Author Response

Reviewer #2 (Public Review):

The work reports a minor modification in the protocol for Prp formation in vitro. Using this the authors evaluate the role of Syntaxin 6 in modulating prion formation in vitro and the toxicity of the amyloid fibrils in cell culture models. The authors show that while prions/amyloids formed by PrP are non-toxic, mixed aggregates formed by Stx6/PrP are toxic; they claim that this is due to the toxic aggregation intermediates that accumulate more in the presence of Stx6. However, the basis of enhanced toxicity of Stx6/PrP mixed aggregates is not clear and doesn't seem to be physiologically relevant; there is no evidence that Stx6 and PrP forms mixed aggregates in vivo. Which is the toxic component of the Stx6/Prp co-aggregate? Is it the Stx6 component or the Prp component? Additionally, the authors do not have mechanistic explanation for the effect of Stx6 on PrP prion formation

We thank the reviewer for his assessment and we agree that more in vivo data was needed to support the physiological relevance of the effect of syntaxin-6 on PrP. We now provide two new key experiments demonstrating interaction of STX6 with PrP in a cell model of prion disease and testing the effect of Stx6 knockout on the replication of infectious RML prions in PMCA assays (Figures 4, 4S1, 4S2). Please refer to our response to reviewer 1, point 1 for more details. We respectfully disagree that the native aggregation assay represents a minor modification of PrP fibril formation protocols. While this statement may be true in the narrow technical sense, it is striking that in more than 25 years of prion research, no aggregation assay under near-native assay conditions had been developed. The conditions of previous assays, which relied on thermal or chemical denaturation to facilitate PrP misfolding, were inherently incapable of assessing the effect of protein modifiers of PrP fibril formation. Therefore, the NAA opens a wide field of new experiments to mechanistically probe modulators of PrP aggregation and toxicity under physiologically relevant conditions. The protein syntaxin-6 proves a test case for this new capability.

The reviewer may have misunderstood the mechanistic hypothesis for neurotoxicicty that is supported by our data. We are not claiming that the co-aggregates between PrP and syntaxin-6 are toxic. As our data demonstrate, aggregation endpoints in the presence of STX6 have little neurotoxicity, as do fibrillar aggregation endpoints without the presence of STX6 (Figures 5 and 5S1). Rather, based on the well-established oligomer toxicity hypothesis, we are concluding that STX6, by delaying or preventing formation of mature amyloid fibrils, caused toxic aggregation intermediates to persist. Our new data from secondary seeding assays (Figure 5S2) demonstrate that at the aggregation time points when the maximum amounts of neurotoxic species are present (20 h), no seeding competent fibrils have yet been formed. The presence of STX6 prolongs this period and therefore increases toxicity (Figures 5 and 5S1). These data directly support the established theories for the basis of amyloid toxicity and, additionally, caution that an intervention to delay amyloid formation can have deleterious effects on toxicity. We have now made this point more clearly in our discussion. Of course, we, like many other protein misfolding laboratories in the world, are also working hard on isolating and characterizing the toxic species in prion and other protein misfolding diseases, which, as the reviewer suggests, will be a very important milestone in understanding these diseases.

Reviewer #3 (Public Review):

The autocatalytic replication mechanism of misfolded Prion-like proteins (PrP) into amyloid aggregates is associated with a plethora of deleterious neurodegenerative diseases. Despite of the huge amount of research, the underlying molecular events of self-replication and identification of the toxic species are not fully understood. Many recent studies have indicated that non-fibrillar oligomeric intermediates could be more neurotoxic compared to the Prion fibrils. Various cellular factors, like the participation of other proteins and chaperone activity, also play an important role in PrP misfolding, aggregation, and neurotoxicity. The present work focuses on understanding the PrP aggregation mechanism with the identification of the associated toxic species and cellular factors. One of the significant strengths of the work is performing the aggregation assay in near-native conditions. In contrast, most in vitro studies use harsh conditions (such as high temperature, denaturant, detergent, low pH, etc.) to promote protein aggregation. The authors successfully observed the well-known seeding property of the PrP in this aggregation assay that bypasses the primary nucleation during aggregation. Moreover, the authors have shown that syntaxin 6 (Stx6), a known risk factor in prion-mediated Creutzfeldt-Jakob disease, delays fibril formation and prolongs the persistence of toxic intermediates, thus playing an anti-chaperone activity. This study will contribute to understanding the molecular mechanism of PrP aggregation and neurotoxicity. However, further studies are required to identify and characterize the toxic intermediate in the near future precisely.

We thank the reviewer for his thoughtful and accurate summary. We fully agree that the nature of the toxic species in protein misfolding diseases is a key challenge of the field and we hope that our study contributes to solving this puzzle.

Author Response:

We would like to express our gratitude to the reviewers for the time and effort dedicated to evaluating our manuscript. We are committed to addressing each of the comments and recommendations they have presented.

It appears that a majority of the feedback emphasizes the need for clarity and expanded explanations. We acknowledge these points and are confident that offering a clearer exposition and delving into further details will notably enhance the manuscript. In our initial draft, our intention was to ensure accessibility to non-mathematical readers by minimizing technical jargon. However, the feedback underscores the importance of certain details, particularly for those well- versed in ODE modelling, and the need to provide complete information.

While we find the reviewers' feedback invaluable, it is worth noting that none of the critiques suggest a fundamental change in our presented analyses. Below, we offer brief responses to the primary critiques mentioned in the public review:

1) The first notable comment pertains to the selection criteria for parameter and initial condition values. This critique is indeed valid. In brief, parameter values were chosen from a range of 10^- 5 to 10^4, representing rates from 10 femtomolar/minute to 10 micromolar/minute, spanning a biologically plausible spectrum. It is conceivable that values outside this range exist but are exceedingly rare. Similarly, initial conditions were chosen within the range 10^0 to 10^4, typically represented in nM.

2) The second comment highlights the challenges in systematically determining a full spectrum of parameter sets with 94 free parameters. In our observations, as we expanded the number of model instances, the distribution of protein dynamics exhibited minimal variation. A doubling of model instances from 100,000 to 200,000 led to less than a 1% error change. This error was calculated based on the differences across every protein species and dynamic category. These findings suggest that examining more than 100,000 model instances neither shifts the dynamic distributions significantly nor unveils new resistance mechanisms. We are committed to presenting these analyses more comprehensively in the revised manuscript.

3) The query about the appropriateness of filtering our models based on computational feasibility is pertinent. Our contention is that this filter does not exclude a significant number of model instances. Furthermore, stiff ODEs generally arise from extremely high reaction rates, which are exceedingly rare in a biological context. Thus, their exclusion only filters out exceedingly rare biological contexts, and only a small proportion of the time.

4 & 5) Clarifications sought about the simulations will be addressed. Though we feel the details were implicitly incorporated, we will make them explicit in the subsequent version.

6) The final major comment underscores the qualitative nature of our validation, which we agree. Currently, we are exploring experimental techniques or datasets for a more robust validation. In our next revision, we will ensure a more in-depth discussion of the validation in the manuscript's discussion section.

Once again, thank you for your valuable feedback. We look forward to submitting a revised version that addresses all concerns and enhances the manuscript's overall quality.

Author Response:

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

We thank the reviewers for their thoughtful and positive evaluation of our work. Below, we have addressed all of the essential revisions and provide point-by-point responses to all of the reviewer comments. Additionally, we include with this resubmission quantification microneme localization, determined by expansion microscopy, which further validates the central role of HOOK in microneme trafficking.

Suggested revisions:

1. Please confirm the interaction between CDPK1 and ROM4 by reciprocal IP.

Prompted by the reviewers suggestions we examined more closely the pulldowns of WT and myristoylation-deficient CDPK1 (cMut). ROM4 had been identified as differentially enriched in the cMut pulldown; however, upon closer examination we realized that the abundance of ROM4 is actually even greater in the untagged control and therefore likely a variable contaminant in the pulldowns. We have re-analyzed the results of those pulldowns to focus on proteins significantly enriched in association with either WT or cMut CDPK1, relative to untagged controls. Among this set of 16 enriched proteins, only three proteins appeared differentially enriched between WT and cMut. None of the proteins associated with CDPK1 inform pathways related to parasite motility and were therefore not pursued further in this study.

2. Please compare the expression of the tagged and complemented (cWT and cMut) CDPK1 with the endogenous expression of the non-tagged and non-complemented gene.

We compared expression levels of CDPK1 using immunoblot with an anti-CDPK1 antibody comparing TIR1, CDPK1-AID, cWT and cMut parasites, which we have included in panel G of Figure 2–figure supplement 1. Endogenous AID tagging of CDPK1 resulted in a decrease in the abundance of CDPK1. cWT and cMut complementation result in similar expression levels to the AID-tagged iKD CDPK1, albeit the cMut complement has marginally higher expression. Since CDPK1 is essential for the lytic cycle, insufficient levels of the cWT expression would have displayed defects in our plaque assays. We have updated our results to reflect this new data:

“Additionally, we compared endogenous CDPK1 expression to mAID-tagged, cWT, and cMut strain (Figure 2–figure supplement 1). Introduction of a mAID tag to CDPK1 led to a reduction in CDPK1 levels, but these levels were equivalent to complementation products in cWT and cMut parasites.”

3. Please attempt to confirm that aerolysin treatment does not impact myristoylation-dependent subcellular partitioning of CDPK1.

The kinase activity in aerolysin-treated parasites was unaffected by the 1B7 inhibitory nanobody, demonstrating that parasites remain impermeable to proteins as small as 15 kDa.  Furthermore, we localize CDPK1 by immunofluorescence in aerolysin-treated parasites to show that the localization of CDPK1 is indistinguishable from that of vehicle-treated parasites, suggesting that overall CDPK1 localization is unaffected by aerolysin treatment. We include this data in panel B in Figure 3–figure supplement 1. Nevertheless, in the manuscript we discuss the limitations of the thiophosphorylation experiment:

“While our approach largely maintains kinases in their subcellular context, aerolysin treatment disrupts native ion concentrations and detaches the plasma membrane from the inner membrane complex (IMC) (Wichroski et al., 2002).”

Because of these limitations we rely on the overlap of CDPK1-dependent targets between our thiophosphorylation and time course experiments.

4. Please confirm the interaction of TGGT1_306920 and TGGT1_316650 with the HOOK and FTS proteins.

In response to this suggestion, we tagged the C termini of TGGT1_306920 and TGGT1_316650 with 3xHA epitopes. Although immunoprecipitation of TGGT1_316650 was unsuccessful, immunoprecipitation of TGGT1_306920 identified HOOK and FTS as significantly enriched proteins. We include this new data in panel C of Figure 5 and have updated our results:

“To further confirm the interaction, we fused a 3xHA tag to the C terminus of TGGT1_306920, performed IP-MS and compared protein enrichment to the HOOK-3xHA IP (Figure 5C). HOOK, FTS, and TGGT1_306920 were significantly enriched across both IP-MS experiments, whereas TGGT1_316650 is only significantly enriched in HOOK and FTS pulldowns. This suggests the presence of multiple HOOK complexes composed of the core HOOK and FTS proteins that bind with either TGGT1_316650 or TGGT1_306920.”

While further interactions with other members of the complex still need to be validated it is not the standard of the field to validate every member of a protein complex by reciprocal IP. Our HOOK and FTS IP-MS results each identified HOOK, FTS, TGGT1_306920, and TGGT1_316650 and our TGGT1_306920 IP-MS identified all members except TGGT1_316650. These interaction partners were found significantly enriched compared to parental controls, which make the observation of the complex robust.

Reviewer #1 (Recommendations For The Authors):

I have only a few minor comments:

1. In the supplemental data section I would include a document of code ( R script) used for the analysis. If this is too cumbersome then I would instead suggest that like done with proteomic data, the code should be deposited in a database that provides a DOI for access, instead of only being provided on request. This can be done by use of an electronic laboratory notebook or via Github.com or a similar service.

Zip files containing R code and CSVs have been included for the sub-minute resolution phosphoproteomics (Supplementary File 11) and thiophosphorylation (Supplementary File 12).

2. It would be useful to expand the discussion of the other 2 proteins identified in the HOOK complex TGGT1_316650 and 306920. Do these have homologs to proteins in other organisms? Based on HOOK in other eukaryotes can you provide a model of the 4 proteins in the complex that you identified? Was any work done on 316650 and 306920 with regards to genetic KO or auxin regulation to see if they also provided a similar phenotype to what was described with HOOK and FTS?

We have included the following information in our discussion:

“It also remains unknown how the HOOK complex binds to micronemes. In H. sapiens and D. melanogaster, RAB5 on vesicles interacts with FHIP in the HOOK complex(Bielska et al., 2014; Gillingham et al., 2014; Guo et al., 2016; Xu et al., 2008; Yao et al., 2014). We speculate that TGGT1_306920 may serve the role of FHIP within the HOOK complex as it is fitness conferring whereas TGGT1_316650 appears dispensable but the complex's binding partner on micronemes remains unknown. RAB5A and RAB5C have been implicated in the biogenesis of micronemes, but their roles during exocytosis have not been explored(Kremer et al., 2013). Understanding how micronemes are recognized may elucidate how cargo specificity is achieved and regulated.”

TGGT1_306920 is conserved amongst coccidians and shares similar localization to HOOK and FTS. TGGT1_316650 is conserved amongst apicomplexans and more broadly in subsets of other eukaryotic phyla. Given our IP-MS data, HOOK and FTS form a core complex that is either bound to TGGT1_316650 or TGGT1_306920. Given that TGGT1_306920 appears to be important for parasite fitness, based on genome-wide screening data (Sidik, Huet, et al. 2016), we speculate this could function to mediate the linkage to microneme organelles. At this time, we have no additional data to present on 316650 and 306920. Additional biochemical studies will be needed to characterize the stoichiometry of complexes and their function; however, we propose that HOOK and FTS are interacting as previously described in opisthokonts (Bielska et al., 2014, Guo et al., 2016 and Zhang et al., 2014). 

3. The myristoylation data section ended with "additional studies will be required to understand how myristoylation influences CDPK1 activity". What studies are required to further this understanding? I assume these studies are difficult and that is why they were not part of this outstanding paper.

The effect of myristoylation is modest during acute phenotypes like egress (see Figure 2H). Moreover there were no significant differences between cWT and cMut that could explain the impact of CDPK1 on microneme secretion, which was the purpose of this study. Further studies would require a phosphoproteomic workup of the cWT and cMut, which is beyond the scope of the present study.

4. In the key resource table, in the first column reagent type I suggest you indicate this as T. gondii RH strain to make it clear the background strain (I know it is encoded in additional information but the first column should also be clear).

We have updated the key resources table to indicate the T. gondii strains used are of RH background.

Reviewer #2 (Recommendations For The Authors):

I have a few minor comments that could be addressed by modification of the current version of the manuscript.

Line 290, where authors classify proteins phosphorylated in CDPK1 dependent manner into five groups, it would be helpful to list at least class 1 (five proteins) and class 2 (four proteins) in the text of the results section. Further since in the same paragraph, the authors are also describing figure 3G, it would be helpful if the groups are identified with roman numerals or as class A, B, C, D, and E. Currently, in fig 3G, the three columns (CDPK1 dependent, CDPK1 independent and fitness scores) are also identified as 1, 2 and 3 and these nomenclatures could be confused with the five different classes of putative substrates.

We thank the reviewer for their helpful suggestion. We have renamed the classes of CDPK1 targets using roman numerals I, II, III, IV, and V. We have also listed out the proteins in Class I and Class II in the results section as follows:

“Class I contains five proteins for which the same phosphorylated site was identified in both the time course and thiophosphorylation experiments and include: TGGT1_227610, TGGT1_221470, TGGT1_235160, TGGT1_273560 (KinesinB), and TGGT1_310060. Class II contains four proteins for which phosphorylated sites identified across both approaches were within 50 amino acid residues of one another and include: TGGT1_289100 (MIC18), TGGT1_309190 (AIP), TGGT1_254870, and TGGT1_259630.”

Line 398, the expansions of the abbreviations FTS and FHIP should be included.

We have included the expansions of the abbreviations for FTS and FHIP:

“In D. melanogaster and mammals, HOOK proteins have been shown to form dimers and bind Fused Toes (FTS) and FTS and HOOK-interacting protein (FHIP) via a C-terminal region that interacts with vesicular cargo (Christensen et al., 2021; Krämer and Phistry, 1996; Lee et al., 2018; Xu et al., 2008).”

The HOOK protein shows CDPK1-dependent phosphorylation at multiple sites S167, S177, and S189-191. In the discussion section, it would be helpful if the authors can speculate about the importance of these phosphorylated residues on the functioning of HOOK.

Prior to engaging parasite motility, micronemes are positioned at the apical third of the parasite, but after an increase in intracellular Ca_2+_, micronemes rapidly traffic to the apical tip of the parasite. Our results indicate that both CDPK1 kinase activity and HOOK are required for microneme trafficking. Given the association of micronemes with tubulin-based structures such as the cortical microtubules and conoid, activation of trafficking along such structures must be rapid, on the time scale of seconds. Cell-free reconstitution assays generated from opisthokonts indicate that activating adaptors like HOOK are necessary to activate processive dynein trafficking along microtubules in addition to conferring cargo selectivity. In intracellular non-motile parasites, HOOK is expressed and localized to the apical end and cytosol prior to the activation of rapid microneme trafficking, consistent with regulation of HOOK activity. We have included reference to this type of regulation and our expectation that CDPK1 activates the HOOK complex as part of the Discussion:

“Phosphorylation has been reported to regulate the function of activating adaptors. In HeLa cells, phosphorylation of BICD2 facilitates recruitment of dynein and dynactin (Gallisà-Suñé et al. 2023). Analogously, phosphorylation of JIP1 mediates the switch between kinesin and dynein motility of autophagosomes in murine neurons (Fu et al. 2014). We therefore speculate that phosphorylation of HOOK by CDPK1 may activate the adaptor by promoting its interaction with dynein and dynactin to initiate trafficking of micronemes.”

Reviewer #3 (Recommendations For The Authors):

1. CDPK1 myristoylation. The loss of myristoylation of CDPK1 appears to increase its interaction with ROM4 which also becomes cytosolic instead of localizing to the plasma membrane. As ROM4 is necessary for microneme discharge after proteolysis it would be interesting to investigate the specific relation between CDPK1 and ROM4 and to confirm the interaction by reciprocal IP.

Please see our response to Suggested Revision #1.

2. CDPK1 myristoylation, Figure 2D. It would be useful to compare the expression of the tagged and complemented (cWT and cMut) CDPK1 with the endogenous expression of the non-tagged and non-complemented gene.

Please see our response to Suggested Revision #2.

3. Thiophosphorylation. The authors used the bacterial toxin aerolysin to semi-permeabilize parasite membranes by forming 3-nm pores. Aerolysin affects the membrane integrity, however, the authors demonstrated that CDPK1 is possibly associated with membrane structures (Figure 2E/F). Could it be possible to transiently destabilize the membrane before to treat with KTPγS or ATP? If not, it would be necessary to confirm that aerolysin treatment does not impact myristoylation-dependent subcellular partitioning of CDPK1 before identifying proteins specifically labelled by CDPK1G and not by CDPK1M (Figure 3C).

Please see our response to Essential Revision #3.

4. IP-MS on HOOK-3xHA parasites. The authors' results suggest that HOOK and FTS form a functional complex implicated in microneme exocytosis. It would be interesting to know if HOOK knockdown can have an effect on FTS expression or localization and reciprocally.

While we agree with the reviewer that this is an interesting question, we focused this paper on the discovery of the complex in relation to CDPK1. Understanding the regulation and interaction of the complex components is the focus of ongoing work and will require generation of new strains and additional mass spectrometry. For those reasons we find these experiments fall beyond the scope of the present study.

5. FTS-Turbo-ID. (Line 443-444) Authors should confirm the interaction of TGGT1_306920 and TGGT1_316650 with the HOOK and FTS proteins, it will give strength to their conclusion. In fact, without confirmation, everything is based on suggestions that were also formulated but not confirmed in humans. The physical existence of this putative complex should be demonstrated by co-IP experiments. In addition, the missing player is a dynein candidate itself, which leaves the model vulnerable. Short of pursuing this experimentally, it should at least be commented on in the Discussion.

Please see our response to Sugegsted Revision #4. Our IP-MS experiments of HOOK-3xHA and FTS-3xHA indicate interactions with HOOK, FTS, TGGT1_316650, and TGGT1_306920. Our FTS-TurboID experiments also suggest an interaction between FTS, HOOK, TGGT1_316650 and TGGT1_306920. Furthermore, our TGGT1_306920 IP-MS data identifies HOOK and FTS, but not TGGT1_316650, suggesting distinct complexes with HOOK and FTS as core components.

6. MIC2 secretion (Fig 5J). The rep represented by the grey dot with a white outline seems like an outlier result compared to the other 2 reps. Basically, without this rep there at least is a strong trend that there is a difference in secretion without EtOH stimulation. That is what actually would be expected, for constitutive secretion! Please carefully reconsider these data - e.g. check for outlier statistics and/or add reps.

We present three independent biological replicates, showing a significant difference in microneme secretion following depletion of CDPK1, HOOK, or FTS. It is expected, based on our prior experience, that microneme secretion will vary day to day. However, the expected trend can be observed in all replicates. We are unclear what the reviewer means by constitutive secretion since some low-level of calcium-dependent microneme discharge is expected even in the absence of stimulation, barring BAPTA-AM treatment. Even in the absence of EtOH stimulation (left graph in Fig. 5J), the trend of diminished basal MIC2 release holds when CDPK1, HOOK, or FTS is knocked down.

7. Apical accumulation of micronemes. A similar observation was made upon manipulation of Ferlin1, which is a manuscript on BioRXivs. Since other BioRXiv manuscripts are cited in the presented work, this is an omission.

We apologize for this omission and have updated the manuscript accordingly:

“It therefore appears that the initial round of microneme discharge during egress depends on CDPK1, and only subsequent rounds require the HOOK complex. Indeed, a fraction of micronemes are already found docked at the apical complex prior to the transition from the replicative to the motile stages, and may constitute the first round of microneme exocytosis (Mageswaran et al., 2021; Sun et al., 2022). Ferlin 1 (FER1) was recently shown to be involved in microneme positioning and overexpression of FER1 was sufficient to initiate an initial round of microneme exocytosis and induce egress (Tagoe et al. 2020).”

Minor comments:

1. Concerning the expression of the HOOK protein in Figures 4B, and C, could the author indicate why they performed the IFA after 24h of auxin treatment and the WB after 40h of treatment?

The difference in timing was for technical reasons. Our immunoblots and additional assays such as microneme secretion require more parasites, such that we harvest at the end of the lytic cycle to increase yields. For the IFAs, we perform these at 24 hrs, which allows for depletion and replication, but captures parasites in small vacuoles that show clear localization patterns. Furthermore, our microneme relocalization studies in Figure 6 were also performed after 24 hrs of auxin treatment, yet exhibit a trafficking defect following  24 hr HOOK depletion.

2. Fig 4H. The color of CDPK1-AID on the left and the HA on the top (HOOK) do correspond but indicate different proteins. Please label HOOK text in teal, not CDPK1.

We have changed the text color of the strain names on 4H to black to avoid confusion with the IFA channel labels.

3. I would like to suggest adding the "Key resources tables" in the supplementary data because it makes the materials & methods harder to read.

The key resources table was included at the beginning of the Materials and Methods section as indicated in eLife’s instructions to the authors.

Author Response

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

eLife assessment:

This study presents a useful inventory of the joint effects of genetic and environmental factors on psychotic-like experiences, and identifies cognitive ability as a potential underlying mediating pathway. The data were analyzed using solid and validated methodology based on a large, multi-center dataset. However, the claim that these findings are of relevance to psychosis risk and have implications for policy changes are only partially supported by the results.

We appreciate the feedback and insightful suggestions from the editor and reviewers, which aided us to improve the manuscript. We believe the concerns initially raised were mostly due to areas that needed further clarification, which we have now clarified in this revised version. Our primary contribution lies in our meticulous analytical approach aimed at minimizing confounding effects and providing more precise estimates of the genetic and environmental impact on children's cognition and psychology. This method differs from the widely used general linear modeling in the field, which, in our opinion, may not be the optimal strategy for large-scale data analysis. Our comprehensive, tutorial-style description of the methods might serve as a valuable resource for the community.

Regarding the critique that our findings 'partially support the relevance to psychosis risk,' we have updated our manuscript to more accurately reflect this feedback. We have altered the narrative to indicate that psychotic-like experiences (PLE) are associated with the risk for psychosis, a connection substantiated by prior studies cited in our manuscript.

Similarly, in response to the comment that our findings 'partially support implications for policy changes,' we have nuanced our conclusion. However, we would like to emphasize our discovery that a negative genetic predisposition impacting cognitive development (i.e., low polygenic scores for cognitive phenotypes) can be counteracted by a positive school and familial environment. We believe that this finding could have meaningful implication for policy making and is robustly supported by our analyses.

We hope this revised manuscript more accurately reflects our research findings and its significances. Lastly, we would like to express our gratitude for your fair and detailed review process. Our experience working with eLife has been incredibly rewarding, and we commend your dedication to an encouraging and progressive publishing culture.  

Public Reviews:

Reviewer #1

This study by Park et al. describes an interesting approach to disentangle gene-environment pathways to cognitive development and psychotic-like experiences in children. They have used data from the ABCD study and have included PGS of EA and cognition, environmental exposure data, cognitive performance data and self-reported PLEs. Although the study has several strengths, including its large sample size, interesting approach and comprehensive statistical model, I have several concerns:

• The authors have included follow-up data from the ABCD Study. However, it is not very clear from the beginning that longitudinal paths are being explored. It would be very helpful if the authors would make their (analysis) approach clearer from the introduction. Now, they describe many different things, which makes the paper more difficult to read. It would be of great help to see the proposed path model in a Figure and refer to that in the Method.

We clarified the longitudinal paths tested in this study in Intro [line 149~159]. We also added a figure of the proposed path model (Figure 1) [Methods: line 231~238].

