Author Response

Reviewer #1 (Public Review):

This article describes the development and refinement of an open-source software framework that is used to track how the COVID-19 pandemic impacted healthcare use in England over a range of key healthcare use indicators.

Important strengths of this study include the high coverage of 99% of practices in England, the development of health care indicators with the input of a clinical advisory group, extensive online documentation, and rigorous safeguards for the protection of patient confidentiality.

Perhaps the largest limitation is that only high-level descriptive data on the monthly volume of health outcomes are presented. It is not clear whether the system could be used to generate more fine-grained or stratified information, ex. weekly or daily data, or data stratified by important characteristics of practices or of patient characteristics. As such, the utility of the system for answering new scientific questions is unclear, and also what the utility and long-term potential uses of this system will be past the COVID-19 pandemic.

OpenSAFELY allows access to the full primary care record for patients registered with a TPP or EMIS practice in England.This includes medical diagnoses, clinical tests, prescriptions, as well as demographic details such as age, sex, ethnicity. Dates attached to these records allow for daily analyses to be performed. This data is updated weekly. Through linkage of other data sources, it also provides information such as hospital admissions, registered deaths or COVID-19 testing data. Detailed subgroup analysis is possible; OpenSAFELY has already been used to understand disease risk 1, monitor vaccination coverage 2,3 and novel treatments 4, assess patient safety 5, inform public health guidance and policy and much more6. These are all widely applicable beyond the COVID-19 pandemic.

Reviewer #3 (Public Review):

This manuscript by Fisher and colleagues documents the change in clinical activity in English general practices during the COVID-19 pandemic according to a set of indicators of clinical activity. The indicators include measures of clinical reviews (e.g. blood pressure, asthma, chronic obstructive pulmonary disease, medication, and cardiovascular risk reviews), blood tests (e.g. cholesterol, liver function, thyroid function, full blood counts, diabetes monitoring blood tests, and kidney function). All these measures saw a drop during the pandemic, to a varying degree, and some recovered afterwards but others did not.

Clinical activity was measured using SNOMED CT codes, which are standard codes used for recording clinical events in UK GP records.

Strengths:

This is a large and comprehensive study including data from 99% of general practices in England. The indicators are clinically relevant, cover a broad range of disease areas, and have been chosen in a sensible manner, involving relevant stakeholders such as GPs, pharmacists, and pathologists.

The OpenSAFELY platform has the ability to enable federated analyses to be run on raw coded data of almost all patients registered with a GP in England.

The study demonstrates the value of OpenSAFELY in being able to monitor clinical activity in general practice at a detailed level, which is essential for planning and improving health services. The statistical methodology is broadly sound.

Weaknesses:

The measures are all related to chronic physical diseases in adults, with a particular focus on cardiometabolic and respiratory conditions. There are no measures related to mental health, maternal or child health.

Results from preliminary analyses of a wider range of clinical conditions can be found in our previous work7. This includes mental health and female and reproductive health with details on why these were not covered by the initial key measures described.

The description of the measures does not distinguish between different types of clinical activity e.g. lab tests, clinical measurements, or diagnoses, and all are lumped together as 'codes'. This is a peculiarity of the way that information is recorded in GP systems - many different types of clinical information (such as diagnoses and lab tests) are recorded using a SNOMED CT 'code', and only the exact code differentiates what type of information is in the record.

Multiple codes of different types can arise from a single encounter, all of which could be indicative of a clinical event of interest. The codelists for each key measure, available at opencodelists.org shows the type of clinical activity (e.g procedure or observable entity) captured by each code within the codelist (see e.g.https://www.opencodelists.org/codelist/opensafely/red-blood-cell-rbc-tests/576a859e/#tree).

The codelists were broad and comprehensive, but it is unclear how necessary this is because for some measures e.g. lab tests, laboratories typically record a particular type of test using a single standardised code. Instead of using a broad set of codes in the analysis, the authors could have initially verified which codes are associated with the clinical activity being measured (e.g. a numerical value of a blood pressure measurement) in all practices, as I would expect the same single or small number of codes would be used in all practices. This would have provided a smaller and simpler final codelist.

Supplementary table 1 shows up to 5 of the most common codes for each key measure across the two electronic health record (EHR) systems used in this analysis. This shows that whilst a single code is often used for many of the clinical activities assessed here, there are exceptions and there can be variation in coded activity between different EHR systems. We have previously described how design features of EHR systems can impact clinical practice 8. Broad codelists allow us to capture activity across multiple EHR systems.

1. Williamson, E. J. et al. Factors associated with COVID-19-related death using OpenSAFELY. Nature 584, 430–436 (2020).
2. Trends and clinical characteristics of 57.9 million COVID-19 vaccine recipients: a federated analysis of patients’ primary care records in situ using OpenSAFELY | British Journal of General Practice. https://bjgp.org/content/early/2021/11/08/BJGP.2021.0376.
3. Parker, E. P. et al. Factors associated with COVID-19 vaccine uptake in people with kidney disease: an OpenSAFELY cohort study. BMJ Open 13, e066164 (2023).
4. Green, A. C. A. et al. Trends, variation, and clinical characteristics of recipients of antiviral drugs and neutralising monoclonal antibodies for covid-19 in community settings: retrospective, descriptive cohort study of 23.4 million people in OpenSAFELY. BMJ Med. 2, (2023).
5. Collaborative, T. O. et al. Potentially inappropriate prescribing of DOACs to people with mechanical heart valves: a federated analysis of 57.9 million patients’ primary care records in situ using OpenSAFELY. 2021.07.27.21261136 https://www.medrxiv.org/content/10.1101/2021.07.27.21261136v1 (2021) doi:10.1101/2021.07.27.21261136.
6. OpenSAFELY Pubmed search results. PubMed https://pubmed.ncbi.nlm.nih.gov/?term=OpenSAFELY.
7. OpenSAFELY NHS Service Restoration Observatory 2: changes in primary care activity across six clinical areas during the COVID-19 pandemic | medRxiv. https://www.medrxiv.org/content/10.1101/2022.06.01.22275674v1.
8. Suboptimal prescribing behaviour associated with clinical software design features: a retrospective cohort study in English NHS primary care | British Journal of General Practice. https://bjgp.org/content/70/698/e636.

Author Response

eLife assessment:

This important study represents a comprehensive computational analysis of Plasmodium falciparum gene expression, with a focus on var gene expression, in parasites isolated from patients; it assesses changes that occur as the parasites adapt to short-term in vitro culture conditions. The work provides technical advances to update a previously developed computational pipeline. Although the findings of the shifts in the expression of particular var genes have theoretical or practical implications beyond a single subfield, the results are incomplete and the main claims are only partially supported.

The authors would like to thank the reviewers and editors for their insightful and constructive assessment. We are particularly glad to read of the technical advances of the methods developed here. We will rephrase parts of the manuscript and move some analysis to the supplementary materials. This will improve the clarity of the results and ensure the main claims are supported.

Reviewer #1 (Public Review):

The authors took advantage of a large dataset of transcriptomic information obtained from parasites recovered from 35 patients. In addition, parasites from 13 of these patients were reared for 1 generation in vivo, 10 for 2 generations, and 1 for a third generation. This provided the authors with a remarkable resource for monitoring how parasites initially adapt to the environmental change of being grown in culture. They focused initially on var gene expression due to the importance of this gene family for parasite virulence, then subsequently assessed changes in the entire transcriptome. Their goal was to develop a more accurate and informative computational pipeline for assessing var gene expression and secondly, to document the adaptation process at the whole transcriptome level.

Overall, the authors were largely successful in their aims. They provide convincing evidence that their new computational pipeline is better able to assemble var transcripts and assess the structure of the encoded PfEMP1s. They can also assess var gene switching as a tool for examining antigenic variation. They also documented potentially important changes in the overall transcriptome that will be important for researchers who employ ex vivo samples for assessing things like drug sensitivity profiles or metabolic states. These are likely to be important tools and insights for researchers working on field samples.

One concern is that the abstract highlights "Unpredictable var gene switching..." and states that "Our results cast doubt on the validity of the common practice of using short-term cultured parasites...". This seems somewhat overly pessimistic with regard to var gene expression profiling and does not reflect the data described in the paper. In contrast, the main text of the paper repeatedly refers to "modest changes in var gene expression repertoire upon culture" or "relatively small changes in var expression from ex vivo to culture", and many additional similar assessments. On balance, it seems that transition to culture conditions causes relatively minor changes in var gene expression, at least in the initial generations. The authors do highlight that a few individuals in their analysis showed more pronounced and unpredictable changes, which certainly warrants caution for future studies but should not obscure the interesting observation that var gene expression remained relatively stable during transition to culture.

Thank you for the suggestion and we are happy to modify the wording to ensure the correct results are presented. We will reword the abstract and emphasise the main change was observed in the core transcriptome. We will also add clarity to the different var transcriptome results presented.

It is important to note this study was in a unique position to assess changes at the individual patient level as we had successive parasite generations. This is not done in most cross-sectional studies and therefore these small changes in the var transcriptome would have been missed.

Reviewer #2 (Public Review):

In this study, the authors describe a pipeline to sequence expressed var genes from RNA sequencing that improves on a previous one that they had developed. Importantly, they use this approach to determine how var gene expression changes with short-term culture. Their finding of shifts in the expression of particular var genes is compelling and casts some doubt on the comparability of gene expression in short-term culture versus var expression at the time of participant sampling. The authors appear to overstate the novelty of their pipeline, which should be better situated within the context of existing pipelines described in the literature.

Other studies have relied on short-term culture to understand var gene expression in clinical malaria studies. This study indicates the need for caution in over-interpreting findings from these studies.

The novel method of var gene assembly described by the authors needs to be appropriately situated within the context of previous studies. They neglect to mention several recent studies that present transcript-level novel assembly of var genes from clinical samples. It is important for them to situate their work within this context and compare and contrast it accordingly. A table comparing all existing methods in terms of pros and cons would be helpful to evaluate their method.

We are grateful for this suggestion and agree that a table comparing the pros and cons of all existing methods would be helpful for the reader, not just malaria researchers. This will also highlight the key benefits of our new approach. This will be included in the updated manuscript as a supplementary table.

Reviewer #3 (Public Review):

This work focuses on the important problem of how to access the highly polymorphic var gene family using short-read sequence data. The approach that was most successful, and utilized for all subsequent analyses, employed a different assembler from their prior pipeline, and impressively, more than doubles the N50 metric.

The authors then endeavor to utilize these improved assemblies to assess differential RNA expression of ex vivo and short-term cultured samples, and conclude that their results "cast doubt on the validity" of using short-term cultured parasites to infer in vivo characteristics. Readers should be aware that the various approaches to assess differential expression lack statistical clarity and appear to be contradictory. Unfortunately there is no attempt to describe the rationale for the different approaches and how they might inform one another.

It is unclear whether adjusting for life-cycle stage as reported is appropriate for the var-only expression models. The methods do not appear to describe what type of correction variable (continuous/categorical) was used in each model, and there is no discussion of the impact on var vs. core transcriptome results.

The reviewer raises a fair point, and we agree the different methods and results of the var transcriptome analysis are difficult to interpret together without further clarification. Var transcript differential expression analysis has been used several times previously and hence was used here. As mentioned above, this study was in a unique position to perform a more focussed analysis of var transcriptional changes across paired samples. This allowed for changes in the var transcriptome to be identified that would have gone unnoticed in the "traditional" differential expression analysis. To address this point, we will add further explanation to the results and move the var differential expression analysis to the supplementary, to allow for comparison with previous studies.

We thank the reviewer for this highly important comment about adjusting for life cycle stage. Var gene expression is highly stage dependent, so any quantitative comparison between samples does need adjustment for developmental stage. Var gene expression was adjusted for in the differential expression analysis by using the mixture model determined proportions as covariates in the design matrix. The var group level analysis and the global var gene expression analysis was also adjusted for life cycle stage using the same proportions, by including them as an independent variable. The rank-expression analysis did not have adjustment for life cycle stage as the values were determined as a percentage contribution to the total var transcriptome.

We will update the methods section to ensure this is clearer.

Author Response

eLife assessment

This important study addresses both the native role of the Plasmodium falciparum protein PfFKBP35 and whether this protein is the target of FK506, an immunosuppressant with antiplasmodial activity. The genetic evidence for the essentiality of FKBP35 in parasite growth is compelling. However, the conclusion that the role of FKBP35 is to secure ribosome homeostasis and the claim that FK506 exerts its antimalarial activity independently of FKBP35 rely on incomplete evidence.<br />

We thank the Reviewers and Editors for their careful evaluation of our manuscript and the constructive criticism. We realized that some of our conclusions may be regarded/misunderstood as overstatements. This was by no means our intention and we apologize for the unnecessary inconvenience. The phenotype of FKBP35 knock-out parasites clearly centers on failing ribosomes and protein synthesis, which in our opinion, provides an important leap towards understanding the role of this drug target in P. falciparum biology. It is however correct that, at this point, we can only make evidence-based hypotheses about direct interaction partners and we will emphasize this more clearly in a revised version of the manuscript. In order to prevent misinterpretation of our work, and as detailed in the point-by-point responses to the reviewer comments, we propose changing the manuscript title to “Genetic validation of Pf_FKBP35 as an antimalarial drug target”. To address the criticism regarding the effects of FK506, we will perform specific additional experiments. We are convinced that this new data set will resolve any remaining ambiguities and allows for a conclusive assessment of FK506 drug activity in _P. falciparum.

Reviewer #1 (Public Review):

In this study, the authors investigate the biological function of the FK506-binding protein FKBP35 in the malaria-causing parasite Plasmodium falciparum. Like its homologs in other organisms, PfFKBP35 harbors peptidyl-prolyl isomerase (PPIase) and chaperoning activities, and has been considered a promising drug target due to its high affinity to the macrolide compound FK506. However, PfFKBP35 has not been validated as a drug target using reverse genetics, and the link between PfFKBP35-interacting drugs and their antimalarial activity remains elusive. The manuscript is structured in two parts addressing the biological function of PfFKBP35 and the antimalarial activity of FK506, respectively.

The first part combines conditional genome editing, proteomics and transcriptomics analysis to investigate the effects of FKBP35 depletion in P. falciparum. The work is very well performed and clearly described. The data provide definitive evidence that FKBP35 is essential for P. falciparum blood stage growth. Conditional knockout of PfFKBP35 leads to a delayed death phenotype, associated with defects in ribosome maturation as detected by quantitative proteomics and stalling of protein synthesis in the parasite. The authors propose that FKBP35 regulates ribosome homeostasis but an alternative explanation could be that changes in the ribosome proteome are downstream consequences of the abrogation of FKBP35 essential activities as chaperone and/or PPIase. It is unclear whether FKBP35 has a specific function in P. falciparum as compared to other organisms. The knockdown of PfFKBP35 has no phenotypic consequence, showing that very low amounts of FKBP35 are sufficient for parasite survival and growth. In the absence of quantification of the protein during the course of the experiments, it remains unclear whether the delayed death phenotype in the knockout is due to the delayed depletion of the protein or to a delayed consequence of early protein depletion. This limitation also impacts the interpretation of the drug assays.

We thank the Reviewer for the compliments regarding our experimental setup and the clarity of our manuscript. We agree that the link between FKBP35 knock-out and ribosome homeostasis is indirect and we now emphasize this more clearly in the revised manuscript. To prevent a general misinterpretation of our manuscript, we will adapt the title accordingly.

We would still like to reiterate that the phenotype of FKBP35 knock-out parasites is best described by their defects in maintaining functional ribosomes. It is for several reasons that we believe the links between FKBP35 and ribosome function are purely evidence driven: First, pre-ribosomal and nucleolar factors are the first proteins (in generation 1 schizonts) to be affected upon knock-out of fkbp35 (Figure 2A, Table S1). We realized that Figure 2A falls short in showing this observation, which is why will update the figure accordingly. Second, the dysregulation of ribosomal factors and the general stall in protein synthesis is dominating the phenotype of FKBP35 knock-out parasites in generation 2. We thus believe it is appropriate to say that knock-out cells are most likely killed in response to defective ribosome maintenance – which is a consequence of reduced FKBP35 levels. We are aware that our experiments (and possibly any other reverse genetics approach) cannot rule out that FKBP35 affects ribosomal factors indirectly. Clearly, more work is required to disentangle this question in more detail in the future.

We agree with the Reviewer that it is not possible to tell if the delayed death-like phenotype is due to a “delayed protein depletion”. We would however like to note that the DiCre/loxP approach allows for an immediate knock-out at the genome level and is thus as precise as possible. Further, in addition to the substantial depletion of FKBP35 in knock-out cells during the phenotypically silent generation, knocking out of fkbp35 at earlier time points (TPs 24-30 and 34-40 hpi in the preceding generation) resulted in the very same phenotype cycle (Figure 1). Here, parasite death was delayed substantially longer, i.e. more than one complete cycle. Together with the dysregulation of early ribosome maturation in generation 1, these findings point towards a delayed death phenotype. It is of course still possible to explain the delayed death-like phenotype by remnant activity of proteins synthetized prior to the genomic knock-out. We address this possibility and describe the two scenarios mentioned by the Reviewer in lines 141-144. Disentangling the two possibilities in future experiments will be difficult, not only with regards to FKBP35, but regarding “delayed death” phenotypes in general.

In the second part, the authors investigate the activity of FK506 on P. falciparum, and conclude that FK506 exerts its antimalarial effects independently of FKBP35. This conclusion is based on the observation that FK506 has the same activity on FKBP35 wild type and knock-out parasites, suggesting that FK506 activity is independent of FKBP35 levels, and on the fact that FK506 kills the parasite rapidly whereas inducible gene knockout results in delayed death phenotype. However, there are alternative explanations for these observations. As mentioned above, the delayed death phenotype could be due to delayed depletion of the protein upon induction of gene knockout. FK506 could have a similar activity on WT and mutant parasites when added before sufficient depletion of FKBP35 protein. In some experiments, the authors exposed KO parasites to FK506 later, presumably when the KO is effective, and obtained similar results. However, in these conditions, the death induced by the knockout could be a confounding factor when measuring the effects of the drug. Furthermore, the authors show that FK506 binds to FKBP35, and propose that the FK506-FKBP35 complex interferes with ribosome maturation, which would point towards a role of FKBP35 in FK506 action. In summary, the study does not provide sufficient evidence to rule out that FK506 exerts its effects via FKBP35.

Noteworthy, we were also very much surprised by data indicating that the antimalarial activity of FK506 is independent of FKBP35. It is for this reason that we conducted a comprehensive set of experiments to disprove our initial observations, but couldn`t find any evidence for an FKBP35-dependent mode of action of FK506:

We were not able to see altered FK506 sensitivity in (i) inducible knock-down parasites, (ii) inducible overexpression parasites and (iii) inducible knock-out parasites. Parasites with altered FKBP35 levels (as assessed by Western blot and quantitative proteomics at 36-42 hpi, respectively) were equally sensitive to FK506. Importantly, at no sub-lethal FK506 concentration did lower FKBP35 levels lead to an altered response of FKBP35KO compared to the wild-type control population. Furthermore, (iv) induction of the knock-out in the cycle preceding FK506 exposure also had no effect on parasite sensitivity. As mentioned by the Reviewer, we also exposed the parasites to FK506 at 30-36 hpi and (v) did not see any effect, even though we measured a 19-fold difference in FKBP35 protein levels between the parasite populations at 36-42 hpi. At this point, parasite death induced by the knock-out cannot be a confounding factor (as it was mentioned by the Reviewer), because the FKBP35 knock-out has no effect on parasite survival in generation 1 in the absence of FK506 (Figure 1F). This demonstrates that the observed effect is only due to drug-mediated killing and not due to the FKBP35 knock-out.

To account for a scenario in which the drop in FKBP35 levels only occurs after 36 hpi, we will perform an additional set of experiments, in which we induce the knock-out at 0-6 hpi and treat the parasites at 36-42 hpi (i.e. the time point at which the 19-fold difference in protein levels was measured by quantitative proteomics). This setup will allow determining whether or not the parasite killing activity of FK506 depends on FKBP35 levels.

So far, our experiments cannot support any scenario in which FK506 kills P. falciparum parasites via inhibiting the essential role of FKBP35 and we would therefore want to insist that this statement is based on highly solid evidence. In this context, it is important to note that our conclusion includes two scenarios: “This indicates that either the binding of FK506 does not interfere with the essential role of _Pf_FKBP35, or that _Pf_FKBP35 is inhibited only at high FK506 concentrations that also inhibit other essential factors.” While this phrase is already present in our initial submission, we will emphasize this point more clearly in the revised manuscript. We are convinced that this information is of high importance for ongoing and future drug development.

Reviewer #2 (Public Review):

The manuscript by Thomen et al. FKBP secures ribosome homeostasis in Plasmodium falciparum and focuses on the importance of PfKBP35 protein, its interaction with the FK506 compound, and the role of PfKBP35 in ribosome biogenesis. The authors showed the interaction of the PfKBP54 with FK506, but the part of the FK506 and PfKBP54 in ribosome biogenesis based on the data is unclear.

The introduction is plotted with two parallel stories about PfKBP35 and FK506, with ribosome biogenesis as the central question at the end. In its current form, the manuscript suffers from two stories that are not entirely interconnected, unfinished, and somewhat confusing. Both stories need additional experiments to make the manuscript(s) more complete. The results from PfFBP35 need more evidence for the proposed ribosome biogenesis pathway control. On the other hand, the results from the drug FK506 point to different targets with lower EC50, and other follow-up experiments are needed to substantiate the authors' claims.

The strengths of the manuscript are the figures and experimental design. The combination of omics methods is informative and gives an opportunity for follow-up experiments.

We thank the Reviewer for the evaluation of the manuscript. We apologize for the fact that the Reviewer found the manuscript to be inaccessible. We will use the comments as an incentive to restructure the manuscript and do our best to clarify the presentation, interpretation and conclusion of the presented data in the revised version. We believe that the FKBP35 data are strongly interlinked with the findings on FK506. We will emphasize these links more clearly and are convinced that the complementary nature of the datasets are a particular strength of the presented work.

Reviewer #3 (Public Review):

The study by Thommen et al. sought to identify the native role of the Plasmodium falciparum FKBP35 protein, which has been identified as a potential drug target due to the antiplasmodial activity of the immunosuppressant FK506. This compound has multiple binding proteins in many organisms; however, only one FKBP exists in P. falciparum (FKBP35). Using genetically-modified parasites and mass spectrometry-based cellular thermal shift assays (CETSA), the authors suggest that this protein is in involved in ribosome homeostasis and that the antiplasmodial activity of FK506 is separate from its activity on the FKBP35 protein. The authors first created a conditional knockdown using the destruction domain/shield system, which demonstrated no change in asexual blood stage parasites. A conditional knockout was then generated using the DiCre system. FKBP35KO parasites survived the first generation but died in the second generation. The authors called this "a delayed death phenotype", although it was not secondary to drug treatment, so this may be a misnomer. This slow death was unrelated to apicoplast dysfunction, as demonstrated by lack of alterations in sensitivity to apicoplast inhibitors. Quantitative proteomics on the FKBP35KO vs FKBP35WT parasites demonstrated enrichment of proteins involved in pre-ribosome development and the nucleolus. Interestingly, the KO parasites were not more susceptible to cycloheximide, a translation inhibitor, in the first generation (G1), suggesting that mature ribosomes still exist at this point. The SunSET technique, which incorporates puromycin into nascent peptide chains, also showed that in G1 the FKBP35KO parasites were still able to synthesize proteins. But in the second generation (G2), there was a significant decrease in protein synthesis. Transcriptomics were also performed at multiple time points. The effects of knockout of FKBP35 were transcriptionally silent in G1, and the parasites then slowed their cell cycles as compared to the FKBP35WT parasites.

The authors next sought to evaluate whether killing by FK506 was dependent upon the inhibition of PfKBP35. Interestingly, both FKBP35KO and FKBP35WT parasites were equally susceptible to FK506. This suggested that the antiplasmodial activity of FK506 was related to activity targeting essential functions in the parasite separate from binding to FKBP35. To identify these potential targets, the authors used MS-CETSA on lysates to test for thermal stabilization of proteins after exposure to drug, which suggests drug-protein interactions. As expected, FK506 bound FKBP35 at low nM concentrations. However, given that the parasite IC50 of this compound is in the uM range, the authors searched for proteins stabilized at these concentrations as putative secondary targets. Using live cell MS-CETSA, FK506 bound FKBP35 at low nM concentrations; however, in these experiments over 50 ribosomal proteins were stabilized by the drug at higher concentrations. Of note, there was also an increase in soluble ribosomal factors in the absence of denaturing conditions. The authors suggested that the drug itself led to these smaller factors disengaging from a larger ribosomal complex, leading to an increase in soluble factors. Ultimately, the authors conclude that the native function of FKBP35 is involved in ribosome homeostasis and that the antiplasmodial activity of FK506 is not related to the binding of FKBP35, but instead results from inhibition of essential functions of secondary targets.

Strengths:

This study has many strengths. It addresses an important gap in parasite biology and drug development, by addressing the native role of the potential antiplasmodial drug target FKBP35 and whether the compound FK506 works through inhibition of that putative target. The knockout data provide compelling evidence that the KBP35 protein is essential for asexual parasite growth after one growth cycle. Analysis of the FKBP35KO line also provides evidence that the effects of FK506 are likely not solely due to inhibition of that protein, but instead must have secondary targets whose function is essential. These data are important in the field of drug development as they may guide development away from structure-based FK506 analogs that bind more specifically to the FKBP35 protein.

Weaknesses:

There are also a few notable weaknesses in the evidence that call into question the conclusion in the article title that FKBP35 is definitely involved in ribosomal homeostasis. While the proteomics supports alterations in ribosome biogenesis factors, it is unclear whether this is a direct role of the loss of the FKBP35 protein or is more related to non-specific downstream effects of knocking down the protein. The CETSA data clearly demonstrate that FK506 binds PfKB35 at low nM concentrations, which is different than the IC50 noted in the parasite; however, the evidence that the proteins stabilized by uM concentrations of drug are actual targets is not completely convincing. Especially, given the high uM amounts of drug required to stabilize these proteins. This section of the manuscript would benefit from validation of a least one or two of the putative candidates noted in the text. In the live cell CETSA, it is noted that >50 ribosomal components are stabilized in drug treated but not lysate controls. Similarly, the authors suggest that the -soluble fraction of ribosomal components increases in drug-exposed parasites even at 37{degree sign}C and suggests that this is likely from smaller ribosomal proteins disengaging from larger ribosomal complexes. While the evidence is convincing that this protein may play a role in ribosome homeostasis in some capacity, it is not sure that the title of the paper "FKBP secures ribosome homeostasis" holds true given the lack of mechanistic data. A minor weakness, but one that should nonetheless be addressed, is the use of the term "delayed death phenotype" with regards to the knockout parasite killing. This term is most frequently used in a very specific setting of apicoplast drugs that inhibit apicoplast ribosomes, so the term is misleading. It is also possible that the parasites are able to go through a normal cycle because of the kinetics of the knockout and that the time needed for protein clearance in the parasite to a level that is lethal.

Overall, the authors set out to identify the native role of FKB35 in the P. falciparum parasites and to identify whether this is, in fact, the target of FK506. The data clearly demonstrate that FKBP35 is essential for parasite growth and provide evidence that alterations in its levels have proteomic but not transcriptional changes. However, the conclusion that FKBP35 actually stabilizes ribosomal complexes remains intermediate. The data are also very compelling that FK506 has secondary targets in the parasite aside from FKBP35; however, the high uM concentrations of the drug needed to attain results and the lack of biological validation of the CETSA hits makes it difficult to know whether any of these are actually the target of the compound or instead are nonspecific downstream consequences of treatment.

We appreciate the detailed and valuable suggestions to improve the manuscript. We agree that CETSA could only identify potential targets of FK506 in the micromolar range, while FK506 showed a high affinity for FKBP35, consistent with earlier reports (2). We would however like to point out that FK506 kills P. falciparum at exactly these relatively high concentrations and not at those presumed from the high affinity interactions between FK506 and FKBP35. The relatively high FK506 concentration required to stabilize potential off target proteins is therefore not a concerning observation, but rather corroborates our conclusion that FK506 fails to inhibit the essential function of FKBP35 at concentrations that leave off targets unaffected. As mentioned in response to Reviewer 1, we will describe and discuss these data more clearly in the revised manuscript.

We thank the Reviewer for pointing out the potential issues regarding the use of the term “delayed death phenotype”. We now refer to the FKBP35 phenotype as “delayed death-like” in the revised manuscript.

We believe that follow-up work on specific FK506 CETSA hits is out of scope of the current and already quite complex manuscript.

As mentioned in the response to Reviewer 1, we realize that the short title of the manuscript can be regarded as an overstatement. Again, this was clearly not our intention and we apologize that the Reviewers had to indicate this issue. While we believe that the message of the title holds true (see response to Reviewer 1), we recognize the misconception that might arise from it, which is why we propose the new title: “Genetic validation of _Pf_FKBP35 as an antimalarial drug target”.

1. Kennedy K, Cobbold SA, Hanssen E, Birnbaum J, Spillman NJ, McHugh E, et al. Delayed death in the malaria parasite Plasmodium falciparum is caused by disruption of prenylation-dependent intracellular trafficking. PLoS Biol. 2019;17(7):e3000376.
2. Kotaka M, Ye H, Alag R, Hu G, Bozdech Z, Preiser PR, et al. Crystal structure of the FK506 binding domain of Plasmodium falciparum FKBP35 in complex with FK506. Biochemistry. 2008;47(22):5951-61.
3. Kasahara K, Nakayama R, Shiwa Y, Kanesaki Y, Ishige T, Yoshikawa H, et al. Fpr1, a primary target of rapamycin, functions as a transcription factor for ribosomal protein genes cooperatively with Hmo1 in Saccharomyces cerevisiae. PLoS Genet. 2020;16(6):e1008865.

Author Response:

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

eLife assessment

This study presents important findings regarding the quantification of dynamics in fish communities in changing ecosystems by combining a large-scale environmental DNA metabarcoding time series with novel statistical approaches. The methods are convincing, with controlled experiments, thorough statistical analyses, and a substantial dataset covering two years of detailed observation, which can provide sufficient power to detect fine-scale ecological interactions. This work is relevant for informing future research on assessing community stability under climate change.

Thank you so much for your careful evaluation of our manuscript. We are very pleased to hear that you found our study important. We have revised our manuscript according to the helpful comments to further improve our manuscript.

Reviewer #1 (Public Review):

[…] Their work provides a highly relevant approach to perform species-interaction strength analysis based on eDNA biodiversity assessments, and as such provides a research framework to study marine community dynamics by eDNA, which is highly relevant in the study of ecosystem dynamics. The models and analytical methods used are clearly described and made available, enabling application of these methods by anyone interested in applying it to their own site and species group of interest.

Thank you so much for your time and effort to evaluate our manuscript. We are very pleased to hear that you found our study interesting. We have further revised the manuscript according to your comments and hope that the revised manuscript is now better than the original one.

Strengths: The authors have a study setup that is suitable to measure the effects of temperature of the eDNA diversity, and have taken a large number of samples and all appropriate controls to be able to accurately measure and describe these dynamics. The applied internal spike in to enable relative eDNA copy number quantification is convincing.

We are happy to hear that you found the study design and the method to estimate eDNA copy number are suitable and convincing.

Weaknesses: The authors aim to study the relationship between species interaction strength and ecosystem complexity, and how temperature will influence this. However, there is only limited ecological context discussed explaining their results, and a link with climate change scenario's is also limited. A further discussion of this would have strengthened the manuscript.

Thank you so much for the comment. We have added discussion about how our study contributes to understanding fish community assembly process and predicting the community-level response under ongoing climate change. We have added one subsection, "Implications for fish community assembly and the effect of global climate change ", at L679. As for the ecological discussion for each specific fish-fish interaction, we provided this in Supplementary file 1c.

The authors were able to find a correlation between water temperature and interaction strengths observed. However, since water temperature is dependent on many environmental variables that are either directly or indirectly influencing ecosystem dynamics, it is hard to prove a direct correlation between the observed changes in community dynamics and the temperature alone.

Thank you for pointing this. We have discussed the possibility of the effects of other environmental variables (e.g., oxygen) and how we could overcome this issue at L661. Some of the sentences were originally in the subsection " Interaction strengths and environmental variables ", but were moved to the subsection " Potential limitations of the present study and future perspectives".

Reviewer #2 (Public Review):

In this work Ushio et al. combine environmental DNA metabarcoding with novel statistical approaches to demonstrate how fish communities respond to changing sea temperatures over a seasonal cycle. These findings are important due to the need for new techniques that can better measure community stability under climate change. The eDNA metabarcoding dataset of 550 water samples over two years is, I feel, of sufficient scale to provide power to detect fine-scale ecological interactions, the experiments are well controlled, and the statistical analysis is thorough.

Thank you so much for your time and effort to evaluate our manuscript. We are happy to hear that you found our study technically sound and important. We have revised the manuscript according to your comments to improve our manuscript further.

The major strengths of the manuscript are: (1) the magnitude of the dataset, which provides densely replicated sampling that can overcome some of the noise associated with eDNA metabarcoding data and scale up the number of data points to make unique inferences; (2) the novel method of transforming the metabarcode reads using endogenous qPCR "spike-in" data from a common reference species to obtain estimates of DNA concentration across other species; and (3) the statistical analysis of time-series and network data and translating it into interaction strengths between species provides a cross-disciplinary dimension to the work.

Thank you for your positive comments. Regarding (1), we are very pleased to hear that (1) our intensive and extensive water sampling, (2) our method for using the common fish species eDNA as "spike-in," and (3) our nonlinear time series analysis were positively evaluated.

I feel like this kind of study showcases the power of eDNA metabarcoding to answer some really interesting questions that were previously unobtainable due to the complexities and cost of such an exercise. Notwithstanding the problems associated with PCR primer bias and PCR stochasticity, the qPCR "spike-in" method is easy to implement and will likely become a standardised technique in the field. Further studies will examine and improve on it.

We must admit that our endogeneous "spike-in" method does not overcome all problems associated with PCR. However, we agree with you and believe that we are heading in a correct direction. The method

does not require the addition of external internal standard DNAs and enables post-hoc evaluation of eDNA absolute concentrations. Although this approach requires an additional experiment (qPCR), the method may be an alternative for quantifying eDNA concentrations.

Overall I found the manuscript to be clear and easy to follow for the most part. I did not identify any serious weaknesses or concerns with the study, although I am not able to comment on the more complex statistical procedures such as the "unified information-theoretic causality" method devised by the authors. The section on limitations of the study is important and acknowledges some issues with interpretation that need to be explained. The methods, while brief in parts, are clear. The code used to generate the results has been made available via a GitHub repository. The figures are clear and attractive.

We are very happy to hear that you found our manuscript clear and not containing any serious weakness.

Reviewer #1 (Recommendations For The Authors):

This is a very nice manuscript discussing highly relevant methods to use eDNA analysis to study interactions in marine ecosystems. There are some minor concerns that we will address below:

- As already mentioned above, based on the statements in the introduction we expected a very elaborate discussion section concerning the ecological interaction observed between species. This is however missing, and a more extensive general discussion of the biological interactions would be appreciated, either based on existing literature, or by suggesting further experiments. Alternatively, the claims made in e.g. line 124-128 (Overcoming these difficulties....) could be amended so this expectation is not raised.

Thank you so much for the comment. As answered in the response above, we have added discussion about how our study contributes to the fish community assembly process and predicting the community-level response under ongoing climate change at L679.

Specifically, we argued that our study provides a piece of evidence that temperature exerts influences on fish-fish interactions under field conditions at a relatively short time scale (weeks to months). We suggested that temperature effects on fish community assembly involve effects at different time scales, and thus, integrating results from different temporal (and spatial) scales are necessary to understand the fish community assembly process in nature. As stated above, we provided the detailed ecological discussion for each specific fish-fish interaction in the Supporting Information.

- A lot of negative controls were taken and described in the material & methods. However, there is no clear mention of what was done with the outcome of these negative controls. How did the results of the negative controls influence your analysis? Or were they all completely negative?

Thank you for pointing this out. The negative controls produced negligible reads (177 ± 665 reads [mean ± S.D.]), which accounted for ca. 0.1% of the positive sample reads. Moreover, all the reads were assigned to non-target taxa, such as fish species that had never been observed in the study region and freshwater fish species. Therefore, we conclude that any contaminations in our experiments were negligible, and we discarded the sequence reads from the negative control samples. We have explained this in L533–L539 in the main text.

- Line 423 states: "..suggesting that weak interactions are key to the maintenance of species-rich communities." We are wondering if this can be stated like this, as it seems the other way around would also be true, since in a species rich community it can be expected that most interactions are weak?

Thank you for pointing this. out We agree that there is a possibility that the high species diversity could be a cause of weak intearctions. To clarify this, we have revised the sentence as follows in L568: " ...suggesting that understanding the causes and effects of weak interactions is key to understanding the maintenance of species-rich communities. "

- There is a correlation between DNA concentration and temperature (e.g. shown in fig. S2b). We wondering what could be an argument to not correct for this temperature effect on eDNA concentrations (as now described) or if it would be better to apply a correction factor for this, as it is also shown that there is a correlation between DNA concentration and interaction strengths.

In the unified information theoretic (UIC) analysis, we took the effect of temperature into account if temperature had statistically clear influence on eDNA dynamics of a particular fish species (L439). This means that temperature was included as a conditional variable in the calculation of TE (i.e., Zt in Eqn. [1]). Other environmental variables were also included if they had statistically clear influence. Similarly, in the MDR S-map, we included temperature or other environmental variables as conditional variables if they had statistically clear influence on eDNA dynamics of a particular fish species. We explained this in L479.

- The models used for the interaction dynamics calculations are extensively discussed in this manuscript, although these details are also present in the original papers describing these models, and therefore the manuscript could be shortened by removing some of this explanation.

Thank you for your suggestion. As you understood, the details of the method (S-map and MDR S-map) are available in Sugihara (1994), Chang et al. (2021), and elsewhere. However, we have kept the explanation so that readers who are not familiar with the methods can briefly understand the methods without the needs to read the detail of the previoius studies.

Reviewer #2 (Recommendations For The Authors):

L50-L72: I feel like the abstract could be snappier, i.e. quicker to read with less detail. Consider reducing it a little.

Thank you for your suggestion. We have deleted some redundant phrases and shortened the abstract a little.

L173-L176: I don't understand exactly what is suggested here. Perhaps rephrase?

We have revised the sentence as follows (L165): " As our eDNA time series was taken twice a month, the interactions detected should also have the same time scale (e.g., the interactions detected may cause changes in the population size at the same time scale), which means that we tend to focus on behavior-level interactions (e.g., schooling) rather than birth-death process in the present study (except for predation)."

L228: How many PCR replicate reactions were undertaken per sample?

We performed eight technical replicates for the same eDNA template. This information is described in the third paragraph of the section "Paired-end library preparation and MiSeq sequencing." This section has been moved from the previous supplementary methods to the main text in the revision.

L236: There is no mention later of how these blanks are used to clean up or filter the dataset from the effects of contamination. Consider adding this information.

Thank you for pointing this. As in the responses above, we have described the negative controls in L533–L539 in the main text. The negative controls generated negligible reads, so we simply discarded the sequence reads.

L252-L253: "Primer sequences were removed from merged reads and reads without the primer sequences underwent quality filtering"? Wouldn't all of the reads not have primers after the primers were trimmed off? Or is something else intended here?

All primer sequences were removed after merging the paired- end reads (see "Sequence analysis"). There is no specific reason for this process, and we think that the primer removal before merging the paired- end reads will generate the same results.

L264-L265: "To refine the above taxon assignments". I assume because there were lots of assignments to species that were not known from the study area? Explain why this was done.

At present, the reference sequences are available for about 70% of 4,500 fish species in Japan. However, due to the unknown degree of intraspecific variation, using a uniform threshold of 98.5% to delineate species can result in over-splitting or over-clustering MOTUs. To solve this issue, the manual refinement of the taxon assignments was performed based on the phylogenetic tree. This has been explained in L335.

L274: More details of the qPCR assay are required, or a citation of previous study or supporting information.

The details of the qPCR assay are provided in the secion "Quantitative PCR and estimation of DNA copy numbers." This section has been moved from the previous supplementary methods to the main text in the revision.

L327: Explain further how seasonality was treated here? This is an important part of the study, so deserves further attention.

We included water temperature (if it had statistically clear influence on fish eDNA dynamics) as a conditional variable z(t) in the calculation of TE, and this took the effect of the seasonality in detecting causation into account. We have described this in L436–444.

L407: Consider giving the code repository a DOI to cite.

We have archived the analysis codes at Zenodo and provided the DOI in L39 and L521.

L411: How many MiSeq runs exactly?

We performed 21 MiSeq runs (often with other eDNA samples). We have described this in the main text (L299).

L411: What proportion of your total sequencing data were assigned to fishes? This is a useful statistic to compare methods between studies.

About 98% of the total sequence reads was assigned to fish. We have described this in the main text (L528).

Figure 2: There does not appear to be a key to the color-coded species ecologies.

We have added a legend for the fish ecology in Figure 2.

Author Response:

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

We thank the editor and reviewers for their careful consideration of our manuscript and very helpful feedback, which guided us in improving our manuscript. We would like to highlight three main areas of improvement in this version:

• Statistical rigor: we have added more detail to justify our 2% cutoff for GLM variable coding, implemented stricter shuffling and cutoffs for value and history coding, and provided more information on the statistical significance of our pairwise comparisons across regions and groups. These go well beyond the field standard for identifying and comparing neural encoding of task features.

• Identification of value coding: we have implemented reviewer suggestions about kernel regression and value coding shuffles, providing even stronger evidence that value signaling among cue neurons is more prevalent than expected by chance, more prevalent than any other cue coding patterns, and present in all recorded regions. The rigor of this analysis is only possible due to our unique task design with 6 cues across two stimulus sets, and our consideration of 153 possible coding models exceeds standard practice for identifying value signals. We now implement population decoding, as well, providing additional support for a robust and widely-distributed value code.

• Stability of value code: we have updated our terminology to better highlight that the value signals in our imaging dataset are indeed identified across days, and we add new analysis to show conservation of value-like signals across training days.

Thanks to the reviewers’ suggestions, our manuscript now has substantially stronger support for the presence of stable and distributed cue value signaling. We address the specific points below.

Excerpts from the Consensus Public Reviews:

One limitation is the lack of focus on population-level dynamics from the perspective of decoding, with the analysis focusing primarily on encoding analyses within individual neurons.

To address this limitation, we now include population-level decoding analysis (new panels, Figs. 3G-H, 4E). This new analysis reveals that, although value neurons can be used to decode cue identity on par with other cue cells, value neurons are more accurate at predicting the value of held out cues (never seen by the model), highlighting the utility of a value signal as a way to consistently represent the value of different stimulus sets.

Moreover, we find comparable value prediction performance when using value neurons from each region (Fig. 4E), adding more support for the similarity of this signal across regions:

The authors use reduced-rank kernel regression to characterize the 5332 recorded neurons on a cell-by-cell basis in terms of their responses to cues, licks, and reward, with a cell characterized as encoding one of these parameters if it accounts for at least 2% of the observed variance. At least 50% of cells met this inclusion criterion in each recorded area. 2% feels like a lenient cutoff, and it is unclear how sensitive the results are to this cutoff, though the authors argue that this cutoff should still only allow a false positive rate of 0.02% (determined by randomly shuffling the onset time of each trial.)

We have provided more information about the 2% cutoff in a new figure, Figure 2-figure supplement 3. We reanalyzed the false positive rate and found that at a cutoff of 2% (but not 0.5% or 1%) there were no false positives (Figure 2-figure supplement 3B). Thus, we are confident that all neurons contain true task-related signals. Moreover, we found that the pattern of results remains largely unchanged as we change the cutoff over a range from 0.5% to 5%. With more stringent cutoffs, we begin to lose neurons with robust task-related responses (Figure 2-figure supplement 3E), so we continue to use the 2% cutoff in this version of the manuscript.

First, they show that the correlation between cell responses on all periods except for the start of day 1 is more correlated with day 3 responses than expected by chance (although the correlation is still quite low, for example, 0.2 on day 2).

We agree that a correlation of 0.2 does not seem like a large effect, however the variability in neuronal responses and noise level of the measurement enforce a ceiling that we can estimate by predicting data from the same session that it was trained on. We have replotted these data (new panel Fig. 7G) with the correlation normalized to the cross-validated performance on the training day’s data. This shows that the models do about half as well in session 1 and session 2 compared to session 3. The original plot is in a new supplementary figure, Figure 7-figure supplement 1B.

To further emphasize the similarity across days, we have added new panels (Fig. 7E and Figure 7-figure supplement 1A) showing that, across mice, a typical neuron was more correlated with its own activity on the subsequent day than with ~90% of the other neurons (shuffle controls, 50%).

Second, they show that cue identity is able to capture the highest unique fraction of variance (around 8%) in day 3 cue cells across three days of imaging, and similarly for lick behavior in lick cells and cue+lick in cue+lick cells. Nonetheless, their sample rasters for all imaged cells also indicate that representations are not perfectly stable, and it will be interesting to see what *does* change across the three days of imaging.

We agree that the representations are not perfectly stable and that is an interesting point of further investigation. One difference we did observe is increased cue coding across training (Figs. 6H, 7H).

Importantly, the authors do not present evidence that value itself is stably encoded across days, despite the paper's title. The more conservative in its claims in the Discussion seems more appropriate: "these results demonstrate a lack of regional specialization in value coding and the stability of cue and lick [(not value)] codes in PFC."

Due to confusing terminology on our part, the reviewers were mistaken about the timing of the experiment where we assess the stability of value coding. In the imaging sessions, odor sets were always presented on separate days. Thus, when we identify value coding in our imaged population, it is across two consecutive days with different odor sets, which is in itself evidence of a stable value code. We have updated our terminology and the text to make this clearer. We also added a new set of plots (Fig. 8H-I) showing the conservation of value-like signaling in cells we tracked across the first three sessions of odor set A, and, as above, that the correlation of these neurons across days is greater than expected by chance. These analyses lend further support to the stability of the value signal.

Additional technical comments:

1) The "shuffle #33" in figure 3B is confusing. The fit kernel in this shuffle shows that the "high" and "medium" responses increase above the pre-stimulus baseline. The "high" response is a combination of set 2 CS+ and set 1 CS50, both of which strongly suppressed the cell's firing over the 2.5-second window shown. Why then does the cue kernel fit these two trials predict an increase in firing rate above baseline at the 2.5-second time point? Is it a consequence of the reduced rank regression process, and if so, how? This strange-looking fit that does not well capture the response of the original cell makes me worry that the high fraction of identified "value" cells may be due to some constraint on the shuffle fits that leads them to often perform poorly.

To address this concern, we refit the value shuffle and its models using a full kernel regression model (rather than reduced ranks). It does improve the appearance of the kernel fits (updated Fig. 3B), and we now use this new approach when fitting cue coding models in the revised manuscript. The regularization inherent in reduced rank constrains the shape of the cue kernel somewhat, which contributed to the shape of the fits (although this did not negatively impact the variance explained); however, because of the importance of the shape of these alternative cue coding models to the interpretation of the analysis, we agree with the reviewers that this was worth improving. The main constraint on the value model and its shuffles, however, is that all cues must use the same template, scaled according to particular values assigned to each cue in each shuffle, which will doubtless lead to compromised (and strange-looking) fits when the shuffled values do not match the ranking of neuron’s cue activity. Critically, this constraint is applied equally to the value model and all the shuffles and would not bias the fits of any one model.

2) The "shuffle" condition when testing for value cells always assumes two high responses, two medium responses, and two low responses. This strategy doesn't account for cells that respond to only a subset of cues, as one might expect in a sparse-coding olfactory region. We suggest adding a set of shuffles where responses are split into two groups, with either 3 conditions per group or 2 in one group and 4 in the other.

We appreciate this valuable suggestion. We added all permutations of models with high responses to 6, 5, 4, 3, 2, or 1 odor cue to the analysis. We still find that the value model is the most frequent best model, displayed in new panels Fig. 3C-D and Figure 3-figure supplement 1A-B. The additional models allowed us to identify other neurons with cue activity best fit by models highly correlated with the ranked value model, which we term “value-like” neurons, including most neurons previously described as “trial-type” neurons. All 153 models and the fraction of neurons best fit by each one are depicted in Figure 3-figure supplement 1.

After implementing the changes to both the method of model fitting (full kernel regression, as noted above) and the possible alternative models, the distribution of value cells has changed slightly. All regions contain value cells, supporting our original conclusion that the value signal is distributed, but there is slight enrichment in PFC when combining these five regions together (Fig. 4A).

We have updated the conclusions of the paper accordingly:

Introduction: “Unexpectedly, in contrast to the graded cue and lick coding across these regions, the proportion of neurons encoding cue value was more consistent across regions, with a slight enrichment in PFC but with similar value decoding performance across all regions.”

Results: “Interestingly, the frequency of value cells was similar across the recorded regions (Fig. 4A). Indeed, despite the regional variability in number of cue cells broadly (Fig. 2F-G), there were very few regions that statistically differed in their proportions of value cells (Fig. 4A, Figure 4-figure supplement 1). Overall, though, there were slightly more value cells across all of PFC than in motor and olfactory cortex (Figs. 4A, Figure 4-figure supplement 1). Although there were the most cue neurons in olfactory cortex, these were less likely to encode value than cue neurons in other regions (Figure 4-figure supplement 2). Value-like cells were also widespread; they were less frequent in motor cortex as a fraction of all neurons, but they were equivalently distributed in all regions as a fraction of cue neurons (Fig. 4B, Figure 4-figure supplement 1, Figure 4-figure supplement 2).”

Discussion: “In contrast to regional differences in the proportion of cue-responsive neurons, cue value cells were present in all regions and could be used to decode value with similar accuracy regardless of region.” AND “The distribution of cue cells with linear coding of value was mostly even across regions, with slight enrichment overall in PFC compared to motor and olfactory cortex, but no subregional differences in PFC. Importantly, cue value could be decoded from the value cells in all regions with similar accuracy.”

3) On pages 11-12, the authors write "value coding is similarly represented across the regions we sampled." I feel this isn't quite what was shown: the authors have shown that all recorded regions contain a roughly comparable number of individual cells that are modulated by value, i.e. "value cells". However, the authors also showed that some recorded cells have mixed selectivity for value and other factors- it is possible that these mixed selectivity cells do vary between brain regions in their quantity or degree of value coding. Regions could potentially also vary in the dynamics of their value response, or in the trial-to-trial variability in the activity of value cells. I suggest the authors revise their original statement, for example by writing "we find a similar proportion of value-specific cells across the regions we sampled."

We thank the reviewer for carefully reviewing our claims. In addition to showing similar proportions of value cells, we also show that the value-related activity is similar (by plotting the first principal component of value and value-like cells, Fig. 4C-D) and that cue value could be decoded from the value cells in all regions with similar accuracy (new panel, Fig. 4E). We have updated the text to more accurately reflect these observations:

“In contrast to regional differences in the proportion of cue-responsive neurons, cue value cells were present in all regions and value could be decoded from them with similar accuracy regardless of region.”

4) We appreciate the authors' idea to introduce a history term to their value cell model but worry that the distinction between history-dependent value cells and lick/cue+lick cells in Figure 4 has gotten fuzzy. At this point, history-dependent value cells are the product of a set of steps: 1) they are identified as "cue" neurons because the cue type accounts for at least 2% of the variance, while the lick rate does not, then 2) among the cue neurons, a subset are identified as "value" neurons because their activity scales with the cue type across both odor sets, and then 3) among value neurons, the "history-dependent" value neurons show a response rate that scales with a model that predicts anticipatory licking. Our concern comes down to this: your conclusion that these cells are not licking cells hinges on the initial point that licking does not account for 2% of the observed variance in cell activity. But if you had dedicated an equal number of model parameters and selection steps to your licking model, might it still not turn out that a licking model predicts their activity as well as the history-dependent cue value model?

What would bolster our confidence here would be a comparison of variance explained: if you compare the predictions of the history-dependent value-encoding cue neuron model to the predictions of a simple lick neuron model, how much better does the former predict what the cells are doing? Are all those extra parameters and selection steps really contributing to an improved description of how neurons will respond?

First, we would like to emphasize that “cue” neurons, as a population, have no discernible modulation by licks, which can be seen when comparing their activity on CS50 trials with and without reward, when licking clearly varies (Figure 2-figure supplement 2D). A new panel, Figure 5E now depicts the improvement in variance explained by the history model over a lick only model. The improvement is robust and universal. This is because even though the number of anticipatory licks per trial is used to fit the weights of our trial value model, these cue neurons have temporal dynamics that are more consistent with cue presentation than the presence of licks. We explain more below in our response to point 7.

5) The paper's title claims that the coding of cue value is both stable and distributed. While the point for value coding being distributed is well supported with analysis, the claim that cue value coding is "stable" is weaker. The authors show in Figure 6 that cue identity best accounts for unique variance among cue cells across three days of imaging, but it does not follow that cue value is similarly stable. Figure 7 shows that on day 3 of imaging, the two odor sets have similar encoding- but this analysis is only performed within day 3, not across days. Why not examine unique variance among value cells over days, as was done for a cue, lick, and both cells in Figure 6G? That seems to be an important missing piece and a logical next step. The Discussion is more conservative in its claims- "these results demonstrate a lack of regional specialization in value coding and the stability of cue and lick [(not value)] codes in PFC." But this subtlety is missing from the paper's title and introduction.

First, an important correction. “This analysis is only performed within day 3, not across days,” is a misunderstanding of our experiment brought on by our confusing terminology, which we have updated. This figure (now Figure 8) analyzes two sessions performed on consecutive days: Odor Set A day 3 (A3) and Odor Set B day 3 (B3), which constitute days 5 and 6 of our experiment (see updated panels Fig. 1B, 6A). This is why identifying value signaling across both of these sessions is justification for a stable code; by definition, it was present on two consecutive days.

A limitation of our imaging experiment prevents us from evaluating value signaling in each individual session (like we did for cues and licks). For the imaging, we only presented one odor set per session (unlike the electrophysiology, where odor sets were presented in blocks). Our method of identifying value signals relies on two odor sets, so we cannot quantify it on a per session basis in the imaging. However, to address this as best we could, we identified CS+-preferring cue cells in session A3 (odor set A day 3) and plotted them for sessions A1-A3 (Fig. 8H), which reveals a conserved value-like signal across days. We also found that the correlation of the activity of these neurons across days was higher than expected by chance (Fig. 8I).

We have edited the discussion text about coding stability, adding in more detail and caveats:

“Previous reports have observed drifting representations in PFC across time (Hyman et al., 2012; Malagon-Vina et al., 2018), and there is compelling evidence that odor representations in piriform drift over weeks when odors are experienced infrequently (Schoonover et al., 2021). On the other hand, it has been shown that coding for odor association is stable in ORB and PL, and that coding for odor identity is stable in piriform (Wang et al., 2020a), with similar findings for auditory Pavlovian cue encoding in PL (Grant et al., 2021; Otis et al., 2017) and ORB (Namboodiri et al., 2019). We were able to expand upon these data in PL by identifying both cue and lick coding and showing separable, stable coding of cues and licks across days and across sets of odors trained on separate days. We were also able to detect value coding common to two stimulus sets presented on separate days, and conserved value features across the three training sessions. Notably, the model with responses only to CS+ cues best fit a larger fraction of imaged PL neurons than the ranked value model, a departure from the electrophysiology results. It would be interesting to know if this is due to a bias introduced by the imaging approach, the slightly reduced CS50 licking relative to CS+ licking in the imaging cohort, or the shorter imaging experimental timeline.

The consistency in cue and lick representations we observed indicates that PL serves as a reliable source of information about cue associations and licking during reward seeking tasks, perhaps contrasting with other representations in PFC (Hyman et al., 2012; Malagon-Vina et al., 2018). Interestingly, the presence of lick, but not cue coding at the very beginning of the first session of training suggests that lick cells in PL are not specific to the task but that cue cells are specific to the learned cue-reward associations. Future work could expand upon these findings by examining stimulus-independent value coding within session across many consecutive days.”

6) Considering licking as the readout of value has pros and cons. Anticipatory licking may be correlated with subjective value, but certainly nonlinearly. After all, licking has a ceiling and floor (bounded rate from 0->10 Hz). Are results consistent with the objective value of the cues (which are 0, .5, 1)? Which measure better explained the data?

Thanks to this important suggestion, we tried fitting another set of models with 0, 0.5, 1 as the cue values. We found the same pattern of results. Overall, the fits were slightly better with 0, 0.5, 1, with 50.6% of potential value neurons (found with either version of the model) better fit by 0, 0.5, 1, and with mean variance explained of 0.265 with 0, 0.5, 1 (compared to 0.264 with the anticipatory lick values). Without strong evidence to choose one model over the other, we decided to use 0, 0.5, 1 because it exactly reflects reward probability, and is more objective as the reviewer notes, whereas before we relied on a noisier estimate of subjective value. We have changed the text accordingly.

7) How can a neuron encode "Cue" in a value-dependent manner and not also encode licking, given they are correlated? If the kernel window includes anticipatory licking, and anticipatory licking is by definition related to value, then how could a licking kernel not at least explain some of that neuron's variance?

The trial estimates of value from the lick linear regression are derived from typical licking patterns across all sessions and do not incorporate the particular number of licks on a given trial or the latency of licking relative to cue onset. Although the trial value model is predicting the number of licks on each trial, it only uses cue identity and reward history to make its prediction, so it is not tightly correlated with the stochastic licks on a given trial. And, importantly, we input the trial value as a cue kernel spanning the entire cue period, whereas lick kernels, per our definition, are restricted to a window around when licking occurs, which generously encompasses neural signals relating to both lick initiation and feedback. Licking can explain some of value and (history) neurons’ variance, which you can see in our new panel Fig. 5E, but it does not contribute any unique variance to the model. That is, with or without licks, the model performs just as well, so the activity of the neuron does not track any of the unique features of licks over cues (like whether or not the mouse licked on trial, when the mouse started licking on a given trial). Without cues, however, the model does worse, which means that the neuron’s activity is modulated by cues separately from when the mouse is licking. Thus, we can conclude the neuron encodes cues, but we have no evidence the neuron encodes licks (beyond the extent to which licks are correlated with cues). In our example fit in 5E, you can see how, although licks track value, they cannot recapitulate the temporal dynamics of this cue neuron. We added more description of this distinction in the manuscript.

8) The ordering analysis with the 89 permutations is very nice for showing across the population the "value ordered" gains are the best explanation of the neural activity. However, it doesn't tell you that any one neuron significantly encodes value, or the strength of this effect if they do. For the former, they could compare to a null distribution of shuffled order of neural vs CS data, and consider neurons for which model is better than chance ( a .05 FDR on a null distribution would be appropriate). This is important for supporting their conclusion of the fraction of neurons encoding value for each region.

In fact, with so many alternative models, the probability of a neuron being best fit by the value model but not encoding value above chance is extremely low. To confirm this, we ran the reviewer’s suggested shuffle analysis, and found that 100% of value neurons performed above the 0.05 FDR. We have added this result to the methods:

“To verify the robustness of value coding in the neurons best fit by the ranked value model, we fit each of those neurons with 1000 iterations of the cue value model with shuffled cue order to create a null distribution. The fits of the original value model exceeded the 98th percentile of the null for all value neurons.”

9) Similarly the 65% cutoff for trial history relative to shuffled is unusually low and therefore not convincing these neurons significantly encode the value. Usually, 95% or 99% is selected to give you a more standard significance criterion (FDR).

We have changed the cutoff to 95%. We originally selected 65% because neurons in the 65% to 95% range had clear history effects, especially at the population level, but we appreciate the importance of rigorous selection. Note this shuffle is very strict, preserving CS+, CS50, CS- ranking but shuffling within-cue fluctuations in value due to trial history. With the stricter value and history shuffling, we now observe fewer history neurons, and they are most prevalent in PFC (Fig. 5I)

10) "Regions with non-overlapping CIs were considered to have significantly different fractions of neurons of that coding type." This isn't a statistical test. Confidence intervals are not the same as significance.

We now perform Bonferroni-corrected pairwise contrasts between all regions in the generalized linear mixed effects model. We added the p-values for all the comparisons that previously relied on non-overlapping confidence intervals in supplementary tables.

Minor comments:

The methods are hard to read. Most of the information seems to be there but in general, paragraphs need to be read over multiple times for meaning to emerge.

We have edited for clarity, and if there are particular sections that remain unclear, we would be happy to know which ones.

Why is there a block predictor in the encoding model?

Because not every odor is present in every block, we did not want our models to use the specific cue predictors to try to account for differences in baseline activity that naturally occur across the session. Thus, each of the six blocks has its own predictor that serves as a constant that can adjust for changing baseline firing rate. Importantly, the block predictor simply marks the passage of blocks and contains no information about the odors present. We added more information about this to the methods:

“For electrophysiology experiments, the model also included 6 constants that identified the block number, accounting for tonic changes in firing rate across blocks. Because not all cues were present in every block, this strategy prevented the cue kernels from being used to explain baseline changes across blocks.”

Did you use an elastic net rather than a lasso? What is the alpha parameter for lasso?

We used an elastic net with alpha = 0.5. We added this information to the methods.

Figure 3F legend doesn't seem to match the figure.

Corrected.

Author Response:

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

Consolidated response to public comments:

We are grateful to the reviewers for their careful examination of our manuscript and for their insights for improving our work. We appreciate that they recognize the potential of the TARDIS approach for diverse transgenesis applications.

We address two primary concerns that the reviewers raise. First is a concern that this approach is not as innovative as stated. We acknowledge that our work builds upon previous studies in the field, such as those by Nonet, Mouridi et al., with Malaiwong coming after our initial preprint. However, we believe that our approach offers a unique contribution, in that prior work does not provide a protocol or process to provide large-scale multiplexed transgenesis. Specifically, our introduction of large sequence library arrays (TARDIS Library Arrays or TLAs). While high throughput multiplexed transgenesis is discussed in Nonet & Mouridi manuscripts, it is never demonstrated. It is the combination of library construction, heritable transmission of the library itself, and then induced transgenesis of library components at a defined location within single individuals that makes this approach particularly useful.

Second, there were concerns that we have not demonstrated that this approach will work beyond C. elegans. We agree that our discussion of the potential application of TARDIS beyond C. elegans is speculative at this point. Our intention was to highlight the potential for future development and application in other systems. In some cases, large integrations into the genome are possible, such as in the case of H11 locus in mice, which could provide a means to inherit a sequence library. We are hopeful that our success in C. elegans will inspire work in other systems. The motivation for this will naturally depend on the usefulness of actual TARDIS implementations, which will be forthcoming in due course.

Reviewer #1 (Recommendations For The Authors):

1. Section titled "Integration from TARDIS array to F1" beginning on line 161 has some missing details that make it difficult to follow. Many of those details are present in the following section titled "Generation and Integration of TARDIS promoter library", but should have been present sooner.<br /> a. How many barcodes were in the array in line PX786?<br /> b. Clarify the use of G-418, heat shock, hygromycin, etc. in this paragraph.<br /> c. Please clarify that the L1 death is due to selection with G-418 - "We found that a portion of the initially plated worms die, likely due to lack of array inheritance." is confusing unless you add that they are selected in this step.<br /> d. "These results suggest that approx. 100-200 worms need to be heat shocked to obtain an integrated line" - the math actually looks like 200-300, and this would be to get a single integrant.<br /> 2. In general, the barcoding study and results reported here read like a teaser/proof-of-concept but do not really robustly demonstrate the application of the method for barcoding and tracing individual lineages in a population of C. elegans. How many barcodes were in the array, and how many ended up in F1s? Would one need to screen for duplicate barcodes after integration?<br /> 3. The promoter library study is impressive but again, rather limited.<br /> 4. The Discussion section about extending this technology to other systems is fairly balanced, acknowledging the limitations that would need to be overcome. The language in the abstract and introduction is less balanced and oversells the current translation of this approach to systems outside C. elegans.

Reviewer #2 (Recommendations For The Authors):

As I mentioned in the Public Review, I appreciate the design of the selection markers for integration. However, I do not see a major advance in the field. The use of barcoding of individuals to address a biological question would change that impression.

Regarding the integration of promoters, I think this is something that anyone could address in diverse forms using existing knowledge.

Suggestions:<br /> - Use one or two more landing pads for barcoding of animals and check numbers, efficacy, enrichments..etc. About 500 sequences overrepresented may be too much for future applications;<br /> - Increase the number of landing pads for inserting promoters. Genomics context matters and this could help to have a better summary of the real expression patterns driven by the promoter of interest;<br /> - Other references about landing pads would be Vicencio et al, Genetics 2019, and Nonet microPublication Biology 2021.

In addition to the general comments, the reviewers provided useful suggestions to the text that we have used to clarify the manuscript.

Author Response:

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

Reviewer #1 (Public Review):

The authors investigated state-dependent changes in evoked brain activity, using electrical stimulation combined with multisite neural activity across wakefulness and anesthesia. The approach is novel, and the results are compelling. The study benefits from an in-depth sophisticated analysis of neural signals. The effects of behavioral state on brain responses to stimulation are generally convincing.

It is possible that the authors' use of "an average reference montage that removed signals common to all EEG electrodes" could also remove useful components of the signal, which are common across EEG electrodes, especially during deep anesthesia. For example, it is possible (in fact from my experience I would be surprised if it is not the case) that under isoflurane anesthesia, electrical stimulation induces a generalized slow wave or a burst of activity across the brain. Subtracting the average signal will simply remove that from all channels. This does not only result in signals under anesthesia being affected more by the referencing procedure than during waking but also will have different effects on different channels, e.g. depending on how strong the response is in a specific channel.

We thank the reviewer for the positive comments and for raising this point. We do not believe that the average reference montage is obscuring an evoked slow wave in the isoflurane-anesthetized mice. Electrical stimulation did elicit a brief activation in nearby neurons that was followed by roughly 200 ms of quiescence, but no significant changes in firing in the other regions we recorded from (Author response image 1).

Author response image 1

ERP and evoked population activity during isoflurane anesthesia do not show evidence of global responses.

(Top). ERP (-0.2 to +0.8 s around stimulus onset) with all EEG electrode traces superimposed. Data represented is the same: red traces have been processed with the average reference montage, black traces have not. (Bottom) Population mean firing rates from the areas of interest from the same experiment as above.

We are familiar with the work from Dasilva et al. (2021), a study similar to ours because they also performed cortical electrical stimulation in mice anesthetized with isoflurane. They show widespread evoked multi-unit activity (derived from LFP) in isoflurane-anesthetized mice in response to electrical stimulation, but critical experimental differences may underlie the conflicting results presented in our study. Both works use similar levels of isoflurane to maintain anesthesia (we use a level roughly equivalent to their “deep” level). However, our experiments use only isoflurane, whereas Dasilva et al. induced anesthesia with ketamine and medetomidine followed by isoflurane. It has been shown that isoflurane and ketamine have different effects on neural dynamics (Sorrenti et al., 2021). Typically, isoflurane causes reduced spontaneous firing rates and decreased evoked response amplitudes compared to wakefulness, whereas ketamine has been shown to increase firing rates and evoked response amplitudes (Aasebø et al., 2017; Michelson & Kozai, 2018). Perhaps a more relevant difference are the electrical stimulation parameters used to perturb the brain. Dasilva et al. used 1 ms pulses of 500 μA, which would have a much larger effect than the stimulation used in this work, 0.2 ms pulses of 10-100 μA.

Additionally, we would like to clarify that the average reference montage is not impacting the main findings of this work. As the reviewer correctly pointed out, the average reference montage does change the appearance of the ERP in the butterfly plots (Top panel in Author response image 1). However, all the quantitative analyses of the EEG-ERPs are performed on the global field power, computed by taking the standard deviation across all EEG channels, which is not affected by the average reference montage.

Reviewer #2 (Public Review):

[…] The conclusions regarding the thalamic contributions to the ERP components are strongly supported by the data.

The spatiotemporal complexity is almost a side point compared to what seems to be the most important point of the paper: showing the contribution of thalamic activity to some components of the cortical ERP. Scalp ERPs have long been regarded as purely cortical phenomena, just like most EEGs, and this study shows convincing evidence to the contrary.

The data presented seemingly contradicts the results presented by Histed et al. (2009), who assert that cortical microstimulation only affects passing fibers near the tip of the electrodes, and results in distant, sparse, and somewhat random neural activation. In this study, it is clear that the maximum effect happens near the electrodes, decays with distance, and is not sparse at all, suggesting that not only passing fibers are activated but that also neuronal elements might be activated by antidromic propagation from the axonal hillock. This appears to offer proof that microstimulation might be much more effective than it was thought after the publication of Histed 2009, as the uber-successful use of DBS to treat Parkinson's disease has also shown.

We thank the reviewer for their positive comments and thoughtful suggestions. We appreciate and agree with the reviewer’s perspective that the thalamic contribution to the cortical ERP is one of the key points of this study. We also thank the reviewer for their comment on the apparently contradictory results reported by Histed et al. (2009). This gives us the opportunity to further highlight the important contribution of our study to the field.

First, we would like to highlight some key experimental differences between the two studies. In our study we used single pulse stimulation with currents between 10 and 100 μA, whereas Histed et al. used trains of pulses (100 ms in duration at 250 Hz) with lower current intensities (between 2 and 50 μA). We varied the depth of stimulation, targeting superficial and deep cortical layers; Histed et al. exclusively stimulated superficial cortical layers. In addition, the two studies used recording methods that are orthogonal in nature. We used Neuropixels probes that record from neurons that span all cortical layers depth-wise while Histed et al. used two-photon calcium imaging to record from a horizontal plane of neurons (again, in the superficial cortical layers).

Because of these important methodological differences, it is more appropriate to compare the Histed et al. results to our results from superficial stimulation at comparable current intensities. In this case, we believe the two studies show similar results: stimulation activated a small fraction of neurons even hundreds of microns away from the stimulating electrode (see Figure 4A from our manuscript). However, our study adds an important observation pointing to the critical role of the depth of the stimulating electrode. We observe significant excitation of local cortical neurons (Figure 4D) and trans-synaptic activation of the thalamus only when we delivered deep stimulation (Figure5A). This effect is likely mediated by activation of large, myelinated cortico-thalamic fibers, which are thought to be more excitable that non-myelinated horizontal fibers (Tehovnik & Slocum, 2013).

To summarize, Histed et al. (2009) concluded that microstimulation causes a sparse activation of a distributed set of neurons with little evidence of synaptically driven activation. Instead, we showed that microstimulation can robustly activate local neurons and trans-synaptically activate distant neurons when stronger stimuli are directed to deep cortical layers. Based on this, we conclude that electrical stimulation is indeed highly effective, and is a valid tool that can be used to probe and characterize the cortico-thalamo-cortical network of any behavioral state.

----------

Reviewer #1 (Recommendations for the authors):

1. I am not clear how "putative pyramidal" or RS and "putative inhibitory" fast-spiking neurons were identified. Please provide some further details on that, including average spike wave shapes, and distribution of firing rates, and it would be interesting to know the proportion of "putative" RS and FS neurons in your recorded population. Obviously, caution is warranted here because, without further work, you cannot be sure that those are indeed pyramidal cells or interneurons! Is this subdivision necessary at all?

We added details regarding the cell-type classification to the Results (lines 136-140) and the Methods section. This classification is common practice in cortical extracellular electrophysiology recordings given that cell-type specific analyses can reveal important differences between the two putative populations (Barthó et al., 2004; Bortone et al., 2014; Bruno & Simons, 2002; Jia et al., 2016; Niell & Stryker, 2008; Sirota et al., 2008). Based on our findings that the two populations respond to electrical stimulation in similar ways (excitation followed by a period of quiescence and rebound excitation), we agree the subdivision is not necessary to support our conclusions. However, we believe that some readers will appreciate seeing the two putative populations presented separately.

2. I wonder how the authors know whether the animals were awake, specifically when they were not running. Did you observe animals falling asleep when head-fixed? Providing some analyses of spontaneous EEG/LFP signals in each state could add some reassurance that only wakefulness was included, as intended.

While we cannot conclusively rule out that mice were asleep during the “quiet wakefulness” periods we analyzed, we believe they are likely to be awake for two main reasons: 1) all the experiments are performed during the dark phase of the light/dark cycle, when the mice are less likely to enter a sleep state (Franken et al., 1999); 2) the animals are not undergoing specific training to promote drowsiness or sleep. Indeed, many sleep-focused studies in head-fixed mice are performed during the light phase of the animal’s cycle to maximize the likelihood of capturing sleep states (Kobayashi et al., 2023; Turner et al., 2020; Yüzgeç et al., 2018; Zhang et al., 2022). We have added this note to the Discussion section (lines 402-406).

Because we do not specifically record during sleep states and our recording does not include electromyography, which is commonly used in conjunction with EEG to classify sleep stages, we cannot accurately perform spectral comparison between “quiet wakefulness” and sleep states in our recordings.

3. I was unsure about the meaning of some of the terminology, specifically "rebound", "rebound spiking", "rebound excitation" etc. Why do you call it "rebound"?

“Rebound” is a term often used to describe a period of enhanced spiking following a period of prolonged silence or inhibition (Guido & Weyand, 1995; Roux et al., 2014). Grenier et al. list “postinhibitory rebound excitation” as an intrinsic property of cortical and thalamic neurons (1998). We added this description to the text (lines 79-80).

Reviewer #2 (Recommendations For The Authors):

Regarding analysis, I would make three main points:

Regarding the CSD analysis, I think the authors have done a good job of circumventing several of the known issues of this technique, especially by using ERPs rather than ongoing activity. However, although I do not immediately have access to the literature to back up this claim, I've heard that many assumptions behind CSD require a laminar structure with electrodes positioned perpendicular to these layers. In Figure 1B it seems like the neuropixels probe is not really perpendicular to the cortical layers, and I wonder if this might be an issue. I am also wondering how to interpret the thalamic CSD, as this structure is not laminar, lacks the mass of neatly stacked neuronal dipoles present in the cortex, and does not have an orderly array of synaptic inputs and outputs. I understand that CSD analysis helps minimize the contributions of volume conduction, but in this case, I also wonder if the thalamic CSD is even necessary to back up the paper's claims.

One-dimensional CSD is computed assuming that the electrode is inserted perpendicular to cortex. This is mainly important for the interpretation of sinks and sources, since CSD can be also computed on radial voltages (e.g., EEG [Tenke & Kayser, 2012]). In general, our Neuropixels probes do not significantly deviate from perpendicular (mean deviation from perpendicular 15.3 degrees, minimum 5.2 degrees, and maximum 36.6 degrees). The probe represented in Figure 1B deviates from perpendicular by 31.2 degrees, which is an outlier compared to the rest of the insertions. Any deviation from perpendicular would result in the “effective” cortical thickness being larger by a factor of 1/cos(angle deviation from perpendicular) and thus would not affect the relative location of sources and sinks. We have added a statement to clarify this in the text (lines 126 and 454-456).

We agree with the statement regarding CSD analysis in the thalamus. We originally included the CSD for the thalamus in Figure 2F for completeness. As the reviewer pointed out, thalamic CSD was not used to perform any subsequent analysis and is, therefore, not necessary to back up any claims. As such, we have removed CSD plot from Figure 2F to avoid any confusion and made a comment to this effect in the legend (lines 1175-1177).

On the merits of using the z-score normalization for spike rates vs. other strategies like standardizing to maximum firing, I am aware that both procedures have limitations, but the z-score changes the range of the firing rate from [0, +Inf] to [-Inf, +Inf]. This does not seem correct considering that negative spiking rates do not exist. The standardization to maximum rate keeps the range within [0, 1], not creating negative rates. Another point that it will be worth discussing is the reported values of the z-scored values. For example, what does it mean to be 54 standard deviations away from the mean? 6 standard deviations is already a big distance from the mean.

For Figure 2, we chose to represent the neural firing rates as z-scores because we found it important to report the magnitude of both the increase and decrease of the evoked firing rates in the post-stimulus period relative to the pre-stimulus rate. The normalization we used helps to visualize the magnitude of the effects of electrical stimulation in neuronal activity for both directions, which is an important result of the study. Despite the differences between the two normalization methods, the normalization based on the maximum firing does not significantly change the qualitative interpretation of Figure 2 in the manuscript (Author response image 2).

Author response image 2

Evoked firing rates for neurons in the areas of interest in response to deep stimulation in MO during the awake state. (Left) Firing rates of all neurons normalized by the average, pre-stimulus firing rate. (Right) Firing rates of all neurons normalized by the maximum post-stimulus firing rate.

Regarding Figure 3 and the associated text, we would like to clarify that the magnitude metric is not simply a z-score value (with units of s.d.) but rather it is the integrated area under the z-scored response over the response window (with units of s.d.∙seconds). This can help explain why we see values of ~50 s.d.∙s. We chose to z-score firing rates, LFP, and CSD to normalize across the different signals and magnitudes of the evoked responses. We often observed the largest responses in the LFP (see Figure 3A), which may be partly due to the signal naturally having a larger dynamic range than the measured neural firing rates. Then we integrated the z-score response time series to capture the dynamic of the signal over the response window, rather than a static value such as the mean or maximum z-score. After performing a thorough literature search, we found no other ways to capture and compare the magnitudes of the different signals. We have added language to clarify the magnitude metric (lines 155-156) and added the appropriate units.

In reporting the p-values, I recommend increasing the number of significant digits to four because the p-value seems to be the same for different tests in several places (e.g.: lines 207 to 218), which seems odd. I also wonder whether this could be an artifact of the z-scoring procedure. In the figures, I would like to advise the use of 1 asterisk to denote "weak evidence to reject the null hypothesis (0.05 > p > 0.01)" and two asterisks to denote "strong evidence to reject the null hypothesis (0.01 > p)", and make a note of it accordingly in the manuscript and/or figure legends.

According to the reviewer’s suggestion, we have changed the statistics language to “* weak evidence to reject null hypothesis (0.05 > p > 0.01), ** strong evidence to reject null hypothesis (0.01 > p > 0.001), *** very strong evidence to reject null hypothesis (0.001 > p)” throughout the manuscript.

We have also increased the number of significant digits to four throughout the manuscript. It is true that some of the p-values reported for Figure 3 (lines 169-180) are the same for different tests. This is not an artifact of the z-scoring, but rather a consequence of performing the Wilcoxon signed-rank test (an ordinal statistical test) with small sample numbers. Because the p-value depends only on the relative ordering, not the continuous distribution of values, the small sample size (N=6-14) increases the likelihood of obtaining the exact same p-value if the relative ordering of samples is the same.

Line 202: If the magnitude corresponds to z-score data, please add "s.d." after the number, as z-scored values are expressed in standard deviation units. Please update this throughout the paper.

As stated above the magnitude metric is the integrated area under the z-scored response over the response window (with units of s.d.∙seconds). We have added the correct units in all places.

Line 214: Please report how the multiple comparisons correction was performed

We have added the test used for multiple comparisons in line 169 (formerly line 214) and in the Methods section (line 770).

Line 462: please replace "Neuropixels activity" with "LFP and single-unit activity".

We changed the wording to specify “LFP, and single neuron responses…” (now line 337).

Line 475: a short explanation of the bi-stability phenomena will be helpful for the reader.

We added the following description: “a state characterized by spontaneous alternation between bouts of activity and periods of silence” (lines 350-351).

Line 601: It is asserted that "Electrical stimulation directly activates local cells and axons that run near the stimulation site via activation of the axon initial segment" and the paper by Histed et al. 2009 is cited. This does not seem like an appropriate citation, as Histed et al. explicitly state that electrical microstimulation does not activate local neuronal bodies near the electrode tip. See my comment above.

Upon further reading, we believe we are seeing evidence of direct axonal activation and subsequent antidromic activation of local cell bodies, as you suggested in your above comment and has been proposed by many including Histed et al. (2009) and Nowak and Bullier (1998). We edited our sentence accordingly, kept the Histed et al. citation, and added other relevant citations (lines 487-490).

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Author Response

Reviewer #1 (Public Review):

The manuscript, "A versatile high-throughput assay based on 3D ring-shaped cardiac tissues generated from human induced pluripotent stem cell-derived cardiomyocytes" developed a unique culture platform with PEG hydrogel that facilitates the in-situ measurement of contractile dynamics of the engineered cardiac rings. The authors optimized the tissue seeding conditions, demonstrated tissue morphology with expressions of cardiac and fibroblast markers, mathematically modeled the equation to derive contractile forces and other parameters based on imaging analysis, and ended by testing several compounds with known cardiac responses.

To strengthen the paper, the following comments should be considered:

1) This paper provided an intriguing platform that creates miniature cardiac rings with merely thousands of CMs per tissue in a 96-well plate format. The shape of the ring and the squeezing motion can recapitulate the contraction of the cardiac chamber to a certain degree. However, Thavandiran et al (PNAS 2013) created a larger version of the cardiac ring and found the electrical propagation revealed spontaneous infinite loop-like cycles of activation propagation traversing the ring. This model was used to mimic a reentrant wave during arrhythmia. Therefore, it presents great concerns if a large number of cardiac tissues experience arrhythmia by geometry-induced re-entry current and cannot be used as a healthy tissue model. It would be interesting to see the impulse propagation/calcium transient on these miniature cardiac rings and evaluate the % of arrhythmia occurrence.

The size is a key factor impacting the electrical propagation within the generated tissues. Our ring-shaped cardiac tissues have a diameter of 360µm, which is largely smaller than other tissues proposed so far, including in Thavandiran et al (PNAS 2013) where circular tissues had a reported size > 1mm. As shown in Figure 4E (and highlighted below in Author Response Figure 1), tissues under basal conditions display regular beating rates without spontaneous arrhythmias. Videos also show that the tissue contraction is homogeneous around the pillar, suggesting that the smaller size favors the electrical propagation and limits the occurrence of spontaneous reentrant waves. Optical mapping measurements will be performed in the future to assess the occurrence of reentrant waves.

Author Response Figure 1: Poincaré plot showing the plots between successive RR intervals (Data from Figure 4E in basal conditions). Linear regression with 95% confidence interval indicates identity.

2) The platform can produce 21 cardiac rings per well in 96-well plates. The throughput has been the highest among competing platforms. The resulting tissues have good sarcomere striation due to the strain from the pillars. Now the emerging questions are culture longevity and reproducibility among tissues. According to Figure 1E, there was uneven ring formation around the pillar, which leads to the tissue thinning and breaking off. There is only 50% survival after 20 days of culture in the optimized seeding group. Is there any way to improve it? The tissues had two compartments, cardiac and fibroblast-rich regions, where fibroblasts are responsible for maintaining the attachment to the glass slides. Do the cardiac rings detach from the glass slides and roll up? The SD of the force measurement is a quarter of the value, which is not ideal with such a high replicate number. As the platform utilizes imaging analysis to derive contractile dynamics, calibration should be done based on the angle and the distance of the camera lens to the individual tissues to reduce the error. On the other hand, how reproducible of the pillars? It is highly recommended to mechanically evaluate the consistency of the hydrogel-based pillars across different wells and within the wells to understand the variance. Figure 2B reports the early results obtained as the system was tested and developed. Since then, we have tested different iPSC lines and confirm that the overall yield is higher (up to 20 tissues at D14 for some cell lines), however dependent of cell lines.

The tissues do not detach from the glass slides. It is very rare to see tissues roll up on the central pillar. As shown in Figure 1B, the pillars have a specific shape to avoid tissues to roll up as they develop and contract.

3) Does the platform allow the observation of non-synchronized beating when testing with compounds? This can be extremely important as the intended applications of this platform are drug testing and cardiac disease modeling. The author should elaborate on the method in the manuscript and explain the obtained results in detail. The arrhythmogenic effect of a drug can be derived from the regularity of the beat-to-beat time. Indeed, we show that dofetilide increases the variability in the beat-to-beat time by plotting for each beat, the beat-to-beat time with the next beat as a function of the beat-to-beat time with the previous beat.

4) The results of drug testing are interesting. Isoproterenol is typically causing positive chronotropic and positive inotropic responses, where inotropic responses are difficult to obtain due to low tissue maturity. It is inconsistent with other reported results that cardiac rings do not exhibit increased beating frequency, but slightly increased forces only. Zhao et al were using electrical pacing at a defined rate during force measurement, whereas the ring constructs are not.

We agree. The difference in the response to isoproterenol with previous papers may be explained by different incubation timing with the drug. In our case, the tissues were incubated for 5 minutes at 37•C before being recorded.

Overall, the manuscript is well written and the designed platform presented the unique advantages of high throughput cardiac tissue culture. Besides the contractile dynamics and IHC images, the paper lacks other cardiac functional evaluations, such as calcium handling, impulse propagation, and/or electrophysiology. The culture reproducibility (high SD) and longevity (<20 days) still remain unsolved.

Since the submission, we have managed to keep some tissues and analyze them up to 32 days. At that time point the tissues are still beating. Nevertheless, a specific study concerning tissue longevity has not been carried out as the tissues were usually fixed after 14 days to be stained and analyze their structure.

Reviewer #2 (Public Review):

The authors should be commended for developing a high throughput platform for the formation and study of human cardiac tissues, and for discussing its potential, advantages and limitations. The study is addressing some of the key needs in the use of engineered cardiac tissues for pharmacological studies: ease of use, reproducible preparation of tissues, and high throughput.

There are also some areas where the manuscript should be improved. The design of the platform and the experimental design should be described in more detail.

It would be of interest to comprehensively document the progression of tissue formation. To this end, it would be helpful to show the changes in tissue structure through a series of images that would correspond to the progression of contractile properties shown in Figure 3.

Our results indicate that the fibroblasts/cardiomyocytes segregation likely happens as soon as the tissue is formed, as the fibroblasts are critical for tissue generation. The change with time in the shape of the contractile ring is reported in Figure 1E, with a series of images which correspond to the timepoints of Figure 3.

The very interesting tissue morphology (separation into the two regions) that was observed in this study is inviting more discussion.

Finally, the reader would benefit from more specific comparisons of the contractile function of cardiac tissues measured in this study with data reported for other cardiac tissue models.

Author Response

We believe that these findings make a significant contribution to the field of CNS endothelial cell biology and blood-brain barrier. We thank you for your time and consideration.

Reviewers' 1 and 2 concern on endothelial cells (ECs) transcription changes on culture.

We would like to express our gratitude to the reviewers for their critical comments. We are pleased to address the concerns raised by performing FACS sorting of the CNS ECs from E-13.5 and adult brain. However, it is important to note that both E-13.5 ECs and adult ECs were cultured in the same media. It is worth mentioning that this work was initiated in 2017, whereas the article mentioned by Reviewer 1 was published in 2020. We went through a series of standardization steps before identifying the Corning endothelial cell culture media (Cat#355054) with 2% FCS as the optimal medium for preserving EC identity in culture. Conversely, if PromoCell media (C-22110) is used, a decrease in the Wnt pathway can be observed, and the use of 5% FCS enhances the Wnt pathway in E-13.5 ECs. The article mentioned by Reviewer 1 (https://elifesciences.org/articles/51276) did not take these differences in culture media into account. Additionally, we did not employ puromycin for obtaining pure ECs, and the ECs were cultured for a maximum of 8 days. Our in vitro study serves as a model for identifying the epigenetic regulators HDAC2 and PRC2 as controllers of BBB gene transcription, which is subsequently validated in an in vivo model.

Reviewer-1 Comment 2- An additional concern is that for many experiments, siRNA knockdowns are performed without validation of the efficacy of the knockdown

In the revised version of this manuscript, we will include validation results to demonstrate the effectiveness of siRNA knockdown experiments.

Reviewer-1 Comment 3- Some experiments in the paper are promising, however. For example, the knockout of HDAC2 in endothelial cells resulting in BBB leakage was striking. Investigating the mechanisms underlying this phenotype in vivo could yield important insights.

We appreciate your positive comment. The in vivo HDAC2 knockout experiment will serve as a validation of our in vitro findings, indicating that the epigenetic regulator HDAC2 can control the expression of endothelial cell (EC) genes involved in angiogenesis, blood-brain barrier (BBB) formation, and maturation. We are actively working on this model, and we plan to publish additional molecular data on epigenetically regulated CNS vascular development and maintenance in our future publications.

Reviewer 2 Comment-2 The use of qPCR assays for quantifying ChIP and transcript levels is inferior to ChIPseq and RNAseq. Whole genome methods, such as ChIPseq, permit a level of quality assessment that is not possible with qPCR methods. The authors should use whole genome NextGen sequencing approaches, show the alignment of reads to the genome from replicate experiments, and quantitatively analyze the technical quality of the data.

We appreciate the reviewer's comment. While it is true that whole-genome methods such as ChIP-seq and RNA-seq provide comprehensive and high-throughput analysis compared to qPCR assays, it would be incorrect to consider qPCR as inferior. qPCR assays offer advantages in terms of sensitivity, specificity, validation, confirmation, and targeted analysis. We agree that performing a comprehensive analysis of HDAC2 and PRC2 targeted endothelial cell (EC) genes is important. We are currently in the process of generating this data, and as soon as it is complete, we will publish it accordingly.

