intend to incorporate a detailed discussion on this matter in the revised manuscript.

(2) We will include a corrections table for entries to provide clarity and transparency regarding any amendments made.

(3) Additional references will be included to elucidate the rationale behind the selection of interact residues definition methods and the set threshold. The threshold is not fixed. In fact, we utilized a 5Å cutoff in current version, listing all residues with distances less than 5Å alongside the corresponding distances. The researchers could screen the residues through distance according to their custom cutoff. To offer researchers flexibility, we will also provide interact residues and corresponding distances with higher cutoffs for custom screening. These enhancements will be detailed in the revised manuscript.

(4)We acknowledge the importance of expanding the database to include a wider range of experimental information and complexes with diverse target sizes. Regrettably, immediate updates to address these limitations are not feasible at this time. Thus, we will give an illustration in the later detail response to reviewers.

eLife Assessment

Mackie and colleagues present a valuable comparison of lateralized gustation in two well-studied nematodes. The evidence they present that ASEL/R lateralization exists and is achieved by different means in P. pacificus compared to C. elegans is solid, but would be strengthened by the inclusion of calcium imaging data for C. elegans and additional verification of P. pacificus AFD neuron identity. This work will be of interest to evolutionary biologists, ecologists, and sensory neurobiologists interested in how small nervous systems make sense of the environment, and how evolution can take multiple paths to asymmetry within a neuron class.

Reviewer #1 (Public review):

Summary:

Mackie and colleagues compare chemosensory preferences between C. elegans and P. pacificus, and the cellular and molecular mechanisms underlying them. The nematodes have overlapping and distinct preferences for different salts. Although P. pacificus lacks the lsy-6 miRNA important for establishing asymmetry of the left/right ASE salt-sensing neurons in C. elegans, the authors find that P. pacificus ASE homologs achieve molecular (receptor expression) and functional (calcium response) asymmetry by alternative means. This work contributes an important comparison of how these two nematodes sense salts and highlights that evolution can find different ways to establish asymmetry in small nervous systems to optimize the processing of chemosensory cues in the environment.

Strengths:

The authors use clear and established methods to record the response of neurons to chemosensory cues. They were able to show clearly that ASEL/R are functionally asymmetric in P. pacificus, and combined with genetic perturbation establish a role for che-1-dependent gcy-22.3 in in the asymmetric response to NH4Cl.

Weaknesses:

The mechanism of lsy-6-independent establishment of ASEL/R asymmetry in P. pacificus remains uncharacterized.

Reviewer #2 (Public review):

Summary:

In this manuscript, Mackie et al. investigate gustatory behavior and the neural basis of gustation in the predatory nematode Pristionchus pacificus. First, they show that the behavioral preferences of P. pacificus for gustatory cues differ from those reported for C. elegans. Next, they investigate the molecular mechanisms of salt sensing in P. pacificus. They show that although the C. elegans transcription factor gene che-1 is expressed specifically in the ASE neurons, the P. pacificus che-1 gene is expressed in the Ppa-ASE and Ppa-AFD neurons. Moreover, che-1 plays a less critical role in salt chemotaxis in P. pacificus than C. elegans. Chemogenetic silencing of Ppa-ASE and Ppa-AFD neurons results in more severe chemotaxis defects. The authors then use calcium imaging to show that both Ppa-ASE and Ppa-AFD neurons respond to salt stimuli. Calcium imaging experiments also reveal that the left and right Ppa-ASE neurons respond differently to salts, despite the fact that P. pacificus lacks lsy-6, a microRNA that is important for ASE left/right asymmetry in C. elegans. Finally, the authors show that the receptor guanylate cyclase gene Ppa-gcy-23.3 is expressed in the right Ppa-ASE neuron (Ppa-ASER) but not the left Ppa-ASE neuron (Ppa-ASEL) and is required for some of the gustatory responses of Ppa-ASER, further confirming that the Ppa-ASE neurons are asymmetric and suggesting that Ppa-GCY-23.3 is a gustatory receptor. Overall, this work provides insight into the evolution of gustation across nematode species. It illustrates how sensory neuron response properties and molecular mechanisms of cell fate determination can evolve to mediate species-specific behaviors. However, the paper would be greatly strengthened by a direct comparison of calcium responses to gustatory cues in C. elegans and P. pacificus, since the comparison currently relies entirely on published data for C. elegans, where the imaging parameters likely differ. In addition, the conclusions regarding Ppa-AFD neuron function would benefit from additional confirmation of AFD neuron identity. Finally, how prior salt exposure influences gustatory behavior and neural activity in P. pacificus is not discussed.

Strengths:

(1) This study provides exciting new insights into how gustatory behaviors and mechanisms differ in nematode species with different lifestyles and ecological niches. The results from salt chemotaxis experiments suggest that P. pacificus shows distinct gustatory preferences from C. elegans. Calcium imaging from Ppa-ASE neurons suggests that the response properties of the ASE neurons differ between the two species. In addition, an analysis of the expression and function of the transcription factor Ppa-che-1 reveals that mechanisms of ASE cell fate determination differ in C. elegans and P. pacificus, although the ASE neurons play a critical role in salt sensing in both species. Thus, the authors identify several differences in gustatory system development and function across nematode species.

(2) This is the first calcium imaging study of P. pacificus, and it offers some of the first insights into the evolution of gustatory neuron function across nematode species.

(3) This study addresses the mechanisms that lead to left/right asymmetry in nematodes. It reveals that the ASER and ASEL neurons differ in their response properties, but this asymmetry is achieved by molecular mechanisms that are at least partly distinct from those that operate in C. elegans. Notably, ASEL/R asymmetry in P. pacificus is achieved despite the lack of a P. pacificus lsy-6 homolog.

Weaknesses:

(1) The authors observe only weak attraction of C. elegans to NaCl. These results raise the question of whether the weak attraction observed is the result of the prior salt environment experienced by the worms. More generally, this study does not address how prior exposure to gustatory cues shapes gustatory responses in P. pacificus. Is salt sensing in P. pacificus subject to the same type of experience-dependent modulation as salt sensing in C. elegans?

(2) A key finding of this paper is that the Ppa-CHE-1 transcription factor is expressed in the Ppa-AFD neurons as well as the Ppa-ASE neurons, despite the fact that Ce-CHE-1 is expressed specifically in Ce-ASE. However, additional verification of Ppa-AFD neuron identity is required. Based on the image shown in the manuscript, it is difficult to unequivocally identify the second pair of CHE-1-positive head neurons as the Ppa-AFD neurons. Ppa-AFD neuron identity could be verified by confocal imaging of the CHE-1-positive neurons, co-expression of Ppa-che-1p::GFP with a likely AFD reporter, thermotaxis assays with Ppa-che-1 mutants, and/or calcium imaging from the putative Ppa-AFD neurons.

(3) Loss of Ppa-che-1 causes a less severe phenotype than loss of Ce-che-1. However, the loss of Ppa-che-1::RFP expression in ASE but not AFD raises the question of whether there might be additional start sites in the Ppa-che-1 gene downstream of the mutation sites. It would be helpful to know whether there are multiple isoforms of Ppa-che-1, and if so, whether the exon with the introduced frameshift is present in all isoforms and results in complete loss of Ppa-CHE-1 protein.

(4) The authors show that silencing Ppa-ASE has a dramatic effect on salt chemotaxis behavior. However, these data lack control with histamine-treated wild-type animals, with the result that the phenotype of Ppa-ASE-silenced animals could result from exposure to histamine dihydrochloride. This is an especially important control in the context of salt sensing, where histamine dihydrochloride could alter behavioral responses to other salts.

(5) The calcium imaging data in the paper suggest that the Ppa-ASE and Ce-ASE neurons respond differently to salt solutions. However, to make this point, a direct comparison of calcium responses in C. elegans and P. pacificus using the same calcium indicator is required. By relying on previously published C. elegans data, it is difficult to know how differences in growth conditions or imaging conditions affect ASE responses. In addition, the paper would be strengthened by additional quantitative analysis of the calcium imaging data. For example, the paper states that 25 mM NH4Cl evokes a greater response in ASEL than 250 mM NH4Cl, but a quantitative comparison of the maximum responses to the two stimuli is not shown.

(6) It would be helpful to examine, or at least discuss, the other P. pacificus paralogs of Ce-gcy-22. Are they expressed in Ppa-ASER? How similar are the different paralogs? Additional discussion of the Ppa-gcy-22 gene expansion in P. pacificus would be especially helpful with respect to understanding the relatively minor phenotype of the Ppa-gcy-22.3 mutants.

(7) The calcium imaging data from Ppa-ASE is quite variable. It would be helpful to discuss this variability. It would also be helpful to clarify how the ASEL and ASER neurons are being conclusively identified during calcium imaging.

(8) More information about how the animals were treated prior to calcium imaging would be helpful. In particular, were they exposed to salt solutions prior to imaging? In addition, the animals are in an M9 buffer during imaging - does this affect calcium responses in Ppa-ASE and Ppa-AFD? More information about salt exposure, and how this affects neuron responses, would be very helpful.

(9) In Figure 6, the authors say that Ppa-gcy-22.3::GFP expression is absent in the Ppa-che-1(ot5012) mutant. However, based on the figure, it looks like there is some expression remaining. Is there a residual expression of Ppa-gcy-22.3::GFP in ASE or possibly ectopic expression in AFD? Does Ppa-che-1 regulate rGC expression in AFD? It would be helpful to address the role of Ppa-che-1 in AFD neuron differentiation.

Author response:

We very much appreciate the reviewers’ and editor’s overall positive responses to our manuscript "Evolution of lateralized gustation in nematodes".

Reviewer #1:

The mechanism of lsy-6-independent establishment of ASEL/R asymmetry in P. pacificus remains uncharacterized. 

We thank the reviewer for recognizing the novel contributions of our work in revealing the existence of alternative pathways for establishing neuronal lateral asymmetry despite the absence of the lsy-6 miRNA in a divergent nematode species. We are certainly encouraged now to search for genetic factors that abolish asymmetric expression of gcy-22.3.

Reviewer #2:

(1) The authors observe only weak attraction of C. elegans to NaCl. These results raise the question of whether the weak attraction observed is the result of the prior salt environment experienced by the worms. More generally, this study does not address how prior exposure to gustatory cues shapes gustatory responses in P. pacificus. Is salt sensing in P. pacificus subject to the same type of experience-dependent modulation as salt sensing in C. elegans? 

Proposed revision: For our live imaging experiments, we had not considered if starved P. pacificus animals in the presence of salt may exhibit responses different from a well-fed state. However, we will venture to address the effect of experience-dependent modulation in P. pacificus chemotaxis behavior using NH4Cl.

(2) A key finding of this paper is that the Ppa-CHE-1 transcription factor is expressed in the Ppa-AFD neurons as well as the Ppa-ASE neurons, despite the fact that Ce-CHE-1 is expressed specifically in Ce-ASE. However, additional verification of Ppa-AFD neuron identity is required. Based on the image shown in the manuscript, it is difficult to unequivocally identify the second pair of CHE-1-positive head neurons as the Ppa-AFD neurons. Ppa-AFD neuron identity could be verified by confocal imaging of the CHE-1-positive neurons, co-expression of Ppa-che-1p::GFP with a likely AFD reporter, thermotaxis assays with Ppa-che-1 mutants, and/or calcium imaging from the putative Ppa-AFD neurons. 

We are happy to provide additional evidence to confirm Ppa-AFD neuron identity since the expression of Ppa-CHE-1 in non-ASE amphid neurons is one of the major differences between the two nematode specie

Proposed revision: We will provide results showing the Ppa-ttx-1::gfp reporter expression in finger-like neuronal endings and Ppa-_TTX-1::ALFA co-localization with _Ppa-che-1::gfp in the putative AFD neurons and discuss the possible role of Ppa-CHE-1 in AFD differentiation. We attempted to obtain AFD markers using several reporter strains. However, Ppa-gcy-8.1p::gfp(csuEx101) (PPA24212) showed no expression while Ppa-gcy-8.2p::gfp(csuEx100) (PPA41407) showed only expression in pharyngeal cells.

(4) The authors show that silencing Ppa-ASE has a dramatic effect on salt chemotaxis behavior. However, these data lack control with histamine-treated wild-type animals, with the result that the phenotype of Ppa-ASE-silenced animals could result from exposure to histamine dihydrochloride. This is an especially important control in the context of salt sensing, where histamine dihydrochloride could alter behavioral responses to other salts. 

Proposed revision: Thank you for noticing this oversight. The control for histamine-treated wild-type worms in the Ppa-ASE silencing experiments was inadvertently left out in the original submission. Because the HisCl transgene is on a randomly segregating transgene array, we have scored worms with and without the transgene expressing the co-injection marker (Ppa-egl-20p::rfp expressed in the tail) to show that the presence of the transgene is necessary for the knockdown of NH4Br attraction.

We will also address most of the other more minor suggestions and clarifications sought by the reviewers.

eLife Assessment

This valuable paper describes the regulatory pathway of rRNA synthesis by Meioc-Piwil1 in germ cell differentiation in zebrafish. Using the molecular genetic and cytological approaches, the authors provide convincing evidence that Meioc antagonizes Piwil1, which downregulates the 45S pre-rRNA synthesis by heterochromatin formation for spermatocyte differentiation. The key results are solid and of use to researchers in the field of germ cell/meiosis as well as RNA biosynthesis and chromatin.

Reviewer #1 (Public review):

Summary:

In this paper Kawasaki et al describe a regulatory role for the PIWI/piRNA pathway in rRNA regulation in Zebrafish. This regulatory role was uncovered through a screen for gonadogenesis defective mutants, which identified a mutation in the meioc gene, a coiled-coil germ granule protein. Loss of this gene leads to redistribution of Piwil1 from germ granules to the nucleolus, resulting in silencing of rRNA transcription.

Strengths:

Most of the experimental data provided in this paper is compelling. It is clear that in the absence of meioc, PiwiL1 translocates in to the nucleolus and results in down regulation of rRNA transcription. the genetic compensation of meioc mutant phenotypes (both organismal and molecular) through reduction in PiwiL1 levels are evidence for a direct role for PiwiL1 in mediating the phenotypes of meioc mutant.

Weaknesses:

Questions remain on the mechanistic details by which PiwiL1 mediated rRNA down regulation, and whether this is a function of Piwi in an unperturbed/wildtype setting. There is certainly some evidence provided in support of the natural function for piwi in regulating rRNA transcription (figure 5A+5B). However, the de-enrichment of H3K9me3 in the heterozygous (Figure 6F) is very modest and in my opinion not convincingly different relative to the control provided. It is certainly possible that PiwiL1 is regulating levels through cleavage of nascent transcripts. Another aspect I found confounding here is the reduction in rRNA small RNAs in the meioc mutant; I would have assumed that the interaction of PiwiL1 with the rRNA is mediated through small RNAs but the reduction in numbers do not support this model. But perhaps it is simply a redistribution of small RNAs that is occurring. Finally, the ability to reduce PiwiL1 in the nucleolus through polI inhibition with actD and BMH-21 is surprising. What drives the accumulation of PiwiL1 in the nucleolus then if in the meioc mutant there is less transcription anyway?

Despite the weaknesses outlined, overall I find this paper to be solid and valuable, providing evidence for a consistent link between PIWI systems and ribosomal biogenesis. Their results are likely to be of interest to people in the community, and provide tools for further elucidating the reasons for this link.

Reviewer #2 (Public review):

Summary:

In this study, the authors report that Meioc is required to upregulate rRNA transcription and promote differentiation of spermatogonial stem cells in zebrafish. The authors show that upregulated protein synthesis is required to support spermatogonial stem cells' differentiation into multi-celled cysts of spermatogonia. Coiled coil protein Meioc is required for this upregulated protein synthesis and for increasing rRNA transcription, such that the Meioc knockout accumulates 1-2 cell spermatogonia and fails to produce cysts with more than 8 spermatogonia. The Meioc knockout exhibits continued transcriptional repression of rDNA. Meioc interacts with and sequesters Piwil1 to the cytoplasm. Loss of Meioc increases Piwil1 localization to the nucleolus, where Piwil1 interacts with transcriptional silencers that repress rRNA transcription.

Strengths:

This is a fundamental study that expands our understanding of how ribosome biogenesis contributes to differentiation and demonstrates that zebrafish Meioc plays a role in this process during spermatogenesis. This work also expands our evolutionary understanding of Meioc and Ythdc2's molecular roles in germline differentiation. In mouse, the Meioc knockout phenocopies the Ythdc2 knockout, and studies thus far have indicated that Meioc and Ythdc2 act together to regulate germline differentiation. Here, in zebrafish, Meioc has acquired a Ythdc2-independent function. This study also identifies a new role for Piwil1 in directing transcriptional silencing of rDNA.

Weaknesses:<br /> There are limited details on the stem cell-enriched hyperplastic testes used as a tool for mass spec experiments, and additional information is needed to fully evaluate the mass spec results. What mutation do these testes carry? Does this protein interact with Meioc in the wildtype testes? How could this mutation affect the results from the Meioc immunoprecipitation?

Reviewer #3 (Public review):

Summary:

The paper describes the molecular pathway to regulate germ cell differentiation in zebrafish through ribosomal RNA biogenesis. Meioc sequesters Piwil1, a Piwi homolog, which suppresses the transcription of the 45S pre-rDNA by the formation of heterochromatin, to the perinuclear bodies. The key results are solid and useful to researchers in the field of germ cell/meiosis as well as RNA biosynthesis and chromatin.

Strengths:

The authors nicely provided the molecular evidence on the antagonism of Meioc to Piwil1 in the rRNA synthesis, which supported by the genetic evidence that the inability of the meioc mutant to enter meiosis is suppressed by the piwil1 heterozygosity.

Weaknesses:

(1) Although the paper provides very convincing evidence for the authors' claim, the scientific contents are poorly written and incorrectly described. As a result, it is hard to read the text. Checking by scientific experts would be highly recommended. For example, on line 38, "the global translation activity is generally [inhibited]", is incorrect and, rather, a sentence like "the activity is lowered relative to other cells" is more appropriate here. See minor points for more examples.<br /> (2) In some figures, it is hard for readers outside of zebrafish meiosis to evaluate the results without more explanation and drawing.<br /> (3) Figure 1E, F, cycloheximide experiments: Please mention the toxicity of the concentration of the drug in cell proliferation and viability.

Author response:

Public Reviews:

Reviewer #1 (Public review):

Summary:

In this paper Kawasaki et al describe a regulatory role for the PIWI/piRNA pathway in rRNA regulation in Zebrafish. This regulatory role was uncovered through a screen for gonadogenesis defective mutants, which identified a mutation in the meioc gene, a coiled-coil germ granule protein. Loss of this gene leads to redistribution of Piwil1 from germ granules to the nucleolus, resulting in silencing of rRNA transcription.

Strengths:

Most of the experimental data provided in this paper is compelling. It is clear that in the absence of meioc, PiwiL1 translocates in to the nucleolus and results in down regulation of rRNA transcription. the genetic compensation of meioc mutant phenotypes (both organismal and molecular) through reduction in PiwiL1 levels are evidence for a direct role for PiwiL1 in mediating the phenotypes of meioc mutant.

Weaknesses:

Questions remain on the mechanistic details by which PiwiL1 mediated rRNA down regulation, and whether this is a function of Piwi in an unperturbed/wildtype setting. There is certainly some evidence provided in support of the natural function for piwi in regulating rRNA transcription (figure 5A+5B). However, the de-enrichment of H3K9me3 in the heterozygous (Figure 6F) is very modest and in my opinion not convincingly different relative to the control provided. It is certainly possible that PiwiL1 is regulating levels through cleavage of nascent transcripts. Another aspect I found confounding here is the reduction in rRNA small RNAs in the meioc mutant; I would have assumed that the interaction of PiwiL1 with the rRNA is mediated through small RNAs but the reduction in numbers do not support this model. But perhaps it is simply a redistribution of small RNAs that is occurring. Finally, the ability to reduce PiwiL1 in the nucleolus through polI inhibition with actD and BMH-21 is surprising. What drives the accumulation of PiwiL1 in the nucleolus then if in the meioc mutant there is less transcription anyway?

Despite the weaknesses outlined, overall I find this paper to be solid and valuable, providing evidence for a consistent link between PIWI systems and ribosomal biogenesis. Their results are likely to be of interest to people in the community, and provide tools for further elucidating the reasons for this link.

The amount of cytoplasmic rRNA in piwi+/- was increased by 26% on average (figure 5A+5B), the amount of ChiP-qPCR of H3K9 was decreased by about 26% (Figure 6F), and ChiP-qPCR of Piwil1 was decreased by 35% (Figure 6G), so we don't think there is a big discrepancy. On the other hand, the amount of ChiP-qPCR of H3K9 in meioc^mo/mo was increased by about 130% (Figure 6F), while ChiP-qPCR of Piwil1 was increased by 50%, so there may be a mechanism for H3K9 regulation of Meioc that is not mediated by Piwil1. As for what drives the accumulation of Piwil1 in the nucleolus, although we have found that Piwil1 has affinity for rRNA (Fig. 6A), we do not know what recruits it. Significant increases in the 18-35nt small RNA of 18S, 28S rRNA and R2 were not detected in meioc^mo/mo testes enriched for 1-8 cell spermatogonia, compared with meioc^+/mo testes. The nucleolar localization of Piwil1 has revealed in this study, which will be a new topic for future research.

Reviewer #2 (Public review):

Summary:

In this study, the authors report that Meioc is required to upregulate rRNA transcription and promote differentiation of spermatogonial stem cells in zebrafish. The authors show that upregulated protein synthesis is required to support spermatogonial stem cells' differentiation into multi-celled cysts of spermatogonia. Coiled coil protein Meioc is required for this upregulated protein synthesis and for increasing rRNA transcription, such that the Meioc knockout accumulates 1-2 cell spermatogonia and fails to produce cysts with more than 8 spermatogonia. The Meioc knockout exhibits continued transcriptional repression of rDNA. Meioc interacts with and sequesters Piwil1 to the cytoplasm. Loss of Meioc increases Piwil1 localization to the nucleolus, where Piwil1 interacts with transcriptional silencers that repress rRNA transcription.

Strengths:

This is a fundamental study that expands our understanding of how ribosome biogenesis contributes to differentiation and demonstrates that zebrafish Meioc plays a role in this process during spermatogenesis. This work also expands our evolutionary understanding of Meioc and Ythdc2's molecular roles in germline differentiation. In mouse, the Meioc knockout phenocopies the Ythdc2 knockout, and studies thus far have indicated that Meioc and Ythdc2 act together to regulate germline differentiation. Here, in zebrafish, Meioc has acquired a Ythdc2-independent function. This study also identifies a new role for Piwil1 in directing transcriptional silencing of rDNA.

Weaknesses:

There are limited details on the stem cell-enriched hyperplastic testes used as a tool for mass spec experiments, and additional information is needed to fully evaluate the mass spec results. What mutation do these testes carry? Does this protein interact with Meioc in the wildtype testes? How could this mutation affect the results from the Meioc immunoprecipitation?

Stem cell-enriched hyperplastic testes came from wild-type adult sox17::GFP transgenic zebrafish. Sperm were found in these hyperplastic testes, and when stem cells were transplanted, they self-renewed and differentiated into sperm. It is not known if the hyperplasias develop due to a genetic variant in the line. We will add the following comment.

“The stem cell-enriched hyperplastic testes, which are occasionally found in adult wildtype zebrafish, contain cells at all stages of spermatogenesis. Hyperplasia-derived SSCs self-renewed and differentiated in the same manner as SSCs of normal testes in transplants of aggregates mixed with normal testicular cells.”

Reviewer #3 (Public review):

Summary:

The paper describes the molecular pathway to regulate germ cell differentiation in zebrafish through ribosomal RNA biogenesis. Meioc sequesters Piwil1, a Piwi homolog, which suppresses the transcription of the 45S pre-rDNA by the formation of heterochromatin, to the perinuclear bodies. The key results are solid and useful to researchers in the field of germ cell/meiosis as well as RNA biosynthesis and chromatin.

Strengths:

The authors nicely provided the molecular evidence on the antagonism of Meioc to Piwil1 in the rRNA synthesis, which supported by the genetic evidence that the inability of the meioc mutant to enter meiosis is suppressed by the piwil1 heterozygosity.

Weaknesses:

(1) Although the paper provides very convincing evidence for the authors' claim, the scientific contents are poorly written and incorrectly described. As a result, it is hard to read the text. Checking by scientific experts would be highly recommended. For example, on line 38, "the global translation activity is generally [inhibited]", is incorrect and, rather, a sentence like "the activity is lowered relative to other cells" is more appropriate here. See minor points for more examples.

Thank you for pointing that out. I will correct the parts pointed out.

(2) In some figures, it is hard for readers outside of zebrafish meiosis to evaluate the results without more explanation and drawing.

We will refine Figure 1A and add schema of spermatogonia culture system in a supplemental figure. 

(3) Figure 1E, F, cycloheximide experiments: Please mention the toxicity of the concentration of the drug in cell proliferation and viability.

When testicular tissue culture was performed at 0.1, 1, 10, 100, 250, and 500mM, abnormal strong OP-puro signals including nuclei were found in cells at 10mM or more. We will add the results in the Supplemental Material. In addition, at 1mM, growth was perturbed in fast-growing 32≤-cell cysts of spermatogonia, but not in 1-4-cell spermatogonia, as described in L122-125.

­eLife Assessment

The authors examine the role of Numb, a Notch inhibitor, in intestinal stem cell self-renewal in Drosophila during homeostasis and regeneration. This is an important study providing evidence of a Numb-mediated, back-up mechanism for intestinal stem cell maintenance. The strength of evidence is convincing as the authors present a carefully constructed series of experiments, robust genetics, large sample sizes, and appropriate statistical analyses.

Reviewer #1 (Public review):

Summary:

By way of background, the Jiang lab has previously shown that loss of the type II BMP receptor Punt (Put) from intestinal progenitors (ISCs and EBs) caused them to differentiate into EBs, with a concomitant loss of ISCs (Tian and Jiang, eLife 2014). The mechanism by which this occurs was activation of Notch in Put-deficient progenitors. How Notch was upregulated in Put-deficient ISCs was not established in this prior work. In the current study, the authors test whether a very low level of Dl was responsible. But co-depletion of Dl and Put led to a similar phenotype as depletion of Put alone. This result suggested that Dl was not the mechanism. They next investigate genetic interactions between BMP signaling and Numb, an inhibitor of Notch signaling. Prior work from Bardin, Schweisguth and other labs has shown that Numb is not required for ISC self-renewal. However the authors wanted to know whether loss of both the BMP signal transducer Mad and Numb would cause ISC loss. This result was observed for RNAi depletion from progenitors and for mad, numb double mutant clones. Of note, ISC loss was observed in 40% of mad, numb double mutant clones, whereas 60% of these clones had an ISC. They then employed a two-color tracing system called RGT to look at the outcome of ISC divisions (asymmetric (ISC/EB) or symmetric (ISC/ISC or EB/EB)). Control clones had 69%, 15% and 16%, respectively, whereas mad, numb double mutant clones had much lower ISC/ISC (11%) and much higher EB/EB (37%). They conclude that loss of Numb in moderate BMP loss of function mutants increased symmetric differentiation which lead caused ISC loss. They also reported that numb15 and numb4 clones had a moderate but significant increase in ISC-lacking clones compared to control clones, supporting the model that Numb plays a role in ISC maintenance. Finally, they investigated the relevance of these observation during regeneration. After bleomycin treatment, there was a significant increase in ISC-lacking clones and a significant decrease in clone size in numb4 and numb15 clones compared to control clones. Because bleomycin treatment has been shown to cause variation in BMP ligand production, the authors interpret the numb clone under bleomycin results as demonstrating an essential role of Numb in ISC maintenance during regeneration.

Strengths:

(i) Most data is quantified with statistical analysis<br /> (ii) Experiments have appropriate controls and large numbers of samples<br /> (iii) Results demonstrate an important role of Numb in maintaining ISC number during regeneration and a genetic interaction between Mad and Numb during homeostasis.

Weaknesses:

(i) No quantification for Fig. 1<br /> (ii) The premise is a bit unclear. Under homeostasis, strong loss of BMP (Put) leads to loss of ISCs, presumably regardless of Numb level (which was not tested). But moderate loss of BMP (Mad) does not show ISC loss unless Numb is also reduced. I am confused as to why numb does not play a role in Put mutants. Did the authors test whether concomitant loss of Put and Numb leads to even more ISC loss than Put-mutation alone.<br /> (iii) I think that the use of the word "essential" is a bit strong here. Numb plays an important role but in either during homeostasis or regeneration, most numb clones or mad, numb double mutant clones still have ISCs. Therefore, I think that the authors should temper their language about the role of Numb in ISC maintenance.

Reviewer #2 (Public review):

Summary:

This work assesses the genetic interaction between the Bmp signaling pathway and the factor Numb, which can inhibit Notch signalling. It follows up on the previous studies of the group (Tian, Elife, 2014; Tian, PNAS, 2014) regarding BMP signaling in controlling stem cell fate decision as well as on the work of another group (Sallé, EMBO, 2017) that investigated the function of Numb on enteroendocrine fate in the midgut. This is an important study providing evidence of a Numb-mediated back up mechanism for stem cell maintenance.

Strengths:

(1) Experiments are consistent with these previous publications while also extending our understanding of how Numb functions in the ISC.<br /> (2) Provides an interesting model of a "back up" protection mechanism for ISC maintenance.

Weaknesses:<br /> (1) Aspects of the experiments could be better controlled or annotated:<br /> (a) As they "randomly chose" the regions analyzed, it would be better to have all from a defined region (R4 or R2, for example) or to at least note the region as there are important regional differences for some aspects of midgut biology.<br /> (b) It is not clear to me why MARCM clones were induced and then flies grown at 18{degree sign}C? It would help to explain why they used this unconventional protocol.

(2) There are technical limitations with trying to conclude from double-knockdown experiments in the ISC lineage, such as those in Figure 1 where Dl and put are both being knocked down: depending on how fast both proteins are depleted, it may be that only one of them (put, for example) is inactivated and affects the fate decision prior to the other one (Dl) being depleted. Therefore, it is difficult to definitively conclude that the decision is independent of Dl ligand.

(3) Additional quantification of many phenotypes would be desired.<br /> (a) It would be useful to see esg-GFP cells/total cells and not just field as the density might change (2E for example).<br /> (b) Similarly, for 2F and 2G, it would be nice to see the % of ISC/ total cell and EB/total cell and not only per esgGFP+ cell.<br /> (c) Fig1: There is no quantification - specifically it would be interesting to know how many esg+ are su(H)lacZ positive in Put- Dl- condition compared to WT or Put- alone. What is the n?<br /> (d) Fig2: Pros + cells are not seen in the image? Are they all DllacZ+?<br /> (e) Fig3: it would be nice to have the size clone quantification instead of the distribution between groups of 2 cell 3 cells 4 cell clones.<br /> (f) How many times were experiments performed?

(4) The authors do not comment on the reduction of clone size in DSS treatment in Figure 6K. How do they interpret this? Does it conflict with their model of Bleo vs DSS?

(5) There is probably a mistake on sentence line 314 -316 "Indeed, previous studies indicate that endogenous Numb was not undetectable by Numb antibodies that could detect Numb expression in the nervous system".

Reviewer #3 (Public review):

Summary:

The authors provide an in-depth analysis of the function of Numb in adult Drosophila midgut. Based on RNAi combinations and double mutant clonal analyses, they propose that Numb has a function in inhibiting Notch pathway to maintain intestinal stem cells, and is a backup mechanism with BMP pathway in maintaining midgut stem cell mediated homeostasis.

Strengths:

Overall, this is a carefully constructed series of experiments, and the results and statistical analyses provides believable evidence that Numb has a role, albeit weak compared to other pathways, in sustaining ISC and in promoting regeneration especially after damage by bleomycin, which may damage enterocytes and therefore disrupt BMP pathway more. The results overall support their claim.

The data are highly coherent, and support a genetic function of Numb, in collaborating with BMP signaling, to maintain the number and proliferative function of ISCs in adult midguts. The authors used appropriate and sophisticated genetic tools of double RNAi, mutant clonal analysis and dual marker stem cell tracing approaches to ensure the results are reproducible and consistent. The statistical analyses provide confidence that the phenotypic changes are reliable albeit weaker than many other mutants previously studied.

Weaknesses:<br /> In the absence of Numb itself, the midgut has a weak reduction of ISC number (Fig. 3 and 5), as well as weak albeit not statistically significant reduction of ISC clone size/proliferation. I think the authors published similar experiments with BMP pathway mutants. The mad1-2 allele used here as stated below may not be very representative of other BMP pathway mutants. Therefore, it could be beneficial to compare the number of ISC number and clone sizes between other BMP experiments to provide the readers with a clearer picture of how these two pathways individually contribute (stronger/weaker effects) to the ISC number and gut homeostasis.

The main weakness of this manuscript is the analysis of the BMP pathway components, especially the mad1-2 allele. The mad RNAi and mad1-2 alleles (P insertion) are supposed to be weak alleles and that might be suitable for genetic enhancement assays here together with numb RNAi. However, the mad1-2 allele, and sometimes the mad RNAi, showed weakly increased ISC clone size. This is kind of counter-intuitive that they should have a similar ISC loss and ISC clone size reduction.

A much stronger phenotype was observed when numb mutants were subject to treatment of tissue damaging agents Bleomycin, which causes damage in different ways than DSS. Bleomycin as previously shown to be causing mainly enterocyte damage, and therefore disrupt BMP signaling from ECs more likely. Therefore, this treatment together with loss of numb led to a highly significant reduction of ISC in clones and reduction of clone size/proliferation. One improvement is that it is not clear whether the authors discussed the nature of the two numb mutant alleles used in this study and the comparison to the strength of the RNAi allele. Because the phenotypes are weak and more variable, the use of specific reagents is important.

Furthermore, the use of possible activating alleles of either or both pathways to test genetic enhancement or synergistic activation will provide strong support for the claims.

Author response:

Reviewer #1 (Public review):

Summary:

By way of background, the Jiang lab has previously shown that loss of the type II BMP receptor Punt (Put) from intestinal progenitors (ISCs and EBs) caused them to differentiate into EBs, with a concomitant loss of ISCs (Tian and Jiang, eLife 2014). The mechanism by which this occurs was activation of Notch in Put-deficient progenitors. How Notch was upregulated in Put-deficient ISCs was not established in this prior work. In the current study, the authors test whether a very low level of Dl was responsible. But co-depletion of Dl and Put led to a similar phenotype as depletion of Put alone. This result suggested that Dl was not the mechanism. They next investigate genetic interactions between BMP signaling and Numb, an inhibitor of Notch signaling. Prior work from Bardin, Schweisguth and other labs has shown that Numb is not required for ISC self-renewal. However the authors wanted to know whether loss of both the BMP signal transducer Mad and Numb would cause ISC loss. This result was observed for RNAi depletion from progenitors and for mad, numb double mutant clones. Of note, ISC loss was observed in 40% of mad, numb double mutant clones, whereas 60% of these clones had an ISC. They then employed a two-color tracing system called RGT to look at the outcome of ISC divisions (asymmetric (ISC/EB) or symmetric (ISC/ISC or EB/EB)). Control clones had 69%, 15% and 16%, respectively, whereas mad, numb double mutant clones had much lower ISC/ISC (11%) and much higher EB/EB (37%). They conclude that loss of Numb in moderate BMP loss of function mutants increased symmetric differentiation which lead caused ISC loss. They also reported that Numb¹⁵ and numb⁴ clones had a moderate but significant increase in ISC-lacking clones compared to control clones, supporting the model that Numb plays a role in ISC maintenance. Finally, they investigated the relevance of these observation during regeneration. After bleomycin treatment, there was a significant increase in ISC-lacking clones and a significant decrease in clone size in numb⁴ and Numb¹⁵ clones compared to control clones. Because bleomycin treatment has been shown to cause variation in BMP ligand production, the authors interpret the numb clone under bleomycin results as demonstrating an essential role of Numb in ISC maintenance during regeneration.

Strengths:

(i) Most data is quantified with statistical analysis

(ii) Experiments have appropriate controls and large numbers of samples

(iii) Results demonstrate an important role of Numb in maintaining ISC number during regeneration and a genetic interaction between Mad and Numb during homeostasis.

Weaknesses:

(i) No quantification for Fig. 1

Thank you for your suggestion. Quantification of Fig.1 will be added.  

(ii) The premise is a bit unclear. Under homeostasis, strong loss of BMP (Put) leads to loss of ISCs, presumably regardless of Numb level (which was not tested). But moderate loss of BMP (Mad) does not show ISC loss unless Numb is also reduced. I am confused as to why numb does not play a role in Put mutants. Did the authors test whether concomitant loss of Put and Numb leads to even more ISC loss than Put-mutation alone.

Thank you for your comment. We have tested the genetic interaction between punt and numb using punt RNAi and numb RNAi driven by esg^ts. According to the results in this study and our previously published data, punt mutant clone or esg^ts> punt RNAi could induce a rapid loss of ISC (whin 8 days). We did not observe further enhancement of stem cell loss phenotype caused punt RNAi by numb RNAi.

(iii) I think that the use of the word "essential" is a bit strong here. Numb plays an important role but in either during homeostasis or regeneration, most numb clones or mad, numb double mutant clones still have ISCs. Therefore, I think that the authors should temper their language about the role of Numb in ISC maintenance.

Thank you. We will revise the language.

Reviewer #2 (Public review):

Summary:

This work assesses the genetic interaction between the Bmp signaling pathway and the factor Numb, which can inhibit Notch signalling. It follows up on the previous studies of the group (Tian, Elife, 2014; Tian, PNAS, 2014) regarding BMP signaling in controlling stem cell fate decision as well as on the work of another group (Sallé, EMBO, 2017) that investigated the function of Numb on enteroendocrine fate in the midgut. This is an important study providing evidence of a Numb-mediated back up mechanism for stem cell maintenance.

Strengths:

(1) Experiments are consistent with these previous publications while also extending our understanding of how Numb functions in the ISC.

(2) Provides an interesting model of a "back up" protection mechanism for ISC maintenance.

Weaknesses:

(1) Aspects of the experiments could be better controlled or annotated:

(a) As they "randomly chose" the regions analyzed, it would be better to have all from a defined region (R4 or R2, for example) or to at least note the region as there are important regional differences for some aspects of midgut biology.

Thank you. Since we mainly focus on region 4, we have added the clarification in the manuscript.

(b) It is not clear to me why MARCM clones were induced and then flies grown at 18{degree sign}C? It would help to explain why they used this unconventional protocol.

To avoid spontaneous clone, we kept the flies under 18°C.

(2) There are technical limitations with trying to conclude from double-knockdown experiments in the ISC lineage, such as those in Figure 1 where Dl and put are both being knocked down: depending on how fast both proteins are depleted, it may be that only one of them (put, for example) is inactivated and affects the fate decision prior to the other one (Dl) being depleted. Therefore, it is difficult to definitively conclude that the decision is independent of Dl ligand.

In our hand, Dl-RNAi is very effective and exhibited loss of N pathway activity as determined by the N pathway reporter Su(H)-lacZ (Fig. 1D). Therefore, the ectopic Su(H)-lacZ expression in Punt Dl double RNAi (fig. 1E) is unlikely due to residual Dl expression. Nevertheless, we will change the statement “BMP signaling blocks ligand-independent N activity” to” Loss of BMP signaling results in ectopic N pathway activity even when Dl is depleted”

(3) Additional quantification of many phenotypes would be desired.

(a) It would be useful to see esg-GFP cells/total cells and not just field as the density might change (2E for example).

We focused on R4 region for quantification where the cell density did not exhibit apparent change in different experimental groups. In addition, we have examined many guts for quantification. It is unlikely that the difference in the esg+ cell number is caused by change in cell density.

(b) Similarly, for 2F and 2G, it would be nice to see the % of ISC/ total cell and EB/total cell and not only per esgGFP+ cell.

Unfortunately, we didn’t have the suggested quantification. However, we believe that quantification of the percentage of ISC or EB among all progenitor cells, as we did here, provides a faithful measurement of the self-renewal status of each experimental group.

(c) Fig1: There is no quantification - specifically it would be interesting to know how many esg+ are su(H)lacZ positive in Put- Dl- condition compared to WT or Put- alone. What is the n?

Quantification will be added.

(d) Fig2: Pros + cells are not seen in the image? Are they all DllacZ+?

Anti-Pros and anti-E(spl)mβ-CD2 were stained in the same channel (magenta).  Pros+ is nuclear dot-like staining, while CD2 outlined the cell membrane of EB cell.

(e) Fig3: it would be nice to have the size clone quantification instead of the distribution between groups of 2 cell 3 cells 4 cell clones.

Thank you for your suggestion. In this study, we have quantified the clone size of each clone and calculated the average size for each genotype. However, the frequency distribution analysis was chosen because it highlights the significance of the clone size differences among genotypes.

(f) How many times were experiments performed?

All experiments are performed 3 times.

(4) The authors do not comment on the reduction of clone size in DSS treatment in Figure 6K. How do they interpret this? Does it conflict with their model of Bleo vs DSS?

numb⁴ clone containing guts treated with DSS exhibited a slight reduction of clone size, evident by a higher percentage of 2-cell clones and lower percentage of > 8 cell clones. This reduction is less significant in guts containing numb¹⁵ clones. However, the percentage of Dl⁺-containing clones is similar between DSS and mock-treated guts. It is possible that ISC proliferation is lightly reduced due to numb⁴ mutation or the genetic background.

(5) There is probably a mistake on sentence line 314 -316 "Indeed, previous studies indicate that endogenous Numb was not undetectable by Numb antibodies that could detect Numb expression in the nervous system".

We will make a correction of the sentence.

Reviewer #3 (Public review):

Summary:

The authors provide an in-depth analysis of the function of Numb in adult Drosophila midgut. Based on RNAi combinations and double mutant clonal analyses, they propose that Numb has a function in inhibiting Notch pathway to maintain intestinal stem cells, and is a backup mechanism with BMP pathway in maintaining midgut stem cell mediated homeostasis.

Strengths:

Overall, this is a carefully constructed series of experiments, and the results and statistical analyses provides believable evidence that Numb has a role, albeit weak compared to other pathways, in sustaining ISC and in promoting regeneration especially after damage by bleomycin, which may damage enterocytes and therefore disrupt BMP pathway more. The results overall support their claim.

The data are highly coherent, and support a genetic function of Numb, in collaborating with BMP signaling, to maintain the number and proliferative function of ISCs in adult midguts. The authors used appropriate and sophisticated genetic tools of double RNAi, mutant clonal analysis and dual marker stem cell tracing approaches to ensure the results are reproducible and consistent. The statistical analyses provide confidence that the phenotypic changes are reliable albeit weaker than many other mutants previously studied.

Weaknesses:

In the absence of Numb itself, the midgut has a weak reduction of ISC number (Fig. 3 and 5), as well as weak albeit not statistically significant reduction of ISC clone size/proliferation. I think the authors published similar experiments with BMP pathway mutants. The mad^1-2 allele used here as stated below may not be very representative of other BMP pathway mutants. Therefore, it could be beneficial to compare the number of ISC number and clone sizes between other BMP experiments to provide the readers with a clearer picture of how these two pathways individually contribute (stronger/weaker effects) to the ISC number and gut homeostasis.

Thank you for your comment. We have tested other components of BMP pathway in our previously study (Tian et al., 2014). More complete loss of BMP signaling (for example, Put clones, Put RNAi, Tkv/Sax double mutant clones or double RNAi) resulted in ISC loss regardless of the status of numb, suggesting a more predominant role of BMP signaling in ISC self-renewal compared with Numb. We speculate that the weak stem cell loss phenotype associated with numb mutant clones in otherwise wild type background could be due to fluctuation of BMP signaling in homeostatic guts.

The main weakness of this manuscript is the analysis of the BMP pathway components, especially the mad^1-2 allele. The mad RNAi and mad^1-2 alleles (P insertion) are supposed to be weak alleles and that might be suitable for genetic enhancement assays here together with numb RNAi. However, the mad^1-2 allele, and sometimes the mad RNAi, showed weakly increased ISC clone size. This is kind of counter-intuitive that they should have a similar ISC loss and ISC clone size reduction.

We used mad^1-2 and mad RNAi here to test the genetic interaction with numb because our previous studies showed that partial loss of BMP signaling under these conditions did not cause stem cell loss, therefore, may provide a sensitized background to determine the role of Numb in ISC self-renewal. The increased proliferation of ISC/ clone size in associated with mad^1-2 and mad RNAi is due to the fact that the reduction of BMP signaling in either EC or EB will non-autonomously induce stem cell proliferation. However, in mad numb double mutant clones, there was a reduction in clone size, which correlated with loss of ISC.

A much stronger phenotype was observed when numb mutants were subject to treatment of tissue damaging agents Bleomycin, which causes damage in different ways than DSS. Bleomycin as previously shown to be causing mainly enterocyte damage,  and therefore disrupt BMP signaling from ECs more likely. Therefore, this treatment together with loss of numb led to a highly significant reduction of ISC in clones and reduction of clone size/proliferation. One improvement is that it is not clear whether the authors discussed the nature of the two numb mutant alleles used in this study and the comparison to the strength of the RNAi allele. Because the phenotypes are weak and more variable, the use of specific reagents is important.

Numb¹⁵ is a null allele, and the nature of numb⁴ has not been elucidated. According to Domingos, P.M. et al., numb¹⁵ induced a more severe phenotype than numb⁴ did. Consistently, we also found that more numb¹⁵ mutant clones were void of stem cell than numb⁴.

Furthermore, the use of possible activating alleles of either or both pathways to test genetic enhancement or synergistic activation will provide strong support for the claims.

Activation of BMP (Tkv^CA) also induced stem cell tumor (Tian et al., 2014), which is not suitable for synergistic activation experiment.

eLife Assessment

This is a useful study that seeks to address the role of the TET family of DNA demethylation enzymes in pancreatic beta cell senescence in the context of type 2 diabetes (T2DM). Although the concepts are novel and of interest, the study presents incomplete evidence to fully support its main conclusions.

Reviewer #1 (Public review):

Summary:

In this manuscript, the authors set out to determine how a DNA demethylation enzyme TET2 regulates beta cell senescence in the context of Type 2 Diabetes and aging. They analyze public RNA-seq data and found upregulation of TET2 coincident with downregulation of MOF and PTEN, genes involved in chromatin regulation and cell cycle. TET2 is upregulated during aging, high-fat diet feeding, high glucose on rat beta cell line INS1E, and in leptin receptor deficient (db/db) mice islets. This was not found for TET1 and TET3. TET2 global KO mice show improved glucose tolerance during aging, but not TET1 or TET3. The authors show improved beta cell identity genes in TET2 KO islets. They they performed DNA methyalation/hydroxymethylation analyses of TET2 KO transformed rat beta cell line INS1E followed by ChIP-seq of Histone H4K16 acetylation to find this mark relies on TET2 expression. Finally they demonstrate in the cell lines that overexpressing TET2 leads to loss of MOF and increased PTEN and p16, linking TET2 to a regulatory mechanism with these factors that may influence senescence.

Strengths:

The study uses a number of orthogonal approaches and evidence from cell lines and the genetic TET2 KO as well as primary islets. The concept is interesting and potentially useful to the field. Efforts were made to examine TET1 and TET3 paralogues to rule out their compensation.

Weaknesses:

The study has several major weaknesses that mean the data presented did not fully support the main conclusions. These include the following:

(1) From the beginning of the manuscript the authors first sentence does not seem to indicate which datasets were analysed, the rationale behind why public datasets were used and what the main conclusions are being drawn from the plots shown throughout Fig. 1. This section of the manuscript was very hard to follow, and lacked rationale and explanation as to what these data show.

(2) All of the metabolic phenotypic data come from global TET2 KO mice, where TET2 is lost from all cells. The authors need to use a beta cell-specific KO of TET2 to ensure that metabolic changes are not due to cross-talk with other tissues (e.g. liver, adipose, even effects on central control of metabolism). No insulin tolerance tests were done to ascertain phenotypes in other metabolic tissues. This was a major weakness of the study. The authors should also provide clear validation of their global TET2 KO mice demonstrating a total lack of protein in islets and metabolic tissues.

(3) TET2 localization and expression pattern in islets was not clearly demonstrated and the data shown are not convincing from Fig 3 and Fig 4. In Fig 3e the staining for TET2 in green looks ubiquitous in acinar tissue (not nuclear) and not in the islet. In Fig 4d there is an increase in nuclear stain shown during aging, but no INS stain is used to show specificity to beta cells. Thus there is not sufficient data to support the expression pattern and localization of TET2 and specificity of the antibody.

(4) In Fig. 5: The effect sizes for the beta cell identity gene expression differences by qRT-PCR between WT and TET2 KO islets shown in Fig 5 are extremely modest so as to be questionable whether they are biologically meaningful. The same is true of the senescence markers quantified from isolated islets by qRT-PCR in Fig 5f. The immunostains for Pdx1 are hard to see and signal should be quantified. The SA-Bgal staining is quantified but no representative image is shown. The p16 immunostaining is not clear and should be quantified. Given that a lack of truly specific p16 antibodies in mouse immunostainings have been a major issue for the field, the authors would be advised to demonstrate specificity of the antibody if possible on mouse KO tissue, or to at least validate the predicted increase in p16 staining comparing young versus old pancreas as has been shown in other studies.

(5) Throughout the manuscript the figures colors are difficult to see and text difficult to read. Text in the p-values above the bars on most Figures is not legible (particularly Figs 4, 5, and 9). The legends simply do not contain sufficient information to interpret the data panels. This is true from Figures 1 through 9. P-value and specific statistical tests are missing from legends as well. For instance, in Fig 6c, what is being shown in LV-Ctrl vs LV-TET2 and why are these sample labels the same for two sets of images with two different outcomes of the staining? How many cells were quantified here?

(6) There is an over-reliance on cell lines throughout the manuscript. INS1E and BTC6 are not truly representative of mature adult mouse or rat beta cells, and hence the connections between H4K16ac/MOF/PTEN and TET2 must be assessed in primary mouse or rat islets to confirm these phenotypes.

(7) In the in vitro studies of senescence markers, it is not convincingly shown that the cells are actually senescent. Even though there changes found in expression of p16 and SA-Bgal in the cultures, the authors did not evaluate key senescence phenotypes such as the actual cell cycle arrest, SASP proteins or apoptosis resistance. Are the cells actually senescent or are these markers simply increasing? Hence much of the changes driven by TET2 overexpression in the in vitro cell lines could likely changes in p16 protein but not actually a senescence phenotype. BTC6, INS1E, and MIN6 are cell lines that are transformed, and while they can undergo some senescence-like changes in response to specific stressors like lipotoxicity, DNA damage, or oxidative stress, the authors did not evaluate these, only senescence genes/proteins in otherwise unstressed cells. Thus the claim that TET2 modifies senescence of beta cells remains unsubstantiated from the in vitro studies. It was not clear how any of these studies related to beta cell senescence in T2DM where there is metabolic and/or gluco-lipotoxic stress. Although it is claimed from Fig 9 that TET2 regulates PTEN/MOF axis to regulate beta cell function, no functional data (e.g. GSIS) are shown.

(8) There were issues and difficulties with the writing in the introduction and discussion in that they did not clearly or adequately describe, discuss or interpret the main conclusions and their significance. The work is not positioned within the current state of the field and it is very difficult to follow the rationales for the study and the advances in knowledge provided.

Reviewer #2 (Public review):

Summary:<br /> Epigenetic regulation is critical for maintaining cellular function, and its dysregulation contributes to senescence and disease. This manuscript investigates the role of TET2 in β cell aging, proposing that TET2-mediated PTEN DNA methylation promotes H4K16 acetylation (H4K16ac) through MOF, driving β cell senescence. Using TET2 inhibitors, RNA interference, lentiviral overexpression, and knockout mouse models, the authors aim to establish TET2 as a key player in β cell aging and a potential therapeutic target in type 2 diabetes mellitus (T2DM).<br /> However, significant limitations reduce the manuscript's impact. Figures are poorly presented, with illegible fonts and unquantified staining panels, while key analyses, such as β cell specificity and senescence inducers, are missing. The rationale for focusing on H4K16ac and MOF is unclear, and the authors fail to address whether β cell identity gene changes reflect altered gene expression or mass. Additionally, critical controls, such as low-fat diet cohorts, are absent, and the writing lacks clarity and coherence. Together, these weaknesses undermine the validity of the findings.

Main Comments<br /> Figures 1 and 2:<br /> The fonts in Figures 1 and 2 are barely visible and should be improved for readability. Additionally, do TET2 protein levels change in mouse and human β cells with aging? Is there evidence from regression analyses using single-cell RNA sequencing on human islets that TET2 expression correlates with age-associated gene signatures in β cells? Are these correlations specific to β cells, or do they extend to other islet cell types? It would also be informative to assess whether TET2 levels increase with senescence inducers such as DNA damage agents (e.g., bleomycin, doxorubicin) or reactive oxygen species (e.g., H₂O₂).<br /> Figure 3:<br /> Why do TET2 protein levels appear stronger in acinar cells? Additionally, the predominant cellular localization of TET2 seems to be cytoplasmic. Can the authors clarify or expand on this observation?<br /> Figure 4:<br /> The data on the impact of TET2 insufficiency in vivo is compelling. There are several quality control experiments to validate their model and main hypothesis (That T2t2 expression increases with aging in beta-cells). Here, authors have the right system to validate their initial Tet2 protein dynamics in the mouse, since they have a KO mouse model. Here, it would be useful to co-stain Tet2 with insulin and glucagon, to infer the dynamics of Tet2 in the two most abundant islet cell types.<br /> Figure 5:<br /> The upregulation of β-cell identity genes in the KO mouse model raises an important question: Is this effect due to an actual increase in gene expression or simply a higher proportion of β cells? Quantifying β-cell mass and performing gene expression analyses on FACS-sorted β cells would help address this. Additionally, the staining panels lack quantification. For instance, GLUT2 staining appears cytoplasmic when it should be membranous. The authors focus on cellular senescence, but does apoptosis increase in wild-type mice under a high-fat diet (HFD)? Including animals on a low-fat diet (LFD) for comparison would add valuable context.<br /> Figure 6:<br /> The data suggest an increase in cell numbers in TET2-overexpressing cells. Does this indicate an effect on β-cell proliferation? Quantification would provide clarity.<br /> Figure 8:<br /> The rationale for focusing on H4K16ac is insufficiently discussed. What is the mechanism linking TET2-induced changes to decreased H4K16ac levels? Including a more thorough explanation in the introduction and discussion would enhance the manuscript.<br /> Figure 9:<br /> The introduction lacks any discussion of H4K16ac or MOF. The discussion paragraph (lines 530-540) that elaborates on these points should instead be moved to the introduction to improve the manuscript's flow. Furthermore, the authors should cite their 2022 paper on H4K16ac as part of the rationale for focusing on this histone modification.

Minor Comments:<br /> The manuscript would benefit from language refinement. Examples include:<br /> Line 183: Replace "the blood included" with a more precise description.<br /> Line 315: "treated with RNA seq" should be rephrased to clarify methodology (e.g., "analyzed via RNA sequencing").<br /> Line 456: Replace "expression of H4K16ac" with "levels of H4K16ac."<br /> Line 496: The phrase "can solve scientific problems from multiple dimensions" sounds vague and overly broad; consider rephrasing to be more specific.

Reviewer #3 (Public review):

Summary:<br /> This study advances the field of β cell dysfunction by unveiling an epigenetic mechanism of β cell senescence. By connecting TET2-mediated DNA methylation to histone acetylation and cellular aging, it opens promising new avenues for therapeutic intervention. In particular, the authors aimed at identifying the mechanisms of pancreatic β cell senescence by epigenetic regulation. They conclude that increased TET2 expression in β cells is associated with ageing and metabolic dysfunction in type 2 diabetes by inducing β cell senescence. The authors further propose that TET2-mediated PTEN promoter methylation promotes β cell senescence by regulating H4K16ac. Last, the authors suggest that this could represent new molecular mechanism and therapeutic target against β cell senescence during type 2 diabetes.

Strengths:<br /> The major strengths of the study are the use of both biased and unbiased experimental tools to approach the topic. The authors also provide in vivo and in vitro mechanistic approaches to answer their questions. All of these approaches are valuable and provides robustness to their study. The authors provide solid evidence that TET2 is associated with ageing and that its absence improves glucose metabolism in ageing and β cell senescence. In addition, the mechanistic studies showing that TET2 regulates the PTEN/MOF/H4K16ac signaling pathway in β cell lines is convincing.

Weaknesses:<br /> Although the use of such a variety of tools is a strength, the outcome of each individual tool is somehow superficial. For instance, the authors focus on very specific targets emanating from their omics studies without a clear or logical justification. In addition, the metabolic studies are inaccurate and the authors do not follow an understandable and rational examination of the ageing versus their obesity cohorts. Last, the mechanistic studies using model cell lines are not validated in the available mouse models.

In my opinion, the evidence that TET2 regulates β cell senescence during obesity is not very strong. This is because the effect of deletion of TET2 in senescence markers is the same under 24weeks of age or 52 weeks of age (16 weeks HFD). Both ageing and HFD promoted the same extent of reduction of senescent markers and increase in β cell markers in the absence of TET2. There is no comparison between young glucose tolerant mice and old glucose intolerant mice. There is also no direct comparison of aged matched lean or obese mice. It may seem as if the mechanism by which TET2 regulates senescence in β cells is independent of the diabetic status but it is more related to ageing. Given that there is evidence that TET2 expression in β cells coordinates inflammatory responses in autoimmune diabetes, it would have been interested to check whether this is also the case for T2DM. Also, considering that expression of TET2 in Figure 3 does not seem to be in β cells in db/db mice but rather in the exocrine pancreas. In addition, senescent marker p16 in Figure 5 in the presence of TET2, seems to be localized in alpha cells or immune cells but not in β cells.<br /> Regarding the mechanistic studies, the authors convincingly show that TET2 regulates the PTEN/MOF/H4K16ac signaling pathway in β cell lines and that this is important for β cell senescence. However, there is no validation of whether this holds true in aged, or prediabetic, mice. Given the availability of mice and model samples, this should be possible and meaningful. Last, in the genome-wide bisulfite sequencing (Figure 7), it seems that the authors are cherry picking for PTEN and in the RNAseq, the same applies for MOF. Thus, although the mechanism seems valid, the lack of in vivo validation, and a proper rational for the selected targets in the omics studies, renders the mechanistic studies rather correlative.

In sum, I believe that the study in its current version, unfortunately, does not bear the conceptual advance or the robustness that is required to offer a strong impact on the field. The methods, on the other hand, mainly the omics analyses provided here, could be of potential benefit for the field of epigenetics in β cell biology. However, in the benefit of the current study, the relevance of this data could be more rigorously assessed experimentally. I believe that the study has the potential to provide the required impact, should the authors work on it further to provide more solid functional and mechanistic validation.

eLife Assessment

Building on earlier evidence that Topoisomerase 2 depletion can extend yeast lifespan, the authors present convincing data demonstrating that reduced Top2 levels confer significant longevity benefits in C. elegans and mice. They show that these benefits encompass not only extended survival but also improved healthspan and mitigation of aging phenotypes, supported by robust transcriptomic, epigenetic, and histological analyses. While the exact mechanistic underpinnings-such as the potential connection to classical aging pathways and the selective down-regulation of genes with active promoters-remain to be fully elucidated, the breadth of these important findings strongly indicates a conserved role for Top2 in aging regulation.

Reviewer #1 (Public review):

Summary

This very interesting article describes extensive work by the authors connecting topoisomerase 2 to aging across multiple model systems. The authors began by analyzing published transcriptomes for genes previously reported to be connected to increased lifespan in S. cerevisiae, focusing on genes whose downregulation is highly correlated with increased lifespan. One of these candidates was topoisomerase 2, which had previously been shown to be connected to lifespan in yeast.

The authors here show that reduction in topoisomerase 2 levels can significantly extend lifespan in yeast (by damp), C. elegans (by RNAi), and mice (by CRISPR CasRx).

Next, the authors demonstrate in both C. elegans and mice that in addition to increased survival times, animals with decreased top2 levels also show increased healthspan, as measured by using rates of body bends and of pharyngeal pumping in C. elegans, and using the Frailty Index (FI) for mice. Further, they report that lowered top2 levels result in less aged tissue phenotypes in multiple tissues in mice as assayed by histology, and positively affect multiple hallmarks of aging in both mouse tissues and human IMR-90 cells.

The authors go on to perform thorough transcriptomic analysis of reduced top2 animals in both C. elegans and mice. Many interesting GO terms are highly overrepresented among both up- and down-regulated transcripts from these experiments, and the authors conclude that in the case of mice there is significant tissue specific biology based on differing results in the tissues they examined.

Given the previously known biological roles of top2, the authors looked at changes in the epigenetic landscape of reduced top2 organisms as evidenced by changes in H3K4me3, H3K9me3 and H3K27me3. Overall, the authors conclude from these data that reduction of top2 "differentially down-regulates genes with active promoters/high abundance".

Overall this well-written manuscript summarizes a great deal of new data that will be of great interest to aging researchers broadly.

The figures and tables are all very clear and well-designed, and all add greatly to the manuscript overall including the use of color which is in all cases justified.

Reviewer #2 (Public review):

Summary:

Previous studies have shown that Topoisomerase 2 (Top2) depletion in yeast can extend the lifespan of the organism, but no known mechanisms have been reported. In the current study, Zhu et al. reported that reduction of Top2 enhances longevity and mitigates aging phenotypes across multiple model organisms, including not yeast, but also C. elegans and mouse. The evidence of reduction of aging phenotypes is particularly strong, which include markers of cellular senescence, nutrient sensing, epigenetic markers, and lysosome biogenesis. They propose that Top2b reduction confers longevity through a conserved mechanism, and may be used a novel therapeutic strategy for countering aging. Overall, their findings should be of broad interest to the fields of Aging and Topoisomerase research. The technical quality of the work is in general solid but can be improved.

Strengths:

Top2 is an essential type II topoisomerase that resolves DNA topological stress generated during transcription, replication, chromosome segregation, and other DNA metabolic processes by introducing transient double-strand breaks (DSBs), passing the DNA strands, and re-ligating them. Top2 is a target for anticancer therapies, but its connection to aging and longevity remains largely unexplored. The authors' findings are notable, as Top2 has been deemed indispensable for normal development. Yet, this study suggests that its reduction confers benefits in the context of healthy aging. Their results convincingly show extended lifespan and improvements in physiological and molecular aging phenotypes, supported by behavioral assays and tissue morphology analyses.

Weaknesses:

Despite these strengths, the manuscript is weak on the proposed "conserved mechanism". The authors proposed in Discussion that Top2/Top2b knockdown may be similar to the classical insulin/IGF1 and the mTORC pathway, but did not provide any genetic evidence to support this.

The authors also mentioned in the Discussion that the potential mechanism could be selective down-regulation of transcription of genes of active promoter and high abundance, such as ribosomal genes, which could be relevant to yeast aging. But there is no evidence in worms or mouse that Top2b directly binds and promotes transcription of certain high abundance genes critical for aging.

I understand that this mechanism issue may be difficult to address, and I do not expect that the authors can fully address this issue. However, as both yeast and worms have been widely-used in aging studies with many tools available, I suggest that the authors can improve their studies by performing the following experiments.

eLife Assessment

This study offers a valuable treatment of how the population of excitatory and inhibitory neurons integrates principles of energy efficiency in their coding strategies. The convincing analysis provides a comprehensive characterisation of the model, highlighting the structured connectivity between excitatory and inhibitory neurons. The role of the many free parameters are discussed and studied in depth.

Reviewer #1 (Public review):

Koren et al. derive and analyse a spiking network model optimised to represent external signals using the minimum number of spikes. Unlike most prior work using a similar setup, the network includes separate populations of excitatory and inhibitory neurons. The authors show that the optimised connectivity has a like-to-like structure, which leads to the experimentally observed phenomenon of feature competition. The authors also examine how various (hyper)parameters-such as adaptation timescale, the excitatory-to-inhibitory cell ratio, regularization strength, and background current-affect the model. These findings add biological realism to a specific implementation of efficient coding. They show that efficient coding explains, or at least is consistent with, multiple experimentally observed properties of excitatory and inhibitory neurons.

As discussed in the first round of reviews, the model's ability to replicate biological observations such as the 4:1 ratio of excitatory vs. inhibitory neurons hinges on somewhat arbitrary hyperparameter choices. Although this may limit the model's explanatory power, the authors have made significant efforts to explore how these parameters influence their model. It is an empirical question whether the uncovered relationships between, e.g., metabolic cost and the fraction of excitatory neurons are biologically relevant.

The revised manuscript is also more transparent about the model's limitations, such as the lack of excitatory-excitatory connectivity. Further improvements could come from explicitly acknowledging additional discrepancies with biological data, such as the widely reported weak stimulus tuning of inhibitory neurons in the primary sensory cortex of untrained animals.

Reviewer #2 (Public review):

Summary:

In this work, the authors present a biologically plausible, efficient E-I spiking network model and study various aspects of the model and its relation to experimental observations. This includes a derivation of the network into two (E-I) populations, the study of single-neuron perturbations and lateral-inhibition, the study of the effects of adaptation and metabolic cost, and considerations of optimal parameters. From this, they conclude that their work puts forth a plausible implementation of efficient coding that matches several experimental findings, including feature-specific inhibition, tight instantaneous balance, a 4 to 1 ratio of excitatory to inhibitory neurons, and a 3 to 1 ratio of I-I to E-I connectivity strength.

Strengths:

While many network implementations of efficient coding have been developed, such normative models are often abstract and lacking sufficient detail to compare directly to experiments. The intention of this work to produce a more plausible and efficient spiking model and compare it with experimental data is important and necessary in order to test these models. In rigorously deriving the model with real physical units, this work maps efficient spiking networks onto other more classical biophysical spiking neuron models. It also attempts to compare the model to recent single-neuron perturbation experiments, as well as some long-standing puzzles about neural circuits, such as the presence of separate excitatory and inhibitory neurons, the ratio of excitatory to inhibitory neurons, and E/I balance. One of the primary goals of this paper, to determine if these are merely biological constraints or come from some normative efficient coding objective, is also important. Lastly, though several of the observations have been reported and studied before, this work arguably studies them in more depth, which could be useful for comparing more directly to experiments.

Weaknesses:

This work is the latest among a line of research papers studying the properties of efficient spiking networks. Many of the characteristics and findings here have been discussed before, thereby limiting the new insights that this work can provide. Thus, the conclusions of this work should be considered and understood in the context of those previous works, as the authors state. Furthermore, the number of assumptions and free parameters in the model, though necessary to bring the model closer to biophysical reality, make it more difficult to understand and to draw clear conclusions from. As the authors state, many of the optimality claims depend on these free parameters, such as the dimensionality of the input signal (M=3), the relative weighting of encoding error and metabolic cost, and several others. This raises the possibility that it is not the case that the set of biophysical properties measured in the brain are accounted for by efficient coding, but rather that theories of efficient coding are flexible enough to be consistent with this regime. With this in mind, some of the conclusions made in the text may be overstated and should be considered in this light.

Conclusions, Impact, and additional context:

Notions of optimality are important for normative theories, but they are often studied in simple models with as few free parameters as possible. Biophysically detailed and mechanistic models, on the other hand, will often have many free parameters by their very nature, thereby muddying the connection to optimality. This tradeoff is an important concern in neuroscientific models. Previous efficient spiking models have often been criticized for their lack of biophysically-plausible characteristics, such as large synaptic weights, dense connectivity, and instantaneous communication. This work is an important contribution in showing that such networks can be modified to be much closer to biophysical reality without losing their essential properties. Though the model presented does suffer from complexity issues which raise questions about its connections to "optimal" efficient coding, the extensive study of various parameter dependencies offers a good characterization of the model and puts its conclusions in context.

Reviewer #3 (Public review):

Summary:

In their paper the authors tackle three things at once in a theoretical model: how can spiking neural networks perform efficient coding, how can such networks limit the energy use at the same time, and how can this be done in a more biologically realistic way than previous work.

They start by working from a long-running theory on how networks operating in a precisely balanced state can perform efficient coding. First, they assume split networks of excitatory (E) and inhibitory (I) neurons. The E neurons have the task to represent some lower dimensional input signal, and the I neurons have the task to represent the signal represented by the E neurons. Additionally, the E and I populations should minimize an energy cost represented by the sum of all spikes. All this results in two loss functions for the E and I populations, and the networks are then derived by assuming E and I neurons should only spike if this improves their respective loss. This results in networks of spiking neurons that live in a balanced state, and can accurately represent the network inputs.

They then investigate in depth different aspects of the resulting networks, such as responses to perturbations, the effect of following Dale's law, spiking statistics, the excitation (E)/inhibition (I) balance, optimal E/I cell ratios, and others. Overall, they expand on previous work by taking a more biological angle on the theory and show the networks can operate in a biologically realistic regime.

Strengths:

* The authors take a much more biological angle on the efficient spiking networks theory than previous work, which is an essential contribution to the field<br /> * They make a very extensive investigation of many aspects of the network in this context, and do so thoroughly<br /> * They put sensible constraints on their networks, while still maintaining the good properties these networks should have

Weaknesses:

* One of the core goals of the paper is to make a more biophysically realistic network than previous work using similar optimization principles. One of the important things they consider is a split into E and I neurons. While this works fine, and they consider the coding consequences of this, it is not clear from an optimization perspective why the split into E and I neurons and following Dale's law would be beneficial. This would be out of scope for the current paper however.<br /> * The theoretical advances in the paper are not all novel by themselves, as most of them (in particular the split into E and I neurons and the use of biophysical constants) had been achieved in previous models. However, the authors discuss these links thoroughly and do more in-depth follow-up experiments with the resulting model.

Assessment and context:

Overall, although much of the underlying theory is not necessarily new, the work provides an important addition to the field. The authors succeeded well in their goal of making the networks more biologically realistic, and incorporate aspects of energy efficiency. For computational neuroscientists this paper is a good example of how to build models that link well to experimental knowledge and constraints, while still being computationally and mathematically tractable. For experimental readers the model provides a clearer link of efficient coding spiking networks to known experimental constraints and provides a few predictions.

Author response:

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

eLife Assessment

This study offers a useful treatment of how the population of excitatory and inhibitory neurons integrates principles of energy efficiency in their coding strategies. The analysis provides a comprehensive characterisation of the model, highlighting the structured connectivity between excitatory and inhibitory neurons. However, the manuscript provides an incomplete motivation for parameter choices. Furthermore, the work is insufficiently contextualized within the literature, and some of the findings appear overlapping and incremental given previous work.

We are genuinely grateful to the Editors and Reviewers for taking time to provide extremely valuable suggestions and comments, which will help us to substantially improve our paper. We decided to do our very best to implement all suggestions, as detailed in the point-by-point rebuttal letter below. We feel that our paper has improved considerably as a result. 

Public Reviews:

Reviewer #1 (Public Review): 

Summary: Koren et al. derive and analyse a spiking network model optimised to represent external signals using the minimum number of spikes. Unlike most prior work using a similar setup, the network includes separate populations of excitatory and inhibitory neurons. The authors show that the optimised connectivity has a like-to-like structure, leading to the experimentally observed phenomenon of feature competition. They also characterise the impact of various (hyper)parameters, such as adaptation timescale, ratio of excitatory to inhibitory cells, regularisation strength, and background current. These results add useful biological realism to a particular model of efficient coding. However, not all claims seem fully supported by the evidence. Specifically, several biological features, such as the ratio of excitatory to inhibitory neurons, which the authors claim to explain through efficient coding, might be contingent on arbitrary modelling choices. In addition, earlier work has already established the importance of structured connectivity for feature competition. A clearer presentation of modelling choices, limitations, and prior work could improve the manuscript.

Thanks for these insights and for this summary of our work.  

Major comments:

(1) Much is made of the 4:1 ratio between excitatory and inhibitory neurons, which the authors claim to explain through efficient coding. I see two issues with this conclusion: (i) The 4:1 ratio is specific to rodents; humans have an approximate 2:1 ratio (see Fang & Xia et al., Science 2022 and references therein); (ii) the optimal ratio in the model depends on a seemingly arbitrary choice of hyperparameters, particularly the weighting of encoding error versus metabolic cost. This second concern applies to several other results, including the strength of inhibitory versus excitatory synapses. While the model can, therefore, be made consistent with biological data, this requires auxiliary assumptions.

We now describe better the ratio of numbers of E and I neurons found in real data, as suggested. The first submission already contained an analysis of how the optimal ratio of E vs I neuron numbers depends in our model on the relative weighting of the loss of E and I neurons and on the relative weighting of the encoding error vs the metabolic cost in the loss function (see Fig. 7E). We revised the text on page 12 describing Fig. 7E. 

To allow readers to form easily a clear idea of how the weighting of the error vs the cost may influence the optimal network configuration, we now present how optimal parameters depend on the weighting in a systematic way, by always including this type of analysis when studying all other model parameters (time constants of single E and I neurons, noise intensity, metabolic constant, ratio of mean I-I to E-I connectivity). These results are shown on the Supplementary Fig. S4 A-D and H, and we comment briefly on each of them in Results sections (pages 9, 10, 11 and 12) that analyze each of these parameters.  

Following this Reviewer’s comment, we now included a joint analysis of network performance relative to the ratio of E-I neuron numbers and the ratio of mean I-I to E-I connectivity (Fig. 7J). We found a positive correlation between optima values of these two ratios. This implies that a lower ratio of E-I neuron numbers, such as a 2:1 ratio in human cortex mentioned by the reviewer, predicts lower optimal ratio of I-I to E-I connectivity and thus weaker inhibition in the network. We made sure that this finding is suitably described in revision (page 13).

(2) A growing body of evidence supports the importance of structured E-I and I-E connectivity for feature selectivity and response to perturbations. For example, this is a major conclusion from the Oldenburg paper (reference 62 in the manuscript), which includes extensive modelling work. Similar conclusions can be found in work from Znamenskiy and colleagues (experiments and spiking network model; bioRxiv 2018, Neuron 2023 (ref. 82)), Sadeh & Clopath (rate network; eLife, 2020), and Mackwood et al. (rate network with plasticity; eLife, 2021). The current manuscript adds to this evidence by showing that (a particular implementation of) efficient coding in spiking networks leads to structured connectivity. The fact that this structured connectivity then explains perturbation responses is, in the light of earlier findings, not new.

We agree that the main contribution of our manuscript in this respect is to show how efficient coding in spiking networks can lead to structured connectivity implementing lateral inhibition similar to that proposed in the recent studies mentioned by the Reviewer. We apologize if this was not clear enough in the previous version. We streamlined the presentation to make it clearer in revision.  We nevertheless think it useful to report the effects of perturbations within this network because these results give information about how lateral inhibition works in our network. Thus, we kept presenting it in the revised version, although we de-emphasized and simplified its presentation. We now give more emphasis to the novelty of the derivation of this connectivity rule from the principles of efficient coding (pages 4 and 6). We also describe better (page 8) what the specific results of our simulated perturbation experiments add to the existing literature.

(3) The model's limitations are hard to discern, being relegated to the manuscript's last and rather equivocal paragraph. For instance, the lack of recurrent excitation, crucial in neural dynamics and computation, likely influences the results: neuronal time constants must be as large as the target readout (Figure 4), presumably because the network cannot integrate the signal without recurrent excitation. However, this and other results are not presented in tandem with relevant caveats.

We improved the Limitations paragraph in Discussion, and also anticipated caveats in tandem with results when needed, as suggested. 

We now mention the assumption of equal time constants between the targets and readouts in the Abstract. 

We now added the analysis of the network performance and dynamics as a function of the time constant of the target (t_x) to the Supplementary Fig S5 (C-E). These results are briefly discussed in text on page 13. The only measure sensitive to t_x is the encoding error of E neurons, with a minimum at t_x =9 ms, while I neurons and metabolic cost show no dependency. Firing rates, variability of spiking as well as the average and instantaneous balance show no dependency on t_x. We note that t_x = t, with t=1/l the time constant of the population readout (Eq. 9), is an assumption we use when we derive the model from the efficiency objective (Eq. 18 to 23). In our new and preliminary work (Koren, Emanuel, Panzeri, Biorxiv 2024), we derived a more general class of models where this assumption is relaxed, which gives a network with E-E connectivity that adapts to the time constant of the stimulus. Thus, the reviewer is correct in the intuition that the network requires E-E connectivity to better integrate target signals with a different time constant than the time constant of the membrane. We now better emphasize this limitation in Discussion (page 16).

(4) On repeated occasions, results from the model are referred to as predictions claimed to match the data. A prediction is a statement about what will happen in the future – but most of the “predictions” from the model are actually findings that broadly match earlier experimental results, making them “postdictions”.

This distinction is important: compared to postdictions, predictions are a much stronger test because they are falsifiable. This is especially relevant given (my impression) that key parameters of the model were tweaked to match the data.

We now comment on every result from the model as either matching earlier experimental results, or being a prediction for experiments. 

In Section “Assumptions and emergent properties of the efficient E-I network derived from first principles”, we report (page 4) that neural networks have connectivity structure that relates to tuning similarity of neurons (postdiction). 

In Section “Encoding performance and neural dynamics in an optimally efficient E-I network” we report (page 5) that in a network with optimal parameters, I neurons have higher firing rate than E neurons (postdiction), that single neurons show temporally correlated synaptic currents (postdiction) and that the distribution of firing rates across neurons is log-normal (postdiction). 

In Section “Competition across neurons with similar stimulus tuning emerging in efficient spiking networks” we report (page 6)  that the activity perturbation of E neurons induces lateral inhibition on other E neurons, and that the strength of lateral inhibition depends on tuning similarity (postdiction). We show that activity perturbation of E neurons induces lateral excitation in I neurons (prediction). We moreover show that the specific effects of the perturbation of neural activity rely on structured E-I-E connectivity (prediction for experiments, but similar result in Sadeh and Clopath, 2020). We show strong voltage correlations but weak spike-timing correlations in our network (prediction for experiments, but similar result in Boerlin et al. 2013). 

In Section “The effect of structured connectivity on coding efficiency and neural dynamics”, we report (page 7) that our model predicts a number of differences between networks with structured and unstructured (random) connectivity. In particular, structured networks differ from unstructured ones by showing better encoding performance, lower metabolic cost, weaker variance over time in the membrane potential of each neuron, lower firing rates and weaker average and instantaneous balance of synaptic currents.

In Section “Weak or no spike-triggered adaptation optimizes network efficiency”, we report (page 9) that our model predicts better encoding performance in networks with adaptation compared to facilitation. Our results suggest that adaptation should be stronger in E compared to I (PV+) neurons (postdiction). In the same section, we report (page 10) that our results suggest that the instantaneous balance is a better predictor of model efficiency than average balance (prediction).