• There is quite a lot of causal language in the paper, particularly in the Discussion. My advice would be to tone this down.

We adjusted and moderated the use of causal languages throughout the manuscript.

• I feel that the limitation section is a bit brief, and can be developed further.

We clearly specified the limitations of our study. These included concerns about the representativeness of the ABCD samples, of the limited scope of longitudinal data, and the use of non-randomized, observational data [line 524~544].

• I like that the assessment of CP and self-reports PEs is of good quality. However, I was wondering which 4 items from the parent-reported CBCL were used and how did they correlate with the child-reported PEs? And how was distress taken into account in the child self-reported PEs measurement? Which PEs measures were used?

Thanks for the clarification question. We report the Pearson’s correlation coefficients between the PLEs [line 198~200]. (The Reviewer #1 may have referred to the prior version of our manuscript submitted elsewhere, for this point has been already addressed in our initial submission to eLife).

• What was the correlation between CP and EA PGSs?

The Pearson’s correlation between CP and EA PGS was 0.4331 (p<0.0001). We added the statistics to the manuscript. [line 214]

• Regarding the PGS: why focus on cognitive performance and EA? It should be made clearer from the introduction that EA is not only measuring cognitive ability, but is also a (genetic) marker of social factors/inequalities. I'm guessing this is one of the reasons why the EA PGS was so much more strongly correlated with PEs than the CP PGS. See the work bij Abdellaoui and the work by Nivard.

We appreciate the reviewer’s insightful feedback. Acknowledging the role of both CP and EA PGSs in our study, we agree with the observation that EA PGS goes beyond gauging cognitive aptitude—it also serves as an indicator of societal influences and inequalities. The multifaceted nature of EA PGS could be the reason underlying the stronger correlation with PLEs compared to CP PGS. In response to this feedback, we revised our introduction to articulate the multifaceted role of EA PGS in more precise terms. For supporting our assertions, we have included references to prior studies (Abdellaoui et al., 2022) [line 131~142].

Abdellaoui, A., Dolan, C. V., Verweij, K. J. H., & Nivard, M. G. (2022). Gene–environment correlations across geographic regions affect genome-wide association studies. Nature Genetics. doi:10.1038/s41588-022-01158-0

• Considering previous work on this topic, including analyses in the ABCD Study, I'm not surprised that the correlation was not very high. Therefore, I don't think it makes a whole of sense to adjust for the schizophrenia PGS in the sensitivity analyses, in other words, it's not really 'a more direct genetic predictor of PLEs'.

We thank the reviewer for the thoughtful comments. We acknowledge that the correlation between schizophrenia PGS and PLE may not be exceedingly high, as evidenced by previous work, including analyses from the ABCD study. However, we would like to emphasize our rationale for adjusting schizophrenia PGS in the sensitivity analyses. Our study design stemmed from the established associations between PLEs and increased risk for schizophrenia. Existing studies have reported significant associations between schizophrenia PGS and cognitive deficits in both psychosis patients (Shafee et al., 2018) and people at risk for psychosis (He et al., 2021). Notable, the PGS for schizophrenia has shown significant associations with PLEs, arguably more so than PGS for PLEs itself (Karcher et al., 2018). Our updated manuscript has incorporated these references to improve clarity. [line 307~309]. By adding this layer of adjustment, we believe that our mixed linear model more precisely examines the relationship between the cognitive phenotype PGS and PLEs, in terms of both sensitivity and specificity.

He, Q., Jantac Mam-Lam-Fook, C., Chaignaud, J., Danset-Alexandre, C., Iftimovici, A., Gradels Hauguel, J., . . . Chaumette, B. (2021). Influence of polygenic risk scores for schizophrenia and resilience on the cognition of individuals at-risk for psychosis. Translational Psychiatry, 11(1). doi:10.1038/s41398-021-01624-z

Karcher, N. R., Paul, S. E., Johnson, E. C., Hatoum, A. S., Baranger, D. A. A., Agrawal, A., . . . Bogdan, R. (2021). Psychotic-like Experiences and Polygenic Liability in the Adolescent Brain Cognitive Development Study. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging. doi:https://doi.org/10.1016/j.bpsc.2021.06.012

Shafee, R., Nanda, P., Padmanabhan, J. L., Tandon, N., Alliey-Rodriguez, N., Kalapurakkel, S., . . . Robinson, E. B. (2018). Polygenic risk for schizophrenia and measured domains of cognition in individuals with psychosis and controls. Translational Psychiatry, 8(1). doi:10.1038/s41398-018-0124-8

• How did the FDR correction for multiple testing affect the results?

Please note that we have clarified our FDR correction in the methods

As detailed in the method section [line 254~255], we applied False Discovery Rate (FDR) correction for multiple testing across nine key variables in the study: PGS (CP or EA), family income, parental education, family’s financial adversity, Area Deprivation Index, years of residence, proportion of population below -125% of the poverty line, positive parenting behavior, and positive school environment. An exception was made in our additional sensitivity analysis, where we included schizophrenia PGS in the linear mixed model for adjustment, thus the FDR correction was applied across ten key variables instead. Overall, the application of FDR correction had minimal impact on our findings. Most associations between the key variables and the outcomes that were originally marked as highly significant sustained their significance after the FDR correction.

Overall, I feel that this paper has the potential to present some very interesting findings. However, at the moment the paper misses direction and a clear focus. It would be a great improvement if the readers would be guided through the steps and approach, as I think the authors have undertaken important work and conducted relevant analyses.

We express our appreciation to the reviewer for the positive feedback and constructive suggestions, which only serve to improve and strengthen our manuscript. We have incorporated the suggested corrections and clarifications in response to the reviewer's suggestions. We believe that these changes will not only enhance the overall readability but also more effectively emphasize the significance and implication of our work.

Reviewer #2 (Public Review):

This paper tried to assess the link between genetic and environmental factors on psychotic-like experiences, and the potential mediation through cognitive ability. This study was based on data from the ABCD cohort, including 6,602 children aged 9-10y. The authors report a mediating effect, suggesting that cognitive ability is a key mediating pathway in the link between several genetic and environmental (risk and protective) factors on psychotic-like experiences.

While these findings could be potentially significant, a range of methodological unclarities and ambiguities make it difficult to assess the strength of evidence provided.

Strengths of the methods:

The authors use a wide range of validated (genetic, self- and parent-reported, as well as cognitive) measures in a large dataset with a 2-year follow-up period. The statistical methods have the potential to address key limitations of previous research.

Weaknesses of the methods:

The rationale for the study is not completely clear. Cognitive ability is probably a more likely mediator of traits related to negative symptoms in schizophrenia, rather than positive symptoms (e.g., psychosis, psychotic-like symptom). The suggestion that cognitive ability might lead to psychotic-like symptoms in the general population needs further justification.

We appreciate the reviewer’s concern regarding the role of cognitive ability in relation to schizophrenia symptoms. We are aware that cognitive ability often serves as a mediator of psychotic-like experiences. However, to our best knowledge, a growing body of research has proposed that cognitive ability can mediate positive symptoms in schizophrenia including psychotic-like experiences. The studies by Howes & Murray (2014) and Garety et al. (2001) suggested that deficits in cognitive ability can potentially contribute to the manifestation of positive symptoms such as psychotic-like experiences. We have elaborated on this aspect in the Introduction section [line 104-115].

Howes, O. D., & Murray, R. M. (2014). Schizophrenia: an integrated sociodevelopmental-cognitive model. The Lancet, 383(9929), 1677-1687. doi:https://doi.org/10.1016/S0140-6736(13)62036-X

Garety, P. A., Kuipers, E., Fowler, D., Freeman, D., & Bebbington, P. E. (2001). A cognitive model of the positive symptoms of psychosis. Psychological Medicine, 31(2), 189-195. doi:10.1017/S0033291701003312

Terms are used inconsistently throughout (e.g., cognitive development, cognitive capacity, cognitive intelligence, intelligence, educational attainment...). It is overall not clear what construct exactly the authors investigated.

We thank the reviewer’s feedback regarding the consistency of terminology in our manuscript. Per the suggestion, we standardized the use of ‘cognitive capacity’ and now consistently refer to it as ‘cognitive phenotypes’ throughout our manuscript. Furthermore, we explicitly stated in the Introduction section that our two PGSs of focus will be termed ‘cognitive phenotypes PGSs’, aligning with terminology used in prior studies (Joo et al., 2022; Okbay et al., 2022; Selzam et al., 2019) [line 140~142].

Joo, Y. Y., Cha, J., Freese, J., & Hayes, M. G. (2022). Cognitive Capacity Genome-Wide Polygenic Scores Identify Individuals with Slower Cognitive Decline in Aging. Genes, 13(8), 1320. doi:10.3390/genes13081320

Okbay, A., Wu, Y., Wang, N., Jayashankar, H., Bennett, M., Nehzati, S. M., . . . Young, A. I. (2022). Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals. Nature Genetics, 54(4), 437-449. doi:10.1038/s41588-022-01016-z

Selzam, S., Ritchie, S. J., Pingault, J.-B., Reynolds, C. A., O’Reilly, P. F., & Plomin, R. (2019). Comparing Within- and Between-Family Polygenic Score Prediction. The American Journal of Human Genetics, 105(2), 351-363. doi:https://doi.org/10.1016/j.ajhg.2019.06.006

Not the largest or most recent GWASes were used to generate PGSes.

We appreciate the reviewer’s observation. Indeed, we were unable to utilize the most recent or the largest GWAS for cognitive performance, educational attainment, and schizophrenia due to the timeline of our study. Regrettably, the commencement of our study preceded the publication of the ‘currently’ the largest or most recent GWAS studies by Okbay et al. (2022) and Trubetskoy et al. (2022). Our research was conducted with the best available data at that time, which was the GWAS of European-descent individuals for educational attainment and cognitive performance (Lee et al, 2018). To eliminate any potential confusion, we adjusted the text to specify that our study used 'a GWAS of European-descent individuals for educational attainment and cognitive performance' rather than the largest GWAS [line 206~208].

It is not fully clear how neighbourhood SES was coded (higher or lower values = risk?). The rationale, strengths, and assumptions of the applied methods are not fully clear. It is also not clear how/if variables were combined into latent factors or summed (weighted by what). It is not always clear when genetic and when self-reported ethnicity was used. Some statements might be overly optimistic (e.g., providing unbiased estimates, free even of unmeasured confounding; use of representative data).

Thank you for pointing this out. Consistent with the illustration of neighborhood SES in the Methods, higher values of neighborhood SES indicate risk [line 217~228]. In the original Figure 2, higher value of neighborhood SES links to lower intelligence (direct effects: β=-0.1121) and higher PLEs (indirect effects: β=-0.0126~ -0.0162). We think such confusion might have been caused by the difference between family SES (higher values = lower risk) neighborhood SES (higher values = higher risk). Thus, we changed the terms to ‘High Family SES’ and ‘Low Neighborhood SES’ in the corrected figure (Figure 3) for clarification.

Considering that shorter duration of residence may be associated with instability of residency, it may indicate neighborhood adversity (i.e., higher risk). This definition of the ‘years of residence’ variable is in line with the previous study by Karcher et al. (2021).

During estimation, the IGSCA determines weights of each observed variable in such a way as to maximize the variances of all endogenous indicators and components. We added this explanation in the description about the IGSCA method [line 266~268].

We deleted overly optimistic statements like ‘unbiased estimates’ and used expressions such as ‘adjustment for observed/unobserved confounding’ instead, throughout our manuscript.

Karcher, N. R., Schiffman, J., & Barch, D. M. (2021). Environmental Risk Factors and Psychotic-like Experiences in Children Aged 9–10. Journal of the American Academy of Child & Adolescent Psychiatry, 60(4), 490-500. doi:10.1016/j.jaac.2020.07.003

It appears that citations and references are not always used correctly.

We thoroughly checked all citations and specified the references for each statement: We deleted Plomin & von Stumm (2018) and Harden & Koellinger (2020) and cited relevant primary studies (e.g., Lee et al., 2018; Okbay et al., 2022; Abdellaoui et al., 2022) instead. We also specified the references supporting the statement that educational attainment PGS links to brain morphometry (Judd et al., 2020; Karcher et al., 2021). As Okbay et al. (2022) use PGS of cognitive intelligence (which mentions the analyses results in their supplementary materials) as well as educational attainment, we decided to continue citing this reference [line 131~141].

Strengths of the results:

The authors included a comprehensive array of analyses.

We thank the reviewer for the positive comment.

Weaknesses of the results:

Many results, which are presented in the supplemental materials, are not referenced in the main text and are so comprehensive that it can be difficult to match tables to results. Some of the methodological questions make it challenging to assess the strength of the evidence provided in the results.

As you rightly identified, we inadvertently failed to reference Table S2 in the main text. We have since corrected this omission in the Results section for the IGSCA (SEM) analysis [line 376]. The remainder of the supplementary tables (Table S1, S3~S7) have been appropriately cited in the main manuscript. We recognize that the quantity of tables provided in the supplementary materials is substantial. However, given the comprehensiveness and complexity of our analyses, which encompass a wide array of study variables, these tables offer intricate results from each analysis. We deem these results, which include valuable findings from sensitivity analyses and confound testing, too significant to exclude from the supplementary materials. That said, we are open to, and would greatly welcome, any further suggestions on how to present our supplementary results in a more clear and digestible format. Your guidance in this matter is highly valued.

Appraisal:

The authors suggest that their findings provide evidence for policy reforms (e.g., targeting residential environment, family SES, parenting, and schooling). While this is probably correct, a range of methodological unclarities and ambiguities make it difficult to assess whether the current study provides evidence for that claim.

We believe that with the improvement we made in this revised manuscript, this concern may have been successfully mitigated.

Impact:

The immediate impact is limited given the short follow-up period (2y), possibly concerns for selection bias and attrition in the data, and some methodological concerns.

We appreciate the feedback provided in the reviewer's impact statement. We added as study limitations [line 524~544] that the impact of our findings may be limited due to the relatively short follow-up period, the possibility of sample selection bias, and the problems of interpreting results from an observational study as causality (despite the novel causal inference methods, designed for non-randomized, observational data, that we used).

As responded above (and also in more detail in the Reviewer #2’s Recommendations For The Authors section below), we made necessary corrections and clarifications for the points suggested by the reviewer. As we are willing to make additional revisions, please feel free to give comments if you feel that our corrections are insufficient or inappropriate.

Nevertheless, we would like to discuss some points. We sincerely hope this following response does not come across as argumentative to the reviewer and the editor. We fully understand the reviewer's perspective on this matter, and we agree that the issues raised about the ABCD study are absolutely valid. However, when evaluating the overall impact of a study, other factors, such as how the field has been assessing the impact of similar studies, should also be considered.

Firstly, the potential selection bias and attrition in the ABCD data may not necessarily limit the conclusions of this study. While recognizing the potential issues with the ABCD data is important, we feel that judging the impact of our findings as "limited" based on these issues may not be entirely fair. This is because no study, particularly those of a nationwide scale such as the UK Biobank, IMAGEN, HEAL, HBCD, etc., is completely free of limitations. Typically, the potential limitations of the data don't undermine the impact of individual studies' findings. Numerous studies using ABCD data have been published in top-tier journals—despite the limitations of the ABCD study—underscoring the scientific merit of the findings. For example, the study by Tomasi, D., & Volkow, N. D. (2021), entitled "Associations of family income with cognition and brain structure in USA children: prevention implications," published in Molecular Psychiatry, might be highly relevant to the limitations of the ABCD study raised by the reviewer. The scientific community, including editors, reviewers, and readers, may have appreciated the impact of this study despite the acknowledged limitations of the ABCD data.

Secondly, the two-year time window of our longitudinal analysis might not impact the aim of this study—an iterative assessment of the associations between genetic and environmental variables with cognitive intelligence and mental health, with a focus on PLE, in preadolescents. Had we aimed to test the developmental trajectory from childhood to adolescence, perhaps a longer timeframe would have made more sense. So, we do not agree with the reviewer’s assessment that the short time window limits the impact of our study.

Suggested revisions based on the combined reviewer feedback:

1) The terminology used should be carefully reviewed and revised

• Please use the correct terminology for the key concepts assessed in this study. For example, authors sometimes conflate PLEs and psychosis, two related but separate constructs. Furthermore, the terms 'good parenting' and 'good schooling' are vague and subjective.

• The authors use multiple terms to refer to cognitive ability (cognitive capacity, intelligence, cognitive intelligence, etc). The term 'cognitive development' in the title and manuscript does not seem to be justified given the focus on different measures of cognitive ability at a single time point (i.e. baseline).

• Please avoid causal language and using statements that cannot be entirely substantiated (e.g. unbiased estimates, free from unmeasured confounding)

Thank you for suggesting this point. We revised all key terminologies used throughout our manuscript.

Per your suggestion, we specified that PLEs indicate the risk of psychosis and often precede schizophrenia. We checked all misused cases of the term ‘psychosis’ and corrected them as ‘PLEs’. We also changed the terms 'good parenting' and 'good schooling' to ‘positive parenting behavior’ and ‘positive school environment’.

We changed the term ‘cognitive development’ to ‘cognitive ability’ throughout our manuscript. We also changed the title to ‘Gene-Environment Pathways to Cognitive Intelligence and Psychotic-Like Experiences in Children’ because we used ‘cognitive intelligence’ for NIH toolbox variable in the text.

We corrected and tone-downed all causal languages used in our manuscript. As mentioned by the reviewers, we deleted statements like ‘unbiased estimates’ and used expressions such as ‘adjustment for observed/unobserved confounding’ instead.

2) A stronger rationale for the focus on PLEs, and the potential mediating role of cognitive ability in genetic and environmental effects on PLES, should be provided

We appreciate the raised concerns that cognitive ability may serve as a mediator of psychotic-like experiences. To our best knowledge, it has been proposed that cognitive ability can be a mediator of positive symptoms in schizophrenia (including psychotic-like experiences), as well as negative symptoms. This mediating role of cognitive ability was proposed in several prior studies on cognitive model of schizophrenia/psychosis. Per your suggestion, we included an additional justification in Intro [line 104~115] where we highlighted that cognitive ability has been proposed as a potential mediator of genetic and environmental influence on positive symptoms of schizophrenia such as psychotic-like experiences. We refer to studies conducted by Howes & Murray (2014) and Garety et al. (2001).

Howes, O. D., & Murray, R. M. (2014). Schizophrenia: an integrated sociodevelopmental-cognitive model. The Lancet, 383(9929), 1677-1687. doi:https://doi.org/10.1016/S0140-6736(13)62036-X

Garety, P. A., Kuipers, E., Fowler, D., Freeman, D., & Bebbington, P. E. (2001). A cognitive model of the positive symptoms of psychosis. Psychological Medicine, 31(2), 189-195. doi:10.1017/S0033291701003312

3) As described in more detail by the reviewers, more information should be provided about the measures used in the study and how they relate to one another (e.g. correlations between PQ-BC and CBCL; PGS-CA and PGS-EA).

Thank you for your suggestion. Although this information was already provided in our initial submission, it appears that the Reviewer #1’s might have referred to the prior version of our manuscript submitted elsewhere before eLife.

To clarify, our findings reveal significant Pearson’s correlation coefficients between PLEs across all time-points (baseline year: r=0.095~0.0989, p<0.0001; 1-year follow-up: r=0.1322~0.1327, p<0.0001; 2-year follow-up: r= 0.1569~0.1632, p<0.0001) and we added this information in the Method section [line 198~200]. We also added the Pearson’s correlation between the two PGSs (r=0.4331, p<0.0001) in the Methods for PGS [line 214].

4) More details are needed regarding the analytical strategies used (e.g. how imputation was performed, why PGS were not based on the largest and most recent GWASes, whether latent or observed variables were examined, what exactly the supplementary materials show and how they relate to information provided in the main text).

We appreciate your feedback. We acknowledge the concerns about the GWAS sources utilized for the study. Unfortunately, our study commenced prior to the publication of the ‘currently’ most recent or largest GWAS by Okbay et al. (2022) and Trubetskoy et al. (2022). Our research was conducted with the best available data at that time, which was the largest GWAS of European-descent individuals for educational attainment and cognitive performance (Lee et al, 2018). We have now clarified this point in the manuscript. [line 206~208]

Also, we specified the use of composite indicators for the PGS, family SES, neighborhood SES, positive family and school environment, and PLEs, while latent factors were used for cognitive intelligence [line 269~285].

We highly appreciate the reviewer’s comments regarding the supplementary materials. We regret overlooking the citation of Table S2 in the main manuscript, and this has now been rectified in the Results section for the IGSCA (SEM) analysis [line 376]. The remaining supplementary tables (Table S1, S3~S7) have been correctly referenced within the manuscript. We acknowledge that the supplementary materials are extensive due to the comprehensive array of study variables and intricate results from each analysis. However, given that our analyses encompass a wide array of study variables, these tables offer intricate results from each analysis. We deem these results, which include valuable findings from sensitivity analyses and confound testing, too crucial to exclude from the supplementary materials. That said, we are open to any further suggestions to make our supplementary results more accessible and digestible. In order to improve the accessibility and clarity of our presentation, we are fully committed to making any necessary changes and look forward to any further recommendations.

5) The limitation section should be expanded and statements regarding the implications of the study findings should be qualified accordingly (e.g. short follow-up period, potential for attrition and selection bias, reverse causation, etc)

We specified additional potential constraints of our study, including limited representativeness, limited periods of follow-up data (baseline year, 1-year, and 2-year follow-up), possible sample selection bias, and the use of non-randomized, observational data [line 524~544].

6) Please ensure that the references provided support the statements in the text to which they are linked to.

Thank you for pointing this out. We thoroughly went over all citations and corrected the inaccurately or vaguely cited references for each statement.

Reviewer #2 (Recommendations For The Authors):

1) Please use terms consistently and correctly. E.g., 'cognitive capacity' is not the same as 'educational attainment'.

We thank the reviewer’s feedback regarding the consistency of terminology in our manuscript. Per the suggestion, we standardized the use of ‘cognitive capacity’ and now consistently refer to it as ‘cognitive phenotypes’ throughout our manuscript. Furthermore, we explicitly stated in the Introduction section that our two PGSs of focus will be termed ‘cognitive phenotypes PGSs’, aligning with terminology used in prior studies (Joo et al., 2022; Okbay et al., 2022; Selzam et al., 2019) [line 140~142].

Joo, Y. Y., Cha, J., Freese, J., & Hayes, M. G. (2022). Cognitive Capacity Genome-Wide Polygenic Scores Identify Individuals with Slower Cognitive Decline in Aging. Genes, 13(8), 1320. doi:10.3390/genes13081320

Okbay, A., Wu, Y., Wang, N., Jayashankar, H., Bennett, M., Nehzati, S. M., . . . Young, A. I. (2022). Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals. Nature Genetics, 54(4), 437-449. doi:10.1038/s41588-022-01016-z

Selzam, S., Ritchie, S. J., Pingault, J.-B., Reynolds, C. A., O’Reilly, P. F., & Plomin, R. (2019). Comparing Within- and Between-Family Polygenic Score Prediction. The American Journal of Human Genetics, 105(2), 351-363. doi:https://doi.org/10.1016/j.ajhg.2019.06.006

2) The authors study 'cognitive performance using seven instruments', but it is not clear how fluid and crystalline intelligence was defined/operationalized.

Thank you for pointing this out. We specified the NIH Toolbox tests used for composite scores of fluid and crystallized intelligence, respectively. “We utilized baseline observations of uncorrected composite scores of fluid intelligence (Dimensional Change Card Sort Task, Flanker Test, Picture Sequence Memory Test, List Sorting Working Memory Test), crystallized intelligence (Picture Vocabulary Task and Oral Reading Recognition Test), and total intelligence (all seven instruments) provided in the ABCD Study dataset” [line 180~187].

3) I don't think Lee 2018 is the largest GWAS for educational attainment. That would be Okbay 2022. It needs to be described how cognitive performance was defined in Lee 2018. Why did the authors not use the Trubetskoy 2022 schizophrenia GWAS?

Thank you for mentioning this point. The reason why we were not able to use the largest GWAS for CP, EA and schizophrenia is because (unfortunately) our study started earlier than the point when the GWAS studies by Okbay et al. (2022) and Trubetskoy et al. (2022) were published. We corrected that our study used ‘a GWAS of European-descent individuals for educational attainment and cognitive performance’ instead of the largest GWAS [line 206~208].

4) It is unclear how neighbourhood SES was coded. The authors seem to suggest that higher values indicate risk, but Figure 2 suggests that higher values links to higher intelligence and lower PLE.

Thank you very much for pointing this out. Consistent with the illustration of neighborhood SES in the Methods section, higher values of neighborhood SES indicate risk. In the original Figure 2, higher values of neighborhood SES links to lower intelligence (direct effects: β=-0.1121) and higher PLEs (indirect effects: β=-0.0126~-0.0162). We think such confusion might have been caused by the difference between family SES (higher values = lower risk) neighborhood SES (higher values = higher risk). Thus, we changed the terms to ‘High Family SES’ and ‘Low Neighborhood SES’ in the corrected figure (Figure 3) for clarification.

5) Also, the 'year of residence' variable is unclearly defined. Does this mean that a shorter duration of residency (even in a good neighbourhood) indicate risk?

Thank you for mentioning this point. Considering that shorter duration of residence may be associated with instability of residency, it may indicate neighborhood adversity (i.e., higher risk). This definition of the ‘years of residence’ variable is in line with the previous study by Karcher et al. (2021).

Karcher, N. R., Schiffman, J., & Barch, D. M. (2021). Environmental Risk Factors and Psychotic-like Experiences in Children Aged 9–10. Journal of the American Academy of Child & Adolescent Psychiatry, 60(4), 490-500. doi:10.1016/j.jaac.2020.07.003

6) Please provide information on how correlated the two PGSes were.

Thank you for your suggestion. We added the Pearson’s correlation between the two PGSs (r=0.4331, p<0.0001) in the Methods section for PGS [line 214].

7) Information on the outcome variable in the 'linear mixed models' section is missing. I assumed it was PLE.