Reviewer 2 Comment-3 Third, the observation that pharmacologic inhibitor experiments and conditional KO experiments targeting HDAC2 and the Polycomb complex perturb EC gene expression or BBB integrity, respectively, is not particularly surprising as these proteins have broad roles in epigenetic regulation in a wide variety of cell types.

We appreciate the comments from the reviewers. Our results provide valuable insights into the specific epigenetic mechanisms that regulate BBB genes It is important to recognize that different cell types possess stage-specific distinct epigenetic landscapes and regulatory mechanisms. Rather than having broad roles across diverse cell types, it is more likely that HDAC2 (eventhough there are several other class and subtypes of HDACs) and the Polycomb complex exhibit specific functions within the context of EC gene expression or BBB integrity.

Moreover, the significance of our findings is enhanced by the fact that epigenetic modifications are often reversible with the assistance of epigenetic regulators. This makes them promising targets for BBB modulation. Targeting epigenetic regulators can have a widespread impact, as these mechanisms regulate numerous genes that collectively have the potential to promote the vascular repair.

A practical advantage is that FDA-approved HDAC2 inhibitors, as well as PRC2 inhibitors (such as those mentioned in clinical trials NCT03211988 and NCT02601950, are already available. This facilitates the repurposing of drugs and expedites their potential for clinical translation.

Please note: illustrations of Fig-1, 4 and 6 are created using Biorender.com, license purchased by Spiros Blackburn. This will be added to the Acknowledgments.

Author Response

eLife assessment

This study presents a potentially valuable discovery which indicates that activation of the P2RX7 pathway can reduce the degree of lung fibrosis caused by other inflammatory pathways. If confirmed, the study could clarify the role of specific immune networks in the establishment and progression of lung fibrosis.

Thanks for this positive comment. Indeed, knowing that lung fibrosis is partly driven by inflammation, with a dysregulated Th1/Th2/Th17 ratio (PMID 20176803, PMID 19682929), we hypothesized that modulating the immune response would be able to attenuate lung fibrosis. To address this issue, we proposed to boost the activation of P2RX7, a purinergic receptor with immunomodulatory properties (PMID 8614837, PMID 11035104), in the well characterized bleomycin-induced lung fibrosis mouse model (PMID 25959210). In this study, we used a pyroglutamic derivative compound (HEI3090) able to specifically enhance P2RX7-dependent biological activities (cationic channel and macropore opening) only in the presence of extracellular ATP, which was qualified as the first representative of an immunotherapy relying on the activation of P2RX7 expressed by dendritic cells (PMID 33510147), and we showed that lung fibrosis is attenuated in mice treated with HEI3090 as compared to vehicle treated mice.

However, the presented data and analyses are incomplete as they rely on limited pharmacological treatments and because there is an absence of key control studies, validation experiments and statistical analyses.

Quantification of lung fibrosis:

Quantification of lung fibrosis was made on the basis of a modified Ashcroft score which assigns 8 grades to quantify lung fibrosis reliably and reproducibly (PMID 18476815). To be even more accurate and not biased by patchy lesions observed in all existing lung fibrosis induced mouse models, the whole lungs (left and right lobes) were divided in section of 880 µm2 and each section was scored individually. A total of 80 to 110 sections were analyzed per mouse. We agree that our text requires clarification. In parallel, the collagen amount given by the polarization intensity of the Sirius red staining of the lung slices was quantified with a homemade ImageJ/Fiji macro program. Further, we recently analyzed by FACS the percentage of PDGFRα (a specific marker of fibroblasts and myofibroblasts) positive cells in lungs isolated from vehicle and HEI3090-treated mice. All these 3 different markers of lung damage show that HEI3090 attenuates bleomycin-induced lung fibrosis and therefore validate the use of the Ashcroft score to accurately study the extend of lung fibrosis. We are going add quantification of collagen fibers in all figures.

Limited pharmacological treatments:

We have designed and characterized HEI3090 in a previous study and have shown that it is a positive modulator of P2RX7 (PMID 33510147).

To test its effect on lung fibrosis, we tested two pharmacological regimens using HEI3090 and have shown that both regimens are effective in limiting the progression of fibrosis. While having shown the requirement of P2RX7 for the activity of HEI3090 (PMID 33510147), we used in this study p2rx7 KO mice which were adoptively transferred with splenocytes isolated from p2rx7 KO mice to demonstrate the involvement of P2RX7 to mediate the antifibrotic effect of HEI3090. This experiment also serves as control to validate the adoptive transfer experiment.

We agree that proving and validating furthermore that activation of the P2RX7/IL-18 pathway can limit the progression of fibrosis requires the use of other activators of P2RX7. However, to date, HEI3090 is the only pharmacological compound described to activate the receptor. Indeed, the other chemical compounds described in the literature are negative allosteric modulator of P2RX7 (PMID 27935479),

Absence of key control studies and validation experiments:

The importance of P2RX7 in the antifibrotic effect of HEI3090 was demonstrated thanks to P2RX7 KO mice (supplementary figures S6B). We are going to implement this figure with additional mice.

The importance of immune cells was demonstrated thanks to adoptive transfer of WT splenocytes (expressing P2RX7) into P2RX7 KO mice. We agree that lung fibrosis is attenuated in vehicle-treated P2RX7 KO mice, but lung fibrosis is still present and could be modulated by treatments as demonstrated by adoptive transfer of splenocytes isolated from IL-1B KO mice who still respond to HEI3090 as shown in Supplementary figure S6C.

As suggested by reviewers we examined the effect of genetic background using two-way Anova test and the result is “the interaction is considered not significant”.

The prevalence of transferred immune cells on endogenous cells is demonstrated in supplementary figure S5, where intravenous injection of splenocytes isolated from P2RX7 KO mice into WT mice abolishes the antifibrotic effect of HEI3090. This experiment further validates the requirement of immune cells and the efficacy of the adoptive transfer approach.

Statistical analyses:

In this study we compared side by side the effect of HEI3090 versus vehicle in different genetic backgrounds in order to characterize the implication of actors of the P2RX7/IL-18 pathway in the antifibrotic effect of HEI3090. We also examined the effect of genetic background using the two-way Anova test. Following European recommendations, and in agreement with the ARRIVE guidelines for mice studies, we performed provisional statistic to evaluate the number of mice required in the study and stopped the experiments when significantly statistical results were observed. We agree that results are heterogeneous, however this heterogeneity does not prevent data analyses as shown in supplementary figure S6D, where adoptive transfer of splenocytes isolated from IL-1B KO mice into P2RX7 KO mice dampens BLM-induced lung fibrosis (with an Ashcroft score of 1.8 versus 3 in WT mice) but still responds to HEI3090, thus indicating that IL-1B is not required to mediate the antifibrotic effect of HEI3090.

Author Response

We thank all reviewers for constructive critiques. We plan to perform new experiments and revise our manuscript accordingly. The text and Figures are currently undergoing the revision process. Below highlights our revision plan.

eLife assessment

The findings of this article provide valuable information on the changes of cell clusters induced by chronic periodontitis. The observation of a new fibroblast subpopulation, which was named as AG fibroblasts, was quite interesting, but needs further evidence. The strength of evidence presented is incomplete.

RESPONSE: We discovered a new subpopulation of gingival fibroblasts, named AG fibroblasts, using non-biased single cell RNA sequencing (scRNA-seq) of mouse gingival samples undergoing the development of ligature-induced periodontitis. AG fibroblasts exhibited a unique gene expression profile: [1] constitutive expression of type XIV collagen; and [2] ligature-induced upregulation of chemokines such as CXCL12. As a biomedical data science experiment, we validated the scRNA-seq observation using immunohistochemical experiment, which showed the presence of type XIV collagen-positive and CXCL12-positive gingival fibroblasts localized immediately under the gingival epithelium and the coronal region of periodontal ligament.

We agree that the functional/pathological role of AG fibroblasts must be further explored. We have hypothesized that AG fibroblasts initially sense the pathological stress including oral microbial stimuli and secrete inflammatory signals through chemokine expression. To address this hypothesis, in this revision, we plan to analyze a separate scRNA-seq data for AG fibroblast gene expression profile derived from mouse gingival tissues that have been stimulated by Toll-Like Receptor 9 (TLR9) ligand (unmethylated CpG oligonucleotide) and TLR2/4 ligand (LPS). This approach mimics the initial pathological stress applied to gingival tissue. The new insight of AG fibroblasts will be presented in the revision.

Reviewer #1 (Public Review):

In this article, the authors found a distinct fibroblast subpopulation named AG fibroblasts, which are capable of regulating myeloid cells, T cells and ILCs, and proposed that AG fibroblasts function as a previously unrecognized surveillant to orchestrate chronic gingival inflammation in periodontitis. Generally speaking, this article is innovative and interesting, however, there are some problems that need to be addressed to improve the quality of the manuscript.

RESPONSE: We appreciate this comment. As suggested, we further investigated the surveillant function of AG fibroblasts by reanalyzing the scRNA-seq data for stress sensing receptors such as Toll-Like Receptors (TLR). Therefore, we analyzed AG fibroblast gene expression profile when the putative ligands to TLR2/4 and TLR9 are applied to mouse gingival tissue instead of ligature placement. We believe that this first step analysis should warrant to dissect further the function of AG fibroblasts in the future.

Results:

1) It is recommended to add HE staining and immunohistochemistry staining to observe the inflammation, tissue damage, and repair status from 0 to 7 days, so that readers can understand cell phenotype changes corresponding to the periodontitis stage. The observation index can include inflammation and vascular related indicators.

RESPONSE: As recommended, representative histological figures will be included. We will further perform new immunohistochemistry experiment of mouse gingival tissue (D0, D1, D4, D7). We plan to highlight the infiltration of CD45+ immune cells. We also plan to highlight the progressive degeneration of gingival collagen fiber by picrosirius red staining.

2) Figure 1A-1D can be placed in the supplementary figure.

RESPONSE: Combining the new data above, Figure 1 will be revised as suggested.

3) I suggest the authors to put the detection of the existence of AG fibroblasts before exploring its relationship with other types of cells.

4) The layout of the picture should be closely related to the topic of the article. It is recommended to readjust the layout of the picture. Figure 1 should be the detection of AG cells and their proportion changes from 0 to 7 days. In other figures, the authors can separately describe the proportion changes of myeloid cells, T cells and ILCs, and explored the association between AG fibroblasts and these cell types.

RESPONSE: As suggested, the presentation order of Figures and text will be revised to bring the information about AG fibroblasts first. The chemokine-receptor analysis is moved below.

Methods:

It is recommended to separately list the statistical methods section. The statistical method used in the article should be one-way ANOVA.

RESPONSE: A separate statistical method section is created. As pointed out, we used one-way ANOVA with post-hoc Tukey test (when multiple groups were compared).

Reviewer #2 (Public Review):

This study proposed the AG fibroblast-neutrophil-ILC3 axis as a mechanism contributing to pathological inflammation in periodontitis. However, the immune response in the vivo is very complex. It is difficult to determine which is the cause and which is the result. This study explores the relevant issue from one dimension, which is of great significance for a deeper understanding of the pathogenesis of periodontitis. It should be fully discussed.

RESPONSE: We agree with this comment. We expanded the current understanding of oral immune signal communication in Discussion and highlight how AG fibroblast may fit to it.

1) Many host cells participate in immune responses, such as gingival epithelial cells. AG fibroblast is not the only cell involved in the immune response, and the weight of its role needs to be clarified. So the expression in the conclusion should be appropriate.

RESPONSE: Following this critique, we revised INTRODUCTION, DISCUSSION and CONCLUSION, to highlight how AG fibroblasts function within a comprehensive immune response network.

2) This study cannot directly answer the issue of the relationship between periodontitis and systemic diseases.

RESPONSE: We agree with this critique. We either deleted or de-emphasized the relationship between periodontitis and systemic diseases throughout the text.

Author Response:

We appreciate the thorough, fair and concise comments and agree with most, if not all, of the interpretations and critiques. We also value the recommendations and guidance for what constitute the most important additional experiments and analyses. Thank you for your hard work and time. Your investment helps improve the impact and clarity of our work and that is very much appreciated. We look forward to submitting a revised version soon.

Author Response:

Reviewer #1 (Public Review):

Overall, I find the work performed by the authors very interesting. However, the authors have not always included literature that seems relevant to their study. For instance, I do not understand why two papers Dunican et al 2013 and Dunican et al 2015, which provide important insight into Lsh/HELLS function in mouse, frog and fish were not cited. It is also important that the authors are specific about what is known and in particular about what is not known about CDCA7 function in DNA methylation regulation. Unless I am mistaken, there is currently only one study (Velasco et al 2018) investigating the effect of CDCA7 disruption on DNA methylation levels (in ICF3 patient lymphoblastoid cell lines) on a genome-wide scale (Illumina 450K arrays). Unoki et al 2019 report that CDCA7 and HELLS gene knockout in human HEK293T cells moderately and extremely reduces DNA methylation levels at pericentromeric satellite-2 and centromeric alpha-satellite repeats, respectively. No other loci were investigated, and it is therefore not known whether a CDCA7-associated maintenance methylation phenotype extends beyond (peri)centromeric satellites. Thijssen et al performed siRNA-mediated knockdown experiments in mouse embryonic fibroblasts (differentiated cells) and showed that lower levels of Zbtb24, Cdca7 and Hells protein correlate with reduced minor satellite repeat methylation, thereby implicating these factors in mouse minor satellite repeat DNA methylation maintenance. Furthermore, studies that demonstrate a HELLS-CDCA7 interaction are currently limited to Xenopus egg extract (Jenness et al 2018) and the human HEK293 cell line (Unoki et al 2019). Whether such an interaction exists in any other organism and is of relevance to DNA methylation mechanisms remains to be determined. Therefore, in my opinion, the conclusion that "Our co-evolution analysis suggests that DNA methylation-related functionalities of CDCA7 and HELLS are inherited from LECA" should be softened, as the evidence for this scenario is not very compelling and seems premature in the absence of molecular data from more species.

We appreciate this reviewer’s thorough reading of our manuscript.

Regarding the citation issues, we will cite Dunican 2013 and Dunican 2015.

As pointed out by the reviewer, the role of CDCA7 in genome DNA methylation was extensively studied in Velasco et al 2018. The result, together with Thijssen et al (2015), and Unoki et al. (2018), supports the idea that ZBTB24, CDCA7 and HELLS act within the same pathway to promote DNA methylation, the pattern of which is overlapping but distinct from DNMT3B-mediated methylation. This observation suggests that a ZBTB24-CDCA7-HELLS mechanism for DNA methylation may involve an alternative DNMT. Interestingly, our analysis of the gene presence-absence pattern revealed that the presence of CDCA7 coincides with DNMT1 more than DNMT3 genes. Indeed, while CDCA7 is lost from diverse branches of eukaryote species, genomes encoding CDCA7 always encode HELLS, and almost always encode DNMT1. Based on this observation, we speculate the role of CDCA7 is tightly linked to HELLS and DNA methylation throughout evolution.

As pointed out by Reviewer 1, the link between CDCA7, HELLS and DNA methylation has not been determined experimentally across these species. However, based on our previously published and unpublished data, we are confident about the functional interaction between CDCA7 and HELLS in Xenopus laevis and Homo sapiens. Furthermore, the importance of HELLS homologs in DNA methylation has been extensively studied in human, mouse and plants. We hope our current study will motivate the field to experimentally test the evolutionary conservation of HELLS-CDCA7 interaction, as well as their importance in DNA methylation, in other species.

The authors used BLAST searches to characterize the evolutionary conservation of CDCA7 family proteins in vertebrates. From Figure 2A, it seems that they identify a LEDGF binding motif in CDCA7/JPO1. Is this correct and if yes, could you please elaborate and show this result? This is interesting and important to clarify because previous literature (Tesina et al 2015) reports a LEDGF binding motif only in CDCA7L/JPO2.

We searched for a LEDGF binding motif ({E/D}-X-E-X-F-X-G-F, also known as IBM described in Tesina et al 2015) in vertebrate CDCA7 proteins, and reported their position in Figure 2A. Examples of identified LEDGF-binding motifs will be presented.

To provide evidence for a potential evolutionary co-selection of CDCA7, HELLS and the DNA methyltransferases (DNMTs) the authors performed CoPAP analysis. Throughout the manuscript, it is unclear to me what the authors mean when referring to "DNMT3". In the Material and Methods section, the authors mention that human DNMT3A was used in BLAST searches to identify proteins with DNA methyltransferase domains. Does this mean that "DNMT3" should be DNMT3A? And if yes, should "DNMT3" be corrected to "DNMT3A"? Is there a reason that "DNMT3A" was chosen for the BLAST searches?

As described in the Methods section, both Human DNMT1 and DNMT3A were used to initially identify any proteins containing a domain homologous to the DNA methyltransferase catalytic domain. Within Metazoa, if their orthologs exist, the top hit from BLAST search using human DNMT1 and DNMT3A show E-value 0.0, and thus their orthology is robust. This is even true for DNMT1 and DNMT3 homologs in the sponge Amphimedon queenslandica, which is one of the earliest-branching metazoan species. For other DNMTs, such as DNMT2, DNMT4, DNMT6, we conducted separate BLAST searches using those proteins as baits as described in Methods. The domain was then isolated using the NCBI conserved domains search. The selected DNMT domain sequences were aligned with CLUSTALW to generate a phylogenetic tree to further classify DNMTs (Figure S6). It has been suggested that vertebrate DNMT3A and DNMT3B are derived from duplication of a DNMT3 gene of chordates ancestor (e.g., Liu et al 2020, PMID 31969623). As such many invertebrates encode only one DNMT3. As previously shown (Yaari et al., 2019, PMID 30962443), plants have two distinct DNMT3-like protein family, the ‘true DNMT3’ and DRM, the plant specific de novo DNMT that is often considered to be a DNMT3 homolog (see Reviewer 2’s comment). Our phylogenetic analysis successfully deviated the clade of DNMT3 and DRM from the rest of DNMTs (Figure S6). Yaari et al noted that PpDNMT3a and PpDNMT3b, the two DNMT3 orthologs encoded by the basal plant Physcomitrella patens, are not orthologs of mammalian DNMT3A and DNMT3B, respectively. Therefore, to minimize such nomenclature confusions, any DNMTs that belong to either the DNMT3 or DRM clades indicated in Figure S6 are collectively referred to as ‘DNMT3’ throughout the paper (see Figure S2 for overview).

CoPAP analysis revealed that CDCA7 and HELLS are dynamically lost in the Hymenoptera clade and either co-occurs with DNMT3 or DNMT1/UHRF1 loss, which seems important. Unfortunately, the authors do not provide sufficient information in their figures or supplementary data about what is already known regarding DNA methylation levels in the different Hymenoptera species to further consider a potential impact of this observation. What is "the DNA methylation status" of all these organisms? This information cannot be easily retrieved from Table S2. A clearer presentation of what is actually known already would improve this paragraph.

As the DNA methylation status of the species in the Hymenoptera clade has not been comprehensively tested, this precluded us from adding this information to Figure 7. However, we have included the published reports of DNA methylation status for these species in Supplementary Table S2 (see column ‘5mC’; species for which 5mC is detected are marked with Y and the relevant PMID). As indicated, DNA methylation was detected in most tested species except for Microplitis demolitor. Many of these data are based on Bewick et al. 2017 (PMID 28025279). During the preparation of this response, we realized that the DNA methylation status reported for some species in Bewick et al. was inferred from the CpG frequency instead of the direct experimental detection of methylated cytosines. Therefore, we have amended Table S2 to indicate the presence of DNA methylation only for those species where this was experimentally tested. As such, we now consider the DNA methylation status of Fopius arisanus, which lacks DNMT1 and CDCA7, to be unknown. In addition, we realized that Bewick et al. reported that DNA methylation is absent in Aphidius ervi. We originally conducted synteny analysis on Aphidius gifuensis, which lacks DNMT1 and CDCA7, since Aphidius ervi protein data were not available in NCBI. By conducting tBLASTn search against the Aphidius ervi genome, we confirmed that the presence and absence pattern of CDCA7, HELLS, DNMT1, DNMT3 and UHRF1 in Aphidius ervi is identical to that of Aphidius gifuensis. In other words, DNA methylation is known to be absent in Aphidius ervi, which has lost DNMT1 and CDCA7. Altogether, among the 17 Hymenoptera species that we analyzed (listed in the amended Table S2), the 6 species that have detectable DNA methylation all encode CDCA7, whereas the 2 species that do not have detectable DNA methylation lack CDCA7. We will note this finding in the revised text.

Furthermore, A. thaliana DDM1, and mouse and human Lsh/Hells are known to preferably promote DNA methylation at satellite repeats, transposable elements and repetitive regions of the genome. On the other hand, DNA methylation in insects and other invertebrates occurs in genic rather than intergenic regions and transposable elements (e.g. Bewick et al 2017; Werren JH PlosGenetics 2013). It would be helpful to elaborate on these differences.

This point was discussed in the third paragraph of the Discussion, but we will better highlight this. It should be noted that, in the Arabidopsis ddm1 mutant, reduction of CG methylation of gene bodies is common (50% of all methylated euchromatic genes) (Zemach et al, 2013). In addition, hypomethylation is not limited to satellite repeats and transposable elements in ICF patients defective in HELLS or CDCA7 (Velasco et al., 2018).

Reviewer #2 (Public Review):

In this manuscript, Funabiki and colleagues investigated the co-evolution of DNA methylation and nucleosome remolding in eukaryotes. This study is motivated by several observations: (1) despite being ancestrally derived, many eukaryotes lost DNA methylation and/or DNA methyltransferases; (2) over many genomic loci, the establishment and maintenance of DNA methylation relies on a conserved nucleosome remodeling complex composed of CDCA7 and HELLS; (3) it remains unknown if/how this functional link influenced the evolution of DNA methylation. The authors hypothesize that if CDCA7-HELLS function was required for DNA methylation in the last eukaryote common ancestor, this should be accompanied by signatures of co-evolution during eukaryote radiation.

[...]

The data and analyses reported are significant and solid. However, using more refined phylogenetic approaches could have strengthened the orthologous relationships presented. Overall, this work is a conceptual advance in our understanding of the evolutionary coupling between nucleosome remolding and DNA methylation. It also provides a useful resource to study the early origins of DNA methylation related molecular process. Finally, it brings forward the interesting hypothesis that since eukaryotes are faced with the challenge of performing DNA methylation in the context of nucleosome packed DNA, loosing factors such as CDCA7-HELLS likely led to recurrent innovations in chromatin-based genome regulation.

Strengths: - The hypothesis linking nucleosome remodeling and the evolution of DNA methylation. - Deep mapping of DNA methylation related process in eukaryotes. - Identification and evolutionary trajectories of novel homologs/orthologs of CDCA7. - Identification of CDCA7-HELLS-DNMT co-evolution across eukaryotes.

Weaknesses: - Orthology assignment based on protein similarity. - No statistical support for the topologies of gene/proteins trees (figure S1, S3, S4, S6) which could have strengthened the hypothesis of shared ancestry.

We appreciate the reviewers’ accurate summary, nicely emphasizing the importance of the our study. We agree that better phylogenetic analysis for orthology assignment will strengthen our conclusion, and we would like to explore this. Having anticipated this weakness, we specifically conducted a CoPAP analysis exclusively for Ecdysozoa species, where orthology assignment is straightforward, which supported our major conclusion. (For example, if we conduct BLAST search the clonal raider ant Oocerea biroi using human HELLS as a query, top 1 hit is a protein sequence annotated as one of three isoforms of ‘lymphoid-specific helicase” (i.e., HELLS), with E value 0.0. Similarly, top BLAST hit from Oocerea biroi using human DNMT1 as a query also returns with isoforms of DNMT1 with E value 0.0. As such, there are little disputes in orthology assignment in Ecdysozoa. Outside of Chordata, regardless of the alternative methods employed for orthology assignment, this will never be perfect (particularly in Excavata and SAR). Our current orthology assignment for the major targets in this study (HELLS, DNMT1, DNMT3, DNMT5) is largely consistent with published results (Ponger et al., 2005 PMID 15689527; Huff et al, 2014 PMID 24630728; Yaari et al., 2019 PMID 30962443; Bewick et al., 2019 PMID 30778188). However, while we are preparing this response and re-crosschecking our assignments with these references, we realized that we erroneously missed DNMT5 orthologs of Leucosporidium creatinivorum, Postia placenta, Armillaria gallica and Saitoella complicata., and DNMT6 ortholog from Fragilariopsis cylindrus. We also had recognized that DNMT4 orthologs were identified in Fragilariopsis cylindrus and Thalassiosira pseudonana In Huff et al 2014 (PMID 24630728), but in our phylogenetic analysis, these proteins form a distinct clade between DNMT1/Dim-2 and DNMT4 (Figure S6). Due to this ambiguity, we did not count them as DNMT1 or DNMT4 in our CoPAP analysis. These minor errors and ambiguity should not affect our presence-absence pattern in our original CoPAP analysis, and thus we feel that further refinement is unlikely to significantly affect our major conclusion.

Author Response:

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

Reviewer #1 (Public Review):

This revised manuscript by Walker et. al. addresses some of the editorial points and conceptual discussion, but in general, most of my suggestions (as the previous reviewer #1) for additional experimentation or addition were not addressed as discussed below. Therefore, my overall review has not changed.

In our previous response, we included i) extra experimental data illustrating the reproducibility of our results and ii) added transcription start site data at the request of this reviewer. We included the information because we agreed with the reviewer that these were important points to address. For the points raised again below, we explained why the additional analysis was unlikely to add much in terms of insight or rigour. We have elaborated further below.   

1) For example, in point 1, the suggested analysis was not performed because it is not trivial. My reason for making this suggestion is that the original manuscript was limited to Vibrio cholerae, and the impact of the manuscript would increase if the findings here were demonstrated to be more broadly applicable. I expect papers published in eLife to have such broad applicability. But no changes were made to the manuscript in this regard. The revised version is still limited to only Vibrio cholerae.

Our paper is focused on the unexpected co-operative interactions between HapR and CRP. Such co-binding of two transcription factors to the same DNA site is unexpected. Consequently, it is this mode of DNA binding that is likely to be of broad interest. With this in mind, we did provide experimental, and bioinformatic, analyses for other regulatory regions and other vibrio species (Figures S3 and S6). This, in our view, is where the “broad applicability” for papers published in eLife comes from.

The analysis the reviewer suggests is not related to the main message of our paper. Instead, the reviewer is asking how many HapR binding sites seen here by ChIP-seq are also seen in other vibrio species by ChIP-seq. This is only likely to be of interest to readers with an extremely specific interest in both vibrio species and HapR. The reviewer states above that we did not make the change “because it is not trivial”. This is an oversimplification of the rationale we presented in our response. The analysis is indeed not straightforward. However, much more importantly, the outcome is unlikely to be of interest to many readers, and has no bearing on the rigour of work. With this in mind, we do not think our position is unreasonable. We also stress that, should a reader with this very specific interest want to explore further, all of our data are freely available for them to do so.

2) For point 2, the activity of FLAG-tag luxO could have been simply validated in a complementation assay. Yes, they demonstrated DNA binding, but that is not the only activity of LuxO.

DNA binding by LuxO is the only activity of the protein with which we are concerned in our paper. Furthermore, LuxO is very much a side issue; we found binding to only the known targets and potentially, at very low levels, one additional target. No further LuxO experiments were done for this reason. Indeed, even if these data were removed completely, our conclusions would not change or be supported any less vigorously. We are happy to remove the LuxO data if the reviewer would prefer but this would seem like overkill.

3) For point 7, the transcriptional fusions were not explored at different times or different media, which is also something that was hinted at by other reviewers. In regard to exploring expression at different time points, this seems particularly relevant for QS regulated genes.

In their previous review, the reviewer did not request that such experiments were done. Similarly, no other reviewer requested these experiments. Instead, this reviewer i) commented that lacZ fusions were not as sensitive as luciferase fusions ii) asked if we had done any time point experiments. We agreed with the first point, whilst also noting that lacZ is not unusual to use as a reporter. For the second point, we responded that we had not done such experiments (which by the reviewer’s own logic would have been complicated using lacZ as a reporter). This seems like a perfectly reasonable way to respond.   

We should stress that these comments all refer to Figure 2a, which was our initial screening of 23 promoter::lacZ fusions, supported by separate in vitro transcription assays. Only one of these fusions was followed up as the main story in the paper. Given that the other 22 fusions were not investigated further, and do not form part of the main story, there would seem little value in now going back to assay them at different time points.

4) For point 13, the authors express that doing an additional CHIP-Seq is outside of the scope of this manuscript. Perhaps that is the case, but the point of the comment is to validate the in vitro binding results with an in vivo binding assay. A targeted CHIP-Seq approach specifically analyzing the promoters where cooperative binding was observed in vitro could have addressed this point.

We did appreciate the original comment, and responded as such, but we do think additional ChIP-seq assays are outside the scope of this paper.

Reviewer #2 (Public Review):

This manuscript by Walker et al describes an elegant study that synergizes our knowledge of virulence gene regulation of Vibrio cholerae. The work brings a new element of regulation for CRP, notably that CRP and the high density regulator HapR co-occupy the same site on the DNA but modeling predicts they occupy different faces of the DNA. The DNA binding and structural modeling work is nicely conducted and data of co-occupation are convincing. The work seeks to integrate the findings into our current state of knowledge of HapR and CRP regulated genes at the transition from the environment and infection. The strength of the paper is the nice ChIP-seq analysis and the structural modeling and the integration of their work with other studies.

We thank the reviewer for the positive comments.

The weakness is that it is not clear how representative these data are of multiple hapR/CRP binding sites

This comment does not consider all data in our paper. We did test our model experimentally at multiple HapR and CRP binding sites. These data are shown in Figure S6 and confirm the co-operative interaction between HapR and CRP at 4 of a further 5 shared binding sites tested. We also used bioinformatics to show the same juxtaposition of CRP and HapR sites in other vibrio species (Figure S3). Hence, the model seems representative of most sites shared by HapR and CRP.

or how the work integrates as a whole with the entire transcriptome that would include genes discovered by others.

At the request of the reviewers, our revision integrated our ChIP-seq data with dRNA-seq data. No other suggestions to ingrate transcriptome data were made by the reviewers. 

Overall this is a solid work that provides an understanding of integrated gene regulation in response to multiple environmental cues.

We thank the reviewer for the positive comment.

—————

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

Reviewer #1 (Public Review):

This manuscript by Walker et. al. explores the interplay between the global regulators HapR (the QS master high cell density (HDC) regulator) and CRP. Using ChIP-Seq, the authors find that at several sites, the HapR and CRP binding sites overlap. A detailed exploration of the murPQ promoter finds that CRP binding promotes HapR binding, which leads to repression of murPQ. The authors have a comprehensive set of experiments that paints a nice story providing a mechanistic explanation for converging global regulation.

We thank the reviewer for their positive evaluation.

I did feel there are some weak points though, in particular the lack of integration of previously identified transcription start sites

For completeness, we have now added the position and orientation or the nearest TSSs to each HapR or LuxO binding peak in Table 1 (based on Papenfort et al.).

the lack of replication (at least replication presented in the manuscript) for many figures,

We assume that the reviewer is referring to gel images rather than any other type of assay output (were error bars, derived from replicates, are shown). As is standard, we show representative gel images. All associated DNA binding and in vitro transcription experiments have been done multiple times. Indeed, comparison between figures reveals several instances of such replication (e.g. Figures 4b & 5d, Figures 4d & 5e). We have added details of repeats done to the methods section.

some oddities in the growth curve

We do not know why cells lacking hapR have a growth curve that appears biphasic. We can only assume that this is due to some regulatory effect of HapR, distinct from the murQP locus. Despite the unusual shape of the growth curve, the data are consistent with our conclusions.

and not reexamining their HapR/CRP cooperative binding model in vivo using ChIP-Seq.

We agree that these would be interesting experiments and, in the future, we may well do such work. Even without these data, our current model is well supported by the data presented (and the reviewer seems to agree with this above).

Reviewer #2 (Public Review):

This manuscript by Walker et al describes an elegant study that synergizes our knowledge of virulence gene regulation of Vibrio cholerae. The work brings a new element of regulation for CRP, notably that CRP and the high density regulator HapR co-occupy the same site on the DNA but modeling predicts they occupy different faces of the DNA. The DNA binding and structural modeling work is nicely conducted and data of co-occupation are convincing. The work could benefit from doing a better job in the manuscript preparation to integrate the findings into our current state of knowledge of HapR and CRP regulated genes and to elevate the impact of the work to address how bacteria are responding to the nutritional environment. Importantly, the focus of the work is heavily based on the impact of use of GlcNAc as a carbon source when bacteria bind to chitin in the environment, but absent the impact during infection when CRP and HapR have known roles. Further, the impact on biological events controlled by HapR integration with the utilization of carbon sources (including biofilm formation) is not explored.

We thank the reviewer for their overall positive evaluation.

The rigor and reproducibility of the work needs to be better conveyed.

Reviewer 1 made a similar comment (see above) and we have modified the manuscript accordingly.

Specific comments to address:

1)  Abstract. A comment on the impact of this work should be included in the last sentence. Specifically, how the integration of CRP with QS for gene expression under specific environments impacts the lifestyle of Vc is needed. The discussion includes comments regarding the impact of CRP regulation as a sensor of carbon source and nutrition and these could be quickly summarized as part of the abstract.

We have added an extra sentence. However, we have used cautious language as we do not show impacts on lifestyle (beyond MurNAc utilisation) directly. These can only be inferred.

2)  Line 74. This paper examines the overlap of HapR with CRP, but ignores entirely AphA. HapR is repressed by Qrrs (downstream of LuxO-P) while AphA is activated by Qrrs. With LuxO activating AphA, it has a significant sized "regulon" of genes turned on at low density. It seems reasonable that there is a possibility of overlap also between CRP and AphA. While doing an AphA CHIP-seq is likely outside the scope of this work, some bioinformatic or simply a visual analysis of the promoters known AphA regulated genes would be interest to comment on with speculation in the discussion and/or supplement.

In short, everything that the reviewer suggests here has already been done and was covered in our original submission (see text towards the end of the Discussion). Also, we would like to point the referee to our earlier publication (Haycocks et al. 2019. The quorum sensing transcription factor AphA directly regulates natural competence in Vibrio cholerae. PLoS Genet. 15:e1008362).

3)  Line 100. Accordingly with the above statement, the focus here on HapR indicates that the focus is on gene expression via LuxO and HapR, at high density. Thus the sentence should read "we sought to map the binding of LuxO and HapR of V. cholerae genome at high density".

Note that expression of LuxO and HapR is ectopic in these experiments (i.e. uncoupled from culture density).

4)  Line 109. The identification of minor LuxO binding site in the intergenic region between VC1142 and VC1143 raises whether there may be a previously unrecognized sRNA here. As another panel in figure S1, can you provide a map of the intergenic region showing the start codons and putative -10 to -35 sites. Is there room here for an sRNA? Is there one known from the many sRNA predictions / identifications previously done? Some additional analysis would be helpful.

We have added an extra panel to Figure S1 showing the position of TSSs relative to the location of LuxO binding. We have altered the main text to accommodate this addition..

5)  Line 117. This sentence states that the CHIP seq analysis in this study includes previously identified HapR regulated genes, but does not reveal that many known HapR regulated genes are absent from Table 1 and thus were missed in this study. Of 24 HapR regulated investigated by Tsou et al, only 1 is found in Table 1 of this study. A few are commented in the discussion and Figure S7. It might be useful to add a Venn Diagram to Figure 1 (and list table in supplement) for results of Tsou et al, Waters et al, Lin et al, and Nielson et al and any others). A major question is whether the trend found here for genes identified by CHIP-seq in this study hold up across the entire HapR regulon. There should also be comments in the discussion on perhaps how different methods (including growth state and carbon sources of media) may have impacted the complexity of the regulon identified by the different authors and different methods.

We have added a list of known sites to the supplementary material (new Table S1). We were unsure what was meant by the comment “A major question is whether the trend found here for genes identified by CHIP-seq in this study hold up across the entire HapR regulon”. We have added the extra comment to the discussion re growth conditions, also noting that most previous studies relied on in vitro, rather than in vivo, DNA binding assays.

6)  The transcription data are generally well performed. In all figures, add comments to the figure legends that the experiments are representative gels from n=# (the number of replicate experiments for the gel based assays). Statements to the rigor of the work are currently missing.

See responses above. We have added a comment on numbers of repeats to the methods section.

7)  Line 357-360. The demonstration of lack of growth on MurNAc is a nice for the impact of the work. However, more detailed comments are needed for M9 plus glucose for the uninformed reader to be reminded that growth in glucose is also impaired due to lack of cAMP in glucose replete conditions and thus minimal CRP is active. But why is this now dependent of hapR? A reminder also that in LB oligopeptides from tryptone are the main carbon source and thus CRP would be active.

We find this point a little confusing and, maybe, two issues (murQP regulation, and growth in general) are being conflated. In particular, we do not understand the comment “growth in glucose is also impaired due to lack of cAMP in glucose replete conditions and thus minimal CRP is active”.

Growth in glucose should indeed result in lower cAMP levels*, and hence less active CRP, but this does not impair growth. This is simply the cell’s strategy for using its preferred carbon source. If the reviewer were instead referring to some aspect of P_murQP_ regulation then yes, we would expect promoter activity to be lower because less active CRP would be available in the presence of glucose. The reviewer also comments “why is this now dependent of hapR?”. We assume that they are referring to some aspect of growth in minimal media with glucose. If so, the only hapR effect is the change in growth rate as cells enter mid-late log-phase (i.e. the growth curve looks somewhat biphasic). A similar effect is seen in all conditions. We do not know why this happens and can only conclude this is due to some unknown regulatory activity of HapR. Overall, the key point from these experiments is that loss if luxO, which results in constitutive hapR expression, lengthens lag phase only for growth with MurNAc as the sole carbon source.

*Although in V. fischeri (PMID: 26062003) cAMP levels increase in the presence of glucose.

8)  A great final experiment to demonstrate the model would have been to show co-localization of the promoter by CRP and HapR from bacteria grown in LB media but not in LB+glucose or in M9+glycerol and M9+MurNAc but not M9+glucose. This would enhance the model by linking more directly to the carbon sources (currently only indirect via growth curves)

This is unlikely to be as straightforward as suggested. The sensitivity of CRP binding to growth conditions is not uniform across different binding sites. For instance, the CRP dependence of the E. coli melAB promoter is only evident in minimal media (PMID: 11742992) whilst the role of CRP at the acs promoter is evident in tryptone broth (PMID: 14651625). Similarly, as noted above, in Vibrio fischeri glucose causes and increase in cAMP levels. (PMID: 26062003).

9) Discussion. Comments and model focus heavily on GlcNAc-6P but HapR has a regulator role also during late infection (high density). How does CRP co-operativity impact during the in vivo conditions?

We really can’t answer this question with any certainty; we have not done any infection experiments in this work.

Does the Biphasic role of CRP play a role here (PMID: 20862321)?

Again, we cannot answer this question with any confidence as experimentation would be required. However, the suggestion is certainly plausible.

Reviewer #3 (Public Review):

Bacteria sense and respond to multiple signals and cues to regulate gene expression. To define the complex network of signaling that ultimately controls transcription of many genes in cells requires an understanding of how multiple signaling systems can converge to effect gene expression and ensuing bacterial behaviors. The global transcription factor CRP has been studied for decades as a regulator of genes in response to glucose availability. It's direct and indirect effects on gene expression have been documented in E. coli and other bacteria including pathogens including Vibrio cholerae. Likewise, the master regulator of quorum sensing (QS), HapR), is a well-studied transcription factor that directly controls many genes in Vibrio cholerae and other Vibrios in response to autoinducer molecules that accumulate at high cell density. By contrast, low cell density gene expression is governed by another regulator AphA. It has not yet been described how HapR and CRP may together work to directly control transcription and what genes are under such direct dual control.

We thank the reviewer for their assessment of our work.

Using both in vivo methods with gene fusions to lacZ and in vitro transcription assays, the authors proceed to identify the smaller subset of genes whose transcription is directly repressed (7) and activated (2) by HapR. Prior work from this group identified the direct CRP binding sites in the V. cholerae genome as well as promoters with overlapping binding sites for AphA and CRP, thus it appears a logical extension of these prior studies is to explore here promoters for potential integration of HapR and CRP. Inclusion of this rationale was not included in the introduction of CRP protein to the in vitro experiments.

We understand the reviewer’s comment. However, the rationale for adding CRP was not that we had previously seen interplay between AphA and CRP (although this is a relevant discussion point, which we did make). Rather, we had noticed that there was an almost perfect CRP site perfectly positioned to activate PmurQP. Hence, CRP was added.

Seven genes are found to be repressed by HapR in vivo, the promoter regions of only six are repressed in vitro with purified HapR protein alone. The authors propose and then present evidence that the seventh promoter, which controls murPQ, requires CRP to be repressed by HapR both using in vivo and vitro methods. This is a critical insight that drives the rest of the manuscripts focus. The DNase protection assay conducted supports the emerging model that both CRP and HapR bind at the same region of the murPQ promoter, but interpret is difficult due to the poor quality of the blot.

There are areas of apparent protection at positions +1 to +15 that are not discussed, and the areas highlighted are difficult to observe with the blot provided.

We disagree on this point. The region between +1 and +15 is inherently resistant to attack by DNAseI and there are only ever very weak bands in this region (lane 1). Other than seeing small fluctuations in overall lane intensity (e.g. lanes 7-12 have a slightly lower signal throughout) the +1 to +15 banding pattern does not change. Conversely, there are dramatic changes in the banding pattern between around -30 and -60 (again, compare lane 1 to all other lanes). That CRP and HapR bind the same region is extremely clear. Also note that this is backed up by mutagenesis of the shared binding site (Figure 4c).

The model proposed at the end of the manuscript proposes physiological changes in cells that occur at transitions from the low to high cell density. Experiments in the paper that could strengthen this argument are incomplete. For example, in Fig. 4e it is unclear at what cell density the experiment is conducted.

Such details have been added to the figure legends and methods section.

The results with the wild type strain are intermediate relative to the other strains tested.

This is correct, and exactly what we would expect to see based on our model.

Cell density should affect the result here since HapR is produced at high density but not low density. This experiment would provide important additional insights supporting their model, by measuring activity at both cell densities and also in a luxO mutant locked at the high cell density. Conducting this experiment in conditions lacking and containing glucose would also reveal whether high glucose conditions mimicking the crp results.

We agree with this idea in principle but note that the output from our reporter gene, β- galactosidase, is stable within cells and tends to accumulate. This is likely to obscure the reduction in expression as cells transition from low to high cell density. Since we have demonstrated the regulatory effects of HapR and CRP both in vivo using gene knockouts, and in vitro with purified proteins, we think that our overall model is very well supported. Further experimental additions may provide an incremental advance but will not alter our overall story. Also note the unexpected increase in intracellular cAMP due to addition of glucose, in Vibrio fischeri (PMID: 26062003).

Throughout the paper it was challenging to account for the number of genes selected, the rationale for their selection, and how they were prioritized. For example, the authors acknowledged toward the end of the Results section that in their prior work, CRP and HapR binding sites were identified (line 321-22).

This is not quite what we say, and maybe the reviewer misunderstood, which is our fault. The prior work identified CRP sites whilst the current work identified HapR sites. We have made a slight alteration to the text to avoid confusion.

It is unclear whether the loci indicated in Table 1 all from this prior study. It would be useful to denote in this table the seven genes characterized in Figure 2 and to provide the locus tag for murPQ.

Again, we are unsure if we have confused the reviewer. The results in Table 1 are all HapR sites from the current work, not a prior study. However, some of these also correspond to CRP binding regions found in prior work.

The reviewer mentions “the seven genes characterised in Figure 2” but 23 targets were characterised in Figure 2a and 9 in Figure 2b. The “VC” numbers used in Figure 2 are the same as used in Table 1 so it is easy to cross reference between the two. We have added a footnote to Table 1, also referred to in the Figure 2 legend, to allow cross referencing between gene names and locus tags (including for murQP and hapR).

Of the 32 loci shown in Table 1, five were selected for further study using EMSA (line 322), but no rationale is given for studying these five and not others in the table.

This is not quite correct, we did not select 5 from the 32 targets listed in Table 1. We selected 5 targets from Table 1 that were also targets for CRP in our prior paper. This was the rationale.

Since prior work identified a consensus CRP binding motif, the authors identify the DNA sequence to which HapR binds overlaps with a sequence also predicted to bind CRP. Genome analysis identified a total of seven sites where the CRP and HapR binding sites were offset by one nucleotide as see with murPQ. Lines 327-8 describe EMSA results with several of these DNA sequences but provides no data to support this statement. Are these loci in Table 1?

This comment is a little difficult to follow, and we may have misunderstood, but we think that the reviewer is asking where the EMSA data referred to on lines 327-328 resides. We can see that the text could be confusing in this regard. We had referred to the relevant figure (Figure S6) on line 324 but did not again include this information further down in the description of the result. We have changed the text accordingly.

Using structural models, the authors predict that HapR repression requires protein-protein interactions with CRP. Electromobility shift assays (EMSA) with purified promoter DNA, CRP and HapR (Fig 5d) and in vitro transcription using purified RNAP with these factors (Figure 5e) support this hypothesis. However, the model proports that HapR "bound tightly" and that it also had a "lower affinity" when CRP protein was used that had mutations in a putative interaction interface. These claims can be bolstered if the authors calculate the dissociation constant (Kd) value of each protein to the DNA. This provides a quantitative assessment of the binding properties of the proteins.

The reviewer is correct that we do not explicitly provide a Kd. However, in both Figures 5d and 5e, we do provide very similar quantification. In 5d, our quantification is the % of the CRP-DNA complex bound by HapR (using either wild type or E55A CRP). Since the % of DNA bound is shown, and the protein concentrations are provided in the figure legend, information regarding Kd is essentially already present. In 5e, we show the % of maximal promoter activity. This is a reasonable way of quantifying the result. Furthermore, Kd is not a metric we can measure directly in this experiment that is not a DNA binding assay.

The concentrations of each protein are not indicated in panels of the in vitro analysis, but only the geometric shapes denoting increasing protein levels.

The protein concentrations are all provided in the figure legend. It is usual to indicate relative concentrations in the body of the figure using geometric shapes.

Panel 5e appears to indicate that an intermediate level of CRP was used in the presence of HapR, which presumably coincides with levels used in lane 4, but rationale is not provided.

There was no particular rationale for this, it was simply a reasonable way to do the experiment.

How well the levels of protein used in vitro compare to levels observed in vivo is not mentioned.

The protein concentrations that we use (in the nM to low μM range) are very typical for this type of work and consistent with hundreds of prior studies of protein-DNA interactions. The general rule of thumb is that 1000 molecules of a protein per bacterial cell equates to a concentration of around 1 μM. However, molecular crowding is likely to increase the effective concentration. Additionally, in vitro, where the DNA concentration is higher.

The authors are commended for seeking to connect the in vitro and vivo results obtained under lab conditions with conditions experienced by V. cholerae in niches it may occupy, such as aquatic systems. The authors briefly review the role of MurPQ in recycling of the cell wall of V. cholerae by degrading MurNAc into GlcNAc, although no references are provided (lines 146-50). Based on this physiology and results reported, the authors propose that murPQ gene expression by these two signal transduction pathways has relevance in the environment, where Vibrios, including V. cholerae, forms biofilms on exoskeleton composed of GlcNAc.

We have added a citation to the section mentioned.

The conclusions of that work are supported by the Results presented but additional details in the text regarding the characteristics of the proteins used (Kd, concentrations) would strengthen the conclusions drawn. This work provides a roadmap for the methods and analysis required to develop the regulatory networks that converge to control gene expression in microbes. The study has the potential to inform beyond the sub-filed of Vibrios, QS and CRP regulation.

As noted above, quantification essentially equivalent to Kd is already shown (% of bound substrate is indicated in figures and all protein concentrations are given in the figure legends).

Reviewer #1 (Recommendations For The Authors):

1.  As similar experiments have been performed in other Vibrios, it would be interesting to do a more detailed analysis of the similarities and differences between the species. Perhaps a Venn diagram showing how many targets were found in all studies versus how many are species specific.

We appreciate this suggestion but would prefer not to make this change. A cross-species analysis would be very time consuming and is not trivial. The presence and absence of each target gene, for all combinations of organisms, would first need to be determined. Then, the presence and absence of binding signals for HapR, or its equivalent, would need to be determined taking this into account. For most readers, we feel that this analysis is unlikely to add much to the overall story. Given the amount of effort involved, this seems a “non-essential” change to make.

2.  Line 101-Are the FLAG tagged versions of LuxO and HapR completely functional? Can they complement a luxO or hapR deletion mutant?

The activity of FLAG tagged HapR (LuxR in other Vibrio species) has been shown previously (e.g. PMIDs 33693882 and 23839217). Similarly, N-terminal HapR tags are routinely used for affinity purification of the protein without ill effect. We have not tested LuxO-3xFLAG for “full” activity, though this fusion is clearly active for DNA binding, the only activity that we have measured here, since all know targets are pulled down.

3.  Line 106-As the authors state later that there are additional smaller peaks for HapR that could be other direct targets, I think a brief mention of the methodology used to determine the cutoff for the 5 and 32 peaks for LuxO and HapR, respectively, would be informative here.

We have added a little more text to the methods section. The added text states “Note that our cut- off was selected to identify only completely unambiguous binding peaks. Hence, weak or less reproducible binding signals, even if representing known targets, were excluded (see Discussion for further details)”.

4.  Line 118-Need a reference here to the prior HapR binding site.

This has been added.

5.  Figs. 1e-What do the numbers on the x-axis refer to? Why not just present these data as bases? The authors also refer to distance to the nearest start codon, but this is irrelevant for 4/5 of the luxO targets as they are sRNAs. They should really refer to the distance to the transcription start site. Likewise, for HapR, distance to the nearest start codon is not as informative as distance to the nearest transcription start site. A recent paper used transcriptomics to map all the transcription start sites of V. cholerae, and these results should be integrated into the author's study rather than just using the nearest start codon (PMID: 25646441).

The numbers are kilo base pairs, this has been added to the axis label. We have also changed “start codon” to “gene start” (since “gene start” is also suitable for genes that encode untranslated RNAs).

Re comparing binding peak positions to transcription start sites (TSSs) rather than gene starts, this analysis would be useful if TSSs could be detected for all genes. However, some genes are not expressed under the conditions tested by PMID: 25646441, so no TSS is found. Consequently, for HapR or LuxO bound at such locations, we would not be able to calculate a meaningful position relative to the TSS. We stress that the point of the analysis is to determine how peaks are positioned with respect to genes (i.e. that sites cluster near gene 5’ ends). Also note that nearest TSSs are now shown in the revised Table 1. In some cases, these are unlikely to be the TSS actually subject to regulation (e.g. because the regulated gene is switched off).

6.  Fig. 1e-Is there directionality to the site? In other words, if a HapR binding site is located between two genes that are transcribed in opposite directions, is there a way to predict which gene is regulated? It looks like this might be the case with the list presented in Table 1, but how such determination is made and what the various symbol in Table 1 mean are not clear to me. This also has ramifications for Fig. 2a as the direction to construct the fusion is critical for the experiment.

The site is a palindrome so lacks directionality. The best prediction re regulation is likely to be positioning with respect to the nearest TSS (which is now included in Table 1). However, this would remain just a prediction and, where TSSs are in odd locations with respect to binding sites (taking into account the caveats above) predictions would be unreliable.

We are unsure which symbol the reviewer refers to in Table 1, a full explanation of any symbols used is provided in the table footnotes.

With respect to Figure 2a, if sites were between divergent genes, and met our other criteria, we tested for regulation in both directions. For example, see the divergent genes VCA0662 (classified inactive) and VCA0663 (classified repressed).

7.  Fig. 2a-It is a little disappointing that the authors use LacZ fusions to measure transcription as this is not the most sensitive reporter gene. Luciferase gene fusions would have been much more sensitive. Also, did the authors examine multiple time points. The methods only describe "mid-log phase" but some of the inactive promoters could be expressed at other time points. Also, it would be simple to repeat this experiment in different media, such as minimal with glucose or another non- CRP carbon source, to expand which promoters are expressed.

The reviewer is correct regarding the sensitivity of β-galactosidase, which is very stable and so accumulates as cells grow. Even so, this reporter has been used very successfully, across thousands of studies, for decades. We did not examine multiple timepoints. We appreciate that the 23 promoter::lacZ fusions could be re-examined using varying growth conditions but this is unlikely to impact the overall conclusions, though it could generate some new leads for future work.

8.  Fig. 2a legend-typos

This has been corrected.

9.  Line 138-I think you mean Fig. 2a here.

This has been corrected.

10.  Fig. 2b and many additional figures quantify band intensity but do not show any replication or error. Therefore, it is impossible to gauge reproducibility of these experiments.

We have added a reproducibility statement (all experiments were done multiple times with similar results) as is standard throughout the literature. Also note that there is a lot of internal replication between figures. Figure 4d and Figure 5e lanes 1-9 show essentially the same experiment (albeit with slightly different protein concentrations) and very similar results. To the same effect, Figure 5e lanes 10-18 and lanes 19-27 show the same experiment for two different mutations of the same CRP residue. Again, the results are very similar. Also see the response to your comment 15 below.

11.  Fig. 4a-lanes 2-4-the footprint does not change with additional CRP. In other words, it looks the same at the lowest concentration of CRP versus the highest concentration of CRP. The footprints for HapR look similar. This is somewhat troubling as in these types of experiments one would like to observe a dose dependent change in the footprint correlating with more DNA occupancy.

For CRP we agree but are not concerned at all by this. The site is simply full occupied at the lowest protein concentration tested. Given that the footprint exactly coincides with a near consensus CRP site (which, when mutated, abolishes CRP binding in EMSAs, and regulation by CRP in vivo) all our results are perfectly consistent. Note that i) our only aim in this experiment was to determine the positions of CRP and HapR binding ii) our conclusions are independently backed up using gel shifts and by making promoter mutations. With respect to HapR, there are changes at the periphery of the main footprint.

12.  Fig. 4e-Why does the transcriptional activation of murQP decrease with increasing concentrations of CRP? This is also seen in Fig. 5e.

In our experience, this often does happen when doing in vitro transcription assays (with CRP and many other activators). The anecdotal explanation is that, at higher concentrations, the regulator can start to bind the DNA non-specifically and so interfere with transcription.

13. The authors demonstrate in vitro that HapR requires binding of CRP to bind the murQP promoter. It would strengthen their model if they demonstrated this in vivo. To do this, the authors only need to repeat their ChIP-Seq experiment in a delta CRP mutant and the HapR signal at murQP would be lost. In fact, such an experiment would experimentally confirm which of the in vivo HapR binding sites are CRP dependent.

We agree, appreciate the comment, and do plan to do such experiments in the future as a wider assessment of interactions between transcription factors. However, doing this does have substantial time and resource implications that we cannot devote to the project at present. We feel that our overall conclusions, regarding co-operative interactions between HapR and CRP at PmurQP, are well supported by the data already provided. This also seems the overall opinion of the reviewers.

14.  Fig. 5b-I am confused by the Venn diagram. The text states that "In all cases, the CRP and HapR targets were offset by 1 bp", but the diagram only shows 7 overlapping sites. The authors need to better describe these data.

We mean that, in all cases where sites overlap, sites are offset by 1 bp (i.e. we didn’t find any sites

overlapping but offset by 2, 3 4 bp etc).

15. Line 287-288 and Fig. 5d-The authors state that HapR binds with less affinity to the CRP E55A mutant protein bound to DNA. There does seem to be a difference in the amount of shifted bands at the equivalent concentrations of HapR, but the difference is subtle. In order to make such a conclusion, the authors should show replication of the data and calculate the variability in the results. The authors should also use these data to determine the actual binding affinities of HapR to WT and the E55A mutant CRP, along with error or confidence intervals.

All of these experiments have been run multiple times and we are absolutely confident of the result. With respect to Figure 5d, this was done many times. We note that not all experiments were exact repeats. E.g. some of the first attempts had fewer HapR concentrations. Even so, the defect in HapR binding to the CRP E55A complex was always evident. The two gels to the left show the final two iterations of this experiment (these are exact repeats). The top image is that shown in Figure 5d. The lower image is an equivalent experiment run a day or so previously. Both clearly show a defect in HapR binding to the CRP E55A complex. We appreciate that our conclusion re these experiments is somewhat qualitative (i.e. that HapR binds the CRP E55A complex less readily) but this is not out of kilter with the vast majority of similar literature and our results are clearly reproducible.

16.  Fig. 6a-It is odd that the locked low cell density mutants have such a growth defect in MurNAc, minimal glucose, and LB. To my knowledge, such a growth defect is not common with these strains. Perhaps this has to do with the specific growth conditions used here, but I can't find that information in the manuscript (it should be there). Furthermore, the growth rate of the luxO and hapR mutants appears to be similar up to the branch point (i.e. slope of the curve), but the lag phage of the luxO mutant is much longer. The authors need to address these issues in relationship to previous published literature and specify their growth conditions because the results are not consistent with their simple model described in Fig 6b.

This comment is a little difficult to pick apart as it covers several different issues. We’ll try and

answer these individually.

a)     The unusual “biphasic growth curve with hapR and hapRluxO cells: We do not know why cells lacking hapR have a growth curve that appears biphasic. We can only assume that this is due to some regulatory effect of HapR, distinct from the murQP locus. Despite the unusual shape of the growth curve, the data are consistent with our conclusions.

b)     The extended lag phase of the luxO mutant in minimal media + MurNAc: We appreciate this comment and had considered possible explanations prior to submission. In the end, we left out this speculation but are happy to include it as part of our response. The extended lag phase might be expected if CRP/HapR regulation is largely critical for controlling the basal transcription of murQP. The locus is likely also regulated by the upstream repressor MurR (VC0204) as in E. coli. So, if deprepression of MurR overwhelms the effect of HapR on murQP, we think you would expect that once the cells start growing on MurNAc, the growth rates are unchanged. But the extended lag is due to the fact that it took longer for those cells to achieve the critical threshold of intracellular MurNAc-6-P necessary to drive murR derepression. Obviously, we can not provide a definitive answer.

c)     We have added further details regarding growth conditions to the methods section and the Figure 6a legend.

17.  Fig. S6-The data to this point with murPQ suggested a model in which CRP binding then enabled HapR binding. But these EMSA suggest that both situations occur as in some cases, such as VCA0691, HapR binding promotes CRP binding. How does such a result fit with the structural model presented in Fig. 5?

This is to be expected and is fully consistent with the model. Cooperativity is a two-way street, and each protein will stabilise binding of the other. Clearly, it will not always be the case that the shared DNA site will have a higher affinity for CRP than HapR (as at PmurQP). Depending on the shared site sequence, expected that sometimes HapR will bind “first” and then stabilise binding of CRP.

18. Line 354-356-The HCD state of V. cholerae occurs in mid-exponential phase and several cell divisions occur before stationary phase and the cessation of growth, at least in normal laboratory conditions. Therefore, there is not support for the argument that QS is a mechanism to redirect cell wall components at HCD because cell wall synthesis is no longer needed.

We did not intent to suggest cell wall synthesis is not needed at all, rather that there is a reduced need. We made a slight change to the discussion to reflect this.

19. Line 357-360-Again, as stated in point 16, the statement that cells locked in the HCD are "defective for growth" is an oversimplification. The luxO mutants have a longer lag phage, but they actually outgrow the hapR mutants at higher cell densities and reach the maximum yield much faster.

In fairness, we do go on to specify that the defect is an extended lag phase. Also see our response above.

Reviewer #2 (Recommendations For The Authors):

Comments to improve the text

1)  Line 103-106, line 130, line 136, etc. Details of the methods and the text directing to presentations of figures should be in the methods and/or figure legends with (Figure x) in citation after the statement. The sentences in lines indicated can be deleted from the results. Although several lines are noted specifically here, this comment should be applied throughout the entire results section.

We appreciate this comment but would prefer not to make this change (it seems mainly an issue of personal stylistic choice). It is sometimes helpful for the reader to include such information as it avoids them having to cross reference between different parts of the manuscript.

2)  Line 115. Recommend a paragraph between content on LuxO and HapR (before "Of the 32 peaks for HapR binding")

We agree and have made this change.

3)  Line 138 and Figure 1a. I am not convinced this gel shows that VC1375 is activated by HapR. Is the arrow pointing to the wrong band? There does seem to be an induced band lower down.

We understand this comment as it is a little difficult to see the induced band. This is because this is a compressed area of the gel and the transcript is near to an additional band.

4)  Line 147. Add the VC0206-VC0207 next to murQP (and the gene name murQP into Table 1).

We have added the gene name to the figure foot note. The text has been changed as requested.

5) Methods. It is essential for this paper to have detailed methods on the bacterial growth conditions. Referring to prior paper, bacteria were grown in LB (add composition...is this high salt LB often used for vibrios or low salt LB often used for E. coli). Growth is to "mid log". Please provide the OD at collection. Is mid log really considered "high density". Provide a reference regarding HapR activity at mid log to support the method. Could the earlier collection of bacteria account for missing known HapR regulated genes? In preparing the requested ç, include growth conditions for other experiments in the legends.

Note that we have included a new supplementary table, rather than a Venn diagram. We have also added further details of growth conditions as mentioned above. Also not that, for the ChIP-seq, HapR and LuxO were expressed ectopically and so uncoupled from the switch between low and high cell density.

6)  Content of Table 1, HapR Chip-seq peaks, needs to be closely double checked to the collected data as there seems to be some errors. Specifically, VC0880 and VC0882 listed under Chromosome I are most likely VCA0880 (MakD) and VCA0882 (MakB), both known HapR induced genes on Chromosome II with VCA0880 previously validated by EMSA. This notable error suggests the table may have other errors and thus requires a very detailed check to assure its accuracy.

We appreciate the attention to detail! We have double checked, thankfully this is not an error, the table is correct (even so, we have also checked all other entries in the table). As an aside, VCA0880 is one of the locations for which we see a weak HapR binding signal below our cut-off (included in the new Table S1). In cross checking between Table 1 and all other data in the paper we noticed that we had erroneously included assay data for VC0620 in Figure 2A. This was not one of our ChIP-seq targets but had been assayed at the same time several years ago. This datapoint, which wasn’t related to any other part of the manuscript, has been removed.

If VCA0880 and VCA0882 are correctly placed on Chr. I, then add comment to text that the Mak toxin genomic island found on Chromosome II in N16961 is on Chr. I in E7946. (See recent references PMID: 30271941, 35435721, 36194176, 34799450).

See above, this is not an error.

7)  Alternatively for both comments 8 & 9, are these problems of present/missing genes or misannotations the result of the annotation of E7946 gene names not aligning with gene names of N16961? (if so, in Table 1, please give the gene name as in E7946 but include a separate column with the N16961 name for cross study comparison)

See above and below, this is not an issue.

8)  Line 126-127. Also regarding Table 1, please add a column with function gene annotation. For example, VC0916 needs to be identified as vpsU. If function is unknown, type unknown in the column. This will help validate the approach of selecting "HapR target promoters where adjacent coding sequence could be used to predict protein function."

We added an extra column to Table 1 in response to a separate reviewer request (TSS locations). This leaves no space for any additional columns. Instead, to accommodate the reviewer’s request, we have added alternative gene names to the footnote.

Not following up on VCA0880 (promoter for the mak operon) is a sad missed opportunity here as it is one of the most strongly upregulated genes by HapR (PMC2677876)

As noted above, this was not an error and VCA0880 was not one of our 32 HapR targets. As such, we would not have followed this up.

9)  Figure Legends. Add a unit to the bar graphs in Figure 1e (should be kb??) This has been corrected.

10) The yellow color text labels in figures 3c, 4a, 4c are difficult to read. Can you use an alternative darker color for CRP.

We have made this slightly darker (although to our eye it is easily reliable). We haven’t changed the colour too much, for consistency with colour coding elsewhere.

11) Figure S3. Binding is misspelled. Add units to the x-axis

This has been corrected.

12) Figure S7. The text in this figure is too small to read. Figure could be enlarged to full page or text enlarged. Are these 4 the only other known regulated promoters? Could all the known alternative promoters linked to HapR be similarly probed?

We have increased the font size and included a new Table S1 for all previously proposed HapR sites.

13) Figure S8. Original images..are any of these the replicate gels (see public comment 6)

We have added a statement regarding reproducibility, and also note the internal reproducibility between different figures in our reviewer response. The gels in Figure S8 are full uncropped versions of those shown in the main figures.

Reviewer #3 (Recommendations For The Authors):

None

Whilst there were no specific recommendations from this reviewer, we have still responded to the public review and made changes if required.

Author Response:

Regarding the two main points emphasized by the eLife assessment:

• Potentially confounding effects of overcrowding: This is indeed an important point, which we avoided, unfortunately without explicitly mentioning it in the manuscript (assuming that it went without saying.) We will point out that our proliferation assays, already part of the original manuscript, indicated that cells were not overcroweded. Nevertheless, we will include additional evidence indicating that our cells were not overcrowded and remained subconfluent.

• Mechanisms: We will mention even more explicitly than we already did that this is beyond the scope of this story and why that is. As we did say, there are lots of factors directly or indirectly involved in translation that depend on Hsp90. Figuring out which one or which ones it might be is a whole new and totally open-ended project.

Regarding some of the other public comments:

• While we did provide quantitative (!) data on changes in cytoplasmic density (e.g. diffusion coefficients, total amount of protein relative to cell size), we will emphasize in the revised manuscript that the changes in cell size, as measured by both flow cytometry and image analyses, are a relative and approximate measure of the 3D changes in cell volume. Although our data on the diffusion coefficients, which report on cytoplasmic density, are directly comparable, our measurements of the amounts of protein relative to cell size (if this is what the comment meant with "cell density") have at least relative value.

• Results of proteomic data not shown in sufficient detail: We recognize that it is not trivial to "read" the data as presented in the paper (volcano plots, full datasets as an Excel file and through ProteomeXchange). We will add subsets of the proteomic data to the Excel file and include some Gene Ontology analyses.

• We did demonstrate that Hsf1 most likely acts transcriptionally to promote the observed cell size increase.

• We acknowledge that a large fraction of our data is "observational", but some experiments clearly go beyond providing correlations. When we manipulate some of the players genetically (KO, knockdown, overexpression) or pharmacologically, we get results that support our conclusions about underlying mechanistic connections.

• GADD34: This protein is not known to be an Hsp90 client (or interactor), which is also supported by our mass spec data since its steady-state levels don't change in Hsp90α or β KO cells compared to wild-type cells.

• Non-dividing cells: it would indeed be exciting to determine whether the same phenomena and mechanisms apply to non-dividing cells. However, there are likely to be substantial technical challenges. We would need primary human (or alternatively murine) cells such as B-cells or hepatocytes, and it is difficult to predict whether they would tolerate mild heat stress for several days. It might also be possible to explore this with a mouse model, but clearly, this must be left to future studies.

Author Response:

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

We’d like to take this opportunity to thank the reviewers and editors for their consideration of our work. As detailed below, we have made the majority of the suggested corrections by the reviewers and believe these have greatly improved our manuscript. The reviewer’s comment are in blue font below and our response to each of these in black font.

Reviewer #1 (Recommendations For The Authors):

Suggestions to improve the manuscript:

-  Line 33 and 34: "This protein" is vague. Please reword to state whether you are referring to TcaA or to WTA

This has been corrected in the revised manuscript (Line 33)

-  Intro: It would be helpful to provide more rationale for testing serum as a surrogate to whole blood in the GWAS screen. Serum is obviously lacking components of the clotting cascade, and some of these components have antimicrobial functions. However, this is easily justified in the text- e.g. to avoid clumping during the screen, to focus only on serum-derived antimicrobial compounds, etc.

This has been edited in the revised manuscript (Line 84-86)

-  Line 120: Please state if the 300 clinical isolates represent 300 distinct patients, or if some of the isolates came from the same patient during sequential collections. If the latter, were there any instances in the which the tcaA SNP appeared during the course of infection?

They each came from individual patients so we were unfortunately unable to look for within host events. This information has been added to the revised manuscript (line 104).

-  Line 133: the closed parenthesis sign is missing after "CC22"

This has been corrected in the revised manuscript (Line 135)

-  Table 1a - NE1296 is misspelled as ME1296. Also there is a typo in the last entry of this table for the locus tag

This has been corrected in the revised manuscript.

-  Table 1b - the authors should comment (in the discussion) on the potential reasons why tcaA was not identified in the CC30 background.

A comment to this effect has been added to the revised manuscript (Lines 553-59)

-  Figure 2a - Why is the mutant with the empty complementation vector not significantly different from WT JE2?

The most widely used and reliable expression plasmid for complementation of mutated phenotypes in S. aureus is the pRMC2 plasmid, which requires chloramphenicol selection and anhydrotetracycline to induce expression of the cloned gene. These antibiotics, and the presence of the plasmid often affect the expression of other genes by the bacteria (as noted by this reviewer). As such, to verify complementation of a mutation the comparison we make is between the strain containing the empty plasmid induced with anhydrotetracycline with a strain with the gene containing plasmid induced with anhydrotetracycline. In that situation, the only difference between those two strains under those conditions is whether the gene is expressed or not. A comment explaining this has been added to the revised manuscript (lines 149-153).

-  Line 188: Statistical analyses should be applied to figure 3C, which also appears to be underpowered.

P values have been added to this in the revised manuscript. We present data point of three biological replicates, which are the mean of three technical replicates, which we believe is sufficiently powers for this analysis.

-  Figure 3 legend - Tecioplanin is mentioned in the title, but the data are not included here

This legend title has been the revised (Line 193).

-  Figure 4 - here is an example where testing the actual tcaA SNP could have been enlightening. For example, what if the selective pressure makes the SNP more relevant to a specific AMP or AA?

While we agree that this would be an interesting experiment to perform, the complementing vector that we would need to use to compare the wild type and SNP contains gene requires antibiotics to select for the plasmid and another to induce expression. As such it becomes quite a complex and messy experiment where synergy between the antimicrobial agents would be likely, the results of which will be difficult to interpret.

-  Lines 317-321 - Suggest moving this to discussion

We have left this here as we felt it a necessary summation/explanation of the results described in that section. It is discussed again later in the discussion section.

-  Line 341 - I believe "serum" should actually be "teicoplanin"

This has been corrected in the revised manuscript (Line 342).

-  Figure 6e - wouldn't it be more powerful to determine the WTA levels in the supernatants of these strains and conditions?

We could have done this both ways, but we focussed here only on how TcaA ligates WTA into the cell wall in the presence of serum.

-  Figure 6 - What is the explanation for the different growth yields for JE2 in tecioplanin in panel A versus panel F? Are these actually two different concentrations? If so, please update the figure legend and the methods.

The concentration used for the A was inhibitory and for F sub-inhibitory. To improve the clarity of this we have now used a table displaying the MICs for the six strains as panel A. We have also included the concentration of teicoplanin used for each experiment in the legend.

-  Line 413: Consider more precise language than "the cell wall is stronger". E.g. More crosslinks?

This has been edited in the revised manuscript (Line 421)

-  Line 415: Consider changing "altered" to a directional term such as increases. It can be difficult for the reader to follow the expected change when you are discussing how the lack of a gene versus the presence of a gene changes susceptibility in one direction and another phenotype in the opposite direction.

This has been edited in the revised manuscript (Line 423).

-  Figure 7: The conclusions made from panels A and B need to be supported by statistical analyses. It is unclear if these lines are truly different from one another.

These have been included in the revised fig 7.

-  Line 426: I believe "tcaA" is missing following "producing"

This has been corrected in the revised manuscript (Line 434).

-  Line 446: "increase" to "increases"

This has been corrected in the revised manuscript (Line 460).

-  Figure 8C: if one goal of the mouse experiment was to look at survival during transit in whole blood, earlier timepoints are indicated based on the described kinetics of bloodstream dissemination in this model.

The primary goal of this experiment was to see if TcaA contributed positively or negatively to the development of the infection. Work on this protein is ongoing, and so we hope in coming years to be able to provide more detail on its activity in vivo.

-  Line 506: "changes to the structural integrity of peptidoglycan" seems overstated without additional studies.

This has been edited in the revised manuscript (Line 524).

-  Line 564: "represents" to "represent"

This has been corrected in the revised manuscript (Line 603).

-  Line 588: The figures all refer to "100 net". Please confirm the concentration used.

This has been corrected in the revised manuscript (Line 628).

-  Line 609: This refers to capsule production? Is this a copy error from a prior paper?

Yes it is, and has been corrected in the revised manuscript (Line 650).

- Line 763: Please provide the concentrations of arachidonic acid used for each experiment.

This has been included in the revised manuscript (Line 805)

- Line 836 and 837: This mentions a time course for blood culture from the infected mice. Where are these data?

Apologies, this is another cut and paste mistake from another paper, and had been removed.

-  Line 870: please discuss how multiple comparisons testing was handled.

This has been included in the revised manuscript (Line 908).

-  Supplemental figure 5 - Please add statistical analyses to support the conclusions in the manuscript. For example, there appears to be no differences for dalbavancin. Please also italicize tcaA in the legend.

These have been included and corrected in the revised manuscript.

Reviewer #2 (Recommendations For The Authors):

Line 65 - I would suggest adding the reference (doi: 10.1128/Spectrum.00116-21), which shows increased mortality in S. aureus bacteremia patients due to agr deficient isolates.

The suggested manuscript shows this effect of Agr dysfunction to be limited to patients with moderate to severe SOFA scores. As such it would require a nuanced description here that we think will detract from the flow of the introduction.

Line 68 - Please add DOI: 10.1016/j.cmi.2022.03.015 as a reference to support the mortality rate in S. aureus bacteremia. A systematic review and meta-analysis provides the highest level of evidence, and this is a contemporary study performed in 2022

This has been included in the revised manuscript (Line 68).

Line 70 - please add supporting reference for this statement

This has been included in the revised manuscript (Line 70).

Figure 2 - This image is low quality and appears pixelated. Please revise

This has been replaced with a higher resolution image in the revised manuscript.

Figure 3c Also appears slightly pixelated

This has been replaced with a higher resolution image in the revised manuscript.

Line 173 - I think it would helpful to mention the catalytic activity encoded by tcaA (aside from mediating sensitivity to glycopeptides) is unknown.

This has been included in the revised manuscript (Line 174)

Line 174 - also confers sensitivity to vancomycin https://doi.org/10.1128/AAC.48.6.1953- 1959.2004

This has been included in the revised manuscript, albeit at a later point than suggested here (Line 406)

Line 209 - did the authors test any other antimicrobial fatty acids such as palmitoleic acid? If common mechanism would also expect decreased sensitivity to other HDFA

No, we focused on arachidonic acid as this is the most relevant antimicrobial fatty acid in serum and it is produced by neutrophils and macrophages during the inflammatory burst.

Figure 4a-D: it would be useful to know what the MIC to these different components is and how that MIC relates to the concentration in human serum

We do not have MICs for all of these compounds tested here but can confirm that the concentrations used are physiologically relevant.

Figure 4b - Can you mention in the legend how the killing assays varied for arachadonic acid versus the other AMPs? I am not immediately clear how this experiment was performed, despite referring to methods

This has been included in the text of revised manuscript (Line 211-213) and the figure legend.

Figure 5 - there is no panel D

This has been corrected in the revised manuscript.

Figure 6a: Lines 328-329 state the experiment was performed in the MIC for each strain. The legend (line 374) states 0.5 ug/ml teicoplanin was used, which is below the MIC for all of the strains tested per supp table 2. Please correct this discrepancy.

This figure has been revised and the additional information included to improve the clarity of this section in the revised manuscript.

Figure 6a: On line 328, the authors state that the tcpA knockout increases the MIC for teicoplanin in each background. Figure 6a is performed in the presence of teicoplanin at 1x the MIC of the wild type (which will be below the MIC for the knockout). Therefore, we know each tcpA mutant will be able to grow in the presence of sub-mic concentrations of teicoplanin. Would a more informative way of conveying this information be to have MIC on the Y axis and background on the X axis?

This has been corrected and clarified in the revised manuscript with a table showing the MICs (fig. 6a).

Figure 6b-c: Similarly, would it be more helpful to show how the MIC varies with the different clinical isolate tcpA mutants?

While MICs have uses in clinical setting, they are a relatively crude and binary (growth V no growth) way to measure and compare sensitivity. For these two groups of isolates the MICs did not vary, which is why we used a concentration that sat that the threshold and quantified growth of all the isolates in this. This information has been added to the legend.

Figure 6e: The figure legends instructs us to refer to supplemental figure 3 to see the densiometry results. However, Figure 6e appears to be 4 conditions (WT and mutant +/- serum) and only examines the cell wall, whereas the supplemental figure refers to two conditions (WT + mutant) and looks at the cell wall and supernatant. I would recommend providing the densitometry data associated with the conditions in figure 6e, especially as differences seem more subtle by eye.

This has been included in the revised manuscript (fig. 6f)

Line 689-691 - description of teicoplanin concentrations used in figure 2. However, no teicoplanin was used in figure 2. Assume is referring to a different figure (figure 6?)

This has been corrected and clarified in the revised manuscript. Line 724.

Please add a section in the methods describing how the MIC was determined for JE2, SH1000 and Newman. Was it performed in CA-MHB or the media that the experiment in figure 6a was performed in. Serum can alter the MIC of several antibiotics

This has been corrected and clarified in the revised manuscript. Line 724-29.

Please add a section to the methods describing the whole blood killing assay, ideally describing how the blood was not frozen and used same day as venipuncture. This is important as freeze/thaw or time periods >12 hours are likely to severely effect the function of phagocytes, especially neutrophils.

This has been corrected and clarified in the revised manuscript. Lines 635-639

Line 588: ng/ul should read ng/µl

This has been corrected in the revised manuscript too ng/ml. Line 628

Reviewer #3 (Recommendations For The Authors):

We have now included a graphical abstract (Fig. 9)

Major:

1-    Line 102: I was not able to find the accession numbers of these 300 genomes, did the authors submit it to any public repository (e.g. NCBI)?

These were submitted previously to a public repository and the associated reference cited, but we have provided these in supplementary Table 1.

Minor:

1 -    Typo in line 133. Fix parenthesis after CC22.

Corrected.

2 -    Typo: Fix figure 5 panels (5e should be 5d).

Corrected.

3 -    Line 276: It is not clear why the extract for this experiment was supplemented at 2% while the other part of the experiment was done with 10%. Clarification is needed.

The experiments at 10% was using overnight supernatant, whereas those with 2% was a purified WTA extract. This has been clarified in the revised manuscript (lines 283 and in the figure legend)

4 -    Line 278: Typo: Figure 6e should be figure 5d.

Corrected. (Line 278)

5 -    Figure 5f: There is no explanation in the text or in the figure legend what the purpose of using mprF was.

A comment has been included in the figure legend.

6 -    Line 328: It would be good if we the authors reports the CC of Newman and SH1000 for a better context for the readers.

This has been added. (Line 332)

7 -    Line 341: Did the authors mean less sensitive to teicoplanin?

Corrected. (Line 342)

8 -    Line 367: Dose dependent effect does not seem to be followed not only in panel H of Supp. Fig. 4(LL37 and EMRDA15) but also panels C, D and G.

Corrected.

9 -    Line 587: Typo: Table 2.

These have all been corrected and/or clarified in the revised manuscript.

Author Response:

First and foremost, we would like to thank all the editors and reviewers for their thoughtful and thorough evaluations of our manuscript. We greatly appreciate their assessment about the novelty and strength in this study and will revise the manuscript according to their recommendations. Here we offer a provisional response to Reviewer 2 to clarify our rationale for using TH-Cre rather than DAT-Cre mice in our study of frontal cortical dopaminergic projections.

We agree with Review 2 that the DAT-Cre line can provide specific labeling of midbrain dopamine neurons projecting to the striatum, as discussed in the cited study (Lammel et al., 2015). But unfortunately, mesocortical dopamine neurons in the VTA are known to express very little DAT (Lammel et al., 2008; Li, Qi, Yamaguchi, Wang, & Morales, 2013; Sesack, Hawrylak, Matus, Guido, & Levey, 1998). This limitation in the use of the DAT-Cre line to target mesocortical dopamine neurons has been acknowledged in the cited publication (Lammel et al., 2015). It is an issue we have also observed when testing the DAT-Cre line in our lab. Additionally, and interestingly, recent extensive evaluation of the DAT-Cre line reported ectopic labeling of multiple non-dopaminergic neuronal populations (Papathanou, Dumas, Pettersson, Olson, & Wallen-Mackenzie, 2019; Soden et al., 2016; Stagkourakis et al., 2018). Our own evaluation of the DAT-Cre line’s utility for cortical imaging also captured sporadic ectopic labeling of cortical cell somas.

Because mesocortical dopamine neurons have stronger TH expression than DAT (Lammel et al., 2008; Lammel et al., 2015; Li et al., 2013; Sesack et al., 1998), TH-Cre lines have been frequently used to study the mesocortical pathway (Ellwood et al., 2017; Gunaydin et al., 2014; Lammel et al., 2012; Lohani, Martig, Deisseroth, Witten, & Moghaddam, 2019; Vander Weele et al., 2018). While TH-Cre expression itself is not restricted to dopaminergic neurons, we targeted our viral injections to the VTA and optogenetic stimulation to the cortical dopaminergic projection target area (Patriarchi et al., 2018) to specifically modulate mesocortical dopaminergic axons. In addition, we tested D1 antagonist’s effects in our manipulations. Although we targeted dopamine neurons in our adolescent stimulation, the final behavioral outcome likely includes contributions from co-released neurotransmitters and non-dopaminergic neurons via network effects. We will revise our discussion and methods sections to clarify these points of interest. Additionally, we will provide DAT-Cre images in the revised supplementary materials to further explain our choice of the TH-Cre line rather than the DAT-Cre line for our study.

References

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Lammel, S., Steinberg, E. E., Foldy, C., Wall, N. R., Beier, K., Luo, L., & Malenka, R. C. (2015). Diversity of transgenic mouse models for selective targeting of midbrain dopamine neurons. Neuron, 85(2), 429-438. doi:10.1016/j.neuron.2014.12.036

Li, X., Qi, J., Yamaguchi, T., Wang, H. L., & Morales, M. (2013). Heterogeneous composition of dopamine neurons of the rat A10 region: molecular evidence for diverse signaling properties. Brain Struct Funct, 218(5), 1159-1176. doi:10.1007/s00429-012-0452-z

Lohani, S., Martig, A. K., Deisseroth, K., Witten, I. B., & Moghaddam, B. (2019). Dopamine Modulation of Prefrontal Cortex Activity Is Manifold and Operates at Multiple Temporal and Spatial Scales. Cell Rep, 27(1), 99-114 e116. doi:10.1016/j.celrep.2019.03.012

Papathanou, M., Dumas, S., Pettersson, H., Olson, L., & Wallen-Mackenzie, A. (2019). Off-Target Effects in Transgenic Mice: Characterization of Dopamine Transporter (DAT)-Cre Transgenic Mouse Lines Exposes Multiple Non-Dopaminergic Neuronal Clusters Available for Selective Targeting within Limbic Neurocircuitry. Eneuro, 6(5). doi:10.1523/Eneuro.0198-19.2019

Patriarchi, T., Cho, J. R., Merten, K., Howe, M. W., Marley, A., Xiong, W. H., ... Tian, L. (2018). Ultrafast neuronal imaging of dopamine dynamics with designed genetically encoded sensors. Science, 360(6396), 1420-+. doi:10.1126/science.aat4422

Sesack, S. R., Hawrylak, V. A., Matus, C., Guido, M. A., & Levey, A. I. (1998). Dopamine axon varicosities in the prelimbic division of the rat prefrontal cortex exhibit sparse immunoreactivity for the dopamine transporter. J Neurosci, 18(7), 2697-2708. doi:10.1523/JNEUROSCI.18-07-02697.1998

Soden, M. E., Miller, S. M., Burgeno, L. M., Phillips, P. E. M., Hnasko, T. S., & Zweifel, L. S. (2016). Genetic Isolation of Hypothalamic Neurons that Regulate Context-Specific Male Social Behavior. Cell reports, 16(2), 304-313. doi:10.1016/j.celrep.2016.05.067

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Author Response:

I appreciate the time and effort of both Reviewers, who have raised important points that I would like to briefly discuss before I start working on a full revision of the paper.

Generality. First, there is the question of how much these conclusions broadly apply across experimental paradigms and subjects, which could give rise to potentially very different TGMs. As the Reviewers mention, I have focussed on one specific TGM that I assumed prototypical, and it could be that these conclusions fit other TGMs less well. Further, the model has quite a few hyperparameters so that it can flexibly accommodate a broad span of scenarios. This flexibility comes at a price, as pointed out by Reviewer 1: that “a different selection of parameters could lead to similar results”, i.e. that other configurations could fit this specific TGM just as well. This is related to the next point, so I will address them jointly.

Lack of quantitative evaluation, “making it hard to draw firm conclusions”. Indeed, I have not explicitly quantified the fit of the hyperparameters to this empirical TGM using a specific measure, and (related to the previous point) I have not made a systematic search through the space of model configurations based on such measure.

There is here a trade-off between generality and specificity. In fact, it is intentional that I did not optimise the hyperparameters to this specific TGM, and that I chose not to show a quantitative measure of fitness. This is because the TGM that I show in the paper is only meant as an example. Instead of focussing on fitting a specific TGM, I aimed at characterising some prominent general features that we often see throughout the literature, which this specific TGM shows in its own specific way. That is, if the paper was meant to focus on a specific paradigm (e.g. passive vision), then the use of a specific metric to fit the model to one or various empirical TGMs would have perhaps been more adequate, but this was not the case here. In future work, when focussing on specific paradigms, I will adapt methods of Bayesian optimisation (Lorenz et al., 2017) for this purpose, as mentioned in the Discussion. Note that doing this right is not trivial and would complicate the paper significantly; for this reason, I feel it should belong to a different piece of work.

I would also like to note that evaluating the different features of the data one by one (“in a stepwise manner”) was necessary for interpretation. One can loosely think of it as a sort of F-test: one is showing how important a feature is by comparing the full model vs. a nested model that does not have that feature. While the Reviewer is right that there might be interactions between the features that we can only unveil through a joint evaluation, my approach is at least valid as a first approximation. I will discuss this limitation in an updated version of the paper in more detail.

In a future revision of the paper, I will argue more specifically why and how these model configurations are, in general terms, necessary to produce these main effects in the TGM, and why other alternative configurations could not easily generate them.

Practical guidelines for researchers. It was suggested to make it clearer how researchers could leverage this model in their own studies to understand their data better and to help relating their TGMs to specific neurobiological mechanisms.

In a future revision of the paper, I will introduce a new section explaining how to use genephys practically, emphasising both opportunities and current limitations.

Neurobiological interpretation. It was criticised that the results were a mere characterisation of sensor space data, and that these were not related clearly to any neurobiological aspect.

In a future revision, I will work toward relating the main findings to existing literature in order to strengthen the neurobiological interpretation of the results, and toward a better justification of how genephys can help shed light on specific brain mechanisms.

Above and beyond these specific points, I intend to restructure the text so that the main goals of the study become clearer. This includes clarifying in the Introduction more unambiguously what is the gap of knowledge this work is specifically tackling.

Again, I would like to thank the Reviewers for helping me realise the limitations of the current version of the paper.

Author Response:

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

In brief, we incorporated all wording and clarity suggestions into the manuscript. We also updated figure legends to include additional details, including replicate numbers. New data have been added in response to requests from the reviewers. Volumetric intake data are included as a supplemental figure (Figure 1–Figure Supplement 1A) and we will include movies of the confocal stacks from our CaMPARI imaging. We worked hard to address all the reviewers’ concerns and provide a detailed response below to the reviewers’ public comments as well as their author-specific comments.

Reviewer #1 (Public Review):

1) All feeding data presented in the manuscript are from the interactions of individual flies with a source of liquid food, where interaction is defined as 'physical contact of a specific duration.' It would be helpful to approach the measurement of feeding from multiple angles to form the notion of hedonic feeding since the debate around hedonic feeding in Drosophila has been ongoing for some time and remains controversial. One possibility would be to measure food intake volumetrically in addition to food interaction patterns and durations (e.g. via the modified CAFE assay used by Ja).

We acknowledge that our FLIC assays address only one dimension of feeding behavior, physical interaction with liquid food. However, there is clear evidence that interactions are strongly predictive of consumption, and it would be technically difficult to measure feeding durations at the resolution of milliseconds using a Café assay.  Nevertheless, we appreciate the spirit of this comment and agree that expanding our inference to other measures of feeding, as well as feeding environments, is an important next step. To this end, we now include measures of feeding on more traditional solid food, using the ConEx assay, and find that flies in the hedonic environment consume twice as much sucrose volume compared to flies in the control environment. These have been added as supplemental data (Figure 1 – Figure Supplement 1A), and the text has been updated to reflect our findings.