In Section “Non-specific currents regulate network coding properties”, we report (page 10) that our model predicts that more than half of the distance between the resting potential and firing threshold is taken by external currents that are unrelated to feedforward processing (postdiction). We also report (page 11) that our model predicts that moderate levels of uncorrelated (additive) noise is beneficial for efficiency (prediction for experiments, but similar results in Chalk et al., 2016, Koren et al., 2017, Timcheck et al. 2022).

In Section “Optimal ratio of E-I neuron numbers and of mean I-I to E-I synaptic efficacy coincide with biophysical measurements”, we predict the optimal ratio of E to I neuron numbers to be 4:1 (postdiction) and the optimal ratio of mean I-I to E-I connectivity to be 3:1 (postdiction). Further, we report (page 13) that our results predict that a decrease in the ratio of E-I neuron numbers is accompanied with the decrease in the ratio of mean I-I to E-I connectivity. 

Finally, in Section “Dependence of efficient coding and neural dynamics on the stimulus statistics”, we report (page 13) that our model predicts that the efficiency of the network has almost no dependence on the time scale of the stimulus (prediction). 

Reviewer #2 (Public Review):

Summary:

In this work, the authors present a biologically plausible, efficient E-I spiking network model and study various aspects of the model and its relation to experimental observations. This includes a derivation of the network into two (E-I) populations, the study of single-neuron perturbations and lateral-inhibition, the study of the effects of adaptation and metabolic cost, and considerations of optimal parameters. From this, they conclude that their work puts forth a plausible implementation of efficient coding that matches several experimental findings, including feature-specific inhibition, tight instantaneous balance, a 4 to 1 ratio of excitatory to inhibitory neurons, and a 3 to 1 ratio of I-I to E-I connectivity strength. It thus argues that some of these observations may come as a direct consequence of efficient coding.

Strengths:

While many network implementations of efficient coding have been developed, such normative models are often abstract and lacking sufficient detail to compare directly to experiments. The intention of this work to produce a more plausible and efficient spiking model and compare it with experimental data is important and necessary in order to test these models.

In rigorously deriving the model with real physical units, this work maps efficient spiking networks onto other more classical biophysical spiking neuron models. It also attempts to compare the model to recent single-neuron perturbation experiments, as well as some longstanding puzzles about neural circuits, such as the presence of separate excitatory and inhibitory neurons, the ratio of excitatory to inhibitory neurons, and E/I balance. One of the primary goals of this paper, to determine if these are merely biological constraints or come from some normative efficient coding objective, is also important.

Though several of the observations have been reported and studied before (see below), this work arguably studies them in more depth, which could be useful for comparing more directly to experiments.

Thanks for these insights and for the kind words of appreciation of the strengths of our work.  

Weaknesses:

Though the text of the paper may suggest otherwise, many of the modeling choices and observations found in the paper have been introduced in previous work on efficient spiking models, thereby making this work somewhat repetitive and incremental at times. This includes the derivation of the network into separate excitatory and inhibitory populations, discussion of physical units, comparison of voltage versus spike-timing correlations, and instantaneous E/I balance, all of which can be found in one of the first efficient spiking network papers (Boerlin et al. 2013), as well as in subsequent papers. Metabolic cost and slow adaptation currents were also presented in a previous study (Gutierrez & Deneve 2019). Though it is perfectly fine and reasonable to build upon these previous studies, the language of the text gives them insufficient credit.

We indeed built our work on these important previous studies, and we apologize if this was not clear enough. We thus improved the text to make sure that credit to previous studies is more precisely and more clearly given (see detailed reply for the list of changes made). 

To facilitate the understanding on how we built on previous work, we expanded the comparison of our results with the results of Boerlin et al. (2013) about voltage correlations and uncorrelated spiking (page 7), comparison with the derivation of physical units of Boerlin et al. (2013) (page 3), discussion of how results on the ratio of the number of E to I neurons relate  to Calaim et al (2022) and Barrett et al. (2016) (page 16), and comment on the previous work by Gutierrez and Deneve about adaptation (page 8).  

Furthermore, the paper makes several claims of optimality that are not convincing enough, as they are only verified by a limited parameter sweep of single parameters at a time, are unintuitive and may be in conflict with previous findings of efficient spiking networks. This includes the following. 

Coding error (RMSE) has a minimum at intermediate metabolic cost (Figure 5B), despite the fact that intuitively, zero metabolic cost would indicate that the network is solely minimizing coding error and that previous work has suggested that additional costs bias the output. 

Coding error also appears to have a minimum at intermediate values of the ratio of E to I neurons (effectively the number of I neurons) and the number of encoded variables (Figures 6D, 7B). These both have to do with the redundancy in the network (number of neurons for each encoded variable), and previous work suggests that networks can code for arbitrary numbers of variables provided the redundancy is high enough (e.g., Calaim et al. 2022). 

Lastly, the performance of the E-I variant of the network is shown to be better than that of a single cell type (1CT: Figure 7C, D). Given that the E-I network is performing a similar computation as to the 1CT model but with more neurons (i.e., instead of an E neuron directly providing lateral inhibition to its neighbor, it goes through an interneuron), this is unintuitive and again not supported by previous work. These may be valid emergent properties of the E-I spiking network derived here, but their presentation and description are not sufficient to determine this.

With regard to the concern that our previous analyses considered optimal parameter sets determined with a sweep of a single parameter at a time, we have addressed this issue in two ways. First, we presented (Figure 6I and 7J and text on pages 11 and 13) results of joint sweeps of variations of pairs of parameters whose joint variations are expected to influence optimality in a way that cannot be understood varying one parameter at a time. These new analyses complement the joint parameter sweep of the time constants of single E and I neurons (t_r^E and t_r^I) that has already been presented in Fig. 5A (former Fig. 4A). Second, we conducted, within a reasonable/realistic range of possible variations of each individual parameter, a Monte-Carlo random joint sampling (10000 simulations with 20 trials each) of all 6 model parameters that we explored in the paper. We presented these new results on Fig. 2 and discuss it on pages 5-6. 

The Reviewer is correct in stating that the error (RMSE) exhibits a counterintuitive minimum as a function of the metabolic constant despite the fact that, intuitively, for vanishing metabolic constant the network is solely minimizing the coding error (Fig. 6B). In our understanding, this counterintuitive finding is due to the presence of noise in the membrane potential dynamics. In the presence of noise, a non-vanishing metabolic constant is needed to suppress “inefficient” spikes purely induced by noise that do not contribute to coding and increase the error. This gives rise to a form of “stochastic resonance”, where the noise improves detection of the signal coming from the feedforward currents. We note that the metabolic constant and the noise variance both appear in the non-specific external current (Eq. 29f in Methods), and, thus, a covariation in their optimal values is expected. Indeed, we find that the optimal metabolic constant monotonically increases as a function of the noise variance, with stronger regularization (larger beta) required to compensate for larger variability (larger sigma) (Fig. 6I). Finally, we note that a moderate level of noise (which, in turn, induces a non-trivial minimum of the coding error as a function of beta) in the network is optimal. The beneficial effect of moderate levels of noise on performance in networks with efficient coding has been shown in different contexts in previous work (Chalk et al. 2016, Koren and Deneve, 2017). The intuition is that the noise prevents the excessive synchronization of the network and insufficient single neuron variability that decrease the performance. The points above are now explained in the revised text on page 11.

The Reviewer is also correct in stating that the network exhibits an optimal performance for intermediate values of the number of I neurons and the number of encoded features. In our understanding, the optimal number of encoded features of M=3 arises simply because all the other parameters were optimized for those values of M. The purpose of those analyses was not to state that a network optimally encodes only a given number of features, but how a network whose parameters are optimized for a given M perform reasonably well when M is varied. We clarify this on page 13 of Results in Discussion on page 16. In the same Discussion paragraph we refer also to the results of Calaim et al mentioned by the Reviewer. 

To address the concern about the comparison of efficiency between the E-I and the 1CT model, we took advantage of the Reviewer’s suggestions to consider this issue more deeply. In revision, we now compare the efficiency of the 1CT model with the E population of the E-I model (Fig. 8H). This new comparison changes the conclusion about which model is more efficient, as it shows the 1CT model is slightly more efficient than the E-I model. Nevertheless, the E-I model performance is more robust to small variations of optimal parameters, e.g., it exhibits biologically plausible firing rates for non-optimal values of the metabolic constant. See also the reply to point 3 of the Public Review of Reviewer 2 for more detail. We added these results and the ensuing caveats for the interpretation of this comparison on Page 14, and also revised the title of the last subsection of Results.  

Alternatively, the methodology of the model suggests that ad hoc modeling choices may be playing a role. For example, an arbitrary weighting of coding error and metabolic cost of 0.7 to 0.3, respectively, is chosen without mention of how this affects the results. Furthermore, the scaling of synaptic weights appears to be controlled separately for each connection type in the network (Table 1), despite the fact that some of these quantities are likely linked in the optimal network derivation. Finally, the optimal threshold and metabolic constants are an order of magnitude larger than the synaptic weights (Table 1). All of these considerations suggest one of the following two possibilities. One, the model has a substantial number of unconstrained parameters to tune, in which case more parameter sweeps would be necessary to definitively make claims of optimality. Or two, parameters are being decoupled from those constrained by the optimal derivation, and the optima simply corresponds to the values that should come out of the derivation.

We thank the reviewer for bringing about these important questions.

In the first submission, we presented both the encoding error and the metabolic cost separately as a function of the parameters, so that readers could get an understanding of how stable optimal parameters would be to the change of the relative weighting of encoding error and metabolic cost. We specified this in Results (page 5) and we kept presenting separately encoding and metabolic terms in the revision.

However, we agree that it is important to present the explicit quantification on how the optimal parameters may depend on g_L. In the first submission, we showed the analysis for all possible weightings in case of two parameters for which we found this analysis was the most relevant – the ratio of neuron numbers (Fig. 7E, Fig. 6E in first submission) and the optimal number of input features M (see last paragraph on page 13 and Fig. 8D). We now show this analysis also for the rest of studied model parameters in the Supplementary Fig. S4 (A-D and H). This is discussed on pages 9, 10,11 and 12.

With regard to the concern that the scaling of synaptic weights should not be controlled separately for each connection type in the network, we agree and we would like to clarify that we did not control such scaling separately. Apologies if this was not clear enough. From the optimal analytical solution, we obtained that the connectivity scales with the standard deviation of decoding weights (s_w^E and s_w^I) of the pre and postsynaptic populations (Methods, Eq. 32). We studied the network properties as a function of the ratio of average I-I to E-I connectivity (Fig. 7 F-I; Supplementary Fig. S4 D-H), which is equivalent to the ratio of standard deviations s_w^I /s_w^E (see Methods, Eq. 35). We clarified this in text on page 12.

Next, it is correct that our synaptic weights are an order of magnitude smaller than the metabolic constant. We analysed a simpler version of the network that has the coding and dynamics identical to our full model (Methods, Eq. 25) but without the external currents. We found that the optimal parameters determining the firing threshold in such a simpler network were biologically implausible (see Supplementary Text 2 and Supplementary Table S1). We considered as another simple solution the rescaling of the synaptic efficacy such as to have biologically plausible threshold. However, that gave implausible mean synaptic efficacy (see Supplementary Text 2).  Thus, to be able to define a network with biologically plausible firing threshold and mean synaptic efficacy, we introduced the non-specific external current. After introducing such current, we were able to shift the firing threshold to biologically plausible values while keeping realistic values of mean synaptic efficacy. Biologically plausible values for the firing threshold are around 15 -– 20 mV above the resting potential (Constantinople and Bruno, 2013), which is the value that we have in our model. A plausible value for the average synaptic strength is between a fraction of one millivolt to a couple of millivolts (Constantinople & Bruno, 2013, Campagnola et al. 2022), which also corresponds to values that the synaptic weights take. The above results are briefly explained in the revised text on page 4.

Finally, to study the optimality of the network when changing multiple parameters at a time, we added a new analysis with Monte-Carlo random joint sampling (10.000 parameter sets with 20 trials for each set) of all 6 model parameters that we explored in the paper. We compared (Fig 2) the so-obtained results of each simulation with those obtained from the understanding gained from varying one or two parameters at a time (optimal parameters reported in Table 1 and used throughout the paper).  We found (Fig. 2) that the optimal configuration in Table 1 was never improved by any other simulations we performed, and that the first three random simulations that came the closest to the optimal one of Table 1 had stronger noise intensity but also stronger metabolic cost than the configuration on Table 1. The second, third and fourth configurations had longer time constants of both E and I single neurons (adaptation time constants). Ratio of E-I neuron numbers and of I-I to E-I connectivity in the second, third and fourth best configuration were either jointly increased or decreased with respect to our configuration. These results are reported on Fig. 2 and in Tables 2-3 and they are discussed in Results (page 5).

Reviewer #3 (Public Review):

Summary:

In their paper the authors tackle three things at once in a theoretical model: how can spiking neural networks perform efficient coding, how can such networks limit the energy use at the same time, and how can this be done in a more biologically realistic way than previous work?

They start by working from a long-running theory on how networks operating in a precisely balanced state can perform efficient coding. First, they assume split networks of excitatory (E) and inhibitory (I) neurons. The E neurons have the task to represent some lower dimensional input signal, and the I neurons have the task to represent the signal represented by the E neurons. Additionally, the E and I populations should minimize an energy cost represented by the sum of all spikes. All this results in two loss functions for the E and I populations, and the networks are then derived by assuming E and I neurons should only spike if this improves their respective loss. This results in networks of spiking neurons that live in a balanced state, and can accurately represent the network inputs.

They then investigate in-depth different aspects of the resulting networks, such as responses to perturbations, the effect of following Dale's law, spiking statistics, the excitation (E)/inhibition (I) balance, optimal E/I cell ratios, and others. Overall, they expand on previous work by taking a more biological angle on the theory and showing the networks can operate in a biologically realistic regime.

Strengths:

(1) The authors take a much more biological angle on the efficient spiking networks theory than previous work, which is an essential contribution to the field.

(2) They make a very extensive investigation of many aspects of the network in this context, and do so thoroughly.

(3) They put sensible constraints on their networks, while still maintaining the good properties these networks should have.

Thanks for this summary and for these kind words of appreciation of the strengths of our work.  

Weaknesses:

(1) The paper has somewhat overstated the significance of their theoretical contributions, and should make much clearer what aspects of the derivations are novel. Large parts were done in very similar ways in previous papers. Specifically: the split into E and I neurons was also done in Boerlin et al (2008) and in Barrett et al (2016). Defining the networks in terms of realistic units was already done by Boerlin et al (2008). It would also be worth it to discuss Barrett et al (2016) specifically more, as there they also use split E/I networks and perform biologically relevant experiments.

We improved the text to make sure that credit to previous studies is more precisely and more clearly given (see rebuttal to the specific suggestions of Reviewer 2 for a full list).

We apologize if this was not clear enough in the previous version. 

With regard to the specific point raised here about the E-I split, we revised the text on page 2. With regard to the realistic units, we revised the text on page 3. Finally, we commented on relation between our results and results of the study by Barrett et al. (2016) on page 16.

(2) It is not clear from an optimization perspective why the split into E and I neurons and following Dale's law would be beneficial. While the constraints of Dale's law are sensible (splitting the population in E and I neurons, and removing any non-Dalian connection), they are imposed from biology and not from any coding principles. A discussion of how this could be done would be much appreciated, and in the main text, this should be made clear.

We indeed removed non-Dalian connections because Dale’s law is a major constraint for biological plausibility. Our logic was to consider efficient coding within the space of networks that satisfy this (and other) biological plausibility constraints. We did not intend to claim that removing the non-Dalian connections was the result of an analytical optimization. We clarified this in revision (page 4).

(3) Related to the previous point, the claim that the network with split E and I neurons has a lower average loss than a 1 cell-type (1-CT) network seems incorrect to me. Only the E population coding error should be compared to the 1-CT network loss, or the sum of the E and I populations (not their average). In my author recommendations, I go more in-depth on this point.

We carefully considered these possibilities and decided to compare only the E population of the E-I model with the 1-CT model. On Fig.8G (7C of the first submission), E neurons have a slightly higher error and cost compared to the 1CT network. In the revision, we compared the loss of E neurons of the E-I model with the loss of the 1-CT model. Using such comparison, we found that the 1CT network has lower loss and is more efficient compared to E neurons of the E-I model. We revised Figure 8H and text on page 14 to address this point. 

(4) While the paper is supposed to bring the balanced spiking networks they consider in a more experimentally relevant context, for experimental audiences I don't think it is easy to follow how the model works, and I recommend reworking both the main text and methods to improve on that aspect.

We tried to make the presentation of the model more accessible to a non-computational audience in the revised paper. We carefully edited the text throughout to make it as accessible as possible. 

Assessment and context:

Overall, although much of the underlying theory is not necessarily new, the work provides an important addition to the field. The authors succeeded well in their goal of making the networks more biologically realistic, and incorporating aspects of energy efficiency. For computational neuroscientists, this paper is a good example of how to build models that link well to experimental knowledge and constraints, while still being computationally and mathematically tractable. For experimental readers, the model provides a clearer link between efficient coding spiking networks to known experimental constraints and provides a few predictions.

Thanks for these kind words. We revised the paper to make sure that these points emerge more clearly and in a more accessible way from the revised paper.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Referring to the major comments:

(1) Be upfront about particular modelling choices and why you made them; avoid talk of a "striking/surprising", etc. ability to explain data when this actually requires otherwise-arbitrary choices and auxiliary assumptions. Ideally, this nuance is already clear from the abstract.

We removed all the "striking/surprising" and similar expressions from the text. 

We added to the Abstract the assumption of equal time constants of the stimulus and of the membrane of E and I neurons and the assumption of the independence of encoded stimulus features.

In revision, we performed additional analyses (joint parameter sweeps, Monte-Carlo joint sampling of all 6 model parameters) providing additional evidence that the network parameters in Table 1 capture reasonably well the optimal solution. These are reported on Figs. 2, 6I and 7J and in Results (pages 5, 11 and 13). See rebuttal to weaknesses of the public review of the Referee 2 for details.

(2) Make even more of an effort to acknowledge prior work on the importance of structured E-I and I-E connectivity.

We have revised the text (page 4) to better place our results within previous work on structured E-I and I-E connectivity.

(3) Be clear about the model's limitations and mention them throughout the text. This will allow readers to interpret your results appropriately.

We now comment more on model's limitations, in particular the simplifying assumption about the network's computation (page 16), the lack of E-E connectivity (page 3), the absence of long-term adaptation (page 10), and the simplification of only having one type of inhibitory neurons (page 16). 

(4) Present your "predictions" for what they are: aspects of the model that can be made consistent with the existing data after some fitting. Except in the few cases where you make actual predictions, which deserve to be highlighted.

We followed the suggestion of the reviewer and distinguished cases where the model is consistent with the data (postdictions) from actual predictions, where empirical measurements are not available or not conclusive. We compiled a list of predictions and postdictions in response to the point 4 of Reviewer 1. In revision, we now comment about every property of the model as either reproducing a known property of biological networks (postdiction) or being a prediction. We improved the text in Results on pages 4, 5, 6, 7, 9, 10, 11, 12 and 13 to accommodate these requests.

Minor comments and recommendations

It's a sizable list, but most can be addressed with some text edits.

(1) The image captions should give more details about the simulations and analyses, particularly regarding sample sizes and statistical tests. In Figure 5, for example, it is unclear if the lines represent averages over multiple signals and, if so, how many. It's probably not a single realization, but if it is, this might explain the otherwise puzzling optimal number of three stimuli. Box plots visualize the distribution across simulation trials, but it's not clear how many. In Figure 7d, a star suggests statistical significance, but the caption does not mention the test or its results; the y-axis should also have larger limits.

All statistical results were computed on 100 or 200 simulation trials, depending on the figure, with duration of the trial of 1 second of simulated time. To compute statistical results in Fig. 1, we used 10 trials with duration of 10 seconds for each trial. Each trial consisted of M independent realizations of Ornstein-Uhlenbeck (OU) processes as stimuli, independent noise in the membrane potential and an independent draw of tuning parameters, such that the results are general over specific realization of these random variables. Realizations of the OU processes were independent across stimulus dimensions and across trials. We added this information in the caption of each figure. 

The optimal number of M=3 stimuli is the result of measuring the performance of the network in 100 simulation trials (for each parameter value), thus following the same procedure as for all other parameters. Boxplots on Fig. 8G-H were also generated from results computed in 100 simulation trials, which we have now specified in the caption of the figure, together with the statistical test used for assessing the significance (twotailed t-test). We also enlarged the limits of Fig. 8H (7D in the previous version).

(2) The Oldenburg paper (reference 62) finds suppression of all but nearby neurons in response to two- photon stimulation of small neural ensembles (instead of single neurons, as in Chettih & Harvey). This isn't perfectly consistent with the model's results, even though the Oldenburg experiments seem more relevant given the model's small size, and strong connectivity/high connection probability between similarly tuned neurons. What might explain the potential mismatch?

We sincerely apologize for not having been precise enough on this point when comparing our model against Chettih & Harvey and Oldenburg et al. We corrected the sentence (page 6) to remove the claim that our model reproduces both. 

We speculate that the discrepancy between perturbing our model and the Oldenburg data may arise from the lack of E-E connectivity in our model. Synaptic connections between E neurons with similar selectivity could create an enhancement instead of suppression between neuronal pairs with very similar tuning. We added a sentence about this in the section with perturbation experiments “Competition across neurons with similar stimulus tuning emerging in efficient spiking networks” (page 7) where we discuss this limitation of our model. We feel that this example shows the utility to derive some perturbation results from our model, as not all networks with some degree of lateral inhibition will show the same perturbation results. Comparing our model's perturbation with real data perturbation results has thus some value to better appreciate strengths and limitations of our approach. 

(3) "Previous studies optogenetically stimulated E neurons but did not determine whether the recorded neurons were excitatory or inhibitory " (p. 11). I believe Oldenburg et al. did specifically image excitatory neurons.

The reviewer is correct about Oldenburg et al. imaging specifically excitatory neurons. We have revised this part of the Discussion (page 15). 

(4) The authors write that efficiency is particularly achieved where adaptation is stronger in E compared to I neurons (p. 7; Figure 4). Although this would be consistent with experimental data (the I neurons in the model seem akin to fast-spiking Pv+ cells), I struggle to see it in the figure. Instead, it seems like there are roughly two regimes. If either of the neuronal timescales is faster than the stimulus timescale, the optimisation fails. If both are at least as slow, optimisation succeeds.

We agree with the reviewer that the adaptation properties of our inhibitory neurons are compatible with Pv+ cells. What is essential for determining the dynamical regime of the network is less the relation to the time constant of the stimulus (t_x) but rather the relation between the time constant of the population readout (t, which is also the membrane time constant) and the time constant of the single neuron (t_r^y for y=E and y=I; see Eq. 23, 25 or 29e). The relation between t and t_r^y determines if single neurons generate spike-triggered adaptation (t_r^y > t) or spike-triggered facilitation (t_r^y < t; see Table 4). In regimes with facilitation in either E or I neurons (or both), the network performance strongly deteriorates compared to regimes with adaptation (Fig. 5A). 

Beyond adaptation leading to better performance, we also found different effects of adaptation in E and I neurons. We acknowledge that the difference of these effects was difficult to see from the Fig. 4B in the first submission. We have now replotted results from previously shown Fig. 4B to focus on the adaptation regime only, (since the Fig. 5A already establishes that this is the regime with better performance). We also added figures showing the differential effect of adaptation in E and I cell type on the firing rate and on the average loss (Fig. 5C-D). Fig. 5B and C (top plots) show that with adaptation in E neurons, the error and the loss increase more slowly than with adaptation in I neurons. Moreover, the firing rate in both cell types decreases with adaptation in E neurons, while this is not the case with adaptation in I neurons (Fig. 5D). These results are added to the figure panels specified above and discussed in text on page 9.

To clarify the relation between neuronal and stimulus timescale, we now also added the analysis of network performance as a function of the time constant of the stimulus t_x (Supplementary Fig. S5 C-E). We found that the model's performance is optimal when the time constant of the stimulus is close to the membrane time constant t. This result is expected, because the equality of these time constants was imposed in our analytical derivation of the model (t_x  = t). We see a similar decrease in performance for values of t_x  that are faster and slower with respect to the membrane time constant (Supplementary Fig. S5C, top). These results are added to the figure panels specified above and discussed in text on page 13.

(5) A key functional property of cortical interneurons is their lower stimulus selectivity. Does the model replicate this feature?

We think that whether I neurons are less selective than E neurons is still an open question. A number of recent empirical studies reported that the selectivity of I neurons is comparable to the selectivity of E neurons (see., e.g., Kuan et al. Nature 2024, Runyan et al. Neuron 2010, Najafi et al. Neuron 2020). In our model, the optimal solution prescribes a precise structure in recurrent connectivity (see Eq. 24 and Fig. 1C(ii)) and structured connectivity endows I neurons with stimulus selectivity. To show this, we added plots of example tuning curves and the distribution of the selectivity index across E and I neurons (Fig. 8E-F) and described these new results in Results (page 14). Tuning curves in our network were similar to those computed in a previous work that addressed stimulus tuning in efficient spiking networks (Barrett et al. 2016). We evaluated tuning curves using M=3 constant stimulus features and we varied one of the features while the two others were kept fixed. We provided details on how the tuning curves and the selectivity index were computed in a new Methods subsection (“Tuning curves and selectivity index”) on page 50.

(6) The final panels of Figure 4 are presented as an approach to test the efficiency of biological networks. The authors seem to measure the instantaneous (and time-averaged) E-I balance while varying the adaptation parameter and then correlate this with the loss. If that is indeed the approach (it's difficult to tell), this doesn't seem to suggest a tractable experiment. Also, the conclusion is somewhat obvious: the tighter the single neuron balance, the fewer unnecessary spikes are fired. I recommend that the authors clearly explain their analysis and how they envision its application to biological data.

We indeed measured the instantaneous (and time-averaged) E-I balance while varying the adaptation parameters and then correlating this with the loss. We did not want to imply that the latter panels of Figure 4 are a means to test the efficiency or biological networks or that we are suggesting new and possibly unfeasible experiments. We see it as a way to better conceptually understand how spike triggered adaptation helps the network’s coding efficiency, by tightening the E I balance in a way that it reduces the number of unnecessary spikes. We apologize if the previous text was confusing in this respect.   We have now removed the initial paragraph of former Results Subsection (including removing the subsection title) and added new text about different effect of adaptation in E and I neurons on Page 9. We also thoroughly revised Figure 5.

(7) The external stimuli are repeatedly said to vary (or be tracked) across "multiple time scales", which might inadvertently be interpreted as (i) a single stimulus containing multiple timescales or (ii) simultaneously presented stimuli containing different timescales. These scenarios are potential targets for efficient coding through neuronal adaptation (reference 21 in the manuscript and Pozzorini et al. Nat. Neuro. 2013), but they are not addressed in the current model. I recommend the authors clarify their statements regarding timescales (and if they're up for it, acknowledge this as a limitation).

We thank the reviewer for bringing up this interesting point. To address the second point raised by the Reviewer (simultaneously presented stimuli containing multiple timescales), we performed new analyses to test the model with simultaneously presented stimuli that have different timescales. We found that the model encodes efficiently such stimuli.  We tested the case with a 3-dimensional stimulus where each dimension is an Ornstein-Uhlenbeck process with a different time constant. More precisely, we kept the time constant in the first dimension fixed (at 10 ms), and varied the time constant in the second and third dimension such that the time constant in the third dimension is doubled with respect to the second dimension. We plotted the encoding error in every stimulus dimension for E and I neurons (Fig. 8B, left plot) as well as the encoding error and the metabolic cost averaged across stimulus dimensions (Fig. 8B, right plot). The results are briefly described with text on page 13.

Regarding the case i) (single stimulus containing multiple timescales), we considered two possibilities. One possibility is that timescales of the stimulus are separable, and in this case a single stimulus containing several time scales can be decomposed in several stimuli with a single time scale each. As we assign a new set of weights for each dimension of the decomposed stimulus, this case is similar to the case ii) that we already addressed. Another possibility is that timescales of the stimulus cannot be separated. This case is not covered in the present analysis and we listed it among the limitations of the model. We revised the text (page 13) around the question of multiple time scales and included the citation of Pozzorini et al. (2013). 

(8) It is claimed that the model uses a mixed code to represent signals, citing reference 47 (Rigotti et al., Nature 2013). But whereas the model seems to use linear mixed selectivity, the Rigotti reference highlights the virtues of nonlinear mixed selectivity. In my understanding, a linearly mixed code does not enjoy the same benefits since it’s mathematically equivalent to a non-mixed code (simply rotate the readout matrix). I recommend that the authors clarify the type of selectivity used by their model and how it relates to the paper(s) they cite.

The reviewer is correct that our selectivity is a linear mixing of input variables, and differs from the selectivity in Rigotti et al. (2013) which is non-linear. We revised the sentence on page 4 to clarify better that the mixed selectivity we consider is linear and we removed Rigotti’s citation. 

(9) Reference 46 is cited as evidence that leaky integration of sensory features is a relevant computation for sensory areas. I don’t think this is quite what the reference shows. Instead, it finds certain morphological and electrophysiological differences between single pyramidal neurons in the primary visual cortex compared to the prefrontal cortex. Reference 46’ then goes on to speculate that these are differences relevant to sensory computation. This may seem like a quibble, but given the centrality of the objectivee function in normative theories, I think it's important to clarify why a particular objective is chosen.

We agree that our reference of Amatrudo et al was not the best reference and that the previous text was confusing. We thus tried to improve on its clarity. We looked at the previous theoretical efficient coding papers introducing this leaky integration and we could not find in the previous theoretical work a justification of this assumption based on experimental papers. However, there is evidence that neurons in sensory structures, and in cortical association areas respond to time varying sensory evidence by summing stimuli over time with a weight that decreases steadily going back in time from the time of firing, which suggests that neurons integrate time-varying sensory features. In many cases, these integration kernels decay approximately exponentially going back in time, and several models explaining successfully perceptual readouts of neural activity work assuming leaky integration. This suggests that the mathematical approximation of leaky integration of sensory evidence, though possibly simplistic, is reasonable.  We revised the text in this respect (page 2).  

(10) The definition of the objective function uses beta as a tuning parameter, but later parts of the text and figures refer to a parameter g_L which might only be introduced in the convex combination of Eq. 40a.

This is correct. Parameter optimization has been performed on a weighted sum of the average encoding error and cost as given by the Eq. 39a (40a in first submission), with the weighting g_L for the error versus the cost, and not the beta that is part of the objective in Eq.10. The convex combination in Eq. 39a allowed us to find a set of optimal parameters that is within biologically realistic parameter ranges, which includes realistic values for the firing threshold. The average encoding error and metabolic cost (the two terms on the right-hand side of Eq. 39a, without weighting with g_L) in our network are of the same order (see Fig 8G for the E-I model where these values are plotted separately for the optimal network). Weighing the cost with optimal beta that is in the range of ~10 would have yielded a network that optimizes almost exclusively the metabolic cost and would bias the results towards solutions with poor encoding accuracy.

To document more fully how the choice of weighting of the error with the cost (g_L) affects the optimal parameters, we now added new analysis (Fig. 8D and Supplementary Fig. S4 A-D and H) showing optimal parameters as a function of this weighting. We commented on these results in the text on pages 9-11 and 12. For further details, please see also the reply to point 1 or Reviewer 1.

(11) Figure 1J: "In E neurons, the distribution of inhibitory and of net synaptic inputs overlap". In my understanding, they are in fact identical, and this is by construction. It might help the reader to state this.

We apologize for an unclear statement. In E neurons, net synaptic current is the sum of the feedforward current and of recurrent inhibition (Eq. 29c and Eq. 42). With our choice of tuning parameters that are symmetric around zero and with stimulus features that have vanishing mean, the mean of the feedforward current is close to zero. Because of this, the mean of the net current is negative and is close to the mean of the inhibitory current. We have clarified this in the text (page 5).

(12) A few typos:

-  p1. "Minimizes the encoding accuracy" should be "maximizes..."

-  p1: "as well the progress" should be something like "as well as the progress"

-  p.11 In recorded neurons where excitatory or inhibitory. ", "where" should be "were" - Fig3: missing parentheses (B)

-  Fig4B: the 200 ticks on the y-scale are cut off.

-  Panel Fig. 5a: "stimulus" should be "stimuli".

-  Ref 24 "Efficient andadaptive sensory codes" is missing a space.

-  p. 26: "requires" should be "required".

-  On several occasions, the article "the" is missing.

We thank the reviewer for kindly pointing out the typos that we now corrected.

Reviewer #2 (Recommendations For The Authors):

I would like to give the authors more details about the two main weaknesses discussed above, so that they may address specific points in the paper. First, there is the relation to previous work. Several published articles have presented very similar results to those discussed here, including references 5, 26, 28, 32, 33, 42, 43, 48, and an additional reference not cited by the authors (Calaim et al. 2022 eLife e73276). This includes:

(1) Derivation of an E-I efficient spiking network, which is found in refs. 28, 42, 43, and 48. This is not reflected in the text: e.g., "These previous implementations, however, had neurons that did not respect Dale's law" (Introduction, pg. 1); "Unlike previous approaches (28, 48), we hypothesize that E and I neurons have distinct normative objectives...". The authors should discuss how their derivation compares to these.

We have now fully clarified on page 3 that our model builds on the seminal previous works that introduced E-I networks with efficient coding (Supplementary text in Boerlin et al. 2013, Chalk et al. 2016, Barrett et al. 2016). 

(2) Inclusion of a slow adaptation current: I believe this also appears in a previous paper (Gutierrez & Deneve 2019, ref. 33) in almost the exact same form, and is again not reflected in the text: "The strength of the current is proportional to the difference in inverse time constants ... and is thus absent in previous studies assuming that these time constants are equal (... ref. 33). Again, the authors should compare their derivation to this previous work.

We thank the reviewer for pointing this out. We sincerely apologize if our previous version did not recognize sufficiently clearly that the previous work of Gutierrez and Deneve (eLife 2019; ref 33) introduced first the slow adaptation current that is similar to spike-triggered adaptation in our model. We have made sure that the revised text recognizes it more clearly. We also explained better what we changed or added with respect to this previous work (see revised text on page 8). 

The work by Gutierrez and Deneve (2019) emphasizes the interplay between single neuron property (an adapting current in single neurons) and network property (networklevel coding through structured recurrent connections). They use a network that does not distinguish E and I neurons. Our contribution instead focuses on the adaptation in an E-I network. To improve the presentation following the Reviewer’s comment, we now better emphasize the differential effect of adaptation in E and in I neurons in revision (Fig. 5 B-D). Moreover, Gutierrez and Deneve studied the effect of adaptation on slower time scales (1 or 2 seconds) while we study the adaptation on a finer time scale of tens of milliseconds. The revised text detailed this is reported on Page 8.

(3) Background currents and physical units: Pg. 26: "these models did not contain any synaptic current unrelated to feedforward and recurrent processing" and "Moreover previous models on efficient coding did not thoroughly consider physical units of variables" - this was briefly described in ref. 28 (Boerlin et al. 2013), in which the voltage and threshold are transformed by adding a common constant, and additional aspects of physical units are discussed.

It is correct that Boerlin et al (2013) suggested adding a common constant to introduce physical units. We now revised the text to make clearer the relation between our results and the results of Boerlin et al. (2013) (page 3). In our paper, we built on Boerlin et al. (2013) and assigned physical units to computational variables that define the model's objective (the targets, the estimates, the metabolic constant, etc.). We assigned units to computational variables in such a way that physical variables (such as membrane potential, transmembrane currents, firing thresholds and resets) have the correct physical units.  We have now clarified how we derived physical units in the section of Results where we introduce the biophysical model (page 3) and specified how this derivation relates to the results in Boerlin et al. (2013).

(4) Voltage correlations, spike correlations, and instantaneous E/I balance: this was already pointed out in Boerlin et al. 2013 (ref 28; from that paper: "Despite these strong correlations of the membrane potentials, the neurons fire rarely and asynchronously") and others including ref. 32. The authors mention this briefly in the Discussion, but it should be more prominent that this work presents a more thorough study of this well-known characteristic of the network.

We agree that it would be important to comment on how our results relate to these results in Boerlin et al. (2013). It is correct that in Boerlin et al. (2013) neurons have strong correlations in the membrane potentials, but fire asynchronously, similarly to what we observe in our model. However, asynchronous dynamics in Boerlin et al. (2013) strongly depends on the assumption of instantaneous synaptic transmission and time discretization, with a “one spike per time bin” rule in numerical implementation. This rule enforces that at most one spike is fired in each time bin, thus actively preventing any synchronization across neurons. If this rule is removed, their network synchronizes, unless the metabolic constant is strong enough to control such synchronization to bring it back to asynchronous regime (see ref. 36). Our implementation does not contain any specific rule that would prevent synchronization across neurons. We now cite the paper by Boerlin and colleagues and briefly summarize this discussion when we describe the result of Fig. 3D on page 7. 

(5) Perturbations and parameters sweep: I found one previous paper on efficient spiking networks (Calaim et al. 2022) which the authors did not cite, but appears to be highly relevant to the work presented here. Though the authors perform different perturbations from this previous study, they should ideally discuss how their findings relate to this one. Furthermore, this previous study performs extensive sweeps over various network parameters, which the authors might discuss here, when relevant. For example, on pg. 8, the authors write “We predict that, if number of neurons within the population decreases, neurons have to fire more spikes to achieve an optimal population readout” – this was already shown in Calaim et al. 2022 Figure 5, and the authors should mention if their results are consistent.

We apologize for not being aware of Calaim et al. (2022) when we submitted the first version of our paper. This important study is now cited in the revised version. We have now, as suggested, performed sweeps of multiple parameters inspired by the work of Calaim. This new analysis is described extensively in reply to Weaknesses in the Public Review of reviewer 2 and is found in Fig 2, 6I and 7J and described on pages 5,11 and 13.

The Reviewer is also correct that the compensation mechanism that applies when changing the ratio of E-I neuron numbers is similar to the one described in Barrett et al. (2016) and related to our claim “if number of neurons within the population decreases, neurons have to fire more spikes to achieve an optimal population readout”. We have now added (page 11) that this prediction is consistent with the finding of Barrett et al. (2016).

With regard to the dependence of optimal coding properties on the number of neurons, we have tried to better describe similarities and differences with our work and that of Calaim et al as well as with the work of Barrett et al. (2016) which reports highly relevant results. These additional considerations are summarized in a paragraph in Discussion (page 16).

(6) Overall, the authors should distinguish which of their results are novel, which ones are consistent with previous work on efficient spiking networks, and which ones are consistent in general with network implementations of efficient and sparse coding. In many of the above cases, this manuscript goes into much more depth and study of each of the network characteristics, which is interesting and commendable, but this should be made clear. In clarifying the points listed above, I hope that the authors can better contextualize their work in relation to previous studies, and highlight what are the unique characteristics of the model presented here.

We made a number of clarifications of the text to provide better contextualization of our model within existing literature and to credit more precisely previous publications. This includes commenting on previous studies that introduced separate objective functions of E and I neurons (page 2), spike-triggered adaptation (page 8), physical units (page 3), and changes in the number of neurons in the network (page 16). 

Next, there are the claims of optimal parameters. As explained on pg. 35 (criterion for determining optimal model parameters), it appears to me that they simply vary each parameter one at a time around the optimal value. This argument appears somewhat circular, as they would need to know the optimal parameters before starting this sweep. In general, I find these optimality considerations to be the most interesting and novel part of the paper, but the simulations are relatively limited, so I would ask the authors to either back them up with more extensive parameter sweeps that consider covariations in different parameters simultaneously (as in Calaim et al. 2022). Furthermore, the authors should make sure that they are not breaking any of the required relationships between parameters necessary for the optimization of the loss function. Again, some of the results (such as coding error not being minimized with zero metabolic cost) suggests that there might be issues here. 

We thank the reviewer for this insightful suggestion. We have now added a joint sweep of all relevant model parameters using Monte-Carlo parameter search with 10.000 iterations. We randomly drew parameter configurations from predetermined parameter ranges that are detailed in the newly added Table 2. Parameters were sampled from a uniform distribution. We varied all the six model parameters studied in the paper (metabolic constant, noise intensity, time constant of single E and I neurons, ratio of E to I neurons and ratio of the mean I-I to E-I connectivity).  We now present these results on a new Figure 2. We did not find any set of parameters with lower loss than the parameters in Table 1 when the weighting of the error with the cost was in the following range: 0.4<g_L<0.81 (Fig. 2C). While our large but finite Monte-Carlo random sampling does not fully prove that the configuration we selected as optimal (on Table 1) is a global optimum, it shows that this configuration is highly efficient. Further, and as detailed in the rebuttal to the Weaknesses of the Public Review of Referee 2, analyses of the near optimal solutions are compatible with the notion (resulting from the join parameter sweep studies that we added to Figures 6 and 7) that network optimality may be influenced by joint covariations in parameters. These new results are reported in Results (page 5, 11 and 13) and in Figure 2, 6I an 7J.

Some more specific points:

(1) In general, I find it difficult to understand the scaling of the RMSE, cost, and loss values in Figures 4-7. Why are RMSE values in the range of 1-10, whereas loss and cost values are in the range of 0-1? Perhaps the authors can explicitly write the values of the RMSE and loss for the simulation in Figure 1G as a reference point.

Encoding error (RMSE), metabolic cost (MC) and average loss for a well performing network are within the range of 1-10 (see Fig. 8G or 7C in the first submission). To ease the visualization of results, we normalized the cost and the loss on Figs. 6-8 in order to plot them on the same figure (while the computation of the optima is done following the Eq. 39 and is without normalization). We have now explicitly written the values of RMSE, MC and the average loss (non-normalized) for the simulation in Fig. 1D on page 5, as suggested by the reviewer. We have also revised Fig. 4 and now show the absolute and not the relative values of the RMSE and the MC (metabolic cost). 

(2) Optimal E-I neuron ratio of 4:1 and efficacy ratio of 3:1: besides being unintuitive in relation to previous work, are these two optimal settings related to one another? If there are 4x more excitatory neurons than inhibitory neurons, won't this affect the efficacy ratio of the weights of the two populations? What happens if these two parameters are varied together?

Thanks for this insightful point. Indeed, the optima of these two parameters are interdependent and positively correlated - if we decrease the E-I neuron ratio, the optimal efficacy ratio decreases as well. To better show this relation we added figures with 2dimensional parameter search (Fig. 7J) where we varied jointly the two ratios. The red cross on the right figure marks the optimal ratios used as optimal parameters in our study. These finding are discussed on page 13.

(3) Optimal dimensionality of M=[1,4]: Again, previous work (Calaim et al. 2022) would suggest that efficient spiking networks can code for arbitrary dimensional signals, but that performance depends on the redundancy in the network - the more neurons, the better the coding. From this, I don't understand how or why the authors find a minimum in Figure 7B. Why does coding performance get worse for small M?

We optimized all model parameters with M=3 and this is the reason why M=3 is the optimal number of inputs when we vary this parameter. Our network shows a distinct minimum of the encoding error as a function of the stimulus dimensionality for both E and I neurons (Fig. 8C, top). This minimum is reflected in the minimum of the average loss (Fig. 8C, bottom). The minimum of the loss is shifted (or biased) by the metabolic cost, with strong weighting of the cost lowering the optimal number of inputs. This is discussed on pages 13-14.

Here are a list of other, more minor points, that the authors can consider addressing to make the results and text more clear:

(1) Feedforward efficient coding models: in the introduction (pg. 1) and discussion (pg. 11) it is mentioned that early efficient coding models, such as that of Olshausen & Field 96, were purely feedforward, which I believe to be untrue (e.g., see Eq. 2 of O&F 96). Later models made this even more explicit (Rozell et al. 2008). Perhaps the authors can either clarify what they meant by this, or downplay this point.

We sincerely apologize for the oversight present in the previous version of the text. We agree with the reviewer that the model in Olshausen and Field (1996) indeed defines a network with recurrent connections, and the same type of recurrent connectivity has been used by Rozell et al. (2008, 2013). The structure of the connectivity in Olshausen and Field (as well as in Rozell et al (2008)) is closely related to the structure of connectivity that we derived in our model. We have corrected the text in the introduction (page 1) to remove these errors.

(2) Pg. 2 - The authors state: "We draw tuning parameters from a normal distribution...", but in the methods, it states that these are then normalized across neurons, so perhaps the authors could add this here, or rephrase it to say that weights are drawn uniformly on the hypersphere.

We rephrased the description of how weights were determined (page 2).

(3) Pg. 2 - "We hypothesize the time-resolved metabolic cost to be proportional to the estimate of a momentary firing rate of the neural population" - from what I can see, this is not the usual population rate, which would be an average or sum of rates across the population.

Indeed, the time-dependent metabolic cost is not the population rate (in the sense of the sum of instantaneous firing rates across neurons), but is proportional to it by a factor of 1/t. More precisely, we can define the instantaneous estimate of the firing rate of a single neuron i as z_i(t) = 1/t_r r_i(t) with r_i(t) as in Eq. 7. We have clarified this in the revised text on page 3. 

(4) Pg. 3: "The synaptic strength between two neurons is proportional to their tuning similarity if the tuning similarity is positive" - based on the figure and results, this appears to be the case for I-E, E-I, and I-I connections, but not for E-E connections. This should be clarified in the text. Furthermore, one reference given in the subsequent sentence (Ko et al. 2011, ref. 51), is specifically about E-E connections, so doesn't appear to be relevant here.

We have now specified that the Eq. 24 does not describe E-E connections. We also agree that the reference (Ko et al. 2011) did not adequately support our claim and we thus removed it and revised the text on page 3 accordingly.

(5) Pg. 3: "the relative weight of the metabolic cost over the encoding error controls the operating regime of the network" and "and an operating regime controlled by the metabolic constant" - what do you mean by operating regime here?

We used the expression “operating regime” in the sense of a dynamical regime of the network.  However, we agree that this expression may be confusing and we removed it in revision. 

(6) Pg. 3: "Previous studies interpreted changes of the metabolic constant beta as changes to the firing thresholds, which has less biological plausibility" - can the authors explain why this is less plausible, or ideally provide a reference for it?

In biological networks, global variables such as brain state can strongly modulate the way neural networks respond to a feedforward stimulus. These variables influence neural activity in at least two distinct ways. One is by changing non-specific synaptic inputs to neurons, which is a network-wide effect (Destexhe and Pare, Nature Reviews Neurosci. 2003). This is captured in our model by changing the strength of the mean and fluctuations in the external currents. Beyond modulating synaptic currents, another way of modulating neural activity is by changing cell-intrinsic factors that modulate the firing threshold in biological neurons (Pozzorini et al. 2013). Previous studies on spiking networks with efficient coding interpreted the effect of the metabolic constant as changes to the firing threshold (Koren and Deneve, 2017, Gutierrez and Deneve 2019), which corresponds to cell-intrinsic factors. Here we instead propose that the metabolic constant modulates the neural activity by changing the non-specific synaptic input, homogeneously across all neurons in the network. Interpreting the metabolic constant as setting the mean of the non-specific synaptic input was necessary in our model to find an optimal set of parameters (as in Table 1) that is also biologically plausible. We revised the text accordingly (page 4).

(7) Pg. 4: Competition across neurons: since the model lacks E-E connectivity, it seems trivial to conclude that there is competition through lateral inhibition, and it can be directly determined from the connectivity. What is gained from running these perturbation experiments?

We agree that a reader with a good understanding of sparse / efficient coding theory can tell that there is competition across neurons with similar tuning already from the equation for the recurrent connectivity (Eq. 24). However, we presume that not all readers can see this from the equations and that it is worth showing this with simulations.

Following the reviewer's comment, we have now downplayed the result about the model manifesting lateral inhibition in general on page 6. We have also removed its extensive elaboration in Discussion.

One reason to run perturbation experiments was to test to what extent the optimal model qualitatively replicates empirical findings, in particular, single neuron perturbation experiments in Chettih and Harvey, 2019, without specifically tuning any of the model parameters. We found that the model reproduces qualitatively the main empirical findings, without tuning the model to replicate the data. We revised the text on page 5 accordingly.

Further reason to run these experiments was to refine predictions about the minimal amount of connectivity structure that generates perturbation response profiles that are qualitatively compatible with empirical observations. To establish this, we did perturbation experiments while removing the connectivity structure of a particular connectivity sub-matrices (E-I, I-I or I-E; Fig. S3 F). This allowed us to determine which connectivity matrix has to be structured to observe results that qualitatively match empirical findings. We found that the structure of E-I and I-E connectivity is necessary, but not the structure of I-I connectivity. Finally, we tested partial removal of the connectivity structure where we replaced the precise (and optimal) connectivity structure and imposed a simpler connectivity rule. In the optimal connectivity, the connection strength is proportional to the tuning similarity. A simpler connectivity rule, in contrast, only specifies that neurons with similar tuning share a connection, and beyond this the connection strength is random. Running perturbation experiments in such a network obeying a simpler connectivity rule still qualitatively replicated empirical results from Chettih and Harvey (2019). This is shown on the Supplementary Fig. S2F on described on page 8.

(8) Pg. 4: "the optimal E-I network provided a precise and unbiased estimator of the multidimensional and time-dependent target signal" - from previous work (e.g., Calaim et al. 2022), I would guess that the estimator is indeed biased by the metabolic cost. Why is this not the case here? Did you tune the output weights to remove this bias?

Output weights were not tuned to remove the bias. On Fig. 1H in the first submission we plotted the bias for the network that minimizes the encoding error. We forgot to specify this in the text and figure caption, for which we apologize. We now replaced this figure with a new one (Fig. 1E) where we plot the bias of the network minimizing the average loss (with parameters as in Table 1). The bias of the network minimizing the error is close to zero, B^E = 0.02 and B^I = 0.03.  The bias of the network minimizing the loss is stronger and negative, B^E = -0.15 and B^I=-0.34. In the text of Results, we now report the bias of both networks (i.e., optimizing the encoding error and optimizing the loss). We also added a plot showing trial-averaged estimates and a time-dependent bias in each stimulus dimension (Supplementary figure S1 F). Note that the network minimizing the encoding error requires a lower metabolic constant (β = 6) than the network optimizing the loss (β=14), however, the optimal metabolic cost in both networks is nonzero. We revised the text and explained these points on page 5.

(9) Pg. 4: "The distribution of firing rates was well described by a log-normal distribution" - I find this quite interesting, but it isn't clear to me how much this is due to the simulation of a finitetime noisy input. If the neurons all have equal tuning on the hypersphere, I would expect that the variability in firing is primarily due to how much the input correlates with their tuning. If this is true, I would guess that if you extend the duration of the simulation, the distribution would become tighter. Can you confirm that this is the stationary distribution of the firing rates?

We now simulated the network with longer simulation time (10 seconds of simulated time instead of 2 seconds used previously) and also iterated the simulation across 10 trials to report a result that is general across random draws of tuning parameters (previously a single set of tuning parameters was used). The reviewer is correct that the distribution of firing rates of E neurons has become tighter with longer simulation time, but distributions remain log-normal. We also recomputed the coefficient of variation (CV) using the same procedure. We updated these plots on Fig. 1F.

(10) Pg. 4: "We observed a strong average E-I balance" - based on the plots in Figure 1J, the inputs appear to be inhibition-dominated, especially for excitatory neurons. So by what criterion are you calling this strong average balance?

The reviewer is correct about the fact that the net synaptic input to single neurons in our optimal network shows excess inhibition and the network is inhibition-dominated, so we revised this sentence (page 5) accordingly.  

(11) Pg. 4: Stronger instantaneous balance in I neurons compared to E neurons - this is curious, and I have two questions: (1) can the authors provide any intuition or explanation for why this is the case in the model? and (2) does this relate to any literature on balance that might suggest inhibitory neurons are more balanced than excitatory neurons?

In our model, I neurons receive excitatory and inhibitory synaptic currents through synaptic connections that are precisely structured. E neurons receive structured inhibition and a feedforward current. The feedforward current consists of M=3 independent OU processes projected on the tuning vectors of E neurons w_i^E. We speculate that because the synaptic inhibition and feedforward current are different processes and the 3 OU inputs are independent, it is harder for E neurons to achieve the instantaneous balance that would be as precise as in I neurons. While we think that the feedforward current in our model reflects biologically plausible sensory processing, it is not a mechanistic model of feedforward processing. In biological neurons, real feedforward signals are implemented as a series of complex feedforward synaptic inputs from downstream areas, while the feedforward current in our model is a sum of stimulus features, and is thus a simplification of a biological process that generates feedforward signals. We speculate that a mechanistic implementation of the feedforward current could increase the instantaneous balance in E neurons.  Furthermore, the presence of EE connections could potentially also increase the instantaneous balance in E neurons. We revised the Discussion about these important questions that lie on the side of model limitations and could be advanced in future work. We could not find any empirical evidence directly comparing the instantaneous balance in E versus I neurons.  We have reported these considerations in the revised Discussion (page 16).

(12) Pg. 5, comparison with random connectivity: "Randomizing E-I and I-E connectivity led to several-fold increases in the encoding error as well as to significant increases in the metabolic cost" and Discussion, pg. 11: "the structured network exhibits several fold lower encoding error compared to unstructured networks": I'm wondering if these comparisons are fair. First, regarding activity changes that affect the metabolic cost - it is known that random balanced networks can have global activity control, so it is not straightforward that randomizing the connectivity will change the metabolic cost. What about shuffling the weights but keeping an average balance for each neuron's input weights? Second, regarding coding error, it is trivial that random weights will not map onto the correct readout. A fairer comparison, in my opinion, would at least be to retrain the output weights to find the best-fitting decoder for the threedimensional signal, something more akin to a reservoir network.

Thank you for raising these interesting questions. The purpose of comparing networks with and without connectivity structure was to observe causal effects of the connectivity structure on the neural activity. We agree that the effect on the encoding error is close to trivial, because shuffling of connectivity weights decouples neural dynamics from decoding weights. We have carefully considered Reviewer's suggestions to better compare the performance of structured and unstructured networks. 

In reply to the first point, we followed the reviewer's suggestion and compared the optimal network with a shuffled network that matched the optimal network in its average balance. This was achieved by increasing the metabolic constant, decreasing the noise intensity and slightly decreasing the feedforward stimulus (we did not find a way to match the net current in both cell types by changing a single parameter). As we compared the metabolic cost between the optimal and the shuffled network with matched average balance, we still found lower metabolic cost in the optimal network, even though the difference was now smaller. We replaced Fig. 3B from the first submission with these new results in Fig. 4B and commented on them in the text (page 7).

In reply to the second point, we followed reviewer’s suggestion and compared the encoding error (RMSE) of the optimal network and the network with shuffled connectivity where decoding weights are trained such as to optimally reconstruct the target signal. As suggested, we now analyzed the encoding error of the networks using decoding weights trained on the set of spike trains generated by the network using linear least square regression to minimize the decoding error. For a fair and quantitative comparison and because we did not train decoding weights of our structured model, we performed this same analysis using spike trains generated by networks with structured and shuffled recurrent connectivity. We found that the encoding error is smaller in the E population and much smaller in the I population in the structured compared to the random network. Decoding weights found numerically in the optimal network approach uniform distribution of weights that we used in our model (Fig. 4A, right). In contrast, decoding weights obtained from the random network do not converge to a uniform distribution, but instead form a much sparser distribution, in particular in I neurons (Supplementary Fig. S3 A). These additional results reported in the above mentioned figures are discussed in text on page 14.  

(13) Pg. 5: "a shift from mean-driven to fluctuation-driven spiking" and Pg. 11 "a network structured as in our efficient coding solution operates in a dynamical regime that is more stimulus-driven, compared to an unstructured network that is more fluctuation driven" - I would expect that the balanced condition dictates that spiking is always fluctuation driven. I'm wondering if the authors can clarify this.

We agree with the reviewer that networks with and without connectivity structure are fluctuation-driven, because in a mean-driven network the mean current must be suprathreshold (Ahmadian and Miller, 2021), which is not the case of either network. We removed the claim of the change from mean to fluctuation driven regime in the revised paper. We are grateful to the Reviewer for helping us tighten the elaboration of our findings.

(14) Pg. 5: "suggesting that variability of spiking is independent of the connectivity structure" - the literature of balanced networks argues against this. Is this not simply because you have a noisy input? Can you test this claim?

We thank the reviewer for the suggestion. We tested this claim by measuring the coefficient of variation in networks receiving a constant stimulus. In particular, we set the same strength in each of the M=3 stimulus dimensions and set the stimulus amplitude such as to match the firing rate of the optimal network in response to the OU stimulus. We computed the coefficient of variation in 200 simulation trials.  The removal of connectivity structure did not cause significant change of the coefficient of variation in a network driven by a constant stimulus (Fig. 4E). These additional results are discussed in text on page 7. 

We also taken the suggestion about variability of spiking being independent of the connectivity structure. We removed this claim in the revision, because we only tested a couple of specific cases where the connectivity is structured with respect to tuning similarity (fully structured, fully unstructured and partially unstructured networks). This is not exhaustive of all possible structures that recurrent connectivity may have.

(15) Pg. 6: "we also removed the connectivity structure only partially, keeping like-to-like connectivity structure and removing all structure beyond like-to-like" - can you clarify what this means, perhaps using an equation? What connectivity structure is there besides like-to-like?

In the optimal model, the strength of the synapse between a pair of neurons is proportional to the tuning similarity of the two neurons, Y_ij proportional to J_ij for Y_ij >0 (see Eq. 24 and Fig. 1C(ii)). Besides networks with optimal connectivity, we also tested networks with a simpler connectivity rule. Such a simpler rule prescribes a connection if the pair of neurons has similar tuning (Y_ij >0), and no connection otherwise. The strength of the connection following this simpler connectivity rule is otherwise random (and not proportional to pairwise tuning similarity Y_ij as it is in the optimal network). We clarified this in the revision (page 8), also by avoiding the term “like-to-like” for the second type of networks, which could indeed be prone to confusion.

(16) Pgs. 6-7: "we indeed found that optimal coding efficiency is achieved with weak adaptation in both cell types" and "adaptation in E neurons promotes efficient coding because it enforces every spike to be error- correcting" - this was not clear to me. First, it appears as though optimal efficiency is achieved without adaptation nor facilitation, i.e., when the time constants are all equal. Indeed, this is what is stated in Table 1. So is there really a weak adaptation present in the optimal case? Second, it seems that the network already enforces each spike to be errorcorrecting without adaptation, so why and how would adaptation help with this?

We agree with the Reviewer that the network without adaptation in E and I neurons is already optimal. It is also true that most spikes in an optimal network should already be error-correcting (besides some spikes that might be caused by the noise). However, regimes with weak adaptation in E neurons remain close to optimality. Spike-triggered facilitation, meanwhile, ads spikes that are unnecessary and decrease network efficiency. We revised the Fig.5 (Fig. 4 in first submission) and replaced 2-dimensional plots in Fig.4 C-F with plots that show the differential effect of adaptation in E neurons (top) and in I neurons (bottom plots) for the measures of the encoding error (RMSE), the efficiency (average loss) and the firing rate (Fig. 5B-D). On the new Fig. 5C it is evident that the loss of E and I population grows slowly with adaptation in E neurons (top) while it grows faster with adaptation in I neurons (bottom). These considerations are explained in revised text on page 9.

(17) Pg. 7: "adaptation in E neurons resulted in an increase of the encoding error in E neurons and a decrease in I neurons" - it would be nice if the authors could provide any explanation or intuition for why this is the case. Could it perhaps be because the E population has fewer spikes, making the signal easier to track for the I population?

We agree that this could indeed be the case. We commented on it in revision (page 9).

(18) Pg. 7: "The average balance was precise...with strong adaptation in E neurons, and it got weaker when increasing the adaptation in I neurons (Figure 4E)" - I found the wording of this a bit confusing. Didn't the balance get stronger with larger I time constants?

By increasing the time constant of I neurons, the average imbalance got weaker (closer to zero) in E neurons (Fig. 5G, left), but stronger (further away from zero) in I neurons (Fig. 5G, right). We have revised the text on page 9 to make this clearer.

(19) Pg. 7: Figure 4F is not directly described in the text.

We have now added text (page 9) commenting on this figure in revision.

(20) Pg. 8: "indicating that the recurrent network dynamics generates substantial variability even in the absence of variability in the external current" -- how does this observation relate to your earlier claim (which I noted above) that "variability of spiking is independent of connectivity structure"?

We agree that the claim about variability of spiking being independent of connectivity structure was overstated and we thus removed it. The observation that we wanted to report is that both structured and unstructured networks have very similar levels of variability of spiking of single neurons. The fact that much of the variability of the optimal network is generated by recurrent connections is not incompatible. We revised the related text (page 11) for clarity.

(21) Pg. 9: "We found that in the optimally efficient network, the mean E-I and I-E synaptic efficacy are exactly balanced" - isn't this by design based on the derivation of the network?

True, the I-E connectivity matrix is the transpose of the E-I connectivity matrix, and their means are the same by the analytical solution. This however remains a finding of our study. We have clarified this in the revised text (page 12).

(22) Pg. 30, eq. 25: the authors should verify if they include all possible connectivity here, or if they exclude EE connectivity beforehand.

We now specify that the equation for recurrent connectivity (Eq. 24, Eq. 25 in first submission) does not include the E-E connectivity in the revised text (page 41).

Reviewer #3 (Recommendations For The Authors):

Essential

(1)  Currently, they measure the RMSE and cost of the E and I population separately, and the 1CT model. Then, they average the losses of the E and I populations, and compare that to the 1CT model, with the conclusion that the 1CT model has a higher average loss. However, it seems to me that only the E population should be compared to the 1CT model. The I population loss determines how well the I population can represent the E population representation (which it can do extremely well). But the overall coding accuracy of the network of the input signal itself is only represented by the E population. Even if you do combine the E and I losses, they should be summed, not averaged. I believe a more fair conclusion would be that the E/I networks have generally slightly worse performance because of needing to follow Dale's law, but are still highly efficient and precise nonetheless. Of course, I might be making a critical error somewhere above, and happy to be convinced otherwise!

We carefully considered the reviewer's comment and tested different ways of combining the losses of the E and I population. We decided to follow the reviewer's suggestion and to compare the loss of the E population of the E-I model with the loss of the one cell type model. As evident already from the Fig. 8G, such comparison indeed changes the result to make the 1CT model more efficient. Also, the sum of losses of E and I neurons results in the 1CT model being more efficient than the E-I model. Note, however, the robustness of the E-I model to changes in the metabolic constant (Fig. 6C, top). The firing rates of the E-I model stay within physiological ranges for any value of the metabolic constant, while the firing rate of the 1CT model skyrocket for the metabolic constant that is lower than optimal (Fig. 8I).

We added to Results (page 14) a summary of these findings.

(2) The methods and main text should make much clearer what aspects of the derivation are novel, and which are not novel (see review weaknesses for specifics).

We specified these aspects, as discussed in more detail in the above reply to point 4 of the public review of Reviewer 1.

Request:

If possible, I would like to see the code before publication and give recommendations on that (is it easy to parse and reproduce, etc.)

We are happy to share the computer code with the reviewer and the community. We added a link to our public repository containing the computer code that we used for simulations and analysis to the preprint and submission (section “Code availability” on page 17). 

Suggestions:

(1) I believe that for an eLife audience, the main text is too math-heavy at the beginning, and it could be much simplified, or more effort could be made to guide the reader through the math.

We tried to do our best to improve the clarity of description of mathematical expressions in the main text.

(2) Generally vector notation makes network equations for spiking neurons much clearer and easier to parse, I would recommend using that throughout the paper (and not just in the supplementary methods).

We now use vector notation throughout the paper whenever we think that this improves the intelligibility of the text. 

(3) In the discussion or at the end of the results adding a clear section summarizing what the minimal requirements or essential assumptions are for biological networks to implement this theory would be helpful for experimentalists and theorists alike.

We have added such a section in Discussion (page 15). 

(5) I think the title is a bit too cumbersome and hard to parse. Might I suggest something like 'Efficient coding and energy use in biophysically realistic excitatory-inhibitory spiking networks' or 'Biophysically constrained excitatory-inhibitory spiking networks can efficiently implement efficient coding'.

We followed reviewer’s suggestion and changed the title to “Efficient coding in biophysically realistic excitatory-inhibitory spiking networks.”

(6) How the connections were shuffled exactly was not clear to me in how it was described now. Did they just take the derived connectivity, and shuffle the connections around? I recommend a more explicit methods section on it (I might have missed it).

Indeed, the connections of the optimal network were randomly shuffled, without repetition, between all neuronal pairs of a specific connectivity matrix. This allows to preserve all properties of the distribution of connectivity weights and only removes the structure of the connectivity, which is precisely what we wanted to test. We now added a section in Methods (“Removal of connectivity structure”) on pages 51-52 where we explain how the connectivity structure is removed.

(7) Figure 1 sub-panel ordering was confusing to read (first up down, then left right). Not sure if re- arranging is possible, but perhaps it could be A, B, and C at the top, with subsublabels (i) and (ii). Might become too busy though.

We followed this suggestion and rearranged the Fig. 1 as suggested by the reviewer. 

(8) Equation 3 in the main text should specify that 'y' stands for either E or I.

This has been specified in the revision (page 3). 

(9) Figure 1D shows a rough sketch of the types of connectivities that exist, but I would find it very useful to also see the actual connection strengths and the effect of enforcing Dale's law.

We revised this figure (now Fig. 1B (ii)) and added connection strengths as well as a sketch of a connection that was removed because of Dale’s law.

(10) The main text mentions how the readout weights are defined (normal distributions), but I think this should also be mentioned in the methods.

Agreed. We indeed had Methods section “Parametrization of synaptic connectivity (page 46), where we explain how readout weights are defined. We apologize if a call on this section was not salient enough in the first submission. We made sure that the revised main text contains a clear pointer to this Methods section for details. 

(11) The text seems to mix ‘decoding weights’ and ‘readout weights’.

Thanks for this suggestion to use consistent language. We opted for ‘decoding weights’ and removed ‘readout weights’.

(12) The way the paper is written makes it quite hard to parse what are new experimental predictions, and what results reproduce known features. I wonder if some sort of 'box' is possible with novel predictions that experimentalists could easily look at and design an experiment around.

We now revised the text. We clarified for every property of the model if this property is a prediction of facts that were not yet experimentally tested or if it accounts for previously observed properties of biological neurons. Please see the reply to point 4 of Reviewer 1. 

(13) Typo's etc.:

Page 5 bottom -- ("all") should have one of the quotes change direction (common latex typo, seems to be the only place with the issue).

We thank the reviewer for pointing out this typo that has been removed in revision.

eLife Assessment

This is a useful study depicting the ultrastructural features of layer 1 of the human temporal cortex, the authors assess various synaptic parameters, astrocytic coverage, and mitochondrial morphology. High-quality data were collected using a solid methodology, however, the analysis of the functional vesicle pools is incomplete, technical issues remaining related to the identification of astrocytic processes and the measurement of vesicle diameters, and reliance solely on electron microscopy limits the scope of the work to structural observation. The work will be of interest to neuroscientists and computational researchers investigating cortical and network function.

Reviewer #1 (Public review):

Summary:

The Authors investigated the anatomical features of the excitatory synaptic boutons in layer 1 of the human temporal neocortex. They examined the size of the synapse, the macular or the perforated appearance and the size of the synaptic active zone, the number and volume of the mitochondria, the number of the synaptic and the dense core vesicles, also differentiating between the readily releasable, the recycling and the resting pool of synaptic vesicles. The coverage of the synapse by astrocytic processes was also assessed, and all the above parameters were compared to other layers of the human temporal neocortex. The Authors conclude that the subcellular morphology of the layer 1 synapses is suitable for the functions of the neocortical layer, i.e. the synaptic integration within the cortical column. The low glial coverage of the synapses might allow the glutamate spillover from the synapses enhancing synpatic crosstalk within this cortical layer.

Strengths:

The strengths of this paper are the abundant and very precious data about the fine structure of the human neocortical layer 1. Quantitative electron microscopy data (especially that derived from the human brain) are very valuable, since this is a highly time- and energy consuming work. The techniques used to obtain the data, as well as the analyses and the statistics performed by the Authors are all solid, strengthen this manuscript, and mainly support the conclusions drawn in the discussion.

Comments on latest version:

The corrected version of the article titled „Ultrastructural sublaminar specific diversity of excitatory synaptic boutons in layer 1 of the adult human temporal lobe neocortex" has been improved thanks to the comments and suggestions of the reviewers. The Authors implemented several of my comments and suggestions. However, many of them were not completed. It is understandable that the Authors did not start a whole new series of experiment investigating inhibitory synapses (as it was a misunderstanding affecting 2 reviewers from the three). But the English text is still very hard to understand and has many mistakes, although I suggested to extensively review the use of English. Furthermore, my suggestion about avoiding many abbreviations in the abstract, analyse and discuss more the perforated synapses, the figure presentation (Figure 3) and including data about the astrocytic coverage in the Results section were not implemented. My questions about the number of docked vesicles and p10 vesicles, as well as about the different categories of the vesicle pools have not been answered neither. Many other minor comments and suggestions were answered, corrected and implemented, but I think it could have been improved more if the Authors take into account all of the reviewers' suggestions, not only some of them. I still have several main and minor concerns, with a few new ones as well I did not realized earlier, but still think it is important.

Main concerns:

(1) Epileptic patients:<br /> As all patients were epileptic, it is not correct to state in the abstract that non-epileptic tissue was investigated. Even if the seizure onset zone was not in the region investigated, seizures usually invade the temporal lobe in TLE. If you can prove that no spiking activity occured in the sample you investigated and the seizures did not invade that region, then you can write that it is presumably non-epileptic. I would suggest to write „L1 of the human temporal lobe neocortical biopsy tissue". See also Methods lines 608-612. Write only „non-epileptic" or „non-affected" if you verified it with EcoG. If this was the case, please write a few sentences about it in the Methods.

(2) About the inhibitory/excitatory synapses.<br /> Since our focus was on excitatory synaptic boutons as already stated in the title we have not analyzed inhibitory SBs.<br /> Now, I do understand that only excitatory synapses were investigated. Although it was written in the title, I did not realized, since all over the manuscript the Authors were writing synapses, and were distinguishing between inhibitory and excitatory syanpses in the text and showing numerous excitatory and inhibitory synapses on Figure 2 and discussing inhibitory interneurons in the Discussion as well. Maybe this was the reason why two reviewers out of the three (including myself) thought you investigated both types of synapses but did not differentiated between them. So, please, emphasize in the Abstract (line 40), Introduction (for ex. line 92-97) and the Discussion (line 369) that only excitatory synaptic boutons were investigated.<br /> As this paper investigated only excitatory synaptic boutons, I think it is irrelevant to write such a long section in the Discussion about inhibitory interneurons and their functions in the L1 of the human temporal lobe neocortex. Same applies to the schematic drawing of the possible wiring of L1 (Figure 7). As no inhibitory interneurons were examined, neither the connection of the different excitatory cells, only the morphology of single synaptic boutons without any reference on their origin, I think this figure does not illustrate the work done in this paper. This could be a figure of a review paper about the human L1, but is is inappropriate in this study.

(3) Perforated synapses<br /> "the findings of the Geinismann group suggesting that perforated synapses are more efficient than non-perforated ones is nowadays very controversially discussed"<br /> I did not ask the Authors to say that perforated synapses are more efficient. However, based on the literature (for ex. Harris et al, 1992; Carlin and Siekievitz, 1982; Nieto-Sampedro et al., 1982) the presence of perforated synapses is indeed a good sign of synapse division/formation - which in turn might be coupled to synaptic plasticity (Geinisman et al, 1993), increased synaptic activity (Vrensen and Cardozo, 1981), LTP (Geinisman et al, 1991, Harris et al, 2003), pathological axonal sprouting (Frotscher et al, 2006), etc. I think it is worth mentioning this at least in the Discussion.

(4) Question about the vesicle pools<br /> Results, Line 271: Still not understandable, why the RRP was defined as {less than or equal to}10 nm and {less than or equal to}20nm. Why did you use two categories? One would be sufficient (for example {less than or equal to}20nm). Or the vesicles between 10 and 20nm were considered to be part of RRP? In this case there is a typo, it should be {greater than or equal to}10 nm and {less than or equal to}20nm.<br /> The answer of the Authors was to my question raised: We decided that also those very close within 10 and 20 nm away from the PreAZ, which is less than a SV diameter may also contribute to the RRP since it was shown that SVs are quite mobile.<br /> This does not clarify why did you use two categories. Furthermore, I did not receive answer (such as Referee #2) for my question on how could you have 3x as many docked vesicles than vesicles {less than or equal to}10nm. The category {less than or equal to}10nm should also contain the docked vesicles. Or if this is not the case, please, clarify better what were your categories.

(5) Astrocytic coverage<br /> On Fig. 6 data are presented on the astrocytic coverage derived from L1 and L4. In my previous review I asked to include this in the text of the Results as well, but I still do not see it. It is also lacking from the Results how many samples from which layer were investigated in this analysis. Only percentages are given, and only for L1 (but how many patients, L1a and/or L1b and/or L4 is not provided). In contrast, Figure 6 and Supplementary Table 2 (patient table) contains the information that this analysis has been made in L4 as well. Please, include this information in the text as well (around lines 348-360).<br /> About how to determine glial elements. I cannot agree with the Authors that glial elements can be determined with high certainty based only on the anatomical features of the profiles seen in the EM. „With 25 years of experience in (serial) EM work" I would say, that glial elements can be very similar to spine necks and axonal profiles.<br /> All in all, if similar methods were used to determine the glial coverage in the different layers of the human neocortex, than it can be compared (I guess this is the case). However, I would say in the text that proper determination would need immunostaining and a new analysis. This only gives an estimatation with the possibility of a certain degree of error.

(6) Large interindividual differences in the synapse density should be discussed in the Discussion.

Reviewer #2 (Public review):

Summary:

The study of Rollenhagen et al examines the ultrastructural features of Layer 1 of human temporal cortex. The tissue was derived from drug-resistant epileptic patients undergoing surgery, and was selected as further from the epilepsy focus, and as such considered to be non-epileptic. The analyses has included 4 patients with different age, sex, medication and onset of epilepsy. The MS is a follow-on study with 3 previous publications from the same authors on different layers of the temporal cortex:

Layer 4 - Yakoubi et al 2019 eLife<br /> Layer 5 - Yakoubi et al 2019 Cerebral Cortex,<br /> Layer 6 - Schmuhl-Giesen et al 2022 Cerebral Cortex

They find, the L1 synaptic boutons mainly have single active zone a very large pool of synaptic vesicles and are mostly devoid of astrocytic coverage.

Strengths:

The MS is well written easy to read. Result section gives a detailed set of figures showing many morphological parameters of synaptic boutons and surrounding glial elements. The authors provide comparative data of all the layers examined by them so far in the Discussion. Given that anatomical data in human brain are still very limited, the current MS has substantial relevance.<br /> The work appears to be generally well done, the EM and EM tomography images are of very good quality. The analyses is clear and precise.

Weaknesses:

The authors made all the corrections required, answered most of my concerns, included additional data sets, and clarified statements where needed.

My remaining points are:

Synaptic vesicle diameter (that has been established to be ~40nm independent of species) can properly be measured with EM tomography only, as it provides the possibility to find the largest diameter of every given vesicle. Measuring it in 50 nm thick sections result in underestimation (just like here the values are ~25 nm) as the measured diameter will be smaller than the true diameter if the vesicle is not cut in the middle, (which is the least probable scenario). The authors have the EM tomography data set for measuring the vesicle diameter properly.

It is a bit misleading to call vesicle populations at certain arbitrary distances from the presynaptic active zone as readily releasable pool, recycling pool and resting pool, as these are functional categories, and cannot directly be translated to vesicles at certain distances. Even it is debated whether the morphologically docked vesicles are the ones, that are readily releasable, as further molecular steps, such as proper priming is also a prerequisite for release.<br /> It would help to call these pools as "putative" correlates of the morphological categories.

Reviewer #3 (Public review):

Summary:

Rollenhagen at al. offer a detailed description of layer 1 of the human neocortex. They use electron microscopy to assess the morphological parameters of presynaptic terminals, active zones, vesicle density/distribution, mitochondrial morphology and astrocytic coverage. The data is collected from tissue from four patients undergoing epilepsy surgery. As the epileptic focus was localized in all patients to the hippocampus, the tissue examined in this manuscript is considered non-epileptic (access) tissue.

Strengths:

The quality of the electron microscopic images is very high, and the data is analyzed carefully. Data from human tissue is always precious and the authors here provide a detailed analysis using adequate approaches, and the data is clearly presented.

Weaknesses:

The text connects functional and morphological characteristics in a very direct way. For example, connecting plasticity to any measurement the authors present would be rather difficult without any additional functional experiments. References to various vesicle pools based on the location of the vesicles is also more complex than it is suggested in the manuscript. The text should better reflect the limitations of the conclusions that can be drawn from the authors' data.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

The authors investigated the anatomical features of the synaptic boutons in layer 1 of the human temporal neocortex. They examined the size of each synapse, the macular or perforated appearance, the size of the synaptic active zone, the number and volume of the mitochondria, and the number of synaptic and dense core vesicles, also differentiating between the readily releasable, the recycling, and the resting pool of synaptic vesicles. The coverage of the synapse by astrocytic processes was also assessed, and all the above parameters were compared to other layers of the human temporal neocortex. The authors conclude that the subcellular morphology of the layer 1 synapses are suitable for the functions of the neocortical layer, i.e. the synaptic integration within the cortical column. The low glial coverage of the synapses might allow increased glutamate spillover from the synapses, enhancing synaptic crosstalk within this cortical layer.

Strengths:

The strengths of this paper are the abundant and very precious data about the fine structure of the human neocortical layer 1. Quantitative electron microscopy data (especially that derived from the human brain) are very valuable since this is a highly time- and energy-consuming work. The techniques used to obtain the data, as well as the analyses and the statistics performed by the authors are all solid, strengthen this manuscript, and mainly support the conclusions drawn in the discussion.

We would like to thank reviewer#1 for his very positive comments on our manuscript stating that such data about the fine structure of the human neocortex are are highly relevant.

Weaknesses:

There are several weaknesses in this work. First, the authors should check and review extensively for improvements to the use of English. Second, several additional analyses performed on the existing data could substantially elevate the value of the data presented. Much more information could be gained from the existing data about the functions of the investigated layer, of the cortical column, and about the information processing of the human neocortex. Third, several methodological concerns weaken the conclusions drawn from the results.