Thank you for notifying us of this point. We added the information on the outcome variables in the section for linear mixed models [line 242~244].

8) In the 'Path Modeling' section, please explain what 'factors and components' concretely refer to. How is this different from a standard SEM with latent factors?

Thank you for your comment on the need to elaborate the IGSCA method. We added that different from standard SEM methods which only uses latent factors, the IGSCA method can use components as well as latent factors as constructs in model estimation. This allows the IGSCA method to control bias more effectively in estimation compared to the standard SEM [line 261~268].

9) The sentence starting line 229 is unclear. Does this mean variables were not used to generate latent factors. And if not, what weights were used to create a 'weighted sum'?

Thank you for mentioning this point. The sentence means that we treated PGSs, family SES, neighborhood SES, positive family and school environment, and PLEs as composite indicators (derived from a weighted sum of relevant observed variables), while general intelligence was represented as a latent factor.

It has been suggested from prior studies that these variables (PGSs, family SES, neighborhood SES, positive family and school environment, and PLEs) are less likely to share a common factor and were assessed as a composite index during analyses. For instance, Judd et al. (2020) and Martin et al. (2015) analyze genetic influence of educational attainment and ADHD as composite indicators. Also, as mentioned in Judd et al. (2020), socioenvironmental influences are often analyzed as composite indicators. Studies on psychosis continuum (e.g., van Os et al., 2009) suggest that psychotic disorders are likely to have multiple background factors instead of having a common factor, and notes that numerous prior research uses composite indices to measure psychotic symptoms. Based on this literature, we used components for these variables.

The IGSCA determines weights of each observed variable to maximize the variances of the endogenous indicators and components [added in line 265~268].

On the other hand, we treated general intelligence as a latent factor/variable underlying fluid and crystallized intelligence. This is based on the extensive literature of classical g theory of intelligence [added in line 269~284].

Judd, N., Sauce, B., Wiedenhoeft, J., Tromp, J., Chaarani, B., Schliep, A., ... & Klingberg, T. (2020). Cognitive and brain development is independently influenced by socioeconomic status and polygenic scores for educational attainment. Proceedings of the National Academy of Sciences, 117(22), 12411-12418.

Martin, J., Hamshere, M. L., Stergiakouli, E., O'Donovan, M. C., & Thapar, A. (2015). Neurocognitive abilities in the general population and composite genetic risk scores for attention‐deficit hyperactivity disorder. Journal of Child Psychology and Psychiatry, 56(6), 648-656.

van Os, J., Linscott, R., Myin-Germeys, I., Delespaul, P., & Krabbendam, L. (2009). A systematic review and meta-analysis of the psychosis continuum: Evidence for a psychosis proneness–persistence–impairment model of psychotic disorder. Psychological Medicine, 39(2), 179-195. doi:10.1017/S0033291708003814

10) It is overall not clear when genetically and when self-reported information of ethnicity was used. This needs to be clearer throughout.

Thank you for mentioning this point. We only used genetically defined ethnicity, and we have not mentioned that we used self-reported ethnicity. Per your suggestion, we clarified that we used ‘genetic ethnicity’ throughout the paper.

11) The sentence starting line 253 is also unclear. How is schizophrenia PGS a 'more direct genetic predictor of PLE' and compared to what other measure?

Thank you for pointing this out. Please note that our adjustment (or sensitivity analyses) was based on the reported associations between PLEs and the risk for schizophrenia: schizophrenia PGS is associated with a cognitive deficit in psychosis patients (Shafee et al., 2018) and individuals at-risk of psychosis (He et al., 2021), and psychotic-like experiences (more so than PGS for psychotic-like experiences) (Karcher et al., 2018). We added these references for clarification [line 307~309]. We believe that because of the adjustment our results from the mixed linear model show the sensitivity and specificity of the association between cognitive phenotype PGS and PLEs.

He, Q., Jantac Mam-Lam-Fook, C., Chaignaud, J., Danset-Alexandre, C., Iftimovici, A., Gradels Hauguel, J., . . . Chaumette, B. (2021). Influence of polygenic risk scores for schizophrenia and resilience on the cognition of individuals at-risk for psychosis. Translational Psychiatry, 11(1). doi:10.1038/s41398-021-01624-z

Karcher, N. R., Paul, S. E., Johnson, E. C., Hatoum, A. S., Baranger, D. A. A., Agrawal, A., . . . Bogdan, R. (2021). Psychotic-like Experiences and Polygenic Liability in the Adolescent Brain Cognitive Development Study. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging. doi:https://doi.org/10.1016/j.bpsc.2021.06.012

Shafee, R., Nanda, P., Padmanabhan, J. L., Tandon, N., Alliey-Rodriguez, N., Kalapurakkel, S., . . . Robinson, E. B. (2018). Polygenic risk for schizophrenia and measured domains of cognition in individuals with psychosis and controls. Translational Psychiatry, 8(1). doi:10.1038/s41398-018-0124-8

12) Please include a statement on the assumptions made when using the method used in this study and developed by Miao 2022, explain what evidence you have to support these assumptions and how this method, which I believe was developed for RCTs, can be applied to observational data.

We specified the assumptions for the causal inference method proposed by Miao et al. (2022) and why it is applicable to our study. Also, we noted that this novel method was developed to identify the causal effects of multiple treatment variables within non-randomized, observational data [line 309~319].

13) Some of the statements are potentially misleading. E.g., I would be very cautious to claim that the methods applied allowed the authors to estimate 'unbiased associations again potential (even unobserved) confounding variables'. There are many concerns such as selection bias, attrition, reverse causation, genetic confounding, etc that cannot be addressed satisfactorily using these data and methods.

Thank you for pointing this out. We deleted statements like ‘unbiased estimates’ and used expressions such as ‘adjustment for observed/unobserved confounding’ instead.

Nevertheless, please note that due to some limitations in the data (e.g., confounders), an analytic approach should be robust enough to handle potential violations of assumptions. This was the point we wanted to emphasize--In contrast to the majority of studies using the ABCD study, which employ simplistic GLM or conventional SEM with only latent variable modeling, our study provides less biased, thus more accurate, estimates through the use of sophisticated modeling for confounding effects (instead of simplistic GLM) and IGSCA (instead of conventional simplistic SEM). We hope our study may help improve our analytical approach in this field.

14) I would be equally cautious to claim that the ABCD study is representative. Please add information on the whole ABCD cohort to Table 1 and describe any relevance with respect to attrition effects or representativeness.

Thank you for highlighting this issue. We previously characterized the ABCD Study as representative of the US population, given its aim to ensure representativeness by recruiting from a broad range of school systems located near each of its 21 research sites, chosen for their geographic, demographic, and socioeconomic diversity. Using epidemiological strategies, a stratified probability sample of schools was selected for each site. This procedure took into account sex, race/ethnicity, socioeconomic status, and urbanicity to reduce potential sampling biases at the school level. Based on these strategies, previous research (e.g., Thompson et al., 2019; Zucker et al., 2018) has referred to the ABCD Study as ‘representative.’ However, we overlooked the fact that “not all 9-year-old and 10-year-old children in the United States had an equal chance of being invited to participate in the study,” and therefore, it should not be deemed fully representative of the US population (Compton et al., 2019). Heeding your suggestion, we have removed all descriptions of the ABCD Study being representative.

Compton, W. M., Dowling, G. J., & Garavan, H. (2019). Ensuring the Best Use of Data: The Adolescent Brain Cognitive Development Study. JAMA Pediatrics, 173(9), 809-810. doi:10.1001/jamapediatrics.2019.2081

Thompson, W. K., Barch, D. M., Bjork, J. M., Gonzalez, R., Nagel, B. J., Nixon, S. J., & Luciana, M. (2019). The structure of cognition in 9 and 10 year-old children and associations with problem behaviors: Findings from the ABCD study’s baseline neurocognitive battery. Developmental Cognitive Neuroscience, 36, 100606. doi:10.1016/j.dcn.2018.12.004

Zucker, R. A., Gonzalez, R., Feldstein Ewing, S. W., Paulus, M. P., Arroyo, J., Fuligni, A., . . . Wills, T. (2018). Assessment of culture and environment in the Adolescent Brain and Cognitive Development Study: Rationale, description of measures, and early data. Developmental Cognitive Neuroscience, 32, 107-120. doi:https://doi.org/10.1016/j.dcn.2018.03.004

15) The imputation methods need to be explained in more detail / more clearly. What concrete variables were included? Why was 50% of the sample excluded despite imputation? How similar is the study sample to the overall ABCD cohort - and to the US population in general (i.e., is this a representative dataset)?

Thank you for mentioning this point. We clarified the method and detailed processes of the imputation (e.g., R package VIM, number of missing observations for each study variables such as genotypes, follow-up observations, and positive environment) [Methods; line 167~176].

The final samples had significantly higher cognitive intelligence, parental education, family income, and family history of psychiatric disorders, lower Area Deprivation Index, percentage of individuals below -125% of the poverty level, and family’s financial adversity (p<0.05). As you have noted above, these results also show the limited representativeness of the data used in our study. We fully acknowledge that our study sample, as well as the overall ABCD cohort, is not representative of the US population in general.

16) There are a range of unclear statements (e.g., 'Supportive parenting and a positive school environment had the largest total impact on PLEs than genetic or environmental factors' - isn't parenting an environmental factor?).

Thank you for mentioning this point. We clarified seemingly vague expressions and unclear statements. We corrected the sentence you noted as ‘Supportive parenting and a positive school environment had the largest total impact on PLEs than any other genetic or environmental factors’ [line 57~58].

17) The authors' conclusion (that these findings have policy implications for improving school and family environmental) are not fully supported by the evidence. E.g., genetic effects were equally large.

Thank you for pointing this out. Our description should be clearer. Our models consistently show that the combined environmental effects of positive family/school environment, and family/neighborhood SES exceeds the genetic effects. We suggest that these findings may have policy implications for “improving the school and family environment and promoting local economic development” [line 62~64].

To clarify, we newly added “Despite the undeniable genetic influence on PLEs, when we combine the total effect sizes of neighborhood and family SES, as well as positive school environment and parenting behavior (∑▒〖|β|〗=0.2718~0.3242), they considerably surpass the total effect sizes of cognitive phenotypes PGSs (|β|=0.0359~0.0502)” [line 510~513]. Based on these results, we suggest that our findings hold potential policy implications for “preventative strategies that target residential environment, family SES, parenting, and schooling—a comprehensive approach that considers the entire ecosystem of children's lives—to enhance children's cognitive ability and mental health” in the Discussion [line 507~510].

Admittedly, our results do not directly demonstrate a causal effect wherein an intervention in the school or family environmental variables would necessarily lead to a significantly meaningful positive impact on a child's cognitive intelligence and mental health. We do not make such a claim in this paper. However, we anticipate that further integrative analyses akin to ours might help identify potential causal or prescriptive effects. We hope this perspective will be recognized as one of the contributions of our study. We leave the final decision to the discerning judgment of the editors and reviewers.

18) Many citations do not support the statements made and are sometimes used rather vaguely. For example, I believe Judd 2020 and Okbay 2022 did not use a PGS of cognitive capacity, but of educational attainment. Plomin 2018 and Harden 2020 are reviews, but the primary studies should be cited instead. Which reference exactly is supporting the statement that cognitive capacity PGS links to brain morphometry?

Thank you very much for your precise observations. We thoroughly checked all citations and updated the references for each statement.

We deleted Plomin & von Stumm (2018) and Harden & Koellinger (2020) and cited relevant original research articles (e.g., Lee et al., 2018; Okbay et al., 2022; Abdellaoui et al., 2022) instead. We also specified the references supporting the statement that educational attainment PGS links to brain morphometry (Judd et al., 2020; Karcher et al., 2021). As Okbay et al. (2022) used the PGS of cognitive intelligence (which presented the analyses results in their supplementary materials) as well as educational attainment, we decided to continue citing this reference [line 131~141].

19) Citations are formatted inconsistently.

We apologize for the inconsistency of the citation formatting. We formatted all citations in APA 7th style, using EndNote v20. We checked that all citations maintain consistency according to the reference style.

20) Re line 281, I believe effect sizes are 'up to twice as large', but not consistently twice as large as suggested in the text.

Thank you for mentioning this point. We corrected the sentence as ‘The effect sizes of EA PGS on children's PLEs were larger than those of CP PGS’ [line 342~343].

21) Please add to the results a short statement on what covariates these analyses were controlled for.

Thank you for giving us this comment. We added that we used sex, age, marital status, BMI, family history of psychiatric disorders, and ABCD research sites as covariates in the Results section [line 329~331].

22) Cho 2020 does not provide recommendations on FIT values (line 315). Please provide another reference and explain how these FIT values should be interpreted.

Thank you for mentioning this point. We added the correct reference for FIT values (Hwang, Cho, & Choo, 2021). We also added that the FIT values range from 0 to 1, and a larger FIT value indicates more variance of all variables is explained by the specified model (e.g., FIT=0.50 denotes that the model explains 50% of the total variance of all variables) [line 291~293].

23) Regarding Figure 2, please add factor loadings to this figure and explain what the difference between the hexagon and circular shapes are. Please also add the autocorrelations between the 3 PLE measures. I assume these were also modelled statistically, given the strong correlations between time points?

Figure 2B needs reworking.

It is unclear what the x-axis of Figure 2C represents. Proportion of R2 or effect size? SM table 2 provides key information, which should be added to Figure 2.

Thank you for pointing this out. We added factor loadings to the corrected figure (Figure 3A and 3B). We also added that the X-axis of Figure 3C represents standardized effect sizes.

24) I suggest adding units directly to Table 1, not in the legend. Was genetic or self-reported ethnicity used in this table? List age in years, not months?

Thank you for your suggestion. We added the units of age and family history of psychiatric disorders directly inside Table 1. We used genetic ethnicity in Table 1, as we only used genetic ethnicity (but not self-reported ethnicity) throughout our study. This is noted on the last row of Table 1. We listed age in chronological months, which is how each child’s age at each point of data collection is coded in the ABCD Study.

25) Please include exact p-values in Table 2.

Thank you for your suggestion. We highly appreciate the reviewer’s comment on the importance of showing exact p-values in the analysis results. Unfortunately, we cannot estimate the standard errors based on normal-theory approximations to obtain the exact p-values of our IGSCA model results. This is described in detail in the original paper of the IGSCA method (Hwang et al., 2021): “Like GSCA and GSCAM, IGSCA is also a nonparametric or distribution-free approach in the sense that it estimates parameters without recourse to distributional assumptions such as multivariate normality of indicators. As a trade-off of no reliance on distributional assumptions, it cannot estimate the standard errors of parameter estimates based on asymptotic (normal-theory) approximations. Instead, it utilizes the bootstrap method (Efron, 1979, 1982) to obtain the standard errors or confidence intervals of parameter estimates nonparametrically.”

Efron, B. (1979). Bootstrap methods: Another look at the jackknife. Annals of Statistics, 7, 1–26. http://dx.doi.org/10.1214/aos/1176344552

Efron, B. (1982). The jackknife, the bootstrap and other resampling plans. Philadelphia, PA: SIAM. http://dx.doi.org/10.1137/1.9781611970319

Hwang, H., Cho, G., Jung, K., Falk, C. F., Flake, J. K., Jin, M. J., & Lee, S. H. (2021). An approach to structural equation modeling with both factors and components: Integrated generalized structured component analysis. Psychological Methods, 26(3), 273-294. doi:10.1037/met0000336

26) There are way too many indigestible tables presented in the supplementary materials, which are also not referenced in the main manuscript.

We appreciate your insightful observation. As you rightly identified, we inadvertently failed to reference Table S2 in the main text. We have since corrected this omission in the Results section for the IGSCA (SEM) analysis [line 376]. The remainder of the supplementary tables (Table S1, S3~S7) have been appropriately cited in the main manuscript. We recognize that the quantity of tables provided in the supplementary materials is substantial. However, given the comprehensiveness and complexity of our analyses, these tables offer intricate results from each analysis. We deem these results, which include valuable findings from sensitivity analyses and confound testing, too significant to exclude from the supplementary materials. That said, we are open to, and would greatly welcome, any further suggestions to ensure clarity and ease of comprehension. Your guidance in this matter is highly valued.

27) Figure S1 is unclear, possibly due to the journal formatting. Is this one figure presented on two pages? Clarify which PGS is listed in Figure S1 and in any case, please add both PGSs.

Thank you for mentioning this point. Figure S1 presents two correlation matrices: the first one is the correlation matrix of component / factor variables in the IGSCA model and the second one is the that of observed variables used to construct the relevant component / factor variables in the IGSCA model. We noted each matrix as Figure S1-A and Figure S1-B. We also corrected the figure legend as “A. Correlation between all component / factor variables of the IGSCA model. B. Correlation between all observed variables used to construct the relevant component / factor variables in the IGSCA model.” Since Figure S1-A presents correlations between the components and latent factors, it lists a single PGS variable constructed from the CP PGS and EA PGS. On the other hand, Figure S1-B presents correlations between the observed variables. Thus, both CP PGS and EA PGS are listed in this correlation matrix.

Author Response

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

We thank the three reviewers for their positive comments and helpful suggestions. We have addressed the issues raised which have helped to improve the manuscript. Below, we address the specific points with detailed responses.

Reviewer #1 (Recommendations For The Authors):

Minor comments

1) Figure 2 - figure supplement 1. The figure states minimal medium while the legend states rich medium.

We have corrected the legend as the experiment was done in minimal medium.

2) Figure 3B - the statements in the text do not seem to match what is in the figure. "Cluster 1 (293 genes, 12 priority unstudied) is enriched for genes showing high expression variability across different conditions (71) and for genes induced during meiotic differentiation (72) and in response to TORC1 inhibitors (29). Cluster 2 (570 genes, 20 priority unstudied) is enriched for phenotypes related to cell mating and sporulation, e.g. 'incomplete cell-wall disassembly at cell fusion site' or 'abnormal shmoo morphology'". These terms (high expression variability, meiotic differentiation, TORC1 inhibitors, cell mating and sporulation/abnormal shmoo morphology" are not seen in the figure.

As stated in the Results, we have carried out analyses with both Metascape and AnGeLi for functional enrichments in different GO and KEGG pathway terms (Figure 3B; Metascape) and/or among genes from published expression or phenotyping studies (AnGeLi). The enrichments for expression variability, meiotic differentiation, TORC1 inhibitors, and cell mating/sporulation/abnormal shmoo morphology are not based on GO terms but on lists from published expression and phenotyping experiments. We have slightly edited the sentence in the Results to make this clearer.

3) The authors could consider citing a systematic screen for sporulation in the introduction (PMID: 292590

We have cited 17 papers for growth screens under different conditions using similar approaches as used by us. Given that we already cite 100 papers, we did not choose to cite numerous other papers reporting screens for more complex phenotypes (cell morphology, mating, meiosis, recombination, etc), which are not directly relevant to our study here.

Reference PMID: 292590 refers to a 1979 paper in the German Dentist Journal.

Reviewer #2 (Recommendations For The Authors):

General comments

1) The authors use their NET-FF approach to predict GO Biological Process and Molecular Function terms (Figure 4). Why was the Cellular Component ontology not included? In general, gene and protein functional characterization is best described by the Biological Process and Cellular Component ontologies, whereas Molecular Function describes the biochemical activity of a protein. In other words, proteins which share Biological Process and/or Cellular Component annotations often function in the same module, which may not be the case for shared Molecular Function annotations.

We did not include Cellular Component because in previous benchmarking of our method using CAFA datasets our approach did not perform well at predicting Cellular Component. This aspect is harder to pick up from homology data and protein network data and is generally the toughest challenge in CAFA. In contrast, our predictions of Biological Process and Molecular Function are competitive with other methods. We have now made the reason for omitting Cellular Component clearer in the Methods.

2) The authors use protein embeddings produced by integrating 6 STRING networks using the deepNF method. One of these networks is the "database" network. According to STRING (https://academic.oup.com/nar/article/47/D1/D607/5198476): "The database channel is based on manually curated interaction records assembled by expert curators, at KEGG, Reactome, BioCyc and Gene Ontology, as well as legacy datasets from PID and BioCarta". If one of the input networks contains information from GO, and then embeddings containing this information are used to predict GO annotations, are the authors not then leaking annotations which could improve downstream GO annotation predictions? It would be valuable to demonstrate to what extent the "database" network is contributing by repeating the GO prediction analyses with this network removed.

We agree and also pointed out this circularity in the manuscript. We used an independent dataset – phenotype data – to benchmark our method, which showed good performance. Note that this study did not aim to develop a completely new method or improve on deepNF and CATH-FunFams but to integrate and exploit their combined power. For that reason, we wanted to keep as many high-quality curated edges in the STRING network as possible. Combining these independent methods brings synergies from their complementary approaches to facilitate interpretation of gene function.

Minor comments

1) Ternary encoding was used as a preprocessing step on the phenotype data before clustering was performed. An explanation of why this encoding was necessary (as opposed to a normalization/standardization approach) would be helpful.

Ternary encoding was not strictly necessary but provided more nuanced and coherent clusters. Some conditions and mutants were associated with much larger phenotypic responses which disproportionately influenced the clustering. After trying different approaches, we followed the recommendations from the R package microbialPhenotypes (https://github.com/peterwu19881230/microbialPhenotypes), which is now specified in the legend of Fig. 3A. Discretizing the data also helped to compare phenotypes across different types of mutants, and we have applied this approach previously in our phenomics study of non-coding RNA mutants (Rodriguez-Lopez et al. eLife 2022). Moreover, this approach allowed us to generate vectors of phenotypes for calculating phenotypic distances between mutants (including hamming distance or Pearson correlations), which supported the posterior cluster analysis using Cytoscape.

2) The authors use a validation set to perform early-stopping on the deepNF model. However, it appears that the validation set proteins are then used in downstream analyses anyway: "After training, weights from the epoch with the lowest validation loss were used to generate embeddings for all proteins" (my emphasis). In the case where the model was being used to generalize to new proteins (such as classification), this analysis would not be a valid way to perform hyperparameter tuning (e.g. early-stopping) since the validation set is then used in downstream analyses. However, deepNF is performing an unsupervised, multi- network encoding on all the available datapoints (proteins). In the case where only deepNF loss is being used to tune the hyperparameters, it's not necessary to use a held-out validation set - it is appropriate to use the full set of proteins to do this.

Our Random Forest consisted of 500 trees with default values for the number of sub- features as √n and partial sampling of 0.7. GO terms were predicted using 5-fold cross- validation. Changing parameters showed that our model was robust to the values of the hyperparameters, so we settled on our initial model.

3) The NET-FF hyperparameter tuning results should be made available in the supplement.

We do not think this would be useful for the reason described in the reply above.

Reviewer #3 (Recommendations For The Authors):

Major points

1) Why were the quantitive colony size data converted to -1, 0, and 1?

It is unclear to me why the authors decided to convert the colony size data to ternary encoding of -1, 0, and 1. The original colony size data seem to be of fairly high precision so that the authors can detect a 5% difference from the wild type. I guess the authors must have tried using the quantitive colony size data for clustering analysis and found the results unsatisfactory. If that is the case, can the authors provide some possible explanations?

A similar query has been raised by Reviewer 2. Ternary encoding provided more nuanced and coherent clusters. Some conditions and mutants were associated with much larger phenotypic responses which disproportionately influenced the clustering. After trying different approaches, we followed the recommendations from the R package microbialPhenotypes, as now specified in the legend of Fig. 3A. Discretizing the data also helped to compare phenotypes across different types of mutants, and we have applied this approach previously in our phenomics study of non-coding RNA mutants (Rodriguez-Lopez et al. eLife 2022). Moreover, this approach allowed us to generate vectors of phenotypes for calculating phenotypic distances between mutants (including hamming distance or Pearson correlations), which supported the posterior cluster analysis using Cytoscape.

2) What do 5% difference and 10% difference look like?

The authors used 5% difference and 10% difference as cutoffs. I am curious whether a 5% difference in colony size is obvious to human eyes. Can the authors show some plate images and label colonies that differ from the wild type by about 5% and 10%? It will help readers understand the thresholds used for determining whether a mutant has a phenotype.

Showing the original ‘raw’ colonies would not be meaningful because all colony sizes have been grid-corrected as described (Kamrad et al. eLife 2020). The grid correction takes care of three issues: (1) it converts colony size into an easily interpretable value by reporting a ratio relative to wild type; (2) it makes results comparable across different plates/batches; and (3) it corrects for within-plate positional effects which become apparent due to the same wild-type grid strain showing different fitness in different plate positions. But in principle, detecting a 5% difference in colony size by eye would be hard, and multiple measurements are required (>10 repeats) to obtain statistically reliable results. Author response image 1 shows the grid colonies in red frames and numbers at bottom right of colonies indicate the corrected effect sizes. Colony 17-8 (top right) is an example of a colony differing by 5% compared to neighbouring colonies 16-8 and 17-9.

Author response image 1.

3) How were the phenotyping conditions chosen?

I am sure that the authors have put a lot of thoughts into designing the 131 phenotyping conditions. It will benefit the readers if the authors can explain how these conditions were chosen. For example, what literature precedents were considered and which conditions have never been examined before in S. pombe research? For drug treatment conditions, were pilot tests done to choose drug doses based on the growth inhibition effects on the wild type?

We have used a wide range of different types of conditions that affect diverse processes (see colour legend on top of Fig. 3A). This was based on our previous experience and selection of conditions in large-scale phenotyping of wild strains (Jeffares et al. Nature Genetics 2015) and non-coding RNA mutants (Rodriguez-Lopez et al. eLife 2022). For previously applied conditions (e.g. oxidants), we used literature precedents for the doses, while for other conditions, we used trial and error to adjust the diose such that wild-type cell growth is barely inhibited. For some drugs and stresses, we assayed both low and high doses, in which wild-type cell growth is normal or inhibited, respectively, to uncover both sensitive or resistant mutants.

Minor points

1) One of the growth condition is "YES_ethanol_1percent_no_glucose". I am curious how this is possible, as S. pombe cannot use ethanol as a carbon source.