2) Some of the statistical analyses were presented in a way that may make understanding the data unnecessarily difficult for readers. Examples include:

a) In Table I the authors present food interaction classifications based on direct observation. These are helpful. However, the classification system is updated or incompletely used as the manuscript progresses, most importantly changing from four categories with seven total subcategories to three categories and no subcategories. In subsequent data analyses, only one or two of these categories are assessed. It would be helpful, especially when moving from direct observation to automated categorization, to quantify the exact correspondences between all of the prior and new classifications, as well as elaborate on the types of data that are being excluded.

We appreciate the feedback on our usage of the behavioral classification system and have made several adjustments to improve it. We renamed some of the behaviors to make them more intuitive (see Reviewer #2, comment #1), and updated the main text and Table 1 to reflect these changes. We updated the text and figures to be more transparent about when we group subcategories into main categories for quantification and when we quantify all subcategories separately. Because these videos required manual scoring by an experimenter, after our initial characterizations we opted to score only main categories (which contain subcategories). We agree that it would be useful to quantify correspondence between subcategories and the automated FLIC signal. However, we believe this task is better suited for more advanced and automated video tracking software, and, incidentally, more sophisticated analysis of FLIC data, which has a very high-dimensional character that has yet to be properly exploited. At the moment, therefore, we are not confident in the ability to understand the data at the desired resolution.

b) The authors switch between a variety of biological and physiological conditions with varying assays, which makes following the train of reasoning nearly impossible to follow. For example, the authors introduce us to circadian aspects of feeding behavior to introduce the concept of 'meal' and 'non-meal' periods of the day. It is then not clear in which of the subsequent experiments this paradigm is used to measure food interactions. Is it the majority of the subsequent figure panels? However, the authors also use starved flies for some assays, which would be incompatible with circadian-locked meals. The somewhat random and incompletely reported use of males and females, which the authors show behave differently, also makes the results more difficult to parse. Finally, the authors are comparing within-fly for the 'control environment' and between flies for their 'hedonic environment' (Figure 3A and subsequent panels), which I believe is not a good thing to do.

We apologize for our difficulties conveying our inference, which was also noted by Reviewer #2.  We have worked hard to improve this component in the revision. With respect to the confusion about circadian feeding, we introduced circadian meal-times to complement starvation as a second (perhaps more natural) way to measure behaviors associated with hunger. Importantly, we do not use circadian meal-times beyond Figure 1; all subsequent FLIC experiments were conducted during non-meal times of day for 6 hours, which avoids confounding our data with circadian-locked meals even when we use starved flies. We have clarified this point in the revision.

The reviewer also points out that we make both within-fly and between-fly comparisons, which is a point that we note. Perhaps some concern arises, again, from the challenges that we faced in properly delineating our inferences about different types of feeding measures (and motivations). Inference about homeostatic feeding was made using within-fly measures, comparing events on sucrose vs. those on yeast.  Inference about hedonic feeding was made using between fly measures (average durations of different flies on 2% vs. 20% sucrose).  Treatment comparisons to control always used measures of the same type, such that inference was not made using between-fly measures for treatment and within-fly for control (i.e., all of our figure panels were either within-fly or between fly). We have worked to clarify this in the revision.

Importantly, our approach to all experiments avoided confounding by used randomized design at multiple levels (e.g., randomizing control and hedonic environments to FLIC DFMs, alternating food choice sidedness in the DFMs), by ensuring that flies in both environments are sibling flies that came from the same vial environment before being tested, and by performing each experiment multiple times.

c) Statistical analyses are not always used consistently. For example, in Figures 3B and C, post hoc test results are shown for sucrose vs. yeast interactions, but no such statistics are given for 3E and 3F, preventing readers from assessing if the assay design is measuring what the authors tell us it is measuring.

We report p-values for two-way ANOVA interaction terms for all appropriate experiments. If (and only if) the interaction term is significant, we conduct post-hoc tests for more detailed statistical analysis and report the p-values. The reviewer points out that we do not perform post-hoc tests in figures 3E and 3F. These figures had a non-significant interaction term, and thus, we did not feel a post-hoc test was warranted.

Reviewer #2 (Public Review):

1) The dissection of feeding into distinct behavioral elements and its correlation with electrical FLIC signals that allow interpreting feeding types is a fundamental new method to dissect feeding in flies. However, the categories of micro-behaviors in Table 1 are not intuitive.

We agree and have updated the Table, figures, and main text. Please see also our response to Reviewer #1, comment #1.

2) The details for the behavioral data analysis are not clear and should be made more obvious. For example, how many males and females were used in each experiment? Were any of the females mated or were they all virgins? If all virgins, why not use mated females? Mating status may have an effect on the feeding drive. If mated and virgin females were used, are there any differences between them? Similarly, for diurnal feeding experiments, it is not immediately clear from the graphs how many animals were used and how the frequencies were obtained (Fig. 1F, presumably averages for each category per fly but that is inconsistent with the legend in the supplement for this figure). Why does the transition heat map not include all micro-behaviors (Fig. 1E, no LQ data which are significant in diurnal feeding)?

We have clarified the number of flies and events for each behavioral experiment in Figure 1, and we updated the figure legend appropriately. We note that these behavioral datasets are non-overlapping, and each time we mention the number of events scored in the text, that number includes only “new” videos. Female and male flies for all experiments were mated, and we have clarified this in the main text and methods.

For the diurnal experiment in Figure 1F, we scored over 700 events from new (non-overlapping) video compilations and updated the number of flies and event number in the figure legend. The diurnal data we present in the supplement for this figure is a separate experiment conducted on 38 flies, intended only to demonstrate the circadian nature of fly feeding.

For the transition heat map, analysis of this sort seems to require a large amount of data to have sufficient power to return a transition matrix. LQ events are relatively low in frequency, so we opted to combine them with L events for this analysis. We have updated the figure and figure legend to reflect this.

3) The CaMPARI images do not look great, particularly in the pan-neuronal condition (Fig. 5A). It would be useful to include the movie of the stack. Did any other brain regions show activity differences, such as SEZ or PI? These regions are known to be involved in feeding so it seems surprising they show no effect.

We find that CaMPARI imaging is subject to high levels of noise and background, especially when using a broad driver as the reviewer has pointed out. This is why we opted to follow-up our pan-neuronal CaMPARI experiment using a more specific mushroom body driver and to test our correlational findings of increased MB activity in hedonic environments with genetic approaches in the remainder of Figure 5. We have included movies of the confocal stacks for both CaMPARI experiments, as requested. 

Reviewer #1 (Recommendations For The Authors): 

Main concern: 

No measurements of intake, either in volume or in caloric value. Hence, 'hedonic' feeding is only indirectly supported. 

I would like to suggest to the authors that they measure intake volumetrically in addition to food interaction patterns and durations. For example, William Ja developed a modified CAFE assay that measures consumption volume in real-time in freely behaving flies (http://dx.doi.org/10.1038/nprot.2017.096). Liming Wang has another capable assay. Additional values of expanding measurement methods for feeding are that it helps tie the research more directly to that of others, and it helps remove the concern that any one assay may introduce unknown biases. 

For the CaMPARI, it would be helpful to provide a demonstration of its effectiveness by recapitulating a deep brain neural pathway known to be engaged by a stimulus by GCaMP or electrophysiology. 

Additional concern: 

The authors assume satiety states during different circadian periods (line 253, for example). It seems critical to directly measure the satiety state. 

Technical concerns: 

Figure 5 A, B: there is reported near zero UV transmission through the head: https://doi.org/10.1364%2FBOE.6.000514, hence the CaMPARI measurements are suspect. It appears that there may be an effect in the optic lobes that may receive greater UV illumination by being more peripheral. A positive control to demonstrate deep brain access by UV is needed. 

Y-axes vary for the same measurement types within figures, for example, Figure 5 C-G. Also Figures 3F, G, I, K, M and Figures 3D, E, H, J, L. This hinders direct comparisons. 

Figure 2: why are there no statistics to distinguish interaction (I) events from F and L? Why are the example graphs presented using different scale x-axes? For A-C, why no averaged response graphs for the classifications? Were there other events that did not fit these classifications? 

In lines 224-226, the claim of statistical significance at p=0.061 makes the reader suspicious of the statistical interpretations throughout the manuscript. 

Figure 3B starved looks the same as Figure 3C sated for females, using the same assay and conditions. This implies a huge amount of variance in behavior between experiments. 

We appreciate the recommendations from Reviewer #1 and have done our best to address many of their concerns. Regarding their main concerns, we have added volumetric feeding data to the manuscript, included movies of the confocal stacks for the CaMPARI experiments, and clarified the circadian timing of our behavioral experiments. These details are outlined in our public response to both reviewers. The reviewer also expressed a few technical concerns, mostly regarding statistical analyses. We agree that there seems to be a large amount of biological variability between experiments, which we do indeed find to be the case with behavioral experiments of this sort. For this reason, we avoid making direct comparisons on absolute values between experiments, as the reviewer suggests, and thus allow our Y-axes to vary for each figure to better facilitate within-experiment comparisons. The reviewer also points out that, in one instance, we refer to a p-value of 0.061 as statistically significant in the text. While we have changed our language to reflect the perceived convention, we note that there is little inferential difference between these values, and we report exact p-values to allow the reader to make an informed decision.

Reviewer #2 (Recommendations For The Authors): The writing and data presentation in this paper is somewhat dense and confusing at times. Comments and questions below are intended to help improve data presentation and resolve questions that will help the reader navigate and understand the data to better appreciate the significance of the findings. 

Comments and questions: 

Line 160 cites Chen et al, 2002 as an example of behavioral characterization that is useful for read-outs of neural states, but no neural states were defined in that work. A better example where a circuit was linked to a specific behavioral category is PMID30415997 (Duistermars et al., 2018). 

Line 171: were the females mated or virgin or was it variable? 

The classification system in Table 1 is a bit confusing. For example, the distinction is made between Fast and Long feeding events as well as interactions with food and other events. FH meet the requirements of F and H, presumably meaning that flies are fast feeding and touching the food with their front legs. Why are front legs and hind legs touching food abbreviated H and FF respectively instead of something more obvious like IF and IH (referring to Interaction with Front legs or Interaction with Hind legs)? 

Also was there never any tasting with the middle legs? In Fig1B, all the I events are grouped. Are most of these H or FF events? The frequency in Fig. 1B is shown as normalized as a frequency of all events. The statistical analyses are all parametric. Are these data normally distributed? 

Lines 224-229: the relative frequency of L-type feeding is increased in starved flies and the relative frequency of F feeding is decreased. Is the relative L- or F-type feeding frequency considered on total behavior or just the sum of long and fast feeding or the sum of all types of feeding? 

The events that are analyzed vary throughout the paper. Line 173 mentions 300 events, line 222, 500 events, and line 257, 700 feeding events. Are these all independent experiments, or are these overlapping data sets analyzed for different parameters? 

For diurnal feeding behavior, the authors analyzed 700 events and found significantly more LQ events during meal time (i.e. at the beginning and end of the day). Based on the figure legend in the supplement to Figure 1, it appears that these data were collected on 38 female flies. But in Fig 1F, there are ~8 points per feeding type (F, L, and LQ) during meal and non-meal conditions. Shouldn't all 38 flies have an average frequency for each type of feeding during meal and non-meal times? Were these females mated or not? Is this effect also true for males? To help the reader understand the data better, it would be helpful to note the number of flies used in each experiment or in each analysis in the different figures and wherever the data are mentioned in the manuscript. It also seems likely that the mating state may have an effect on feeding so knowing the result in mated versus unmated would be a useful analysis. 

It is interesting that there is a difference in feeding in starved flies versus diurnal feeding in the presumably hungry versus sated phase (meal versus non-meal phases). As mentioned by the authors earlier in the manuscript, starved flies have a relative increase in L-type feeding. However, they perform less LQ feeding than sated flies, and yet LQ feeding is the only significantly different type of feeding in the hungry state of diurnal feeding. In the morning, the transition to feeding is very abrupt compared to the gradual increase in the evening. Is there any difference between the type of feeding or the transition matrix in the evening versus morning meal times? Also, why is LQ feeding not included as a category in the transition matrix in Fig 1E? 

In Fig 2, the authors examine FLIC signals with video data to identify feeding types from FLIC signals. Why are there signal durations for F-type feeding that are longer than 3 seconds when it is defined as 1-3 sec of the proboscis contact with food and conversely signals of L-type feeding shorter than 4 seconds when it is defined as >4 seconds of continuous proboscis contact? Does this mean that signal can be longer or shorter than the actual time the proboscis is in the food? 

With these parameters, the authors develop an assay to identify homeostatic and hedonic feeding by applying the signal analysis to food choices representing homeostatic (2% sucrose versus yeast) and hedonic (2% sucrose versus 20%) conditions. In Fig 3C, they show that fully-fed females show a stronger preference for yeast food than sugar food compared to males (line 335). Is this in fully fed animals? The yeast preference in females looks almost the same as in the starved females in Fig 3B. 

The CaMPARI images shown in Fig 5A (and to a lesser extent Fig 5B) are not particularly convincing although the quantification looks clear. Providing the movies of the stacks may help the reader better appreciate the difference in MB red signal in the hedonic state. It would also help to show the number of flies that were tested in these experiments as well as the sex and mating status. Provide the n in the figure legend and in the relevant sections in the text. 

Were the mushroom bodies the only brain region with significant, measurable activity changes? One might expect changes in other feeding areas, such as the subesophageal zone (SEZ) and the peptidergic regions of the brain (PI), which are both known to affect feeding in flies. This may also be a useful method to examine differences in mated versus unmated flies. 

In Fig 5C the caption reads MB lambda lobe inhibition. Shouldn't this be gamma lobe inhibition as suggested in the figure legend? 

The paper largely distinguishes homeostatic from hedonic feeding only. It may be useful to discuss other non-homeostatic mechanisms as well or at least make the distinction in the introduction and or discussion.

We thank reviewer #2 for their thoughtful suggestions to improve the clarity of the manuscript. They suggest several improvements, which we implemented, including that we improve the classification system in Table 1 to make it more intuitive, state how we normalized observed behavioral frequencies, clarify that the number of events we cite for each experiment are non-overlapping, and explain the use of circadian meal vs. non-meal times. We also noticed, as did this reviewer, that the usage of L vs. LQ events differs between starved flies and flies observed during meal-time. We agree that it may be interesting to sort out the nuances of why and how these differences occur, as it suggests that starvation may in some ways be different from physiological hunger. However, our method of manually observing flies would make this difficult at present. We hope to utilize more advanced video tracking software in the future to investigate this question. The reviewer also posed several questions about the hunger/satiety state of flies that we used for each experiment, which we clarified throughout the main text, figure legends, and methods.

This reviewer points out two technical concerns, which we have addressed. The concerns about our CaMPARI imaging are noted, and we have discussed them in response to reviewer #1 and in our public response. We now include movies of the confocal stacks, as requested. There was also a question about FLIC durations of F and L events in Figure 2, with some visually identified F events producing FLIC signals longer than 4 seconds and some L events producing FLIC signals shorter than 4 seconds. Although we show that population averages from the FLIC can reliably recapitulate our visual metrics, there is occasional noise at the individual level. For example, although a fly may have contact of its proboscis with the food for less than 4 seconds, the FLIC signal may persist slightly beyond that interaction due to sustained contact with a non-proboscis body part or due to liquid food contacting the signal pad. We also occasionally observed L events that we visually identified to last longer than 4 seconds, but nevertheless did not produce a FLIC signal of equal length. This can occur when a fly feeds on the liquid food but transiently loses contact with the signal pad. Although there is some noted technical noise, we show that population-level data is sufficient to reflect our visual observations.

Author Response

Reviewer 1:

The reviewer indicated the data convincingly demonstrates absence of Perlecan causes a severe perturbation of the ECM-based neural lamella, that synaptic terminals degenerate, and that axons and even entire nerve bundles break. The reviewer noted that future studies will be important to define the precise source of Perlecan and the underlying mechanism for axonal breakage, and suggested several follow-up experiments. We address these comments below.

1. The reviewer noted our data indicate Perlecan’s role in synaptic retraction is not due to its absence from neurons and that some of the wording is confusing in this regard.

We’ve tried to make it clear throughout the manuscript that Perlecan functions non-cell autonomously, as our failure to rescue with neuronal re-expression or recapitulate the phenotype with neuronal-only RNAi indicates. As such, we agree that the phenotypes are not due to Perlecan loss within neurons, consistent with our data showing breakdown of the neural lamella ECM and subsequent axonal breakage. These phenotypes do manifest in neurons, but the defect is triggered non-cell autonomously as described in our study and stated by the reviewer here.

1. The reviewer suggested future experiments to resolve the source(s) of Perlecan secretion from defined tissues that control neuronal stability, noting that showing ubiquitous rescue with a pan-cellular Gal4 driver would be useful.

We did do pan-cellular rescue and overexpression experiments with the ubiquitous Tubulin-Gal4 driver, but expression of our two UAS-trol transgenes with this strong driver resulted in lethality. This observation indicates too much Perlecan expression is also detrimental for ECM function. Interestingly, we found that NMJ synapses do not retract following ubiquitous Perlecan overexpression in wildtype larvae, so another aspect of ECM dysfunction is responsible for lethality under this condition. As reported in the manuscript, we found driving a Trol RNAi with multiple Gal4 lines expressed in specific cell populations was unable to recapitulate the synaptic retraction phenotype, including pan-neuronal (elavC155), neuronal and muscle (elavC155 and mef2-Gal4), glial (repo-Gal4), fat body (ppl-Gal4, Lsp2-Gal4), hemocytes (Hml-Gal4), and fat body and hemocytes (c564-Gal4) driven expression. These data suggest Perlecan secretion is required by multiple cell types to achieve sufficient accumulation in the ECM to prevent neuronal instability.

1. The reviewer indicates future studies of the blood-brain barrier might reveal insights into the pathology and axonal breakage we observe. The reviewer also suggests we perform a detailed timeline of the axonal breakage timeline.

We agree with the reviewer that examination of the blood-brain barrier and glial dysfunction will be exciting experiments for future studies. For the phenotypic timeline, this was an important component of our study and was done in two ways and described in the manuscript. In Figure 4, we describe serial in vivo imaging of synapses with briefly anesthetized larvae over 4 full days of imaging. In Figure 9, we describe fixed imaging of larval axons at specific developmental stages (2nd, early 3rd, wandering 3rd instar). This set of experiments provided a detailed timeline for synaptic retraction and axonal breakage. As suggested, we also used single neuron drivers (MN1-Ib) to label a single motoneuron and examine axonal breakage and synaptic retraction at this scale. This data is shown in Figure 9E. Together, these experiments provided a timeline for the biology we observe – disruptions of the neural lamella ECM, disorganization of the axonal microtubule cytoskeleton, followed by axonal breakage and fragmentation (usually in a hemi-segment coordinated manner), with subsequent synaptic retraction at NMJs.

1. The reviewer indicates the final model in Figure 10 may not be fully representative.

We feel this model best describes our complete dataset on the Trol mutant. We provide evidence for each of these phenotypic events in detail in the paper. The disruptions to the neural lamella are described in Figure 8. The onset of synaptic retraction does occur in the 3rd instar stage and not the 2nd instar stage – Figure 4 shows this with serial in vivo imaging where we see normal synaptic morphology on Day 1 (2nd instar stage) and degeneration over the 3rd instar period (Days 2-4). The figure does not indicate Perlecan functions for synaptic stability by residing at the NMJ, only that synaptic retraction occurs. Indeed, as stated in the text, we argue against a role for Perlecan function directly at the NMJ for the phenotypes we describe, but rather as a downstream consequence of ECM disruption and following axonal breakage.

Reviewer 2:

The reviewer noted the work provided a strong and thorough genetic analysis of the role of Perlecan in neuronal stability and axonal retraction. The reviewer provided some suggestions for future experiments and requested a few clarifications.

1. The reviewer wondered whether mutations in other neural lamella components also cause synaptic retraction and potential genetic interactions between Trol and Vkg.

We agree further genetic studies of other neural lamella components will be of interest. In the case of Vkg, null mutations in the locus result in embryonic lethality, suggesting it plays a more critical role in overall ECM function. Although we did not perform genetic interaction studies between the two mutants (for example trans-heterozygotes), they have been shown to interact in multiple other contexts as described in the manuscript.

1. The reviewer noted the lack of whole animal Trol rescue.

As described in point #2 above, we did do pan-cellular rescue experiments with the ubiquitous driver Tubulin-Gal4, but driving our two UAS-trol transgenes resulted in lethality, indicating a strong-dosage sensitivity to Perlecan function.

1. The reviewer indicated the hyperactive Mhc mutant was an interesting experiment but only examines one alternative. They wondered if we could reduce muscle contraction and see if that "rescues" the trol phenotype. The Mhc1 null mutant is embryonic lethal, and the retraction phenotypes do not occur until the 3rd instar stage, so that experiment would not be possible. However, we did attempt to block muscle contraction by expressing a UAS-tetanus toxin to eliminate evoked neurotransmitter release with our MN1-Ib Gal4 driver (pan-neuronal expression of tetanus is lethal). This did not alter the synaptic retraction phenotype, but it was difficult to make strong conclusions for this experiment as the co-innervating Is motoneuron was not expressing tetanus toxin. As such, we did not include this data in the manuscript, though it does generally support the model that synaptic retraction is independent of muscle contraction and rather occurs downstream of the axonal breakage that we highlight.

2. The reviewer wondered whether other Wnt signaling manipulations might be useful to test interactions with the Trol retraction phenotype.

Given we used the same Sgg-CA that was used to block the previously reported ghost bouton phenotype in Trol mutants and saw no effect on retraction, we did not feel that was a fruitful pathway to keep pushing on. Indeed, all our evidence point to a non-Wnt role, with neural lamella disruption and axonal breakage being the key insults.

Reviewer 3:

The reviewer indicated the work described an interesting and important role for Perlecan in motor neuron axon maintenance. The reviewer suggested experiments to elucidate the mechanism of action of Perlecan would benefit the study.

1. The reviewer indicated it would be beneficial to validate the Wnt and Wallerian degeneration transgenic lines used in the study to provide a positive control.

Our study used previously published and well-established Sarm RNAi and Sgg-CA transgenic lines (Sarm RNAi from the DiAntonio lab) and Sgg-CA from Kamimura et al., 2013, via BDSC) that have been published multiple times and are well-validated in the field. These were not new lines that we generated. We also blocked Wallerian degeneration with a number of other perturbations to the pathway and did not see rescue of synaptic retraction in these cases either. Sarm is an upstream pathway component and thus the manipulation we included in the manuscript.

1. The reviewer notes similar questions on cell-autonomy that we addressed in point 2 to Reviewers 1 and 2 above.

The reviewer noted it would be helpful to show that the single cell-type RNAi experiments are working by western blotting for Perlecan. We performed a similar approach by examining knockdown of the endogenous Trol-GFP by the RNAi with immunostaining. Pan-cellular knockdown with Tubulin-Gal4 eliminates the staining (validating the RNAi line, Figure 1D-I), while knockdown with the individual drivers does not (Figure 5C-G). Although we used well-established cell-type specific Gal4 drivers that have been used to many other studies, we cannot eliminate strength of expression of the driver as an issue for failure to recapitulate the phenotypes. However, other experiments we performed and presented in the figures supports a non-cell autonomous role for Perlecan in axonal breakage and synaptic retraction.

1. The reviewer suggested a similar approach that Reviewer 2 did above in point 3 about the role of muscle contraction.

We agree eliminating muscle contraction altogether would be a nice assay for the role of mechanical stress, but we don’t have muscle specific drivers to eliminate contraction from only a single muscle (eliminating it everywhere is lethal). However, we did attempt to block muscle contraction by expressing a UAS-tetanus toxin to block evoked neurotransmitter release with our MN1-Ib Gal4 driver as described above. Future experiments with the newly described BoNT-C toxin produced by the Dickman lab might be a promising approach for a full elimination of all motoneuron release to achieve a similar effect and test in the Trol mutant.

1. The reviewer wondered what other components of the ECM are affected beyond Vkg in the Trol mutant.

This is an exciting question to pursue in future studies. Together with genetic interaction experiments with other ECM components, as well as a detailed analysis of the effects on glia that surround larval nerves, such studies will further refine mechanistic actions on how loss of Perlecan triggers axonal breakage and downstream synaptic retraction.

Author Response

Reviewer #1 (Public Review):

In the present study, Yasuko Isoe, Ryohei Nakamura & colleagues follow a lineage analysis study aiming at identifying the clonal organization of the dorsal telencephalon. The authors use the teleost fish medaka to conduct their experiments since it displays a clearly delineated dorsal pallium. After identifying the clonal units that constitute the dorsal telencephalon, they analyze the epigenetic landscape in each unit. The authors identify then differential open chromatin patterns that they relate to functional aspects of each unit, and additionally, use the epigenetic landscape to infer the identity of transcription factors operating as putative regulators. Overall, the study consists of an impressive amount of data that shed light on the organization of a central brain region in vertebrates.

The findings in the manuscript are organized into two main sections: lineage analysis and epigenetic organization. The authors combine genetic tools with laser dissections of specific clones and ATAC-seq and RNA-seq analysis in multiple samples, an approach that is very elegant and follows high technical standards. For lineage analysis, the authors used a basic, but appropriate, tool to induce and follow clones generated in early embryos, with the side note that lineages are followed using a non-ubiquitous promoter so that the authors restrict their analysis to neural progenitors. My overall impression is that the authors have collected a massive amount of high-quality data, which unfortunately is not properly integrated or discussed in the manuscript. There is only a superficial effort in incorporating the two main findings, which contrasts with the depth of acquired data.

The observation of clonal sectors in the pallium is a great finding that deserves a more detailed analysis in terms of their developmental and evolutionary origin: How many progenitors are used to set up the entire pallium? What is the smallest clone that contributes to it? Is there any laterality bias in the clonal architecture?

Thank you for the question. We interpret the first question as, “how many neural progenitors (or neural stem cells) at the early developmental stage contribute to the adult pallium?”. Based on the number of clonal units visualized in the pallium, we assume that there are around 50 neural stem cells at the neurula stage that provide cells in the pallium.

In terms of the smallest clone, we found a dozen of cells in the anterior lateral pallium region (Dla) as the smallest clone. But since the HuC promoter activity is not strong in Dla (shown in Figure 1 – figure supplement 2B), we didn’t observe the clones in a reproducible way, so we removed the clones in Dla from the comprehensive structural analysis. The second smallest clone is the cells in the Dcpm, in which only a few dozens of cells were labeled at once.

And for the last question, we didn’t find any lateral bias in the clonal architecture in the telencephalon (shown in Figure 1- figure supplement 3A, 3C)

We added the explanation above in the revised manuscript. (page 29, line 591 - 595)

Is the clonal architecture exclusive for progenitors or does it extend to neurons as well?

Though we used HuC promoter to visualize the clones which should label the neural progenitors, we observed long axonal projections from Dp to the olfactory bulb, which suggest that this transgenic line labels both neural progenitors and young mature neurons, at least in some brain regions. So yes, we assume this clonal architecture extends to neurons as well, and we added descriptions to the revised manuscript. (page 10, line 205-207)

How has the clonal architecture impacted the morphological diversity of the pallium among teleosts? What are possible evolutionary paths to explain this phenomenon? The authors' discussion on this point circles around one concept, illustrated in the following sentence: " (The clonal architecture) ... possibly explains how the difference in diversity between the pallium and subpallium has emerged: the subpallium is conserved because cells belong to various clonal units intertwined with each other, which has constrained their modification during evolution; whereas the pallium is diverse because of the modular nature of the clonal units which allows for the emergence of diversity". This is the concept that I have the most problems with. The authors' reason that a more defined clonal structure (pallium) makes a system more prone to evolutionary novelties, while a region where clones intermingle (subpallium) is more rigid and therefore more conserved between species. Is there experimental data that backs up this statement in any other systems? If there is, I urge the authors to share these here. If this is just a speculation, then the argument would benefit from an explanation of how this clonal organization allows for evolutionary novelty.

We appreciate the reviewer’s question. In order to make our point, we added the following paragraph to the revised manuscript,

“Our structural analysis in the adult medaka telencephalon revealed that the clonal architecture between the pallium and subpallium differs in the distribution of cells in clonal units: clonal units in the subpallium intertwine with each other, whereas the pallium is formed by the compartmentalized clonal units, giving rise to a modular structure. Modular structure is frequently seen in the animal body, including brain; central complex in insect 40, cerebellum in vertebrates 41. And the modularity of cell populations or organs is generally thought to contribute to evolutionary flexibility; one module can acquire a new phenotype without impacting the others.42, 43, 44 . We assume that the modular nature of the clonal units in the pallium plays a key role in the diversity across teleost.” (page 23, line 448-452)

Would it happen by the appearance of more clones at the early stages of development? The authors leave this central point untouched even when discussing the evolutionary origin of the pallium in teleosts.

Thank you for the comment. As shown in the previous report, when the Cre-loxp recombination was induced at the early developmental stage, a wider expression of GFP is observed across the whole brain (Okuyama et al. 2013). This suggests that the neural stem cells at the earlier developmental stage generate daughter neural stem cells which produce neural progenitors later. We added a few sentences mentioning this in the revised manuscript. (page 7, line 146-149)

Having shown the clonal architecture of the pallium and conducted a detailed epigenetic analysis in clones, the authors could also speculate on what is special about this type of organization. Particularly, how they envision that cells belonging to the same clone inherit a common epigenetic landscape that will define their function later on.

Thank you for the comment. To explain the epigenetic feature of this pallial organization, we added the following paragraph in the revised manuscript.

“As shown in mammals, the epigenetic landscape can be inherited from apical progenitors, which have a multipotency, to the late neural progenitors during development 37. Since the teleost exhibit post-hatch neurogenesis in the entire life, we think that the common epigenetic landscape is inherited in each clonal unit in the adult medaka telencephalon. And as a result, we make the assumption that function and characteristic of each clonal unit is defined already in progenitors by specific regulators (e.g. TFs), and those progenitors continuously produce neurons that possess the same property to function in a coordinated manner.” (page 22, line 433-439)

There is little analysis of the cellular organization of each clone, mainly because the authors labeled only a subset of the real, genetic clone. The authors present images of entire brains and optical horizontal and transverse sections, which largely sustain their claims for a clonal organization. The difference in the clonal arrangements between the Dld and the Vd is clear, but the authors could provide a higher-resolution image of some clones in the telencephalon to get an idea of the cellular composition of the regions they use for their analysis.

Here, we added a new panel in Figure 2 which is a combination of previous supplemental figures S3-1,2,3 to show our analysis on the cellular organization of each clone. We showed how the pallial regions, other than Dld, are formed by multiple genetic clones in different colors, and also the projection from each clone. (page 9, Figure 2B)

What is the extent of non-GFP cells in the regions they use for RNAseq and ATACseq? From the images shown it is very difficult to realize whether all cells in the clonal sector do indeed belong to the clone.

Thank you for your question. In our revised manuscript, we analyzed the ratio of cells labeled in this transgenic line (HuC:loxp-DsRed-loxp-GFP). We found that a large portion of cells (around 60-70% cells) are DsRed positive in our transgenic line (Figure 1 - figure supplement 2B). (page 7, line 142-143)

Reviewer #2 (Public Review):

In this study, Isoe and team produced an atlas of the telencephalon of the adult medaka fish with which they better defined pallial and subpallial regions, characterized the expression of neurotransmitters, and performed clonal analysis to address their organization and maintenance during the continuous neurogenesis. They show that pallial anatomical regions are formed by independent clonal units. Furthermore, the authors demonstrate that pallial compartments exhibit region-specific chromatin landscapes, suggesting that gene expression is differentially regulated. Specifically, synaptic genes have a distinct chromatin landscape and expression in one of the regions of the dorsal pallium, the Dd2. Using the region-specific RNA expression and chromatin accessibility data they have generated; the authors propose several transcription factors as candidate regulators of Dd2 specification. Lastly, the authors use the enrichment of transcription factor binding motifs to establish homology between medaka and human telencephalon, aiming to describe an evolutionary origin for the Dd2 region.

Overall, the study carefully describes diverse aspects of neurogenesis in the telencephalon of the adult medaka fish. As such, the manuscript has the potential to contribute insights to the understanding of circuits and neurogenesis in teleosts and the medaka fish, as well as the evolution of cellular heterogeneity and organization of the telencephalon. Furthermore, the atlas, if easily accessible to the broader community, could be a substantial resource to researchers interested in medaka and teleosts neuroscience. However, there are some conceptual and technical concerns that should be addressed to strengthen this work.

Improving the atlas: The different interpretations of the imaging data generated remain isolated or fragmented and could be better integrated to describe anatomical, connectivity, and ontogeny differences through pallial and subpallial regions.

In the revision process, we described the details of anatomical, and connectivity differences in the adult pallial and subpallial regions in Table 2. This document includes the description of comparing the brain regions with previous atlases.

In terms of the ontogeny differences, we described the neural stem cells localization in the telencephalon in Figure 1 figure supplement 4. “The cell-body distribution in the pallium and subpallium is consistent with the pattern of the neural stem cell (radial glial) (Figure 1 – figure supplement 4). In the teleost telencephalon, the cell bodies of radial glia are located in the surface of the hemispheres and project inside the telencephalon 15. Since neural progenitors migrate along those axons, it is consistent that the cell bodies of the pallial clonally-related units are clustered along those axons in a cylindrical way.”(page 8, line 175-179; page 22, line 427-431))

Molecular differences across regions and species: Differential gene expression and chromatin accessibility throughout regions should be better and more deeply characterized and presented, exhibiting more region-specific features, and leading to a better description of candidate transcription factors that could differentially regulate regional gene expression.

The comparison between medaka fish and human telencephalon regions would benefit from a more extensive molecular analysis. Comparison of gene expression and accessible regions could expand the analysis together with TF-binding motif enrichment.

In order to check the gene expression across brain regions in the different vertebrate species, we examined the mammal gene expression data (in situ hybridization) from the Allen Institute database. We analyzed the expression of all the Dd-specific expressing genes (809 genes) across the mammalian brain regions (12 regions), but we could not observe strong correlations with any specific brain regions in mice. Therefore, we have revised our conclusions regarding the correspondence between medaka's Dd2 and mammalian brain regions to be more cautious. (page 20, line 396 - page 21, line 401)

Lineage tracing: The authors claim that the functional compartmentalization of the pallium relies on different cell lineages, which also mostly share connectivity patterns and, at least to some extent, expression patterns. It would be interesting to see how homogenous these lineages are at the molecular level and whether their compartmentalization is retained when neurons reach maturity.

Thank you for the comment. We think single-cell RNA-seq in cell lineages in the future will allow us to see how homogenous cells that derived from the same lineages are at the molecular level and to assess the cell-type of the cells.

Author Response

Reviewer #2 (Public Review):

The paper by Arribas et al. examines the coding properties of adult-born granule cells in the hippocampus at both single cell and network level. To address this question, the authors combine electrophysiology and modeling. The main findings are:

Noisy stimulus patterns produce unreliable spiking in adult-born granule cells, but more reliable responses in mature granule cells.

Analysis of spike patterns with a spike response model (SRM) demonstrates that adult-born and mature GCs show different coding properties.

Whereas mature GCs are better decoders on the single cell level, heterogeneous networks comprised of both mature and adult-born cells are better encoders at the network level.

Based on these results, the authors conclude that granule cell heterogeneity confers enhanced encoding capabilities to the dentate gyrus network.

Although the manuscript contains interesting ideas and initial data, several major points need to be addressed.

Major points:

1) The authors use and noisy stimulation paradigm to activate granule cells at a relatively high frequency. However, in the intact network in vivo, granule cells fire much more sparsely. Furthermore, granule cells often fire in bursts. How these properties affect the coding properties of granule cells proposed in the present paper remains unclear. At the very least, this point needs to be better discussed.

In vivo whole cell recordings of granule cells are very scarce. In our study, we based the design of our stimulus on recordings from the intact network in vivo (PerniaAndrade and Jonas 2014), which show that granule cells receive a wide range of frequencies, with a power spectrum that exhibits a power law decay. These properties are built in our noisy stimuli. These in vivo recordings have also reported the presence of theta oscillations, showing a peak in the spectrum. However, in our approach we deliberately removed these oscillations from our stimuli because it is best to fit GLMs using white noise or noise with an exponentially decaying autocorrelation (Paninski et al. 2004).

Thus, our choice of the stimuli is far from arbitrary, but rooted on experimental evidence from intact network in vivo recordings, together with previous knowledge about GLM/SRM fitting. This comment reveals to us that we did not clarify this enough in the manuscript. We are grateful to the reviewer for revealing this omission, since this is in fact an important aspect of the study strategy. In the revised manuscript, we brought these points up front in the results section when we introduce the stimulus for the first time, and more thoroughly discussed it in the Methods section that describes the stimulus.

Still, the bursts observed in granule cells are an important feature and they have been observed to be phase locked to the theta-gamma oscillations in vivo (Pernia-Andrade and Jonas 2014). In the revised version of the manuscript we included new experiments and simulations with stimuli that include a peak in theta frequency. We found that immature neurons also improve decoding performance with these theta modulated stimuli.

2) The authors induce spiking in granule cells by injection of current waveforms. However, in the intact network, neurons are activated by synaptic conductances. As current and conductance have been shown to affect spike output differently, controls with conductance stimuli need to be provided. Dynamic clamp is not a miracle anymore these days.

The use of dynamic clamp sounds in principle like a good suggestion. However, in the manuscript we have taken a different approach to enable the use of a single neuron GLM that uses currents as inputs. To control for the differences between mature and immature neurons we used currents with amplitude normalized by the input resistance, and both types of neurons were measured with the same technique to allow for the comparison.

Importantly, the GLM type model that we use assumes that the membrane potential is a linear convolution of the input, which permits a straightforward and robust fitting approach. We argue that this is not a minor issue, since using dynamic clamp would require a drastic modification of the model. Furthermore, the use of conductance stimuli would not allow for the straightforward model fitting we perform with our approach. The key point here is that the membrane potential would not be correctly approximated as a linear function of the conductance stimulus, precluding the fitting strategy.

Finally, at the moment we do not have the equipment to perform the suggested experiment, so this suggestion would require a big amount of time to acquire the equipment and set up the experiments in mature and immature neurons. In addition, we would have to change the model and develop a different fitting strategy. With the controls that we already have in the manuscript, we do not think dynamic clamp experiments would fundamentally change the conclusions of the manuscript. Thus, we argue that this is beyond a reasonable timeframe for this revision, but could be something to further explore in future. We now mention this possibility in the discussion.

3) The greedy procedure is a good idea, but there are several issues with its implementation. First, it is unclear how the results depend on the starting value. What we end up with the same mixed network if we would start with adult-born cells? Second, the size of the greedy network is very small. It is unclear whether the main conclusion holds in larger networks, up to the level of biological network size (1 million). Finally, the fraction of adult-born granule cells in the optimal network comes out very large. This is different from the biological network, where clearly four or five-week-old granule cells cannot represent the majority. Much more work is needed to address these issues.

The reviewer approves the greedy procedure that we apply in our manuscript and poses three issues for consideration.

First, the reviewer queries what would be the result of starting the procedure with a different pool of simulated neurons, and whether we would obtain “the same mixed network if we would start with adult-born cells”. Let us remark that the outcome of the greedy procedure is not always the same mixed population of neurons. For each different mature neuron that we use to start the procedure, the trajectory (see Fig. 4A) of selected neurons will be different. Thus, the final population (network) will be different, and this is reflected in the error bars that we obtain in Fig. 4. Presumably, starting with adult-born cells will change the outcome of the greedy procedure. However, note that this is not the point of the approach. The motivation to start with mature neurons is to ask whether adult-born cells can contribute something to decoding, given that mature cells on their own perform better.

Second, the reviewer questions the size of the population that we reach with the greedy procedure. Note that for the population sizes that we show in the manuscript the decoding performance already begins to saturate, Fig. 4F-H. Furthermore, it is unfeasible to construct a 1M neurons population due to the computational cost –the time it takes to run the algorithm. These two facts motivated us to stop at 12 neurons as it strikes a good balance between computational time and saturation. Importantly, as we expand below, the aim of the greedy procedure simulation is not reconstructing the actual network of the dentate gyrus. Rather, we seek to understand whether immature neurons could improve coding in a population.

Third, the reviewer observes that the fraction of adult born cells in the reconstructed populations using the greedy procedure are large as compared to the biological network. Again, here note that the aim of the whole in-silico experiment is not to recover the biological network, where other aspects are at play. More simply, we query the possible contribution of adult born cells to coding. In fact, if we obtained the same proportion it would be by chance, since we do not think that adult-born cells in the dentate gyrus are chosen according to the greedy algorithm.

Still, this comment from the reviewer motivated us to include further simulations of the greedy procedure with constraints. In the revised manuscript we show new results using the greedy procedure, but constraining the fraction of immature neurons in the resulting populations, see Figure 4-figure supplement 2.

More generally, we think that these comments reveal a possible misunderstanding about the approach, its purpose and the interpretation of the results. The point of the greedy procedure is to show that immature neurons do in fact contribute to improve the decoding, despite being generally worse individually. We do not claim that the population obtained with the greedy procedure faithfully reflects the actual shape of the in vivo network. We are aware that it does not. We see that this may have not been clear in the original version. In the revised version, we now explain the purpose of the greedy procedure when we introduced it. Additionally, we comment on the proportion of immature neurons in the same paragraph.

4) Likewise, the idea of dynamic pattern separation seems quite nice. However, the authors focus on the differences between mixed and pure networks, which are extremely small. Furthermore, the correlation coefficients of "low", "medium", and "high" correlation groups are chosen completely arbitrarily. A correlation coefficient of 0.99, considered low here, would seem extremely high in other contexts. Whether dynamic pattern separation is possible over a wider range of input correlation coefficients is unclear (see O'Reilly and McClelland, 1995, Hippocampus, for a possible relationship). Finally, aren't code expansion and lateral inhibition the key mechanisms underlying pattern separation? None of these potential mechanisms are incorporated here.

The reviewer positively appreciates the idea of the pattern separation task that we propose in the manuscript, and poses some questions concerning the extent of the contribution of adult-born neurons.

We agree that code expansion and lateral inhibition are key mechanisms for pattern separation in the DG, and we do not claim that adult-born neurogenesis is the key mechanism behind pattern separation. Rather, in our work we explore the role of adultborn immature neurons in coding in general, and in pattern separation in particular, given that it’s a commonly attributed function to the DG.

We note that the correlation in O'Reilly and McClelland 1994 (actually, what they call pattern overlap) is of a very different nature than the one we compute in our work. They compute the overlap between different patterns of activation in a population of neurons, that is the probability that a single neuron is active in two different patterns of activation. In our manuscript we compute the correlation between different continuous time-varying stimuli that stimulate single neurons.

Importantly, previous work has shown that ablating neurogenesis particularly affects fine spatial discrimination, that is when the separation between patterns is small, but not when it is large (Clelland 2009, Science). Hence, we were actually expecting the impact of adult-born neurons to be important only for relatively large correlation coefficient values.

In the revised manuscript, we now explain the rationale for the choice of correlation values, both in the main text when we introduce the task, and in the Methods when we set the values for the low, medium and high correlation classes. We also added a sentence to the discussion on pattern separation, bringing in the importance of the ideas of lateral inhibition, code expansion, and the work of O’Reilly 1994.

5) A main conclusion of the paper is that while mature GCs are better decoders on the single cell level, heterogeneity in mixtures improves coding in neuronal networks. However, this seems to be true only for r^2 as a readout criterion (Fig. 4F). For information, the result is less clear (Fig. 4G). The results must be discussed in a more objective way. Furthermore, intuitive explanations for this paradoxical observation are not provided. Saying that "this is an interesting open question for future work" is not enough.

This is an interesting point raised by the Reviewer. While r^2 is quantified by comparing the decoded stimuli with the true stimuli, mutual information is related to the uncertainty about the decoding. That is, it quantifies the correspondence between decoded and true stimuli, but does not tell us whether it is a good approximation to it. For example, a decoder could achieve perfect mutual information but result in a poor reconstruction by performing a perfectly scrambled one-to-one mapping of the true stimulus [Schneidman et al. 2003], see also our reply to point [5] by Reviewer #1 above.

We agree that this is an important point and we realize that it was not clear in the original version of the manuscript. In the revised manuscript we added some sentences to clarify this point.

6) The authors ignore possible differences in the output of mature and adult-born granule cells in their thinking. If mature and adult-born granule cells had different outputs, this could affect their contributions to the code (either positively or negatively). At the very least, this possibility should be discussed.

Newborn neurons contact the same targets as mature neurons, born during development: pyramidal cells in CA3, and interneurons in CA3 and the DG. During the maturation, there is a sequence of connectivity with CA3 and within the DG (Toni et a. 2008). At 4 weeks, newborn cells are already contacting their postsynaptic targets. Still, there may be subtle differences in the strength of these connections compared to mature neurons.

So, although the targets are the same, there may be quantitative differences in the way they contribute to the code. Thus the point raised by the reviewer is interesting, so we decided to discuss it further in the revision.

Author Response

Reviewer #1 (Public Review):

This manuscript by Toshima et al. describes a study of the organization of traffic in the endomembrane system of the budding yeast Saccharomyces cerevisiae. The authors address the relation between endocytosis and the Golgi (TGN: a collection of maturing membrane elements derived from the trans-Golgi). The study builds on a previous article by the group of Benjamin Glick. In that study (Day et al., 2018), it was postulated that the TGN is the first destination for yeast endocytic traffic after internalization from the plasma membrane. Additionally, Day et al. had shown that endocytic recycling traffic towards the plasma membrane departs from the TGN as well. Therefore, early endosome and recycling endosome compartments would be identical to the TGN or contained within it. Here, Toshima et al. use super-resolution confocal live imaging microscopy (SCLIM) to refine a model of endocytic pathway organization. This powerful imaging technology allows them to show that out of two partially overlapping TGN markers, namely Tlg2 and Sec7, the syntaxin Tlg2 correlates better with the arrival of fluorescently labeled endocytic cargo than alternative TGN marker Sec7. Building on this main finding, the authors conclude that a specific part of the TGN (an "independent sub-compartment") functions as the early endosome. Further experiments in mutants for GGA clathrin adaptors, required for departure of endocytic cargo from the TGN to the Rab5-positive prevacuolar endosome, show again that endocytosed cargo accumulation correlates better with Tlg2 than with Sec7. Furthermore, in GGA mutants the overlap between Tlg2 and Sec7 is decreased, suggesting that GGA is required for maturation of this Tlg2 sub-compartment.

The study is well conducted and its main conclusion that a Tlg2 subregion within the TGN functions as the early endosome seems well supported by the superb live imaging and the analysis of GGA mutants.

Although a technical feat in live superresolution imaging, this single kind of data (moving, shape-shifting blobs of fluorescently-labeled proteins) does not totally fill with meaning the terms "compartment", "sub-compartment", or "independent sub-compartment". This, I think, is the main limitation of the study. Are these compartments or sub-compartments individuated membrane elements, collections of vesicles, regions of the same cisterna or saccule? For this, electron microscopy would be needed.

We are very grateful for the reviewer’s favorable evaluation of our study. In accordance with the editors’ judgment in "Essential Revision", we have not performed electron microscopy analysis for this revision. However, we have addressed all other valuable comments.

Reviewer #2 (Public Review):

In this manuscript Toshima et al document the use of sophisticated microscopy - with powerful spatial and time resolution - to image markers of the yeast endosomal system.

The initial work documented in this paper does a good job of defining the compartment endocytic cargoes internalise to. This is convincingly shown to be a compartment that is not marked by Sec7 but is instead a distinct (sub)compartment marked by the SNARE protein Tlg2. This agrees with many previous studies, (including biochemical experiments and microscopy of cargoes in a series of membrane trafficking mutants) but has different conclusions to another study (Day et al 2018 - Developmental Cell). Although the microscopy techniques used in the two studies are different, the yeast system and many of the reporters (FP tagged Tlg1, Sec7, Vps21 and fluorescently labelled mating factor) are the same. The Day et al study is suitably referenced throughout the manuscript but as to why the authors have come to fundamentally different answers about endocytic cargoes internalising to a Sec7+ compartment, is not discussed.

According to the reviewer's suggestion, we have added a paragraph discussing about this (line 533-539).

The work goes on to show endocytic carriers (marked by Abp1) and endocytic cargoes like fluorescently labelled mating factor internalise to the Tlg2+ compartment. The forward trafficking of these molecules is then observed to transit to a later endosome compartment labelled by Vps21. The super-resolution and time lapse imaging, sometimes even using 3 colours - is of very high quality and fully support the model presented at the end of the paper for this trafficking itinerary. Trafficking mutants are also used (such as a defective allele of arp3 and deletion of VPS21 / YPT52 GTPases) to interrupt trafficking routes and define the pathways followed by endocytosed mating factor.

The endocytic trafficking from Tlg2+ to Vps21 compartments is shown to be defective in mutants lacking GGA adaptors (gga1∆ gga2∆), with cargoes accumulating in the Tlg2+ compartment and other clathrin adaptor mutants not causing this defect. This research avenue also reveals that the GGA proteins are required to maintain the distinct Tlg2 sub compartment.

The final section of the paper uses the same tools to analyse the localisation of the recycling v-SNARE protein Snc1. This is arguably the most important set of experiments in the paper, not only is Snc1 a putative v-SNARE that functionally interacts with Tlg2, but this cargo, unlike pheromone, allows the investigation of recycling back to the PM from TGN/endosomes. However, the authors do not comment on the fact that Snc1 does not localise to the plasma membrane in either experiments using different microscopy techniques (Figure 5A + 5B), calling into question whether the recycling pathway is operating properly or that the FP-tagged machinery has disrupted processing? The steady state localisation of Snc1 in WT cells only looks normal in Supplemental figure, this discrepancy should be discussed or addressed.

As the reviewer points out, fluorescent protein-tagged Snc1p usually localizes to the plasma membrane in addition to cytosolic puncta, as shown in Fig. 6–figure supplement 1A. In Fig. 6A, localization of GFP-Snc1p is demonstrated by focusing on the cell surface using a TIRF microscope, which differs from that focusing on the medial focal plane. Therefore, Fig. 6A shows that GFP-Snc1p localizes to the plasma membrane, albeit with evident punctate localization.

Localization of mCherry-Snc1p to the plasma membrane was also observed in the images obtained by SCLIM. However, since the intracellular signals of mCherry-Snc1p are partially blocked by those around the plasma membrane, in Fig. 6B the intracellular localization has been emphasized by modulating the contrast, thereby reducing the fluorescence signals at the plasma membrane. In the new manuscript, we have added an image with only slight contrast (Fig. 6–figure supplement 1C) in the same cell as that shown in Fig. 6B.

Reviewer #3 (Public Review):

The manuscript by JY Toshima et al. is an excellent and important study that demonstrates very clearly the existence of an endosomal compartment in yeast, distinct from the trans-Golgi network, to which endocytic vesicles fuse upon internalization. They show that this compartment is enriched in the SNARE protein Tlg2, a yeast homologue of syntaxin, and is segregated from the Golgi-localized Sec7-containing compartment, indicating that the organization of the endocytic system in yeast is similar to that of animal cells. Furthermore, they demonstrate the trafficking machinery required for maturation of this compartment, and that it is also a station on the pathway back to the plasma membrane. Because there have been conflicting reports in the literature as to the existence of an endosomal compartment in yeast distinct from the trans-Golgi network, this paper is of great importance for the cell biology community.

Major strengths of this study are the cutting-edge imaging technology used, and the careful, quantitative analyses carried out. The authors use a super-resolution live cell imaging approach that allows them to discriminate to a high resolution different compartments and membrane domains of highly dynamic yeast organelles, and to follow an internalizing cargo over time. With their manuscript, they have provided a full set of movies, along with quantifications, to support their conclusions.

The authors use fluorescent-protein-labelled endocytic cargo (alpha-factor) and florescent-protein-labelled compartment markers, assaying them in high resolution and super-resolution live cell imaging microscopy systems. In this way, they are able to follow trafficking of cargo through compartments in real time. The authors first demonstrate that the alpha-factor cargo substantially colocalized with the SNARE protein Tlg2, a marker of early endosomes, but very little with Sec7. They also show that Tlg2 marks a sub-compartment distinct from the Sec7 compartment, but adjacent to it. Furthermore, they demonstrate using super-resolution microscopy and triple color 4D imaging that endocytosed alpha-factor cargo structures make contact with the Tlg2 compartment, adjacent to the Sec7 compartment, then disappear, supporting the conclusion that endocytic vesicles first fuse with the Tlg2 compartment. Next the authors show that alpha factor is transported from the Tlg2 compartment to the Vps21 compartment, a process that requires the GGA adaptors Gga1 and Gga2. Finally, the authors show that recycling of the endocytic R-SNARE Snc1 also occurs by passage through the Tlg2 compartment.

The use of mutants that affect different stages of endosomal trafficking is a strength of the manuscript, as it allows elucidation of the mechanism of transport through successive compartments. Importantly, using a gga1-delta gga2-delta mutant, the authors demonstrate convincingly that the GGA adaptors Gga1 and Gga2 are required for alpha factor transport from the Tlg2 compartment to the Vps21 compartment.

Throughout this study, the authors use fluorescent protein-labelled cargo and compartment markers (EGFP, mCherry, iRFP), but don't explicitly state to what extent these fusion proteins are functional compared to the endogenous proteins. They could cite previous publications or their results describing the functionality of the fusion proteins used.

According to the reviewer's suggestion, we have cited previous publications for GFP-Tlg2 (Seron et al., MBoC, 1998), Sec7-GFP/-mCherry (Seron et al, MBoC, 1998; Llinares et al., Sci Rep, 2015), Abp1-mCherry (Kaksonen et al., Cell, 2003; Picco et al., eLife, 2015), GFP-Vps21 (Toshima et al., Nat. Comm, 2016), Gga2-mCherry (Daboussi et al., NCB, 2012), GFP-Snc1p (Lewis et al., MBoC, 2000), and GFP-Ypt31 (Kim et al., Dev Cell, 2016). We have also added data showing the functionality of Abp1-mCherry (Fig. 2–figure supplement 1A), Sec7-iRFP (Fig. 1–figure supplement 1F), Gga2-mCherry (Fig. 5–figure supplement 2G), and GFP-Ypt31p (Fig. 7–figure supplement 1A) in the new manuscript.

Author Response

Reviewer #1 (Public Review):

Luu et al. have developed a genome-edited African elite rice variety, Komboka. The work was initiated in response to the outbreak in Eastern Africa by Xanthomonas oryzae strains that are phylogenetically related to Asian strains and carry TALes, similar to strains from China, possessing an expanded repertoire of TALes compared to those in endemic strains. As these emerging strains contain TALe targeting SWEET11a, as well as that suppressing Xa1, pthXo1, and iTALes, the authors have generated edited lines targeting promoter regions of SWEET11a, 13 and 14 in African elite rice variety, Komboka. The same team has previously generated genome-edited lines targeting the promoter regions of SWEET11a, 13, and 14 in varieties Kitaake, IR64, and Ciherang-Sub1. Bacterial blight outbreaks and emerging pathogen lineages remain to be a threat to rice production. Thus, efforts in targeting pathogen weaknesses to generate genome-edited varieties possessing broad-spectrum resistance are required. The survey, collection of isolates, and strain characterization studies on >800 strains are commendable. This study has taken advantage of this ongoing collection to stay ahead in the arms race to deploy broad-spectrum resistance in an elite rice variety using TALe targets.

Overall conclusions presented here are supported to some extent; however, I have listed some of my comments and concerns below.

1) Data in supplementary table 2 suggests that Komboka is still a moderately resistant variety under field conditions in Africa, with a disease severity scale of 2 i.e. 4-6% disease severity, compared to other varieties having a disease severity scale of 5. Thus, I am not convinced that emerging strains are of concern on the Komboka variety under field conditions, thus, question the justification of Komboka being a choice for editing to tackle emerging strains.

We apologize, because the Table 2 is admittedly hard to read with the geo data. We have thus added a new figure 1 with maps. Please note that the data in this Table are from 2022. If you look at for example the Morogoro region (Dakawa and Lunkege), it appears that also there, the initial scale (number of plants infected) was low and became more severe in the subsequent years as one might expect. We thus hypothesize, that in the upcoming analyses, the scale will also become much higher, thus this snapshot cannot serve as a measure of general susceptibility. As we noted in the response to the Editor, the Kaufmann clipping assays are widely used by breeders to evaluate resistance in greenhouse conditions, and since the assays uses severe wounding and extremely high bacterial inocula, this assay is a reliable measure of susceptibility. Note also, that Komboka was chosen before the outbreak was characterized. Our data show that Komboka is highly susceptible to Asian strains, as well as to the introduced strains. Note also that we characterized the R gene outfit as far as feasible, an found two R genes that can explain the resistance to the endemic African strains. Note that single, double and triple R gene mutant combinations have been broken in India, thus we deemed it necessary to create a rational approach that prevents SWEET gene recruitment to generate broad spectrum resistance. xa13 has likely only been broken by circumventing SWEET11a (by using SWEET13 or 14), but still stands up in quintuple breeding combinations in India. Thus, we expect that our lines will be rather robust, which will have to be tested in future field trials in Kenya where this variety is highly cultivated. We added text to Results, Discussion sections and a new section on sampling in Methods with respective references that show the correlation of data from assays with the same strains in greenhouse and field.

2) Is Xa4 from Komboka related to Xa4_Teqing? The breakdown of Xa4T was due to the mutant allele of avrXa4 in virulent Xoo CR6. But this breakdown was accompanied by a fitness penalty and residual QTL had a significant residual effect on virulent strains. Would this be why Komboka carrying Xa1 (Xa45(t) and Xa4 under field conditions still showed moderate resistance? But Xoo strains showed susceptibility in leaf clipping assays.

We apologize, this was a typo that has been corrected. Komboka is a high yielding variety, we thus cannot comment on any yield penalty here, it is superior and widely accepted now in Kenya. And we responded regarding on the moderate resistance in the previous paragraph. Komboka is fully susceptible to the Asian strains that induce SWEET11a.

3) I felt a bit of a disconnect in sections on phenotypic assays confirming the virulence profile of strains on Komboka and then understanding mechanisms underlying virulence since the same strains used in path data were not the ones mentioned in WGS and TALe analysis, leaving the readers with the only one strain to support the hypothesis of the basis for higher disease severity on Komboka due to new TALes, pthXo1, and iTALe. Do authors have pathogenicity data for African strains T19, Dak16, and Xoo3-1 that grouped with endemic African strains on Komboka? Authors present data on CIX4457, 4458, and 4462 being virulent on Komboka, and show that they cluster with Asian strains. However, in the tree, 4462 is the only one shown to be closely related to Chinese strains. Where are 4457 and 4458 placed? Do 4457 and 4458 also contain pthXo1 and iTALe? Authors could also provide path data for 4506/4509 that they included in TALe figure and in the phylogenetic tree.

We had initiated WGS of 8 strains (3 from Dakawa and 5 from Lukenge), but at the time of submission, not all genomes were fully polished. Although not all are in a publishable state by now, we were able to determine the similarity as well as presence of pthXo1 and iTALes. The number of SNPs among the 8 strains is extremely low (between 1 and 4), strongly intimating that they are siblings. They are so similar, that we can at present not trace the origin. All eight strains isolated in Dakawa in 2019 and in Lukenge in 2021 contain iTALes and the PthXo1B variant. With near certainty that they are all derived from a single introduction event. We fully understand your comment. We apologize, since we should not have used the CIX nomenclature, which was introduced to obtain a more reliable code for the strains. We have introduced a clearer nomenclature while keeping the code for the database. We added a new Figure 2-supplement 1 which shows that Komboka is susceptible not only to the three strains isolated in Dakawa in 2019, but also to one of the strains isolated from Lukenge in 2021. We replaced Fig. 3 with a new phylogenetic tree including the eight strains and provide more detailed information on the relation of those strains. In principle it would be sufficient to use a single isolate in this case. We now provide, as far as possible the new data (analysis is ongoing) as well as new data for some strains collected in 2022 and conclude that also the strains identified in 2022 are derivatives from an initial introduction in the Morogoro region. It is also clear from Fig. 2 and supplement that Komboka is fully susceptible to the strains isolated from Dakawa and Lukenge, as susceptible as to the Philippine reference strain PXO99A, which also uses PthXo1.

4) The authors present pathogenicity data on EBE-edited T0, T1, and T2 lines of Komboka which are promising against the Tanzanian strains carrying new TALes. The cas9/cpf1 system developed here to target multiple EBEs will be a valuable contribution to the scientific community. What are the agronomic characteristics of these edited lines? As the edited lines have not been tested against a diversity panel of Asian and African strains, I would be skeptical of the choice of the term "broad-spectrum" yet.

Virulence of Xoo depends critically on the recruitment of at least one of the three SWEETs (11a, 13 or 14). Single R genes, such as xa13 can be overcome by using SWEET13 or 14. All strains that are virulent carry at least one TALe that targets a SWEET. Thus, by blocking all known EBEs, we obtain broad spectrum resistance. We have not observed a single case yet where this is not working. Note that in the case of EBE edited Kitaake, we tested about 100 different strains from a world-wide collection, for IR64 and Ciherang-Sub 1 also many representative strains, and we now show data for Komboka and additional varieties. Thus, based on the current knowledge, including the information gained from Xoo genome sequences that have been published, e.g., recently from India, there is at present no strain known that can overcome this resistance.

Regardless of my comment earlier on Komboka being moderately resistant under field conditions and thus a questionable choice for EBE-editing here, the genome-edited lines in any variety imparting resistance to bacterial blight remain to be a valuable contribution to managing disease outbreaks.

We commented on the interpretation of moderate resistance above, but appreciate the comment that these lines will be valuable.

5) As this manuscript utilizes the diversity of African strains to generate edited lines, it would be good to make diagnostics and path data for 833 strains available to the scientific community (instead of select strains as indicated in the supplementary table), especially for the fact stated here in the manuscript about scarce data on Xoo in Africa and the goal of systematic comparison of the pathogen population.

We are currently preparing a manuscript that will include an extensive analysis of these strains, and focus on the diversity of African Xoo strains, i.e., MLVA-based diversity of the collection. This manuscript, which is in preparation, will include the requested data.

Reviewer #2 (Public Review):

This study describes the emergence of virulent strains of the rice bacterial blight pathogen Xanthomonas oryze pv. oryzae in the Morogoro rice-growing region in Tanzania. The aims of the study were to describe the virulence features of the emerging population, as compared to previous bacterial blight outbreaks in Africa, and generate an elite rice variety that is resistant to both pathogen populations. To achieve these aims, the authors characterized the genetic basis of the virulence of these new strains by sequencing the genomes of three representative strains and phenotyping using excellent genetic resources for identifying the susceptibility gene targets of this pathogen in rice. They then used two rounds of hybrid CRISPR-Cas9/Cpf1 to successfully edit six targets of the pathogen in an East African rice variety, which conferred resistance to all strains tested.

The strengths of this paper are the systematic analysis of the virulence of emerging pathogen strains relative to strains from previous outbreaks and the successful creation of edited lines that will form the basis for continued efforts to gain regulatory approval for the introduction of resistant rice in East Africa. The creation of the edited line is a substantial and important contribution, indeed, the authors include strains collected in 2021 and include disease severity data from 2022 in the supplementary data, illustrating the urgent need for solutions.

The weaknesses of the study are largely related to the quick turnaround between data collection and manuscript submission.

1) Different strains are used for different experimental work and sequence analysis, making relationships between different parts of the work unclear and also more challenging for the reader to follow because of changing strain designations. CIX4457, CIX4458, and CIX4462 were virulent on rice near-isogenic-lines, CIX4457 and CIX4505 were used for identifying SWEET targets and phenotyping edited lines, while whole genome sequencing was conducted with CIX4462, CIX4506, CIX4509.

We added new information which demonstrates that the strains isolated in 2019 in Dakawa and the strains from Lukenge (2021) are very closely related and differ only by a 1 to 4 core genome SNPs (see new supp Fig. 3A). We added a new Figure2-supplementary Figure 1 and expanded Table 1 to show that the strains from Lukenge and Dakawa behave in a similar manner. We are aware of the differences in the figures but hopefully have now addressed them in an acceptable manner, we did not want to combine data from different experiments. The differences in strain use are due to i) the different timing of strains sampling and isolation (those from 2019 were isolated first and the long and tedious work of leaf-clipping the whole set of NILs with all the diversity strain panel did therefore not include Tanzanian strains from 2021 that were isolated much later also due to long delay in having the infected leaf material sent out; including them in the NILs testing would have taken us another year given the volume of this experiment), and ii) the variable quality of whole genome sequencing of the strains. Overall, we have sequenced the genome of 8 newly introduced strains including 3 from Dakawa_2019 and 5 from Lukenge_2021 (see new suppl. Table 3 that gives a detailed overview of the genomic analysis of these strains). The best genome sequences were obtained for strains CIX4462, CIX4506 and CIX4509 (renamed in the revised version of this MS and for sake of clarity as iTzDak19-3, iTzLuk21-1 and iTzLuk21-2) of which a circularized chromosome could be generated. Unfortunately, these were not the strains that we had selected for SWEET characterization and phenotyping of edited lines, whereby one representative strain of each collection had been randomly picked, namely CIX4457 and CIX4505 (now iTzDak19-1 and iTzLuk21-3, respectively). To reconcile these two sets of data and show that strains from Dakawa and Lukenge are actually extremely similar, we have performed a SNP-based phylogenetic analysis of the 8 strains demonstrating that they all cluster as one homogeneous genetic lineage, in line with a scenario whereby all these strains result of a single introduction event from Asia. Careful analysis of these additional genomes also confirmed the presence of a pthXo1like allele (pthXo1B) and iTALes in all Tanzanian strains introduced from Asia. One exception is strain iTzLuk21-3 (CIX4505) where the poor quality of the pthXo1B sequence with potential frameshifts prevented any confirmatory analysis. Taken together, these data support the hypothesis that all new isolates, irrespectively of the year of sampling, are genetically very close and share the same virulence characteristics.

2) Disease survey results from 2022 are listed in Supplementary Table 2, but it is challenging for the reader to summarize across many lines of data, which appear to represent individual samples.

We agree that this was not the best way to show the data. In addition to the new suppl. Tables 1 and 3 we have now generated a new Figure 1 which contains maps of the disease distribution and severity across Tanzania in the different years as well as photos from the fields in Dakawa from 2019 and Lukenge in 2021 that highlight the massive infections.

3) The focus of the editing is Komboka but bacterial blight in 2022 was mostly on other varieties. It would be helpful to have more context on this variety and what has prevented adoption by the growers in the Morogoro region to date.

The variety was chosen several years ago after extensive consultations with breeders from IRRI, IRRI Africa, and India, since it is high yielding, and was specifically generated for Kenya where it has a high level of adoption. Tanzania has apparently not yet adopted this variety as you can see from Table 2. Also, Tanzania does NOT have any regulations for genome edited crops and we can thus NOT provide the lines to Tanzania. By contrast, Kenya has established a regulatory framework by which the local government authorities can import transgene-free edited lines. We are currently segregating the transgenes out and have established a through set of measures to validate whether the lines still contain transgenes (including vector backbone and T-DNA remnants). Tanzania will have to establish suitable guidelines. We would like to note that establishing protocols for different elite varieties is challenging and time consuming and we had early on, in 2019, decided to initiate work on transformation protocols for this variety. If Tanzania also adopt regulations, it would be possible to provide the lines to Tanzania as well, and possibly by then Tanzania has a higher level of adoption of Komboka. If you look at the maps we show, it is very likely that the disease will spread to all neighboring countries, including Kenya. Thus, our lines may become one possible measure to try to address the outbreak.

Reviewer #3 (Public Review):

One key finding of this work is the identification of Xanthomonas oryzae pv. oryzae (Xoo) strains in Africa, based on their genomes sequence and their TALE repertoires, have high similarity with Asian strains. Asian Xoo strains typically overcome NLR-mediated recognition of TALEs in rice by so-called iTALEs. Moreover, some Asian strains contain a TALE resembling PthXo1, a TALE protein that was shown to overcome xa5 resistance.

The authors now found that some of the newly identified African strains have iTALEs and PthXo1-like TALEs. Such newly evolved African strains were found to be fully virulent on the African rice elite variety Komboka, which is resistant to a broad panel of African Xoo strains.

Previous studies have shown that TALEs bind to effector binding elements (EBEs) present in promoters of rice SWEET genes to promote disease. Work from the lab of the authors and other labs has shown that TALEs can no longer promote the disease if matching EBEs are changed or deleted by CRISPR or TALEN-mediated mutagenesis. In fact, pioneering work by Bing Yang, one of the authors of this article published about ten years ago a Nature Biotechnology article where he showed that rice plants with mutated EBEs are resistant to Xoo. Recently, a combined effort of the Yang and Frommer labs resulted in two further Nature Biotechnology publications (2019), in which they described along with other useful tools rice lines where multiple EBEs were mutagenized in parallel and that provide broad spectrum resistance.

The work under review describes now CRISPR mutagenesis of an African elite cultivar resulting in a line that mediates resistance to Asian and newly evolved African strains.

Overall, the work is technically sound. Yet, the approach that has been described - mutagenesis of multiple EBEs - has been used before and is a routine procedure for labs that are focused on such undertakings. While such approaches do not provide new insights for fundamental research, they nevertheless are certainly important and useful in translational research, as demonstrated here.

We thank reviewer for the comments. If we may, we would like to add aspects of novelty. We detected an outbreak that is spreading. We determined the disease mechanism, and we used CPF1 to obtain ‘optimal’ mutations at all sites (massive improvement over 2019 publication, which used Cas9) and we try to provide a solution for the outbreak when it spreads to Kenya, or when Tanzania and neighboring Countries adopt similar guidelines. This seems highly urgent das Reviewer 2 points out.

Author Response

Reviewer #1 (Public Review):

This study used intersectional genetic approaches to stimulate a specific brainstem region while recording swallow/laryngeal motor responses. These results, coupled with histology, demonstrate that the PiCo region of the IRt mediates swallow/laryngeal behaviors, and their coordination with breathing. The data were gathered using solid methods and difficult electrophysiological techniques. This study and its findings are interesting and relevant. The analysis (and/or the presentation of the analysis) is incomplete, as there are analyses that need to be added to the manuscript. The interpretation of the data is mostly valid, but there are claims that are too speculative and are not well-supported by the results. The introduction and discussion would benefit from more citations and a deeper exploration of how this study relates to other work - especially a thorough accounting of and comparison to other studies concerning putative swallow gates.

General/major concerns:

The field of respiratory control is far from unified regarding the role of PiCo in breathing or any other laryngeal behaviors. If anything, the current consensus does not support the triple-oscillator hypothesis (in which PiCo is one of 3 essential respiratory oscillators). The name "PiCo", short for "post-inspiratory complex", suggests a function that has not been well-supported by data - it is a putative post-inspiratory complex, at best. I suggest putting this area in context with other discussions i.e. IRt (such as in Toor et al., 2019) or Dhingra et al. 2020 showed broad activation of many brainstem sites at the post-I period (including pons, BotC, NTS)

The reviewer’s comment refers to our previous publication and not the present one. With all due respect to the reviewer, the submitted study investigates PiCo’s involvement in swallow and laryngeal activation and its coordination with breathing.

We did not feel that it is appropriate for us to critique the Dhingra paper in the present study. However, since this seems to be important to this reviewer, we would like to clarify: Because of filter characteristics, and the low temporal and spatial resolution of these field recordings, the approach used by Dhingra is inappropriate for providing insights into the presence or absence of PiCo. We therefore developed an alternative approach, which provides more detailed insights into population activity, the Neuropixel approach. This Neuropixel recording from PiCo (black trace) exemplifies how field recordings (yellow) fail to pick up post-I activity. We could provide many more examples, but as stated above, addressing the study by Dhingra is tangential to the present study.

We would also emphasize that the study by Dhingra was never designed to provide negative evidence, and Dhingra et al. never claimed that their study demonstrates the absence of PiCo. Unfortunately, the data by Dhingra were misinterpreted by Swen Hülsmann in his Journal of Physiology editorial which created considerable confusion, but also sensation in the field. Objectively, Toor et al reproduced the Anderson study in rats as we will elaborate below. Unfortunately, Toor et al added to the confusion, by renaming the PiCo area into IRt. The field of respiration would have also been confused if the first study reproducing the Smith et al. 1991 study in a different rodent species would have refused to call this area preBötC and instead would have called it e.g. ventrolateral reticular field.

Did you perform control experiments in which the opto stimulations were done on animals without the genetic channels (for example, WT or uncrossed ChAT-ires-cre, etc.), or in mice with the genetic channels that weren't crossed (uncrossed Ai32 mice)? If so, please include. If not, why?

Yes, we performed many control experiments. Aside of many recordings in which viral injections were targeted outside PiCo, we also performed optogenetic stimulations in mice lacking channelrhodopsin. We have now added the following statements and supplemental figure.

Optogenetic stimulation in mice lacking channelrhodopsin

Stimulation of PiCo, across all stimulation durations, in 3 Ai32+/+ mice and 4 ChATcre:Vglut2FlpO:ChR2 mice where the ChR2 did not transfect ChATcre:Vglut2FlpO, as confirmed by a post-hoc histological analysis, resulted in no response (Fig. S3).

How do you know that your opto activations simulate physiological activation? First, the intensive optical activation at the stim site does not occur in those neurons naturally.

This seems like a generic critique of the optogenetic approach. In none of the 10,000+ published optogenetic studies is it known to what extent optogenetic activation stimulates exactly the same neurons and the same degree of activity as during a natural behavior. What we know is that PiCo neurons are activated during postinspiration (Anderson et al. 2016) and that optogenetic activation stimulates these neurons and that this activation evokes the same muscles in the same temporal sequence as a water-evoked swallow. We assume that the reviewer’s comment does not intend to imply that “swallows” evoked by nonspecifically stimulating the SLN is more physiological than the optogenetically-evoked swallows of a specific neuron population? From the reviewer’s other comments, it is obvious that the reviewer has no problems with the results of the Toor study that used exclusively SLN stimulations, an approach which is known to be very non-specific.

Doing a natural (water) stim for comparison is good, but it cannot necessarily be directly compared to the opto stim. The water stim would activate many other brainstem regions in addition to PiCo.

Can the reviewer provide any hard evidence that “many other brainstem regions” are activated by water stimulation in comparison to optogenetic stimulation?

A caveat is that opto PiCo stim =/= water stim (in terms of underlying mechanisms) should be included. Second, in looking at the differences between water vs opto swallows in Table S2: it appears that the ChAT animals (S2A) have something weaker than a swallow with opto stim. For the Vglut2 and ChAT/Vglut2 (S2B&C), the opto swallows also aren't as "strong" as the water swallows (the X and EMG amplitudes are smaller). The interpretation/discussion attributes this to the lack of sensory input during opto stim, but does not mention the strong possibility that there is a difference in central mechanisms occurring. It also seems to be dismissed with the characterization of the swallow as "all-or-none" (see note on Fig 3 results).

With all due respect, we are somewhat surprised that the reviewer dismisses the entire paragraph in the discussion that specifically addresses the comparison between water-swallows and PiCo-stimulated swallows. We discussed the possibility that PiCo stimulated swallows may not activate the full pathway/mechanism as does the water swallow. We carefully compared and confirmed that PiCo-stimulated swallows have the same temporal motor sequence of the same muscles as those activated in water swallows. As already stated, it is surprising that the reviewer has no problem with accepting the validity of previously published methods like electrical non-specific stimulations of the cNTS or SLN, a frequently used and accepted model to produce and study swallow.

The writing needs extensive copy editing to improve clarity and precision, and to fix errors.

Thank you for this comment, we have revised and reviewed the writing.

Results/Fig 1: What proportion had no/other motor response (non-swallow, non-laryngeal) to the opto stim? I can extrapolate by subtraction, but it would be nice to see the "no/other response" on the plot.

With all due respect to this reviewer, but it is not possible to address this question. Specifically, it is not possible to know if a “No response” (meaning “no behavioral output” occurred in response to PiCo stimulation), would have resulted in a swallow or laryngeal activation. However, figure 2 contains responses other than swallows, i.e. “non swallows”, which includes both laryngeal activation as well as “no responses” meaning “no behavioral response” in response to PiCo stimulation. This was determined to assess how the respiratory rhythm is affected when a swallow is not produced by PiCo stimulation.

The explanation of genetics is too spread out and confusing. There needs to be more detail about all the genetic tools used, using the standard language for such tools, in one spot. Please also provide a clear explanation of what those tools accomplish. Include a figure if necessary.

We apologize for creating confusion. We added more explanations to the text.

Pick a conventional designator/abbreviation for the different strains, define them in the methods and in the first paragraph of the results section, and use those abbreviations throughout. I think that using ChAT as an abbreviation for your ChAT-ires-cre x Ai32 mice is confusing because it makes it sound like you're talking about the enzyme rather than the specific strain/neurons. Saying "ChAT stimulated swallows... swallows evoked by water or ChAT" makes it sound like the enzyme choline acetyltransferase itself is stimulating swallow. As is convention, pick a more precise abbreviation like ChAT-cre/Ai32 or ChAT:Ai32 or ChAT-ChR2 or ChAT/EYFP. This goes for the other strains as well.

Thank you for pointing this out. To avoid confusion the strains/neurons are now referred to as: ChATcre:Ai32, Vglut2cre:Ai32, and ChATcre:Vglut2FlpO:ChR2

For Fig S2C&D, why does it say mCherry? Isn't it tdTomato? Is it just an anti-ChAT antibody and then the tdTomato Ai65 is only labeling Vglut2? I don't see this in the methods section.

Thank you for pointing this out. We apologize for our mistake, and we have corrected the manuscript to say tdTomato.

I also don't see methods for all the staining in Fig S3. The photomicrograph says Vglut2-cre Ai6, but there's no mention of Ai6 anywhere else. Which mice are these? Did you cross Vglut2-cre with an Ai6 reporter mouse? How can you image an Ai6 mouse (which I assume expresses ZsGreen? and that you excited at 488?) and a 488 anti-goat in the same section (that's the only secondary listed in the methods that would work with your goat anti-ChAT)? Is there an error in listing the fluorophores in the methods? Please give more details on the microscopy including which filters were used for the triple staining.

We have decided to remove the CTb data from the manuscript.

Regarding the staining: I would expect the staining/maps in for the 2 different ChAT/Vglut2 intersectional strains to be similar (Fig 5A/B and S2C/D). The photomicrographs look very different to me, while the heat maps (this goes for all the heat maps in the paper) have barely distinguishable differences. In Fig 5, the staining looks much stronger than in Fig S2C. Why does it look like there are so many more transfected neurons in Fig 5A2 than there are red neurons in the corresponding panel Fig S2C2? And for Fig 5A4 and Fig S2C44? The plot and results text for Fig 5 says the avg number of neurons was 123+¬11. The plot for Fig S2D says 112+¬15, but the results text says 242+¬12 (not sure which is the correct number).

Thank you for your comments. Previously the heat maps had different scale bars if you compare Fig 5A/B and S2C/D (now figure S4C/D). We changed the heat maps keeping the same scale for all of them. Discussing the representative photomicrography, even figure Fig 5A/B and S4C/D represents the same cluster of cells (PiCo Chat/Vglut+). Figure S4D states 242 ± 12 neurons (also stated in the results section).

However, we want to point out that there are several technical differences between both, 1) figure 5A represents the transfection promoted by the virus injection, impacting the number of cells stained/transfected (133 ± 16 neurons), 2) figure S4C/D represents a intersectional mouse ChATcre: Vglut2FlpO: Ai65; (242 ± 12 neurons). In this case, we have more tdTomato positive cells because this genetic approach is able to detect most of the Chat and Vglut2 cells. The difference between figures is considered normal for anatomical studies, in some studies the same bregma can show different number of cells. Thus, the differences are due to the differences in the type of approaches (viral expressions vs. intersectional approach).

We have also added additional experiments to figure 5 (now N=7) which has been reflected in the text and figures.

The results text for Fig S2C also says the staining is "similar to the previous ChAT staining...", which I assume refers to S2A/B. The plot and results text for Fig S2B reports 403+¬39 neurons, while S2D is either 112 or 242 (not sure?). The plots have different Y scales, which should be changed to be the same. But why do the photomicrographs and the heat maps look so similar? I would expect far fewer neurons to be stained in the intersectional mice (Fig 5 and Fig S2C/D) than in the ChAT staining (Fig S2A/B). I am having trouble reconciling the different presentations/quantifications and making sense of the data in these histology figures.

We removed “similar to the previous ChAT staining” and we have reviewed the heat maps. Since the original submission, we performed more experiments and now added more animals to the analysis (now N=7), each heat map represents the correct number of neurons in PiCo, respectively to each experiment.

The Y scales has been adjust to better demonstrate the Chat staining vs. the intersectional mice triple conditioned.

How can you distinguish PiCo from non-PiCo in the histology, especially in the ChAT-only staining? It seems that you have arbitrarily defined the PiCo region, and only counted neurons within that very constrained area.

Even in ChAT-only staining, the N.ambiguus is very distinct from the cholinergic neurons located more medial to the N.ambiguus. This can be unambiguously be confirmed by combining ChAT with glutamatergic in situ staining as done in the Anderson et al. study, or unambiguously be demonstrated with the viral approach as done in the present study. Thus, we don’t see why it is arbitrary to define the distribution of PiCo neurons. What is arbitrary is the definition of the preBötC, yet the field of respiration seems to have no problem with this. We assume that the reviewer knows that Dbx1 neurons are spread along the entire ventral respiratory column and dorsal portion of the PreBötzinger Complex up to the level of the XII nucleus. Yet it is commonly accepted for authors to refer to the PreBötzinger Complex by counting dbx1 neurons within a constrained area of what is believed to be the PreBötzinger Complex, even though the borders are arbitrary. It is e.g. known that some of the ventrally located preBötC neurons are presumed rhythmogenic while the more dorsally located Dbx1 neurons are premotor. The transition from rhythmogenic to premotor is gradual. Similarly, NK1 staining, or SST staining is not restricted to the preBötC and it is arbitrary to define where preBötC begins and what to include. Indeed, our PNAS paper indicates that inspiratory bursts can be generated by optogenetically stimulating Dbx1 neurons along the entire VRC column – so it is not clear where the rhythmogenic portion of the preBötC begins rostrocaudally and dorsoventrally and where the rhythmogenic portion and preBötC itself ends. Thus, we want to re-iterate and emphasize, that for the present study, we developed a method using the cre/FlpO approach to unambiguously define the PiCo region. It is surprising that this reviewer does not acknowledge this technical advance that added significantly more specificity to the anatomical and physiological characterization of PiCo, than the Toor et al. study, and also the Anderson et al. study.

I can see stained neurons in the area immediately outside of PiCo, and I'd like to see lower-magnification images that show the staining distribution in a broader region surrounding PiCo as well, especially in the rest of the reticular formation.

We characterized the PiCo area based on the histological phenotype and in vitro and in vivo experiments performed by Anderson et al., 2016. PiCo is an area located close to the NAmb, presenting the same ChATcre phenotype. As stated above, the distribution and agglomeration of the NAmb is clearly very compact, and different then the observed ChATcre: Vglut2FlpO: Ai65 neurons located outside of NAmb. It is also important to emphasize, that like is the case for the preBötC, other transmitter phenotypes of neurons are also present in the PiCo region (i.e. GABA or Dbx1). However, the study performed by Anderson et al, 2016 paper, described only the functions of cholinergic neurons located in PiCo, and we always planned to publish a paper of the other neurons within PiCo – this area e.g. contains pacemaker neurons etc. But, I hope that the reviewer acknowledges that many investigators have studied the preBötC for the past 30 years. Hence, much more information has been accumulated on this region (which btw was at least as controversial at the beginning), and it will likely take at least another 30 years to fully identify and characterize PiCo.

Similarly, how can you be sure you're stereotaxically targeting PiCo precisely (600um in diameter?) with your opto fiber (200um in diameter). Wouldn't small variations in anatomy put the fiber outside the tiny PiCo area?

We assume the reviewer means “stereotactically”. And yes, the reviewer is correct, it is necessary to position the laser at a consistent anatomical location. Placement of the optical fibers outside of this area does not result in activation of PiCo. We have added an additional supplemental figure (Figure S6) to address this.

Please put N's and stats results in Table S1 for both swallow and laryngeal activity. I took what I assume to be the Ns (10, 11, and 4) and did some stats like the ones you presented for the laryngeal duration. The differences between vagus duration for 40 and 200 ms pulse durations are all significant for each strain, by my calculations. Also, I think there must be an error in the orange swallow plot in Fig 3A. The orange dots don't correspond to the table values. I plotted all the Table S1 values for each strain. Each line looks similar to the blue laryngeal activation plot in Fig 3A. The slopes of the Vglut2 were less than the other strains, and the slopes for the swallow behavior were less than the laryngeal behavior for all strains. Otherwise, they all look similar. Please double-check your values/stats to address these discrepancies. If it is indeed true that the stim pulse duration affects swallow duration, revise the interpretations and manuscript accordingly.

We thank the reviewer for the diligence in reviewing our manuscript. But, with all due respect, the reviewer is incorrect and misunderstood the data. To clarify: Table S1 is only presenting data for laryngeal activation, swallow data is presented in Table S2. The orange data points in Fig 3A are not detailed in Table S1 or S2. Table S2 is the average of all swallows across all laser pulse durations since the laser pulse duration does not affect swallow behavior duration. All data will be publically available after publication of the manuscript.

Figure 3A is only representing the ChATcre:Vglut2FlpO:ChR2 column of Table S1

The N’s have been added to table S1

Please add more details on stats in general, including the specific tests that were performed, F values and degrees of freedom, etc.

Thank you, this has been added throughout the results section. Please refer to the results section for this addition. However below we have provided an example.

An example: A two-way ANOVA revealed a significant interaction between time and behavior (p<0.0001, df= 4, F= 23.31) in ChATcre:Vglut2FlpO:ChR2 mice (N=7).

How do you know that you're not just activating motoneurons in the NA when you stimulate your ChAT animals, especially given the results in Fig 1B? In this case, the phase-specific results could be explained by inhibitory inputs (during inspiration) to motoneurons in the region of the opto stim.

As stated in this paper as well as the Anderson et al 2016 paper (and for that matter also the Toor et al study) this is a caveat. This major caveat motivated the development and use of the ChATcre:Vglut2FlpO:ChR2 (specifically targeting the PiCo neurons that co-express ChAT and Vglut2, not laryngeal motor neurons) experiments that have mostly the same response as the ChATcre:Ai32 mice. We cannot say this is due to inhibitory inputs to laryngeal motoneurons, since the cre/FlpO specific experiments are not directly activating laryngeal motoneurons. But we do not want to entirely exclude that some premotor mechanisms may also occur in PiCo. The reviewer may know that there is overlap of rhythmogenic and premotor functions for the Dbx1 neurons in the PreBötC, But, addressing this issue is beyond the scope of this study. In fact, we are working on a separate connectivity study using novel, still unpublished antegrade and retrograde vectors that do not reveal any direct connections to laryngeal motoneurons. Hence, we expect that the connectivity from PiCo to laryngeal motoneurons is more complex and addressing this question cannot be done as a simple add-on to an already complex study. Again, we would refer to the PreBötzinger complex, where nobody expects that one study can resolve all the physiological and anatomical characterizations that have been accumulated over 30 years in one study. We would argue that in some ways, our cre/FlpO approach is more specific than the Dbx1 stimulations which activates not only rhythmogenetic PreBötzinger complex neurons, but also pre motoneurons as well as glia cells, and many neurons rostral and caudal to the PreBötzinger complex. We are aware of these caveats, and we have discussed this in the original submission, and also in the revision.

While the study from Toor et al is cited, there needs to be a much more thorough discussion of how their findings relate to the current study.

Many thanks for asking for a more thorough discussion of Toor et al., which we are happy to provide here. Perhaps we were too polite in our original manuscript to emphasize all the problems in that study.

They demonstrated that PiCo isn't necessary for the apneic portion of swallow. Inhibiting this region also didn't affect TI.

Please note – the fact that Toor et al did not find an effect on TI confirms Anderson et al. 2016: In Figure 3G,3F of the Nature paper, the reviewer will find that injections of DAMGO and SST into PiCo inhibited post-I activity without affect inspiratory duration. This figure also shows that the inspiratory burst can terminate in the absence of postinspiratory activity.

The reviewer states: “They demonstrated that PiCo isn't necessary for the apneic portion of swallow”. With all due respect to this reviewer, this is NOT correct. Toor et al showed that inhibiting PiCo did block SLN-evoked fictive-swallows but not the apnea caused by SLN stimulation. This is not the apnea caused by swallows (which was never studied by Toor), but by the SLN stimulation. The apnea evoked by SLN stimulation has most likely nothing to do with the apnea caused by swallows. Unfortunately, the Toor et al. makes the same misleading claim as the reviewer.

PiCo cannot be the sole source of post-I timing, and the evidence overwhelmingly favors the major involvement of other regions such as the pons.