We would like to thank the reviewer for his critical and thus helpful comments on our manuscript. We took the first comment of the reviewer concerning the English and have thus improved our manuscript by rephrasing and shortening sentences. Secondly, according to the reviewer several additional analyses should be performed on the existing data, which could substantially elevate the value of the data presented. We will implement some of the suggestions in the improved version of the manuscript where appropriate. We will address a more detailed answer to the reviewer’s queries in her/his suggestions to the authors (see below). However, the reviewer states himself: “The techniques used to obtain the data, as well as the analyses and the statistics performed by the authors are all solid, strengthen this manuscript, and mainly support the conclusions drawn in the discussion”.

Reviewer #2 (Public review):

Summary:

The study of Rollenhagen et al. examines the ultrastructural features of Layer 1 of the human temporal cortex. The tissue was derived from drug-resistant epileptic patients undergoing surgery, and was selected as far as possible from the epilepsy focus, and as such considered to be non-epileptic. The analyses included 4 patients with different ages, sex, medication, and onset of epilepsy. The manuscript is a follow-on study with 3 previous publications from the same authors on different layers of the temporal cortex:

Layer 4 - Yakoubi et al 2019 eLife

Layer 5 - Yakoubi et al 2019 Cerebral Cortex

Layer 6 - Schmuhl-Giesen et al 2022 Cerebral Cortex.

They find, that the L1 synaptic boutons mainly have a single active zone, a very large pool of synaptic vesicles, and are mostly devoid of astrocytic coverage.

Strengths:

The manuscript is well-written and easy to read. The Results section gives a detailed set of figures showing many morphological parameters of synaptic boutons and glial elements. The authors provide comparative data of all the layers examined by them so far in the Discussion. Given that anatomical data in the human brain are still very limited, the current manuscript has substantial relevance. The work appears to be generally well done, the EM and EM tomography images are of very good quality. The analysis is clear and precise.

We would like to thank the reviewer for his very positive evaluation of our paper and the comments that such data have a substantial relevance, in particular in the human neocortex. In contrast to reviewer#1, this reviewer’s opinion is that the manuscript is well written and easy to read.

Weaknesses:

One of the main findings of this paper is that "low degree of astrocytic coverage of L1 SBs suggests that glutamate spillover and as a consequence synaptic cross-talk may occur at the majority of synaptic complexes in L1". However, the authors only quantified the volume ratio of astrocytes in all 6 layers, which is not necessarily the same as the glial coverage of synapses. In order to strengthen this statement, the authors could provide 3D data (that they have from the aligned serial sections) detailing the percentage of synapses that have glial processes in close proximity to the synaptic cleft, that would prevent spillover.

We agree with the reviewer that we only quantified the volume ratio of the astrocytic coverage but not necessarily the percentage of synapses that may or not contribute to the formation of the ‘tripartite’ synapse. As suggested, we will re-analyze our material with respect to the percentage of coverage for individual synaptic boutons in each layer and will implement the results in the improved version of the manuscript. However, since this is a completely new analysis that is time-consuming we would like to ask the reviewer for additional time to perform this task.

A specific statement is missing on whether only glutamatergic boutons were analyzed in this MS, or GABAergic boutons were also included. There is a statement, that they can be distinguished from glutamatergic ones, but it would be useful to state it clearly in the Abstract, Results, and Methods section what sort of boutons were analyzed. Also, what is the percentage of those boutons from the total bouton population in L1?

We would like to thank the reviewer for this comment. Although our title clearly states, we focused on quantitative 3D-models of excitatory synaptic boutons, we will point out that more clearly in the Methods and Result chapters. Our data support recent findings by others (see for example Cano-Astorga et al. 2023, 2024; Shapson-Coe et al. 2024) that have evaluated the ratio between excitatory vs. inhibitory synaptic boutons in the temporal lobe neocortex, the same area as in our study, which was between 10-15% inhibitory terminals but with a significant layer and region specific difference. We will include the excitatory vs. inhibitory ratio and the corresponding citations in the Results section.

Synaptic vesicle diameter (that has been established to be ~40nm independent of species) can properly be measured with EM tomography only, as it provides the possibility to find the largest diameter of every given vesicle. Measuring it in 50 nm thick sections results in underestimation (just like here the values are ~25 nm) as the measured diameter will be smaller than the true diameter if the vesicle is not cut in the middle, (which is the least probable scenario). The authors have the EM tomography data set for measuring the vesicle diameter properly.

We partially disagree with the reviewer on this point. Using high-resolution transmission electron microscopy, we measured the distance from the outer-to-outer membrane only on those synaptic vesicles that were round in shape with a clear ring-like structure to avoid double counts and discarded all those that were only partially cut according to criteria developed by Abercrombie (1946) and Boissonnat (1988). We assumed that within a 55±5 nm thick ultrathin section (silver to gray interference contrast) all clear-ring-like vesicles were distributed in this section assuming a vesicle diameter between 25 to 40nm. For large DCVs, double-counts were excluded by careful examination of adjacent images and were only counted in the image where they appeared largest.

In addition, we have measured synaptic vesicles using TEM tomography and came to similar results. We will address this in Material and Methods that both methods were used.

It is a bit misleading to call vesicle populations at certain arbitrary distances from the presynaptic active zone as readily releasable pool, recycling pool, and resting pool, as these are functional categories, and cannot directly be translated to vesicles at certain distances. Indeed, it is debated whether the morphologically docked vesicles are the ones, that are readily releasable, as further molecular steps, such as proper priming are also a prerequisite for release.

We thank the reviewer for this comment. However, nobody before us tried to define a morphological correlate for the three functionally defined pools of synaptic vesicles since synaptic vesicles normally are distributed over the entire nerve terminal. As already mentioned above, after long and thorough discussions with Profs. Bill Betz, Chuck Stevens, Thomas Schikorski and other experts in this field we tried to define the readily releasable (RRP), recycling (RP) and resting pools by measuring the distance of each synaptic vesicle to the presynaptic density (PreAZ). Using distance as a criterion, we defined the RRP including all vesicles that were located within a distance (perimeter) of 10 to 20 nm from the PreAZ that is less than an average vesicle diameter (between 25 to 40 nm). The RP was defined as vesicles within a distance of 60-200 nm away, still quite close but also rapidly available on demand and the remaining ones beyond 200 nm were suggested to belong to the resting pool. This concept was developed for our first publication (Sätzler et al. 2002) and this approximation since then is very much acknowledged by scientist working in the field of synaptic neuroscience and computational neuroscientist. We were asked by several labs worldwide whether they can use our data of the perimeter analysis for modeling. We agree that our definition of the three pools can be seen as arbitrary but we never claimed that our approach is the truth but nothing as the truth. Concerning the debate whether only docked vesicles or also those very close the PreAZ should constitute the RRP we have a paper in preparation using our perimeter analysis, EM tomography and simulations trying to clarify this debate. Our preliminary results suggest that the size of the RRP should be reconsidered.

Tissue shrinkage due to aldehyde fixation is a well-documented phenomenon that needs compensation when dealing with density values. The authors cite Korogod et al 2015 - which actually draws attention to the problem comparing aldehyde fixed and non-fixed tissue, still the data is non-compensated in the manuscript. Since all the previous publications from this lab are based on aldehyde fixed non-compensated data, and for this sake, this dataset should be kept as it is for comparative purposes, it would be important to provide a scaling factor applicable to be able to compare these data to other publications.

We thank the reviewer for his suggestion. However, for several reasons we did not correct for shrinkage caused by aldehyde fixation. There are papers by Eyre et al. (2007) and the mentioned paper by Korogod et al. 2015 that have demonstrated that cryo-fixation reveals larger numbers of docked synaptic vesicles, a smaller glial volume, and a less intimate glial coverage of synapses and blood vessels compared to chemical fixation. Other structural subelements such as active zone size and shape and the total number of synaptic vesicles remained unaffected. In two further publications Zhao et al. (2012a, b) investigating hippocampal mossy fiber boutons using cryo-fixation and substitutions came to similar results with respect to bouton and active zone size and number and diameter of synaptic vesicles compared to aldehyde-fixation as described by Rollenhagen et al. 2007 for the same nerve terminal. This was one of the reasons not correcting for shrinkage. In addition, all cited papers state that chemical fixation in general provides a much better ultrastructural preservation of tissue samples when compared with cryo-fixation and substitution where optimal preservation is only regional within a block of tissue and therefore less suitable for large-scale ultrastructural analyses as we performed.

Reviewer #3 (Public review):

Summary:

Rollenhagen et al. offer a detailed description of layer 1 of the human neocortex. They use electron microscopy to assess the morphological parameters of presynaptic terminals, active zones, vesicle density/distribution, mitochondrial morphology, and astrocytic coverage. The data is collected from tissue from four patients undergoing epilepsy surgery. As the epileptic focus was localized in all patients to the hippocampus, the tissue examined in this manuscript is considered non-epileptic (access) tissue.

Strengths:

The quality of the electron microscopic images is very high, and the data is analyzed carefully. Data from human tissue is always precious and the authors here provide a detailed analysis using adequate approaches, and the data is clearly presented.

We are very thankful to the reviewer upon his very positive comments about our data analysis and presentation.

Weaknesses:

The study provides only morphological details, these can be useful in the future when combined with functional assessments or computational approaches. The authors emphasize the importance of their findings on astrocytic coverage and suggest important implications for glutamate spillover. However, the percentage of synapses that form tripartite synapses has not been quantified, the authors' functional claims are based solely on volumetric fraction measurements.

We thank the reviewer for his critical comments on our findings concerning the layer-specific astrocytic coverage as also suggested by reviewer#2. As already stated above we will analyze the astrocytic coverage and the layer-specific percentage of astrocytic contribution to the ‘tripartite’ synapse in more detail. We are, however, a bit puzzled about the comment that structural anatomists usually receive that our study only provides morphological details. Our thorough analysis of structural and synaptic parameters of synaptic boutons underlie and might even predict the function of synaptic boutons in a given microcircuit or network and will thus very much improve our understanding and knowledge about the functional properties of these structures, in particular in the human brain where such studies are still quite rare. The main goal of our studies in the human neocortex was the quantitative morphology of synaptic boutons and thus the synaptic organization of the cortical column, layer by layer which to our knowledge is the first such detailed study undertaken in the human brain. Our efforts have set a golden standard in the analysis of synaptic boutons embedded in different microcircuits und is meanwhile internationally very well accepted.

The distinction between excitatory and inhibitory synapses is not clear, they should be analyzed separately.

As already stated above in response to reviewer#1 our study focused on excitatory synaptic boutons since they represent the majority of synapses. However, in the improved version of our manuscript in the Material and Method section we included a paragraph with structural criteria to distinguish excitatory from inhibitory terminals (see also our comment to reviewer#1 concerning this point) including appropriate citations.

The text connects functional and morphological characteristics in a very direct way. For example, connecting plasticity to any measurement the authors present would be rather difficult without any additional functional experiments. References to various vesicle pools based on the location of the vesicles are also more complex than suggested in the manuscript. The text should better reflect the limitations of the conclusions that can be drawn from the authors' data.

We thank the reviewer for this comment. However, it has been shown by meanwhile numerous publications that the shape and size of the active zone together with the pool of synaptic vesicles and the astrocytic coverage critically determines synaptic transmission and synaptic strength, but can also contribute to the modulation of synaptic plasticity (see also citations within the text). It has been shown that synaptic boutons can switch upon certain stimulation conditions to different modes of release (uni- vs. multiquantal, uni- vs multivesicular release) and from asynchronous to synchronous release leading also to the modulation of synaptic short- and long-term plasticity. To the second comment: When we started with our first paper about the Calyx of Held – principal neuron synapse in the MNTB (Sätzler et al. 2002) we tried to define a morphological correlate for the three functionally defined pools. As already mentioned above in our reply to the other two reviewers, this is rather difficult since synaptic vesicles are normally distributed over the entire nerve terminal. After long and thorough discussions with Bill Betz, Chuck Stevens and other leading scientist in the field of synaptic neuroscience, we together with Bert Sakmann tried to define a morphological correlate for the functionally defined pools using a perimeter analysis. We defined the readily releasable pool as vesicles 10 to 20 nm away from the presynaptic active zone, the recycling pool as those in 60-200 nm distance and the remaining as those belonging to the resting pool. However, it has been shown by capacitance measurements (see for example Hallermann et al 2003), FM1-43 investigations (see for example Henkel et al. 1996) and high-resolution electron microscopy (see for example Schikorski and Stevens 2001; Schikorski 2014) that our estimate of the RRP nearly perfectly matches with the functionally defined pools at hippocampal and cortical synapses (Silver et al. 2003). In addition, in one of our own papers (Rollenhagen et al. 2018) we also estimated the RP functionally from trains of EPSPs using an exponential fit analysis and came to similar results upon its size using the perimeter analysis.

Of course, as stated by the reviewer the scenario could be more complex, using other criteria but we never claimed that our morphologically defined pools are the truth but nothing as the truth but we believe it offers a quite good approximation.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

Abstract:

Avoid the numerous abbreviations in the abstract. The paragraph describing the results obtained in this study is too short. Include more results, such as the size of the active zone, the proportion of perforated synapses, the ratio of synapses terminating on dendrites/spines, the percentage of volume occupied by mitochondria, etc. In the last paragraph, compare the layer-specific data to other layers of the neocortex before writing the concluding sentence.

To meet the word limits of the abstract (150 words) defined by eLife we had to use abbreviations. We followed the suggestions by the reviewer and expanded our abstract by adding the proportion of macular vs. perforated active zone and the percentage of mitochondria within an SB. However, we did not include the comparison of structural parameters in the Abstract since this is discussed thoroughly in the MS at other places (see Results and Discussion).

Results:

First of all, wonderful data! Lots of work, very valuable quantitative electron microscopy results.

Main concerns:

Adding several analyses would give much more information about the cortical synaptic organization. It would be very useful to differentiate between excitatory and inhibitory terminals (and give their ratio) and include this information in all different analyses, such as in the SV number, SV pool analysis, mitochondrion analysis, etc., that would give functional information as well. You have all the data for this, and you know how to differentiate between inhibitory and excitatory synapses, it can be done. We could see the possible morphological differences between excitatory and inhibitory synapses (maybe one is larger/has more SVs, etc. than the other). Based on these possible differences conclusions could be drawn about functional hypotheses, such as one or the other is more efficient in inducing postsynaptic potentials, excitation or inhibition is more pronounced in layer 1, etc. Furthermore, looking at the ratio of perforated synapses, we could gain information about the formation of new synapses. Maybe there is a difference between excitatory and inhibitory circuits in this point of view.

To the first point: Since our focus was on excitatory synaptic boutons as already stated in the title we have not analyzed inhibitory SBs. To do so, we have to re-analyze our complete data which is time-consuming and an additional workload. However, we can give a ratio excitatory vs. inhibitory synaptic boutons which was between 10-15% but with layer-specific differences. Our finding are in good agreement with a recent publication in Science by the Lichtman group (Shapson-Coe et al. 2024) and work by the DeFelipe group (Cano-Astorga et al. 2023, 2024) estimating the number of inhibitory boutons in different layers of the temporal lobe neocortex as we did by 10-15%. We included a small paragraph about inhibitory synapses, their percentage and included the citations in our Results section. Concerning the ratio between macular, non-perforated vs. perforated active zones we stated the majority of synaptic boutons were of the macular, non-perforated type (~75%; see improved version of the MS). If perforated, this was found predominantly on the postsynaptic site, but quite rare in L1 SBs. Since GABAergic terminals had only a small or no clearly visible PSD this would be hard to look at.

To the last point, it has been demonstrated that the number of dense core vesicles and their fusion with the presynaptic density could be a critical factor in the build-up of the active zone. In addition, the findings of the Geinismann group suggesting that perforated synapses are more efficient than non-perforated ones is nowadays very controversially discussed since other factors such as size of the active zone (see for example Matz et al. 2010; Holderith et al. 2012) and the astrocytic coverage contribute to synaptic efficacy and strength.

Related to this topic: although in the case of rat CA1 pyramidal cells all inhibitory synapses terminated on dendritic shafts (Megias et al., Neuroscience 2001), please be aware that both excitatory and inhibitory synapses can terminate on both dendritic shafts and spines in humans (inhibitory synapses are though rare on spines, usually less than 10%, but they do exist, see for example Wittner et al, Neuroscience, 2001). Please, define the excitatory/inhibitory nature of the synapses based on morphological features (not on their postsynaptic target), i.e., flattened vesicles and thin postsynaptic density for GABAergic synapses, whereas larger, round vesicles and thick postsynaptic density for glutamatergic synapses. Anyway, the ratio of excitatory and inhibitory synapses on dendrites and spines in the two sublamina would also give useful information about the synaptic organization of the human neocortical layer 1.

We are aware that not all terminals targeting on spines are excitatory, in turn it has been shown that not all terminals on shafts were inhibitory as long thought (Silver et al. 2003). However, as stated by the reviewer their abundancy on spines is rather low. At the moment it is rather unclear which functional impact inhibitory terminals on spines have, despite a local inhibition (see for example Kubota et al. eLife 2015), and thus their role is rather speculative since excitatory synapses are the predominant class on dendritic spines. As already stated above the ratio of excitatory vs. inhibitory terminals is between 10-15% and not significantly different between the two sublaminae. We are willing to add this in the results section (see in the improved version of the manuscript).

(2) About the glial coverage: Please, specify how glial elements were determined. What were the morphological features specific to astroglial processes? In Figure 5, how could we know whether the glial element marked by green is not a spine neck? The lack of morphological features specific to glial processes makes this analysis weak. The most accurate would be to make it with the aid of GFAP staining. I know this is not possible with your existing data, but at least, provide information on how glial processes were identified.

We used the criteria first described by Peters et al. (1991) and Ventura and Harris (1999) identifying astrocytic profiles by their irregular stellate shape, relatively clear cytoplasm, numerous glycogen granules and bundles of intermediate filaments. After more than 20 years of structural investigations, we hope that the reviewers will believe us that we can identify astrocytic processes at the high-resolution TEM level. In some of our publications (Rollenhagen et al. 2007; 2015; 2018; Yakoubi et al. 2019a) we have used glutamine synthetase pre-embedding immunhistochemistry to identify astrocytic processes, but a disadvantage of this method is the reduction of the ultrastructural preservation of the tissue. We have included the criteria to identify astrocytic processes of glial coverage in our manuscript together with the two citations (see improved version of the manuscript).

(3) The authors state that the total number of SVs was very variable. How was the distribution of the number of SVs? Homogenous distribution suggests that different types of synapses cannot be distinguished based on their morphological features, whereas distribution with more than one peak would suggest that different types of synapses are present in L1, and that they can be differentiated by their appearance (number of SVs, for example). This might be also related to the type of synapse (i.e., excitatory or inhibitory). The same applies to the number of RP and resting pool SVs.

To look for differences in structural and synaptic parameters that can further classify synaptic boutons we have performed a hierarchical cluster and multivariance analysis. However, it turned out that according to structural and functional parameters no further classification into subtypes could be done.

(4) The authors should check and review extensively for improvements to the use of English. The Results and Discussion sections contain many sentences which are not easy to understand. They have either a too complicated structure, or they are incomplete and hard to follow. Few examples: "The RRP/PreAZ at p20 nm criterium was on average 19.05 {plus minus} 17.23 SVs (L1a: 25.04 {plus minus} 21.09 SVs and L1b: 13.07 {plus minus} 13.87SVs) and thus nearly 2-fold larger for L1a." If you take out the parenthesis, the sentence has no meaning. "The majority of SBs in L1 of the human TLN had a single at most three AZs that could be of the non-perforated macular or perforated type comparable with results for other layers in the human TLN but by ~1.5-fold larger than in rodent and non-human primates." Rephrase these types of sentences, please.

We partially agree with the reviewer. We have improved our manuscript by rephrasing and shortening sentences.

Other suggestions:

(1) Put the synaptic density part after the description of the neuronal and synaptic composition part, it is more logical this way (i.e., first qualitative description, the distinction between sublayers, then quantitative data). Please write down in the description of the neuronal and synaptic composition part how L1a and L1b were differentiated (see also my comment on Figure 1).

We agree with the reviewer and did the change according to the suggestion. For a better understanding, we have also expanded the neuronal and synaptic description of the two sublaminae in L1.

(2) Introduce a list of abbreviations at the beginning, that would help.

It is quite unusual to provide a list of abbreviations in eLife. However, when used first the full meaning of the abbreviations is now given.

(3) What is cleft width? Usually, it refers to the distance between the pre- and the postsynaptic membrane, but here, I think it refers to the size (diameter) of the active zone. Please, clarify in the Result section (as it appears earlier than the Methods section, where it is explained). I would probably use the expression "synaptic cleft size" instead of "synaptic cleft width" to avoid misunderstanding.

We thank the reviewer for the suggestion and used synaptic cleft size for better clarity and have transferred the sentence from the Material and Methods to the Results section.

(4) The description of the different SVs (RRP, RP, etc.) is not clear in lines 236-242. What does it mean, that RRP vesicles are located {less than or equal to}10 nm and {less than or equal to}20 nm from the active zone? Explain, why the two different distance criteria were used. Furthermore, how were the vesicles located at p20-p60 defined? Why were these vesicles not considered in the determination of the different pools?

As stated in the public review to the reviewers concern we have tried to define a morphological correlate to the three functionally defined pools. After thorough discussions, with leading scientists in the field of synaptic neuroscience we have decided to use the distance of individual vesicles from the PreAZ and sort vesicles upon these criteria. One can argue that this approach is random, however, these distance criteria were described by Rizzoli and Betz (2004, 2005) and Denker and Rizzoli (2010). As also stated in the public review there is still a controversial discussion whether only docked or omega-shaped SVs constitute the RRP. We decided that also those very close within 10 and 20 nm away from the PreAZ, which is less than a SV diameter may also contribute to the RRP since it was shown that SVs are quite mobile.

(5) Please, explain how the number of docked vesicles can be 3x larger in L1b, than the number of vesicles located at p10? Docked vesicles are the closest (with the membrane touching the PreAZ)... if this comes from the fact that another pool of boutons was used for the EM tomography analysis, then the entire pool of boutons analyzed, then it means that the selection of boutons for the EM tomography is highly biased. This also implies that EM tomography data are most probably not valid for the entire L1b. The difference might also come from the different ratios of dendrite/spine synapses included in the two different analyses. In this case, it would be helpful to distinguish between synapses terminating on dendrites/spines and analyse them separately (same as for inhibitory/excitatory, which is not exactly the same as dendrite/spine!). Different n numbers of synapses are given in the text (n=25, 25, 25 25) and in Table 2 (n=91, 98, 87, and 84) for the analysis of the docked vesicles, please, correct this.

This is a correct value and thus there is a nearly 3-fold difference. The TEM tomography was carried out on the same blocks that have been used for our 3D-volume reconstructions. To carry out TEM tomography we had to use thicker sections (250 nm) to look for complete SBs as we also did in our serial sections, but of course, we could not quantify the same SBs. The completeness of SBs was one of our main criteria to reconstruct structural and synaptic parameters. The second was that the synaptic cleft was cut perpendicular. Only SBs that met these criteria were chosen for further quantitative analysis. In this respect we are of course biased in both methods.

Secondly, as already stated we did not quantify inhibitory terminals in serial sections. However, we did not find significant differences between shaft vs. spine synapses.

Finally, in Table 2 the total number of ‘docked’ SVs is given analyzed from the total number of SBs analyzed.

Discussion:

Please include the recent findings of human L1 neurons, including the "rosehip" cells in the L1 neuronal network, see Boldog et al., Nat Neurosci 2018. It would be also useful to consider in the discussion the human-specific cortical synchrony and integration phenomena derived from in vitro data (Mansvelder, Lein, Tamas, Wittner, Larkum, Huberfeld labs, etc.), and how the synaptic morphology can be related to these.

We thank the reviewer and include the reference in our chapter functional significance.

Figures and Tables:

Figure 1: In the legend, it is written that CR cells are marked by an asterisk, but on the figure it is marked by arrowheads. H: I would put the dashed line slightly lower, just above the two neuronal cell bodies. Now it looks like in the middle of the astrocytic layer. One of the asterisks marking the CR cell is not above the nucleus of that cell. I: the gabaergic neuron is outside of the framed area. I would delete the frame, anyway, the arrowheads and the asterisk are enough to show what the authors want to show.

We have changed the Figure according to the suggestions raised by the reviewer.

Figure 3: The transparent yellow is not visible. It is a bit disturbing that the contours of the boutons are not visible, I would make the transparent yellow stronger (less transparent). The SVs in green/magenta will be still visible.

We wanted to highlight the internal subelements of SBs and thus made the covering transparent but we think it is still visible.

Figure 6C: The data concerning other layers than L1 are most probably taken from other publications of the research group. One is cited (for L6), but not the others. Please correct this, or if not, then write this in the Results and Methods.

We changed the citation in the improved version of the manuscript. We overlooked that the values for L4 and L5 were already published in Schmuhl-Giesen et al. 2022.

Table 1: What does central and lateral cleft width mean in Table 1? Furthermore, please, give the name for abbreviations CV and IQR in Tables 1 and 2.

The measurements of the synaptic cleft are now described in detail in the Results section. We now have given the full names for CV and IQR in the legends of tables 1 and 2.

Supplemental Figures 1 and 2: Why Hu01 and Hu02 are twice? What is the difference? Based on the figure legend, it is L1a and L1b? If yes, please, indicate on the figure or in the legend.<br /> Supplemental Table 1: What is TLE in the case of Hu_04? If it is temporal lobe epilepsy, then why age at epilepsy onset is missing?

Yes, Hu01 and Hu02 were selected for both L1a and L1b in separate serial sections preparations each. We indicated this now in the figure legend. Concerning Hu_04, unfortunately we do not have any further information about the medical background of the patient.

Supplemental Table 1 (Patient table), that there are many abbreviations explained which do not appear in the table (lBAZ: Brivaracetam CBZ: Carbamazepine; CLB: Clobazam; ESL: Eslicarbazepin; GGL: Ganglioglioma, etc.), please check and correct.

We have removed the unnecessary abbreviations.

Other minor suggestions:

What is Pr? Please, give the name a first appearance (line 368).

We explained Pr (release probability) when used for the first time.

Give the name for t-LDT, please (lines 442-443).

We explained t-LTD (timing-dependent long-term depression) when used for the first time.

Typo in line 169: DCW instead of DCV (dense core vesicle), DCV is used in the figure legends.

We changed DCW to DCV.

Typo in line 190: Yokoubi instead of Yakoubi (reference).

We changed Yokoubi to Yakoubi.

Typo in line 237: Rizzoloi instead of Rizzoli (reference).

We changed Rizzoloi to Rizzoli.

Line 229-230: One reference is not inserted properly - Piccolo and Bassoon.

The reference of Schoch and Gundelfinger and Murkherjee to the build-up of the active zone and the role of DCV containing Piccolo and Bassoon are properly cited in the text.

Typo in line 398: exit instead of exist.

Corrected

Typo in line 700: Reynolds (1063) instead of 1963.

Corrected

Reviewer #2 (Recommendations for the authors):

Abstract:

The last sentence seems far-fetched, and unrelated to the manuscript. How mostly single active zone boutons can "mediate, integrate and synchronize contextual and cross-modal information, enabling flexible and state-dependent processing of feedforward sensory inputs from other layers of the cortical column"? Which of the anatomical findings of the manuscript led to these conclusions?

According to the review by Schuman et al. (2021) layer 1 is regarded as a layer that mediate, integrate and synchronize contextual and cross-modal information, enabling flexible and state-dependent processing of feedforward sensory inputs from other layers of the cortical column to which the structural quantitative 3D- models of SBs contribute since they are an integral element connecting neurons and building networks.

I am also puzzled by the authors' statement in more than one place of the manuscript that "L1a can be characterized as a predominantly astrocytic sublamina". If the L1 contains the lowest measured volume ratio of glial processes (Figure 6), then this description does not seem to hold. Please rephrase.

The reviewer is right and we rephrased the sentences for more clarity in the improved version of our manuscript.

Results:

The authors find large inter-patient variability in the synapse density at L1, which raises the issue of what were the criteria to include certain patients in the analyses. Apparently, these are different from the ones analysed in their previous papers, and all the provided parameters were different (sex, age, medication, onset of epilepsy), and any of them can result in altered synapse density.

First, we have not used all patients for this study. Secondly, it was not possible to use all patients for all six layers.

It would be useful to add a panel for Figure 1 with synapse density across the different layers, as they provide this data in the Discussion.

We implemented a Supplementary Table 1 with the synaptic density values over all layers compared in the Discussion.

I cannot find Source Data 1 in the manuscript although it is referred to in more than 1 place (e.g. page 5 line 100).

Source data were uploaded when our manuscript was submitted directly to eLife as Supplemental Material. However, as stated by bioRxiv ‘any Supplemental Materials associated with this manuscript have not been transferred to bioRxiv to avoid the posting of potentially sensitive information’ all source data have not been uploaded to the preprint server.

Page 5 line 100 the correct value is 7.3*107 or rather 108?

We corrected the value in the improved version of the MS.

It would be nice to put the synapse density values into context by comparing them to e.g. mouse, rat, or monkey data.

Since we are working on the human temporal lobe neocortex we avoided to compare those data with those estimated in experimental animals. In addition as discussed by DeFelipe et al. (1999) different methods were used to quantify synaptic density in experimental animals so these results are difficult to compare.

Page 5 Line 117 CR-cells stands for Cayal-Retzius cells?

CR-cells is the abbreviation for Cajal-Retzius cells.

Page 6 Line 146 repeated sentence.

We deleted the repeated sentence.

Page 7 Line 154 "file-scale TEM" ??

We replaced file-scale by fine-scale.

Page 7 Line 164 "GABAergic synapses identified by the smaller more spherical SVs". With this fixation condition, GABAergic vesicles are more ovoid than glutamatergic ones. What were the criteria to distinguish them?

To our knowledge in meanwhile numerous publications using the same fixation inhibitory terminals contain more spherical and smaller and not roundish synaptic vesicles and showed no clear prominent PSDs as described in our paper. We have addressed that more clearly in the results section of the improved version of the MS.

Page 8 line 197 "The majority (~98%) of SBs in L1a and L1b had only a single (Figures 2C-E, 3A-C, E) at most two or three AZs" is in striking contrast with the other statement from page 7 Line 163 "Numerous SBs in both sublaminae were seen to establish either two or three synaptic contacts on the same spine or dendrite". Which of these statements is valid? Please provide exact quantification for this statement and decide which one is true.

It is true that the majority of synaptic boutons had a single active zone. However, for example on a spine not only a single but also two or three SBs can be found. We have rephrased this sentence for more clarity.

Page 9 Line 206 "L1 AZs did not show a large variability in size as indicated by the low SD, CV, and variance (Table 1)" Is this inter-patient variance of mean values? As in Supplementary Figure 1, both the SBs volume and PreAZ area show large variability in a given patient sample. Only the inter-patient variability of mean values seems low. Please state it clearly throughout the MS for other datasets as well.

For clarity concerning the variability between patients and structural parameters we have generated box plots (Suppl. Figures 1 and 2).

Page 9 Line 208 data is on Figure 5A and not 8A.

We thank the reviewer and corrected the citation of the Figure

Page 12 Line 295 how can the number of docked vesicles for L1b be larger than the one measured by the perimeter p10 nm? This later should contain the docked and PreAZ membrane proximal pool as well. This difference is even larger if we assume, that at EM tomography only partial AZs were analysed in a 200 nm thick section, not the entire AZ as for the perimeter measurement. Can the authors provide density estimates by dividing the docked / p10 nm vesicle numbers with the AZ area and comparing them?

This is a result comparing both methods. To the second concern: As stated in the text only synaptic boutons were the active zone can be followed from the beginning to its end and were the synaptic cleft was cut perpendicular were included in the TEM tomography sample as we also did in our 3D-volume reconstructions.

Methods:

Page 25 Line 624 While the PSD area can be equivocally measured, due to the dense appearance of the PSD on the EM images, the PreAZ is more difficult to outline due to lack of evident anatomical markers except the synaptic cleft (the dense material is much thinner). That is why in many publications the PreAZ area is considered to be identical to the PSD area. What are the anatomical criteria used here for the PreAZ? Why do the authors correct the PSD area, which is easy to measure with the PreAZ area that is much less certain to outline?

As stated in material and Methods both the pre- and postsynaptic densities are not defined by placing a closed contour in both densities because one can’t be certain that the dense accumulation of particles defining both areas since the impregnation (staining) and contrast of both structures critically depends on the uranyl and lead staining which could led to misinterpretation due to different staining results. That’s why we have drawn a contour line from the beginning to the end of the presynaptic density and extrapolated that for the postsynaptic density (for details see Material and Methods). In our samples both the pre- and postsynaptic densities were always clearly visible in those boutons further analyze.

Page 26 Line 640 vesicle density measurement: All the synaptic vesicles that are in the 50 nm thick section in their entirety are missed, and there are methods based on EM tomography to correct these estimations. One can not assume, that the error caused by "double counts" of vesicles cancels for the lost ones. There are stereological methods to estimate both types of error please include them and correct the values.

We would like to point out that the whole body of our work to structural analysis of vesicle pools is based on image data stemming from transmission electron microscopy (TEM) generating a projection of the entire volume of the ultra-thin section and NOT from scanning electron microscopy (SEM) where only a small volume close to the surface of the section would be captured. Operating in TEM mode ensures that no vesicle is missed only because it is embedded in its entirety in the section as postulated by the reviewer. Hence, EM tomography, which is basically a TEM operating from different incident angles in relation to the specimen or section, does not provide any advantage in detecting these vesicles. It does, however, help to better position a 3D object within the section volume itself and therefore allows to detect objects that could overlap from one viewing angle by using another angle. As the average vesicle diameter is of similar size compared to the section thickness, the possibility of a complete overlap to happen, however, is almost zero. And as we only count clear ring-like structures, a stereological correction factor calculated according to Abercrombie (1946) would underestimate real counts (see also Saetzler et al. 2002). If there is, however, relevant literature on "methods based on EM tomography" and "stereological methods to estimate both types of error" (over- and underestimates) that we are missing out on, we would appreciate the reviewer providing us with the corresponding references so that we can include such calculations in our paper.

Page 27 Line 664 and 665 "sections" are still tissue blocks, as sectioning comes after if the process is correctly written. Please correct.

We have corrected this according to the reviewer’s comment.

Page 43 Figure 4 D Data for L1b is missing, only the correlation line is visible.

Corrected in a new Figure.

Page 44 Figure 5 C arrowheads are in the correct places? Some of them do not seem to point to the edge of the synapse.

We carefully checked the Figure and adjusted the arrowheads.

Figure 5 E lower arrowhead labels something, that is difficult to identify but does not seem to be a vesicle.

We agree with the reviewer on this point and changed the figure accordingly.

Figure 5 F, the upper vesicle is at least 10 nm apart from the PreAZ membrane. Did the authors consider it as docked (indicated with arrowhead, according to the legend it labels docked vesicles)?

We agree with the reviewer on this point and changed the figure accordingly.

Page 45 Figure 6 B one of the 2 synaptic boutons (sb), sb2 has a tangential active zone that precludes the identification of the pre- and post-synaptic membranes, still 2 "docked vesicles" are labeled. How were they classified as docked? Please remove these tangential synapses from the dataset, as membranes can not be identified.

The reviewer is right that the active zone is tangentially cut, however, the two vesicles are associated with the AZ. In addition, we did not use this AZ for vesicle data analysis.

Page 46 Line 1124 interneuron axon labelled in green not brown.

Corrected as suggested by the reviewer.

Line 1129 SStC is missing.

Changed according to the reviewer’s comment.

Page 48 Table 2 Number of docked vesicles Median values are rounded to integer values? If yes why?

The statistic package used rounded to the given values.

Page 51 Supplementary Table 1 Hu_04 Histopathology, what does TLE stands for?

TLE: temporal lobe epilepsy. We included the abbreviation in the legend of Supplementary Table1, that is now table 2.

Reviewer #3 (Recommendations for the authors):

(1) Reanalysis of astrocytic coverage based on the % of synapses that form tripartite synapses.

We have reanalyzed the data concerning this point (new Figure 6D).

(2) Segregation of excitatory and inhibitory synapses.

We have now included a paragraph in our results section to distinguish between excitatory and inhibitory synapses.

(3) Better explanation of the limits of the study to assess functional parameters.

We disagree with the reviewer on this point and have not included an explanation concerning the limits of this study.

eLife Assessment

This valuable study uses high-field fMRI to test the hypothesized involvement of subcortical structures, particularly the striatum, in updating working memory. The study overcomes limitations of prior work by applying high-field imaging with a more precise definition of regions of interest in the brain. Thus, the empirical observations are of use to specialists interested in working memory gating or the reference back task specifically. The evidence is generally solid, but strong conclusions on dopaminergic contributions must await additional work using molecular imaging or related techniques.

Reviewer #1 (Public review):

Summary:

Trutti and colleagues used 7T fMRI to identify brain regions involved in subprocesses of updating the content of working memory. Contrary to past theoretical and empirical claims that the striatum serves a gating function when new information is to be entered into working memory, the relevant contrast during a reference-back task did not reveal significant subcortical activation. Instead, the experiment provided support for a role of subcortical (and cortical) regions in other subprocesses.

Strengths

The use of high-field imaging optimized for subcortical regions in conjunction with the theory-driven experimental design mapped well to the focus on a hypothetical striatal gating mechanism.

Consideration of multiple subprocesses and the transparent way of identifying these, summarized in a table, will make it easy for future studies to replicate and extend the present experiment.

Weaknesses:

The reference-back paradigm seems to only require holding a single letter in working memory (X or O; Fig 1). It remains unclear how such low demand on working memory influences associated fMRI updating responses. It is also not clear whether reference-switch trials with 'same' response truly taxes working-memory updating (and gate opening), as the working-memory content/representation does not need to be updated in this case. These potential design issues, together with the rather low number of experimental trials, raise concerns about the demonstrated absence of evidence for striatal gate opening. Adding an experiment with higher working-memory demand and additional trials could strengthen the evidence for the authors present claim

The authors provide a motivation for their multi-step approach to fMRI analyses. Still, the three subsections of fMRI results (3.2.1; 3.2.2; 3.3.3) for 4 subprocesses each (gate opening, gate closing, substitution, updating mode) made the Results section complex and it was not always easy to understand why some but not other approaches revealed significant effects (as the midbrain in gate opening).<br /> It could be helpful to readers to further revise the Results section and/or more clearly convey the analytic strategy.

The many references to the role of dopamine are interesting, but the discussion of dopaminergic pathways and signals remains speculative and must be confirmed in future studies (e.g., with PET imaging).

Several relevant studies were not cited (e.g., Dahlin et al., 2008, Science; Bäckman et al., 2011, Science).

Reviewer #2 (Public review):

Summary:

The study reported by Trutti et al. uses high-field fMRI to test the hypothesized involvement of subcortical structure, particularly striatum, in WM updating. Specifically, participants were scanned while performing the Reference Back task (e.g., Rac-Lubashevsky and Kessler, 2016), which tests constructs like working memory gate opening and closing and substitution. While striatal activation was involved in substitution, it was not observed in gate opening.

While there have been prior fMRI studies of the reference back task (Nir-Cohen et al., 2020), the present study overcomes limitations in prior work, particularly with regard to subcortical structures, by applying high-field imaging with more precise definition of ROIs. And, the fMRI methods are careful and rigorous, overall. Thus, the empirical observations here are useful and will be of interest to specialists interested in working memory gating or the reference back task specifically. I do not have additional concerns about this contribution.

Author response:

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

eLife Assessment 

This useful study uses high-field fMRI to test the hypothesized involvement of subcortical structure, particularly the striatum, in WM updating. It overcomes limitations in prior work by applying high-field imaging with a more precise definition of ROIs. Thus, the empirical observations are of use to specialists interested in working memory gating or the reference back task specifically. However, evidence to support the broader implications, including working memory gating as a construct, is incomplete and limited by the ambiguities in this task and its connection to theory. 

We would like to express our gratitude to the editor and the reviewers for their time and effort in providing insightful and valuable comments. We greatly value the critical perspective on the relationship between fMRI contrasts and the PBWM model. We hope to have addressed all the last critical points and changed the manuscript according to the reviewers’ suggestions. Furthermore, we would like to point out that the behavioral results section was edited, as a double-check of the results section revealed some erroneous descriptive statistics.

Public Reviews:

Reviewer #1:

Summary: 

Trutti and colleagues used 7T fMRI to identify brain regions involved in subprocesses of updating the content of working memory. Contrary to past theoretical and empirical claims that the striatum serves a gating function when new information is to be entered into working memory, the relevant contrast during a reference-back task did not reveal significant subcortical activation. Instead, the experiment provided support for the role of subcortical (and cortical) regions in other subprocesses. 

Strengths: 

The use of high-field imaging optimized for subcortical regions in conjunction with the theory-driven experimental design mapped well to the focus on a hypothetical striatal gating mechanism. 

Consideration of multiple subprocesses and the transparent way of identifying these, summarized in a table, will make it easy for future studies to replicate and extend the present experiment.   

Weaknesses: 

The reference-back paradigm seems to only require holding a single letter in working memory (X or O; Figure 1). It remains unclear how such low demand on working memory influences associated fMRI updating responses. It is also not clear whether reference-switch trials with 'same' response truly tax working-memory updating (and gate opening), as the working-memory content/representation does not need to be updated in this case. These potential design issues, together with the rather low number of experimental trials, raise concerns about the demonstrated absence of evidence for striatal gate opening. 

We acknowledge that a limitation of our study is that the task involved relatively low working memory demands. It remains to be clarified whether the same neural mechanisms would be engaged under a higher working memory load, and this is an important consideration for future research.

We also fully agree that it is uncertain whether reference-switch trials requiring a ‘same’ (or ‘match’ ) response truly engage working memory updating (or gate opening), as the working memory content or representation does not need to be altered in these cases. This concern is addressed in detail in the discussion section titled “No Support for Striatal Gate Opening” (see second paragraph).

Regarding our references to dopamine, we completely agree with the reviewer about the speculative nature of these discussions. In response, we thoroughly reviewed the manuscript and made revisions where necessary to ensure that we consistently emphasize the speculative nature of our commentary on dopamine and dopaminergic pathways.

Finally, we acknowledge the concerns about the design and the relatively low number of trials. However, our fMRI analyses of other reference-back task contrasts did reveal activity in the striatum and other subcortical ROIs. This suggests that our scanning protocol and task design are sufficiently sensitive to detect striatal activity, even with the limited number of trials.

The authors provide a motivation for their multi-step approach to fMRI analyses. Still, the three subsections of fMRI results (3.2.1; 3.2.2; 3.3.3) for 4 subprocesses each (gate opening, gate closing, substitution, updating mode) made the Results section complex and it was not always easy to understand why some but not other approaches revealed significant effects (as the midbrain in gate opening). 

We thank the reviewer for this important remark and the opportunity to clarify our approach. We conducted whole-brain general linear models (GLMs) to generate a comprehensive wholebrain map of brain activity for each contrast. However, the whole-brain statistical parametric mappings (SPMs) involve data smoothing, which–while improving signal detection–reduces spatial precision. This is especially problematic in smaller or closely adjacent regions, where spatial blurring can merge distinct activations or make localized signals appear more widespread.

Additionally, the statistical thresholds in whole-brain analyses may detect weak or borderline significant effects, whereas ROI-wise GLMs, which assume uniform behavior across the entire region, may miss the same effects if the signal is weak or inconsistent across the ROI.

Since our primary focus was on the subcortex, we relied more heavily on ROI-wise GLMs, which were limited to subcortical regions. We prioritized findings that were supported by either the ROI-wise GLMs or by both GLM analyses. For instance, the midbrain activations found in our whole-brain analysis but not in the ROI analysis may result from smoothing (where activation from neighboring regions spreads into midbrain voxels) or from functional heterogeneity within the ROI, which can obscure localized activations when averaged in the ROI-wise GLMs. Inferences from each GLM approach, along with their discrepancies, are discussed for each contrast throughout the discussion, with additional details on the clusterbased ROI analysis in the discussion section titled “Dopaminergic involvement in working memory substitution” (see third paragraph).

We acknowledge that the results section may seem complex, and we apologize for any inconvenience this may cause.

Reviewer #2:

Summary: 

The study reported by Trutti et al. uses high-field fMRI to test the hypothesized involvement of subcortical structure, particularly striatum, in WM updating. Specifically, participants were scanned while performing the Reference Back task (e.g., Rac-Lubashevsky and Kessler, 2016), which tests constructs like working memory gate opening and closing and substitution. While striatal activation was involved in substitution, it was not observed in gate opening. This observation is cited as a challenge to cortico-striatal models of WM gating, like PBWM (Frank and O'Reilly, 2005). 

Strengths: 

While there have been prior fMRI studies of the reference back task (Nir-Cohen et al., 2020), the present study overcomes limitations in prior work, particularly with regard to subcortical structures, by applying high-field imaging with a more precise definition of ROIs. And, the fMRI methods are careful and rigorous, overall. Thus, the empirical observations here are useful and will be of interest to specialists interested in working memory gating or the reference back task specifically. 

Weaknesses: 

I am less persuaded by the more provocative points regarding the challenge it presents to models like PBWM, made in several places by the paper. As detailed below, issues with conceptual clarity of the main constructs and their connection to models, like PBWM, along with some incomplete aspects of the results, make this stronger conclusion less compelling. 

(1) The relationship of the Nir-Cohen et al. (2020) task analysis of the reference back task, with its contrasts like gate opening and closing, and the predictions of PBWM is far from clear to me for several reasons. 

First, contrasts like gate opening and gate closing make strong finite state assumptions. As far as I know, this is not an assumption of PBWM, certainly not for gate opening. At a minimum, PBWM is default closed because of the tonic inhibition of cortico-thalamic dynamics by the globus pallidus. Indeed, this was even noted in the discussion of this paper, which seems to acknowledge this discrepancy, but then goes on to conclude that they have challenged the PBWM model anyway.  

We thank the reviewer for this remark and agree that the reference-back task contrasts do not perfectly align with the predictions of the PBWM model. In the discussion section "No support for striatal gate opening," we note that our data support the PBWM model by emphasizing the central role of the basal ganglia in working memory processes. However, we acknowledge that it may not have been sufficiently clear in the manuscript that the way the reference-back task is operationalised does not allow for a precise test of the PBWM's gating predictions. To address this, we have revised the manuscript to shift focus away from framing it as a direct challenge to the PBWM model. Below, some edits are highlighted.

‘This contrasts with the findings of Nir-Cohen et al. (2020) and raises questions about the relationship between the gate opening process in the reference back task and the indirect striatal gating mechanism described in the PBWM model (Frank et al., 2001; Hazy et al., 2007; O’Reilly & Frank, 2006) and other neurocomputational theories (Hazy et al., 2007; Jongkees, 2020). According to these models, a dopaminergic signal in the striatum is required to trigger gating. Although the orthogonal contrasts in the referenceback task are intended to isolate working memory subprocesses inspired by models of working memory, the two gating contrasts do not fully capture the gating mechanism as originally proposed in neurocomputational models (Frank et al., 2001; Hazy et al., 2007; O’Reilly & Frank, 2006).’ (line 721-730)

‘Another explanation for the lack of enhanced striatal activity in gate opening challenges the conceptualization of the gating mechanism in the reference-back task, which does not accurately map onto the PBWM predictions.’ (line 746)

‘Moreover, despite the lack of striatal involvement during gate opening, our findings do not rule out the possibility that the PBWM model's predictions about striatal gating in working memory are correct, given the misalignment between the gate opening contrast and the PBWM’s proposal regarding striatal gating. It remains unclear whether the absence of striatal activation during gate opening trials is specific to low-demand tasks, like the reference-back task, which does not require as much gating compared to high working memory-demand tasks involving preparation for updating. Or whether the gate opening contrast does not sufficiently capture the PBWM proposed gating mechanism. Further investigation is needed to determine whether (dopamine-driven) striatal gating occurs in high-demand working memory tasks, where the gating process plays a more critical role.’

Second, as far as I know, PBWM emphasizes go/no-go processes around constructs of input- and output-gating, rather than state shifts between gate opening and closing. While this relationship is less clear in reference back, substituting task-relevant items into working memory does appear to be an example of input gating, as modeled by PBWM. Thus, it is not clear to me why the substitution contrast would not be more of a test of input gating than the gate opening contrast, which requires assumptions that are not clear are required by the model, as noted above. 

We fully agree with the reviewer, which is why we proposed that neural mechanisms involving the midbrain and striatum are more likely to be observed in the substitution contrast rather than the gate opening contrast.

Third, PBWM relies on striatal mechanisms to solve the problem of selective gating, inputting, or outputting items in memory while also holding on to others. Selective gating contrasts with global gating, in which everything in memory is gated or nothing. The reference back task is a test of global gating. It is an important distinction because non-striatal mechanisms that can solve global gating, cannot solve selective gating. Indeed, this limitation of non-striatal mechanisms was the rationale for PBWM adding striatum. The connectivity of the striatum with the cortex permits this selectivity. It is not clear that the reference back task tests these selective demands in the first place. That limitation in this task was the rationale behind the recent Rac-Lubashevsky and Frank (2022) paper using the reference back 2 procedure that modifies the original reference back for selective gating. 

We thank the reviewer for highlighting this excellent reference. We believe it holds exciting potential for future high-field fMRI studies that explore the neural mechanisms underlying selective gating.

So, if the primary contribution of the paper is to test PBWM, as suggested by the first line of the abstract, then it is not clear that the reference back task in general, or the gate opening contrast in particular, is the best test of these predictions. Other contrasts (substitution), or indeed, tasks (reference back 2) would have been better suited. 

We agree with the reviewer that the gate opening contrast may not be the optimal test for the PBWM model predictions. However, previous studies have found evidence of striatal gateopening mechanisms using the reference-back task, which cannot be overlooked. We hypothesized that striatal mechanisms are likely active only when working memory content requires replacement, as seen in the substitution contrast in line with the PBWM model. Additionally, the reference-back 2 task (Rac-Lubashevsky & Frank, 2021) had not yet been published when we began data collection. Exploring this task in future studies, particularly with a 7 T fMRI protocol optimized for subcortical regions, would be an exciting avenue for further investigation.

Finally, in response to the reviewer’s remark, we have revised the abstract to remove the emphasis on challenging the PBWM model.

(2) In general, observations of univariate activity in the striatum have been notoriously variable in the context of WM. Indeed, Chatham et al. (2014) who tested working memory output gating - notably in a direct test of the predictions of PBWM - noted this variability. They too did not observe univariate activation in the striatum associated with selective output gating. Rather they found evidence of increased connectivity between the striatum and cortex during selective output gating. They argued that one account of this difference is that striatal gating dynamics emerge from the balance between the firing of both Go and NoGo cell populations that decide whether to gate or not. It is not always clear how this balance should relate to univariate activation in the striatum. Thus, the present study might also test cortico-striatal connectivity, rather than relying exclusively on univariate activation, in their test of striatal involvement in these WM constructs. 

We appreciate the reviewer’s insightful observation regarding the variability of univariate activity in the striatum, particularly in the context of working memory and the challenges noted by Chatham et al. (2014). We agree that striatal gating dynamics likely reflect a balance between Go and NoGo cell populations, which may not always manifest in univariate activation alone. In line with the reviewer’s suggestion, examining cortico-striatal connectivity could provide a more comprehensive understanding of striatal involvement in working memory processes, particularly selective gating.

While our current study focused primarily on univariate activity, we recognize the importance of connectivity-based approaches and plan to incorporate functional connectivity analyses in future studies to further explore these dynamics. Such an approach, especially when combined with ultra-high-field fMRI, may offer valuable insights into the interaction between the striatum and cortex during working memory tasks.

(3) It is concerning that there was no behavioral cost for comparison switch vs. repeat trials. This differs from with prior observations from the reference back (e.g., Nir-Cohen et al., 2020), and in general, is odd given the task switch/cue interpretation component. This failure to observe a basic behavioral effect raises a concern about how participants approached this task and how that might differ from prior reports of the reference back. If they were taking an unusual strategy, it further complicates the interpretation of these results and the implications they hold for theory. 

We understand the reviewer’s concern regarding the lack of behavioral response time costs for comparison switch versus repeat trials, which does indeed differ from previous findings in studies such as Nir-Cohen et al. (2020). It is possible that this results from our fMRI task design, such as increased inter-trial intervals compared to behavioral studies. While this is certainly a point of concern, we believe that the neural data still provide valuable insights into the mechanisms underlying working memory gating despite the absence of a clear behavioral effect.

In future studies, we aim to increase the number of trials and more closely align our task design with previous studies to mitigate this issue. We agree that further investigation is necessary to ensure the robustness of these effects and their theoretical implications.

In summary, the present observations are useful, particularly for those interested in the reference back task. For example, they might call into question verbal theories and task analyses of the reference back task that tie constructs like gate-opening to striatal mechanisms. However, given the ambiguities noted above, the broader implications for models like PBWM, or indeed, other models of working memory gating, are less clear.

eLife Assessment

This important study successfully applies an innovative chemogenetic tool to investigate cerebellar function to advance our understanding of the contributions of Purkinje cell populations to postural control in larval zebrafish. The evidence supporting the conclusions is convincing and supported by rigorous statistical analysis. The study highlights the power of combining genetically targeted perturbations with quantitative high-throughput behavioral analysis and original microscopy tools.

Reviewer #1 (Public review):

This study uses a variety of approaches to explore the role of cerebellum, and in particular Purkinje cells (PCs), in the development of postural control in larval zebrafish. A chemogenetic approach is used to either ablate PCs or disrupt their normal activity and a powerful, high-throughput behavioural tracking system then enables quantitative assessment of swim kinematics. Using this strategy, convincing evidence is presented that PCs are required for normal postural control in the pitch axis. Calcium imaging further shows that PCs encode tilt direction. Evidence is also presented that suggests the role of the cerebellum changes over the course of early development, although this claim is less robust. Finally, the authors build on their prior work showing that both axial muscles and pectoral fins contribute to "climbs" and show convincing evidence that PCs are required for speed-dependent engagement of the fins during this behavior. Overall, establishing a role for cerebellum in postural control is not very surprising. However, a clear motivation of this study was to establish a robust experimental platform to investigate the changing role of cerebellar circuits in the development of postural control in the highly experimentally accessible zebrafish larvae and in this regard the authors have certainly succeeded.

This revised version of the manuscript incorporates several improvements. In particular, additional analysis and methodological detail is provided regarding the chemogenetic manipulation, there is expanded analysis of the speed-dependency of pectoral fin engagement, and aspects of the decoding analysis are clearer. However, it is still not certain that the emergence of a dive phenotype over development (from 7 to 14 day post fertilisation) really represents changing role for the cerebellum as opposed to changing sensitivity of Purkinje cells to the chemogenetic treatment.

Reviewer #2 (Public review):

Franziska Auer et al. successfully applied the TRPV1/capsaicin tool to study the contribution of Purkinje cells to postural control. They leveraged the ability of this tool to both activate and ablate neurons within the same construct and tested its effects using their smart, high-throughput behavioral setup for postural control monitoring. With Purkinje cells ablated, balance did not appear to be disrupted; however, postural control was clearly modified along the pitch axis, with larval zebrafish maintaining, on average, a more nose-down posture compared to controls. While this effect is subtle, it is statistically robust and consistent with the group's previous findings using KillerRed-mediated ablation of Purkinje cells, where the observed postural angle change was explained by a disruption in cerebellar-mediated fin-trunk coordination. Here, the authors present a novel insight, demonstrating that this coordination is swim-speed dependent.

Furthermore, the authors convincingly activated Purkinje cells at 7 dpf, and reported modifications in posture pitch angle comparable to those observed when ablating Purkinje cells. The authors suggest a potential desynchronization of Purkinje cells to explain this observation. Future characterization and application of this activation method to other developmental time points could be of major interest. The authors successfully validated the transfer of the TRPV1/capsaicin method for targeted cell ablation and activation to the study of cerebellar functions and reinforced our current understanding of the role of Purkinje cells in postural control.

This study also explores the developmental evolution of cerebellar function in postural control by comparing the effects of Purkinje cell ablation at 7 dpf and 14 dpf. Interestingly, only dive bout posture showed differential effects across these time points, with no significant impact at 7 dpf but a significant change in postural pitch angle at 14 dpf. In contrast, the effect of Purkinje cell ablation on the climbing bout postural angle remained comparable at both ages. Including additional developmental time points would further strengthen this critical characterization of cerebellar maturation in the context of postural control.

To examine whether Purkinje cell activity encodes postural tilt angle, the authors performed calcium imaging on 31 cells from 8 fish using their Tilt In Place Microscope (TIPM). They found that tilt-angle could be decoded from individual neurons with highly tuned responses, as well as from neurons that were not obviously tuned when pooling their data. The authors refer to this effect as pseudo-population coding because recordings were performed non-simultaneously across animals.

This study successfully integrates cutting-edge genetic tools, high-throughput behavioral assays, and advanced optical microscopy to investigate the role of populations of Purkinje cells in postural control. The authors have not only validated these powerful tools but have also provided novel insights into the cerebellar involvement in postural control, including the swim-speed dependence of fin-trunk coordination.

This work represents an important step toward a detailed understanding of cerebellar contributions to postural control and highlights the potential of combining genetically targeted perturbation with quantitative behavioral analysis.

The authors have addressed my previous concerns, and I congratulate them for their excellent work.

Reviewer #3 (Public review):

Summary:

This paper uses a new chemogenetic tool to investigate the role of cerebellar Purkinje cells in postural control. Using a high-throughput behavioral assay, they show that activation or ablation of Purkinje cells affects various aspects of postural control in zebrafish larvae during spontaneous swimming, and that the effects are more pronounced at later developmental time points, where the Purkinje cell number is much greater. Using a sophisticated imaging assay, they record Purkinje cell activity in response to tilt of the fish, and show that some Purkinje cells are tuned to tilt direction, and that the direction can even be decoded from untuned neurons.

Strengths:

Overall the study is nice, using a variety of genetic tools and behavioral analysis to address a fundamental question about the role of the cerebellum in postural control in fish

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

This study uses a variety of approaches to explore the role of the cerebellum, and in particular Purkinje cells (PCs), in the development of postural control in larval zebrafish. A chemogenetic approach is used to either ablate PCs or disrupt their normal activity and a powerful, high-throughput behavioural tracking system then enables quantitative assessment of swim kinematics. Using this strategy, convincing evidence is presented that PCs are required for normal postural control in the pitch axis. Calcium imaging further shows that PCs encode tilt direction. Evidence is also presented that suggests the role of the cerebellum changes over the course of early development, although this claim is rather less robust in the current version of the paper. Finally, the authors build on their prior work showing that both axial muscles and pectoral fins contribute to "climbs" and show evidence that suggests PCs are required for correct engagement of the fins during this behaviour. Overall, establishing a role for the cerebellum in postural control is not very surprising. However, a clear motivation of this study was to establish a robust experimental platform to investigate the changing role of cerebellar circuits in the development of postural control in the highly experimentally accessible zebrafish larvae, and in this regard, the authors have certainly succeeded.

Overall, I consider this an excellent paper, with some room for improvement in aspects of presentation, discussion, and some aspects of the data analysis..

We thank the reviewer for their kind comments and support. In the revision we have addressed their concerns regarding data presentation and analysis. Additionally, we have expanded our introduction and discussion to address questions of presentation.  

Reviewer #2 (Public Review):

Summary:

Franziska Auer et al. investigate the role of cerebellar Purkinje cells in controlling posture in larval zebrafish using the chemogenetic tool TRPV1/capsaicin to bidirectionally manipulate (i.e., activate or ablate) these cells. This tool has been developed for zebrafish previously but has not been applied to Purkinje cells.

High-throughput behavioral experiments are presented to monitor how body posture is affected by these perturbations. The analysis of postural control focuses on a specific subaspect of posture: the body tilt-angle relative to horizontal just before a swim bout is executed, quantified separately for pre-ascent and pre-dive bouts. They report a broad bimodal distribution of pre-ascent bout posture ranging from -20 to +40 degrees, while the pre-dive bout posture was more Gaussian, ranging between -40 and 0 degrees. The treatment effect is quantified as the change in the median of these distributions.

Purkinje cell activation and ablation in 7 days post-fertilization (dpf) fish shifted the median of the ascending bout posture distributions to positive values. The authors hypothesize that the stochastic nature of the activation process might desynchronize Purkinje cell activity, thus abolishing Purkinje cells' role in postural control, similar to ablation. However, this does not explain why dive bout posture decreased upon activation but was unaffected by ablation. 

To test whether the role of Purkinje cells in postural control matures over development, the authors repeated the ablation experiments at 14 dpf. They state that "at 14 dpf, the effects of Purkinje cell lesions on posture were more widespread than at 7 dpf." However, this effect size is comparable to that observed at 7 dpf, suggesting no further maturation of the role of Purkinje cells in pre-ascending bout postural control. The median pre-dive bout posture decreased at 14 dpf, contrasting with no effect at 7 dpf, yet this change was comparable in effect size to the activation effect on Purkinje cells at 7 dpf. The current data breadth may not be sufficient to conclude that signatures of emerging cerebellar control of posture across early development were uncovered.

The study's exploration of activating Purkinje cells in freely swimming fish using TRPV1/ capsaicin is of special interest, but the practicability of this method is unclear from the current presentation. It would be beneficial to present the distribution of the percentage of activatable Purkinje cells across animals and time points to provide insight into the method's efficiency. Discussing this limitation and potential improvements would aid in evaluating the method, especially since the authors report that the activation experiments were labor-intensive, limiting repeat experiments. This may explain why the activation experiment at 7 dpf is the only data presented with cell activation, with other analyses performed using the cell ablation capabilities of the TRPV1/capsaicin method.

Another data point at 14dpf would significantly strengthen the conclusions.

The authors analyze Purkinje cell-controlled fin-trunk coordination by examining ascending bout posture across different swim bout speeds. They make the important finding that pectoral fin movements contribute significant lift for median and fast swim bouts but not for slow ones, and that Purkinje cell ablation disrupts lift generation at all speeds.

Finally, the authors examined whether Purkinje cell activity encodes postural tilt-angle by performing calcium imaging on 31 cells from 8 fish using their Tilt In Place Microscope (TIPM). They report that they could decode the tilt-angle from individual neurons with a highly tuned response, and also from neurons that were not obviously tuned when pooling them and analyzing the population response. However, due to the non-simultaneous recordings across animals, definitive conclusions about populationlevel encoding should be made cautiously, it might be better to suggest potential population encoding that needs confirmation with more targeted experiments involving simultaneous recordings.

Strengths:

- The study introduces a novel application of the chemogenetic tool TRPV1/capsaicin to study cerebellar function in zebrafish.

- High-throughput behavioral experiments provide detailed analysis of postural control.

- The further investigation of Purkinje cell-controlled fin-trunk coordination offers new insights into motor control mechanisms.

- The use of calcium imaging to decode postural tilt-angle from Purkinje cell activity presents interesting preliminary results on neuronal population encoding.

Weaknesses:

- The term "disruption" for postural control effects may lead to misleading expectations.

- The supporting data show only subtle median shifts in postural angle, raising questions about the significance of observed effects. Statistical methods that account for the hierarchical structure of the data might be required to support the conclusions.

- The study's data breadth may not be sufficient to conclude emerging cerebellar postural control across early development.

- The current presentation does not adequately detail the practicability and efficiency of the TRPV1/capsaicin method for activating Purkinje cells, and the labor-intensive nature of these experiments constrains the ability to replicate and validate the findings.

- Non-simultaneous recordings in calcium imaging necessitate cautious interpretation of population-level encoding results.

We appreciate the reviewer's thoughtful and detailed feedback. In response, we have made several changes to highlight key points in our manuscript. We have adjusted our wording to more accurately reflect the scope of our findings. Finally, we have clarified and expanded the methods used.

Reviewer #3 (Public Review):

Summary:

This paper uses a new chemogenetic tool to investigate the role of cerebellar Purkinje cells in postural control. Using a high-throughput behavioral assay, they show that activation or ablation of Purkinje cells affects various aspects of postural control in zebrafish larvae during spontaneous swimming and that the effects are more pronounced at later developmental time points, where the Purkinje cell number is much greater. Using a sophisticated imaging assay, they record Purkinje cell activity in response to the tilt of the fish and show that some Purkinje cells are tuned to tilt direction and that the direction can even be decoded from untuned neurons.

Strengths:

Overall the study is nice, using a range of tools to address a fundamental question about the role of the cerebellum in postural control in fish.

Weaknesses:

(1) The data in Figure 1 that establishes the method seems to be based on a very small number of experiments and lacks some statistical analysis.

(2) The choice and presentation of the statistical and analysis methods used in Figures 2-5 could be improved.

We thank the reviewer for their comments.  We have added additional statistical analyses for the activation experiments, and improved data presentation .

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Overall I think this is a great paper.

* Introduction and Discussion.

The Introduction (and Discussion) do little to explain what is understood about cerebellar control of posture and what major outstanding questions remain. The first paragraph of the Introduction seems to argue that the role of the cerebellum in control of posture is well established and line 24 attempts to motivate the present study by virtue of the fact that terrestrial locomotion is "complex". This might be true but is not necessarily a major obstacle given the suite of powerful approaches available in rodent neuroscience. What are the major challenges that are hard to tackle in rodents and what specific questions can the larval zebrafish help to answer? What about development (which gets no mention at all)? I'm not suggesting a comprehensive review of every aspect of cerebellar physiology, but I think the Introduction should attempt to outline the current hypotheses in a little more detail and highlight what we still need to understand.

We take the Reviewer’s point that there is more to say in the Introduction. We feel that multi-dimensional limb biomechanics and proprioception are two aspects of terrestrial locomotion that support our use of the word “complexity.” However, we don’t dwell on this point because, as the reviewer correctly states, the suite of tools for rodent neuroscience & behavior is expansive and, in our opinion, not a limiting factor. Instead, we said what we felt we could regarding the potential contribution of the larval zebrafish in the last paragraph of the Discussion. In the revision, we have added details about the development of cerebellum to the introduction (though this, of course, is an expansive topic and well-beyond the scope of the Introduction), highlighted some of the historical limitations in rodent posture analysis, and set up the .

* Figure 2: 'Arrows denote the shift towards more nose-up postures'. I think the distribution is quite easy to interpret without these arrows; I suggest removing them.

We have removed the arrows.  

* IQR is sometimes stated as a single number and sometimes as a range. It should be consistent and unless eLife has guidance to the contrary, I suggest that it be the latter.

Thank you for pointing that out. We now report it as the value at the 25&75th %ile for all IQRs.  

* Figure S2: For 14 dpf fish the axes are labelled PC2/3 - is this an error?

We have changed it to a 3-dimensional plot for both 7 and 14 dpf data to show comparable plots for both ages (now Figure S5 F and G). For the analysis in the 14dpf fish the clearest separation was in the space defined by the 2nd and 3rd principal component.  

* In the methods, there is insufficient detail given about fluorescent imaging.

We added additional information to how the fluorescent imaging was performed to the ‘Confocal imaging’ section as well as to the ‘Functional imaging section’

* Abstract

In my opinion, the statement "Here, we used a powerful chemogenetic tool (TRPV1/ capsaicin) to *define the role of Purkinje cells*..." is too strong. Whilst the evidence that PCs are required for postural control is certainly strong, what exactly these cells do in the service of postural control is far from clear (as the authors indeed acknowledge in the Discussion). As such, I wouldn't say their role has been "defined".

We change the word to “describe” to better reflect our findings

* aldoca transgenic.

This appears to be a beautiful transgenic line but the data showing the extent of its expression and evidence that in the cerebellum it exclusively labels PCs isn't clear enough.

(i) Ideally Figure 1A would show an image of a whole animal to provide an overview of transgene expression but instead it seems to be (the legend is unclear) a cartoon with a confocal projection of part of the brain overlaid.

We have updated the figure legend to be clearer that we show a cartoon of a larval zebrafish with the confocal image overlaid. The aldoca promotor has been previously described and exclusively labels Purkinje cells (10.1523/JNEUROSCI.3352-10.2010)

(ii) Figure 1B shows expression in the cerebellum, but how are we to understand that all the labelled cells are PCs? Are all PCs labelled, or only a subset? Perhaps a double labelling with a PC in situ marker could be done to demonstrate colocalisation?

As above, the aldoca promotor has been previously described; to the best of our knowledge in the Hibi lab’s hands (and ours) it labels Purkinje cells exclusively, and it labels all of them (10.1523/JNEUROSCI.3352-10.2010)  

* Chemogenetic validation.

Overall, the chemogenetic approach to abrogate PC function looks to be very powerful. The authors state in several places that a contribution of this paper is in its "establishing the validity of TRPV1/capsaicin-mediated perturbations". However, the data in Figure 1, along with various comments in other parts of the paper raise some questions:

(i) For experiments depolarising PCs with 1µM CSn, the same size is tiny: Two transgenic animals and one control. Moreover, it is stated 'in one fish ... we observed a small number of neurons at the 9h timepoint with bright, speckled fluorescence suggestive of cell death". Was this one out of two transgenics?! In the discussion, I didn't understand the statement "ensure adequate brightness levels *to achieve sufficient depolarization without excitotoxicity*". Does this "excitotoxicity" relate to the specked fluorescence observation?

Overall, the very small sample size and comments about excitotoxicity and cell death raise concerns about the approach that I think warrant clearer treatment in the results (including information about the assessment of transgene expression, % embryos judged to have suitable expression), especially as this paper is seeking to establish the validity of the method.

We note first that the method has been previously validated (https://doi.org/10.1038/ nmeth.3691) and that we build on this work. For the experiment described, the point was to identify an acceptable duration for exposure. To that end, we analyzed 6 animals for up to 6h (including the washout experiments in Figure S1B) where we never observed any speckled fluorescence; we limited our behavioral experiments to 6h accordingly. We thought it would be worth including the observation of speckled fluorescence at 9h timepoint for future reference. To directly address the comment we have increased the number of analyzed cells and fish for the 1uM capsaicin experiments and added statistical analysis (lines 65-67).

When screening for transgene expression we selected for fish that had clearly visible expression, but that did not look overly bright, and used the same criteria when screening fish for the GCaMP imaging and for behavior. Around a quarter of the fish that had aldoca:TRPV1-tagRFP expression had a usable expression level for the activation experiment. We have added this information to the Results (line 62) and Methods (line 369-372)

(ii) The authors note "capsaicin could sporadically activate subsets of Purkinje cells" and further speculate about PC activity and synchrony in the discussion. Figure 1 seems to rely on single images at widely spaced time points but given that they are set up to do 2-photon calcium imaging, why didn't they collect continuous time series data and analyse the temporal patterns of activity across the transgenic PC population?

We have added time series data for calcium imaging after 1uM of Capsaicin in TRPV1-  and TRPV1+ cells to Supplementary Figure S1A. Here too we see sporadic increases in calcium levels at similar rates: 0% for TRPV1- and 15-19% for TRPV1+ (see also Figure S1 legend)

(iii) The axonopathy and cell death resulting from 10 µM Csn is quite dramatic.

However, here the authors do not appear to have included a TRPV1 negative control (although oddly they did for 1 µM treatment) so it is currently unclear whether or not a high conc of Csn alone might be cytotoxic.

Chen et al (https://doi.org/10.1038/nmeth.3691) have established the TRPV1/capsaicin method in zebrafish with broad neuronal label and did not see any effect with high doses of capsaicin in TRPV1 negative fish.  

* Behavioural assessment - stats

Overall, the disruption of postural stability after PC manipulations is convincing.

However, I have a few queries about the statistics:

(i) In this section, the statistical unit was not clear. The tables, which are otherwise very useful, give no indication of N. The legend text does report "8 repeats/149 control fish" and "across experimental repeats" suggesting the statistical unit might be the repeats rather than animals, but this should be clarified. In Figure 2G, individual data points should be plotted if N=8, or a representation of the distribution (eg violin or box and whisker plots) if N = 149.

We apologize for the confusion. Given the variable numbers of bouts, a single experimental repeat does not allow for an accurate estimate of expected value. Below we simulated how accurately the median can be estimated based on increasing sample sizes (Author response image 1). Given that large numbers of bouts are necessary to accurately estimate the median we pool the data for all experiments and use resampling statistics to estimate bias in our estimate.

Author response image 1.

Median estimation based on increasing sample size

(ii) Related to the above, I hope it might be easier to interpret the unexpected change in climb posture in ablation controls once the data for individual repeats is shown.

When we analyze the data as single repeats we see considerable variability between different repeats due to undersampling. We tested the medians for the single repeats for outliers to ensure that the shift is not due to a single repeat skewing the distribution. We did not detect any outliers in the pre-lesion control or in the post-lesion control group. (Outliers were determined as deviating more than 3 times the scaled median absolute deviation (MAD) from the median. A scaling factor of 1.4826 was used to ensure that MAD-based outlier detection is consistent with other methods like Z-scores.) We added this information to line 133-134 and the method section under Statistics. 

(iii) In some parts of this section, including the Tables, the authors report the 95% CI of the median, rather than IQR. In this case, they should report the z-value used for 95% CI estimation.

As we are using resampling to estimate the 95% confidence interval of the median there is no z-value as in a traditional normal distribution based confidence interval; Instead, we explicitly define the 2.5th and 97.5th percentiles from the bootstrapped sample distribution, which captures the middle 95% of the data, representing the 95% confidence interval.

* It is stated that "fish adopted more nose-up postures before *and throughout* climb bouts". Figure 2F seems to show posture before the climb, but where is the "throughout" data? It would be useful if Figure 2E, J could be extended to make a bit clearer these two phases of postural assessment.

We removed the phrase ‘throughout climb bouts’ as we are not showing the posture throughout the bout and to avoid over complicating the interpretation.  

* Why were PCs not activated at 14 dpf (eg using 1 µM Csn)?

Due to shifts in priorities the first author will not be continuing this series of experiments, and so this additional experiment will have to wait for someone to pick up this line of inquiry

* The authors appear to claim that the difference in phenotype in 7 versus 14 dpf animals following high conc Csn treatment is indicative of a changing role for cerebellar PCs over this developmental period. For instance, in reference to the 14 dpf ablation phenotype, the authors write "reveals the functional emergence of Purkinje cell control of dives" and in the abstract they talk about "emerging control of posture across early development". However, can they rule out that the phenotypic differences might instead reflect differential sensitivity of the relevant PC (sub)populations to CSn at the two ages? If this caveat cannot be discounted then I suggest it is acknowledged e.g. in the discussion.

As previously established, all Purkinje cells are labeled in the aldoca line (10.1523/ JNEUROSCI.3352-10.2010). Fluorescence is brighter at 14dpf compared to 7dpf, suggesting higher levels of TRPV1. We therefore assume that at 14 dpf, the high concentration of Csn is sufficient to ablate Purkinje cells. At 14 dpf, cerebellar damage is visible under a standard dissecting microscope.The preponderance of evidence therefore speaks against a previously undiscovered subpopulation of TRPV1expressing Purkinje cells that are, by mechanisms yet unknown, resistant to high doses of capsaicin. 

* Fin-body "coordination"

The ideas and data around fin-body coordination are very intriguing.

(i) The statement "fin engagement is speed-dependent" would benefit from a stats test to show this is indeed significant. The data in Figure 4B suggest a rather high degree of variance.

This is an important point; we appreciate the Reviewer’s attention. We have added statistics to show this is speed dependent to line 167-169 and show the corresponding plot in the supplement in Figure S4.  "Here, we observed that fin engagement is speeddependent, with faster bouts producing greater lift for a given axial rotation (Spearman correlation coefficient: control 0.2193; 10uM capsaicin: 0.0397; Z-test after ztransformation: p < 0.001)  

(ii) The statement "After capsaicin exposure, the slopes of the medium fast speed bins were significantly lower (Figure 4C), reflecting *a loss of speed-dependent modulation*" is not convincing. The slope is likely a function of both speed and Csn treatment, and the comparisons in Figure 4C appear to be testing the latter, not the former.

We understand the reviewer’s point. However, the slope for the slow bouts remains unchanged. We therefore conclude that the reduction in fin-body slope is speed dependent and not a speed independent reduction of slope overall. 

We have made this more clear by adding Supplementary Figure S4 and changing the text in line 177-179. 

(iii) I'd like to understand more about the phenotype of the fin-amputated animals. Were any "bout" parameters changed? Did the animals still attempt climbs and was the distribution of the upward rotation parameter similar to controls? The text states "the slope of the relationship between upward rotation and lift was indistinguishable from zero" but the stats reported in the text are comparisons between groups while Table 5 shows 95% CIs that don't span zero. Some clarification would be useful here.

We appreciate the Reviewer’s interest. We’ve studied climbing in fin-amputated animals at length here: https://doi.org/10.7554/eLife.45839 and here: https://doi.org/10.1016/ j.celrep.2023.112573 and have added these references in line 183.

(iv) The authors repeatedly refer to fin-body *coordination* but it is not clear whether the loss of lift after PC ablation is a result of an explicit coordination defect (i.e. changes in the relative timing and/or kinematics between fins and axial motion components), versus a simple reduction in pectoral fin engagement. Either result could be interesting, but this should be clarified.

Thank you for pointing that out. In the fastest speed bin, we observed an increase in upward rotation and a decrease in average fin lift. In contrast, the medium speed bin showed no significant changes in average fin lift or upward rotation (see Author response image 2 and Tables 4 and 5), yet already displayed coordination deficits. Based on these observations, we argue that Purkinje cell lesions primarily affect coordination, rather than simply reducing one specific parameter such as lift or rotation (line 293-298).

We have added fin lift and rotation values from Author response image 2 for all speed bins to tables 4 and 5.  

Author response image 2.

Fin lift and rotation for slow, medium and fast bouts

* PC activity and decoding of pitch direction.

The clever TIPM method is used to collect calcium data that convincingly shows that individual PCs can encode pitch-tilt direction. However, a population of "not tuned" cells are also identified, and here I found the analysis of their responses and the argument that they encode pitch direction at a population level difficult to follow.

(i) First, although the naming of the cells implies that individual neurons do not encode pitch direction, I did not find this convincing. Figures 5F/G suggest that several "not tuned" cells in fact show quite consistent differences in activity across trial types and indeed in terms of their average responses sit as far from the unity line as do several "tuned" cells.

The Reviewer’s comment helped us clarify some key points. First, tuned and untuned cells were categorized based on a Directionality Index threshold of 0.35; some cells might look similar in 5F/G but the highly variable responses of Purkinje cells have highly variable response so overall there was no consistent tuning. We have clarified this in the text in line 203-207 Below we have plotted the Up versus Down responses for the 10 least tuned cells (sorted by directionality index). While some cells have higher responses on average to one direction we think that the variability makes it difficult to support a claim for “tuning.” We have also tested the support vector machine on the least tuned cells to confirm that the chosen cutoff for tuned/untuned is not affecting our claim that untuned cells can encode position.(see also Author response image 4)

Author response image 3.