We assume that the cells contain sufficient internal glucose to fuel growth and division for a few cycles before running out of glucose. Thus, cells showed some residual growth on this medium, but growth is indeed very limited. Nevertheless, we could identify both sensitive and resistant mutants in this condition.

2) Abstract "over 900 new proteins affected the resistance to oxidative stress". This sentence should be rephrased. Perhaps it is better to say "over 900 proteins were newly implicated in the resistance to oxidative stress".

Yes, we have edited the sentence as suggested.

3) Page 4 "S. pombe encodes 641 'unknown' genes (PomBase, status March 2023). " "Among these 643 unknown proteins, many are apparently found only in the fission yeast clade, but 380 are more widely conserved. " Which number is correct, 641 or 643?

These numbers keep changing slightly. We now consistently use 641, the number from March 2023.

4) Page 4 "These priority unstudied proteins have not been directly studied in any organism but can be assumed to have pertinent biological roles conserved over 500 million years of evolution. " According to http://timetree.org/, S. pombe and H. sapiens diverged about 1275 million years ago.

We have now changed ‘over 500 million’ to ‘over 1000 million’, although there are of course different estimates for these times.

5) "Using these potent wet and dry methods, we obtained 103,520 quantitative phenotype datapoints for 3,492 non-essential genes across 131 diverse conditions."

I think "quantitative phenotype datapoints" are generated using wet methods, not dry methods. Yes, we have now deleted ‘Using these potent wet and dry methods,’ and start the sentence with ‘We obtained…’

6) Abstract "We assayed colony-growth phenotypes to measure the fitness of deletion mutants for all 3509 non-essential genes"

Page 6 "We performed colony-based phenotyping of the deletion mutants for all non- essential S. pombe genes"

It is not clear to me how the authors can claim that the 3509 non-essential genes correspond to "all non-essential S. pombe genes". The authors should explain how they classify S. pombe genes into essential genes and non-essential genes. The deletion project papers (Kim et al. 2010 and Hayles et al. 2013) provided binary classification for most but not all genes, as there are genes whose deletion mutants were not generated by the deletion project. PomBase does not use a binary classification and there are a number of genes deemed "Gene Deletion Viability: Depends on conditions" by PomBase.

We used the latest deletion library (Bioneer Version 5) as well as additional deletion mutants published by Kathy Gould and colleagues, which together should capture all non- essential genes. But we agree that non-essentiality is not that clear-cut and context- dependent. So we have deleted ‘all’ in the two sentences highlighted above.

7) Page 20 "Other clusters contained mostly genes involved in vacuolar/endosomal transport and peroxisome function, along with poorly characterized genes (Figure 6B)."

This sentence needs rephrasing. Perhaps it is better to say "Cluster 31 and cluster 22 contained respectively mostly genes involved in vacuolar/endosomal transport and peroxisome function, along with poorly characterized genes (Figure 6B)."

We have edited this sentence to ‘Cluster 31 and Cluster 22 contained mostly genes involved in vacuolar/endosomal transport and peroxisome function, respectively, along with poorly characterized genes (Figure 6B).’

8) Legend of Figure 2-figure supplement 1A

"Left: Volcano plot of mutant colony sizes for priority unstudied genes (green) and all other genes (grey) growing in rich medium. " I think "rich medium" should be "minimal medium".

Yes, we have now corrected this.

Author Response

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

eLife assessment

This important study expands on current knowledge of allosteric diversity in the human kinome by C-terminal splicing variants using as a paradigm DCLK1. The authors provide solid evolutionary and some mechanistic evidence how C-terminal isoform specific variants generated by alternative splicing can regulate catalytic activity by means of coupling specific phosphorylation sites to dynamical and conformational changes controlling active site and substrate pocket occupancy, as well as protein-protein interactions. The data will be of interest to researchers in the kinase and signal transduction field.

We thank the editor for coordinating the review of our manuscript and the reviewers for their valuable feedback. We have significantly revised the manuscript in response to the reviewer’s comments. Our point-by-point response to each comment is present below. We have uploaded both a clean draft of our revised manuscript as well as a version with the revisions highlighted in yellow. We hope the revised manuscript is now acceptable for publication in eLife. We have additionally updated the preprint on bioRxiv and have included the link: We thank the editor for coordinating the review of our manuscript and the reviewers for their valuable feedback. We have significantly revised the manuscript in response to the reviewer’s comments. Our point-by-point response to each comment is present below. We have uploaded both a clean draft of our revised manuscript as well as a version with the revisions highlighted in yellow. We hope the revised manuscript is now acceptable for publication in eLife. We have additionally updated the preprint on biorxiv and have included the link here: https://www.biorxiv.org/content/10.1101/2023.03.29.534689v2.

Reviewer #1

Summary

In the study by Venkat et al. the authors expand the current knowledge of allosteric diversity in the human kinome by c-terminal splicing variants using as a paradigm DCLK1. In this work, the authors provide evolutionary and some mechanistic evidence about how c-terminal isoform specific variants generated by alternative splicing can regulate catalytic activity by means of coupling specific phosphorylation sites to dynamical and conformational changes controlling active site and substrate pocket occupancy, as well as interfering with protein-protein interacting interfaces that altogether provides evidence of c-terminal isoform specific regulation of the catalytic activity in protein kinases.

The paper is overall well written, the rationale and the fundamental questions are clear and well explained, the evolutionary and MD analyses are very detailed and well explained. The methodology applied in terms of the biochemical and biophysical tools falls a bit short in some places and some comments and suggestions are given in this respect. If the authors could monitor somehow protein auto-phosphorylation as a functional readout would be very useful by means of using phospho-specific antibodies to monitor activity. Overall I think this is a study that brings some new aspects and concepts that are important for the protein kinase field, in particular the allosteric regulation of the catalytic core by c-terminal segments, and how evolutionary cues generate more sophisticated mechanisms of allosteric control in protein kinases. However a revision would be recommended.

Major Comments

The authors explain in the introduction the role of T688 autophosphorylation site in the function of DCLK1.2. This site when phosphorylated have a detrimental impact on catalytic activity and inhibits phosphorylation of the DCX domain. allowing the interaction with microtubules. In the paper they show how this site is generated by alternative splicing and intron skipping in DCLK1.2. However there is no further functional evidence along the functional experiments presented in this study.

1) What is the effect of a non-phosphorylable T688 mutant in terms of stability and enzymatic activity? What would be the impact of this mutant in the overall auto-phosphorylation reaction?

The role of T688 phosphorylation on DCLK1 functions has been explored in previous studies (Agulto et al, 2020: PMID: 34310279), although only relevant to DCLK1.2 splice variants, since this site is lacking in DCLK1.1. These studies showed that mutation of T688 to an alanine increases total kinase autophosphorylation (ie autoactivity) and the subsequent phosphorylation of DCX domains, which in turn decreases microtubule binding. Given this information, our goal was to use an evolutionary perspective to investigate this, alongside less-well characterized aspects of DCLK autoregulation, including co-conserved residues in the catalytic domain and C-terminal tail. However, to address the reviewers question of a non-phosphorylatable T688 mutant, we performed MD simulations of T688A and T688E (a phosphomimic) mutant and include a new supplementary figure (Figure 5-supplement 3) which show the two mutants slightly destabilize the C-tail relative to wt (1 and 2 angstrom increase in RMSF for T688E and T688A respectively), but by themselves cannot dislodge the C-tail from the ATP binding pocket. Thus, other co-conserved interactions as revealed by our analysis, are likely to contribute to the autoregulation of the kinase domain by the C-terminal tail. We have incorporated these observations into the revised results section.

Furthermore, to address the reviewer’s question in terms of site-specific autophosphorylation as a marker of DCLK1.2 activity, we have now performed a much-more detailed phosphoproteomic analysis of a panel of purified DCLK1.2 proteins after purification from E.coli (Figure 8-figure supplement 2). This showed that we are only able to detect Thr 688 phosphorylated in our ‘activated’ DCLK1.2 mutants, and not in the autoinhibited WT DCLK1.2 version of the protein. This apparent contradiction does not necessary discount Thr 688 as an important regulatory hotspot, but, together with the MD simulations, may imply a decreased contribution of pThr 688 in facilitating/maintaining DCLK1.2 auto-inhibition than previously anticipated, especially in the context of the numerous other stabilizing amino acid contacts that we describe between the C-tail and the ATP-binding pocket. We do, however, propose a mechanism for pThr688 as a potential ATP mimic based on MD analysis. However, we only found MS-based evidence for phosphorylation at this (and other sites in the same peptide) in highly active DCLK1.2 mutants, in which the C-tail remains uncoupled from the ATP-binding site, even in the presence of this regulatory PTM. We acknowledge that better understanding of DCLK biology will require a detailed appraisal of how the DCLK auto-inhibited states are subsequently physiologically regulated (PTMs, protein-protein interaction etc.), but this is beyond the scope of our current evolutionary investigation, and the absence of phosphospecific antibodies makes this challenging currently. We intend to expand upon our current work by assessing the relative contribution of multiple DCLK phosphorylation sites (including, but not limited to, Thr 688) with regard to cellular DCLK auto-regulation in future studies, in part by generating such site-specific phospho-antibodies.

2) Have the authors made an equivalent T687/688 tanden in DCLK1.1 instead of the two prolines?

This is a good point. We have not considered introducing a T687/688 tandem mutation into DCLK1.1 (at the equivalent position to that of DCLK1.2), primarily because the amino acid composition of their respective C-tail domains are so highly divergent across the tail (due to alternative splicing, as discussed in our paper). As discussed in our present study, there are numerous contacts made between specific amino acids in the regulatory C-tail and the kinase domain of DCLK1.2, which functionally occlude ATP binding, and thus change catalytic output. It is these contacts, which are determined by the specific amino acid sequence identity, and not the extended length of the DCLK1.2 C-tail per se, that drives autoinhibition. The alternate amino acid sequence identity of the C-tail of DCLK1.1 does not enable such contacts to form, which we believe explains the different activities of the two isoforms.

Furthermore, our mutational analysis reveals clearly that Thr688 and several other sites are more highly autophosphorylated in the artificially activated DCLK1.2 constructs than WT DCLK1.2, and as such it remains our hypothesis that introduction of the tandem phosphorylation sites into DCLK1.1 is unlikely to be sufficient to impose an auto-inhibitory conformation of the enzyme.

3) Could T688 autophosphorylation be used as a functional readout to evaluate DCLK1.2 activity?

We agree with the reviewer’s suggestion about using autophosphorylation (including potentially Thr688 for DCLK1.2) as a functional read out for DCLK1 activity. In our present study, we identify phosphorylated peptides containing pThr688 only in the mutationally activated DCLK1.2 variants. We have now taken this analytical approach further and performed a detailed comparative phosphoproteomic characterisation of all of our DCLK1 constructs, where we observe marked differences in the overall phosphorylation profiles of the mutant DCLK1.2 (and DCLK1.1) proteins relative to the less phosphorylated WT DCLK1.2 kinase. This manifests as a depletion in the total number of confidently assigned phosphorylation sites within the kinase domain and C-tail of WT DCLK1.2, and also as a depletion in the abundance of phosphorylated peptides for a given site. To help visualise this, individual phosphorylation sites have been schematically mapped onto DCLK1, which has been included as a new extended supplementary figure (Figure 8-figure supplement 2). For comparative analysis of phosphosite abundance, we could only select peptides that could be directly compared between all mutants (identical amino acid sequences) and those found to be phosphorylated in all proteins (these are Ser660 and Thr438); these are now shown in figure supplement 2 as a table. These site occupancies follow what we see with respect to the increased catalytic activity between DCLK1.1 and DCLK1.2 mutants versus DCLK1.2. We also detect increased phosphorylation of DCLK1.1 and activated DCLK1.2 mutants in comparison to (autoinhibited) DCLK1.2, supporting the hypothesis that these mutants are relieving the autoinhibited conformation.

4) What are the evidences of the here described c-terminal specific interactions to be intra-molecular rather than inter-molecular? Have the authors looked at the monodispersion and molecular mass in solution of the different protein evaluated in this study? Basically, are the proteins in solutions monomers or dimers/oligomers?

Analysis of symmetry mates in the crystal structure of DCLK1.2 (PDB ID: 6KYQ) provide no evidence for inter-molecular interactions. Furthermore, to evaluate oligomerization status in solution, we conducted an analytical size exclusion chromatography (SEC) and our analysis reveals that both DCLK1.1 and DCLK1.2 predominantly exist as monomers in solution (Figure 3-Supplements 1-3). These results suggest that the C-terminal tail interactions are primarily intra-molecular.

5) (Figure 3) Did the authors look at the mono-dispersion of the protein preparation? The sec profile did result in one single peak or multiple peaks? Could the authors show the chromatogram? how many species do you have in solution? Was the tag removed from the recombinant proteins or not?

Yes, as mentioned above, the SEC profile resulted in a single peak for both DCLK1.1 and DCLK1.2, which was confirmed as DCLK1 by subsequent SDS-PAGE. We have included the chromatogram and gels in supporting data (Figure 3-supplements 1-3) in the revised manuscript and updated the Methods section. ‘The short N-terminal 6-His affinity tag present on all other DCLK1 proteins described in this paper was left in situ on recombinant proteins, since it does not appear to interfere with DSF, biochemical interactions or catalysis.’

6) Authors should do Michaelis-Menten saturation kinetics as shown in Figure 3C with the WT when comparing all the functional variant analysed in the study. So we can compared the catalytic rates and enzymatic constants (depicted in a table also) kcat, Km and catalytic efficiency constants (kcat/Km)

Thank you for your suggestion. We have performed the requested comparative kinetics analyses for selected functional DCLK1 variants at the same concentration as suggested, using our real-time assay to determine Vmax for peptide phosphorylation as a function of ATP, but at a fixed substrate concentration (we are unable to assess Vmax above 5 µM peptide for technical reasons). The results of these analyses have been included in the revised version of Figure 8-Supplement 1, where they support differences in both Vmax and Km[ATP]; the ratio of these values very clearly points to differences in activities falling into ‘low’ or ‘high’. This kinetic analysis fully supports our initial activity assays, where mutations predicted to uncouple the auto-inhibitory C-tail rescue DCLK1.2 activity to levels similar to DCLK1.1 towards a common substrate.

Minor Comments

It is very interesting how the IBS together with the pT688 mimics ATP in the case of DCLK1.2 to reach full occupancy of the active site. On Figure 8 you evaluate residues of the GRL and IBS interface to probe such interactions.

1) Did the authors look at the T688 non-phosphorylable mutant?

See our response to Major Comment 1 above. In addition, due to the absence of T688 in DCLK1.1, we did not look at the T688A mutant of DCLK1.2 biochemically, partially because it has been characterized in previous studies, but partially because this site is preceeded by another Thr residue. The lack of a selective antibody towards this site makes it difficult to evaluate the role of T688 phosphorylation specifically with respect to DCLK cellular functions and interactions. Therefore, we focused our in vitro efforts to understand how mutations in the IBS impact the catalytic activity of DCLK1.2 by comparing different variants to DCLK1.1.

2) Classification of DCLK C-terminal regulatory elements.

It would be useful to connect the different regulatory elements described in this study to a specific functional and biological setting where these different switches play a role e.g. microtubule interactions and dynamics, cell cycle, cancer, etc..

While the primary focus of our paper is on the mechanism of allosteric regulation of DCLK1, we have indeed touched upon the potential implications of the various regulatory elements of the tail on functions such as microtubule binding and phenotypic effects like cancer progression. However, we acknowledge that a comprehensive understanding of these effects would necessitate a more detailed investigation. This could potentially involve the integration of RNA-seq data with extensive cell assays to evaluate phenotypic effects. We believe that such a future study would be a valuable extension of our current work and could provide further insights into the functional roles of DCLK1.

3) (Figure 3) Could the authors explain the differences in yield between the WT and the D531A mutant. Apparently, it [the yield] does not appear to be caused by a lower stability as indicated by the Tm. Could the authors comment on this? It is important to compare different samples in parallel, in the same experiment and side by side. This applies to the thermal shift data comparing WT and a D531A mutant on panel D and also on panel C a comparison between WT and D531A as negative control should be shown.

WT and D533A (kinase-dead) were indeed analysed in parallel, but have been split in two panels to make the data easier to interpret. The modest differences in yield is likely explained by experimental prep-to-prep variations. Our experience shows that many protein kinase yields vary between kinase and kinase-dead variants, likely due to bacterial toxicity related to enzyme activity. In regards to thermal stability, we would like to emphasize that Differential Scanning Fluorimetry (DSF) is to our mind a more informative and quantitative measure of protein stability than yield from bacteria, because both assess purified proteins at the same concentration. We believe that the DSF data provide a more accurate representation of the real stability differences between the WT and D533A mutant.  

Author Response

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

We gratefully thank the editors and all reviewers for their time spend making their constructive remarks and useful suggestions, which has significantly raised the quality of the manuscript and has enable us to improve the manuscript. Each suggested comment brought forward by the reviewers was accurately considered. The manuscript has been revised in consideration of all suggestions.

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 extended 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 (Exp Hematol, 2021 May;97:32-46). As such, the differentiation to monocyte is reduced under hypoxic conditions, which may subsequently cause a decrease in migration to 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.

Author response image 1.

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.

Author response image 2.

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.

Author response image 3.

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 S1 (panels L and M).

Author response image 4.

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 Author response image 5).

Author response image 5.

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).

Author response image 6.

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 (7 and 14 days). It is currently unclear whether the phagocytic capacity is amplified during the acute phase of HH exposure, especially on the first day. Considering that the spleen contraction on the next day of HH leads to the release of stored RBCs into the bloodstream, and whether this initial reaction leads to ferroptosis, and the phagocytic capacity of RBCs is subsequently weakened after 7 or 14 days under sustained 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.

Author response image 7.

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.

Author response image 8.

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.

Author response image 9.

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.

Author response image 10.

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.

Author response image 11.

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.

Recommendations for the authors: please note that you control which, if any, revisions, to undertake.

Thank you for your comments and recommendations. We appreciate your understanding that the choice of implementing revisions ultimately rests with us. However, we also value your expertise and will seriously consider your suggestions as they can provide additional perspectives to our work and contribute to the overall quality and robustness of our study.

We strive to produce research that meets the highest scientific standards and we believe that constructive criticism, such as yours, helps us to achieve this objective. We will carefully review your comments and consider the appropriate changes to make in order to address your concerns and improve our manuscript.

Reviewer #1 (Recommendations For The Authors):

Minor:

1) HCV in text is a typo, should be HCT. Please edit.

Thanks for the correction. We’ve revised it.

1. Fig 2D is not useful beyond the more accurate measure of HCT in Fig 2G and should be removed.

Thank you for your feedback and suggestion about Fig. 2D. We understand your point regarding the comparative accuracy of HCT in Fig. 2G. However, our intention in including Fig. 2D was to provide a more intuitive visual representation of the erythrocyte position levels, which we believe complements the more precise HCT data. We have observed that the erythrocyte positions significantly increased for 14 days after HH splenectomy, and this trend is visually depicted in Fig. 2D. While HCT provides a more accurate measure, Fig. 2D provides a snapshot that can be more immediately graspable, especially for readers who may prefer visual data. Nevertheless, we appreciate your perspective and will reassess whether the inclusion of Fig. 2D adds enough value to the overall understanding of our findings. If we find that it indeed does not contribute significantly, we will consider removing it in line with your suggestion.

1. What is the purpose of performing splenectomy? It is well established that reticuloendothelial cells of the liver perform a redundant function to splenic macrophages and since these cells are not being evaluated, data following splenectomy is of limited value. Please remove or move to supplement. Alternatively, evaluate what happens in the liver in response to hypoxia. Is there an increase in erythroblasts? Is there a decrease in liver macrophages in the same way as in the spleen in non-splenectomized mice? The minimally increased HCT in hypoxic splenectomized mice (relative to non-splenectomized mice) suggests that the spleen does the primary work of clearance but not exclusively since there is still a major increase in response to hypoxia in splenectomized mice. The sentence (page 16, line 292) states that the spleen is essential which is not the case based on this data.

Thank you for your comments and recommendations. In reality, we have been consistently studying the liver's response to hypobaric hypoxia (HH) exposure. Nevertheless, the changes observed in the liver are contrary to those in the spleen, including an increase in macrophage count and the capacity for erythrophagocytosis, as well as processing heme iron (refer to the above figure for details).

It is widely accepted that HH exposure predominantly induces erythropoiesis by stimulating bone marrow production. The primary objective of this study was not to refute this central mechanism behind erythrocytosis. Instead, our intent was to supplement this understanding by proposing that impaired clearance of red blood cells (RBCs) could potentially exacerbate erythrocytosis. We believe this additional perspective could significantly enhance our understanding of the complex dynamics involved in RBC production and clearance under hypoxic conditions.

Reviewer #2 (Recommendations For The Authors):

The following questions and remarks should be considered by the authors:

1). The methods should clearly state whether the HH was discontinued during the 7- or 14-day exposure for cleaning, fresh water etc. Moreover, how was CO2 controlled? The procedure for splenectomy needs to be described in the methods.

Thank you for your insightful comments and questions. We apologize for any lack of clarity in our original description. To address your questions:

During the 7- or 14-day HH exposure, the HH was not discontinued for cleaning or providing fresh water. We ensured that the cage was thoroughly cleaned, and food and water were sufficiently stocked before placing the mice into the HH chamber. The design of the cage and the HH chamber allowed the mice to have continuous access to food and water during the entire exposure period.

Regarding the control of CO2, the HH chamber was equipped with a CO2 scrubbing system. The system utilized soda lime to absorb excess CO2 produced by the mice, and the air inside the chamber was exchanged with the air outside 25 times per hour to maintain a stable atmospheric concentration and ensure adequate oxygen supply.

As for the procedure for splenectomy, we apologize for the omission in the original manuscript. The mice were anesthetized using isoflurane, and a small incision was made in the left flank to expose the spleen. The spleen was then gently exteriorized, ligated, and excised. The incision was sutured, and the mice were allowed to recover under close monitoring. We ensured that all procedures were performed in accordance with our institution's guidelines for animal care.

2) The lack of changes in MCH needs explanation? During stress erythropoiesis some limit in iron availability should cause MCH decrease particularly if the authors claim that macrophages for rapid iron recycling are decreased. Fig 1A is dispensable. Fig 1G NN control 14 days does not make sense since it is higher than 7 days of HH.

Thank you for your insightful comments and queries. Regarding the lack of changes in Mean Corpuscular Hemoglobin (MCH), our hypothesis is that the decrease in iron recycling in the spleen following HH is potentially compensated by the increased iron absorption or supply from the liver, thus maintaining the iron requirement for erythropoiesis. This may explain why MCH levels did not significantly change after HH exposure. We have indeed observed an increase in macrophage numbers and their erythrophagocytosis/heme iron processing ability after HH exposure for 7 or 14 days in liver (please refer to the above figure for details), suggesting a compensatory mechanism to ensure adequate iron for erythropoiesis.

Regarding your comment on Fig 1A, we included this figure to provide a baseline of the experimental condition before any treatment. However, we understand your point and will consider removing it if it does not contribute significantly to the interpretation of our results. As for Fig 1G, we agree that the control at 14 days being higher than 7 days of HH may seem counterintuitive. We believe this could be due to individual variations among the mice or potential experimental errors. However, considering recommendations from other reviewers, we have removed this result from the revised manuscript.

3) Fig 2, the difference between sham and splenectomy is really marginal and not convincing. Is there also a difference at 7 days? Why does the spleen size decrease between 7 and 14 days?

We understand your concerns regarding the observed differences in Fig. 2 between sham and splenectomy groups. We acknowledge that while the absolute numerical differences may appear marginal, it is important to consider the unit of measurement. In the case of RBC count, the unit is 1012/L, hence even slight numerical differences can translate to significant variations in the actual count of RBCs.

We did not examine alterations occurring 7 days post-splenectomy in our study. The discernible trend of spleen size diminution between the 7th and 14th days is indeed compelling. It is plausible that this might be attributable to the body's adaptive response to hypobaric hypoxia (HH) exposure, wherein spleen size initially enlarges (at day 7) in response to compensatory erythropoiesis, followed by a reduction (at day 14) as the body acclimatizes to the HH conditions. Nevertheless, we did not identify a statistically significant difference between the measurements at day 7 and day 14, suggesting that this observation warrants further scrutiny.

4) Fig 3B, the clusters should be explained in detail. If the decrease in macrophages in Fig 3K/L is responsible for the effect, why does splenectomy not have a much stronger effect? How do the authors know which cells died in the calcein stained population in Fig 3D?

Thank you for your insightful queries and comments. Regarding Fig. 3B, we apologize for not providing sufficient detail on the clusters in the original manuscript. We will ensure that we include a comprehensive explanation of the clusters, including the specific cell types and their respective markers, in our revision. (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 for the decrease in macrophages observed in Fig. 3K/L, it's important to note that the spleen is a complex organ comprising numerous cell types, all of which can contribute to its overall function. While macrophages play a crucial role in iron recycling and erythropoiesis, other cell types and factors may also influence these processes. Therefore, while splenectomy results in the removal of all splenic cells, the overall impact on these processes may not be as pronounced as the specific reduction in macrophages due to compensatory mechanisms from other tissues and cells.

Concerning Fig. 3D, we acknowledge the ambiguity in the initial interpretation. The calcein staining was utilized to determine cell viability, but it doesn't identify the specific cell types that have died. To address this, we performed a single-cell analysis, which can provide a more accurate identification of the specific cell types affected.

5) Is the reduced phagocytic capacity in Fig4B significant? Erythrophagocytosis is compromised due to the considerable spontaneous loss of labelled erythrocytes; could other assays help? (potentially by a modified Chromium release assay?). Is it necessary to stimulated phagocytosis to see a significant effect?

We express our gratitude for your insightful queries and recommendations. In response to your initial question, the observed reduction in phagocytic capacity illustrated in Fig. 4B was indeed statistically significant. However, in alignment with feedback from other reviewers, we have elected to exclude the phagocytic results from this revised manuscript, as they predominantly reflect immune function rather than erythrophagocytosis of macrophages.