This comment seems to be unrelated to the main thrust of this paper that studies PiCo’s involvement in swallow and laryngeal activation in coordination with breathing. However, since this comment seems to discredit the Ramirez lab in general, we would like to clarify that inhibiting PiCo with DAMGO and SST inhibits post-I activity (Anderson et al 2016, Fig.3G,3F). Thus, we don’t understand the rationale or actual data for the reviewer’s conclusion that PiCo cannot be the sole source of post-I timing? We also don’t understand the basis for the reviewer’s conclusion that “the evidence overwhelmingly favors the major involvement of other regions such as the pons”. We also want to add, that no-where in the Anderson et al. study did we state that the pons plays NO role. Indeed, we specifically stated: “In this context it will be interesting to resolve the role of the PiCo in specific postinspiratory behaviors and to identify how the PiCo interacts with other neural networks such as the Kolliker-Fuse nucleus, a pontine structure that has been hypothesized to gate postinspiratory activity and the periaqueductal grey a structure involved in vocalization and the control of postinspiration”.

They also showed that inhibition of all neurons (not just ChAT/Vglut) in the PiCo region suppresses post-I activity in eupnea. This suppression was overcome by the increased respiratory drive during hypoxia.

Before comparisons are made with Toor et al. it is important to note the species and methodological differences between Toor et al. rat anesthetized, vagotomized, paralyzed and artificially ventilated model which evaluated fictive swallows (deafferented and paralyzed). By contrast this study uses a mouse anesthetized, vagal intact, freely breathing model and evaluates natural physiologic swallow via water and central stimulation. It seems that the reviewers does not acknowledge one of the main innovations of this study. For this study we introduced a genetic approach to specifically target and activate ChATcre/Vglut2FlpO PiCo neurons. This has never been done before, and developing this approach took more than 4 years of breeding and crossing and testing different options.

As for Toor et al., these authors pharmacologically, bilaterally inhibited neurons in the area of PiCo with isoguvacine, a specific GABA-A agonist. Even though this pharmacological intervention does not specifically inhibit cholinergic/glutamatergic neurons in PiCo, these authors essentially confirm the study by Anderson et al. We do not find this finding controversial. Perhaps the reviewer finds the definition of PiCo “controversial”, because Toor et al called the identical area IRt instead of PiCo, even though they exactly reproduce the finding by Anderson. Toor et al. not only arrive at the same conclusion as Anderson but they added more details – none of which is contradicting the results by Anderson et al.: Here are excerpts from the Toor study “We therefore conclude that the ongoing activity of neurons in the IRt contributes to eupneic respiratory and sympathetic post-I activities without exerting significant control on other respiratory or cardiovascular parameters” “IRt significantly inhibited the post-I components of VNA” “IRt inhibition was also associated with a reduction in PNA” “increase in respiratory cycle frequency” “due to a reduction in TE“ “with no effect on TI observed”. “Bilateral microinjection of isoguvacine selectively reduced the magnitudes of post-I VNA and rSNA, but not PNA responses to acute hypoxemia”.

In this statement the reviewer probably refers to one particular aspect, i.e. the fact that Toor et al. did not significantly block some of the post-I activity – they state: “had no significant effect on the AUC of post-I rSNA (305+/- 24 vs 230+/- 28,p=0.16,n=6)”. Please note that there is a tendency, a reduction from 305-230. Perhaps the Toor study was not sufficiently powered to fully block the effect, perhaps the drug did not inhibit the entire PiCo. These are all open questions that a critical reader should know. The reviewer will agree that it is as difficult if not more difficult to demonstrate the absence of an effect. To arrive at a negative conclusion experiments should be done with the same scrutiny than to demonstrate a positive result. We also assume that the reviewer is familiar with animal experiments and will understand that pharmacological injections are often difficult to interpret, in particular in case of local in vivo injections. It is possible that Toor et al is inhibiting e.g. parts of the Bötzinger complex.

We have added to the manuscript the following statement: It is important to note that SLN stimulation does not only trigger swallows, but also changes in the overall stiffness and tension of the vocal cords (Chhetri et al., 2013) as well as prolonged hypoglossal activation independent of swallowing (Jiang, Mitchell, & Lipski, 1991). It has been hypothesized that inhibition of the IRt blocks fictive swallow but not swallow-related apnea. Yet this apnea was generated by SLN stimulation and not by a natural swallow stimulation (Ain Summan Toor et al., 2019). It is known that SLN stimulation causes endogenous release of adenosine that activates 2A receptors on GABAergic neurons resulting in the release of GABA on inspiratory neurons and subsequent inspiratory inhibition (Abu-Shaweesh, 2007), suggesting that the SLN evoked apnea may not be the same as a swallow related apnea. Moreover, microinjections of isoguvacine into the Bötzinger complex attenuated the apneic response but not the ELM burst activity (Sun, Bautista, Berkowitz, Zhao, & Pilowsky, 2011), suggesting the Bötzinger complex, not PiCo, could be involved in modulating apnea.

We would also like to add that our current study characterized swallow-related specific muscles and nerves in both water-triggered and PiCo-triggered swallows to better characterize the physiological properties of this swallow behavior. By contrast, Toor et al. only characterized nerve activities that are involved in multiple upper airway activities and breathing. It is somewhat surprising that the reviewer did not consider the fact that Toor et al. characterized putative swallows that were triggered by SLN stimulation and that Toor et al. were content with nerve-recordings and failed to confirm that the behavior that they evoked is actually a physiological swallow. Which, according to the comments from this reviewer (see above), indicates the possibility of differences in central mechanisms occurring between fictive swallow and physiological swallows.

While we have cited Toor et al and their truly excellent work in the broad iRt we did not feel it is appropriate to critique them for the fact that they are confusing the field by using a different anatomical term for the area that was clearly defined by us as an area containing cholinergic-glutamatergic neurons. We also did not feel it is appropriate to discuss results that are similar to comparing Apples and Oranges. Toor et al. never specifically manipulated glutamatergic-cholinergic neurons, thus their entire results rest on indirect stimulation affecting this general area – which will unavoidably also include laryngeal motoneurons. We don’t want to criticize this approach, since PiCo is heterogenous, which is another misunderstanding that we find in the reviewers’ critique. We used cholinergic-glutamatergic neurons to define this area. However, like the preBötC, PiCo is also heterogenous. This region contains inhibitory neurons, it also contains glutamatergic neurons that are not cholinergic, and cholinergic neurons that are not glutamatergic. Because of this heterogeneity we compared the effects of stimulating glutamatergic neurons and cholinergic neurons as well as cholinergic-glutamatergic neurons. This is an approach that is generally accepted in the field. As already stated, there is not a single marker that uniquely characterizes the PreBötC. Thus, when stimulating Dbx1 neurons, glutamatergic neurons, or Somatostatin neurons it only captures subpopulations of this region. The recently published study by Menuet et al. in eLife, used even more indirect methods to inhibit preBötC. They used a pan-neuronal CBA promotor that targets neurons irrespective of phenotype. It is not our intention to discredit this very elegant study, but we object the statement that we “have arbitrarily defined the PiCo region”.

This study has not demonstrated some of the things that are depicted in Fig 7 and included in the discussion. While swallow can inhibit inspiration, there are many mechanisms by which this can happen other than a direct inhibitory connection from the DGS to PreBotC. You cite Sun et al., 2011 findings of "a group of neurons that inhibits inspiration" during SLN stim, but don't mention that it is the BotC and that the paper shows that swallow apnea is dependent on BotC. That is also supported by the Toor study. I don't understand how post-I (aka E) can be discussed without discussion of the BotC - this is a glaring omission.

We have removed figure 7, which was only meant as a hypothetical schematic.

Why is it necessary for PiCo to innervate the cNTS?

This was a hypothesis based on CTb data that we have now removed.

That is true if the conjecture that PiCo gates swallowing is true, as the cNTS is the only known region for central swallow gating. However, PiCo could influence afferent input to the NTS less directly, and therefore not function as a gating hub per se. The experimental evidence does not warrant the claim that PiCo gates swallowing. The definition of a swallow gate(s) is a topic of much debate and no conclusive experimental evidence has emerged for swallow gating regions to exist anywhere except in the NTS. The current study's evidence also does not meet the criteria necessary to conclusively call PiCo a swallow gate. The authors should soften this claim and language throughout the manuscript.

Although we do not know of any studies that has optogenetically gated swallow in the cNTS, it seems the reviewer objects our use of the word “gate”. We have revised the manuscript and removed any wording stating PiCo is a swallow “gate”. It would be interesting to know whether the reviewer has the same objections of the use of the word “relay” as done by Toor et al.?

It is also unclear that PiCo acts directly on the swallow pattern generator to gate swallowing. It is not just "conceivable that the gating mechanism involves" the pons, but nearly certain. Swallow gating by respiratory activity may not be able to be ascribed to one particular location. At a minimum, it likely involves the NTS/DSG, pons, and possibly IRt (inclusive of PiCo). The authors are correct that "further studies are necessary to understand the interaction between PiCo and the pontine respiratory group on the gating swallow and other airway protective behaviors." This is why it shouldn't be stated that "this small brainstem microcircuits acts as a central gating mechanism for airway protective behaviors."

We have removed all language stating PiCo is a swallow gate.

PiCo is likely part of the VSG (and thus the swallow pattern generator). PiCo, as part of the IRt/VSG could indeed be surveilling afferent information and providing output that affects swallow or other laryngeal activation and the coordination of these behaviors with breathing. However, this is not the responsibility of PiCo alone. This role is likely shared by other parts of the SPG, and may require distributed SPG network participation to be functional. If one were to stim other regions of the distributed SPG, similar results might be expected. When Toor et al silenced the PiCo area (and locations that lie at least lightly beyond the borders of what the present study defines as PiCo), stim-evoked fictive swallows were greatly suppressed. However, swallow-related apnea was unaffected. This supports the role of PiCo as a premotor relay for swallow motor activation, but not as the site that terminates inspiration. Therefore, it cannot be called a gate.

We already addressed the issue that Toor never demonstrated that the “swallow-related” apnea was unaffected. Toor et al only demonstrated that the SLN-evoked apneas were unaffected, and their conclusions were only based on nerve recordings under fictive conditions (deafferented and paralyzed). Also, to the best of our knowledge, many aspects of the putative swallow pattern generator that this reviewer mentions are purely hypothetical. However, to avoid further arguments, we have removed the word gate and Figure 7 from this manuscript.

Similarly, Fig 7 does not accurately depict things that are already well-supported by evidence. PiCo should be included as part of the swallow pattern generator (VSG), not as a separate entity acting on it. The BotC and pons are glaring omissions. This study has not demonstrated the labeled inhibitory connection from DSG to PreBotC. The legend states speculations as fact and needs to be dialed way back to either include statements with solid experimental evidence or to clearly mark things as putative/speculative.

We have removed figure 7.

The discussion of expiratory laryngeal motoneurons needs to be expanded and integrated better into the discussion of swallow, post-I, and other laryngeal motor activation. Why can't PiCo just be premotor to ELMs?

If PiCo would “only” or “just” be premotor to ELM then it would not be expected that it could trigger an all-or-none swallow response with a temporal activity pattern similar to the one of a water-evoked swallow. We would also not expect that the activation of the activity pattern is independent of the laser stimulation duration as demonstrated in Figure 3. This was our reasoning why we originally called PiCo a “gate” because at the correct phase it will gate/trigger a complex swallow sequence. But, as stated above, we avoid the word gate in the revised manuscript.

Concerning the discussion of "PiCo's influence as a gate for airway protective behaviors is blurred...": The incomplete swallow motor sequence didn't seem super different in timing or duration compared to the fully transfected animals (comparing plots from Fig 6 to Fig S1, and Table S2 to Table S3. The values for swallow durations (XII and X) for each group for water and opto seem within similar ranges, as do the differences between water & opto-evoked swallows between strains. While the motor pattern is distinctive from the normal swallow, with laryngeal activity rather than submental activity leading, one might not even be able to call that a swallow. Is it evidence against a classic all-or-nothing swallow behavior any more than the graded swallow results from (fully transfected) Table S1?

We fully agree that it is possible that this unidentified behavior may not be a swallow. We have changed the name of this behavior to “upper airway motor activity.” However we also cannot rule out the possibility of this being some portion of a graded swallow which would argue that a graded swallow response is exact evidence against the classic all or nothing swallow behavior.

Please expand on this point and put it into context with others' results: "This brings into question whether this is the first evidence against the classic dogma of swallow as an "all or nothing" behavior, and/or whether this is an indication that activating the cholinergic/glutamatergic neurons in PiCo is not only gating the SPG, but is actually involved in assembling the swallow motor pattern itself."

This has been expanded and included citation of other studies. The following paragraph can be found in the discussion

Swallow has been thought of as an “all or nothing” response as early as 1883 (Meltzer, 1883). Whether modulating spinal or vagal feedback (Huff A, 2020b), central drive for swallow/breathing (Huff, Karlen-Amarante, Pitts, & Ramirez, 2022) or lesions in swallow related areas of the brainstem (Car, 1979; Robert W Doty, Richmond, & Storey, 1967; Wang & Bieger, 1991) swallow either occurred or did not. Swallows are thought to be a fixed action pattern, with duration of stimulation having no effect on behavior duration (Fig. 3) (Dick, Oku, Romaniuk, & Cherniack, 1993). Thus, it was particularly interesting that in instances when few PiCo neurons were transfected, either unilateral or bilateral, an unknown activation of upper airway activity occurred. Motor activity no longer outlasted laser stimulation rather was contained within, and the timing of the motor sequence was reversed in comparison to a water or PiCo evoked swallow (Fig. 6). Thus, if insufficient numbers of neurons are activated, PiCo’s influence to specifically activate swallow or laryngeal activation is blurred, resulting in the uncoordinated activation of muscles involved in both behaviors. This brings possible evidence against the classic dogma of swallow as an “all or nothing” behavior, or the presence of an entirely different behavior. We are not the first to bring possible evidence against the classic dogma, “small swallows” were described but failed to be discovered if this was in-fact a partial or incomplete swallow (Miller & Sherrington, 1915). The SPG is thought to consist of bilateral circuits (hemi-CPGs) that govern ipsilateral motor activities, but receive crossing inputs from contralateral swallow interneurons in the reticular formation, thought to coordinate synchrony of swallow movements (Kinoshita et al., 2021; Sugimoto, Umezaki, Takagi, Narikawa, & Shin, 1998; Sugiyama et al., 2011). Incomplete activation of PiCo activates the muscular components of a swallow, without establishing the coordinated timing and sequence of the pattern. It is possible that PiCo is involved in assembling the swallow motor pattern itself and unilateral activation of PiCo could either desynchronize swallow interneurons or activates only one side of the SPG. Since we did not record bilateral swallow related muscles and nerves this question needs to be further examined.

Reviewer #3 (Public Review):

Huff et.al further characterise the anatomy and function of a population of excitatory medullary neurons, the Post-inspiratory Complex (PiCo), which they first described in 2016 as the origin of the laryngeal adduction that occurs in the post-inspiratory phase of quiet breathing. They propose an additional role for the glutamatergic and cholinergic PiCo interneurons in coordinating swallowing and protective airway reflexes with breathing, a critical function of the central respiratory apparatus, the neural mechanics of which have remained enigmatic. Using single allelic and intersectional allelic recombinase transgenic approaches, Huff et al. selectively excited choline acetyltransferase (ChAT) and vesicular glutamate transporter-2 (VGluT2) expressing neurons in the intermediate reticular nucleus of anesthetised mice using an optogenetic approach, evoking a stereotyped swallowing motor pattern (indistinguishable from a water-induced swallow) during the early phase of the breathing cycle (within the first 10% of the cycle) or tonic laryngeal adduction (which tracked tetanically with stimulus length) during the later phase of the breathing cycle (after 70% of the cycle).

They further refine the anatomical demarcation of the PiCo using a combination of ChAT immunohistochemistry and an intersectional transgenic strategy by which fluorescent reporter expression (tdTomato) is regulated by a combinatorial flippase and cre recombinase-dependent cassette in triple allelic mice (Vglut2-ires2-FLPO; ChAT-ires-cre; Ai65).

Lastly, they demonstrate that the PiCo is anatomically positioned to influence the induction of swallowing through a series of neuroanatomical experiments in which the retrograde tracer Cholera Toxin B (CTB) was transported from the proposed location of the putative swallowing pattern generator within the caudal nucleus of the solitary tract (NTS) to glutamatergic ChAT neurons located within the PiCo. We would like to thank the reviewer for acknowledging the technical advances of the present study and for the positive statements in general.

Methods and Results

The experimental approach is appropriate and at the cutting edge for the field: advanced neuroscience techniques for neuronal stimulation (virally driven opsin expression within a genetically intersecting subset of neurons) applied within a sophisticated in vivo preparation in the anaesthetized mouse with electrophysiological recordings from functionally discrete respiratory and swallowing muscles. This approach permits selective stimulation of target cell types and simultaneous assessment of gain-of-function on multiple respiratory and swallowing outputs. This intersectional method ensures PiCo activation occurs in isolation from surrounding glutamatergic IRt interneurons, which serve a diverse range of homeostatic and locomotor functions, and immediately adjacent cholinergic laryngeal motor neurons within the nucleus ambiguous (seen by some as a limitation of the original study that first described the PiCo and its roll in post-I rhythm generation Anderson et al., 2016 Nature 536, 76-80). These experiments are technically demanding and have been expertly performed.

Again, we would like to thank the reviewer for these positive comments acknowledging the advances of the present study.

The supplemental tracing experiments are of a lower standard. CTB is a robust retrograde tracer with some inherent limitations, paramount of which is the inadvertent labelling of neurons whose axons pass through the site of tracer deposition, commonly leading to false positives. In the context of labelling promiscuity by CTB, the small number of PiCo neurons labelled from the NTS (maybe 5 or 6 at most in an optical plane that features 20 or more PiCo neurons) is a concern. Even assuming that only a small subset of PiCo neurons makes this connection with the presumed swallowing CPG within the cNTS, interpretation is not helped by the low contrast of the tracer labelling (relative to the background) and the poor quality of the image itself. The connection the authors are trying to demonstrate between PiCo and the cNTS could be solidified using anterograde tracing data the authors should already have at hand (i.e. EYFP labelling driven by the con-fon AAV vectors from PiCo neurons (shown in Fig5), which should robustly label any projections to the cNTS).

We fully agree with the reviewer that the CTB staining is of a lower standard and have removed this approach.

The retrograde labelling from laryngeal muscles seems unnecessary: the laryngeal motor pool is well established (within the nAmb and ventral medulla), and it would be unprecedented for a population of glutamatergic neurons to form direct connections with muscles (beyond the sensory pool).

The authors support their claim that PiCo neurons gate laryngeal activity with breathing through the demonstration that selective activation of glutamatergic and cholinergic PiCo neurons is sufficient to drive oral/pharyngeal/laryngeal motor responses under anaesthesia and that such responses are qualitatively shaped by the phase of the respiratory cycle within which stimulation occurs. Optical stimulation within the first 10% of the respiratory cycle was sufficient to evoke a complete, stereotyped swallow that reset the breathing cycle, while stimuli within the later 70% of the cycle, evoked discharge of the laryngeal muscles in a stimulus length-dependent manner. Induced swallows were qualitatively indistinguishable from naturalistic swallow induced by the introduction of water into the oral cavity. The authors note that a detailed interpretation of induced laryngeal activity is probably beyond the technical limits of their recordings, but they speculate that this activity may represent the laryngeal adductor reflex. This seems like a reasonable conclusion.

We thank the reviewer for this comment. Unfortunately, we felt compelled to remove the word “gating” based on the statements by reviewer 1.

The authors propose a model whereby the PiCo impinges upon the swallowing CPG (itself a poorly resolved structure) to explain their physiological data. The anatomical data presented in this study (retrograde transport of CTB from cNTS to PiCo) are insufficient to support this claim. As suggested above, complementary, high-quality, anterograde tracing data demonstrating connectivity between these structures as well as other brain regions would help to support this claim and broaden the impact of the study.

We fully agree with this reviewer. We have been working on a thorough anatomical characterization for more than 3 years using cutting edge anterograde and retrograde viruses in collaboration with vector experts at the University of Irvine. But these are partly novel, unpublished techniques that are in development, and require many careful controls and characterization. We feel that this is a separate study as it doesn’t relate to swallowing coordination and also includes partly different authors. We hope to submit this as a separate study later this year.

This study proposes that the PiCo in addition to serving as the site of generation of the post-I rhythm also gates swallowing and respiration. The scope of the study is small, and limited to the subfields of swallowing and respiratory neuroscience, however, this is an important basic biological question within these fields. The basic biological mechanisms that link these two behaviors, breathing and swallowing, are elusive and are critical in understanding how the brain achieves robust regulation of motor patterning of the aerodigestive tract, a mechanism that prevents aspiration of food and drink during ingestion. This study pushes the PiCo as a key candidate and supports this claim with solid functional data. A more comprehensive study demonstrating the necessity of the PiCo for swallow/breathing coordination through loss of function experiments (inhibitory optogenetics applied in the same transgenic context) along with robust connectivity data would solidify this claim.

Thanks again for the positive assessment of our study.

Author Response

Reviewer #1 (Public Review):

This paper explores the potential regulatory role of a previously unstudied phosphorylation site in the Src kinase SH3 domain. The data presented conclusively demonstrate that a phosphomimetic mutation of this site, src90E, causes an elevation in Src kinase activity, changes the structure of the Src catalytic domain as determined with a FRET sensor, disrupts certain SH3 domain interactions, causes changes in kinase intracellular dynamicity, and promotes cell invasiveness. Based on the behavior of the phosphomimetic mutant, the idea that constitutive phosphorylation of Y90 could have all of these effects is well-supported by the data. However, in wild-type cells or cells transformed by activated forms of Src, there is no constitutive phosphorylation of this site. Therefore, the question remains whether Y90 phosphorylation occurs to any significant extent in cells, and the data suggesting that it could do so is limited. It also remains to be conclusively established whether Y90 phosphorylation occurs via autophosphorylation.

Major comments:

1) Y90 was identified as a site of phosphorylation in Luo et al. It would be helpful if more information were provided about its significance relative to other sites identified in that study. Was it detected in non-transformed cells? Was it a major site? How does it relate to Y416 in abundance? The reference to the identification of the site in a different study from the White lab is made in the discussion but not in the introduction (this should be corrected). How abundant was it that study? A fuller description of its detection would strengthen the rationale for this study. Any additional phosphoproteomics studies that identified it should also be included.

As indicated in the manuscript (Figure 3C and new 3D), the amount of Y90 phosphorylation increases with the level of Src activation. Standard proteomic/phosphoproteomic data cannot be quantified in absolute values for technical reasons, only relative quantification is possible to some extent. To overcome this issue and address the question of the amount of Y90 phosphorylation, we newly prepared the corresponding stable isotope-labeled phosphopeptides and used them as internal standards. To the best of our knowledge, this allowed us to quantify for the first time the amount of specific tyrosine phosphorylation of Src kinase in cells. We found that in case of WT Src, the major phosphorylation site localized in the activation loop of the kinase domain, Y416, is phosphorylated in 22 % of molecules. In activated Src, this pool of Y416-phosphorylated molecules increases 2,5 times to 57 %. Y90 is phosphorylated in approximately 1 % of WT Src molecules but becomes 5 times more abundant in case of the activated kinase (5,3 % of phosphorylated molecules). This newly added data of absolute Src tyrosine phosphorylation (Figure 3D) is consistent with values we obtained from relative MS quantification of Src variants differing in catalytic activity (Figure 3C). Although the enrichment of Y90 phosphorylation in the catalytically active kinase is lower compared to Y416 phosphorylation in terms of percentage of phosphorylated molecules, it’s increment with respect to the basal state is significantly higher. We believe that this broader dynamic range of Y90 phosphorylation is in agreement with the demonstrated regulatory function of Y90 phosphorylation. We incorporated these new results and methodological approach into the revised manuscript. We also extended the original description of the MS protocol to include a description of relative quantification, which was included in the original manuscript.

Phosphorylation of Y90 was only detected in Luo et al. and Johnson et al. phosphoproteomic screens. However, phosphorylation of tyrosines homologous to Src Y90 was described in a vast number of proteins. Some of them are mentioned in the discussion e.g., Btk, Abl, p130Cas or Src family kinases Yes and Fyn. The presence of phosphorylation on homologous tyrosines and the evolutionary conserved nature of Y90 in SH3 domains supports relevance of Src Y90 phosphorylation despite the small number of studies that were able to identify it. In our opinion, this can be attributed to its low abundance in the basal state and the technical difficulties of its detection, as discussed below in response to point 2.

We emphasize the Luo et al. study in the introduction because it was the only study reporting Y90 phosphorylation at the time of the project’s initiation and led us to study Y90 further. Both studies are then mentioned in the discussion, which we believe is appropriate and sufficient.

2) Related to point 1, is there evidence from the literature indicating a significant site of phosphorylation in Src has been overlooked? Or, was this site only identified because of the recent advances in MS technology and increased sensitivity of this methodology? An introduction to these points would also enhance the rationale for the study.

In the manuscript discussion, we mention an early study (Erpel et al., 1995) which mapped conserved residues within the binding surface of the Src SH3 domain. It showed that mutation of Y90 to alanine led to partially deregulated Src and reduced affinity of the SH3 domain. Although they acknowledged the importance of Y90 for SH3 domain binding ability, they did not probe or discuss the effect of Y90 phosphorylation status. Furthermore, the level of Src Y90 phosphorylation in untransformed cells is relatively low (20-fold lower than Y416 phosphorylation). It is therefore not surprising that it has not been identified in most general phosphoproteomic studies performed on untransformed cells. In fact, in many of these studies, Y416 phosphorylation was not detected either. The detection of Y90 phosphorylation by Luo et al. likely reflects the fact that it was performed in Src527F-transformed cells, similarly Johnson et al. used HGF-activated cells. Last, we also cannot exclude that the tryptic peptides with Y90/pY90 are less detectable in MS depending on the experimental conditions. In fact, the "heavy" Y90 peptide was consistently much less (10-80 times less) detectable in our hands than the Y416 peptide. This could be because of its worse ionizability, stability in vacuum or some other technical reasons.

In our approach, we used immunoprecipitated Src molecules to maximize the amount of Src in the sample and targeted MS, which allowed us to specifically detect even low abundant ions/peptides. This represented the critical technical approach that allowed us to consistently detect Y90 phosphorylation in untransformed cells.

3) The explanation of the MS experiment designed to show that Y90 phosphorylation happens in cells is insufficient in the text. It is not clear why the SYF cells were not used and not clear why the FRET sensor constructs were used. It is also not clear whether or how the proteins were purified before MS analysis. Also, rather than showing the MS data as a relative level, it would be preferable to provide the number of spectra obtained for each peptide/phosphopeptide and compare this also to Y416. A fuller comparison between the phosphorylation of Y90 to that of Y416 is necessary in order to place the potential Y90-mediated phosphoregulation in context.

We are sorry for the confusing description. With the new quantification data, we have rewritten this section and hopefully made it clearer. We kept the original relative quantification data as they nicely show that abundance of Y90 phosphorylation increases with enhanced activity of Src. However, we added new MS analysis of Src tyrosine phosphorylation performed with labeled peptides as internal standards that provides absolute numbers of Y416 and Y90 phosphorylation in cells. The new <br /> measurements confirm the original data showing increased Y90 phosphorylation in activated Src variants and suggest that Y90 phosphorylation is not a rare event but represents an important regulatory element in Src activation. Our approach of MS quantification of phosphorylation events using labeled peptides as standards, allowed us, to the best of our knowledge, for the first time, to measure absolute quantities of Y416 and Y90 phosphorylation and therefore also the amount of activated Src molecules in cells.

For technical reasons, the SrcFRET biosensor was used in all these experiments. We attempted to analyze endogenous Src in several cell lines to assess its Y90 phosphorylation. However, in our hands, the amount of Src efficiently precipitated was never sufficient to detect the "very elusive" phosphopeptide containing Y90. We believe this was not caused by low amounts of Src in the cells, <br /> but rather because the anti-Src antibody performed much worse than the anti-GFP antibody used for SrcFRET biosensor (two high affinity epitopes) immunoprecipitation. We have previously shown that the SrcFRET biosensor functions in the same way as endogenous Src (Koudelková et al., 2019), and therefore we presume that it is phosphorylated in a similar manner and rate as endogenous Src.

4) I would like to see conclusive evidence that Y90 phosphorylation is due to autophosphorylation. This would involve relatively simple experiments. As one possibility, an IP kinase assay followed by immunoblotting with a site-specific antibody or MS or other types of phosphopeptide visualization/identification.

We further addressed the question of Y90 autophosphorylation using a kinase dead version of Src527F bearing K295M substitution. To quantify the amount of phosphorylated Src we applied the identical approach with labeled standards and measured phosphorylation levels of Y416 and Y90. Compared to Src WT and Src527F, phosphorylation of both tyrosines in the kinase dead variant was negligible despite the presence of endogenous Src and other SFKs in the U2OS cells we used for the experiments. These results suggest that phosphorylation of Y90 does indeed depend on the intrinsic kinase activity of Src and is therefore very likely autophosphorylation.

We have tried to address the question of Src autophosphorylation on Y90 by analyzing the level of Y90 phosphorylation in cells expressing a kinase-inactive SrcFRET construct with open conformation (527FKD) by quantitative MS. Despite the open conformation, SrcFRET527F-KD did not display any significant phosphorylation of neither Y90 nor Y416, even though we used U2OS cells which express endogenous Src and other SFKs. These results suggest that phosphorylation of Y90 depends on catalytic activity of the kinase rather than on compactness of its conformation and is therefore very likely autophosphorylation.

5) A few other mutations would be interesting to examine in both kinase and transformation assays for comparison to the mutants that were: Y527F Y416F; Y527F Y416F Y90E. The first is a low activity control and the second is for understanding whether Y90E could overcome the lack of Y416 phosphorylation.

Due to lack of time, we did not perform these experiments. However, we analyzed our new kinasedead 527F mutant for FRET and found that despite its inactive kinase domain and lack of Y416 phosphorylation, it still retains an open conformation. We believe that this is a strong indication that the Y90E kinase-dead mutant would behave the same way, maintaining an open conformation albeit the kinase domain is inactive.

6) I recommend that the results are discussed in a more circumspect manner. The results presented in Figure 7 on the double mutant, Y527F Y90F, suggest that phosphorylation of Y90 is not a very significant component of Src kinase regulation, at least in these biological contexts. That Y90 phosphorylation isn't a major regulatory factor does not diminish the value of the work describing Y90 phosphorylation. However, it does alter the interpretations. I encourage a more conservative discussion of its importance and a broader discussion of why it isn't a major site of Src phosphorylation, particularly if it is due to autophosphorylation.

We believe that given our new quantifications showing that Y90 phosphorylation is indeed considerably present and utilized in cells, the original discussion is consistent with the new data and does not need to be changed.

Reviewer #2 (Public Review):

The manuscript "Phosphorylation of tyrosine 90 in SH3 domain is a new regulatory switch controlling Src kinase" describes efforts to understand how phosphorylation of tyrosine (Y90) in the SH3 domain of Src affects the activity and function of this multi-domain kinase. The authors find that an Src variant containing a phospho-mimetic mutation (Glu) at position 90 demonstrates elevated activation levels in lysates and cells (Figure 1) and adopts a less compact autoinhibited conformation within the context of a SrcFRET biosensor in lysates (Figures 3A, 3B). A series of pulldown experiments with an isolated SH3 domain (Figure 2A, 2B) or full-length Src (Figure 2C, 2D) that contain the phospho-mimetic Y90E mutation demonstrates that phosphorylation of Tyr90 would likely disrupt the interaction of Src's SH3 domain with intermolecular binding partners and the linker that couples SH2 domain/C-tail binding to autoinhibition, which provides a mechanistic basis for the observed elevated kinase activity of Src Y90E. By performing a series of imaging experiments with a SrcFRET biosensor, the authors show that the Y90E mutation does not show enhanced localization at focal adhesions like a hyperactivated Src mutant (Y527F) that contains a non-phosphorylatable C-tail (Figure 4A). However, using ImFCS combined with TIRF microscopy (Figure 4B), the authors demonstrate that Src Y90E shows similarly reduced mobility (relative to the WT SrcFRET biosensor) at the plasma membrane (especially at focal adhesions) as Src Y527F. Consistent with the elevated kinase activity of Src Y90E, the authors go on to demonstrate that the Src Y90E variant shows an ability to transform fibroblasts-at levels that are intermediate between wild-type Src and the hyperactive Src mutant Y527F (Figure 5). Similarly, Src Y90E confers an intermediate level (between wild-type Src and Src Y527F) of invasiveness and ability to form spheroids. Together, these comprehensive experiments with a Y90 phospho-mimetic strongly support a model where phosphorylation of Src's SH3 domain at Tyr90 would lead to a more intramolecularly disengaged SH3 regulatory domain and enhanced kinase activity in cells.

Most of the conclusions in this paper are well supported by solid data, but confidence in several assays would be higher if additional technical detail or controls were provided and the biological significance of these findings would be higher if the role that Y90 phosphorylation plays in Src regulation and function were better delineated.

1) The kinase activity assays in Figures 1C,1D, and 7A need to be scaled to the Src variant levels present in the lysate (quantification of relative Src levels is not provided).

For kinase activity measurements, we used lysates of equal protein concentrations prepared from cell lines stably expressing Src variants. These cell lines were sorted and repeatedly tested for equal expression of Src constructs using immunodetection of Src on Western blots. We corrected the <br /> methods section and added this information to the description of kinase assays experimental setup.

2) More details are required for the experiments quantifying Y90 phosphorylation levels in Figure 3C. The experimental states that equal amounts of IP'd proteins were used for these analyses but there are no details on how this was confirmed. In addition, the experimental states that normalized intensities were used for your quantifying the Y90 phospho-peptide but no details are provided on how normalization was performed (the legend states that a base peptide was used but it is unclear what this means).

The paragraph on mass spectrometry analysis in the Materials and Methods section has been updated with the required information.

3) A key question is whether Y90 phosphorylation serves a regulatory role in Src's cellular activity and, if so, what is the regulatory network that mediates this phospho-event. Using a mass spectrometry readout with three Src variants (wild type vs. Y527F vs. E381G) that possess differing kinase activities, the authors demonstrate that Y90 phosphorylation levels correlate to Src's kinase activity (Figure 3C), which they suggest is an indication that this residue is an autophosphorylation site (or phosphorylated by another Src family kinase). However, as Src's kinase activity correlates with SH3 domain disengagement (which leads to a more accessible Y90), it is also entirely possible that another tyrosine kinase is responsible for this phosphorylation event. More importantly, it is unclear under which signaling regime Y90 phosphorylation would play a significant regulatory role. This phospho-event was observed in a previous phospho-proteomic study but it is unclear whether the phosphorylation levels of this site occur high enough stoichiometry to modulate the intracellular function of Src and whether there is a regulatory signaling network that influences Y90 phosphorylation levels.

We have tried to address the question of Src autophosphorylation on Y90 by analyzing the level of Y90 phosphorylation in cells expressing a kinase-inactive SrcFRET construct with open conformation (527F-KD) by quantitative MS. Despite the open conformation, SrcFRET527F-KD did not display any significant phosphorylation of neither Y90 nor Y416, even though we used U2OS cells which express endogenous Src and other SFKs. These results suggest that phosphorylation of Y90 depends on catalytic activity of the kinase rather than on compactness of its conformation and is therefore very likely autophosphorylation.

To further support our data on relevance of Y90 phosphorylation in cells, we performed a new MS analysis of Y90 and Y416 phosphorylation in WT and activated Src. This time we used corresponding stable isotope-labeled peptides and phosphopeptides as internal standards for MS quantification. This allowed us to measure absolute amounts of phosphorylated molecules and changes in their numbers, which is information that cannot be acquired by standard biochemical or proteomic approaches and is usually lacking for the majority of known phosphorylation sites. We found that in case of WT Src, the major phosphorylation site localized in the activation loop of the kinase domain, Y416, is phosphorylated in 22,6 % of molecules. In activated Src, this pool of Y416-phosphorylated molecules increases 2,5 times to 57 %. Y90 is phosphorylated in approximately 1 % of WT Src molecules but becomes 5,1 times more abundant in case of the activated kinase (5,3 % of phosphorylated molecules). This newly added data of absolute Src tyrosine phosphorylation (Figure 3D) is consistent with values we obtained from relative MS quantification of Src variants differing in catalytic activity (Figure 3C). Although the enrichment of Y90 phosphorylation in the catalytically active kinase is lower compared to Y416 phosphorylation in terms of percentage of phosphorylated molecules, it’s increment with respect to the basal state is significantly higher. We believe that this broader dynamic range of Y90 phosphorylation is consistent with and reflects the demonstrated regulatory function of Y90 phosphorylation. We incorporated these new results and methodological approach into the revised manuscript.

Author Response

Reviewer #1 (Public Review):

This manuscript focuses on a set of neurons from the border between the central and medial amygdala (AMGc/m-PAG ) that project to neurons in the periaqueductal gray (PAG) that gate ultrasonic vocalizations (USVs). These neurons suppress vocal production and are active in contexts where vocalizations would be inappropriate (e.g. in the presence of predator cues, or aggressive encounters with conspecifics). They then further characterized these neurons, demonstrating that like in males, these neurons are GABAergic in females and in both sexes, half of these neurons express estrogen receptor alpha (Esr1). To examine the inputs into these neurons, the authors performed monosynaptically-restricted transsynaptic rabies tracing and identified numerous cortical and subcortical projections. Of particular interest, neurons from the preoptic area of the hypothalamus (POA) in addition to terminating on PAG-USV neurons also project to AMGc/m-PAG neurons. Imaging the terminals of these neurons revealed elevated activity during vocalization-promoting contexts and optogenetically stimulating them resulted in evoking USVs. Together, these experiments further identify and quantify a circuit incorporating external factors (e.g. predatory factors, social interactions) in the drive to produce vocalizations.

The authors are commended for use of male and female mice, demonstrating that even though they produce USVs in different social contexts, AMGc/m-PAG neurons share a function in suppressing USV production in both sexes. They do this convincingly with a variety of methodologies while incorporating appropriate controls (e.g. light-only and GFP-control in optogenetic experiments). The experiments are performed in a logical order and the data generated is elaborate.

We appreciate the reviewer’s commendations and for their appreciation of the convincing insights provided by our study. We provide detailed responses to their recommendations in the following section. We hope the reviewer finds these revisions satisfactorily address their concerns.

Reviewer #2 (Public Review):

The existence of PAG-USV-producing neurons has been recently established, alongside two independent pathways, POA->PAG, and AMG->PAG, that promote and inhibit the production of ultrasound vocalizations in female and male mice, respectively. Because vocalizations can be modulated in a variety of contexts, such as in the presence of a predator, the authors first show that the AMG->PAG pathway is activated in situations where mice stop vocalizing, such as in the presence of a predator or aggressive conspecifics, and can inhibit natural vocalizations in contexts where females vocalize (extending to their previous findings in male mice). Interestingly, AMG->PAG neurons also receive input from POA neurons that are known to promote vocalizations via their connection to PAG interneurons that inhibit PAG-USV-producing neurons. This POA->AMG and PAG pathway is inhibitory and therefore its capacity to promote vocalizations via these two parallel pathways might be achieved by its inhibition of AMG and PAG neurons that inhibit the PAG-USV producing neurons. While these results hint at possible mechanisms that could underlie the hierarchical control of vocalization, and how different external signals impinge on existing pathways to produce behavior flexibility, the study is missing important elements to draw such conclusions. Overall, the study is also missing important information on how experiments were performed.

We appreciate the reviewer’s efforts to evaluate our manuscript and provide constructive feedback. In the following section, we have responded to all the reviewer’s comments and concerns and provide all but one of the previously missing elements and information. We also maintain that the results and additional analysis we provide in this manuscript go beyond merely hinting at possible mechanisms, and instead provide explicit synaptic mechanisms by which vocal-promoting and vocal suppressing signals interact in the mouse’s brain.

Author Response

Reviewer #1 (Public Review):

The authors worked towards a better understanding of the functional diversification of flavodoxins among diatoms, and this represents a quantum contribution building on the initial findings of Whitney, Lins, Hughes, Wells, Chappelle, and Jenkins (2011), with the inclusion of metatranscriptomic and other data from field collections and on-deck incubation experiments, relatively new genomic and transcriptomic datasets, and the adoption of reverse genetics tools that are not yet widely used in T. pseudonana. They hypothesize that clade I flavodoxins play a role in mitigating oxidative stress, while additional clade II flavodoxins would respond according to canon, in response to low iron availability.

The authors embarked on several field campaigns across environmental gradients where iron-responsive and oxidative stress-responsive flavodoxins were expected to show differential expression. The use of metatranscriptomics allowed taxa-specific assignment of relative transcript expression levels, and the results of both measurements across the environmental gradient and manipulative incubation experiments show the widespread taxonomic distribution of iron-responsive clade II flavodoxin. The fieldwork was well thought out, and biogeochemical trends comported to expectations. It's worth noting that the concomitant inclusion of geochemical data such as dissolved iron further strengthened the work. The authors also found clade I flavodoxins were not iron-responsive (as expected), but rather exhibited diel patterns in transcript abundance that suggest responses to photo-oxidative stress. Taken together, these field data are stunning.

We thank the reviewer for this kind assessment.

Lab experiments with five diatom species grown under varied iron and induced oxidative (H2O2) stress and transcript abundances for flavodoxin genes are reported. One reservation concerns the untoward and unknown effects of inducing outright iron starvation with the strong chelator, DFB (as opposed to achieving steady-state growth rate limitation from low iron by use of weak chelators such as EDTA). With DFB it is also difficult to predict sample timing (when cells have hit that "correct" and reproducible iron-limited space) when independent replicates are collected on different dates. Similarly, the use of DFB also makes it difficult to sample low and high iron cells at the same density or to maintain densities among replicate samples collected on different dates. pH and CO2 availability change with density unless special measures are taken.

We agree with the reviewer that DFB is a strong iron chelator that may affect diatom physiology in inadvertent ways. We designed the DFB experiments to allow us to screen multiple diatoms for whether they transcribed clade I and II flavodoxins in a short-term response to iron limitation.

We added the logic behind this experimental design (L177-179):

“In order to screen multiple diatoms for whether they transcribed clade I and II flavodoxins in response to iron limitation, we used the strong iron-chelator desferrioxamine B (DFB) and enhanced short-term iron limitation.”

Additionally, we now discuss the possible effect of DFB in our discussion (L395-410):

“Notably, we used the strong iron chelator DFB to enhance iron limitation in a variety of diatoms, as previously described (Andrew et al., 2019; Kranzler et al., 2021; Lampe et al., 2018; Timmermans et al., 2001; Wells, 1999), while recognizing that undesirable effects of DFB, that are not related to iron-limitation per se cannot be ruled out. Here, DFB was used in experiments designed to test whether transcription of the two flavodoxin clades differentially responded to iron limitation. The results from T. oceanica, and T. pseudonana agree with the literature, in which DFB was not added. In T. oceanica only the expression of one clade II flavodoxin was induced (Figure 2B-C, as in Lommer et al., 2012). The minor induction in mRNA of T. pseudonana clade I flavodoxin in response to iron limitation was detected in both long- and short-term adaptation to low iron, without added DFB (Goldman et al., 2019; Thamatrakoln et al., 2012). This flavodoxin seems to have diel regulation, and the observed induction might be specific to the circadian time and the setting of the diel cycle (Goldman et al., 2019).”

Based on the reviewer comments, we realized that our transcriptome sampling protocol was not clear. Because the diatom species have different growth rates, as well as different rates of growth-inhibition by iron limitation, we adjusted the sampling day for each species based on cell counts and photosynthetic efficiency. Importantly, the 9 samples (triplicates of 3 conditions) of each species were sampled together, at the same date and time. We also ensured that the biological replicates of each species and treatment had similar cell density at the time of harvest.

We clarified these concerns in the Results section (L188-206):

“For each diatom 6 replicates were grown in iron-replete conditions and 3 replicates in iron-limiting conditions until the low-iron cultures displayed a decrease in maximum photochemical yield of photosystem II (Fv/Fm), 3-6 days (depending on species, Figure 2 -figure supplement 1A-C, Figure 2A, supplementary file 1c), indicative of iron limitation, at which point transcriptome samples were collected for both the iron-limited and iron-replete conditions. Three of the iron-replete replicates were exposed to oxidative stress, mimicked by a lethal dose of H2O2, and transcriptome samples were collected about 1.5 h after exposure, when the cell phenotype (Fv/Fm or cell abundance) was unaltered from control.”

In the Materials and Methods section (L542-545)

"Cells were harvested by filtration onto 0.22 µm filters. Full details of the number of cells harvested per treatments, per species, and samples that failed library preparation are indicated in supplementary file 1c. The 9 samples of each diatom species were sampled together, at the same date and time. Filters were snap-frozen…”

A second set of lab experiments involved the (non-trivial) establishment and use of "knock out" clones of the clade I flavodoxin gene in the model diatom T. pseudonana to test the oxidative stress hypothesis. This is an exciting idea and the data suggest this flavodoxin may confer resistance to oxidative stress. The conclusion would be greatly strengthened if different phenotypes could be observed between WT and KO clones in response to environmentally relevant oxidative stress (such as supra-optimal irradiance), rather than exogenous H2O2 addition.

Based on the reviewer suggestion, we conducted a preliminary experiment with irradiation of up to 500 µE. As with the light level originally tested, there were no differences in growth rate or Fv/Fm between the WT and KO lines. We agree that future study of these knock-lines a series of much higher irradiation levels, photosynthetic-inhibitors, and other environmental stresses is interesting, but it is out of the scope of the current study.

We now also mention this in the revised manuscript (L417-419):

“Future studies in which the oxidative stress is driven by other environmental conditions as supra-optimal irradiation, UV radiation or biotic interactions are needed to further support the role of clade I flavodoxins in oxidative stress.”

We clarify that our use of exogenous H2O2 additions was based on previous studies with Phaeodactylum and T. pseudonana that indicate that exogenous addition of micromolar range of H2O2 is representative for other oxidative stress-responses (Graff van Creveld, 2015, Volpert 2018, Mizrachi 2019) (L185-188):

“Oxidative stress was induced by the lowest lethal dose of H2O2 (200-250 µM), as similar treatment was shown to be representative to other environmentally-relevant oxidative stressors in T. pseudonana and Phaeodactylum (Graff van Creveld et al., 2015; Mizrachi et al., 2019; Volpert et al., 2018).”

The relationship between the experimental conditions and results in Figure 3C and Supplemental Figure 3H was not clear.

Figure 3C summarize parts of Figure S3H information, Figure S3D-I present the individual clones, while Figure 3 only shows WT vs Flav-KO.

According to the reviewer comments, we modified Figure S3H (it is now Figure S3I), and specify this relationship in the legend:

“H-I. Percentage of Sytox Green-positive (dead) cells, measured by flow cytometry 24 h after treatment with H2O2 treatment. Orange and gray box plots represent a Flav-KO and WT respectively, single measurements are marked, color-coded by the individual colonies. H. Results of a single dose-response experiment. I. Results from additional experiments, experiments marked with an asterisk are summarized in main Figure 3C.”

In the introduction, the authors suggest that Fe-S-containing proteins are particularly sensitive to damage via oxygen and ROS and that reliance on ferredoxin (Fd) for electron shuttling carries an enhanced sensitivity to the ROS generated during photosynthesis. References would be helpful here. Fe-S cluster-containing proteins are not monolithic regarding their behavior or susceptibility towards ROS. My limited understanding is that (i) several 4Fe-4S cluster proteins (such as aconitase, isopropylmalate isomerase) are particularly sensitive but that (ii) this is less so for canonical 2Fe-2S cluster ferredoxins; (iii) in some phototrophs Fd catalyzes the reduction of molecular oxygen to superoxide, as part of a mechanism that keeps the electron transport chain less reduced under extremely high light. Thus, ferredoxins may not necessarily be susceptible to in vivo ROS-mediated damage.

Thank you for these comments.

We modified our original sentence (L37-39):

“Moreover, iron-sulfur-containing proteins are particularly sensitive to damage via oxygen and reactive oxygen species (ROS).”

Corrected sentence:

“Moreover, iron containing proteins are sensitive to damage via oxygen and reactive oxygen species (ROS), and Fd is down-regulated in response to oxidative stress (Singh et al., 2010, 2004).”

Reviewer #2 (Public Review):

In their manuscript, Van Creveld et al. set out to demonstrate divergent functions for two clades of flavodoxin in diatoms. To achieve their goals, the authors combined metatranscriptomic results originating from three separate research cruises in the North Pacific Ocean with laboratory experiments with a clade I flavodoxin knock-out mutant in the diatom Thalassiosira pseudonana. Overall, their field study confirmed that Clade II flavodoxin is mostly up-regulated under iron limitation in most diatoms that were represented in their metatranscriptomic data (Figure 5 A-F). Their field study also demonstrated that clade I flavodoxin is expressed at levels that are several orders of magnitude lower than clade II flavodoxin (figure 5H). The lower expression of clade I flavodoxin was also observed in laboratory culture experiments (Figure 2). The laboratory experiments also demonstrated that the clade I flavodoxins were responsive to iron limitation in some of the species studied (Their Figure 2C), such that the assignment of function based solely on the clade I and clade II flavodoxin classification may not always be straight forward, and that exceptions will likely be found as more diatom species are studied.

In their quest to determine whether Clade I flavodoxin plays a role in adaptation to oxidative stress, the authors created several knock-out mutants where the clade I flavodoxin is not functional. These mutant strains responded to iron limitation in the same way as the WT strains. However, the mutant strains defective in the clade I flavodoxin were more slightly more sensitive to oxidative stress (created by exposure to lethal doses of hydrogen peroxide) than the wild-type strains. The results of the oxidative stress challenges would have been stronger if a broader concentration range of hydrogen peroxide had been used in the experiments leading to a dose-response curve for both the mutant and wild-type strains.

Thank you for this suggestion. We now tested a broader range of H2O2 concentrations on the WT and KO strains and added a new Figure S3H, which includes responses to 0, 25, 50, 75, 100, 150, 200, 250 µM H2O2.

The supplemental information provided in the main manuscript holds a lot of important information. Take for example Figure S4 showing the placement of reads for Clade I and Clade II in a Maximum-likelihood tree for flavodoxin in the North Pacific Ocean. The results show that clade II flavodoxin is much more commonly found in the transcripts than clade I flavodoxin.

Perhaps different results would have been obtained by conducting a similar sampling of metatranscriptome in the Atlantic Ocean that is less subject to iron limitation.

We agree completely and would love to analyze metatranscriptomes from the Atlantic Ocean in the future.

Overall, the authors have provided results that support a role for Clade I flavodoxin in alleviating oxidative stress in Thalassiosira pseudonana, however, whether or not this role is universal for clade I flavodoxin in other diatom species will require further studies.

We agree with this assessment that additional experiments with additional diatoms is a fruitful research area into the future.

Author Response

Reviewer #1 (Public Review):

In their study Mas Sandoval and colleagues estimate, from human genomic data, two important parameters that measure how intermarriages have been affected by social stratification in the Americas: sex-biased admixture (SB), which refers to sex differences in the chances to intermarry with another ethnic group, and ancestry-based assortative mating (AM), which refers to the higher probability of partners to intermarry when they carry similar genetic ancestries. To do so, the authors train a deep neural network (DNN) with simulations of admixture with non-random mating and use ancestry tract length distributions to infer the two parameters. They show that their approach estimates SB and AM parameters with a relatively good accuracy in a number of scenarios. When applying the DNN to empirical data, they find solid evidence that social stratification has constrained the admixture processes in the Americas for the last centuries.

In contrast with the vast majority of population genetic studies, which assume random mating, this study assesses if mating has been random or not in American populations. Furthermore, the study is very valuable because it leverages, for the first time, a deep learning approach and local ancestry inference to co-estimate the extent of SB and AM from genomic data.

One limitation of the study, however, is that it assumes that (i) the admixture date in the simulations is known and equals 19 generations and (ii) admixture started at the same time in all admixed American populations. The authors also implicitly assume that the variance of the difference between male and female ancestry proportions only depends on AM, and not admixture timing. This may be problematic, as it has been shown that linkage disequilibrium between local ancestry tracts depends both on AM and admixture timing (Zaitlen et al., Genetics 2017).

To clarify the assumption of fixed admixture date, we have added the following sentence in the results section (line 170) where the model is firstly described: “In both models we assume a continuous admixture process that starts 19 generations ago, knowing that the populations analysed trace the first contact of Native American and European populations in the first half of 16th century and assuming a generation time of 26 .9 years (Wang et al., 2023). In contrast with the approaches that aim to find an admixture date assuming random mating, we assume that the admixture process starts with the contact, and it is continuous and modulated with the mating parameters.”

We thank the reviewer for such an important reference we had not included in our manuscript, whose findings support the basis of our approach. It is now included on line 70 to justify the analysis of the length of the ancestry tracts: Herein, we argue that the tract length information can measure the non-randomness of mating associated with genetic ancestry and, therefore, it can also monitor the permeability of socioeconomic and cultural barriers between subpopulations with different genetic ancestries (Zaitlen et al., 2017)

This is also suggested by the authors' results, showing that AM estimates are much lower in admixed Americans under the two-pulse model, relative to the one-pulse model, i.e., when admixture extends over time. Estimates of AM in admixed Americans may thus be biased, if admixture actually started less (or more) than 19 generations ago.

We evaluated the resemblance of the footprints left by either assortative mating or gene flow, by testing how a neural network trained on models with gene flow due to a second migration pulse predicts migration size on data generated by models without a second migration pulse but assortative mating only . We then tested how neural networks trained on models with assortative mating detect assortative mating from data with no assortative mating but only migration. Results are summarised in Figures 4 – supplement 1 – supplement 2 and show a strong correlation of the predicted size of the second migration pulse and the simulated level of assortative mating. Parallelly, there is also a strong correlation between the predicted assortative mating level and the size of the second migration pulse. Below, we respond to the reviewers in more detail regarding this question.

Another potential limitation concerns local ancestry inference. The authors assume that RFMix makes no errors when inferring ancestry tracts. This can be a concern, as recent studies have shown that RFMix has reduced accuracy compared to other methods (Hilmarsson et al., bioRxiv 2022).

In response to this comment, we performed a local ancestry analysis with Gnomix and generated the tract length profile according to the results obtained. One possible issue shared by Gnomix and RFMix is that they may infer a higher fraction of short tracts (at the expense of breaking longer ones). This issue was reported by Gravel et al. (2012). In this study, authors decided to filter out the short tracts because these tracts showed a high rate of false positives and false negatives. Therefore, we conducted an experiment to test if filtering out the shortest tract length window (i) improve the accuracy of the predictions of the simulated test data through the Mean Squared Error (MSE), ii) decrease the uncertainty of the estimations, and (iii) increase the correlation between Gnomix and RFMix-based estimates through the generalised variance.

We also tested a modification of the tract lengths profile by dividing (or not) the tract lengths profile by the total amount of tracts in either the Autosomes or the X chromosome. Our goal was to force the neural network to focus on the profile shape rather than on the absolute value of tracts at each window to mitigate the possible bias in the tract length profile. Our experimental set-up consisted of three combinations of modifications of the tract length profile, in addition to the non-modified one.

In Figures 4 supplement 3 – supplement 7, we show the predicted mating parameters using the modifications of the tract length profile outcoming from the local ancestry inference. Each point represents a prediction using RFMix and Gnomix tract length profiles (x and y axis, respectively) as input for each of the 1000 trained neural networks with the same architecture. We evaluated the uncertainty of the estimations for both Gnomix and RFMix and the correlation between them through the Generalised Variance. The Generalised Variance is the determinant of the covariance matrix, which increases with low values of covariance of the bivariate distribution and high values of the respective variances.The estimations of the parameters based on the tract length profile normalised by dividing by the total amount of fragments in Autosomes or X chromosome had both low values of Generalised Variances in the Gnomix-RFmix bivariate distribution of predicted parameters and low values of MSE in the prediction of simulated test data. These results indicate that by normalising the tract length profile by the total amount of fragments, the distribution is still informative and less sensitive to possible biases introduced by errors in the local ancestry analysis .

Therefore, we present the results obtained from this RFMix profile in the main figures and tables, while showing the other predictions in the supplementary figures.

In addition, the authors do not report a measure of uncertainty for the estimation of SB and AM, which is another important weakness. Interpretation of parameter estimates is limited if no measures of uncertainty are provided.

We now provide the 95% CI for each parameter obtained from the distribution of predicted parameters from the 1000 trained neural networks, for both RFMix and Gnomix for the tract length profile.

Finally, the authors compare the likelihood of two competing models, assuming a single or two admixture pulses, but do not determine the accuracy of their model choice procedure.

We now include the confidence intervals of the composite likelihood by replicating the test for each of the 1000 bootstrapped tract length profiles for each population. None of the 95% confidence intervals includes both negative and positive results and all of them support either the one pulse or the two pulses model, except for the sub-Saharan ancestry in the Columbian (CLM) population.

Overall, besides these methodological limitations, I expect that the study by Mas Sandoval and colleagues could be of great and broad interest for the scientific community studying population genetics, anthropology, sociology and history.

Reviewer #2 (Public Review):

This paper introduces a method to quantify how genetic ancestry drives non-random mating in admixed populations. Admixed American populations are structured by racial, gender, and class hierarchies. This has the potential to cause both ancestry-related assortative mating, in which the ancestry of mates tends to be correlated, and ancestry-related sex bias, in which individuals have a preference for mates with a particular ancestry composition. By applying their method to several African American and Latin American populations, Sandoval et al. further our understanding of ancestry-based population structure in this region more broadly.

Strengths

As many others have recently done, Sandoval et al. leverage the ability of a neural network to predict demographic parameters from high-dimensional population genomic data. Sandoval et al. first develop a clever probabilistic model of mating by defining the probability of a male and female mating as a function of the difference in ancestry between the individuals. They use this model to simulate population genomic data under various demographic scenarios, and then train a neural network on these simulated data. Finally, they apply the neural network to empirical data and learn the parameters of the underlying probability distribution, which can be related back to assortative mating and sex bias.

One clear strength of this paper is their ability to jointly assess assortative mating and sex bias, as well as their ability to apply their model to multiple contemporary admixed populations.

Importantly, the authors couch their results in an intersectional understanding of populations and consistently refer to research from historians and other social scientists throughout their paper, which reflects a very thoughtful awareness of the interdisciplinary nature of this research.

Weaknesses

The definition of assortative mating is conceptually confusing - in the text, assortative mating is introduced as genetic similarity between mates, i.e. positive assortative mating. However, based on the definition of assortative mating in their model, a population can have high assortative mating for a particular ancestry component even when there is non-zero sex bias for that component (e.g. males with low Native American ancestry are more likely to mate with females with high Native American ancestry). Fundamentally, this scenario cannot reflect positive assortative mating; rather, it reflects negative assortative mating (i.e. there is structured genetic dissimilarity between mates). However, the authors do not discuss the fact that the interpretation of the assortative mating parameter changes with the value of the sex bias parameter.

We acknowledge that our definition of assortative mating requires more clarity. We now define it on line 155 as: The AM parameter measures the non-randomness of mating associated to a genetic ancestry. This includes both positive assortative mating -genetic similarity between mates- (when SB is zero) and negative assortative mating -genetic dissimilarity between mates- (when SB is not zero). This approach allows accounting for the male-female way of negative assortative mating through SB parameter.

In addition, the results of the inference in ASW are difficult to interpret. They find that males of high African ancestry are more likely to mate with females of low African ancestry. This result seems counterintuitive given the body of literature that suggests sex-biased admixture in African Americans has greater male European and female African contributions. The authors do not suggest potential explanations for this observation.

We agree that results regarding the ASW population can be confusing. Our hypothesis to explain such results is that the sex bias parameter captures both sex-biased migrations and sex-biased admixture. Therefore, it is difficult to accommodate the complex genetic history of ASW. We have extended the discussion on this aspect as follows on line 380:

In addition, African American populations might have a complex genetic history involving on one hand male-biased sub-Saharan migration and on the other hand an admixture femalebiased in the sub-Saharan ancestry. However, our current model can only accommodate this demographic scenario with a single sex-bias parameter, and the results regarding this population should be interpreted with caution.

Lastly, the authors have not done any simulations to assess how accurate parameter estimates are if the demographic model is misspecified, which weakens the interpretability of the results.

We have performed a new analysis where we vary AM to generate tract length profiles to predict GFR, and viceversa. The results of this analysis are shown in the new figure 4Supplement 1. Results show how the footprint in the genome of the admixing populations of assortative mating and multiple pulse migration is similar. In the discussion we argue that both One Pulse and Two pulse models must be considered because they are supported by results obtained using X chromosome and Autosomes, respectively. We discuss how accounting for migration reduces AM values and how the resulting admixture dynamics resemble in both cases.

Author Response

Reviewer 1 (Public Review):

1) In Figure 2, electron microscopy images represent n=1 cell, making it hard to know how generalizable the mitochondrial phenotypes are. It would be useful to see a quantitative summary of a larger dataset indicating how frequently the mitochondrial defects are seen.

As requested, we performed quantitative analysis of mitochondrial ultrastructure in a larger dataset (n=163 analyzed in WT and n=206 in the KO) confirming that this finding is very consistent. This additional quantitative analysis that we included in the revised manuscript confirms a very significant and diffuse alteration of mitochondrial ultrastructure in Parl-/- vs WT spermatocytes (p=0.0002).

2) In Figure 3, representative images are shown for a single field from n=1 animal. It is hard to decisively conclude that the phenotype of Pink1-/-;Pgam5-/- and Ttc19-/- testes is completely normal based on this limited data. There may be other tubules outside the field of view that are abnormal, or more subtle changes in cell ratios. This conclusion would be significantly strengthened by cell counting (e.g. # round spermatids per Sertoli cell per tubule and # spermatocytes per Sertoli cell per tubule) or other quantitation. Likewise, the similarities in phenotype between Parl-/-, Parl-/-;Pink2-/-, and Parl-/-;Pgam5-/- should be more thoroughly documented. At least some additional images should be shown.

The goal of figure 3 is to indicate that WT, Pink1-/-;Pgam5-/- and Ttc19-/- have no gross morphological abnormality and have preserved sperm production in sharp contrast with Parl/-, Parl-/-;Pink1-/-, and Parl-/-;Pgam5-/- and the TKO that show total lack of sperm in the tubular lumen, indicating that the loss of Parl alone or in combination drives this phenotype. To strengthen these conclusions we performed additional work. We stained testis sections from all strains with an antibody for AIF-1, a marker of post-mitotic spermatids/spermatozoa included in Fig3-figure supplement 1. This additional experiment clearly confirms that production of differentiated germ cells occurs only in WT, Pink1-/-;Pgam5-/- and Ttc19-/-, but not in Parl-/, Parl-/-;Pink1-/-, and Parl-/-;Pgam5-/-. These results are consistent with the reproductive capacity of these mouse lines (the first group is fertile, the second is infertile). We acknowledge we cannot rule out minimal subclinical differences in reproductive fitness between the fertile mouse groups, but this is beyond the goal of our study.

3) In Figure 4, it looks like there is a significant decrease in CIV-driven respiration in Parl knockouts, but the text describes this as "did not significantly enhance" - that is, the absence of an increase. This result is difficult to interpret without further explanation.

We recognize this might be confusing but it is specified in the text that CIV driven (TMPD+ascorbate) respiration- relying on endogenous cytochrome c- is diminished (line 195) in Parl-/- testis mitochondria. This test reflects cytochrome c oxidase respiratory capacity/activity. We performed then an additional experiment just after the previous where we add exogenous cytochrome c in the cuvette to test the integrity of the outer mitochondrial membrane and checked if CIV-driven respiration increases or not after,compared to before, the addition of cytc. Exogenous cytochrome c does not cross intact mitochondrial outer membranes, so the test is performed to verify the good quality of mitochondrial preparations and/or pathological changes by looking if of the outer membrane integrity, not the function of CIV. CIV driven respiration increases only modestly after compared to before the addition of cytc and to a similar extent in both WT and Parl-/- indicating a good quality of the mitochondrial preparations and that the outer mitochondrial membrane of these mitochondria is overall well preserved in both WT and KO.

4) In Figure 5B, there is some variation in band intensity between replicates. Quantifying the band intensity relative to the loading control would help to increase confidence in the conclusion that coQ levels are reduced.

We performed this quantification, as suggested by the reviewer, and added the quantification in figure 5B. Quantification of the band intensity relative to the loading control confirms a significant difference between WT and KO. Moreover, we performed quantitative immunofluorescence of COQ4 in SCP-1 positive cells included now in Fig 5-figure supplement 1, which confirms a significantly decreased expression of COQ4 in Parl-/- primary spermatocytes.

5) GPX4 is not a Parl substrate, and no explanation is provided for why it might be reduced in Parl-/- testes. This makes the result and model difficult to interpret.

We thank the reviewer for pointing this out. We acknowledged this limitation in the discussion. We mentioned in the discussion that decreased GPX4 levels have been observed in other conditions (chemical inhibition, pathological conditions, etc.) and no mechanism has so far been demonstrated to our knowledge, but some evidence raises a possible link with CoQ deficiency that we discussed. Potential mechanisms including protein degradation are likely although unproven. This remains an important and intriguing issue to address in future studies.

6) Since Parl knockout induces necrosis in the brain, necrosis could be a contributing factor to cell death in spermatocytes alongside ferroptosis. No data is presented that can exclude this possibility.

Ferroptosis is actually considered, by some authors, a form of regulated necrosis (Seibt TM FRBM 2019). Therefore, we can affirm that PARL deletion leads to regulated necrosis in testis via ferroptosis through specific ferroptosis pathways that do not appear to be activated in the brain, or at least not overtly. Importantly, there is no recognized marker or specific molecular pathway for generic «accidental» necrosis that can be tested to differentiate between the 2 different cell death modalities.

7) The severe spermatogenesis phenotype implies that Parl knockout males should be infertile, but the fertility status is not described in the manuscript. It may be difficult to test fertility in these animals due to the neurodegeneration phenotype; if so, this can be clarified. If it is feasible to test fertility, demonstration of a fertility phenotype would significantly strengthen the conclusion that loss of Parl leads to spermatogenic arrest.

We specify in the text that Parl-/- mice are sterile due to total lack of sperm production caused by arrested spermatogenesis, as evidenced by detailed histological analysis and AIF1 staining. This is not due to the neurodegeneration since Parl-Ncre knockout have normal production of sperm as presented in the paper. Fertility in Parl-/- cannot be tested in vitro since these mice have no sperm due to the complete block of spermatogenesis, nor in vivo since they die young due to neurodegeneration. With these limitation Parl-/- males and WT females are kept together and in no single exception since the beginning of the colonies a pregnancy has ever been observed. Parl-/- mice are sterile.

Author Response

Reviewer #1 (Public Review):

The authors tried to measure the accuracy of the decision-making of honey bees by carrying out behavioural experiments in which they trained the bees to forage on artificial flowers of 5 different colours that offered different levels of reward. Subsequently, the bees' decision-making behaviour was tested with flowers of the same or different colours, with no reward present. The authors found that bees tend to approach a flower only when they are highly certain of a reward, and these decisions are made quickly. The majority of flowers were rejected by the bees. Based on the results of the tests, the authors created a model to identify what circuit elements or connections would be necessary to mimic the bees' decisions. This model could be potentially used for robotics.

The study is well supported by the signal detection theory and the experiments are well designed which is a major strength. However, the methods are not completely clear, so would be better to make a clearer description. Another weakness is the lack of clear explanations of the importance and relevance of the model.

Given the experimental design was optimal, the authors could potentially achieve the aims of this study.

Thank you for expressing your interest and providing constructive inputs. Based on your suggestions, we have thoroughly revised our manuscript to offer a more comprehensive explanation of the rationale behind our approach, as well as its comparison to existing knowledge and methods in the field. We believe that these revisions will significantly enhance the comprehensibility of our study and facilitate a better understanding of our findings.

Reviewer #2 (Public Review):

By elegantly designing experiments, MaBouDi et al. elucidated honeybee's behavioral strategy to quantitatively associate sensory cues with valences. The description is simple and concise enough to understand the logic. Particularly, the authors clearly demonstrated how sensory evidence and reward likelihood quantitatively affect the decision-making process and animals' response time. Their behavioral characterization approach and proposed model could also be helpful for studies using higher animal species. I have a few doubts regarding the definition of rejection behavior and the structure of the model that is critical to lead their main conclusions.

Thank you for your interest and valuable feedback. We greatly appreciate your input, and as a result, we have thoroughly reviewed your comments and implemented significant revisions to our manuscript. We have taken care to provide more comprehensive explanations of our methods, results, and the proposed model in order to enhance the overall comprehensibility of our study. Our intention is to ensure that readers can better understand our findings through these revisions.

Author Response

Reviewer #1 (Public Review)

Using in vitro assays that take advantage of thymic slices, with or without the ability to present pMHC antigens, the authors define an early period in which CCR4 expression is induced, which induces their migration to the medulla and likely encounter with cDC2 and other APCs. Notably, the timing for CCR4 expression precedes that of CCR7 and illustrates the potential role for this early expression to initiate the movement of post-positive selection thymocytes to the medulla. The evidence for supporting a role for CCR4, as well as CCR7, in sequential tolerance induction is provided using multiple approaches, and although the observed changes amount to small percent changes, the significance is clear and likely biologically relevant over the lifespan of a developing T cell repertoire. Overall, the model provides a holistic view of how tolerance to self-antigens is likely induced during T cell development, which makes this work highly topical and influential to the field.

We thank the reviewer for their comments and for highlighting the significance of identifying distinct roles for CCR4 and CCR7 in promoting medullary localization and inducing self-tolerance of thymocytes at different stages of T-cell development.

Reviewer #2: (Public Review )

This manuscript describes that CCR4 and CCR7 differentially regulate thymocyte localization with distinct outcomes for central tolerance. Overall, the data are presented clearly. The distinct roles of CCR4 and CCR7 at different phases of thymocyte deletion (shown in Figure 6C) are novel and important. However, the conclusion that expression profiles of CCR4 and CCR7 are different during DP to SP thymocyte development was documented previously. More importantly, the data presented in this manuscript do not support the conclusion that CCR7 is uncoupled from medullary entry. Moreover, it is unclear how the short-term thymus slice culture experiments reflect thymocyte migration from the cortex to the medulla.

We thank the reviewer for pointing out the significance of our finding that CCR4 and CCR7 regulate different phases of thymocyte deletion. We agree that prior reports, including our own (Cowan et al. 2014, Hu et al., 2015) have shown that CCR4 and CCR7 are expressed by different post-positive selection thymocytes. However, the expression data we present here provides a higher resolution perspective on the specific thymocyte subsets that express these two receptors, as well as the different timing with which the receptors are expressed after positive selection. These data, coupled with chemotaxis assays of the granular thymocyte subsets responding to CCR4 versus CCR7 ligands, and 2-photon imaging data showing that CCR4 and CCR7 are required for medullary accumulation of distinct thymocyte subsets, are critical for delineating the unexpectedly distinct roles of these two chemokine receptors in promoting medullary entry and central tolerance.

The reviewer raises an important question about our conclusion that CCR7 is “uncoupled” from medullary entry. We think there was likely a misunderstanding of our intended meaning, as we did not mean to imply that CCR7 does not promote medullary entry of thymocyte subsets; we have modified the wording of the abstract to replace “uncoupled” to clarify. As we detail in the Introduction, the role of CCR7 in directing chemotaxis of single-positive thymocytes towards the medulla and inducing their medullary accumulation is well established (Ehrlich et al., 2009; Kurobe et al., 2006; Kwan & Killeen, 2004; Nitta et al., 2009; Ueno et al., 2004). Instead, our data demonstrate that 1) the most immediate post-positive selection thymocyte subset (DP CD3loCD69+) does not require CCR7 for medullary entry, and 2) the next stage of post-positive selection thymocytes (CD4SP SM) express CCR7, but CCR7 recruits these cells only modestly into medulla. In contrast, CCR7 promotes robust medullary accumulation of more mature thymocyte subsets (CD4SP M1+M2), in keeping with the well-known role of CCR7 in promoting thymocyte medullary localization. We think these findings are highly significant for the field because currently, there is a widely held assumption that post-positive selection thymocytes that do not express CCR7 are located in the cortex, while those that express CCR7 are located in the medulla. Our data show that neither of these assumptions is true: CCR4 drives medullary accumulation of cells that do not yet express CCR7, and the earliest post-positive selection cells that express CCR7 continue to migrate in both the cortex and medulla. These findings form the basis of our statement that CCR7 expression is “not synonymous with” medullary localization. The finding that thymocytes do not robustly accumulate in the medulla in a CCR7-dependent manner until more the mature SP stages has important implications for central tolerance, as localization of thymocytes in the cortex versus medulla will impact which APCs and self-antigens they encounter when testing their TCRs for self-reactivity.

The reviewer also raised concerns about whether short-term thymus slice cultures reflect physiological thymocyte migration. Short-term live thymic slice cultures have been widely used to investigate the development, localization, migration, and positive and negative selection of thymocytes, as they have been shown to faithfully reflect these in vivo processes, including confirming the role of CCR7 in inducing chemotaxis of mature thymocytes from the cortex into the medulla (Au-Yeung et al., 2014; Dzhagalov et al., 2013; Ehrlich et al., 2009; Lancaster et al., 2019; Melichar et al., 2013; Ross et al., 2014). However, we acknowledge that thymic slices are not equivalent to intact thymuses and have now discussed limitations of this system in our revised Discussion.

Comment 1: Differential profiles in the expression of chemokine receptors, including CCR4, CCR7, and CXCR4, during DP to SP thymocyte development were well documented. Previous papers reported an early and transient expression of CCR4, a subsequent and persistent expression of CCR7, and an inverse reduction of CXCR4 (Campbell, et al., 1999, Cowan, et al., 2014, and Kadakia, et al. 2019). The data shown in Figures 1, 2, and 3 are repetitive to previously published data.

The expression profile of CCR4, CCR7 and CXCR4 on thymocytes has been documented previously in the studies cited above and in our prior publication (Hu et al., 2015). Campbell et al. (Campbell, Haraldsen, et al., 1999) investigated chemotactic effects of chemokines, but did not directly address expression of chemokine receptors by thymocyte subsets. Cowan et al. (Cowan et al., 2014) examined the expression of CCR4 versus CCR7 on DP and CD4SP thymocytes. However, our data provide a more detailed analysis of expression of these distinct chemokine receptors by subsets of DP, CD4SP, and CD8SP thymocyte subsets along the trajectory of differentiation after positive selection, using a gating scheme inspired by a study published after the above-cited papers (Breed et al., 2019). Our more nuanced evaluation of CCR4 versus CCR7 expression sets the stage for finding that they play distinct roles in promoting medullary entry and central tolerance of early- versus late-stage post-positive selection thymocytes. Without examining CCR4 and CCR7 expression patterns by distinct thymocyte subsets in detail, we would not have made the unexpected observation that although CCR7 is expressed at high levels by many CD4SP SM thymocytes, it does not induce strong chemotaxis or medullary accumulation of this subset, relative to its role in more mature SP thymocyte subsets. This finding has important implications for which APCs thymocytes encounter as they are tested for self-reactivity to enforce central tolerance. As we were working on these studies, Kadakia et al. reported that extinguishing CXCR4 expression was important for enabling medullary entry (Kadakia et al., 2019). Thus, we thought it was important to place CXCR4 in the context of CCR4 and CCR7 expression on thymocyte subsets in our study, and in doing so found another example of asynchronous chemokine receptor expression and function, further indicating that expression of a chemokine receptor alone is not a reliable marker of functional activity or thymocyte localization, as cells migrate dynamically between the cortex and medulla.

Through more extensive gating and simultaneous investigation of chemokine receptor expression and function, our data have provided new insights into how thymocytes respond to chemokine cues at different time points during their post-positive selection development. Moreover, our refined gating scheme (Figure 1) can be used to distinguish thymocyte subsets at different development stages without relying on chemokine receptor expression, thus providing an unbiased way of investigating chemokine receptor expression at different developmental stages.

Comment 2: The manuscript describes the lack of CCR7 at early stages during DP to SP thymocyte development (Figure 1-3). However, CCR7 expression is detected insensitively in this study. Unlike CCR4 detection with a wide fluorescence range between 0 and 2x104 on the horizontal axis, CCR7 detection has a narrow range between 0 and 2x103 on the vertical axis (Figure 1C, 1D, 4B, 4C, 6B, S2, S3), so that flow cytometric CCR7 detection in this study is 10-times less sensitive than CCR4 detection. It is therefore likely that the "CCR7-negative" cells described in this manuscript actually include "CCR7-low/intermediate" thymocytes described previously (for example, Figure S5A in Van Laethem, et al. Cell 2013 and Figure 6 in Kadakia, et al. J Exp Med 2019).

We provide new data to address the possibility that we were failing to detect low levels of CCR7 expression on early post-positive selection DPs (CD3loCD69+). We agree that CCR7 immunostaining of mouse cells is known to be more challenging than immunostaining of other chemokine receptors, including CCR4 and CXCR4. CCR7 immunostaining needs to be carried out at 37°C, which we did throughout our studies. We provide new data comparing CCR7 expression by Ccr7+/+ versus Ccr7-/- thymocyte subsets (Figure 1—figure supplement 2A-B), which confirm that CCR7 is not expressed at detectable levels by CD3loCD69+ DP cells above the background seen in CCR7-deficient cells. As thymocytes transition to theCD4SP SM stage, low/intermediate to high expression of CCR7 can be detected (Figure 1—figure supplement 2A). To further test whether we were failing to detect low levels of CCR7 by post-positive selection DPs, we incubated thymocytes at 37°C for up to 2 hours prior to immunostaining for CCR4 and CCR7, as a prior study indicated in vitro culture would enable increased cell surface expression of CCR7 by alleviating ligand-mediated CCR7 internalization (Britschgi et al., 2008). However, we did not observe increased CCR7 (or CCR4) expression by any thymocyte subset incubated at 37°C (Figure 1—figure supplement 2C-D). Lack of expression of CCR7 by CD3loCD69+ DP cells is consistent with their failure to undergo chemotaxis to CCR7 ligands in vitro, and initial expression of CCR7 by CD4SP SM is consistent with their chemotaxis towards CCR7 ligands in vitro (now show in greater detail in Figure 2—figure supplement 1), albeit at a much lower migration index than subsequent thymocyte subsets.

Comment 3: Low levels of CCR7 expression could be functionally evaluated by the chemotactic assay as shown in Figure 2. However, the data in Figure 2 are unequally interpreted for CCR4 and CCR7; CCR4 assays are sensitive where a migration index at less than 1.5 is described as positive (Figure 2A and 2B), whereas CCR7 assays are dismissal to such a small migration index and are only judged positive when the migration index exceeds 10 or 20 (Figure 2C and 2D). CCR7 chemotaxis assays should be carried out more sensitively, to equivalently evaluate the chemotactic function of CCR4 and CCR7 during thymocyte development.

We thank the reviewer for his insight about the possibility that we could have overlooked CCR7-mediated chemotaxis at lower migration indexes. When data from the chemotaxis assays were evaluated separately for each thymocyte subset, CCR7-mediated chemotaxis of CD4SP SM and subsequent DP CD3+CD69+ co-receptor reversing thymocytes could be detected. However, DP CD3loCD69+ thymocytes still did not undergo CCR7-meidated chemotaxis, but were responsive to the CCR4 ligand CCL22 (Figure 2—figure supplement 1).

We did not detect CCR7-mediated chemotaxis of CD4SP SM and DP CD3+CD69+ subsets in our previous analysis because their lower-level chemotactic index relative to mature thymocytes did not reach statistical significance when chemotaxis of all subsets were compared simultaneously (Figure 2D). We note that the magnitude of difference in the responsiveness of CD4SP SM cells compared to mature CD4SP and CD8SP M1 & M2 thymocytes (Figure 2D) is likely physiologically important as CCR7 deficiency results in severely reduced medullary accumulation of CD4SP M1+M2 cells, but only a very mild reduction in medullary accumulation of CD4SP SM cells, which is only detected with our new paired analyses in Figure 5C. We feel these new analyses provide important new insights and thank the reviewer for this suggestion.

Comment 4: Together, this manuscript suffers from the poor sensitivity for CCR7 detection both in flow cytometric analysis and chemotactic functional analysis. Conclusions that CCR7 is absent at early stages of DP to SP thymocyte development and that CCR7 is uncoupled from medullary entry are the overinterpretation of those results with the poor sensitivity for CCR7. The oversimplified scheme in Figure 3D is misleading.

We agree that the scheme in Figure 3D, as previously constructed, did not ideally display the difference in scale between thymocyte responses to CCR7 ligands versus CCR4 and CXCR4 ligands (as detected in vitro). Thus, we have now modified the schematic to include the mild response to CCR7 ligands that we observed in CD4SP SM thymocytes (comment 3) and to emphasize the higher chemotactic response of mature thymocytes to CCR7 ligands than of DPs and CD4SP SM to CCR4 ligands. Likewise, we have modified the manuscript to clarify the importance of CCR7 expression in the medullary entry and accumulation of mature thymocyte subsets.

We respectfully disagree that the sensitivity of CCR7 detection was poor in our flow cytometry and chemotactic analyses. Our CCR7 stains identified a range of CCR7 expression levels, from no expression by pre- and post-positive positive selection DP cells to high expression by CD4SP M1 cells, and we now provide new data confirming our ability to detect CCR7 expression (Figure 1—figure supplement 2), as described in response to Comment 3. Our chemotaxis assays detected CCR7 responses over a range of migration indexes from ~ 2 up to 100, showing our sensitive ability to detect CCR7-mediated chemotaxis in vitro (Figure 2 and Figure 2—figure supplement 1). In live thymic slices, we were also able to capture a range of biologic activities of CCR7, from mediating modest medullary accumulation of CD4SP SM cells to robust medullary accumulation of CD4SP M1+M2 cells (Figure 5A-C). Importantly, our results demonstrate that CCR7 is not the only chemokine receptor responsible for medullary entry and accumulation of thymocytes. Complex spatiotemporal regulation of thymocytes at distinct stages of development is achieved through tight orchestration of expression and signaling through multiple chemokine receptors, including CCR4, as shown by our data. However, our study does not negate an important role for CCR7 in mediating medullary entry of thymocytes, which we have clarified in the text.

Comment 5: The short-term thymus slice culture experiments should be described more carefully in terms of selection events during DP to SP thymocyte development, which takes at least 2 days for CD4 lineage T cells and approximately 4 days for CD8 lineage T cells (Saini, et al. Sci Signal 2010 and Kimura, et al. Nat Immunol 2016). The slice culture experiments in this manuscript examined cellular localization within 12 hours and chemokine receptor expression within 24 hours (Figures 4, 5) even for the development of CD8 lineage T cells (Figure S2), which are too short to examine entire events during DP to SP thymocyte development and are designed to only detect early phase events of thymocyte selection.

Experiments in Figures 4 and 5 were indeed designed to capture behaviors of thymocytes relatively early after introduction onto thymic slices. Figure 4 (and Figure 4—figure supplement 1) shows that the timing of CCR4 versus CCR7 expression after positive selection is dramatically different: CCR4 is expressed within hours of positive selection, concomitant with medullary entry, while CCR7 expression takes several days in the slices (sufficient time for CD8SP development, Figure 4—figure supplement 1). Figure 5 shows that medullary accumulation of CD4SP M1+M2 cells occurs robustly in the medulla of thymic slices within a couple of hours after introduction into the slices, and this localization is CCR7 dependent, while CCR4 induces more mild medullary accumulation of post-positive selection DPs. As indicated by the reviewer, it has been shown that it takes days for DP thymocytes to develop into mature CD4SP and CD8SP cells (Kimura et al., 2016; Lutes et al., 2021; Saini et al., 2010), as recapitulated in the thymus slice system (Figure 4—figure supplement 1) (Lutes et al., 2021). The relatively short time frame of our time-course experiments (up to 12 hours after addition of pre-positive selection thymocytes to positively selecting thymic slices) allowed us to detect expression of CCR4 within a few hours after positive selection and to determine that this timing correlated with medullary entry. Thus, the 12-hour time-course was important for temporal resolution of chemokine receptor expression and medullary localization after initial stages of positive selection.

Comment 6: It is unclear what the medullary density alteration measured in the thymus slice culture experiments represents. Although the manuscript describes that the increase in the medullary density reflects the entry of cortical thymocytes to the medulla (Figure 4E and S2E), this medullary density can be affected by other mechanisms, including different survival of the cells seeded on the top of different thymus microenvironments. Thymocytes seeded on the medulla may be more resistant to cell death than thymocytes seeded in the cortex, for example, because of the rich supply of cytokines by the medullary cells. So, the detected alterations in the medullary density may be affected by the differential survival of thymocytes seeded in the cortex and the medulla. Also, the medullary density is measured only within a short period of up to 12 hours. The use of MHC-II-negative slices and CCR4- or CCR7-deficient thymocytes in the thymus slice cultures may verify whether the detected alteration in the medullary density is dependent on TCR-initiated and chemokine-dependent cortex-to-medulla migration.

We thank the reviewer for pointing out these possibilities. The purpose of the positive selection timing experiment (Figure 4) was to establish the early correlation between receiving a positive selection signal, upregulating CCR4, and migrating into the medulla. In this system, cells only enter only the cortex in the first hour after migration in the slice, consistent with prior studies of localization of pre-positive selection thymocytes to the cortex (Ehrlich et al., 2009; Ross et al., 2014); subsequently, they move into the medulla. Because CCR7 is widely accepted to be essential for medullary entry, we feel it is important to demonstrate the disconnect between the timing of medullary entry and CCR7 expression in multiple ways. The timing experiment design utilized MHCII-/- and β2m-/- slices to show that positive selection was necessary for expression of CCR4. To test whether CCR4 or CCR7 were required for medullary entry of early post-positive selection DPs, we evaluated medullary accumulation of this subset from WT, Ccr4-/-, Ccr7-/-, and Ccr4-/-Cc7-/- mice. This experiment provided a more robust means of determining the extent to which CCR4 deficiency impacted medullary localization of a large cohort of cells that had passed positive selection (Figure 5), and again showed that the post-positive selection thymocytes, which express CCR4 but not CCR7, accumulate in the medulla in a CCR4-dependent manner. We note that in Figure 5, we show that all Ccr4-/-Ccr7-/- thymocyte subsets imaged have medullary:cortical density ratios of ~1, indicating an even distribution across cortex and medulla, which is highly consistent with an essential role for these two chemokine receptors in cooperating to mediate medullary accumulation of different stages of developing T cells.

The reviewer makes an interesting point that survival cues could differ in the cortex versus medulla. However, if thymocytes lacking one or both chemokine receptors had impaired survival because they didn’t enter a region of the thymus efficiently to receive survival cues, we would expect to detect increased apoptosis in Ccr4-/-, Ccr7-/-and Ccr4-/-Cc7-/- thymocytes. However, we found that chemokine receptor deficiencies resulted in diminished apoptosis of different thymocyte subsets (Figure 6). This finding is more consistent with reduced negative selection of these subsets due to reduced clonal deletion. We nonetheless discuss this possibility in our revised manuscript, as it important to consider that chemokine-mediated migration of thymocytes into different microenvironments could alter their access cytokines and other pro-survival cues.

Reviewer #3 (Public Review)

In this manuscript, Li et al. examine how the expression of the chemokine receptor CCR4 impacts the movement of thymocytes within the thymus. It is currently known that the chemokine receptor CCR7 is important for developing thymocytes to migrate from the cortical region into the medullary region and CCR7 expression is therefore often used to define medullary localization. This is important because key developmental outcomes, like enforcing tolerance to self-antigens amongst others, occur in the medullary environment. The authors demonstrate that the chemokine receptor CCR4 is induced on thymocytes prior to expression of CCR7 and thymocytes exhibit responsiveness to CCR4 ligands earlier in development. Using elegant live confocal microscopy experiments, the authors demonstrate that CCR4 expression is important for the entry and accumulation of specific thymocyte subsets while CCR7 expression is needed for the accumulation of more mature thymocyte subsets. The use of cells deficient in both CCR4 and CCR7 and competitive migration/accumulation experiments provide strong support for this conclusion. The elimination of CCR4 expression results in decreases in apoptosis of thymocyte subsets that have been signalled through their antigen receptor and are responsive to CCR4 ligands. As expected, more mature thymocyte subsets show decreased apoptosis when CCR7 is absent. Distinct antigen-presenting cells in the thymus express CCR4 ligands supporting a model where CCR4 expressing thymocytes can interact with thymic antigen-presenting cells for induction of apoptosis. The absence of CCR4 results in an increase in peripheral T cells that can respond to self-antigens presented by LPS-activated antigen-presenting cells providing further support for the model. Collectively, the manuscript convincingly demonstrates a previously unappreciated role for CCR4 in directing a subset of thymocytes to the medulla.

We thank the reviewer for appreciating the novelty of the finding that CCR4 directs distinct subsets of thymocytes into the medulla relative to CCR7, as supported by multiple lines of evidence.