Trial-by-trial variability

(ii) It is therefore not very surprising that PCA (and the SVM decoder) distinguishes trial type. I would guess that PCA assigns the largest weights to these most tuned of the "not tuned" cells, and the 3-5 cell decoders do well when these cells happen to be sampled.

Author response image 4.

Decoding accuracy of the 3/5/7 least tuned cells

This was an interesting idea. To rule out that it is only the most tuned cells that contain the information, we tested the decoder on the 3/5/7 least tuned cells; here too, 5 and more cells are better able to accurately decode the direction. We have add the decoding accuracy to the text in line 221-224

(iii) As I understand the analysis, Figure 5G shows responses for "not tuned" cells over 21 trials (of each type) but these are not the same trials for the different cells? How then is population coding being assessed?

We have updated the text and refer to this data as a “pseudo-population” in lines 216 and 218 for all experiments where we combined cells from different fish. For technical reasons, when we perform TIPM at eccentric angles we must use sparsely labelled fish to ensure that we can find the same cells over a 60 degree range. We have repeated our analyses for TIPM centered at the horizon, where we can record from entire populations from a single fish.  

(iv) Furthermore, Figure S2 shows a somewhat different analysis with decoding accuracy measured on a fish-by-fish basis. In this case, are these decoders for simultaneously imaged neurons? Is this a cross-validated measure of decoding accuracy?

Yes, as above, Figure S4 (former S2) looks at fish-by-fish basis of simultaneous recorded neurons. Yes, it was 5-fold cross validated. We have updated the text in line 490-494.

Reviewer #2 (Recommendations For The Authors):

- Postural control involves various aspects such as balance, coordination, relative body part orientations, and stability. Discussing these and presenting in this context the specific subaspect characterized in this study would help clarify which aspect of postural control the work focuses on.

The Reviewer makes an interesting point, but we think their description of what constitutes postural control is overly broad. Specifically, control of “relative body part orientations in space” by definition requires coordination, and subserves balance and stability. We acknowledge, of course, that different aspects can be and often are treated independently. While interesting, a full treatment of what comprises “postural control” is beyond the scope of the paper, as it would require reconciling the terms across taxa, effectors, environments and well over a century of experiments.

We contend that posture — particularly underwater — is best defined as the relative orientation of body parts in space. For fish, those parts consist of predominantly axial muscles and secondarily fins. We present these definitions in the Introduction and thank the Reviewer for encouraging us to more clearly shape our findings.

- Disruption of posture or postural control: The use of the word "disruption" could lead to misleading expectations. While it may not be incorrect, it suggests a significant loss of equilibrium, an obvious increase in postural variability, or at least a noticeable effect when observing an individual animal's behavior. However, the supporting data show only a subtle median shift in postural angle within a very broad distribution averaged over many individuals. This effect was only significant when comparing fish with a control group, not when comparing fish posture before and after the treatment.

Replacing "disruption" with "modification" would be more cautious.

We take the Reviewer’s point and have adjusted our wording to "modifies postural control.” In lines 137, 266, and 283

- Statistical significance: Consider aligning the asterisk notation with conventional standards (e.g., * for p < 0.05, ** for p < 0.01, *** for p < 0.001) to enhance clarity for readers. On the other hand, the individual measurements might not be independent (e.g., measurements from the same fish, or the same tank are likely to be correlated), so using the Wilcoxon rank-sum test (Mann-Whitney U test) on pooled data might lead to incorrect conclusions. Methods that account for the hierarchical structure of the data might be required to support the conclusions.

We take the Reviewer’s point about the importance of conventions, however we have never found “more stars = more significant” to be all that helpful in evaluating claims. Instead, we’ve opted to have both a significance and effect size criteria; a “star” here reflects our considered confidence in the difference we observe. 

We agree that the hierarchical nature of pooled data is worth considering/presenting.

We performed a two-way analysis of variance (ANOVA) on the interquartile ranges (IQRs) of the single experimental repeats for the 7 days post-fertilization (dpf) activation, 7dpf lesion, and 14dpf lesion experiments. The ANOVA revealed no significant main effects, supporting the strategy of pooling experimental repeats to estimate distributions.

The results of the ANOVA, along with the IQRs for all experimental repeats, are presented in Tables 6-11. We have also clarified this in the methods section in lines 505-509.

- Data representation: All data of postural angles should be represented in the form of violin plots to show the underlying distributions of the postural angles, especially given that the effect size is small relative to the dispersion of the distribution of the postural angle and that this distribution is also not Gaussian but bimodal, and different before and after the treatments.

We take the Reviewer’s point that seeing the full distribution can be useful. We have added plots of the raw distributions for the data in Figure 3 as supplemental Figure S3.

- Showing the distributions will provide the necessary information for the reader to evaluate the importance of the effect. For all data shown in Table 1, the distributions should be presented in the supplementary information.

As requested, we have added the distributions of the data in Table 1 to the supplement (Figure S2)

- Roll posture: A statement about whether roll posture is perturbed by Purkinje cell manipulation would be a piece of important additional information helping to understand how strong the 'disruption' of posture is.

We haven’t assessed roll posture, as this is not practical in the current version of the SAMPL apparatus. We have added this limitation to the results (line 116) but also note that as our manipulations are bilateral, we don’t anticipate any systematic changes to roll.   

- Comparison with other methods: Add a discussion on how the TRPV1/capsaicin method compares with other methods, such as using nitroreductase (Ntr) for targeted pharmaco-genetic ablation of cells by treatment with metronidazole or the the possibility to to ablate Purkinje cells by KillerRed as the author lab has done previously. Both methods have been applied to ablate Purkinje cells in larval zebrafish. What are the advantages of the TRPV1 method compared to these when neglecting the activation possibility?

Thank you for that suggestion, we have added a section to the discussion where we compare the TRPV1/capsaicin lesion to other lesion methods (lines 334-336)

- Describe the decoding algorithm: The decoding algorithm used could be described more in detail in the methods section.

We have described the decoding algorithm in more detail in the methods under ‘Functional GCaMP imaging in Purkinje cells.’ Line 488+ 

We used a support vector machine (SVM) with a linear kernel. The SVM model was trained using k-fold cross-validation, which splits the data into k subsets (folds). At each iteration, the model was trained on k-1 folds and tested on the remaining fold, ensuring that the model performance was evaluated on unseen data in each fold. Permutations were performed on randomized trial identity as a null hypothesis (5-fold cross-validation; 100 shuffles for randomization). Accuracy was calculated as 1 minus the classification loss.  

- Availability of code: The link to the data and code repository is not working.

Thank you for pointing that out, we have fixed it now. In the lower right of the page you can see the history of all changes to the repository, including the entry on 2023-09-08 where the corresponding author set it to “public.” When we checked thanks to your comment, it had been set to “private,” without any record of when/why. We have reset it 2024-10-17. We will continue to check it periodically in the future and apologize in advance if it is unavailable; this is the first time we’ve seen that happen.

- Electrophysiological Control: Including an electrophysiological characterization of the activation of Purkinje cells by the TRPV1/capsaicin would significantly strengthen the validity of the method.

We take the Reviewer’s point that electrophysiological characterization is a way to strengthen the validity of the method. However, Chen et al (h"ps://doi.org/10.1038/ nmeth.3691) have performed electrophysiology during neuronal activation and concluded that TRPV1 activation with capsaicin indeed increases neuronal activity and firing rates increased. Our calcium imaging and lesion experiments amply demonstrate that Purkinje cells are sensitive to TRPV1-mediated currents. We therefore do not believe that the additional information gained by arduous electrophysiological evaluation is merited here.

- Describe more in detail how climb and dive bouts are defined. The height difference between consecutive bouts measured 250ms before the bout of executions.

Climb and Dive bouts are split by the angle of their trajectory. If the fish moves up (i.e. trajectory larger 0) it is considered a climb bout and vice versa for dive bouts. 250ms prior to the maximum speed is roughly the time the fish initiate a bout, so the pre-bout posture is measured when at this point. The time-courses of bouts are dissected extensively in Zhu et. al. 2023. We have added a definition for climb and dive bouts to the method section under ‘Behavior analysis’ line 453 and 454.  

- Figure 1H: Why can't you ablate all Purkinje cells but only about 80%?

This is an excellent question. We opted for an extremely conservative count, and included everything that was still resembling a cell, even if it might not be functional/ already dying. Our counts are therefore likely an underestimate of the percentage of cells that were lost. We have added this point to the text in lines 393 395

- Figure 2C: The method is not fully clear. At 8dpf 0.1uM capsaicin is added to the chamber. At what time after the application of capsaicin did the behavioral recording start?

We recorded after about 10-15min after adding the 1uM Csn to the chambers. The fish were fed after the 6h in capsaicin. We have added this information to the method section line 404 - 408.

- Figure 2F: What indicates the shown confidence interval? Also median with a 95% confidence interval calculated over the experiments in parallel?

The distributions shown in Figure 2F take data from all experiments pooled. We use resampling methods to determine the variability in our estimates. The distribution plots are showing the median and the 25th and 75th percentile of the resampled distribution. We have added this information to the figure legends.

- Figure 3: Subtitles on panel D and E indicating <climb bout posture> and would facilitate reading.

We have added the subtitles to those panels.

- Figure 4: Describe in the methods how recordings from individual fish were mapped onto each other to superimpose the Purkinje cell locations recorded from the 8 fish.

We have added the respective section to the methods: Line 481 - 483

“To map the anatomical locations of the recorded cells, we imaged overview stacks for each fish. These stacks were manually aligned in Illustrator, and the cells included in the analysis were reidentified and color-coded according to their tuning properties.”

Reviewer #3 (Recommendations For The Authors):

Major points:

(1) Lines 74-81. The data presented here and in later experiments to argue for an effect of capsaicin on neural activity lacks statistical rigor because of the apparently very small numbers of animals/cells assessed. For example, the control appears to involve 4 cells assessed from 1 animal, and the experimental group is just 2 animals. Given that the interpretation of the paper depends upon this result, it is worthwhile to show the result more clearly, and with some statistical analysis. They argue in the discussion that "Our imaging assay established that 1 µM of capsaicin would stochastically activate subsets of Purkinje cells" which seems a stretch from the data as presented.

We appreciate this point, which was shared by Reviewer 1. We have added more data and performed statistical analysis (line 63 - 67 as well as Figure S1A)

(2) I found the practice of sorting effects by a mixture of effect size and p-value to be a little arbitrary, although in this case, it seems likely that it identified the most relevant effects. I would have preferred to see some attempt to correct for multiple comparisons (e.g. by resampling with the identities of fish shuffled to estimate the distribution of each measurement for this population size), followed by filtering for effect size after establishing a corrected threshold for significance.

We take the Reviewer’s point, though we note that critical values for effect size and pvalue are inevitably “a little arbitrary.” We can’t do the exact analysis the Reviewer suggests as we do not measure data from individual fish for these experiments. However, we did calculate new critical p-values (added to the Tables) that account for multiple comparisons using Šidák’s method.

(3) Figure 4. The data here is a little strange in that the slope in the control condition for medium speed is given as much larger than for slow, but the data in the two cases appears largely overlapping for most of the range of behavior, only diverging for the most extreme rotations. It seems perhaps that the measurement of slope is strongly dependent on these most extreme values. The authors might want to consider the use of robust regression methods which might mitigate these effects.

This is an interesting observation and we appreciate the Reviewer’s thoughtful suggestion. We now use a robust regression method (bisquare weighting of residuals).

We have adjusted all values in lines 175 - 177  and added the regression method to the Methods section line 520.

(4) Figure 5. The 'principal component analysis' description is extremely unclear. The text says that PCA 'showed near-complete segregation of trial types' but it is not explained how this was achieved with PCA or how this was quantified. Figure panels show the data plotted using different pairs of PCs showing visual evidence of segregation. In the methods, it is stated that "We performed principal component analysis" and that "cells were used for principal component analysis and subsequent support vector machine decoding analysis". What is meant exactly by 'performed PCA'? Was PCA used in a dimensionality reduction step? And if so, how many and which PCs were chosen and why? For visualization of the separation, the authors show arbitrary pairs of PCs. Could it be better to use a method more suited to that purpose such as linear discriminant analysis?

PCA was used to define a subspace to qualitatively evaluate if different trials could be separated. Once it became clear that it could, we next trained a binary decoder on the complete dataset (i.e. no dimensionality reduction). We did not perform linear discriminant analysis as the unsupervised PCA already showed separation of trial types.  We have made this clearer in lines 212 - 214.

(5) Why does the decoding analysis use only untuned cells? Isn't it equally, or more, interesting to know how well tilt can be encoded using all cells? It is unclear to me what we learn by selecting only untuned cells for this analysis (although I agree it is interesting that this does work).

We focused exclusively on untuned cells because including even a single highly tuned cell for the population coding will lead to excellent results. By using untuned cells we test if there is some directionality information that is not visible just by looking at the up/ down responses of single cells. We have made this clear in lines 217 - 218

Minor points and corrections:

(1) Maybe consider losing the words 'powerful' (I think it is overused and not well defined) and 'reagent'. Reagent is normally used for something that participates in a reaction. It is a bit odd to use it to refer to a transgenic animal. Later it is called a 'tool' which seems better.

We have changed the wording and refer to it as tool for the whole paper.  

(2) Figure 1D. Please use a color bar to indicate the scale.

We have added a color scale to the panel

(3) Saying that 'posture' increases is confusing, although the meaning can be inferred from the overall context and the definitions in the Methods - could Posture be capitalized to indicate a specific definition is being used rather than the general meaning?

This suggestion agrees with those made by Reviewer 2. We have changed the wording to “postural angle.” 

(4) The arrowheads in Figure 2FHK are unnecessary and confusing (why are some horizontal and some vertical?).

Thank you for that suggestion, we have removed the arrowheads.

(5) Figure 3 The legend should indicate that the image is shown with an inverted lookup table.

We have updated the legend

(6) Figure 3 D and E Titles would be helpful, so it is not necessary to refer to the legend to understand the difference.

We have added titles to the figure panels

(7) The dwell time for the 2-photon experiments is given in the manuscript, but I think the authors meant microseconds?

Thank you for pointing that out. We have corrected it to microseconds.

eLife Assessment

This important study provides convincing evidence that developing neurons in the neocortex regulate glial cell development. The data demonstrates that the transcription factor FOXG1 negatively regulates gliogenesis by controlling the expression of a member of the FGF ligand family and by suppressing the receptor for this ligand in developing neurons. This study leads to a new understanding of the cascade of events regulating the timing of glial development in the neocortex.

Reviewer #1 (Public review):

Summary:

In this paper, Bose et al. investigated the role of Foxg1 transcription factor in the progenitors at late stages of cerebral cortex development.

They discover that Foxg1 is a repressor of gliogenesis and has a dual function, first as a repressor of Fgfr3 receptor in progenitors, and second as a suppressor of the Fgf ligands in young neurons.

They found that the inactivation of Foxg1 in cortical progenitors causes premature astrogliogenesis at the expense of neurogenesis. They identify Fgfr3 as a novel FOXG1 target. They show that suppression of Fgfr3 by FOXG1 in progenitors is required to maintain neurogenesis. On the other hand, they also show that FOXG1 negatively regulates the expression of Fgf gliogenic secreted factors in young neurons suppressing gliogenesis cells extrinsically.

Strengths:

The authors used time-consuming in vivo experiments utilizing several mouse strains including Foxg1-MADM in combination with RNA-Seq and ChIP to convincingly show that Foxg1 acts upstream of FGF signalling in the control of gliogenesis onset. The conclusions of this paper are mostly well supported by data.

Reviewer #2 (Public review):

Summary:

We have known for some time that neural progenitors in the cerebral cortex switch their output from cortical neurons to glia at late embryonic stages, however little is known about how this switch is regulated at the molecular level. Bose et al present a convincing set of findings, demonstrating that the transcription factor Foxg1 plays a key role in this process, mediated through FGF signalling. Foxg1 cell-autonomously inhibits gliogenesis in progenitor cells (thereby promoting neuronal identity), and lower Foxg1 expression in postnatal neurons leads to increased expression of FGF ligand, promoting glial development from nearby progenitors.

Strengths:

The study is very well designed, having a systematic, thorough, and logical approach. The data is convincing. The authors make full use of a range of existing transgenic strains, published 'omics data, and elegant genetic approaches such as MADM. This combination of approaches is particularly rigorous, lending significant weight to the study. The manuscript is well-written, clear, and easy to follow.

Impact

This manuscript identifies a previously unknown role for Foxg1 in forebrain development and a mechanism underlying the neurogenic-to-gliogenic switch that occurs at late embryonic stages of cortex development. These findings will stimulate further research to uncover more details of how this important switch is controlled and may provide useful insight into some of the symptoms experienced by children with FOXG1 Syndrome.

Author response:

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

Weaknesses (Reviewer 1):

The role of Fgf signaling in gliogenesis and Foxg1 in neurogenesis is well known. It is not clear if Fgf18 is a direct target of Foxg1.

We agree with the reviewer- Fgf signaling is an established pro-gliogenic pathway (Duong et al 2019) and Foxg1 overexpression is known to promote neurogenesis in cultured neural stem cells (Branacaccio et al 2019). Our study links these two mechanisms, as the Reviewer has summarized: (a) we demonstrate that FOXG1 works via modulating Fgf signaling cell-autonomously within progenitors by regulating the levels of Fgfr3. (b) Loss of Foxg1 in postmitotic neurons results in the upregulation of Fgf ligand expression (possibly via indirect mechanisms) and this non-cell autonomously increases Fgf signaling in progenitors_. Our study is entirely performed _in vivo.

Revision: We have revised the manuscript to reflect that Fgf18 may be an indirect target of FOXG1 in postmitotic neurons.

Weaknesses (Reviewer 2):

It wasn't clear to me why the authors chose postnatal day 14 to examine the effects of Foxg1 deletion at E15 - this is a long time window, giving time for indirect consequences of Foxg1 deletion to influence development and thereby potentially complicating the interpretation of findings. For example, the authors show that there is no increased proliferation of astrocytes or death of neurons lacking Foxg1 shortly after cre-mediated deletion, but it remains formally possible (if perhaps unlikely) that these processes could be affected later during the time window. The rationale underlying the choice of this time point should be explained.

I don't agree with the statement in the very last sentence of the results section that "neurogenesis is not possible in the absence of [Foxg1]" as there are multiple reports in the literature demonstrating the presence of neurons in Foxg1-/- mice (eg: Xuan et al., 1995; Hanashima et al., 2002, Martynoga et al., 2005, Muzio and Mallamaci 2005). Perhaps the statement refers specifically to late-born cortical neurons. This point also arises in the discussion section.

Revisions:

(a) We have revised the manuscript to explain why we chose postnatal day 14 to examine the effects of Foxg1 deletion at E15.

●  We have examined the transcriptomic dysregulation after Foxg1 deletion at E17.5, which is a reasonable period to identify potential direct targets. Furthermore, FOXG1 occupies the Fgfr3 locus in ChIP-seq performed at E15.5. Together, these support the interpretation that Fgfr3 is a direct target of Foxg1.

● As the Reviewer notes, we have investigated the possibility of increased proliferation of astrocytes and death of neurons and found no evidence suggesting these phenomena occur in the 3 days after loss of Foxg1. Cortical neurons are postmitotic and differentiated by E18.5, the stage at which we examined CC3 staining and found no difference in cell death in control and mutants (Supplementary Figure S2C, C’). The majority of progenitors (PAX6+ve cells) that lose Foxg1 at E15.5 express the gliogenic transcription factor NFIA by E18.5 (Figure 2C, C’), but hardly any express intermediate (neurogenic) progenitor marker TBR2 (Supplementary Figure S2B, B’). It is therefore unlikely that neurons are born from Foxg1 mutant progenitors and then die at a later stage.

● The cellular consequences of loss of Foxg1 require additional time to detect e.g. it takes ~ 5 days for GFAP to be detected in astrocytes once they are born. The P14 timepoint permits the assessment of oligogenesis which begins after astrogliogenesis and therefore permits a comprehensive assessment of the lineage of E15.5 Foxg1 null progenitors.

(b) Thank you for pointing out that the last sentence of the results section implied (incorrectly) that ALL neurogenesis is not possible in the absence of Foxg1 We have modified this (and the discussion) to reflect that this applies to E14/15 progenitors and late-born cortical neurons.

Recommendations for the authors (Reviewer 2):

(c) We thank the reviewer for this suggestion. We will modify the schematic (Figure 7) to remove any ambiguity regarding Foxg1 expression.

eLife Assessment

The work by Han and collaborators describes valuable findings on the role of Akkermansia muciniphila during ETEC infection. If confirmed, these findings will add to a growing list of beneficial properties of this organism. The strength of the evidence used to justify the conclusions in the manuscript is solid, as the analyses broadly support the claims with only minor weaknesses.

Reviewer #3 (Public review):

Summary:

The manuscript by Ma et al. describes a multi-model (pig, mouse, organoid) investigation into how fecal transplants protect against E. coli infection. The authors identify A. muciniphila and B. fragilis as two important strains and characterize how these organisms impact the epithelium by modulating host signaling pathways, namely the Wnt pathway in lgr5 intestinal stem cells.

Strengths:

The strengths of this manuscript include the use of multiple model systems and follow up mechanistic investigations to understand how A. muciniphila and B. fragilis interacted with the host to impact epithelial physiology.

Weaknesses:

As in previous revisions, there remains concerning ambiguity in the methodology used for microbiota sequence analysis and it would be difficult to replicate the analysis in any meaningful way. In this revision, concerns about the rigor and reproducibility of this component of the manuscript have been increased. Readers should be cautious with interpretation of this data.

(1) In previous versions of the manuscript it would appear the correct bioproject accession was listed but, the actual link went to an unrelated project. The updated accession link appears to contain raw data; however, the authors state they used an Illumina HiSeq 2500. This would be an unusual choice for V3-V4 as it would not have read lengths long enough to overlap. Inspection of the first sample (SRR19164796) demonstrates that this is absolutely not the raw data, as there is a ~400 nt forward read, and a 0 length reverse read. All quality scores are set to 30. There is no logical way to go from HiSeq 2500 raw data and read lengths to what was uploaded to the SRA and it was certainly not described in the manuscript.

(2) No multiple testing correction was applied to the microbiome data.

Author response:

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

Those comments are all valuable and very helpful for revising and improving our paper, as well as the important guiding significance to our researches. We have studied comments carefully and have made correction which we hope meet with approval.

Reviewer #3 (Public review):

Summary:

The manuscript by Ma et al. describes a multi-model (pig, mouse, organoid) investigation into how fecal transplants protect against E. coli infection. The authors identify A. muciniphila and B. fragilis as two important strains and characterize how these organisms impact the epithelium by modulating host signaling pathways, namely the Wnt pathway in lgr5 intestinal stem cells.

Strengths:

The strengths of this manuscript include the use of multiple model systems and follow up mechanistic investigations to understand how A. muciniphila and B. fragilis interacted with the host to impact epithelial physiology.

Weaknesses:

As in previous revisions, there remains concerning ambiguity in the methodology used for microbiota sequence analysis and it would be difficult to replicate the analysis in any meaningful way. In this revision, concerns about the rigor and reproducibility of this component of the manuscript have been increased. Readers should be cautious with interpretation of this data.

(1) In previous versions of the manuscript it would appear the correct bioproject accession was listed but, the actual link went to an unrelated project. The updated accession link appears to contain raw data; however, the authors state they used an Illumina HiSeq 2500. This would be an unusual choice for V3-V4 as it would not have read lengths long enough to overlap. Inspection of the first sample (SRR19164796) demonstrates that this is absolutely not the raw data, as there is a ~400 nt forward read, and a 0 length reverse read. All quality scores are set to 30. There is no logical way to go from HiSeq 2500 raw data and read lengths to what was uploaded to the SRA and it was certainly not described in the manuscript.

What we uploaded to the SRA was Contigs files for sample, we have modified the description on line 694.

(2) No multiple testing correction was applied to the microbiome data.

The alpha diversity indexes were tested using T-test and wilcox test, and we showed the result of T-test in FigureS1B. The p-values were corrected for multiple testing using the Benjamini-Hochberg method, we have modified the description on line 322.

---------

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

Public Reviews:

Reviewer #2 (Public Review):

Ma X. et al proposed that A. muciniphila was a key strain that promotes the proliferation and differentiation of intestinal stem cells through acting on the Wnt/β-catenin signaling pathway. They used various models, such as piglet model, mouse model and intestinal organoids to address how A. muciniphila and B. fragilis offer the protection against ETEC infection. They showed that FMT with fecal samples, A. muciniphila or B. fragilis protected piglets and/or mice from ETEC infection, and this protection is manifested as reduced intestinal inflammation/bacterial colonization, increased tight junction/Muc2 proteins, as well as proper Treg/Th17 cells. Additionally, they demonstrated that A. muciniphila protected basal-out and/or apical-out intestinal organoids against ETEC infection via Wnt signaling.

Comments on revised version:

Please add proper references to indicate the invasion of ETEC into organoids after 1 h of infection.

We have added references on line 211.

References:

Xiao K, Yang Y, Zhang Y, Lv QQ, Huang FF, Wang D, Zhao JC, Liu YL. 2022. Long-chain PUFA ameliorate enterotoxigenic Escherichia coli-induced intestinal inflammation and cell injury by modulating pyroptosis and necroptosis signaling pathways in porcine intestinal epithelial cells. Br. J. Nutr. 128(5):835-850.

Qian MQ, Zhou XC, Xu TT, Li M, Yang ZR, Han XY. 2023. Evaluation of Potential Probiotic Properties of Limosilactobacillus fermentum Derived from Piglet Feces and Influence on the Healthy and E. coli-Challenged Porcine Intestine. Microorganisms. 11(4).

Reviewer #3 (Public Review):

Summary:

The manuscript by Ma et al. describes a multi-model (pig, mouse, organoid) investigation into how fecal transplants protect against E. coli infection. The authors identify A. muciniphila and B. fragilis as two important strains and characterize how these organisms impact the epithelium by modulating host signaling pathways, namely the Wnt pathway in lgr5 intestinal stem cells.

Strengths:

The strengths of this manuscript include the use of multiple model systems and follow up mechanistic investigations to understand how A. muciniphila and B. fragilis interacted with the host to impact epithelial physiology.

Weaknesses:

After an additional revision, the bioinformatics section of the methods has changed significantly from previous versions and now indicates a third sequencer was used instead: Ion S5 XL. Important parameters required to replicate analysis have still not been provided. Inspection of the SRA data indicates a mix of Illumina MiSeq and Illumina HiSeq 2500. It is now unclear which sequencing technology was used as authors have variably reported 4 different sequencers for these samples. Appropriate metadata was not provided in the SRA, although some groups may be inferred from sample names. These changing descriptions of the methodologies and ambiguity in making the data available create concerns about rigor of study and results.

Due to confusing the sequencing method of this experiment with other experiment samples, we apologize for the multiple incorrect modifications of the method description. We have modified the method for microbiome sequencing technology on line 304. The sequencing technology is Illumina HiSeq 2500. The SRA metadata can be viewed at https://www.ncbi.nlm.nih.gov/sra/PRJNA837047. The sample names ep1-6 and ef1-6 were correspond to the EP and EF groups, respectively.

Recommendations For the Authors:

As in the previous revision:

-provide important parameters required to replicate analysis

-ensure that reporting of sequencing technology is correct as data listed on SRA appears to be derived from Illumina sequencers, and was deposited indicating as such.

-update SRA metadata such that experimental groups are clear and match the nomenclature used in the manuscript (Particularly for samples which are labelled [A-Z][0-9]

- The multiple testing correction wasn’t applied.

-Due to confusing the sequencing method of this experiment with other experiment samples, we apologize for the multiple incorrect modifications of the method description. We have modified the method for microbiome sequencing technology on line 304. The sequencing technology is Illumina HiSeq 2500.

- The SRA metadata can be viewed at https://www.ncbi.nlm.nih.gov/sra/PRJNA837047. The sample names ep1-6 and ef1-6 were correspond to the EP and EF groups, respectively.

eLife Assessment

This study tested the specific hypothesis that age-related changes to hearing involve a partial loss of synapse connections between sensory cells in the ear and the nerve fibers that carry information about sounds to the brain, and that this interferes with the ability to discriminate rapid temporal fluctuations in sounds. Physiological, behavioral, and histological analyses provide a powerful combination to test this hypothesis in gerbils. Contrary to previous suggestions, it was found that chemically-induced isolated synaptopathy (at similar levels as observed in aged gerbils) did not result in worse performance on a behavioral task measuring sensitivity to fine-structure. Further, altered neural coding of rapid fluctuations produced no perceptual deficits in either these gerbils or in aged gerbils. These findings are important for understanding age-related changes to hearing; however, the evidence provided is incomplete due to problems in interpretation and the discussion of possible confounds and/or limitations of these data that currently limits mechanistic insight.

Reviewer #1 (Public review):

Summary:

The authors investigate the effects of aging on auditory system performance in understanding temporal fine structure (TFS), using both behavioral assessments and physiological recordings from the auditory periphery, specifically at the level of the auditory nerve. This dual approach aims to enhance understanding of the mechanisms underlying observed behavioral outcomes. The results indicate that aged animals exhibit deficits in behavioral tasks for distinguishing between harmonic and inharmonic sounds, which is a standard test for TFS coding. However, neural responses at the auditory nerve level do not show significant differences when compared to those in young, normal-hearing animals. The authors suggest that these behavioral deficits in aged animals are likely attributable to dysfunctions in the central auditory system, potentially as a consequence of aging. To further investigate this hypothesis, the study includes an animal group with selective synaptic loss between inner hair cells and auditory nerve fibers, a condition known as cochlear synaptopathy (CS). CS is a pathology associated with aging and is thought to be an early indicator of hearing impairment. Interestingly, animals with selective CS showed physiological and behavioral TFS coding similar to that of the young normal-hearing group, contrasting with the aged group's deficits. Despite histological evidence of significant synaptic loss in the CS group, the study concludes that CS does not appear to affect TFS coding, either behaviorally or physiologically.

Strengths:

This study addresses a critical health concern, enhancing our understanding of mechanisms underlying age-related difficulties in speech intelligibility, even when audiometric thresholds are within normal limits. A major strength of this work is the comprehensive approach, integrating behavioral assessments, auditory nerve (AN) physiology, and histology within the same animal subjects. This approach enhances understanding of the mechanisms underlying the behavioral outcomes and provides confidence in the actual occurrence of synapse loss and its effects. The study carefully manages controlled conditions by including five distinct groups: young normal-hearing animals, aged animals, animals with CS induced through low and high doses, and a sham surgery group. This careful setup strengthens the study's reliability and allows for meaningful comparisons across conditions. Overall, the manuscript is well-structured, with clear and accessible writing that facilitates comprehension of complex concepts.

Weaknesses:

The stimulus and task employed in this study are very helpful for behavioral research, and using the same stimulus setup for physiology is advantageous for mechanistic comparisons. However, I have some concerns about the limitations in auditory nerve (AN) physiology. Due to practical constraints, it is not feasible to record from a large enough population of fibers that covers a full range of best frequencies (BFs) and spontaneous rates (SRs) within each animal. This raises questions about how representative the physiological data are for understanding the mechanism in behavioral data. I am curious about the authors' interpretation of how this stimulus setup might influence results compared to methods used by Kale and Heinz (2010), who adjusted harmonic frequencies based on the characteristic frequency (CF) of recorded units. While, the harmonic frequencies in this study are fixed across all CFs, meaning that many AN fibers may not be tuned closely to the stimulus frequencies. If units are not responsive to the stimulus further clarification on detecting mistuning and phase locking to TFS effects within this setup would be valuable. Given the limited number of units per condition-sometimes as few as three for certain conditions - I wonder if CF-dependent variability might impact the results of the AN data in this study and discussing this factor can help with better understanding the results. While the use of the same stimuli for both behavioral and physiological recordings is understandable, a discussion on how this choice affects interpretation would be beneficial. In addition a 60 dB stimulus could saturate high spontaneous rate (HSR) AN fibers, influencing neural coding and phase-locking to TFS. Potentially separating SR groups, could help address these issues and improve interpretive clarity.

A deeper discussion on the role of fiber spontaneous rate could also enhance the study. How might considering SR groups affect AN results related to TFS coding? While some statistical measures are included in the supplement, a more detailed discussion in the main text could help in interpretation.

Although Figure S2 indicates no change in median SR, the high-dose treatment group lacks LSR fibers, suggesting a different distribution based on SR for different animal groups, as seen in similar studies on other species. A histogram of these results would be informative, as LSR fiber loss with CS-whether induced by ouabain in gerbils or noise in other animals-is well documented (e.g., Furman et al., 2013).

Although ouabain effects on gerbils have been explored in previous studies, since these data already seems to be recorded for the animal in this study, a brief description of changes in auditory brainstem response (ABR) thresholds, wave 1 amplitudes, and tuning curves for animals with cochlear synaptopathy (CS) in this study would be beneficial. This would confirm that ouabain selectively affects synapses without impacting outer hair cells (OHCs). For aged animals, since ABR measurements were taken, comparing hearing differences between normal and aged groups could provide insights into the pathologies besides CS in aged animals. Additionally, examining subject variability in treatment effects on hearing and how this correlates with behavior and physiology would yield valuable insights. If limited space maybe a brief clarification or inclusion in supplementary could be good enough.

Another suggestion is to discuss the potential role of MOC efferent system and effect of anesthesia in reducing efferent effects in AN recordings. This is particularly relevant for aged animals, as CS might affect LSR fibers, potentially disrupting the medial olivocochlear (MOC) efferent pathway. Anesthesia could lessen MOC activity in both young and aged animals, potentially masking efferent effects that might be present in behavioral tasks. Young gerbils with functional efferent systems might perform better behaviorally, while aged gerbils with impaired MOC function due to CS might lack this advantage. A brief discussion on this aspect could potentially enhance mechanistic insights.

Lastly, although synapse counts did not differ between the low-dose treatment and NH I sham groups, separating these groups rather than combining them with the sham might reveal differences in behavior or AN results, particularly regarding the significance of differences between aged/treatment groups and the young normal-hearing group.

Reviewer #2 (Public review):

Summary:

Using a gerbil model, the authors tested the hypothesis that loss of synapses between sensory hair cells and auditory nerve fibers (which may occur due to noise exposure or aging) affects behavioral discrimination of the rapid temporal fluctuations of sounds. In contrast to previous suggestions in the literature, their results do not support this hypothesis; young animals treated with a compound that reduces the number of synapses did not show impaired discrimination compared to controls. Additionally, their results from older animals showing impaired discrimination suggest that age-related changes aside from synaptopathy are responsible for the age-related decline in discrimination.

Strengths:

(1) The rationale and hypothesis are well-motivated and clearly presented.

(2) The study was well conducted with strong methodology for the most part, and good experimental control. The combination of physiological and behavioral techniques is powerful and informative. Reducing synapse counts fairly directly using ouabain is a cleaner design than using noise exposure or age (as in other studies), since these latter modifiers have additional effects on auditory function.

(3) The study may have a considerable impact on the field. The findings could have important implications for our understanding of cochlear synaptopathy, one of the most highly researched and potentially impactful developments in hearing science in the past fifteen years.

Weaknesses:

(1) My main concern is that the stimuli may not have been appropriate for assessing neural temporal coding behaviorally. Human studies using the same task employed a filter center frequency that was (at least) 11 times the fundamental frequency (Marmel et al., 2015; Moore and Sek, 2009). Moore and Sek wrote: "the default (recommended) value of the centre frequency is 11F0." Here, the center frequency was only 4 or 8 times the fundamental frequency (4F0 or 8F0). Hence, relative to harmonic frequency, the harmonic spacing was considerably greater in the present study. By my calculations, the masking noise used in the present study was also considerably lower in level relative to the harmonic complex than that used in the human studies. These factors may have allowed the animals to perform the task using cues based on the pattern of activity across the neural array (excitation pattern cues), rather than cues related to temporal neural coding. The authors show that mean neural driven rate did not change with frequency shift, but I don't understand the relevance of this. It is the change in response of individual fibers with characteristic frequencies near the lowest audible harmonic that is important here.

The case against excitation pattern cues needs to be better made in the Discussion. It could be that gerbil frequency selectivity is broad enough for this not to be an issue, but more detail needs to be provided to make this argument. The authors should consider what is the lowest audible harmonic in each case for their stimuli, given the level of each harmonic and the level of the pink noise. Even for the 8F0 center frequency, the lowest audible harmonic may be as low as the 4th (possibly even the 3rd). In human, harmonics are thought to be resolvable by the cochlea up to at least the 8th.

(2) The synapse reductions in the high ouabain and old groups were relatively small (mean of 19 synapses per hair cell compared to 23 in the young untreated group). In contrast, in some mouse models of the effects of noise exposure or age, a 50% reduction in synapses is observed, and in the human temporal bone study of Wu et al. (2021, https://doi.org/10.1523/JNEUROSCI.3238-20.2021) the age-related reduction in auditory nerve fibres was ~50% or greater for the highest age group across cochlear location. It could be simply that the synapse loss in the present study was too small to produce significant behavioral effects. Hence, although the authors provide evidence that in the gerbil model the age-related behavioral effects are not due to synaptopathy, this may not translate to other species (including human). This should be discussed in the manuscript.

It would be informative to provide synapse counts separately for the animals who were tested behaviorally, to confirm that the pattern of loss across the group was the same as for the larger sample.

(3) The study was not pre-registered, and there was no a priori power calculation, so there is less confidence in replicability than could have been the case. Only three old animals were used in the behavioral study, which raises concerns about the reliability of comparisons involving this group.

Reviewer #3 (Public review):

This study is a part of the ongoing series of rigorous work from this group exploring neural coding deficits in the auditory nerve, and dissociating the effects of cochlear synaptopathy from other age-related deficits. They have previously shown no evidence of phase-locking deficits in the remaining auditory nerve fibers in quiet-aged gerbils. Here, they study the effects of aging on the perception and neural coding of temporal fine structure cues in the same Mongolian gerbil model.

They measure TFS coding in the auditory nerve using the TFS1 task which uses a combination of harmonic and tone-shifted inharmonic tones which differ primarily in their TFS cues (and not the envelope). They then follow this up with a behavioral paradigm using the TFS1 task in these gerbils. They test young normal hearing gerbils, aged gerbils, and young gerbils with cochlear synaptopathy induced using the neurotoxin ouabain to mimic synapse losses seen with age.

In the behavioral paradigm, they find that aging is associated with decreased performance compared to the young gerbils, whereas young gerbils with similar levels of synapse loss do not show these deficits. When looking at the auditory nerve responses, they find no differences in neural coding of TFS cues across any of the groups. However, aged gerbils show an increase in the representation of periodicity envelope cues (around f0) compared to young gerbils or those with induced synapse loss. The authors hence conclude that synapse loss by itself doesn't seem to be important for distinguishing TFS cues, and rather the behavioral deficits with age are likely having to do with the misrepresented envelope cues instead.

The manuscript is well written, and the data presented are robust. Some of the points below will need to be considered while interpreting the results of the study, in its current form. These considerations are addressable if deemed necessary, with some additional analysis in future versions of the manuscript.

Spontaneous rates - Figure S2 shows no differences in median spontaneous rates across groups. But taking the median glosses over some of the nuances there. Ouabain (in the Bourien study) famously affects low spont rates first, and at a higher degree than median or high spont rates. It seems to be the case (qualitatively) in Figure S2 as well, with almost no units in the low spont region in the ouabain group, compared to the other groups. Looking at distributions within each spont rate category and comparing differences across the groups might reveal some of the underlying causes for these changes. Given that overall, the study reports that low-SR fibers had a higher ENV/TFS log-z-ratio, the distribution of these fibers across groups may reveal specific effects of TFS coding by group.

Threshold shifts - It is unclear from the current version if the older gerbils have changes in hearing thresholds, and whether those changes may be affecting behavioral thresholds. The behavioral stimuli appear to have been presented at a fixed sound level for both young and aged gerbils, similar to the single unit recordings. Hence, age-related differences in behavior may have been due to changes in relative sensation level. Approaches such as using hearing thresholds as covariates in the analysis will help explore if older gerbils still show behavioral deficits.

Task learning in aged gerbils - It is unclear if the aged gerbils really learn the task well in two of the three TFS1 test conditions. The d' of 1 which is usually used as the criterion for learning was not reached in even the easiest condition for aged gerbils in all but one condition for the aged gerbils (Fig. 5H) and in that condition, there doesn't seem to be any age-related deficits in behavioral performance (Fig. 6B). Hence dissociating the inability to learn the task from the inability to perceive TFS 1 cues in those animals becomes challenging.

Increased representation of periodicity envelope in the AN - the mechanisms for increased representation of periodicity envelope cues is unclear. The authors point to some potential central mechanisms but given that these are recordings from the auditory nerve what central mechanisms these may be is unclear. If the authors are suggesting some form of efferent modulation only at the f0 frequency, no evidence for this is presented. It appears more likely that the enhancement may be due to outer hair cell dysfunction (widened tuning, distorted tonotopy). Given this increased envelope coding, the potential change in sensation level for the behavior (from the comment above), and no change in neural coding of TFS cues across any of the groups, a simpler interpretation may be -TFS coding is not affected in remaining auditory nerve fibers after age-related or ouabain induced synapse loss, but behavioral performance is affected by altered outer hair cell dysfunction with age.

Emerging evidence seems to suggest that cochlear synaptopathy and/or TFS encoding abilities might be reflected in listening effort rather than behavioral performance. Measuring some proxy of listening effort in these gerbils (like reaction time) to see if that has changed with synapse loss, especially in the young animals with induced synaptopathy, would make an interesting addition to explore perceptual deficits of TFS coding with synapse loss.

Author response:

Public Reviews:

Reviewer #1 (Public review):

Summary:

The authors investigate the effects of aging on auditory system performance in understanding temporal fine structure (TFS), using both behavioral assessments and physiological recordings from the auditory periphery, specifically at the level of the auditory nerve. This dual approach aims to enhance understanding of the mechanisms underlying observed behavioral outcomes. The results indicate that aged animals exhibit deficits in behavioral tasks for distinguishing between harmonic and inharmonic sounds, which is a standard test for TFS coding. However, neural responses at the auditory nerve level do not show significant differences when compared to those in young, normal-hearing animals. The authors suggest that these behavioral deficits in aged animals are likely attributable to dysfunctions in the central auditory system, potentially as a consequence of aging. To further investigate this hypothesis, the study includes an animal group with selective synaptic loss between inner hair cells and auditory nerve fibers, a condition known as cochlear synaptopathy (CS). CS is a pathology associated with aging and is thought to be an early indicator of hearing impairment. Interestingly, animals with selective CS showed physiological and behavioral TFS coding similar to that of the young normal-hearing group, contrasting with the aged group's deficits. Despite histological evidence of significant synaptic loss in the CS group, the study concludes that CS does not appear to affect TFS coding, either behaviorally or physiologically.

We agree with the reviewer’s summary.

Strengths:

This study addresses a critical health concern, enhancing our understanding of mechanisms underlying age-related difficulties in speech intelligibility, even when audiometric thresholds are within normal limits. A major strength of this work is the comprehensive approach, integrating behavioral assessments, auditory nerve (AN) physiology, and histology within the same animal subjects. This approach enhances understanding of the mechanisms underlying the behavioral outcomes and provides confidence in the actual occurrence of synapse loss and its effects. The study carefully manages controlled conditions by including five distinct groups: young normal-hearing animals, aged animals, animals with CS induced through low and high doses, and a sham surgery group. This careful setup strengthens the study's reliability and allows for meaningful comparisons across conditions. Overall, the manuscript is well-structured, with clear and accessible writing that facilitates comprehension of complex concepts.

Weaknesses:

The stimulus and task employed in this study are very helpful for behavioral research, and using the same stimulus setup for physiology is advantageous for mechanistic comparisons. However, I have some concerns about the limitations in auditory nerve (AN) physiology. Due to practical constraints, it is not feasible to record from a large enough population of fibers that covers a full range of best frequencies (BFs) and spontaneous rates (SRs) within each animal. This raises questions about how representative the physiological data are for understanding the mechanism in behavioral data. I am curious about the authors' interpretation of how this stimulus setup might influence results compared to methods used by Kale and Heinz (2010), who adjusted harmonic frequencies based on the characteristic frequency (CF) of recorded units. While, the harmonic frequencies in this study are fixed across all CFs, meaning that many AN fibers may not be tuned closely to the stimulus frequencies.

We chose the stimuli for the AN recordings to be identical to the stimuli used in the behavioral evaluation of the perceptual sensitivity. Only with this approach can we directly compare the response of the population of AN fibres with perception measured in behaviour. We will address this more clearly in the revision.

If units are not responsive to the stimulus further clarification on detecting mistuning and phase locking to TFS effects within this setup would be valuable.

It is unclear to us what the reviewer alludes to. We ask to rephrase the question.

Given the limited number of units per condition-sometimes as few as three for certain conditions - I wonder if CF-dependent variability might impact the results of the AN data in this study and discussing this factor can help with better understanding the results. While the use of the same stimuli for both behavioral and physiological recordings is understandable, a discussion on how this choice affects interpretation would be beneficial. In addition a 60 dB stimulus could saturate high spontaneous rate (HSR) AN fibers, influencing neural coding and phase-locking to TFS. Potentially separating SR groups, could help address these issues and improve interpretive clarity.

In the discussion of a revised version of the manuscript, we will point out the pros and cons of using fixed-level stimuli that were not adjusted in frequency to the BF.

A deeper discussion on the role of fiber spontaneous rate could also enhance the study. How might considering SR groups affect AN results related to TFS coding? While some statistical measures are included in the supplement, a more detailed discussion in the main text could help in interpretation. We do not think that it will be necessary to conduct any statistical analysis in addition to that already reported in the supplement.

We will consider moving some supplementary information back into the main manuscript when revising.

Although Figure S2 indicates no change in median SR, the high-dose treatment group lacks LSR fibers, suggesting a different distribution based on SR for different animal groups, as seen in similar studies on other species. A histogram of these results would be informative, as LSR fiber loss with CS-whether induced by ouabain in gerbils or noise in other animals-is well documented (e.g., Furman et al., 2013).

We will add information on the distribution when revising.

Although ouabain effects on gerbils have been explored in previous studies, since these data already seems to be recorded for the animal in this study, a brief description of changes in auditory brainstem response (ABR) thresholds, wave 1 amplitudes, and tuning curves for animals with cochlear synaptopathy (CS) in this study would be beneficial. This would confirm that ouabain selectively affects synapses without impacting outer hair cells (OHCs). For aged animals, since ABR measurements were taken, comparing hearing differences between normal and aged groups could provide insights into the pathologies besides CS in aged animals. Additionally, examining subject variability in treatment effects on hearing and how this correlates with behavior and physiology would yield valuable insights. If limited space maybe a brief clarification or inclusion in supplementary could be good enough.

We do indeed have data on ABR amplitudes and the wave 1 growth functions but only in response to broadband clicks. For more frequency-specific information, mass-potential recordings are available, obtained before and after ouabain treatment. Regarding neural tuning, we did not obtain full frequency-threshold curves but do have bandwidths for response curves recorded close to threshold. We are in the process of analyzing all these data further and will consider how to best incorporate them into the manuscript, to address the reviewer’s concerns.

Another suggestion is to discuss the potential role of MOC efferent system and effect of anesthesia in reducing efferent effects in AN recordings. This is particularly relevant for aged animals, as CS might affect LSR fibers, potentially disrupting the medial olivocochlear (MOC) efferent pathway. Anesthesia could lessen MOC activity in both young and aged animals, potentially masking efferent effects that might be present in behavioral tasks. Young gerbils with functional efferent systems might perform better behaviorally, while aged gerbils with impaired MOC function due to CS might lack this advantage. A brief discussion on this aspect could potentially enhance mechanistic insights.

Our provisional response below will be integrated in similar form into the Discussion.

Olivocochlear efferent activity is a potential modulator of OHC gain (by medial olivocochlear neurons, MOC) and afferent activity (by lateral olivocochlear neurons, LOC). Beyond this general observation it is, however, difficult to speculate about its specific role in the TFS1 test, as almost nothing is known about efferent activity under naturalistic conditions in a behaving animal (reviewed by Lauer et al., 2022). We note, however, that efferent activity is believed to be reduced under general anesthesia (reviewed by Guinan, 2011, DOI 10.1007/978-1-4419-7070-1_3) and possibly abnormal in other ways, considering the potential top-down inputs to the efferent neurons from extensive brain networks (reviewed by Schofield, 2011, DOI 10.1007/978-1-4419-7070-1_9; Romero and Trussell, 2022, DOI: 10.1016/j.heares.2022.108516). Thus, it is reasonable to assume a reduced efferent influence in our auditory-nerve data, compared to the behavioral test situation. In contrast, we assume more comparable efferent influences in young-adult and old gerbils. It was recently shown that, despite age-related losses in both MOC and LOC cochlear innervation, this basically reflected the loss of efferent target structures (OHC and type-I afferents), with the surviving cochlear circuitry remaining largely normal (Steenken et al., 2024, DOI: 10.3389/fnsyn.2024.1422330). The main difference was an increased proportion of OHC without any efferent innervation, predominantly in low-frequency cochlear regions (Steenken et al., 2024). Such OHC are thus not under efferent control, and they are more numerous (about 10 – 30%) in old gerbils.

Lastly, although synapse counts did not differ between the low-dose treatment and NH I sham groups, separating these groups rather than combining them with the sham might reveal differences in behavior or AN results, particularly regarding the significance of differences between aged/treatment groups and the young normal-hearing group. For maximizing statistical power, we combined those groups in the statistical analysis. These two groups did not differ in synapse number and had quite similar ABR wave 1 growth functions.

Reviewer #2 (Public review):

Summary:

Using a gerbil model, the authors tested the hypothesis that loss of synapses between sensory hair cells and auditory nerve fibers (which may occur due to noise exposure or aging) affects behavioral discrimination of the rapid temporal fluctuations of sounds. In contrast to previous suggestions in the literature, their results do not support this hypothesis; young animals treated with a compound that reduces the number of synapses did not show impaired discrimination compared to controls. Additionally, their results from older animals showing impaired discrimination suggest that age-related changes aside from synaptopathy are responsible for the age-related decline in discrimination.

We agree with the reviewer’s summary.

Strengths:

(1) The rationale and hypothesis are well-motivated and clearly presented.

(2) The study was well conducted with strong methodology for the most part, and good experimental control. The combination of physiological and behavioral techniques is powerful and informative. Reducing synapse counts fairly directly using ouabain is a cleaner design than using noise exposure or age (as in other studies), since these latter modifiers have additional effects on auditory function.

(3) The study may have a considerable impact on the field. The findings could have important implications for our understanding of cochlear synaptopathy, one of the most highly researched and potentially impactful developments in hearing science in the past fifteen years.

Weaknesses:

(1) My main concern is that the stimuli may not have been appropriate for assessing neural temporal coding behaviorally. Human studies using the same task employed a filter center frequency that was (at least) 11 times the fundamental frequency (Marmel et al., 2015; Moore and Sek, 2009). Moore and Sek wrote: "the default (recommended) value of the centre frequency is 11F0." Here, the center frequency was only 4 or 8 times the fundamental frequency (4F0 or 8F0). Hence, relative to harmonic frequency, the harmonic spacing was considerably greater in the present study. By my calculations, the masking noise used in the present study was also considerably lower in level relative to the harmonic complex than that used in the human studies. These factors may have allowed the animals to perform the task using cues based on the pattern of activity across the neural array (excitation pattern cues), rather than cues related to temporal neural coding. The authors show that mean neural driven rate did not change with frequency shift, but I don't understand the relevance of this. It is the change in response of individual fibers with characteristic frequencies near the lowest audible harmonic that is important here.

The auditory filter bandwidth of the gerbil is about double that of human subjects. Because of this, the masking noise has a larger overall level than in the human studies in the filter. This precludes that the gerbils can use excitation patterns, especially in the condition with a center frequency of 1600 Hz and a fundamental of 200 Hz and in the condition with a center frequency of 3200 Hz and a fundamental of 400 Hz.

The case against excitation pattern cues needs to be better made in the Discussion. It could be that gerbil frequency selectivity is broad enough for this not to be an issue, but more detail needs to be provided to make this argument. The authors should consider what is the lowest audible harmonic in each case for their stimuli, given the level of each harmonic and the level of the pink noise. Even for the 8F0 center frequency, the lowest audible harmonic may be as low as the 4th (possibly even the 3rd). In human, harmonics are thought to be resolvable by the cochlea up to at least the 8th.

Because of the gerbil’s broader auditory filters, with the exception of the condition with center frequency of 1600 Hz and fundamental of 400 Hz harmonics are are not resolved. We will expand the topic of potential excitation pattern cues in the discussion of the revised version and add results on modeled excitation patterns to the supplement.

(2) The synapse reductions in the high ouabain and old groups were relatively small (mean of 19 synapses per hair cell compared to 23 in the young untreated group). In contrast, in some mouse models of the effects of noise exposure or age, a 50% reduction in synapses is observed, and in the human temporal bone study of Wu et al. (2021, https://doi.org/10.1523/JNEUROSCI.3238-20.2021) the age-related reduction in auditory nerve fibres was ~50% or greater for the highest age group across cochlear location. It could be simply that the synapse loss in the present study was too small to produce significant behavioral effects. Hence, although the authors provide evidence that in the gerbil model the age-related behavioral effects are not due to synaptopathy, this may not translate to other species (including human). This should be discussed in the manuscript.

Our provisional response below will be integrated in similar form into the Discussion.

The observed extent of age-related or noise-induced loss of type-I afferent synapses on IHC varies widely between species and studies. For example, in ageing CBA/CaJ mice, mean losses of between 20 and 50% of afferent synapses (depending on cochlear location and precise age) were reported (Sergeyenko et al., 2013, DOI: 10.1523/JNEUROSCI.1783-13.2013; Kobrina et al., 2020, DOI: 10.1016/j.neurobiolaging.2020.08.012). Humans showed more pronounced losses of peripheral axons, of 40–100%, again depending on cochlear location, precise age, and noise history (Wu et al., 2019, DOI: 10.1016/j.neuroscience.2018.07.053; 2021, DOI: 10.1523/JNEUROSCI.3238-20.2021). The age-related and induced synapse losses in our gerbils were in a more moderate range, around 20% (Steenken et al., 2021, DOI: 10.1016/j.neurobiolaging.2021.08.019; this study). Thus, it is possible that a more severe, induced synaptopathy would have resulted in behavioral deficits in young-adult gerbils. However, in the absence of additional noise or pharmacologically induced damage, our study provides strong evidence for other factors causing temporal processing problems with advancing age. Our 3-year-old gerbils are approximately comparable to a 60-year-old human (Castano-Gonzalez et al., 2024, DOI: 10.1016/j.heares.2024.108989) with beginning but not yet clinically relevant hearing loss (Hamann et al., 2002, DOI: 10.1016/S0378-5955(02)00454-9).

It would be informative to provide synapse counts separately for the animals who were tested behaviorally, to confirm that the pattern of loss across the group was the same as for the larger sample.

Yes, the pattern was the same for the subgroup of behaviorally tested animals. We will add this information to the revised version of the manuscript.

(3) The study was not pre-registered, and there was no a priori power calculation, so there is less confidence in replicability than could have been the case. Only three old animals were used in the behavioral study, which raises concerns about the reliability of comparisons involving this group.

The results for the three old subjects differed significantly from those of young subjects and young ouabain-treated subjects. This indicates a sufficient statistical power, since otherwise no significant differences would be observed.

Reviewer #3 (Public review):

This study is a part of the ongoing series of rigorous work from this group exploring neural coding deficits in the auditory nerve, and dissociating the effects of cochlear synaptopathy from other age-related deficits. They have previously shown no evidence of phase-locking deficits in the remaining auditory nerve fibers in quiet-aged gerbils. Here, they study the effects of aging on the perception and neural coding of temporal fine structure cues in the same Mongolian gerbil model.

They measure TFS coding in the auditory nerve using the TFS1 task which uses a combination of harmonic and tone-shifted inharmonic tones which differ primarily in their TFS cues (and not the envelope). They then follow this up with a behavioral paradigm using the TFS1 task in these gerbils. They test young normal hearing gerbils, aged gerbils, and young gerbils with cochlear synaptopathy induced using the neurotoxin ouabain to mimic synapse losses seen with age. In the behavioral paradigm, they find that aging is associated with decreased performance compared to the young gerbils, whereas young gerbils with similar levels of synapse loss do not show these deficits. When looking at the auditory nerve responses, they find no differences in neural coding of TFS cues across any of the groups.

However, aged gerbils show an increase in the representation of periodicity envelope cues (around f0) compared to young gerbils or those with induced synapse loss. The authors hence conclude that synapse loss by itself doesn't seem to be important for distinguishing TFS cues, and rather the behavioral deficits with age are likely having to do with the misrepresented envelope cues instead.

We agree with the reviewer’s summary.

The manuscript is well written, and the data presented are robust. Some of the points below will need to be considered while interpreting the results of the study, in its current form. These considerations are addressable if deemed necessary, with some additional analysis in future versions of the manuscript.

Spontaneous rates - Figure S2 shows no differences in median spontaneous rates across groups. But taking the median glosses over some of the nuances there. Ouabain (in the Bourien study) famously affects low spont rates first, and at a higher degree than median or high spont rates. It seems to be the case (qualitatively) in Figure S2 as well, with almost no units in the low spont region in the ouabain group, compared to the other groups. Looking at distributions within each spont rate category and comparing differences across the groups might reveal some of the underlying causes for these changes. Given that overall, the study reports that low-SR fibers had a higher ENV/TFS log-z-ratio, the distribution of these fibers across groups may reveal specific effects of TFS coding by group.

As the reviewer points out, our sample from the group treated with a high concentration of ouabain showed very few low-spontaneous-rate auditory-nerve fibers, as expected from previous work. However, this was also true, e.g., for our sample from sham-operated animals, and may thus well reflect a sampling bias. We are therefore reluctant to attach much significance to these data distributions. We will consider moving some supplementary information back into the main manuscript when revising.

Threshold shifts - It is unclear from the current version if the older gerbils have changes in hearing thresholds, and whether those changes may be affecting behavioral thresholds. The behavioral stimuli appear to have been presented at a fixed sound level for both young and aged gerbils, similar to the single unit recordings. Hence, age-related differences in behavior may have been due to changes in relative sensation level. Approaches such as using hearing thresholds as covariates in the analysis will help explore if older gerbils still show behavioral deficits.

Unfortunately, we did not obtain behavioral thresholds that could be used here. The ABR thresholds, although not directly comparable to behavioral thresholds, suggest that our old animals had at most a moderate threshold increase in quiet. Furthermore, we want to point out that the TFS 1 stimuli had an overall level of 68 dB SPL, and the pink noise masker would have increased the threshold more than expected from the moderate, age-related hearing loss in quiet. Thus, the masked thresholds for all gerbil groups are likely similar and should have no effect on the behavioral results.

Task learning in aged gerbils - It is unclear if the aged gerbils really learn the task well in two of the three TFS1 test conditions. The d' of 1 which is usually used as the criterion for learning was not reached in even the easiest condition for aged gerbils in all but one condition for the aged gerbils (Fig. 5H) and in that condition, there doesn't seem to be any age-related deficits in behavioral performance (Fig. 6B). Hence dissociating the inability to learn the task from the inability to perceive TFS 1 cues in those animals becomes challenging.

Even in the group of gerbils with the lowest sensitivity, for the condition 400/1600 the animals achieved a d’ of on average above 1. Furthermore, stimuli were well above threshold and audible, even when no discrimination could be observed. Finally, as explained in the methods, different stimulus conditions were interleaved in each session, providing stimuli that were easy to discriminate together with those being difficult to discriminate. This approach ensures that the gerbils were under stimulus control, meaning properly trained to perform the task. Thus, an inability to discriminate does not indicate a lack of proper training.

Increased representation of periodicity envelope in the AN - the mechanisms for increased representation of periodicity envelope cues is unclear. The authors point to some potential central mechanisms but given that these are recordings from the auditory nerve what central mechanisms these may be is unclear. If the authors are suggesting some form of efferent modulation only at the f0 frequency, no evidence for this is presented. It appears more likely that the enhancement may be due to outer hair cell dysfunction (widened tuning, distorted tonotopy). Given this increased envelope coding, the potential change in sensation level for the behavior (from the comment above), and no change in neural coding of TFS cues across any of the groups, a simpler interpretation may be -TFS coding is not affected in remaining auditory nerve fibers after age-related or ouabain induced synapse loss, but behavioral performance is affected by altered outer hair cell dysfunction with age.

A similar point is made by Reviewer #1. As indicated above, we do have limited data on neural bandwidths and will explore if these are sufficient to address the reviewers’ questions about potential, age-related changes in neural tuning in our sample. Previous work found no substantial OHC losses (Tarnowski et al., 1991, DOI: 10.1016/0378-5955(91)90142-V; Adams and Schulte, 1997, DOI: 10.1016/S0378-5955(96)00184-0; Steenken et al., 2024, DOI: 10.3389/fnsyn.2024.1422330) nor any deterioration in neural frequency tuning (Heeringa et al., 2020, DOI: 10.1523/JNEUROSCI.2784-18.2019), in quiet-aged gerbils of similar age as the ones used here.

Emerging evidence seems to suggest that cochlear synaptopathy and/or TFS encoding abilities might be reflected in listening effort rather than behavioral performance. Measuring some proxy of listening effort in these gerbils (like reaction time) to see if that has changed with synapse loss, especially in the young animals with induced synaptopathy, would make an interesting addition to explore perceptual deficits of TFS coding with synapse loss.

This is an interesting suggestion that we will explore in the revision of the manuscript. Reaction times were recorded for responses that can be used as a proxy for listening effort.

eLife Assessment

This valuable study shows that a very slow (infraslow) oscillation occurs in voltage recordings from the dentate gyrus of the adult mouse. The authors suggest that it is related to sleep stage and serotonin acting at one type of serotonin receptor in the dentate gyrus. The ideas are potentially significant because they suggest that this oscillation affects memory through serotonin receptors in the dentate gyrus. Solid evidence is provided to broadly support the main claims, although analytical weaknesses remain that could be improved by clarification of methods, analyses, and data shown in the figures.

Reviewer #1 (Public review):

Turi, Teng and the team used state-of-the-art techniques to provide convincing evidence on the infraslow oscillation of DG cells during NREM sleep, and how serotonergic innervation modulates hippocampal activity pattern during sleep and memory. First, they showed that the glutamatergic DG cells become activated following an infraslow rhythm during NREM sleep. In addition, the infraslow oscillation in the DG is correlated with rhythmic serotonin release during sleep. Finally, they found that specific knockdown of 5-HT receptors in the DG impairs the infraslow rhythm and memory, suggesting that serotonergic signaling is crucial for regulating DG activity during sleep. Given that the functional role of infraslow rhythm still remains to be studied, their findings deepen our understanding on the role of DG cells and serotonergic signaling in regulating infraslow rhythm, sleep microarchitecture and memory.

Reviewer #2 (Public review):

Summary:

The authors investigated DG neuronal activity at the population and single cell level across sleep/wake periods. They found an infraslow oscillation (0.01-0.03 Hz) in both granule cells (GC) and mossy cells (MC) during NREM sleep. The important findings are 1) the antiparallel temporal dynamics of DG neuron activities and serotonin neuron activities/extracellular serotonin levels during NREM sleep, and 2) the GC Htr1a-mediated GC infraslow oscillation.

Strengths:

(1) The combination of polysomnography, Ca-fiber photometry, two-photon microscopy and gene depletion is technically sound. The coincidence of microarousals and dips in DG population activity is convincing. The dip in activity in upregulated cells is responsible for the dip at the population level.<br /> (2) DG GCs express excitatory Htr4 and Htr7 in addition to inhibitory Htr1a, but deletion of Htr1a is sufficient to disrupt DG GC infraslow oscillation, supporting the importance of Htr1a in DG activity during NREM sleep.

Weaknesses:

(1) The current data set and analysis are insufficient to interpret the observation correctly.<br /> a. In Fig 1A, during NREM, the peaks and troughs of GC population activities seem to gradually decrease over time. Please address this point.<br /> b. In Fig 1F, about 30% of Ca dips coincided with MA (EMG increase) and 60% of Ca dips did not coincide with EMG increase. If this is true, the readers can find 8 Ca dips which are not associated with MAs from Fig 1E. If MAs were clustered, please describe this properly.<br /> c. In Fig 1F, the legend stated the percentage during NREM. If the authors want to include the percentage of wake and REM, please show the traces with Ca dips during wake and REM. This concern applies to all pie charts provided by the authors.<br /> d. In Fig 1C, please provide line plots connecting the same session. This request applies to all related figures.<br /> e. In Fig 2C, the significant increase during REM and the same level during NREM are not convincing. In Fig 2A, the several EMG increasing bouts do not appear to be MA, but rather wakefulness, because the duration of the EMG increase is greater than 15 seconds. Therefore, it is possible that the wake bouts were mixed with NREM bouts, leading to the decrease of Ca activity during NREM. In fact, In Fig 2E, the 4th MA bout seems to be the wake bout because the EMG increase lasts more than 15 seconds.<br /> f. Fig 5D REM data are interesting because the DRN activity is stably silenced during REM. The varied correlation means the varied DG activity during REM. The authors need to address it.<br /> g. In Fig 6, the authors should show the impact of DG Htr1a knockdown on sleep/wake structure including the frequency of MAs. I agree with the impact of Htr1a on DG ISO, but possible changes in sleep bout may induce the DG ISO disturbance.

(2) It is acceptable that DG Htr1a KO induces the reduced freezing in the CFC test (Fig. 6E, F), but it is too much of a stretch that the disruption of DG ISO causes impaired fear memory. There should be a correlation.

(3) It is necessary to describe the extent of AAV-Cre infection. The authors injected AAV into the dorsal DG (AP -1.9 mm), but the histology shows the ventral DG (Supplementary Fig. 4), which reduces the reliability of this study.

Comments on revisions:

In the first revision, I pointed out the inappropriate analysis of the EEG/EMG/photometry data and gave examples. The authors responded only to the points raised and did not seem to see the need to improve the overall analysis and description. In this second revision, I would like to ask the authors to improve them. The biggest problem is that the detection criteria and the quantification of the specific event are not described at all in Methods and it is extremely difficult to follow the statement. All interpretations are made by the inappropriate data analysis; therefore, I have to say that the statement is not supported by the data.

Please read my following concerns carefully and improve them.

(1) The definition of the event is critical to the detection of the event and the subsequent analysis. In particular, the authors explicitly describe the definition of MA (microarousal), the trough and peak of the population level of intracellular Ca concentrations, or the onset of the decline and surge of Ca levels.

(1-1) The authors categorized wake bouts of <15 seconds with high EMG activity as MA (in Methods). What degree of high EMG is relevant to MA and what is the lower limit of high EMG? In Fig 1E, there are some EMG spikes, but it was unclear which spike/wave (amplitude/duration) was detected as MA-relevant spike and which spike was not detected. In Fig 2E, the 3rd MA coincides with the EMG spike, but other EMG spikes have comparable amplitude to the 3rd MA-relevant EMG spike. Correct counting of MA events is critical in Fig 1F, 2F, 4C.

(1-2) Please describe the definition of Ca trough in your experiments. In Fig 1G, the averaged trough time is clear (~2.5 s), so I can acknowledge that MA is followed by Ca trough. However, the authors state on page 4 that "30% of the calcium troughs during NREM sleep were followed by an MA epoch". This discrepancy should be corrected.

(1-3) Relating comment 1-2, I agree that the latency is between MA and Ca through in page 4, as the authors explain in the methods, but, in Fig 1G, t (latency) is labeled at incorrect position. Please correct this.

(1-4) The authors may want to determine the onset of the decline in population Ca activity and the latency between onset and trough (Fig 1G, latency t). If so, please describe how the onset of the decline is determined. In Fig 1G, 2G, S6, I can find the horizontal dashed line and infer that the intersection of the horizontal line and the Ca curve is considered the onset. However, I have to say that the placement of this horizontal line is super arbitrary. The results (t and Drop) are highly dependent on the position of horizontal line, so the authors need to describe how to set the horizontal line.

(1-5) In order to follow Fig 1F correctly, the authors need to indicate the detection criteria of "Ca dip (in legend)". Please indicate "each Ca dip" in Fig 1E. As a reader, I would like to agree with the Ca dip detection of this Ca curve based on the criteria. Please also indicate "each Ca dip" in Fig 2E and 2F. In the case of the 2nd and 3rd MAs, do they follow a single Ca dip or does each MA follow each Ca dip? This chart is highly dependent on the detection criteria of Ca dip.

As I mentioned above, most of the quantifications are not based on the clear detection criteria. The authors need to re-analyze the data and fix the quantification. Please interpret data and discuss the cellular mechanism of ISO based on the re-analyzed quantification.

Reviewer #3 (Public review):

Summary:

The authors employ a series of well-conceived and well-executed experiments involving photometric imaging of the dentate gyrus and raphe nucleus, as well as cell-type specific genetic manipulations of serotonergic receptors that together serve to directly implicate serotonergic regulation of dentate gyrus (DG) granule (GC) and mossy cell (MC) activity in association with an infra slow oscillation (ISO) of neural activity has been previously linked to general cortical regulation during NREM sleep and microarousals.

Strengths:

There are a number of novel and important results, including the modulation of dentage granule cell activity by the infraslow oscillation during NREM sleep, the selective association of different subpopulations of granule cells to microarousals (MA), the anticorrelation of raphe activity with infraslow dentate activity.

The discussion includes a general survey of ISOs and recent work relating to their expression in other brain areas and other potential neuromodulatory system involvement, as well as possible connections with infraslow oscillations, micro arousals, and sensory sensitivity.

Weaknesses:

- The behavioral results showing contextual memory impairment resulting from 5-HT1a knockdown are fine, but are over-interpreted. The term memory consolidation is used several times, as well as references to sleep-dependence. This is not what was tested. The receptor was knocked down, and then 2 weeks later animals were found to have fear conditioning deficits. They can certainly describe this result as indicating a connection between 5-HT1a receptor function and memory performance, but the connection to sleep and consolidation would just be speculation. The fact that 5-HT1a knockdown also impacted DG ISOs does not establish dependency. Some examples of this are:<br /> o The final conclusion asserts "Together, our study highlights the role of neuromodulation in organizing neuronal activity during sleep and sleep-dependent brain functions, such as memory.", but the reported memory effects (impairment of fear conditioning) were not shown to be explicitly sleep-dependent.<br /> o Earlier in the discussion it mentions "Finally, we showed that local genetic ablation of 5-HT1a receptors in GCs impaired the ISO and memory consolidation". The effect shown was on general memory performance - consolidation was not specifically implicated.

- The assertion on page 9 that the results demonstrate "that the 5-HT is directly acting in the DG to gate the oscillations" is a bit strong given the magnitude of effect shown in Fig. 6D, and the absence of demonstration of negative effect on cortical areas that also show ISO activity and could impact DG activity (see requested cortical sigma power analysis).

- Recent work has shown that abnormal DG GC activity can result from the use of the specific Ca indicator being used (GCaMP6s). (Teng, S., Wang, W., Wen, J.J.J. et al. Expression of GCaMP6s in the dentate gyrus induces tonic-clonic seizures. Sci Rep 14, 8104 (2024). https://doi.org/10.1038/s41598-024-58819-9). The authors of that study found that the effect seemed to be specific to GCaMP6s and that GCaMP6f did not lead to abnormal excitability. Note this is of particular concern given similar infraslow variation of cortical excitability in epilepsy (cf Vanhatalo et al. PNAS 2004). While I don't think that the experiments need to be repeated with a different indicator to address this concern, you should be able to use the 2p GCaMP7 experiments that have already been done to provide additional validation by repeating the analyses done for the GCaMP6s photometry experiments. This should be done anyway to allow appropriate comparison of the 2p and photometry results.

- While the discussion mentions previous work that has linked ISOs during sleep with regulation of cortical oscillations in the sigma band, oddly no such analysis is performed in the current work even though it is presumably available and would be highly relevant to the interpretation of a number of primary results including the relationship between the ISOs and MAs observed in the DG and similar results reported in other areas, as well as the selective impact of DG 5-HT1a knockdown on DG ISOs. For example, in the initial results describing the cross correlation of calcium activity and EMG/EEG with MA episodes (paragraph 1, page 4), similar results relating brief arousals to the infraslow fluctuation in sleep spindles (sigma band) have been reported also at .02 Hz associated with variation in sensory arousability (cf. Cardis et al., "Cortico-autonomic local arousals and heightened somatosensory arousability during NREMS of mice in neuropathic pain", eLife 2021). It would be important to know whether the current results show similar cortical sigma band correlations. Also, in the results on ISO attenuation following 5-HT1 knockdown on page 7 (fig. 6), how is cortical EEG affected? is ISO still seen in EEG but attenuated in DG?

- The illustrations of the effect of 5-HT1a knockdown shown in Figure 6 are somewhat misleading. The examples in panels B and C show an effect that is much more dramatic than the overall effect shown in panel D. Panels B and C do not appear to be representative examples. Which of the sample points in panel D are illustrated in panels B, C? it is not appropriate to arbitrarily select two points from different animals for comparison, or worse, to take points from the extremes of the distributions. If the intent is to illustrate what the effect shown in D looks like in the raw data, then you need to select examples that reflect the means shown in panel D. It is also important to show the effect on cortical EEG, particularly in sigma band to see if the effects are restricted to the DG ISOs. It would also be helpful to show that MAs and their correlations as shown in Fig 1 or G as well as broader sleep architecture are not affected.

- On page 9 of the results it states that GCs and MCs are upregulated during NREM and their activity is abruptly terminated by MAs through a 5-HT mediated mechanism. I didn't see anything showing the 5-HT dependence of the MA activity correlation. The results indicate a reduction in ISO modulation of GC activity but not the MA correlated activity. I would like to see the equivalent of Fig 1,2 G panels with the 5-HT1a manipulation.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

This study provides convincing evidence on the infraslow oscillation of DG cells during NREM sleep, and how serotonergic innervation modulates hippocampal activity pattern during sleep and memory.

Strengths and Weaknesses:

The authors used state-of-the-art techniques to carry out these experiments. Given that the functional role of infraslow rhythm still remains to be studied, this study provides convincing evidence of the role of DG cells in regulating infraslow rhythm, sleep microarchitecture, and memory.

I have a few minor comments.

(1) Decreased infraslow rhythm during NREMs in the 5ht1a KO mice is striking. It would be helpful to know whether sleep-wake states, MAs, and transitions to REMs are changed.

We agree with the reviewer that serotonin receptors may be involved in sleep regulation therefore it is important to analyze the effect of their manipulation. We would also like to bring to the attention of the reviewer that in this case we restricted the 5ht1a manipulation to the hippocampus which does not have a known impact on sleep-wake regulation. The analysis of our recorded dataset from these mice confirmed this notion, because we did not see any changes in sleep metrics (see: supplementary figure 6A).

(2) It would be interesting to discuss whether the magnitude in changes of infraslow rhythm strength is correlated with memory performance (Figure 6).

We agree with the reviewer that this could be an interesting point. In our experiments we wanted to minimize the impact of the surgical procedures on the behavior, thus we used separate cohorts to record the photometry and to carry out the behavior experiments, therefore we are unable to correlate behavior and infraslow oscillatory amplitudes in our dataset.

However, a similar experiment was carried out in a recent paper where the authors discovered that the norepinephrine system also displays infraslow oscillatory cycles during NREM sleep (Kjaerby et al 2022). The authors of that paper gradually decreased the magnitude of the NE pulses during NREM by optogenetic manipulation of the locus coeruleus which led to a fragmented sleep phenotype characterized by increased micro arousal occurrence, decreased REM and reduced spindle activity. They also tested the memory performance of the mice in a novel object recognition task and found diminished performance level in the opto group. Serotonin has multiple roles in the brain, many of them show overlap with proposed functions of the noradrenergic system including regulation of plasticity, signaling reward or fearful stimuli. Therefore, we speculate that the modification of serotonin dynamics during sleep will most likely interfere with memory performance.

We inserted this paragraph in the discussion part of our paper.

(3) The authors should cite the Oikonomou Neuron paper that describes slow oscillatory activity of DRN SERT neurons during NREM sleep.

Thank you for the suggestion, we inserted this paper in the manuscript.

(4) The authors should clarify how they define the phasic pattern of the photometry signal.

We have added the details in the Methods.

Reviewer #2 (Public review):

Summary:

The authors investigated DG neuronal activity at the population and single-cell level across sleep/wake periods. They found an infraslow oscillation (0.01-0.03 Hz) in both granule cells (GC) and mossy cells (MC) during NREM sleep.

The important findings are:

(1) The antiparallel temporal dynamics of DG neuron activities and serotonin neuron activities/extracellular serotonin levels during NREM sleep, and

(2) The GC Htr1a-mediated GC infraslow oscillation.

Strengths:

(1) The combination of polysomnography, Ca-fiber photometry, two-photon microscopy, and gene depletion is technically sound. The coincidence of microarousals and dips in DG population activity is convincing. The dip in activity in upregulated cells is responsible for the dip at the population level.

(2) DG GCs express excitatory Htr4 and Htr7 in addition to inhibitory Htr1a, but deletion of Htr1a is sufficient to disrupt DG GC infraslow oscillation, supporting the importance of Htr1a in DG activity during NREM sleep.

Weaknesses:

(1) The current data set and analysis are insufficient to interpret the observation correctly.

a. In Figure 1A, during NREM, the peaks and troughs of GC population activities seem to gradually decrease over time. Please address this point.

Thank you for the suggestion. We have analyzed and compared the magnitude of the oscillatory signals in the first and last minute of the NREM sleep epochs in Dock10-Cre mice and found no significant difference. However, we did observe that the ISO amplitude is smaller in the early stage of the first NREM epochs, defined as those with the prior wakefulness longer than 5 minutes (new supplementary figure 1).

b. In Figure 1F, about 30% of Ca dips coincided with MA (EMG increase) and 60% of Ca dips did not coincide with EMG increase. If this is true, the readers can find 8 Ca dips which are not associated with MAs from Figure 1E. If MAs were clustered, please describe this properly.

We did not find evidence that MAs were clustered in our dataset (see a representative example in supplementary figure 1A). We replaced the example trace with a new one which shows calcium dips with and without MAs. We believe this new trace better represents the data.

c. In Figure 1F, the legend stated the percentage during NREM. If the authors want to include the percentage of wake and REM, please show the traces with Ca dips during wake and REM. This concern applies to all pie charts provided by the authors.