With respect to your proposal of potential alternatives to the erythrophagocytosis assay, we concur that the spontaneous loss of labeled erythrocytes could have influenced our results. Your suggestion of implementing a modified Chromium release assay is indeed an intriguing possibility that warrants further exploration.

Regarding the requirement for stimulating phagocytosis, we employed stimulation as a mechanism to investigate the potential for augmenting erythrophagocytosis and iron processing within the red pulp. Our findings suggest that increased phagocytosis in the spleen contributes positively to these processes. As part of the Tuftsin injection experiment, we assessed the RBC count and hemoglobin content. Despite an observed reduction trend, there were no statistically significant alterations. We are uncertain if the observation period was insufficiently long. Nevertheless, we concur that it would be worthwhile to explore inherent changes without external stimulation, and we will take this into consideration in our future research.

6) Can the observed ferroptosis be influenced by bi- and not trivalent iron chelators?

Thank you for your insightful question. Indeed, the role of iron chelators in the observed ferroptosis is an important aspect to explore. Ferroptosis is a form of regulated cell death characterized by an iron-dependent accumulation of lipid peroxides, and the role of different iron chelators could potentially influence this process.

In the case of bi- versus trivalent iron chelators, their influence on ferroptosis could be distinct due to their specificities for different forms of iron. However, we have not yet investigated this in our current study.

Your suggestion has highlighted a valuable direction for our future research. We agree that examining the influence of bi- and trivalent iron chelators on the observed ferroptosis would provide a deeper understanding of the iron-dependent mechanisms involved in this process. We will consider this important aspect in our subsequent investigations.

Reviewer #3 (Recommendations For The Authors):

Methodology:

1) Several syntax and grammatical errors, and unclear phrasing. Some factual errors as well: eg, line 380-81 the authors wrote that hypoxia increased viable cell numbers and phagocytosis ability, although their data suggest the opposite. Lines in Discussion 454-55 and in the Results 346-47 convey opposite messages.

We appreciate your attention to detail and your feedback on the language and factual discrepancies within the manuscript.

Upon revisiting lines 380-381, we would like to clarify that we had made a mistake. Our data indeed suggest that hypoxia led to a reduction in viable cell numbers and phagocytosis ability, not an increase as originally stated. We sincerely apologize for the confusion and will correct this statement in our revised manuscript.

As for the opposing messages between lines 454-455 in the Discussion and 346-347 in the Results, we apologize for any confusion caused. We understand that it is crucial to maintain consistent interpretation of our data throughout the manuscript. We will carefully reevaluate these sections and adjust our phrasing to ensure that our interpretations accurately reflect our results.

2) It is not clear why the authors investigated CD47 expression.

Thank you for your question regarding our investigation of CD47 expression. CD47, also known as integrin-associated protein, is ubiquitously expressed on many cell types, including red blood cells (RBCs). In the context of our study, we used CD47 expression as an indicator of young RBCs, as CD47 is known to be highly expressed on newly produced RBCs. Our intention was to use CD47 positive cells as a proxy for new RBC production, which would give us insights into erythropoiesis under hypobaric hypoxia conditions. This marker thus provides valuable information about the rate and effectiveness of erythropoietic response to hypoxic stress. However, according to others reviewers’ suggestion, we removed this part of results in the revised manuscript.

Minor:

1) Y axis is often labeled without sufficient detail.

2) The legends do not specify the exact statistical tests.

3) Some in vivo exp contain n=3 which is relatively low for mouse-based studies.

Some suggestions for the text:

Line 60: is the main cause of erythrocytosis which in turn alleviates..

62-66 - argumentation is not clear/grammatically correct and should be rephrased (eg, „RBC homeostasis is disturbed and never formed into a homeostasis status" - „homeostasis.. is never formed into a homeostasis status" sounds incorrect.

Ref # 8 - does not fit, I assume this was a mistake and the authors aimed to cite a Review article by Slusarczyk and Mleczko-Sanecka in Genes. However, this reference seems appropriate to be discussed in the Discussion section as it is very directly connected to the content of the present manuscript

76-78 - unclear/incomplete sentence (binding of iron to Tf and Tf-Fe delivery to the erythroid compartment is missing in this sentence, please, rephrase)

80 - iron is not stored ON FtL

90 - should be written: important role in iron recycling from RBCs

94 - phrasing 'damage of erythrophagocytosis' is incorrect

96-97 - should be written, for example: 'followed by eryptosis and iron recycling defects in the spleen'

282 - the sentence is grammatically incorrect and unclear.

292-94 - the statement is completely unclear, what can 'inhibit the excessive proliferation of RBCs'? What does it mean?

Reference to tuftsin was not provided (Am J Gastroenterol, 1999;94:391-397; PLoS One. 2012;7(4):e34933)

How quantification of microscopy images for F4/80 signal was performed?

In Figure 5, more explanation is required for the readers regarding the measured genes/proteins - why the patter of gene expression changes suggest ferroptosis?

Writing that ferroptosis INHIBITS phagocytosis is incorrect

Line 460 is unclear

468 - erythrocytophagy is not a commonly used term/

We are grateful for your keen eye and the time you have taken to provide such thorough feedback. It will undoubtedly help us to significantly enhance the clarity and completeness of our research. We have modified the corresponding sections in our manuscript to include these details. The comments have helped us ensure that our methodology is transparent and our findings are presented clearly. We have taken all your comments into consideration in our revision. we also have revised our manuscript to discuss these alternative interpretations more clearly and to acknowledge the potential limitations of our data.

Author Response

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

We would like to thank both reviewers and the editor for their time and effort in carefully reviewing and comprehending our manuscript. We are grateful for their thorough assessment, as well as the insightful questions and suggestions they have provided. We have taken into account the questions and comments raised by the reviewers, and we have incorporated the necessary revisions accordingly. In the following pages the reviewers’ comments are italicized. Our replies are in normal script.

In addition to revisions suggested by reviewers we also added a new summary schematic (Fig 8) and minor changes to acknowledgments.

Reviewer 1

This is a very strong study with few concerns. Regarding DN1+ T cell function, the authors assessed IFN-γ and activation markers, but it is unclear if the cells are polyfunctional (produced high levels of other cytokines at 6 weeks) or if there were changes in the humoral response (serum Ab titers or size/ number of germinal centers.)

Thank you for your thorough assessment of our work and your kind comments.

a. We observed a decreased IFN-γ and TNF-α production in antigen experienced DN1 T cells compared to naïve DN1 T cells, which is consistent with findings in Tfh cells.

b. We tested for anti-MA IgM and IgG production but did not observe an increase in these antibodies in the vaccinated setting. It is possible that additional inflammatory stimulation, such as from an adjuvant or infection, may be necessary to trigger sufficient antibody level for detection using ELISA.

c. We did not measure the number or size of germinal centers in this study, but future investigations could explore this aspect.

Reviewer 2

1. Authors elaborate the introduction solely highlighting the relevance of antigen persistence in the context of vaccination. However, it is well known that several mycobacterial antigens (Lipids and proteins) can cause detrimental responses when overexposed to the immune system. In this regard, it would be appropriate to introduce the possibility of the occurrence of exhaustion when prolonged exposure to antigens is happening, which is the main theme of this paper.

Thank you for bringing these points to our attention. We have added a paragraph in the discussion section (page 15-16, line 372-386), addressing the implications of our findings in relation to exhaustion in the context of antigen persistence during chronic viral infections. We have also provided an example involving the lipid trehalose 6,6’-dibehenateled (TDM), a known virulence factor for Mtb, which has been utilized in several subunit vaccines without demonstrating significant toxicity.

1. Authors need to provide more information about the source of MA. It is briefly mentioned in the materials and methods section that it was obtained from Sigma. If that is the case, it would be ideal to show the integrity of the polysaccharide in term of balance and abundance between different MA species.

We obtained M. tuberculosis MA from Sigma, which comprises α-, keto-, and methoxy MA forms with an average combined lipid tail length of 80 carbons. MA-specific T cells preferentially recognize these three forms of MA have been identified in humans. We have provided more detailed information regarding the MA in the Materials and Methods section (page 17, line 429-431).

1. Building up on the previous comment, MA is a complex mixture of polysaccharides including multiple lengths of fatty acids and modifications. Could the authors comments on the potential variability of MA structure and potential impact on immune responses?

The binding capacities of Group 1 CD1-restricted T cells can be influenced by various factors, including specific head groups, lipid tail length, and structure of the lipid tail. Notably, DN1 T cells have been shown to have higher binding affinities towards keto and methoxy MA, while displaying weaker binding to α-MA (Van Rhijn et al., 2017, Eur. J. Immunol. 47:1525). In our study, we successfully utilized a mixture of MA to activate DN1 T cells, indicating that the required subtypes of MA were present in sufficient quantities to elicit this activation. In future investigations focusing on the polyclonal immune response, incorporating a mixture of MA and possibly other Mtb lipid antigens will enable a broader spectrum of T cell activation. This, in turn, is expected to enhance the overall effectiveness and robustness of protection in challenge experiments.

1. How do the authors explain the lack of stimulation of cell proliferation induced by MA-PLGA formulation? Does this result contradict previous findings?

This study represents the first instance of utilizing PLGA as a delivery system for a lipid antigen via a pulmonary vaccine route, despite its previous applications in numerous other vaccine formulations. Therefore, we do not think our findings contradicts any existing research in the field. It is worth noting that the immunogenicity of PLGA can be influenced by the specific polymer chemistry and formulation, which may account for potential variations in the observed effects. We have added additional text to the discussion (page 13, line 310 – 313) to address this point.

1. Fig 3. Authors switch to IT administration simply arguing against the limitation of IN delivery regarding its low volume. However, administration via IN could be done in an iterative manner. According to this change, this reviewer asks whether the performance of MA-PLGA could now be comparable to BCN-MA using IT instead.

PLGA possesses an inherent background adjuvant effect, which may not be ideal for precisely stimulating group 1 CD1-restricted T cells, as a considerable proportion of these T cells exhibit some level of autoreactivity (Li, et al, 2011, Blood 118:3870, De Lalla et al., 2011, Eur. J. Immunol. 41:602; de Jong et al, 2010, Nat. Immunol. 11:1102). Notably, our observations revealed that blank PLGA-NP exerted a significant stimulatory effect on both mouse (DN1) and human (M11) MA-specific T cells (Fig. 2A-D). This underscores the advantage of the BCN system, which lacks detectable adjuvant effects and enables a more controlled, dose-dependent augmentation of T cell responses with increasing concentrations of loaded MA. Therefore, we did not further evaluate the impact of PLGA-MA using the IT route of vaccination.

1. What would be the reasons of the no role of encapsulating NP in the persistence of MA?

In this study, we have provided evidence to support the notion that encapsulation plays a role in antigen persistence, as demonstrated in Fig. 5A-C. Specifically, we directly compared the persistence of MA when delivered encapsulated in BCNs versus without encapsulation in BCNs, using DC pulsing and IT vaccination as the delivery methods. Our results indicate that at 6 weeks post-vaccination, MA encapsulated in BCNs can activate DN1 T cells, while free MA does not. These findings may initially appear to be contradictory to those depicted in Fig. 5D-F, where antigen persistence is observed following vaccination with attenuated Mtb. However, we propose that the attenuated Mtb bacteria may function similarly to nanoparticles by encapsulating and containing MA, thereby facilitating its persistence within the host. We appreciate the opportunity to clarify these points (page 15, line 364-367). Encapsulation within PEG-PPS NP may also contribute to two additional mechanisms. First, we have demonstrated that PEG-PPS NPs target myeloid cell populations (Burke et al., 2022, Nat. Nano. 17:319), such as alveolar macrophages, that can serve as antigen persistence depots as well as present CD1b/MA complexes on their surfaces. NPs allow more efficient delivery to these cells, whereas otherwise the lipid would bind to albumin, HDL, LDL, and other lipid carriers in blood for a broader, non-specific biodistribution, which would include cells less efficient at antigen persistence or presentation. Second, we previously demonstrated that the BCN nanostructure is highly stable within cells, supporting a slow intracellular release (Bobbala et al., 2020, Nanoscale 12:5332). This could assist with a more sustained presentation of lipid antigen by targeted cells in contrast to free form lipid or NPs (like PLGA) that rapidly degrade within cells. Indeed, low levels of fluorescently tagged BCNs were still detectable 6 weeks post-vaccination (Fig. 6B). Our future studies will further investigate this hypothesis.

1. Authors need to discuss to what extent the MA location into AM is route dependent.

The localization of MA within alveolar macrophages (AMs) in the lung is likely specific to intratracheal (IT) vaccination. Therefore, mice vaccinated subcutaneously (SC) or intravenously (IV) may possess distinct antigen persistence depots. We have made modifications to the discussion section to further emphasize this point (page 15, line 359-364).

1. Also, AM are programmed to sustain low immune responses because of their unique location in the lung. In fact, Mtb uses this to replicate while immune response is mounted. In this regard, accumulation of MA into this compartment may not be relevant for the overall immune response. In other words, what would be the contribution of this population to the T cell activation?

It is likely that AMs primarily function as antigen depots and do not directly contribute to the activation of DN1 T cells. This assertion is supported by our findings, as co-culturing AMs with DN1 T cells alone did not result in T cell activation (Fig. 6E). However, we observed that the presence of hCD1Tg-expressing bone marrow-derived dendritic cells was necessary for DN1 T cell activation in vitro, which likely reflects a similar phenomenon occurring in vivo.

1. Could the T cells responses measured be due to the reduced fraction of DC loaded with BCN-MA at initial time points?

Regarding the T cell response observed in Fig. 5A-C, where we used DCs to deliver either free MA or MA-BCN, we took steps to address potential differences in loading capacity between the two at initial time points. Specifically, DCs were pulsed with a concentration of 10 𝜇g/mL for free MA and 5 𝜇g/mL of MA-BCN (the figure legend has been modified to clarify this point, page 37, line 962 - 963). To ensure approximate equivalence in loading, we examined the immune response one week after vaccination and found no statistically significant difference between the two methods.

Author response

We appreciate the responses from the editors and reviewers and will submit a revised manuscript addressing all of the main points raised. We are glad to see broad agreement that we took a careful approach and addressed a clear question.

There were questions raised about the framing of the study vis-à-vis prior literature. One question was whether low frequency signals always have larger point spread functions, thereby making our result unsurprising. A second question was whether the notion of alpha oscillations as having wide-spread coherence and relating to system-general states was out-of-date. We appreciate these comments and agree that they could use further discussion. Our view is that neither of these points weakens the study, but our framing could be clearer regarding these two important issues. We will improve discussion of these topics in the revision.

A second criticism mentioned by two reviewers is the lack of null-hypothesis testing. The value of null hypothesis statistical testing (NHST) in biomedicine is hotly debated, with many statisticians and scientists arguing that NHSTs add little to no value (Gigerenzer & Marewski, 2015; McShane et al., 2019; Meehl, 1978). Others of course disagree (Mogie, 2004). Our goal was not to try to rule out null hypotheses, but rather to make systematic measurements and to report the reliable patterns. We generally focused on observations where the results were well above the noise, obviating the need to test the null. Nonetheless, we can (and will) improve the clarity of our arguments in terms of how we rely on specific statistical analyses to support particular conclusions, as well as how to deal with the issue of multiple electrodes coming from small numbers of subjects, an important point raised by R3. We will clarify these issues in the revision.

Reviewer 1 also made an interesting point about visual maps having an oculomotor component. We will do our best to incorporate this interesting issue into our revision.

In addition to the public review, the reviewers made a number of useful recommendations for the revision. We appreciate these recommendations and will carefully consider each of them.

Gigerenzer, G., & Marewski, J. N. (2015). Surrogate science: The idol of a universal method for scientific inference. Journal of management, 41(2), 421-440.

McShane, B. B., Gal, D., Gelman, A., Robert, C., & Tackett, J. L. (2019). Abandon statistical significance. The American Statistician, 73(sup1), 235-245.

Meehl, P. E. (1978). Theoretical risks and tabular asterisks: Sir Karl, Sir Ronald, and the slow progress of soft psychology. Journal of Consulting and Clinical Psychology, 46(4), 806-834. https://doi.org/10.1037/0022006X.46.4.806

Mogie, M. (2004). In support of null hypothesis significance testing. Proc Biol Sci, 271 Suppl 3(Suppl 3), S82-84. https://doi.org/10.1098/rsbl.2003.0105

Author Response

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

eLife assessment

This study provides potentially important, new information about the combination of information from the two eyes in humans. The data included frequency tagging of each eye's inputs and measures reflecting both cortical (EEG) and sub-cortical processes (pupillometry). Binocular combination is of potentially general interest because it provides -in essence- a case study of how the brain combines information from different sources and through different circuits. The strength of supporting evidence appears to be solid, showing that temporal modulations are combined differently than spatial modulations, with additional differences between subcortical and cortical pathways. However, the manuscript's clarity could be improved, including by adding more convincing motivations for the approaches used.

We thank the editor and reviewers for their detailed comments and suggestions regarding our paper. We have implemented most of the suggested changes. In doing so we noticed a minor error in our analysis code that affected the functions shown in Figure 2e (previously Figure 1e), and have fixed this and rerun the modelling. Our main results and conclusions are unaffected by this change. We have also added a replication data set to the Appendix, as this bears on one of the points raised by a reviewer, and included a co-author who helped run this experiment.

Reviewer #1 (Public Review):

In this paper, the interocular/binocular combination of temporal luminance modulations is studied. Binocular combination is of broad interest because it provides a remarkable case study of how the brain combines information from different sources. In addition, the mechanisms of binocular combination are of interest to vision scientists because they provide insight into when/where/how information from two eyes is combined.

This study focuses on how luminance flicker is combined across two eyes, extending previous work that focused mainly on spatial modulations. The results appear to show that temporal modulations are combined in different ways, with additional differences between subcortical and cortical pathways.

1. Main concern: subcortical and cortical pathways are assessed in quite different ways. On the one hand, this is a strength of the study (as it relies on unique ways of interrogating each pathway). However, this is also a problem when the results from two approaches are combined - leading to a sort of attribution problem: Are the differences due to actual differences between the cortical and subcortical binocular combinations, or are they perhaps differences due to different methods. For example, the results suggest that the subcortical binocular combination is nonlinear, but it is not clear where this nonlinearity occurs. If this occurs in the final phase that controls pupillary responses, it has quite different implications.

At the very least, this work should clearly discuss the limitations of using different methods to assess subcortical and cortical pathways.

The modelling asserts that the nonlinearity is primarily interocular suppression, and that this is stronger in the subcortical pathway. Moreover the suppression impacts before binocular combination. So this is quite a specific location. We now say more about this in the Discussion, and also suggest that fMRI might avoid the limits on the conclusions we can draw from different methods.

1. Adding to the previous point, the paper needs to be a better job of justifying not only the specific methods but also other details of the study (e.g., why certain parameters were chosen). To illustrate, a semi-positive example: Only page 7 explains why 2Hz modulation was used, while the methods for 2Hz modulation are described in detail on page 3. No justifications are provided for most of the other experimental choices. The paper should be expanded to better explain this area of research to non-experts. A notable strength of this paper is that it should be of interest to those not working in this particular field, but this goal is not achieved if the paper is written for a specialist audience. In particular, the introduction should be expanded to better explain this area of research, the methods should include justifications for important empirical decisions, and the discussion should make the work more accessible again (in addition to addressing the issues raised in point 1 above). The results also need more context. For example, why EEG data have overtones but pupillometry does not?

We now explain the choice of frequency in the final paragraph of the introduction as follows:

‘We chose a primary flicker frequency of 2Hz as a compromise between the low-pass pupil response (see Barrionuevo et al., 2014; Spitschan et al., 2014), and the relatively higher-pass EEG response (Regan, 1966).’

We also mention why the pupil response is low-pass:

‘The pupil response can be modulated by periodic changes in luminance, and is temporally low-pass (Barrionuevo et al., 2014; Spitschan et al. 2014), most likely due to the mechanical limitations of the iris sphincter and dilator muscles’.

Reviewer #2 (Public Review):

Previous studies have extensively explored the rules by which patterned inputs from the two eyes are combined in the visual cortex. Here the authors explore these rules for un-patterned inputs (luminance flicker) at both the level of the cortex, using Steady-State Visual Evoked Potentials (SSVEPs) and at the sub-cortical level using pupillary responses. They find that the pattern of binocular combination differs between cortical and sub-cortical levels with the cortex showing less dichoptic masking and somewhat more binocular facilitation.

Importantly, the present results with flicker differ markedly from those with gratings (Hou et al., 2020, J Neurosci, Baker and Wade 2017 cerebral cortex, Norcia et al, 2000 Nuroreport, Brown et al., 1999, IOVS). When SSVEP responses are measured under dichoptic conditions where each eye is driven with a unique temporal frequency, in the case of grating stimuli, the magnitude of the response in the fixed contrast eye decreases as a function of contrast in the variable contrast eye. Here the response increases by varying (small) magnitudes. The authors favor a view that cortex and perception pool binocular flicker inputs approximately linearly using cells that are largely monocular. The lack of a decrease below the monocular level when modulation strength increase is taken to indicate that previously observed normalization mechanism in pattern vision does not play a substantial role in the processing of flicker. The authors present a computational model of binocular combination that captures features of the data when fit separately to each data set. Because the model has no frequency dependence and is based on scalar quantities, it cannot make joint predictions for the multiple experimental conditions which is one of its limitations.

A strength of the current work is the use of frequency-tagging of both pupil and EEG responses to measure responses for flicker stimuli at two anatomical levels of processing. Flicker responses are interesting but have been relatively neglected. The tagging approach allows one to access responses driven by each eye, even when the other eye is stimulated which is a great strength. The tagging approach can be applied at both levels of processing at the same time when stimulus frequencies are low, which is an advantage as they can be directly compared. The authors demonstrate the versatility of frequency tagging in a novel experimental design which may inspire other uses, both within the present context and others. A disadvantage of the tagging approach for studying sub-cortical dynamics via pupil responses is that it is restricted to low temporal frequencies given the temporal bandwidth of the pupil. The inclusion of a behavioral measure and a model is also a strength, but there are some limitations in the modeling (see below).

The authors suggest in the discussion that luminance flicker may preferentially drive cortical mechanisms that are largely monocular and in the results that they are approximately linear in the dichoptic cross condition (no effect of the fixed contrast stimulus in the other eye). By contrast, prior research using dichoptic dual frequency flickering stimuli has found robust intermodulation (IM) components in the VEP response spectrum (Baitch and Levi, 1988, Vision Res; Stevens et al., 1994 J Ped Ophthal Strab; France and Ver Hoeve, 1994, J Ped Ophthal Strab; Suter et al., 1996 Vis Neurosci). The presence of IM is a direct signature of binocular interaction and suggests that at least under some measurement conditions, binocular luminance combination is "essentially" non-linear, where essential implies a point-like non-linearity such as squaring of excitatory inputs. The two views are in striking contrast. It would thus be useful for the authors could show spectra for the dichoptic, two-frequency conditions to see if non-linear binocular IM components are present.

This is an excellent point, and one that we had not previously appreciated the importance of. We have generated a figure (Fig 8) showing the IM response in the cross frequency conditions. There is a clear response at 0.4Hz in the pupillometry data (2-1.6Hz), and at 3.6Hz in the EEG data (2+1.6Hz). We therefore agree that this shows the system is essentially nonlinear, despite the binocular combination appearing approximately linear. We now say in the Discussion:

‘In the steady-state literature, one hallmark of a nonlinear system is the presence of intermodulation responses at the sums and differences of fundamental flicker frequencies (Baitch & Levi, 1988; Tsai et al., 2012). In Figure 8 we plot the amplitude spectra of conditions from Experiment 1 in which the two eyes were stimulated at different frequencies (2Hz and 1.6Hz) but at the same contrast (48%; these correspond to the binocular cross and dichoptic cross conditions in Figures 2d,e and 3d,e). Consistent with the temporal properties of pupil responses and EEG, Figure 8a reveals a strong intermodulation difference response at 0.4Hz (red dashed line), and Figure 8b reveals an intermodulation sum response at 3.6Hz (red dashed line). The presence of these intermodulation terms is predicted by nonlinear gain control models of the type considered here (Baker and Wade, 2017; Tsai et al., 2012), and indicates that the processing of monocular flicker signals is not fully linear prior to the point at which they are combined across the eyes.’

If the IM components are indeed absent, then there is a question of the generality of the conclusions, given that several previous studies have found them with dichoptic flicker. The previous studies differ from the authors' in terms of larger stimuli and in their use of higher temporal frequencies (e.g. 18/20 Hz, 17/21 Hz, 6/8 Hz). Either retinal area stimulated (periphery vs central field) or stimulus frequency (high vs low) could affect the results and thus the conclusions about the nature of dichoptic flicker processing in cortex. It would be interesting to sort this out as it may point the research in new directions.

This is a great suggestion about retinal area. As chance would have it, we had already collected a replication data set where we stimulated the periphery, and we now include a summary of this data set as an Appendix. In general the results are similar, though we obtain a measurable (though still small) second harmonic response in the pupillometry data with this configuration, which is a further indication of nonlinear processing.

Whether these components are present or absent is of interest in terms of the authors' computational model of binocular combination. It appears that the present model is based on scalar magnitudes, rather than vectors as in Baker and Wade (2017), so it would be silent on this point. The final summation of the separate eye inputs is linear in the model. In the first stage of the model, each eye's input is divided by a weighted input from the other eye. If we take this input as inhibitory, then IM would not emerge from this stage either.

We have performed the modelling using scalar values here for simplicity and transparency, and to make the fitting process computationally feasible (it took several days even done this way). This type of model is quite capable of processing sine waves as inputs, and producing a complex output waveform which is Fourier transformed and then analysed in the same way as the experimental data (see e.g. Tsai, Wade & Norcia, 2012, J Neurosci; Baker & Wade, 2017, Cereb Cortex). However our primary aim here was to fit the model, and make inferences about the parameter values, rather than to use a specific set of parameter values to make predictions. We now say more about this family of models and how they can be applied in the methods section:

“Models from this family can handle both scalar contrast values and continuous waveforms (Tsai et al., 2012) or images (Meese and Summers, 2007) as inputs. For time-varying inputs, the calculations are performed at each time point, and the output waveform can then be analysed using Fourier analysis in the same way as for empirical data.This means that the model can make predictions for the entire Fourier spectrum, including harmonic and intermodulation responses that arise as a consequence of nonlinearities in the model (Baker and Wade, 2017). However for computational tractability, we performed fitting here using scalar contrast values.”