Author Response

Reviewer #1 (Public Review):

The sustainability of vaccination programs is subject to multiple threats, from a pandemic like COVID-19 to political changes. The present study assesses different strategies, including gender-neutral vaccination, to better respond to threats in HPV national immunization programs. The authors showed that vaccinating boys against HPV (compared to vaccinating girls alone), would not only prevent more cases of cervical cancer but also limit the impact of disruptions in the program. Moreover, it would help attain the goal set by the World Health Organization of eliminating cervical cancer as a public health problem sooner, even in the case of disruptions.

Strengths and weaknesses: I found the manuscript well-written and easy to read. Decision-makers may find the results helpful in policy development and other researchers may use the study as an example to investigate similar scenarios in their local contexts. Nevertheless, there are some limitations. First, it should be considered that the present study is only applicable to India and other countries with a similar HPV context. Second, because it is a study based on a mathematical model, errors might arise from the assumptions considered for its construction. It also relies on the quality of the data used to construct and calibrate the model.

Models are important tools for decision-making, they allow us to assess different scenarios when obtaining real-world data is not feasible. They also allow to carried-out multiple sensitivity analyses to test the strengths of the results. The study carries out a necessary assessment of different vaccination strategies to minimize the impact on cervical cancer prevention due to disruptions in the HPV immunization program. By using a mathematical model, the authors are able to assess different scenarios regarding vaccination coverage rates, disruption time, and cervical cancer incidence. Therefore, decision-makers can consider the scenario which best represents their current situation.

The present study is not only valuable for decision-making, but also from a methodological point of view as future research can be conducted exploring more in deep the impact of vaccination disruptions and prevention measures.

The conclusions of this paper are mostly well supported by data, but some aspects of the methodology need clarification; furthermore, some aspects of the calculations can be improved. It would be more informative, and better for comparisons between the four scenarios, to have relative measures instead of the absolute numbers of cases prevented.

We thank the reviewer for the kind acknowledgement of the merits of the paper. We have tried to address the suggestions and questions as much as possible in the revised manuscript.

We agree to the points of weaknesses raised by the reviewer regarding the applicability of our study results is limited to other countries and the possible errors arising from a using a mathematical model. We have added more elaborate discussion of these points in the manuscript, as follows: - Page 15 lines 310-312: “Extrapolation of the results of this study to other populations will be limited to those sharing similar patterns of demography, social norms, and cervical cancer epidemiology as India.” - Page 17 lines 361-363: “…, within the limitations of our model, the modelbased estimates show that shifting from GO to GN vaccination may improve the resilience of the Indian HPV vaccination programme while also enhancing progress towards the elimination of cervical cancer.”

Furthermore, we have tried to clarify the rationale, advantages, and limitations of the measure of resilience we have adopted.

Reviewer #2 (Public Review):

This study evaluated the effect of population-based HPV vaccination programs in India which is suffering from the disease burden of cervical cancer. The authors used model simulations for estimating the outcomes by adopting the latest available data in the literature. The findings provide evidence-based support for policymakers to devise efficient strategies to reduce the impacts of cervical cancer in the country.

Strengths.

The study investigated the potential impact of cervical cancer elimination when HPV vaccination was disrupted (e.g., during the COVID-19 pandemic) and for meeting the WHO's initiatives. The authors considered several settings from the low to high effects of vaccination disruption when concluding the findings. The natural history was calibrated to local-specific epidemiological data which helps highlight the validity of the estimation.

Weaknesses.

Despite the importance and strengths, the current study may likely be improved in several directions. First, the study considered the scenario of using a recently developed domestic HPV vaccine but assuming vaccine efficacy based on another foreign HPV vaccine that has been developed and used (overseas) for more than 10 years. More information should be provided to support this important setting.

Second, the authors are advised to discuss the vaccine acceptability and particularly the feasibility to achieve high coverage scenarios in relatively conservative countries where HPV vaccines aim to prevent sexually transmitted infection. Third, as the authors highlighted, the health economics of gender-neutral strategies, which is currently missing in the manuscript, would be a substantial consideration for policymakers to implement a national, population-based vaccination program.

We thank the reviewer for the kind acknowledgement of the merits and strengths of the paper.

We have tried to address the reviewer’s three points of weaknesses as comprehensively as possible in the revised manuscript.

Regarding the first two points of weaknesses, we have provided more background information about the current situation of HPV introduction and screening in India (see the more specific replies below for where changes have been made), and some data of observed coverage in India in the states where HPV vaccination has been introduced.

Regarding the reviewer’s third point about the health economics of genderneutral strategies, we agree fully that it is an important aspect to consider for the local policymakers. However, a health economic assessment is out of the scope of the present paper. In the present paper, we are interested in highlighting the potential health benefits on GN HPV vaccination. Given the current context of HPV vaccination in India we think it is too early to provide a realistic assessment of the health-economic balance of GN vaccination. Please note that one manuscript (de Carvalho et al., MedRxiv, doi: https://doi.org/10.1101/2023.04.14.23288563) based on the same modelling exercise and reporting a health economic assessment of girls-only (routine and catch-up) HPV vaccination in India is currently submitted for peer-review.

Reviewer #3 (Public Review):

The authors put together a rigorous study to model the impact of HPV vaccine programme disruptions on cervical cancer incidence and meeting WHO elimination goals in a low-income country - using India as an example. The study explores possible scenarios by varying HPV vaccination strategies for 10-year-old children between a) increasing vaccine coverage in a girls-only vaccination programme and b) vaccinating boys in addition to girls (i.e a gender-neutral vaccination programme).

The main strength of this study is the strength of the modelling methodology in helping to make predictions and in contingency planning. The study methodology is rigorous and uses models that have been validated in other settings. The study employs a high level of detail in calibrating and adapting the model to the Indian context despite poor data availability. The detailed methodology allows future studies to employ the model and techniques with locally-contextualised parameters to study the potential impact of HPV vaccine programme disruptions in other countries.

The work in this field can begin to help lower-income countries explore varying HPV vaccination strategies to reduce cervical cancer incidence, keeping in mind the potential for future supply chains or other related disruptions. However, the scenarios could be better sculpted to model potentially realistic scenarios to guide policymakers to make decisions in situations with limited vaccine supplies - in other words comparing scenario alternatives based on a fixed number of vaccines being available. Using comparative alternatives will help policymakers grapple with the decisions that need to be made regarding planning national HPV vaccination programmes. The results could afford to provide readers with a clearer measure of vaccine strategy 'resilience'.

In all, the authors are able to successfully explore the potential impact of varying HPV vaccination strategies on cervical cancer cases prevented in the context of vaccine disruptions, and make valid conclusions. The results produced are rich in information and are worthy of deeper discussion.

We thank the reviewer for the kind acknowledgement of the merits and strengths of the paper.

Author Response

Reviewer #3 (Public Review):

The strongest aspects of this study are the structural analysis of the 90 residue KER domain. This is an important advance, discovering a founding member of a novel class of DNA binding motifs, termed a SAH-DBD (single alpha helix-DNA binding domain). Interestingly, they define a subregion of KER (termed "middle-A", residues 155-204 of Cac1) that has nearly the same DNA binding affinity and confers similar in vivo phenotypes as the full KER domain.

This study also shows that the biological role of KER partially overlaps compensatory factors in vivo, both within the same Cac1 protein subunit (e.g. the WHD domain) and also with other proteins acting in parallel (e.g. Rtt106). That is, the presence of either WHD or Rtt106 renders the drug-resistance and silencing assays employed here insensitive to loss of the KER domain.

However, the drug resistance and gene silencing phenotypes are inherently indirect measures of the most important claim of this work, that KER is a molecular ruler for DNA for the purpose of ensuring sufficiently large templates deposition of histone H3/H4 cargoes. Therefore, this study would be of greater impact if the authors more directly tested this measurement idea in assays that directly assess histone deposition. There are multiple options. Since the authors have in hand recombinant wild-type and mutant CAF-1 complexes, one could examine the number and/or spacing of nucleosomes formed during in vitro deposition reactions. Complementary in vivo experiments using the authors' existing mutant strains could be based on the finding that CAF-1 is particularly important for histone deposition onto nascent Okazaki fragments during DNA replication (Smith and Whitehouse, 2012; pmid: 22419157), and that the spacing pattern of nucleosomes on this DNA is greatly perturbed in cac1-delete cells.

Thank you for the suggestion of approaches to obtain data that more directly addresses changes in nucleosome assembly due to CAF-1 KER mutants. We considered using an in vitro nucleosome assembly assay, such as the reconstitution of nucleosomes onto gapped DNA using purified components developed by Kadyrova et al., 2013 (doi: 10.4161/cc.26310). However, they found defects only in the amount of nucleosome assembly and not changes in nucleosome spacing without CAF-1. In addition, we didn’t have the system set up and knew that it would be unlikely to produce data in the time needed for a revision of the manuscript, or even show spacing changes in nucleosomes at all. Therefore, we chose an assay system in yeast that already has been used to assess the impact of CAF-1 DNA binding mutants on nucleosome assembly (Smith and Whitehouse, 2012; pmid: 22419157 and Mattiroli et al., 2017 doi: 10.7554/eLife.22799). This approach, developed by Smith and Whitehouse, uses a degradable Ligase I system in yeast, which reveals Okazaki fragment lengths, and shows a defect when CAF-1 activity is knocked out (Smith and Whitehouse, 2012). This assay also showed that mutations or deletions in the Cac1 WHD DNA binding domain, led to increased lengths of Okazaki fragments (Mattiroli et al., 2017). As the WHD DBD impacts Okazaki fragment lengths, we reasoned that mutations in the KER DBD might also.

We generated numerous new yeast strains that included the degradable Ligase I system and collaborated with Dr. Duncan Smith of (Smith and Whitehouse, 2012; pmid: 22419157) to detect nascent Okazaki fragments in various CAC1 mutants in strains that were RTT106 or rtt106∆. We found that the Okazaki fragment lengths from cac1∆ yeast were larger and less discrete than from CAC1 yeast (as Dr Smith published previously) and that the Okazaki fragments from the cac1∆ rtt106∆ strain were barely detectable, presumably because they were too long to be resolved on the gel. However, the assay did not have sufficient resolution to detect changes between the Okazaki fragment length distribution between wild type CAC1 or the ∆KER, ∆middle-A and 2xKER mutants of CAC1, in either the RTT106 or rtt106∆ background. Therefore, we were unable to detect direct effects of the KER mutants on Okazaki-fragment lengths. We considered using the combination of KER mutants with the WHD mutants, but as this would not directly assess the effects of the KER mutants and CAF-1 proteins lacking the KER and the WHD don’t bind to DNA (Figure 3 in Mattiroli et al., 2017), we didn’t pursue it. As the complete deletion of the KER, shortening of the KER and lengthening of the KER did not give detectable changes in this assay, we also did not pursue the other mutants tested in the manuscript. Although, we are disappointed the experiment did not reveal effects that we had hoped for, this experiment provides support for the redundant functions of CAF-1 and Rtt106 in nucleosome assembly, which has not been shown using this assay. As such, we have added Figure 1-figure supplement 1g and text to the results section, methods section and strain table. We have included Prof. Duncan Smith and his student Anne Seck as authors.

Added text lines 195 to 207: “Finally, to assess the impact of deleting the KER more directly on nucleosome assembly in vivo, we examined histone deposition onto nascent Okazaki fragments during DNA replication as we have shown previously that the length of Okazaki fragment lengths are determined by histone deposition into nucleosomes and is disrupted upon deletion of CAC1 (Smith and Whitehouse, 2012). We compared CAF-1 mutants in the WT yeast background and in yeast lacking Rtt106. We found that the Okazaki fragment length distributions of the ∆KER mutant was indistinguishable from that of WT while that of cac1∆ was disrupted (Figure 1-figure supplement Figure 1-figure supplement 3g). That we did not detect effects on Okazaki-fragment lengths for the yCAF-1 mutants lacking the intact KER is consistent with the results of the viability and silencing assays for KER mutants, which also retained the WHD. Strikingly, the Okazaki fragments from rtt106∆ cac1∆ yeast were highly disrupted (Figure 1-figure supplement Figure 1-figure supplement 3g) further highlighting the redundancy between Rtt106 and Cac1 for assembling histones onto newly replicated DNA. Therefore, t”

Author Response

Reviewer #3 (Public Review):

The authors investigated the mechanism of transport of the GLUT5 sugar porter using enhanced sampling molecular dynamics simulations and biochemical analysis. The results suggest a possible general mechanism by which binding to a transported substrate stabilizes an occluded intermediate conformation between outward and inward-facing states of the alternating access conformational change of the protein, thereby enabling transport.

The authors also identified key elements of this transition, associated with residues involved in sugar binding, and through elegant biochemical experiments demonstrated how mutations of the latter affect the protein function, including mutations of gating residues that can recover the function of inactive mutants.

The general computational methodology used by authors is appropriate for addressing these questions and compared to other techniques has the advantage of bringing forth an unbiased molecular description of the transport process. The results are overall qualitatively in line with the proposed conclusions.

A major weakness of this work is that, in contrast to previous studies with the same type of methodology, the authors do not report error analysis or careful statistical assessment of the computational results. Therefore, it is not clear whether the latter is solid or if they support the proposed conclusions. The computational data might generally benefit from an improved methodological design, such as including more degrees of freedom (or collective variables) in the description of the minimum free energy pathway, e.g. the salt-bridges.

This has now been addressed in the essential revisions above.

Another weakness is that some of the details of the computational analysis are not reported, therefore other investigators would not know how to reproduce the results.

We have extended the methods section to include much more detail about the MSM construction and other computational analysis. Data files needed for reproduction are now found in a public repository with links provided in the Methods section.

Author Response

Reviewer #1 (Public Review):

This manuscript presents an inference technique for estimating causal dependence between pairs of neurons when the population is driven by optogenetic stimulation. The key issue is how to mitigate spurious correlations between unconnected neurons that can arise due to polysynaptic and other network-level effects during stimulation. The authors propose to leverage each neuron's refractory period (which begins at approximately random times, assuming Poisson-distributed spikes and conditional on network state) as an instrumental variable, allowing the authors to tease apart causal dependence by considering how the postsynaptic neuron fires when the presynaptic neuron must be muted (i.e., is in its refractory period). The idea is interesting and novel, and the authors show that their modified instrumental variable method outperforms similar approaches.

We wish to thank the reviewer for this positive assessment.

However, the scope of the technique is limited. The authors' results suggest that the proposed technique may not be practical because it requires considerable amounts of data (more than 10^6 trials for just 200 neurons, resulting in stimulation of more than 5000 times per neuron). Even with such data sizes, the method does not appear to converge to the true solution in simulations. The method is also not tested on any experimental data, making it difficult to judge how well the assumptions of the technique would be met in real use-cases. While the manuscript offers a unique solution to inferring causal dependence, its applicability for experimental data has not yet been convincingly demonstrated, and would, therefore primarily be of interest to those looking to build on these theoretical results for further method development.

We thank the reviewer for this assessment and agree that the requirement for this many trials makes the estimators practically unsuitable for identifying causal interactions in large systems. However, in the revised manuscript, we can observe that the IV estimator can be beneficial after even a few thousand trials when introducing a newly improved error measurement (which we discovered thanks to these reviews). Moreover, we agree that this work will be of interest to the more theoretically oriented community for methodological improvements; we believe that the methods and causal inference framework will be interesting and useful for the wider neuroscience community. For example, considering the first (new) example in the introduction, even under two-photon single-neuron stimulation, the IV framework should be used to avoid bias amplification.

Reviewer #2 (Public Review):

Lepperød et al. consider the problem of inferring the causal effect of a single neuron's activity on its downstream population. While modern methods can perturb neuronal activity, the authors focus on the issue of confounding that arises when attempting to infer the causal influence of a single neuron while stimulating many neurons together. The authors adapt two basic methods from econometrics that were developed to address causal inference in purely observational data: instrumental variables and difference-in-differences, both of which help correct for unobserved correlations that confound causal inference. The authors propose an experimental procedure where neurons have spike times measured with millisecond precision and a subset of neurons are optogenetically activated. As an instrumental variable, the authors propose using the refractoriness of a stimulated neuron, resulting in absent or delayed spiking which can be used to infer its causal effect in otherwise matched conditions.

Based on this, they develop a collection of estimators to measure the pairwise causal relationship of one neuron on another. By simulating a variety of small networks, the authors show that, provided enough data is present, the proposed causal methods provide estimates that better match underlying connectivity than methods based on ordinary least squares or naive cross-correlograms (CCHs). However, the methods proposed require extensive data and highly targeted stimulation to converge.

Strengths:

The value of the paper comes from its attempt to find neuroscience applications for methods from fields where causal analysis of observational data is required. Moreover, as the field develops improved methods of measuring anatomical neuronal connectivity using molecular, physiological, and structural approaches, the question of the causal influence of one neuron's spiking on another remains vital. The authors thoughtfully lay out the necessary conditions - and difficulties - required to establish this type of causal functional influence and suggest one potential approach. The collection of models tested highlighted both the strengths and difficulties of the suggested approaches.

We wish to thank the reviewer for the positive feedback, we are delighted to share your view that obtaining methodology for estimating causal influence is vital.

Weaknesses:

1) I found the paper's introduction to its analysis techniques to be very confusingly written, particularly as it is designed to bridge fields. It is vital that the ideas are communicated more clearly. Some topics are explained multiple times, even after being used previously, other ideas and notations are introduced and immediately dropped (e.g. the "do operator", the ratio of covariances in the introduction to instrumental variables), and still others are introduced with no clear explanation (e.g. the weight term w, the "|Y->Y-Y*" notation, and the notation in the methods with "Y(Z=0)").

We thank the reviewer to point out this lack of clarity and we extensively rewrote the paper to make it more accessible. The do operator is used in the methods to define Y(Z=0), but is now removed from the introduction to reduce the number of concepts introduced early in the text. The w term is now defined from the generative model. The difference in differences notation is written out fully to be clear and a sketch of the method intuition is added to Figure 1.

1) Of particular importance, the introduction of the Z,X, and Y variables in the first full paragraph on page five, it could be made much more clear that this method is pairwise: Z and X reference the spiking of one specific stimulated neuron at two time points and Y references one specific downstream neuron. 2) In the third paragraph of the same page, the authors refer to the "refractoriness of X" and "spiking of X onto Y", but this language confuses the neurons with variables in a way that took considerable time to unpack. 3) This was not helped by Figure 1b, which suggested that Z_i, X_i, and Y_i applied to all neurons and merely reflected time points around stimulation. 4) Similarly, the introduction of the Y* variable in the difference of differences method, which the authors view as one of the main contributions, is given little clear explanation or intuition. I assume "shifted on window-size left" means measuring the presence of spiking at the same time step as X, but I see no clear definition of this. 5) The confusion about variables remains when, in Figure 1d, a "transmission probability" goes below 0 and above 1.

1) Thank you for pointing out this lack of clarity, the suggested explanation of the variables XYZ is adopted.

2) The language is clarified such that variables and neurons are separated.

3) Figure is fixed such that variables refer to the neurons they represent.

4) We have now improved the explanation of DiD with a figure for intuition.

5) We have now redefined the “transmission probability” to effective connectivity to reduce confusion.

I also found the network models studied after the first section and the relevant variables difficult to understand with the detail necessary to interpret the results. For example, the cartoon in Figure 2a does not seem to match the text description. I see no explanation for the external "excitatory confounder" and "inhibitory confounder" terms, nor what is done to control the (undefined) \sigma_max/\sigma_min term. I don't see anything in the methods about distinct inhibitory and excitatory neurons either. Further, the violin plots (e.g. Fig 2d) seem quite noisy (e.g. is Br, DiD really bimodal?), and it is not clear what distribution is being covered by them. If this is computational simulations, I would imagine more samples could be generated. The same vagueness issues hold for the networks in section 2.4 and 2.7.

We have now clarified the implementation of the excitatory and inhibitory confounder and how we distinguish between excitatory and inhibitory neurons and defined the condition number. The violin plots were removed in Fig 2 since the large variance represented changes across external drive which produced largely incomparable statistics. To illustrate variance, we now show the standard deviation of the absolute error in line plots 2e and 2g.

2) Broadly speaking, the causal estimates appear better in the sense of having smaller errors, but it's not clear to me if they are actually good or not. What does an error of 0.4 mean in terms of your ability to estimate the causal structure, and what exactly does the Error(w{greater than or equal to}0) notion refer to? It would be useful to see actual reconstructions of ground truth versus causally inferred connectivity to better understand the method's strengths and weaknesses.

To improve clarity, we have added a paragraph in the text before figure 2 explaining a new error measure. Since the estimators give the transmission probability and not the inferred connection strength directly, we previously computed a regressed error as in Das & Fiete 2020. This error measure is equivalent to the sine of the angle between $W$ and $\hat{W}$. This error measure is not ideal and gives an indirect population measure with deviations scaled during the error regression. Upon further reflection, we realized that we could define the error directly using our definition of effective connectivity on the generative model to obtain a much cleaner and more interpretable measure. This further led us to remove one of the proposed methods (brew) as it did not perform well under this new error measure. All error measurements are updated in all figures. Error(w{greater than or equal to}0) means that we only look at positive weights; now clarified in the text

3) I found the section on optogenetic modeling to be unsatisfying in its realism. The general result that 1 photon excitation hits a wide collection of neurons is undisputed, but the simulation does not account for a number of key factors - optogenetic receptor expression is distributed across the axons and dendrites of a cell, not only soma, scattering in tissue greatly affects transmission, etc. Moreover, experiments that attempt to do highly targeted activation have other methods for exactly this reason, such as multiphoton activation or electrophysiology. The message of decreasing performance as a function of stimulus size is important, but I struggle with the idea of the model being "realistic".

We thank the reviewer for pointing out this unsatisfactory comparison with realistic scenarios. To mitigate we have changed the wording, but kept the simulation as is. As the reviewer pointed out optogenetic receptor expression is distributed, and here we have assumed an expression that only affects soma (experimentally plausible according to Grødem et al 2023 (10.1038/s41467-023-36324-3)), scattering in tissue is included according to the Kubelka-Munk model.

4) The authors spend a great deal of analysis of stimulation, but little time on measurement. It seems like this approach demands a highly precise measure of spike time to know if a neuron is firing or not at a given millisecond due specifically being in a refractory state. A stimulated but refractory neuron will still likely spike as soon as it can after the momentary delay, and given the noise in the network this difference might not be easily detectable in the delay-to-spike of the downstream neuron, even assuming one spike in the presynaptic neuron is likely to cause a spike in the downstream. It would be useful to see this aspect considered with the same detail as the rest of the study.

We thank the reviewer for pointing out this. We have now added a paragraph discussing this: “As outlined in \citep{ozturk2000ill}, ill-conditioning can affect statistical analysis in three ways and therefore similarly in inverse connectivity estimates from measured activity. First, measurement errors such as a temporal shift in spike time estimate e.g. due to low sampling frequency, inaccurate spike sorting, or general noisy measurement due to animal movement etc. In the presence of ill-conditioning the outputs will be sensitive (unstable) to small input changes. If errors are included in some variables, the inference procedures will require information about the distributional properties of these errors. Second, optimized inference can give misleading results in the presence of ill-conditioning, caused by bad design or sampling.

There will always exist a natural variability in the observations which necessitates the assessment of ill-conditioning before performing statistical analysis. Third, rounding errors can lead to small changes in input under ill-conditioning. This numerical problem is often not considered in neuroscience but will become evermore relevant when large-scale recordings require large-scale inferences.”

Author Response

Reviewer #1 (Public Review):

HCN channels are atypically opened by the downward movement of gating charges during hyperpolarisation and have such weak coupling between the VSD and pore domain, and in the absence of an open state structure, extracting mechanistic information has been difficult. This manuscript is a continuation of a previous study on HCN channel gating that revealed how hyperpolarisation causes a downward movement of the VSD's S4, with breakage into two helices. The authors explore gating motions and the coupling between VSD and the pore domain using atomistic simulations. This includes microsecond MD with and without very strong -1V applied potentials to try to drive VSD-TMD changes to open the channel. In the end, however, the authors used a biased simulation approach (adiabatic bias) to enforce conformational change from resting to an open homology model of HCN based on hERG/rEAG. This microsecond simulation followed three interaction distances that were suggested to change between resting and open states based on free MD. This simulation caused pore opening and allowed a description of changes that may occur during gating, including a competition of S5-S6 and S6-S6 contacts and lipid binding locations, which may suggest lipid-dependent function and explain an unexpected closed structure at 0mV in micelles. While I feel the manuscript is written for the HCN expert audience, the mechanistic information in terms of hyperpolarisation-induced voltage gating makes it of much interest. The manuscript is presented at a high level, though there are a couple of points to address, including reproducibility of simulations and potential for more relation to experimental findings.

We appreciate the comments, thank you, please find a detailed answer below.

The authors carried out 1μs-MD simulations of the resting, activated, and a Y289D mutant at 0 mV, and then tried to drive the conformational change with a very large -1V voltage (double that studied previously). In 1 us MD, is the membrane stable with such a big voltage, as it would likely not be experimentally? Even with a volt applied, there was incomplete activation of the voltage sensors, despite timescales approaching that of activation.

This reviewer is correct in cautioning against membrane rupturing effects in simulations with a voltage of this magnitude. We have indeed checked that the membrane and the protein remains intact under these conditions and can confirm that no poration occurs. As membrane poration is stochastic, it could indeed occur over microsecond timescales under 1V, but the probability remains low, and we were lucky to not face this situation herein. Note that whereas potentials of this magnitude could not be applied in experiments, they are relatively routinely used in MD simulations to speed up processes that are driven by changes in transmembrane potentials.

Interestingly, other work from our lab (Rems et al. Biophysical Journal 119 (1) 190-205 (2020)) has shown that HCN1 voltage sensor domains are less prone to poration than those from other voltage sensor domains, for reasons that remain to be determined.

Author Response Figure 1. Final snapshots from the simulations of the resting (blue), intermediate (yellow) and activated (red) states. The representation of the solvent (water+ions) in cyan showed no membrane poration at the end of the 1us simulations.

For the pulling/ driving simulations (adiabatic bias MD) to change suspected interaction distances (V390-I302, N300-W281, and D290-K412), it seems to be just 1 simulation, without reproducibility. One has to wonder, if the simulation was redone from a very different initial conformation, would the results be the same (in addition to the distances themselves that were enforced by the ABMD). Moreover, the authors had to model the open state, such that the results depend on a homology model based on other CNBD channels, hERG / rEAG. Although the model stayed open for a microsecond, what other measures of accuracy of the homology model are there, such as preserved distances according to mutants/double mutants?

The ABMD simulations were repeated, please refer to the response to essential revisions point 1 for details.

For reasons mentioned by the reviewer as well as a reconsideration of our strategy to model channel opening, we have decided to omit homology models from the revised version of the paper.

The authors find that activation involves hydrophobic forces that strengthen the intra-subunit S4/S5/S6 interface, as well as lipid headgroups that make contact with hydrophilic residues at this interface, with lipid tails also contributing to hydrophobic contacts. The authors see bending and rotation of the lower S4 and a displacement of S1 away from S4 that exposes the VSD-pore interface to lipids, with increased lipid contacts at S4 and S5 during activation. This indicates lipid tails may play a role in coupling in HCN1 and may explain the closed state micelle structure at 0mV. Two sites of lipid contact are identified, one engaging VSD residues and the other polar or charged residues on S5 and S6. No experiments are presented or proposed to test the predicted lipid sites. e.g. Mutation of key residues, such as the arginine and histidine seen binding lipid headgroups could be tested as proof of their involvement, or perhaps experiments with varied phosphate moieties? In the absence of new experiments, is there existing data that could help validate the findings?

We thank this reviewer for this comment. As noted in the response to essential revisions point 3, such experiments are challenging, and have not been reported so far in HCN channels. We do agree that aspects of the mechanism we propose remain hypothetical awaiting further work, but are happy to report that importance of lipid interactions with the crucial salt bridge pair mentioned in the response to essential revisions point 3 has been completely independently validated, thus strengthening our mechanistic hypothesis substantially.

During free MD simulation, the authors see tilting of S5 caused by activation of the Y289D mutation that brings D290 and K412 positions into proximity. How do we know that the adjacent mutant of Y289 to aspartate has not caused this, or was this interaction also seen in wild-type simulation? Fig.3c might suggest the wt activated simulation may see such an interaction, but it is unclear given the large C_alpha distances, as opposed to H-bonding distances.

Indeed, Figure 3 appears to indicate that this interaction between D290 and K412 is present in the activated state when the mutation is reverted to the WT sequence. We have recalculated the interaction propensity using all atoms of the residues and present an updated Figure 3c in response.

The authors predict that a D290-K412 salt bridge may be important for gating and sought to experimentally validate the interaction in the activated-open state using cysteine cross-bridging. As this is the only experimental backing in the paper, it is important to be able to judge its ability to report on the D290-K412 salt bridge. A comparison experiment demonstrating other crosslinks that do not favour the open state would have been helpful in this regard e.g. if crossbridging at similar locations (but not predicted to change interaction during gating) had little effect on I/Imax, then the result may be bolstered. Are there existing mutagenesis experiments that may suggest the importance of these residues (as well as for other key interaction distances identified)?

Negative results in cross bridging and cysteine accessibility studies in general are difficult to interpret as the lack of a cadmium-specific effect may be due to inaccessibility of the site to cadmium, pairwise distance too far to bridge by cadmium, or bridging or the specified site without a functional effect. However, as reviewer 2 pointed out below, the Yellen group has performed extensive cross bridging experiments in the S4-S5 to Clinker region in spHCN and in most of these positions, the pairs favoring the open state are closer together in our models than pairs favoring the closed state or those without functional effect. We have added Videos 1-6 to highlight this comparison on our open state models and describe in our updated discussion section.

Rotation of the V390 side chain from a position facing the pore lumen to a position facing I302 on S5 is coupled to an increase of the pore radius at V390, an increased hydration of the pore intracellular gate, and K+ ion movement. Perhaps 5 or 6 ions cross in that single simulation. As K channel ion permeation can depend critically on starting ion configs (as well as the model/force field), reproducibility of this finding is important but does not appear to have been tested. How can we be sure that periods of permeation or no permeation in individual simulations are reliable?

As mentioned in our response to essential revisions point 1, we have modified the collective variable set used in ABMD, and repeated the simulations in 4 replicates. Whereas the number of permeation events is low in each simulation (Figure 4 S1), the consistency across repeats indicates that these open pore models indeed represent conductive states. Given how short the simulations are, however, it appears unreasonable to infer conductance values from these observations.

Reviewer #3 (Public Review):

In this work, Elbahnsi and colleagues use enhanced sampling MD simulation, to recapitulate step by step, the electromechanical coupling between VSD and the pore in HCN1 channels. Building on the available cryoEM structures of HCN1 with the VSD in resting and active state, the authors characterize by MD a subset of interactions that seemingly stabilize the open channel. This subset is, in turn, used in enhanced-sampling simulations to guide channel opening. The main findings are that S4 movement induces a rearrangement of the hydrophobic interaction at the level of S1- S4- and S5 interfaces. Occupancy of lipids seems therefore statedependent and highlights their regulatory role in HCN gating.

The approach is rather innovative, and it apparently allows the reconstruction of the whole mechanism of gating, pushing the predictive power of MD simulation well beyond its actual temporal limitations. At the same time, the initial choice of interactions is crucial for this approach, because the result cannot differ from the inputs. And reading the paper it does not emerge clearly how the correctness of the reconstructed gating pathway can be verified, if not by functional validation.

We thank the reviewer for this thoughtful review. It has pushed us to reconsider our approach to enhance the sampling of channel activation and gating. Please refer to the detailed response below as well as the response in particular to essential revisions point 1.

Here are my comments on the main interactions that were used to feed the final MD simulation:

1) W281-N300: this interaction, previously identified and studied in SpH channels (Ramentol et al, 2020; Wu et al, 2021), has been elegantly confirmed in this paper. Its inclusion in the initial subset seems appropriate. In the other two cases, the choice of interactions requires further explanations and experimental validation.

2) D290 and K412: the validation of this interaction shown in Figure 3 and suppl Figure 1 is missing a control, i.e., the effect of the addition of Cd++ on the wt channel. Please add.

We have performed the control suggested. Please also refer to the answer to essential revisions point 2.

3) Modelling the open state of HCN1 pore (page 18), is done on the structure of the distantly related hERG rather than on the available open pore structure of HCN4. This choice is justified as follows by the authors:

a) "Available structures in the CNBD channel family for which representative structures have been solved in closed and open states".

b) "The structural mechanism of pore gating (i.e. the ⍺ to 𝜋 helix occurring at the glycine657 hinge in hERG) observed in rEAG/hERG may be a conserved gating transition in the CNBD family of channels"

I encourage the authors to consider the following:

a) The structure of hERG channel is not available in the closed/open configuration, indeed the comparison must be done with the closed configuration of the related channel rEAG. On the contrary, HCN4 is available in the closed/open configurations. Moreover, one of the open pore structures shows S4-S5-S6 in a very similar conformation to the lock open mutant (F186C/S264C) of HCN1 (Saponaro et al, 2021). With an available HCN4 open structure, forcing HCN1 to the open pore structure of hERG channel (which opens in depolarization and is not regulated by cAMP) seems not necessary.

In response to this point, we reconsidered our approach and chose to instead use a biasing distance that is consistently increased in CNBD channels of resolved structures, that between neighboring and cross-subunits V390. We have detailed our rationale in the response to essential revisions point 1.

To my knowledge, hERG is the only channel of the CNBD family for which the transition ⍺ to 𝜋 helix reported by the Authors, occurs in S6. It is not reported for other CNBD family members, in particular for the CNG channels mentioned by the Authors (Zheng et al., 2020; Xue et al., 2021, 2022). Task 4 (Zheng et al) does not show it. Its pore opens by a right-handed twist of S6 at glycine 399, a conserved glycine in all CNG. Human CNGA1 too, opens the pore by a rotational movement of S6 hinged at the equivalent glycine (glycine 385) (Xue et al, 2021). And the same occurs in the non-symmetrical channel CNGA1/B1 (Xue te al, 2022). So, it seems that CNG channels do not show the ⍺ to 𝜋 helix transition in the open pore. Moreover, hERG excluded, all other members of the CNBD family, CNG, EAG, and HCN4 included, do not bend at the hinge glycine 657 of hERG, but at another glycine (gly 648 in hERG numbering) located upstream. Further, their opening is due to a rotation of S6 associated with an outward movement, rather than to the lifting of the lower part of S6, as in hERG.

After considering this reviewer’s comment, we were surprised to see that HCN1 is apparently prone to secondary structure deformation in S6, even when biasing the aforementioned distances, and thus enforcing no rotation at all in S6. We are intrigued by this observation and eagerly await experimental validation or disproval.<br /> In the meantime, we have made clear in the text that this hypothesis remains based exclusively on modeling work.

4) V390-I302: this interaction is predicted to stabilize the open pore configuration and was included in the subset. The contact between V390 on S6 and I302 on S5 is observed in the homology model discussed above when the S6 is twisted at the glycine hinge, rotating the preceding residue (V390) out of its pore-lining position and is. Again, I can only disagree with this hypothesis because it has been experimentally demonstrated (Cheng et al, J Pharmacol Exp Ther. 2007 Sep;322(3):931-9) that the side chain of Valine390 is inside the cavity of the open pore of HCN1 channels as it controls the affinity for the pore blocker ZD7288.

In accordance with other comments above, we have eliminated the bias applied to the V390I302 distance. However, the new ABMD simulations with bias applied to encourage dilation at position 390 still involve rotation of V390 away from the central pore axis, albeit with bending of S6 at the upper glycine mentioned by this reviewer. The degree of rotation is lower than in our previous simulations so that V390 still lines the inner vestibule in the open state, consistent with the observation that this position influences the apparent affinity of open pore blockers.

In conclusion, modelling the open state pore of HCN1 on hERG rather than on that of HCN4 seems not justified based on accumulated evidence in the published literature. Therefore, the choice of the authors to use it as the open pore model of HCN1 channels needs to be experimentally validated. One possibility is to mutate the glycine hinge, gly391 in HCN1, into an Alanine in order to remove the flexible hinge. If this mutation alters pore gating, it will support the choice of the Authors.

Once more, we thank the reviewer for the comments, which have led us to reconsider a larg part of our modeling work.

Author Response:

We would like to thank the reviewers for their thorough evaluation of the presented manuscript and herewith would like to address their comments and suggestions.

This study was funded by a NSF-grant awarded to Prof. Celio. The animal experimentation license (including animal husbandry, breeding and experiments) that is required by law to perform animal experiments was also issued to Prof. Celio. Therefore, with the retirement of Prof. Celio, the funding for the project was discontinued and the animal license was terminated. We are thus unable to answer the reviewers’ open questions with follow-up experiments. We would however like to discuss some of the reviewers’ open questions or concerns and hope this might be insightful to the interested reader.

Reviewer #1 (Public Review):

“First, they reported that chemogenetic activation of Foxb1 hypothalamic cell groups led to tachypnea. The authors tend to attribute this effect to the activation of hM3Dq expressed in the parvofox Foxb1 but did not rule out the participation of the PMd Foxb1 cell group which may as well have expressed hM3Dq, particularly considering the large volume (200 nl) of the viral construct injected. It is also noteworthy that the activation of the Foxb1hypothalamic cell groups in this experiment did not alter the gross locomotor activity, such as time spent immobile state.”

Because an AAV2 serotype was used for expression of the chemogenetic tools, the spread of viral infection was much more restricted to the injection site in chemogenetic animals than was observed the AAV5-based expression of optogenetic tools. The more restricted spread of viral infection with AAV2 serotypes has previously been shown by a range of other groups (e.g. see https://doi.org/10.3389/fnana.2019.00093). This limited spread of the AAV2 serotype in our chemogenetic animals, together with the absence of the very strong locomotor phenotype observed during optogenetic stimulation experiments makes us hypothesize, that the respiratory phenotype is largely attributable to the ParvafoxFoxb1 neurons.

“In the second experiment, the authors applied optogenetic ChR2-mediated excitation of the Foxb1+ cell bodies' axonal endings in the dlPAG leading to freezing […]. Here it is important to consider that optogenetic ChR2-mediated excitation of the axonal endings is likely to have activated the cell bodies originating these fibers, and one cannot ascertain whether the behavioral effects are related to the activation of the terminals in the PAGdl or the cell bodies originating the projection.”

We did not consider the possibility of backpropagation induced by optogenetic axon terminal stimulation at the time of experiments. We acknowledge that this is the major limitation of our optogenetic experiments that would have to be investigated with further animal experiments.

Reviewer #2 (Public Review):

“3) Fig. 5, a great effort has been made to illustrate the point that CCK and Foxb1 are differentially expressed. Why not just perform a double in situ experiment to directly illustrate the point?”

We came across the publication in which the Cck-expressing PMd neurons’ control escape behaviors, only when we were drafting the manuscript. Because this was already after the retirement of Prof. Celio and we were not able to conduct further experiments involving animals, we leveraged on in silico methods and the publicly available high-quality dataset on the gene expression of the posterior hypothalamic area. The applied in silico method of dimensionality reduction and cluster assignment is well established and widely accepted. We believe in the quality of the dataset and the reliability of these in silico results but we agree with the reviewer that an alternative would have been to illustrate the expression patterns of Cck and Foxb1 by in-situ hybridisation.

“4) Fig. 7 data on optogenetic stimulation on immobility and breathing, since not all mice showed the same phenotype, what is the criterion for allocating these mice to hit or no hit groups?"

We defined the group allocation criteria in the section titled “Optogenetic modulation of Foxb1 terminal in the dlPAG induces immobility” as follows:

“OnTarget_antPAG animals had the tip of the optic fiber implant located above the dlPAG at an anterior-posterior level AP-4.04mm (from bregma) or proxymal. The OffTarget group contains animals with fiber tips located below (i.e. ventral to) the dlPAG and/or located more distal than AP -4.04mm.”

Author Response

Reviewer #1 (Public Review):

The manuscript of Parab et al. reports a beautiful phenotype analysis of the vascular brain/meningeal anatomy in a variety of reporter lines and mutants for Wnt/β-catenin signaling and angiogenic cues (Vegfaa, Vegfab Vegfc, Vegfd) during zebrafish development.<br /> The present study extends the previous work of the same Parab, Quick, and Matsuoka, that focused on fenestrated vessel formation in the zebrafish myelencephalic choroid plexus (mCP). Vegfs were shown to regulate fenestrated vessel formation in combination, but not individually, and with only little effect on neighboring non-fenestrated brain vessel development. The fenestrated endothelium is thus known to have specific angiogenic requirements.

The scale of investigation has now changed, and fenestrated vessel formation has been examined throughout the brain, in both circumventricular organs (organum vasculosum of lamina terminalis) and other choroid plexuses (CPs) including the diencephalic CP and its interface with the pineal gland, the eye choroid (choriocapillaris), and the hypophysis vasculature. The original finding is that a regionspecific code of angiogenic cues controls fenestrated vessel formation. The authors show that fenestrated vessels form independently of Wnt/β-catenin signaling and BBB vascular development but require different combinations of Vegfa and Vegfc/d-dependent angiogenesis within and across brain regions. A previously unappreciated function of autocrine and paracrine Vegfc signaling is demonstrated in this brain region-specific regulation of fenestrated capillary development.

Twenty-one different fish lines accurately genotyped and characterized and including a new Reck mutant, have been instrumental to conduct vascular pattern analysis, using confocal and stereomicroscopy imaging combined with transmission EM. High-quality illustration and robust quantification methods, previously validated, have been used. The study is well organized and reflects the high expertise and strong methodology of the investigators. Data are presented in nine dense figures and the contribution of angiogenic ligands to fenestrated vessel formation can hardly be studied more indepth.

However, and this will be my only main concern, no information is provided on the regional diversity of angiogenic receptor expression that may correlate with the regional angiogenic factor code. Without asking for a spatial transcriptomic study, the combination of Vegfr-reporter lines or in situ hybridization with a combination of receptor probes would allow for generating a comprehensive set of ligand/receptor data relative to the regional angiogenic signaling pattern involved in fenestrated vessel formation.

We appreciate this reviewer’s positive and encouraging comments highlighting both the quality and significance of our study. As we commented in response to the Essential Revisions point #1, we anticipate that a detailed expression analysis of all four Vegf receptors at different developmental stages during CP and CVO vascularization will be best addressed with new technologies combined with optimizations of existing tools/protocols. Thus, we have provided a paragraph of discussion on our perspectives for potential Vegf receptors involved in CP and CVO vascularization in the current study.

We address each of the points raised by the reviewer below.

Reviewer #2 (Public Review):

Building on their previous studies, Parab et al used a larger collection of genetically modified zebrafish lines to map the precise expression domains of different VEGF isoforms in the brain and demonstrated that different combinations of VEGF isoforms differentially control the formation of fenestrated vessels at different locations in the brain.

The authors used three Wnt signaling mutants to convincingly show wnt signaling is essential for parenchymal angiogenesis, but not required for fenestrated vessel development, such as those in choroid plexus, suggesting fenestrated vessel and barrier vessel are differentially regulated. The previous work from this group has established that VEGF isoforms are critical for myelencephalic choroid plexus development. In this study, they carefully documented the developmental vessel patterning in the diencephalic choroid plexus/pineal gland interface. They also documented the local expression pattern of VEGF isoforms with a set of BAC transgenic fish, together with the phenotype of a series of VEGF mutant fish, the data well support that different combinations of VEGF isoforms regulate fenestrated vessel development at different brain locations.

Given a larger temporal and spatial domain, VEGFs are critical for all forms of vessel development, there are potential redundancy mechanisms to maintain hemostasis of VEGF signaling, in this study, no data is provided to address whether LOF of one form of VEGF affects the expression of other isoforms.

This work provided detailed evidence of different isoform combinations of VEGF regulate formation/patterning of the fenestrated vessel at CP, OVLT, and NH in zebrafish. It will be interesting to follow in the mammalian system, how well these findings are conserved, for example, which isoform of VEGF is critical for vascular patterning during the developmental stages of the pineal gland? How VEGF isoforms participate in choroid plexus development at different ventricle regions and subsequence secretory function maintenance. However, these tasks are challenging without a good genetic tool to locally manipulate VEGF isoform expression during mammalian brain vessel development.

We appreciate this reviewer’s favorable and encouraging comments highlighting both the quality and impact of our study. We also acknowledge the great importance of the points raised by the reviewer, including the Vegf redundancy mechanisms and also our results’ conservation in mammals.

Reviewer #3 (Public Review):

Parab et al. investigate the requirement of specific Vegf ligands during the embryonic development of new blood vessels in different brain regions. The authors implement their previously published experimental paradigm (Parab et al 2021 eLife) combined with new transgenic and mutant zebrafish lines to show that vegf ligands (vegfaa, vegfab, vegfc, and vegfd) are required in various combinations to drive angiogenesis in distinct brain regions. Specifically, they show that individual loss of different vegf ligands causes either undetectable or partial effects in angiogenesis, while combined loss of vegf ligands results in severe defects in brain region-specific angiogenesis. As different blood vessel types (i.e. arteries, veins, lymphatics) require specific angiogenic cues, this study provides interesting new data on how the combination of these signals drives brain region-specific vascular development.

While the conclusions of the paper are generally well supported by the data, the authors overstate some of their findings, particularly with respect to the development of fenestrated capillaries. In this study, the authors use the zebrafish transgenic reporter line, plvap:EGFP, as an indicator of fenestrations. However, the authors do not provide any evidence of fenestrations of the blood vessels of the choroid plexuses or the cranial vessels used for quantification (Figures 1, 3, and 4). While expression of Plvap protein is often used as a marker for non-blood brain barrier endothelial cells, as Plvap is the major component of the diaphragms of fenestrated capillaries, plvap:EGFP expression alone does not indicate fenestrations. This is an important point because previous work has demonstrated that targeted deletion of Plvap does not cause a loss of fenestrations, but instead a loss of the diaphragms associated with fenestrations (Stan et al 2012 Dev Cell; Gordon et al 2019 Development). Similarly, Plvap expression alone does not necessarily indicate fenestrations as an expression of Plvap is not sufficient for fenestration formation. In fact, Plvap has initially been expressed in brain endothelial cells during initial angiogenesis to the brain without evidence of fenestrations, and subsequently, Plvap expression disappears during the maturation of the BBB. Thus, to conclude that specific vegf ligands are required for the development of fenestrated capillaries, transmission electron microscopy (TEM) should be used on the capillaries examined in this study or the language describing the results should be modified accordingly. Conversely, the authors did show TEM for the choriocapillaris (Figure 5A-C) but did not show plvap:EGFP expression in these vessels.

Additionally, the authors' usage of the phrase "development of fenestrated vessels" suggests that the study was examining signals that regulate the formation of fenestrations and not angiogenesis of vessels that may become fenestrated as demonstrated here. Therefore, as Plvap expression does not necessarily equate fenestrations (and vice-versa), the title and some of the major claims of the study are somewhat overstated.

We appreciate this reviewer’s constructive comments and suggestions to improve this study. We agree with the reviewer that the descriptions of our findings in the original manuscript were not strictly accurate in some aspects. We have now addressed the concern of the Tg(plvap:EGFP) reporter specificity by conducting additional molecular and functional characterizations of Tg(plvap:EGFP)+ vs Tg(glut1b:mCherry)+ brain vasculature, as we have commented in response to the Essential Revisions point #2. In addition, we have made substantial revisions in describing our findings, including 1) the change of the phrase "development of fenestrated vessels" into a more appropriate phrase and 2) the clarification of the primary focus of this manuscript on “angiogenesis/vascularization”. We believe that our revised manuscript now more clearly conveys the finding of signals involved in angiogenesis/vascularization of CP and CVO vascular beds.

Author Response:

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

We are very glad that the editor and reviewers found our paper of broad interest to the community of population, evolutionary, and ecological genetics. We thank them for their positive feedback and insightful comments and suggestions. We have revised our manuscript to address some of the issues raised by the review. The main change we made was providing a detailed discussion of limitations of simulated genomes, focusing on considerations one needs to make when selecting a demographic model. This can be found in a new section “Limitations of simulated genomes” (pages 9-10). We made a few additional adjustments in other parts of the text based on the reviewers’ suggestions. They are all listed in the detailed point-by-point response to reviewers comments and questions below.

Editor:

1) It was noted that demographic models (or genomic parameters) that are inferred based on certain aspects of the genomic data (eg., site frequency spectrum, haplotype structure) may not recapitulate other aspects of the data. In other words, any inferred demographic models are expected to reliably reproduce only some aspects of the genetic variation data but not necessarily all. It would be helpful to emphasize this limitation in the manuscript and to include a table summarizing the types of variation that the demographic models for the catalogued species were based on.

This is a very important point, which we addressed in the revision by adding a section entitled “Limitations of simulated genomes”. This section discusses the considerations that one should make when selecting an inferred demographic model to implement in simulation. This includes the samples used in analysis, the method used for inference, as well as various filters. In this section we also point to the documentation page of the stdpopsim catalog, which provides information about each demographic model that can help users decide whether it is appropriate for their needs. We decided not to summarize this information in a succinct table in the manuscript because it is not straightforward to summarize the strengths and potential limitations of each model in a table. Instead, we will expand the summary provided for each demographic model in the documentation page to provide additional information. See response to the second reviewer’s comment on this topic for more details.

2) It will make stdpopsim more user-friendly to include an automated module that can visualize a demographic model given the corresponding parameters (or simulation scripts).

As mentioned in the response to the first reviewer’s comment on this subject, the documentation page of the stdpopsim catalog provides a brief summary for each demographic model, including a graphical representation. See response below for more details.

Reviewer #1:

In the introduction, the authors cite numerous efforts to generate high-quality reference genomes. That's not an issue in itself, but leading with this might send the message to some readers that it is these reference genome efforts that are driving the need for population genomics analysis and simulation tools, which is not really the case - why not instead give some citation attention to actual population genomics projects aiming to address the types of evolutionary questions this paper is concerned with? The reference genome citations would fit better in the section dealing with reference genomes, where they already appear.

Indeed, the desire to answer complex evolutionary questions is the main motivation for sequencing these genomes and also for generating realistic genome simulations. The reason we chose to lead with the genome-sequencing efforts is that high quality genome data is an important prerequisite for obtaining parameters for chromosome-scale simulations. So, with that perspective, these efforts which we cite are the driving force behind expansion of stdpopsim in the near future. Thus, we decided to leave these citations in the introduction. To balance things out, we now start the introduction with a statement about board questions in population genetics. Moreover, after we list the genome sequencing efforts, we added a list of specific types of questions that can be addressed by these newly emerging genomes, with relevant citations. The beginning of the introduction now reads:

“Population genetics allows us to answer questions across scales from deep evolutionary time to ongoing ecological dynamics, and dramatic reductions in sequencing costs enable the generation of unprecedented amounts of genomic data that can be used to address these questions (Ellegren, 2014). Ongoing efforts to systematically sequence life on Earth by initiatives such as the Earth Biogenome (Lewin et al., 2022) and its affiliated project networks, such as Vertebrate Genomes (Rhie et al., 2021), 10,000 Plants (Cheng et al., 2018) and others (Darwin Tree of Life Project Consortium, 2022), are providing the backbone for enormous increases in the amount of population-level genomic data available for model and non-model species. These data are being used, among other things, in inference of population history and demographic parameters (Beichman et al., 2018), studying adaptive introgression (Gower et al., 2021), distinguishing adaptation from drift (e.g. Hsieh et al., 2021), and understanding the implications of deleterious variation in populations of conservation concern (e.g. Robinson et al., 2023).”

Something that would be useful for the stdpopsim resource in general, though not necessarily something for the paper, would be some kind of more human-friendly representation of the demographic models implemented in the curated library. Perhaps I'm not looking in the right place, but as far as I can tell, if I want to study the curated demographic models, I need to go into the Python scripts on the stdpopsim GitHub page (e.g.

https://github.com/popsim-consortium/stdpopsim/tree/main/stdpopsim/catalog/BosTau). Here the various parameters and demographic events are hard-coded into the scripts. To understand the model being implemented, one thus needs to go dig into these scripts - something which is not necessarily very accessible to all researchers. Visual representations, such as the one for Anopheles gambiae in Fig 2. in the paper, are more widely accessible. I wonder if such figures could be produced for all the curated models and included in the GitHub folders alongside the scripts, perhaps aided by an existing model visualization software such as POPdemog. Again, I would not suggest that this is necessary for the paper, but if practically feasible I think it would be a useful addition to the resource in the longer term.

This is a very good point. The stdpopsim catalog actually has a documentation page that provides a brief summary for each demographic model, including a graphical representation. This graphical representation is generated using demesdraw applied to the demographic model object implemented in the code. Thus, potential users do not have to dig through the Python code to figure out the details of the demographic model. We used a similar approach to generate the image of the demographic history of A. gambiae for Fig. 2 of the paper. The documentation page is an important part of the stdpopsim catalog, and we now added a link to it in section “Data availability”, and we mention it in key places in the manuscript, such as the caption of Fig 2.

Reviewer #2:

An important update to the stdpopsim software is the capacity for researchers to annotate coding regions of the genome, permitting distributions of fitness effects and linked selection to be modeled. However, though this novel feature expands the breadth of processes that can be evaluated as well as is applicable to all species within the stdpopsim framework, the authors do not provide significant detail regarding this feature, stating that they will provide more details about it in a forthcoming publication. Compared to this feature, the additions of extra species, finite-site substitution models, and non-crossover recombination are more specialized updates to the software.

It would be helpful to provide additional information regarding the coding annotation (and associated distribution of fitness effects and linked selection) that is implemented in the current version of stdpopsim, but will be detailed in a forthcoming paper. This is not to take away from the forthcoming paper, but I believe this is the most important update to the software, and the current manuscript only brushes over it.

We agree that implementation of selection in simulations is a significant addition to stdpopsim. However, our intention in this manuscript is to focus on the separate effort we made in the last two years to expand the utility of stdpopsim to a more diverse set of species. We think the manuscript stands firmly even without discussing in detail the new features that allow modeling selection. The main reason we briefly mention these features in sections “Additions to stdpopsim” and “Basic setup for chromosome-level simulations” is because the released version of stdpopsim contains implemented DFEs for a few species, and we did not want to completely ignore this. We thus added a brief comment at the end of the “Basic setup” section (page 8) mentioning the three model species for which the stdpopsim catalog currently has annotations and implemented DFE models. We think that a more detailed description of how these features and how they should be used is best left to the manuscript that the PopSim community is currently writing (preprint expected later this year).

When it comes to simulating realistic genomic data, the authors clearly lay out that parameters obtained from the literature must be compatible, such as the same recombination and mutation rates used to infer a demographic history should also be used within stdpopsim if employing that demographic history for simulation. This is a highly important point, which is often overlooked. However, it is also important that readers understand that depending on the method used to estimate the demographic history, different demographic models within stdpopsim may not reproduce certain patterns of genetic variation well. The authors do touch on this a bit, providing the example that a constant size demographic history will be unable to capture variation expected from recent size changes (e.g., excess of low-frequency alleles). However, depending on the data used to estimate a demographic history, certain types of variation may be unreliably modeled (Biechman et al. 2017; G3, 7:3605-3620). For example, if a site frequency spectrum method was used to estimate a demographic history, then the simulations under this model from y stdpopsim may not recapitulate the haplotype structure well in the observed species. Similarly, if a method such as PSMC applied to a single diploid genome was used to estimate a demographic history, then the simulations under this model from stdpopsim may not recapitulate the site frequency spectrum well in the observed species. Though the authors indicate that citations are given to each demographic model and model parameter for each species, this may not be sufficient for a novice researcher in this field to understand what forms of genomic variation the models may be capable of reliably producing. A potential worry is that the inclusion of a species within stdpopsim may serve as an endorsement to users regarding the available simulation models (though I understand this is not the case by the authors), and it would be helpful if users and readers were guided on the type of variation the models should be able to reliably reproduce for each species and demographic history available for each species. It would be helpful to include a table with types of observed variation that the current set of 21 species (and associated demographic histories) are likely and unlikely to recapitulate well.

This is a very important point, which we now address in the section “Limitations of simulated genomes”, which we added to the manuscript. In this section, we expand on this topic and discuss various things that will affect the way simulated genomes reflect true sequence variation. This includes the choice of demographic inference method, but also the analyzed samples, and various filters. The main message of this section is that one should consider various things when deciding to implement a demographic model in simulation (or selecting a model among those implemented in stdpopsim). We also cite studies (including Beichman, et al. 2017), which compared different approaches to demography inference. However, we note that the conclusions of these comparisons are not as straightforward as the reviewer suggests. In particular, methods that make use of the site frequency spectrum (such as dadi) should be able to capture some aspects of haplotype structure, because this information is encoded in the demographic history. Furthermore, a demographic history inferred from a single genome (e.g., using PSMC) should do a reasonable job approximating some aspects of the site frequency spectrum. In other words, the aspects of genetic variation not modeled well by a given demographic inference method are not always predicted in a straightforward way. This is why we avoid summarizing this information in a table in the manuscript. The 2nd paragraph of the “Limitations of simulated genomes” section addresses some of these subtle considerations. In particular, we suggest that considering a demographic model for simulation requires some familiarity with the inference method and the way it was applied to data. Regarding the demographic models currently implemented in stdpopsim, we provide some information about each model in the documentation page of the catalog. When selecting a demographic model from the catalog, users should make use of this documentation to guide their decision. This is mentioned in the 3rd paragraph of the “Limitations of simulated genomes” section. Following-up on this issue, we intend to review the documentation and make sure it provides sufficient information for each demographic model. See this GitHub issue.

Reviewer #3:

- p5, 2nd paragraph: I think many Biologists, myself included, will think of horizontal gene transfer mostly as plasmids being transferred among bacteria and adding extra genetic material, not as homologous bacterial recombination. This made me confused about modelling horizontal gene transfer in the same way as gene conversion. It may be helpful for some readers if you specify that you are modelling this particular type of horizontal gene transfer. Some explanation along the lines of what is in Cury et al (2022) would be enough.

This is a good point. We modified the text in that sentence in the 2nd paragraph on page 5 to clarify that we are modeling non-crossover homologous recombination, and not incorporation of exogenous DNA (e.g., via plasmid transfer). The relevant part of the text now says:

“In bacteria and archaea, genetic material can be exchanged through horizontal gene transfer, which can add new genetic material (e.g., via the transfer of plasmids) or replace homologous sequences through homologous recombination (Thomas and Nielsen, 2005; Didelot and Maiden, 2010; Gophna and Altman-Price, 2022). However, the initial version of stdpopsim used crossover recombination to stand in for these processes. Although we cannot currently simulate varying gene content (as would be required to simulate the addition of new genetic material by horizontal gene transfer), the msprime and SLiM simulation engines now allow gene conversion, which has the same effect as non-crossover homologous recombination.

Following (Cury et al., 2022), we use this to include non-crossover homologous recombination in bacterial and archaeal species.”

- p5, 3rd paragraph: When you say gene conversion is turned off by default, you could refer to table 1 and briefly mention the consequence of ignoring gene conversion.

We agree that it is important to note that avoiding to model gene conversion may lead to faulty lengths of shared haplotypes across individuals. This is implied by the statement we make in the beginning of the 3rd paragraph on page 5, where we lay out the motivation for modeling gene conversion in simulation. Following the reviewer’s suggestion, we now added a statement about this in the end of that paragraph:

“Note that ignoring gene conversion may result in a slightly skewed distribution of shared haplotypes between individuals (see Table 1)”

-  p7, item 1 and p9, 1st paragraph: I am not sure what you mean by genetic map here, can you define this term? I am not sure if it is synonymous with gene annotations, a recombination map, or something else. The linkage map doesn't seem to make sense to me here.

The term ‘genetic map’ referred to the recombination map whenever it was used in the manuscript. To avoid any confusion, we now removed all mentions of ‘genetic map’, and use ‘recombination map’ instead. The recombination map is relevant in item 1 of page 7 because in species with poor assemblies you will not be able to reliably estimate recombination maps, making chromosome-scale simulations less effective. In the 1st paragraph of page 9, we discuss the issue of lifting over coordinates from one assembly to another, and if you have a recombination map estimated in one assembly, you might need to lift it over to another assembly to apply it in your simulation.

-  Table 1, last row, middle column: when you say "simulated population", I think it is a bit ambiguous. You mean "the true population that we are trying to simulate", but could be read as "the population data that was generated by simulation". I would delete the word simulated here.

What we mean here is that the selected effective population size should reflect the observed genetic diversity in real genomic data. We realize that the previous wording was confusing, and changed this to the following:

“Set the effective population size (Ne) to a value that reflects the observed genetic diversity”

-  Figure 2, and other places when you refer to mutation and recombination rate (eg p11, last paragraph), can you include the units (e.g. per base pair, per generation)?

Throughout the manuscript, rates are always specified per base per generation. In Figure 2, this is specified in the caption (3rd line). We added units in other places in section “Examples of added species” on pages 12-13, where they were indeed missing.

-  p11, "default effective population size": can you use a more descriptive word instead of the default? Maybe the historical average? Also, what is this value used for in the simulations when there is a demographic model specified (as in the case of Anopheles)?

We think that “default effective population size” is the most appropriate term to use here, since we are referring to the parameter in the species model in stdpopsim. It is correct that the value of this parameter should reflect the historical average size in some sense, but it is really unclear what this should be in the case of a species like Bos taurus, which experienced a very dramatic bottleneck in the recent past. We address this subtle, yet important, issue in the sentence preceding this one. If a demographic model is specified in simulation, it overrides the default effective population size, and its value is ignored (which is why we refer to it as ‘default’). We added a short sentence clarifying this in the 2nd paragraph of the “Bos Taurus” section (now page 12).

“Note that the default Ne is only used in simulation if a demographic model is not specified.”

-  p8, when you say "Such simulations are useful for a number of purposes, but they cannot be used to model the influence of natural selection on patterns of genetic variation.": You may want to bring up the discussion that many of these neutral parameters taken from the literature could have been estimated assuming genome-wide neutrality, and thus ignoring the effect of background selection. Therefore the parameter values might reflect some effect of background selection that was unaccounted for during their estimation.

This is an important subtle point, which we now address in the section “Limitations of simulated genomes”, which we added to the revised manuscript. In that section, we discuss various limitations of simulations, focusing on inferred demographic models. We address the potential influence of the segments selected for analysis toward the end of 2nd paragraph in that section (page 9):

“... all methods assume that the input sequences are neutrally evolving. This implies that technical choices, such as the specific genomic segments analyzed and various filters, may also influence the inferred model and its ability to model observed genetic variation.”

Interestingly, background selection in itself typically does not have a strong effect on the inferred model. This is something that is examined in the forthcoming publication that presents simulations with natural selection in stdpopsim.

-  Why are some concepts written in bold (eg effective population size, demographic model)? Were you planning to make a vocabulary box? I think this is a good idea given that you are aiming for a public that can include people who are not very familiar with some population genetics concepts.

In the “Examples of added species” section, we use boldface fonts to highlight the model parameters that were determined for each species. We added a statement clarifying this in the beginning of this section (page 11), and made sure that all the relevant parameters were consistently highlighted throughout this section. In other sections, we use boldface fonts only for titles. A few cases that did not conform to this rule were removed in the current version. We did not intend on adding a vocabulary box, but considered this when revising the manuscript, due to the reviewer’s suggestion. However, we found it difficult to converge on a small (yet comprehensive) set of terms with accurate and succinct definitions. We think that the important terms are adequately defined within the text of the manuscript, providing sufficient information also for readers who are not expert population geneticists.

- p4, 2nd paragraph: Are these automated scripts that are used to compare models publicly available? If you are suggesting that people use this approach generally when coming up with a simulation model (p8, penultimate paragraph), it would be helpful to have access to these automated scripts.

The scripts are part of the public stdpopsim repository on GitHub, and may be used by anyone. Some components of these scripts are more easy to apply in general, such as comparing a demographic model with one implemented separately by the reviewer. This step, for example, is achieved by application of the Demography.is_equivalent method in msprime. Other parts of the comparison depend on the specific structure of python objects used by stdpopsim, so they are not likely to be useful when implementing simulations outside the framework of stdpopsim.

-  p9, 1st paragraph, and p.12 2nd paragraph: instead of adjusting the mutation rate to fit the demographic model (and using an old estimate of the mutation rate), would it be ok to adjust the demographic model to fit the new mutation rate? E.g. with a new mutation rate that is the double of a previous estimate, would it be ok to just divide Ne by 2 such that Ne*mu is constant (in a constant population size model)? I imagine this could get complicated with population size changes.

In principle, this could be done if you were simulating neutrally evolving sequences (without modeling natural selection). Since the coalescence is scale-free, then you can scale down all population sizes and divergence times by a multiplicative factor, and scale up migration rates and the mutation rate by the same factor, and you get the exact same distribution over the output sequences. However, making sure you get the scaling right is tricky and is quite error-prone. Especially considering the fact that you have to do this every time the mutation rate of a species is updated. Moreover, once you start modeling natural selection, this scale-free property no longer holds. Thus, the simple solution we came up with in stdpopsim is to attach to each demographic model the mutation rate used in its inference.

Author Response:

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

We sincerely thank all the editors and reviewers for taking the time to evaluate this study. Here is our point-by-point response to the reviewers’ comments and concerns.

Reviewer #1 (Public Review):The study by Oikawa and colleagues demonstrates for the first time that a descending inhibitory pathway for nociception exists in non-mammalian organisms, such as Drosophila. This descending inhibitory pathway is mediated by a Drosophila neuropeptide called Drosulfakinin (DSK), which is homologous to mammalian cholecystokinin (CCK). The study creates and uses several Drosophila mutants to convincingly show that DSK negatively regulates nociception. They then use several sophisticated transgenic manipulations to demonstrate that a descending inhibitory pathway for nociception exists in Drosophila.

[…]

Weaknesses:

A minor weakness in the study is that it is unclear how DSK negatively regulates nociception. An earlier study at the Drosophila nmj shows that loss of DSK signaling impairs neurotransmission and synaptic growth. In the current study, loss of CCKLR-17D1 in Goro neurons seems to increase intracellular calcium levels in the presence of noxious heat. An interesting future study would be the examination of the underlying mechanisms for this increase in intracellular calcium.

We thank the reviewer for the kind and very positive evaluation of our manuscript. We agree that this study has not elucidated the intracellular molecular pathway(s) downstream of CCKLR-17D1 that are involved in the regulation of the activity of Goro neurons, and we think that it would definitely be an interesting topic for future research.       

Reviewer #1 (Recommendations For The Authors):

The response latencies for the control yw larvae seem large, with many larvae appearing to be insensitive to the thermal stimulus. Is this just an effect of the yw genetic background? A brief discussion of this might be helpful.

We thank the reviewer for pointing this out. We have also noticed that the yw control larvae tend to show longer response latencies than the other control strains, and in the revised manuscript, we have added the following sentence in the Result section (Lines 91–94):

“We have noticed that the yw control strain, which was used by us to generate the dsk and receptor deletion mutants, showed relatively longer response latencies to the 42 °C probe compared to the other control strains used in this study. This may be attributed to the effect of the genetic background, although, presently, the cause for this difference is unknown.”

Reviewer #2 (Public Review):_

This is an exceptional study that provides conclusive evidence for the existence of a descending pathway from the brain that inhibits nociceptive behavioral outputs in larvae of Drosophila melanogaster. […] The study raises many interesting questions for future study such as what behavioral contexts might depend on this pathway. Using the CAMPARI approach, the authors do not find that the DSK neurons are activated in response to nociceptive input but instead suggest that these cells may be tonically active in gating nociception. Future studies may find contexts in which the output of the DSK neurons is inhibited to facilitate nociception, or contexts in which the cells are more active to inhibit nociception._

Reviewer #2 (Recommendations For The Authors):I have no recommendations for the authors as this is a very complete and thoroughly executed study. The writing is crystal clear.

We thank the reviewer for the kind and very positive evaluation of our manuscript. We are happy to know that our current manuscript was deemed to be clear and convincing by the reviewer.

Reviewer #3 (Public Review):[…] Overall the authors use clean logic to establish a role for DSK and its receptor in regulating nociception. I have made a few suggestions that I believe would strengthen the manuscript as this is an important discovery.

Major comments:

1. It's not completely clear why the authors are staining animals with an FLRFa antibody. Can the authors stain WT and DSK KO animals with a DSK antibody? Also, can the authors show in supplemental what antigen the FLRFa antibody was raised against, and what part of that peptide sequence is retained in the DSK sequence? This overall seems like a weakness in the study that could be improved on in some way by using DSK-specific tools.

We thank the reviewer for this query. We would like to clarify that we first tried the FLRFa antibody to visualize an RFamide-type neuropeptide other than DSK in Drosophila and found that the staining pattern is quite similar to that of anti-DSK, as shown by Nichols et al. [1]. According to the original paper describing the anti-FLRFa antisera [2] (already cited in the reviewed manuscript), the antigen used to raise it was the Phe-Met-Arg-Phe-NH2 peptide conjugated with succinylated thyroglobulin, and the study experimentally shows that the antibody well binds to peptides containing Met-Arg-Phe-NH2 or Leu-Arg-Phe-NH2 sequence and has 100% cross-reactivity to FLRFa. As DSK contains Met-Arg-Phe-NH2 sequence [3], the cross-reaction of this antibody to DSK is consistent with the description of the original study.    

Although we were unable to use an antibody specific to DSK, our staining data with dsk deletion mutants and the expression pattern of DSK-2A-GAL4 corroborate each other (Figure 2 and Figure 2-figure supplement 1), which we believe provides compelling evidence for the specific expression of DSK in MP1 and Sv neurons, and for that DSK-2A-GAL4 is a reasonably effective tool to specifically manipulate DSK-expressing neurons.

2. What is the phenotype of DSK-Gal4 x UAS-TET animals? They should be hyper-reactive. If it's lethal maybe try an inducible approach.

We thank the reviewer for this question. Unfortunately, we have not attempted this experiment, although we agree that this would be a nice addition to further strengthen the study if TET worked well in the DSKergic neurons.

3. Figure 9. This was not totally clear, but I think the authors were evaluating spontaneous (i.e. TRPA1-driven) rolling at 35C. The critical question is "does activating DSK-expressing neurons suppress acute heat nociception" and this hasn't really been addressed. The inclusion of PPK Gal4 + DSK Gal4 in the same animal kind of clouds the overall conclusions the reader can draw. The essential experiment is to express UAS-dTRPA1 in DSK-Gal4 or GORO-Gal4 cells, heat the animals to ~29C, and then test latency to a thermal heat probe (over a range of sub and noxious temperatures). Basically prove the model in Figure 10 showing ectopic activation or inhibition for each major step, then test heat probe responses.

We thank the reviewer for suggesting ideas for alternative experiments to potentially strengthen our conclusion. Regarding experiments using heat probes, previous studies have demonstrated that (i) Blocking ppk1.9-GAL4-positive C4da neurons almost completely abolishes the larval nociceptive response to local heat stimulations [4]; (ii) Local heat stimuli above 39 °C readily activate C4da neurons and larval nociceptive rolling [5-9]; and (iii) Thermogenetically or optogenetically activating these neurons is sufficient to trigger Goro neurons and larval rolling [4, 10-12]. Thus, it has now been made clear that heat probes induce larval nociceptive rolling via excitation of the C4da pathway, and we believe that our experiments using thermogenetic activation of C4da neurons can be safely interpreted as an alternative to experiments using heat probes. Using heat probes demands a more complicated experimental set-up to be combined with CaMPARI imaging experiments, and this is another reason why we preferred to take the thermogenetic approach.

We have also considered the experiment using Goro-GAL4 instead of ppk-GAL4. However, if dTRPA1 artificially activates Goro neurons far downstream of the neuronal mechanism by which MP1 activation suppresses Goro neuron activity, the effect of MP1 activation may be bypassed and masked. As we currently do not know the epistasis between dTRPA function and the effect of MP1 activation in modulating the activity of Goro neurons, we rather chose to activate C4da neurons by using ppk-GAL4, which likely resulted in more natural activation of Goro neurons than dTRPA1-triggered direct activations.

4. It would also then be interesting to see how strong the descending inhibition circuit is in the context of UV burn. If this is a real descending circuit, it should presumably be able to override sensitization after injury.

Indeed, this is an interesting avenue to explore in future studies to understand the type of situation in which the DSKergic descending system functions to control nociception.

Reviewer #3 (Recommendations For The Authors):Overall this is a good story and the claims are generally supported with experimental evidence. The way to really improve this study would be to use more precise and definitive tools, like specific antibodies, specifically targeted genes, and better temporal control of the descending circuit to prove this is inducible sufficient to suppress acute thermal nociception and this occurs only via a descending pathway, etc. However this would be exponentially more work, and so the authors I guess need to weigh the cost-benefit of definitive proof vs. strong evidence for their claims. Overall I think this study will be the beginning of a new line of inquiry in the field that has the potential to guide our understanding also of mammalian descending pathways, and as such, this study is of value to the community.

We appreciate the reviewer’s multiple interesting ideas for experiments that could have been performed to further reinforce our findings. We agree that some experiments that the reviewer suggested would potentially strengthen this work if supplemented. However, as aforementioned, in our humble opinion, we think that the experiments that the reviewer suggested are either outside the scope of this paper or have no significant benefits over the experiments that were already conducted, and hence are not essential to the present study.

References

1. Nichols, R. and I.A. Lim, Spatial and temporal immunocytochemical analysis of drosulfakinin (Dsk) gene products in the Drosophila melanogaster central nervous system. Cell Tissue Res, 1996. 283(1): p. 107-16.

2. Marder, E., et al., Distribution and partial characterization of FMRFamide-like peptides in the stomatogastric nervous systems of the rock crab, Cancer borealis, and the spiny lobster, Panulirus interruptus. J Comp Neurol, 1987. 259(1): p. 150-63.

3. Nassel, D.R. and M.J. Williams, Cholecystokinin-like peptide (DSK) in Drosophila, not only for satiety signaling. Front Endocrinol, 2014. 5.

4. Hwang, R.Y., et al., Nociceptive neurons protect Drosophila larvae from parasitoid wasps. Curr Biol, 2007. 17(24): p. 2105-2116.

5. Tracey, W.D., Jr., et al., painless, a Drosophila gene essential for nociception. Cell, 2003. 113(2): p. 261-73.

6. Xiang, Y., et al., Light-avoidance-mediating photoreceptors tile the Drosophila larval body wall. Nature, 2010. 468(7326): p. 921-6.

7. Burgos, A., et al., Nociceptive interneurons control modular motor pathways to promote escape behavior in Drosophila. eLife, 2018. 7.

8. Honjo, K. and W.D. Tracey, Jr., BMP signaling downstream of the Highwire E3 ligase sensitizes nociceptors. PLoS Genet, 2018. 14(7): p. e1007464.

9. Im, S.H., et al., Tachykinin acts upstream of autocrine Hedgehog signaling during nociceptive sensitization in Drosophila. eLife, 2015. 4: p. e10735.

10. Ohyama, T., et al., A multilevel multimodal circuit enhances action selection in Drosophila. Nature, 2015. 520(7549): p. 633-9.

11. Honjo, K., R.Y. Hwang, and W.D. Tracey, Jr., Optogenetic manipulation of neural circuits and behavior in Drosophila larvae. Nat Protoc, 2012. 7(8): p. 1470-8.

12. Zhong, L., et al., Thermosensory and non-thermosensory isoforms of Drosophila melanogaster TRPA1 reveal heat sensor domains of a thermoTRP channel. Cell Rep, 2012. 1(1): p. 43-55.

Author Response:

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

We’d like to thank the three reviewers for reviewing in depth our work and providing insightful comments and suggestions.

Reviewer #1 (Recommendations For The Authors):

1) The evidence that MS023 is actually working in vivo in their last experiment (Fig 6) needs to be strengthened. This could be due to the timing of the experiment. Tail tips were collected 48 h after the final injection and analyzed by Western for ADMA and SDMA levels. They do see subtle changes, in the right directions, of SDMA and ADMA (but these changes are really not very obvious). Perhaps the inhibitor has already been largely metabolized two days after injection. Have they looked at MMA levels?

We have quantified the ADMA and SDMA levels of Fig. S6. We have not measured MMA levels. The text has been edited as follows:

“The average ADMA relative expression was 0.95 for vehicle treated mice and 0.83 for MS023 treated mice (p < 0.00041). The average SDMA relative expression was 0.92 for vehicle treated mice and 0.94 for MS023 treated mice (p < 0.17). These whole-body measurements as measured by tail biopsies show MS023 promotes the decrease of proteins with ADMA and a slight increase in proteins with SDMA. It is known that inhibition of type I PRMTs or PRMT1 deletion diminishes ADMA and increases SDMA due to substrate scavenging (Dhar et al, 2013).”

2) The authors need to explain why they would expect an increase in SDMA levels in these mice after MS023-treatment. 

We have edited the text as follows:

“It is known that inhibition of type I PRMTs or PRMT1 deletion diminishes ADMA and increases SDMA due to substrate scavenging (Dhar et al, 2013).”

3) In the discussion, it would be valuable to address the types of CRISPR-screens that could be performed in these MS023-expanded MSCs. They mention this as a benefit in the introduction, but to expand on this idea in the discussion.

The idea here was not necessarily to perform a CRISPR screen on the MS023-treated cells (although it is an interesting idea), but rather to correct the genetic mutation by CRISPR-Cas9 to enhance the success of genetically corrected autologous cell transplantation. The addition of MS023 to MuSC in vitro would allow to expand the cells while maintaining their self-renewal potential, thereby providing the opportunity to correct the mutation on the dystrophin gene using technologies such as CRISPR prime editing (Mbakam et al., 2022 Mol Ther Nucleic Acids 30:272-285). Our results demonstrating that MS023 enhances cell engraftment suggest that this method could be used to improve autologous cell transplantation efficiency. We have edited the text in the discussion as follows:

“Our findings suggest that type I PRMT inhibitors may have therapeutic potential for treating certain skeletal muscle diseases. For instance, to improve the efficacy of autologous cell therapy, the dystrophin-deficient MuSCs collected from DMD patient and corrected by CRISPR prime editing (Happi Mbakam et al, 2022) could be treated with MS023 to maintain their stemness and enhance their cell engraftment capacity.”

4) Also, could they address the potential value of MSC culture and expansion using a combination of SETD7 inhibition and PRMT1 inhibition?

Agreed. We have edited the text as follows:

“These findings suggest that inhibiting methyltransferases can affect MuSC fate and perhaps a combination of Setd7 and MS023 inhibitors would provide a more favorable combination to promote the expansion of MuSCs while maintaining their stem cell-like properties.”

Reviewer #2 (Recommendations For The Authors): 

In figure 2 the authors show that upon removal of MS023, the cells differentiate more efficiently. In figure 5E-F they show that the mice that received MS023-treated cells had more GFP mature muscle fibers. However, in figure 5C-D these cells have the same capacities to terminally differentiate. This reviewer was wondering if these cells would differentiate faster? Have the authors look into this?

The reviewer raises an interesting point. Our in vitro experiments shown in Supplemental Figure S1 indicate that MS023-treated cells are actively more cycling (more ki67+ cells) and are less committed to differentiation (less Pax7-MyoD+ cells), which would suggest that they would need to exit the cell cycle and differentiate faster to reach the same fusion capacity after 3 days of differentiation without MS023. Future experiments with a time course including earlier time points will be needed to confirm if these cells differentiate faster.

Reviewer #3 (Recommendations For The Authors): 

1) MS023 is a non-selective inhibitor of type I PRMTs. It has comparable IC50 values for PRMT1 and PRMT4 (CARM1), and lower IC50 values for PRMT6 and PRMT8. The authors argue that the cellular phenotype caused by MS023 is solely mediated via PRMT1, since the specific PRMT4-inhibitor TP-064 has no effects on MuSC expansion. TP-064 treatment was not used as a control for the transplantation and muscle strength measurement experiments. Are PRMT6 and PRMT8 expressed in MuSC and are thy inhibited by the applied concentrations of MS023? Kawabe et al reported that CARM1 methylates Pax7, thereby inducing Myf5 transcription during the asymmetric division of MuSC (PMID: 22863532). Is the expression of Myf5 reduced upon MS023 treatment? scRNAseq of MuSC 4-day after culture is too late to address this question, since the majority of the cells are already committed to differentiation. Staining for Myf5 using ex vivo cultured fibers or regenerating muscles in vivo should be used. 

Indeed, we mention throughout the text that MS023 is a type I PRMT inhibitor. We have edited the text as follows suggesting the effect are most likely mediated by inhibition of PRMT1 in vivo.

“Treatment of MuSCs with MS023 resulted in metabolic reprogramming of MuSCs, supporting a role for type I PRMTs as metabolic regulators. In vitro, MS023 has been shown to inhibit several type I enzymes at nM concentrations (Eram et al., 2016). It is well-documented that PRMT1 is the major cellular type I enzyme (Pawlak et al, 2000) and this is why PRMT1, but not the other type I PRMTs are embryonic lethal in mice (Guccione & Richard, 2019). The numerous published data in cellulo with MS023 are thus far only reproduced by PRMT1-deficiency by siRNA or knockout, suggesting that MS023 actions in vivo are predominantly mediated by inhibiting PRMT1 (Gao et al, 2019; Plotnikov et al, 2020; Wu et al, 2022; Zhu et al, 2019). Thus, the effects of MS023 on MuSCs are most likely mediated by inhibition of PRMT1.”

Moreover, we investigated the expression of other type I PRMTs as suggested by the reviewer. We investigated their expression from publicly available single cell RNAseq dataset (Oprescu SN et al, iScience 2020, 23:100993), which performed analysis on skeletal muscle at different time points post-cardiotoxin injury (uninjured, and 0.5, 2, 3.5, 5, 10, 21 days post-injury). The findings show that Prmt1 is by far the most expressed type I PRMT in MuSCs at every time point tested. Carm1 (Prmt4) is expressed at high level in a small/moderate subset of cells, especially during regeneration. Prmt6 is expressed at low level in a small proportion of cells, while Prmt8 expression was not detected. These findings are coherent with our observation that Prmt1 is the predominant type I Prmt in MuSCs, which further support our hypothesis that it is the main target of MS023. These findings were added in Suppl. Fig 1B.

The expression of Myf5 during asymmetric division is indeed well characterized on muscle fiber-associated MuSCs (Dumont et al., 2015 Nat Med 21:1455; Kawabe et al., 2012 Cell Stem Cell 11:333). As the reviewer states, the 4-day time point is too late to investigate Myf5 expression. Additionally, these cells were cultured ex-vivo and were not fiber-associated. Therefore, scRNAseq is not an ideal method to address the question of whether MS023 treatment modulates Myf5 expression, and further experiments would be required to examine Myf5 in an appropriate context (i.e. on ex-vivo cultured myofibers).

2) Figure 2 is not very informative, while the second paragraph of the result parts is excessive and too complicated. The extensive description of differential gene expression in each potential subpopulation is neither very informative nor helpful to convince the reader that the M3/M5 population has acquired more stemness-like features due to the MS023 treatment. From my point of view, the data just reflect the increased proliferative capacity of MS023-treated cells with elevated cell cycle markers, ribosomal protein, and metabolic state. Do the M1-M5 populations show any different distribution along the trajectory? The authors need to show cell trajectories for each sample and cluster in Figure S3A. It is also imperative to present the distribution of signature genes for each individual cluster. Essentially, M1-M5 all located together in one cloud. What justifies segregation into different subclusters? The color code for the different clusters (including the trajectories) to allow better distinction. 

MS023 treated MuSCs contain a subpopulation with higher Pax7 expression (Supplementary Figure S2F, S2G), which is consistent with the IF results in Figure 1 and emphasized in the abstract. Why are these data in the supplements and not in a main figure (e.g. in figure 2)?

We appreciate the thoughtful and detailed comments on our single-cell data. Please see below for a response to each point:

To address the concern that the results section is excessive, our intention was to simply provide the reader with a descriptive overview of the identity of each subcluster that the software identified. In fact, to ensure clarity and conciseness, we elected to provide only the names of a select few cluster markers rather than list all of the significant cluster markers that were generated. We kindly refer the reviewer to Supplementary Table S1 for a more extensive list of markers.

In response to the reviewer’s comment: “The color code for the different clusters (including the trajectories) to allow better distinction,” we agree that colour-coding is helpful, please refer to Figure 2A for a colour-coded map of the clusters.

To address the reviewer’s question regarding what justifies segregation into different subclusters for M1-M5, refer to Supplementary Table S1 for a list of uniquely enriched markers for each cluster. This list was filtered to include marker genes that were present only in a given cluster, thus contributing to its uniqueness and explains why that cluster was identified as being distinct from another given cluster.

Lastly, since the elevated Pax7 levels in MS023-treated MuSCs was already presented and discussed thoroughly in Figure 1, we elected to avoid repetition in the main Figures and presented the ridge plots showing elevated Pax7 in the Supplementary Material for Figure 2

3) The same group has reported previously that PRMT1-deficient MSCs show reduced expression of MyoD due to disruption of Eya1/Six1 recruitment to the MyoD promoter (PMID: 27849571). However, the scRNAseq result does reflect this finding. MyoD levels are not significantly changed in d4 MS023 compared with d4 (Supplementary figure S2G). The authors need to provide an explanation. Furthermore, the authors previously described that "the majority of PRMT1-deficient MSCs repressed Pax7 expression at day 3 while being Ki67 positive (Fig. 5B). How does that fit to the current observations, which indicate an increase of Pax7+ cells after MS023 treatment? This discrepancy needs to be resolved. 

While the scRNAseq does not show a reduction in overall MyoD expression in MS023-treated MUSCs, there is indeed a reduction in the proportion of MyoD+ myofiber-associated MuSCs (Figure 1C, 1D). Supplemental Figure S2G further shows a subpopulation in the d4MS023 group with lower MyoD expression that was not present in the d4 group, reflective of the findings in Figures 1C and 1D. Therefore, although the average expression was not shifted significantly with MS023, there was indeed a subpopulation of MuSCs with lower MyoD expression.

The reviewer additionally points out that Fig. 5B from a previous study (Blanc et al., 2017 MCB 37:e00457) performed by our group, shows that Pax7 expression was repressed at day 3 of culture in PRMT1-null MuSCs. However, this quantification was based on immunofluorescence staining where cells are marked positive or negative for Pax7 expression and does not look at the intensity of Pax7 expression levels. In our current study, we examine the expression levels of Pax7 in discrete subpopulations of MuSCs and found that there is a subpopulation of MuSCs that emerges with MS023 treatment that has higher Pax7 expression than untreated counterparts. Therefore, the results of the two experiments are not directly comparable. 

4) I do have a major problem with the interpretation of the metabolic changes in MS023-treated MuSC. In the abstract, the authors wrote, "These findings suggest that type I PRMT inhibition metabolically reprograms MuSCs resulting in improved self-renewal and muscle regeneration fitness." There is simply no causal evidence to support this claim, which is solely based on a correlation. If the authors want to maintain this claim they either need to stimulate OXPHOS and glycolysis by other means to see whether such a manipulation recapitulates the effects of MS023 or attenuate OXPHOS and glycolysis to see whether this abrogates the effects of MS023. To prove whether increased OXPHIS is a cause for improved self-renewal, the authors might simply co-treat MuSC with MS023 and an OXPHIS inhibitor and analyze consequences for the Pax7+/MyoD- population. 

We thank the reviewer for the excellent suggestions of experiments that would solidify a causal relationship between increased metabolism and increased self-renewal. We will certainly consider them for future studies. We agree that the relationship in the present study is correlative, and the text has been modified in the abstract as follows:

“Single cell RNA sequencing (scRNAseq) of ex vivo cultured MuSCs revealed the emergence of subpopulations in MS023-treated cells which are defined by elevated Pax7 expression and markers of MuSC quiescence, both features of enhanced self-renewal. Furthermore, the scRNAseq identified MS023-specific subpopulations to be metabolically altered with upregulated glycolysis and oxidative phosphorylation (OxPhos). Transplantation of MuSCs treated with MS023 had a better ability to repopulate the MuSC niche and contributed efficiently to muscle regeneration following injury. Interestingly, the preclinical mouse model of Duchenne muscular dystrophy had increased bilateral grip strength 10 days after a single intraperitoneal dose of MS023. Our  findings show that inhibition of type I PRMTs increased the proliferation capabilities of MuSCs with altered cellular metabolism, while maintaining their stem-like properties such as self-renewal and engraftment potential.”

5) Ryall et al reported that MuSCs undergo a metabolic switch from fatty acid oxidation to glycolysis with reduced intracellular NAD+ levels and reduced activity of SIRT1, leading to elevated H4K16 acetylation. Here, both OXPHOS and glycolysis are increased after treatment of MuSC with MS023. Are the NAD+ and H4K16ac levels changed in MS023-treated MuSC? 

This is another excellent study that would help to support a causal relationship between MS023 treatment and increase OXPHOS and glycolysis and could certainly be addressed in future studies.