Figure 1F (and all other pie charts) shows the outcome of brain states following a calcium-dip episode. That is, we found that the Ca-dips during NREM were followed by MAs in 30% of the cases, 59% of the Ca-dips led to the maintenance of NREM (no MAs) while in 2% and 9% of the cases we detected either REM state or wakening of the animal. These numbers correspond very well with similar analysis done in a recent paper which looked at the infraslow oscillatory behavior of the norepinephrine system (Kjaerby et al 2022) during NREM sleep. We apologize if the wording in the manuscript was misleading, we modified the figure legends to clarify what the pie charts represent. 

d. In Figure 1C, please provide line plots connecting the same session. This request applies to all related figures.

We have replaced the dot plots in all related figures with the line plots. 

e. In Figure 2C, the significant increase during REM and the same level during NREM are not convincing. In Figure 2A, the several EMG increasing bouts do not appear to be MA, but rather wakefulness, because the duration of the EMG increase is greater than 15 seconds. Therefore, it is possible that the wake bouts were mixed with NREM bouts, leading to the decrease of Ca activity during NREM. In fact, In Figure 2E, the 4th MA bout seems to be the wake bout because the EMG increase lasts more than 15 seconds.

We have replaced the Figure 2C with line plots as suggested above. It is clear that MC activity during REM sleep is higher, compared to that in NREM sleep, whereas the overall difference between wake and NREM is not significant (some increased, some decreased). Regarding the MAs, we have added a trace of averaged EMG signals in Figure 2G, showing that the averaged EMG bursts during MA are shorter than 5 seconds.

f. Figure 5D REM data are interesting because the DRN activity is stably silenced during REM. The varied correlation means the varied DG activity during REM. The authors need to address it.

We thank the reviewer for this suggestion. We have added this point to the discussion. We speculate that inputs from the supramammillary nucleus or entorhinal cortex to the DG during REM sleep may both contribute to this variability.

g. In Figure 6, the authors should show the impact of DG Htr1a knockdown on sleep/wake structure including the frequency of MAs. I agree with the impact of Htr1a on DG ISO, but possible changes in sleep bout may induce the DG ISO disturbance.

As suggested, we have performed sleep analysis in the Htr1a knockdown experiments including MA quantification. We have found no significant difference between Hrt1-knockdown and control mice in any of the sleep metrics (see: supplemental figure 6). Our interpretation is that the lack of changes in sleep/wake cycles is likely due to the hippocampus not being directly involved in regulating these brain states.

(2) It is acceptable that DG Htr1a KO induces the reduced freezing in the CFC test (Figure 6E, F), but it is too much of a stretch that the disruption of DG ISO causes impaired fear memory. There should be a correlation.

We have modified the discussion accordingly.

(3) It is necessary to describe the extent of AAV-Cre infection. The authors injected AAV into the dorsal DG (AP -1.9 mm), but the histology shows the ventral DG (Supplementary Figure 4), which reduces the reliability of this study.

The histology image shown in the manuscript was taken from the -2.5 mm anteroposterior level, which we still consider to be part of the dorsal DG. For additional clarity, we have replaced the figure with new histology images slightly more anterior position (AP~2.0mm). 

Reviewer #3 (Public review):

Summary:

The authors employ a series of well-conceived and well-executed experiments involving photometric imaging of the dentate gyrus and raphe nucleus, as well as cell-type specific genetic manipulations of serotonergic receptors that together serve to directly implicate serotonergic regulation of dentate gyrus (DG) granule (GC) and mossy cell (MC) activity in association with an infra slow oscillation (ISO) of neural activity has been previously linked to general cortical regulation during NREM sleep and microarousals.

Strengths:

There are a number of novel and important results, including the modulation of dentage granule cell activity by the infraslow oscillation during NREM sleep, the selective association of different subpopulations of granule cells to microarousals (MA), the anticorrelation of raphe activity with infraslow dentate activity.

The discussion includes a general survey of ISOs and recent work relating to their expression in other brain areas and other potential neuromodulatory system involvement, as well as possible connections with infraslow oscillations, micro-arousals, and sensory sensitivity.

Weaknesses:

(1) The behavioral results showing contextual memory impairment resulting from 5-HT1a knockdown are fine but are over-interpreted. The term memory consolidation is used several times, as well as references to sleep-dependence. This is not what was tested. The receptor was knocked down, and then 2 weeks later animals were found to have fear conditioning deficits. They can certainly describe this result as indicating a connection between 5-HT1a receptor function and memory performance, but the connection to sleep and consolidation would just be speculation. The fact that 5-HT1a knockdown also impacted DG ISOs does not establish dependency. Some examples of this are:

a. The final conclusion asserts "Together, our study highlights the role of neuromodulation in organizing neuronal activity during sleep and sleep-dependent brain functions, such as memory.". However, the reported memory effects (impairment of fear conditioning) were not shown to be explicitly sleep-dependent.

We thank the reviewer for this comment. We have revised the sentence.

b. Earlier in the discussion it mentions "Finally, we showed that local genetic ablation of 5-HT1a receptors in GCs impaired the ISO and memory consolidation". The effect shown was on general memory performance - consolidation was not specifically implicated.

We have revised the sentence.

(2) The assertion on page 9 that the results demonstrate "that the 5-HT is directly acting in the DG to gate the oscillations" is a bit strong given the magnitude of effect shown in Figure 6D, and the absence of demonstration of negative effect on cortical areas that also show ISO activity and could impact DG activity (see requested cortical sigma power analysis).

We have revised the sentence.

(3) Recent work has shown that abnormal DG GC activity can result from the use of the specific Ca indicator being used (GCaMP6s). (Teng, S., Wang, W., Wen, J.J.J. et al. Expression of GCaMP6s in the dentate gyrus induces tonic-clonic seizures. Sci Rep 14, 8104 (2024). https://doi.org/10.1038/s41598-024-58819-9). The authors of that study found that the effect seemed to be specific to GCaMP6s and that GCaMP6f did not lead to abnormal excitability. Note this is of particular concern given similar infraslow variation of cortical excitability in epilepsy (cf Vanhatalo et al. PNAS 2004). While I don't think that the experiments need to be repeated with a different indicator to address this concern, you should be able to use the 2p GCaMP7 experiments that have already been done to provide additional validation by repeating the analyses done for the GCaMP6s photometry experiments. This should be done anyway to allow appropriate comparison of the 2p and photometry results.

We would like to thank the reviewer for this comment. We also analyzed the two-photon data in the same manner as the photometry data. However, the only supportive evidence that might be related to ISO in the two-photon data, recorded at the somatic level, was decreased fluorescence during MAs in the NREM-upregulated cell group (see Figure 3 D, E). We are unsure why this discrepancy exists, but we have discussed it in the manuscript and offered some alternative explanations. One hypothesis we are currently exploring relates to the different subcellular compartments sampled by the two imaging techniques. The photometry probe was implanted above the dentate gyrus, and since light collection efficiency declines sharply with distance from the probe tip (Pisano et al., 2019), we hypothesize that ISO is stronger at the dendritic level which directly receive the inputs from entorhinal cortex, and which is closest to the probe's tip. We are now conducting multiplane two-photon imaging experiments in our labs to test this hypothesis.

(4) While the discussion mentions previous work that has linked ISOs during sleep with regulation of cortical oscillations in the sigma band, oddly no such analysis is performed in the current work even though it is presumably available and would be highly relevant to the interpretation of a number of primary results including the relationship between the ISOs and MAs observed in the DG and similar results reported in other areas, as well as the selective impact of DG 5-HT1a knockdown on DG ISOs. For example, in the initial results describing the cross-correlation of calcium activity and EMG/EEG with MA episodes (paragraph 1, page 4), similar results relating brief arousals to the infraslow fluctuation in sleep spindles (sigma band) have been reported also at .02 Hz associated with variation in sensory arousability (cf. Cardis et al., "Cortico-autonomic local arousals and heightened somatosensory arousability during NREMS of mice in neuropathic pain", eLife 2021). It would be important to know whether the current results show similar cortical sigma band correlations. Also, in the results on ISO attenuation following 5-HT1 knockdown on page 7 (Figure 6), how is cortical EEG affected? Is ISO still seen in EEG but attenuated in DG?

Thank you for this valuable comment. We performed the analysis and found a positive correlation between cortical sigma band activity and DG activity during NREM sleep (see supplementary figure 1C-1E). Additionally, we conducted further analyses using the local 5-HT1a KO mouse model but did not observe significant changes in sleep architecture or MA frequency (see supplementary figure 6A). It is also important to note that ISO was only analyzed using calcium signals, not EEG signals. The standard filtering settings in our EEG data collection (0.5-500 Hz) do not allow us to analyze signals in such a low-frequency range.

(5) The illustrations of the effect of 5-HT1a knockdown shown in Figure 6 are somewhat misleading. The examples in panels B and C show an effect that is much more dramatic than the overall effect shown in panel D. Panels B and C do not appear to be representative examples. Which of the sample points in panel D are illustrated in panels B and C? It is not appropriate to arbitrarily select two points from different animals for comparison, or worse, to take points from the extremes of the distributions. If the intent is to illustrate what the effect shown in D looks like in the raw data, then you need to select examples that reflect the means shown in panel D. It is also important to show the effect on cortical EEG, particularly in sigma band to see if the effects are restricted to the DG ISOs. It would also be helpful to show that MAs and their correlations as shown in Figure 1 or G as well as broader sleep architecture are not affected.

We agree with the reviewer that the chosen example may appear somewhat exaggerated. However, we must point out that visually assessing missing or downregulated frequency components can be challenging. To provide a more objective presentation, we included Supplementary Figure 6B-C, in which we performed analysis similar to that in Fig1G in 5HT1a mice. These figures show a significant decrease in ISO amplitude, though the blockade is not complete, due to the incomplete nature of genetic manipulation with viral injection (see Suppl Fig 5). Furthermore, recent studies (Dong et al., 2023; Zhang et al., 2024; Kjaerby et al., 2022) have identified several other neuromodulatory and peptidergic systems that might affect DG activity during MAs.

To explore this further, we conducted pharmacological experiments. We administered 8-hydroxy-DPAT, a 5-HT1a agonist (i.p. 1 mg/kg) in Dock10-Cre mice injected with AAV-FLEX-GcaMP6s in the DG. Since 5-HT1a receptors act as autoreceptors on raphe 5-HT neurons, this treatment effectively silences the serotonergic system, thereby “removing” 5-HT signaling from the brain. The results, shown in Author response image 1, indicate that pharmacological suppression of 5-HT dampens the ISO in the DG during subsequent sleep intervals, with ISO recovering after the drug is washed out. These findings are consistent with the results obtained with the more specific local genetic manipulation. We have not included this result in the manuscript because we believe that the local downregulation is a cleaner experiment whose interpretation is more straightforward.

Author response image 1.

Finally, we also performed sleep analysis in 5-HT1a KO mice, showing that the local downregulation of 5-HT1a receptors had no significant effect on sleep metrics (Suppl Fig 6A). The hippocampus is not typically involved in regulating sleep-wake cycles, so we believe this result is consistent with that understanding.

(6) On page 9 of the results it states that GCs and MCs are upregulated during NREM and their activity is abruptly terminated by MAs through a 5-HT mediated mechanism. I didn't see anything showing the 5-HT dependence of the MA activity correlation. The results indicate a reduction in ISO modulation of GC activity but not the MA-correlated activity. I would like to see the equivalent of Figure 1,2 G panels with the 5-HT1a manipulation.

We agree with the reviewer on this point. We did not conduct any pharmacological or genetic manipulation in 2-photon calcium imaging experiments. We have removed that statement. As for the suggested analysis, please see our explanation above (Suppl Fig 6B-C).

Recommendations for the authors:

Reviewer #2 (Recommendations for the authors):

(1) Since the authors did not monitor DG neuronal activity with an electrophysiological tool, please rephrase the following sentence: "In this study, we investigated the neuronal activity of the dentate gyrus (DG) with electrophysiological and optical imaging tools during sleep-wake cycles." in the Abstract.

We have rephrased the sentence as suggested.

(2) Since the authors did not manipulate the serotonin release during sleep to investigate whether serotonin release modulates DG ISO, please edit the following sentence: "Further experiments revealed that the infraslow oscillation in the DG is modulated by rhythmic serotonin release during sleep" in the Abstract.

We have rephrased the sentence as suggested.

(3) Single-cell recording in DG with two-photon microscopy may address the issue raised in the 4th paragraph of the Discussion. In addition, in Fig 6C, the photometry has only captured the diminished oscillation in Htr1a KO, but cannot distinguish whether the activity levels of GC remain at high or low, which is a clear disadvantage of photometry.

We agree with the reviewer, and have added text to the discussion.

Reviewer #3 (Recommendations for the authors):

(1) Some of the figures are missing labels in the spectrogram panels (e.g. no freq units in Figures 4 and 6).

We have added information in those figures.

(2) Missing specific locations for EEG electrodes/screws. The text states "we predrilled 2 holes on the right side of the skull (1.5 mm posterior of the Bregma) for implanting recording electrodes". 2 holes on the right side of the skull are pretty vague.

We have added this information in the Methods.

(3) Some additional work that could be cited particularly when discussing the serotonergic impact on hippocampal function as it might relate to sleep and memory would include work linking mesopontine activity (both serotonergic and non-serotonergic) to memory-associated hippocampal sharp-wave ripple activity (e.g. Jelitai et al. Front. Neural Circ. 2021, Wang et al Nat. Neuro. 2015).

We have cited these papers.

(4) The work cited at the beginning of the Results describing higher population calcium activity during sleep states (15,18,30) is generally appropriate but not explicitly related to GCamP imaging. Pilz et al. "Functional Imaging of Dentate Granule Cells in the Adult Mouse Hippocampus", J.Neurosci. 2016 might be a more relevant citation.

We have added the citation.

eLife Assessment

This important study introduces a new cortical circuit model for predictive processing. Simulations effectively illustrate that, with appropriate synaptic plasticity, a canonical layer 2/3 cortical circuit - comprising two classes of interneurons providing subtractive and divisive inhibition - can generate uncertainty-modulated prediction errors by pyramidal neurons. The model is compelling; although it relies on many assumptions and has not yet been compared directly to data, the model does align with empirical observations and yields a range of testable predictions. The study is expected to be of great interest to those involved in cortical and predictive processing research.

Reviewer #2 (Public review):

Summary:

This computational modeling study addresses the observation that variable observations are interpreted differently depending on how much uncertainty an agent expects from its environment. That is, the same mismatch between a stimulus and an expected stimulus would be less significant, and specifically would represent a smaller prediction error, in an environment with a high degree of variability than in one where observations have historically been similar to each other. The authors show that if two different classes of inhibitory interneurons, the PV and SST cells, (1) encode different aspects of a stimulus distribution and (2) act in different (divisive vs. subtractive) ways, and if (3) synaptic weights evolve in a way that causes the impact of certain inputs to balance the firing rates of the targets of those inputs, then pyramidal neurons in layer 2/3 of canonical cortical circuits can indeed encode uncertainty-modulated prediction errors. To achieve this result, SST neurons learn to represent the mean of a stimulus distribution and PV neurons its variance.

The impact of uncertainty on prediction errors in an understudied topic, and this study provides an intriguing and elegant new framework for how this impact could be achieved and what effects it could produce. The ideas here differ from past proposals about how neuronal firing represents uncertainty. The developed theory is accompanied by several predictions for future experimental testing, including the existence of different forms of coding by different subclasses of PV interneurons, which target different sets of SST interneurons (as well as pyramidal cells). The authors are able to point to some experimental observations that are at least consistent with their computational results. The simulations shown demonstrate that if we accept its assumptions, then the authors' theory works very well: SSTs learn to represent the mean of a stimulus distribution, PVs learn to estimate its variance, firing rates of other model neurons scale as they should, and the level of uncertainty automatically tunes the learning rate, so that variable observations are less impactful in a high uncertainty setting.

Strengths:

The ideas in this work are novel and elegant, and they are instantiated in a progression of simulations that demonstrate the behavior of the circuit. The framework used by the authors is biologically plausible and matches some known biological data. The results attained, as well as the assumptions that go into the theory, provide several predictions for future experimental testing. The authors have taken into account earlier review comments to revise their paper in ways that enhance its clarity.

Weaknesses:

One weakness could be that the proposed theory does rely on a fairly large number of assumptions. However, there is at least some biological support for these. Importantly, the authors do lay out and discuss their key assumptions in the Discussion section, so readers can assess their validity and implications for themselves.

Comments on revisions:

I have no further suggestions for the authors.

Reviewer #4 (Public review):

Summary:

Wilmes and colleagues develop a model for the computation of uncertainty modulated prediction errors based on an experimentally inspired cortical circuit model for predictive processing. Predictive processing is a promising theory of cortical function. An essential aspect of the model is the idea of precision weighting of prediction errors. There is ample experimental evidence for prediction error responses in cortex. However, a central prediction of the theory is that these prediction error responses are regulated by the uncertainty of the input. Testing this idea experimentally has been difficult due to a lack of concrete models. This work provides one such model and makes experimentally testable predictions.

Strengths:

The model proposed is novel and well-implemented. It has sufficient biological accuracy to make useful and testable predictions.

Weaknesses:

One key idea the model hinges on is that stimulus uncertainty is encoded in the firing rate of parvalbumin positive interneurons. While this assumption is rather speculative, the model also here makes experimentally testable predictions.

Comments on revisions:

Congratulations on a very nice paper.

Author response:

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

Public Reviews:

Reviewer #2 (Public Review):

Summary:

This computational modeling study addresses the observation that variable observations are interpreted differently depending on how much uncertainty an agent expects from its environment. That is, the same mismatch between a stimulus and an expected stimulus would be less significant, and specifically would represent a smaller prediction error, in an environment with a high degree of variability than in one where observations have historically been similar to each other. The authors show that if two different classes of inhibitory interneurons, the PV and SST cells, (1) encode different aspects of a stimulus distribution and (2) act in different (divisive vs. subtractive) ways, and if (3) synaptic weights evolve in a way that causes the impact of certain inputs to balance the firing rates of the targets of those inputs, then pyramidal neurons in layer 2/3 of canonical cortical circuits can indeed encode uncertainty-modulated prediction errors. To achieve this result, SST neurons learn to represent the mean of a stimulus distribution and PV neurons its variance.

The impact of uncertainty on prediction errors in an understudied topic, and this study provides an intriguing and elegant new framework for how this impact could be achieved and what effects it could produce. The ideas here differ from past proposals about how neuronal firing represents uncertainty. The developed theory is accompanied by several predictions for future experimental testing, including the existence of different forms of coding by different subclasses of PV interneurons, which target different sets of SST interneurons (as well as pyramidal cells). The authors are able to point to some experimental observations that are at least consistent with their computational results. The simulations shown demonstrate that if we accept its assumptions, then the authors’ theory works very well: SSTs learn to represent the mean of a stimulus distribution, PVs learn to estimate its variance, firing rates of other model neurons scale as they should, and the level of uncertainty automatically tunes the learning rate, so that variable observations are less impactful in a high uncertainty setting.

Strengths:

The ideas in this work are novel and elegant, and they are instantiated in a progression of simulations that demonstrate the behavior of the circuit. The framework used by the authors is biologically plausible and matches some known biological data. The results attained, as well as the assumptions that go into the theory, provide several predictions for future experimental testing. The authors have taken into account earlier review comments to revise their paper in ways that enhance its clarity.

Weaknesses:

One weakness could be that the proposed theory does rely on a fairly large number of assumptions. However, there is at least some biological support for these. Importantly, the authors do lay out and discuss their key assumptions in the Discussion section, so readers can assess their validity and implications for themselves.

Thank you very much, we are very satisfied with this public review.

Reviewer #4 (Public Review):

Summary:

Wilmes and colleagues develop a model for the computation of uncertainty modulated prediction errors based on an experimentally inspired cortical circuit model for predictive processing. Predictive processing is a promising theory of cortical function. An essential aspect of the model is the idea of precision weighting of prediction errors. There is ample experimental evidence for prediction error responses in cortex. However, a central prediction of the theory is that these prediction error responses are regulated by the uncertainty of the input. Testing this idea experimentally has been difficult due to a lack of concrete models. This work provides one such model and makes experimentally testable predictions.

Strengths:

The model proposed is novel and well-implemented. It has sufficient biological accuracy to make useful and testable predictions.

Weaknesses:

One key idea the model hinges on is that stimulus uncertainty is encoded in the firing rate of parvalbumin positive interneurons. This assumption, however, is rather speculative and there is no direct evidence for this.

Thank you very much for this nice description. With regard to the weakness: it is true that the key idea hinges on uncertainty being encoded in the firing of inhibitory neurons. If it turns out that these inhibitory neurons are not PV neurons, however, the theory does not break down. The suggestion of PV neurons is fueled by the observation that PV neurons implement shunting and hence divisive inhibition and by the connectivity of PVs in the circuit. We discuss this in the discussion section: "To provide experimental predictions that are immediately testable, we suggested specific roles for SSTs and PVs, as they can subtractively and divisively modulate pyramidal cell activity, respectively. In principle, our theory more generally posits that any subtractive or divisive inhibition could implement the suggested computations. With the emerging data on inhibitory cell types, subtypes of SSTs and PVs or other cell types may turn out to play the proposed role."

Recommendations for the authors:

Reviewer #4 (Recommendations For The Authors):

(1) Line numbers would simplify reviewing.

We will add line numbers to our next submission.

(2) The existence of positive and negative PE was already suggested by Rao & Ballard.

We added the citation to the sentence "Because baseline firing rates are low in layer 2/3 pyramidal cells () positive and negative prediction errors were suggested to be represented by distinct neuronal populations [44,66],[...]" in the section "Computation of UPEs in cortical microcircuits".

(3) wekk should probably read well.

Indeed, thank you. We fixed it.

(4) Figure 4. legends A-C are mixed up. What are the two values of ¦s-u¦ in F and I - the same as in D and F.

Thank you, we fixed this.

(5) "representation neurons, the activity of which reflects the internal model". For consistency with the original definitions this should read "the activity of which reflects the internal representation". The internal "model" is the synaptic weights (or transformation between areas) - the activity of representation neurons (as the name implies) is the internal "representation".

Thank you, we changed it.

(6) "Mice trained in a predictable environment [...] [4]." This should read "reared" in an unpredictable environment, etc. Relatedly, the problem with this argument is that, the referenced paper argues that the mice never learned to predict and the reduced PE responses are a consequence of a reduction in prediction strength (these mice never - in life - had experience of visuomotor coupling). Better evidence might be the acute changes observed in normal mice (see e.g. Figure 3B in https://pubmed.ncbi.nlm.nih.gov/22681686/ However, another finding from the paper referenced is that in mice reared without visuomotor coupling, MM responses of SST interneurons are unchanged, while those in PV interneurons are completely absent. Would the authors model come to similar results if trained in an environment with (very) high uncertainty and then tested in a low uncertainty environment?

Thank you for pointing us to Figure 3B of Keller et al. 2012. We are now citing this result as it is indeed better evidence.

Thank you very much for your illuminating question and for pointing out that a mouse that never experienced a predictable visual flow may not have formed a model of the visual flow, and hence may not have any prediction about its visual experience. We haven’t considered this scenario in our paper before. So far, we only considered scenarios, in which it is possible to learn a prediction, i.e. to infer the mean from the sensory input. We now consider this other scenario in which the mouse that was reared in an unpredictable environment did not form a prediction and compare SST (1) and PV (2) activity in this mouse to one that learned to form a prediction, and added it to the section "Predictions for different cell types":

"Second, prediction error activity seems to decrease in less predictable, and hence more uncertain, contexts: in mice reared in a predictable environment [where locomotion and visual flow match, 42], error neuron responses to mismatches in locomotion and visual flow decreased with each day of experiencing these unpredictable mismatches. Third, the responses of SSTs and PVs to mismatches between locomotion and visual flow [4] are in line with our model (note that in this experiment the mismatches are negative prediction errors as visual flow was halted despite ongoing locomotion): In this study, SST responses decreased during mismatch, i.e. when the visual flow was halted, and there was no difference between mice reared in a predictable or unpredictable environment. In line with these observations, the authors concluded that SST responses reflected the actual visual input. In our model negative PE circuit, SSTs also reflect the actual stimulus input, which in our case was a whisker stimulus (SST rates in Fig. 6C and I reflect the stimuli (black and grey bar) in A and G, respectively) and SST rates are the same for high and low uncertainty (corresponding to mice reared in a predictable or unpredictable environment). In the same study, PV responses were absent towards mismatches in animals reared in an unpredictable environment [4]. The authors argued that mice reared in an unpredictable environment did not learn to form a prediction. In our model, the missing prediction corresponds to missing predictive input from the auditory domain (e.g. due to undeveloped synapses from the predictive auditory input). If we removed the predictive input in our model, PVs in the negative PE circuit would also be silent as they would not receive any of the excitatory predictive inputs."

(7) "Our model further posits the existence of two distinct subtypes of SSTs in positive and negative error circuits." There is some evidence for this: Figure 5a in https://pubmed.ncbi.nlm.nih.gov/36747710/

Thank you, we added this citation to the corresponding section.

eLife Assessment

This valuable study reports the induction of supernumerary inner hair cells in the mouse cochlea upon reducing the expression level of a tight-junction protein (claudin-9) at developmental stages. Although these ectopic hair cells are functional and persists through adulthood, the evidence supporting some of the claims is incomplete, particularly regarding the underlying mechanisms of cell differentiation and the potential of the approach for hair-cell regeneration. The work will be of interest to scientists working in the development and regeneration of hair cells in the inner ear.

Reviewer #1 (Public review):

The focus of this manuscript was to investigate the role of Cldn9 in the development of the mammalian cochlea. The main rationale of the study is the fact that cochlear hair cells do not regenerate, so when damaged they are lost forever, causing irreparable hearing loss. The authors have attempted to address this problem by inducing the ectopic production of additional hair cells and test whether they acquire the morphological and functional characteristics of native hair cells. They show that downregulation of Cldn9 using a well-established genetic manipulation of transgenic mice led to the production of extra numerary inner hair cells, which were able to survive for several months. By performing a large battery of experiments, the authors were able to determine that the native and ectopic inner hair cells have comparable morphological and physiological characteristics. There are several conclusions highlighted by the authors in different parts of the manuscript, including the key role of Cldn9 in coordinating embryonic and postnatal development, the differentiation of supporting cells into inner hair cells, and the possible use of Cldn9 to induce inner hair cell differentiation following deafness induced by hair cell loss.

Comments on revised version:

The authors have addressed the following points raised during the first submission: statistical analysis and wave 1 analysis. However, very little was done to address the other key aspects of my report, which are essential for the interpretation of the results. As mentioned in my previous report, some aspects of the work are not justified by the current data and will require either a tone-down of the claims or further experiments.

For example, one puzzling finding that is not addressed in the manuscript is the lack of functional benefit from these additional inner hair cells. In fact, it appears to be detrimental based on the increased ABR thresholds and EP. So, it is not clear to this reviewer the advantage of this approach.

It is not clear what direct evidence there is, apart from some immunostaining, indicating that the ectopic inner hair cells derive from the supporting cells. This part would benefit from a more careful consideration and maybe an attempt at a more direct experimental approach. Alternatively, the text should be modified accordingly.

One point that should be made clear throughout the manuscript is that the ectopic inner hair cells are generated in a cochlea that is undergoing normal maturation. Thus, there is no guarantee that modulating the expression levels of Cldn9 in a deaf mouse lacking hair cells would produce the same result as that shown in this study. This point should be at least discussed.

Reviewer #3 (Public review):

The study by Chen et al reports an interesting and previously unknown phenomenon of generation of supernumerary inner hair cells (IHCs) in response to downregulation of Cldn9 during embryonic or postnatal development. The authors developed an inducible doxycycline (dox)-tet-OFF-Cldn9 transgenic mice to regulate expression levels of Cldn9 and show that downregulation of Cldn9 resulted in additional, although incomplete row of IHCs immediately adjacent to the original IHC row. These induced extra IHCs had similar well-developed hair bundles, able to mechanotransduce and were innervated by auditory neurons, resembling wild-type IHCs. In addition, the authors knock down Cldn9 postnatally using shRNA injections in P1-7 mice with similar induction of extranumerary IHCs next to the original row of IHCs. The conclusions of this paper are mostly well supported by the data. However, some data analyses are limited, and some important controls are not shown.<br /> The data from this study are important and promising for future gene therapy applications. The generation of extra IHCs postnatally using downregulation of Cldn9 by shRNA could potentially be used as a replacement of IHCs lost after noise-induced trauma, ototoxic agents, or other environmental trauma. However, it is not clear if downregulation of CLDN9 in adult mice would lead to extranumerary IHCs. On the other hand, the replacement of lost inner hair cells due to various genetic mutations by inducing supernumerary mutant IHCs with the same abnormalities would not be reasonable.

The authors show that postnatally generated ectopic IHCs are viable and mechanotransducive, but the hearing function of the mice with ectopic hair cells is not improved. However, the ectopic hair cells seems to be generated from supporting cell trans-differentiation, and the intricate mosaic of the organ of Corti is altered (the extra row of IHCs seems to be positioned immediately adjacent to the original IHC row), which could by itself lead to hearing issues. It is not clear if the newly formed unusual junctions between the ectopic and original IHCs are sufficiently tight to prevent leakage of the endolymph to the basolateral surface of IHCs. Also, it is not clear if the other organ of Corti tight junctions could lose their tightness due to the downregulation of Cldn9, which could over time affect the endocochlear potential and hearing abilities as shown by this study.

Overall, the manuscript could be of interest to scientists working in the inner ear development and regeneration field, and to the hearing researchers in general and perhaps developmental biologists and cell biologists interested in tight junction proteins and their function.

Strength

The methodologies used are solid and convincing. There is a great potential for practical use of these valuable findings and new knowledge on IHC developmental regulation by Cldn9 expression.

Weakness

Some of the data in this study would benefit from showing corresponding negative controls and higher-resolution images of CLDN9 localization, which the authors chose not to show in the revised manuscript. Importantly, CLDN9 immunofluorescence staining data look different from previously published observations and show cytoplasmic staining of supporting cells only and did not show the staining of tight junctions between the OHCs and supporting cells as well as between the IHCs and supporting cells as reported previously (Kitajiri et al., 2004; Nakano et al., 2009, Ramzan et al., 2021). The organ of Corti schematics showing CLDN9 expression reflects the authors' immunostaining data but is unusual considering that CLDN9 localizes to the tight junctions of the reticular lamina as was shown by immuno-EM in this study and described in previous publications (Kitajiri et al., 2004; Nakano et al., 2009, Ramzan et al., 2021). However, the authors did not provide an explanation for these discrepancies in the Discussion of the manuscript.

Also, more detailed investigations would in some instances clarify the data. For example, it is not clear if the downregulation of Cldn9 affects the other genes known to participate in cell fate determination, and why downregulation of Cldn9 expression resulted in production of extranumerary inner hair cells only and not the other cell types, like OHCs, for example.

Reviewer #4 (Public review):

The work by Yingying Chen, Jeong Han Lee, and co-authors summarizes the morphological and functional outcomes of Cldn9 loss in the inner ear, particularly in the organ of Corti. While the study does not provide mechanistic insights into how the developmental loss of Cldn9 leads to ectopic hair cell formation, the phenomenon itself is curious. The work primarily focuses on a detailed characterization of the ectopic hair cells, which is well done. Despite the lack of mechanistic insights, the study will be of interest to the inner ear field if several major issues with the manuscript are addressed.

(1) The title, "Genetic and pharmacologic alterations of claudin9 levels suffice to induce functional and mature inner hair cells," is misleading. First, both manipulations (knockout and knockdown) are genetic, and no pharmacology is involved. Second, both manipulations are carried out during the embryonic and neonatal periods, and there is no evidence of mature hair cell regeneration in this study. The title should be revised to reflect this. A more accurate title could be: "Developmental loss of Cldn9 results in functional ectopic inner hair cells that persist through adulthood."<br /> (2) Contact-mediated lateral inhibition in hair cell fate determination is one of the most well-studied phenomena in the inner ear field, and numerous groups have shown that it is mediated by Notch signaling. This must be added to the introduction.<br /> (3) A large body of literature has demonstrated that Notch inhibition alone is not sufficient to regenerate hair cells in adult mice. Therefore, if the loss of claudins disrupts Notch signaling-the proposed mechanisms in the discussion - it is unlikely to be a viable therapeutic strategy for hair cell regeneration in the adult ear. Furthermore, no hair cell ablation experiments were conducted to demonstrate what could be considered true regeneration. These speculative statements should be removed or revised accordingly.<br /> (4) Cldn9 is a tight junction protein and should localize to the membrane. Yet, the data presented show what appears to be diffuse cytoplasmic staining, which is concerning.

Author response:

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

Reviewer #1 (Public Review):

Summary:

The focus of this manuscript was to investigate the role of Cldn9 in the development of the mammalian cochlea. The main rationale of the study is the fact that cochlear hair cells do not regenerate, so when damaged they are lost forever, causing irreparable hearing loss. The authors have attempted to address this problem by inducing the ectopic production of additional hair cells and testing whether they acquire the morphological and functional characteristics of native hair cells. They show that downregulation of Cldn9 using a well-established genetic manipulation of transgenic mice led to the production of extra numerary inner hair cells, which were able to survive for several months. By performing a large battery of experiments, the authors were able to determine that the native and ectopic inner hair cells have comparable morphological and physiological characteristics. There are several conclusions highlighted by the authors in different parts of the manuscript, including the key role of Cldn9 in coordinating embryonic and postnatal development, the differentiation of supporting cells into inner hair cells, and the possible use of Cldn9 to induce inner hair cell differentiation following deafness induced by hair cell loss.

Strengths:

Several of the conclusions in this study are well supported by the experimental work.

Weaknesses:

Some aspects of the data and its interpretation needs better explanation and requires further investigation.

(1) The Results section is the most difficult part to read and understand. It contains a very limited, and in some places confusing and repetitive, description of the data. Statistical analysis is missing for some of the key data (e.g., ABRs), and in some places the text contradicts the data presented in the figures (e.g., Figure 8). I am sure carefully revising the text would clarify some of these issues.

We thank the reviewer for the suggestion. We revised parts of the results section and added the statistical analysis to the ABRs and DPOAE (lines 151-159; Page 29, lines 846-880). 

(2) One puzzling finding that is not addressed in the manuscript is the lack of functional benefit from these additional inner hair cells. In fact, it appears to be detrimental based on the increased ABR thresholds. Maybe it would be useful to analyze the wave 1 characteristics.

We thank the reviewer for the suggestion. We added the wave 1 characteristics as S8.

(3) It is not clear what direct evidence there is, apart from some immunostaining, indicating that the ectopic inner hair cells derive from the supporting cells. This part would benefit from a more careful consideration and maybe an attempt at a more direct experimental approach.

We thank the reviewer for the suggestion. We intend to investigate the origin of the ectopic inner hair cells using (for example, a qRT-PCR, sm FISH, etc.) in our future study.

(4) One point that should be made clear throughout the manuscript is that the ectopic inner hair cells are generated in a cochlea that is undergoing normal maturation. Thus, there is no guarantee that modulating the expression levels of Cldn9 in a deaf mouse lacking hair cells would produce the same result as that shown in this study. My guess is that it probably won't, but I am sure this could be tested (maybe in the future) using the excellent experimental approach applied in this study.

That is a great point. We will explore it in our future experiments.

Reviewer #2 (Public Review):

Summary:

The generation of functional extranumerary inner hair cells (IHCs) in postnatal mice, particularly with virus-mediated knockdown of Cldn9 mRNA expression in the neonatal cochlear duct, is an important observation. It is significant because not many studies exist that report molecular manipulations of the neonatal organ of Corti that result in the generation of new hair cells that remain functional and appear to be intact for an extended time, here more than one year. Overall, this is a carefully conducted study; the observations are clear, and the methods are solid. Two independent methods for reducing the expression of Cldn9 mRNA were used: a conditional transgenic model and AAV-mediated knockdown with shRNA. The lack of a functional explanation of how the reduced expression of Cldn9 specifically leads to the formation of extranumerary IHCs leaves open questions. For example, it is not clear whether there is indeed a fate change happening and whether Cldn9 reduction affects developmental processes. The discussion of how Cldn9 reduction potentially affects Notch signaling, without hard evidence, is handwaving.

Strengths:

It is a very interesting observation and somewhat unexpected in its specificity for inner hair cells. Using two different approaches to manipulate Cldn9 expression provides a strong experimental foundation. The study is conducted quantitatively and with care.

Weaknesses:

The lack of mechanistic insight results in an open-ended story where at least the potential interaction of Cldn9 reduction with known and well-characterized signaling pathway components should have been investigated. This missed opportunity limits the scope of the study and should be addressed: How does Cldn9 downregulation affect the expression levels of other known genes linked to hair cell production and cell fate decisions? Quantitative RT-PCR works well for the authors, and comparing the expression of Notch or other known pathway components could provide mechanistic insight.

We thank the reviewer for the suggestion. We did quantitative RT-PCR to compare the expression of Notch or other known pathway components in our future work. Besides, we used smFISH with ccnd1 probe and cdkn1b probe to detect cyclin D1 and cyclin-dependent kinase inhibitor 1B (p27) separately in the mouse cochlea. GAPDH was selected as a reference gene. The quantification results showed no significant difference between Cldn9^+/T mice and Cldn9^+/+ mice at P2, P7, and P14.

It is unclear how P21 inner hair cells were identified for the patch-clamp experiments shown in Fig 4E-H. This is a challenging endeavor without the possibility of using specific markers.

We did not have a specific marker for IHCs. However, one with experience in hair bundle morphology and knowledge of their location in the epithelia can identify IHCs from the upright microscope.

Please also address the numerous minor points outlined below; it will improve the paper's readability.

Thanks. Please find the point-to-point answers below.

Please include page numbers and line numbers in a revised manuscript.

We include page numbers and line numbers in a revised manuscript.

Reviewer #3 (Public Review):

This important study by Chen et al help in advancing our knowledge about the regulation of inner hair cell (IHC) development and revealed the role of Cldn9 in IHC embryonic and postnatal induction by transdifferentiation from the supporting cells. The authors developed an inducible doxycycline (dox)-tet-OFF-Cldn9 transgenic mice to regulate expression levels of Cldn9 and show that downregulation of Cldn9 resulted in additional, although incomplete row of IHCs immediately adjacent to the original IHC row. These induced extra IHCs had similar well developed hair bundles, able to mechanotransduce and were innervated by auditory neurons resembling wild-type IHCs. In addition, the authors knock down Cldn9 postnatally using shRNA injections in P1-7 mice with similar induction of extranumerary IHC next to the original row of IHCs. The conclusions of this paper are mostly well supported by the data, but some data analysis needed to be clarified and some crucial controls should be provided to improve the confidence in the presented results. There is a great potential for practical use of these valuable findings and new knowledge on IHC developmental regulation to design Cldn9 gene therapy in the future.

The described by Chen et al mechanisms of extra hair cell generation by suppression of the tight junction protein Cldn9 expression level are very interesting and previously unknown. In particular, the generation of extra IHCs postnatally using downregulation of Cldn9 by shRNA could potentially be very useful as a replacement of HCs lost after noise-induced trauma, ototoxic agents, or other environmental trauma. On the other hand, the replacement of lost hair cells due to various genetic mutations by inducing a supernumerary IHCs with the same abnormalities would not be reasonable.

The authors show that postnatally generated ectopic IHCs are viable and mechanotransducive, but it would be nice to show the maturation steps of ectopic IHC during this postnatal period. For example, stereocilia bundles of the ectopic hair cells should mature later than the original IHCs. A few days after viral delivery of shRNA, you should be able to observe immature IHC bundles that unequivocally will define newly generated IHCs. Unfortunately, the authors show only examples of already mature ectopic IHCs at P21 and in 5-6 weeks old mice and at relatively low resolution. Also, during maturation, IHCs usually have transient axo-somatic synapses that are not present in mature IHCs. It would be great to see if, in 5-6 weeks old mouse, the ectopic IHCs still have axo-somatic synapses or not, and if the majority of the ectopic IHCs have innervation. Some of the data in this study would benefit from showing corresponding controls and some - from higher resolution imaging.

We appreciate the reviewer's suggestion. The objective of the paper is to report the phenomenon and present the coarse features of the Cldn9-mediated induced ectopic hair cells. The systematic details are for future studies, which are ongoing and out of the current scope.

In the mammalian cochlea, each HC is separated from the next by intervening supporting cells, forming an invariant and alternating mosaic along the cochlea's length. Cochlear supporting cells in some conditions can divide and trans-differentiate into HCs, serving as a potential resource for HC differentiation, using transcription and other developmental signaling factors.

However, when ectopic hair cells are generated from supporting cell trans-differentiation, the intricate mosaic of the organ of Corti is altered, which could by itself lead to hearing issues. In case of downregulation of Cldn9, the extra row of IHCs seems to be positioned immediately adjacent to the original IHC row. It is not clear if the newly formed unusual junctions between the ectopic and original IHCs are sufficiently tight to prevent leakage of the endolymph to the basolateral surface of IHCs. Also, it is not clear if the other organ of Corti tight junctions could lose their tightness due to the downregulation of Cldn9, which could over time affect the endocochlear potential as shown by this study and hearing abilities.

There was a slightly increased ABR threshold (5 dB -15 dB) (Fig. 4A) and a decrease in the magnitude of the EP and the rise in the K⁺ concentration in the endolymph and perilymph of Cldn9+/T mice compared to from age-matched littermates (S10) indicated there might be a compromised epithelium tight junction. The downregulation of Cldn9 affected the endocochlear potential and hearing abilities ((Fig. 4A, S10) after 2m, suggesting an age-dependent effect. The effective downregulation of Cldn9 would require proper titration of Cldn9 levels to induce extra hair cells with intact epithelial integrity; work may require additional studies.

Importantly, CLDN9 immunofluorescence staining data that show cytoplasmic staining of supporting cells should be revisited and the organ of Corti schematics showing CLDN9 expression should be corrected, considering that CLDN9 localizes to the tight junctions of the reticular lamina as was shown by immunoEM in this study and described in previous publications (Kitajiri et al., 2004; Nakano et al., 2009, Ramzan et al., 2021). While the current version of the manuscript will interest scientists working in the inner ear development and regeneration field, it could be more valuable to hearing researchers outside this immediate field and perhaps developmental biologists and cell biologists after proper revision.

We appreciate the reviewer's comments. We were concerned about the observation, but the results were consistent. Indeed, that was the motivation for performing the immunoEM (S3). A follow-up report may address it further.

Recommendations for the authors:

Reviewer #2 (Recommendations For The Authors):

Please address the points I made about the presentation (word choice, inconsistencies in labeling, etc). It ultimately helps a reader to understand and to follow your logic. This is an important observation.

We corrected the inconsistencies in labeling and addressed the points you suggested.

Making the extra effort to investigate a possible interaction between Cldn9 and Notch signaling would substantially increase the significance of the work.

Thanks for the suggestions. We will explore it in our future work.

Minor points:

Some sentences would benefit from revision:

- The abstract argues that hearing loss is incurable because mammalian hair cells are terminally differentiated (3rd sentence). This is not accurate.

Mammalian HCs are terminally differentiated by birth, making HC loss challenging to replace.

- The second sentence of the second paragraph of the introduction, "Cochlear SCs can divide and trans-differentiate into HCs, serving as a potential resource for HC differentiation, using transcription and developmental signaling factors (White et al., 2006)," should be referenced in the context of the animal's age. This feature of supporting cells is transient and only observed in neonatal mice. The following sentences in the same paragraph would also benefit from being placed into the same context when appropriate.

We thank the reviewer for the suggestion. These sentences have been corrected.

- Introduction: "But functional features of the newly developed HC are circumspect." The authors probably meant "circumspect," but is this the appropriate word? Also, please use the plural of HC = HCs.

The sentence has been corrected to “but the functional features of the newly developed HCs are circumspect”.

- Introduction: Isn't an essential function of tight junctions in the organ of Corti the separation of fluid-filled spaces? Perhaps additional functions of tight junction proteins are unclear, but at least this one function appears clear.

We thank the reviewer for the suggestion. We added the “additional” before the “function” in this sentence.

- Introduction: "using shRNA injection in postnatal (P) days (P1-7) mice." This is a rather vague statement that could be better defined. Perhaps mention that the injections targeted the round window and that an AAV-based method was used. Also, it is not clear from the methods whether the injection needle pierced the round window. Please clarify. Likewise, the methods state that these experiments were conducted in P1-P15 mice, but the main text says P1-P7. Later, in the results section and in the figure legend for Fig 7, the mice are between P1-P7 and P14; the figure itself is labeled with P1 and P14. However, data is presented (Fig 6) for injections at P2, P4, P7, and P14. In the text referring to Fig 6B in the results section, it is stated, "By contrast, the P14-21 inner ear transfected with Cldn9-shRNA produced no detectable increase..." Only data for P2, P4, P7, and P14 injections are presented. These are minor issues, but please check the inconsistencies because they make it difficult to follow.

We corrected this sentence to “Analogous additional putative IHCs differentiation was observed when Cldn9-shRNA was injected through the round window to postnatal (P) days (P2-7, and P14) mice…”.  The label in Fig 7A has been changed to P2-7, and the text referring to Fig 6B in the result section has been changed to “the P14 inner ear transfected with Cldn9-shRNA produced no detectable increase...".

- Last statement of the Introduction: "making Cldn9 a viable target for generating transformed IHCs." It is not clear what transformed IHCs are.

We replaced the transformed with supernumerary.

- To understand the Southern Blot analysis in Fig 1E, the location of BstAPI and BamHI restriction sites and the probe need to be illustrated in Fig 1D.

The restriction sites BstAPI, (Bst), and BamHI (Bam) are indicated (Fig. 1D).

- Please define the purple arrows and arrowheads in Fig 1D. What do the different colors for the backbone mean? I see red and green, but also orange and yellow in the floxed allele. In Fig 1F, is "Knock-in" synonymous with homozygote? Would it be clearer to use the nomenclature Cldn9(T/T), Cldn9(T/+), and Cldn9(+/+), which is used later in the text?

We have made the changes as requested.

- Results, first paragraph: "Results of RT-PCR..." This refers to quantitative RT-PCR; please add the word "quantitative."

Thanks. We added “quantitative” to the sentence.

- Results and Fig S1. Is the strong upregulation of Cldn9 mRNA (S1A) also reflected in stronger Cldn9 immunoreactivity?

Yes, the strong upregulation of Cldn9 mRNA showed higher cldn9 immunoreactivity.

- Results, Fig 1. Please add a schematic drawing showing all elements of the inducible gene expression cassette in the final transgenic allele, and please illustrate how the system works. This helps the reader to understand the strong Cldn9 mRNA upregulation in Cldn9(T/T) mice, where expression is likely driven by the CMV promoter and reciprocally, in the presence of doxycycline, the suppression of transcription by binding of the tTA-dox protein to the TRE elements of the modified CMV promoter. Is this a correct assumption?

Yes, this is a correct assumption

- Results, about Fig S3. Why is it important to investigate Cldn6 and ILDR1 levels in the context of Cldn9 downregulation? Also, that is meant with "no comparative differences in others?". If a potential compensatory effect is suspected, why are the authors not systematically characterizing the expression of other tight junction proteins with quantitative RT-PCR? The results shown in S3 are anecdotal, without proper quantification, and lack context.

The goal is to examine the potential compensatory changes in other TJ proteins. It was not to examine all possible TJ proteins localized in the inner ear.

Results, section headed with "Downregulation of..." First sentence. Fig. 2A-C à Fig. 2A-E.

Thanks. We corrected the sentence “5-week-old mice Cldn9^+/T cochleae displayed a notable row of ectopic HCs (Fig. 2A-C).” to “5-week-old mice Cldn9^+/T cochleae displayed a notable row of ectopic HCs (Fig. 2A-E).”

The same section: "were negatively labeled with anti-prestin antibody." Consider "were not labeled with antibody to prestin." Likewise, a few sentences below, please consider rephrasing "the ectopic HCs ... reacted positively to otoferlin antibodies". Also, "...expressed multiple CtBP2 labeling..." - this reads like an incomplete sentence.

Thanks for the suggestions. We have corrected the three sentences mentioned.

The phrase "putative ectopic" lacks clarity because "putative" could refer to "ectopic" (like an adverb). Consider swapping the two words and writing "ectopic putative IHCs" or simply "ectopic IHCs."

Thanks for the suggestions. We replaced the “putative ectopic IHCs” with “ectopic IHCs” in all contexts.

Please use more precise figure labels when referring to a specific figure panel. For example, "Additionally, the ectopic HCs show IHC bundle features (Fig. 2)," - Bundles are shown in Fig 2D and Fig 2E. Please check all instances where a full figure is mentioned, but the specific reference is to a panel of the figure. Another example, "... using quantitative RT-PCR (S7)..." would be more specific if Fig S7A is referred to.

Thanks for the suggestions. We checked all instances and corrected the labels. Thanks!

"IHC counts at different ages (P2-P21) and the cochlear frequency segments (4-32 kHz) demonstrate..."- the figure shows data for 8 kHz and 32 kHz; please revise: "segments (8 kHz and 32 kHz) demonstrate."

This sentence has been revised based on your suggestion. Thanks!

Please add a legend to Fig. 3C (like the one shown in Fig. 2F).

Thanks for the reminder. The legend for Fig. 3C was modified.

Fig 4A and Fig 4B. It is impossible to distinguish the open/closed circles and the many lines. Please consider a different format or an extended supplemental figure. Also, drawing a line connection between the 32 kHz and click data points in 4A is inappropriate.

Instead of the open/closed circles, the dashed line means Cldn9^+/+ mice, and solid lines represent Cldn9^+/T mice. We added the line labels. The line connecting between 32 kHz and click data points was removed.

Fig 4, legend. Please define BHB and BHC levels.

BHB and BHC are defined.

The paragraph "Synaptic features of PE IHCs match original IHCs" is confusing because it states the following: "The synapses between the IHCs and auditory neurons at the apical, middle, and basal cochlear locations from 5-week-old Cldn9+/+ and Cldn9+/T mice show substantial differences." The meaning of the heading, therefore, does not match what is ultimately shown and discussed.

We have changed the title to “Synaptic features of ectopic IHCs and original IHCs”.

Moreover, no actual features of synapses are investigated; CtBP2/Homer pairs were used to identify afferent synapses, which this reviewer would argue provides a reasonable estimate of the number of synapses where pre- and post-synaptic markers are detected in close vicinity. It would be helpful to describe the method for counting juxtaposed CtBP2 and Homer-labeled puncta with more detail.

The method section now includes more information about the synapse count, which this reviewer would argue provides a reasonable estimate of the number of synapses where pre- and post-synaptic markers are detected in close proximity.

The final concluding sentence of the section also suggests that synaptic transmission from PE IHCs might be compromised because significant differences in synapse numbers were identified. It would be important to mention this.

Thanks for the reminder. We added this information to the final concluding sentence.

Fig. 5C, 5D; legend. Is "co-expressed" the right word choice? Consider "colocalized" or "juxtaposed".

The "co-expressed" has been replaced with "colocalized".

Voltage-clamp recordings of P21 inner hair cell mechanoelectrical transduction currents. This reviewer cannot identify a previous publication describing the details of this method on P21 cochlear inner hair cells; this seems like an excellent methodological advance.

Yes, we can record data from older mice. Thanks for pointing it out.

"Transfection in vivo of Cldn9 shRNA," the P14-21 inner ear transfected with Cldn9-shRNA." Plus, additional use of the word "transfection." Transfection generally means the introduction of plain nucleic acid into cells. The word refers to methods that do not use viruses. In contrast, "transduction" is the term used for virus-mediated gene transfer. The authors used AAVs. Please correct for appropriate scientific terminology.

Thanks for the clarification. This information has been corrected accordingly.

"A slight decline in the amplitude of the EP and a substantial rise in perilymph K+ was detected in 8-month-old Cldn9+/T (S7)." Probably Fig. S8A,B is meant.

Yes, it referred to Fig. S8 A, B. We corrected it in the result section. Thanks!

Heading "Discussions" -> "Discussion"

The focus of the second part of the discussion on potential interactions between Cldn9 suppression and known signaling pathways is essential. The logic that is presented with respect to Notch signaling, however, is not clear and misleading. For example, it is not obvious what is meant by "Cldn9 subserves the signaling catalyst to activate NICD cascades" and whether this statement is supported by any published data.

The statement was a suggestion and has been qualified with a “may” clause (line 299).

The authors might consider discussing whether the observed effect caused by Cldn9 elimination is a specific role of the Cldn9 protein itself or is an epiphenomenon resulting from cytomechanical changes in the developing and maturing organ of Corti. This would add a potential Notch-independent component for a possible interpretation of the observations.

We state lines 302-304 “Alternatively, Cldn9 levels disruption may alter the mechanical properties of the developing and maturing organ of Corti that may trigger ectopic IHC differentiation, an epiphenomenon independent of the Notch signaling“.

Methods:

"Deletion of the selection marker in the tTA cassette by crossing the F1 mouse with the embryonic Cre line (B6.129S4-Meox2tm1(cre)Sor/J)." This sentence seems to be incomplete.

Thanks for pointing it out. This sentence has been rewritten.

"Images were captured under a confocal microscope." Consider writing "with a confocal microscope".

This sentence has been corrected. Thanks!

RNA extraction and... How many mice were used per experiment? 10-15 or just 10?

The mice number for the RNA extraction is between 10 and 15. Thanks

Reviewer #3 (Recommendations For The Authors):

Below are my suggestions, questions, and criticisms.

(1) The red outline on Fig1A schematic does not correspond to the previously published expression pattern of CLDN9 in the organ of Corti reticular lamina tight junctions (Kitajiri et al, 2004, Nakano et al., 2009, Ramzan et al., 2021). Also, there are no tight junctions all around the pillar cells. The tight junctions are restricted to the sites of tight attachments between two cells. The immunofluorescence staining using CLDN9 antibody looks rather cytoplasmic (Fig 1 and Fig S1) than associated with the tight junctions as it was shown by immunoEM data here and reported previously (Kitajiri et al, 2004; Nakano et al, 2009; Ramzan et al, 2021). Please correct the schematic and explain your data.

We have redrawn the diagram (Fig. 7).

(2) The CLDN9 staining in Figure 1, B and C, highlights the cytoplasm of the supporting cells, and hair cells devoid of the staining. From the images in Fig. S1C, it also looks like CLDN9 is present only in supporting cells and not in hair cells? How would the authors reconcile their data with Cldn9 expression data from the gEAR database and Ramzan et al.'s 2021 RNAscope data? Please provide the validation of the antibody used in this study.

We recognize the reviewer’s concern but RNA and protein levels are not always in parallel.

(3) Figure 1D. The dash lines from the targeting vector to the wt allele seem to indicate a recombination event. Please do not show the recombination event, instead just show what part of the targeting vector was incorporated to replace wt Cldn9. There is no description in the figure 1 legend what purple arrows and arrowheads mean and what yellow and orange line segments in the floxed allele schematic indicate. Please also show where the BstAPI and BamHI restriction enzyme sites are.

We have provided supplement Fig 1., and have noted the BstAPI and BamHI restriction enzyme sites in Fig. 1D.

(4) What does the organ of Corti that has 40-to-55-fold increase in Cldn9 mRNA expression looks like before dox treatment? Any abnormalities at all? How is CLDN9 protein localization looks in the Cldn9+/T untreated mice? Do they have normal number of IHCs? Cldn9+/T untreated mice should be used as another control at least in Figure S1. What does the organ of Corti that has a 40-to-55-fold increase in Cldn9 mRNA expression look like before dox treatment? Are there any abnormalities at all?

The untreated Cldn9^+/T mice can grow normally but are not fertile. So, we used a very low concentration of dox water (0.1 mg/ml) instead of normal water to keep the breeding pairs. The protein level increased in the Cldn9^+/T mice compared with Cldn9^+/+mice. With 0.1 mg/ml dox water, they also showed ectopic IHCs.

(5) It is interesting that decline of 0.4-0.6-fold in mRNA level leads to about 8-fold decrease in protein level based on your immunoEM data on tight junctions of IHC with supporting cells. Do you observe the same effect in OHC-SC tight junctions, or the decrease was observed selectively around IHCs?

The reviewer is alluding to matching RNA and protein levels. It appears that for Clnd9 one cannot expect a closely matched relationship.

(6) The quality of the immunoEM data is great, but a control of secondary antibody alone staining in wt and Cldn9+/T dox treated should be shown and compared to the Cldn9+/T treated sample.

We thank the reviewer for raising the issue. Secondary antibodies are used as a control in all immunoEMs in the laboratory. We opted not to show negative results.

(7) The authors observed a decrease in Cldn6 expression albeit not quantitative in response to Cldn9 downregulation. How were the immunofluorescence signals compared and evaluated? Please provide a detailed description of the method used. Did the authors used the same image acquisition parameters? Was the Cldn9 and Cldn6 immunostaining done using same protocol with the same aliquot and dilution of the secondary antibodies, etc.? The staining for CLDN6 seems to be concentrated in the cytoplasm of supporting cells, and not in the tight junctions, similar to CLDN9 immunoreactivity shown in Fig. S1C and to the ILDR1 pattern of staining in Fig. S3. How can the authors explain this? How were the antibodies validated?

The Cldn9 and Cldn6 immunostaining were done using the same protocol with the same aliquot and dilution of the secondary antibodies.

(8) CLDN14 is also expressed in the organ of Corti tight junctions. What happened to this TJ protein during CLDN9 downregulation?

We detected Cldn14 with immunostaining in the Cldn9+/T mice and Cldn9+/+ mice fed with 0.25 mg/ml dox water, and the results showed increased expression of Cldn14 in Cldn9+/T mice. Detail alterations of other TJ proteins have been reserved for future studies. 

(9) When supernumerary IHCs were observed in Cldn9+/T mice, have the authors noticed a corresponding decrease in supporting cells surrounding IHCs? Quantification of the IHCs supporting cells would be useful. Do the ectopic IHCs have apical tight junctions with original IHCs or they are surrounded by supporting cells?

We quantified the SCs around the IHCs but did not detect significant differences among the groups.

(10) The authors indicated that viable PE IHCs were observed in 15 months old Cldn9+/T dox treated mice. How stereocilia bundles look in these ectopic hair cells? Are they preserved similar to the original IHCs or degenerated? It is hard to see this in Fig 3, phalloidin panel. High-resolution SEM would show this better.

For the remaining ectopic IHCs in 15 months, we did not detect apparent differences in hair bundles compared with the original IHCs.

(11) Interestingly, the authors indicate that the highest number of the ectopic IHCs were developed in the apical turn and the higher elevation of ABR threshold was also observed at low frequencies end. This may indicate that extra IHCs do not help hearing function.

The extra IHCs showed along the whole cochlea, even though it is more obvious in the apical turn. The declined hearing may have resulted from the leakage of the endolymph K+ to the perilymph and EP decline.

(12) No age-matched wt control is shown for decreased expression of Cldn9 after shRNA injection at P2 (Fig. 6A).

As indicated earlier, we opted to state but did not show negative results.

(13) Figure 6C. The better- quality SEM images showing a longer stretch of IHCs are needed to convince readers that there are ectopic IHCs that are well preserved in 5-6 weeks old mice in all cochlear turns after GFP-Cldn9 shRNA treatment at P2-P7.

In S4, we showed that there are ectopic IHCs along the cochlear axis.

(14) Do scrambled shRNA control samples had some ectopic IHCs? This control is missing in Fig.6D.

No scrambled shRNA controls did not show ectopic IHCs. We have stated it.

(15) Figure 7B, lower schematic. There are no known continuous tight junctions and CLDN9 expression around the OHCs and IHCs. CLDN9 is known to be concentrated at the reticular lamina tight junctions which separate the endolymph from perilymph. Please, correct all schematics accordingly.

We have made the changes as requested.

Minor comments:

(1) Page 1, Abstract. I would not say "making HC loss incurable" since recent gene therapy results show some advances in this direction. Please rephrase more accurately.

We have made the changes as requested.

(2) Page 4, Results, line 5; please rephrase "PCR of tail tissue samples performed genotyping."

It has been corrected to “The genotyping was performed by the PCR with the tail tissue.”

(3) Fig. 1 legend, panel B, replace "showing IHC stained myosin7a" with "showing IHC stained by myosin7a". Also, in the same sentence, "phalloidin, actin (green) antibodies," Phalloidin is not an antibody; please change this.

Thanks. We have corrected this information.

(4) Fig 2C, IHC label obscures the view of IHCs, please move this label out and use an arrow to point to IHCs.

We have made the changes as requested.

(5) Figure 4, title. Replace "currents elicited original" with "current elicited from original".

This sentence has been corrected. Thanks.

(6) Figure 4, panel A. It is hard to see the open symbols on the graph. Are they associated with the dash lines? Please make them more visible or indicate what dash lines are. "ABR threshold for (n=12)" should be "ABR threshold for Cldn9+/+(n=12)"?

Yes, they are associated with the dash lines. We added the labels for the solid lines and dash lines. "ABR threshold for (n=12)" was corrected to "ABR threshold for Cldn9+/+(n=12)."

(7) Figure 4, legend. "Within each wt and heterozygote mice, there was no significant shift...". Do you mean within each group of mice? Also "Mean DPOAE threshold for 2-8 mos (n=9) was tested,..." Do you mean (n=9) for each group or what group?

Yes, "Within each wt and heterozygote mice, there was no significant shift..." has been revised. The number of mice in each group for the DPOAE test was clarified in the Fig. 4B legend. Thanks.

(8) Please label the X axis in Figure 4D.

The X-axis has been labeled (Time (s))

(9) Figure 4 B, do the colors of the lines indicate the same age groups as in Fig 4A? Do the dash lines associate with open symbols? Please state this clearly in the figure's legend.

Yes. We added this information in Fig. 4B legend.

(10) Figure 4D. Please label the X axis of the fluorescence intensity graph.

The X-axis has been labeled (Time (s))

(11) Figure 4G, legend. Replace "(mean +std)" with "(mean +SD)" for consistency here and in Figure 5 legend.

Thanks. We replaced "(mean +std)" with "(mean +SD) in the legend of Fig. 4G and Fig.5 and Fig.6.

(12) Figure 5B, legend. Replace "makers" with "markers".

Thanks. This information was corrected.

(13) Figure 6A, legend. There is no downregulation of Cldn9 by shRNA shown in "S5". Do the authors mean Figure S7? Please, correct "S5" to "Fig. S7".

This information was corrected. Thanks.

(14) Figure 6A, legend. There is no reduced CLDN9 protein expression shown in Fig. 1C. Do the authors mean Fig. 6A, third panel? Please correct the phrase "reduced protein expression (Fig. 1C) is shown in the 3rd Panel (Cldn9, red)" accordingly, and do not capitalize "p" in the "3rd Panel".

This information was corrected. Thanks (line 917-918).

(15) Also there, replace "The right Panel shows two rows of IHCs (marked HC marker, Myo7a (cyan), and the merged photomicrograph" with "The right panel shows the merged image with two rows of IHCs stained with HC marker Myo7a (cyan) and the expression of Ad-GFP-mCldn9 shRNA (green) in the adjacent row of supporting cells". Please indicate in what cells Ad-GFP-mCldn9 shRNA (green) is expressed. It looks like only one row of supporting cells has this green signal.

This information was corrected.

(16) Figure 6B, legend. Replace "Examples of photomicrographs of sections of the whole-mount cochlea of P2, P4, P7, and P14 Cldn9 shRNA injected mice" with "Examples of phalloidin stained whole-mount organ of Corti samples from cochleae of the wild-type mice injected at P2, P4, P7 and P14 with Cldn9 shRNA"

This sentence has been modified based on your suggestions. Thanks!

(17) Replace "action labeling" with "actin labeled."

Thanks!  The "action labeling" has been replaced with "actin labeled." Line 924

(18) Figure 6C. Insert "C" before SEM images description in the legend. The authors stated that SEM images of "5-6-wks-old mice" are shown. Please indicate the exact age of mice shown on each image and at what age these mice received the virus injection.

Thanks!  The “C” has been added. We have noted that the SEM images are from 5-week-old mice" in the legend, and the virus was injected at P2.

(19) Figure 6D, legend. Last sentence: move "are significantly different" and insert this between "IHCs" and "at P2 apex".

This information was corrected.

(20) Figure S7, legend. Replace "(sram)" with "(scram)" as in the figure itself. Also, Indicate the age of samples at the harvesting time for imaging and the age at injection of Cldn9 shRNA.

"(sram)" has been replaced with "(scram)". The age of samples at the harvesting time for imaging and the age at injection of Cldn9 shRNA are indicated.

(21) Figure S8. Replace "4 mos-old" and "8 mos-old" with "4 months-old" and "8 months-old" everywhere in the legend and in the figure labels.

We have made the changes as suggested.

(22) Page 8, 5th lane from the bottom. Change "EP and K+ concentration endolymph" to "EP and K+ concentration of the endolymph".

It has been corrected. Thanks.

(23) Page 8, next to the last sentence before the Discussion. Wrong figure number, please replace "(S7)" with "Fig. S8".

It has been corrected. Thanks.

eLife Assessment

What makes one member of the species behave differently from another? This is a core problem in behavioral neuroscience. This valuable study seeks an answer for the specific case of the fruit fly expressing preferences for one odor over another. By a combination of behavioral measurements, neurophysiology, and network modeling, the authors find solid evidence for at least one locus of individuality in the peripheral olfactory system.

Joint Public Review:

Summary:

The authors aimed to identify the neural sources of behavioral variation in fruit flies deciding between odor and air, or between two odors.

Strengths:

- The question is of fundamental importance.<br /> - The behavioral studies are automated, and high-throughput.<br /> - The data analyses are sophisticated and appropriate.<br /> - The paper is clear and well-written aside from some initially strong wording.<br /> - The figures beautifully illustrate their results.<br /> - The modeling efforts mechanistically ground observed data correlations.

Weaknesses:

-The correlations between behavioral variations and neural activity/synapse morphology are statistically significant but relatively weak.

Author response:

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

Joint Public Review:

Summary:

The authors aimed to identify the neural sources of behavioral variation in fruit flies deciding between odor and air, or between two odors.

Strengths:

- The question is of fundamental importance.

- The behavioral studies are automated, and high-throughput.

- The data analyses are sophisticated and appropriate.

- The paper is clear and well-written aside from some initially strong wording.

- The figures beautifully illustrate their results.

- The modeling efforts mechanistically ground observed data correlations.

Weaknesses:

- The correlations between behavioral variations and neural activity/synapse morphology are relatively weak, and sometimes overstated in the wording that describes them.

We sincerely thank the reviewers for these evaluations.

Recommendations for the authors:

Line 56: "We hypothesize that as sensory cues are encoded and transformed to produce motor outputs, their representation in the nervous system becomes increasingly idiosyncratic and predictive of individual behavioral responses". This seems obvious a priori. The sensory stimuli are the same, but the motor responses are different. Along the way there has to be a progression from same to different. Is there an alternative hypothesis? If so, perhaps state the alternative.

We added text to the first paragraph of the introduction (lines 58-60) laying out an alternative hypothesis that individuality emerges through biomechanical differences and environmental interactions, and we have altered our motivating question to assess whether circuit elements in which activity is predictive of individual behavior exist, and if so, where (lines 60-62).

Line 157: typo "remaining"

We changed “remaining” to “remain” (line 160).

Line 163: why report r sometimes and R^2 other times? Better to use R^2 throughout.

We changed all instances of r to R², notably when reporting combined train/test statistics for calcium - behavior models (line 162). We also reframed the outputs (medians + 90% confidence intervals) of the supplemental analysis inferring the strength of the latent calcium-behavior relationship to be in terms of R² (lines 166, 173-175, 241, 252; modified text in Inference of correlation between latent calcium and behavior states in Materials and Methods; adjusted figure and caption for Figure 1 – figure supplement 9).

Line 182: "odorant". Should be "odorant receptors"?

We respectfully disagree – our ORN and PN calcium data are responses to odorants in 5 glomerulus/odorant receptor types. When we group PCA loadings by glomerulus for both ORN and PN calcium, the consistency within groups is much stronger than when we group the loadings by odorant (Figure 1 – figure supplement 8). Additionally, “odorant receptor organization” would mean the same thing as “glomerular organization,” since all ORNs expressing the same odorant receptor project to a single glomerulus.

Line 331: "harbor". Maybe more modestly "contribute to"?

We changed “harbor” to “contribute to” (line 334) and added additional moderating language that the difference in DC2 and DM2 activations in PNs explains a large portion of the individuality signal (lines 337-339).

Line 403: typo "is"

We retained “is” as the corresponding verb for “the net effect,” but we adjusted the position of the reference to Gomez-Marin and Ghazanfar, 2019 for more clarity (lines 406-408).

eLife Assessment

The study evaluates the feasibility, safety, and tolerability of neoadjuvant radiotherapy followed by a CDK4/6 inhibitor (dalpiciclib) and hormonal therapy in treatment-naive patients with unilateral early-stage HR+/HER2- breast cancer. The findings are convincing, with a strong scientific rationale supported by integrated correlative studies. The trial is considered to be important as the outcomes could inform the design of larger, future studies. The strength of the conclusions should be tempered as the study included only a small cohort of patients (n=12) and was not adequately powered to definitively assess the efficacy or safety of this combinatorial treatment approach.

Reviewer #1 (Public review):

Summary:

This manuscript details the results of a small pilot study of neoadjuvant radiotherapy followed by combination treatment with hormone therapy and dalpiciclib for early-stage HR+/HER2-negative breast cancer.

Strengths:

The strengths of the manuscript include the scientific rationale behind the approach and the inclusion of some simple translational studies.

Weaknesses:

The main weakness of the manuscript is that overly strong conclusions are made by the authors based on a very small study of twelve patients. A study this small is not powered to fully characterize the efficacy or safety of a treatment approach, and can, at best, demonstrate feasibility. These data need validation in a larger cohort before they can have any implications for clinical practice, and the treatment approach outlined should not yet be considered a true alternative to standard evidence-based approaches.

I would urge the authors and readers to exercise caution when comparing results of this 12-patient pilot study to historical studies, many of which were much larger, and had different treatment protocols and baseline patient characteristics. Cross-trial comparisons like this are prone to mislead, even when comparing well powered studies. With such a small sample size, the risk of statistical error is very high, and comparisons like this have little meaning.

Reviewer #2 (Public review):

The author and his team explored a novel neoadjuvant strategy of radiotherapy followed by CDK4/6 inhibitor and exemestane for HR+/HER2- breast cancer. This strategy interestingly reached an ORR of 91.7% and RCB 0-I of 16.7%, with satisfying tolerance.

There are several questions for your further consideration.

Firstly, as this is a single-arm preliminary study, we are curious about the order of radiotherapy and the endocrine therapy. Besides, considering the radiotherapy, we also concern about the recovery of the wound after the surgery and whether related data were collected.

Secondly, in the methodology, please describe the sample size estimation of this study and follow up details.

Thirdly, in Table 1, the item HER2 expression, it's better to categorise HER2 into 0, 1+, 2+ and FISH-.

Author response:

Reviewer #1(Public review):

Summary:

This manuscript details the results of a small pilot study of neoadjuvant radiotherapy followed by combination treatment with hormone therapy and dalpiciclib for early-stage HR+/HER2-negative breast cancer.

Strengths:

The strengths of the manuscript include the scientific rationale behind the approach and the inclusion of some simple translational studies.

Weaknesses:

The main weakness of the manuscript is that overly strong conclusions are made by the authors based on a very small study of twelve patients. A study this small is not powered to fully characterize the efficacy or safety of a treatment approach, and can, at best, demonstrate feasibility. These data need validation in a larger cohort before they can have any implications for clinical practice, and the treatment approach outlined should not yet be considered a true alternative to standard evidence-based approaches.

I would urge the authors and readers to exercise caution when comparing results of this 12-patient pilot study to historical studies, many of which were much larger, and had different treatment protocols and baseline patient characteristics. Cross-trial comparisons like this are prone to mislead, even when comparing well powered studies. With such a small sample size, the risk of statistical error is very high, and comparisons like this have little meaning.

We greatly appreciate your evaluation of our study and fully agree with the limitations you have pointed out. We have clearly stated the limitations of the small sample size and emphasized the need for a larger population to validate our preliminary findings in the discussion section (Lines 311-316).

We acknowledge that this small sample size is not powered to characterize this regimen as a promising alternative regimen in the treatment of patients with HR-positive, HER2-negative breast cancer. Therefore, we have revised the description of this regimen to serve as a feasible option for neoadjuvant therapy in HR-positive, HER2-negative breast cancers both in the discussion (Lines 317-320) and the abstract (Lines 71-72).

We agree with you that cross-trial comparisons should be approached with caution due to differences in study designs and patient populations. In our discussion section, we acknowledge that small sample size limited the comparison of our data with historical data in the literature due to the potential bias (Lines 312-313). We clearly state that such comparisons hold limited significance (Lines 313-314) and suggest a larger population to validate our preliminary findings.

• Why was dalpiciclib chosen, as opposed to another CDK4/6 inhibitor?

Thank you for your comments. The rationale for selecting dalpiciclib over other CDK4/6 inhibitors in our study is primarily based on the following considerations:

(1) Clinical Efficacy: In several clinical trials, including DAWNA-1 and DAWNA-2, the combination of dalpiciclib with endocrine therapies such as fulvestrant, letrozole, or anastrozole has been shown to significantly extend the progression-free survival (PFS) in patients with hormone receptor-positive, HER2-negative advanced breast cancer (1-2).

(2) Tolerability and Management of Adverse Reactions: The primary adverse reactions associated with dalpiciclib are neutropenia, leukopenia, and anemia. Despite these potential side effects, the majority of patients are able to tolerate them, and with proper monitoring and management, these reactions can be effectively mitigated (1-2).

(3) Comparable pharmacodynamic with other CDK4/6 inhibitors: The combination of CDK4/6 inhibitors, including palbociclib, ribociclib, and abemaciclib, with aromatase inhibitors has demonstrated an enhanced ability to suppress tumor proliferation and increase the rate of clinical response in neoadjuvant therapy for HR-positive, HER2-negative breast cancer (3-5). Furthermore, preclinical studies have shown that dalpiciclib has comparable in vivo and in vitro pharmacodynamic activity to palbociclib, suggesting its potential effectiveness in similar treatment regimens (6).

(4) Accessibility and Regulatory Approval: Dalpiciclib has gained marketing approval in China on December 31, 2021, which facilitates the accessibility of this medication, making it a more convenient option when considering treatment plans.

References:

(1) Zhang P, Zhang Q, Tong Z, et al. Dalpiciclib plus letrozole or anastrozole versus placebo plus letrozole or anastrozole as first-line treatment in patients with hormone receptor-positive, HER2-negative advanced breast cancer (DAWNA-2): a multicentre, randomised, double-blind, placebo-controlled, phase 3 trial(J). The Lancet Oncology, 2023, 24(6): 646-657.

(2) Xu B, Zhang Q, Zhang P, et al. Dalpiciclib or placebo plus fulvestrant in hormone receptor-positive and HER2-negative advanced breast cancer: a randomized, phase 3 trial(J). Nature medicine, 2021, 27(11): 1904-1909.

(3) Hurvitz S A, Martin M, Press M F, et al. Potent cell-cycle inhibition and upregulation of immune response with abemaciclib and anastrozole in neoMONARCH, phase II neoadjuvant study in HR+/HER2− breast cancer(J). Clinical Cancer Research, 2020, 26(3): 566-580.

(4) Prat A, Saura C, Pascual T, et al. Ribociclib plus letrozole versus chemotherapy for postmenopausal women with hormone receptor-positive, HER2-negative, luminal B breast cancer (CORALLEEN): an open-label, multicentre, randomised, phase 2 trial(J). The lancet oncology, 2020, 21(1): 33-43.

(5) Ma C X, Gao F, Luo J, et al. NeoPalAna: neoadjuvant palbociclib, a cyclin-dependent kinase 4/6 inhibitor, and anastrozole for clinical stage 2 or 3 estrogen receptor–positive breast cancer(J). Clinical Cancer Research, 2017, 23(15): 4055-4065.

(6) Long F, He Y, Fu H, et al. Preclinical characterization of SHR6390, a novel CDK 4/6 inhibitor, in vitro and in human tumor xenograft models(J). Cancer science, 2019, 110(4): 1420-1430.

• The eligibility criteria are not consistent throughout the manuscript, sometimes saying early breast cancer, other times saying stage II/III by MRI criteria.

criteria in our manuscript. We deeply apologize for any confusion caused by these inconsistencies. We have revised the term from “early-stage HR-positive, HER2-negative breast cancer” to “early or locally advanced HR-positive, HER2-negative breast cancer” (Lines 128 and 150). The term “early or locally advanced” encompasses two different stages of breast cancer, whereas “Stage II/III by MRI criteria” refers to specific stages within the TNM staging system.

• The authors should emphasize the 25% rate of conversion from mastectomy to breast conservation and also report the type and nature of axillary lymph node surgery performed. As the authors note in the discussion section, rates of pathologic complete response/RCB scores are less prognostic for hormone-receptor-positive breast cancer than other subtypes, so one of the main rationales for neoadjuvant medical therapy is for surgical downstaging. This is a clinically relevant outcome.

We appreciate your constructive comments. Based on your suggestions, we have made the following revisions and additions to the article.

The breast conservation rate serves as a secondary endpoint in our study (Line 62 and 179). We have highlighted the significant 25% conversion rate from mastectomy to breast conservation in both the results (Lines 229-230) and discussion sections (Lines 290-292).

In our study, all patients underwent lymph node surgery, including sentinel lymph node biopsy or axillary lymph node dissection. Among them, 58.3% of patients (7/12) underwent sentinel lymph node biopsies.

We agree with your point that the prognostic value of pathologic complete response/RCB score is lower for hormone receptor-positive breast cancer compared to other subtypes, we have revised the discussion section to clarify that one of the principal objectives for neoadjuvant therapy in this patient population is to facilitate downstaging and enhance the rate of breast conservation (Lines 289-290). And also emphasized that this neoadjuvant therapeutic regiment appeared to improve the likelihood of pathological downstaging and achieve a margin-free resection, particularly for those with locally advanced and high-risk breast cancer (Lines 293-295).

Reviewer #2 (Public review):

Firstly, as this is a single-arm preliminary study, we are curious about the order of radiotherapy and the endocrine therapy. Besides, considering the radiotherapy, we also concern about the recovery of the wound after the surgery and whether related data were collected.

Thanks for the comments. The treatment sequence in this study is to first administer radiotherapy, followed by endocrine therapy. A meta-analysis has indicated that concurrent radiotherapy with endocrine therapy does not significantly impact the incidence of radiation-induced toxicity or survival rates compared to a sequential approach (1). In light of preclinical research suggesting enhanced therapeutic efficacy when radiotherapy is delivered prior to CDK4/6 inhibitors, we have opted to administer radiotherapy before the combination therapy of CDK4/6 inhibitors and hormone therapy (2).

In our study, we collected data on surgical wound recovery. All 12 patients had Class I incisions, which healed by primary intention. The wounds exhibited no signs of redness, swelling, exudate, or fat necrosis.