As a side point, there are quite a lot of ways to produce intermodulation terms, meaning they are not as diagnostic as one might suppose. We demonstrate this in Author response image 1, which shows the Fourier spectra produced by a toy model that multiplies its two inputs together (for an interactive python notebook that allows various nonlinearities to be explored, see here). Intermodulation terms also arise when two inputs of different frequencies are summed, followed by exponentiation. So it would be possible to have an entirely linear binocular summation process, followed by squaring, and have this generate IM terms (not that we think this is necessarily what is happening in our experiments).

Author response image 1

Related to the model: One of the more striking results is the substantial difference between the dichoptic and dichoptic-cross conditions. They differ in that the latter has two different frequencies in the two eyes while the former has the same frequency in each eye. As it stands, if fit jointly on the two conditions, the model would make the same prediction for the dichoptic and dichoptic-cross conditions. It would also make the same prediction whether the two eyes were in-phase temporally or in anti-phase temporally. There is no frequency/phase-dependence in the model to explain differences in these cases or to potentially explain different patterns at the different VEP response harmonics. The model also fits independently to each data set which weakens its generality. An interpretation outside of the model framework would thus be helpful for the specific case of differences between the dichoptic and dichoptic-cross conditions.

As mentioned above, the limitations the reviewer highlights are features of the specific implementation, rather than the model architecture in general. Furthermore, although this particular implementation of the model does not have separate channels for different phases, these can be added (see e.g. Georgeson et al., 2016, Vis Res, for an example in the spatial domain). In future work we intend to explore the phase relationship of flicker, but do not have space to do this here.

Prior work has defined several regimes of binocular summation in the VEP (Apkarian et al.,1981 EEG Journal). It would be useful for the authors to relate the use of their terms "facilitation" and "suppression" to these regimes and to justify/clarify differences in usage, when present. Experiment 1, Fig. 3 shows cases where the binocular response is more than twice the monocular response. Here the interpretation is clear: the responses are super-additive and would be classed as involving facilitation in the Apkarian et al framework. In the Apkarian et al framework, a ratio of 2 indicates independence/linearity. Ratios between 1 and 2 indicate sub-additivity and are diagnostic of the presence of binocular interaction but are noted by them to be difficult to interpret mechanistically. This should be discussed. A ratio of <1 indicates frank suppression which is not observed here with flicker.

Operationally, we use facilitation to mean an increase in response relative to a monocular baseline, and suppression to mean a decrease in response. We now state this explicitly in the Introduction. Facilitation greater than a factor of 2 indicates some form of super-additive summation. In the context of the model, we also use the term suppression to indicate divisive suppression between channels, however this feature does not always result in empirical suppression (it depends on the condition, and the inhibitory weight). We think that interpretation of results such as these is greatly aided by the use of a computational modelling framework, which is why we take this approach here. The broad applicability of the model we use in the domain of spatial contrast lends it credibility for our stimuli here.

Can the model explore the full range of binocular/monocular ratios in the Apkarian et al framework? I believe much of the data lies in the "partial summation" regime of Apkarian et al and that the model is mainly exploring this regime and is a way of quantifying varying degrees of partial summation.

Yes, in principle the model can produce the full range of behaviours. When the weight of suppression is 1, binocular and monocular responses are equal. When the weight is zero, the model produces linear summation. When the weight is greater than 1, suppression occurs. It is also possible to produce super-additive summation effects, most straightforwardly by changing the model exponents. However this was not required for our data here, and so we kept these parameters fixed. We agree that the model is a good way to unify the results across disparate experimental paradigms, and that is our main intention with Figure 7i.

Reviewer #3 (Public Review):

This manuscript describes interesting experiments on how information from the two eyes is combined in cortical areas, sub-cortical areas, and perception. The experimental techniques are strong and the results are potentially quite interesting. But the manuscript is poorly written and tries to do too much in too little space. I had a lot of difficulty understanding the various experimental conditions, the complicated results, and the interpretations of those results. I think this is an interesting and useful project so I hope the authors will put in the time to revise the manuscript so that regular readers like myself can better understand what it all means.

Now for my concerns and suggestions:

The experimental conditions are novel and complicated, so readers will not readily grasp what the various conditions are and why they were chosen. For example, in one condition different flicker frequencies were presented to the two eyes (2Hz to one and 1.6Hz to the other) with the flicker amplitude fixed in the eye presented to the lower frequency and the flicker amplitude varied in the eye presented to the higher frequency. This is just one of several conditions that the reader has to understand in order to follow the experimental design. I have a few suggestions to make it easier to follow. First, create a figure showing graphically the various conditions. Second, come up with better names for the various conditions and use those names in clear labels in the data figures and in the appropriate captions. Third, combine the specific methods and results sections for each experiment so that one will have just gone through the relevant methods before moving forward into the results. The authors can keep a general methods section separate, but only for the methods that are general to the whole set of experiments.

We have created a new figure (now Fig 1) that illustrates the conditions from Experiment 1, and is referenced throughout the paper. We have kept the names constant, as they are rooted in a substantial existing literature, and it will be confusing to readers familiar with that work if we diverge from these conventions. We did consider separating out the methods section, but feel it helps the flow of the results section to keep it as a single section.

I wondered why the authors chose the temporal frequencies they did. Barrionuevo et al (2014) showed that the human pupil response is greatest at 1Hz and is nearly a log unit lower at 2Hz (i.e., the change in diameter is nearly a log unit lower; the change in area is nearly 2 log units lower). So why did the authors choose 2Hz for their primary frequency? And why did the authors choose 1.6Hz which is quite close to 2Hz for their off frequency? The rationale behind these important decisions should be made explicit.

We now explain this in the Introduction as follows:

‘We chose a primary flicker frequency of 2Hz as a compromise between the low-pass pupil response (see Barrionuevo et al., 2014; Spitschan et al., 2014), and the relatively higher-pass EEG response (Regan, 1966).’

It is a compromise frequency that is not optimal for either modality, but generates a measurable signal for both. The choice of 1.6 Hz was for similar reasons - for a 10-second trial it is four frequency bins away from the primary frequency, so can be unambiguously isolated in the spectrum.

By the way, I wondered if we know what happens when you present the same flicker frequencies to the two eyes but in counter-phase. The average luminance seen binocularly would always be the same, so if the pupil system is linear, there should be no pupil response to this stimulus. An experiment like this has been done by Flitcroft et al (1992) on accommodation where the two eyes are presented stimuli moving oppositely in optical distance and indeed there was no accommodative response, which strongly suggests linearity.

We have not tried this yet, but it’s on our to-do list for future work. The accommodation work is very interesting, and we now cite it in the manuscript as follows:

‘Work on the accommodative response indicates that binocular combination there is approximately linear (Flitcroft et al. 1992), and can even cancel when signals are in antiphase (we did not try this configuration here).’

Figures 1 and 2 are important figures because they show the pupil and EEG results, respectively. But it's really hard to get your head around what's being shown in the lower row of each figure. The labeling for the conditions is one problem. You have to remember how "binocular" in panel c differs from "binocular cross" in panel d. And how "monocular" in panel d is different than "monocular 1.6Hz" in panel e. Additionally, the colors of the data symbols are not very distinct so it makes it hard to determine which one is which condition. These results are interesting. But they are difficult to digest.

We hope that the new Figure 1 outlining the conditions has helped with interpretation here.

The authors make a strong claim that they have found substantial differences in binocular interaction between cortical and sub-cortical circuits. But when I look at Figures 1 and 2, which are meant to convey this conclusion, I'm struck by how similar the results are. If the authors want to continue to make their claim, they need to spend more time making the case.

Indeed, it is hard to make direct comparisons across figures - this is why Figure 4 plots the ratio of binocular to monocular conditions, and shows a clear divergence between the EEG and pupillometry results at high contrasts.

Figure 5 is thankfully easy to understand and shows a very clear result. These perceptual results deviate dramatically from the essentially winner-take-all results for spatial sinewaves shown by Legge & Rubin (1981); whom they should cite by the way. Thus, very interestingly the binocular combination of temporal variation is quite different than the binocular combination of spatial variation. Can the pupil and EEG results also be plotted in the fashion of Figure 5? You'd pick a criterion pupil (or EEG) change and use it to make such plots.

We now cite Legge & Rubin. We see what you mean about plotting the EEG and pupillometry results in the same coordinates as the matching data, but we don’t think this is especially informative as we would end up only with data points along the axes and diagonal of the plot, without the points at other angles. This is a consequence of how the experiments were conducted.

My main suggestion is that the authors need to devote more space to explaining what they've done, what they've found, and how they interpret the data. I suggest therefore that they drop the computational model altogether so that they can concentrate on the experiments. The model could be presented in a future paper.

We feel that the model is central to the understanding and interpretation of our results, and have retained it in the revised version of the paper.

Reviewer #2 (Recommendations For The Authors):

I found the terms for the stimulus conditions confusing. I think a simple schematic diagram of the conditions would help the reader.

Now added (the new Fig 1).

In reporting the binocular to monocular ratio, please clarify whether the monocular data was from one eye alone (and how that eye was chosen) or from both eyes and then averaged, or something else. It would be useful to plot the results from the dichoptic condition in this form, as well.

These were averaged across both eyes. We now say in the Methods section:

‘We confirmed in additional analyses that the monocular consensual pupil response was complete, justifying our pooling of data across the eyes.’

Also, clarify whether the term facilitation is used as above throughout (facilitation being > 2 times monocular response under binocular condition) or if a different criterion is being used. If we take facilitation to mean a ratio > 2, then facilitation depends on temporal frequency in Figure 4.

We now explain our use of these terms in the final paragraph of the Introduction:

‘Relative to the response to a monocular signal, adding a signal in the other eye can either increase the response (facilitation) or reduce it (suppression).’

The magnitude of explicit facilitation attained is interesting, but not without precedent. Ratios of binocular to mean monocular > 2, have been reported previously and values of summation depend strongly on the stimulus used (see for example Apkarian et al., EEG Journal, 1981, Nicol et al., Doc Ophthal, 2011).

We now mention this in the Discussion as follows:

‘(however we note that facilitation as substantial as ours has been reported in previous EEG work by Apkarian et al. (1981))’

In Experiment 3, the authors say that the psychophysical matching results are consistent with the approximately linear summation effects observed in the EEG data of Experiment 1. In describing Fig. 3, the claim is that the EEG is non-linear, e.g. super-additive - at least at high contrasts. Please reconcile these statements.

We think that the ‘superadditive’ effects are close enough to linear that we don’t want to make too much of a big deal about them - this could be measurement error, for example. So we use terms such as near-linear, or approximately linear, when referring to them throughout.

Reviewer #3 (Recommendations For The Authors):

Let me make some more specific comments using a page/paragraph/line format to indicate where in the text they're relevant.

1/2 (middle)/3 from end. "In addition" seems out of place here.

Removed.

1/3/4. By "intensities" do you mean "contrasts"?

Fixed.

1/3/last. "... eyes'...".

Fixed.

2/5/3. By "one binocular disc", you mean into "one perceptually fused disc".

Rewritten as: ‘to help with their perceptual fusion, giving the appearance of a single binocular disc’

3/1/1. "calibrated" seems like the wrong word here. I think you're just changing the vergence angle to enable fusion, right?

Now rewritten as: ‘Before each experiment, participants adjusted the angle of the stereoscope mirrors to achieve binocular fusion’

3/1/1. "adjusting the angles...". And didn't changing the mirror angles affect the shapes of the discs in the retinal images?

Perhaps very slightly, but this is well within the tolerance of the visual system to compensate for in the fused image, especially for such high contrast edges.

3/3/5. "fixed contrast" is confusing here because it's still a flickering stimulus if I follow the text here. Reword.

Now ‘fixed temporal contrast’

3/4/1. It would be clearer to say "pupil tracker" rather than "eye tracker" because you're not really doing eye tracking.

True, but the device is a commercial eye tracker, so this is the appropriate term regardless of what we are using it for.

3/5/6. I'm getting lost here. "varying contrast levels" applies to the dichoptic stimulus, right?

Yes, now reworded as ‘In the other interval, a target disc was displayed, flickering at different contrast levels on each trial, but with a fixed interocular contrast ratio across the block.’

3/5/7. Understanding the "ratio of flicker amplitudes" is key to understanding what's going on here. More explanation would be helpful.

Addressed in the above point.

4/3/near end. Provide some explanation about why the Fourier approach is more robust to noise.

Added ‘(which can make the phase and amplitude of a fitted sine wave unstable)’

Figure 1. In panel a, explain what the numbers on the ordinate mean. What's zero, for example? Which direction is dilation? Same question for panel b. It's interesting in panel c that the response in one eye to 2Hz increases when the other eye sees 1.6Hz. Would be good to point that out in the text.

Good idea about panel (a) - we have changed the y-axis to ‘Relative amplitude’ for clarity, and now note in the figure caption that ‘Negative values indicate constriction relative to baseline, and positive values indicate dilation.’ Panel (b) is absolute amplitude, so is unsigned. Panel (c) only contains 2Hz conditions, but there is some dichoptic suppression across the two frequencies in panels (d,e) - we now cover this in the text and include statistics.

6/2/1. Make clear in the text that Figure 1c shows contrast response functions for the pupil.

Now noted in the caption.

Figure 3. I'm lost here. I feel like I should be able to construct this figure from Figures 1 and 2, but don't know how. More explanation is needed at least in the caption.

Done. The caption now reads:

‘Ratio of binocular to monocular response for three data types. These were calculated by dividing the binocular response by the monocular response at each contrast level, using the data underlying Figures 2c, 3c and 3f. Each value is the average ratio across N=30 participants, and error bars indicate bootstrapped standard errors.’

9/1/1-2. I didn't find the evidence supporting this statement compelling.

We now point the reader to Figure 4 as a reminder of the evidence for this difference.

9/1/6-9. You said this. But this kind of problem can be fixed by moving the methods sections as I suggested above.

As mentioned, we feel that the results section flows better with the current structure.

Figure 4. Make clear that this is EEG data.

Now added to caption.

Figure 5 caption. Infinite exponent in what equation?

Now clarified as: ‘models involving linear combination (dotted) or a winner-take-all rule (dashed)’

Figure 6. I hope this gets dropped. No one will understand how the model predictions were derived. And those who look at the data and model predictions will surely note (as the authors do) that they are rather different from one another.

As noted above, we feel that the model is central to the paper and have retained this figure. We have also worked out how to correct the noise parameter in the model for the number of participants included in the coherent averaging, which fixes the discrepancy at low contrasts. The correspondence between the data and model in is now very good, and we have plotted the data points and curves in the same panels, which makes the figure less busy.

12/1. Make clear in this paragraph that "visual cortex" is referring to EEG and perception results and that "subcortical" is referring to pupil. Explain clearly what "linear" would be and what the evidence for "non-linear" is.

Good suggestion, we have added qualifiers linking to both methods. Also tidied up the language to make it clearer that we are talking about binocular combination specifically in terms of linearity, and spelled out the evidence for each point.

12/2/6-9. Explain the Quaia et al results enough for the reader to know what reflexive eye movements were studied and how.

We now specify that these eye movements are also known as the ‘ocular following response’ and were measured using scleral search coils.

12/2/9-10. Same for Spitchan and Cajochen: more explanation.

Added:

“(melatonin is a hormone released by the pineal gland that regulates sleep; its production is suppressed by light exposure and can be measured from saliva assays)”

12/3/2-3. Intriguing statements about optimally combining noisy signals, but explain this more. It won't be obvious to most readers.

We have added some more explanation to this section.

13/1. This is an interesting paragraph where the authors have a chance to discuss what would be most advantageous to the organism. They make the standard argument for perception, but basically punt on having an argument for the pupil.

Indeed, we agree that this point is necessarily speculative, however we think it is interesting for the reader to consider.

13/2/1. "Pupil size affects the ..." is more accurate.

Fixed.

13/2/2 from end. Which "two pathways"? Be clear.

Changed to ‘the pupil and perceptual pathways’

Author Response:

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

Reviewer #3 (Recommendations For The Authors):

In response to my comment about Col10a1 expression in the dermal SFCs (Fig 3B, I), the authors provide additional text to clarify but also state that "Col2 genes were not detected robustly". I think this comment on the absence of Col2 transcripts should be explicitly included in that paragraph as it is a reasonable and expected question given the cartilage angle the authors begin the paragraph with. Including this in no way weakens their point, rather adds clarity.

This version includes some modifications to fix typos and add a sentence in response to the concern above.

Author Response:

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

Reviewer #1 (Public Review):

In their study, Aman et al. utilized single cell transcriptome analysis to investigate wild-type and mutant zebrafish skin tissues during the post-embryonic growth period. They identified new epidermal cell types, such as ameloblasts, and shed light on the effects of TH on skin morphogenesis. Additionally, they revealed the important role of the hypodermis in supporting pigment cells and adult stripe formation. Overall, I find their figures to be of high quality, their analyses to be appropriate and compelling, and their major claims to be wellsupported by additional experiments. Therefore, this study will be an important contribution to the field of vertebrate skin research. Although I have no major concerns, I would like to offer a few minor comments for the authors to consider.

1) The discovery of ameloblasts in the zebrafish skin is a fascinating finding that could potentially provide a new research model for understanding the development and regeneration of vertebrate teeth. It would be beneficial if the authors could provide further elaboration on this aspect and discuss how the zebrafish scale model could be utilized by researchers to better understand the morphogenesis of vertebrate teeth and/or hair.

We have provided additional discussion points regarding epidermal EMP+ cells with ameloblast-like transcriptional profiles. We believe that further studies of scale matrix composition and the material properties endowed by various collagenous and non-collagen matrix proteins will be useful for understanding fundamental mechanisms of biomineralization. This section of the discussion now reads:

“We systematically assessed the expression of genes encoding non-collagen calcified matrix proteins throughout the skin during squamation, leading to the discovery of a transcriptionally distinct population of basal epidermal cells that express EMP transcripts, likely corresponding to epidermal secretory cells proposed to participate in scale matrix formation based on ultrastructure (Sire et al., 1997). These cells also express dlx3a, dlx4a, runx2b and msx2a but not sp7, a transcription factor suite that is shared with ameloblasts that form tooth enamel. While these transcription factors are not exclusive to ameloblasts and have been reported in osteoblasts and odontoblasts, in addition to cell types that do not produce calcified matrix, such as neurons, their co-expression along with EMP-encoding transcripts in basal epidermal cells is consistent with a common origin of ameloblasts and the EMP+ epidermal cells reported here. One alternative hypothesis is that co-expression of these gene products arose convergently and can be explained by mechanistic linkages among them. Future work aimed at functionally dissecting the regulatory mechanisms that govern EMP gene expression in a variety of organisms may clarify these issues either by providing evidence of additional commonalities, supporting a shared ancestor, or by revealing diverse, lineage-specific regulatory architectures, supporting convergent evolution of superficial enamel deposition in teeth and fish skin appendages.”

2) While the overexpression-rescue experiments (i.e., fgf20a and pdafaa) provide crucial evidence to support the author's conclusions, it is important to note that overexpression driven by the heat-shock promoter is not spatially regulated. Therefore, it should be acknowledged that the rescue effects may not be cell-autonomous, as suggested in the current version.

The reviewer is correct that hsp70l promotor is not spatially regulated and F0 transgenics have random mosaic expression. Importantly, since we were testing specific hypotheses regarding signaling interactions between basal epidermal cells and dermal cells, we applied stringent selection and only analyzed individuals with transgene expression in basal epidermal cells. This approach enabled us to assay the results of basal cell expression of signaling ligands in eda mutant and hypo-thyroid backgrounds. The original manuscript omitted this crucial aspect of our experimental design, and we thank the reviewer for noticing this omission. We have revised the following parts of the results section.

“Indeed, heatshock-driven expression in F0 mosaics stringently selected for basal epidermal expression of Fgf20a in the skin of Eda mutants led to localized rescue of scales where transgene expression was detectable (Figure 5D).”

“When we forced expression of Pdgfaa in basal cells of epidermis by heatshock induction and stringent selection of basal epidermal expression in F0 mosaics, we found, as predicted, a recruitment of dermal cells in hypoTH skin, leading to a locally stratified dermis (Figure 6E) similar to that of the wild-type (Figure 4C).”

We additionally revised the legends for Figure 5 and Figure 6 to mention stringent selection of basal epidermal expression of fgf20a and pdgfaa, respectively.

3) Figure 7D. The authors used the ET37:EGFP lines to visualize hypodermis. Based on the absence of EGFP signal in the deep dermis of bnc2 mutants, the authors concluded that the hypodermis may be missing, suggesting the importance of the hypodermis in pigment cell formation. However, since the EGFP evidence is indirect, it is crucial to confirm the absence of the hypodermis structure with histology.

It is indeed conceivable that hypodermal cells physically persist in bnc2 mutants yet have sufficiently altered gene expression that they neither cluster with wild-type hypodermal cells in single cell RNA-seq analyses nor initiate or maintain the broadly expressed dermal reporter ET37:GFP that we used to assess the presence or absence of such cells in a defined anatomical position. Though we believe this to be somewhat unlikely (hence our original interpretation), we have added a caveat referencing this formal possibility in the revised manuscript:

“It is possible that hypodermal cells physically persist in bnc2 mutants but have sufficiently altered transcriptional profiles such that they no longer cluster together with wild-type hypodermal cells or express the ET37:EGFP transgene. Nevertheless, these analyses suggest that ET37:EGFP+ hypodermal cells likely play a role in pigment pattern development.”

We believe this issue raises interesting philosophical questions about the definition of a “cell-type.” If cells constituting the deep surface of the dermis physically persist, but have a profoundly altered transcriptional profile and no longer perform the biological functions of their wild-type counterparts, are they still the original cell type, or was the wild-type cell type lost? As researchers continue to discover new cell types and deepen our understanding of cell-state plasticity in normal and pathological conditions, the community will need to articulate new rubrics of categorization to ensure that “cell-type” remains a rigorous and useful concept (if, indeed, it has been one).

4) As the dataset is expected to be a valuable asset to the field, please provide Excel tables summarizing the key genes and their corresponding expression levels for each major cluster that has been identified.

This table has been provided in the revised manuscript (Supplementary file 2 – Table 5.)

Reviewer #2 (Public Review):

The authors used single cell transcriptome analysis of zebrafish skin cells and characterized various types of cells that are involved in scale formation and stripe patterning. The methods employed in this study is highly powerful to provide mechanistic explanation of these fundamental biological issues and will be a good example for many researchers studying other biological issues. Furthermore, the results characterizing differences in gene expression patterns among various types of cells will be informative for other researchers in the field.

For scale formation, it is known that mineralized tissues may significantly differ in rayfins and lobefins since sox9, col2a1, and col10a1 are all expressed in osteoblasts, in addition to chondrocytes, in zebrafish and gar (Eames et al., 2012, BMC Evol. Biol.). Furthermore, in mammals, Col10 is expressed in chondrocytes in mature cartilage that undergoes ossification. Thus, unlike the authors argue, col10a1 expression is not apparently relevant to the elasticity of scales.

The authors also state that the expression of dlx4a, msx2a, and runx2b characterize cells homologous to mammalian ameloblasts. However, dlx4, runx2, and msx2 are all duplicated in zebrafish, and the function of duplicated genes in teleost fishes may differ from that of single ancestral gene. Moreover, none of Dlx4, Msx2, and Runx2 is expressed specifically by ameloblasts in mammals. Indeed, both Msx2 and Runx2 are expressed in osteoblasts, while the expression of Dlx4 in ameloblasts is not reported. These results, together with the expression of an enamel gene, enam, in dermal cells (SFC), do not appear to support the homology of the surface tissue of mammalian teeth and zebrafish scales.

We appreciate the reviewers’ comments and have provided caveats to our interpretation in the revised manuscript (see our response to Reviewer #1, item 1, above). In the revised manuscript, we also display results for an additional Dlx gene, dlx3b, that is coexpressed in EMP+ basal epidermal cells (Figure 3C), although dlx4 has been reported in mammalian tooth germs and elasmobranch tooth and odontode epithelia (Pemberton et al., 2007; Debiais-Thibaud et al., 2011 ; Woodruff et al., 2022).

More generally, expression of specific genes can be useful characters for testing hypotheses of homology. The operant inference depends on a parsimony assumption: if a transcriptional profile is shared between celltypes in disparate organisms, one explanation is that this transcriptional profile was inherited from a common ancestor. This inference is not impacted by the teleost whole genome duplication. If the common ancestor had one ortholog and a subset of modern animals have two, the homology hypothesis predicts that at least one ortholog will be expressed in common in the tissue that descended from the common ancestor. This interpretation is entirely compatible with our understanding of the mechanisms that underlie retention of duplicated genes in animal genomes. Additionally, exclusivity is not necessarily predicted by homology hypotheses. Indeed, all the transcription factors used here as characters for evaluating homology have pleiotropic roles in many cell types.

In this specific case, we found two EMP genes, ambn and enam, co-expressed with a complement of transcription factors that is also co-expressed in ameloblasts. These findings are consistent with a model in which both ameloblasts and EMP+ epidermal cells associated with zebrafish scales inherited this transcriptional profile from a common ancestral cell type. Given the temporal and phylogenetic continuity of superficial enameling in the fossil record of skin appendages, and the dual origin of mineralized matrices in extant skin appendages and teeth, we continue to favor the model where these traits are shared and conserved among vertebrates. Nevertheless, we have acknowledged in the revised manuscript the limitations of homology testing by analyses of gene expression and the possibility that these traits might have evolved convergently; we suggest additional research avenues for testing this hypothesis further (response to Reviewer #1, item 1, above).