6) In Ryall et al.'s results, there was no difference in the basal mitochondrial OCR between freshly isolated MuSCs and cultured MuSCs. Importantly, stimulation of OXPHOS will increase ROS concentration, resulting in premature differentiation of MuSC (PMID: 30106373). Furthermore, increased ROS levels will most likely enhance DNA damage rather than improve self-renewal. The authors have to address these issues and also monitor ROS and DNA damage levels. 

The lack of cell death upon treatment with MS023 in the present study would indicate that there is no major ROS-induced DNA damage occurring. Additionally, the propensity of MS023-treated MuSCs to retain their stemness while in long-term culture (Supplemental figure S1E) would indicate that in this context, premature differentiation is not a concern.

7) The authors used FACS-analysis of MuSCs three weeks after transplantation to demonstrate that MS023 treatment enables better engraftment into the MuSC niche. The six-fold increase of transplanted cells in the MuSC niche is difficult to understand, Why shall transplanted cells compete so efficiently with endogenous MuSC for repopulation of the niche? Is it possible that some of the transplanted MuSC are still lingering within the interstitium and erroneously counted as bona fide MuSC? The authors have to determine the localization of transplanted MuSC. Are all transplanted cells indeed situated in the proper niche or are they also present outside the basal lamina of muscle fibers? 

The hindlimbs which received the engraftment were irradiated 24h prior to engraftment, therefore the ability of endogenous MuSCs to compete is compromised. Additionally, Figure 5E shows that the regenerated muscle indeed has GFP negative fibers that would have been generated from endogenous MuSCs, indicating that MS023-treated MuSCs did not fully outcompete endogenous MuSCs.

8) The authors reported that an only 3-day treatment with MS023 is sufficient to dramatically improve muscle function in mdx mice even 30 days later, which is hard to swallow. What is the evidence that such strong effects are primarily mediated by stimulation of MuSC expansion? Are there other pathways or cells that respond to MS023 treatment and stimulate muscle strength? To support the claim of a 'better' stem cell function as the major cause for MS023-dependent stimulation of muscle strength in mdx mice, the authors need to determine the total number of Pax7+ cells, Pax7+/Ki67+, Pax7+/MyoD+, Pax7+/MyoD-, Pax7-/MyoD+ and myonuclei. It is also absolutely mandatory to include wildtype controls in the muscle strength measurements. Does MS023 treatment also increase muscle strength in wild-type controls? 

Agreed. We cannot exclude if the effect is mediated by an expansion of the MuSC pool or by an effect on other cell types, such as a direct impact on the myofibers. The manuscript has been modified to include the following text:

“Furthermore, our findings show that injection of MS023 in the dystrophic mouse model mdx led to enhanced muscle strength with effects lasting up to 30 days.  We cannot exclude if the effect of MS023 was mediated by an expansion of the MuSC pool or by an effect on other cell types, such as a direct impact on the myofibers. The goal of this experiment was to provide a therapeutic perspective for the possible use of type I PRMT inhibitor for the treatment of DMD.”

The goal of this figure was to provide a therapeutic perspective for the use of type I PRMT inhibitor for the treatment of DMD. Muscle wasting/weakness in DMD is a complex and multifactorial process (e.g., myofiber fragility, MuSC defects, chronic inflammation, fibrofatty accumulation). If MS023 can target multiple aspects of the physiopathology of the disease it would increase its therapeutic applicability. Further studies will be needed to determine the exact mechanism by which MS023 mediate its beneficial effect. These future studies could include the use of wild type control, as the reviewer suggests, to investigate the role of MS023 in a non-muscle degenerative context.

9) Ideally, a genetic inactivation-reactivation of PRMT1 should be done to validate the results with MS023 and to make sure that indeed the transient inhibition of PRMT1 is responsible for the beneficial effects of MS023. Of course, this would be a major effort when done in genetically manipulated mice and therefore is not adequate to ask for. However, it should be possible to use PRMT1-deficient MuSC, which the authors have in hand, and re-express PRMT1 in these cells with an AAV or a lentivirus. 

We agree that genetic ablation of PRMT1 is a key experiment to validate MS023 results. Please refer to previous work from our group, which shows that PRMT1-KO MuSCs have an enhanced self-renewal phenotype (Blanc et al., 2017 MCB 37:e00457), similar to what was observed in the present study with MS023 treatment.

10) Some claims are overstated and/or to aggressive. E.g.: "Therefore, through repression of type I PRMTs with MS023, we have reprogramed MuSCs to acquire a unique and previously uncharacterized identity." I do not see clear evidence that MS023 treatment 'reprograms' MuSC to a 'unique identity'. The observed changes are in large parts compatible with a simple stimulation of proliferation. 

The unique finding in our data is that treatment with MS023 resulted in a shift in identity as compared to the DMSO-treated proliferating MuSCs (M1, M2 and M4), creating transcriptionally distinct M3 and M5 clusters. M3 and M5 had elevated markers for metabolism (E.g. Eno1, Atp5k, etc) and early activation (E.g. Fos, Jun), while the untreated MuSCs in clusters M1, M2 and M4 did not. Furthermore, M3 and M5 had higher baseline levels of Pax7 expression when compared to untreated cells. Together, these findings describe a transitional subpopulation of MuSCs unique to MS023 treatment which not only harbour stem like/early activation markers Pax7, Fos and Jun, but also elevated proliferative markers related to cell cycle and energy metabolism. This particular combination of characteristics is unique to the MS023-treated MuSCs, thus identifying a unique subtype of MuSC identity. In accordance with our scRNAseq data, we validated experimentally that MS023-treated cells have higher energy metabolism and increased self-renewal potential, thereby confirming that the unique transcriptomic signature of these cells also lead to a different cell fate decision.

Author Response:

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

1) l. 80: "evolved from a fourth domain of cellular life": I am worried a little bit about putting together what I believe are too distinct hypothesis: (i) NCLDV deriving from a complex (ancestral) cellular life form (possibly proto-eukaryotic) by reductive evolution, and (ii) NCLDV forming or deriving from a fourth domain of cellular life. To clarify for non-expert reader, I would suggest rephrasing as "evolved reductive evolution, possibly from a fourth domain of cellular life...".

Following the reviewer’s recommendation, we have clarified the sentence by writing: “These observations are at odds with the suggestion that NCLDVs originated by reductive evolution, possibly from a fourth domain of cellular life (Colson et al., 2018; Legendre et al., 2012; Patil and Kondabagil, 2021).”.

2) l. 187-198: Please provide more information on which tool (with version number and parameter) was used to search genomes for MCPs. When I downloaded the HMM model and the faa file for the MCP from the figshare repository and tried to match the two, only a small number (4) of the MCP sequences actually matched the MCP HMM model with significant e-value, but I am not sure why? (for reference, I was using hmmsearch 3.3.2, default parameters)

We used HMMER version 3.3.2 using the default parameters (hmmbuild and hmmsearch algorithms). We now include this information in the relevant section of the Methods: “Next, we constructed a set of Hidden Markov Models (HMMER version 3.3.2, hmmbuild/hmmsearch using the default parameters) for each of the 4 core proteins involved in virion morphogenesis”.

We were able to reproduce the reviewer’s observation that the Major capsid curated HMM model returns 4 significant hits when used on the Major capsid multiple alignment file provided in FigShare (significant matches: 1. maverick2_NW_021681489.1_105940131438, 2. ncbincldv_NC_011335.1, 3. ncbincldv_NC_038553.1, 4. yutin_PLVACE1). This curated HMM model was one of the models used for searching homologous protein sequences and was built from a preliminary multiple sequence alignment comprising a different set of taxa (N. taxa = 48). In contrast, the multiple sequence alignment provided in Figshare is the final multiple sequence alignment of major capsid proteins that was used in phylogenetic analyses (N. taxa = 54). Therefore, we should not expect an exact match between the two files.

We have updated the Figshare repository with a compressed file containing all the HMMs used for searching protein homologues (n = 38), which can be validated on hmmsearch on the European Bioinformatic Institute’s website (https://www.ebi.ac.uk/Tools/hmmer/search/hmmsearch).) A separate compressed file contains the final multiple sequence alignments that were used in phylogenetic inference and hypothesis testing.

3) Figure 4: The acronyms should be explained in the legend (pPOLB, MCP, mCP, pro, atp, int, TIRs, etc)

We now provide an explanation of the acronyms used for the traits matrix on Figure 4: “Acronyms refer to genes and genomic features present in the viral genomes: pPOLB (protein-primed DNA polymerase B), MCP (major capsid protein), mCP (minor capsid protein), int (rve-type integrase), pro (adenoviral-like protease), atp (FtsK/HerA DNA packaging ATPase), TIRs (terminal inverted repeats).”

4) Figure 4: I believe that "TIRs" should be "Present in some members" for the virophages, based on https://doi.org/10.1186/s13062-015-0054-9? Interestingly, this group is typically the one that branches the deepest within virophages, which would be consistent with TIRs being an ancestral trait of the Maveriviricetes class (formerly Lavidaviridae family).

As suggested, we updated the terminal inverted repeats (TIRs) trait for virophages to “Present in some members” to account for the Rumen virophages described by Yutin, Kapitonov and Koonin (2015, doi: 10.1186/s13062-015-0054-9).

Additional changes:

1) Figure 1 has been updated and now shows a polytomy between Mavericks 1/2 and PLVs. This reflects more closely the conceptual framework for our analyses since the specific branching of these groups was not specified in the phylogenetic models.

2) We have added an Acknowledgements section to the end of the manuscript:

Acknowledgements

We wish to thank Peter Simmonds and Alexander Suh for their critical reading and comments on the manuscript, which served to improve this work. We also thank the reviewers for their recommendations and feedback. This work was supported by a doctoral scholarship (Dr. Jose Gregorio Hernandez Award) to JGNB made by the National Academy of Medicine of Venezuela and Pembroke College, Oxford.

Author Response

eLife assessment

Tilk and colleagues present a valuable computational analysis of tumor transcriptomes to investigate the hypothesis that the large number of somatic mutations in some tumors is detrimental such that these detrimental effects are mitigated by an up-regulation by pathways and mechanisms that prevent protein misfolding. The authors address this question by fitting a model that explains the log expression of a gene as a linear function of the log number of mutations in the tumor and show that specific categories of genes (proteasome, chaperones, ...) tend to be upregulated in tumors with a large number of somatic mutations. Some of the associations presented could arise through confounding, but overall the authors present solid evidence that mutational load is associated with higher expression of genes involved in mitigation of protein misfolding - an important finding with general implications for our understanding of cancer evolution.

We thank the reviewers for these kind words. The summary statement and public review highlight our work in understanding how human tumors phenotypically respond to mutational load by assessing changes in gene expression. This work provides a mechanistic underpinning to our previous finding that the accumulation of passenger mutations in tumors creates a substantial cost because even substantially damaging passenger mutations can fix in non-recombining clonal tumor lineages. At the same time, we believe the summary statement and the public review do not mention a key remaining part of our paper that validates our findings and establishes causal connections between protein misfolding due to coding passenger mutations and tumor fitness. Specifically, we replicate and cross-validate our findings in human tumors by examining expression responses in an independent dataset of cancer cell lines (CCLE), where we demonstrate similar expression responses to an accumulation of mutations, indicating generic, cell intrinsic responses. We then establish a causal link by demonstrating that mitigation of protein misfolding through protein degradation and re-folding is necessary for high mutational load cancer cells to maintain viability through perturbation experiments via shRNA known-down and treatment with targeted agents. These analyses and results are important because they show that the adaptive responses we observe are evidence of a generic, cell intrinsic phenomenon that cannot be explained by organismal effects, such as aging, changes in the immune system or microenvironment. 

Joint Public Review:

Tilk and colleagues present a computational investigation of tumor transcriptomes to investigate the hypothesis that the large number of somatic mutations in some tumors is detrimental and that these detrimental effects are mitigated by an up-regulation by pathways and mechanisms that prevent protein misfolding.

The authors address this question by fitting a model that explains the log expression of a gene as a linear function of the log number of mutations in the tumor and additional effects for tumor homogeneity and type. This analysis identified a large number of genes (5000) that are more highly expressed at high mutational load at a FDR of 0.05. These genes are enriched in many core categories, most prominently in the proteasome, translation, and mitochondral translation. The authors then proceed to investigate specific categories of upregulated genes further.

The individual reviews, and the discussion among the reviewers, raised several issues that could potentially undermine or weaken some of the findings presented in this paper.

1) Systematic differences in expression of some genes from one tumor class to another might generate spurious associations with mutational load (ML), which would affect the results presented in Figs 1 and 3. The case of a causal link between ML and over-expression of genes that mitigate deleterious effects of misfolding would be stronger if these results were replicated within single cancer types with many samples with different ML (similar to how Fig S6 relates to Fig 3). A related concern might be an association between increased variance of expression and ML. The compositional nature of expression data could generate trends like the ones shown in Fig. 2 with changing variance.

We agree with the reviewers that possible confounders should be considered since TCGA data is heterogeneous. In this paper, we investigated possible confounders such as multicollinearity with different mutational types (SNVs and CNVs), controlled for expression responses within cancer types in the GLMM, and used the jackknifing procedure to ensure that no one cancer type dominates the signal. However, in principle unknown hidden confounders could remain, which is why a large part of our paper was focused on validating these effects in an independent dataset (CCLE) where many other covariates are not relevant (immune system, donor variability, stage, age, sex, etc.). Importantly, we also used data from perturbation screens that are completely orthogonal to expression responses in CCLE to get at a cause and effect. 

Our reasoning for using all of the data in Figure 1 while controlling for differences due to cancer type in the GLMM was to maximize the variation in mutational load across all of the samples in this dataset to identify what genes increase in expression as mutational load increases over 5 orders of magnitude. As noted here, we also already further validated that the signal we observe in Figure 1 is still robust for our gene sets of interest within cancer types in Supplemental Figure 6.

2) Fig 4, Fig S5 and Fig S8 show results for the regression coefficient of expression on ML after leaving out one cancer at a time. All of us initially read this as results for 'one cancer at a time', rather than 'leave-one-out'. These figures are used to argue that the results are not driven by specific cancer types. However, this analysis would not reveal if the signal was driven by a (small) subset of cancer types. To justify claims like "significant negative relationship between mutational load and cell viability across almost all cancer types", one needs to analyze individual cancer types. Results for specific genes, rather than broad groups would also help interpret these results.

Our reasoning for grouping together genes in Figure 4 was because the shRNA screen was done on a single gene at a time, and we were interested in measuring the joint effect on viability after knocking down all of the genes in a given complex. 

Given that the expression responses in Figure 3 already validate within cancer types in TCGA in Supplemental Figure 6, we believe that it’s very unlikely that the signal we observe is driven by individual cancer types or smaller groups of cancer types. In addition, we did not perform a within cancer analysis in CCLE for Figure 4, because not all available cancer types in CCLE were profiled evenly in the shRNA screen (Total < 300). The vast majority of cancer types in CCLE for the shRNA screen (23/26) have sample sizes <20 within each group that we believe are unlikely to lead to meaningful results that are not driven by noise.

3) You use different model architecture for the TCGA and CCLE analysis because you suspect that the sample size imbalance in the latter might mean that a GLMM can not capture the different variance components accurately. Did you test this? Could you downsample to avoid this? Cancer type is likely a strong confounder of ML.

That was indeed our reasoning, that within group sample sizes in CCLE are too low to robustly estimate variance within cancer types. Given that many cancer types have <20 samples within each group, we don’t think that evenly downsampling would enable us to get an estimate not driven by noise. As noted above, our approach to control for this was to perform a jackknifing procedure that eliminates a single cancer type at a time and re-estimates the effect. 

4) In the splicing analysis (Fig 2 and Fig S4), you report a 10% variation in splicing for a 100-fold variation in ML. This weak trend is replicated in very similar ways for many different types of alternative splicing events. It is not clear why different events (exon skipping, intron retention, etc) should respond in the same way to ML. A weak but homogeneous effect like the one shown here might result from some common confounder (see point 1). Similarly, it is not clear why with increasing intron retention PSI threshold the fraction of under-expressed transcripts would decrease and not increase.

We agree that the effects of all the different alternative splicing effects are complex. Our focus was on intron retention, which is known to occur in cancer (Lindeboom, et. al 2016, Nature Genetics), and our analysis is consistent with the idea that damaging passenger mutations can shift cellular phenotypic states that require the use of many different mechanisms to mitigate protein misfolding.

For Figure S4, as the PSI threshold for calling an alternative splicing event increases, fewer samples are called as having an intron retention event in the gene. This uniformly decreases the numerator across all the mutational load bins, so that when the threshold is increased the fraction of under-expressed transcripts with intron retention events is lower.

Author Response

Reviewer #1 (Public Review):

Wang and all present an interesting body of work focused on the effects of high altitude and hypoxia on erythropoiesis, resulting in erythrocytosis. This work is specifically focused on the spleen, identifying splenic macrophages as central cells in this effect. This is logical since these cells are involved in erythrophagocytosis and iron recycling. The results suggest that hypoxia induces splenomegaly with decreased number of splenic macrophages. There is also evidence that ferroptosis is induced in these macrophages, leading to cell destruction. Finally, the data suggest that ferroptosis in splenic red pulp macrophages causes the decrease in RBC clearance, resulting in erythrocytosis aka lengthening the RBC lifespan. However, there are many issues with the presented results, with somewhat superficial data, meaning the conclusions are overstated and there is decreased confidence that the hypotheses and observed results are directly causally related to hypoxia.

Major points:

1) The spleen is a relatively poorly understood organ but what is known about its role in erythropoiesis especially in mice is that it functions both to clear as well as to generate RBCs. The later process is termed extramedullary hematopoiesis and can occur in other bones beyond the pelvis, liver, and spleen. In mice, the spleen is the main organ of extramedullary erythropoiesis. The finding of transiently decreased spleen size prior to splenomegaly under hypoxic conditions is interesting but not well developed in the manuscript. This is a shortcoming as this is an opportunity to evaluate the immediate effect of hypoxia separately from its more chronic effect. Based just on spleen size, no conclusions can be drawn about what happens in the spleen in response to hypoxia.

Thank you for your insightful comments and questions. The spleen is instrumental in both immune response and the clearance of erythrocytes, as well as serving as a significant reservoir of blood in the body. This organ, characterized by its high perfusion rate and pliability, constricts under conditions of intense stress, such as during peak physical exertion, the diving reflex, or protracted periods of apnea. This contraction can trigger an immediate release of red blood cells (RBCs) into the bloodstream in instances of substantial blood loss or significant reduction of RBCs. Moreover, elevated oxygen consumption rates in certain animal species can be partially attributed to splenic contractions, which augment hematocrit levels and the overall volume of circulating blood, thereby enhancing venous return and oxygen delivery (Dane et al. J Appl Physiol, 2006, 101:289-97; Longhurst et al. Am J Physiol, 1986, 251: H502-9). In our investigation, we noted a significant contraction of the spleen following exposure to hypoxia for a period of one day. We hypothesized that the body, under such conditions, is incapable of generating sufficient RBCs promptly enough to facilitate enhanced oxygen delivery. Consequently, the spleen reacts by releasing its stored RBCs through splenic constriction, leading to a measurable reduction in spleen size.

However, we agree with you that further investigation is required to fully understand the implications of these changes. Considering the comments, we propose to extend our research by incorporating more detailed examinations of spleen morphology and function during hypoxia, including the potential impact on extramedullary hematopoiesis. We anticipate that such an expanded analysis would not only help elucidate the initial response to hypoxia but also provide insights into the more chronic effects of this condition on spleen function and erythropoiesis.

2) Monocyte repopulation of tissue resident macrophages is a minor component of the process being described and it is surprising that monocytes in the bone marrow and spleen are also decreased. Can the authors conjecture why this is happening? Typically, the expectation would be that a decrease in tissue resident macrophages would be accompanied by an increase in monocyte migration into the organ in a compensatory manner.

We appreciate your insightful query regarding the observed decrease in monocytes in the bone marrow and spleen, particularly considering the typical compensatory increase in monocyte migration into organs following a decrease in tissue resident macrophages.

The observed decrease in monocytes within the bone marrow is likely attributable to the fact that monocytes and precursor cells for red blood cells (RBCs) both originate from the same hematopoietic stem cells within the bone marrow. It is well established that exposure to hypobaric hypoxia (HH) induces erythroid differentiation specifically within the bone marrow, originating from these hematopoietic stem cells. As such, we postulate that the differentiation into monocytes is reduced under hypoxic conditions, which may subsequently cause a decrease in migration to the spleen.

Furthermore, we hypothesize that an increased migration of monocytes to other tissues under HH exposure may also contribute to the decreased migration to the spleen. The liver, which partially contributes to the clearance of RBCs, may play a role in this process. Our investigations to date have indeed identified an increased monocyte migration to the liver. We were pleased to discover an elevation in CSF1 expression in the liver following HH exposure for both 7 and 14 days. This finding was corroborated through flow cytometry, which confirmed an increase in monocyte migration to the liver.

Consequently, we propose that under HH conditions, the liver requires an increased influx of monocytes, which in turn leads to a decrease in monocyte migration to the spleen. However, it is important to note that these findings will be discussed more comprehensively in our forthcoming publication, and as such, the data pertaining to these results have not been included in the current manuscript.

3) Figure 3 does not definitively provide evidence that cell death is specifically occurring in splenic macrophages and the fraction of Cd11b+ cells is not changed in NN vs HH. Furthermore, the IHC of F4/80 in Fig 3U is not definitive as cells can express F4/80 more or less brightly and no negative/positive controls are shown for this panel.

We appreciate your insightful comments and critiques regarding Figure 3. We acknowledge that the figure, as presented, does not definitively demonstrate that cell death is specifically occurring in splenic macrophages. While it is challenging to definitively determine the occurrence of cell death in macrophages based solely on Figure 3D-F, our single-cell analysis provides strong evidence that such an event occurs. We initially observed cell death within the spleen under hypobaric hypoxia (HH) conditions, and to discern the precise cell type involved, we conducted single-cell analyses. Regrettably, we did not articulate this clearly in our preliminary manuscript. In the revised version, we have modified the sequence of Figure 3A-C and Figure 3D-F for better clarity. Besides, we observed a significant decrease in the fraction of F4/80hiCD11bhi macrophages under HH conditions compared to NN. To make the changes more evident in CD86 and CD206, we have transformed these scatter plots into histograms in our revised manuscript.

Considering the limitations of F4/80 as a conclusive macrophage identifier, we have concurrently presented the immunohistochemical (IHC) analyses of heme oxygenase-1 (HO-1). Functioning as a macrophage marker, particularly in cells involved in iron metabolism, HO-1 offers additional diagnostic accuracy. Observations from both F4/80 and HO-1 staining suggested a primary localization of positively stained cells within the splenic red pulp. Following exposure to hypoxia-hyperoxia (HH) conditions, a decrease was noted in the expression of both F4/80 and HO-1. This decrease implies that HH conditions contribute to a reduction in macrophage population and impede the iron metabolism process. In the revised version of our manuscript, we have enhanced the clarity of Figure 3U to illustrate the presence of positive staining, with an emphasis on HO-1 staining, which is predominantly observed in the red pulp.

4) The phagocytic function of splenic red pulp macrophages relative to infection cannot be used directly to understand erythrophagocytosis. The standard approach is to use opsonized RBCs in vitro. Furthermore, RBC survival is a standard method to assess erythrophagocytosis function. In this method, biotin is injected via tail vein directly and small blood samples are collected to measure the clearance of biotinilation by flow; kits are available to accomplish this. Because the method is standard, Fig 4D is not necessary and Fig 4E needs to be performed only in blood by sampling mice repeatedly and comparing the rate of biotin decline in HH with NN (not comparing 7 d with 14 d).

We appreciate your insightful comments and suggestions. We concur that the phagocytic function of splenic red pulp macrophages in the context of infection may not be directly translatable to understanding erythrophagocytosis. Given our assessment that the use of cy5.5-labeled E.coli alone may not be sufficient to accurately evaluate the phagocytic function of macrophages, we extended our study to include the use of NHS-biotin-labeled RBCs to assess phagocytic capabilities. While the presence of biotin-labeled RBCs in the blood could provide an indication of RBC clearance, this measure does not exclusively reflect the spleen's role in the process, as it fails to account for the clearance activities of other organs.

Consequently, we propose that the remaining biotin-labeled RBCs in the spleen may provide a more direct representation of the organ's function in RBC clearance and sequestration. Our observations of diminished erythrophagocytosis at both 7 and 14 days following exposure to HH guided our subsequent efforts to quantify biotin-labeled RBCs in both the circulatory system and spleen. These measurements were conducted during the 7 to 14-day span following the confirmation of impaired erythrophagocytosis. Comparative evaluation of RBC clearance rates under NN and HH conditions provided further evidence supporting our preliminary observations, with the data revealing a decrease in the RBC clearance rate in the context of HH conditions. In response to feedback from other reviewers, we have elected to exclude the phagocytic results and the diagram of the erythrocyte labeling assay. These amendments will be incorporated into the revised manuscript. The reviewers' constructive feedback has played a crucial role in refining the methodological precision and coherence of our investigation.

5) It is unclear whether Tuftsin has a specific effect on phagocytosis of RBCs without other potential confounding effects. Furthermore, quantifying iron in red pulp splenic macrophages requires alternative readily available more quantitative methods (e.g. sorted red pulp macrophages non-heme iron concentration).

We appreciate your comments and questions regarding the potential effect of Tuftsin on the phagocytosis of RBCs and the quantification of iron in red pulp splenic macrophages. Regarding the role of Tuftsin, we concur that the literature directly associating Tuftsin with erythrophagocytosis is scant. The work of Gino Roberto Corazza et al. does suggest a link between Tuftsin and general phagocytic capacity, but it does not specifically address erythrophagocytosis (Am J Gastroenterol, 1999;94:391-397). We agree that further investigations are required to elucidate the potential confounding effects and to ascertain whether Tuftsin has a specific impact on the phagocytosis of RBCs. Concerning the quantification of iron in red pulp splenic macrophages, we acknowledge your suggestion to employ readily available and more quantitative methods. We have incorporated additional Fe2+ staining in the spleen at two time points: 7 and 14 days subsequent to HH exposure (refer to the following Figure). The resultant data reveal an escalated deposition of Fe2+ within the red pulp, as evidenced in Figures 5 (panels L and M) and Figure 7 (panels L and M).

6) In Fig 5, PBMCs are not thought to represent splenic macrophages and although of some interest, does not contribute significantly to the conclusions regarding splenic macrophages at the heart of the current work. The data is also in the wrong direction, namely providing evidence that PBMCs are relatively iron poor which is not consistent with ferroptosis which would increase cellular iron.

We appreciate your insightful critique regarding Figure 5 and the interpretation of our data on peripheral blood mononuclear cells (PBMCs) in relation to splenic macrophages. We understand that PBMCs do not directly represent splenic macrophages, and we agree that any conclusions drawn from PBMCs must be considered with caution when discussing the behavior of splenic macrophages.

The primary rationale for incorporating PBMCs into our study was to investigate the potential correspondence between their gene expression changes and those observed in the spleen after HH exposure. This was posited as a working hypothesis for further exploration rather than a conclusive statement. The gene expression in PBMCs was congruous with changes in the spleen's gene expression, demonstrating an iron deficiency phenotype, ostensibly due to the mobilization of intracellular iron for hemoglobin synthesis. Thus, it is plausible that NCOA4 may facilitate iron mobilization through the degradation of ferritin to store iron.

It remains ambiguous whether ferroptosis was initiated in the PBMCs during our study. Ferroptosis primarily occurs as a response to an increase in Fe2+ rather than an overall increase in intracellular iron. Our preliminary proposition was that relative changes in gene expression in PBMCs could potentially mirror corresponding changes in protein expression in the spleen, thereby potentially indicating alterations in iron processing capacity post-HH exposure. However, we fully acknowledge that this is a conjecture requiring further empirical substantiation or clinical validation.

7) Tfr1 increase is typically correlated with cellular iron deficiency while ferroptosis consistent with iron loading. The direction of the changes in multiple elements relevant to iron trafficking is somewhat confusing and without additional evidence, there is little confidence that the authors have reached the correct conclusion. Furthermore, the results here are analyses of total spleen samples rather than specific cells in the spleen.

We appreciate your astute comments and agree that the observed increase in transferrin receptor (TfR) expression, typically associated with cellular iron deficiency, appears contradictory to the expected iron-loading state associated with ferroptosis. We understand that this apparent contradiction might engender some uncertainty about our conclusions.

In our investigation, we evaluated total spleen samples as opposed to distinct cell types within the spleen, a factor that could have contributed to the seemingly discordant findings. An integral element to bear in mind is the existence of immature RBCs in the spleen, particularly within the hematopoietic island where these immature RBCs cluster around nurse macrophages. These immature RBCs contain abundant TfR which was needed for iron uptake and hemoglobin synthesis. These cells, which prove challenging to eliminate via perfusion, might have played a role in the observed upregulation in TfR expression, especially in the aftermath of HH exposure. Our further research revealed that the expression of TfR in macrophages diminished following hypoxic conditions, thereby suggesting that the elevated TfR expression in tissue samples may predominantly originate from other cell types, especially immature RBCs (refer to subsequent Figure).

Reviewer #2 (Public Review):

The authors aimed at elucidating the development of high altitude polycythemia which affects mice and men staying in the hypoxic atmosphere at high altitude (hypobaric hypoxia; HH). HH causes increased erythropoietin production which stimulates the production of red blood cells. The authors hypothesize that increased production is only partially responsible for exaggerated red blood cell production, i.e. polycythemia, but that decreased erythrophagocytosis in the spleen contributes to high red blood cells counts.

The main strength of the study is the use of a mouse model exposed to HH in a hypobaric chamber. However, not all of the reported results are convincing due to some smaller effects which one may doubt to result in the overall increase in red blood cells as claimed by the authors. Moreover, direct proof for reduced erythrophagocytosis is compromised due to a strong spontaneous loss of labelled red blood cells, although effects of labelled E. coli phagocytosis are shown. Their discussion addresses some of the unexpected results, such as the reduced expression of HO-1 under hypoxia but due to the above-mentioned limitations much of the discussion remains hypothetical.

Thank you for your valuable feedback and insight. We appreciate the recognition of the strength of our study model, the exposure of mice to hypobaric hypoxia (HH) in a hypobaric animal chamber. We also understand your concerns about the smaller effects and their potential impact on the overall increase in red blood cells (RBCs), as well as the apparent reduced erythrophagocytosis due to the loss of labelled RBCs.

Erythropoiesis has been predominantly attributed to the amplified production of RBCs under conditions of HH. The focus of our research was to underscore the potential acceleration of hypoxia-associated polycythemia (HAPC) as a result of compromised erythrophagocytosis. Considering the spontaneous loss of labelled RBCs in vivo, we assessed the clearance rate of RBCs at the stages of 7 and 14 days within the HH environment, and subsequently compared this rate within the period from 7 to 14 days following the clear manifestation of erythrophagocytosis impairment at the two aforementioned points identified in our study. This approach was designed to negate the effects of spontaneous loss of labelled RBCs in both NN and HH conditions. Correspondingly, the results derived from blood and spleen analyses corroborated a decline in the RBC clearance rate under HH when juxtaposed with NN conditions.

Apart from the E. coli phagocytosis and the labeled RBCs experiment (this part of the results was removed in the revision), the injection of Tuftsin further substantiated the impairment of erythrophagocytosis in the HH spleen, as evidenced by the observed decrease in iron within the red pulp of the spleen post-perfusion. Furthermore, to validate our findings, we incorporated RBCs staining in splenic cells at 7 and 14 days of HH exposure, which provided concrete confirmation of impaired erythrophagocytosis (new Figure 4E).

As for the reduced expression of heme oxygenase-1 (HO-1) under hypoxia, we agree that this was an unexpected result, and we are in the process of further exploring the underlying mechanisms. It is possible that there are other regulatory pathways at play that are yet to be identified. However, we believe that by offering possible interpretations of our data and potential directions for future research, we contribute to the ongoing scientific discourse in this area.

Reviewer #3 (Public Review):

The manuscript by Yang et al. investigated in mice how hypobaric hypoxia can modify the RBC clearance function of the spleen, a concept that is of interest. Via interpretation of their data, the authors proposed a model that hypoxia causes an increase in cellular iron levels, possibly in RPMs, leading to ferroptosis, and downregulates their erythrophagocytic capacity. However, most of the data is generated on total splenocytes/total spleen, and the conclusions are not always supported by the presented data. The model of the authors could be questioned by the paper by Youssef et al. (which the authors cite, but in an unclear context) that the ferroptosis in RPMs could be mediated by augmented erythrophagocytosis. As such, the loss of RPMs in vivo which is indeed clear in the histological section shown (and is a strong and interesting finding) can be not directly caused by hypoxia, but by enhanced RBC clearance. Such a possibility should be taken into account.

Thank you for your insightful comments and constructive feedback. In their research, Youssef et al. (2018) discerned that elevated erythrophagocytosis of stressed red blood cells (RBCs) instigates ferroptosis in red pulp macrophages (RPMs) within the spleen, as evidenced in a mouse model of transfusion. This augmentation of erythrophagocytosis was conspicuous five hours post-injection of RBCs. Conversely, our study elucidated the decrease in erythrophagocytosis in the spleen after both 7 and 14 days.

Typically, macrophages exhibit an enhanced phagocytic capacity in the immediate aftermath of stress or stimulation. Nonetheless, the temporal points of observation in our study were considerably extended (seven and fourteen days). It remains uncertain whether phagocytic capability was amplified during the acute phase of HH exposure—particularly within the first day, considering that splenoconstriction under HH for one day results in the release of stored RBCs into the bloodstream—and whether this initial response could precipitate ferroptosis and subsequently diminished erythrophagocytosis at the 7 or 14 day marks under continued HH conditions.

Major points:

1) The authors present data from total splenocytes and then relate the obtained data to RPMs, which are quantitatively a minor population in the spleen. Eg, labile iron is increased in the splenocytes upon HH, but the manuscript does not show that this occurs in the red pulp or RPMs. They also measure gene/protein expression changes in the total spleen and connect them to changes in macrophages, as indicated in the model Figure (Fig. 7). HO-1 and levels of Ferritin (L and H) can be attributed to the drop in RPMs in the spleen. Are any of these changes preserved cell-intrinsically in cultured macrophages? This should be shown to support the model (relates also to lines 487-88, where the authors again speculate that hypoxia decreases HO-1 which was not demonstrated). In the current stage, for example, we do not know if the labile iron increase in cultured cells and in the spleen in vivo upon hypoxia is the same phenomenon, and why labile iron is increased. To improve the manuscript, the authors should study specifically RPMs.

We express our gratitude for your perceptive remarks. In our initial manuscript, we did not evaluate labile iron within the red pulp and red pulp macrophages (RPMs). To address this oversight, we utilized the Lillie staining method, in accordance with the protocol outlined by Liu et al., (Chemosphere, 2021, 264(Pt 1):128413), to discern Fe2+ presence within these regions. The outcomes were consistent with our antecedent Western blot and flow cytometry findings in the spleen, corroborating an increment in labile iron specifically within the red pulp of the spleen.

However, we acknowledge the necessity for other supplementary experimental efforts to further validate these findings. Additionally, we scrutinized the expression of heme oxygenase-1 (HO-1) and iron-related proteins, including transferrin receptor (TfR), ferroportin (Fpn), ferritin (Ft), and nuclear receptor coactivator 4 (NCOA4) in primary macrophages subjected to 1% hypoxic conditions, both with and without hemoglobin treatment. Our results indicated that the expression of ferroptosis-related proteins was consistent with in vivo studies, however the expression of iron related proteins was not similar in vitro and in vivo. It suggesting that the increase in labile iron in cultured cells and the spleen in vivo upon hypoxia are not identical phenomena. However, the precise mechanism remains elusive.

In our study, we observed a decrease in HO-1 protein expression following 7 and 14 days of HH exposure, as shown in Figure 3U, 5A, and S1A. This finding contradicts previous research that identified HO-1 as a hypoxia-inducible factor (HIF) target under hypoxic conditions (P J Lee et al., 1997). Our discussion, therefore, addressed the potential discrepancy in HO-1 expression under HH. According to our findings, HO-1 regulation under HH appears to be predominantly influenced by macrophage numbers and the RBCs to be processed in the spleen or macrophages, rather than by hypoxia alone.

It is challenging to discern whether the increased labile iron observed in vitro accurately reflects the in vivo phenomenon, as replicating the iron requirements for RBCs production induced by HH in vitro is inherently difficult. However, by integrating our in vivo and in vitro studies, we determined that the elevated Fe2+ levels were not dependent on HO-1 protein expression, as HO-1 levels was increased in vitro while decreasing in vivo under hypoxic/HH exposure.

2) The paper uses flow cytometry, but how this method was applied is suboptimal: there are no gating strategies, no indication if single events were determined, and how cell viability was assessed, which are the parent populations when % of cells is shown on the graphs. How RBCs in the spleen could be analyzed without dedicated cell surface markers? A drop in splenic RPMs is presented as the key finding of the manuscript but Fig. 3M shows gating (suboptimal) for monocytes, not RPMs. RPMs are typically F4/80-high, CD11-low (again no gating strategy is shown for RPMs). Also, the authors used single-cell RNAseq to detect a drop in splenic macrophages upon HH, but they do not indicate in Fig. A-C which cluster of cells relates to macrophages. Cell clusters are not identified in these panels, hence the data is not interpretable).

Thank you for your comments and constructive critique regarding our flow cytometry methodology and presentation. We understand the need for greater transparency and detailed explanation of our procedures, and we acknowledge that the lack of gating strategies and other pertinent information in our initial manuscript may have affected the clarity of our findings.

In our initial report, we provided an overview of the decline in migrated macrophages (F4/80hiCD11bhi), including both M1 and M2 expression in migrated macrophages, as illustrated in Figure 3, but did not specifically address the changes in red pulp macrophages (RPMs). Based on previous results, it is difficult to identify CD11b- and CD11blo cells. We will repeat the results and attempt to identify F4/80hiCD11blo cells in the revised manuscript. The results of the reanalysis are now included (Figure 3M). However, single-cell in vivo analysis studies may more accurately identify specific cell types that decrease after exposure to HH.

Furthermore, we substantiated the reduction in red pulp, as evidenced by Figure 4J, given that iron processing primarily occurs within the red pulp. In Figure 3, our initial objective was merely to illustrate the reduction in total macrophages in the spleen following HH exposure.

To further clarify the characterization of various cell types, we conducted a single-cell analysis. Our findings indicated that clusters 0,1,3,4,14,18, and 29 represented B cells, clusters 2, 10, 12, and 28 represented T cells, clusters 15 and 22 corresponded to NK cells, clusters 5, 11, 13, and 19 represented NKT cells, clusters 6, 9, and 24 represented cell cycle cells, clusters 26 and 17 represented plasma cells, clusters 21 and 23 represented neutrophils, cluster 30 represented erythrocytes, and clusters 7, 8, 16, 20, 24, and 27 represented dendritic cells (DCs) and macrophages, as depicted in Figure 3E.

3) The authors draw conclusions that are not supported by the data, some examples: a) they cannot exclude eg the compensatory involvement of the liver in the RBCs clearance (the differences between HH sham and HH splenectomy is mild in Fig. 2 E, F and G).

Thank you for your insightful comments and for pointing out the potential involvement of other organs, such as the liver, in the RBC clearance under HH conditions. We concur with your observation that the differences between the HH sham and HH splenectomy conditions in Fig. 2 E, F, and G are modest. This could indeed suggest a compensatory role of other organs in RBC clearance when splenectomy is performed. Our intent, however, was to underscore the primary role of the spleen in this process under HH exposure.

In fact, after our initial investigations, we conducted a more extensive study examining the role of the liver in RBC clearance under HH conditions. Our findings, as illustrated in the figures submitted with this response, indeed support a compensatory role for the liver. Specifically, we observed an increase in macrophage numbers and phagocytic activity in the liver under HH conditions. Although the differences in RBC count between the HH sham and HH splenectomy conditions may seem minor, it is essential to consider the unit of this measurement, which is value*1012/ml. Even a small numerical difference can represent a significant biological variation at this scale.

b) splenomegaly is typically caused by increased extramedullary erythropoiesis, not RBC retention. Why do the authors support the second possibility? Related to this, why do the authors conclude that data in Fig. 4 G,H support the model of RBC retention? A significant drop in splenic RBCs (poorly gated) was observed at 7 days, between NN and HH groups, which could actually indicate increased RBC clearance capacity = less retention.

Prior investigations have predominantly suggested that spleen enlargement under hypoxic conditions stems from the spleen's extramedullary hematopoiesis. Nevertheless, an intriguing study conducted in 1994 by the General Hospital of Xizang Military Region reported substantial exaggeration and congestion of splenic sinuses in high altitude polycythemia (HAPC) patients. This finding was based on the dissection of spleens from 12 patients with HAPC (Zou Xunda, et al., Southwest Defense Medicine, 1994;5:294-296). Moreover, a recent study indicated that extramedullary erythropoiesis reaches its zenith between 3 to 7 days (Wang H et al., 2021).

Considering these findings, the present study postulates that hypoxia-induced inhibition of erythrophagocytosis may lead to RBC retention. However, we acknowledge that the manuscript in its current preprint form does not offer conclusive evidence to substantiate this hypothesis. To bridge this gap, we further conducted experiments where the spleen was perfused, and total cells were collected post HH exposure. These cells were then smeared onto slides and subjected to Wright staining. Our results unequivocally demonstrate an evident increase in deformation and retention of RBCs in the spleen following 7 and 14 days of HH exposure. This finding strengthens our initial hypothesis and contributes a novel perspective to the understanding of splenic responses under hypoxic conditions.

c) lines 452-54: there is no data for decreased phagocytosis in vivo, especially in the context of erythrophagocytosis. This should be done with stressed RBCs transfusion assays, very good examples, like from Youssef et al. or Threul et al. are available in the literature.

Thanks. In their seminal work, Youssef and colleagues demonstrated that the transfusion of stressed RBCs triggers erythrophagocytosis and subsequently incites ferroptosis in red pulp macrophages (RPMs) within a span of five hours. Given these observations, the applicability of this model to evaluate macrophage phagocytosis in the spleen or RPMs under HH conditions may be limited, as HH has already induced erythropoiesis in vivo. In addition, it was unclear whether the membrane characteristics of stress induced RBCs were similar to those of HH induced RBCs, as this is an important signal for in vivo phagocytosis. The ambiguity arises from the fact that we currently lack sufficient knowledge to discern whether the changes in phagocytosis are instigated by the presence of stressed RBCs or by changes of macrophages induced by HH in vivo. Nonetheless, we appreciate the potential value of this approach and intend to explore its utility in our future investigations. The prospect of distinguishing the effects of stressed RBCs from those of HH on macrophage phagocytosis is an intriguing line of inquiry that could yield significant insights into the mechanisms governing these physiological processes. We will investigate this issue in our further study.

d) Line 475 - ferritinophagy was not shown in response to hypoxia by the manuscript, especially that NCOA4 is decreased, at least in the total spleen.

Drawing on the research published in eLife in 2015, it was unequivocally established that ferritinophagy, facilitated by Nuclear Receptor Coactivator 4 (NCOA4), is indispensable for erythropoiesis. This process is modulated by iron-dependent HECT and RLD domain containing E3 ubiquitin protein ligase 2 (HERC2)-mediated proteolysis (Joseph D Mancias et al., eLife. 2015; 4: e10308). As is widely recognized, NCOA4 plays a critical role in directing ferritin (Ft) to the lysosome, where both NCOA4 and Ft undergo coordinated degradation.

In our study, we provide evidence that exposure to HH stimulates erythropoiesis (Figure 1). We propose that this, in turn, could promote ferritinophagy via NCOA4, resulting in a decrease in NCOA4 protein levels post-HH exposure. We will further increase experiments to verify this concern. This finding not only aligns with the established understanding of ferritinophagy and erythropoiesis but also adds a novel dimension to the understanding of cellular responses to hypoxic conditions.

4) In a few cases, the authors show only representative dot plots or histograms, without quantification for n>1. In Fig. 4B the authors write about a significant decrease (although with n=1 no statistics could be applied here; of note, it is not clear what kind of samples were analyzed here). Another example is Fig. 6I. In this case, it is even more important as the data are conflicting the cited article and the new one: PMCID: PMC9908853 which shows that hypoxia stimulates efferocytosis. Sometimes the manuscript claim that some changes are observed, although they are not visible in representative figures (eg for M1 and M2 macrophages in Fig. 3M)

We recognize that our initial portrayal of Figure 4B was lacking in precision, given that it did not include the corresponding statistical graph. While our results demonstrated a significant reduction in the ability to phagocytose E. coli, in line with the recommendations of other reviewers, we have opted to remove the results pertaining to E. coli phagocytosis in this revision, as they primarily reflected immune function. In relation to PMC9908853, which reported metabolic adaptation facilitating enhanced macrophage efferocytosis in limited-oxygen environments, it is worth noting that the macrophages investigated in this study were derived from ER-Hoxb8 macrophage progenitors following the removal of β-estradiol. Consequently, questions arise regarding the comparability between these cultured macrophages and primary macrophages obtained fresh from the spleen post HH exposure. The characteristics and functions of these two different macrophage sources may not align precisely, and this distinction necessitates further investigation.

5) There are several unclear issues in methodology:

• what is the purity of primary RPMs in the culture? RPMs are quantitatively poorly represented in splenocyte single-cell suspensions. This reviewer is quite skeptical that the processing of splenocytes from approx 1 mm3 of tissue was sufficient to establish primary RPM cultures. The authors should prove that the cultured cells were indeed RPMs, not monocyte-derived macrophages or other splenic macrophage subtypes.

Thank you for your thoughtful comments and inquiries. Firstly, I apologize if we did not make it clear in the original manuscript. The purity of the primary RPMs in our culture was found to be approximately 40%, as identified by F4/80hiCD11blo markers using flow cytometry. We recognize that RPMs are typically underrepresented in splenocyte single-cell suspensions, and the concern you raise about the potential for contamination by other cell types is valid.

We apologize for any ambiguities in the methodological description that may have led to misunderstandings during the review. Indeed, the entirety of the spleen is typically employed for splenic macrophage culture. The size of the spleen can vary dependent on the species and age of the animal, but in mice, it is commonly approximately 1 cm in length. The spleen is then dissected into minuscule fragments, each approximately 1 mm3 in volume, to aid in enzymatic digestion. This procedure does not merely utilize a single 1 mm3 tissue fragment for RPMs cultures. Although the isolation and culture of spleen macrophages can present considerable challenges, our method has been optimized to enhance the yield of this specific cell population.

• (around line 183) In the description of flow cytometry, there are several missing issues. In 1) it is unclear which type of samples were analyzed. In 2) it is not clear how splenocyte cell suspension was prepared.

1) Whole blood was extracted from the mice and collected into an anticoagulant tube, which was then set aside for subsequent thiazole orange (TO) staining. 2) Splenic tissue was procured from the mice and subsequently processed into a single-cell suspension using a 40 μm filter. The erythrocytes within the entire sample were subsequently lysed and eliminated, and the remaining cell suspension was resuspended in phosphate-buffered saline (PBS) in preparation for ensuing analyses.

We have meticulously revised these methodological details in the corresponding section of the manuscript to ensure clarity and precision.

• In line 192: what does it mean: 'This step can be omitted from cell samples'?

The methodology employed for the quantification of intracellular divalent iron content and lipid peroxidation level was executed as follows: Splenic tissue was first processed into a single cell suspension, subsequently followed by the lysis of RBCs. It should be noted that this particular stage is superfluous when dealing with isolated cell samples. Subsequently, a total of 1 × 106 cells were incubated with 100 μL of BioTracker Far-red Labile Fe2+ Dye (1 mM, Sigma, SCT037, USA) for a duration of 1 hour, or alternatively, C11-Bodipy 581/591 (10 μM, Thermo Fisher, D3861, USA) for a span of 30 minutes. Post incubation, cells were thoroughly washed twice with PBS. Flow cytometric analysis was subsequently performed, utilizing the FL6 (638 nm/660 nm) channel for the determination of intracellular divalent iron content, and the FL1 (488 nm/525 nm) channel for the quantification of the lipid peroxidation level.

• 'TO method' is not commonly used anymore and hence it was unclear to this Reviewer. Reticulocytes should be analyzed with proper gating, using cell surface markers.

We are appreciative of your astute observation pertaining to the methodology we employed to analyze reticulocytes in our study. We value your recommendation to utilize cell surface markers for effective gating, which indeed represents a more modern and accurate approach. However, as reticulocyte identification is not the central focus of our investigation, we opted for the TO staining method—due to its simplicity and credibility of results. In our initial exploration, we adopted the TO staining method in accordance with the protocol outlined (Sci Rep, 2018, 8(1):12793), primarily owing to its established use and demonstrated efficacy in reticulocyte identification.

• The description of 'phagocytosis of E. coli and RBCs' in the Methods section is unclear and incomplete. The Results section suggests that for the biotinylated RBCs, phagocytosis? or retention? Of RBCs was quantified in vivo, upon transfusion. However, the Methods section suggests either in vitro/ex vivo approach. It is vague what was indeed performed and how in detail. If RBC transfusion was done, this should be properly described. Of note, biotinylation of RBCs is typically done in vivo only, being a first step in RBC lifespan assay. The such assay is missing in the manuscript. Also, it is not clear if the detection of biotinylated RBCs was performed in permeablized cells (this would be required).

Thanks for the comments. In our initial methodology, we employed Cy5.5-labeled Escherichia coli to probe phagocytic function, albeit with the understanding that this may not constitute the most ideal model for phagocytosis detection within this context (in light of recommendations from other reviewers, we have removed the E. coli phagocytosis results from this revision, as they predominantly mirror immune function). Our fundamental aim was to ascertain whether HH compromises the erythrophagocytic potential of splenic macrophages. In pursuit of this, we subsequently analyzed the clearance of biotinylated RBCs in both the bloodstream and spleen to assess phagocytic functionality in vivo.

In the present study, instead of transfusing biotinylated RBCs into mice, we opted to inject N-Hydroxysuccinimide (NHS)-biotin into the bloodstream. NHS-biotin is capable of binding with cell membranes in vivo and can be recognized by streptavidin-fluorescein isothiocyanate (FITC) after cells are extracted from the blood or spleen in vitro. Consequently, biotin-labeled RBCs were detectable in both the blood and spleen following NHS-biotin injection for a duration of 21 days.

Ultimately, we employed flow cytometry to analyze the NHS-biotin labeled RBCs in the blood or spleen. This method facilitates the detection of live cells and is not applicable to permeabilized cells. We believe this approach better aligns with our investigative goals and offers a more robust evaluation of erythrophagocytic function under hypoxic conditions.

Author Response

“It is unclear whether the Ter sites integrated by a single copy plasmid have any effect on the replication of this region but the data show that the observed effects are dependent on expression of the Tus protein.”

-The lack of perturbation of the TerB sequence on fork progression has extensively been studied previously in both Willis et al, 2014 and Larsen et. al, 2014. Furthermore, as the detection of the SMARD signal at the TerB sites is dependent on the 7.5kb probe that spans the TerB sites (orange probe, Fig 2B & 2D), it would be impossible to study the effect on replication in this region, with and without the integration of the single copy plasmid.

“The SMARD data do not reveal what proportion of forks are arrested at Tus/Ter, or how long the fork delay is imposed.”

-The percentage of fork stalling at the TerB sites, with and without Tus expression, has been quantified in Figure 2E & 2F. Essentially, 36% forks stall at the TerB block, i.e. 18% of the forks stall in both the 5’ to 3’ (orange) and 3’ to 5’ (blue) direction when the Tus-TerB block is active.

“It is not shown whether the replication inhibitor HU leads to the same widely spread gamma H2AX response.”

-While we have not shown gH2AX accumulation via ChIP after HU treatment, Supplementary Figure 5A & 5B clearly show increased gH2AX foci when the cells are treated with HU, suggesting a global replication stress response that is in stark contrast to the response to Tus-TerB.

Author Response

First, we would like to thank eLife and the reviewers for the positive assessment of our manuscript and for providing the medium to expand the scientific dialogue on the present study. We greatly appreciate the reviewers´ assessment and will revise the manuscript considering their input. Please see our provisional response below.

Reviewer #1’s main concern is centered on the evidential strength of the study’s conclusion that age-specific effects of birth weight on brain structure are more localized and less consistent across cohorts than age-uniform, stable effects. More specifically, the reviewer points out the evidence (or lack of such) for age-specific effects. The reviewer specifies four methodological concerns: that #1 no direct statistical comparisons are conducted between samples (beyond the spin-tests) and that #2 the differential composition of samples in terms of age distribution leads to the possibility that lack of results is explained by methodological differences. Further, he/she adds that #3 some datasets have a narrow age range precluding detection of age-related effects and #4 that the modeling strategy does not allow for non-linear interaction between age and BW suggesting the use of spline models instead in a _mega-_analytical fashion. Reviewer #1 also asks for greater clarity regarding the statistical models and the provision of effect-size maps.

As a response to the reviewer’s comments, we will submit a revised version of the manuscript with a revised description of the statistical models and submit effect-size maps in an accompanying repository. We will tackle the first two concerns by performing additional statistical analyses. The planned analyses will include across-sample reliability and within-sample reliability for the time*birth weight analyses. These analyses will address the concerns that the lack of age-specific effects is due to sample differences rather than a lack of biological effects (#2) and provide further explicit statistical comparisons across samples (#1).

We do not believe concern #3 is critically problematic since time*birth weight refers to a within-subject contrast, e.g. longitudinal-only based contrast. Birth weight, even when self-reported is a highly reliable measure and the sample sizes are relatively large (n = 635, 1759, and 3324 unique individuals). Note that the smaller dataset does have longer follow-up times and more observations per participant increasing the reliability of estimations in individual change. Structural MRI measures have very high reliability (often ICC > .95). Clearly, longitudinal brain change is less reliable, yet the present sample size and the high reliability of birth weight should provide enough statistical power to capture even small time-varying effects of birth weight on brain structure.

We agree that some - if not most - brain structures follow non-linear trajectories throughout life (#4). In the present study, age regressors are used only for accounting for variance in the data rather than capturing any effect of interest. Rather, it is the time*birth weight regressor that captures age-varying changes in brain structure. Time reflects within-subject follow-up time. In any case, the inclusion of non-linear regressors is superfluous in ABCD given the small age range and will not  regress out additional variance. Likewise, published data on UKB has shown that most age trajectories of cortical data follow grossly linear fits (age range in the UKB: 45 – 85 years). In LCBC, however, we recognize that non-linear modeling may be able to capture additional variance since it is a lifespan dataset. This may provide better estimates for birth weight and time*birth weight though it is unlikely that it has a big effect on the slope. In any case, #4 is a valid concern and we will repeat the main analysis using «spline models to fit age We will not follow the reviewer's suggestion for a mega-analytical approach. It is a valid approach, but it is best suited to other configurations of data than those used here where we used longitudinal data only. The use of cross-sectional data for deriving differential age trajectories is also not without problems.

We see Reviewer #2´s  concerns mainly being: #1 The translation of birth-weight effects on brain volume to years of aging is inadequate and not fully supported by the data. #2 Lack of data regarding the functional relevance of brain weight effects on brain structure; suggesting the inclusion of cognitive data and (birth weight – brain structure – cognition) mediation analyses. #3 The degree to which the association between birth weight and cortical area/volume is explained by overall somatic growth. #4 The lack of non-linear regressors for fitting age.

1 We will modify the section on translation of birth weight effects into years of aging. While it is often used - see for example the literature on brain age - and is relatable, it can be easily misinterpreted and does not reflect any objective measure of effect size. #2 The degree to which birth weight relates to cognition throughout the lifespan through individual variations in brain structure is a key question in the field for which currently we only have indirect evidence. Unfortunately, the study of this triple relationship is out of the scope of the current study. This, we agree with the reviewer, warrants further research. We disagree, however, in that mediation analyses are able to provide a satisfactory answer to this question. Also, we believe that the conclusions of the present study are not affected by the omission of cognitive data beyond what has been commented on in #1. The relationship between birth weight, structure, and cognition will be further discussed in the revised version of the manuscript.

3 Indeed, part of birth weight effects on brain structure can be explained by overall somatic growth. This study does not provide - and is not designed to provide - a specific mechanism for which birth weight is associated with brain structure. Yet, research has also shown that differences in birth weight partially reflect factors associated with variations in neurodevelopmental processes in gestation. Mechanisms are thought to be partially genetic (including geneticly influenced potential for somatic growth) and partially environmental prenatal influences. The amount of variance explained by such mechanisms is unknown and may differ across samples (e.g. population samples vs. monozygotic twins). In our view, the association is no less meaningful if mostly attributed to somatic growth if it is associated with partly modifiable variance in brain growth. The revised manuscript will include further considerations on the possible mechanisms explaining the association between birth weight and cortical structure throughout the lifespan.

Concern #4 is equivalent to Reviewer #1, concern #4. In short, the age regressor is used only to account for variance. Linear modeling in the ABCD and UKB datasets has been shown to fit relatively well the different cortical regions due to the samples’ narrow age range. In LCBC, however, we recognize that non-linear modeling may be able to capture additional variance since it is a lifespan dataset. This may provide better estimates for birth weight and time*birth weight though it is unlikely that has a big effect on the slope of the effects. We will repeat the main analysis using «spline models to fit age.

Author Response:

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

We thank the reviewers for their positive remarks, which we have addressed in detail below and which we have considered in our revised manuscript.

Reviewer #1 (Recommendations For The Authors):

The authors claim several times to have documented electrogenic chloride/oxalate exchange mediated by human SLC26A6. However, they fail to detect whole cell currents in SLC26A6-expressing HEK293 cells in oxalate bath, despite robust, saturable Cl- efflux from proteoliposomes into extracellular oxalate solution, as detected by AMCA fluorescence decay.

We interpret the low, and essentially non-detectable currents for Cl-/oxalate exchange as a consequence of the slow kinetics of transport. This lack of sensitivity is not unusual for electrogenic secondary-active transport processes recorded by patch-clamp electrophysiology in mammalian cells, which renders the recording in large X. laevis oocytes by two-electrode voltage clamp the preferred method for such investigations. In contrast to the non-detectable activity in electrophysiology, the pronounced signal in the ACMA assay reflects the influx of H+ as a consequence of the negative membrane potential established by the influx of the divalent anion oxalate, which we assume to occur in exchange with the monovalent Cl-.

Instances in the manuscript include:

Abstract Line 17 overstates the paper's findings as "we have characterized SLC26A6 as a strictly coupled exchanger of chloride with either bicarbonate or oxalate". To the extent that "strictly coupled" implies 1:1 stoichiometry, the authors conclude Cl-/bicarbonate exchange is electroneutral based on its lack of exchange current. In contrast, the lack of Cl/oxalate exchange current does not lead the authors to the same conclusion of electroneutrality for Cl-/oxalate exchange. The data presented do not measure the stoichiometry of Cl-/oxalate exchange.

We agree that our ACMA experiments do not strictly discriminate between coupled and uncoupled oxalate transport. However, it should be emphasized that, assuming that transport proceeds by an alternate access mechanism, uncoupled oxalate transport would require the change of the unloaded transporter between inward- and outward-facing conformations, which was shown to be unfavorable in Figure 1D.

We have reworded the sentence in the abstract to:

“Here we have determined the structure of the closely related human transporter SLC26A6 and characterized it as a coupled exchanger of chloride with bicarbonate and presumably also oxalate.”

Line 264 claims that "the paper's functional data has defined SLC26A6 as a coupled transporter that exchanges Cl- with either HCO3- or oxalate at equimolar stoichiometry."

We have changed the sentence to:

“Whereas our functional data has defined SLC26A6 as a coupled antiporter that exchanges Cl- with HCO3- and presumably also oxalate with equimolar stoichiometry…”

In lines 299-302, the authors claim to have "detected strict equimolar exchange of anions"...leading to the reasonable conclusion of electroneutral Cl-/HCO3- exchange and the reasonable but unsupported conclusion of coupled Cl/oxalate exchange.

We have reworded the sentence to:

“In the case of Cl-/HCO3- transport, we detect a strict equimolar exchange of anions binding to a conserved site in the mobile core domain of the transmembrane transport unit (Figure 4B, H). Although not shown unambiguously, we assume an analogous mechanism also for Cl-/oxalate exchange.”

Lines 505-508 in Methods claim that the AMCA proteoliposome assay "measured electrogenic oxalate transport." However, the assay documented extracellular oxalate- dependent anion transport that was most simply interpreted as coupled exchange.

The assay has detected H+ uptake into proteoliposomes as a consequence of electrogenic anion influx. In these experiments, oxalate is the only anion on the outside of vesicles and it requires to be transported to be able to observe any fluorescent change. The claim of electrogenic oxalate transport is thus justified. As described above, the assay does under the applied conditions not discriminate between uncoupled and coupled oxalate transport, however uncoupled oxalate transport would require the conformational change of an unloaded transporter, which was shown to be kinetically disfavored.

In contrast, other parts of the manuscript acknowledge that the evidence presented falls short of documenting stoichiometric chloride/oxalate exchange.

Results Line 151 sets out to "investigate a potentially electrogenic Cl-/oxalate exchanger. Similarly, results line 160 conservatively claims that Cl-/oxalate exchange occurs "presumably" with a 1:1 stoichiometry. This more accurate language needs to be used throughout the paper, replacing the more absolute but unjustified descriptions summarized earlier above.

We have now introduced the requested clarifications throughout.

I have otherwise only Minor points to suggest. Abstract:

"Among the eleven paralogs in humans.... ". This should be "at least 10," as the original

status of human SLC26A10 as a transcribed pseudogene vs. a truncated protein-expressing gene remains unresolved. The authors recognize this in the introduction, where on p. 3 they acknowledge "ten functional SLC26 paralogs in humans."

We have changed to ‘ten functional paralogs’

Introduction:

p. 4 line 45: membrane-inserted

We have introduced the correction.

Methods:

Construct Generation:

p. 25 lines 380-2: Add a sentence describing any C-terminal sequence extension added after C3 cleavage product, and whether/how it modified the PDZ-binding domain sequence. Has the modification been tested for PDZ-binding activity?

We have introduced the following sentence:

“As a consequence of FX cloning, expressed constructs include an additional serine at the N- terminus and an alanine at the C-terminus. Following C3-cleavage, SLC26A6 carries a seven residues long C-terminal extension (of sequence ALEVLFQ).”

We have not tested PDZ-domain binding but expect that the added residues interfere with interaction with the C-terminal binding motif.

Liposome Reconstitution:

p.28: lines 453-4: Please clarify the meaning of: "absorbance at 540 nm was used to detect liposome destabilization," followed immediately by "After the formation of stabilized liposomes".... Does destabilization mean liposomal leak of Eu.L1+ chromophore, with decline of absorbance? What is practically meant in terms of the number of 10 mL additions of 10% TTX-100 routinely added to generate stabilized liposomes without generating destabilized liposomes? Did this number vary from trial to trial? How did you know when to stop adding aliquots of TTX-100?

We have added the following sentence:

“For protein incorporation, 10 µl aliquots of 10% Triton X-100 were added in order to destabilize liposomes and permit protein incorporation. After reaching a plateau of the light scattering measured at 540 nm, 4 additional aliquots of Triton X-100 were added. The number of additions required for destabilization did not vary between reconstitutions.”

p.28 line 463: "dissolved" should be "suspended."

We have introduced the correction.

Bicarbonate Transport Assay

p. 29 line 480-1. How many repetitions represented by the phrase "sequential ultracentrifugation steps"- please provide a number or a range, as applicable.

We have defined the number of ultracentrifugation steps (two).

Pp 29-30, lines 485-7: define "cycles" - are these fluorometric excitation-emission cycles?

We have defined cycles as fluorometric excitation/emission cycles.

p. 30 line 489: delete "by"

We have deleted ‘by’

Name the fluorimeter used.

We have named the fluorimeter used as Tecan Infinite M1000 Pro microplate reader.

AMCA assay

Pp 30-31, lines 505-8: Add composition of extraliposomal oxalate-containing buffer. In Fig 1 Suppl Fig. 1 panels H and I, and Methods lines 505-508, with 150 mM oxalate substituting for 150 mM Cl- how was osmotic balance maintained in the external chloride solution?

We have added the composition of the oxalate-containing buffer. The osmolarity of the extracellular solution was not balanced.

Electrophysiology

p.32 line 532: What fold-increase of SLC26 protein levels was produced by inclusion of 3 mM valproic acid?

We consistently see an increase of expression upon addition of valproic for different membrane proteins acid but did not quantify it in this case.

Results:

Functional characterization of SLC26A6

Line 91: "comparably" to what? Otherwise, perhaps, "comparatively" was intended here?

We have changed to ‘comparatively’

Fig 1E legend: line 763 "time- and concentration-dependent". Same for line 791, line 799

We have introduced the correction.

Fig. 1G: Change Y axis legend to "Normalized [Eu.L1+] emission." Add bath ion composition for "neg" condition (black trace).

We have corrected the label on the Y-axis and added ion composition for neg.

Fig. 1H legend sentence 2 "in a concentration-dependent manner for liposomes (Mock) in

75 mM oxalate (n=5) and for SLC26A6 proteoliposomes in extracellular oxalate concentrations of 9.4 mM (n=3) etc

We have reworded the sentence:

“Traces show mean quenching of ACMA fluorescence in a time- and concentration-dependent manner for SLC26A6 proteoliposomes with outside oxalate concentrations of 9.4 mM (n = 3), 37.5 mM (n = 5), 75 mM (n = 6), 150 mM (n = 8, all from two independent reconstitutions). Neg. refers to liposomes not containing SLC26A6 assayed upon addition of 75 mM oxalate as defined in Figure1-figure supplement 1G.”

Fig. 1 Fig Suppl. 1. p.45 line 790: change "chemical formulas" to "2-D chemical structures"

We have introduced the change.

lines 799: Time-

We have introduced the change.

Fig 1 Fig Suppl. 1. p 46 lines 809-810: dashed lines indicating 0 pA are indeed red in panels A and B, but black in panels H and I.

We explicitly refer to recordings, where dashed lines at 0 pA are consistently in red.

Fig 2 Fig Suppl.1 p. 50, line 832: Two additional multi-class.

We have introduced the change.

Fig 2 Suppl Fig 2B, p. 51: Please label the residue numbers of the side chains coordinating the chloride binding site. Can those residues be indicated in Fig 2 Suppl Fig 2A in the appropriate helices? These residues might also be asterisked in the primary sequence alignment of Fig 2 Suppl Fig 3A.

We have labeled the residues in Fig. 2-figure supplement 2B but not 2A where the focus is on the general quality of the density in different parts of the protein. We have also labeled the same residues in Fig. 2-figure supplement 3A.

Fig.4 legend p. 59 line 882 – Deviating residues in SLC26A9 (typo A6) are highlighted in violet.

We have introduced the correction.

p. 60 lines 888-9: Please clarify the individual meaning of green and purple asterisks on defining the substrate cavity diameter; How do purple and green asterisks relate to the yellow and green lines in the graph? Should the asterisks be two green and two yellow asterisks, or should they be black? What is the meaning of the purple and green asterisks at the two upper corners of panel G with respect to the substrate cavity radii?

Please specify if y axis label "radius" refers to substrate cavity radius, and whether X axis label "distance" refers to axial distance along helix alpha10, alpha1, or of the helical pair. Is value "0 A" on the X axis anchored at the top of the helices as depicted in panels D-F? Is X-axis value 10.5A sited at the bottom of the helices? Please indicate on the panel G curves the x-y value range depicted in the inset images- or clarify that the inset images present the entire curves of panel G.

We have clarified these remarks in a revised legend:

“The radius of the substrate cavity of either protein is mapped along a trajectory connecting a start position at the entrance of each cavity (distance 0 Å) and an end position located outside of the cavity in the protein region (distance 10 Å). Both points are defined by asterisks in insets showing the substrate cavities for either transporter and they are indicated in the graph (green, cavity entrance towards the aqueous vestibule; violet, protein region).”

Fig. 4 p . 61-2 panel H and lines 907-8: Addition to the panel of the A5 "buried Cl- binding site" would be helpful, if possible to do without obscuring the A6 and A9 Cl-s.

Panels H and I show the cavity harboring the ion binding site in two orientations, including the surrounding residues. We prefer to show all surrounding residues for both orientations, even if this somewhat obscures the view on the ion in the left panels. An unobscured view of the ion in its cavity is provided in panels D and E.

p. 12. Results line 234: Please specify that "both proteins" here refers to A6 and A5 vs A6 and A9

We have specified this.

p. 13 Results lines 268-72: R404 is "ubiquitous in other mammalian paralogs..." should be changed to "shared by most but not all mammalian paralogs".

We have changed the text accordingly.

Fig 4C should have a red or purple asterisk placed under the yellow column corresponding to R404 of SLC26A6, so that the discussion can refer to it. It would also be helpful to remind the reader that R404 corresponds to conserved position 6 in Fig 4 Fig Suppl. 1 panels   D-G.

Here the authors might note that sulfate -transporting SLC26A1 and -A2 have the shorter side chain K residue.

We have marked the position in Figure 4C with an asterisk and added the following sentence to its legend:

Asterisk marks position that harbors a basic residue in all family members except for SLC26A9 where the residue is replaced by a valine. Whereas most paralogs, including the ones operating as bicarbonate exchangers, have an arginine at this site, the sulfate transporters SLC26A1 and 2 contain a smaller lysine.

We have added the following statement to the legend of Figure4-figure supplement 1:

“‘6’ indicates the position which contains a basic residue in all family members except for SLC26A9.”

Fig 5B legend p. 63 line 916. Please specify if the 14 independent experiments include both the symmetric Cl- conditions and the asymmetric Cl-/HCO3- conditions or only one condition.

The 14 independent experiments were only recorded in symmetric chloride conditions. We have changed the legend accordingly.

Fig 5C. It would be useful for readers to add the I-V trace of WT SLC26A6 taken from Fig 1 Suppl 1B (perhaps in gray), to document the specificity of the very low magnitude R404V whole cell current. Alternatively, please note (if the case) that WT SLC26A6 currents (Fig 1 Supple 1B) are indistinguishable from the blacked dashed zero current density line.

We have now displayed the I-V trace of WT SLC26A6 as grey dashed line for comparison and added a new panel that show the differences between the currents of R404V and WT recorded at 100 mV (Fig. 5D). Although the currents for R404V were consistently lager than for WT, the difference is not statistically significant. We have explicitly mentioned this in the text and the figure legend.

Fig 5E depiction of decline in ACMA fluorescence is missing from the legend. Legend references to panels E and F seem to correspond to Fig 5 panels F and G (lucigenin fluorescence), as noted in Results p 14 lines 280-3.

We have added the legend.

Chernova et al (2005) reported electroneutral human and mouse A6-mediated Cl/HCO3- exchange in Xenopus oocytes. They also observed electrogenic Cl-/oxalate exchange by mouse SLC26A6, but detected no current generated by human SLC26A6-mediated Cl-/Oxalate exchange. That paper (already cited) might be referred to more explicitly in connection to the authors' current findings of electroneutral Cl-/HCO3- exchange by human SLC26A6 as well as their inability to detect human SLC26A6-mediated Cl-/oxalate exchange current in HEK-293 whole cell recordings.

We now have included the reference in the discussion:

“Consequently, transport would be electroneutral in case of the monovalent HCO - and electrogenic in case of the divalent oxalate (Figure 1E-H), which was already proposed in a previous study (Chernova et al., 2005).   We also want to re-emphasize that the inability to measure discernable currents does not necessarily imply that the transport might not be electrogenic as, due to their slow kinetics, transport-mediated currents might be below the detection limit of patch-clamp electrophysiology.”

Reviewer #2 (Recommendations For The Authors):

-  It would be helpful if the authors briefly clarify the depiction/scheme of the hypothetical SLC26A6 outward-facing conformation. Is this gleaned from a prior structure of a related SLC family or distant homolog? Functional data? Biochemical/biophysical data? As well, I would also recommend labeling this within the figure (Figure 3-figure supplement 1D labeling, for instance - inward, hypothetical outward).  

As mentioned in the legend of Figure 3-figure supplement 1D, the outward conformation is hypothetical. We have also now mentioned this in the title. The displayed outward- conformation was constructed by manually moving the area depicting the core domain relative to the fixed gate domain.  

-  Have the authors attempted to block SLC26A6-mediated transport with the addition of a known inhibitor, such as niflumic acid? I understand that this may be technically challenging, but it would strengthen the transport assay data, especially in Figure 5D with the ACMA assay testing the SLC26A6 R404V mutant.  

We have not attempted to block the currents by addition of niflumic acid.  

-  It could be helpful to the reader to move the schematics in Figure 1-figure supplement 1C into Figure 1.  

We have now displayed the schematics illustrating the principle of the respective transport assays next to the data in Figure 1, but kept Figure 1-figure supplement 1C for a more detailed description of the assays.  

-  Figure 3-figure supplement 1D legend, should be "hypothetical" instead of "hypothetic."  

We have introduced the correction.

-  I might consider coloring the Cl- ion something that is distinct from the model colors that are used in the figures (see Figure 4 and Figure 4-figure supplement 1). This would help to clarify Figure4-figure supplement 1H, where I believed that the Cl- ions at first were from the SLC26A6 model at first glance.

We have used the green color for chloride throughout the manuscript and would prefer to keep it that way for consistency.

-  Labeling in Figure 5 legend (E, F) do not match the Figure (F,G). The description of the ACMA assay is absent from the figure legend (the real Figure 5E).

This has been corrected.

Reviewer #3 (Recommendations For The Authors):

None.  This  is  a  well-done  manuscript  and  I  have  no  further  suggestions.

Author Response

Reviewer #3 (Public Review):

In this manuscript, Man et al. describe a new signaling pathway for regulation of the voltage-gated calcium channel Cav1.2 and show that it can modulate synaptic plasticity in the hippocampus. Studies with specific inhibitors, phosphopecific antibodies, and gene knockdown show that activation of alpha-1 adrenergic receptors induces downstream activation of the serine/threonine protein kinase PKC and the tyrosine protein kinases Pyk2 and Src, which bind to the Cav1.2 channel through its large intracellular segment connecting domains II and III. This signaling complex leads to tyrosine phosphorylation of Cav1.2 and increased channel activity. Block of this novel signaling pathway in hippocampal slices with specific inhibitors of Pyk2 and Src reduced a specific component of long-term potentiation whose induction requires Cav1.2 channel activity.

This work is an important advance, as it presents a novel signaling pathway through which the ubiquitous neurotransmitter norepinephine and the neurohormone epinephrine can regulate synaptic plasticity, attention, learning, and memory. The experiments are comprehensive, carefully done, and clearly presented. The authors should consider revisions and responses to the points below.

1) Figure 2B, D. Inhibitors reduce Ica below control. Is there endogenous stimulation of this regulatory pathway under control conditions?

We now explicitly state in the Discussion: “Inhibitors of PKC, Pyk2, and Src reduce under nearly all conditions Cav1.2 baseline activity and also tyrosine phosphorylation of Cav1.2, Pyk2, and Src even when activators for alpha1 AR and PKC were present. Especially notable is the strong reduction of channel activity way below the control conditions by the Src inhibitor PP2 as well as the PKC inhibitor chelerythrine in Figure 2C. This effect is consistent with PP2 strongly reducing down below control conditions tyrosine phosphorylation of Src (Figure 8J), Pyk2 (Figure 8L), and Cav1.2 (Figure 9E) even with the PKC activator PMA present. These findings suggest that Pyk2 and Src experience significant although clearly by far not full activation under basal conditions as reflected by their own phosphorylation status, which translates into tyrosine phosphorylation of Cav1.2 under such basal conditions.” Because there are multiple ways Pyk2 and Src can be activated including Ca influx and cell-matrix interactions, defining the cause of this baseline activity has to remain beyond the scope of the current work.

2) As noted by the authors, it would be interesting to know if peptides from the linker between domains II and III block this signaling pathway. This would be an important result because, without this information, it is not clear if this is the correct functional site of interaction for this regulatory complex.

Briefly, we were not able to identify shorter loopII/III-derived peptides that would constitute the Pyk2 binding site and thus cannot displace Pyk2 from loop II/III either acutely with peptides or through mutagenesis of the binding site.

3) Figure 4B. The Brain IP for Src has a weak signal. The authors should replace this panel with a more convincing immunoblot.

We provide now the uncropped version in the raw dataset, which clearly illustrates clean, monospecific detection of the Src band over the full length of the blot. Also, please note that earlier work already reported that showed that Src binds to the C-terminus of Cav1.2 (Bence-Hanulec et al., 2000).

4) Scatter plots are provided for the electrophysiological results but not immunoblots. For immunoblots that are quanitified, it would be valuable to add a scatter plot of the replicates.

We now also provide scatter plots for the biochemical analysis.

Author Response

Reviewer #1 (Public Review):

The paper addresses why and how odor discrimination ability achieved after learning occurs in select contexts. The finding is that two related odors trigger near identical Kenyon cell responses when tested in isolation, but trigger different responses to the second odor if these are experienced in sequence within a small temporal window. The authors argue that this template comparison requires some activity downstream of Kenyon cells, that is recruited by MBONs. Overall, the experiments provide very nice physiological evidence for a neural mechanism that underlies a contextual basis for the precision of memory recall.

The experiments were well designed and done. The findings are interesting, but the pitch (e.g. the last paragraph of the discussion and the title of the paper) seems to both ignore the main finding of the paper and overstate the novelty of the idea that memory recall can be flexibly regulated by context. There should be more space dedicated to clearly articulated statements/descriptions of hypotheses and candidate mechanisms to explain the interesting phenomenon described here. For instance, explaining "enhanced template mismatch detection" by potential " real-time and delay line summation" of MBON activity is not super useful for the reader as seems to use one abstraction to explain another. The authors cite Lin et al, 2014 from Miesenbock's lab which shows a key role for GABAergic APL neurons in discrimination. Is there increased activation of APL neurons when similar odourants are being compared and discrimination is required? This seems like a simple physically embodied mechanism that could/ should be examined.

Overall, I think the idea that memories are recalled with high precision (less generalisation) only when increased precision is demanded, is a fact that sure is well appreciated by behavioral biologists even beyond the two papers cited here (Campbell et al., J Neurosci 2013; Xu and Südhof, Science 2013). The new findings fill in a physiological gap in this phenomenology. I think the paper would be greatly improved if the authors highlighted what and focused on the physiological correlate uncovered, and tried to communicate (or test) possible mechanistic origins for this in more physically accessible terms.

We thank Reviewer #1 for their appreciation of our findings. We are grateful for this extremely constructive feedback on re-focussing the pitch of the paper and have extensively revised the manuscript along these lines, particularly changing the Title, Introduction and Discussion. As suggested, we now highlight how similar stimuli can be categorized together, or apart, depending on the stimulus choices animals are presented during recall.

Reviewer #2 (Public Review):

One of the key questions in circuit neuroscience is how learned information guides behavior. Modi et al. investigated this question in Drosophila's mushroom bodies (MBs), where olfactory memory traces are formed during pavlovian olfactory conditioning. They have used optogenetics to restrict the formation of memory traces in selective output compartments of the Kenyon cell (KC) axon terminals, the principal intrinsic neurons of the MB, and tested how flies use these 'minimal memories' during learned olfactory discrimination. They found that memory traces formed in some compartments support discrimination between similar odor pairs, whereas others do not. They then investigated the neural basis of this difference by comparing the responses of relevant output neurons (MBONs) to similar and dissimilar odor pairs. They discovered that MBONs' responses could predict behavioral outcomes if odor presentation profiles during calcium imaging mimic olfactory experience during behavior. This paper and previous works support the idea that flies use olfactory memory templates flexibly to suit their behavioral needs. However, one key difference between this paper and the previous works is the site of discrimination. While previous studies using intensity discrimination have pointed towards spike-latency and on and off responses of the KCs as the main mechanism behind discrimination, Modi et al. have not detected any response difference for similar odor pairs among the KCs. Therefore, they concluded that a hitherto unknown mechanism creates these context-specific responses at the MBONs. The findings will advance our understanding of how memories are recalled during behavior. However, the authors need to bolster their data by including some critical controls that are currently missing.

We thank Reviewer #2 for highlighting how our work contributes to the literature and for pointing out the gaps in our discussion of previous work, as well as the missing controls.

Reviewer #3 (Public Review):

This manuscript by Modi et al represents a novel and significant advance in the neurobiology of memory retrieval. The authors employ a novel behavioral paradigm in order to investigate memory generalization and discrimination. They investigate the role of two different populations of dopamine neurons (DANs) targeting different compartments involved in aversion learning, i.e. α3 (MB630B) and γ2α'1 (MB296B).

The behavioral platform is clear and convincing but lacks natural reinforcement comparisons. The entire paper uses optogenetic reinforcement of said DAN populations.

The authors identify that the gamma DANs can enable both easy and hard odor discrimination, while the alphas DANs can only do easy.

The odors can be separated by calcium imaging analysis of Kenyon cells. Subsequent calcium imaging of the gamma DANs themselves showed that a single training event was insufficient to enable easy odor discrimination at the gamma DAN level, but strangely not for the hard discrimination that gamma DANs can mediate. Seemingly, this is due to the lack of the temporal contiguity of odors (present in behavioral experiments but not in the initial imaging experiments. However, in gamma DANs, Odour transitions enabled discrimination of odors in hard discrimination, based on the depression of calcium activity in DANs after training that was odor-specific. The same was not true for alpha DANs, though the authors used natural electric shock pairings instead of optogenetic stimulation of DANs for the alpha experiment. However, statistical comparisons are done within group and need also be provided for between the groups for both pre and post-training. The authors persuasively show that hard discrimination can only happen in transitions. They also argue that the same engram can be read in two different ways. This is convincing overall, but they claim it is happening downstream of the Kenyon cells just because they do not see it in the Kenyon cells, and I cannot comment on the modeling in Figure 5 (expertise).

Experimental methods used are appropriate, as are data analysis strategies.

The manuscript itself is well written in parts, though at times paragraphs are quite patchy, especially in the discussion. There are also a visible number of typos. The figures are well constructed, and generally well organized. The overall document is concise and has sufficient detail.

We appreciate the reviewer’s comments on the novelty and significance of our study.

Author Response

Reviewer #2 (Public Review):

In Rey et al., the authors goal was to characterize the development of a myelin-like (lacunar) expansion of glial membrane in Drosophila. Although myelin is largely considered a vertebrate innovation, there are a handful of invertebrate models that have been described with glial-derived "myelin," though these systems are not amenable to the same genetic control as Drosophila. To that end, the authors first newly-developed genetics and antibodies to characterize the presence of an axon initial segment (AIS) for adult Drosophila motor neurons that is present at the border between the central and peripheral nervous systems. They show that both sodium (Para) and potassium (Shal) channels, which are typically enriched at the AIS in mammalian neurons, are enriched at this border specifically on motor neurons. They then used multiple types of transmission electron microscopy to visualize this region and found that along with clustering of channels, there is an expansion of membranes from wrapping glia that is reminiscent of myelin. At times, this expansion spirally wraps around larger axons. Finally, they show that genetic ablation of wrapping glia results in an upregulation and redistribution of Para.

Major strengths of this manuscript include the creation of new genetic tools for visualization of subcellular features (e.g. channels) by both light microscopy and electron microscopy.

While this manuscript provides an interesting set of data, but suffers from a lack of quantification and annotation to allow the reader to judge whether this is a robust phenomenon. To increase the reader's confidence in these studies, substantially more quantification of the data is required.

Furthermore, to improve the accessibility of this manuscript, I have the following suggestions:

1) Please label the panels throughout the figures with an abbreviated genotype and what the fluorophores signify. Similarly, the presence of scale bars in uneven across the figures.

This was all corrected.

2) For panels where only one channel is shown, please show these in black and white, which is easier for the visually-impaired.

We have not done this, since the color adds another layer of information (e.g. paramCherry is in magenta, whereas anti-Para staining is in green) which in our view helps to make the complex figures easier to understand.

Author Response

Reviewer #1 (Public Review):

Inhibition of translation has been found as a conserved intervention to extend lifespan across a number of species. In this work, the authors systematically investigate the similarities and differences between pharmacological inhibition of protein synthesis at the initiation or elongation steps on longevity and stress resistance. They find that translation elongation inhibition is beneficial during times when proteostasis collapse is the primary phenotype such as proteasome dysfunction, hsf-1 mutants, and heat shock, but this intervention does not extend the lifespan of wt worms. While translation initiation inhibition extends the lifespan of wt worms and heat shock, but in an HSF-1 dependent manner. This work shows that a simple explanation of just inhibiting total protein synthesis and reduced folding load cannot explain all of the phenotypes seen from protein synthesis inhibition, as initiation and elongation inhibition repress overall translation similarly, but have different effects depending on the experiment tested. Using multiple interventions that target both initiation and elongation lends further support to their findings. These experiments are important for conceptualizing how translation inhibition actually extends lifespan and promotes proteostasis.

Major Comment:

The authors acknowledge that lifespan extension must not necessarily arise just from reducing protein synthesis, as elongation inhibition reduced protein synthesis but did not extend lifespan. Yet for the converse effects from elongation inhibition they seem to suggest that it arises from reducing protein synthesis. For example, regarding how elongation inhibition extends lifespan in an hsf-1 mutant, the authors suggest that "inhibition of elongation lowers the production of newly synthesized proteins and thus reduces the folding load on the proteostasis machinery", even though initiation inhibitors do not extend lifespan in an hsf-1 background (while presumably lowering the production of newly synthesized proteins).

Thank you for this excellent comment. It led us to conduct a crucial experiment with a new finding that is now Figure 6. As suggested, we asked if initiation inhibitors lower the concentration of newly synthesized protein in the hsf-1(sy441) background. The surprising answer is that initiation inhibitors lower the concentration of newly synthesized proteins in N2 but dramatically increase it in hsf-1(sy441). The failure to lower the concentration of newly synthesized proteins was true for the pharmacological inhibitors as well as RNAi against ifg-1. Therefore, inhibition of initiation requires HSF1 to lower the protein concentration. These new findings enable us to make a much more precise statement now added to the discussion:

Lines 372: “The inability of translation-initiation inhibitors to reduce the concentration of newly synthesized proteins in hsf-1(sy441) mutants and the inability to extend their lifespan shows that lowering the concentration of newly synthesized proteins is necessary for the beneficial effects. On the other hand, the finding that elongation inhibitors protect from proteotoxic stress but does not extend lifespan shows that lowering the concentration of newly synthesized proteins is sufficient to protect from proteotoxic stress but is not sufficient to extend lifespan in wild-type, which appears to require selective translation.”

Reviewer #2 (Public Review):

In this manuscript, Clay et al. investigate the underlying effects of reduced mRNA translation beneficial on protein aggregation and aging. They aim to test two pre-existing hypotheses: The selective translation model proposes that downregulation of overall translation increases the capacity of ribosomes to translate selected factors that in turn increase stress resistance against toxicity. The reduced folding load model suggests that during high mRNA translation rates, newly synthesized peptides and proteins can overwhelm the protein folding capacity of the cell and therefore cause protein toxicity. By generally lowering mRNA translation, lower loads of newly synthesized proteins should cause less protein folding stress and hence protein toxicity.

To understand how reduced mRNA translation mediates its beneficial effects in the context of the proposed models, the authors use different drugs established previously in other in vitro and in vivo systems to inhibit selected steps of translation. The systemic effects of translation initiation versus elongation inhibition in C. elegans are compared during heat shock, specific protein aggregation stresses and aging. These phenotypes are further tested for dependence on hsf-1, as contradictory data on the effect of translation inhibition during thermal stress in the context of hsf-1 dependency exist.

The data show that inhibition of translation initiation protects from heat stress and age-associated protein aggregation but on the contrary further sensitizes animals to protein toxicity induced by a misfunctioning proteasome. Further, inhibition of translation initiation increases lifespan in WT animals. The survival phenotypes observed during heat shock and regular lifespan assays are dependent of HSF-1, supporting the selective translation model. As stated in the manuscript, these findings themselves are not new, given that similar observations were made before using genetic models. Interestingly, the inhibition of translation elongation protects from heat stress, but, unlike initiation inhibition, also proteasome-misfunction-induced protein toxicity. Both phenotypes were observed to be independent of hsf-1. The authors further find that inhibiting elongation does not reduce protein aggregation in aged worms and does not prolong lifespan in wild-type animals. It does increase lifespan in short-lived hsf-1 mutants, where protein homeostasis is compromised. To a degree, these findings support the reduced folding load model. Overall, from these observations the authors summarize that the systemic consequences of lowering translation depend on the step in which translation is inhibited as well as the environmental context. The authors conclude that different ways to inhibit translation can protect from different insults by independent mechanisms.

Impact, strengths and weaknesses:

mRNA translation and its regulation is one of the most studied mechanisms connected to lifespan extension. However, gaps behind the protective effects of translation inhibition are so far unresolved, as stated by the authors. Therefore, testing existing hypotheses explaining the beneficial effects of translation inhibition is of great interest, not only for C. elegans researchers but a broad community working on the effects of misregulated translation during aging and disease. Overall, the conclusions made by the authors are generally supported by the data shown in this manuscript. However, some major gaps remain and need to be clarified and extended.