References:

(1) Li Y F, Chang L, Li W H, et al. Radiotherapy concurrent versus sequential with endocrine therapy in breast cancer: A meta-analysis(J). The Breast, 2016, 27: 93-98.

(2) Petroni G, Buqué A, Yamazaki T, et al. Radiotherapy delivered before CDK4/6 inhibitors mediates superior therapeutic effects in ER+ breast cancer(J). Clinical Cancer Research, 2021, 27(7): 1855-1863.

Secondly, in the methodology, please describe the sample size estimation of this study and follow up details.

Thanks for pointing out this crucial omission. Sample size estimation for this study and follow-up details have been added in the methodology section. The section on sample size estimation has been revised to state in Statistical analysis: “This exploratory study involves 12 patients, with the sample size determined based on clinical considerations, not statistical factors (Lines 210-211).” The section on follow up has been revised to state in Procedures section “A 5-year follow-up is conducted every 3 months during the first 2 years, and every 6 months for the subsequent 3 years. Additionally, safety data are collected within 90 days after surgery for subjects who discontinue study treatment (Lines 169-172).”

Thirdly, in Table 1, the item HER2 expression, it's better to categorise HER2 into 0, 1+, 2+ and FISH-.

Thank you very much for pointing out this issue. The item HER2 expression in Table 1 has been revised from “negative, 1+, 2+ and FISH-” to “0, 1+, 2+ and FISH-”.

eLife Assessment

This valuable study uses zebrafish as a model to reveal a role for the cell cycle protein kinase CDK2 as a negative regulator of type I interferon signaling. The evidence supporting the authors' claims is convincing, including both in vivo and in vitro investigative approaches that corroborate a role for CDK2 in regulating TBK1 degradation. In this latest version, the authors included data addressing concerns raised by the reviewers at the first peer review round that strengthens the conclusions. This work will interest cell biologists, immunologists, and virologists.

Reviewer #1 (Public review):

Summary:

The authors set out to evaluate the regulation of interferon (IFN) gene expression in fish, using mainly zebrafish as a model system. Similar to more widely characterized mammalian systems, fish IFN is induced during viral infection through the action of the transcription factor IRF3 which is activated by phosphorylation by the kinase TBK1. It has been previously shown in many systems that TBK1 is subjected to both positive and negative regulation to control IFN production. In this work, the authors find that the cell cycle kinase CDK2 functions as a TBK1 inhibitor by decreasing its abundance through recruitment of the ubiquitinylation ligase, Dtx4, which has been similarly implicated in the regulation of mammalian TBK1. Experimental data are presented showing that CDK2 interacts with both TBK1 and Dtx4, leading to TBK1 K48 ubiqutinylation on K567 and its subsequent degradation by the proteasome.

Strengths:

The strengths of this manuscript are its novel demonstration of the involvement of CDK2 in a process in fish that is controlled by different factors in other vertebrates and its clear and supportive experimental data.

Weaknesses:

The weaknesses of the study include the following. 1) It remains unclear how CDK is regulated during viral infection and how it specifically recruits E3 ligase to TBK1. 2) The implications and mechanisms for a relationship between the cell cycle and IFN production will be a fascinating topic for future studies.

Reviewer #1 (Public review):

Summary:

The authors set out to evaluate the regulation of interferon (IFN) gene expression in fish, using mainly zebrafish as a model system. Similar to more widely characterized mammalian systems, fish IFN is induced during viral infection through the action of the transcription factor IRF3 which is activated by phosphorylation by the kinase TBK1. It has been previously shown in many systems that TBK1 is subjected to both positive and negative regulation to control IFN production. In this work, the authors find that the cell cycle kinase CDK2 functions as a TBK1 inhibitor by decreasing its abundance through recruitment of the ubiquitinylation ligase, Dtx4, which has been similarly implicated in the regulation of mammalian TBK1. Experimental data are presented showing that CDK2 interacts with both TBK1 and Dtx4, leading to TBK1 K48 ubiqutinylation on K567 and its subsequent degradation by the proteasome.

Strengths:

The strengths of this manuscript are its novel demonstration of the involvement of CDK2 in a process in fish that is controlled by different factors in other vertebrates and its clear and supportive experimental data.

Weaknesses:

The weaknesses of the study include the following. 1) It remains unclear how CDK is regulated during viral infection and how it specifically recruits E3 ligase to TBK1. 2) The implications and mechanisms for a relationship between the cell cycle and IFN production will be a fascinating topic for future studies.

Author response:

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

Reviewer #1 (Public Review):

Weaknesses:

The weaknesses of the study include the following. 

(1) It remains unclear whether the function described for CDK2 is regulatory, that is, it affects TBK1 levels during physiological responses such as viral infection or cell cycle progression, or if it is homeostatic, governing the basal abundance of TBK1 but not responding to signaling.

The regulation of TBK1 by CDK2 described in this article occurs during viral infection. Simultaneously, we also investigated the effects of CDK2 overexpression and knockdown on TBK1 levels under non-infected state and observed a slight reduction, as shown in Figure 4K and 4L. Thus, we speculate that the regulation of TBK1 by CDK2 serves, on one hand, to maintain cellular homeostasis and, on the other hand, to respond to signaling triggered by viral infection.

(2) The authors have not explored whether the catalytic activity of CDK2 is required for TBK1 ubiquitinoylation and, if so, what its target specificity is.

We found that the ubiquitination modification of TBK1 was not affected by treatment with a CDK2 kinase activity inhibitor (SNS-032), as demonstrated in the results below (Author response image 1).

Author response image 1.

(3) Given the multitude of CDK isoforms in fish, it remains unexplored whether the identified fish CDK2 homolog is a requisite cell cycle regulator or if its action in the cell cycle is redundant with other CDKs.

A comparison of the protein sequences of fish CDK2 and human CDK2 revealed a 90% similarity (Author response image 2). It has also been reported that the kinase activity of goldfish CDK2 significantly increases during oocyte maturation (ref. 1). Furthermore, UHRF1 phosphorylation by cyclin A2/CDK2 is crucial for zebrafish embryogenesis (ref. 2). Additionally, Red grouper nervous necrosis virus (RGNNV) infection activated the p53 pathway, leading to the upregulation of p21 and downregulation of cyclin E and CDK2, which forces infected cells to remain in the G1/S replicative phase (ref. 3). All these evidences suggest that fish CDK2 plays a vital role in cell cycle regulation, and there have been no reports of other CDKs demonstrating CDK2-like functions.

References:

(1) Hirai T, et al. (1992) Isolation and Characterization of Goldfish Cdk2, a Cognate Variant of the Cell-Cycle Regulator Cdc2. Developmental biology 152(1):113-120.

(2) Chu J, et al. (2012) UHRF1 phosphorylation by cyclin A2/cyclin-dependent kinase 2 is required for zebrafish embryogenesis. Molecular biology of the cell 23(1):59-70. 

(3) Mai WJ, Liu HX, Chen HQ, Zhou YJ, & Chen Y (2018) RGNNV-induced cell cycle arrest at G1/S phase enhanced viral replication via p53-dependent pathway in GS cells. Virus Res 256:142-152.

Author response image 2.

Reviewer #2 (Public Review):

Weaknesses:

(1) While the study focuses on fish, the broader implications for other lower vertebrates and higher vertebrates are not extensively discussed.

Thanks to your comment, we have added a paragraph to the Discussion section of the manuscript regarding the implications of the negative regulation of IFN expression by fish CDK2 for other vertebrates (lines 398-403). The details are as follows: first, we selected representative species from each of the six major vertebrate groups and compared their CDK2 protein sequences, finding that they are over 90% similar to one another (Author response image 3). This suggests that the function of CDK2 may be conserved to some extent across vertebrates. Additionally, CDK2 inhibition has been shown to enhance anti-tumor immunity by increasing the IFN response to endogenous retroviruses (ref. 1). Our studies provide evidence that fish CDK2 inhibits the IFN response by promoting the ubiquitination and degradation of TBK1, strongly supporting the role of CDK2 in the regulation of the immune response.

Reference:

(1) Chen Y, et al. (2022) CDK2 Inhibition Enhances Antitumor Immunity by Increasing IFN Response to Endogenous Retroviruses. Cancer Immunol Res 10(4):525-539.

Author response image 3.

(2) The study heavily relies on specific fish models, which may limit the generalizability of the findings across different species.

Thank you for your comment. First, we compared the amino acid sequences of CDK2 proteins from fish and other vertebrates, which show over 90% similarity. Moreover, the small size, low cost, and external development of zebrafish make it an excellent model for vertebrate developmental biology. It has been reported that due to the high genomic and molecular similarities between zebrafish and other vertebrates, including humans, many significant discoveries in zebrafish development are relevant to humans (ref. 2). Our study concentrated on CDK2 in zebrafish, and the findings should be valuable for other vertebrates.

Reference:

(2) Veldman MB & Lin S (2008) Zebrafish as a Developmental Model Organism for Pediatric Research. Pediatr Res 64(5):470-476.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

The following additional data/discussion could improve the manuscript.

(1) Investigate whether the catalytic activity of CDK2 is required to regulate TBK1 abundance. It is common for E3 ligases to be directed towards phosphorylated substrates, so it would be of interest to know if CDK2 phosphorylates TBK1 to facilitate its recognition for ubiquitinylation.

We examined the effect of CDK2 on the TBK1 protein after inhibiting its kinase activity with SNS-032 treatment and found that it could still affect TBK1 expression, as shown in the results below (Figure R4). Our previous experiments investigating the effect of CDK2 on TBK1 did not show that CDK2 caused the migration of TBK1 bands (typically, proteins that undergo phosphorylation exhibit band migration). Furthermore, in this study, CDK2 did not function as an E3 ligase; instead, it recruited the E3 ligase Dtx4 to ubiquitinate TBK1.

Author response image 4.

(2) Investigate how CDK2 abundance is regulated by viral infection and whether viral infection impacts cell cycle progression in a CDK2-dependent manner.

In fact, as illustrated in Figure 1, we investigated the changes in CDK2 at both the mRNA and protein levels following viral infection. Our findings revealed that SVCV infection resulted in an increase in CDK2 mRNA and protein expression. Additionally, our earlier reports have indicated that SVCV infection can induce alterations in the cell cycle, resulting in a notable increase in the S phase (Figure 1 of ref. 1). However, whether SVCV infection impacts cell cycle progression in a CDK2dependent manner will be explored in our upcoming study.

Reference:

(1) Li S, et al. Spring viraemia of carp virus modulates p53 expression using two distinct mechanisms. PLoS Pathog 15, e1007695 (2019).

(3) Provide data/discussion concerning the role of fish CDK2 in the regulation of cell cycle progression and whether this process is impacted by viral infection (part 1). Are TBK1 abundance and interferon production differentially regulated across the cell cycle due to the action of CDK2 (part 2).

Thank you for your advice. This concern is addressed in two parts, as follows: 

For part 1: To date, there has been limited research conducted on fish CDK2 in the regulation of cell cycle progression. The details are as follows: It has been reported that the kinase activity of goldfish CDK2 significantly increases during oocyte maturation (ref. 1). Furthermore, UHRF1 phosphorylation by cyclin A2/CDK2 is crucial for zebrafish embryogenesis (ref. 2). Additionally, a novel CDK2 homolog has been identified in Japanese lamprey, which plays a crucial role in apoptosis (ref. 3). Red grouper nervous necrosis virus (RGNNV) infection activates the p53 pathway, leading to the upregulation of p21 and downregulation of cyclin E and CDK2, which forces infected cells to remain in the G1/S replicative phase (ref. 4). All this evidence suggests that fish CDK2 plays a vital role in cell cycle regulation, and this process is also impacted by viral infection. Relevant content has been added to the Discussion section in the revised manuscript (lines 389-398).

References:

(1) Hirai T, et al. (1992) Isolation and Characterization of Goldfish Cdk2, a Cognate Variant of the Cell-Cycle Regulator Cdc2. Developmental biology 152(1):113-120.

(2) Chu J, et al. (2012) UHRF1 phosphorylation by cyclin A2/cyclin-dependent kinase 2 is required for zebrafish embryogenesis. Molecular biology of the cell 23(1):5970.

(3) Xu Y, Tian Y, Zhao H, Zheng N, Ren KX, Li QW. A novel CDK-2 homolog identified in lamprey, with roles in apoptosis. Fish Physiol Biochem 47, 189-189 (2021). 

(4) Mai WJ, Liu HX, Chen HQ, Zhou YJ, & Chen Y (2018) RGNNV-induced cell cycle arrest at G1/S phase enhanced viral replication via p53-dependent pathway in GS cells. Virus Res 256:142-152.

For part 2: TBK1 plays a crucial role in regulating IFN production. Variations in CDK2 activity during different phases of the cell cycle may lead to changes in the expression and function of TBK1. Our findings suggest that heightened CDK2 activity may suppress TBK1 expression, thereby hindering the cell's capacity to produce IFN. Conversely, during the late phase of the cell cycle or in an inhibited state, TBK1 expression may rise, enhancing IFN synthesis and release. In summary, CDK2 is involved in intracellular signaling by modulating TBK1 levels and IFN production, affecting the cellular immune response and cycle regulation—two processes that are notably distinct at various stages of the cell cycle. Relevant content has been added to the Discussion section in the revised manuscript (lines 377-384).

Minor suggestions:

(1) The authors introduce their study with the consideration that knowledge of fish signaling pathways can inform mammalian biology because mammals evolved from fish. This is not strictly true, since mammals and fish both evolved from an ancient common ancestor and the diversification of signaling in each species likely occurred in response to distinct evolutionary selective pressures.

Thank you for your suggestion. We have revised the statement in the manuscript to eliminate the notion that mammals evolved from fish (lines 98-99). The immune systems of higher vertebrates (e.g., humans) and lower vertebrates (e.g., fish) generally exhibit some consistency, although there are notable differences.

(2) On line 210 and line 276, the authors appear to have misstated the data. CDK2 knockout increases not decreases TBK1 and Dtx4 knockdown abrogated rather than restored CDK2 suppression of TBK1.

Thanks for your reminder, I jumped to the wrong conclusions in these two places (line 204 and line 267) and have changed them as you suggested.

Reviewer #2 (Recommendations For The Authors):

The manuscript has some shortcomings that, if addressed, could improve the overall quality of the article.

(1) Line 63-72, line 77-79, line 88-90- please add additional references for these sentences.

Thanks to your comment, we have added references for these sentences (Line 63-72, line 77-79, line 88-90).

(2) It is of the utmost importance to quantify the data presented in Figures 4J and 5D, as this will facilitate the visualization of the immunoblot.

Thank you for your comment. We have quantified the data presented in Figures 4J and 5D to enhance the clarity of the immunoblot.

(3) The scale in Figure 4E is difficult to discern.

Thanks for your comment. To improve the visual clarity of the image, we have enlarged the scale label in Figure 4E.

(4) In Figure 3B, shCDK2 is shown in italics, preferably in line with other standards such as Figures 3C and 3F.

Thank you for your comment. We have revised the shCDK2 in Figure 3B.

(5) The functions of CDK family members in immunity are hoped to be discussed.

Thanks for your suggestion. We have discussed the functions of CDK family members in immunity (lines 363-387). The details are as follows: Recent studies have demonstrated that CDK activity is crucial for virus-induced innate immune responses. Reports indicate that CDKs are involved in the Toll-like receptor (TLR) signaling pathway, the nuclear factor-κB (NF-κB) signaling pathway, and the JAK-STAT signaling pathway. For instance, CDK8 and/or CDK19 enhanced the transcription of inflammatory genes, such as IL-8 and IL-10, in cells following TLR9 stimulation. CDKs and NF-κB establish a remarkable paradigm where CDKs can act directly on substrate proteins rather than depending solely on transcriptional control. It has been reported that CDK1 serves as a positive regulator of the IFN-I signaling pathway, facilitating STAT1 phosphorylation, which subsequently boosts the expression of ISGs. Furthermore, inhibiting CDK activity has been shown to obstruct STAT phosphorylation, proinflammatory gene activation, and ISG mRNA induction in response to SeV infection. It is important to note that no evidence suggests the involvement of CDKs in RLR signaling pathways. This study has shown that fish CDK2 functions as a negative regulator of the key kinase TBK1, which is involved in the RLR signaling pathway. A better understanding of the relationship between CDK2 and RLR signaling pathways will enhance our grasp of the regulatory mechanisms of CDKs in antiviral innate immunity.

eLife Assessment

In this important work, Lodhiya et al. provide evidence that excessive ATP underlies the killing of the model organism Mycobacterium smegmatis by two mechanistically-distinct antibiotics. The data are generally solid as the authors deploy multiple, orthogonal readouts and methods for manipulating reactive oxygen species and ATP. The work will be of interest to those studying antibiotic mechanisms of action.

Reviewer #1 (Public review):

Summary:

Lodhiya et al. demonstrate that antibiotics with distinct mechanisms of action, norfloxacin and streptomycin, cause similar metabolic dysfunction in the model organism Mycobacterium smegmatis. This includes enhanced flux through the TCA cycle and respiration as well as a build-up of reactive oxygen species (ROS) and ATP. Genetic and/or pharmacologic depression of ROS or ATP levels protect M. smegmatis from norfloxacin and streptomycin killing. Because ATP depression is protective, but in some cases does not depress ROS, the authors surmise that excessive ATP is the primary mechanism by which norfloxacin and streptomycin kill M. smegmatis. In general, the experiments are carefully executed; alternative hypotheses are discussed and considered; the data are contextualized within the existing literature.

Strengths:

The authors tackle a problem that is both biologically interesting and medically impactful, namely, the mechanism of antibiotic-induced cell death.

Experiments are carefully executed, for example, numerous dose- and time-dependency studies; multiple, orthogonal readouts for ROS; and several methods for pharmacological and genetic depletion of ATP.

There has been a lot of excitement and controversy in the field, and the authors do a nice job of situating their work in this larger context.

Inherent limitations to some of their approaches are acknowledged and discussed e.g., normalizing ATP levels to viable counts of bacteria.

Weaknesses:

All of the experiments performed here were in the model organism M. smegmatis. As the authors point out, the extent to which these findings apply to other organisms (most notably, slow-growing pathogens like M. tuberculosis) is to be determined.

At first glance, some of the results in the manuscript seem to conflict with what has been previously reported in the (referenced) literature. In their response to reviewers, the authors addressed these concerns. Ideally they would have addressed them in the main manuscript too.

Figs. 9 and 10A-B and associated text make the manuscript significantly longer and more descriptive. They are more appropriate to the beginning of a new story rather than the end of the current one.

Reviewer #2 (Public review):

Summary:

The authors are trying to test the hypothesis that ATP bursts are the predominant driver of antibiotic lethality of Mycobacteria

Strengths:

No significant strengths in the current state as it is written.

Weaknesses:

A major weakness is that M. smegmatis has a doubling time of three hours and the authors are trying to conclude that their data would reflect the physiology of M. tuberculossi which has a doubling time of 24 hours. Moreover, the authors try to compare OD measurements with CFU counts and thus observe great variabilities.

Comments on revisions:

The authors confirm they are using CFU counts, but then Figure 1 has 0 as the first data point on the Y-axis. This should be somewhere between 10e5 or 10e6. CFU would not start at 0, your initial inoculum has to be more than 0 to have something to challenge.

Author response:

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

Reviewer #1 (Public review):

Summary:

Lodhiya et al. demonstrate that antibiotics with distinct mechanisms of action, norfloxacin and streptomycin, cause similar metabolic dysfunction in the model organism Mycobacterium smegmatis. This includes enhanced flux through the TCA cycle and respiration as well as a build-up of reactive oxygen species (ROS) and ATP. Genetic and/or pharmacologic depression of ROS or ATP levels protect M. smegmatis from norfloxacin and streptomycin killing. Because ATP depression is protective, but in some cases does not depress ROS, the authors surmise that excessive ATP is the primary mechanism by which norfloxacin and streptomycin kill M. smegmatis. In general, the experiments are carefully executed; alternative hypotheses are discussed and considered; the data are contextualized within the existing literature.

We thank the reviewer for the very comprehensive summary of the study.

Strengths:

The authors tackle a problem that is both biologically interesting and medically impactful, namely, the mechanism of antibiotic-induced cell death.

Experiments are carefully executed, for example, numerous dose- and time-dependency studies; multiple, orthogonal readouts for ROS; and several methods for pharmacological and genetic depletion of ATP.

There has been a lot of excitement and controversy in the field, and the authors do a nice job of situating their work in this larger context.

Inherent limitations to some of their approaches are acknowledged and discussed e.g., normalizing ATP levels to viable counts of bacteria.

We thank the reviewer for the encouraging comments.

Weaknesses:

All of the experiments performed here were in the model organism M. smegmatis. As the authors point out, the extent to which these findings apply to other organisms (most notably, slow-growing pathogens like M. tuberculosis) is to be determined. To avoid the perception of overreach, I would recommend substituting "M. smegmatis" for Mycobacteria (especially in the title and abstract).

At first glance, a few of the results in the manuscript seem to conflict with what has been previously reported in the (referenced) literature. In their response to reviewers, the authors addressed my concerns. It would also be ideal to include a few lines in the manuscript briefly addressing these points. (Other readers may have similar concerns).

In the first round of review, I suggested that the authors consider removing Figs. 9 and 10A-B as I believe they distract from the main point of the paper and appear to be the beginning of a new story rather than the end of the current one. I still hold this opinion. However, one of the strengths of the eLife model is that we can agree to disagree.

We acknowledge the reviewer’s concern and have changed title of the manuscript by including Mycobacterium smegmatis instead of Mycobacteria. The abstract already mentioned the same.

In the discussion section of the revised manuscript, we have already addressed and analysed our results extensively within the context of the available literature, regardless of whether our findings aligned with or differed from previous studies. We still believe that the mentioned discussion will help suffice to explain our results to the readers.

In this manuscript we also sought to assess the bacteria's ability to counteract drug induced stresses, contributing to our understanding of how antibiotic tolerance develop in Mycobacterium smegmatis. Results presented in Figure 9 clearly demonstrate that M.smegmatis attempt to reduce respiration by decreasing flux through the complete TCA cycle, thereby mitigating ROS and ATP production in response to antibiotics.  Additionally, the bacterial response also included increased expression of the protein Eis, which is exemplar for intrinsic drug resistance, with a concomitant increase in mutation frequency, thereby hinting at the development of antibiotic tolerance followed by resistance. We still believe that these data should be included to support our observations and they make the study more comprehensive.

Reviewer #2 (Public review):

Summary:

The authors are trying to test the hypothesis that ATP bursts are the predominant driver of antibiotic lethality of Mycobacteria

Strengths:

No significant strengths in the current state as it is written.

Weaknesses:

A major weakness is that M. smegmatis has a doubling time of three hours and the authors are trying to conclude that their data would reflect the physiology of M. tuberculossi that has a doubling time of 24 hours. Moreover, the authors try to compare OD measurements with CFU counts and thus observe great variabilities.

Comments on revisions:

I am surprised that the authors simply did not repeat the study in figure one with CFU counts and repeated in triplicate. Since this is M. smegmatis, it would take no longer than two weeks to repeat this experiment and replace the figure. I understand that obtaining CFU counts is much more laborious than OD measurements but it is necessary. Your graph still says that there is 0 bacteria at time 0, yet in your legend it says you started with 600,000 CFU/ml. I don't understand why this experiment was not repeated with CFU counts measured throughout. This is not a big ask since this is M. smegmatis but it appears that the authors do not want to repeat this experiment. Minimally, fix the graph to represent the CFU.

We acknowledge the reviewer’s concern and have changed title of the manuscript by specifying Mycobacterium smegmatis instead of Mycobacteria.

It is still not clear to the authors what the reviewer mean by OD measurements. All the data presented in the entire manuscript , including in Figure 1 are solely based on CFU measurements. So, as suggested by the reviewer, all experiments are already presented in terms of CFU.

eLife Assessment

This fundamental work extends our understanding of the role of TGFβ2 as a modulator of mechanosensing in the eye and identifies the TRPV4 ion channel as a common regulator of Trabecular Meshwork (TM) contractility and pathological OHT. The data and evidence are convincing, with some minor limitations. This work will clearly be of interest to researchers investigating the role of mechanosensors in the TM and may underpin future research into treatments that aim to lower intra ocular pressure. This work will additionally be of interest to the growing field of researchers investigating the regulation of force sensing via ion channels and their roles in health and disease, in particular the ion channel TRPV4.

Reviewer #1 (Public review):

Summary:

This comprehensive study employed molecular, optical, electrophysiological and tonometric strategies to establish the role of TGFβ2 in transcription and functional expression of mechanosensitive channel isoforms alongside studies of TM contractility in biomimetic hydrogels, and intraocular pressure regulation in a mouse model of TGFβ2 -induced ocular hypertension. TGFβ2 upregulated expression of TRPV4 and PIEZO1 transcripts and time-dependently augmented functional TRPV4 activation. TRPV4 activation induced TM contractility whereas pharmacological inhibition suppressed TGFβ2-induced hypercontractility and abrogated ocular hypertension in eyes overexpressing TGFβ2. Trpv4-/- mice resisted TGFβ2-driven increases in IOP. These data establish a fundamental role of TGFβ as a modulator of mechanosensing and identifies TRPV4 channel as a common mechanism for TM contractility and pathological ocular hypertension.

Strengths:

The manuscript is very well written and details the important function of TRPV4 in TM cell function. These data provide novel therapeutic targets and potential for disease-altering therapeutics.

Weaknesses:

The experimental rigor and design of the noctural IOP experiments was weak with low n values and differing methods of IOP measurement (conscious versus anesthetized). The same method of IOP measurement needs to be used for all measurements to make any conclusions on the circadian patterns of IOP in each condition.

Reviewer #2 (Public review):

The manuscript by Christopher N. Rudzitis et al. describes the role of TGFβ2 in the transcription and functional expression of mechanosensitive channel isoforms, alongside studies on TM contractility in biomimetic hydrogels and intraocular pressure. Overall, it is a very interesting study, nicely designed, and will contribute to the available literature on TRPV4 sensitivity to mechanical forces.

I have the following comment for the authors to address.

Figure 1A-C.<br /> Often there is a difference between the massage and transcript data. I recommend the authors to confirm with qPCR data with another mode of protein measurements.<br /> Does direct TRPV4 activation also induce the expression of these markers? Does inhibition of TRPV4, after TGF-β treatment, prevent the expression of these markers? Is TRPV4 acting downstream of this response?

Figure 1D. Beta tubulin is not a membrane marker. Having staining of b tubulin in membrane fraction shows contamination from the cytoplasm.<br /> Does the overall expression also increase?

Figure 4A: it is not very clear. I recommend including a zoom image or better resolution image.

Figure 5B and 6B.<br /> Why there is a difference between groups in pre-injection panel. As Figure 5A, in pre-injection, there is no difference between LV-TGFβ and LV-control while in 5B there is a significant difference between these groups.<br /> Discussion section.

Line 279, . "TRPV4 channels in cells treated with TGFβ2 are likely to be constitutively active" ... needs to be discussed further.

Line 280: "The residual contractility in HC-06-treated cells may reflect TGFβ2-mediated contributions from Piezo1."<br /> Piezo1 has a low threshold for mechanosensitivity. How do the authors discuss the observation that, in the presence of Piezo1, TRPV4 has a more prominent mechanosensory function? Is this tied to TGFβ signalling?

Author response:

We thank the editors and reviewers for the constructive assessment. We plan to address the comments as follows:

Reviewer #1 (Public review):

We are generating a new cohort of Lv-TGFB2 overexpressing mice in which IOP will be compared under the anesthesia conditions that are identical for diurnal and nocturnal states. Parenthetically, we used the awake (diurnal) and isoflurane (nocturnal) anesthesia to mirror the conditions in the Patel et al (2021) PNAS study.

Reviewer #2 (Public review):

We are not sure what the Reviewer means by the “difference between the message and transcript data” and are not sure whether providing evidence about the TRPV4-dependence of the expression of fibrotic genes and canonical TGFb2 pathway genes fits within the scope of our study (which focuses on the TGFB2-dependence of TRPV4 expression and IOP regulation). We propose to address this by including new data about the TGFb2- and TRPV4 dependence of TRPV4 and Piezo1 expression. We could include information about the effect of TGFB2 on fibrosis-related genes from a (submitted study) in which we used RNASeq to investigate TGFB2 and TGFB2 + HC067047-dependence of gene expression in TM cells on a confidential basis but not include it in the revised manuscript.

- Re:  b-tubulin comment  [b-tubulin associates with the plasma membrane by binding to integral membrane proteins in the plasma and organellar membranes, through palmitoylation and attachment to linker proteins and as an integral component of exocytotic vesicles (Wolff, BBA 2009; Hogerheide et al., PNAS 2017). Together with b-actin and Gapdh it is often used as a loading control to assess cellular TRPV4 protein expression (e.g., https://www.cellsignal.com/products/primary-antibodies/trpv4-antibody/65893; Grove et al., Science Signaling 2019 and Moore et al., PNAS 2013).  Our qPCR and RNASeq studies show that TGFB2 does not affect b-tubulin expression]

- We will provide a higher resolution image for Fig. 4A

- Will address the Fig 5A and 6A comment [We thank the Reviewer for noticing the ambiguity and revised Figure Legends to clarify that “pre-injection” in Figures 5B and 6B refers to IOP measurements before the intracameral injection of HC-06  not pre-injection of lentiviral constructs].

-  We will address the issue of constitutive TRPV4 activity and Piezo1 involvement in the revised Discussion.

We hope this is sufficient information at this point but would be more than happy to provide more information if needed.

Thank you, we are very impressed by the eLife review protocols.

eLife Assessment

The findings are important and intriguing, with theoretical or practical implications beyond a single subfield. The computational methods employed are clever and sophisticated and the strength of evidence is convincing. However, all three reviewers also highlight a lack of focus and clarity, as well as missing information about model comparison, fit statistics and group comparison of parameters from different models.

Reviewer #1 (Public review):

Summary:

The authors use a sophisticated task design and Bayesian computational modeling to test their hypothesis that information generalization (operationalized as a combination of self-insertion and social contagion) in social situations is disrupted in Borderline Personality Disorder. Their main finding relates to the observation that two different models best fit the two tested groups: While the model assuming both self-insertion and social contagion to be present when estimating others' social value preferences fit the control group best, a model assuming neither of these processes provided the best fit to BPD participants.

Strengths:

The strengths of the presented work lie in the sophisticated task design and the thorough investigation of their theory by use of mechanistic computational models to elucidate social decision-making and learning processes in BPD.

Weaknesses:

The manuscript's primary weakness relates to the number of comparisons conducted and a lack of clarity in how those comparisons relate to the authors' hypotheses. The authors specify a primary prediction about disruption to information generalization in social decision making & learning processes, and it is clear from the text how their 4 main models are supposed to test this hypothesis. With regards to any further analyses however (such as the correlations between multiple clinical scales and eight different model parameters, but also individual parameter comparisons between groups), this is less clear. I recommend the authors clearly link each test to a hypothesis by specifying, for each analysis, what their specific expectations for conducted comparisons are, so a reader can assess whether the results are/aren't in line with predictions. The number of conducted tests relating to a specific hypothesis also determines whether multiple comparison corrections are warranted or not. If comparisons are exploratory in nature, this should be explicitly stated.

Furthermore, the authors present some measures for external validation of the models, including comparison between reaction times and belief shifts, and correlations between model predicted accuracy and behavioural accuracy/total scores. However it would be great to see some more formal external validation of how the model parameters relate to participant behaviour, e.g., the correlation between the number of pro-social choices and ß-values, or the correlation between the change in absolute number of pro-social choices and the change in ß. From comparing the behavioural and computational results it looks like they would correlate highly, but it would be nice to see this formally confirmed.

The statement in the abstract that 'Overall, the findings provide a clear explanation of how self-other generalisation constrains and assists learning, how childhood adversity disrupts this through separation of internalised beliefs' makes an unjustified claim of causality between childhood adversity and separation of self - and other beliefs, although the authors only present correlations. I recommend this should be rephrased to reflect the correlational nature of the results.

Currently, from the discussion the findings seem relevant in explaining certain aberrant social learning and -decision making processes in BPD. However, I would like to see a more thorough discussion about the practical relevance of their findings in light of their observation of comparable prediction accuracy between the two groups.

Relatedly, the authors mention that a primary focus of mentalization based therapy for BPD is 'restoring a stable sense of self' and 'differentiating the self from the other'. These goals are very reminiscent of the findings of the current study that individuals with BPD show lower uncertainty over their own and relative reward preferences, and that they are less susceptible to social contagion. Could the observed group differences therefore be a result of therapy rather than adverse early life experiences?

Regarding partner similarity: It was unclear to me why the authors chose partners that were 50% similar when it would be at least equally interesting to investigate self-insertion and social contagion with those that are more than 50% different to ourselves? Do the authors have any assumptions or even data that shows the results still hold for situations with lower than 50% similarity?

Reviewer #2 (Public review):

Summary:

The paper investigates social-decision making, and how this changes after observing the behaviour of other people, in borderline personality disorder. The paper employs a task including three phases, the first where participants make decision on how to allocate rewards to oneself and to a virtual partner, the second where they observe the same task performed by someone else, and a third phase equivalent to phase one, but with a new partner. Using sophisticated computational modelling to analyse choice data, the study reports that borderline participants (versus controls) are more certain about their preferences in phase one, used more neutral priors and are less flexible during phase two, and are less influenced by partners in phase three.

Strengths:

The topic is interesting and important, and the findings are potentially intriguing. The computational methods employed is clever and sophisticated, at the cutting edge of research in the field.

Weaknesses:

There are two major weaknesses. First, the paper lacks focus and clarity. The introduction is rather vague and, after reading it, I remained confused about the paper's aims. Rather than relying on specific predictions, the analysis is exploratory. This implies that it is hard to keep track, and to understand the significance, of the many findings that are reported. Second, although the computational approach employed is clever and sophisticated, there is important information missing about model comparison which ultimately makes some of the results hard to assess from the perspective of the reader.

Reviewer #3 (Public review):

In this paper, the authors use a three-phase economic game to examine the tendency to engage in prosocial versus competitive exchanges with three anonymous partners. In particular, they consider individual differences in the tendency to infer about others' tendencies based on one's preferences and to update one's preferences based on observations of others' behavior. The study includes a sample of individuals diagnosed with borderline personality disorder and a matched sample of psychiatrically healthy control participants.

On the whole, the experimental design is well-suited to the questions and the computational model analyses are thorough, including modern model-fitting procedures. I particularly appreciated the clear exposition regarding model parameterization and the descriptive Table 2 for qualitative model comparison. My broad question about the experiment (in terms of its clinical and cognitive process relevance): Does the task encourage competition or give participants a reason to take advantage of others? I don't think it does, so it would be useful to clarify the normative account for prosociality in the introduction (e.g., some of Robin Dunbar's work).

The finding that individuals with BPD do not engage in self-other generalization on this task of social intentions is novel and potentially clinically relevant. The authors find that BPD participants' tendency to be prosocial when splitting points with a partner does not transfer into their expectations of how a partner will treat them in a task where they are the passive recipient of points chosen by the partner. In the discussion, the authors reasonably focus on model differences between groups (Bayesian model comparison), yet I thought this finding -- BPD participants not assuming prosocial tendencies in phase 2 while CON participant did -- merited greater attention. Although the BPD group was close to 0 on the \beta prior in Phase 2, their difference from CON is still in the direction of being more mistrustful (or at least not assuming prosociality). This may line up with broader clinical literature on mistrustfulness and attributions of malevolence in the BPD literature (e.g., a 1992 paper by Nigg et al. in Journal of Abnormal Psychology). My broad point is to consider further the Phase 2 findings in terms of the clinical interpretation of the shift in \beta relative to controls.

On the conceptual level, I had two additional concerns. First, the authors note that they have "proposed a theory with testable predictions" (p. 4 but also elsewhere) but they do not state any clear predictions in the introduction, nor do they consider what sort of patterns will be observed in the BPD group in view of extant clinical and computational literature. Rather, the paper seems to be somewhat exploratory, largely looking at group differences (BPD vs. CON) on all of the shared computational parameters and additional indices such as belief updating and reaction times. Given this, I would suggest that the authors make stronger connections between extant research on intention representation in BPD and their framework (model and paradigm). In particular, the authors do not address related findings from Ereira (2020) and Story (2024) finding that in a false belief task that BPD participants *overgeneralize* from self to other. A critical comparison of this work to the present study, including an examination of the two tasks differ in the processes they measure, is important.

In addition, perhaps it is fairer to note more explicitly the exploratory nature of this work. Although the analyses are thorough, many of them are not argued for a priori (e.g., rate of belief updating in Figure 2C) and the reader amasses many individual findings that need to by synthesized.

Second, in the discussion, the authors are too quick to generalize to broad clinical phenomena in BPD that are not directly connected to the task at hand. For example, on p. 22: "Those with a diagnosis of BPD also show reduced permeability in generalising from other to self. While prior research has predominantly focused on how those with BPD use information to form impressions, it has not typically examined whether these impressions affect the self." Here, it's not self-representation per se (typically, identity or one's view of oneself), but instead cooperation and prosocial tendencies in an economic context. It is important to clarify what clinical phenomena may be closely related to the task and which are more distal and perhaps should not be approached here.

On a more technical level, I had two primary concerns. First, although the authors consider alternative models within a hierarchical Bayesian framework, some challenges arise when one analyzes parameter estimates fit separately to two groups, particularly when the best-fitting model is not shared. In particular, although the authors conduct a model confusion analysis, they do not as far I could tell (and apologies if I missed it) demonstrate that the dynamics of one model are nested within the other. Given that M4 has free parameters governing the expectations on the absolute and relative reward preferences in Phase 2, is it necessarily the case that the shared parameters between M1 and M4 can be interpreted on the same scale? Relatedly, group-specific model fitting has virtues when believes there to be two distinct populations, but there is also a risk of overfitting potentially irrelevant sample characteristics when parameters are fit group by group.

To resolve these issues, I saw one straightforward solution (though in modeling, my experience is that what seems straightforward on first glance may not be so upon further investigation). M1 assumes that participants' own preferences (posterior central tendency) in Phase 1 directly transfer to priors in Phase 2, but presumably the degree of transfer could vary somewhat without meriting an entirely new model (i.e., the authors currently place this question in terms of model selection, not within-model parameter variation). I would suggest that the authors consider a model parameterization fit to the full dataset (both groups) that contains free parameters capturing the *deviations* in the priors relative to the preceding phase's posterior. That is, the free parameters $\bar{\alpha}_{par}^m$ and $\bar{\beta}_{par}^m$ govern the central tendency of the Phase 2 prior parameter distributions directly, but could be reparametrized as deviations from Phase 1 $\theta^m_{ppt}$ parameters in an additive form. This allows for a single model to be fit all participants that encompasses the dynamics of interest such that between-group parameter comparisons are not biased by the strong assumptions imposed by M1 (that phase 1 preferences and phase 2 observations directly transfer to priors). In the case of controls, we would expect these deviation parameters to be centred on 0 insofar as the current M1 fit them best, whereas for BPD participants should have significant deviations from earlier-phase posteriors (e.g., the shift in \beta toward prior neutrality in phase 2 compared to one's own prosociality in phase 1). I think it's still valid for the authors to argue for stronger model constraints for Bayesian model comparison, as they do now, but inferences regarding parameter estimates should ideally be based on a model that can encompass the full dynamics of the entire sample, with simpler dynamics (like posterior -> prior transfer) being captured by near-zero parameter estimates.

My second concern pertains to the psychometric individual difference analyses. These were not clearly justified in the introduction, though I agree that they could offer potentially meaningful insight into which scales may be most related to model parameters of interest. So, perhaps these should be earmarked as exploratory and/or more clearly argued for. Crucially, however, these analyses appear to have been conducted on the full sample without considering the group structure. Indeed, many of the scales on which there are sizable group differences are also those that show correlations with psychometric scales. So, in essence, it is unclear whether most of these analyses are simply recapitulating the between-group tests reported earlier in the paper or offer additional insights. I think it's hard to have one's cake and eat it, too, in this regard and would suggest the authors review Preacher et al. 2005, Psychological Methods for additional detail. One solution might be to always include group as a binary covariate in the symptom dimension-parameter analyses, essentially partialing the correlations for group status. I remain skeptical regarding whether there is additional signal in these analyses, but such controls could convince the reader. Nevertheless, without such adjustments, I would caution against any transdiagnostic interpretations such as this one in the Highlights: "Higher reported childhood trauma, paranoia, and poorer trait mentalizing all diminish other-to-self information transfer irrespective of diagnosis." Since many of these analyses relate to scales on which the groups differ, the transdiagnostic relevance remains to be demonstrated.

eLife Assessment

This valuable study tests a methodology for the discovery of new honey bee-repellent odorants via machine learning. The conclusions of the study are supported by solid evidence, with predicted compounds validated in the lab and the field. This work will be of interest to researchers in ecology, pest control and olfactory neuroscience.

Reviewer #1 (Public review):

Summary:

This manuscript reports a very interesting, novel and important research angle to add to the now enormous interest in how pesticides can be toxic to beneficial insects like the honey bee. Many studies have reported on how pesticides in standard use formulations show both lethality as well as sublethal negative effects on behavior and reproduction. The authors propose to use machine learning algorithms to identify new volatile compounds that can be tested for repellency. They use as input chemical structures that are derived from chemicals that have known repellent effects as identified in their initial behavioral assays.

Strengths:

The conclusion is that such chemicals specific to repelling bees and not pest insects (using the fruit fly as a model for the latter) can be identified using the ML approach. Have a list of such chemicals that can be rotated among in any field application would be a benefit because of the honey bees' ability to learn its way around any kind of stimulus designed to keep it from nectar and pollen, even when they may be tainted by pesticide.

Weaknesses:

The use of machine learning seems well-executed and legitimate. But this is beyond my expertise. So other reviewers can maybe comment more on that.

The behavioral data report on the use of a two-choice assay for bees in small Petrie plates. Bess can feed from two small wells place of filter paper impregnated with control or the control containing a chemical. The primary behavior, for ex in Fig 2C, is the first choice by one of the five bees in the plate of which well to feed from. For some chemical compound, there seems to be a 50:50 choice, indicating no repellent effects. In other cases the first bee making the choice chose the control, indicating possible repellent effects of the test chemical. Choices in this assay were validated in a free flying assay.

Concerns with the choice assay:<br /> - 50-70 microliters amounts to what one hungry bee will drink. Did the first bee drink most of it, such that measures of bait consumed reflect a single bee or multiple bees?<br /> - How many bees were repelled to the control side? Was it just the one bee? Were other measures considered? E.g. time to first approach; the number of bees feeding at different time points; the total number of bees observed feeding per unit time.

Reviewer #2 (Public review):

Summary:

The search for new repellent odors for honey bees has significant practical implications. The authors developed an iterative pipeline through machine learning to predict honey bee-repellent odors based on molecular structures. By screening a large number of candidate compounds, they identified a series of novel repellents. Behavioral tests were then conducted to validate the effectiveness of these repellents. Both the discovery and the methodological approach hold value for related fields.

Strengths:

* The study demonstrates that using molecular structures and a relatively small training dataset, the model could predict repellents with a reasonably high success rate. If the iterative approach works as described, it could benefit a wide range of olfaction-related fields.<br /> * The effectiveness of the predicted repellents was validated through both laboratory and field behavioral tests.

Weaknesses:

The small size of the training dataset poses a common challenge for machine learning applications. However, the authors did not clearly explain how their iterative approach addresses this limitation in this study. Quantitative evidence demonstrating improvements achieved in the second round of training would strengthen their claims. For instance, details on whether the success rate of predictions or the identification of higher-affinity components would be helpful. Furthermore, given that only 15 new components were added for the second round of training, it is surprising that such a small dataset could result in significant improvements.

Reviewer #3 (Public review):

The manuscript of Kowalewski et al. titled "Machine learning of honey bee olfactory behavior identifies repellent odorants in free flying bees in the field" did machine learning to predict potential candidates for honeybee repellents, which may keep foraging bees from pesticides. This is a pilot research with strong significance in the research of olfactory behavior and in pest control. However, some major issues need to be addressed to enhance the manuscript's clarity, strength, and overall coherence.

(1) Drosophila melanogaster is not considered as a true agricultural pest. The manuscript would be more compelling if using true pests, for example, Drosophila suzukii or others.<br /> (2) For repellency test, the result relies on dosage. An attractant may become a repellent at high concentration. Test a range of concentrations for each chemicals and compare responses between honeybees and pests.<br /> (3) Be more clear about bee behavior data and their scores (as in Page 4 Results "184 training chemicals and later for 203 chemicals" and Page 10 Methods). I suggest that authors add a supplemental table with each chemical and its behavioral score, feature and reference - which ones were used for training, and which ones for testing. Also add your own behavioral test data (second input) to this table.<br /> (4) The AUC in the first validation was 0.88 (Page 4), and in Page 5, "As expected, the computational validation results based on the AUC values, show an improvement." However, there were no other AUC values to show improvement.<br /> (5) Show plots of ROC AUC curves from Round 1 and Round 2.<br /> (6) In the Discussion, the authors mentioned olfactory receptors in honeybees. It would be useful to provide a general review of the current understanding of these receptors and their (potential) functions.<br /> (7) I suggest combining Fig. 1 and Fig. 3A as one pipeline for this work.<br /> (8) Figure 2C, some sample sizes are very small, such as 2-piperidone: 1 first-choice control vs 0 first-choice repellent? Increase sample size and do statistical analysis.<br /> (9) In general, to assist reviewers, include line numbers to the manuscript.

eLife Assessment

This is a fundamental body of work reporting anatomical, molecular, and functional mapping of the central complex in Drosophila. The tools generated and the findings, which include characterization of neuromodulators used by different cells, will undoubtedly serve as a foundation for future studies of this brain region. Overall, the data are compelling and likely to have a major impact.

Reviewer #1 (Public review):

Summary:

This work is meant to help create a foundation for future studies of the Central Complex, which is a critical integrative center in the fly brain. The authors present a systematic description of cellular elements, cell type classifications, behavioral evaluations and genetic resources available to the Drosophila neuroscience community.

Strengths:

The work contributes new, useful and systematic technical information in compelling fashion to support future studies of the fly brain. It also continues to set a high and transparent standard by which large-scale resources can be defined and shared.

Weaknesses:

manuscript p. 1<br /> "The central complex (CX) of the adult Drosophila melanogaster brain consists of approximately 2,800 cells that have been divided into 257 cell types based on morphology and connectivity (Scheer et al., 2020; Hulse et al. 2021; Wolff et al., 2015)."<br /> The 257 accumulated cell types have informational names (e.g., PBG2‐9.s‐FBl2.b‐NO3A.b) in addition to their associations with specific Gal4 lines and specific EM Body IDs. All this is very useful. I have one suggestion to help a reader trying to get a "bird's eye view" of such a large amount of detailed and multi-layered information. Give each of the 257 CX cell types an arbitrary number: 1 to 257. In fact, Supplemental File 2 lists ~277 cell types each with a number in sequence, so perhaps in principle, it is there. This could expedite the search function when a reader is trying to cross-reference CX cell type information from the text, to the Figures and/or to the Supplemental Figures. Also, the use of (arbitrary) cell type numbers could expedite the explanation of which cell types are included in any compilation of information (e.g., which ones were tested for specific NT expression).

manuscript p 2<br /> "Figure 2 and Figure 2-figure supplements 1-4 show the expression of 52 new split-GAL4 lines with strong GAL4 expression that is largely limited to the cell type of interest. .... We also generated lines of lesser quality for other cell types that in total bring overall coverage to more than three quarters of CX cell types."<br /> This section describes the generation and identification of specific split Gal4 lines, and the presentation is generally excellent. It represents an outstanding compendium of information. My reading of the text suggests ~200 cell types have Gal4 lines that are of immediate use (having high specificity or v close-to-high). Use of an arbitrary number system (mentioned above) could augment that description for the reasons stated. For example, which of the 257 cell types are represented by split Gal4 lines that constitute the ~1/3 representing "high-quality lines "? A second comment relates to this study 's functional analysis of the contributions of CX cell types to sleep physiology. The recent literature contains renewed interest in the specific expression patterns of Gal4 lines that can promote sleep-like behaviors. In particular Gal4 line expression outside the brain (in the VNC and outside the CNS) have been raised as important elements that need be included for interpretation interpretation of sleep regulation. This present study offers useful information about a large number of expression patterns, as well as a basis with which to seek additional information., including mention of VNC expression in many cases However, perhaps I missed it, but I could not find a short description of the over-all strategy used to describe the expression patterns and feel that could be helpful. Were all Gal4 lines studied for expression in the VNC? and in the peripheral NS? It is probably published elsewhere, but even a short reprise would still be useful.

manuscript p 9<br /> Neurotransmitter expression in CX cell types<br /> "To determine what neurotransmitters are used by the CX cell types, we carried out fluorescent in situ hybridization using EASI-FISH (Eddison and Irkhe, 2022; Close et al., 2024) on brains that also expressed GFP driven from a cell-type-specific split GAL4 line. In this way, we could determine what neurotransmitters were expressed in over 100 different CX cell types based on ...."<br /> Reading this description, I was uncertain whether the >100 cell types mentioned were tested with all the NT markers by EASI-FISH? Also, assigning arbitrary numbers to the cell types (same suggestion as above) could help the reader more readily ascertain which were the ~100 cell types classified in this context.

manuscript p 10<br /> "Our full results are summarized below, together with our analysis of neuropeptide expression in the same cell types."<br /> I recommend specifying which Figures and Tables contain the "full results" indicated.

NP expression in CX cell types<br /> Similar to the comments regarding studies of NT expression: were all ~100 cell types tested with each of the 17 selected NPs? Arbitrary numerical identifies could be useful for the reader to determine which cell types/ lines were tested and which were not yet tested

manuscript p. 11<br /> "The neuropeptide expression patterns we observed fell into two broad categories."<br /> This section presents information that is extensive and extremely useful. It supports consideration of peptidergic cell signaling at a circuits level and in a systematic fashion that will promote future progress in this field. I have two comments. First, regarding the categorization of two NP expression patterns, discernible by differences in cell number: this idea mirrors one present in prior literature. Recently the classification of the transcription factor DIMM summarizes this same two-way categorization (e.g., doi: 10.1371/journal.pone.0001896). That included the fact that a single NP can be utilized by cell of either category.

Second, regarding this comment:<br /> "In contrast, neuropeptides like those shown in Figure 6 appear to be expressed in dozens to hundreds of cells and appear poised to function by local volume transmission in multiple distinct circuits."<br /> Signaling by NPs in this second category (many small cells) suggests more local diffusion, a smaller geographic expanse compared to "volume" signaling by the sparser larger peptidergic cells. Given this, I suggest re-consideration in using the term "volume" in this instance, perhaps in favor of "local" or "paracrine". This is only a suggestion and in fact rests almost entirely on speculation/ interpretation, as the field lacks a strong empirical basis to say how far NPs diffuse and act. A recent study in the fly brain of peptide co-transmitters (doi: 10.1016/j.cub.2020.04.025) provides an instructive example in which differences between the spatial extents of long-range (peptide 1) versus short-range (peptide 2) NP signaling may be inferred in vivo.

Spab was mentioned (Figure 6 legend) but discarded as a candidate NP to include based on a personal communication, as was Nplp1. The manuscript did not include reasons to do so, nor include a reference to spab peptide. I suggest including explicit reasons to discard candidate NPs.

In Fig 9-supplement 1, neurotransmitter biosynthetic enzymes were measured by RNA-seq for given CX cell types to augment the cell type classification. The same methods could be used to support cell type classification regarding putative peptidergic character (in Figure 9 supplement 2) by measuring expression levels of critical, canonical neuropeptide biosynthetic enzymes. These include the proprotein convertase dPC2 (amon); the carboxypeptidase dCPD/E (silver); and the amidating enzymes dPHM; dPal1; dPal2. PHM is most related to DBM (dopamine beta monooxygenase), the rate limiting enzyme for DA production, and greater than 90% of Drosophila neuropeptides are amidated. If the authors are correct in surmising widespread use of NPs by CX cell types (and I expect they are), there could be diagnostic value to report expression levels of this enzyme set across many/most CX cell types.

Comment #6<br /> Screen of effects on Sleep behavior<br /> This work is large in scope and as suggested likely presents excellent starting points for many follow-up studies. I again suggest assigning stable number identities to the elements described. In this case, not cell types, but split Gal4 lines. This would expedite the cross-referencing of results across the four Supplemental Files 3-6. For example, line SS00273 is entry line #27 in S Files 3 and 4, but line entry #18 in S Files 5 and 6.

manuscript p 26<br /> Clock to CX<br /> "Not surprisingly, the connectome reveals that many of the intrinsic CX cell types with sleep phenotypes are connected by wired pathways (Figure 12 and Figure 12-figure supplement 1)."<br /> Do intrinsic CX cells with sleep phenotypes also connect by wired pathways to CX cells that do not have sleep phenotypes?

"The connectome also suggested pathways from the circadian clock to the CX. Links between clock output DN1 neurons to the ExR1 have been described in Lamaze et al. (2018) and Guo et al. (2018), and Liang et al. (2019) described a connection from the clock to ExR2 (PPM3) dopaminergic neurons."<br /> The introduction to this section indicates a focus on connectome-defined synaptic contacts. Whereas the first two studies cited featured both physiological and anatomic evidence to support connectivity from clock cells to CX, the third did not describe any anatomical connections, and that connection may in fact be due to diffuse not synaptic signaling

I could not easily discern the difference between Figs 12 and 12-S1? These appear to be highly-related circuit models, wherein the second features more elements. Perhaps spell out the basis for the differences between the two models to avoid ambiguity.

"...the cellular targets of Dh31 released from ER5 are unknown, however previous work (Goda et al., 2017; Mertens et al., 2005) has shown that Dh31 can activate the PDF receptor raising the possibility of autocrine signaling."<br /> Regarding pharmacological evidence for Dh31 activation of Pdfr: strong in vivo evidence was developed in doi: 10.1016/j.neuron.2008.02.018: a strong pdfr mutation greatly reduces response to synthetic dh31 in neurons that normally express Pdfr

manuscript p 30<br /> "Unexpectedly, we found that all neuropeptide-expressing cell types also expressed a small neurotransmitter."<br /> Did this conclusion apply only to CX cell types? - or was it also true for large peptidergic neurons? Prior evidence suggests the latter may not express small transmitters (doi: 10.1016/j.cub.2009.11.065). The question pertains to the broader biology of peptidergic neurons, and is therefore outside the strict scope of the main focus area - the CX. However, the text did initially consider peptidergic neurons outside the CX, so the information may be pertinent to many readers.

Reviewer #2 (Public review):

Summary:

In this paper, Wolff et al. describe an impressive collection of newly created split-GAL4 lines targeting specific cell types within the central complex (CX) of Drosophila. The CX is an important area in the brain that has been involved in the regulation of many behaviors including navigation and sleep/wake. The authors advocate that to fully understand how the CX functions, cell-specific driver lines need to be created. In that respect, this manuscript will be of very important value to all neuroscientists trying to elucidate complex behaviors using the fly model. In addition, and providing a further very important finding, the authors went on to assess neurotransmitter/neuropeptides and their receptors expression in different cells of the CX. These findings will also be of great interest to many and will help further studies aimed at understanding the CX circuitries. The authors then investigated how different CX cell types influence sleep and wake. While the description of the new lines and their neurochemical identity is excellent, the behavioral screen seems to be limited.

Strengths:

(1) The description of dozens of cell-specific split-GAL4 lines is extremely valuable to the fly community. The strength of the fly system relies on the ability to manipulate specific neurons to investigate their involvement in a specific behavior. Recently, the need to use extremely specific tools has been highlighted by the identification of sleep-promoting neurons located in the VNC of the fly as part of the expression pattern of the most widely used dorsal-Fan Shaped Body (dFB) GAL4 driver. These findings should serve as a warning to every neurobiologist, make sure that your tool is clean. In that respect, the novel lines described in this manuscript are fantastic tools that will help the fly community.<br /> (2) The description of neurotransmitter/neuropeptides expression pattern in the CX is of remarkable importance and will help design experiments aimed at understanding how the CX functions.

Weaknesses:

(1) I find the behavioral (sleep) screen of this manuscript to be limited. It appears to me that this part of the paper is not as developed as it could be. The authors have performed neuronal activation using thermogenetic and/or optogenetic approaches. For some cell types, only thermogenetic activation is shown. There is no silencing data and/or assessment of sleep homeostasis or arousal threshold. The authors find that many CX cell types modulate sleep and wake but it's difficult to understand how these findings fit one with the other. It seems that each CX cell type is worthy of its own independent study and paper. I am fully aware that a thorough investigation of every CX neuronal type in sleep and wake regulation is a herculean task. So, altogether I think that this manuscript will pave the way for further studies on the role of CX neurons in sleep regulation.<br /> (2) Linked to point 1, it is possible that the activation protocols used in this study are insufficient for some neuronal types. The authors have used 29{degree sign} for thermogenetic activation (instead of the most widely used 31{degree sign}) and a 2Hz optogenetic activation protocol. The authors should comment on the fact that they may have missed some phenotypes by using these mild activation protocols.<br /> (3) There are multiple spelling errors in the manuscript that need to be addressed.

Reviewer #3 (Public review):

Summary:

The authors created and characterized genetic tools that allow for precise manipulation of individual or small subsets of central complex (CX) cell types in the Drosophila brain. They developed split-GAL4 driver lines and integrated this with a detailed survey of neurotransmitter and neuropeptide expression and receptor localization in the central brain. The manuscript also explores the functional relevance of CX cell types by evaluating their roles in sleep regulation and linking circadian clock signals to the CX. This work represents an ambitious and comprehensive effort to provide both molecular and functional insights into the CX, offering tools and data that will serve as a critical resource for researchers.

Strengths:

(1) The extensive collection of split-GAL4 lines targeting specific CX cell types fills a critical gap in the genetic toolkit for the Drosophila neuroscience community.<br /> (2) By combining anatomical, molecular, and functional analyses, the authors provide a holistic view of CX cell types that is both informative and immediately useful for researchers across diverse disciplines.<br /> (3) The identification of CX cell types involved in sleep regulation and their connection to circadian clock mechanisms highlights the functional importance of the CX and its integrative role in regulating behavior and physiological states.<br /> (4) The authors' decision to present this work as a single, comprehensive manuscript rather than fragmenting it into smaller publications each focusing on separate central complex components is commendable. This decision prioritizes accessibility and utility for the broader neuroscience community, which will enable researchers to approach CX-related questions with a ready-made toolkit.

Weaknesses:

While the manuscript is an outstanding resource, it leaves room for more detailed mechanistic exploration in some areas. Nonetheless, this does not diminish the immediate value of the tools and data provided.

Appraisal:

The authors have succeeded in achieving their aims of creating well-characterized genetic tools and providing a detailed survey of neurochemical and functional properties in the CX. The results strongly support their conclusions and open numerous avenues for future research. The work effectively bridges the gap between genetic manipulation, molecular characterization, and functional assessment, enabling a deeper understanding of the CX's diverse roles.

Impact and Utility

This manuscript will have a significant and lasting impact on the field, providing tools and data that facilitate new discoveries in the study of the CX, sleep regulation, circadian biology, and beyond. The genetic tools developed here are likely to become a standard resource for Drosophila researchers, and the comprehensive dataset on neurotransmitter and neuropeptide expression will inspire investigations into the interplay between neuromodulation and classical neurotransmission.

Additional Context

By delivering an integrated dataset that spans anatomy, molecular properties, and functional relevance, the authors have created a resource that will serve the neuroscience community for years to come.

eLife Assessment

This important study investigates how hummingbird hawkmoths integrate stimuli from across their visual field to guide flight behavior. Cue conflict experiments provide solid evidence for an integration hierarchy within the visual field: hawkmoths prioritize the avoidance of dorsal visual stimuli, potentially to avoid crashing into foliage, while they use ventrolateral optic flow to guide flight control. With a more systematic quantification of specific parameter combinations, this paper would be of broad interest to enthusiasts of visual neuroscience and ethology.

Reviewer #1 (Public review):

Summary:

Recent work has demonstrated that the hummingbird hawkmoth, Macroglossum stellatarum, like many other flying insects, use ventrolateral optic flow cues for flight control. However, unlike other flying insects, the same stimulus presented in the dorsal visual field elicits a directional response. Bigge et al., use behavioral flight experiments to set these two pathways in conflict in order to understand whether these two pathways (ventrolateral and dorsal) work together to direct flight and if so, how. The authors characterize the visual environment (the amount of contrast and translational optic flow) of the hawkmoth and find that different regions of the visual field are matched to relevant visual cues in their natural environment and that the integration of the two pathways reflects a priortiziation for generating behavior that supports hawkmoth safety rather than than the prevalence for a particular visual cue that is more prevalent in the environment.

Strengths:

This study creatively utilizes previous findings that the hawkmoth partitions their visual field as a way to examine parallel processing. The behavioral assay is well-established and the authors take the extra steps to characterize the visual ecology of the hawkmoth habitat to draw exciting conclusions about the hierarchy of each pathway as it contributes to flight control.

Weaknesses:

The work would be further clarified and strengthened by additional explanation included in the main text, figure legends, and methods that would permit the reader to draw their own conclusions more feasibly. It would be helpful to have all figure panels referenced in the text and referenced in order, as they are currently not. In addition, it seems that sometimes the incorrect figure panel is referenced in the text, Figure S2 is mislabeled with D-E instead of A-C and Table S1 is not referenced in the main text at all. Table S1 is extremely important for understanding the figures in the main text and eliminating acronyms here would support reader comprehension, especially as there is no legend provided for Table S1. For example, a reader that does not specialize in vision may not know that OF stands for optic flow. Further detail in figure legends would also support the reader in drawing their own conclusions. For example, dashed red lines in Figures 3 and 4 A and B are not described and the letters representing statistical significance could be further explained either in the figure legend or materials to help the reader draw their own conclusions.

Reviewer #2 (Public review):

Summary:

Bigge and colleagues use a sophisticated free-flight setup to study visuo-motor responses elicited in different parts of the visual field in the hummingbird hawkmoth. Hawkmoths have been previously shown to rely on translational optic flow information for flight control exclusively in the ventral and lateral parts of their visual field. Dorsally presented patterns, elicit a formerly completely unknown response - instead of using dorsal patterns to maintain straight flight paths, hawkmoths fly, more often, in a direction aligned with the main axis of the pattern presented (Bigge et al, 2021). Here, the authors go further and put ventral/lateral and dorsal visual cues into conflict. They found that the different visuomotor pathways act in parallel, and they identified a 'hierarchy': the avoidance of dorsal patterns had the strongest weight and optic flow-based speed regulation the lowest weight.

Strengths:

The data are very interesting, unique, and compelling. The manuscript provides a thorough analysis of free-flight behavior in a non-model organism that is extremely interesting for comparative reasons (and on its own). These data are both difficult to obtain and very valuable to the field.

Weaknesses:

While the present manuscript clearly goes beyond Bigge et al, 2021, the advance could have perhaps been even stronger with a more fine-grained investigation of the visual responses in the dorsal visual field. Do hawkmoths, for example, show optomotor responses to rotational optic flow in the dorsal visual field?

Reviewer #3 (Public review):

The central goal of this paper as I understand it is to extract the "integration hierarchy" of stimulus in the dorsal and ventrolateral visual fields. The segregation of these responses is different from what is thought to occur in bees and flies and was established in the authors' prior work. Showing how the stimuli combine and are prioritized goes beyond the authors' prior conclusions that separated the response into two visual regions. The data presented do indeed support the hierarchy reported in Figure 5 and that is a nice summary of the authors' work. The moths respond to combinations of dorsal and lateral cues in a mixed way but also seem to strongly prioritize avoiding dorsal optic flow which the authors interpret as a closed and potentially dangerous ecological context for these animals. The authors use clever combinations of stimuli to put cues into conflict to reveal the response hierarchy.

My most significant concern is that this hierarchy of stimulus responses might be limited to the specific parameters chosen in this study. Presumably, there are parameters of these stimuli that modulate the response (spatial frequency, different amounts of optic flow, contrast, color, etc). While I agree that the hierarchy in Figure 5 is consistent for the particular stimuli given, this may not extend to other parameter combinations of the same cues. For example, as the contrast of the dorsal stimuli is reduced, the inequality may shift. This does not preclude the authors' conclusions but it does mean that they may not generalize, even within this species. For example, other cue conflict studies have quantified the responses to ranges of the parameters (e.g. frequency) and shown that one cue might be prioritized or up-weighted in one frequency band but not in others. I could imagine ecological signatures of dorsal clutter and translational positioning cues could depend on the dynamic range of the optic flow, or even having spatial-temporal frequency-dependent integration independent of net optic flow.

The second part of this concern is that there seems to be a missed opportunity to quantify the integration, especially when the optic flow magnitude is already calculated. The discussion even highlights that an advantage of the conflict paradigm is that the weights of the integration hierarchy can be compared. But these weights, which I would interpret as stimulus-responses gains, are not reported. What is the ratio of moth response to optic flow in the different regions? When the moth balances responses in the dorsal and ventrolateral region, is it a simple weighted average of the two? When it prioritizes one over the other is the response gain unchanged? This plays into the first concern because such gain responses could strongly depend on the specific stimulus parameters rather than being constant.

The authors do explain the choice of specific stimuli in the context of their very nice natural scene analysis in Fig. 1 and there is an excellent discussion of the ecological context for the behaviors. However, I struggled to directly map the results from the natural scenes to the conclusions of the paper. How do they directly inform the methods and conclusions for the laboratory experiments? Most important is the discussion in the middle paragraph of page 12, which suggests a relationship with Figure 1B, but seems provocative but lacking a quantification with respect to the laboratory stimuli.

The central conclusion of the first section of the results is that there are likely two different pathways mediating the dorsal and the ventrolateral response. This seems reasonable given the data, however, this was also the message that I got from the authors' prior paper (ref 11). There are certainly more comparisons being done here than in that paper and it is perfectly reasonable to reinforce the conclusion from that study but I think what is new about these results needs to be highlighted in this section and differentiated from prior results. Perhaps one way to help would be to be more explicit with the open hypotheses that remain from that prior paper.

eLife Assessment

This important Research Advance presents convincing evidence on the neuroprotective effects of reserpine in a well-established model of retinitis pigmentosa (P23H-1). This study builds on previous work establishing reserpine as a neuroprotectant in models of Leber congenital amaurosis. Here authors show reserpine's disease gene-independent influence on photoreceptor survival and emphasizes the importance of considering biological sex in understanding inherited retinal degeneration and the impact of drug treatments on mutant retinas. The work will be of interest to vision researchers as well as a broad audience in translational research.

Reviewer #1 (Public review):

Summary:

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

Strengths:

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

Weaknesses:

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

Reviewer #2 (Public review):

Summary:

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

Strengths:

(1) Relevance to Clinical Challenges: The study addresses a critical limitation in inherited retinal degeneration (IRD) therapies by exploring a gene-agnostic approach. It emphasizes sex-specific responses, which aligns with recent NIH mandates on sex as a biological variable.<br /> (2) Multi-dimensional Methodology: Combining electroretinography (ERG), optical coherence tomography (OCT), histology, and transcriptomics strengthens the study's findings.<br /> (3) Novel Insights: The transcriptomic analysis uncovers sex-specific pathways impacted by reserpine, laying the foundation for personalized approaches to retinal disease therapy.

Weaknesses:

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

Statistical Analysis

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

Sex-Specific Pathways

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

The study does not explore why female rats respond better to reserpine. Potential factors such as hormonal differences, retinal size, or differential drug uptake are not discussed.<br /> It remains open, whether observed transcriptomic trends (e.g., proteostasis network genes) correlate with sex-specific functional outcomes.

Author response:

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

eLife Assessment:

This study provides valuable insights, addressing the growing threat of multi-drug-resistant (MDR) pathogens by focusing on the enhanced efficacy of colistin when combined with artesunate and EDTA against colistin-resistant Salmonella strains. The evidence is solid, supported by comprehensive microbiological assays, molecular analyses, and in vivo experiments demonstrating the effectiveness of this synergic combination. However, the discussion on the clinical application challenges of this triple combination is incomplete, and it would benefit from addressing the high risk associated with using three potential nephrotoxic agents in vivo.

The development of novel pharmaceutical dosage forms, pharmacokinetic, pharmacodynamic and safety analysis of the triple combination will be further conducted in our next study to provide a theoretical basis for the next clinical drug use. The discussion of potential toxicity of AS, colistin, EDTA and the triple combination have been added in line 318 to 337.

Public Reviews:

Reviewer #1 (Public Review):

(1) The study focuses on a limited number of Salmonella strains, and broader testing on various MDR pathogens would strengthen the findings.

The number of COL-resistant clinical strains that actually used was larger than that mentioned in our original article, when evaluating the antimicrobial activities of AS, EDTA, COL alone or drug combinations. But, considering that there were superfluous results of mcr-1 positive Salmonella strains, we omitted these results (Table supplement 7 and 8 in revised supplement materials) to avoid redundant data presentation in the original article. Additionally, much more gram-negative and -positive MDR bacteria, such as Klebsiella pneumoniae, Pseudomonas aeruginosa and Staphylococcus aureus will be selected for the next study including the development of novel pharmaceutical dosage forms, pharmacokinetic, pharmacodynamic and safety analysis et al.

(2) While the study elucidates several mechanisms, further molecular details could provide deeper insights into the interactions between these drugs and bacterial targets.

In our next study, further molecular details will be focused on the regulatory targets of CheA and SpvD-related pathways, as well as the precise inhibition targets of MCR protein by the triple combination, through the generation of deletion or point mutations, and analysis of intermolecular interactions.

(3) The time-kill experiment was conducted over 12 hours instead of the recommended 24 hours. To demonstrate a synergistic effect among the drugs, a reduction of at least 2 log10 in colony count should be shown in a 24-hour experiment. Additionally, clarifying the criteria for selecting drug concentrations is important to improve the interpretation of the results.

The time-kill experiment of 24 hours have been re-executed and could be used to replace the Figure 1 in the original paper. The New Figure 1 has been uploaded and the change do not affect our interpretation of the result.

Although in vitro studies have determined that with increasing dose of AS and EDTA, the antibacterial synergistic activity was gradually enhanced, and meanwhie, may also resulting in more toxic side effects. Thus, in our study, the 1/8 MICs of AS and EDTA were selected to ensure excellent antibacterial activity whereas minimize the potential toxicity. The instructions on the selection of drug concentration have been added in line 323 to 326.

(4) While the combination of EDTA, artesunate, and colistin shows promising in vitro results against Salmonella strains, the clinical application of this combination warrants careful consideration due to potential toxicity issues associated with these compounds.

The development of novel pharmaceutical dosage forms, pharmacokinetic, pharmacodynamic and safety analysis of the triple combination will be further conducted in our next study to provide a theoretical basis for the next clinical drug use.

Reviewer #2 (Public Review):

(1) The study by Zhai et al describes repurposing of artesunate, to be used in combination with EDTA to resensitize Salmonella spp. to colistin. The observed effect applied both to strains with and without mobile colistin resistance determinants (MCR). It was already known that EDTA in combination with colistin has an inhibitory effect on MCR-enzymes, but at the same time, both colistin and EDTA can contribute to nephrotoxicity, something which is also true for artesunate. Thus, the triple combination of three nephrotoxic agents has significant challenges in vivo, which is not particularly discussed in this paper.

The discussion of potential toxicity of triple combination has been added in line 318 to 337.

(2) The selection of strains is not very clear. Nothing is known about the sequence types of the strains or how representative they are for strains circulating in general. Thus, it is difficult to generalize from this limited number of isolates, although the studies done in these isolates are comprehensive.

The tested strains in this study were all COL-resistant clinical isolates, and the genome sequencing and comparative analysis of these strains have not been analyzed. The antibacterial activities of different antimicrobial drugs against the S16 and S30 strains have been measured and listed in the Table supplement 9 within revised supplement materials. Considering that the number of COL-resistant clinical strains that actually used was larger than that mentioned in our original article (see the NO.1 response to the Public Reviewer #1), we think that the results obtained in this study could be representative to some extent.

(3) Nothing is known about the susceptibility of the strains to other novel antimicrobial agents. Colistin has a limited role in the treatment of gram-negative infections, and although it can be used sometimes in combination, it is not clear why it would be combined with two other nephrotoxic agents and how this could have relevance in a clinical setting.

The antibacterial activities of different antimicrobial drugs against the S16 and S30 strains have been measured and listed in the Table supplement 9 within revised supplement materials. Additionally, the discussion of potential toxicity of triple combination has been added in line 318 to 337.

(4) It is not clear whether their transcriptomics analysis should at least be carried out in duplicate for reasons of being able to assess reproducibility. It is also not clear why the samples were incubated for 6 hours - no discussion is presented on the selection of a time point for this.

As it can be seen from the time kill curves that the survival number of bacteria started to decrease after 4 h incubation of drug combinations. If the incubation time is too short (for example less than 4 h), the differentially expressed genes can not be fully revealed, while too long incubation time (such as 8 h and 12 h) may lead to a significant CFU reduction of bacteria, and result in inaccurate sequencing results. Therefore, we selected the incubation time 6 h, at which point drugs exhibited  significant antibacterial effects and there were also enough survival bacteria in the sample for transcriptome analysis. Each sample had three replications to preserve the accuracy of results.

(5) Discussion is lacking on the reproducibility and selection of details for the methodology.

The results obtained in this paper have been repeated several times, which indicated that the detailed operation steps described in the materials and methods section were reproducibility. To avoid redundancy, we did not include too much details in the discussion section.

Reviewer #3 (Public Review):

(1) Number of strains tested.

The number of COL-resistant clinical strains that actually used was larger than that mentioned in our original article (see the NO.1 response to the Public Reviewer #1)

(2) Response to comment: Lack of data on cytotoxicity.

The pharmacokinetic, pharmacodynamic and safety analysis of the triple combination will be further conducted in our next study to provide a theoretical basis for the next clinical drug use.

Recommendations For The Authors:

Reviewer #1 (Recommendations For The Authors):

(1) Introduction:

The introduction should provide more context about the pathogen Salmonella, its significance in both human and veterinary medicine, and the impact of colistin resistance in these pathogens. Salmonella is a leading cause of foodborne illnesses worldwide, resulting in substantial morbidity and mortality. It can cause a range of diseases, from gastroenteritis to more severe systemic infections like typhoid fever and invasive non-typhoidal salmonellosis. In veterinary medicine, Salmonella infections can lead to significant economic losses in livestock industries due to illness and death among animals, as well as through the contamination of animal products.

The description has been added in the introduction section in line 47 to 53.

(2) Results and Discussion:

(1) While the combination of EDTA, artesunate, and colistin shows promising in vitro results against Salmonella, the clinical application of this combination warrants careful consideration due to potential toxicity issues associated with these compounds. Colistin is known for nephrotoxicity and neurotoxicity, limiting its use to severe cases where the benefits outweigh the risks. EDTA, as a chelating agent, can disrupt essential metal ions in the body, posing risks of metabolic imbalances. Although it has clinical applications, primarily in cases of heavy metal poisoning, its use as an adjuvant in antibiotics may present risks. Although generally well-tolerated for malaria, interactions of artesunate with other drugs and long-term safety in combined therapies require thorough investigation.

The discussion of potential toxicity of triple combination has been added in line 318 to 337.

(2) Table 1: The manuscript mentions that some strains used in the study are mcr-positive and mcr-negative. It is important to indicate in Table 1, in addition to the identification of Salmonella species, which strains are mcr-positive or mcr-negative.

The relevant information has been added in Table 1.

(3) Figure 2: What is the authors' hypothesis regarding the growth curves labeled "a" and "e" where strains JS and S16 resume growth 12 hours after treatment with AS? In the legend of Figure 2, describe what was used as the "positive control group."

The growth curves labeled “a” and “e” were in Figure 1. After incubated with AC for 8 h, the survival CFUs of JS and S16 strains showed a slightly reduction, but there were still living cells. Since the bactericidal activity of AC is not strong enough to exert sustained bactericidal activity, these remaining living cells will resume growth after treatment with AC for 12 h. The “positive control group” in the legend of Figure 2 has been indicated in line 724.

(4) What is the authors' hypothesis for the differences observed in the transcriptome and metabolome?

The changes in gene transcription level may cause corresponding changes in protein level, but these proteins are not all involved in the bacterial metabolic process. For example, MCR protein  is encoded by the COL resistance related gene mcr, which mediates the modification of lipid A, but are not involved in the cellular metabolic process. Therefore, the transcriptome change of mcr gene may affect the protein production of MCR, nor the bacterial metabolic processes, so there are differences observed in the transcriptome and metabolome.

(5) In some parts of the text, the authors state that artesunate and EDTA potentiate the action of colistin, which is a bacteriostatic drug. However, in other parts, the authors describe the effect of the AEC combination as bacteriostatic (Abstract: line 32; Results: line 179). How do the authors explain this inconsistency?

The artesunate and EDTA could be regarded as “adjuvants” for the bacteriostatic drug colistin. Adjuvants itself act no or weak antibacterial effect on bacteria. For antimicrobial drugs, the “adjuvants” are compounds that generally used in combination with antibacterial drugs to re-sensitizing bacteria that have developed drug resistance. Thus, in this paper the AEC combination could be regared as bacteriostatic.

(6) According to Brennan & Kirby (2019; doi: 10.1016/j.cll.2019.04.002), to evaluate the synergism between different drug combinations, bacterial growth curves need to be assessed over 24 hours. If the colony count is {greater than or equal to} 2 log10 lower than that of the most active antimicrobial alone, the combination is considered synergistic. Based on the growth curve results shown in Figure 1, the experiment was conducted for 12 hours, and in some cases, only a small reduction in growth was observed, even at the maximum concentration of colistin. Moreover, in some cases, the curve resumes rising between 8 and 12 hours. What is the authors' hypothesis in this case? It is important to conduct the assay over 24 hours to confirm the synergism between these drugs.

The time-kill experiment of 24 hours have been re-executed and could be used to replace the Figure 1 in the original paper. Additionally, the phenomenon that “the curve resumes rising between 8 and 12 hours” has been explained in the response to comment of “Reviewer #1 (Recommendations For The Authors), Results and Discussion, (3) Figure 2”.

(7) To prove that CheA and SpvD play a critical role in the effect of the AEC combination, deletion of these genes should be performed, and the mutant strains should be tested.

The deletion of cheA and spvD will be carried out in our next study.

(8) To demonstrate that the flagellum is no longer assembled, a transmission electron microscopy image using antibodies against flagellin should be performed, along with motility tests.

The motility assays have been performed and displayed as Figure supplement 5 in the revised supplement materials.

(9) Figure 7: In the X-axis legend, specify what "model" refers to.