Reviewer #3 (Public Review):

This work describes transcriptome profiling of dissected skin of zebrafish at post-embryonic stages, at a time when adult structures and patterns are forming. The authors have used the state-of-the-art combinatorial indexing RNA-seq approach to generate single cell (nucleus) resolution. The data appears robust and is coherent across the four different genotypes used by the authors.

The authors present the data in a logical and accessible manner, with appropriate reference to the anatomy. They include helpful images of the biology and schematics to illustrate their interpretations.

The datasets are then interrogated to define cell and signalling relationships between skin compartments in six diverse contexts. The hypotheses generated from the datasets are then tested experimentally. Overall, the experiments are appropriate and rigorously performed. They ask very interesting questions of interactions in the skin and identify novel and specific mechanisms. They validate these well.

The authors use their datasets to define lineage relationships in the dermal scales and also in the epidermis. They show that circumferential pre-scale forming cells are precursors of focal scale forming cells while there appeared a more discontinuous relationship between lineages in the epidermis.

The authors present transcriptome evidence for enamel deposition function in epidermal subdomains. This is convincingly confirmed with an ameloblastin in situ. They further demonstrate distinct expression of SCPP and collagen genes in the SFC regions.

The authors then demonstrate that Eda and TH signalling to the basal epidermal cells generates FGF and PDGF ligands to signal to surrounding mesenchyme, regulating SFC differentiation and dermal stratification respectively.

Finally they exploit RNA-seq data performed in parallel in the bnc2 mutants to identify the hypodermal cells as critical regulators of pigment patterning and define the signalling systems used.

Whilst these six interactions in the skin are disparate, the stories are unified by use of the sci-RNA-seq data to define interactions. Overall, it's an assembly of work which identifies novel and interesting cell interactions and cross-talk mechanisms. There are some aspects that require clarification:

With respect to the discontinuous relationship noted in Figure 2I in the epidermis, the authors did not make mention of the fact that there are in fact two independent sources of periderm in the zebrafish. The first periderm derives from the EVL, is segregated a gastrulation, and gradually replaced from the basal epidermis during post-embryonic stages. Could this residual EVL-derived periderm have reduced sensitivity of the trajectory mapping from basal to periderm? The authors should comment whether their transcriptome dataset likely had residual EVL-derived periderm and if this might have impacted their trajectory continuity interpretation.

While dual origin of periderm may impact the single cell analysis, this should not be an issue for suprabasal cells, which also show no continuity with their basal cell progenitors in UMAP space. We thank the reviewer for bringing this issue up and comment on the dual origin of periderm in the revised manuscript.

“During this stage of development, basal epidermal cells are the stem cell population that differentiate into both suprabasal and periderm cells, and each of the three major epidermal cell types are well represented in our dataset (Figure 2H,I; Figure 1—figure supplement 3)(Guzman et al., 2013; Lee et al., 2014). While periderm cells at the sampled stage are likely of dual origin, representing a mixture of early embryonic and stem cell derived cells, suprabasal cells are entirely derived from basal cells (Kimmel et al., 1990; Guzman et al., 2013; Lee et al., 2014).”

During this stage of development, basal epidermal cells are the stem cell population that differentiate into both suprabasal and periderm cells, and each of the three major epidermal cell types are well represented in our dataset (Figure 2H,I; Figure 1—figure supplement 3)(Guzman et al., 2013; Lee et al., 2014). While periderm cells at the sampled stage are likely of dual origin, representing a mixture of early embryonic and stem cell derived cells, suprabasal cells are entirely derived from basal cells (Kimmel et al., 1990; Guzman et al., 2013; Lee et al., 2014).

The authors ask if dermal SFCs express proteins associated with cartilage formation and use Col10a1 orthologues as markers (Fig 3B, I). I wonder if these are the best transcripts to answer this question as this has also been described to label osteoblasts in certain contexts in the fish and the authors might want to refer to Li et al 2009 or Avaron et al 2005. Were other markers of cartilage formation present such as collagen2 genes? These may be more definitive. The authors might want to reinterrogate their datasets for true cartilage markers or reframe their question.

In the revised manuscript, we have clarified and moderated inferences from col10a1 ortholog expression. Col2 genes were not detected robustly in our dataset. This section now reads:

“Scale elasmoidin is a flexible, collagenous ECM, material properties that are similar to cartilage (Quan et al., 2020). We therefore wondered whether dermal SFCs express matrix proteins associated with cartilage formation. Col10a1 is a major structural molecule in collagen, although its expression has also been documented in osteoblasts (Gu et al., 2014; Yang et al., 2014; Kawasaki et al., 2021). The zebrafish genome harbors genes encoding two Col10a1 orthologs (col10a1a and col10a1b) and we found both transcripts in SFCs representing distinct steps of maturation (Figure 3B,I; Figure 2—figure supplement 1F,I).”

Finally, of interest, were there any clear clusters on the UMAP plots (Fig 1 Supp3A) of unassigned identity? Even comment on these and how they were dealt with would be of significant interest to the field, as it is highly unlikely all cell types in the skin have been defined. This dataset promises to be a critical reference for defining these in the future.

Thanks for raising this issue. We provide a new figure (Figure 1 – supplement 4) displaying the unsupervised clustering of the wild-type dataset and a new table (Supplementary file 2 – table 5) with gene expression information for these clusters.

Minor clarification:

Fig 2E top. The authors interpret that fate-mapped SFCs at the posterior margin are progressively displaced towards the scale focus. This is confusing as the margin SFC in Fig 2E seems to show them staying largely at the margin. Please clarify if this is what you meant.

In Figure 2E, a new row of newly differentiated, non-photoconverted SFC were added, displacing the existing row of cells towards the scale focus. Since these cells are all very thin, the net displacement was not as dramatic as the displacement found for sub-marginal SFCs. This point has been clarified in the figure legend in the revised manuscript. This figure legend now reads:

“Figure 2. Postembryonic skin cell lineage relationships are not reflected in UMAP space. (A) UMAP visualization showing distribution of differentiated SFC expressing sp7 and pre-SFC progenitors expressing runx2b. (B) In-situ hybridization of sp7 and runx2b shows that a halo of pre-SFC progenitors surround the growing scale (arrows). (C) sp7:nEOS expressing differentiated SFC (magenta), were labelled by photoconversion on Day 1. Over the following two days, newly differentiated, un-photoconverted SFC appeared at the scale margin (arrows; n = 5 fish). (D) Schematic representation of differentiated SFC (purple) and the associated halo of pre-SFC (blue). (E) Photoconversion of small groups of SFC in the scale margin and sub-margin; and single-cell photoconversion of focus SFCs (arrows) showed that SFC are progressively displaced toward the scale focus and that SFC in all these regions are capable of cell division (arrows, n ≥ 4 fish for each region tested). Margin SFCs were displaced towards the posterior by newly differentiated, un-photoconverted SFCs (arrowheads). (F) SFCs in UMAP space colored by “pseudotime” rooted in the SFCs. (G) SFCs in UMAP space colored by the ratio of a mesenchymal (migratory) signature to an epithelial signature (Supplementary file 2—Table 3). (H) Schematic representation of epidermis with major substrata. (I) UMAP visualization of wild-type epidermis, subclustered independently of other cell types and displaying expression of the epidermal basal cell marker tp63 (blue) and the periderm marker krt4 (red). Scale bars, 50 μm (B,C,E); 25 μm, (C, lower). (J) The fraction of cells from panel H that pass a minimum threshold for expression of tp63, krt4 or both genes. .”

References

Debiais-Thibaud M, Oulion S, Bourrat F, Laurenti P, Casane D, Borday-Birraux V. 2011. The homology of odontodes in gnathostomes: insights from Dlx gene expression in the dogfish, Scyliorhinus canicula. BMC Evolutionary Biology 11:307. doi: 10.1186/1471-2148-11-307,

Pemberton TJ, Li FY, Oka S, Mendoza-Fandino GA, Hsu YH, Bringas P, Jr., Chai Y, Snead ML, Mehrian-Shai R, Patel PI. 2007. Identification of novel genes expressed during mouse tooth development by microarray gene expression analysis. Dev Dyn 236:2245-57. doi: 10.1002/dvdy.21226, PMID: 17626284

Woodruff ED, Kircher BK, Armfield BA, Levy JK, Bloch JI, Cohn MJ. 2022. Domestic cat embryos reveal unique transcriptomes of developing incisor, canine, and premolar teeth. Journal of Experimental Zoology Part B: Molecular and Developmental Evolution 338:516-31. doi: https://doi.org/10.1002/jez.b.23168

Author Response

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

eLife assessment:

This important study used a battery of cutting-edge technologies including whole exosome sequencing, knockout/knockdown animal models and comparative proteomics to define the physiological roles of ZMYND21 in the regulation of sperm flagellar development and male fertility. The data supporting the conclusion are solid, although inclusion of more patients and ultrastructural studies would have further strengthened the study. This work will be of interest to clinicians and researchers who work on male fertility, but also those working on organs/systems containing motile cilia (e.g., trachea, oviduct, ventricular ependymal cells).

We thank the eLife editorial board for these very positive comments.

The MMAF sperm phenotype is rare and, as for all rare diseases, the number of affected patients remains low. Moreover, the most prevalent genes have already been identified. In such case, the identification of four unrelated patients with pathogenic mutations in the same new gene is thus significant, especially as compared to most studies on the same phenotype. We agree that ultrastructural studies could provide valuable information. However, the amount of sperm cells available did not allow us to consider such experiments at this time. The production and study of the Trypanosoma enabled us to overcome these limitations.

Reviewer #1 (Public Review):

The goal of the authors is to use whole-exome sequencing to identify genomic factors contributing to asthenoteratozoospermia and male infertility. Using whole-exome sequencing, they discovered homozygous ZMYND12 variants in four unrelated patients. They examined the localization of key sperm tail components in sperm from the patients. To validate the findings, they knocked down the ortholog in Trypanosoma brucei. They further dissected the complex using coimmunoprecipitation and comparative proteomics with samples from Trypanosoma and Ttc29 KO mice. They concluded that ZMYND12 is a new asthenoteratozoospermia-associated gene, biallelic variants of which cause severe flagellum malformations and primary male infertility.

The major strengths are that the authors used the cutting-edge technique, whole-exome sequencing, to identify genes associated with male infertility, and used a new model organism, Trypanosoma brucei to validate the findings; together with other high-throughput tools, including comparative proteomics to dissect the protein complex essential for normal sperm formation/function. The major weakness is that limited samples could be collected from the patients for further characterization by other approaches, including western blotting and TEM. In general, the authors achieved their goal and the conclusion is supported by their results. The findings not only provide another genetic marker for the diagnosis of asthenoteratozoospermia but also enrich the knowledge in cilia/flagella.

We thank the reviewer for these positive comments that are helpful for improving our paper. Concerning the remark about the low amount of sperm cells available, most patients allowed us to use excess sperm samples not used for ART treatment but are generally reluctant to perform a new sperm collection. Therefore, we often have to prioritize the most relevant and suitable experiments with the amount of sperm cells available.

Reviewer #2 (Public Review):

The manuscript "Novel axonemal protein ZMYND12 interacts with TTC29 and DNAH1, and is required for male fertility and flagellum function" by Dacheux et al. interestingly reported homozygous deleterious variants of ZMYND12 in four unrelated men with asthenoteratozoospermia. Based on the immunofluorescence assays in human sperm cells, it was shown that ZMYND12 deficiency altered the localization of DNAH1, DNALI1, WDR66 and TTC29 (four of the known key proteins involved in sperm flagellar formation). Trypanosoma brucei and mouse models were further employed for mechanistic studies, which revealed that ZMYND12 is part of the same axonemal complex as TTC29 and DNAH1. Their findings are solid, and this manuscript will be very informative for clinicians and basic researchers in the field of human infertility.

We thank the reviewer for these positive comments that are helpful for improving our paper.

Reviewer #3 (Public Review):

In this study, the authors identified homozygous ZMYND12 variants in four unrelated patients. In sperm cells from these individuals, immunofluorescence revealed altered localization of DNAH1, DNALI1, WDR66, and TTC29. Axonemal localization of ZMYND12 ortholog TbTAX-1 was confirmed using the Trypanosoma brucei model. RNAi knock-down of TbTAX-1 dramatically affected flagellar motility, with a phenotype similar to ZMYND12-variant-bearing human sperm. Co-immunoprecipitation and ultrastructure expansion microscopy in T. brucei revealed TbTAX-1 to form a complex with TTC29. Comparative proteomics with samples from Trypanosoma and Ttc29 KO mice identified a third member of this complex: DNAH1. The data presented revealed that ZMYND12 is part of the same axonemal complex as TTC29 and DNAH1, which is critical for flagellum function and assembly in humans, and Trypanosoma. The manuscript is informative for the clinical and basic research in the field of spermatogenesis and male infertility.

We thank the reviewer for these positive comments that are helpful for improving our paper.

Reviewer #1 (Recommendations For The Authors):

The manuscript was very well written, and very easy to follow. Most data were presented in high quality. I only have a few minor issues with some figures.

1. The signals in some IF images (Fig 1E, Fig. 2B are too weak;

The figures were improved and modified accordingly.

1. In some IF images, strong dot-like signals are observed (Fig. 1B, Fig. 2D, Fig. 2F). Are they specific signals or non-specific? Please specify. If they are non-specific, please replace these images.

These figures were improved and modified accordingly. Indeed, the dot-like signals were non-specific.

Reviewer #2 (Recommendations For The Authors):

Here further revisions are suggested.

1) Description of ZMYND12 genotypes of the patients and the sperm cell samples:<br /> In the title of Table 1, it is suggested to mention "homozygous" for ZMYND12 variants in the patients, since the heterozygous carriers should be unaffected.

It was done as suggested

In the Abstract ("with a phenotype similar to ZMYND12-variant-bearing human sperm"), it is suggested to use "with a phenotype similar to the sperm from men bearing homozygous ZMYND12 variants", since the sperm phenotypes are dependent on the biallelic genotypes of human individuals (not the monoallelic genotype of the sperm cells). Please check the whole manuscript and revise the similar points.

It was done as suggested

2) The database accession number for ZMYND12:<br /> There are three different numbers (NM_032257.5 vs NM_032257 vs ENSTxxxx) on Page 5 and Figure 1B. Please use NM_032257.5 for consistency.

It was done as suggested

3) For the exonic deletion variant, is it possible to predict the coding consequence of ZMYND12 protein?

No serious and reliable in silico prediction could be perform due to the absence of the exact breakpoints of the exon deletion. mRNA (or WB) studies could precise this point, however no additional sperm samples from this patient was available.

4) Please italicize the gene symbols. For example, TTC29 on Page 8 and Figure S4, Ttc29-/- KO on Page 13.

It was done as suggested

5) In Figure 2, there are too many panels that cannot be merged into one page. Some of the data can be shown as supplemental data.

We modified the figure 2 as suggested. The new figure 2 now includes only four panels (A, B, C and D) and we added a new figure S4 with the two remaining panels. We modified the text, figure legends and numeration accordingly.

6) Some of the references are duplicated. Please delete one of them.<br /> For example, there are two Broadhead et al., two Coutton et al. (Nat Commun), and two Dacheux et al.

Sorry for the duplicates. It was corrected

7) The information on some references is incomplete (missing volume and/or page numbers). For example, Touré et al and Wang et al. (2010).

It was corrected

Reviewer #3 (Recommendations For The Authors):

However, I have several points as the following:

1. The sperm concentrations of ZMYND12_3 in patient 3 and patient 4 are significantly different from the other two patients. Do you think it is just due to phenotype heterogeneity?

We have no formal explanations about these observations but we think that such difference in sperm concentration are more likely due to patient heterogeneity.

1. There is no record for detailed semen parameters of ZMYND12_ 4, and readers cannot see that the proportion of short flagella in Table 1 is 70%. Please provide complete semen routine information for this case.

Unfortunately, no additional information about the semen parameters of this patient are available at this time.

1. In this study, no immunostaining for DNAH1, DNALI1, or WDR66 was detected in sperm from individual ZMYND12_3, and subsequent validation found that TTC29 interacted with ZMYND12 in Trypanosoma brucei. DNAH1 and DNALI1 both interact with TTC29 in mice. The author concluded that ZMYND12 is part of the same axonemal complex as TTC29 and DNAH1 and plays a critical role in flagellum function and assembly. If it is possible, the author can add an experiment on the interaction between ZMYND12 and DNAH1 to make this theory more complete.

Our study focuses on characterizing protein-protein interactions using IPs (Immunoprecipitations). We were able to demonstrate that the protein ZYMIND12, along with TTC29, DNAH1, and DNALI1, belongs to the same complex, IAD-4. However, this technique does not allow us to draw conclusions about direct interactions for any of the identified proteins.

Our Co-IP results in T.brucei indicate that the orthologue of DNAH1 (Tb927.11.8160 orthologs) and TTC29 co-immunoprecipitate with TAX-1 (ZYMIND12), thereby complementing the study conducted in Chlamydomonas by Yamamoto et al., 2008. As suggested by reviewer 3, direct interactions between each protein could provide valuable insights into the organization of the intracomplex protein interactome. This aspect will be addressed in a separate study, as it requires the use of direct interaction techniques such as Y2H (Yeast Two-Hybrid) or DuoLink.

1. Please check the reference section. Some references have duplication, and the content of the literature also needs to be standardized. For example,

Broadhead R., Dawe HR, Farr H, Griffiths S, Hart SR, Portman N, Shaw MK, Ginger ML, Gaskell SJ, McKean PG, Gull K. 2006. Flagellar motility is required for the viability of the bloodstream trypanosome. Nature 440:224-7.

Broadhead Richard, Dawe HR, Farr H, Griffiths S, Hart SR, Portman N, Shaw MK, Ginger ML, Gaskell SJ, McKean PG, Gull K. 2006. Flagellar motility is required for the viability of the bloodstream trypanosome. Nature 440:224-227. doi:10.1038/nature04541

Ersfeld K, Gull K. 2001a. Targeting of cytoskeletal proteins to the flagellum of Trypanosoma brucei. J Cell Sci 114:141-148.

Ersfeld K, Gull K. 2001b. Targeting of cytoskeletal proteins to the flagellum of Trypanosoma brucei. J Cell Sci 114:141-148. doi:10.1242/jcs.114.1.141

Sorry for the duplicates, it was corrected.

Author Response

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

Combined Public Review:

It has been shown previously that maternal aging in mice is associated with an increase in accumulation of damaged mitochondria and activation of parkin-mediated autophagy (see DOI: 10.1080/15548627.2021.1946739). It has also been shown that C-natriuretic peptide (CNP) regulates oocyte meiotic arrest and that its use during in vitro oocyte maturation can improve parameters associated with decreased oocyte quality. Here the authors tested whether use of CNP treatment in vivo could improve oocyte quality and fertility of aged mice, for which they provided convincing evidence. They also attempted to determine how CNP improves oocyte developmental competence. They showed a correlation between CNP use in vivo and the appearance (and some functional qualities) of cytoplasmic organelles more closely approximating those of oocytes from young mice. However, this correlation could not be interpreted to imply causation. Additional experiments performed using CNP during in vitro maturation were not properly controlled and so are not possible to interpret.

A strength of the manuscript is that the authors use an in vivo treatment to improve oocyte quality rather than just using CNP during oocyte maturation in vitro as has been done previously. This strategy provides more potential for improving oocyte quality - over the course of oocyte growth and maturation - rather than just the final few hours of maturation alone. This strategy also has the potential to be translated into a more generally useful clinical therapeutic method that using CNP during in vitro maturation. However, it is difficult to glean information regarding how CNP might have its effects in vivo. A range of models are used in the manuscript with a mix of in vivo studies with in vitro experiments, which results in some disconnect between systemic CNP and its reported intrafollicular action as well as in the short-term versus longer-term actions of CNP on oocyte quality. Specifically, CNP was shown to be reduced in the plasma of aged mice, but this was not shown in the granulosa cells, which are the reported source of CNP that acts on oocytes. Whether the ovarian source of CNP is reduced in aged females was not demonstrated, and CNP is not known to act on oocytes through an endocrine effect. In vivo treatments with CNP by i.p. injection were performed, but the dose (120 ug/kg) and time (14 days) of treatment were not validated by any prior experiments to give them physiological relevance.

Thank you for the summary and for highlighting our manuscript’s strengths and weaknesses.

Weaknesses:

1. There are errors in the manuscript writing that make the Results difficult to follow. Reference to the Figures in the Results section does not match what is shown in the Figure panels. For example, the Results text reports differences in CNP levels in aged and young mice shown in Figure 1C, but the relevant panel is actually shown in Figure 1F. Other Figures have the same problem.

Thanks for the valuable suggestion. All the mistakes have been corrected in the revised manuscript.

1. The Results section is not always clear regarding what CNP treatment was done - in vivo injections or in vitro maturation. For example, what is the difference, if any, between Figures 2C-D and Figures S2A-B?

Thank you for pointing out the potential confusion regarding the experimental procedures in Figures 2C-D and Figures S2A-B. In the revised manuscript, we have included additional explanations to clarify that Figures 2C-D represent in vivo injections, while Figures S2A-B depict in vitro maturation. In brief, the results presented in the Supplementary Material (Figures S1-S7) are derived from in vitro CNP treatment.

1. Immature oocytes from aged females (~1 year) were treated with a two-step culture system with a pre-IVM step with CNP. Controls included oocytes from young (6-8 weeks) females or oocytes from aged females treated by conventional IVM. The description of these methods suggests that control oocytes did not receive an equivalent pre-IVM culture, hence the relevance of comparisons of CNP-treated versus control oocyte is questionable. It was observed that aged oocytes pre-cultured in CNP improved polar body extrusion rates and meiotic spindle morphology compared to oocytes in conventional IVM, as has been well established. The description of statistical methods does not make clear whether the PBE rate in CNP-treated old oocytes remained significantly lower than young controls.

Statistical analyses were performed using GraphPad Prism 8.00 software (GraphPad, CA, United States). Differences between two groups were assessed using the t-test. Indeed, CNP is unlikely to fully restore the PB1 rate in aged mice to the same level as in the young group. PB1 rate in CNP-treated aged oocytes remained significantly lower than young controls (P<0.05).

1. The main effect of the CNP 2-week treatment appears to be increasing the number of follicles that grow into secondary and antral stages, but there is no attempt made to discover the mechanism by which this occurs and therefore to understand why there might be an increase in the number of ovulated eggs, quality of the eggs, and litter size. It is also not clear how an intraperitoneal injection can guarantee its effectiveness because the half-life of CNP is very short, only a few minutes.

The 2-week treatment of CNP had a significant impact, leading to an increase in the number of follicles progressing to secondary and antral stages, as well as an increase in the number of ovulated eggs, improved egg quality, and enhanced litter size. Previous studies (references: 10.1530/REP-18-0470; 10.1210/me.2012-1027) have demonstrated the crucial role of CNP as an upstream regulator in stimulating preantral follicle growth and promoting the ovulation rate. These studies have also identified the influence of CNP on the expression of key ovarian genes involved in cell growth and steroidogenic enzymes. Consistent with these findings, our study provides further evidence supporting CNP as a critical regulator of preantral follicle growth and oocyte quality. Furthermore, it is important to note that oocyte-derived paracrine factors play essential roles in follicular development. CNP may regulate the communication between oocytes and somatic cells, contributing to folliculogenesis and follicular development. We are considering this aspect for further investigation in another ongoing study.

To ensure the effectiveness of CNP, given its short half-life (a few minutes), aged mice (58 weeks old) received daily intraperitoneal injections of CNP (120 μg/kg body weight; Cat#B5441, ApexBio) for a duration of 14 days.

1. Meiotic spindle morphology, as well as a number of putative markers of cytoplasmic maturation are also suggested to be improved after pre-culture with CNP. In each case a subjective interpretation of "normal" morphology of these markers is derived from observations of the young controls and the proportions of oocytes with normal or abnormal appearance is evaluated. However, parameters that define abnormal patterns of these markers appear to be subjective judgements, and whether these morphological patterns can be mechanistically attributed to the differences in developmental potential cannot be concluded.

Oocyte cytoplasmic maturation involves a remarkable reorganization of the oocyte cytoplasm, encompassing the movement of vesicles, mitochondria, Golgi apparatus, and endoplasmic reticulum. This dynamic process occurs during the transitions from the germinal vesicle breakdown (GVBD) stage to the metaphase I (MI), polar body extrusion (PBE), and metaphase II (MII) stages (reference: 10.1093/humupd/dmx040). In our study, we observed that CNP treatment partially rescued cytoplasmic maturation events in aged oocytes by maintaining normal distribution patterns of cortical granules (CG), endoplasmic reticulum (ER), and Golgi apparatus. However, further experiments are needed to investigate the specific action of CNP on the function of CG, ER, and Golgi apparatus. These experiments are beyond the scope of this manuscript, but we acknowledge the importance of this aspect and will consider it for future research. In this study, our main focus was to examine the effects of CNP on mitochondria distribution and function. Therefore, we analyzed the localization patterns of mitochondria, mitochondrial membrane potential, oocyte ATP content, and ROS levels. These experiments were aimed at elucidating the impact of CNP on mitochondrial dynamics and metabolism, which are crucial for oocyte quality and development.

1. In addition to the localization patterns of mitochondria, the mitochondrial membrane potential, oocyte ATP content and ROS levels were assessed through more objective quantitative methods. These are well known to be defective in oocytes of aged females and CNP treatment improved these measures. Mitochondrial dysfunction is the most obvious link between oocyte apoptosis, autophagy, cytoplasmic organelle miss-localization and aberrant spindle morphology. Among the most intriguing results is the finding that CNP mediated a cAMP-dependent protein kinase (PKA) dependent reduction in mitochondrial autophagy mediators PINK and Parkin and reduced the recruitment of Parkin to mitochondria in oocytes. However, it may not be possible to directly link this observation to the improvements in IVM oocyte quality, since PINK/Parkin assessments were performed in oocytes from cultured follicles treated with CNP for 6 days.