Thank you for your generous comments and thorough review.

Reviewer #3 (Public Review):

Clay and colleagues investigate the proteostasis and longevity benefits derived from translation inhibition in C. elegans by examining the impacts of chemical translation initiation inhibitors (IIs) and translation elongation inhibitors (EIs) on thermotolerance, protein folding stress, aggregation and longevity. They observe somewhat distinct impacts by the two chemical groups. IIs increased longevity in wild-type animals in an hsf-1 dependent manner, whereas, EIs only extended hsf-1 mutants' lifespan. Only EIs protected against proteasome dysfunction. Both protected against heat stress but with differing hsf-1 dependence. The authors utilize these observations to derive conclusions regarding two dominant points of view on the mechanism by which translation inhibition improves lifespan and proteostasis.

The study is based on interesting observations and several promising avenues of further investigation can be identified. However, the manuscript appears somewhat preliminary in nature, with many of the observations, while interesting, only explored superficially for mechanistic insights. The rationale behind some of the interpretations was also difficult to interpret. For example, the authors make conclusions about 'selective translation' being adopted upon IIs treatment without directly testing this. Protein aggregation, while possibly predictive, is not a reliable readout for selective translation of some mRNAs. Similarly, the evidence for a reduction in 'newly-synthesized protein load' by EIs is thin based on one reporter. Previous studies from the Blackwell lab have identified differential impacts of SKN-1 on select cytoprotective genes' expression and proteasomal gene expression based on inhibition of translation initiation or elongation. So there is precedence for both the differential impact of initiation vs. elongation inhibition as well as genetic background. There are several other such studies that reduce the impact of the observations presented here. With limited novelty and mechanistic insight, the impact of the study on the field is likely to be moderate.

We thank the reviewer for the thorough analysis and candid summary. Some of the criticisms rang true, and we have made considerable efforts to address them, both increasing the thoroughness of our study by establishing that these inhibitors inhibit initiation and elongation (new Figure 1) and by providing a novel mechanism showing that the ability of the initiation machinery requires HSF1 to lower the concentration of newly synthesized proteins.

Before we go into the specific criticisms, we would like to note that of the 30-40 eukaryotic translation inhibitors used in cell culture and yeast, very few have been validated in C. elegans. The go-to inhibitor was cycloheximide which, in our hands, is reliable in cell culture but unreliable in C. elegans, most likely due to its poor pharmacokinetics (data now added to the supplementary figures). To our knowledge, no C. elegans study investigating translation has made sure to equalize the concentration of newly synthesized proteins or could have because of a lack of validation of the chemical tools used in other organisms. Thus, the comment of reviewer #3 that we did not go far enough with the validation struck home, and in the revised version of Figure 1 we added more validation.

We are of the opinion that it is essential to ensure that both mechanisms reduce the concentration of newly synthesized proteins to the same degree to study mechanistic differences. Otherwise, one cannot deconvolute if any phenotypic difference is caused by the mechanistic difference or the degree of translation inhibition. The importance of monitoring the level of inhibition became evident in our new Figure 6, which shows that inhibition of the translation initiation machinery no longer reduces the concentration of newly synthesized proteins but increases it in the absence of HSF1.

Author Response

Reviewer #1 (Public Review):

The role of HCO3 (or possibly CO2) in regulating sACs is well established yet its physiological context is less clear. The heart is indeed an excellent choice of organ to study this. Isolated mitochondria offer a tractable model for studying the model, although are not without limitations. The quality of recordings is very high, as judged by the consistency of results (i.e. lack of clustering between biological repeats). My primary concern is about distinguishing the effect of pH and HCO3. A rise in HCO3 will also raise pH unless this had been compensated by CO2. It is unclear, from the legend or results, if the bicarbonate effect is due to HCO3 or pH. Was pH controlled by matching the rise in HCO3 with an appropriate level of CO2? The swings in pH are likely to be very large and, potentially, a confounding factor. Certainly, there will be an effect on the proton motive force. A more informative test would compare the effect of 0 CO2/0HCO3 at a pH set to say 7.2, 2.5% CO2/7.5 mM HCO3, and then 5% CO2/15 mM HCO3, etc. Control experiments would then repeat these observations over a range of pH (at zero CO2/HCO3) and over a range of CO2 (at constant HCO3). Data for zero bicarbonate are not informative, as this will never be a physiological setting (results claim 0-15 mM bicarb to represent physiology). Importantly, there seems to be no significant difference in 2A between 10 v 15 mM bicarb, i.e. the physiological range.

Thank you for your clear discussion and suggestions. We agree that pH must be controlled in these experiments to avoid the confounding situation you describe. In fact, pH was carefully controlled but this was not described adequately. To make this clearer the methods section was modified.

We agree that bicarbonate concentration is above 0 in living tissue. We used that value only as a reference in Figure 2A to examine which type of adenylyl cyclase (AC) is inside mitochondria, i.e., bicarbonate-activated soluble AC as opposed to transmembrane AC which is not bicarbonate activated. The wording has been corrected to better describe this. The question of what the physiological consequences are require an assay with higher signal-to-noise ratio. In effect, this is achieved in the experiments of Fig. 2B-C which show that physiologically relevant changes in bicarbonate have a large and significant influence on mitochondrial ATP production.

There is also a question on the validity of the model. A rise in respiratory rate will produce more CO2 in the matrix. This may raise matrix HCO3, and stimulate sACs therein, but the authors claim sACs are in the IMS, rather than the matrix. Since HCO3 is impermeable, it is unclear how sACs would detect HCO3 beyond the IMM. CO2 escaping the matrix will enter the continuum of the cytoplasmic space, which has finely controlled pH. Since membranes (including IMM) are highly permeable to CO2, the gradient between matrix and cytoplasm will be small (i.e. you only need a small gradient to drive a big flux, if the permeability is massive). Since CO2 can dissipate over a large volume, it is unlikely to accumulate to any degree. CO2 will be in equilibrium with HCO3 and pH (because there are carbonic anhydrases available). Since the cytoplasm has near-constant pH, [HCO3] must also be close to constancy. It is therefore difficult to imagine how HCO3 could change dramatically to meaningfully affect sACs and hence cAMP. Evidence for major changes in IMS pH in intact cells during swings of respiratory activity would be required to make this point. Indeed, for that reason, it would be more sensible to anchor sACS in the matrix, as there, HCO3 could rise to high levels, as it is impermeable, i.e. could be confined within the mitochondrion. I am therefore not convinced the numbers are favorable to the proposed mechanism to be meaningful physiologically.

The question of how CO2/bicarbonate signaling can work in the intermembrane space (IMS) is explicitly addressed in the revisions in the results and discussion sections. CO2, a membrane permeable gas, can easily cross the IMM (permeability coefficient of 0.01 to 0.33 cm/s). Once in the IMS, CO2 can combine with water to produce bicarbonate, a fast reaction in a physiological context (i.e. mM/s in physiological saline) that can be accelerated to nearly diffusion limits by carbonic anhydrase if present. The assumption that all the “CO2 escaping the matrix will enter the continuum of the cytoplasmic space” is not supported by the structure of cardiomyocyte mitochondria. As illustrated in new Fig 2H, only a small fraction (9-15%) of the IMS occurs along the mitochondrial periphery adjacent to the outer membrane and cytosol. Most of the IMS is contained inside the cristae (the intracristal space or ICS), which in interfibrillar mitochondrion are composed of closely packed extended flat membranes that create scores of alternating layers of matrix and ICS ideal for rapid gas exchange between compartments. The cristae are connected to the peripheral IMS through narrow “crista junction” openings that restrict solute diffusion between the peripheral IMS and ICS. Thus, rather than being dominated by the ionic equilibria of the cytosol, the crista compartments are functionally distinct from the peripheral IMS region and cytosol. We cite recent publications using super-resolution light microscopy in which gradients of 0.3-0.4 pH units (dependent on metabolic state) have been detected along the cristae of respiring cells and between the peripheral IMS and crista interiors. These diffusion effects would likely be even more pronounced for cardiac muscle mitochondria, which have larger, more densely packed lamellar cristae than other cell types. Thus, the microenvironment of the cristae provide confined spaces in close communication with the matrix CO2 production, and ideal for operation of a sAC signaling system.

Reviewer #2 (Public Review):

The authors explore the role of bicarbonate-regulated soluble adenylate cyclase in modulating cardiac mitochondrial energy supply. In isolated rat mitochondria, they show that cyclic AMP (but not the permeable cAMP analog 8-Br-cAMP) increases ATP production via a Ca-independent mechanism at a location in the intermembrane space of the mitochondria, rather than in the matrix, as previously reported. Moreover, they show that inhibition of EPAC, but not PKA, inhibits the response. The effect required supplementing the mitochondria with GTP and GDP to facilitate activation of the EPAC effector GTPase Rap1. The study provides interesting new information about how the heart might adapt to changes in energy supply and demand through complementary regulatory processes involving both Ca and cyclic AMP.

The authors nicely demonstrate that soluble adenylate cyclase is localized to mitochondria. They argue, based on the effects of cyclic AMP, which is accessible to the mitochondrial intermembrane space (IMS) but not the matrix, that the signalling pathway is located in the IMS. They also find that EPAC/Rap1 is the likely downstream effector of cyclic AMP, through yet unknown targets regulating oxidative phosphorylation.

A weakness is that the components of signaling (sAC, EPAC, and rap1) are not definitively localized to a specific mitochondrial compartment using the superresolution imaging methods employed.

Thank you for the concise summary of key findings. While the super-resolution data indicates sAC and CA are localized to the interior of the mitochondria (possibly co-localized in the same subspace), identification of the particular microcompartment is not possible from the imaging data alone. We explain clearly in the manuscript that the functional experiments are critical to the conclusion that the sAC signaling pathway most likely operates in the IMS.

Author Response

Reviewer #1 (Public Review):

This manuscript is interesting because of the exploration of a novel model organisms utilizing next-generation sequencing approaches, such as single-cell-RNA-seq. Despite the authors' efforts the manuscript lacks a cohesive narrative and suffers from being extremely preliminary in nature. For example, most of the figures are cut and pasted directly from the computational programs with very little formatting or thought to creating new knowledge from the data generated. Essentially the manuscript consists of 2-3 experiments where the authors performed single-cell-RNA-seq on different anatomical locations in the pig and also on a couple of different pig types (The Chenghua and Large White). The authors used standard computational pipelines consisting of Seurat, Monocle, Cell Chat, and others to characterize differences in their data.

There is potential in this manuscript but the authors should improve upon the manuscript by mining the data better and generating a better understanding of anatomical positions of pig skin by evaluating the Hox genes.

(1) Thanks for the reviewer's positive evaluation for our article and providing valuable feedback to improve the quality of our manuscript. To provide a more cohesive narrative, we have edited throughout the manuscript.

(2) Meanwhile, we also modified and formatted some figures including Figures 2-6, Figure 4—figure supplement 1 and 2, Figure 5—figure supplement 1 and 2, and Figure 6—figure supplement 1.

(3) We have analyzed these data of regional- or species-based differences more extensively, and the added content are in Result Section of “Heterogeneity of skin FBs in different anatomic sites” and “Heterogeneity of skin cells in different pig populations”.

(4) However, in our study, we did not identify any Hox gene among these differentially expressed genes in skin fibroblasts from both different anatomical sites and different pig populations. The differences of Hox code expression patterns might come from the heterogeneity of different species.

Reviewer #2 (Public Review):

The authors aimed to analyze different dermal compositions of various skin regions, focusing on fibroblast, endothelium and smooth muscle cells. They collect skin samples from six different skin regions of adult pig skin including the head, ear, shoulder, back, abdomen, and leg skins. After dissociating the tissues into single cells, they perform single-cell RNA analyses. A total of 215 thousand cells were analyzed. The authors identified distinct cell clusters, enriched molecules within each cell cluster, and the dynamic of cell cluster transition and interactions. Based on their findings, they conclude that tenascin N, collagen 11A1, and inhibin A are candidate genes for facilitating extracellular matrix accumulation.

Strength:

The methodology they used to prepare scRNA data is appropriate. Bioinformatic analyses are solid. The authors emphasize the heterogeneous phenotypes and composition ratios of smooth muscle cells, endothelial cells and fibroblasts in each skin region. They identify potential cell communication pathways among cell clusters. Expression of selective molecules on tissue sections were done.

Weakness:

While tenascin, collagen and inhibin are highlighted as genes important for ECM accumulation, there is no functional evaluation data. The discussion section is a compilation of comparisons, and is somewhat fragmentary. More significance from this dataset could have been extracted.

(1) We appreciate the reviewer's suggestions for evaluating the functional significance further. In our next research, we will perform some experiments in vitro and in vivo to explore the functions of these identified key genes.

(2) The discussion section have been greatly modified and it shall be more logical and readable.

Author Response

Reviewer #1 (Public Review):

Wu et al. provide a powerful cross-species approach to better understand brain cell-type specific responses to mutant tau and aging. Therefore, they use scRNAseq of established Drosophila models that they had previously used for bulk RNAseq (Mangleburg et al., 2020) at 1, 10 and 20 days of age, which thus allows them to study the contribution of pathogenic tau (R406W-mutant) in isolation in an experimentally highly controllable manner. They find a large overlap between tau-induced and aging-induced deregulated genes, however different cell-types were primarily affected, suggesting that expression of tau does not simply induce accelerated aging. When assessing cell number abundance in response to tau expression the authors noted that certain excitatory neurons were preferentially lost. They then examined innate immune pathways downstream of NFkB, which they had already uncovered in their previous bulk studies to be associated with tau expression. Also at the scRNAseq level, they find these pathways to be deregulated after expression of tau. In addition, in control cell types that are lost when tau is expressed, they find an inverse correlation of the expression of these pathways and cellular loss, suggesting they might be predictors of neurodegeneration severity. Finally, they use this finding uncovered in Drosophila and reexamined human Alzheimer's disease snRNAseq datasets, were they also find the NFkB pathway to be deregulated.

This study has several strengths. It demonstrates the power of studying taueffects in a tractable model and then using the obtained knowledge to pin-point relevant pathways in cross-sectional studies of human tauopathy, which are otherwise not easy to interpret given the overlayed effects of other disease triggers. By examining the single-cell level they uncover cell type specific effects, which would otherwise be hidden. This study also represents a valuable resource. Given that the authors have included multiple time points the dataset provides an opportunity to understand the evolution of cell-type specific tau effects over time. The authors have also included a replication dataset, which confirms the results of the primary analysis of neuronal loss. I also appreciate the efforts to understand the apparent increase in glia cell number after expression of tau. By combining computational and experimental methods the authors reach the well supported conclusion that in fact glial cell numbers remain constant but only appear increased due to the proportional nature of the scRNAseq data and profound loss of some neurons. Overall, it is interesting that the authors nominate the innate immunity and NFkB pathways in tauopathy, based on deregulated genes and also based on vulnerable neurons. Nevertheless, this is a correlative finding and as such does not proof that it is causal.

As noted [R3], above, we agree that our findings of NFkB dysregulation are correlative. We have performed new experiments to directly test the hypothesis that neuronal immune pathways are causally linked to tau-mediated neurodegeneration; however, the results were negative. These data are included in the revision and we also carefully discuss published work from other fly models of aging and neurodegeneration as well as mouse tauopathy that strongly suggest NFkB can directly modulate neurodegeneration.

The authors correctly point out the importance of aging as a risk factor for Alzheimer's disease. However, it is unclear whether their models actually capture age-dependent neurodegeneration. Alternatively, they might represent neurodevelopmental tau toxicity. In Figure 1B it can be seen that all vulnerable cell types are already lost at day 1, most notably a'/b'-KC, a/b-KC and G-KC with a >4-fold decrease. This raises the question whether the lost cells might developmentally have not correctly formed, as suggested by a study that the authors cite (Kosmidis et al., 2010). This distinction is important in order to strengthen the translational value of the study to human tauopathies.

The elav>tauR406W model manifests both developmental toxicity and age-dependent neurodegenerative changes. Our revision includes new data highlighting specific examples and includes a more balanced discussion of these issues.

The analysis of tau expression levels relative to its impact across cell types in Figure S8 is interesting, however has caveats. The profound neuronal loss makes the interpretation of the correlation analysis of tau levels vs. neuronal vulnerability difficult - since it might be that the individual surviving a'/b'KC, a/b-KC and G-KC cells are the ones that expressed little amounts of tau, while those that are missing used to express high tau. In addition, it is unclear from the methods whether the 3' UTR from the transformation vector to generate the models was included in the counting. The majority of reads would be expected to be there.

As suggested, we have repeated the alignment and analysis of MAPT expression including the short SV40 3’UTR (135bp) from the transformation vector. The result appears very similar to that from the previous analysis, and we have updated Figure 3–figure supplement 4 with these data. Based on the feedback from Reviewer 2, we also include a new plot highlighting the non-relation between MAPT expression and cell abundance changes (Figure 3–figure supplement 4B). We acknowledge the caveat that “missing” / dead cells may have previously expressed high levels of tau, leaving behind survivors with low tau levels. We have added mention of this possible caveat in the Results (lines 197-198). However, while this scenario might impact our interpretation of cell abundance changes, it is a less likely to confound our analyses of differential expression, in which the number of differentially expressed genes show very poor correlation, if any, with MAPT transgene expression (Figure 3–figure supplement 4C).

It would be relevant to know whether the animals were in the same genetic background. I.e. is UAS-TauR406W in the same background of the fly that was crossed to elav-Gal4 to serve as the control. This is not mentioned in the paper and also not in Mangleburg et al., 2020 which the authors refer to. There is a lot of tau-induced DEGs (~1/3 of the detected genes) and it would be relevant to know whether some of them might be due to genetic background.

Our experimental design mitigates the possibility of a substantial impact from genetic background; however, we have added text in the revision noting that this is an important consideration and possible confounder.

The finding of the authors that NFkB pathways are higher in cell types that degenerate more is interesting. However, in Figure 4D it is also apparent that multiple cell types that do not degenerate have comparably high expression. Therefore, it is not a sufficient factor to explain why some neurons are vulnerable vs. others are not, but rather predicts amongst the vulnerable neurons how much they will be lost. It would be helpful to make this distinction clear in the text.

We agree with the reviewer’s interpretation, and we have tried to make this more clear in both the results and discussion (lines 286-288; 387-390). Indeed, the NFkB expression level seems to be a marker for the severity of tau-triggered cell loss among the vulnerable cells.

Reviewer 2 (Public Review):

Wu et al. conducted longitudinal single-nucleus RNA sequencing in a Drosophila transgenic line expressing pathogenic tau (Arg406 ->Trp) and control to study presenile degenerative dementia with bitemporal atrophy. Their data is consistent with previous findings on Tau neurotoxicity, which significantly affects excitatory neurons in human brain samples and transgenic mice. Authors identify aging-like signatures, and an innate immune glial response, including the NFKB pathway, in the transgenic animals.

Strength: This is a great resource for the dissection of dynamic, age-dependent gene expression changes at cellular resolution for the fly community. The article's conclusions are largely supported by the data.

We thank the reviewer for recognizing the value of this work as a resource for the field.

Weakness: No additional orthogonal validation is done on the identified pathways using immunohistochemistry. Also, the authors hypothesized that innate immune signatures might serve as predictors of neuronal subtype vulnerability in tauopathies. Although their data support stronger immune responses in the mutant lines, these findings are not validated. Moreover, the Authors need to use appropriate control animals to compare the mutant Tau animals.

Our original manuscript included experimental validation demonstrating that (1) the apparent increases in glial cell abundance is likely due to changes in cell proportions, and we also (2) confirmed the expression of Relish in both neurons and glia of the adult fly brain. For our revision, we were guided by the requested Essential Revision #3 [R3]. We have therefore performed additional experiments directly testing whether manipulation of Relish/NFkB in neurons alters tau-induced neurodegeneration. While the results of these experiments were negative, we have incorporated them into the results and discussion, along with discussion of related published studies that support a causal role for NFkB immune pathways in tauopathy. Lastly, in our response to Essential Revision #4 [R4], we address concerns about the conservation of neurotoxic mechanisms between mutant and wildtype froms of MAPT and the use of mutant MAPT transgenic models for investigations of AD, along with the caveats. New analyses have been performed and textual revisions have been made in response this feedback

Reviewer3 (Public Review):

Understanding the changes in the brain during the progression of neurodegenerative diseases may provide a critical entry point towards medical treatments. Many genes have been directly or indirectly implemented in an array of neurodegenerative diseases, including the microtubule associated protein tau (MAPT). Various studies have shown that misexpression of tau can cause behavioral, genetic as well as molecular phenotypes that display properties of human neurodegenerative diseases connected to tauopathies. Here the authors use the fruit fly as model to assess phenotypic defects at single-cell resolution. Pan-neuronal misexpression of a mutant form of tau (R406W) and single-cell RNAseq at different time points provides the basis for the investigation.

The authors assess which cell-types are affected (by comparing it with previously described brain cell atlas identities) and find that certain cell types are missing (or less abundant) while other appear unaffected. They do this comparison in relative abundance; both neurons and glia cells are affected.

As next step they compare this with the cell-cluster changes during aging and compare both types of analysis; the investigation here includes the analysis of differentially expressed genes in defined cell clusters. One particularly affected pathway in response to tau is the NFκB signaling pathway. The authors investigate the gene expression changes of the NFκB signaling pathway in the current dataset in more detail. In the last section the authors compare singlecell transcriptomic analyses between fly and human postmortem tissue, showing that the NFκB signaling pathway might be a conserved aspect of neurodegeneration.

The manuscript is overall an elegant example of how single-cell RNAseq can be employed as tool to study the impact of genetic modulators of neurodegeneration (in this case tau) and that it allows direct comparison with human tissues. The results are clean, logically presented and accordingly discussed. It shows that such approaches are indeed powerful for genetic dissection of mechanisms at a descriptive level and opening doors for functional studies.

We thank the reviewer for this positive summary of our manuscript.

Author Response

Reviewer #1 (Public Review):

The goal of the authors was to understand how the kinase, hpk-1, could regulate and interrogate different aspects of cellular stress resilience. To this end, the authors uncovered that hpk-1 is coexpressed with several transcription factors known to regulate different stress responses and this coregulation only appears to occur in the nervous system. Taking a deeper dive, they convincingly find that hpk-1 overexpression in either serotonergic of GABAergic neurons can protect animals from heat stress or toxic protein aggregates. Interesting, it appears that hpk-1 functions in serotonergic neurons differently from GABAergic neurons in the induction of the heat shock response and autophagy.

Overall, the experiments and results are solid and the conclusions drawn reflect the result. The model suggests that the receiving cell deciphers that either heat shock response or autophagy can be induced in the same cell, but the data suggest otherwise. Perhaps the model should be reworked to reflect this point.

We thank the reviewer for their kind assessment and suggestion to refine our model. Indeed, we did not intend to imply that that the receiving cell/tissues were the same after each stimulus, but were attempting to simplify the diagram and condense space. In the revised manuscript we have altered the model (Figure 9B) to reflect that the recipient tissues are distinct.

Reviewer #2 (Public Review):

Lazaro-Pena et al. investigated how a conserved kinase called homeodomain interacting protein kinase (HPK-1), helps to preserve neuronal function, motlity and stress resilience during aging in the metazoan, C. elegans. HPK-1 is a member of the HIPK kinases that, in mammalian systems, regulate the activity of transcription factors (TFs), chromatin modifiers, signaling molecules and scaffolding proteins in response to cellular stress. The group finds that in C. elegans, HPK-1 depletion causes a premature shortening of lifespan and decreases motility and stress resilience in the whole animal. Conversely, increasing active, but not enzymatically dead, HPK-1 levels in the nervous system alone is sufficient to extend lifespan and mitigate the accumulation of aging-associated protein aggregates. The authors then identify a subset of neurons and cell stress response pathways that could be responsible for the contribution of HPK-1 to lifespan and neuronal health. This leads the authors to propose a hypothesis whereby HPK-1 activity in specific neurons preserves protein homeostasis and neuronal integrity, and thus limits the aging-induced decline in organismal function.

Overall, the authors test several functional readouts for neuronal activity to support their claim that HPK-1 activity limits functional decline during aging. These experiments are solid, and the use of a kinase dead HPK-1 in these experiments adds strong support to their claim that HPK-1 activity preserves organismal health. However, weaknesses in the experimental layout and rigor, and the statistical analyses of the publicly available data, limit the inferences that can be made, and further experimental evidence would be required to confirm the working model proposed by the authors.

We thank the reviewer for their thoughtful and balanced assessment of our study.

Author Response

The authors would like to thank the reviewers and editors for this thorough and constructive assessment of our paper. We look forward to addressing their suggestions for improvement of our work in a revised manuscript. In particular: (i) Reviewer 1 raises interesting questions regarding the potential impact of intrinsic cortical and mesh morphology on interpolation, smoothing and the resultant patterns of gene expression. We will test these ideas by developing a null model framework. (ii) Reviewer 2 suggests re-creating dense expression maps using an alternative Gaussian Processes for interpolation. We will implement this suggestion and compare the resulting maps with those generated by the current interpolation method. Notwithstanding these helpful lines of further enquiry, we believe our study provides a meaningful step forwards in multiscale analysis of the human brain by generating. validating, describing and annotating dense gene expression maps which can accelerate translation between neuroimaging and genomic analysis of the human cortical sheet.

Author Response

Reviewer #1 (Public Review):

We thank the Reviewer for their comments.

Reviewer #2 (Public Review):

1) In Figure 4, the authors injected a retrograde tracer in the NA and an anterograde tracer in DCN to find potential "nodes" of overlap. From this experiment, the authors identify the VTA and regions of the thalamus as potential areas of tracer overlap, but it is unclear how many other brain regions were examined. Did the authors jump straight to likely locations of overlap based on previous findings, or were large swaths of the brain examined systematically? If other brain regions were examined, which regions and how was this done? A table listing which brain regions were examined and the presence/intensity of ctb-Alexa568 and GFP fluorescence would be helpful.

We thank the Reviewer for their comments. Exhaustive characterizations of inputs into nucleus accumbens (NAc) as well as of direct outputs of the deep cerebellar nuclei (DCN) have appeared elsewhere (e.g, Ma et al., 2020 doi: 10.3389/fnsys.2020.00015; Novello et al., 2022 doi: 10.1007/s12311-022-01499-w). Our anatomical investigations with retrograde and anterograde tracers were focused on putative intermediary nodal regions with robust inputs from the DCN, clear outputs to NAc, and limbic functionality. Only a handful of brain regions fulfill these criteria, and from those, we chose to target the VTA and intralaminar thalamus based on the observation that cerebellar activation induces dopamine release in the NAc medial shell and core (Holloway et al., 2019 doi: 10.1007/s12311-019-01074-w; Low et al., 2021 10.1038/s41586-021-04143-5) and on our previous work on DCN projections to the midline thalamus (Jung et al., 2022 doi: 10.3389/fnsys.2022.879634).

2) In Figure 5, the authors inject AAV1-Cre in DCN and AAV-FLEX-tdTomato in VTA or thalamus. This is an interesting experiment, but controls are missing. An important control is to inject AAV-FLEX-tdTomato in the VTA or thalamus in the absence of AAV1-Cre injection in DCN. Cre-independent expression of tdTomato should be assessed in the VTA/thalamus and the NA.

We thank the reviewer for bringing up this important control. We routinely perform this control experiment to test for any “leakiness” of floxed vectors prior to use but we did not include it in the paper. In response to the Reviewer’s comment, we show results from this control below. Briefly, we performed a large injection (300 nl) of AAV-FLEX-tdTomato in the thalamus area together with AAV-EGFP (to visualize the injection). No Cre-expressing virus was injected anywhere in the brain. PFA-fixed brain slices were obtained at 3 weeks post-injection and imaged for EGFP and tdTomato. Author Response Figure 1 shows images of the injected thalamus area. No tdTomato expression (Fig. 1C, red) was observed despite abundant EGFP expression (Fig. 1B, green), which confirms that in the absence of Cre the floxed construct does not “leak”.

Author Response Figure 1 (related to Fig. 5 of manuscript). Control experiment for “leakiness” of floxed tdTomato. A, Epifluorescence image of thalamus region in brain slice with EGFP (green) and tdTomato (red) channels merged. Gain settings in the red channel were increased intentionally, to ensure detection of any “leaky” cells. B, Cellular EGFP expression marks successful viral injection. C, No cellular expression of tdTomato without Cre. Note diffuse red signal from background fluorescence.

Reviewer #3 (Public Review):

1) The novelty of this paper lies in the mapping of projections from the interposed and the lateral nuclei of the cerebellum, as the authors themselves mention. However, in some of the experiments the medial nucleus is also clearly injected (Fig. 4B and 6B). In those experiments, it is impossible to distinguish which nucleus these projections come from, and they could be the ones from the medial nucleus that were previously described (see above).

We thank the Reviewer for their comments. As stated in the Results and in the legend of Fig. 4, in addition to experiments with injections in all DCN (Fig. 4B-D), we also performed experiments with injections in only the lateral nucleus (Fig. 4E and F). The results of these experiments support the existence of lateral DCN projections that overlap with NAc-projecting neurons in VTA and thalamus. This finding is further corroborated by our transsynaptic experiments with lateral DCN-only injections (Fig. 5E,F). Regarding the optophysiological experiments, as mentioned in the Results, all DCN were injected (Fig. 6B) in order to maximize ChR2 expression and the chances of successful stimulation of projections.

2) A strength of the paper is the use of both electrical and optogenetic stimulation. However, the responses to the two in the NAc are very different - electrical stimulation results in both excitation and inhibition, whereas opto stimulation mostly results in only excitation.

We thank the Reviewer for this comment. At least two different explanations could account for the observed differences in the prevalence of inhibitory responses elicited by optogenetic vs electrical stimulation: i) inhibitory response prevalence is sensitive to stimulation intensity (Table 1 and Fig. 2B). Because of inherent differences between optogenetic and electrical stimulation, it is not possible to directly compare intensities between the two modalities in order to determine at which intensities, if at all, the prevalence of responses should match. The observation that, at least in the VTA, the prevalence of inhibitory responses elicited by 1 mW light intensity (the lowest intensity that we tested) was comparable to the prevalence of inhibitory responses elicited by 100 µA electrical stimulation is in line with this explanation; ii) DCN electrical stimulation is not node-specific. To our knowledge, there is currently no evidence to support mesoscale topographic organization in lateral and interposed DCN that is node-specific. Consequently, electrical stimulation of DCN could, in principle, result in NAc responses through various polysynaptic pathways and/or nodes. This possibility would still exist even if electrical stimulation had limited spread of a few hundred microns (as in our experiments) and is at least partly supported by the observed long latencies of electrically-evoked inhibitory responses (NAcCore: 268 ± 25 ms (10th percentile: 42 ms), NAcMed: 259 ± 14 ms (10th percentile: 60 ms). Our recognition of this intrinsic limitation of DCN electrical stimulation was the impetus behind the node-specific optogenetic experiments.

3) The stimulation frequency at which the electrical stimulation in Fig 1 is done to identify responses in the NAc is 200 Hz for 25 ms. Is this physiological? In addition, responses in the NAc are measured for 500 ms after, which is a very long response time.

Regarding stimulation frequency, DCN neurons readily reach firing rates between 100-200 Hz in vivo and ex vivo (e.g., Beekhof et al., 2021 doi.org/10.3390/cells10102686; Sarnaik & Raman, 2018 doi:10.7554/eLife.29546; Canto et al., 2016 doi:10.1371/journal.pone.0165887). Regarding the duration of the response window, at the outset of our experiments we were agnostic to the type of responses that our stimulation protocols would evoke in NAc. We therefore established a response time window that would allow us to capture both fast neurotransmitter-mediated responses as well as neuromodulatory (e.g., dopaminergic) responses, which are known to occur at hundred-millisecond latencies or longer (Wang et al., 2017 doi.org/10.1016/j.celrep.2017.02.062; Chuhma et al., 2014 doi:10.1016/j.neuron.2013.12.027; Gonon, 1997). A posteriori analysis indicated that even if we reduced the response time window by 50%, the ratio of DCN-evoked excitatory vs inhibitory responses in NAc would not change substantially (E/I500: 4.3 vs E/I250: 5).

4) Previous studies have described how different cell types within the DCN have different downstream projections (Fujita et al. 2020). However, the experiments here bundle together all this known heterogeneity.

We agree with the Reviewer that dissecting the contributions of specific DCN cell types to NAc circuits is an important next step, which we are excited to undertake in future studies. Here we have broken new ground by identifying for the first time nodes and functional properties of DCN-NAc connectivity. Performing these studies with DCN cell type-specificity was not justified or feasible, given that the molecular identity of participating DCN neurons is currently unknown.

5) Previous studies have also highlighted the importance of different cell types within the NAc and how input streams are differentially targeted to them. Here, that heterogeneity is also obscured.

Along the same lines as #4, we agree with the Reviewer about the importance of examining the cellular identity of NAc neurons that participate in DCN-NAc circuitry. We are excited to undertake these examinations using ex vivo approaches, which are well suited to dissect cellular and molecular mechanisms.

6) In Fig. 4C, E and F, the experiments on overlap between anterograde and retrograde tracers are not particularly convincing - it's hard to see the overlap.

We thank the reviewer for this comment and have included revised figure panels 4C5, E3, Author Response Figure 1 and Figure 2 below. Single optical sections with altered color scheme and orthogonal confocal views are presented in order to show the overlap between DCN projections and NAc-projecting nodal neurons more clearly. The findings of these imaging experiments are corroborated by the results of our transsynaptic labeling approach (Fig. 5), which we have validated elsewhere (Jung et al., 2022 doi:10.3389/fnsys.2022.879634; and Author Response Figure 1).

Author Response Figure 2 (related to Fig. 4 of manuscript). Co-localization of NAc-projecting neurons with DCN axonal projections in VTA and thalamus. A-D, Single optical sections and orthogonal views are shown. Green: EGFP-expressing DCN axons; white: ctb- Alexa 568; red: anti-TH (A-B; VTA) or NeuN (C-D; thalamus). White arrows in orthogonal views point to regions of overlap.

Author Response

Reviewer #1 (Public Review):

First, we thank the reviewer for his instructive remarks. In the following we address the queries of Reviewer 1.

1.1) At several points, the authors make claims that I believe extend beyond the data presented here. For instance, in the Abstract (line 27), the authors state "the development of adult songs requires restructuring the entire HVC, including most HVC cell types, rather than altering only neuronal subpopulations or cellular components." The gene ontology analyses performed do suggest that there is a progression from cellular transcriptional changes to organ-level changes, however caution should be taken in claiming that "most HVC cell types" exhibit transcriptional changes. In fact, according to Fig. 3D most of the transcriptional changes appear restricted to neurons. As the authors themselves note elsewhere, claims at this resolution are difficult without support from single-cell approaches. I do not suggest that the authors need to perform single-cell RNA-seq for this work, but strong claims like this should be avoided.

We have revised our claim to more accurately reflect our findings. Our intended message is that testosterone treatment leads to extensive transcriptional changes in the HVC, likely affecting a majority of neuronal subpopulations rather than solely targeting specific cellular components. The revised text in lines 29-32 now reads: "Thus, the development of adult songs stimulated by testosterone results in widespread transcriptional changes in the HVC, potentially affecting a majority of neuronal subpopulations, rather than altering only specific cellular components."

1.2) Similarly the Abstract states that parallel regulation "directly" by androgen and estrogen receptors, as well as the transcription factor SP8, "lead" to the transcriptional and neural changes observed after testosterone treatment of females. However, experiments that demonstrate such a causal role have not been performed. The authors do perform a set of bioinformatic analyses that point in this direction - enrichment of androgen and estrogen receptor binding sites in the promoters of differentially expressed genes, high coexpression of SP8 with other genes, and the enrichment of predicted SP8 binding sites in coexpressed genes. However, further support for direct regulation, at the level that the authors claim, would require some form of transcription factor binding assay, e.g. ChIP-seq or CUT&RUN. I am fully aware that these assays are enormously challenging to perform in this system (and again I don’t suggest that these experiments need to be done for this work); however, statements of direct regulation should be tempered. This is especially true for the role of SP8. This does appear to be a compelling target, but without some manipulation of the activity of SP8 (e.g. through knockdowns) and subsequent analysis of gene expression, it is too much to claim that this transcription factor is a regulatory link in the testosterone-driven responses. SP8 does appear to be a highly connected hub gene in correlation network analysis, but this alone does not indicate that it acts as a hub transcription factor in a gene regulatory network.

We appreciate the reviewer's comment and have revised the statement concerning the role of SP8. Indeed, we document the coexpression of ESR2 and SP8, and our bioinformatics analysis suggests that SP8 might play an important role in transcriptomics. We have rephrased the statement in line 29-32 as follows: "Parallel gene regulation directly by androgen and estrogen receptors, potentially amplified by coexpressed transcription factors that are themselves steroid receptor regulated, leads to substantial transcriptomic and neural changes in specific behavior-controlling brain areas, resulting in the gradual seasonal occurrence of singing behavior." In addition, we have included discussions regarding limitations of promoter sequence analyses (lines 414 to 427).

1.3. Along these lines, the in-situ hybridizations of ESR2 and SP8 presented in Figure 5 need significant improvement. The signals in the red and green channels, SP8 and ESR2, look suspiciously similar, showing almost identical subcellular colocalization. This signal pattern usually suggests bleed-through during image acquisition, as it’s highly unlikely that the mRNA of both genes would show this degree of overlap. I would suggest that control ISHs be run with one probe left out, either SP8 or ESR2, and compare these ISHs with the dual label ISHs to determine if signal intensity and cellular distribution look similar. Furthermore, on lines 354-356 the authors write, "The fact that the two genes were expressed nearby in the same cell may indicate physical interactions between the gene pair and warrant further investigation into the nature of their relationship.". Yet, even if the overlap between ESR2 and SP8 shown in Figure 5 is confirmed, close localization of transcripts does not imply that the protein products physically interact. The STRING bioinformatic analysis is more convincing that there is a putative regulatory interaction between ESR2 and the SP8 locus, and this suggestion of protein-protein interaction is weak and should be omitted. In addition, the authors note that ESR2 has not been detected in the songbird HVC in a previous study. To further demonstrate the expression of ESR2 (and SP8) in HVC, it would be useful to plot their expression from the microarray data across the different testosterone conditions.

We repeated the coexpression study using confocal microscopy and fluorescent RNAScope in situ hybridization, which is now reflected in the revised Figure 5 and a new Figure 5 - Supplement Figure 1. We have also moderated our statement regarding the sparse co-expression of ESR2 and SP8 in HVC neurons. While the presence of co-expressing neurons may provide some anatomical basis for the bioinformatic findings, we have been cautious in our interpretation and have stated that "SP8 and ESR2 mRNAs exhibited low expression levels in HVC, co-localizing in a subset of cells, predominantly GABAergic cells" (lines 369-370). We have removed the speculation about potential protein interaction based on mRNA distribution. Additionally, we have highlighted that SP8 and ESR2 were differentially upregulated at T14d (lines 362-363).

1.4) My final concern lies in the interpretation of these results as generalizable to other sex hormone-modualated behaviors. On lines 452-455, the authors write, "This suggests that the testosterone (or estrogen)-triggered induction of adult behaviors, such as parental behavior and courtship, requires a much more extensive reorganization of the transcriptome and the associated biological functions of the brain areas involved than previously thought.". The experiments and argument likely apply to other neural systems to undergo large seasonal fluctuations in sex hormones and similar morphological changes. However, the authors argue that the large number of transcriptional changes seen here may generalize broadly to sex hormone modulated adult behaviors. I think there are a couple of problems with this argument. First, as described here and in past work, testosterone drives major morphological changes the song system of adult canaries; such dramatic changes are not seen for instance in sex hormone-receptive areas underlying mating behavior in adult mammals. Similarly, the study introduced testosterone into female birds which drives a greater morphological change in HVC relative to similar manipulations in males, which again may account for the large number of differentially expressed genes. I would temper the generality of these results and note how the experimental and biological differences between this system and other sex hormone-responsive systems and behaviors may contribute to the observed transcriptional differences.

We modified this statement in lines 473-478: “The testosterone-driven changes in female HVC morphology and function represent some of the most notable modifications known in the vertebrate brain. However, how this extensive, testosterone-induced gene regulation in the HVC applies to other seasonally testosterone-sensitive brain areas remains to be seen. Endpoint analysis of testosterone-induced singing in male canaries during the non-reproductive season also indicates considerable regulation of HVC transcriptomes (Frankl-Vilches et al., 2015; Ko et al., 2021)”.

Reviewer #2 (Public Review):

First, we would like to express our gratitude to Reviewer #2 for the constructive feedback. We have addressed the concerns in detail below:

2.1). The bulk of the manuscript details WGCNA, GO terms, and promoter ARE/ERE motif abundance, using the initial pairwise comparisons for each timepoint as input lists. However, there are no p/adjp values provided for these pair-wise comparisons that form the basis of all subsequent analyses. Nor are there supplementary tables to indicate how consistent the replicates are within each group or how abundantly the genes-of-interest are expressed. With the statistical tests used here, and the lack of relevant information in the supplementary tables, I cannot determine if the data support the authors’ conclusions. These omissions mar what is otherwise a conceptually intriguing line of investigation.

We appreciate the reviewer’s concerns. Please refer to our response addressing this point and the subsequent one (2.2) together in the section below.

Reviewer #3 (Public Review):

We appreciate the positive feedback from the reviewer and below addressed the issues pointed out by the reviewer.

3.1) My biggest concern is the sample size. Most of the time points only have 5 or 6 individuals represented, and I question whether these numbers provide sufficient statistical power to uncover the effects the authors are trying to explore. This is a particular problem when it comes to evaluating the supposed "transient" of testosterone on gene expression. There is currently little basis for distinguishing such effects from noise that accrues because of low power. This can be a major problem with studies of gene expression in non-model species, like canaries, where among-individual variability in transcript abundance is quite high. Thus, it is possible that one or two outliers at a given time point cause the effect testosterone at this time point to become indistinguishable from the controls; if so, then a gene may get put into the transient category, when in fact its regulation was not likely transient.

We acknowledge that our sample sizes may appear moderate. To address the concern regarding temporal regulation analysis, we followed Reviewer 3's suggestion and conducted a probe-level power analysis (point 2 of recommendations for the authors; labelled as point 3.9 below). We then excluded differentially expressed genes with a power less than 0.8 prior to conducting temporal classification. Consequently, 93% of our differentially expressed genes demonstrated a power ≥ 0.8 (9025/9710). Following further classification by temporal regulation pattern, we identified 29 constantly upregulated, 41 constantly downregulated, 39 dynamically regulated, and 8916 transiently regulated genes. If we apply a stricter constraint by requiring each differentially expressed gene to have at least two probe-sets with a power ≥ 0.8, 83% of differentially expressed genes (8033/9710) still have sufficient power.

We recognize that our sample size may not be sufficient to detect weakly differentially expressed genes. However, we have intentionally excluded these genes from the beginning (those with |log2(fold change)| ≤ 0.5 were excluded).

The scenario outlined by the reviewer, where outliers might cause the effect of testosterone to blend with controls, leading to misclassification, is indeed plausible. This could occur either because the genes are weakly regulated, or because the power to detect differential expression is insufficient, thus preventing these genes from surpassing the threshold to be deemed significantly differentially expressed. However, this also illustrates that the effect of testosterone does not regulate every gene in the same way.

We have appended a column indicating high power genes (≥ 0.8) in the DiffExpression.tsv file, available in the Dryad repository. The power analysis has been incorporated to the method section at lines 801-808 and result section at lines 188-192.

3.2) More on the transient categorization. Would a gene whose expression is not immediately upregulated (within 1 hour), but is upregulated later on (say in the 14d group) be considered transient? If so, this seems problematic. Aren’t the authors setting the null expectation of "non-transient" as a gene that does not increase immediately after 1 hour of treatment? The authors even recognize that it is quite surprising that gene expression changes after an hour. It may be that some genes whose regulation is classified as transient are simply slower to upregulate; but, really, would we say their expression in transient per se? Maybe I’m misunderstanding the categorizations?

We appreciate the reviewer's insightful discussion regarding the transient categorization. We understand that it is indeed more challenging for a gene to be classified as constantly regulated than transiently regulated, due to smaller effects by testosterone or being undetectable owing to low power. To address this concern, we further dissected the transiently regulated category by reporting the number of time points at which a gene is differentially expressed in Figure 2 - Figure supplement 1. Approximately half of the transiently regulated genes were only regulated at one time point, further illustrating that the effect of testosterone on gene expression was not constant during the time window we examined (see lines 184 - 187).

3.3) The authors don’t fully explain the logic for using females in this study to measure a "male-typical" behavior (singing). My understanding is that females have underlying circuitry to sign, and T administration triggers it; thus, this situation that creates a natural experiment in which we can explore T’s on brain and behavior, unlike in males which have fluctuating T. First, it might be good to clarify this logic for readers, unless perhaps I’m misunderstanding something. Second, I found myself questioning this logic a little. Our understanding of basic sex differences and the role that steroid hormones play in generating them has changed over the last few decades. There are, for example, a variety of genetic factors that underlie the development of sex differences in the brain (I’m especially thinking about the incredible work from Art Arnold and many others that harness the experimental power of the four core genotype mice). Might some of these factors influence female development, such that T’s effects on the female brain and subsequent ability to increase HVC size and sing is not the same as males.

Indeed, sex-chromosome dosage compensation is absent in birds leading to higher Z-chromosomal gene expression in males. We demonstrated substantial sex differences in gene expression in our earlier work [Ko, M.-C., Frankl-Vilches, C., Bakker, A., Gahr, M., 2021. The Gene Expression Profile of the Song Control Nucleus HVC Shows Sex Specificity, Hormone Responsiveness, and Species Specificity Among Songbirds. Frontiers in Neuroscience 15].

We have revised the introduction (lines 96-98) to clarify our rationale for using female canaries as a model for adult behavioral development, not as a model for male canaries. After testosterone treatment, these females start to sing, with song structure developing over time, similar to male seasonal progression. This approach eliminates the confounding effect of fluctuating testosterone levels seen in males, supported by distinct HVC transcriptomes in testosterone-implanted singing female canaries compared to males (Ko et al., 2021).

The revised paragraph reads as below: Female canaries (Serinus canaria) are typically non-singers, with their spontaneous songs displaying less complexity than their male counterparts (Hartley et al., 1997; Herrick and Harris, 1957; Ko et al., 2020; Pesch and Güttinger, 1985). Despite their infrequent singing, these females possess the necessary underlying circuitry that can be activated by testosterone. Following testosterone treatment, these females start to produce simple songs, which gradually evolve in structure over weeks—paralleling the seasonal progression of male singing (Hartog et al., 2009; Ko et al., 2020; Shoemaker, 1939; Vallet et al., 1996; Vellema et al., 2019). Moreover, testosterone induces the differentiation of song control-related brain nuclei in adult female canaries, a critical step for song development (Fusani et al., 2003; Madison et al., 2015; Nottebohm, 1980). In this study, we focus on these testosterone-treated female canaries as a model for adult behavioral development rather than a model for male canaries. This unique model allows us to examine transcriptional cascades in parallel with the differentiation of the song control system and the progression of song development, without the confounding impact of fluctuating testosterone levels seen in males, which often results in considerable individual differences in the non-reproductive season baseline singing behavior. This approach is backed by the observation that the HVC transcriptomes of testosterone-implanted singing female canaries are distinct from those of singing males (Ko et al., 2021).

3.4) I was surprised by the authors assertion that testosterone would only influence several tens or hundreds of genes. My read of the literature says that this is low, and I would have expected 100s, if not 1,000s, of genes to be influenced. I think that the total number of genes influenced by T is therefore quite consistent with the literature.

We apologize for any confusion caused by our statement. We did not mean to imply that testosterone only influences several tens or hundreds of genes, but rather that we did not expect such an extensive transcriptional regulation in the HVC by testosterone. We have clarified this in our revised manuscript, specifically in lines 450-451. Thank you for helping us to clarify this point.

3.5) I found the GO analyses presented herein uncompelling. As the authors likely know, not all GO terms are created equally. Some GO terms are enriched by hundreds of genes and thus reflect broad functional categories, whereas other GO terms are much more specific and thus are enriched by only a few genes. The authors report broad GO terms that don’t tell us much about what is happening in the HVC functionally. This is particularly the case when a good 50% of the genome is being differentially regulated.

We appreciate the reviewer's comment. We have added KEGG pathway enrichment analysis in Figure 3 - Figure supplement 1 as an alternative. However, we believe that the GO term enrichment results still provide valuable insights, and therefore we have retained them in Fig. 3.

3.6) The Genomatix analyses are similarly uncompelling. This approach to finding putative response elements can uncover many false positives, and these should always be validated thoroughly. Don’t get me wrong-I appreciate that these validations are not trivial, and I value the authors response element analysis.

We appreciate the reviewer's comment on the presence of AR or ER motifs in promoters and acknowledge that in mammals, AR and ER predominantly bind at distal enhancers rather than promoters. Our analysis focused on promoter regions due to the limitations of available tools and resources for our study species. We understand that this approach may not capture the full complexity of AR and ER regulation. We have revised our manuscript to note the limitations of our approach and clarify that the presence of AREs and EREs alone is not indicative of active receptor binding or direct regulation (lines 416-427).

3.7) I’m sceptical about the section of the paper that speculates about modification of steroid sensitivity in the HVC. These conclusions are based on analyses of mRNA expression of AKR1D1, SRD5A2, and the like. However, this does not reflect a different in the capacity to metabolize steroids, or at least there is little evidence to suggest this. Note that many of these transcripts have different isoforms, which could also influence steroidal metabolism.

We agree that mRNA expression levels of AKR1D1, SRD5A2, and other transcripts involved in steroid metabolism do not necessarily reflect changes in steroid metabolizing capacity. However, we believe that these changes in mRNA expression are indicative of potential changes in steroid sensitivity in the HVC, which could affect the neural response to steroids. We acknowledge that isoform differences of these transcripts may influence steroid metabolism and further studies are necessary to confirm our findings and elucidate the mechanisms underlying the observed changes in gene expression. In response to this comment, we have amended the text in lines 245-249 to reflect this consideration.

Author Response

Reviewer #1 (Public Review):

The authors have compiled and analysed a unique dataset of patients with treatment-resistant aggressive behaviours who received deep brain stimulation (DBS) of the posterior hypothalamic region. They used established analysis pipelines to identify local predictors of clinical outcomes and performed normative structural and functional connectivity analyses to derive networks associated with treatment response. Finally, Gouveia et al. perform spatial transcriptomics to determine the molecular substrates subserving the identified circuits. The inclusion of data from multiple centres is a notable strength of this retrospective study, but there are current limitations in the methodology and interpretation of findings that need to be addressed.

1) The validation of findings is heterogeneous and inconsistent across analysis pipelines. While the authors performed non-parametric permutation testing during sweet-spot mapping, structural and functional connectivity were validated using a 'four-fold consistency analysis'. The latter consists of a visual representation of streamlines and peak intensities after randomly dividing data into four groups, the findings were not validated quantitatively. If possible, the authors should apply permutation analysis in alignment with sweet-spot mapping and demonstrate the predictive ability of their identified networks in a LOO or k-fold cross-validation paradigm as carried out by similar studies. Given that the data has been derived from multiple centers, the prediction of left-out cohorts based on models generated by the remaining cohorts could be another means of validation. If validation is not possible, the authors should clearly state the limitations of their approach.

We appreciate the comment. We have now improved the validation of our connectomics analyses and removed the four-fold consistency analysis. For the functional connectivity analysis, we performed a 1000 permutation test (p<0.05). Similar brain areas were detected in the corrected and uncorrected maps. For the structural connectivity analysis, we used False Discovery Rate (FDR) correction at a significant level of p<0.001, as it is not feasible to perform a 1000 permutation test with this data. The structural connectome is composed of 12 million fibres, and every single permutation takes approximately 4 hours to be completed using our most powerful computational system. To perform 1000 permutations, it would take at least 4000 hours (i.e. 167 days or 5.5 months) of uninterrupted analysis to complete the test. However, it is important to highlight that an FDR correction at the level of p<0.001 is an extremely stringent method. This means that of the 23,000 fibres detected as being touched by the VATs, only 23 would be incorrect, while the remaining 22,977 are correct. Here again, we observed many similarities between the uncorrected and corrected maps, with the main anatomical structures being detected in both. The Methods section and Figures 4 and 5 were revised to reflect these changes.

2) In addition to a 'four-fold consistency analysis', functional connectivity was evaluated using LOOCV in a priori identified ROIs. Their network analysis, however, revealed a far more extensive network encompassing cortical, subcortical, and cerebellar structures. To avoid selection bias the authors should incorporate identified structures into their analysis and apply appropriate means of validation.

We thank the reviewer for this valuable suggestion. We originally did not explore the various significant areas but performed a more focused analysis intended to demonstrate that regions of the known ‘aggression network’ are indeed implicated in our findings. We performed a new analysis exploring the correlation between symptom improvement and the functional connectivity of all the areas described in Figure 5 (i.e., functional connectivity map). To this aim, we extracted individual connectivity values from the peak within each significant region and performed the same additive linear model, incorporating the functional connectivity of each area as well as the age of the patients to estimate individual symptomatic improvement. In addition, we performed a complete exploratory analysis considering the connectivity of any 2 brain structures and age. The resulting matrix shows to what extent functional connectivity to any two areas can be used to estimate clinical outcomes. Interestingly, this new analysis revealed the Periaqueductal Grey matter (PAG) to be the most important functionally connected area when investigated alone or in combination with brain structures critically involved in the regulation of emotional responses, namely the amygdala, anterior cingulate cortex, bed nucleus of the stria terminalis, nucleus accumbens, orbitofrontal cortex and fusiform gyrus. Also, the significance of the PAG connectivity was retained during leave-one-out cross-validation (LOOCV). The Methods, Results, Discussion and Figure 6 were revised. In addition, we added a new Table 2 and Supplementary File 1 to describe the new analysis and results.

3) Functional connectivity mapping: how were R-maps generated? The authors mention that patient-specific R-maps were p-thresholded and corrected for multiple comparisons, but it is not clear how group-level maps were generated. How did the authors perform regression on these maps? Were voxels that did not survive thresholding excluded?

This is a multiple-step analysis. First, it is necessary to localize the electrodes in each patient’s brain and estimate the volume of activated tissue (VAT) observed when stimulation parameters associated with symptomatic improvement are used. The VATs are then used as seeds for the next steps, during which we investigate how much functional influence the VTAs have on the other areas of the brain (i.e., individual r-map). This is done by correlating the BOLD time course of the VAT’s seed with the BOLD time course of all other voxels in the brain. The individual r-maps are then corrected for multiple comparisons to exclude voxels with potentially spurious correlations, resulting in an individual r-map that only included voxels surviving Bonferroni correction at the level of p<0.05. Finally, to create group-level maps, a voxel-wise linear regression analysis was performed to investigate whether each voxel of the map exerts more or less influence (corrected individual r-map with the functional connectivity of the patient’s VAT) or is more or less related to the clinical outcome (i.e. individual improvement). The last step is a permutation correction resulting in a significant group-level functional connectivity map (ppermute<0.05). We modified the Methods section and added a new Figure 1-figure supplement 1 illustrating this analysis.

4) The authors determined that age was a significant prédictor of the outcome, but it is unclear whether certain age groups presented with distinct etiologies underlying their aggressiveness. For example, aggression in epilepsy may show a better response to DBS as opposed to schizophrenia. How does patient outcome change when stratifying according to etiology? How does model performance change when controlling for etiology? The authors should include the etiology of aggressiveness in Table 1.

This is an interesting point. We observed a similar distribution between the pediatric and adult populations in relation to the most common etiologies reported. Epilepsy was the most frequent diagnosis in both populations (pediatric: 50%, adult: 62%), followed by autism spectrum disorder (pediatric: 34%, adult: 24%). The remaining etiologies were largely composed of single cases. A similar proportion of intellectual disability was also observed in pediatric and adult populations. Severe cases were observed in 75% of pediatric and 85% of adult patients. Moderate disability was present in 25% of pediatric and 15% of adult patients. Since several diagnoses were unique to some patients, the addition of this information to Table 1 could result in the identification of the patient. Thus, to preserve anonymity, the diagnoses were added to the end of Table 1 from more to less frequent. We have also revised the Results and Discussion sections to address this concern.

5) Stimulation parameters. The authors report average pulse widths of 219 µs and 142µs respectively, which is up to 4-fold higher as compared to DBS settings used conventionally in movement disorders and will significantly alter the volume of activated tissue. Did the authors account for the drastic increases in pulse width during VAT modeling?

We thank the reviewer for raising this point about the volume of activated tissue (VAT) modelled and the unusual pulse width observed in some patients in this cohort. These patients presented stimulation-induced sympathetic side effects when DBS was set with higher frequencies (e.g. increased heart rate and blood pressure). The chosen final parameters were the ones associated with a clinical benefit without generating side effects. There are a multitude of ways to estimate the VATs, from advanced axon cable models – the gold standard, which simulate axon membrane dynamics and require patient-specific diffusion-weighted imaging and tremendous computing power 1 - to simple heuristics-based models that estimate the rough extent of a VAT based on stimulation parameters without constructing an actual spatial model 2–4. The model employed in our study (and in a number of previous publications by our group 5–10) was the FieldTripSimBio ‘E-field norm’ finite element method (FEM) model. This model, which was first described by Horn et al. 11 and is freely available in Lead-DBS (https://www.lead-dbs.org/), strikes a balance between the sophisticated axon cable models and the simpler heuristic models. In particular, it constructs an electric field (E-field, by applying an electric field strength threshold, or activation threshold) and calculates the VAT associated with specific voltage settings and contact configurations, taking into account the conductivity of surrounding brain tissue and electrode components. Notably, studies comparing VAT modelling techniques 12 showed that ‘E-field norm’ FEM models closely approximate (<0.1 mm difference) the gold standard axon cable models in terms of the size of VATs constructed for monopolar stimulation settings. However, it should be acknowledged that the FieldTripSimBio model in Lead-DBS does not allow the user to specifically enter values for pulse width. Instead, it employs a standard activation/electric field strength threshold (0.2 V/mm) that reflects a combination of commonly modelled axon diameters (roughly 3.5 μm) and pulse width values (i.e., 60-90 μs). This threshold is based on work by researchers such as Astrom et al. 13 and reflects a ‘middle ground’ value that takes into account the fact that any VAT model will necessarily be an imperfect approximation of how electrical stimulation interfaces with brain tissue, depending heavily on aspects such as the diameter of local axons. Nonetheless, it is certainly understood that increased pulse width does meaningfully increase the effective range of stimulation (thus translating to a larger VAT) by lowering the activation threshold of nearby axons 12.

Given that our patient cohort included a small number of patients who were stimulated with higher pulse widths than the values assumed by our model (90 μs), it is reasonable to wonder whether we underestimated the size of these patients’ VATs. To address this aspect, we modelled these patients’ VATs using a simpler heuristic model 2 that does allow specific pulse width values to be selected by the user. More specifically, we computed a range of VATs for these patients using varied pulse width values (ranging from 90 μs up to their actual values). Not surprisingly, this endeavour did yield larger VATs when higher pulse widths were used. On average, the absolute difference in VAT diameter between 90 μs and 450 μs (the largest pulse width observed in this cohort) versions of these patients’ VATs was 2 mm. To check whether or not this difference could have potentially impacted our results, we repeated our probabilistic mapping analysis using altered VATs (specifically, VATs that were enlarged by 2 mm in diameter) for the patients with higher pulse widths. This new repeat analysis yielded a very similar average map to the original analysis: the overall map pattern and location/values of the peak corresponding to the most efficacious area for maximal symptom alleviation remaining unaltered, and only a few voxels on the periphery of the map changing in value by a couple of percentage points. This new supplementary analysis indicates that our results were not meaningfully altered by the unusual pulse width observed in these patients. We modified the Methods section to address some of these aspects and added a new Figure 3-figure supplement 2 illustrating both voxel efficacy maps.

6) Imaging transcriptomics. The methods described lack detail: How did the authors account for differences in expression across donors, samples, and regions during preprocessing of the Allen Human Brain Atlas? How was expression data collapsed into regions of interest? Did the authors apply any normalization? Recent publications have introduced reproducible workflows for processing and preparing the AHBA expression data for analysis that is publicly available.

7) 'genes with similar patterns of spatial distribution to the TFCE map were compiled in an extensive list'. It is unclear why authors used TFCE maps for spatial transcriptomics as opposed to the functional connectivity map featured in Figure 5. How was similarity measured between the TFCE map and the AHBA? How were candidate genes identified? Please provide a more comprehensive description of the analysis pipeline.

We apologize for the short description of this analysis. We performed a gene set analysis using the abagen toolbox (https://abagen.readthedocs.io/en/stable/index.html) to investigate genes with a spatial pattern distribution similar to one of clinically relevant functional connectivity. For this analysis, we used the Allen Human Brain Atlas (https://alleninstitute.org/) microarray data describing the cortical, subcortical, brainstem and cerebellar localization of over 20,000 genes in the human brain (3702 anatomical locations from 6 neurotypical adult brains) 14–17, along with a cell-specific aggregate gene set 18. These data are provided preprocessed, with gene expression values normalized across all brains, and registered to standard MNI space, allowing for a direct comparison between the spatial pattern of gene expression and the functional connectivity map (https://human.brain-map.org/microarray/search) 15. The TFCE maps were used to create clusters of clinically relevant functional connectivity with a spatial extent that overlaps with the anatomical locations from which microarray data was obtained. We parcellated both datasets (results of functional connectivity analysis and Allen Gene Atlas) according to the Harvard-Oxford brain atlas and correlated the spatial distribution of gene expression with the spatial distribution of the results of the functional connectivity mapping. The resultant list of candidate genes was used as input in gene ontology tools to investigate the associated biological processes and cell types. It is important to highlight that this process involves 2 corrections for multiple comparisons using FDR at q<0.005; one correction occurs at the level of the gene list to include only the most significant genes in the gene ontology analysis; a second correction occurs at the level of the gene ontology analysis to consider only the most significant biological processes. We have included some of these details in the revised Methods section.

8) What do the bar plots in Figure 7 (left) represent? P-values? The authors should label the axes to make this clear to the reader.

9) Interprétation of imaging transcriptomics: The authors identify a therapeutic circuit associated with deep brain stimulation of the posterior hypothalamic area, however, it is unclear how to reconcile genes associated with hormones, inflammation, and plasticity in this context. The authors mention and discuss genes implicated in hormonal processing, specifically oxytocin. The results provided in Figure 7, however, do not support this finding and it is unclear how the authors identified genes linked to oxytocin. In addition, the authors identified reductions in the number of microglia and astrocytes, while oligodendrocytes were overexpressed relative to the expected distribution of genes per cell type. These findings were attributed to DBS effects, however, both connectomic and transcriptomic data are acquired from healthy subjects, which suggests a physiological deficit/enrichment in a therapeutic circuit. How do the authors interpret findings given that no electrode implantation and stimulation were performed?

The analysis of normative datasets (functional and structural connectomics and spatial transcriptomics) is based on the idea of better understanding mechanisms of treatment considering our current knowledge of the average human brain. Unlike patient-specific studies in which imaging is acquired from a single patient or genetic profiles are extracted from tissue samples, these normative analyses rely on high-quality “atlases” derived from healthy subjects. In the case of functional and structural connectivity, these atlases are calculated from very large cohorts of subjects (around 1000 brain scans). Thus, imaging connectomics investigates the pattern of brain activity and structural connectivity related to a specific area of the brain (in this case, the volume of tissue activated (VATs) with DBS) and correlate these data with clinical outcomes to shed light on potential mechanisms of action. Similarly, the spatial transcriptomic analysis identifies spatial correlations between patterns of gene expression and brain characteristics detected by MRI 19 (in this case, the spatial pattern of functional connectivity) to investigate possible genetic underlying mechanisms. It is important to highlight that previous studies have shown that normative analyses yield results that are similar to the ones observed using patient-specific data 20–22. In the specific case of imaging connectomics, It has been shown that normative datasets can be used to create probabilistic models of optimal connectivity associated with patients’ outcomes that are meaningful to predict outcomes in patient-specific connectivity data 21. Thus, these exploratory data-driven approaches strive to simulate the presumed fingerprint that a particular patient’s individualized DBS intervention might modulate. They also allow the investigation of possible mechanisms of action in a large, previously inaccessible cohort of patients whose individual data are available. We apologize for the inaccuracy in Figure 7. Along with improving the Discussion section of the manuscript, we included the label for the bar plots in the left panel to improve the clarity of the graph and added the missing result from the KEGG 2021 Human Library that shows the oxytocin signalling pathway.

10) Data availability. Code used for data processing should be made openly available or shared as source data along with the Figures that were generated using the code. Sweet-spot, structural, and functional connectivity maps should be shared openly.

All tools and codes necessary for localizing the electrodes, estimating the volume of activated tissues, and analyzing imaging connectomics are freely available in Lead-DBS (https://www.lead-dbs.org/), a toolbox designed for DBS electrode reconstructions and computer simulations based on postoperative imaging. All codes for spatial transcriptomics are freely available in abagen (https://abagen.readthedocs.io/en/stable/), a toolbox designed to analyze the Allen Brain Atlas genetics data. Along with the codes, the websites for these tools provide manuals describing the step-by-step procedure for successful analysis. The datasets were made freely available at Zenodo (doi: 10.5281/zenodo.7344268). We improved our Data Availability Statement to address this concern.

Reviewer #2 (Public Review):

Deep brain stimulation (DBS) is an important, relatively new approach for treating refractory psychiatric illnesses including depression, addiction, and obsessive-compulsive disorder. This study examines the structural and functional connections associated with symptom improvement following DBS in the posterior hypothalamus (pHyp-DBS) for severe and refractory aggressive behavior. Behavioral assessments, outcome data, electrode placements, and structural and functional (resting-state) imaging data were collected from 33 patients from 5 sites. The results show structural connections of the effective electrodes (91% of patients responded positively) were with sensorimotor regions, emotional regulation areas, and monoamine pathways. Functional connectivity between the target, periaqueductal gray, and amygdala was highly predictive of treatment outcome.

Strengths.

This dataset is interesting and potentially valuable.

Weaknesses.

The figures seem to indicate that electrodes and symptom improvement is located lateral to the hypothalamus, perhaps in the subthalamic nucleus (STN). This is might explain why the streamlines from the tractography are strongest in motor regions. The inclusion of the monoaminergic based on the tractography is not warranted, as the resolution is not sufficient to demonstrate the distinction between the MFB (a relatively small bundle) and others flowing through this region to the brainstem.

This is an interesting point. The sweet spot identified in this work is indeed located in the posterior-inferior-lateral region of the posterior hypothalamic area, reaching the most superior part of the red nucleus, without including the STN. It is important to highlight that the voxel-efficacy mapping only shows voxels associated with a minimum of 50% symptomatic improvement following treatment. Thus, the areas not touching the red nucleus are also associated with excellent symptom alleviation. Although the structural connectivity mapping revealed tracts involved in motor and sensory information, it also showed tracts known to be involved in the regulation of emotions, such as the MFB, the Amygdalofugal Pathway and the ALIC. It is worth noting that this analysis is excellent for segregating the fibre tracts as relevant or not associated with a clinical improvement, but it is not capable of tearing apart the system to determine which of those are necessary for symptom alleviation. As a result, it is not possible to determine whether the motor projections are stronger or more relevant than others. However, the structural connectivity analysis presented here contributes to the body of knowledge on the network of aggressive behaviour and provides clinically relevant data that can be useful to improve future patient outcomes.

We agree with the reviewer that the engagement of the motor system is indeed highly relevant for the reduction of aggressive behaviours, as we have previously shown that aggressive behaviour is highly correlated with motor agitation 23,24. Additionally, in the context of ASD, self-injury behaviour is defined as a type of repetitive/stereotypic behaviour that results in physical injury to the patient’s own body. In relation to the involvement of the monoaminergic system, we would like to apologize for not being clear in the discussion of our findings. Although the functional and structural connectivity maps are related, they provide different means of exploring distinct aspects of the connectivity profile of each VAT. While the structural connectivity map may elucidate symptom improvement via direct fibre modulation (i.e. fibres that touch vs fibres that do not touch the VAT), the functional connectivity map investigates the functional dynamics of the network via BOLD signals (functional MRI). In this manuscript, we showed the functional connectivity (not fibre tracts) of the VATs with areas known to regulate monoamine production, such as the Raphe nuclei and the Substantia Nigra. Both serotonin and dopamine are critically involved in the control of aggressive behaviours, being the target of the main medications used to treat these symptoms in several patient populations. To address all the raised concerns, we incorporated a few sentences in the discussion, highlighting the relevance of the motor system and some limitations of our analysis. We also added a new Figure 3-figure supplement 1 and a discussion on the position of the sweet spot in relation to the red nucleus and subthalamic nucleus.

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Author Response

Reviewer #1 (Public Review):

The strength of the manuscript is highlighted by the application of fractal formalism, which is commonly used in colloidal systems, in conjunction with MD simulation to study the phase separation of an IDP. The weakness lies in the fact that this study does not provide any discussion on how our understanding of the network structure and dynamical behavior of biomolecular condensates and their biological significance improves through this study. The experimental part remains weak, without any measurements of the dynamics of the condensates. Whether and how the formalism can distinguish between phase-separated condensates (WT) and classical protein aggregates (Y to A variant) remains unclear.

We thank the Reviewer for their careful reading of the manuscript and their appreciation of the link between IDP phase separation and colloid chemistry. Establishment of a quantitative framework behind this link, as given by the fractal formalism, and a multiscale model of the spatial organization of a biomolecular condensate, derived from MD simulations in combination with fractal scaling, are indeed two of our main contributions. In particular, to the best of our knowledge, ours is the first atomistically resolved model of the spatial organization of a biomolecular condensate at an arbitrary scale. The key features of the proposed model, as elaborated in the Discussion of the revised manuscript (p. 18, 20-21), are the coexistence of differently sized clusters inside a condensate, and a quantitative prediction of a particular scaling of mass with cluster size (Figure 5A), as further discussed below. Moreover, our results also point to the possible formation of pre-percolation clusters with sizes below the resolution limit of typical microscopy experiments, in agreement with recent observations (https://doi.org/10.1073/pnas.2202222119).

We agree that the full understanding of biomolecular condensates also requires a detailed treatment of the dynamical aspects. Following the Reviewer’s comments, we have provided significant new results in this regard and included an experimental characterization of fusion behavior (Videos 1, 2) and condensate dynamics by FRAP (Figure 1D, E and Figure 1—figure supplement 2) as well as a detailed analysis of diffusion and viscosity in the simulated systems (Figure 4C and Figure 4—figure supplement 1D-F). The newly performed FRAP experiments provide a direct measure of the condensate dynamics. Importantly, the measured recovery half-times for WT and R>K condensates resemble those of other well-characterized in vitro condensates. We have occasionally observed elongated, amorphous Y>A precipitates, albeit in low number and only at 50-fold higher concentration than the wild-type (45 mM and above, Figure 1C). While this may be consistent with the predictions of the fractal model and hint at the differences in mesoscopic organization between the WT and R>K condensates and the Y>A precipitates, the latter are rare and we are reluctant to draw major conclusions.

Furthermore, we could show that the WT diffusion coefficient is lower than for either mutant (Figure 4C, and Supplementary File 2). Clearly, this difference is not due to the effect of protein size or a higher solvent viscosity, but primarily indicates protein slow-down due to the more extensive interactions with partners (reflected also in higher average valency, Figure 2D, or probability of interactions Figure 2—figure supplement 1D). The fact that the WT diffusion coefficient drops by about 20% over the last 0.3 µs of the MD trajectory also correlates with the formation of a single percolating cluster in the system (Figure 2C). This is an expected effect on protein diffusion upon crossing the percolation threshold (https://doi.org/10.1038/ncomms11817, https://doi.org/10.1021/acs.jpcb.7b08785). Moreover, the difference in the recovery dynamics observed for WT and R>K mutant can be interpreted using the proposed model. Namely, accurate fitting of FRAP data was only possible if using at least two components (Figure 1—figure supplement 2). According to https://doi.org/10.1016/j.tcb.2004.12.001, these components indicate the contribution of particle diffusion and interaction (binding). Thus, recovery of the centrally bleached condensates is faster for WT than for the R>K mutant, which can be related to the higher compactness of the WT particles across scales as compared to R>K. On the other hand, the FRAP results for the condensates bleached in the peripheral area highlight the contribution of the binding component. Indeed, the recovery is about 3-fold faster for the R>K mutant, which could potentially be related to the lower valency of the interactions and the ease of the replacement of inactivated fluorescent species and/or exchange with proteins in the bulk. A further connection of the developed model and condensate dynamics concerns the multimodal description of diffusion in biomolecular condensates, together with multimodal fitting of FCS and FRAP data as used recently for interpreting single particle tracking results (https://doi.org/10.1016/j.bpj.2021.01.001). Namely, the polydisperse nature of the protein phase as suggested by the model translates to multimodal diffusion, reflecting the dynamics of protein clusters of different size. For instance, regularization fits used for DLS autocorrelation curves assume a multimodal character of the diffusion and are interpreted to reflect a multimodal distribution of cluster sizes in condensates (https://doi.org/10.1073/pnas.2202222119).

Finally, a way of testing the model prediction, which would merit a study in its own right, would involve static light scattering (SLS): a linearly decreasing scattering intensity as a function of the scattering vector in a log-log representation, as frequently seen for different colloidal systems, is expected by the fractal model. In fact, fractal dimension dF could directly be estimated from SLS experiments (https://doi.org/10.1038/339360a0) from the limiting value of scattering intensity for high values of the product of the scattering vector q and the average cluster size <Rg>. As a direct test of the predictions of the model, the experimental value of dF could then be compared with the predicted one. Moreover, techniques such as DLS and MALS could be used to measure independently masses and sizes of biomolecular condensates in vitro at different scales in order to test the validity of the particular scaling predicted by the fractal model. Such experiments are not trivial and are out of scope of the present study.

Reviewer #2 (Public Review):

A key aspect of the work is to use the simulations to explain differences between (i) dilute and dense phases and (ii) wild-type and mutant variants. Here, it would be important with a clearer analysis of convergence and errors to quantify which differences are significant.

Following the Reviewer’s suggestion, we now provide an analysis of convergence and statistical significance. Specifically, in Supplementary File 1 “Technical summary” we now report the average value, standard deviation and a block-average measure of convergence for all the key observables analyzed, including radius of gyration (Rg), valency (n), and compactness (), for all modeled systems. Furthermore, in the revised manuscript, we now also include the analysis of protein translational diffusion constants and solution viscosity for all modeled systems to assess the ability of the simulations to capture protein dynamics realistically (Figure 4C, Figure 4—figure supplement 1D-F, Supplementary File 2, see also above). Moreover, we include in the revised version a new figure depicting time evolution of average compactness in the 24-copy systems (Figure 4—figure supplement 1C). Thus, it can be seen that the two key model parameters derived from MD simulations of the 24-copy system – protein valency and compactness – reach a stable plateau over the last 0.3 µs (Figure 2D and Figure 4—figure supplement 1C), which were used for final analyses, with block-averaged deviations of less than 10% throughout (see below for details). All the differences in these parameters between single-copy and 24-copy simulations, as well as those between WT and mutation simulations, were found to be significant with p-values < 2.2 10-16 according to the Wilcoxon rank sum test with continuity correction (details in Supplementary File 1). Finally, considering the sampling limitations implicit in most MD studies, we clearly recognize the possibility that with longer simulation times or more protein copies per simulation box, the simulated systems may show a qualitatively different behavior. However, we emphasize that our derivation of the formalism that links the features of simulated ensembles on the scale of 10s of nanometers with their behavior on the scale of 100s of nanometers and beyond is independent of such limitations. Once longer, larger and more accurate simulations become available, one will be able to apply the formalism without alteration and obtain a model of the spatial organization of the condensate on an arbitrary scale, starting just from the local features of individual proteins. We now discuss these details on pp. 10, 11, 13 of the revised manuscript.

It would also be useful with a clearer description of how the analytical model is predictive, of which properties, and how they have been/can be validated. Which measurable quantities does the model predict?

As pointed above, the model predicts the existence and provides a quantitative description of pre-percolation finite-size clusters (https://doi.org/10.1016/j.molcel.2022.05.018, https://doi.org/10.1073/pnas.2202222119). More generally, the model provides the fractal dimension (dF) of protein clusters and enables evaluation of different scale-dependent properties of clusters of arbitrary size, including protein density as a function of cluster size (Figure 5—figure supplement 1C, Figure 5C). Importantly, the fractal dimension can be used in combination with local MD simulations and cluster–cluster aggregation algorithms to derive a detailed model of the 3D organization of fractal clusters of a chosen size at atomistic resolution (Figure 5A, B, and Videos 4, 5, and 6). Such detailed structural understanding of the interior organization of a condensate can, for example, be used to evaluate cavity sizes and interpret partitioning experiments. Since the differences in the morphology of WT and mutant protein clusters propagate across length scales, they can even be qualitatively characterized by the analysis of microscopic images (e.g. circularity, Figure 1—figure supplement 1C, see also discussion above). Finally, static light scattering (SLS) experiments give the possibility to test the model directly, which will be the subject of our future work. Namely, the fractal formalism predicts linear behavior in the log-log representation of the SLS intensity vs. scattering vector curves, while dF, which can directly be evaluated from such experiments, providing a quantitative point of comparison between theoretical predictions and experiment (see above).

In addition to these overall questions, a number of more specific suggestions follow below.

Major:

p. 7, line 120 (Fig. S1B) The proteins do not appear particularly pure based on the presented SDS PAGE analysis. How pure is the protein estimated to be, and is the presence of the other bands expected to affect e.g. the data presented in Fig. 1?

We have quantified the purity of the constructs by densitometry of the Coomassie stained gels and included it in Figure 1—figure supplement 1A: in the case of WT and R>K, we achieve purity higher than 91%. Importantly, the observed LLPS behavior of the constructs is consistent with the simulation and in agreement with other studies on R>K substitutions (https://doi.org/10.1073/pnas.2000223117; https://doi.org/10.1016/j.molcel.2020.01.025; https://doi.org/10.1073/pnas.2200559119; https://doi.org/10.1016/j.jmb.2019.08.008). In the case of Y>A, we have obtained the least pure protein (~65%), and must note that the precipitates observed in the experiments of Figure 1C are only present at the protein concentrations that are 50-fold higher as compared to WT (45 mM and above). Therefore, at such high total protein concentration, we cannot exclude the possibility that there might be some contamination affecting the behavior of this construct.

p. 7 & 8, lines 138-159: Has the method and energy function used to calculate the interact potential been validated by comparison to experiments, including studying the effect of varying the solvent? I see the computed error bars are very small, but am more interested in the average error when comparing to experiments. The numbers in water appear different from those e.g. reported by Krainer et al (https://doi.org/10.1038/s41467-021-21181-9), though the latter are also not immediately compared to experiments. Thus, it would be useful to know how much to trust these numbers.

We thank the Reviewer for raising this important point. To the best of our knowledge, the absolute binding free energies between Y-Y, Y-R or Y-K sidechain analogs or complete amino acids have never been determined experimentally, preventing a direct validation of the computed values and/or an evaluation of the average error when comparing to experiments. On the other hand, we did compare our data against the PMF curves presented by Krainer et al. (https://doi.org/10.1038/s41467-021-21181-9) for R-Y and Y-Y and the general trends are largely similar. In particular, in both analyses the R-Y interaction is stronger than the Y-Y interaction across different conditions, except at zero salt in Krainer et al. where the two are similar. When it comes to exact quantitative differences between the studies, it should first be pointed out that Krainer et al. studied capped amino-acids, while we used amino-acid side-chain analogs. The difference in the observed binding strengths is in part certainly related to the contribution of the capped backbone. Second, the values in Krainer et al. refer to the depth of the free energy minimum in the obtained PMFs and not to the resulting G values, as in our method. The latter includes integration over the PMF and an assumption of a standard-state concentration, which could also lead to significant differences. Finally, the differences could also be due to the intrinsic properties of the interaction potentials used. In particular, the prominent free-energy minima for the R-Y pair in the Krainer et al. study could only be obtained after refitting of the original AMBERff03ws charges on the Y bound to R via semi-empirical quantum-chemical calculations. On the other hand, the interaction potential used in our study was not adjusted to the system at hand, but rather comes from a published, widely used force field, the OPLS-AA (https://doi.org/10.1021/ja9621760), that was independently tested and validated experimentally in multiple studies. For example, OPLS-AA exhibits the low average error in absolute hydration free energy of ~0.5 kcal/mol, errors of only ~2% for heats of vaporization and densities (https://doi.org/10.1021/ja9621760), and a close agreement with osmotic coefficients (https://doi.org/10.1021/acs.jcim.9b00552) or a large range of organic compounds. This raises our confidence in the accuracy of the derived binding free energies, which directly or indirectly depend on these fundamental thermodynamic properties.

Regarding the method to evaluate PMF profiles, we have used a classical all-atom Monte Carlo approach originally developed by Jorgensen and coworkers (see, e.g., https://doi.org/10.1021/ar00161a004 and https://doi.org/10.1021/ja00168a022), as implemented in the widely used BOSS program (v. 4.8) (https://doi.org/10.1002/jcc.20297). This approach has been extensively tested against experimental data on ΔΔG values of various compounds in environments of different polarity (e.g., 2). Moreover, we have previously successfully applied this methodology in studies of the free energy of association of amino acid residues (https://doi.org/10.1021/jp803640e) and other biologically important groups (https://doi.org/10.1021/acs.jcim.9b00193). The results obtained have been compared with the available experimental data and demonstrated a good agreement. As for the small error bars in the plots, the fairly good convergence achieved in our PMF calculations is a result of extensive sampling combined with small system size, although obviously this is not always the case – see, for example, PMFs in our recent work (https://doi.org/10.1021/acs.jcim.9b00193).

The above points have been discussed on pp 7-8 of the revised manuscript.

p. 8, lines 149-154: Following up on the above, the authors also write "Importantly, only in the latter case are the R-Y interactions slightly more favorable than the K-Y ones (Figure S1C). While this can potentially contribute to increasing of Csat for the R>K mutant as compared to WT, the estimated thermodynamic effect is not too strong, especially if one considers that these interactions take place in an environment with largely water-like polarity. Therefore, the effect of R>K substitution on LLPS should be further explored in the context of protein-protein interactions." In the absence of estimates of the accuracy of the predictions, these sentences are somewhat unclear. Also, it is unclear what the authors mean by that the effect of R>K should be studied; there are already several examples of this (https://doi.org/10.1016/j.cell.2018.06.006 [already cited], https://doi.org/10.1038/s41557-021-00840-w & https://doi.org/10.1073/pnas.2000223117 come to mind, but there are likely more).

As pointed above, the free-energy values were obtained using well-established computational techniques and are expected to reflect realistic trends. However, considering that there exist no equivalent experimental results to assess the accuracy of the predicted free energies, they indeed must clearly be understood as predictions. This is now stated on pp. 7-8 of the revised manuscript. Furthermore, it seems that the vague phrasing on our part in the above paragraph resulted in a misunderstanding. Namely, when we talk about “further exploration”, we only meant it in relation to our study, i.e. a connection with the MD part, and not in relation to a wider literature on the topic. In other words, we simply wanted to refer to the fact that our binding free energies for individual residues do not provide sufficient information about interactions between Lge11-80 protein chains. Following the Reviewer’s comment, we have rephrased this part and included additional references on the known role of R and K residues on phase separation.

p. 8, lines 161-162: The authors perform MD simulations of Lge1 and variants using 24 copies and a box that gives them protein concentrations "in the mM concentration range". I realize that there's a concern about what is computationally feasible, but it would be important with an argument for this choice. Why is 24 expected to be enough to represent a condensate (I expect that there could be substantial finite-size effects)? What is the exact protein concentration in the simulations of the 24 chains [and of the 1-chain simulations]? How does this protein concentration compare to that in the condensates? The authors performed simulations in the NPT ensemble; how stable were the box dimensions?

The effective protein concentration for different 24-copy systems is 6-7 mM, depending on the system (Figure 2—figure supplement 1A). This concentration range was selected in order to get a reasonable system size for microsecond all-atom MD simulations, while still being approximately one order of magnitude lower than the semi-dilute regime of the protein at hand. As a testament to the internal consistency of our framework, the fractal model predicts the concentration inside WT condensates of the size observed in the experiment to indeed be in the mM range. Moreover, as seen in many other systems, the concentration inside the observed droplets is expected to be significantly higher than Csat (https://doi.org/10.1101/2020.10.25.352823). Here, we should again emphasize that we did not aim to model the process of phase separation in our all-atom MD. We rather use multicopy simulations for the analyses of the organization of the protein crowded phase and specifically, the mode of intermolecular interactions, and then use the fractal scaling to derive a model of the internal organization of condensates at arbitrary scales.

Regarding the experimental determination of the protein concentration in the condensates, we have used different approaches to estimate Csat and CD values: spin-down analyses (https://doi.org/10.1126/science.aaw8653), volumetry analysis (https://doi.org/10.1038/nchem.2803), estimation of concentration by fluorescent intensity of the condensates (https://doi.org/10.1016/j.molcel.2018.12.007; https://doi.org/10.1016/j.cell.2019.08.008), FCS (https://doi.org/10.1038/nchem.2803; https://doi.org/10.1016/j.cell.2019.10.011; https://doi.org/10.1126/science.aaw8653). However, different approaches yield values that vary by several orders of magnitude. That is the reason why we did not report definitive numbers. In general, there are uncertainties in the field about how to reliably measure protein concentrations in a condensate, necessitating the development of new approaches (https://doi.org/10.1101/2020.10.25.352823).

With regard to the convergence and potential finite-size effects, we agree that this is an important issue and have addressed it in the revised version. In general, the convergence of our observables such as valency or compactness (Figure 2C, D and Figure 4—figure supplement 1C) gives confidence that the simulations are at least in a local equilibrium, especially when it comes to short-range properties such as contact preferences as further elaborated in our reply to the Reviewer’s specific comment about convergence below (please, see also above for our response to Editor’s comment #5). Importantly, in all 24-copy systems, the average separation between protein images lies in the 12-15 nm range, and no instances of self-interaction between images due to PBC were observed (Supplementary File 1). Finally, analysis of fluctuations in box dimensions shows that they are all in the range of picometers and largely negligible when it comes to the analysis at hand (Supplementary File 1).

In order to highlight the realistic behavior of the simulated systems in the revised version, we now also report a detailed analysis of protein translational diffusion in MD simulations (Figure 4C and Figure 4—figure supplement 1D-F and Supplementary File 2). According to this analysis, single-molecule translational diffusion coefficients of Lge11-80 variants obtained from fitting of MSD curves with applied finite-size PBC correction and rescaling by the solvent viscosity (see Methods for details) are typically in the range of 100 µm2/s (Figure 4C and Supplementary File 2), which corresponds to experimentally measured values for different proteins of similar size. Importantly, the requisite finite-size corrections applied in the case of 24-copy systems are relatively small and amount to about 35-60%, while this is almost an order of magnitude higher (450-530%) for the single-copy simulations (Supplementary File 2). Please, see also the reply above to the Editor’s statements above for more details.

Also, did the authors include the Strep- and His-tags in the simulations? If not, why not?

We did not simulate the constant part of the constructs in order to: 1. expedite computation and 2. more directly expose the effect of different mutations. Since our comparison between simulation and experiment concerned largely qualitative observables, we have primarily focused on the relative differences between the three Lge11-80 variants. Importantly, the effect of mutations on the full-length protein and its different variants was analyzed in vivo in a previous publication (https://doi.org/10.1038/s41586-020-2097-z).

Throughout: One of my major concerns about this work is the general lack of analysis of convergence of the simulations. The authors must present some solid analysis of which results are robust given the relatively short simulations and potential for bias from the chosen starting structures.

First, we would like to emphasize that we did not attempt to capture the process of phase separation or characterize two coexisting phases, for which much larger ensembles and/or simulation times would be needed. Rather, our aim was to study the conformational behavior of individual protein chains in the context of a crowded protein mixture, taken as a model for the dense phase, and then use fractal scaling to provide a model of spatial organization of a condensate at an arbitrary length scale. Having said this, it is absolutely important to address how converged the key observables are, given the finite size of the all-atom simulation setup and the limited sampling used. In the revised manuscript, we have included an additional analysis of convergence of our simulations and could show that both key MD-derived parameters required by the fractal model, protein compactness and valency, display convergent behavior over the last third of 0.3 µs MD in the 24-copy systems (Figure 4—figure supplement 1C) and all analyses were performed over this region. In particular, the block averages of compactness and valency exhibit a standard deviation of only 2-4% and 4-8%, respectively, over the last 0.3 µs of MD simulations. Moreover, since we are interested in single-chain features in the context of a crowded mixture, our sampling corresponds effectively to 24 x 0.3 µs = 7.2 µs. Finally, a detailed analysis of convergence in conformational sampling was performed for single-copy simulations using calculations of configurational entropy as evaluated by the MIST formalism (Figure 4—figure supplement 1B). For instance, in the case of the weakly self-interacting Y>A, we do observe a close convergence in terms of the configurational entropy between two independent replicas on 1 µs MD trajectory (Figure 4—figure supplement 1B). However, we still recognize the possibility that with longer simulation times and/or more protein copies per simulation, the simulated systems may show a qualitatively different behavior, as discussed on pp. 10, 11, and 13 of the revised manuscript. Finally, we would like to reiterate the point that our derivation of the formalism that links the features of simulated ensembles on the scale of 10s of nanometers with their behavior on the scale of 100 s of nanometers and beyond is independent of such limitations. Once longer, larger and more accurate simulations become available, one will be able to apply the formalism without alteration and obtain a model of the spatial organization of the condensate on an arbitrary scale, starting just from the local features of individual proteins. We now discuss these details on pp. 10, 11, and 13 of the revised manuscript.