The “model” refers to the PBS control group that mice were treated with PBS after the intraperitoneal injection of 100 µL bacterial solution (1.31 × 10⁵ CFU).

(10) Figure 8 Legend: In the legend of Figure 8 (line 717), are the authors referring to E. coli or Salmonella?

It referred to Salmonella, which has already been illustrated in the headline of Figure 8 in the revised manuscript.

(3) Materials and Methods:

(1) Bacterial Strains and Agents: It would be beneficial to include in the table the species of the strains used in the study, as well as the concentrations of colistin, artesunate, and EDTA utilized (lines 321 - 332).

We have ever tried to add the above information to Table 1, but the addition of this information would make the table too large and beyond the margins, which is not conducive to the layout design of the table, so we chose to display these information in the materials and methods section instead of the table.

(2) Antibacterial Activity In Vitro: Ensure clarity and well-defined ranges for the concentrations of colistin, EDTA, and artesunate used separately and in combinations (lines 335 - 344).

The drug concentrations have been listed in line 369 to 371.

(3) Time-Kill Assays: Clarify the criteria for selecting concentrations, whether based on MICs or peak and trough concentrations relevant to human and animal treatments with colistin (lines 345 - 351).

Although in vitro studies have determined that with increasing dose of AS and EDTA, the antibacterial synergistic activity was gradually enhanced, and meanwhie, may also resulting in more toxic side effects. Thus, in our study, the 1/8 MICs of AS and EDTA were selected to ensure excellent antibacterial activity whereas minimize the potential toxicity. The instructions on the selection of drug concentration have been added in line 323 to 326.

(4) General Corrections: Throughout the manuscript, correct typographical errors and consistently include the concentration values in mg/L alongside the MIC fractions. Specify the strains used for all experiments to ensure clarity. In the manuscript, the term "medication regimens" is used to describe the experimental setups involving different combinations of drugs tested in vitro. To improve accuracy and clarity, it is recommended to use the term "drug combination" instead. This term is more appropriate for in vitro experiments and will help avoid confusion with clinical treatment protocols.

The typographical errors have been checked and corrected throughout the manuscript, and the “medication regimens” have been replaced by “drug combinations”.

Reviewer #2 (Recommendations For The Authors):

Please see above for recommendations on what can be done to improve the manuscript.

While other omics analyses have been conducted herein, the authors do not comment on the genomic analysis of their own strains. It would have been a natural step to sequence all the strains used in the experiments.

Due to limited program funding, the genome sequencing and comparative analysis of these strains have not been analyzed. The antibacterial activities of different antimicrobial drugs against the S16 and S30 strains have been measured and listed in the Table supplement 9 within revised supplement materials.

Some minor comments:

(1) There are some spelling errors - e.g. "bacteria strains" instead of "bacterial strains".

The grammar and spelling errors have been corrected throughout the manuscript.

(2) I would avoid words like "unfortunately".

The word “unfortunately” has been changed.

(3) Some MIC-values in Table 1 seem incorrect - e.g. 24 mg/L. This is not a 2-log value - the value should be 32 mg/L if the dilution series has been carried out correctly.

We are so sorry for the mistake. The data has been corrected, and we also checked other data.

Reviewer #3 (Recommendations For The Authors):

Below are some suggestions.

(1) Sentences L47 & L48 "Infections with antibiotic-resistant pathogens, especially carbapenemase-producing Enterobacteriaceae, represent an impending catastrophe of a return to the pre-antibiotic era" - this is slightly exaggerated! I also wonder if we need to use Enterobacterales instead of Enterobacteriaceae.

The sentences in L47 & L48 have been changed. We googled the “carbapenemase-producing Enterobacteriaceae” and found it is a high-frequency word in numerous reports.

(2) L48. The drying up of the antibiotic discovery pipeline is NOT necessarily the reason to use colistin as a drug of last resort!

The sentence has been revised.

(3) The manuscript requires extensive English editing but has merit based on the strong compilation of data.

We have optimized and revised the writing of the whole article.

(4) I suggest the authors have some data on the cytotoxicity of AS alone, colistin alone, and both of them against eucaryotic cells (Caco-) and if possible determine IS (index selectivity). This additional experiment will strengthen the quality of the manuscript. The authors must also explain how to put such tri-therapy into practice.

The development of novel pharmaceutical dosage forms, pharmacokinetic, pharmacodynamic and safety analysis of the triple combination will be further conducted in our next study to provide a theoretical basis for the next clinical drug use. The discussion of potential toxicity of AS, colistin, EDTA and the triple combination have been added in line 318 to 337.

eLife Assessment

This valuable study addresses the growing threat of multi-drug-resistant (MDR) pathogens by focusing on the enhanced efficacy of colistin when combined with artesunate and EDTA against colistin-resistant Salmonella strains. The evidence is solid, supported by comprehensive microbiological assays, molecular analyses, and in vivo experiments demonstrating the effectiveness of this synergic combination.

Reviewer #1 (Public review):

Summary:

The study addresses the growing threat of multi-drug-resistant (MDR) pathogens, focusing on the efficacy of colistin (COL), a last-resort antibiotic, and its enhanced activity when combined with artesunate (AS) and ethylenediaminetetraacetic acid (EDTA) against colistin-resistant Salmonella strains. The researchers aim to explore whether these combinations can restore the effectiveness of colistin and understand the underlying mechanisms. The study used a combination of microbiological and molecular techniques to evaluate the antibacterial activity and mechanisms of action of COL, AS, and EDTA. Key methods included: (i) Antimicrobial Susceptibility Testing: Determining minimum inhibitory concentrations (MICs) of COL, AS, and EDTA, both alone and in combination, against various Salmonella strains; (ii) Time-Kill Assays: Measuring bacterial growth inhibition over time with different drug combinations; (iii) Fluorescent Probe-Permeability Assays: Assessing cell membrane integrity using fluorescent dyes; (iv) Proton Motive Force Assay: Evaluating the impact on the electrochemical proton gradient (PMF); (v) Reactive Oxygen Species (ROS) Measurement: Quantifying intracellular ROS levels; (vi) Scanning Electron Microscopy (SEM): Observing morphological changes in bacterial cells; and (vii) Omics Analysis: Transcriptome and metabolome profiling to identify differentially expressed genes (DEGs) and significant differential metabolites (SDMs). The combination of COL, AS, and EDTA (AEC) showed significant antibacterial activity against colistin-resistant Salmonella strains, reducing the MICs and enhancing bacterial killing compared to individual treatments. The AEC treatment caused extensive damage to both the outer and inner bacterial membranes, as evidenced by increased fluorescence of membrane-impermeant dyes and SEM images showing deformed cell membranes. AEC treatment selectively collapsed the Δψ component of PMF, indicating disruption of vital cellular processes. The combination therapy increased intracellular ROS levels, contributing to bacterial killing. Transcriptome data revealed changes in genes related to two-component systems, flagellar assembly, and ABC transporters. Metabolome analysis highlighted disruptions in pathways such as arachidonic acid metabolism. The findings suggest that AS and EDTA can potentiate the antibacterial effects of colistin by disrupting bacterial membranes, collapsing PMF, and increasing ROS levels. This combination therapy could serve as a promising approach to combat colistin-resistant Salmonella infections.

Strengths:

- The study employs a wide range of techniques to thoroughly investigate the antibacterial mechanisms and efficacy of the drug combinations.<br /> - The results are consistent across multiple assays and supported by both in vitro and in vivo data.<br /> - Combining AS and EDTA with COL represents a novel strategy to tackle antibiotic resistance.

Weaknesses:

- The methodology used for interpreting and reporting time-kill assay results.

Comments on revised version:

Overall, the authors have adequately addressed the suggestions provided.

Reviewer #2 (Public review):

The study by Zhai et al describes repurposing of artesunate, to be used in combination with EDTA to resensitize Salmonella spp. to colistin. The observed effect applied both to strains with and without mobile colistin resistance determinants (MCR). It is known since earlier that EDTA in combination with colistin has an inhibitory effect on MCR-enzymes, but at the same time both colistin and EDTA can contribute to nephrotoxicity, something which is also true for artesunate. Thus, the triple combination of three nephrotoxic agents has significant challenges in vivo, which is not particularly discussed in this paper.

The study is sound from a methodological point of view and has many interesting angles to address mechanistically how the three compounds can synergize.

Comments on revised version:

After having read the revised version, I have the following comments:

(1) The antimicrobials tested in Figure 9 are not really very relevant. I would want to see carbapenems and novel beta-lactam/beta-lactamase inhibitors rather than many old drugs with a debatable role in the treatment of Gram-negative infections. At least the authors should be able to test carbapenem resistance<br /> (2) The genomics analysis of the strains should be fairly quick - both in terms of characterizing the mobile resistome and the sequence types. There are publicly available databases for this purpose

The rest of my comments have been addressed in the revised version. There are still some remaining valid points from other reviewers that could be debatable whether they should be address. The authors refer to plans of studying these aspects in subsequent studies, but it could be discussed whether some of the data could be expected already in this study.

eLife Assessment

This study provides important insights into how IL-1 cytokines protect cells against SARS-CoV-2 infection. By inducing a non-canonical RhoA/ROCK signaling pathway, IL-1beta inhibits the ability of SARS-CoV-2 infected cells to fuse with uninfected cells and produce syncytia. Convincing evidence underlies the identification of the key signaling components required for this inhibitory phenotype, and suggests that this process may also function to inhibit SARS-CoV-2 infection in vivo.

Reviewer #1 (Public review):

Summary:

SARS-CoV-2 infection induces syncytia formation, which promotes viral transmission. In this paper, the authors aimed to understand how host-derived inflammatory cytokines IL-1α/β combat SARS-CoV-2 infection.

Strengths:

First, they used a cell-cell fusion assay developed previously to identify IL-1α/β as the cytokines that inhibit syncytia formation. They co-cultured cells expressing the spike protein and cells expressing ACE2 and found that IL-1β treatment decreased syncytia formation and S2 cleavage.

Second, they investigated the IL-1 signaling pathway in detail, using knockouts or pharmacological perturbation to understand the signaling proteins responsible for blocking cell fusion. They found that IL-1 prevents cell-cell fusion through MyD88/IRAK/TRAF6 but not TAK1/IKK/NF-κB, as only knocking out MyD88/IRAK/TRAF6 eliminates the inhibitory effect on cell-cell fusion in response to IL-1β. This revealed that the inhibition of cell fusion did not require a transcriptional response and was mediated by IL-1R proximal signaling effectors.

Third, the authors identified RhoA/ROCK activation by IL-1 as the basis for this inhibition of cell fusion. By visualizing a RhoA biosensor and actin, they found a redistribution of RhoA to the cell periphery and cell-cell junctions after IL-1 stimulation. This triggered the formation of actin bundles at cell-cell junctions, preventing fusion and syncytia formation. The authors confirmed this molecular mechanism by using constitutively active RhoA and an inhibitor of ROCK.<br /> Diverse Cell types and in vivo models were used, and consistent results were shown across diverse models. These results were convincing and well-presented.

In summary, the authors have provided compelling evidence regarding how IL-1 signaling induces a prophylactic response to viral infection. While the mechanistic details of how IL-1R and MyD88 induce RhoA/Rock pathway to mediate actin remodeling remain unclear, this manuscript serves as the basis for future studies.

Reviewer #2 (Public review):

Summary:

In this study, Zheng et al investigated the role of inflammatory cytokines in protecting cells against SARS-CoV-2 infection. They demonstrate that soluble factors in the supernatants of TLR stimulated THP1 cells reduce fusion events between HEK293 cells expressing SARS-CoV-2 S protein and the ACE2 receptor. Using qRT-PCR and ELISA, they demonstrate that IL-1 cytokines are (not surprisingly) upregulated by TLR treatment in THP1 cells. Further, they convincingly demonstrate that recombinant IL-1 cytokines are sufficient to reduce cell-to-cell fusion mediated by the S protein. Using chemical inhibitors and CRISPR knock-out of key IL-1 receptor signaling components in HEK293 cells, they demonstrate that components of the myddosome (MYD88, IRAK1/4, and TRAF6) are required for fusion inhibition, but that downstream canonical signaling (i.e., TAK1 and NFKB activation) is not required. Instead, they provide evidence that IL-1-dependent non-canonical activation of RhoA/Rock is important for this phenotype. Importantly, the authors demonstrate that expression of a constitutively active RhoA alone is sufficient to inhibit fusion and that chemical inhibition of Rock could reverse this inhibition. The authors followed up these in vitro experiments by examining the effects of IL-1 on SARS-COV-2 infection in vivo and they demonstrate that recombinant IL-1 can reduce viral burden and lung pathogenesis in a mouse model of infection. Use of a ROCK inhibitor in IL-1 treated mice restored the ability of SARS-CoV-2 to spread in the lung, suggesting that this inhibitory process functions in vivo.

Strengths:

(1) The bioluminescence cell-cell fusion assay provides a robust quantitative method to examine cytokine effects on viral glycoprotein-mediated fusion.

(2) The study identifies a new mechanism by which IL-1 cytokines can limit virus infection.

(3) The authors tested IL-1 mediated inhibition of fusion induced by many different coronavirus S proteins and several SARS-CoV-2 strains.

(4) The authors demonstrate that recombinant IL-1 mediated inhibition of SARS-CoV-2 infection in mice is dependent on the RhoA/Rock pathway.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

SARS-CoV-2 infection induces syncytia formation, which promotes viral transmission. In this paper, the authors aimed to understand how host-derived inflammatory cytokines IL-1α/β combat SARS-CoV-2 infection.

Strengths:

First, they used a cell-cell fusion assay developed previously to identify IL-1α/β as the cytokines that inhibit syncytia formation. They co-cultured cells expressing the spike protein and cells expressing ACE2 and found that IL-1β treatment decreased syncytia formation and S2' cleavage.

Second, they investigated the IL-1 signaling pathway in detail, using knockouts or pharmacological perturbation to understand the signaling proteins responsible for blocking cell fusion. They found that IL-1 prevents cell-cell fusion through MyD88/IRAK/TRAF6 but not TAK1/IKK/NF-κB, as only knocking out MyD88/IRAK/TRAF6 eliminates the inhibitory effect on cell-cell fusion in response to IL-1β. This revealed that the inhibition of cell fusion did not require a transcriptional response and was mediated by IL-1R proximal signaling effectors.

Third, the authors identified RhoA/ROCK activation by IL-1 as the basis for this inhibition of cell fusion. By visualizing a RhoA biosensor and actin, they found a redistribution of RhoA to the cell periphery and cell-cell junctions after IL-1 stimulation. This triggered the formation of actin bundles at cell-cell junctions, preventing fusion and syncytia formation. The authors confirmed this molecular mechanism by using constitutively active RhoA and an inhibitor of ROCK.

Diverse Cell types and in vivo models were used, and consistent results were shown across diverse models. These results were convincing and well-presented.

Weaknesses:

As the authors point out in the discussion, whether IL-1-mediated RhoA activation is specific to viral infection or regulates other RhoA-regulated processes is unclear. We would also require high-magnification images of the subcellular organization of the cytoskeleton to appreciate the effect of IL-1 stimulation.

Thanks for the suggestions. We tested the role of IL-1β in other RhoA-regulated processes, and found that IL-1β-mediated RhoA activation also reduced cell migration in a cell scratch assay (see Author response image 1). We also provided high-magnification images in the revised Figures 4 and 5, as well as their respective figure supplements.

Author response image 1.

(A) Cell scratch assay images of HEK293T cells treated with PBS or IL-1β. (B) Quantification of cell migration in (A).

Reviewer #2 (Public Review):

Summary:

In this study, Zheng et al investigated the role of inflammatory cytokines in protecting cells against SARS-CoV-2 infection. They demonstrate that soluble factors in the supernatants of TLR-stimulated THP1 cells reduce fusion events between HEK293 cells expressing SARS-CoV-2 S protein and the ACE2 receptor. Using qRT-PCR and ELISA, they demonstrate that IL-1 cytokines are (not surprisingly) upregulated by TLR treatment in THP1 cells. Further, they convincingly demonstrate that recombinant IL-1 cytokines are sufficient to reduce cell-to-cell fusion mediated by the S protein. Using chemical inhibitors and CRISPR knock-out of key IL-1 receptor signaling components in HEK293 cells, they demonstrate that components of the myddosome (MYD88, IRAK1/4, and TRAF6) are required for fusion inhibition, but that downstream canonical signaling (i.e., TAK1 and NFKB activation) is not required. Instead, they provide evidence that IL-1-dependent non-canonical activation of RhoA/Rock is important for this phenotype. Importantly, the authors demonstrate that expression of a constitutively active RhoA alone is sufficient to inhibit fusion and that chemical inhibition of Rock could reverse this inhibition. The authors followed up these in vitro experiments by examining the effects of IL-1 on SARS-COV-2 infection in vivo and they demonstrate that recombinant IL-1 can reduce viral burden and lung pathogenesis in a mouse model of infection. However, the contribution of the RhoA/Rock pathway and inhibition of fusion to IL-1-mediated control of SARS-CoV-2 infection in vivo remains unclear.

Strengths:

(1) The bioluminescence cell-cell fusion assay provides a robust quantitative method to examine cytokine effects on viral glycoprotein-mediated fusion.

(2) The study identifies a new mechanism by which IL-1 cytokines can limit virus infection.

(3) The authors tested IL-1 mediated inhibition of fusion induced by many different coronavirus S proteins and several SARS-CoV-2 strains.

Weaknesses:

(1) The qualitative assay demonstrating S2 cleavage and IL-1 mediated inhibition of this phenotype is extremely variable across the data figures. Sometimes it appears like S2 cleavage (S2') is reduced, while in other figures immunoblots show that total S2 protein is decreased. Based on the proposed model the expectation would be that S2 abundance would be rescued when cleavage is inhibited.

In our present manuscript, IL-1-mediated changes of the full-length spike showed some variation between authentic SARS-CoV-2 infection model and HEK293T-S + HEK293T-ACE2 coculture model, while IL-1 inhibited S2’ cleavage accompanied by a reduction of S2 subunit in both models.

In the authentic SARS-CoV-2 infection model, we observed that IL-1 inhibited S2' cleavage accompanied with a reduction in both S2 subunit and full-length spike protein. This is likely because the S2 subunit and full-length spike protein in this model are not only from infected cells, but also from intracellular viral particles. IL-1 inhibited SARS-CoV-2 induced cell-cell fusion and reduced the viral load in host cells, therefore the abundance of S2 subunit and full-length spike proteins were both reduced.

In the HEK293T-based co-culture model, IL-1 inhibited S2' cleavage accompanied with a reduction in S2 subunit, while the full-length spike protein was more or less rescued. Based on our previous study, R685A and ΔRRAR spike mutants cannot generate the S2 subunit, but still generated S2′ fragment to induce cell-cell fusion, and the S2' fragment produced from R685A and ΔRRAR spike mutants were only slightly reduced compared to wild-type spike protein, suggesting that the S2' fragment is mainly derived from the full-length spike directly, and to a minimal extent from the S2 subunit (Fig. 4B and 4G, PMID: 34930824). Thus, inhibition of S2’ cleavage by IL-1 mainly rescued the full-length spike protein.

(2) The text referencing Figure 1H suggests that TLR-stimulated THP-1 cell supernatants "significantly" reduce syncytia, but image quantification and statistics are not provided to support this statement.

Thanks for pointing out this issue. We have provided fluorescence image quantification and statistics in the revised version of our manuscript (Figure 1D, Figure 1-figure supplement 1A, Figure 1H-1I, Figure 2H-2I, Figure 1-figure supplement 1D-1E, Figure 1-figure supplement 1H-1I, Figure 2-figure supplement 1C-1D, Figure 2-figure supplement 2B-2E, Figure 2-figure supplement 2G-2H, Figure 2-figure supplement 6A-6B, Figure 2-figure supplement 7F-7G).

(3) The authors conclude that because IL-1 accumulates in TLR2-stimulated THP1 monocyte supernatants, this cytokine accounts for the ability of these supernatants to inhibit cell-cell fusion. However, they do not directly test whether IL-1 is required for the phenotype. Inhibition of the IL-1 receptor in supernatant-treated cells would help support their conclusion.

Thanks for the suggestion. Accordingly, we performed experiment and found that IL-1RA treatment reduced the inhibitory effect of PGN-stimulated THP-1 cell culture supernatant on cell-cell fusion, suggesting that IL-1 is required for the inhibition. This result has been added in our revised manuscript (Figure 2J and Figure2-figure supplement 4C).

(4) Immunoblot analysis of IL-1 treated HEK293 cells suggests that this cytokine does not reduce the abundance of ACE2 or total S protein in cells. However, it is possible that IL-1 signaling reduces the abundance of these proteins on the cell surface, which would result in a similar inhibition of cell-cell fusion. The authors should confirm that IL-1 treatment of their cells does not change Ace2 or S protein on the cell surface.

Thanks for the suggestion. Accordingly, we applied Wheat Germ Agglutinin (WGA) to stain cell surface in HKE293T cells and observed that IL-1β treatment did not change ACE2 or Spike protein on the cell surface. This result has been added in our revised manuscript (Figure 5-figure supplement 3A-D).

(5) In Figure 5A, expression of constitutively active RhoA appears to have profound effects on how ACE2 runs by SDS-PAGE, suggesting that RhoA may have additional effects on ACE2 biology that might account for the decreased cell-cell fusion. This phenotype should be addressed in the text and explored in more detail.

Thanks for pointing out this. We also noticed that the occurrence of cell-cell fusion reduced the amount of ACE2, whereas inhibition of cell-cell fusion restored the ACE2 abundance. Take the original Figure 5A (revised Figure 4-figure supplement 2B) as example, the increased ACE2 protein should be attributed to the decreased cell-cell fusion upon RhoA-CA transfection, as Spike binding with ACE2 leads to clathrin- and AP2-dependent endocytosis, resulting in ACE2 degradation in the lysosome (PMID: 36287912).

In addition, we have examined the potential effect of RhoA-CA on ACE2, and found that RhoA-CA did not affect ACE2 expression, nor Spike binding to ACE2 (revised Figure 5-figure supplement 2E); it did not affect ACE2 distribution on cell surface either (revised Figure 5-figure supplement 2F and G).

(6) The experiments linking IL-1 mediated restriction of SARS-COV-2 fusion to the control of virus infection in vivo are incomplete. The reported data demonstrate that recombinant IL-1 can restrict virus replication in vivo, but they fall short of confirming that the in vitro mechanism described (reduced fusion) contributes to the control of SARS-CoV2 replication in vivo. A critical piece of data that is missing is the demonstration that the ROCK inhibitor phenocopies IL-1RA treatment of SARS-COV-2 infected mice (viral infection and pathology).

Thanks for this suggestion. Accordingly, we applied the ROCK inhibitor in vivo to confirm its role in SARS-CoV-2-infected mice, and found similar phenotype as the IL-1RA treatment experiment. That is to say, Y-26732 treatment prevented the formation of IL-1β-induced actin bundles at cell-cell junctions, thus promoted syncytia formation and further viral transmission in vivo (revised Figure 7).

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

I suggest providing single-channel images in a supplementary figure for the live-cell images in Figures 4 and 5. Higher magnification images would also help distinguish the subcellular details of the cytoskeleton organization.

Thanks for the suggestion. We have provided the single channel images and higher magnification images in the revised Figures 4 and 5, as well as their respective figure supplements.

In Figure 4, the authors showed that IL-1 activates RhoA and induces the accumulation of activated RhoA at the cell-cell junctions. They also showed that IL-1 promotes the formation of actin bundles at cell-cell junctions. However, the authors have not shown any connection between RhoA and actin yet, but in lines 263-264, they claim that actin bundle formation is induced by RhoA. Evidence for this part was shown in later results, but at this moment, it is lacking. The same applies to lines 282-284; I think this conclusion that IL-1-induced actin bundle formation is through the RhoA-ROCK pathway should come after showing how RhoA affects actin bundle formation at cell-cell junctions. To this end, I suggest moving Supplementary Figures S12B and S12D to the main figure, as they provide strong evidence of the IL-1-RhoA-ROCK-actin pathway.

We appreciate these valuable comments. As suggested, we have moved the respective supplementary figures to the main figures to support our findings in the revised manuscript (Figure 4E and Figure 4-figure supplement 2B; Figure 5C and Figure 5-figure supplement 2A), the text has also been adjusted accordingly.

eLife Assessment

This fundamental study demonstrates the role of TRPV4 channels in mechano-transduction during carcinoma progression, showing how high cell confluence in DCIS cells activates pathways regulating calcium homeostasis, cell volume reduction, and invasiveness. The authors addressed all prior concerns, with the shRNA knockdown providing compelling evidence for TRPV4's role in metastasis.

Reviewer #1 (Public review):

Summary:

In this study, Bu et al examined the dynamics of TRPV4 channel in cell overcrowding in carcinoma conditions. They investigated how cell crowding (or high cell confluence) triggers a mechano-transduction pathway involving TRPV4 channels in high-grade ductal carcinoma in situ (DCIS) cells that leads to large cell volume reduction (or cell volume plasticity) and pro-invasive phenotype.

In vitro, this pathway is highly selective for highly malignant invasive cell lines derived from a normal breast epithelial cell line (MCF10CA) compared to the parent cell line, but not present in another triple-negative invasive breast epithelial cell line (MDA-MB-231). The authors convincingly showed that enhanced TRPV4 plasmamembrane localization correlates with high-grade DCIS cells in patient tissue samples. Specifically in invasive MCF10DCIS.com cells they showed that overcrowding or over-confluence leads to a decrease in cell volume and intracellular calcium levels. This condition also triggers the trafficking of TRPV4 channels from intracellular stores (nucleus and potentially endosomes), to the plasma membrane (PM). When these over-confluent cells are incubated with a TRPV4 activator, there is an acute and substantial influx of calcium, attesting the fact that there are high number of TRPV4 channels present on the PM. Long-term incubation of these over-confluent cells with the TRPV4 activator results in the internalization of the PM-localized TRPV4 channels.

In contrast, cells plated at lower confluence primarily have TRPV4 channels localized in the nucleus and cytosol. Long-term incubation of these cells at lower confluence with a TRPV4 inhibitor leads to the relocation of TRPV4 channels to the plasma membrane from intracellular stores and a subsequent reduction in cell volume. Similarly, incubation of these cells at low confluence with PEG 3000 (a hyperosmotic agent) promotes the trafficking of TRPV4 channels from intracellular stores to the plasma membrane.

Strengths:

The study is elegantly designed and the findings are novel. Their findings on this mechano-transduction pathway involving TRPV4 channels, calcium homeostasis, cell volume plasticity, motility and invasiveness will have a great impact in the cancer field and potentially applicable to other fields as well. Experiments are well-planned and executed, and the data is convincing. Authors investigated TRVP4 dynamics using multiple different strategies- overcrowding, hyperosmotic stress, pharmacological and genetic means, and showed a good correlation between different phenomena.

All of my previous concerns have been addressed. The quality of the manuscript has improved significantly.

Reviewer #2 (Public review):

Summary:

The metastasis poses a significant challenge in cancer treatment. During the transition from non-invasive cells to invasive metastasis cells, cancer cells usually experience mechanical stress due to a crowded cellular environment. The molecular mechanisms underlying mechanical signaling during this transition remain largely elusive. In this work, the authors utilize an in vitro cell culture system and advanced imaging techniques to investigate how non-invasive and invasive cells respond to cell crowding, respectively.

The results clearly show that pre-malignant cells exhibit a more pronounced reduction in cell volume and are more prone to spreading compared to non-invasive cells. Furthermore, the study identifies that TRPV4, a calcium channel, relocates to the plasma membrane both in vitro and in vivo (patient's samples). Activation and inhibition of TRPV4 channel can modulate the cell volume and cell mobility. These results unveil a novel mechanism of mechanical sensing in cancer cells, potentially offering new avenues for therapeutic intervention targeting cancer metastasis by modulating TRPV4 activity. This is a very comprehensive study, and the data presented in the paper are clear and convincing. The study represents a very important advance in our understanding of the mechanical biology of cancer.

Author response:

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

Reviewer #1 (Public review):

Summary:

In this study, Bu et al examined the dynamics of TRPV4 channel in cell overcrowding in carcinoma conditions. They investigated how cell crowding (or high cell confluence) triggers a mechano-transduction pathway involving TRPV4 channels in high-grade ductal carcinoma in situ (DCIS) cells that leads to large cell volume reduction (or cell volume plasticity) and proinvasive phenotype.

In vitro, this pathway is highly selective for highly malignant invasive cell lines derived from a normal breast epithelial cell line (MCF10CA) compared to the parent cell line, but not present in another triple-negative invasive breast epithelial cell line (MDA-MB-231). The authors convincingly showed that enhanced TRPV4 plasma membrane localization correlates with highgrade DCIS cells in patient tissue samples.

Specifically in invasive MCF10DCIS.com cells, they showed that overcrowding or overconfluence leads to a decrease in cell volume and intracellular calcium levels. This condition also triggers the trafficking of TRPV4 channels from intracellular stores (nucleus and potentially endosomes), to the plasma membrane (PM). When these over-confluent cells are incubated with a TRPV4 activator, there is an acute and substantial influx of calcium, attesting to the fact that there are a high number of TRPV4 channels present on the PM. Long-term incubation of these over-confluent cells with the TRPV4 activator results in the internalization of the PMlocalized TRPV4 channels.

In contrast, cells plated at lower confluence primarily have TRPV4 channels localized in the nucleus and cytosol. Long-term incubation of these cells at lower confluence with a TRPV4 inhibitor leads to the relocation of TRPV4 channels to the plasma membrane from intracellular stores and a subsequent reduction in cell volume. Similarly, incubation of these cells at low confluence with PEG 3000 (a hyperosmotic agent) promotes the trafficking of TRPV4 channels from intracellular stores to the plasma membrane.

Strengths:

The study is elegantly designed and the findings are novel. Their findings on this mechanotransduction pathway involving TRPV4 channels, calcium homeostasis, cell volume plasticity, motility, and invasiveness will have a great impact in the cancer field and are potentially applicable to other fields as well. Experiments are well-planned and executed, and the data is convincing. The authors investigated TRVP4 dynamics using multiple different strategies- overcrowding, hyperosmotic stress, and pharmacological means, and showed a good correlation between different phenomena.

Weaknesses:

A major emphasis in the study is on pharmacological means to relate TRPV4 channel function to the phenotype. I believe the use of genetic means would greatly enhance the impact and provide compelling proof for the involvement of TRPV4 channels in the associated phenotype.

In this regard, I wonder if siRNA-mediated knockdown of TRPV4 in over-confluent cells (or knockout) would lead to an increase in cell volume and normalize the intracellular calcium levels back to normal, thus ultimately leading to a decrease in cell invasiveness.

We greatly appreciate the positive feedback regarding the design of our study and the novelty of our findings. We also acknowledge the valuable suggestion to complement our pharmacological approaches with genetic manipulation of TRPV4.

In response to the comment regarding siRNA-mediated knockdown or knockout of TRPV4, we fully agree that this would further substantiate our findings. In the revised manuscript, we implemented shRNA targeting TRPV4 to investigate its functional effects on intracellular calcium level changes, cell volume plasticity, and invasiveness phenotypes, assessed through singlecell motility assays under cell crowding or hyperosmotic stress. These results have been incorporated into the revised manuscript, and detailed descriptions of these findings are included below.

Using the shRNA approach that resulted in ~50% reduction of TRPV4 expression

(Supplementary Figure 6A and 6B show TRPV4 expression levels via IF and immunoblots, respectively), we examined the effect of reduced TRPV4 on intracellular calcium levels in MCF10DCIS.com cells under normal density (ND) and stress conditions (confluent; Con and hyperosmotic; PEG) using Fluo-4 AM imaging (Fig. 4S-X). We found that shRNA TRPV4 slightly decreased calcium levels in ND cells, likely due to fewer active calcium channels at the plasma membrane resulting from lower TRPV4 expression (as shown in the summary plot in Fig. 4W). With fewer active calcium channels, cells treated with shRNA TRPV4 exhibited less reduction in intracellular calcium levels under cell crowding conditions compared to control cells. Additionally, hyperosmotic stress using PEG 300 induced smaller calcium spikes in shRNA cells compared to the significant spike observed in control cells. This reduced calcium response to Con and hyperosmotic stress in shRNA cells was reflected in the decreased cell volume reduction by PEG 300 shown in Fig. 4Y. Consequently, shRNA-mediated TRPV4 reduction impaired cell volume plasticity in MCF10DCIS.com cells and abolished the pro-invasive mechanotransduction capability involving cell volume reduction, as evidenced by no increase in cell motility (both cell diffusivity and directionality) under hyperosmotic conditions (Fig. 5H-J). These findings demonstrate the critical role of TRPV4 in conferring pro-invasive

mechanotransduction capability to MCF10DCIS.com cells through cell volume reduction.

Reviewer #2 (Public review):

Summary:

The metastasis poses a significant challenge in cancer treatment. During the transition from non-invasive cells to invasive metastasis cells, cancer cells usually experience mechanical stress due to a crowded cellular environment. The molecular mechanisms underlying mechanical signaling during this transition remain largely elusive. In this work, the authors utilize an in vitro cell culture system and advanced imaging techniques to investigate how non-invasive and invasive cells respond to cell crowding, respectively.

Strengths:

The results clearly show that pre-malignant cells exhibit a more pronounced reduction in cell volume and are more prone to spreading compared to non-invasive cells. Furthermore, the study identifies that TRPV4, a calcium channel, relocates to the plasma membrane both in vitro and in vivo (patient samples). Activation and inhibition of the TRPV4 channel can modulate the cell volume and cell mobility. These results unveil a novel mechanism of mechanical sensing in cancer cells, potentially offering new avenues for therapeutic intervention targeting cancer metastasis by modulating TRPV4 activity. This is a very comprehensive study, and the data presented in the paper are clear and convincing. The study represents a very important advance in our understanding of the mechanical biology of cancer.

Weaknesses:

However, I do think that there are several additional experiments that could strengthen the conclusions of this work. A critical limitation is the absence of genetic ablation of the TRPV4 gene to confirm its essential role in the response to cell crowding.

We are deeply grateful for the positive assessment of our study and its contribution to advancing our understanding of mechanical signaling in cancer progression. We also greatly appreciate the suggestion to incorporate genetic ablation experiments to further validate the role of TRPV4 in cell crowding responses.

As noted in our response to Reviewer #1, we employed an shRNA approach to investigate the functional effects of TRPV4 knockdown on intracellular calcium level changes, cell volume plasticity, and invasiveness phenotypes. We assessed these effects using Fluo-4 AM calcium assay, single-cell volume measurements, and single-cell motility assays under cell crowding or hyperosmotic stress. These results have been incorporated into the revised manuscript and are described in detail in our response to Reviewer #1's "weaknesses" comment.

Reducing TRPV4 expression levels by shRNA diminished mechanosensing intracellular calcium changes under cell crowding and hyperosmotic conditions using PEG 300 treatment. Furthermore, a significantly reduced cell volume plasticity was observed under hyperosmotic conditions in shRNA treated cells compared to control cells (Fig. 4S-X). This diminished mechanosensing capability abolished the pro-invasive mechanotransduction effect, as assessed by single cell motility under hyperosmotic conditions (Fig. 5H-J). These findings demonstrate the critical role of TRPV4 in conferring pro-invasive mechanotransduction capability to MCF10DCIS.com cells through cell volume reduction.

Reviewer #1 (Recommendations for the authors):

The way the results or discussion section is written. It was a little confusing for me to relate to some phenomena. For example, it is not clear how TRPV4 inhibition (due to overcrowding) leads to a decrease in intercellular calcium levels, especially when TRPV4 channels were intercellular (not on the PM) to begin with (in normal density (ND) conditions). Along the same lines, how GSK219 causes a dip in calcium levels in ND cells when TRPV4 channels are primarily intercellular (Figure 4E). If most of the TRPV4 channels that are translocated to the PM in response to cell crowding are in an inactive state, how do they confer enhanced cell volume plasticity relative to non-invasive cell lines?

Thank you very much for raising this important point. We fully agree with your concern and have significantly revised the manuscript to clarify this aspect. Specifically, we have emphasized that a modest level of TRPV4 channels are constitutively active at the plasma membrane in normal density (ND) cells. This is now discussed in detail in the context of Fig. 4:

Page 14: “Considering these factors, we hypothesized that cell crowding might inhibit calcium-permeant ion channels that are constitutively active at the plasma membrane, including TRPV4, which would then lower intracellular calcium levels and subsequently reduce cell volume via osmotic water movement.”

Page 16-17: “… However, the temporal profile of Fluo-4 intensity in Fig. 4E, which corresponds to the time points marked in Fig. 4D (t₁: baseline and t₂: dip), clearly shows the dip at t₂, indicated by ΔCa (the vertical dashed line between the dip and baseline). This modest Fluo-4 dip at t₂ represents the inhibition of activity by GSK219 on a small population of constitutively active TRPV4 channels at the plasma membrane under ND conditions.

In Con cells, 1 nM GSK219 caused a smaller dip in Fluo-4 intensity compared to the one observed in ND cells, with no subsequent changes. This is likely due to fewer constitutively active TRPV4 at the plasma membrane in Con cells than in ND cells. …These findings suggest that a substantial portion of TRPV4 channels relocated to the plasma membrane under cell crowding was inactive, and some constitutively active TRPV4 channels already present in the membrane became inactive as a result of cell crowding.”

'Internalization' might be a better word than 'uptake' in the following line in the results section

"...activating TRPV4 under cell crowding conditions triggered channel uptake, indicating that TRPV4 trafficking depended on the channel's activation status."

Thank you very much for this suggestion. As recommended, we replaced ‘uptake’ with internalization’ on page 18: 

“However, in Con cells, where a large number of inactive TRPV4 channels are likely located at the plasma membrane, GSK101 treatment notably reduced plasma membrane-associated TRPV4 in a dose-dependent manner through internalization (Fig. 4O, 4Q), consistent with previous findings65. These data suggest that plasma membrane TRPV4 levels were largely

regulated by the channel activity status. Specifically, channel activation led to the internalization of TRPV4, while channel inhibition promoted the relocation of TRPV4 to the plasma membrane.”

1. Out of curiosity:

2. Is there any information on what the intercellular TRPV4 channels are doing in the cytosol and in the nucleus? Is there any role of intercellular calcium stores in the proposed pathway?

We greatly appreciate this insightful question. Although we were unable to find studies specifically exploring the roles of cytosolic TRPV4, a recent study (Reference 74) identified a role for nuclear TRPV4 in regulating calcium within the nucleus. We speculate that when TRPV4 activity is severely impaired, such as with additional TRPV4 inhibition under cell crowding conditions, some TRPV4 channels may be redirected to the nucleus. This redistribution could help maintain nuclear calcium homeostasis.

This discussion is included on page 18 of the manuscript:

“These findings suggest that further TRPV4 inhibition under crowding conditions triggers a distinct trafficking alteration. Recent studies have implicated nuclear TRPV4 in regulating nuclear Ca2+ homeostasis and Ca2+-regulated transcription74. In light of this study and our findings, TRPV4 may relocate to the nucleus as a compensatory mechanism to maintain nuclear calcium regulation. This relocation could reflect an adaptive response to preserve calcium-dependent transcriptional programs or other nuclear processes essential for cell survival under mechanical stress.”

One recommendation is to add some explanation or some minor details for the convenience of the reader. For example:

At normal or lower confluence, cells show an acute large dip in intercellular calcium when an inhibitor is applied implying that there are a few TRPV4 channels on the PM and they are constitutively active.

Thank you very much for highlighting this important point and for the helpful suggestion to improve clarity. We have significantly revised the text associated with Fig. 4 to ensure this point is clear. Specifically, we have added the following explanation on page 16:

"This modest Fluo-4 dip at t2 represents the inhibition of activity by GSK219 on a small population of constitutively active TRPV4 channels at the plasma membrane under ND conditions."

Reviewer #2 (Recommendations for the authors):

(1) Figure 1. The authors frequently change the medium to prevent acidification in overconfluent cultures. A cell viability assay should be performed to ensure that the over-confluent cells remain healthy and viable during the experiments. There are commercial kits that can be easily used to quantify the number of viable cells and the extent of cell toxicity. The number of viable cells would provide a more reliable basis for comparison between normal density and overconfluent conditions.

Thank you very much for raising this important point. We have consistently observed that cell crowding does not induce significant cell death in MCF10DCIS.com cells. To address your recommendation, we performed a viability assay using propidium iodide (PI) to selectively stain dead cells and WGA-488 to stain all live cells. Cell death was quantified under normal density (ND) conditions and at 1, 3, 5, 7, and 10 days post-confluence.

Our results indicate that cells remain similarly viable post-confluence, with minimal cell death

(~1.5%) compared to ND cells (~0.75%). These findings are summarized in Supplementary Figure 2, demonstrating that over-confluent cultures remain healthy and viable during the experiments.

(2) Figure 2. To determine whether the reduction in cell volume is reversible, over-confluent cells can be further diluted back to normal density. Additionally, the reversibility of TRPV4 channel trafficking to the plasma membrane should be assessed under these conditions in IF experiments and cell surface biotinylation.

Thank you for this suggestion. We reseeded the previously overcrowded (OC) cells at normal density and observed that their TRPV4 distribution predominantly returned to being intracellular, with only modest plasma membrane localization, as shown by line analysis (Supplementary Figure 10A-C, page 13). Furthermore, their invasiveness decreased to levels comparable to the original normal density (ND) cells (Supplementary Figure 3C and 3E, page 6). These results demonstrate the reversibility of TRPV4 trafficking changes and the increase in invasiveness under mechanical stress.

Page 6. "The enhanced invasiveness of MCF10DCIS.com cells under cell crowding was largely reversible. When OC cells were reseeded at normal density for invasion assays, their invasive cell fraction decreased to approximately 15%, slightly lower (p = 0.012) than the initial value of around 24% (Suppl. Fig. 3C, 3E)."

Page 13. “We investigated whether TRPV4 relocation to the plasma membrane induced by cell crowding is reversible, as suggested by its impact on invasiveness (Suppl. Fig. 3E). To test this, previously OC MCF10DCIS.com cells were reseeded under ND conditions. We then assessed TRPV4 localization via immunofluorescence (IF) imaging to determine if most channels returned to the cytoplasm and could be relocated to the plasma membrane under mechanical stress, such as hyperosmotic conditions. Consistent with their initial ND state, reseeded ND MCF10DCIS.com cells displayed intracellular TRPV4 distribution (Suppl. Fig. 10A). Upon exposure to hyperosmotic stress (74.4 mOsm/Kg PEG300), TRPV4 was again relocated to the plasma membrane (Suppl. Fig. 10B). These findings, quantified through line analysis (Suppl. Fig. 10C), demonstrate that the mechanosensing response of MCF10DCIS.com cells is reversible.”

(3) Figure 3B. A control using intracellular proteins such as GAPDH or Tubulin is missing. Including this control would help exclude the possibility of cell rupture or compromised cell membranes in crowded environments, which is very common in a cell crowding environment.

Thank you very much for pointing this out. The control lanes (GAPDH) were already included in the full gel results shown in Supplementary Figure 5. For the immunoprecipitation and immunoblotting of surface-biotinylated cell lysates, we did not expect to detect GAPDH; however, some GAPDH signals were still observed. As shown for MCF10DCIS.com cells, less GAPDH was detected under OC conditions, but the immunoprecipitated samples displayed significantly higher levels of TRPV4 on the cell surface compared to ND cells (Supplementary Figure 5A). For the whole cell lysates, TRPV4 protein levels were comparable across different cell lines based on the immunoblot results, with consistent GAPDH signals serving as a loading control (Supplementary Figure 5B).

(4) Figure 4. To convincingly demonstrate TRPV4 relocation to the plasma membrane, IF should be performed under non-permeable conditions (i.e., without detergents like saponin). This approach ensures that only plasma membrane proteins are accessible to antibodies, reducing intracellular background. The same approach should be applied to Piezo1 and TfR.

Thank you for this suggestion. We observed that under non-permeable conditions, primary antibodies could still access intracellular proteins. To address this issue, we employed extracellular-binding TRPV4 antibodies to selectively detect TRPV4 relocation to the plasma membrane under hyperosmotic conditions (74.4 mOsm/kg PEG 300) in live MCF10DCIS.com cells, as shown in Supplementary Figure 9. These results clearly demonstrate the plasma membrane relocation of TRPV4 under hyperosmotic conditions, distinguishing it from control conditions. Unfortunately, we were unable to identify high-affinity extracellular-binding antibodies for Piezo1 and TfR. Nevertheless, our findings strongly support the mechanosensing plasma membrane relocation of TRPV4.

Essential Weakness:

Throughout the study, only TRPV4 inhibitors and activators were used to show that TRPV4 relocation is associated with intracellular calcium concentration and cell size changes. It is crucial to use TRPV4 KO or KD cells to confirm that the observed effects are specific to TRPV4 and not due to off-target effects on other proteins. Additionally, fusing a plasma membrane targeting sequence to TRPV4 to make a constitutive plasma membrane-localized construct could demonstrate the opposite effect.

Thank you very much for this important comment. As noted in our response to Reviewer #1, we employed an shRNA approach to investigate the functional effects of TRPV4 knockdown on intracellular calcium level changes, cell volume plasticity, and invasiveness phenotypes. We assessed these effects using Fluo-4 AM calcium assay, single-cell volume measurements, and single-cell motility assays under cell crowding or hyperosmotic stress. These results have been incorporated into the revised manuscript and are described in detail in our response to Reviewer #1's "weaknesses" comment.

Reducing TRPV4 expression levels by shRNA diminished mechanosensing intracellular calcium changes under cell crowding and hyperosmotic conditions using PEG 300 treatment. Furthermore, a significantly reduced cell volume plasticity was observed under hyperosmotic conditions in shRNA treated cells compared to control cells (Fig. 4S-X). This diminished mechanosensing capability abolished the pro-invasive mechanotransduction effect, as assessed by single cell motility under hyperosmotic conditions (Fig. 5H-J). These findings demonstrate the critical role of TRPV4 in conferring pro-invasive mechanotransduction capability to MCF10DCIS.com cells through cell volume reduction.

Minor Points:

The introduction section is poorly written; many results currently included in the introduction would be more appropriately placed in the discussion section. The long redundant introduction makes the article hard to read through.

Thank you very much for pointing this out. In the revised introduction, we have significantly reduced references to the results, streamlining the section to make it more concise and focused. This adjustment ensures the introduction is clearer and avoids redundancy, improving the readability of the manuscript.

eLife Assessment

In an important fMRI study with an elegant experimental design and rigorous cross-decoding analyses, this work shows a convincing dissociation between two parietal regions in visually processing actions. Specifically, aIPL is found to be sensitive to the causal effects of observed actions, while SPL is sensitive to the patterns of body motion involved in those actions. The work will be of broad interest to cognitive neuroscientists, particularly vision and action researchers.

Reviewer #1 (Public review):

Summary:

The authors report a study aimed at understanding the brain's representations of viewed actions, with a particular aim to distinguish regions that encode observed body movements, from those that encode the effects of actions on objects. They adopt a cross-decoding multivariate fMRI approach, scanning adult observers who viewed full-cue actions, pantomimes of those actions, minimal skeletal depictions of those actions, and abstract animations that captured analogous effects to those actions. Decoding across different pairs of these action conditions allowed the authors to pull out the contributions of different action features in a given region's representation. The main hypothesis, which was largely confirmed, was that the superior parietal lobe (SPL) more strongly encodes movements of the body, whereas the anterior inferior parietal lobe (aIPL) codes for action effects of outcomes. Specifically, region of interest analyses showed dissociations in the successful cross-decoding of action category across full-cue and skeletal or abstract depictions. Their analyses also highlight the importance of the lateral occipito-temporal cortex (LOTC) in coding action effects. They also find some preliminary evidence about the organisation of action kinds in the regions examined, and take some steps to distinguishing the differences and similarities of action-evoked patterns in primary visual cortex and the other examined regions.

Strengths:

The paper is well-written, and it addresses a topic of emerging interest where social vision and intuitive physics intersect. The use of cross-decoding to examine actions and their effects across four different stimulus formats is a strength of the study. Likewise the a priori identification of regions of interest (supplemented by additional full-brain analyses) is a strength. Finally, the authors successfully deployed a representational-similarity approach that provides more detailed evidence about the different kinds of action features that seem to be captured in each of the regions that were examined.

Weaknesses:

Globally, the findings provide support for the predicted anatomical distinctions, and for the distinction between body-focused representations of actions and more abstract "action effect structures". Viewed more narrowly, the picture is rather complex, and the patterns of (dis)similarity in the activity evoked by different action kinds do not always divide neatly. Probably, examining many more kinds of actions with the multi-format decoding approach developed here will be needed to more effectively disentangle the various contributions of movement, posture, low-level visual properties, and action outcomes/effects.

Author response:

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

eLife Assessment

In an important fMRI study with an elegant experimental design and rigorous cross-decoding analyses, this work shows a solid dissociation between two parietal regions in visually processing actions. Specifically, aIPL is found to be sensitive to the causal effects of observed actions, while SPL is sensitive to the patterns of body motion involved in those actions. Additional analysis and explanation would help to determine the strength of evidence and the mechanistic underpinnings would benefit from closer consideration. Nevertheless, the work will be of broad interest to cognitive neuroscientists, particularly vision and action researchers.

We thank the editor and the reviewers for their assessment and their excellent comments and suggestions. We really believe they helped us to provide a stronger and more nuanced paper. In our revision, we addressed all points raised by the reviewers. Most importantly, we added a new section on a series of analyses to characterize in more detail the representations isolated by the action-animation and action-PLD cross-decoding. Together, these analyses strengthen the conclusion that aIPL and LOTC represent action effect structures at a categorical rather than specific level, that is, the type of change (e.g., of location or configuration) rather than the specific effect type (e.g. division, compression). SPL is sensitive to body-specific representations, specifically manuality (unimanual vs. bimanual) and movement kinematics. We also added several other analyses and addressed each point of the reviewers. Please find our responses below.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The authors report a study aimed at understanding the brain's representations of viewed actions, with a particular aim to distinguish regions that encode observed body movements, from those that encode the effects of actions on objects. They adopt a cross-decoding multivariate fMRI approach, scanning adult observers who viewed full-cue actions, pantomimes of those actions, minimal skeletal depictions of those actions, and abstract animations that captured analogous effects to those actions. Decoding across different pairs of these actions allowed the authors to pull out the contributions of different action features in a given region's representation. The main hypothesis, which was largely confirmed, was that the superior parietal lobe (SPL) more strongly encodes movements of the body, whereas the anterior inferior parietal lobe (aIPL) codes for action effects of outcomes. Specifically, region of interest analyses showed dissociations in the successful cross-decoding of action category across full-cue and skeletal or abstract depictions. Their analyses also highlight the importance of the lateral occipito-temporal cortex (LOTC) in coding action effects. They also find some preliminary evidence about the organisation of action kinds in the regions examined.

Strengths:

The paper is well-written, and it addresses a topic of emerging interest where social vision and intuitive physics intersect. The use of cross-decoding to examine actions and their effects across four different stimulus formats is a strength of the study. Likewise, the a priori identification of regions of interest (supplemented by additional full-brain analyses) is a strength.

Weaknesses:

I found that the main limitation of the article was in the underpinning theoretical reasoning. The authors appeal to the idea of "action effect structures (AES)", as an abstract representation of the consequences of an action that does not specify (as I understand it) the exact means by which that effect is caused, nor the specific objects involved. This concept has some face validity, but it is not developed very fully in the paper, rather simply asserted. The authors make the claim that "The identification of action effect structure representations in aIPL has implications for theories of action understanding" but it would have been nice to hear more about what those theoretical implications are. More generally, I was not very clear on the direction of the claim here. Is there independent evidence for AES (if so, what is it?) and this study tests the following prediction, that AES should be associated with a specific brain region that does not also code other action properties such as body movements? Or, is the idea that this finding -- that there is a brain region that is sensitive to outcomes more than movements -- is the key new evidence for AES?

Thank you for raising this important issue. We reasoned that AES should exist to support the recognition of perceptually variable actions, including those that we have never experienced before. To the best of our knowledge, there is only indirect evidence for the existence of AES, namely that humans effortlessly and automatically recognize actions (and underlying intentions and feelings) in movements of abstract shapes, as in the famous Heider and Simmel (1949) animations. As these animations do not contain any body posture or movement information at all, the only available cues are the spatiotemporal relations between entities and entity parts in the perceived scene. We think that the effortless and automatic attribution of actions to these stimuli points toward an evolutionary optimized mechanism to capture action effect structures from highly variable action instantiations (so general that it even works for abstract animations). Our study thus aimed to test for the existence of such a level of representation in the brain. We clarified this point in the introduction.

In our revised manuscript, we also revised our discussion of the implications of the finding of AES representations in the brain:

"The identification of action effect structure representations in aIPL and LOTC has implications for theories of action understanding: Current theories (see for review e.g. Zentgraf et al., 2011; Kemmerer, 2021; Lingnau and Downing, 2024) largely ignore the fact that the recognition of many goal-directed actions requires a physical analysis of the action-induced effect, that is, a state change of the action target. Moreover, premotor and inferior parietal cortex are usually associated with motor- or body-related processing during action observation. Our results, together with the finding that premotor and inferior parietal cortex are similarly sensitive to actions and inanimate object events (Karakose-Akbiyik et al., 2023), suggest that large parts of the 'action observation network' are less specific for body-related processing in action perception than usually thought. Rather, this network might provide a substrate for the physical analysis and predictive simulation of dynamic events in general (Schubotz, 2007; Fischer, 2024). In addition, our finding that the (body-independent) representation of action effects substantially draws on right LOTC contradicts strong formulations of a 'social perception' pathway in LOTC that is selectively tuned to the processing of moving faces and bodies (Pitcher and Ungerleider, 2021). The finding of action effect representation in right LOTC/pSTS might also offer a novel interpretation of a right pSTS subregion thought to specialized for social interaction recognition: Right pSTS shows increased activation for the observation of contingent action-reaction pairs (e.g. agent A points toward object; agent B picks up object) as compared to two independent actions (i.e., the action of agent A has no effect on the action of agent B) (Isik et al., 2017). Perhaps the activation reflects the representation of a social action effect - the change of an agent's state induced by someone else's action. Thus, the representation of action effects might not be limited to physical object changes but might also comprise social effects not induced by a physical interaction between entities. Finally, not all actions induce an observable change in the world. It remains to be tested whether the recognition of, e.g., communication (e.g. speaking, gesturing) and perception actions (e.g. observing, smelling) similarly relies on structural action representations in aIPL and LOTC"

On a more specific but still important point, I was not always clear that the significant, but numerically rather small, decoding effects are sufficient to support strong claims about what is encoded or represented in a region. This concern of course applies to many multivariate decoding neuroimaging studies. In this instance, I wondered specifically whether the decoding effects necessarily reflected fully five-way distinction amongst the action kinds, or instead (for example) a significantly different pattern evoked by one action compared to all of the other four (which in turn might be similar). This concern is partly increased by the confusion matrices that are presented in the supplementary materials, which don't necessarily convey a strong classification amongst action kinds. The cluster analyses are interesting and appear to be somewhat regular over the different regions, which helps. However: it is hard to assess these findings statistically, and it may be that similar clusters would be found in early visual areas too.

We agree that in our original manuscript, we did not statistically test what precisely drives the decoding, e.g., specific actions or rather broader categories. In our revised manuscript, we included a representational similarity analysis (RSA) that addressed this point. In short, we found that the action-animation decoding was driven by categorical distinctions between groups of actions (e.g. hit/place vs. the remaining actions) rather than a fully five-way distinction amongst all action kinds. The action-PLD decoding was mostly driven by , specifically manuality (unimanual vs. bimanual)) and movement kinematics; in left and right LOTC we found additional evidence for action-specific representations.

Please find below the new paragraph on the RSA:

"To explore in more detail what types of information were isolated by the action-animation and action-PLD cross-decoding, we performed a representational similarity analysis.

We first focus on the representations identified by the action-animation decoding. To inspect and compare the representational organization in the ROIs, we extracted the confusion matrices of the action-animation decoding from the ROIs (Fig. 5A) and compared them with different similarity models (Fig. 5B) using multiple regression. Specifically, we aimed at testing at which level of granularity action effect structures are represented in aIPL and LOTC: Do these regions encode the broad type of action effects (change of shape, change of location, ingestion) or do they encode specific action effects (compression, division, etc.)? In addition, we aimed at testing whether the effects observed in EVC can be explained by a motion energy model that captures the similarities between actions and animations that we observed in the stimulus-based action-animation decoding using motion energy features. We therefore included V1 in the ROI analysis. We found clear evidence that the representational content in right aIPL and bilateral LOTC can be explained by the effect type model but not by the action-specific model (all p < 0.005; two-sided paired t-tests between models; Fig. 5C). In left V1, we found that the motion energy model could indeed explain some representational variance; however, in both left and right V1 we also found effects for the effect type model. We assume that there were additional visual similarities between the broad types of actions and animations that were not captured by the motion energy model (or other visual models; see Supplementary Information). A searchlight RSA revealed converging results, and additionally found effects for the effect type model in the ventral part of left aIPL and for the action-specific model in the left anterior temporal lobe, left dorsal central gyrus, and right EVC (Fig. 5D). The latter findings were unexpected and should be interpreted with caution, as these regions (except right EVC) were not found in the action-animation cross-decoding and therefore should not be considered reliable (Ritchie et al., 2017). The motion energy model did not reveal effects that survived the correction for multiple comparison, but a more lenient uncorrected threshold of p = 0.005 revealed clusters in left EVC and bilateral posterior SPL.

To characterize the representations identified by the action-PLD cross-decoding, we used a manuality model that captures whether the actions were performed with both hands vs. one hand, an action-specific model as used in the action-animation RSA above, and a kinematics model that was based on the 3D kinematic marker positions of the PLDs (Fig. 6B). Since pSTS is a key region for biological motion perception, we included this region in the ROI analysis. The manuality model explained the representational variance in the parietal ROIs, pSTS, and LOTC, but not in V1 (all p < 0.002; two-sided paired t-tests between V1 and other ROIs; Fig. 6C). By contrast, the action-specific model revealed significant effects in V1 and LOTC, but not in pSTS and parietal ROIs (but note that effects in V1 and pSTS did not differ significantly from each other; all other two-sided paired t-tests between mentioned ROIs were significant at p < 0.0005). The kinematics model explained the representational variance in all ROIs. A searchlight RSA revealed converging results, and additionally found effects for the manuality model in bilateral dorsal/medial prefrontal cortex and in right ventral prefrontal cortex and insula (Fig. 6D).”

We also included an ROI covering early visual cortex (V1) in our analysis. While there was significant decoding for action-animation in V1, the representational organization did not substantially match the organization found in aIPL and LOTC: A cluster analysis revealed much higher similarity between LOTC and aIPL than between these regions and V1:

(please note that in this analysis we included the action-PLD RDMs as reference, and to test whether aIPL shows a similar representational organization in action-anim and action-PLD; see below)

Given these results, we think that V1 captured different aspects in the action-animation cross-decoding than aIPL and LOTC. We address this point in more detail in our response to the "Recommendations for The Authors".

Reviewer #2 (Public Review):

Summary:

This study uses an elegant design, using cross-decoding of multivariate fMRI patterns across different types of stimuli, to convincingly show a functional dissociation between two sub-regions of the parietal cortex, the anterior inferior parietal lobe (aIPL) and superior parietal lobe (SPL) in visually processing actions. Specifically, aIPL is found to be sensitive to the causal effects of observed actions (e.g. whether an action causes an object to compress or to break into two parts), and SPL to the motion patterns of the body in executing those actions.

To show this, the authors assess how well linear classifiers trained to distinguish fMRI patterns of response to actions in one stimulus type can generalize to another stimulus type. They choose stimulus types that abstract away specific dimensions of interest. To reveal sensitivity to the causal effects of actions, regardless of low-level details or motion patterns, they use abstract animations that depict a particular kind of object manipulation: e.g. breaking, hitting, or squashing an object. To reveal sensitivity to motion patterns, independently of causal effects on objects, they use point-light displays (PLDs) of figures performing the same actions. Finally, full videos of actors performing actions are used as the stimuli providing the most complete, and naturalistic information. Pantomime videos, with actors mimicking the execution of an action without visible objects, are used as an intermediate condition providing more cues than PLDs but less than real action videos (e.g. the hands are visible, unlike in PLDs, but the object is absent and has to be inferred). By training classifiers on animations, and testing their generalization to full-action videos, the classifiers' sensitivity to the causal effect of actions, independently of visual appearance, can be assessed. By training them on PLDs and testing them on videos, their sensitivity to motion patterns, independent of the causal effect of actions, can be assessed, as PLDs contain no information about an action's effect on objects.

These analyses reveal that aIPL can generalize between animations and videos, indicating that it is sensitive to action effects. Conversely, SPL is found to generalize between PLDs and videos, showing that it is more sensitive to motion patterns. A searchlight analysis confirms this pattern of results, particularly showing that action-animation decoding is specific to right aIPL, and revealing an additional cluster in LOTC, which is included in subsequent analyses. Action-PLD decoding is more widespread across the whole action observation network.

This study provides a valuable contribution to the understanding of functional specialization in the action observation network. It uses an original and robust experimental design to provide convincing evidence that understanding the causal effects of actions is a meaningful component of visual action processing and that it is specifically localized in aIPL and LOTC.

Strengths:

The authors cleverly managed to isolate specific aspects of real-world actions (causal effects, motion patterns) in an elegant experimental design, and by testing generalization across different stimulus types rather than within-category decoding performance, they show results that are convincing and readily interpretable. Moreover, they clearly took great care to eliminate potential confounds in their experimental design (for example, by carefully ordering scanning sessions by increasing realism, such that the participants could not associate animation with the corresponding real-world action), and to increase stimulus diversity for different stimulus types. They also carefully examine their own analysis pipeline, and transparently expose it to the reader (for example, by showing asymmetries across decoding directions in Figure S3). Overall, this is an extremely careful and robust paper.

Weaknesses:

I list several ways in which the paper could be improved below. More than 'weaknesses', these are either ambiguities in the exact claims made, or points that could be strengthened by additional analyses. I don't believe any of the claims or analyses presented in the paper show any strong weaknesses, problematic confounds, or anything that requires revising the claims substantially.

(1) Functional specialization claims: throughout the paper, it is not clear what the exact claims of functional specialization are. While, as can be seen in Figure 3A, the difference between action-animation cross-decoding is significantly higher in aIPL, decoding performance is also above chance in right SPL, although this is not a strong effect. More importantly, action-PLD cross-decoding is robustly above chance in both right and left aIPL, implying that this region is sensitive to motion patterns as well as causal effects. I am not questioning that the difference between the two ROIs exists - that is very convincingly shown. But sentences such as "distinct neural systems for the processing of observed body movements in SPL and the effect they induce in aIPL" (lines 111-112, Introduction) and "aIPL encodes abstract representations of action effect structures independently of motion and object identity" (lines 127-128, Introduction) do not seem fully justified when action-PLD cross-decoding is overall stronger than action-animation cross-decoding in aIPL. Is the claim, then, that in addition to being sensitive to motion patterns, aIPL contains a neural code for abstracted causal effects, e.g. involving a separate neural subpopulation or a different coding scheme. Moreover, if sensitivity to motion patterns is not specific to SPL, but can be found in a broad network of areas (including aIPL itself), can it really be claimed that this area plays a specific role, similar to the specific role of aIPL in encoding causal effects? There is indeed, as can be seen in Figure 3A, a difference between action-PLD decoding in SPL and aIPL, but based on the searchlight map shown in Figure 3B I would guess that a similar difference would be found by comparing aIPL to several other regions. The authors should clarify these ambiguities.

We thank the reviewer for this careful assessment. The observation of action-PLD cross-decoding in aIPL is indeed not straightforward to interpret: It could mean that aIPL encodes both body movements and action effect structures by different neural subpopulations. Or it could mean that representations of action effect structures were also activated by the PLDs, which lead to successful decoding in the action-PLD cross-decoding. Our revision allows a more nuanced view on this issue:

First, we included the results of a behavioral test show that PLDs at least weakly allow for recognition of the specific actions (see our response to the second comment), which in turn might activate action effect structure representations. Second, the finding that also the cross-decoding between animations and PLDs revealed effects in left and right aIPL (as pointed out by the reviewer in the second comment) supports the interpretation that PLDs have activated, to some extent, action effect structure representations.

On the other hand, if aIPL encodes only action-effect-structures, that were also captured in the action-PLD cross-decoding, we would expect that the RDMs in aIPL are similar for the action-PLD and action-animation cross-decoding. However, the cluster analysis (see our response to Reviewer 1 above) does not show this; rather, all action-PLD RDMs are representationally more similar with each other than with action-animation RDMs, specifically with regard to aIPL. In addition, the RSA revealed sensitivity to manuality and kinematics also in aIPL. This suggests that the action-PLD decoding in aIPL was at least partially driven by representations related to body movements.

Taken together, these findings suggest that aIPL encodes also body movements. In fact, we didn't want to make the strong claim that aIPL is selectively representing action effect structures. Rather, we think that our results show that aIPL and SPL are disproportionally sensitive to action effects and body movements, respectively. We added this in our revised discussion:

"The action-PLD cross-decoding revealed widespread effects in LOTC and parietal cortex, including aIPL. What type of representation drove the decoding in aIPL? One possible interpretation is that aIPL encodes both body movements (isolated by the action-PLD cross-decoding) and action effect structures (isolated by the action-animation cross-decoding). Alternatively, aIPL selectively encodes action effect structures, which have been activated by the PLDs. A behavioral test showed that PLDs at least weakly allow for recognition of the specific actions (Tab. S2), which might have activated corresponding action effect structure representations. In addition, the finding that aIPL revealed effects for the cross-decoding between animations and PLDs further supports the interpretation that PLDs have activated, at least to some extent, action effect structure representations.  On the other hand, if aIPL encodes only action effect structures, we would expect that the representational similarity patterns in aIPL are similar for the action-PLD and action-animation cross-decoding. However, this was not the case; rather, the representational similarity pattern in aIPL was more similar to SPL for the action-PLD decoding, which argues against distinct representational content in aIPL vs. SPL isolated by the action-PLD decoding. In addition, the RSA revealed sensitivity to manuality and kinematics also in aIPL, which suggests that the action-PLD decoding in aIPL was at least partially driven by representations related to body movements. Taken together, these findings suggest that aIPL encodes not only action effect structures, but also representations related to body movements. Likewise, also SPL shows some sensitivity to action effect structures, as demonstrated by effects in SPL for the action-animation and pantomime-animation cross-decoding. Thus, our results suggest that aIPL and SPL are not selectively but disproportionally sensitive to action effects and body movements, respectively."

A clarification to the sentence "aIPL encodes abstract representations of action effect structures independently of motion and object identity": Here we are referring to the action-animation cross decoding only; specifically, the fact that because the animations did not show body motion and concrete objects, the representations isolated in the action-animation cross decoding must be independent of body motion and concrete objects. This does not rule out that the same region encodes other kinds of representations in addition.

And another side note to the RSA: It might be tempting to test the "effects" model (distinguishing change of shape, change of location and ingest) also in the action-PLD multiple regression RSA in order to test whether this model explains additional variance in aIPL, which would point towards action effect structure representations. However, the "effect type" model is relatively strongly correlated with the "manuality" model (VIF=4.2), indicating that multicollinearity might exist. We therefore decided to not include this model in the RSA. However, we nonetheless tested the inclusion of this model and did not find clear effects for the "effects" model in aIPL (but in LOTC). The other models revealed largely similar effects as the RSA without the "effects" model, but the effects appeared overall noisier. In general, we would like to emphasize that an RSA with just 5 actions is not ideal because of the small number of pairwise comparisons, which increases the chance for coincidental similarities between model and neural RDMs. We therefore marked this analysis as "exploratory" in the article.

(2) Causal effect information in PLDs: the reasoning behind the use of PLD stimuli is to have a condition that isolates motion patterns from the causal effects of actions. However, it is not clear whether PLDs really contain as little information about action effects as claimed. Cross-decoding between animations and PLDs is significant in both aIPL and LOTC, as shown in Figure 4. This indicates that PLDs do contain some information about action effects. This could also be tested behaviorally by asking participants to assign PLDs to the correct action category. In general, disentangling the roles of motion patterns and implied causal effects in driving action-PLD cross-decoding (which is the main dependent variable in the paper) would strengthen the paper's message. For example, it is possible that the strong action-PLD cross-decoding observed in aIPL relies on a substantially different encoding from, say, SPL, an encoding that perhaps reflects causal effects more than motion patterns. One way to exploratively assess this would be to integrate the clustering analysis shown in Figure S1 with a more complete picture, including animation-PLD and action-PLD decoding in aIPL.

With regard to the suggestion to behaviorally test how well participants can grasp the underlying action effect structures: We indeed did a behavioral experiment to assess the recognizability of actions in the PLD stick figures (as well as in the pantomimes). In short, this experiment revealed that participants could not well recognize the actions in the PLD stick figures and often confused them with kinematically similar but conceptually different actions (e.g. breaking --> shaking, hitting --> swiping, squashing --> knitting). However, the results also show that it was not possible to completely eliminate that PLDs contain some information about action effects.

Because we considered this behavioral experiment as a standard assessment of the quality of the stimuli, we did not report them in the original manuscript. We now added an additional section to the methods that describes the behavioral experiments in detail:

"To assess how much the animations, PLD stick figures, and pantomimes were associated with the specific action meanings of the naturalistic actions, we performed a behavioral experiment. 14 participants observed videos of the animations, PLDs (without stick figures), and pantomimes in three separate sessions (in that order) and were asked to describe what kind of actions the animations depict and give confidence ratings on a Likert scale from 1 (not confident at all) to 10 (very confident). Because the results for PLDs were unsatisfying (several participants did not recognize human motion in the PLDs), we added stick figures to the PLDs as described above and repeated the rating for PLD stick figures with 7 new participants, as reported below.

A general observation was that almost no participant used verb-noun phrases (e.g. "breaking a stick") in their descriptions for all stimulus types. For the animations, the participants used more abstract verbs or nouns to describe the actions (e.g. dividing, splitting, division; Tab. S1). These abstract descriptions matched the intended action structures quite well, and participants were relatively confident about their responses (mean confidences between 6 and 7.8). These results suggest that the animations were not substantially associated with specific action meanings (e.g. "breaking a stick") but captured the coarse action structures. For the PLD stick figures (Tab. S2), responses were more variable and actions were often confused with kinematically similar but conceptually different actions (e.g. breaking --> shaking, hitting --> turning page, squashing --> knitting). Confidence ratings were relatively low (mean confidences between 3 and 5.1). These results suggest that PLD stick figures, too, were not substantially associated with specific action meanings and additionally did not clearly reveal the underlying action effect structures. Finally, pantomimes were recognized much better, which was also reflected in high confidence ratings (mean confidences between 8 and 9.2; Tab. S3). This suggests that, unlike PLD stick figures, pantomimes allowed much better to access the underlying action effect structures."

We also agree with the second suggestion to investigate in more detail the representational profiles in aIPL and SPL. We think that the best way to do so is the RSA that we reported above. However, to provide a complete picture of the results, we also added the whole brain maps and RDMs for the animation-pantomime, animation-PLD, pantomime-PLD, and action-pantomime to the supplementary information.

(3) Nature of the motion representations: it is not clear what the nature of the putatively motion-driven representation driving action-PLD cross-decoding is. While, as you note in the Introduction, other regions such as the superior temporal sulcus have been extensively studied, with the understanding that they are part of a feedforward network of areas analyzing increasingly complex motion patterns (e.g. Riese & Poggio, Nature Reviews Neuroscience 2003), it doesn't seem like the way in which SPL represents these stimuli are similarly well-understood. While the action-PLD cross-decoding shown here is a convincing additional piece of evidence for a motion-based representation in SPL, an interesting additional analysis would be to compare, for example, RDMs of different actions in this region with explicit computational models. These could be, for example, classic motion energy models inspired by the response characteristics of regions such as V5/MT, which have been shown to predict cortical responses and psychophysical performance both for natural videos (e.g. Nishimoto et al., Current Biology 2011) and PLDs (Casile & Giese Journal of Vision 2005). A similar cross-decoding analysis between videos and PLDs as that conducted on the fMRI patterns could be done on these models' features, obtaining RDMs that could directly be compared with those from SPL. This would be a very informative analysis that could enrich our knowledge of a relatively unexplored region in action recognition. Please note, however, that action recognition is not my field of expertise, so it is possible that there are practical difficulties in conducting such an analysis that I am not aware of. In this case, I kindly ask the authors to explain what these difficulties could be.

Thank you for this very interesting suggestion. We conducted a cross-decoding analysis that was based on the features of motion energy models as described in Nishimoto et al. (2011). Control analyses within each stimulus type revealed high decoding accuracies (animations: 100%, PLDs: 100%, pantomimes: 65%, actions: 55%), which suggests that the motion energy data generally contains information that can be detected by a classifier. However, the cross-decoding between actions and PLDs was at chance (20%), and the classification matrix did not resemble the neural RDMs. We also tested optical flow vectors as input to the decoding, which revealed similarly high decoding for the within-stimulus-type decoding (animations: 75%, PLDs: 100%, pantomimes: 65%, actions: 40%), but again at-chance decoding for action-PLD (20%), notably with a very different classification pattern:

Author response image 1.

Given these mixed results, we decided not to use these models for a statistical comparison with the neural action-PLD RDMs.

It is notable that the cross-decoding worked generally less well for decoding schemes that involve PLDs, which is likely due to highly different feature complexity of actions and PLDs: Naturalistic actions have much richer visual details, texture, and more complex motion cues. Therefore, motion energy features extracted from these videos likely capture a mixture of both fine-grained and broad motion information across different spatial frequencies. By contrast, motion energy features of PLDs are sparse and might not match the features of naturalistic actions. In a way, this was intended, as we were interested in higher-level body kinematics rather than lower-level motion features. We therefore decided to use a different approach to investigate the representational structure found in the action-PLD cross-decoding: As the PLDs were based on kinematic recordings of actions that were carried out in exactly the same manner as the naturalistic actions, we computed the dissimilarity of the 5 actions based on the kinematic marker positions. Specifically, we averaged the kinematic data across the 2 exemplars per PLD, vectorized the 3D marker positions of all time points of the PLDs (3 dimensions x 13 markers x 200 time points), computed the pairwise correlations between the 5 vectors, and converted the correlations into dissimilarity values by subtracting 1 - r. This RDM was then compared with the neural RDMs extracted from the action-PLD cross-decoding. This was done using a multiple regression RSA (see also our response to Reviewer 1's public comment 2), which allowed us to statistically test the kinematic model against other dissimilarity models: a categorical model of manuality (uni- vs. bimanual) and an action-specific model that discriminates each specific action from each other with equal distance.

This analysis revealed interesting results: the kinematic model explained the representational variance in bilateral SPL and (particularly right) pSTS as well as in right fusiform cortex and early visual cortex. The action-specific model revealed effects restricted to bilateral LOTC. The manuality model revealed widespread effects throughout the action observation network but not in EVC.

(4) Clustering analysis: I found the clustering analysis shown in Figure S1 very clever and informative. However, there are two things that I think the authors should clarify. First, it's not clear whether the three categories of object change were inferred post-hoc from the data or determined beforehand. It is completely fine if these were just inferred post-hoc, I just believe this ambiguity should be clarified explicitly. Second, while action-anim decoding in aIPL and LOTC looks like it is consistently clustered, the clustering of action-PLD decoding in SPL and LOTC looks less reliable. The authors interpret this clustering as corresponding to the manual vs. bimanual distinction, but for example "drink" (a unimanual action) is grouped with "break" and "squash" (bimanual actions) in left SPL and grouped entirely separately from the unimanual and bimanual clusters in left LOTC. Statistically testing the robustness of these clusters would help clarify whether it is the case that action-PLD in SPL and LOTC has no semantically interpretable organizing principle, as might be the case for a representation based entirely on motion pattern, or rather that it is a different organizing principle from action-anim, such as the manual vs. bimanual distinction proposed by the authors. I don't have much experience with statistical testing of clustering analyses, but I think a permutation-based approach, wherein a measure of cluster robustness, such as the Silhouette score, is computed for the clusters found in the data and compared to a null distribution of such measures obtained by permuting the data labels, should be feasible. In a quick literature search, I have found several papers describing similar approaches: e.g. Hennig (2007), "Cluster-wise assessment of cluster stability"; Tibshirani et al. (2001) "Estimating the Number of Clusters in a Data Set Via the Gap Statistic". These are just pointers to potentially useful approaches, the authors are much better qualified to pick the most appropriate and convenient method. However, I do think such a statistical test would strengthen the clustering analysis shown here. With this statistical test, and the more exhaustive exposition of results I suggested in point 2 above (e.g. including animation-PLD and action-PLD decoding in aIPL), I believe the clustering analysis could even be moved to the main text and occupy a more prominent position in the paper.

With regard to the first point, we clarified in the methods that we inferred the 3 broad action effect categories after the stimulus selection: "This categorization was not planned before designing the study but resulted from the stimulus selection."

Thank you for your suggestion to test more specifically the representational organization in the action-PLD and action-animation RDMs. However, after a careful assessment, we decided to replace the cluster analysis with an RSA. We did this for two reasons:

First, we think that RSA is a better (and more conventional) approach to statistically investigate the representational structure in the ROIs (and in the whole brain). The RSA allowed us, for example, to specifically test the mentioned distinction between unimanual and bimanual actions, and to test it against other models, i.e., a kinematic model and an action-specific model. This indeed revealed interesting distinct representational profiles of SPL and LOTC.

Second, we learned that the small number of items (5) is generally not ideal for cluster analyses (absolute minimum for meaningful interpretability is 4, but to form at least 2-3 clusters a minimum of 10-15 items is usually recommended). A similar rule of thumb applies to methods to statistically assess the reliability of cluster solutions (e.g., Silhouette Scores, Cophenetic Correlation Coefficient, Jaccard Coefficient). Finally, the small number of items is not ideal to run a permutation test because the number of unique permutations (for shuffling the data labels: 5! = 30) is insufficient to generate a meaningful null distribution. We therefore think it is best to discard the cluster analysis altogether. We hope you agree with this decision.

(5) ROI selection: this is a minor point, related to the method used for assigning voxels to a specific ROI. In the description in the Methods (page 16, lines 514-24), the authors mention using the MNI coordinates of the center locations of Brodmann areas. Does this mean that then they extracted a sphere around this location, or did they use a mask based on the entire Brodmann area? The latter approach is what I'm most familiar with, so if the authors chose to use a sphere instead, could they clarify why? Or, if they did use the entire Brodmann area as a mask, and not just its center coordinates, this should be made clearer in the text.