As an example, on p. 8 the authors discuss a potential asymmetry between the interactions found in the dilute (single-copy) and dense (24-mer) phases. These observations are somewhat in contrast to other observations in the field, namely that it is the same interactions that drive compaction of monomers as those that drive condensate formation.

Obviously, both the results in the literature and those presented here could be true. But in order to substantiate the statements made here, the authors should show some substantial statistical analyses to make it clear which differences are robust. The above holds for all parts of the computational/simulation work (e.g. other aspects of Fig. 2)

Note: this comment by the Reviewer echoes in several respects the comment 7 by the Editor. Because of this, our reply in some parts is identical to that given above to the Editor. We have decided to include it here for the ease of reading and completeness.

An expectation of the symmetry between intra- and intermolecular modes of interaction emerged from the background of polymer theory, which was primarily aimed to describe the behavior of homopolymers. In the case of heteropolymers such as proteins, the asymmetry in the aforementioned modes is rather intuitive. For instance, if there is only a single Y in a protein, then Y-Y contacts will not be possible in the intramolecular context, but could occur in multichain interactions. However, we agree with the Reviewer that this is an important issue and have deepened the analysis of this phenomenon in the revised manuscript.

First, our analysis shows that the observed asymmetry between intra- and intermolecular contexts is statistically significant and is likely not a consequence of limited sampling (pp. 10-11, Figure 3—figure supplement 1B-C). Moreover, the observed symmetry breaking is in line with the recent studies by Bremer et al. (https://doi.org/10.1038/s41557-021-00840-w) and Martin et al. (https://doi.org/10.1126/science.aaw8653), which have delineated the key requirements for the symmetry between single-chain and collective phase behavior to hold. Specifically, we have compared in detail the sequence composition of Lge11-80 with that of A1-LCD variants studied by Bremer et al. When it comes to aromatic composition, Lge1 is most similar to the -12F+12Y mutant of A1-LCD, and by this token, i.e. the high frequency of stickers tyrosines, should exhibit a strong coupling between single-chain and phase behavior. However, the net charge per residue (NCPR) in Lge11-80 of 0.075 is greater than that of A1-LCD (0.059) and this could contribute to the extent of decoupling, as suggested by Bremer et al. Moreover, Lge1 is extremely abundant in Arg (13.5 % as compared to 7.4 % in A1-LCD), and is in this sense most similar to the +7R A1-LCD mutant, which showed the greatest degree of decoupling between single-chain and phase behavior in Bremer et al., in agreement with what we see here. While these authors have demonstrated that NCPR is the primary determinant of decoupling in the case of A1-LCD mutants, their analysis showed that the nature of positive and negative residues involved also makes a significant difference. In particular, the significant excess of Arg residues, as context-dependent auxiliary stickers, could create the asymmetry between interactions that determine single-chain dimensions vs. collective phase behavior.

Furthermore, Martin et al. (https://doi.org/10.1126/science.aaw8653) have shown that an approximately uniform distribution of stickers along the sequence is required for the correspondence between the driving forces behind coil-to-globule transitions and phase separation to hold. We have analyzed the patterning of Tyr residues along the Lge11-80 sequence using Waro parameter used by Martin et al. (note that Tyr is the only aromatic in the Lge11-80 sequence). Interestingly, Lge11-80 exhibits a highly non-uniform patterning of Tyr residues, with Waro of the native Lge1 sequence (0.47) falling in the middle of the distribution for its shuffled variants (p=0.57). This is in contrast to the highly patterned sequences such as that of A1-LCD with p>0.99. Taken together, in addition to the relatively high NCPR, symmetry breaking in the case of Lge11-80 could be a consequence of its complex sequence composition, including both the non-uniform patterning of tyrosines and a high abundance of arginines. Provided that our simulations are long enough to provide an equilibrium picture and are on the length-scale of a single protein not strongly influenced by finite-size effects (these potential artifacts cannot be discounted), they actually can be seen as a demonstration of such symmetry breaking in a heteropolymer.

Furthermore, analysis of pairwise contacts suggests that intra- and intermolecular interactions rely on a similar pool of contacts by amino-acid type, but differ significantly if one analyzes specific sequence location of the interacting residues involved (Figure 2—figure supplement 1B and C). For example, one observes a high correlation between the frequencies of different contacts by amino-acid type when comparing intramolecular contacts in single-copy simulations and intermolecular contacts in 24-copy simulations (Figure 3—figure supplement 1B). This correlation is completely lost (Figure 3—figure supplement 1C) if one analyzes position-resolved statistics (2D pairwise contacts maps) or statistically defined interaction modes (Figure 3A, and Figure 3—figure supplement 1A). For example, although Tyr-Tyr interactions dominate in both cases, in single-copy simulations of WT Lge11-80 the C-terminal Tyr80 barely participates in any intramolecular interactions with other residues (Figure 3—figure supplement 1A), while in 24-copy simulations it is one of the most intermolecularly interactive residues (Figure 3). In other words, while the symmetry between intra- and intermolecular interactions can be observed at the level of pairwise contact types (similar type contact used for both), the distribution of these contacts along the peptide sequence is clearly different in the two cases. Finally, it should be mentioned that the parallel between single-copy and phase behavior in both homopolymers and heteropolymers is observed primarily at the level of thermodynamic variables such as LLPS critical temperature (Tc), coil-to-globule transition temperature (Tq) or the Boyle temperature (TB). It is possible that the noted correspondence extends primarily to such and similar thermodynamic variables, while and more structural, topological features of the globule in the single-molecule case and the network in the collective phase case remain uncoupled.

Interestingly, the core of intramolecular interactions observed for a single molecule at infinite dilution and in the crowded context remain approximately the same as reflected in the high correlation between intramolecular modes obtained in single and multichain simulations. Namely, proteins keep core self-contacts and establish new ones with neighbors, but do not donate everything to the intermolecular network losing “self-identity”, as in homopolymer melts. Similar effects have also been observed elsewhere: https://doi.org/10.1073/pnas.2000223117, https://doi.org/10.1073/pnas.1804177115.

Similarly, how were the errors of the radius of gyration for WT, R>K and Y>A mutants calculated? Is the Rg for WT significantly smaller than the values for the two mutants? And are the differences in Rg between single-copy and multi-copy simulations statistically significant? I am asking since converging the Rg of IDPs of this length in all-atom MD is not easy.

The errors for Rg values correspond to the standard deviations of the underlying distributions and are reported in Figure 4A and B, together with the corresponding means and an assessment of statistical significance of the difference. In particular, the character of the distributions (especially, for 24-copy systems) also suggests significant differences. In order to deepen this part in the revised version, we have added a new supplementary table (Supplementary File 1 “Technical summary) where we have included the average values of Rg together with the standard deviations for all modeled systems. Due to distributions being non-Gaussian, we have estimated the significance of the differences in Rgs between single-copy and multicopy simulations, as well as WT and mutants, using Wilcoxon rank sum test with continuity correction, with the resulting p-values < 2.2 10-16 for all cases.

p. 12, line 251: Has the MIST formalism been validated for IDPs; if so please provide a reference.

In the present work, we have evaluated the configurational entropy using a mutual information expansion approach with maximum-spanning-tree (MIST) approximation in internal-coordinate (bond-angle-torsion) representation. The latter is particularly well-suited for the analysis of IDPs as it allows one to avoid a number of artifacts (e.g., due to fitting of disordered ensembles to the average structure) associated with the more widely-used Cartesian-coordinate-based quasi-harmonic approaches. In particular, the MIST approach was used previously for the analysis of disordered protein ensembles (https://doi.org/10.1021/acs.jctc.8b00100). Here it should also be noted that, since intramolecular couplings are in general lower in IDPs, this makes them even better suited for MIST as compared to globular proteins. We have highlighted these points on p. 13 of the revision.

p. 5, line 105, p. 16 line 334 and p. 18 line 283: It is not completely clear what the predictions are and what/which experiments they are compared to. On p. 16, exactly what does the analytical model predict? As far as I understand, the results from the MD simulations are input to the model, but I am probably missing something. Which concrete and testable predictions does the model enable?

A key contribution of the present work is the development of a quantitative model that treats the spatial organization of a biomolecular condensate across scales using two key properties of individual polymer chains in the condensate - their average valency and compactness. The main predictions of the model concern the presence of a particular scaling of condensate mass with its radius, M(R), as captured by the fractal dimension, and the consequences this has on condensate morphology across scales. In the present manuscript, we have taken the first steps in testing these predictions in four different contexts. First, we could show that the MD simulations indeed match the predictions of fractal scaling for the three smallest clusters, which relates to the discussion on p. 16 that the Reviewer refers to. Here, it is important to understand that MD simulations in the first instance just give the average valency and compactness of individual chains in the dense phase. These values are then input into the fractal scaling formalism, which is conceptually fully independent from MD simulations, to obtain the dependence of condensate mass on its radius, M(R), at any desired length scale. The analysis presented in Figure 5—figure supplement 1B and discussed on p. 16 shows that the predictions of fractal scaling for the first three smallest clusters indeed correspond to what is seen in MD. This is a non-trivial correspondence and can be taken as direct evidence that fractal organization is present even at the shortest scale, i.e. at the level of MD simulation boxes.

Second, the model was used to reconstruct the spatial organization of clusters of arbitrary size at the atomistic level (Figure 5A and B, Videos 4, 5, and 6), enabling a structural understanding of the organization of condensate interior. One direct practical application of such understanding concerns the nature of cavity sizes and interpretation of dextran partitioning experiments (p. 20). Moreover, as pointed above, differences in morphology of protein clusters propagate across scales, and can be qualitatively characterized by the analysis of microscopic images (see also discussion above). In particular, the model correctly predicts the difference in the behavior of WT and R>K as opposed to Y>A variants, solely based on the predicted fractal dimension they exhibit. Ultimately, however, static light scattering experiments would give the best possibility to test the model directly and will be the topic of our future work. In particular, the fractal formalism predicts significant regions of linear behavior in such curves in log-log representation, while the fractal dimension df, provides a quantitative point of comparison between theoretical predictions and experimental measurements (Figure 5C). These points have been further discussed on p. 21 of the revised manuscript.

p. 19, lines 408-411: The authors find that when building clusters of Y>A from the simulations they find filamentous structures that they suggest explain the aggregation of the Y>A variant at high concentrations. While that sounds like an intriguing suggestion, it would be useful with a bit more detail about the robustness of this observation. For example, the simulations of Y>A appear similar to that of R>K; are the differences in topology really significantly different?

Fractal dimension, dF, is the key parameter that defines self-similar organization of differently sized protein clusters according to the fractal model. Consequently, the difference in morphology between R>K and Y>A mutants is reflected in different values of dF for the two. In particular, with a dF of 1.63, the Y>A mutant is predicted to form low-dimensional clusters, straddling the range between a linear (1-dimensional) and a planar (2-dimensional object), unlike WT and R>K variants, which both exhibit dF values greater than 2. The qualitative behavior of the three variants, whereby WT and R>K result in spherical condensates and Y>A does not, is consistent with this. Notably, we have observed sporadic precipitates at high protein concentration in the Y>A mutant, which may be consistent with the predictions of the fractal model. However, the material properties and possible influence of sample impurities in the Y>A case at high concentrations remain unclear. Moreover, the sporadic nature of Y>A precipitates prevents an adequate statistical analysis. Hence, in the revised manuscript we refrain from commenting on these infrequently observed precipitates.

Regarding MD simulations, the morphological differences between Y>A and R>K proteins can already be seen at the level of individual proteins in multicopy simulations, highlighted by the significantly different distribution of Rg (Figure 4B). This distribution in the case of Y>A has a prominently long tail, which indicates the possibility of adopting significantly more elongated configurations. Due to the self-similarity principle, such differences in morphology may propagate across length scales. Importantly, a recent publication included the experimental study of the possibility of IDRs to form low dimensional fractal systems upon disruption of the LLPS tendency by polyalanine insertion in synthetic elastin-like polypeptides (Roberts et al., Nature Materials, 2018).

Finally, I would suggest that the authors make their code and data available in electronic format.

All sharable data has been made available as part of the article package. Due to the heterogeneous character of our analysis, we do not have a single master code to be shared, but rather a collection of different scripts in combination with different software packages as indicated in the Methods section of the manuscript (GROMACS, MATLAB, R, FracVAL).

Author Response

Reviewer #1 (Public Review):

This manuscript confirms previous studies suggesting a great deal of heterogeneity of gene expression at the neural plate border in early vertebrate embryos, as neural, placodal, neural crest, and epidermal lineages gradually segregate. Using scRNA-seq, the study expands previous studies by using far larger numbers of genes as evidence of this heterogeneity. The evidence for this heterogeneity and the change in heterogeneity over time is compelling.

Many studies have suggested that there is considerable heterogeneity of gene expression in the developing neural plate border as the neural, neural crest, placodal and epidermal lineages segregate. Although the evidence for such heterogeneity was strong, until the advent of scRNA-seq, the extent of this heterogeneity was not appreciated. By using scRNA-seq at different stages of chick development, the authors sought to characterize how this heterogeneity develops and resolves over time.

The work is technically sound, and the level of analysis of gene expression, clustering, synexpression groups, and dynamic changes in gene modules over time is state-of-the-art. A weakness of the results as they stand now is that the conclusions of the analysis are not tested by the authors and thus, are over-interpreted. Such tests could be performed in future studies either by gain- and loss-of-function experiments or by using lineage tracing to demonstrate that the cell states the authors observe - especially the "unstable progenitors" they characterize - are biologically meaningful. The data will nevertheless be a useful resource to investigators interested in understanding the development of different cell lineages at the neural plate border.

We thank the reviewer for the positive assessment of our work. We agree that our models will need to be tested experimentally in the future, however, this will require a substantial amount of work. We therefore opted to share our data as a resource to be used by the community.

Reviewer #2 (Public Review):

The study of Thiery et al. aims to elucidate how cells undergo fate decisions between neural crest and (pan-) placodal cells at the neural plate border (NPB). While several previous single-cell RNA-Seq studies in vertebrates have included neural plate border cells (e.g. Briggs et al., 2018; Wagner et al., 2018; Williams et al., 2022), these previous studies did not provide conclusive insights on cell fate decisions between neural crest and placodes, due to either the limited number of genes recovered, the limited number of cells sampled or the limited numbers of stages included. The present study overcomes these limitations by analyzing almost 18,000 cells at six stages of development ranging from gastrulation until after neural tube closure (8 somite-stage), with an average depth of almost 4000 genes/cell. Using this extensive and high-quality data set, the study first describes the timing of segregation of neural crest and placodal lineages at the NPB suggesting that at late neural fold stages (somite stage 4) most cells have decided between placodal and neural crest fates. It then identifies gene modules specific for neural crest and placodal lineages and characterizes their temporal and spatial expression. Focusing on an NPB-specific subset of cells, the study then shows that initially most of these cells co-express neural crest and placodal gene modules suggesting that these are undecided cells, which they term "border-located unstable progenitors" (BLUPs). The proportion of BLUPs decreases over time, while cells classified as placodal or neural crest cells increases, with few BLUPs remaining at late neural fold stages (and a few scattered BLUPs even at somite stage 8). Based on these findings, the authors propose a new model of cell fate decisions at the NPB (termed the "gradient border model"), according to which the NPB is not defined by a specific transcriptional state but is rather a region of undecided cells, which diminishes in size between gastrulation and neural fold stages due to more and more cells committing to a placodal or neural crest fate based on their mediolateral position (with medial cells becoming specified as neural crest and lateral cells as placodal cells).

The study of Thiery et al. provides an unprecedentedly detailed, methodologically careful, and well-argued analysis of cell fate decisions at the NPB. It provides novel insights into this process by clearly demonstrating that the NPB is an area of indecision, in which cells initially co-express gene modules for ectodermal fates (neural crest and placodes), which subsequently become segregated into mutually exclusive cell populations. The paper is very well written and largely succeeds in presenting the very complex strategy of data analysis in a clear way. By addressing the earliest cell fate decisions in the ectoderm and one of the earliest cell fate decisions in the developing vertebrate embryo, this study will have a significant impact and be of interest to a wide audience of developmental biologists. There are, two conceptual issues raised in the paper that require further discussion.

We thank the reviewer for the positive comments on our work and its significance; we have addressed the conceptual issues below and in the revised version of the manuscript.

First, the authors suggest that their data resolve a conflict between two previously proposed models, the "binary competence model" and the "neural plate border model". The authors correctly describe, that the binary competence model proposed by Ahrens and Schlosser (2005) and Schlosser (2006) suggests that the ectoderm is first divided into two territories (neural and non-neural), which differ in competence, with the neural territory subsequently giving rise to the neural plate and neural crest and the non-neural territory giving rise to placodes and epidermis (sequence of cell-fate decisions: ([neural or neural crest]-[epidermal or placodal]). This model was proposed as an alternative to a "neural plate border state model", which instead suggests that initially the NPB is induced as a territory characterized by a specific transcriptional state, from which then neural crest and placodes are induced by different signals (sequence of cell fate decisions: neural-[placodal or neural crest]-epidermal) (see Schlosser, 2006, 2014). Instead in this paper, the authors contrast the binary competence model with a model they call the "neural plate border" model according to which the NPB can give rise to all four ectodermal fates with equal probability. However, I think this misses the main point of contention since all previously proposed models are in agreement that initially the neural plate border region is unspecified and can give rise to all four fates and that lineage restrictions only appear over time. "Binary competence" and "Neural plate border state" model, differ, however, in their predictions about the sequence, in which these fate restrictions occur.

We appreciate the reviewer's thoughtful feedback, but respectfully disagree with their comment regarding the sequence of events predicted by the neural plate border (NPB) model. While the NPB model does suggest that the NPB is a transcriptionally distinct state, it does not make specific predictions about the sequence of fate decisions. Although several papers cited in the Schlosser 2006 and 2014 reviews suggest that the NPB gives rise to all four ectodermal fates, none of them (and, to the best of our knowledge, no other primary paper referring to the NPB model) specifically defines the sequence of fate specification from the NPB.

The key points of the NPB model are that the NPB is defined by overlapping expression of early neural/non-neural markers (which is also observed in Xenopus – see Pieper et al., 2012 supplementary material), contains progenitors for all four ectodermal fates, and that this "state" exists prior to the emergence of definitive neural crest and placodal cells.

To investigate the heterogeneity in the order of cell fate decisions at the NPB, we carried out additional pairwise co-expression analyses of forebrain, mid-hindbrain, neural crest, and placodal gene modules, which reveals multiple different hierarchies of cell fate choice depending on a cell's axial positioning, as shown in Figure 6-figure supplement 1.

Considering these findings, we have expanded our discussion of the previously proposed binary competence and neural plate border models to highlight how neither of these models is sufficient to fully characterize the heterogeneity in cell fate decisions observed in our study. We hope this clarification will help address any concerns the reviewer may have had about the NPB model and its implications for our results.

Second, the authors should be more careful when relating their data to the specification or commitment of cells. Questions of specification and commitment can only be tested by experimental manipulation and cannot be inferred from a transcriptome analysis of normal development. So the conclusion that the activation of placodal, neural and neural crest-specific modules in that sequence suggests a sequence of specification in the same temporal order (lines 706-709) is not justified. Studies from the authors' own lab previously showed that epiblast cells from pre-gastrula stages are specified to express a large number of NPB border markers including neural crest and panplacodal markers, when cultured in vitro (Trevers et al., 2018; see also Basch et al., 2006 for early specification of the neural crest), which is not easily reconciled with this interpretation. I am not aware of any experimental evidence that shows that a panplacodal regulatory state is specified prior to neural crest in the chick (although I may have missed this). In Xenopus, experimental studies have shown instead that neural crest is specified and committed during late gastrulation, while the panplacodal states are specified much later, at neural fold stages (Mancilla and Mayor, 2006; Ahrens and Schlosser, 2005). It may well be the case that the relative timing of neural crest and panplacodal specification is different between species (and such easy dissociability may even be expected from the perspective of the binary competence model).

We very much agree with the reviewer that the definitions and correct terminology is important and apologise for lack of clarity. We have reworded the text carefully.

The reviewer is correct: specification of neural crest, placodes and neural plate is observed very early in chick, prior to gastrulation. However, in specification experiments tissue is removed from its normal environment to reveal what it does autonomously in the absence of additional signals. In the current study, we assess the activation of gene modules in normal development. We have therefore reworded the text to avoid ‘specification’ in this context.

Reviewer #3 (Public Review):

The goal of this work was to better understand how cell fate decisions at the neural plate border (NPB) occur. There are two prevailing models in the field for how neural, neural crest and placode fates emerge: (i) binary competence which suggests initial segregation of ectoderm into neural/neural crest versus placode/epidermis; (ii) neural plate border, where cells have mixed identity and retain the ability to generate all the ectodermal derivatives until after neurulation begins.

The authors use single-cell sequencing to define the development of the NPB at a transcriptional level and suggest that their cell classification identified increased ectodermal cell diversity over time and that as cells age their fate probabilities become transcriptionally similar to their terminal state. The observation of a placode module emerging before the neural and neural crest modules is somewhat consistent with the binary competence model but the observation of cells with potentially mixed identity at earlier stages is consistent with the neural plate border model.

Differences in the timing of analyses and techniques used can account for the generation of these two original models, and in essence, the authors have found some evidence for both models, possibly due to the period over which they performed their studies. However, the authors propose recognizing the neural plate border as an anatomical structure, containing transcriptionally unstable progenitors and that a gradient border model defines cell fate choice in concert with spatiotemporal positioning.

The idea that the neural plate border is an anatomical structure is not new to most embryologists as this has been well-recognized in lineage tracing and transplantation assays in many different species over many decades.

We appreciate the reviewers comment and agree that the neural plate border has previously been characterised anatomically. However, many studies have applied the term literally in reference to a transcriptional state which is specified through the expression of ‘neural plate border specifiers’, prior to segregation of the placodes and neural crest. Here we highlight that treating the neural plate border as a definitive transcriptional state which can be identified through the expression of ‘neural plate border specifiers’ is false. Instead, we find these ‘specifiers’ are upregulated within either neural crest, placodal or neural cell lineages over time. Cells at the neural plate border co-express these alternate lineage markers and therefore predicted to be undecided.

The authors don't provide molecular evidence for transcriptional instability in any cells. It's a molecular term and phenomenon inaccurately applied to these cells that are simply bipotential progenitors.

We thank the reviewer for pointing this out; we have therefore refrained from using the term unstable and instead refer to the cells as ‘undecided’ as suggested by reviewer 2.

Lastly, there's no evidence of a gradient that fits the proper biochemical or molecular definition. Graded or sequential are more appropriate terms that reflect the lineage determination or segregation events the authors characterize, but there's no data provided to support a true role for a gradient such as that achieved by a concentration or time-dependent morphogen.

We agree with the reviewer that ‘gradient’ was misleading. We have now replaced ‘gradient’ with ‘graded’ and expanded figure 6 to highlight the graded co-expression of gene modules associated with alternate fates. We have changed the title to reflect this.

A limitation of the study is that much of it reads like a proof-of-principle because validation comes primarily from known genes, their expression patterns in vivo, and their subsequent in vivo functions. Thus, the authors need to qualify their interpretations and conclusions and provide caveats throughout the manuscript to reflect the fact that no functional testing was performed on any novel genes in the emerging modules classified as placode versus neural or neural crest.

We agree with the reviewer that we do not provide any functional data to validate our predictions; it is for this reason that we submitted the manuscript as a ‘resource’ to make our data available to the community.

Lastly, a limitation of gene expression studies is that it provides snapshots of cells in time, and while implying they have broad potential or are lineage fated, do not actually test and confirm their ultimate fate. Therefore, in parallel with their studies, the authors really need to consider, the wealth of lineage tracing data, especially single-cell lineage tracing, which has been performed using the embryos of the same stage as that sequenced in this study, and which has revealed critical data about the potential cells through when and where lineage segregation and cell fate determination occurs.

The reviewer rightly points out the significance of the classical experiments in the context of the neural plate border. However, only one of the mentioned studies (Bronner-Fraser and Fraser, 1989), analyses cells at a single-cell level and does not assess placodes, while the remaining studies use tissue transplantation or cell population labelling. Although these studies provide valuable insights, they do not examine the fate or potential of single cells, nor do they reveal the transcriptional signature of these progenitors.

Our findings emphasize the transcriptional heterogeneity at the neural plate border, suggesting that distinct subsets of neural plate border progenitors undergo varying sequences of fate restrictions. The upcoming challenge will be to conduct clonal analysis alongside scRNAseq to determine if neural plate border progenitors with similar transcriptional signatures experience the same fate restrictions or if external factors, such as cell-cell signalling, dictate cell fate choices.

We have amended the manuscript to clarify that predictions of fate decisions require future validation through lineage tracing. Additionally, we have acknowledged in the introduction that previous studies have demonstrated the intermingling of neural, neural crest, and placodal progenitors at the neural plate border.

Author Respone

Reviewer #1 (Public Review):

This article describes the application of a computational model, previously published in 2021 in Neuron, to an empirical dataset from monkeys, previously published in 2018 in eLife. The 2021 modeling paper argued that the model can be used to determine whether a particular task depends on the perirhinal cortex as opposed to being soluble using ventral visual stream structures alone. The 2018 empirical paper used a series of visual discrimination tasks in monkeys that were designed to contain high levels of 'feature ambiguity' (in which the stimuli that must be discriminated share a large proportion of overlapping features), and yet animals with rhinal cortex lesions were unimpaired, leading the authors to conclude that perirhinal cortex is not involved in the visual perception of objects. The present article revisits and revises that conclusion: when the 2018 tasks are run through the 2021 computational model, the model suggests that they should not depend on perirhinal cortex function after all, because the model of VVS function achieves the same levels of performance as both controls and PRC-lesioned animals from the 2018 paper. This leads the authors of the present study to conclude that the 2018 data are simply "non-diagnostic" in terms of the involvement of the perirhinal cortex in object perception.

We appreciate the Reviewer’s careful reading and synthesis of the background and general findings of this manuscript.

The authors have successfully applied the computational tool from 2021 to empirical data, in exactly the way the tool was designed to be used. To the extent that the model can be accepted as a veridical proxy for primate VVS function, its conclusions can be trusted and this study provides a useful piece of information in the interpretation of often contradictory literature. However, I found the contribution to be rather modest. The results of this computational study pertain to only a single empirical study from the literature on perirhinal function (Eldridge et al, 2018). Thus, it cannot be argued that by reinterpreting this study, the current contribution resolves all controversy or even most of the controversy in the foregoing literature. The Bonnen et al. 2021 paper provided a potentially useful computational tool for evaluating the empirical literature, but using that tool to evaluate (and ultimately rule out as non-diagnostic) a single study does not seem to warrant an entire manuscript: I would expect to see a reevaluation of a much larger sample of data in order to make a significant contribution to the literature, above and beyond the paper already published in 2021. In addition, the manuscript in its current form leaves the motivations for some analyses under-specified and the methods occasionally obscure.

We believe that our comments outline our rationale for focusing our current analysis on data from Eldridge et al. In brief, these data provide compelling evidence against PRC involvement in perception, and are the only such data with PRC-lesioned/-intact macaques that we were able to secure the stimuli for. As such, data from Eldridge et al. provide a singular opportunity to address discrepancies between human and macaque lesion data. For this reason, we propose the current work as a Research Advance Article type, building off of a manuscript that was previously published in eLife.

Reviewer #2 (Public Review):

The goal of this paper is to use a model-based approach, developed by one of the authors and colleagues in 2021, to critically re-evaluate the claims made in a prior paper from 2018, written by the other author of this paper (and colleagues), concerning the role of perirhinal cortex in visual perception. The prior paper compared monkeys with and without lesions to the perirhinal cortex and found that their performance was indistinguishable on a difficult perceptual task (categorizing dog-cat morphs as dogs or cats). Because the performance was the same, the conclusion was that the perirhinal cortex is not needed for this task, and probably not needed for perception in general, since this task was chosen specifically to be a task that the perirhinal cortex might be important for. Well, the current work argues that in fact the task and stimuli were poorly chosen since the task can be accomplished by a model of the ventral visual cortex. More generally, the authors start with the logic that the perirhinal cortex gets input from the ventral visual processing stream and that if a task can be performed by the ventral visual processing stream alone, then the perirhinal cortex will add no benefit to that task. Hence to determine whether the perirhinal cortex plays a role in perception, one needs a task (and stimulus set) that cannot be done by the ventral visual cortex alone (or cannot be done at the level of monkeys or humans).

There are two important questions the authors then address. First, can their model of the ventral visual cortex perform as well as macaques (with no lesion) on this task? The answer is yes, based on the analysis of this paper. The second question is, are there any tasks that humans or monkeys can perform better than their ventral visual model? If not, then maybe the ventral visual model (and biological ventral visual processing stream) is sufficient for all recognition. The answer here too is yes, there are some tasks humans can perform better than the model. These then would be good tasks to test with a lesion approach to the perirhinal cortex. It is worth noting, though, that none of the analyses showing that humans can outperform the ventral visual model are included in this paper - the papers which showed this are cited but not discussed in detail.

Major strength:

The computational and conceptual frameworks are very valuable. The authors make a compelling case that when patients (or animals) with perirhinal lesions perform equally to those without lesions, the interpretation is ambiguous: it could be that the perirhinal cortex doesn't matter for perception in general, or it could be that it doesn't matter for this stimulus set. They now have a way to distinguish these two possibilities, at least insofar as one trusts their ventral visual model (a standard convolutional neural network). While of course, the model cannot be perfectly accurate, it is nonetheless helpful to have a concrete tool to make a first-pass reasonable guess at how to disambiguate results. Here, the authors offer a potential way forward by trying to identify the kinds of stimuli that will vs won't rely on processing beyond the ventral visual stream. The re-interpretation of the 2018 paper is pretty compelling.

We thank the Reviewer for the careful reading of our manuscript and for providing a fantistics synthesis of the current work.

Major weakness:

It is not clear that an off-the-shelf convolution neural network really is a great model of the ventral visual stream. Among other things, it lacks eccentricity-dependent scaling. It also lacks recurrence (as far as I could tell).

We agree with the Reviewer completely on this point: there is little reason to expect that off-the-shelf convolutional neural networks should predict neural responses from the ventral visual stream, for the reasons outlined above (no eccentricity-dependent scaling, no recurrence) as well as others (weight sharing is biologically implausible, as well as the data distributions and objective functions use to optimize these models). Perhaps surprisingly, these models do provide quantitatively accurate accounts of information processing throughout the VVS; while this is well established within the literature, we were careless to simply assert this as a given without providing an account of these data. We appreciate the Reviewer for making this clear and we have changed the manuscript in several critical ways in order to avoid making unsubstantiated claims in the current version. We hope that these changes also make it easier for the casual reader to appreciate the logic in our analyses. First, in the introduction, we outline some of the prior experimental work that demonstrates how deep learning models are effective proxies for neural responses throughout the VVS. We also demonstrate this model-neural fit in the current paper using electrophysiological recordings, but also including comments about the limitation of these models raised by the Reviewer.

In the introduction we also more clearly demarcate prior contributions from our recent computational work, and highlight how models approximate the performance supported by a linear readout of the VVS, but fail to reach human-level performance.

Results from these analyses were essential to understanding the logic of the paper but previously (as noted by the Reviewer) this critical evidence was cited but not directly presented. We include a description to these we describe these data in the introduction more thoroughly, and substantial change Figure 1, in order to visualize these data (b).

Moreover, we include a over of the methods and data used to generate these plots in the results and methods sections.

While there is little reason to expect that off-the-shelf convolutional neural networks should predict neural responses from the ventral visual stream, we believe that these modifications to the manuscript (to the introduction and figure one, as well as the results and methods sections) make clear that these models are nonetheless useful methods for predicting VVS responses and the behaviors that depend on the VVS.

To the authors' credit, they show detailed analysis on an image-by-image basis showing that in fine detail the model is not a good approximation of monkey choice behavior. This imposes limits on how much trust one should put in model performance as a predictor of whether the ventral visual cortex is sufficient to do a task or not. For example, suppose the authors had found that their model did more poorly than the monkeys (lesioned or not lesioned). According to their own logic, they would have, it seems, been led to the interpretation that some area outside of the ventral visual cortex (but not the perirhinal cortex) contributes to perception, when in fact it could have simply been that their model missed important aspects of ventral visual processing. That didn't happen in this paper, but it is a possible limitation of the method if one wanted to generalize it. There is work suggesting that recurrence in neural networks is essential for capturing the pattern of human behavior on some difficult perceptual judgments (e.g., Kietzmann et al 2019, PNAS). In other words, if the ventral model does not match human (or macaque) performance on some recognition task, it does not imply that an area outside the ventral stream is needed - it could just be that a better ventral model (eg with recurrence, or some other property not included in the model) is needed. This weakness pertains to the generalizability of the approach, not to the specific claims made in this paper, which appear sound.

We could not agree more with the Reviewer on these points. It could have been the case that these models' lack of correspondence with known biological properties (e.g. recurrence) led them to lack something important about VVS-supported performance, and that this would derail the entire modeling effort here. Surprisingly, this has not been the case, as is evident in the clear correspondence between model performance and monkey data in Eldridge et al. 2018. Nonetheless, we would expect that other experimental paradigms should be able to reveal these model failings. And future work evaluating PRC involvement in perception must contend with this very problem in order to move forward with this modeling framework. That is, it is of critical importance that these VVS models and the VVS itself exhibit similar failure modes, otherwise it is not possible to use these models to isolate behaviors that may depend on PRC.

A second issue is that the title of the paper, "Inconsistencies between human and macaque lesion data can be resolved with a stimulus-computable model of the ventral visual stream" does not seem to be supported by the paper. The paper challenges a conclusion about macaque lesion data. What inconsistency is reconciled, and how?

It appears that this point was lost in the original manuscript; we have tried to clarify this idea in both the abstract and the introduction. In summary, the cumulative evidence from the human lesion data suggest that PRC is involved in visual object perception, while there are still studies in the monkey literature that suggest otherwise (e.g. Eldridge et al. 2018). In this manuscript, we suggest that this apparent inconsistency is, in fact, simply a consequence of reliance on information interpretations of the monkey lesion data.

We have made substantive changes to the abstract so this is an obvious, central claim.

We have also made substantive changes to the introduction to make resolving this cross-species discrepancy a more central aim of the current manuscript.

Author Response

Reviewer #1 (Public Review):

This manuscript describes experiments that lead to a potentially impactful result and most of the data seem very nice. The authors conducted a mutant screen to find the gene BbCrpa from a fungus resistant to cyclosporine A (CsA). Microscopy indicates that the mode of action is likely sequestration of the toxin in vacuoles, mediated through the P4-ATPase pathway. They also show that expression of BbCrpa in Verticillium renders that fungus resistant to CsA. The paper then takes a very large jump across kingdoms and toxins and asks if BbCrpa, expressed in plants, will confer resistance to a different toxin (cinnamon acetate) that is produced by Verticillium. They conduct disease assays on Arabidopsis and cotton and show promising results, but these assays are less thoroughly completed. They provide microscopic evidence that the transgenics accumulate CIA in vacuoles, which is consistent with the mode of action of the other systems. Overall, my assessment of the paper is that the authors may have a nice story, but the transition to plants needs to be better described and potentially supported by additional experiments. For example, the authors seem to conclude that this resistance mechanism will be a very broad spectrum. Is there a second toxin-producing pathogen that could be used to assess whether this is true?

Thanks for your advice! To answer the question that "Is there a second toxin-producing pathogen that could be used to assess whether this is true?", we added the data of another t toxin-producing pathogens, Fusarium oxysporum and another V. dahliae race, L2-1, to support the conclusion that BbCrpa can confer resistance of plants against pathogens. As expected, the expression of BbCRPA in Arabidopsis and cotton could also significantly increase the resistance to the pathogens we tested. New data were shown in Figure 5-figure supplement 1G-J.

Reviewer #2 (Public Review):

The fungus B. bassiana is one of few fungal species resistant to cyclosporine A and tacrolimus, naturally occurring microbial compounds with antifungal and immunosuppressive properties. The authors studied the mechanism of this resistance and found a novel vesicle-mediated transport pathway that directs the compounds to vacuoles for degradation. This hitherto unknown mode of detoxification is initiated by the activity of a phospholipid flippase of the P4-ATPase type. Interestingly, transgenically expressing the fungal flippase in plant model systems induces a similar detoxification pathway and makes the plants resistant to certain fungal toxins of secondary metabolism.

Strengths

The genetic screening, isolation of cyclosporine A (CsA) resistant mutants, and characterization of the causative gene BbCrpa are very solid with two independent alleles, a synthetic knockout strain, and rescue of the mutant phenotype.

BbCrpa protein function in detoxification is demonstrated convincingly by expression in another CsA-sensitive strain, as is its reliance on sites conveying ATPase activity for proper function. It is also functionally different from a relatively closely related P4-ATPase from yeast.

Using fluorescently labeled CsA and tacrolimus (FK506), it is nicely demonstrated how the compounds are going through the anterograde pathway all the way to the vacuole.

The authors demonstrate that vacuolar targeting is key for the detoxifying function of BbCrpa and identify the targeting motif that contains a ubiquitination site.

A trans-species approach (actually, trans-kingdom) confers that BbCrpa can also enhance vacuolar targeting of small toxic compounds to vacuoles in plants, which is quite astounding, given that plant endomembrane transport has quite a number of differences from that of fungi.

Weaknesses

It is not clear at which temporal scale CsA is going through the different endosomal compartments.

Thanks for your comments! We agree your idea that it is better to provide indication of the temporal scale of CsA entering into different endosomal compartments. Actually, we had tried to trace the distribution of CsA in cells many times. Unfortunately, the fluorescence of 5-FAM is weak and decreases fast compared with eGFP or mRFP protein, which made us difficult to capture the transient localization and moving trace of CsA in the cells. Nevertheless, the trail of BbCrpa, which carried CsA from the vesicles to early/late endosome, and vacuoles, can reflect the pathway of the cargo (Figure 3K, Supplementary file 1).

Can it be ruled out that the fluorescently labeled CsA and the GFP-tagged BbCrpa are stripped off their label and we are seeing the free label only?

Thanks! We accept your comments. In order to rule out the interference from the cleaved fluorescent proteins (i.e., eGFP and mRFP) or chemical compound (i.e. 5-FAM), we took eGFP/mRFP and 5FAM as control. New data about the localization of eGFP/mRFP and 5-FAM in B. bassiana hypha were provided in the revised manuscript. Our observation indicated that the distribution of fluorescent materials alone is different with the labeled ones, confirming the bona fide localization of the fusion proteins or compound. Please see Figure 2-figure supplement 2.

Reviewer #3 (Public Review):

In this manuscript, the authors have attempted to determine the molecular mechanisms underlying the resistance of an insect fungal pathogen Bauveria barbicans to cyclosporine A (CsA) and tacrolimus (FK506), known antifungal secondary metabolites that are also used extensively as immunosuppressing agents in medicine. By screening the random insertion mutant library of this pathogen, they identified the gene responsible for conferring resistance to CsA and FK506. The amino acid sequence of the gene identified it to be P4-ATPase, designated BbCrpa, which was hypothesized to be involved in vesicle-mediated transport. The identity of this gene as a CsA resistance gene was confirmed by demonstrating that disruption of this gene in B. barbicans confers susceptibility to CsA and FK506 and the expression of the wild-type gene in the BbCRPA knockout strain restores resistance to these compounds. In addition, expression of this gene in a plant pathogen Verticillium dahliae confers resistance to CsA and FK506.

The authors hypothesized that CsA/FK506 detoxification in the resistant B. barbiana strain used is through the BbCRPA-mediated vesicle transport process transporting these toxic metabolites to vacuoles through trans-Golgi (TGN)-early endosome (EE)-late endosomes (LE) pathway. To test this hypothesis, they employed a dual labeling system using 5-carboxyfluorescein fluorescently labeled CsA and FK506 and fusions of red fluorescent proteins (RFP) with BbRab5 GTPase (a marker for early endosomes), BbRab7 GTPase (a marker for late endosome) and pleckstrin homology domain of human oxysterol binding protein (PHOSBP) (a marker for trans-Golgi). By looking at the distribution of fluorescein-labeled CsA and FK506 in the wild-type and ΔBbCRPA cells using confocal microscopy, the authors have provided compelling evidence that these metabolites are transported to the vacuole. The co-localization of CsA with endocytic marker proteins also appears to be convincing for the most part. The co-localization of CsA with mRFP:: PHOSBP as shown in Fig. 2D seems less compelling. Also, in the confocal micrographs presented in Fig. 2, the distinction between early and late endosomes seems less convincing. It seems that there is significant heterogeneity in the early endosome and late endosome populations in the fungal cells.

1) The co-localization of CsA with mRFP::PHOSBP as shown in Fig. 2D seems less compelling.

Thanks a lot! According to your suggestion, we repeated the observation and replaced the original figure with new one (Figure 2D). The new figures clear indicates the co-localization of CsA with mRFP::PHOSBP.

2) Also, in the confocal micrographs presented in Fig. 2, the distinction between early and late endosomes seems less convincing. It seems that there is significant heterogeneity in the early endosome and late endosome populations in the fungal cells.

Thanks! We agree with your comments! Rab5 is widely used as a marker for early endosomes, while Rab7 is used as a marker for late endosomes. Nevertheless, early endosome and late endosome are hardly to be distinguished strictly. According to your suggestion, we repeated the observation, and replaced the Figure 2F and 2L, and Figure 3E with new ones. Our results indicated that mRFP::BbRab5 appeared largely in the lumen of vacuoles and some punctaes (early endosomes), and mRFP::BbRab7 locates to vacuolar membrane and late endosomal compartments. These can be seen in our observations in Figure 3D and 3E, which are consistent with the observations in Fusarium graminearum described by Zheng et al. (Zheng et al., 2018, New Phytologist, 219: 654671, DOI: 10.1111/nph.15178).

The authors addressed the question of whether BbCrpa acts as a component involved in vesicle trafficking through the trans-Golgi-endosomes to vacuoles. Ten different eGFP-BbCrpa fusion proteins were constructed and shown to provide detoxification of CsA and FK506. The BbCrpa is localized to the apical plasma membrane and spitzenkorper region of the germ tube. The evidence for localization of BbCrpa in trans-Golgi and vacuole is clear. However, the experimental data shown in Fig. 3D-F claiming localization of BbCrpa in EEs and LEs are somewhat difficult for this reviewer to interpret. It is also not clear to this reviewer why the two FM4-64 staining patterns in Fig. 3C and Fig. 3F are strikingly different. The evidence for co-localization of the fluorescein-labeled CsA or FK506 with RFP-labeled BbCrpa in vacuoles (Fig.3 H and J) is convincing. Figs. 3L-M depicting dynamic trafficking of BbCrpa from TGN to vacuoles using timelapse microscopy is interesting. In Fig. 3M, eGFP should be labeled eGFP::Drs2p. The authors have identified the N-terminal vacuole targeting motif in BbCrpa and shown that the C-terminal sequence from aa1326 to aa1359 is important for detoxification of CsA and FK506 in B. barbiana. In particular, the importance of three Tyr residues located in the C-terminal domain of the enzyme for CsA resistance is interesting.

1) The experimental data shown in Fig. 3D-F claiming localization of BbCrpa in EEs and LEs are somewhat difficult for this reviewer to interpret.

Thanks for your comments! P4-ATPases are implicated in the initiation of vesicle biogenesis and moves along with the vesicle (Panatala et al., 2015, Journal of Cell Science, 128: 2021-2032, DOI: 10.1242/jcs.102715; van der Mark et a., 2013, International Journal of Molecular Sciences, 14, 7897-7922, DOI: 10.3390/ijms14047897). In this study, the crucial issue to be addressed was the journey of CsA to the vacuole, which might be through BbCrpa-mediated TGN-EE-LE vesicle transport pathway. Therefore, we observed the localization of BbCrpa in TGN, EEs and LEs. It has been reported that small GTPase Rab5 is localized to the early endosomes (Bucci et al., 1992, Cell, 70: 715-728, DOI: 10.1016/0092-8674(92)90306-w), while Rab7 is to the late endosomal compartment (Vitelli et al., 1997, The Journal of Biological Chemistry, 272: 4391-4397, DOI: 10.1074/jbc.272.7.4391). Thus, Rab5 and Rab7 are used as marker proteins to indicate EEs and LEs, respectively. Nevertheless, Rab5 and Rab7 could also be observed in MVB (multivesicular bodies) or vacuoles (Toshima J.Y.,et al., 2014, Bifurcation of the endocytic pathway into Rab5-dependent and -independent transport to the vacuole, Nature Communication, 5:3498, DOI: 10.1038/ncomms4498; Zheng et al., 2018, New Phytologist, 219: 654-671, DOI: 10.1111/nph.15178). According to your suggestion, we repeated our observation and replaced Figure 3E with new one. In Figure3D, we can see mRFP::BbRab5 appeases largely in the lumen of vacuoles and some punctaes (early endosomes), and in Figure 3E mRFP::BbRab7 locates to late endosomal compartments and vacuolar membrane, which are consistent with the observations in Fusarium graminearum described by Zheng et al.(Zheng et al., 2018, New Phytologist, 219: 654671, DOI: 10.1111/nph.15178).

2) It is also not clear to this reviewer why the two FM4-64 staining patterns in Fig. 3C and Fig. 3F are strikingly different.

Thanks for your comments! FM4-64 is a styryl dye that can bind to the outer lipid leaflet of the plasma membrane and enter into cells through endocytosis (Scheuring et al., 2015, Methods in Molecular Biology, 1242:83-92, DOI: 10.1007/978-1-4939-1902-4_8). When the dye is internalized, it can be observed firstly in the membrane of vesicles and endosomal compartments, and then appears in the vacuolar membrane (Jelníková et al., 2010, Plant Journal, 61(5): 883-892, DOI: 10.1111/j.1365-313X.2009.04102.x; Löfke et al., 2013, Journal of Integrative Plant Biology, 55(9): 864-875, DOI: 10.1111/jipb.12097). In Figure 3C, we tried to show the evidence that eGFP::BbCrpa appears in vesicles that are stained by FM4-64, while in Figure 3F, we aimed to indicate eGFP::BbCrpa accumulates in mature vacuoles. Hence, FM4-64 staining patterns in Figure 3C and Figure 3F are somehow different.

3) In Fig. 3M, eGFP should be labeled eGFP::Drs2p.

Thanks for your reminder! We have modified it according to the suggestion. Please see Figure 3M.

Finally, the authors overexpressed BbCrpa gene in transgenic Arabidopsis and cotton plants to show that transgenic plants expressing this enzyme are protected from the toxic effects of the toxin cinnamyl acetate (CA) produced by the fungal pathogen Verticillium dahlia which causes vascular wilt disease in these plants. The data reported in Fig. 5A show that the transgenic Arabidopsis seed is able to germinate in presence of CA, whereas the nontransgenic control seed is not able to germinate. Evidence is presented that CA accumulates in the vacuole in transgenic Arabidopsis. However, the seedlings emerging from transgenic seeds are only partially protected from CA (Fig. 5A). It is also clear from the data presented in Figs. 5B-G that expression of the BbCrpa gene in transgenic Arabidopsis and cotton affords protection from infection by V. dahlia although no evidence for the expression of this gene at the protein level is presented. However, it seems likely that the transgenic lines only show delayed disease symptoms and are not truly resistant to this pathogen. The authors did not state clearly if Verticillium wilt disease resistance assays were performed on homozygous transgenic plants and their corresponding null segregants as negative controls. They also fail to provide evidence that the transgenic Arabidopsis and cotton challenged with the pathogen are able to grow to maturity and set viable seeds.

1）However, the seedlings emerging from transgenic seeds are only partially protected from CA (Fig. 5A).

Thanks for your comments! In this study, at the concentration of 50 μg/ml, the germination of wildtype seeds of Arabidopsis was severely inhibited while the transgenic seeds were still able to germinate. However, the growth of transgenic seedling was suppressed obviously compared with that of the untreated seedlings (Figure 5A). According to your suggestion, in the revised manuscript, we used “tolerance”, rather than “resistance” to weaken the statement.

2) However, it seems likely that the transgenic lines only show delayed disease symptoms and are not truly resistant to this pathogen. The authors did not state clearly if Verticillium wilt disease resistance assays were performed on homozygous transgenic plants and their corresponding null segregants as negative controls. They also fail to provide evidence that the transgenic Arabidopsis and cotton challenged with the pathogen are able to grow to maturity and set viable seeds.

Thanks for your comments! In our routine procedure for generating transgenic plant lines, we identified non-transgenic plants in the segregative generation (usually in T1 generation) of transformats, and then used these non-transgenic plants (null lines) as control to rule out the somatic variation from tissue culture. In the meantime, the homologous transgenic plants were identified in the segregative generation and propagated by selfing. Relevant descriptions were added in the section of Methods & Materials (Lines 783-795).

We agree with your opinion. The resistance displayed in seedlings does not always match that in maturity stage, because the resistance of host to Verticilium pathogen can be affected by environmental conditions, for example, temperature and nutrition. Nevertheless, in the case of our transgenic plants, the resistance to Verticilium disease is endowed from the detoxic function of BbCrpa. Theoretically, such transgenic traits will not be significantly affected by the environmental conditions if the expression of transgenes is stable. We had detected the expression level of BbCRPA during plants growth and in deferent generation (to T5 generation). The expression of BbCRPA gene was stable and the resistance to the diseases is descendible.

Author Response

Reviewer #1 (Public Review):

This is a well-conceived and well-executed investigation of how activation loop autophosphorylation and IN-box autophosphorylation synergistically activate AURKB/INCENP. An elegant chemical ligation strategy allowed construction of the intermediate phospho-forms so that the contributions of each phosphorylation event to structure, dynamics, and activity could be dissected. Autophosphorylation at both sites serves to rigidify both AURKB and the IN-box, and to coordinate opening, twisting, and activation loop movements. Consistent with previous findings, both sites are necessary for enzymatic activity; further, this work finds that activation loop autophosphorylation occurs slowly in cis while INbox autophosphorylation occurs quickly in trans.

Due to abundant previous work in the field, many of the conclusions of this paper were expected. However, that does not diminish the quality of the work, and the addition of how kinase dynamics contribute to activation is important for AURKB and many other kinases. The experimental results are clear and interpreted appropriately, with good controls. The computational work is also clearly explained and directly tied to the function of the enzyme, making it highly complementary to the experimental findings and to previously published structures.

We thank the reviewer for positive words about our work.

Some minor limitations of the study:

1) Of note when interpreting the HDX data, there is no coverage of the peptide containing the activation loop autophosphorylation site T248 (Fig S2A), and as mentioned in the Discussion, the time scale of HDX is not able to capture differences in exchange in very flexible regions like the activation loop.

The peptides spanning the region containing the phosphorylated Aurora BThr248 are not shown in our coverage map because they do not meet the stringent quality criteria for peptides that we used for HDX analysis (see Material and Methods). However, we have compared these peptides in phosphorylated and unphosphorylated enzyme complex manually and added a paragraph 4 on page 4.

“The peptides spanning the region containing the phosphorylated Aurora BThr248 are not shown in our coverage map (Figure 1-figure supplement 2A) because they did not pass the stringent peptide quality filter based on intensity, and redundancy of the peptide. However, upon manual analysis, we did not detect any changes in deuterium uptake between the phosphorylated and unphosphorylated forms in this region. Deuterium exchange in this part of the protein (which is also observed in the peptides immediately upstream of Aurora BThr248, see Supplementary file 5) is very rapid, independently of enzyme phosphorylation, so that complete exchange occurs even at the earliest time points. This is in contrast to the second part of the activation loop, which includes the Aurora BaEF helix (labeled region 3 in Figure 1A; peptide 254-260 in Supplementary file 5), where we clearly see HDX protection upon phosphorylation. It is possible that phosphorylation causes dynamic changes on a very fast scale (seconds or faster) in the part of the protein encompassing Aurora BThr248, but we could not detect them due to the limitation of our approach, which operates on the scale of minutes.“

Also, we analyzed the peptides covering this region to confirm the extent of phosphorylation in the loop (Figure 3-figure supplement 2A).

2) Some data lack robust statistical analysis, which would make the findings more compelling.

We have now included statistical analysis throughout the paper where it was possible.

3) One point that might be clarified is how the occupancy of T248 was confirmed to be either fully phosphorylated in the [AURKB/IN-box]IN-deltaC or fully dephosphorylated in the IN-box K846N/R827Q mutant. Especially because T248 autophosphorylation is found to occur in cis, it is unclear how incubating the [AURKB/IN-box]IN-deltaC with traces of wild-type [AURKB/IN-box]all-P would ensure that T248 is phosphorylated.

We confirmed phosphorylation occupancy of Aurora BThr248 by mass spectrometry in [Aurora B/IN-box]allP and [Aurora B/IN-box]loop-P but not for [AURKB/IN-box]no-P (Figure 3-figure supplement 2A). To achieve complete phosphorylation in the activation loop of [Aurora B/IN-box]IN-DC, we incubated this construct with fully active [Aurora B/IN-box]all-P. This is because, according to previous kinetic analysis, cisphosphorylation of Aurora BThr248 is only obligatory when the entire enzyme population is in the nonphosphorylated state. Once a fraction of the Aurora B population has been partially or fully activated, phosphorylation of Aurora BThr248 can also occur in trans, by the already activated enzyme. In other words, our model proposes an obligatory initial intramolecular step followed by propagation of activation in trans as reported by (Zaytsev, Segura-Peña at al, eLife.2016). We have now clarified this on page 11, paragraph 2 where we explain the results of the autoactivation kinetics.

“It is noteworthy that phosphorylation of the activation loop in cis is the first necessary step in the autoactivation process, assuming a completely unphosphorylated enzyme pool. However, a partially active or fully active enzyme can phosphorylate the activation loop in trans. This type of activation mechanism with an initial intramolecular activation step followed by an intermolecular step of activation have been previously reported for PAK2 (J. Wang et al., 2011) and for Aurora B (Zaytsev et al., 2016).”

Reviewer #2 (Public Review):

This study presents a dynamic, multi-step model for the activation of Aurora-B kinase through the interaction with INCENP and autophosphorylation. This interaction is critical to the proper execution of chromosome segregation, and key details of the mechanism are not resolved. The study is an advance on previous studies on Aurora-B and the related kinase Aurora-C, primarily because it clarifies the roles of the different phosphorylation sites. However, major differences in the details of the molecular interactions are presented that are not clearly backed up by the evidence due to limitations in the approach, when compared to previous work based on crystal structures.

Strengths. The experimental approach to the analysis of the Aurora-B/INCENP interaction is sound and novel and it is striking example of preparation of proteins in specific phosphorylation states, and of using HDX to characterise localised changes in the structural dynamics of a protein complex. The authors have generated two intermediate phosphorylation states of the complex, enabling them to dissect their contributions to the regulation of structural dynamics and activity of the complex.

Weaknesses. The major weakness of the study is the molecular dynamics simulation. The resulting model of the complex differs from the crystal structure of the Aurora-C/IN-box structure in key details, and these are neither described clearly nor explained. The challenges/limitations of simulation of phosphorylated proteins should be described.

We thank the reviewer for the positive words about our work and the criticism that helped us to improve our manuscript. We have now extended the MD studies that confirm our original observations regarding the entropic nature of the IN-box and the effect of phosphorylation on the structure and dynamics of [Aurora B/IN-box]. We clarify that the conformation of [Aurora B/IN-box]all-P observed in the simulations is not the final folded state, but a productive intermediate in the activation pathway of [Aurora B/IN-box]. For this reason, the differences from the [Aurora C/IN-box] crystal structure do not indicate flaws in the simulations. On the contrary, we believe that the data from the MD simulations provide crucial insights into the dynamical properties of the system that could not otherwise be assessed.

Author Response

Reviewer #2 (Public Review):

1) It has been reported that PHD fingers can bind to DNA in addition to lysine-methylated histone H3. Can the authors address whether or not the enhanced selectivity of PHD-nucleosome interactions over PHD-peptide interactions is due to PHD-DNA binding?

We apologize for not making this clearer in our initial manuscript. We did test the ability of our PHD readers to bind nucleosomal DNA of various lengths and observed no significant engagement (Figure 1 - figure supplement 1B). This is emphasized in the revised text.

2) What's the binding affinities of PHD-nucleosome interactions and PHD-peptide interactions, respectively?

The relative EC50 (EC50rel) for these interactions (Figure 1 - figure supplement 1C-D and Figure 2 - figure supplement 1H) are consistent with others using Alpha/dCypher technologies (e.g. doi.org/10.1101/ 2022.02.21.481373v1; which also contains a detailed description of EC50rel calculation and the difference between this value and an equilibrium Kd).

3) Histone H4K5acK8ac is a well-known site-specific histone acetylation mark for gene transcriptional activation, much more so than histone H3 acetylation. Does H4K5K8 acetylation enhance PHD-H4K3me3 binding in nucleosome?

We appreciate the reviewer for asking this question. In our studies, we tested H4K5ac and H4K8ac binding individually but do not have a nucleosome with the dual H4K5ac8ac nucleosome. Given the limited amount of time and resources we had for making more nucleosomes, we felt our efforts were better spent on developing heterotypic nucleosomes to answer the more striking cis vs. trans question posed by both reviewers.

4) The authors provided the data showing cis histone H3 tail lysine acetylation effects on PHDH4K3me3 binding. What about trans histone H3 lysine acetylation effects?

Thank you for this suggestion. To address this, we expended considerable resources to create new fully PTM-defined heterotypic (to accompany our homotypic) nucleosomes (note nomenclature to minimize confusion with asymmetric/symmetric DNA methylation) to directly test whether MLL1’s activity enhancement in the context of H3 tail acetylation occurs in cis or in trans. As shown in Figure 2D, enhancement of H3K4 methylation only occurs with heterotypic nucleosomes that have an available H3K4 residue with tail acetylation in cis (H3K4me3 • H3K9acK14acK18ac (hereafter H3triac)) and is not seen in H3K4 methylatable nucleosomes with tail acetylation in trans (H3 -> H3K4acK9acK14acK18ac (hereafter H3tetraac)). These exciting new findings greatly strengthen our study and provide more definitive mechanistic details of H3ac → H3K4me regulation.

Author Response

Reviewer #2 (Public Review):

This manuscript reports an experiment involving learning and imaging of neural activity in rats. The goal was to test if scopolamine, which is an antagonist of acetylcholine receptors, could cause memory loss (amnesia). Two types of learning were tested: first, rats learned to prefer a short path, compared to a detour path, between two rewarded locations in a linear maze; second, in a subset of the experimental sessions, a shock zone was activated in the middle of the short path, and rats had to learn to avoid it. As a control, some sessions had a clear plexiglass barrier placed in the middle of the short path, which should not have aversive properties. The order of sessions was different for different groups of rats, but shock learning was always followed by a number of 'extinction' sessions without shock. In some groups, shock learning was accompanied by a systemic (intraperitoneal) scopolamine injection, 30 min before the start of the session. This manipulation was performed on most rats once but at a different slot in the sequence of sessions (sometimes before the drug-free shock learning, sometimes after it, sometimes in the absence of preceding barrier sessions, sometimes after them). In what follows, I might use the terms 'control group' and 'test group' to refer to sessions without scopolamine and sessions with it, respectively.

The main behavioural results are that rats increase their visits to the short path with learning, then visit less the short path once the shock zone is active or when the barrier is there. When re-tested in later sessions, rats trained in the absence of the scopolamine injection still avoid the short path, while most of the rats that were given the scopolamine injection do not avoid it, suggesting a deficit in encoding or recall of the shock zone memory.

In addition to these behavioural manipulations, the authors image the activity of dorsal CA1 hippocampal neurons using calcium imaging. They detect the existence of place cells, which increase their firing on specific portions of the short path of the maze (the long path data is not analysed). When comparing data before the shock training to during shock training, control place cells were more stable (i.e. had increased between-session correlations) and had more recurrent place fields (i.e. spatially active in one session then still active in another session) with respect to test data (rats injected with scopolamine). When comparing the pre-shock session and the first extinction session, place cell activity was less similar in the control rats (no scopolamine) compared to the scopolamine rats; but note that for scopolamine rats, extinction occurred earlier, so instead of using the first extinction session, the 4th session post-shock training was used to match the control data. Place cell activity has been shown to allow decoding of the animal's position; here, position decoding accuracy was lower around the shock zone in the control group compared to the scopolamine group.

From these analyses, the manuscript proposes the following findings: 1) scopolamine injections impair avoidance learning, and 2) scopolamine affects the long-term response of place cells to the aversive experience (less "remapping"). These findings are interpreted to support the idea that 3) place cell remapping is involved in avoidance/aversive learning and 4) that scopolamine, as an antagonist of the muscarinic acetylcholine receptors in the hippocampus (or elsewhere?), produces amnesia.

These findings, if properly supported, would be very interesting to a wide range of researchers interested in the neural bases of memory and learning, specifically aversive memory and spatial learning. The manuscript is well-written, has the advantage of using both male and female rats (which have consistent results), is one of the rare studies to date to perform calcium imaging of the hippocampus in rats, and records along learning of two simple tasks which seems relatively ecological and produce robust learning effects, and uses an experimental manipulation (injection of scopolamine) instead of purely correlational measures. The authors make some analytical effort in equalizing the number of trials across different sessions (even though I do not believe this fixes the existing confounds). I particularly appreciated the nice '3D' trajectory plots that show the unfolding of behaviour along a given session and that each individual's data are generally shown in the figures.

However, the experiment and its analysis seem to have some major flaws both in the experimental design (which may be difficult to fix) as well as in the analysis (which might be easier to fix), which prevent proper interpretation of the results. Specifically:

• To demonstrate finding 1 (that scopolamine specifically impairs avoidance learning), a control would be needed to show that the injection procedures do not impair general behaviour (e.g. motivation, attention, level of stress) as well as other forms of learning. Indeed, the control rats - as far as I understood - are not injected with saline, which would have been an appropriate control. One example of non-specific confounding effects of scopolamine is that it could, for example, reduce sensitivity to pain, thus to some extent decreasing the relevance of the shock to the rats, but many other interpretations are possible; most revolve around the idea that instead of scopolamine impairing learning, scopolamine might impair behaviour, which might, in turn, impair learning. Indeed, the scopolamine injection is shown to decrease running speed and the number of trials run, even before exposure to the shock. Related to this, the "short path preference" does not seem to be quantified properly: instead of simply using the number of visits to the short path, a better measure would be to compute a relative preference index quantifying visits to the short path with respect to visits to the long path [e.g. (num short - num long) / num total], to focus on the preference regardless of global changes in behavioural activity levels. In summary: the proposition that scopolamine specifically impairs avoidance learning has not been convincingly demonstrated; the possibility that it even impairs any form of learning is not currently demonstrated either.

We have run an additional saline-only control group (along with additional scopolamine groups) to demonstrate that scopolamine does impair avoidance learning. As shown in the Supplement to Fig. 1, rats receiving saline alone show significantly greater post-training avoidance of the short path than mice receiving scopolamine.

• For similar reasons, finding 2 (that the place cell response to aversive learning is affected by the scopolamine injection) is subject to the same lack of controls and existence of possible confounds noted above. Specifically, running more slowly and running fewer laps would have affected the overall amount of excitation of place cells during the session, which might affect plasticity, as well as the amount of reactivations/replay at the reward sites, which is likely to have effects in terms of memory consolidation. One way to potentially control for this would be to have the control group run the same amount of trials as the test group (but then session durations would be different); it is unclear how to prevent the difference in running speed. To be able to claim that the effects of scopolamine are specific to aversive learning, a control with either no learning (perhaps the long path data would be useful for this?) or appetitive learning (e.g. of a reward location, which also involves place field reorganization in some cases) would be useful.

We recognize and understand the referee’s valid concerns about these potential confounds. The revised paper makes more clear that the scopolamine condition is designed as a control for whether an aversive stimulus is remembered or forgotten, and the barrier condition is a control for whether a novel path-blocking stimulus is aversive or neutral. As the referee points out, there are other confounding variables that may covary with these manipulated factors. The revised discussion (ll. 544-559) acknowledges potential confounds arising from learning-induced behavior changes, and argues that even though it is not possible to perfectly control for such changes, our current study does so more effectively than most prior studies because the rat’s behavior during isolated beeline trials is highly stereotyped and thus more similar across experimental conditions than in any prior study that we know of. The revised discussion also acknowledges the difficulty of dissociating acquisition versus extinction effects upon remapping (ll. 633-644). The significance of the barrier manipulation is now acknowledged more clearly in the abstract, introduction, and discussion.

• Statement 3 implies a causal link between the two first statements, suggesting that place cell remapping would be necessary for the memory of an aversive experience or aversive location. Given the weakness of the arguments supporting the first 2 statements, this is also non convincingly demonstrated. In any case, the current paradigm would not be sufficient to make a causal link, but it might be sufficient to show a correlational link by showing a correlation between the amount of remapping and memory performance, such as presented in supplementary figure 5 (which would still be informative even if results from the scopolamine sessions were removed?).

We agree with the referee’s point that our study’s evidence is correlational in nature. The revised manuscript prominently acknowledges this in the concluding sentence of the discussion’s opening paragraph (ll. 500-503), which now reads: “While these results do not definitively prove that place cell remapping is causally necessary for storing memories of aversive encounters, they provide correlational evidence that remapping occurs selectively under conditions where a motivationally significant (rather than neutral) stimulus occurs and is subsequently remembered rather than forgotten.”

• Statement 4 - that scopolamine causes amnesia - is both not fully defined (what form of amnesia?) and not supported by the findings for the reasons mentioned above.

It is unclear what remedy the referee is recommending for this concern. We do not present the idea that scopolamine is an amnesic drug as a novel conclusion of our study; rather, this is a widely accepted view in the literature that motivated our experimental design decision to use scopolamine as a tool for dissociating whether an aversive event was remembered or forgotten. We recognize that scopolamine’s effects on memory may vary with experimental conditions. In the revised discussion, we extensively compare and contrast our experimental findings with acute and chronic results from numerous prior studies using scopolamine and other cholinergic drugs (ll. 561-678). We hope this is sufficient to address the referees concerns.

• In addition to these concerns, a study cited here (Sun et al 2021) mentions a few references in the discussion regarding how muscarinic receptor agonists might affect the link between spikes and calcium signals. Scopolamine is a muscarinic receptor antagonist and might thus have related/reversed effects. Thus, the technique used (calcium imaging) does not seem the best to address questions related to scopolamine. The current manuscript also mentions some findings that were not replicated (e.g. lack of over-representation of the shock zone) which are probably due to the fact that the finding relied on extra-field isolated spikes, which are less likely to be detected via calcium imaging.

This is an excellent point which is now addressed in the revised discussion; it is acknowledged (ll. 536-539) that mAChRs can regulate calcium signals and thus that scopolamine may have different effects on spikes detected from calcium imaging versus electrophysiology, which in turn could account for discrepancies between our finding that place fields do not migrate to aversively reinforced locations and Milad et al.’s (2019) findings that they do. The Sun et al. (2021) paper used calcium imaging methods similar to ours, so this factor is less likely to account for the discrepancy between their prior finding that place fields were acutely disrupted by scopolamine and our current finding that they were not.

• If anything, perhaps targeted injections in a specific brain region (e.g. dorsal CA1), instead of systemic injection, might give a more precise picture of the effects of scopolamine on place cells and spatial memory, but I do not know if this is technically possible.

Unfortunately the necessary placement of the GRIN lens above the recording location prevented the direct application of scopolamine there via cannulae. To date only one series of experiments has demonstrated single unit place cell recordings with direct microdialysis (Brazhnik et al. 2003, 2004). To study the effects of aversive learning across many days, we needed to utilize a recording method capable of tracking many cells across long time periods. However, systemic scopolamine has been widely used to study both learning (Anagnostaras et al. 1999; Huang et al. 2011; Svoboda et al. 2017) and its effects on place cells (Douchamps et al. 2013; Newman et al. 2017; Sun et al. 2021), thus by utilizing this method we can directly compare our findings with previous work. We have added a paragraph to the discussion (ll. 665-678) in which it is explained why the main conclusions of our study (namely, that place cell cell remapping is related to storage of memories for aversive events) do not depend upon whether or not scopolamine’s pharmacological actions were localized to the hippocampus (almost certainly they were not).

My conclusion would be that the experiment either needs to be redesigned to address the original question (effect of scopolamine on place cell firing and aversive learning) or that some of the data could be still used to address different questions which have not been addressed with calcium imaging before, e.g. learning of the short path, activity on the short path vs long path, effects on behaviour and place cell activity of learning & extinction of the barrier and shock zone avoidance; perhaps without focusing on the scopolamine manipulations, which seem to introduce many confounds.

Author Response

Reviewer #2 (Public Review):

Kim et al. examined the properties of neuronal connections responsible for inhibitory cell activation to show that the characteristics examined were similar in humans and rodents. This is important, as it suggests that the many rodent studies carried out over the past decades are physiologically relevant to humans.

Strengths

1) Human brain tissues are difficult to obtain, hence the study provides valuable insights

2) An impressive multipronged approach was used for cell classifications

3) Despite the lack of novel findings, the revelation of the similarities between human and rodent synapses is important and has far-reaching implications. This important finding suggests the knowledge generated from rodent research is, at least partly, physiologically relevant to and transferrable to humans.

Weaknesses

1) The study is descriptive by design, and hence provides limited conceptual advances, especially with the retrospect that synaptic properties are similar between humans and rodents (although see strength #3). For example, very similar findings and techniques have already recently been reported by a number of the same authors in the Campagnola et al., Science 2022 paper.

We agreed that stimulus protocols of connectivity assays with multiple patch-clamp recordings in this study had been adapted from the recent publication (Campagnola et al., Science 2022). In this previous study, especially for human synaptic connectivity data, the main cell type categorization was at the level of excitatory and inhibitory neurons which identified based on morphological features and observed PSP characteristics (e.g., direction of membrane potential changes) when it connected each other. However, we went further to identify interneuron subclasses in the connectivity assays using virally labeled slice cultures and post-hoc HCR staining in addition to intrinsic classifier, which is not investigated from the recent publication (Campagnola et al., 2022). Therefore, following scientific findings and their implications are not the same shown in the previous study and we think this study provides a significant advance of our understanding in human cortical circuits organization.

2) Despite the fact that normal physiology was reported, the use of pathological human brain tissue could affect the results.

We agreed that the use of pathological human brain tissue to investigate normal physiology is not ideal, however, as mentioned in the METHODS below (section of “Acute slice preparation”), our surgically resected neocortical tissues show minimal pathology, and we believe these tissue preparations can be used to address normal physiological properties of human neurons. Importantly, we saw no effect of disease state (epilepsy vs. tumor) on the intrinsic or synaptic properties that we measured. Our METHODS state that “Surgically resected neocortical tissue was distal to the pathological core (i.e., tumor tissue or mesial temporal structures). Detailed histological assessment and using a curated panel of cellular marker antibodies indicated a lack of overt pathology in surgically resected cortical slices (Berg et al., 2021).”. We also state in the RESULTS that “These tissues were distal to the epileptic focus or tumor, and have shown minimal pathology when examined (Berg et al., 2021). Brain pathology was evaluated using six histological markers that were independently scored by three pathologists. Surgically resected tissues have been used extensively to characterize human cortical physiology and anatomy (Berg et al., 2021).”. Lastly, this is the best possible human tissue available for us to conduct physiological experiments. It is an unavoidable caveat of this work that our healthy brain tissue was derived from a donor brain exhibiting a serious disease.

3) The manuscript may not be easy to understand for the uninvited, because many concepts and abbreviations were not properly introduced.

Thank you for pointing this oversight out. We updated our manuscript and made sure that we fully describe all abbreviations. We now changed the abbreviation of MPC back to multiple patch-clamp recording, and some other abbreviations such as LAMP5, SLC17A7, DLX are now better explained. We have also changed the order of multiple figures (i.e., Figure 5 – Figure supplements to Figure 3 – Figure supplements) and removed some complicated figures (e.g., Figure 1 – Figure supplement 1) to present the data in a fashion that can be understood by a more general reader.

4) The statistical treatment is not ideal, so some conclusions may not be valid.

We performed additional statistical analyses as suggested and implemented in the text of the RESULTS.

Furthermore, we also made additional Figure supplements (Figure 4 – Figure supplement 3, Figure 4 – Figure supplement 4, Figure 6 – Figure supplement 2, and Figure 6 – Figure supplement 3) to support our conclusions.

5) The mixed usage of acute and cultured slices is not ideal and likely affects the outcome.

We agree that the mixed usage of acute and cultured slices is not ideal, and it could affect the interpretation of outcome. Therefore, we performed additional analyses to see if there is any correlated change of synaptic property (i.e., paired pulse ratio) along the days after slice culture (now implemented in Figure 4 – Figure supplement 4 and Figure 6 – Figure supplement 3) and we didn’t find any significant correlation. However, we noticed the short-term synaptic dynamics are rather differentiated between acute and slice culture condition shown in Figure 4 – Figure supplement 1d. We think this is due to sampling bias rather than tissue preparation difference and these points are now more carefully described in the DISCUSSION as “This difference we observed in this study, i.e., more facilitating synapses were detected in slice cultures than in acute slices, could either reflect an acute vs. slice culture difference. However, we believe it is more likely to reflect a selection bias for PVALB neurons when patching in unlabeled acute slices, and that the AAV-based strategy with a pan-GABAergic enhancer allows a more unbiased sampling of interneuron subclasses whose properties are preserved in culture. In support of this, PPR analysis as a function of days after slice culture shows no relationship to acute versus slice culture preparation (Figure 4 – Figure supplement 4, Figure 6 – Figure supplement 3). Furthermore, we have observed that viral targeting of GABAergic interneurons greatly facilitates sampling of the SST subclass in the human cortex compared to unbiased patch-seq experiments (Lee et al., 2022), and this selection bias likely explains synapse type sampling differences in cultured slices compared to acute preparations.”.

Author Response

We would like to extend our thanks to the reviewers who took the time to carefully read our paper and provide thoughtful insights and suggestions on how to strengthen our conclusions. All reviewers agreed that our study presented strong data supporting a role for triglyceride lipase brummer (bmm) in regulating testis lipid droplets and spermatogenesis in Drosophila, and that our findings advance our understanding of lipid biology during sperm development. Reviewers also made several helpful suggestions on how to strengthen our manuscript even further. Below, we provide a brief outline of our plans to revise this manuscript in response to reviewer comments.

The majority of reviewer comments will be addressed by text changes, rearranging figures to add images, and making a model to visually represent our findings. Together, these changes will ensure we clearly communicate our data and conclusions with readers, and properly contextualize our findings. See below for details on our planned revisions.

Reviewer #1 (Public Review):

In this study, the authors investigate the role of triglycerides in spermatogenesis. This work is based on their previous study (PMID: 31961851) on triglyceride sex differences in which they showed that somatic testicular cells play a role in whole body triglyceride homeostasis. In the current study, they show that lipid droplets (LDs) are significantly higher in the stem and progenitor cell (pre-meiotic) zone of the adult testis than in the meiotic spermatocyte stages. The distribution of LDs anti-correlates with the expression of the triglyceride lipase Brummer (Bmm), which has higher expression in spermatocytes than early germline stages. Analysis of a bmm mutant (bmm[1]) - a P-element insertion that is likely a hypomorphic - and its revertant (bmm[rev]) as a control shows that bmm acts autonomously in the germline to regulate LDs. In particular, the number of LDs is significantly higher in spermatocytes from bmm[1] mutants than from bmm[rev] controls. Testes from males with global loss of bmm (bmm[1]) are shorter than controls and have fewer differentiated spermatids. The zone of bam expression, typically close to the niche/hub in WT, is now many cell diameters away from the hub in bmm[1] mutants. There is an increase in the number of GSCs in bmm[1] homozygotes, but this phenotype is probably due to the enlarged hub. However, clonal analyses of GSCs lacking bmm indicate that a greater percentage of the GSC pool is composed of bmm[1]-mutant clones than of bmm[rev]-clones. This suggests that loss of bmm could impart a competitive advantage to GSCs, but this is not explored in greater detail. Despite the increase in number of GSCs that are bmm[1]-mutant clones, there is a significant reduction in the number of bmm[1]-mutant spermatocyte and post-meiotic clones. This suggests that fewer bmm[1]-mutant germ cells differentiate than controls. To gain insights into triglyceride homeostasis in the absence of bmm, they perform mass spec-based lipidomic profiling. Analyses of these data support their model that triglycerides are the class of lipid most affected by loss of bmm, supporting their model that excess triglycerides are the cause of spermatogenetic defects in bmm[1]. Consistent with their model, a double mutant of bmm[1] and a diacylglycerol O-acyltransferase 1 called midway (mdy) reverts the bmm-mutant germline phenotypes.

There are numerous strengths of this paper. First, the authors report rigorous measurements and statistical analyses throughout the study. Second, the authors utilize robust genetic analyses with loss-of-function mutants and lineage-specific knockdown. Third, they demonstrate the appropriate use of controls and markers. Fourth, they show rigorous lipidomic profiling. Lastly, their conclusions are appropriate for the results. In other words, they don't overstate the results.

We thank the Reviewer for their positive assessment of our paper.

There are a few weaknesses. Although the results support the germline autonomous role of bmm in spermatogenesis, one potential caveat that the mdy rescue was global, i.e., in both somatic and germline lineages. The authors did not recover somatic bmm clones, suggesting that bmm may be required for somatic stem self-renewal and/or niche residency. While this is beyond the scope of this paper, it is possible that somatic bmm does impact germline differentiation in a global bmm mutant.

In the revised manuscript, we will more clearly delineate when we used global versus germline-only loss of mdy to rescue bmm mutant phenotypes in the testis. We will also acknowledge the possibility that somatic bmm may play a role in germline differentiation in a global bmm mutant.

Regarding data presentation, I have a minor point about Fig. 3L: why aren't all data shown as box plots (only Day 14 bmm[rev] does). Finally, the authors provide a detailed pseudotime analysis of snRNA-seq of the testis in Fig. S2A-D, but this analysis is not sufficiently discussed in the text.

We will make text and presentation changes in the revised manuscript to describe our data more clearly, and will add text to describe our pseudotime analysis of single-cell RNA seq data in more detail.

Overall, the many strengths of this paper outweigh the relatively minor weaknesses. The rigorously quantified results support the major aim that appropriate regulation of triglycerides are needed in a germline cell-autonomous manner for spermatogenesis.

This paper should have a positive impact on the field. First and foremost, there is limited knowledge about the role of lipid metabolism in spermatogenesis. The lipidomic data will be useful to researchers in the field who study various lipid species. Going forward, it will be very interesting to determine what triglycerides regulate in germline biology. In other words, what functions/pathways/processes in germ cells are negatively impacted by elevated triglycerides. And as the authors point out in the discussion, it will be important to determine what regulates bmm expression such that bmm is higher in later stages of germline differentiation.

We agree with the reviewer about the many interesting future directions for this project. We will therefore add a model figure in the revised manuscript to visualize our findings and highlight remaining questions about how bmm and triglycerides support normal spermatogenesis in Drosophila.

Reviewer #2 (Public Review):

Summary:

Here, the authors show that neutral lipids play a role in spermatogenesis. Neutral lipids are components of lipid droplets, which are known to maintain lipid homeostasis, and to be involved in non-gonadal differentiation, survival, and energy. Lipid droplets are present in the testis in mice and Drosophila, but not much is known about the role of lipid droplets during spermatogenesis. The authors show that lipid droplets are present in early differentiating germ cells, and absent in spermatocytes. They further show a cell autonomous role for the lipase brummer in regulating lipid droplets and, in turn, spermatogenesis in the Drosophila testis. The data presented show that a relationship between lipid metabolism and spermatogenesis is congruous in mammals and flies, supporting Drosophila spermatogenesis as an effective model to uncover the role lipid droplets play in the testis.

We thank the Reviewer for their positive assessment of our paper.

Strengths and weaknesses:

The authors do a commendably thorough characterization of where lipid droplets are detected in normal testes: located in young somatic cells, and early differentiating germ cells. They use multiple control backgrounds in their analysis, including w[1118], Canton S, and Oregon R, which adds rigor to their interpretations. The authors employ markers that identify which lipid droplets are in somatic cells, and which are in germ cells. The authors use these markers to present measured distances of somatic and germ cell-derived lipid droplets from the hub. Because they can also measure the distance of somatic and germ cells with age-specific markers from the hub, these results allow the authors to correlate position of lipid droplets with the age of cells in which they are present. This analysis is clearly shown and well quantified.

The quantification of lipid droplet distance from the hub is applied well in comparing brummer mutant testes to wild type controls. The authors measure the number of lipid droplets of specific diameters, and the spatial distribution of lipid droplets as a function of distance from the hub. These measurements quantitatively support their findings that lipid droplets are present in an expanded population of cells further from the hub in brummer mutants. The authors further quantify lipid droplets in germline clones of specified ages; the quantitative analysis here is displayed clearly, and supports a cell autonomous role for brummer in regulating lipid droplets in spermatocytes.

Data examining testis size and number of spermatids in brummer mutants clearly indicates the importance of regulating lipid droplets to spermatogenesis. The authors show beautiful images supported by rigorous quantification supporting their findings that brummer mutants have both smaller testes with fewer spermatids at both 29 and 25C. There is also significant data supporting defects in testis size for 14-day-old brummer mutant animals compared to controls. The comparison of number of spermatids at this age is not significant, which does not detract from the the story but does not support sperm development defects specifically caused by brummer loss at 14 days. Their analysis clearly shows an expanded region beyond the testis apex that includes younger germ cells, supporting a role for lipid droplets influencing germ cell differentiation during spermatogenesis.

We thank the reviewer for pointing out this inaccuracy in our manuscript. In the revised manuscript we will choose more precise language to describe defects in sperm development in 14-day-old bmm mutants.

The authors present a series of data exploring a cell autonomous role for brummer in the germline, including clonal analysis and tissue specific manipulations. The clonal data indicating increased lipid droplets in spermatocyte clones, and a higher proportion of brummer mutant GSCs at the hub are convincing and supported by quantitation. The authors also show a tissue specific rescue of the brummer testis size phenotype by knocking down mdy specifically in germ cells, which is also supported by statistically significant quantitation. The authors present data examining the number of spermatocyte and post-meiotic clones 14 days after clonal induction. While data they present is significant with a 95% confidence interval and a p value of 0.0496, its significance is not as robust as other values reported in the study, and it is unclear how much information can be gained from that specific result.

We thank the reviewer for raising this point. In the revised manuscript we will display the p-value clearly to ensure our statistical output is clear for readers to evaluate our conclusions regarding bmm mutant clones 14 days after clone induction.

The authors do a beautiful job of validating where they detect brummer-GFP by presenting their own pseudotime analysis of publicly available single cell RNA sequencing data. Their data is presented very clearly, and supports expression of brummer in older somatic and germline cells of the age when lipid droplets are normally not detected. The authors also present a thorough lipidomic analysis of animals lacking brummer to identify triglycerides as an important lipid droplet component regulating spermatogenesis.

Impact:

The authors present data supporting the broad significance of their findings across phyla. This data represents a key strength of this manuscript. The authors show that loss of a conserved triglyceride lipase impacts testis development and spermatogenesis, and that these impacts can be rescued by supplementing diet with medium-chain triglycerides. The authors point out that these findings represent a biological similarity between Drosophila and mice, supporting the relevance of the Drosophila testis as a model for understanding the role of lipid droplets in spermatogenesis. The connection buttresses the relevance of these findings and this model to a broad scientific community.

Reviewer #3 (Public Review):

In this manuscript, Chao et al seek to understand the role of brummer, a triglyceride lipase, in the Drosophila testis. They show that Brummer regulates lipid droplet degradation during differentiation of germ and somatic cells, and that this process is essential for normal development to progress. These findings are interesting and novel, and contribute to a growing realisation that lipid biology is important for differentiation.

We thank the Reviewer for their positive assessment of our manuscript.

Major comments:

1) The data in Figs 1 and 2, while helpful in setting the scene, do not add much to what was previously shown by the same group, namely that lipid droplets are present in both early germ cells and early somatic cells in the testis, and that Bmm regulates their degradation (PMID: 31961851). Measuring the distance of lipid droplets from the hub, while helpful in quantifying what is apparent, that only stem and early differentiated stages have lipid droplets, is not as informative as the way data are presented later (Fig. 2I), where droplets in specific stages are measured. Much of this could be condensed without much overall loss to the manuscript.

We thank the reviewer for this comment and will condense the first part of the paper in our revised manuscript.

2) It would be important to show images of the clones from which the data in Fig. 2I are generated. The main argument is that Bmm regulates lipid droplets in a cell autonomous manner; these data are the strongest argument in support of this and should be emphasised at the expense of full animal mutants (which could be moved to supplementary data).

We thank the reviewer for this comment, and will add an image in our revised manuscript showing lipid droplets in bmm mutant spermatocyte clones.

Similarly, the title of Fig. S2 ("brummer regulates lipid droplets in a cell autonomous manner") should be changed as the figure has no experiments with cell (or cell-type)-specific knockdowns/mutants. This figure does show changes in lipid droplets in both lineages in bmm mutants, so an appropriate title could be "brummer regulates lipid droplets in both germ and soma".

We thank the reviewer for this comment, and will adjust the S2 figure legend title in the revised manuscript.

3) Interestingly, the clonal data show that bmm is dispensable in germ cells until spermatocyte stages, as no increase in lipid droplet number is seen until then. This should be more clearly stated, as it indicates that the important function of Bmm is to degrade lipid droplets at the transition from spermatogonial to spermatocyte stages. This is consistent with the phenotypes observed in which late stage germ cells are reduced or missing. However, the effect on niche retention of the mutant GSCs at the expense of neighbouring wildtype GSCs is hard to explain. Are lipid droplets in mutant GSCs larger than in control? Is there any discernible effect of bmm mutation on lipids in GSCs? Additionally, bam expression is delayed, suggesting that bmm may have roles on cell fate in earlier stages than its roles that can be detected on lipid droplets.

We thank the reviewer for this comment. We will include more text in the revised manuscript to clarify the key role bmm plays in regulating lipid droplets at the spermatogonia-spermatocyte transition. We will also add more detail and potentially data to our description of how bmm affects lipid droplets in cells at the earliest stages of germline development.

4) The bmm loss-of-function phenotype could be better described. Some of the data is glossed over with little description in the text (see for example the reference to Fig. 3A-C). For instance, in the discussion, the text states "loss of bmm delays germline differentiation leading to an accumulation of early-stage germ cells" (p13, l.259-60). However, this accumulation has not been clearly shown, or at least described in the manuscript. Most of the data show a reduction (or almost complete absence) of differentiated cell types. This could indeed be due to delayed differentiation, or alternatively to a block in differentiation or to death of the differentiated cells. The clonal data presented show a decrease in the number of cells recovered, but do not allow inferences as to the timing of differentiation, making it hard to distinguish between the various possibilities for the lack of differentiated spermatids. Apart from data showing that GSCs are more likely to remain at the niche, no further data are shown to support the fact that mutant germ cells accumulate in early stages. While additional experiments could help resolve some of these issues, much of this could also be resolved by tempering the conclusions drawn in the text.

We thank the reviewer for these comments. In the revised manuscript we will temper our conclusions regarding bmm’s precise role in spermatogenesis by discussing different mechanisms (e.g. differentiation or death) that could lead to the phenotypes we observe.

5) In the discussion (p.14, l-273 onwards), the authors suggest that products of triglyceride breakdown are important for spermatogenesis. However, an alternative interpretation of the results presented here (especially those using the midway mutant) could be that triglycerides impede normal differentiation directly. Indeed, preventing the cells' ability to produce triglycerides in the first place can rescue many of the defects observed. A better discussion of these results with a model for the function of triglycerides and their by-products would be a great improvement to this manuscript.

We thank the reviewer for this comment. To ensure our data is clearly communicated with readers, we will add a model to the paper suggesting how triglyceride and its by-products influence spermatogenesis.

Together, these changes will strengthen our overall finding that bmm-mediated regulation of testis triglyceride is important for normal sperm development. Because our findings in flies align with and extend data from rodent models, the developmental mechanisms we uncovered about how triglyceride lipase bmm regulates testis lipid droplets and sperm development will likely operate in other species.