We indeed used a sphere around the center coordinate of the Brodmann areas. This was done to keep the ROI sizes / number of voxels constant across ROIs. Since we aimed at comparing the decoding accuracies between aIPL and SPL, we thereby minimized the possibility that differences in decoding accuracy between ROIs are due to ROI size differences. The approach of using spherical ROIs is a quite well established practice that we are using in our lab by default (e.g. Wurm & Caramazza, NatComm, 2019; Wurm & Caramazza, NeuroImage, 2019; Karakose, Caramazza, & Wurm, NatComm, 2023). We clarified that we used spherical ROIs to keep the ROI sizes constant in the revised manuscript.

Reviewer #3 (Public Review):

This study tests for dissociable neural representations of an observed action's kinematics vs. its physical effect in the world. Overall, it is a thoughtfully conducted study that convincingly shows that representations of action effects are more prominent in the anterior inferior parietal lobe (aIPL) than the superior parietal lobe (SPL), and vice versa for the representation of the observed body movement itself. The findings make a fundamental contribution to our understanding of the neural mechanisms of goal-directed action recognition, but there are a couple of caveats to the interpretation of the results that are worth noting:

(1) Both a strength of this study and ultimately a challenge for its interpretation is the fact that the animations are so different in their visual content than the other three categories of stimuli. On one hand, as highlighted in the paper, it allows for a test of action effects that is independent of specific motion patterns and object identities. On the other hand, the consequence is also that Action-PLD cross-decoding is generally better than Action-Anim cross-decoding across the board (Figure 3A) - not surprising because the spatiotemporal structure is quite different between the actions and the animations. This pattern of results makes it difficult to interpret a direct comparison of the two conditions within a given ROI. For example, it would have strengthened the argument of the paper to show that Action-Anim decoding was better than Action-PLD decoding in aIPL; this result was not obtained, but that could simply be because the Action and PLD conditions are more visually similar to each other in a number of ways that influence decoding. Still, looking WITHIN each of the Action-Anim and Action-PLD conditions yields clear evidence for the main conclusion of the study.

The reviewer is absolutely right: Because the PLDs are more similar to the actions than the animations, a comparison of the effects of the two decoding schemes is not informative. As we also clarified in our response to Reviewer 2, we cannot rule out that the action-PLD decoding picked up information related to action effect structures. Thus, the only firm conclusion that we can draw from our study is that aIPL and SPL are disproportionally sensitive to action effects and body movements, respectively. We clarified this point in our revised discussion.

(2) The second set of analyses in the paper, shown in Figure 4, follows from the notion that inferring action effects from body movements alone (i.e., when the object is unseen) is easier via pantomimes than with PLD stick figures. That makes sense, but it doesn't necessarily imply that the richness of the inferred action effect is the only or main difference between these conditions. There is more visual information overall in the pantomime case. So, although it's likely true that observers can more vividly infer action effects from pantomimes vs stick figures, it's not a given that contrasting these two conditions is an effective way to isolate inferred action effects. The results in Figure 4 are therefore intriguing but do not unequivocally establish that aIPL is representing inferred rather than observed action effects.

We agree that higher decoding accuracies for Action-Pant vs. Action-PLD and Pant-PLD could also be due to visual details (in particular of hands and body) that are more similar in actions and pantomimes relative to PLDs. However, please note that for this reason we included also the comparison of Anim-Pant vs. Anim-PLD. For this comparison, visual details should not influence the decoding. We clarified this point in our revision.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

It struck me that there are structural distinctions amongst the 5 action kinds that were not highlighted and may have been unintentional. Specifically, three of the actions are "unary" in a sense: break(object), squash(object), hit(object). One is "binary": place(object, surface), and the fifth (drink) is perhaps ternary - transfer(liquid, cup, mouth)? Might these distinctions be important for the organization of action effects (or actions generally)?

This is an interesting aspect that we did not think of yet. We agree that for the organization of actions (and perhaps action effects) this distinction might be relevant. One issue we noticed, however, is that for the animations the suggested organization might be less clear, in particular for "drink" as ternary, and perhaps also for "place" as binary. Thus, in the action-animation cross-decoding, this distinction - if it exists in the brain - might be harder to capture. We nonetheless tested this distinction. Specifically, we constructed a dissimilarity model (using the proposed organization, valency model hereafter) and tested it in a multiple regression RSA against an effect type model and two other models for specific actions (discriminating each action from each other with the same distance) and motion energy (as a visual control model). This analysis revealed no effects for the "valency" model in the ROI-based RSA. Also a searchlight analysis revealed no effects for this model. Since we think that the valency model is not ideally suited to test representations of action effects (using data from the action-animation cross-decoding) and to make the description of the RSA not unnecessarily complicated, we decided to not include this model in the final RSA reported in the manuscript.

In general, I found it surprising that the authors treated their LOTC findings as surprising or unexpected. Given the long literature associating this region with several high-level visual functions related to body perception, action perception, and action execution, I thought there were plenty of a priori reasons to investigate the LOTC's behaviour in this study. Looking at the supplementary materials, indeed some of the strongest effects seem to be in that region.

(Likewise, classically, the posterior superior temporal sulcus is strongly associated with the perception of others' body movements; why not also examine this region of interest?)

One control analysis that would considerably add to the strength of the authors' conclusions would be to examine how actions could be cross-decoded (or not) in the early visual cortex. Especially in comparisons of, for example, pantomime to full-cue video, we might expect a high degree of decoding accuracy, which might influence the way we interpret similar decoding in other "higher level" regions.

We agree that it makes sense to also look into LOTC and pSTS, and also EVC. We therefore added ROIs for these regions: For EVC and LOTC we used the same approach based on Brodmann areas as for aIPL and SPL, i.e., we used BA 17 for V1 and BA 19 for LOTC. For pSTS, we defined the ROI based on a meta analysis contrast for human vs. non-human body movements (Grobras et al., HBM 2012). Indeed we find that the strongest effects (for both action effect structures and body movements) can be found in LOTC. We also found effects in EVC that, at least for the action-animation cross-decoding, are more difficult to interpret. To test for a coincidental visual confound between actions and animations, we included a control model for motion energy in the multiple regression RSA, which could indeed explain some of the representational content in V1. However, also the effect type model revealed effects in V1, suggesting that there were additional visual features that caused the action-animation cross-decoding in V1. Notably, as pointed out in our response to the Public comments, the representational organization in V1 was relatively distinct from the representational organization in aIPL and LOTC, which argues against the interpretation that effects in aIPL and LOTC were driven by the same (visual) features as in V1.

Regarding the analyses reported in Figure 4: wouldn't it be important to also report similar tests for SPL?

In the analysis of implied action effect structures, we focused on the brain regions that revealed robust effects for action-animation decoding in the ROI and the searchlight analysis, that is, aIPL and SPL. However, we performed a whole brain conjunction analysis to search for other brain regions that show a profile for implied action effect representation. This analysis (that we forgot to mention in our original manuscript; now corrected) did not find evidence for implied action effect representations in SPL.

However, for completeness, we also added a ROI analysis for SPL. This analysis revealed a surprisingly complex pattern of results: We observed stronger decoding for Anim-Pant vs. Anim-PLD, whereas there were no differences for the comparisons of Action-Pant with Action-PLD and Pant-PLD:

This pattern of results is not straightforward to explain: First, the equally strong decoding for Action-Pant, Action-PLD, and Pant-PLD suggests that SPL is not substantially sensitive to body part details. Rather, the decoding relied on the coarse body part movements, independently of the specific stimulus type (action, pantomime, PLD). However, the stronger difference between Anim-Pant and Anim-PLD suggests that SPL is also sensitive to implied AES. This appears unlikely, because no effects (in left aIPL) or only weak effects (in right SPL) were found for the more canonical Action-Anim cross-decoding. The Anim-Pant cross-decoding was even stronger than the Action-Anim cross-decoding, which is counterintuitive because naturalistic actions contain more information than pantomimes, specifically with regard to action effect structures. How can this pattern of results be interpreted? Perhaps, for pantomimes and animations, not only aIPL and LOTC but also SPL is involved in inferring (implied) action effect structures. However, for this conclusion, also differences for the comparison of Action-Pant with Action-PLD and for Action-Pant with Pant-PLD should be found. Another non-mutually exclusive interpretation is that both animations and pantomimes are more ambiguous in terms of the specific action, as opposed to naturalistic actions. For example, the squashing animation and pantomime are both ambiguous in terms of what is squashed/compressed, which might require additional load to infer both the action and the induced effect. The increased activation of action-related information might in turn increase the chance for a match between neural activation patterns of animations and pantomimes.

In any case, these additional results in SPL do not question the effects reported in the main text, that is, disproportionate sensitivity for action effect structures in right aIPL and LOTC and for body movements in SPL and other AON regions. The evidence for implied action effect structures representation in SPL is mixed and should be interpreted with caution.

We added this analysis and discussion as supplementary information.

Statistical arguments that rely on "but not" are not very strong, e.g. "We found higher cross-decoding for animation-pantomime vs. animation-PLD in right aIPL and bilateral LOTC (all t(23) > 3.09, all p < 0.0025; one-tailed), but not in left aIPL (t(23) = 0.73, p = 0.23, one-tailed)." Without a direct statistical test between regions, it's not really possible to support a claim that they have different response profiles.

Absolutely correct. Notably, we did not make claims about different profiles of the tested ROIs with regard to implied action effect representations. But of course it make sense to test for differential profiles of left vs. right aIPL, so we have added a repeated measures ANOVA to test for an interaction between TEST (animation-pantomime, animation-PLD) and ROI (left aIPL, right aIPL), which, however, was not significant (F(1,23)=3.66, p = 0.068). We included this analysis in the revised manuscript.

Reviewer #2 (Recommendations for The Authors):

(1) I haven't found any information about data and code availability in the paper: is the plan to release them upon publication? This should be made clear.

Stimuli, MRI data, and code are deposited at the Open Science Framework (https://osf.io/am346/). We included this information in the revised manuscript.

(2) Samples of videos of the stimuli (or even the full set) would be very informative for the reader to know exactly what participants were looking at.

We have uploaded the full set of stimuli on OSF (https://osf.io/am346/).

(3) Throughout the paper, decoding accuracies are averaged across decoding directions (A->B and B->A). To my knowledge, this approach was proposed in van den Hurk & Op de Beeck (2019), "Generalization asymmetry in multivariate cross-classification: When representation A generalizes better to representation B than B to A". I believe it would be fair to cite this paper.

Absolutely, thank you very much for the hint. We included this reference in our revised manuscript.

(4) Page 3, line 70: this is a very nitpicky point, but "This suggests that body movements and the effects they induce are at least partially processed independently from each other." is a bit of an inferential leap from "these are distinct aspects of real-world actions" to "then they should be processed independently in the brain". The fact that a distinction exists in the world is a prerequisite for this distinction existing in the brain in terms of functional specialization, but it's not in itself a reason to believe that functional specialization exists. It is a reason to hypothesize that the specialization might exist and to test that hypothesis. So I think this sentence should be rephrased as "This suggests that body movements and the effects they induce might be at least partially processed independently from each other.", or something to that effect.

Your reasoning is absolutely correct. We revised the sentence following your suggestion.

(5) Page 7, line 182: the text says "stronger decoding for action-animation vs. action-PLD" (main effect of TEST), which is the opposite of what can be seen in the figure. I assume this is a typo?

Thanks for spotting this, it was indeed a typo. We corrected it: “…stronger decoding for action-PLD vs. action-animation cross-decoding..”

(6) Page 7, Figure 3B: since the searchlight analysis is used to corroborate the distinction between aIPL and SPL, it would be useful to overlay the contours of these ROIs (and perhaps LOTC as well) on the brain maps.

We found that overlaying the contours of the ROIs onto the decoding searchlight maps would make the figure too busy, and the contours would partially hide effects. However, we added a brain map with all ROIs in the supplementary information.

(7) Page 9, Figure 4A: since the distinction between the significant difference between anim-pant and anim-PLD is quite relevant in the text, I believe highlighting the lack of difference between the two decoding schemes in left aIPL (for example, by writing "ns") in the figure would help guide the reader to see the relevant information. It is generally quite hard to notice the absence of something.

We added “n.s.” to the left aIPL in Fig. 4A.

(8) Page 11, line 300: "Left aIPL appears to be more sensitive to the type of interaction between entities, e.g. how a body part or an object exerts a force onto a target object" since the distinction between this and the effect induced by that interaction" is quite nuanced, I believe a concrete example would clarify this for the reader: e.g. I guess the former would involve a representation of the contact between hand and object when an object is pushed, while the latter would represent only the object's displacement following the push?

Thank you for the suggestion. We added a concrete example: “Left aIPL appears to be more sensitive to the type of interaction between entities, that is, how a body part or an object exerts a force onto a target object (e.g. how a hand makes contact with an object to push it), whereas right aIPL appears to be more sensitive to the effect induced by that interaction (the displacement of the object following the push).”

(9) Page 12, line 376: "Informed consent, and consent to publish, was obtained from the participant in Figure 2." What does this refer to? Was the person shown in the figure both a participant in the study and an actor in the stimulus videos? Since this is in the section about participants in the experiment, it sounds like all participants also appeared in the videos, which I guess is not the case. This ambiguity should be clarified.

Right, the statement sounds misleading in the “Participants” section. We rephrased it and moved it to the “Stimuli” section: “actions…were shown in 4 different formats: naturalistic actions, pantomimes, point light display (PLD) stick figures, and abstract animations (Fig. 2; informed consent, and consent to publish, was obtained from the actor shown in the figure).”

(10) Page 15, line 492: Here, "within-session analyses" are mentioned. However, these analyses are not mentioned in the text (only shown in Figure S2) and their purpose is not clarified. I imagine they were a sanity check to ensure that the stimuli within each stimulus type could be reliably distinguished. This should be explained somewhere.

We clarified the purpose of the within session decoding analyses in the methods section: "Within-session decoding analyses were performed as sanity checks to ensure that for all stimulus types, the 5 actions could be reliably decoded (Fig. S2)."

(11) Page 20, Figure S1: I recommend using the same color ranges for the two decoding schemes (action-anim and action-PLD) in A and C, to make them more directly comparable.

Ok, done.

Reviewer #3 (Recommendations For The Authors):

(1) When first looking at Figure 1B, I had a hard time discerning what action effect was being shown (I thought maybe it was "passing through") Figure 2 later clarified it for me, but it would be helpful to note in the caption that it depicts breaking.

Thank you for the suggestion. Done.

(2) It would be helpful to show an image of the aIPL and SPL ROIs on a brain to help orient readers - both to help them examine the whole brain cross-decoding accuracy and to aid in comparisons with other studies.

We added a brain map with all ROIs in the supplementary information.

(3) Line 181: I'm wondering if there's an error, or if I'm reading it incorrectly. The line states "Moreover, we found ANOVA main effects of TEST (F(1,24)=33.08, p=7.4E-06), indicating stronger decoding for action-animation vs. action-PLD cross-decoding..." But generally, in Figure 3A, it looks like accuracy is lower for Action-Anim than Action-PLD in both hemispheres.

You are absolutely right, thank you very much for spotting this error. We corrected the sentence: “…stronger decoding for action-PLD vs. action-animation cross-decoding..”

(4) It might be useful to devote some more space in the Introduction to clarifying the idea of action-effect structures. E.g., as I read the manuscript I found myself wondering whether there is a difference between action effect structures and physical outcomes in general... would the same result be obtained if the physical outcomes occurred without a human actor involved? This question is raised in the discussion, but it may be helpful to set the stage up front.

We clarified this point in the introduction:

In our study, we define action effects as induced by intentional agents. However, the notion of action effect structures might be generalizable to physical outcomes or object changes as such (e.g. an object's change of location or configuration, independently of whether the change is induced by an agent or not).

(5) Regarding my public comment #2, it would perhaps strengthen the argument to run the same analysis in the SPL ROIs. At least for the comparison of Anim-Pant with Anim-PLD, the prediction would be no difference, correct?

The prediction would indeed be that there is no difference for the comparison of Anim-Pant with Anim-PLD, but also for the comparison of Action-Pant with Action-PLD and for Action-Pant with Pant-PLD, there should be no difference. As explained in our response to the public comment #2, we ran a whole brain conjunction (Fig. 4B) to test for the combination of these effects and did not find SPL in this analysis. However, we did found differences for Anim-Pant vs. Anim-PLD, which is not straightforward to interpret (see our response to your public comment #2 for a discussion of this finding).

eLife Assessment

Wang et al. presented visual (dot) motion and/or the sound of a walking person and found solid evidence that EEG activity tracks the step rhythm, as well as the gait (2-step cycle) rhythm, with some demonstration that the gait rhythm is tracked superadditively (power for A+V condition is higher than the sum of the A-only and V-only condition). The valuable findings will be of wide interest to those examining biological motion perception and oscillatory processes more broadly. Some of the theoretical interpretations concerning entrainment must remain speculative when the authors cannot dissociate evoked responses from entrained oscillatory effects.

Reviewer #1 (Public review):

Summary:

Shen et al. conducted three experiments to study the cortical tracking of the natural rhythms involved in biological motion (BM), and whether these involve audiovisual integration (AVI). They presented participants with visual (dot) motion and/or the sound of a walking person. They found that EEG activity tracks the step rhythm, as well as the gait (2-step cycle) rhythm. The gait rhythm specifically is tracked superadditively (power for A+V condition is higher than the sum of the A-only and V-only condition, Experiments 1a/b), which is independent of the specific step frequency (Experiment 1b). Furthermore, audiovisual integration during tracking of gait was specific to BM, as it was absent (that is, the audiovisual congruency effect) when the walking dot motion was vertically inverted (Experiment 2). Finally, the study shows that an individual's autistic traits are negatively correlated with the BM-AVI congruency effect.

Strengths:

The three experiments are well designed and the various conditions are well controlled. The rationale of the study is clear, and the manuscript is pleasant to read. The analysis choices are easy to follow, and mostly appropriate.

Weaknesses:

On revision, the authors are careful not to overinterpret an analysis where the statistical test is not independent from the data (channel) selection criterion.

Reviewer #2 (Public review):

Summary:

The authors evaluate spectral changes in electroencephalography (EEG) data as a function of the congruency of audio and visual information associated with biological motion (BM) or non-biological motion. The results show supra-additive power gains in the neural response to gait dynamics, with trials in which audio and visual information was presented simultaneously producing higher average amplitude than the combined average power for auditory and visual conditions alone. Further analyses suggest that such supra-additivity is specific to BM and emerges from temporoparietal areas. The authors also find that the BM-specific supra-additivity is negatively correlated with autism traits.

Strengths:

The manuscript is well-written, with a concise and clear writing style. The visual presentation is largely clear. The study involves multiple experiments with different participant groups. Each experiment involves specific considered changes to the experimental paradigm that both replicate the previous experiment's finding yet extend it in a relevant manner.

In the first revisions of the paper, the manuscript better relays the results and anticipates analyses, and this version adequately resolves some concerns I had about analysis details. In a further revision, it is clarified better how the results relate to the various competing hypotheses on how biological motion is processed.

Weaknesses:

Still, it is my view that the findings of the study are basic neural correlate results that offer only minimal constraint towards the question of how the brain realizes the integration of multisensory information in the service of biological motion perception, and the data do not address the causal relevance of observed neural effects towards behavior and cognition. The presence of an inversion effect suggests that the supra-additivity is related to cognition, but that leaves open whether any detected neural pattern is actually consequential for multi-sensory integration (i.e., correlation is not causation). In other words, the fact that frequency-specific neural responses to the [audio & visual] condition are stronger than those to [audio] and [visual] combined does not mean this has implications for behavioral performance. While the correlation to autism traits could suggest some relation to behavior and is interesting in its own right, this correlation is a highly indirect way of assessing behavioral relevance. It would be helpful to test the relevance of supra-additive cortical tracking on a behavioral task directly related to the processing of biological motion to justify the claim that inputs are being integrated in the service of behavior. Under either framework, cortical tracking or entrainment, the causal relevance of neural findings toward cognition is lacking.

Overall, I believe this study finds neural correlates of biological motion that offer some constraint toward mechanism, and it is possible that the effects are behaviorally relevant, but based on the current task and associated analyses this has not been shown (or could not have been, given the paradigm).

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

Shen et al. conducted three experiments to study the cortical tracking of the natural rhythms involved in biological motion (BM), and whether these involve audiovisual integration (AVI). They presented participants with visual (dot) motion and/or the sound of a walking person. They found that EEG activity tracks the step rhythm, as well as the gait (2-step cycle) rhythm. The gait rhythm specifically is tracked superadditively (power for A+V condition is higher than the sum of the A-only and V-only condition, Experiments 1a/b), which is independent of the specific step frequency (Experiment 1b). Furthermore, audiovisual integration during tracking of gait was specific to BM, as it was absent (that is, the audiovisual congruency effect) when the walking dot motion was vertically inverted (Experiment 2). Finally, the study shows that an individual's autistic traits are negatively correlated with the BM-AVI congruency effect.

Strengths:

The three experiments are well designed and the various conditions are well controlled. The rationale of the study is clear, and the manuscript is pleasant to read. The analysis choices are easy to follow, and mostly appropriate.

Weaknesses:

There is a concern of double-dipping in one of the tests (Experiment 2, Figure 3: interaction of Upright/Inverted X Congruent/Incongruent). I raised this concern on the original submission, and it has not been resolved properly. The follow-up statistical test (after channel selection using the interaction contrast permutation test) still is geared towards that same contrast, even though the latter is now being tested differently. (Perhaps not explicitly testing the interaction, but in essence still testing the same.) A very simple solution would be to remove the post-hoc statistical tests and simply acknowledge that you're comparing simple means, while the statistical assessment was already taken care of using the permutation test. (In other words: the data appear compelling because of the cluster test, but NOT because of the subsequent t-tests.)

We are sorry that we did not explain this issue clearly before, which might have caused some misunderstanding. When performing the cluster-based permutation test, we only tested whether the audiovisual congruency effect (congruent vs. incongruent) between the upright and inverted conditions was significantly different [i.e., (UprCon – UprInc) vs. (InvCon – InvInc)], without conducting extra statistical analyses on whether the congruency effect was significant in each orientation condition. Such an analysis yielded a cluster with a significant interaction between audiovisual integration and BM orientation for the cortical tracking effect at 1Hz (but not at 2Hz). However, this does not provide valid information about whether the audiovisual congruency effect at this cluster is significant in each orientation condition, given that a significant interaction effect may result from various patterns of data across conditions: such as significant congruency effects in both orientation conditions (Author response image 1a), a significant congruency effect in the upright condition and a non-significant effect in the inverted condition (Author response image 1b), or even non-significant yet opposite effects in the two conditions (Author response image 1c). Here, our results conform to the second pattern, indicating that cortical tracking of the high-order gait cycles involves a domain-specific process exclusively engaged in the AVI of BM. In a similar vein, the non-significant interaction found at 2Hz does not necessarily indicate that the congruency effect is non-significant in each orientation condition (Author response image 1f&e). Indeed, the congruency effect was significant in both the upright and inverted conditions at 2Hz in our study despite the non-significant interaction, suggesting that neural tracking of the lower-order step cycles is associated with a domain-general AVI process mostly driven by temporal correspondence in physical stimuli.

Therefore, we need to perform subsequent t-tests to examine the significance of the simple effects in the two orientation conditions, which do not duplicate the clusterbased permutation test (for interaction only) and cause no double-dipping. Results from interaction and simple effects, put together, provide solid evidence that the cortical tracking of higher-order and lower-order rhythms involves BM-specific and domaingeneral audiovisual processing, respectively.

To avoid ambiguity, we have removed the sentence “We calculated the audiovisual congruency effect for the upright and the inverted conditions” (line 194, which referred to the calculation of the indices rather than any statistical tests) from the manuscript. We have also clarified the meanings of the findings based on the interaction and simple effects together at the two temporal scales, respectively (Lines 205-207; Lines 213-215).

Author response image 1.

Examples of different patterns of data yielding a significant or nonsignificant interaction effect.

Reviewer #2 (Public review):

Summary:

The authors evaluate spectral changes in electroencephalography (EEG) data as a function of the congruency of audio and visual information associated with biological motion (BM) or non-biological motion. The results show supra-additive power gains in the neural response to gait dynamics, with trials in which audio and visual information was presented simultaneously producing higher average amplitude than the combined average power for auditory and visual conditions alone. Further analyses suggest that such supra-additivity is specific to BM and emerges from temporoparietal areas. The authors also find that the BM-specific supra-additivity is negatively correlated with autism traits.

Strengths:

The manuscript is well-written, with a concise and clear writing style. The visual presentation is largely clear. The study involves multiple experiments with different participant groups. Each experiment involves specific considered changes to the experimental paradigm that both replicate the previous experiment's finding yet extend it in a relevant manner.

Weaknesses:

In the revised version of the paper, the manuscript better relays the results and anticipates analyses, and this version adequately resolves some concerns I had about analysis details. Still, it is my view that the findings of the study are basic neural correlate results that do not provide insights into neural mechanisms or the causal relevance of neural effects towards behavior and cognition. The presence of an inversion effect suggests that the supra-additivity is related to cognition, but that leaves open whether any detected neural pattern is actually consequential for multi-sensory integration (i.e., correlation is not causation). In other words, the fact that frequency-specific neural responses to the [audio & visual] condition are stronger than those to [audio] and [visual] combined does not mean this has implications for behavioral performance. While the correlation to autism traits could suggest some relation to behavior and is interesting in its own right, this correlation is a highly indirect way of assessing behavioral relevance. It would be helpful to test the relevance of supra-additive cortical tracking on a behavioral task directly related to the processing of biological motion to justify the claim that inputs are being integrated in the service of behavior. Under either framework, cortical tracking or entrainment, the causal relevance of neural findings toward cognition is lacking.

Overall, I believe this study finds neural correlates of biological motion, and it is possible that such neural correlates relate to behaviorally relevant neural mechanisms, but based on the current task and associated analyses this has not been shown.

Thank you for providing these thoughtful comments regarding the theoretical implications of our neural findings. Previous behavioral evidence highlights the specificity of the audiovisual integration (AVI) of biological motion (BM) and reveals the impairment of such ability in individuals with autism spectrum disorder. However, the neural implementation underlying the AVI of BM, its specificity, and its association with autistic traits remain largely unknown. The current study aimed to address these issues.

It is noteworthy that the operation of multisensory integration does not always depend on specific tasks, as our brains tend to integrate signals from different sensory modalities even when there is no explicit task. Hence, many studies have investigated multisensory integration at the neural level without examining its correlation with behavioral performance. For example, the widely known super-additivity mode for multisensory integration proposed by Perrault and colleagues was based on single-cell recording findings without behavioral tasks (Perrault et al., 2003, 2005). As we mentioned in the manuscript, the super-additive and sub-additive modes indicate non-linear interaction processing, either with potentiated neural activation to facilitate the perception or detection of near-threshold signals (super-additive) or a deactivation mechanism to minimize the processing of redundant information cross-modally (subadditive) (Laurienti et al., 2005; Metzger et al., 2020; Stanford et al., 2005; Wright et al., 2003). Meanwhile, the additive integration mode represents a linear combination between two modalities. Distinguishing among these integration modes helps elucidate the neural mechanism underlying AVI in specific contexts, even though sometimes, the neural-level AVI effects do not directly correspond to a significant behavioral-level AVI effect (Ahmed et al., 2023; Metzger et al., 2020). In the current study, we unveiled the dissociation of multisensory integration modes between neural responses at two temporal scales (Exps. 1a & 1b), which may involve the cooperation of a domain-specific and a domain-general AVI processes (Exp. 2). While these findings were not expected to be captured by a single behavioral index, they revealed the multifaceted mechanism whereby hierarchical cortical activity supports audiovisual BM integration. They also advance our understanding of the emerging view that multi-timescale neural dynamics coordinate multisensory integration (Senkowski & Engel, 2024), especially from the perspective of natural stimuli processing.

Meanwhile, our finding that the cortical tracking of higher-order rhythmic structure in audiovisual BM specifically correlated with individual autistic traits extends previous behavioral evidence that ASD children exhibited reduced orienting to audiovisual synchrony in BM (Falck-Ytter et al., 2018), offering new evidence that individual differences in audiovisual BM processing are present at the neural level and associated with autistic traits. This finding opens the possibility of utilizing the cortical tracking of BM as a potential neural maker to assist the diagnosis of autism spectrum disorder (see more details in our Discussion Lines 334-346).

However, despite the main objective of the current study focusing on the neural processing of BM, we agree with the reviewer that it would be helpful to test the relevance of supra-additive cortical tracking on a behavioral task directly related to BM perception, for further justifying that inputs are being integrated in the service of behavior. In the current study, we adopted a color-change detection task entirely unrelated to audiovisual correspondence but only for maintaining participants’ attention. The advantage of this design is that it allows us to investigate whether and how the human brain integrates audiovisual BM information under task-irrelevant settings, as people in daily life can integrate such information even without a relevant task. However, this advantage is accompanied by a limitation: the task does not facilitate the direct examination of the correlation between neural responses and behavioral performance, since the task performance was generally high (mean accuracy >98% in all experiments). Future research could investigate this issue by introducing behavioral tasks more relevant to BM perception (e.g., Shen et al., 2023). They could also apply advanced neuromodulation techniques to elucidate the causal relevance of the cortical tracking effect to behavior (e.g., Ko sem et al., 2018, 2020).

We have discussed the abovementioned points as a separate paragraph in the revised manuscript (Lines 322-333). In addition, since the scope of the current study does not involve a causal correlation with behavioral performance, we have removed or modified the descriptions related to "functional relevance" in the manuscript (Abstract; Introduction, lines 101-103; Results, lines 239; Discussion, line 336; Supplementary Information, line 794、803). Moreover, we have strengthened the descriptions of the theoretical implications of the current findings in the abstract.

We hope these changes adequately address your concern.

References

Ahmed, F., Nidiffer, A. R., O’Sullivan, A. E., Zuk, N. J., & Lalor, E. C. (2023). The integration of continuous audio and visual speech in a cocktail-party environment depends on attention. Neuroimage, 274, 120143. https://doi.org/10.1016/j.neuroimage.2023.120143

Falck-Ytter, T., Nystro m, P., Gredeba ck, G., Gliga, T., Bo lte, S., & the EASE team. (2018). Reduced orienting to audiovisual synchrony in infancy predicts autism diagnosis at 3 years of age. Journal of Child Psychology and Psychiatry, 59(8), 872–880. https://doi.org/10.1111/jcpp.12863

Ko sem, A., Bosker, H., Jensen, O., Hagoort, P., & Riecke, L. (2020). Biasing the Perception of Spoken Words with Transcranial Alternating Current Stimulation. Journal of Cognitive Neuroscience, 32, 1–10. https://doi.org/10.1162/jocn_a_01579

Ko sem, A., Bosker, H. R., Takashima, A., Meyer, A., Jensen, O., & Hagoort, P. (2018). Neural Entrainment Determines the Words We Hear. Current Biology, 28(18), 2867-2875.e3. https://doi.org/10.1016/j.cub.2018.07.023

Laurienti, P. J., Perrault, T. J., Stanford, T. R., Wallace, M. T., & Stein, B. E. (2005). On the use of superadditivity as a metric for characterizing multisensory integration in functional neuroimaging studies. Experimental Brain Research, 166(3), 289–297. https://doi.org/10.1007/s00221-005-2370-2

Metzger, B. A., Magnotti, J. F., Wang, Z., Nesbitt, E., Karas, P. J., Yoshor, D., & Beauchamp, M. S. (2020). Responses to Visual Speech in Human Posterior Superior Temporal Gyrus Examined with iEEG Deconvolution. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience, 40(36), 6938–6948. https://doi.org/10.1523/JNEUROSCI.0279-20.2020

Perrault, T. J., Vaughan, J. W., Stein, B. E., & Wallace, M. T. (2003). Neuron-Specific Response Characteristics Predict the Magnitude of Multisensory Integration. Journal of Neurophysiology, 90(6), 4022–4026. https://doi.org/10.1152/jn.00494.2003

Perrault, T. J., Vaughan, J. W., Stein, B. E., & Wallace, M. T. (2005). Superior Colliculus Neurons Use Distinct Operational Modes in the Integration of Multisensory Stimuli. Journal of Neurophysiology, 93(5), 2575–2586. https://doi.org/10.1152/jn.00926.2004

Senkowski, D., & Engel, A. K. (2024). Multi-timescale neural dynamics for multisensory integration. Nature Reviews Neuroscience, 25(9), 625–642. https://doi.org/10.1038/s41583-024-00845-7

Shen, L., Lu, X., Wang, Y., & Jiang, Y. (2023). Audiovisual correspondence facilitates the visual search for biological motion. Psychonomic Bulletin & Review, 30(6), 2272–2281. https://doi.org/10.3758/s13423-023-02308-z

Stanford, T. R., Quessy, S., & Stein, B. E. (2005). Evaluating the Operations Underlying Multisensory Integration in the Cat Superior Colliculus. Journal of Neuroscience, 25(28), 6499–6508. https://doi.org/10.1523/JNEUROSCI.5095-04.2005

Wright, T. M., Pelphrey, K. A., Allison, T., McKeown, M. J., & McCarthy, G. (2003). Polysensory Interactions along Lateral Temporal Regions Evoked by Audiovisual Speech. Cerebral Cortex, 13(10), 1034–1043. https://doi.org/10.1093/cercor/13.10.1034

eLife Assessment

This study empirically investigates the neural noise hypothesis of developmental dyslexia using electroencephalography (EEG) during a spoken language task and 7T magnetic resonance spectroscopy (MRS). The convincing findings indicate no evidence of an imbalance between excitatory and inhibitory (E/I) brain activity in adolescents and young adults with dyslexia compared to controls, thereby challenging the neural noise hypothesis. This research is valuable for advancing our understanding of the neural mechanisms underlying dyslexia and offers broader insights into the neural processes involved in reading development.

Reviewer #1 (Public review):

Summary:

"Neural noise", here operationalized as an imbalance between excitatory and inhibitory neural activity, has been posited as a core cause of developmental dyslexia, a prevalent learning disability that impacts reading accuracy and fluency. This is study is the first to systematically evaluate the neural noise hypothesis of dyslexia. Neural noise was measured using neurophysiological (electroencephalography [EEG]) and neurochemical (magnetic resonance spectroscopy [MRS]) in adolescents and young adults with and without dyslexia. The authors did not find evidence of elevated neural noise in the dyslexia group from EEG or MRS measures, and Bayes factors generally informed against including the grouping factor in the models. Although the comparisons between groups with and without dyslexia did not support the neural noise hypothesis, a mediation model that quantified phonological processing and reading abilities continuously revealed that EEG beta power in the left superior temporal sulcus was positively associated with reading ability via phonological awareness. This finding lends support for analysis of associations between neural excitatory/inhibitory factors and reading ability along a continuum, rather than as with a case/control approach, and indicates the relevance of phonological awareness as an intermediate trait that may provide a more proximal link between neurobiology and reading ability. Further research is needed across developmental stages and over a broader set of brain regions to more comprehensively assess the neural noise hypothesis of dyslexia, and alternative neurobiological mechanisms of this disorder should be explored.

Strengths:

The inclusion of multiple methods of assessing neural noise (neurophysiological and neurochemical) is a major advantage of this paper. MRS at 7T confers an advantage of more accurately distinguishing and quantifying glutamate, which is a primary target of this study. In addition, the subject-specific functional localization of the MRS acquisition is an innovative approach. MRS acquisition and processing details are noted in the supplementary materials using according to the experts' consensus recommended checklist (https://doi.org/10.1002/nbm.4484). Commenting on rigor the EEG methods is beyond my expertise as a reviewer.<br /> Participants recruited for this study included those with a clinical diagnosis of dyslexia, which strengthens confidence in the accuracy of the diagnosis. The assessment of reading and language abilities during the study further confirms the persistently poorer performance of the dyslexia group compared to the control group.<br /> The correlational analysis and mediation analysis provide complementary information to the main case-control analyses, and the examination of associations between EEG and MRS measures of neural noise is novel and interesting.<br /> The authors follow good practice for open science, including data and code sharing. They also apply statistical rigor, using Bayes Factors to support conclusions of null evidence rather than relying only on non-significant findings. In the discussion, they acknowledge the limitations and generalizability of the evidence and provide directions for future research on this topic.

Appraisal:

The authors present a thorough evaluation of the neural noise hypothesis of developmental dyslexia in a sample of adolescents and young adults using multiple methods of measuring excitatory/inhibitory imbalances as an indicator of neural noise. The authors concluded that there was not support for the neural noise hypothesis of dyslexia in their study based on null significance and Bayes factors. This conclusion is justified, and further research is called for to more broadly evaluate the neural noise hypothesis in developmental dyslexia.

Impact:

This study provides an exemplar foundation for the evaluation of the neural noise hypothesis of dyslexia. Other researcher may adopt the model applied in this paper to examine neural noise in various populations with/without dyslexia, or across a continuum of reading abilities, to more thoroughly examine evidence (or lack thereof) for this hypothesis. Notably, the lack of evidence here does not rule out the possibility for a role of neural noise in dyslexia, and the authors point out that presentation with co-occurring conditions, such as ADHD, may contribute to neural noise in dyslexia. Dyslexia remains a multi-faceted and heterogenous neurodevelopmental condition, and many genetic, neurobiological and environmental factors play a role. This study demonstrates one step toward evaluating neurobiological mechanisms that may contribute to reading difficulties.

Reviewer #2 (Public review):

Summary:

This study utilized two complimentary techniques (EEG and 7T MRI/MRS) to directly test a theory of dyslexia: the neural noise hypothesis. The authors report finding no evidence to support an excitatory/inhibitory balance, as quantified by beta in EEG and Glutamate/GABA ratio in MRS. This is important work and speaks to one potential mechanism by which increased neural noise may occur in dyslexia.

Strengths:

This is a well-conceived study with in depth analyses and publicly available data for independent review. The authors provide transparency with their statistics and display the raw data points along with the averages in figures for review and interpretation. The data suggest that an E/I balance issue may not underlie deficits in dyslexia and is a meaningful and needed test of a possible mechanism for increased neural noise.

Weaknesses:

The researchers did not include a visual print task in the EEG task, which limits analysis of reading specific regions such as the visual word form area, which is a commonly hypoactivated region in dyslexia. This region is a common one of interest in dyslexia, yet the researchers measured the I/E balance in only one region of interest, specific to the language network.

Reviewer #3 (Public review):

Summary:

This study by Glica and colleagues utilized EEG (i.e., Beta power, Gamma power, and aperiodic activity) and 7T MRS (i.e., MRS IE ratio, IE balance) to reevaluate the neural noise hypothesis in Dyslexia. Supported by Bayesian statistics, their results show convincing evidence of no differences in EI balance between groups, challenging the neural noise hypothesis.

Strengths:

Combining EEG and 7T MRS, this study utilized both the indirect (i.e., Beta power, Gamma power, and aperiodic activity) and direct (i.e., MRS IE ratio, IE balance) measures to reevaluate the neural noise hypothesis in Dyslexia.

Author response:

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

Reviewer #1 (Public review):

Summary:

"Neural noise", here operationalized as an imbalance between excitatory and inhibitory neural activity, has been posited as a core cause of developmental dyslexia, a prevalent learning disability that impacts reading accuracy and fluency. This is study is the first to systematically evaluate the neural noise hypothesis of dyslexia. Neural noise was measured using neurophysiological (electroencephalography [EEG]) and neurochemical (magnetic resonance spectroscopy [MRS]) in adolescents and young adults with and without dyslexia. The authors did not find evidence of elevated neural noise in the dyslexia group from EEG or MRS measures, and Bayes factors generally informed against including the grouping factor in the models. Although the comparisons between groups with and without dyslexia did not support the neural noise hypothesis, a mediation model that quantified phonological processing and reading abilities continuously revealed that EEG beta power in the left superior temporal sulcus was positively associated with reading ability via phonological awareness. This finding lends support for analysis of associations between neural excitatory/inhibitory factors and reading ability along a continuum, rather than as with a case/control approach, and indicates the relevance of phonological awareness as an intermediate trait that may provide a more proximal link between neurobiology and reading ability. Further research is needed across developmental stages and over a broader set of brain regions to more comprehensively assess the neural noise hypothesis of dyslexia, and alternative neurobiological mechanisms of this disorder should be explored.

Strengths:

The inclusion of multiple methods of assessing neural noise (neurophysiological and neurochemical) is a major advantage of this paper. MRS at 7T confers an advantage of more accurately distinguishing and quantifying glutamate, which is a primary target of this study. In addition, the subject-specific functional localization of the MRS acquisition is an innovative approach. MRS acquisition and processing details are noted in the supplementary materials using according to the experts' consensus recommended checklist (https://doi.org/10.1002/nbm.4484). Commenting on rigor the EEG methods is beyond my expertise as a reviewer.

Participants recruited for this study included those with a clinical diagnosis of dyslexia, which strengthens confidence in the accuracy of the diagnosis. The assessment of reading and language abilities during the study further confirms the persistently poorer performance of the dyslexia group compared to the control group.

The correlational analysis and mediation analysis provide complementary information to the main case-control analyses, and the examination of associations between EEG and MRS measures of neural noise is novel and interesting.

The authors follow good practice for open science, including data and code sharing. They also apply statistical rigor, using Bayes Factors to support conclusions of null evidence rather than relying only on non-significant findings. In the discussion, they acknowledge the limitations and generalizability of the evidence and provide directions for future research on this topic.

Weaknesses:

Though the methods employed in the paper are generally strong, the MRS acquisition was not optimized to quantify GABA, so the findings (or lack thereof) should be interpreted with caution. Specifically, while 7T MRS affords the benefit of quantifying metabolites, such as GABA, without spectral editing, this quantification is best achieved with echo times (TE) of 68 or 80 ms in order to minimize the spectral overlap between glutamate and GABA and reduce contamination from the macromolecular signal (Finkelman et al., 2022, https://doi.org/10.1016/j.neuroimage.2021.118810). The data in the present study were acquired at TE=28 ms, and are therefore likely affected by overlapping Glu and GABA peaks at 2.3 ppm that are much more difficult to resolve at this short TE, which could directly affect the measures that are meant to characterize the Glu/GABA+ ratio/imbalance. In future research, MRS acquisition schemes should be optimized for the acquisition of Glutamate, GABA, and their relative balance.

As the authors note in the discussion, additional factors such as MRS voxel location, participant age, and participant sex could influence associations between neural noise and reading abilities and should be considered in future studies.

We have modified Figure 2 and revised the paragraph discussing the MRS methodological limitations in accordance with Reviewer #1's recommendations. Additionally, we have included the CRLB and linewidth thresholds in the Results section. Furthermore, a new figure showing the correlations between EEG and MRS biomarkers has been added (Figure 3).

Appraisal:

The authors present a thorough evaluation of the neural noise hypothesis of developmental dyslexia in a sample of adolescents and young adults using multiple methods of measuring excitatory/inhibitory imbalances as an indicator of neural noise. The authors concluded that there was not support for the neural noise hypothesis of dyslexia in their study based on null significance and Bayes factors. This conclusion is justified, and further research is called for to more broadly evaluate the neural noise hypothesis in developmental dyslexia.

Impact:

This study provides an exemplar foundation for the evaluation of the neural noise hypothesis of dyslexia. Other researcher may adopt the model applied in this paper to examine neural noise in various populations with/without dyslexia, or across a continuum of reading abilities, to more thoroughly examine evidence (or lack thereof) for this hypothesis. Notably, the lack of evidence here does not rule out the possibility for a role of neural noise in dyslexia, and the authors point out that presentation with co-occurring conditions, such as ADHD, may contribute to neural noise in dyslexia. Dyslexia remains a multi-faceted and heterogenous neurodevelopmental condition, and many genetic, neurobiological and environmental factors play a role. This study demonstrates one step toward evaluating neurobiological mechanisms that may contribute to reading difficulties.

Reviewer #2 (Public review):

Summary:

This study utilized two complimentary techniques (EEG and 7T MRI/MRS) to directly test a theory of dyslexia: the neural noise hypothesis. The authors report finding no evidence to support an excitatory/inhibitory balance, as quantified by beta in EEG and Glutamate/GABA ratio in MRS. This is important work and speaks to one potential mechanism by which increased neural noise may occur in dyslexia.

Strengths:

This is a well conceived study with in depth analyses and publicly available data for independent review. The authors provide transparency with their statistics and display the raw data points along with the averages in figures for review and interpretation. The data suggest that an E/I balance issue may not underlie deficits in dyslexia and is a meaningful and needed test of a possible mechanism for increased neural noise.

Weaknesses:

The researchers did not include a visual print task in the EEG task, which limits analysis of reading specific regions such as the visual word form area, which is a commonly hypoactivated region in dyslexia. This region is a common one of interest in dyslexia, yet the researchers measured the I/E balance in only one region of interest, specific to the language network.

Reviewer #3 (Public review):

Summary:

This study by Glica and colleagues utilized EEG (i.e., Beta power, Gamma power, and aperiodic activity) and 7T MRS (i.e., MRS IE ratio, IE balance) to reevaluating the neural noise hypothesis in Dyslexia. Supported by Bayesian statistics, their results show convincing evidence of no differences in EI balance between groups, challenging the neural noise hypothesis.

Strengths:

Combining EEG and 7T MRS, this study utilized both the indirect (i.e., Beta power, Gamma power, and aperiodic activity) and direct (i.e., MRS IE ratio, IE balance) measures to reevaluating the neural noise hypothesis in Dyslexia.

eLife Assessment

In this valuable study, the authors studied a novel Zn2+- and NAD+-independent KDAC protein, AhCobQ, in Aeromonas hydrophila, which lacks homology with eukaryotic counterparts, thus underscoring its unique evolutionary trajectory within the bacterial domain. They attempt to demonstrate deacetylase activity, however, whilst the revised manuscript has been improved, significant aspects of the data are still incomplete and require further refinement. The work will be of interest to microbiologists studying metabolism and post-translational modifications.

Reviewer #1 (Public review):

Summary:

This study by Wang et al. identifies a new type of deacetylase, CobQ, in Aeromonas hydrophila. Notably, the identification of this deacetylase reveals a lack of homology with eukaryotic counterparts, thus underscoring its unique evolutionary trajectory within the bacterial domain.

Strengths:

The manuscript convincingly illustrates CobQ's deacetylase activity through robust in vitro experiments, establishing its distinctiveness from known prokaryotic deacetylases. Additionally, the authors elucidate CobQ's potential cooperation with other deacetylases in vivo to regulate bacterial cellular processes. Furthermore, the study highlights CobQ's significance in the regulation of acetylation within prokaryotic cells.

Weaknesses:

The problem I raised has been well resolved. I have no further questions.

Reviewer #2 (Public review):

In recent years, lots of researchers tried to explore the existence of new acetyltransferase and deacetylase by using specific antibody enrichment technologies and high resolution mass spectrometry. Here is an example for this effort. Yuqian Wang et al. studied a novel Zn2+- and NAD+-independent KDAC protein, AhCobQ, in Aeromonas hydrophila. They studied the biological function of AhCobQ by using biochemistry method and MS identification technology to confirm it. These results extended our understanding of the regulatory mechanism of bacterial lysine acetylation modifications. However, I find this conclusion is a little speculative, and unfortunately it also doesn't totally support the conclusion as the authors provided.

Major concerns:

-It is a little arbitrary to come to the title "Aeromonas hydrophila CobQ is a new type of NAD+- and Zn2+-independent protein lysine deacetylase in prokaryotes." It should be modified to delete the "in the prokaryotes" except that the authors get new more evidence in the other prokaryotes for the existence of the AhCobQ.<br /> -I was confused about the arrangement of the supplementary results. Because there are no citations for Figures S9-S19.<br /> -Same to the above, there are no data about Tables S1-S6.<br /> -All the load control is not integrated. Please provide all of the load controls with whole PAGE gel or whole membrane western blot results. Without these whole results, it is not convincing to come the conclusion as the authors mentioned in the context.<br /> -Thoroughly review the materials & methods section. It is unclear to me what exactly the authors describe in the method. All the experimental designs and protocols should be described in detail, including growth conditions, assay conditions, and purification conditions, etc.<br /> -Include relevant information about the experiments performed in the figure legends, such as experimental conditions, replicates, etc. Often it is not clear what was done based on the figure legend description.

eLife Assessment

This work shows that newborn Thbs4-positive astrocytes generated in the adult subventricular zone (SVZ) respond to middle carotid artery occlusion (MCAO) by secreting hyaluronan at the lesion penumbra, and that hyaluronin is a chemoattractant to SVZ astrocytes. These findings are valuable, despite being mostly descriptive, as they point to a relevant function of SVZ newborn astrocytes in the modulation of the glial scar after brain ischemia. The methods, data and analyses are convincing and broadly support the claims made by the authors with only some weaknesses.

Reviewer #1 (Public review):

Summary:

The authiors show that SVZ derived astrocytes respond to a middle carotid artery occlusion (MCAO) hypoxia lesion by secreting and modulating hyaluronan at the edge of the lesion (penumbra) and that hyaluronin is a chemoattractant to SVZ astrocytes. They use lineage tracing of SVZ cells to determine their origin. They also find that SVZ derived astrocytes express Thbs-4 but astrocytes at the MCAO-induced scar do not. Also, they demonstrate that decreased HA in the SVZ is correlated with gliogenesis. While much of the paper is descriptive/correlative they do overexpress Hyaluronan synthase 2 via viral vectors and show this is sufficient to recruit astrocytes to the injury. Interestingly, astrocytes preferred to migrate to the MCAO than to the region of overexpressed HAS2.

Strengths:

The field has largely ignored the gliogenic response of the SVZ, especially with regards to astrocytic function. These cells and especially newborn cells may provide support for regeneration. Emigrated cells from the SVZ have been shown to be neuroprotective via creating pro-survival environments, but their expression and deposition of beneficial extracellular matrix molecules is poorly understood. Therefore, this study is timely and important. The paper is very well written and the flow of results logical.

Comments on revised version:

The authors have addressed my points and the paper is much improved. Here are the salient remaining issues that I suggest be addressed.

The authors have still not shown, using loss of function studies, that Hyaluronan is necessary for SVZ astrogenesis and or migration to MCAO lesions.

(1) The co-expression of EGFr with Thbs4 and the literature examination is useful.

(2) Too bad they cannot explain the lack of effect of the MCAO on type C cells. The comparison with kainate-induced epilepsy in the hippocampus may or may not be relevant.

(3) Thanks for including the orthogonal confocal views in Fig S6D.

(4) The statement that "BrdU+/Thbs4+ cells mostly in the dorsal area" and therefore they mostly focused on that region is strange. Figure 8 clearly shows Thbs4 staining all along the striatal SVZ. Do they mean the dorsal segment of the striatal SVZ or the subcallosal SVZ? Fig. 4b and Fig 4f clearly show the "subcallosal" area as the one analysed but other figures show the dorsal striatal region (Fig. 2a). This is important because of the well-known embryological and neurogenic differences between the regions.

(5) It is good to know that the harsh MCAO's had already been excluded.

(6) Sorry for the lack of clarity - in addition to Thbs4, I was referring to mouse versus rat Hyaluronan degradation genes (Hyal1, Hyal2 and Hyal3) and hyaluronan synthase genes (HAS1 and HAS2) in order to address the overall species differences in hyaluronan biology thus justifying the "shift" from mouse to rat. You examine these in the (weirdly positioned) Fig. 8h,i. Please add a few sentences on mouse vs rat Thbs4 and Hyaluronan relevant genes.

(7) Thank you for the better justification of using the naked mole rat HA synthase.

Reviewer #3 (Public review):

Summary:

The authors aimed to study the activation of gliogenesis and the role of newborn astrocytes in a post-ischemic scenario. Combining immunofluorescence, BrdU-tracing and genetic cellular labelling, they tracked the migration of newborn astrocytes (expressing Thbs4) and found that Thbs4-positive astrocytes modulate the extracellular matrix at the lesion border by synthesis but also degradation of hyaluronan. Their results point to a relevant function of SVZ newborn astrocytes in the modulation of the glial scar after brain ischemia. This work's major strength is the fact that it is tackling the function of SVZ newborn astrocytes, whose role is undisclosed so far.

Strengths:

The article is innovative, of good quality, and clearly written, with properly described Materials and Methods, data analysis and presentation. In general, the methods are designed properly to answer the main question of the authors, being a major strength. Interpretation of the data is also in general well done, with results supporting the main conclusions of this article.

In this revised version, the points raised/weaknesses were clarified and discussed in the article.

Author response:

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

Reviewer #1 (Public Review):

The authors show that SVZ-derived astrocytes respond to a middle carotid artery occlusion (MCAO) hypoxia lesion by secreting and modulating hyaluronan at the edge of the lesion (penumbra) and that hyaluronan is a chemoattractant to SVZ astrocytes. They use lineage tracing of SVZ cells to determine their origin. They also find that SVZ-derived astrocytes express Thbs-4 but astrocytes at the MCAO-induced scar do not. Also, they demonstrate that decreased HA in the SVZ is correlated with gliogenesis. While much of the paper is descriptive/correlative they do overexpress Hyaluronan synthase 2 via viral vectors and show this is sufficient to recruit astrocytes to the injury. Interestingly, astrocytes preferred to migrate to the MCAO than to the region of overexpressed HAS2.

Strengths:

The field has largely ignored the gliogenic response of the SVZ, especially with regard to astrocytic function. These cells and especially newborn cells may provide support for regeneration. Emigrated cells from the SVZ have been shown to be neuroprotective via creating pro-survival environments, but their expression and deposition of beneficial extracellular matrix molecules are poorly understood. Therefore, this study is timely and important. The paper is very well written and the flow of results is logical.

Weaknesses:

The main problem is that they do not show that Hyaluronan is necessary for SVZ astrogenesis and or migration to MCAO lesions. Such loss of function studies have been carried out by studies they cite (e.g. Girard et al., 2014 and Benner et al., 2013). Similar approaches seem to be necessary in this work. 

We appreciate the comments by the reviewer. The article is, indeed, largely descriptive since we attempt to describe in detail what happens to newborn astrocytes after MCAO. Still, we have not attempted any modification to the model, such as amelioration of ischemic damage. This is a limitation of the study that we do not hide. However, we use several experimental approaches, such as lineage tracing and hyaluronan modification, to strengthen our conclusions.

Regarding the weaknesses found by the reviewer, we do not claim that hyaluronan is necessary for SVZ astrogenesis. Indeed, we observe that when the MCAO stimulus (i.e. inflammation) is present, the HMW-HA (AAV-Has2) stimulus is less powerful (we discuss this in line 330-332). We do claim, and we believe we successfully demonstrate, the reverse situation: that SVZ astrocytes modulate hyaluronan, not at the SVZ but at the site of MCAO, i.e. the scar. However, regarding whether hyaluronan is necessary for SVZ astrogenesis, we only show a correlation between its degradation and the time-course of astrogenesis. We suggest this result as a starting point for a follow-up study. We have included a phrase in the discussion (line 310), stating that further experiments are needed to fully establish a link between hyaluronan and astrogenesis in the SVZ.

Major points:

(1) How good of a marker for newborn astrocytes is Thbs4? Did you co-label with B cell markers like EGFr? Is the Thbs4 gene expressed in B cells? Do scRNAseq papers show it is expressed in B cells? Are they B1 or B2 cells?

We chose Thbs4 as a marker of newborn astrocytes based on published research (Beckervordersanforth et al., 2010; Benner et al., 2013; Llorens-Bobadilla et al. 2015, Codega et al, 2014; Basak et al., 2018; Mizrak et al., 2019; Kjell et al., 2020; Cebrian-Silla et al., 2021). From those studies, at least 3 associate Thbs4 to B-type cells based on scRNAseq data (LlorensBobadilla et al. 2015; Cebrian-Silla et al., 2021; Basak et al., 2018). We have included a sentence about this and the associated references, in line 92. 

We co-label Thbs4 with EGFR, but in the context of MCAO. We observed an increase of EGFR expression with MCAO, similar to the increase in Thbs4 alongside ischemia (see author ). We did not include this figure in the manuscript since we did not have available tissue from all the time points we used (7d, 60d post-ischemia). 

Author response image 1.

Thbs4 cells, in basal and ischemic conditions, only represent a small amount of IdU-positive cells (Fig 3F), suggesting that they are mostly quiescent cells, i.e., B1 cells. However, the scRNAseq literature is not consistent about this.

(2) It is curious that there was no increase in Type C cells after MCAO - do the authors propose a direct NSC-astrocyte differentiation?

Type C cells are fast-proliferating cells, and our BrdU/IdU experiment (Fig. 3) suggests that Thbs4 cells are slow-proliferating cells. Some authors suggest (Encinas lab, Spain) that when the hippocampus is challenged by a harsh stimulus, such as kainate-induced epilepsy, the NSCs differentiate directly into reactive astrocytes and deplete the DG neurogenic niche (Encinas et al., 2011, Cell Stem Cell; Sierra et al., 2015, Cell Stem Cell). We believe this might be the case in our MCAO model and the SVZ niche, since we observe a decrease in DCX labeling in the olfactory bulb (Fig S5) and an increase in astrocytes in the SVZ, which migrate to the ischemic lesion. We did not want to overcomplicate an already complicated paper, dwelling with direct NSC-astrocyte differentiation or with the reactive status of these newborn astrocytes. 

(3) The paper would be strengthened with orthogonal views of z projections to show colocalization.

We thank the reviewer for this observation. We have now included orthogonal projections in the critical colocalization IF of CD44 and hyaluronan (hyaluronan internalization) in Fig S6D, and a zoomed-in inset. Hyaluronan membrane synthesis is already depicted with orthogonal projection in Fig 6F.

(4) It is not clear why the dorsal SVZ is analysed and focused on in Figure 4. This region emanates from the developmental pallium (cerebral cortex anlagen). It generates some excitatory neurons early postnatally and is thought to have differential signalling such as Wnt (Raineteau group).

We decided to analyze in depth the dorsal SVZ after the BrdU experiment (Fig S3), where we observed an increase in BrdU+/Thbs4+ cells mostly in the dorsal area. Hence, the electrodes for electroporation were oriented in such a way as to label the dorsal area. We appreciate the paper by Raineteau lab, but we assume that this region may potentially exploit other roles (apart from excitatory neurons generated early postnatally) depending on the developmental stage (our model is in adults) and/or pathological conditions (MCAO). 

(5) Several of the images show the lesion and penumbra as being quite close to the SVZ. Did any of the lesions contact the SVZ? If so, I would strongly recommend excluding them from the analysis as such contact is known to hyperactivate the SVZ.

We thank the referee for the suggestion to exclude the harsher MCAO-lesioned animals from the analysis. Indeed, the MCAO ischemia, methodologically, can generate different tissue damages that cannot be easily controlled. Thus, based on TTC staining, we had already excluded the more severe tissue damage that contacted the SVZ, based on TTC staining.

(6) The authors switch to a rat in vitro analysis towards the end of the study. This needs to be better justified. How similar are the molecules involved between mouse and rat?

We chose the rat culture since it is a culture that we have already established in our lab, and that in our own hands, is much more reproducible than the mouse brain cell culture that we occasionally use (for transgenic animals only). Benito-Muñoz et al., Glia. 2016; Cavaliere et al., Front Cell Neurosci. 2013. It is true that there could be differences between the rat and mouse Thbs4-cell physiology, despite a 96% identity between rat and mouse Thbs4 protein sequence (BLASTp). In vitro, we only confirm the capacity of astrocytes to internalize hyaluronan, which was a finding that we did not expect in our in vivo experiments. Indeed, these observations, notwithstanding the obvious differences between in vivo and in vitro scenarios, suggest that the HA internalization by astrocytes is a cross-species event, at least in rodents. Regarding HA, hyaluronan is similar in all species, since it’s a glycan (this is why there are no antibodies against HA, and ones has to rely on binding proteins such as HABP to label it).

(7) Similar comment for overexpression of naked mole rat HA.

We chose the naked mole rat Hyaluronan synthase (HAS), because it is a HAS that produces HA of very high molecular weight, similar to the one found accumulated in the glial scar, at the lesion border. The naked-mole rat HAS used in mice (Gorbunova Lab) is a known tool in the ECM field. (Zhang et al, 2023, Nature; Tian et al., 2013, Nature).

Reviewer 1 (Recommendation to authors):

(1) Line 22: most of the cells that migrate out of the SVZ are not stem cells but cells further along in the lineage - neuroblasts and glioblasts.

We thank the reviewer for this clarification. We have modified the abstract accordingly. 

(2) In Figure 3d the MCAO group staining with GFAP looks suspiciously like ependymal cells which have been shown to be dramatically activated by stroke models.

The picture does show ependymal cells, which are located next to the ventricle and are indeed very proliferative in stroke. However, these cells do not express Thbs4 (Shah et al., 2018, Cell). In the quantifications from the SVZ of BrdU and IdU injected animals (Fig 3e and f), we only take into account Thbs4+ GFAP+ cells, no GFAP+ only. 

(3) The TTC injury shown in Figure 5c is too low mag.

We apologize for the low mag. We have increased the magnification two-fold without compromising resolution. The problem might also have arisen from the compression of TIF into JPEG in the PDF export process. We will address this in the revised version by carefully selecting export settings. The images we used are all publication quality (300 ppi).

(4) How specific to HA is HABP?

Hyaluronic Acid Binding Protein is a canonical marker for hyaluronan that is used also in ELISA to quantify it specifically, since it does not bind other glycosaminoglycans. The label has been used for years in the field for immunochemistry, and some controls and validations have been published: Deepa et al., 2006, JBC performed appropriate controls of HABP-biotin labeling using hyaluronidase (destroys labeling) and chondroitinase (preserves labeling). Soria et al., 2020, Nat Commun checked that (i) streptavidin does not label unspecifically, and (ii) that HABP staining is reduced after hyaluronan depletion in vivo with HAS inhibitor 4MU.

(5) A number of images are out of focus and thus difficult to interpret (e.g. SFig. 4e).

This is true. We realized that the PDF conversion process for the preprint version has severely compressed the larger images, such as the one found in Fig. S4e. We have submitted a revised version in a better-quality PDF (the final paper will have the original TIFF files). We apologize for the technical problem.

(6) "restructuration" is not a word.

We apologize for the mistake and thank the reviewer for the correction. We corrected “restructuration” with “reorganization” in line 67.

(7) While much of the manuscript is well-written and logical it could use an in-depth edit to remove awkward words and phrasings.

A native English speaker has revised the manuscript to correct these awkward phrases. All changes are labeled in red in the revised version.

(8) Please describe why and how you used skeleton analysis for HABP in the methods, this will be unfamiliar to most readers. The one-sentence description in the methods is insufficient.

We have modified the text accordingly, explaining in depth the logic behind the skeleton analysis. (Line 204). We also added several lines of text describing in detail the image analysis (CD44/HABP spots, fractal dimension, masks for membranal HABP, among others, in lines 484494) 

Reviewer #2 (Public Review)

Summary:

In their manuscript, Ardaya et al have addressed the impact of ischemia-induced gliogenesis from the adult SVZ and their effect on the remodeling of the extracellular matrix (ECM) in the glial scar. They use Thbs4, a marker previously identified to be expressed in astrocytes of the SVZ, to understand its role in ischemia-induced gliogenesis. First, the authors show that Thbs4 is expressed in the SVZ and that its expression levels increase upon ischemia. Next, they claim that ischemia induces the generation of newborn astrocyte from SVZ neural stem cells (NSCs), which migrate toward the ischemic regions to accumulate at the glial scar. Thbs4-expressing astrocytes are recruited to the lesion by Hyaluronan where they modulate ECM homeostasis.

Strengths:

The findings of these studies are in principle interesting and the experiments are in principle good.

Weaknesses:

The manuscript suffers from an evident lack of clarity and precision in regard to their findings and their interpretation.

We thank the reviewer for the valuable feedback. We hope the changes proposed improve clarity and precision throughout the manuscript.

(1) The authors talk about Thbs4 expression in NSCs and astrocytes, but neither of both is shown in Figure 1, nor have they used cell type-specific markers.

As we reported also to Referee #1 (major point 1), Thbs4 is widely considered in literature as a valid marker for newly formed astrocytes (Beckervordersanforth et al., 2010; Benner et al., 2013; Llorens-Bobadilla et al. 2015, Codega et al, 2014; Basak et al., 2018; Mizrak et al., 2019; Kjell et al., 2020; Cebrian-Silla et al., 2021). Some of the studies mentioned here and discussed in the manuscript text, also associate Thbs4 to B-type cells based on scRNAseq data (LlorensBobadilla et al. 2015; Cebrian-Silla et al., 2021; Basak et al., 2018). Moreover, we also showed colocalization of Thbs4 with activated stem cells marker nestin (Fig.2), glial marker GFAP (Fig. 3) and with dorsal NSCs marker tdTOM (from electroporation, Fig. 4). 

(2) Very important for all following experiments is to show that Thbs4 is not expressed outside of the SVZ, specifically in the areas where the lesion will take place. If Thbs4 was expressed there, the conclusion that Thbs4+ cells come from the SVZ to migrate to the lesion would be entirely wrong.

In Figure 1a, we show that Thbs4 is expressed in the telencephalon, exclusively in the neurogenic regions like SVZ, RMS and OB, together with cerebellum and VTA, which are likely not directly topographically connected to the damaged area (cortex and striatum). Regarding the origin of Thbs4+ cells, we demonstrated their SVZ origin by lineage tracking experiments after in vivo cell labeling (Fig. 4).

(3) Next, the authors want to confirm the expression level of Thbs4 by electroporation of pThbs4-eGFP at P1 and write that this results in 20% of total cells expressing GFP, especially in the rostral SVZ. I do not understand the benefit of this sentence. This may be a confirmation of expression, but it also shows that the GFP+ cells derive from early postnatal NSCs.

Furthermore, these cells look all like astrocytes, so the authors could have made a point here that indeed early postnatal NSCs expressing Thbs4 generate astrocytes alongside development. Here, it would have been interesting to see how many of the GFP+ cells are still NSCs.

We thank the reviewer for this useful remark. We have rephrased this paragraph in the results section (Line 99).

(4) In the next chapter, the authors show that Thbs4 increases in expression after brain injury. I do not understand the meaning of the graphs showing expression levels of distinct cell types of the neuronal lineage. Please specify why this is interesting and what to conclude from that.

Also here, the expression of Thbs4 should be shown outside of the SVZ as well.

In Fig 2, we show the temporal expression of two markers (besides Thbs4) in the SVZ. Nestin and DCX are the gold standard markers for NSCs, with DCX present in neuroblasts. This is already explained in line 119. What we didn’t explain, and now we say in line 124, is that Nestin and DCX decrease immediately after ischemia (7d time-point). This probably means that the NSCs stop differentiating into neuroblast to favor glioblast formation. This is also supported by the experiments in the olfactory bulb depicted in Fig. S5C-H.

(5) Next, the origin of newborn astrocytes from the SVZ upon ischemia is revealed. The graphs indicate that the authors perfused at different time points after tMCAO. Did they also show the data of the early time points? If only of the 30dpi, they should remove the additional time points indicated in the graph. In line 127 they talk about the origin of newborn astrocytes. Until now they have not even mentioned that new astrocytes are generated. Furthermore, the following sentences are imprecise: first they write that the number of slow proliferation NSCs is increased, then they talk about astrocytes. How exactly did they identify astrocytes and separate them from NSCs? Morphologically? Because both cell types express GFAP and Thbs4.

The same problem also occurs throughout the next chapter.

We thank the reviewer for this interesting comment. The experiment in Fig 3 combines BrdU and IdU. This is a tricky experiment, since chronic BrdU is normally analyzed after 30d, since the experimenter must wait for the wash out of BrdU (it labels slow-proliferating cells). Since we also wanted to label fast proliferative cells with IdU, we used IP injections of this nucleotide at the different time points, and perfused the day after. It wouldn’t make sense to show BrdU at earlier time points. We do so in Fig 3e, just to colocalize with Thbs4 to read the tendency of the experiment. However, the quantification of BrdU (not of IdU) is done only at 30 DPI, which is explained in the methods (line 407).

“In line 127, they talk about the origin of newborn astrocytes…” 

Indeed, we wanted to introduce in the paragraph title that ischemia induced the generation of new astrocytes, which is more clearly described in the text. We changed the paragraph title with “Characterization of Ischemia-induced cell populations”

“How exactly did they identify astrocytes and separate them from NSC?” 

With this experiment and using two different protocols to label proliferating cells (BrdU vs IdU) we wanted to track the precursor cells that derivate to astrocytes and that already expressed the marker Thbs4. Indeed, the different increase and rate of proliferation is only related to the progenitor cells that lately will differentiate in astrocytes. In this experiment we only referred to the astrocytes in the last sentence “These results suggest that, after ischemia, Thbs4positive astrocytes derive from the slow proliferative type B cells”

(6) "These results suggest that ischemia-induced astrogliogenesis in the SVZ occurs in type B cells from the dorsal region, and that these newborn Thbs4-positive astrocytes migrate to the ischemic areas." This sentence is a bit dangerous and bares at least one conceptual difficulty: if NSCs generate astrocytes under normal conditions and along the cause of postnatal development (which they do), then local astrocytes  (expressing the tdTom because they stem from a postnatal NSC ), may also react to MCAO and proliferate locally. So the astrocytes along the scar do not necessarily come from adult NSCs upon injury but from local astrocytes.  If the authors state that NSCs generate astrocytes that migrate to the lesion, I would like to see that no astrocytes inside the striatum carry the tdTom reporter before MCAO is committed.

We understand the referee’s concern about the postnatal origin of astrocytes that can also be labeled with tdTom. Our hypothesis, tested at the beginning of the paper, is that SVZ-derived astrocytes derive from slow proliferative NSC. Thus, it is reasonable that Tom+ cells can reach the cortical region in such a short time frame. This is why we assumed that local astrocytes can’t be positive for tdTom. We characterized the expression of tfTom in sham animals and we observed few tdTom+ cells in the cortex and striatum (Author response image 2 and Figure S4). The expression of tdTom mainly remains in the SVZ and the corpus callosum under physiological conditions. However, proliferation of local astrocytes labeled with tdTom expression (early postnatally astrocytes) could explain the small percentage of tdTom+ cells in the ischemic regions that do not express Thbs4, even though this percentage could represent other cell types such as OPCs or oligodendrocytes. 

Author response image 2.

(7) If astrocytes outside the SVZ do not express Thbs4, I would like to see it.  Otherwise, the discrimination of SVZ-derive GFAP+/Thbs4+ astrocytes and local astrocytes expressing only GFAP is shaky.

Regarding Thbs4 outside the SVZ, we already answered this in point 2 (please refer to Fig 1A). We also quantified the expression of Thbs4+/GFAP+ astrocytes in the corpus callosum, cortex and striatum of sham and MCAO mice (Figure S5a-b) and we did not observe that local astrocytes express Thbs4 under physiological conditions.

(8) Please briefly explain what a Skeleton analysis and a Fractal dimension analysis is, and what it is good for.

We apologized for the brief information on Skeleton and Fractal dimension analysis. We included a detailed explanation of these analyses in methods (line 484-494).

(9) The chapter on HA is again a bit difficult to follow. Please rewrite to clarify who produces HA and who removes it by again showing all astrocyte subtypes (GFAP+/Thbs4+ and GFAP+/Thbs4-).

We apologize for the lack of clarity. We rewrote some passages of those chapters (changes in red), trying to convey the ideas more clearly. We also changed a panel in Figure S6b-c to clarify all astrocytes subtypes that internalize hyaluronan (Thbs4+/GFAP+ and Thbs4-/GFAP+). See Author response image 3.

Author response image 3.

(10) Why did the authors separate dorsal, medial, and ventral SVZ so carefully? Do they comment on it? As far as I remember, astrogenesis in physiological conditions has some local preferences (dorsal?)

We performed the electroporation protocol in the dorsal SVZ based on previous results (Figure 3 and Figure S3). NSC produce specific neurons in the olfactory bulb according to their location in the SVZ. However, postnatal production of astrocytes mainly occurs through local astrocytes proliferation and the SVZ contribution is very limited at this time point. 

Reviewer #3 (Public Review)

Summary:

The authors aimed to study the activation of gliogenesis and the role of newborn astrocytes in a post-ischemic scenario. Combining immunofluorescence, BrdU-tracing, and genetic cellular labelling, they tracked the migration of newborn astrocytes (expressing Thbs4) and found that Thbs4-positive astrocytes modulate the extracellular matrix at the lesion border by synthesis but also degradation of hyaluronan. Their results point to a relevant function of SVZ newborn astrocytes in the modulation of the glial scar after brain ischemia. This work's major strength is the fact that it is tackling the function of SVZ newborn astrocytes, whose role is undisclosed so far.

Strengths:

The article is innovative, of good quality, and clearly written, with properly described Materials and Methods, data analysis, and presentation. In general, the methods are designed properly to answer the main question of the authors, being a major strength. Interpretation of the data is also in general well done, with results supporting the main conclusions of this article.

Weaknesses:

However, there are some points of this article that still need clarification to further improve this work.

(1) As a first general comment, is it possible that the increase in Thbs4-positive astrocytes can also happen locally close to the glia scar, through the proliferation of local astrocytes or even from local astrocytes at the SVZ? As it was shown in published articles most of the newborn astrocytes in the adult brain actually derive from proliferating astrocytes, and a smaller percentage is derived from NSCs. How can the authors rule out a contribution of local astrocytes to the increase of Thbs4-positive astrocytes? The authors also observed that only about one-third of the astrocytes in the glial scar derived from the SVZ.

We thank the reviewer for the interesting comment. We have extended the discussion about this topic in the manuscript, (lines 333-342), including the statement about a third of glial scar astrocytes being from the SVZ and not downplaying the role of local astrocytes.  Whether the glial scar is populated by newborn astrocytes derived from SVZ or from local astrocytes is under debate, since there are groups that found astrocytes contribution from local astrocytes (Frisèn group, Magnusson et al., 2014) but there are others that observed the opposite (Li et al., 2010; Benner et al., 2013; Faiz et al., 2015; Laug et al., 2019 & Pous et al., 2020). 

In our study we observed that Thbs4 expression is almost absent in the cortex and striatum of sham mice. To demonstrate that new-born astrocytes are derived from SVZ we used two techniques: the chronic BrdU treatment and the cell tracing which mainly labels SVZ neural stem cells. Fast proliferating cells lose BrdU quickly so local astrocytes under ischemic conditions do not express BrdU. In addition, we injected IdU the day before perfusion in order to see if local astrocytes express Thbs4 when they respond to the brain ischemia. However, we did not observe proliferating local astrocytes expressing Thbs4 after MCAO (see Author response image 4)

Author response image 4.

As mentioned in the response for reviewer 2, the cell tracing technique could label early postnatal astrocytes. We characterized the technique and only a small percentage of tdTom expression was found in the cortex and striatum of sham animals.  This tdTom population could explain the percentage of tdTom+ cells in the ischemic regions that do not express Thbs4 even though this percentage could represent other cell types such as OPCs or oligodendrocytes. Taking all together, evidences suggest that Thbs4+ astrocyte population derived from the SVZ. 

We indeed observed a small contribution of Thbs4+ astrocytes to the glial scar. However, Thbs4+ astrocytes arrive at the lesion at a critical temporal window - when local hyper-reactive astrocytes die or lose their function. We hypothesized that Thbs4+ astrocytes could help local astrocytes or replace them in reorganizing the extracellular space and the glial scar, an instrumental process for the recovery of the ischemic area. 

(2) It is known that the local, GFAP-reactive astrocytes at the scar can form the required ECM. The authors propose a role of Thbs4-positive astrocytes in the modulation, and perhaps maintenance, of the ECM at the scar, thus participating in scar formation likewise. So, this means that the function of newborn astrocytes is only to help the local astrocytes in the scar formation and thus contribute to tissue regeneration. Why do we need specifically the Thbs4positive astrocytes migrating from the SVZ to help the local astrocytes? Can you discuss this further?

Unfortunately, we could not demonstrate which molecular machinery is involved in these mechanisms, and we can only speculate the functional meaning of a second wave of glial activation. We added a lengthy discussion in lines 333-342.

(3) The authors observed that the number of BrdU- and DCX-positive cells decreased 15 dpi in all OB layers (Fig. S5). They further suggest that ischemia-induced a change in the neuroblasts ectopic migratory pathway, depriving the OB layers of the SVZ newborn neurons. Are the authors suggesting that these BrdU/DCX-positive cells now migrate also to the ischemic scar, or do they die? In fact, they see an increase in caspase-3 positive cells in the SVZ after ischemia, but they do not analyse which type of cells are dying. Alternatively, is there a change in the fate of the cells, and astrogliogenesis is increased at the expense of neurogenesis?  The authors should understand which cells are Cleaved-caspase-3 positive at the SVZ and clarify if there is a change in cell fate. Also please clarify what happens to the BrdU/DCX-positive cells that are born at the SVZ but do not migrate properly to the OB layers.

Actually, we cannot demonstrate the fate of missing BrdU/DCX cells in the OB. We can reasonably speculate that following the ischemic insult, the neurogenic machinery steers toward investing more energy in generating glial cells to support the lesion. We didn’t analyze the fate of the DCX that originally should migrate and differentiate to the OB, whether they die or if there is a shift in the differentiation program in the SVZ, since we consider that question is out of the study’s scope.   

(4) The authors showed decreased Nestin protein levels at 15 dpi by western blot and immunostaining shows a decrease already at 7div (Figure 2). These results mean that there is at least a transient depletion of NSCs due to the promotion of astrogliogenesis. However, the authors show that at 30dpi there is an increase of slow proliferating NSCs (Figure 3). Does this mean, that there is a reestablishment of the SVZ cytogenic process?  How does it happen, more specifically, how NSCs number is promoted at 30dpi?  Please explain how are the NSCs modulated throughout time after ischemia induction and its impact on the cytogenic process.

Based on the chronic BrdU treatment, results suggested a restoration of SVZ cytogenic process (also observed in the nestin and DCX proteins expression at 30dpi). However, we did not analyze how it happens (from asymmetric or symmetric divisions). As suggested by Encinas group, we hypothesized that the brain ischemia induces the exhaustion of the neurogenic niche of the SVZ by symmetric divisions of NSC into reactive astrocytes.

(5) The authors performed a classification of Thbs4-positive cells in the SVZ according to their morphology. This should be confirmed with markers expressed by each of the cell subtypes.

We thank the referee for the comment. Classifying NSC based on different markers could also be tricky because different NSC cell types share markers. This classification was made considering the specific morphology of each NSC cell type. In addition, Thbs4 expression in Btype cells is also observed in other studies (Llorens-Bobadilla et al. 2015; Cebrian-Silla et al.,

2021; Basak et al., 2018).

(6) In Figure S6, the authors quantified HABP spots inside Thbs4-positive astrocytes. Please show a higher magnification picture to show how this quantification was done.