The beneficial effects of CNP on oocyte quality have been extensively demonstrated through in vivo experiments (Figure 1 and 4) and “two-step” in vitro culture experiments (Figure S1 and S7). In this study, our primary focus is to analyze the signaling pathway and mechanism by which CNP inhibits mitophagy in oocytes. Previous studies have highlighted the significant role of cAMP-PKA activity in reducing mitochondrial recruitment of Parkin and mitophagy (reference: 10.1038/s42003-020-01311-7). Consistent with these findings, our study revealed that aged oocytes exhibited lower concentrations of cAMP compared to young oocytes. However, upon administration of CNP, we observed a substantial increase in intraoocyte cAMP levels. To investigate the involvement of PKA in CNP-mediated oocyte mitophagy, we conducted further experiments. We isolated preantral follicles (80-100 µm diameter) from the ovaries of aged mice and subjected them to in vitro culture with either 100 nM CNP or a combination of 100 nM CNP and 10 µM H89, a PKA inhibitor. Monitoring the growth dynamics of the follicles revealed that treatment with 100 nM CNP significantly increased follicle diameter, while H89 treatment inhibited the promotive effect of CNP on preantral follicle growth (Figure 6 K and L). Western blot analysis demonstrated that CNP supplementation led to a significant decrease in PINK1 and Parkin expression levels, which were abrogated by H89 treatment (Figure 6 M-O). It is well-established that the cAMP-PKA pathway plays a crucial role in inhibiting Parkin recruitment to damaged mitochondria (Akabane et al., 2016). Therefore, we aimed to investigate whether PKA inhibition regulates Parkin recruitment. To assess the effects of CNP on mitochondria, we performed double staining for Parkin and translocase of outer mitochondrial membrane 20 (TOMM20). The results clearly demonstrated that CNP inhibited the mitochondrial localization of Parkin, while PKA inhibition with H89 led to Parkin translocation to mitochondria, as indicated by the overlap of the two staining signals (Figure 6 P and Q). Collectively, our data suggest that the suppression of Parkin recruitment through the cAMP-PKA axis represents an important mechanism underlying the protective effect of CNP against oxidative injury in maternally aged mouse oocytes.

1. The gold standard assay for oocyte quality is embryo transfer and live birth. The authors assessed the impact of maturing oocytes in vitro in the presence of CNP on oocyte quality by less robust assays (e.g., preimplantation embryo development in vitro), so the impact on oocyte quality is less certain.

We appreciate the Revierer’s suggestion to assay live birth rates by transfer embryos obtained from IVM oocytes. However, we decided not to pursue this option for this revision because of the current technical challenges that make it difficult to get a precise result of live birth rates from IVM oocyte. Thank you for your very valuable suggestion, we have discovered the shortcomings in my current work, and I will follow your suggestions in my future work to improve the level of scientific research and achieve more results.

1. The terminology used to describe many of the Results exaggerates the findings. For example, the authors claim that many of their immunofluorescent markers of the various organelles have a pattern that is "restored" by CNP. However, in most cases the pattern is "improved" toward the control condition but is not fully restored.

We acknowledge the confusion caused by the wording of the mechanism of action of CNP in the original version. In the resubmission, we have made significant improvements by providing critical information that clarifies the action of CNP. We believe that these revisions will enhance the understanding of the mechanism of CNP and its implications. Thank you for pointing out this issue, and we appreciate your feedback in helping us improve the clarity of our work.

1. The numbers of embryos should have been corrected for the number of eggs fertilized as a starting point so that the percentage that developed to each stage could be expressed as a percentage of successfully fertilized eggs rather than overall percentages. As currently shown in the Figures and described in the Legend, there is no information regarding what the percentage on the y-axis means. For example, does Figure 4B show the number of 2C embryos divided by the number of eggs inseminated? Or is it divided by the number of successfully fertilized eggs, and if so, how was that assessed?

The embryonic development rates (Figure 4 B-F) were calculated based on the total number of oocytes, and the percentages of oocytes that developed to each stage were expressed as overall percentages.

1. When fewer eggs are fertilized, the numbers of embryos per group are lower and so the impact of culturing multiple embryos together is lost. As a result, it is possible that culture conditions rather than oocyte quality drove the differences in the numbers of embryos that achieved each stage of development.

The embryonic development rate was calculated based on the total number of oocytes. Each group included a minimum of 50 oocytes with three replicates (Young: 51, aged: 53, CNP+aged: 50). The embryo culture conditions were consistent across all groups.

1. Not all claims in the Discussion are supported by the evidence provided. For example, "In addition, the findings demonstrated that CNP improved cytoplasmic maturation events by maintaining normal CG, ER and Golgi apparatus distribution and function in aged oocytes" but it was never demonstrated that the altered distribution had any functional impact.

Oocyte cytoplasmic maturation involves a remarkable reorganization of the oocyte cytoplasm, including the movement of vesicles, mitochondria, Golgi apparatus, and endoplasmic reticulum. Extensive remodeling and repositioning of intracellular organelles occur during the transitions from GVBD to MI, PBE, and MII stages (10.1093/humupd/dmx040). Our findings indicate that CNP partially rescued cytoplasmic maturation events in aged oocytes by preserving normal distribution of CG, ER, and Golgi apparatus, as well as maintaining mitochondrial function. We acknowledge the importance of considering the impact of CNP on the function of CG, ER, and Golgi apparatus for future research. In summary, these findings demonstrate that CNP improves cytoplasmic maturation events in aged oocytes by facilitating the reorganization of CG, ER, and Golgi apparatus.

1. Incompleteness and errors in the Methods section reduce confidence in many of the results reported.

We will enhance the readability of the entire Methods section for the resubmission.

1. The methods used for Statistical Analysis are never explained in either the Methods or the Figure legends. It is unclear whether appropriate analyses were done, and it is frequently unclear what was the sample size and how many times a particular experiment was repeated. These weaknesses detract from confidence in the data.

Statistical analyses were performed using GraphPad Prism 8.00 software (GraphPad, CA, United States). Differences between two groups were assessed using the t-test. Data were reported as means ± SEM. Results of statistically significant differences were denoted by asterisk. (P < 0.05 denoted by , P < 0.01 denoted by , P < 0.001 denoted by , and P < 0.0001 denoted by **).

Recommendations for the authors: please note that you control which revisions to undertake from the public reviews and recommendations for the authors

1. The introduction does not provide critical information regarding what is already known about the mechanism of action of CNP, what other tissues are impacted by CNP treatment, and how it might affect oocyte growth. Providing this information would make it much easier to understand what is novel about the current manuscript.

We acknowledge that the mechanism of action of CNP was unclear in the original version. We have now included essential information to clarify the action of CNP.

1. Comparison of the RNAseq dataset to robust datasets from young vs aged mice would strengthen the analysis (e.g., the dataset in DOI: 10.1111/acel.13482).

Thank you for your professional suggestion. According to the suggestion from you, we will make comparison of the RNAseq dataset to robust datasets from young vs aged mice in my future work .

1. Please explain what is "Dr. Tom" that was used for RNA sequencing analysis, in the Methods.

Dr. Tom is a web-based solution that offers convenient analysis, visualization, and interpretation of various types of RNA data, including mRNA, miRNA, and lncRNA. It also supports the interpretation of single-cell RNA-seq data and WGBS data. Developed by a team of expert scientists and bioinformaticians at BGI, who have extensive experience in numerous research projects, Dr. Tom provides a wide range of intuitive and interactive data visualization tools tailored to save time in conducting differential expression or pathway analysis research. Moreover, its powerful analysis tools and advanced algorithms enable users to extract new insights and derive additional value from their data beyond what is available through standard RNA analysis services. The integration of data from leading databases worldwide allows users to reference and cross-check their results and findings. Dr. Tom is already trusted by tens of thousands of scientists and researchers, serving as a valuable and essential tool alongside their own internal data curation and analysis efforts. To learn more, please visit: Dr. Tom website https://www.bgi.com/global/service/dr-tom.

1. The Results state that single-cell transcriptomics was performed, but the Methods state that 5 oocytes were collected from each mouse. The actual Method used should be clarified.

Single-cell RNA-seq is a powerful technique that enables digital transcriptome analysis at the single-cell level using deep-sequencing methods. With this approach, even a single cell can be isolated and processed through various steps to generate sequencing libraries. Given the limited availability of oocyte samples, we employed a single-cell RNA-seq library construction protocol, allowing us to analyze the transcriptomes of individual oocytes. As a result, we collected and analyzed five oocytes from each mouse in our study.

1. The raw RNAseq data should be deposited into a publicly accessible database and reported by an accession number. It is not sufficient to state that the data is included in the manuscript and supporting information.

The RNA-seq data has been submitted as supporting information and is now accessible to all readers.

1. The image in Figure 1G is not very clear.

Thank you for bringing this to our attention. We will enhance the readability of all our figures for the resubmission.

Author Response

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

Reviewer #1 (Public Review):

The authors report a study, where they have sequenced whole genomes of four individuals of an extinct species of butterfly from western North America (Glaucopsyche xerces), along with seven genomes of a closely related species (Glaucopsyche lygdamus), mainly from museum specimens, several to many decades old. They then compare these fragmented genomes to a high-quality, chromosome-level assembly of a genome of a European species in the same genus (Glaucopsyche alexis). They find that the extinct species shows clear signs of declining population sizes since the last glacial period and an increase in inbreeding, perhaps exacerbating the low viability of the populations and contributing to the extinction of the species.

The study really highlights how museum specimens can be used to understand the genetic variability of populations and species in the past, up to a century or more ago. This is an incredibly valuable tool, and can potentially help us to quickly identify whether current populations of rare and declining species are in danger due to inbreeding, or whether at least their genetic integrity is in good condition and other factors need to be prioritised in their conservation. In the case of extinct species, sequencing museum specimens is really our only window into the dynamics of genomic variability prior to extinction, and such information can help us understand how genetic variation is related to extinction.

I think the authors have achieved their goal admirably, they have used a careful approach to mapping their genomic reads to a related species with a high-quality genome assembly. They might miss out on some interesting genetic information in the unmapped reads, but by and large, they have captured the essential information on genetic variability within their mapped reads. Their conclusions on the lower genetic variability in the extinct species are sound, and they convincingly show that Glaucopyche xerces is a separate species to Glaucopsyche lygdamus (this has been debated in the past).

We thank the reviewer for his/her positive assessment and we hope to have contributed to both the knowledge of this iconic extinct species and also the possibility of applying our observations to other, endangered insects.

Reviewer #2 (Public Review):

The Xerces Blue is an iconic species, now extinct, that is a symbol for invertebrate conservation. Using genomic sequencing of century-old specimens of the Xerces Blue and its closest living relatives, the authors hypothesize about possible genetic indicators of the species' demise. Although the limited range and habitat destruction are the most likely culprits, it is possible that some natural reasons have been brewing to bring this species closer to extinction.

The importance of this study is in its generality and applicability to any other invertebrate species. The authors find that low effective population size, high inbreeding (for tens of thousands of years), and higher fraction of deleterious alleles characterize the Xerces colonies prior to extinction. These signatures can be captured from comparative genomic analysis of any target species to evaluate its population health.

It should be noted that it remains unclear if these genomic signatures are indeed predictive of extinction, or populations can bounce back given certain conditions and increase their genetic diversity somehow.

Methods are detailed and explained well, and the study could be replicated. I think this is a solid piece of work. Interested researchers can apply these methods to their chosen species and eventually, we will assemble datasets to study extinction process in many species to learn some general rules.

We thank the reviewer for his/her observations and suggestions for improvement and we agree that endangered species show conflicting signals sometimes associated to decreasing genetic diversity (some species are very low in numbers and yet they keep reasonably high diversity levels as compare to others); however, this aspect remains to be explored in detail in insects that have demographic dynamics to a large extent impossible to compare to those observed in vertebrates. We agree there is a full range of cases and circumstances in declining insects to be explored in the future.

Several small questions/suggestions:

1) The authors reference a study concluding that Shijimiaeoides is Glaucopsyche. Their tree shows the same, confirming previous publications. And yet they still use Shijimiaeoides, which is confusing. Why not use Glaucopsyche for all these blues?

We have decided, for the sake of clarity, to change it to Glaucopsyche divina in Figure 1, as suggested by the reviewer.

2) Plebejus argus is a species much more distant from P. melissa than Plebejus anna (anna and melissa are really very close to each other), and yet their tree shows the opposite. What is the problem? Misidentification? Errors in phylogenetic analyses?

The reviewer is right and we think there is a mixture of potential problems here that deserve a more in depth analysis of this genus. We used MN974526 as a proxy for P. argus and we suspect now this is probably a case of misidentification (but we cannot verify it without a morphological examination of the original specimen and likely additional genomic data). MN974526 shows a 99.33% identity to the sequence by Vila et al. (2011) code NGK02C411, defined as P. melissa; as the true status of this mitogenome cannot be totally clarified (it is likely that it is in fact P. idas), we have decided to attribute it to “Plebejus sp” in the Figure 1 and explained this in the text.

3) Wouldn't it be nicer to show the underside of butterfly pictures that reveals the differences between xerces and others? Now, they all look blue and like one species, no real difference.

This is a good suggestion, and we have now included the underside of different species, including Xerces Blue.

4) The authors stated that one of five xerces specimens failed to sequence, and yet they show 5 specimens in the tree. Was the extra specimen taken from GenBank?

Yes, the extra specimen is the one reported in Grewe et al. 2021; we have marked in Figure 1 with an * this specific mitogenome (and mentioned in the legend), which clusters nicely within the set of Xerces Blue mtDNA diversity we have generated.

Reviewer #1 (Recommendations For The Authors):

I am curious why the authors did not attempt to do a de novo assembly of the extinct species' genomes. In our work on museum specimen genomes, we have successfully used a de novo approach to extract protein coding genes from such highly fragmented genomes. We used SPAdes to assemble the museum genomes and then assessed BUSCO completeness, finding anything from 50% to 90% BUSCO completeness. The genome assemblies themselves are pretty poor with N50s around a few thousand bp at best, but the information we can extract from such highly fragmented genomes is very useful, especially with regard to protein coding gene exons. Perhaps worth trying?

Thanks for the comment. In our approach, and considering the expected low quality from some museum specimens in the lower part of the conservation spectrum, we used the standard approach based on the variant calling of short read data mapped to a close assembly. This method has been shown to be precise enough in cross species mapping (Kuderna et al. Science 2023). Local assemblies of exons and genes, while potentially informative, particularly for structural preservation, was not the priority in our objectives where only the base pair mutations were explored. Nevertheless, we are planning to generate in the near future an assembly for the closest living relative of Xerces, Glaucopsyche lygdamus, and once we get it, we will consider the possibility of undertaking the suggested approach with this new reference to explore the genomic architecture of Xerces Blue in more detail.

Author Response

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

Reviewer #1 Public Review

“First, I agree with the authors of this manuscript that conformational changes in the XFEL structures with 2.8 A resolution are not reliable enough for demonstrating the subtle changes in the electron transfer events in this bacterial photosynthesis system. Actually, the data statistics in the paper by Dods et al. showed that the high-resolution range of some of the XFEL datasets may include pretty high noise (low CC1/2 and high Rsplit) so the comparison of the subtle conformational changes of the structures is problematic.

The manuscript by Gai Nishikawa investigated time-dependent changes in the energetics of the electron transfer pathway based on the structures by Dods et al. by calculating redox potential of the active and inactive branches in the structures and found no clear link between the time-dependent structural changes and the electron transfer events in the XFEL structures published by Dods, R.et al. (2021). This study provided validation for the interpretation of the structures of those electrontransferring proteins.

The paper was well prepared.”

Thank you very much for your positive and insightful comment. We greatly appreciate your suggestion regarding the high noise levels of the XFEL structures. Including this information in the Introduction section will draw readers’ attention to the concerns about the reliability of these XFEL structures. We have incorporated it into the Introduction section.

Reviewer #2 Public Review

“The manuscript by Nishikawa et al. addresses time-dependent changes in the electron transfer energetics in the photosynthetic reaction center from Blastochloris viridis, whose time-dependent structural changes upon light illumination were recently demonstrated by time-resolved serial femtosecond crystallography (SFX) using X-ray free-electron laser (XFEL) (Dods et al., Nature, 2021). Based on the redox potential Em values of bacteriopheophytin in the electron transfer active branch (BL) by solving the linear Poisson-Boltzmann equation, the authors found that Em(HL) values in the charge-separated 5-ps structure obtained by XFEL are not clearly changed, suggesting that the P+HL- state is not stabilized owing to protein reorganization. Furthermore, chlorin ring deformation upon HL- formation, which was expected from their QM/MM calculation, is not recognized in the 5ps XFEL structure. Then the authors concluded that the structural changes in the XFEL structures are not related to the actual time course of charge separation. They argued that their calculated changes in Em and chlorin ring deformations using the XEFL structures may reflect the experimental errors rather than the real structural changes; they mentioned this problem is due to the fact that the XFEL structures were obtained at not high resolutions (mostly at 2.8 Å). I consider that their systematic calculations may suggest a useful theoretical interpretation of the XFEL study. However, the present manuscript insists as a whole negatively that the experimental errors may hamper to provide the actual structural changes relevant to the electron transfer events.”

Thank you for your feedback on our manuscript. We appreciate your positive assessment of our systematic calculations and theoretical interpretation of the XFEL study. We have carefully considered your comments and made the necessary revisions to address your concerns.

Reviewer #2 Recommendations for the authors

“The authors have satisfied my concerns mostly, in particular by providing the Em(QA) changes, which seem to be more attractive in the present form. However, the Em(QA) value(s), at least in the dark structure, should be provided, and the procedure of the calculation for the Em(QA) value(s) should be described in METHODS "Calculation of Em".

The calculated Em(QA) values for dataset a and dataset b in the dark structure are –223 mV and – 209 mV, respectively, using the reference Em value of –256 mV versus NHE for menaquinone-2 in water [Photosynth. Res. 134 (2017) 193]. These calculated values are comparable to experimentally measured values of –150 mV for PbRC from Blastochloris viridis (naphtoquinone) [Biochim. Biophys. Acta 440 (1976) 622] and –180 mV for PbRC from Rhodobacter sphaeroides (ubiquinone) [Arch. Biochem. Biophys 172 (1976) 329].

We have now provided this information in the Method (“Calculation of Em”) and Results and Discussion (“Relevance of structural changes observed in XFEL structures”) sections.

Author Response

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

Reviewer #1 (Public Review):

“First, I agree with the authors of this manuscript that conformational changes in the XFEL structures with 2.8 A resolution are not reliable enough for demonstrating the subtle changes in the electron transfer events in this bacterial photosynthesis system. Actually, the data statistics in the paper by Dods et al. showed that the high-resolution range of some of the XFEL datasets may include pretty high noise (low CC1/2 and high Rsplit) so the comparison of the subtle conformational changes of the structures is problematic.

The manuscript by Gai Nishikawa investigated time-dependent changes in the energetics of the electron transfer pathway based on the structures by Dods et al. by calculating redox potential of the active and inactive branches in the structures and found no clear link between the time-dependent structural changes and the electron transfer events in the XFEL structures published by Dods, R.et al. (2021). This study provided validation for the interpretation of the structures of those electron-transferring proteins.

The paper was well prepared.”

Thank you very much for your positive and insightful comment. We greatly appreciate your suggestion regarding the high noise levels of the XFEL structures, as indicated by the low CC1/2 and high Rsplit values reported by Dods et al. Including this information in the Introduction section will draw readers’ attention to the concerns about the reliability of these XFEL structures. We have incorporated the following sentences into the Introduction section:

“Furthermore, the data statistics provided by Dods et al. indicate that the high-resolution range of some XFEL datasets exhibit high levels of noise, as evidenced by low CC1/2 and high Rsplit values. These observations raise concerns about the reliable comparison of subtle conformational changes among these structures. Hence, caution must be exercised when interpreting these XFEL structures in terms of their ability to accurately capture relevant conformational changes.”

The following sentences have also been added to the Conclusions section:

“Hence, it is crucial to exercise caution when interpreting time-dependent XFEL structures, especially in the absence of comprehensive evaluations of the energetics and accompanying structural changes. This cautionary note should serve as a counterargument in the future, highlighting the potential pitfalls associated with presenting time-dependent XFEL structures of insufficient quality and drawing conclusive interpretations of protein structural changes that may not be distinguishable from significant experimental errors.”

Recommendations for the authors

“Figure 1 needs clear labels or detailed notes in the figure legend for the labels such as M, L, Pm, Pl, etc.”

In Figure 1, we have increased the size of the labels to improve visibility. Additionally, we have expanded the figure legend to include detailed explanations of the abbreviations used, such as M, L, PM, PL, etc. We believe that these modifications have significantly improved the clarity and comprehensibility of Figure 1.

Reviewer #2 (Public Review):

“The manuscript by Nishikawa et al. addresses time-dependent changes in the electron transfer energetics in the photosynthetic reaction center from Blastochloris viridis, whose time-dependent structural changes upon light illumination were recently demonstrated by time-resolved serial femtosecond crystallography (SFX) using X-ray free-electron laser (XFEL) (Dods et al., Nature, 2021). Based on the redox potential Em values of bacteriopheophytin in the electron transfer active branch (BL) by solving the linear Poisson-Boltzmann equation, the authors found that Em(HL) values in the charge-separated 5-ps structure obtained by XFEL are not clearly changed, suggesting that the P+HL- state is not stabilized owing to protein reorganization. Furthermore, chlorin ring deformation upon HL- formation, which was expected from their QM/MM calculation, is not recognized in the 5-ps XFEL structure. Then the authors concluded that the structural changes in the XFEL structures are not related to the actual time course of charge separation. They argued that their calculated changes in Em and chlorin ring deformations using the XEFL structures may reflect the experimental errors rather than the real structural changes; they mentioned this problem is due to the fact that the XFEL structures were obtained at not high resolutions (mostly at 2.8 Å). I consider that their systematic calculations may suggest a useful theoretical interpretation of the XFEL study. However, the present manuscript insists as a whole negatively that the experimental errors may hamper to provide the actual structural changes relevant to the electron transfer events. My concerns are the following two points:

Is the premise of the authors for the electron transfer energetics obviously valid?

Could the authors find any positive aspect(s) in the XFEL study?

The authors' argument is certainly due to their premise "Em(HL) is expected to be exclusively higher in the 5-ps and 20-ps structures than in the other XFEL structures due to the stabilization of the [PLPM]•+HL•- state by protein reorganization" as noted in the Results and Discussion (p. 12, lines 180-182); however, it is unknown whether this premise can be applied to the ps-timescale electron transfer events. The above premise is surely based on the Marcus theory, as the authors also noted in the Introduction "The anionic state formation induces not only reorganization of the protein environment (ref. 5: Marcus and Sutin, 1985) but also out-of-plane distortion of the chlorin ring (ref. 6: two of the authors, Saito and Ishikita, co-authored, 2012)"; however, it is unknown whether protein reorganization can follow the ps-timescale electron transfer events. Indeed, Dods et al. mentioned in the Nature paper (2021) "The primary electron-transfer step from SP (special pair PLPM) to BPhL (HL) occurs in 2.8 {plus minus} 0.2 ps across a distance of 10 Å by means of a two-step hopping mechanism via the monomeric BChL molecule and is more rapid than conventional Marcus theory". It was also mentioned, "By contrast, the 9 Å electron-transfer step from BPhL to QA has a single exponential decay time of 230 {plus minus} 30 ps, which is consistent with conventional Marcus theory". As for the primary electron-transfer step from PLPM to HL, Wang et al. (2007, Science 316, 747; cited as ref. 8 in the Nature paper 2021) reported, by monitoring tryptophan absorbance changes in various reaction centers in which the driving forces (namely, the Em gaps between PLPM and HL) are different, that the protein relaxation kinetics is independent of the charge separation kinetics on the picosecond timescale. On the other hand, in the EPR study cited by the authors as ref. 7 (Muh et al. (1998) Biochemistry 37, 13066), although the authors described "two distinct conformations of HL- were reported in spectroscopic studies" (p. 3, lines 44-45), it should be noted that conformation of HL- was formed by 1 or 45 s illumination prior to freezing, and hence the second-order reorganized conformations may differ from picosecond-order conformations observed by the XFEL study (Nature, 2021) and/or the transient absorption spectroscopy (Science, 2007).

Therefore, I consider there is a possibility that the authors' findings may reflect not experimental errors but the actual ps-timescale phenomena presented by the first-time XFEL study on the timescale of the primary charge-separation reactions of photosynthesis. Thus I would like to suggest that the authors reconsider the premise for the electron transfer energetics on the picosecond timescale.

In any case, to discuss the experimental errors in the XFEL study, it is better to calculate the Em(QA) changes in the 300-ps and 8-us XFEL structures, which showed distinctive structural changes even at the 2.8 Å resolution as discussed by Dods et al. Then, if the Em(QA) values are changed as expected from theoretical calculations, such calculated results may suggest a useful theoretical interpretation of the XFEL study as a positive aspect. If the Em(QA) values are not higher in the 300-ps and 8-us structures than in the other structures, it may be argued that the experimental errors would be so large that the XFEL structures are irrelevant to the electron transfer events expected from theoretical calculations.”

We appreciate the reviewer's constructive suggestions, which significantly contributed to the improvement of our manuscript. We have performed additional calculations to address the reviewer's suggestion. We calculated the changes in Em(QA) in the XFEL structures. The Em(QA) values in the 300-ps and 8-μs structures were not significantly higher than those in the other structures (Figure 8).

These findings align with the scenario proposed by the reviewer, suggesting that the experimental errors are substantial, rendering the XFEL structures irrelevant to the electron transfer events. The results further reinforce our argument that the observed structural changes in the XFEL structures are not directly linked to the expected changes in electron transfer events.

We have incorporated these important points into the revised version as follows:

“One might argue that the loss of the link between the formation of the charge-separated state and the Em(HL) change (Figure 5) is not due to experimental errors but rather represents the actual ps-timescale phenomena during the primary charge-separation reactions (e.g., Dods et al. noted that “the primary electron-transfer step to HL is more rapid than conventional Marcus theory” 8). However, even if this were the case, this hypothesis regarding the relevance of the XFEL structures to the electron-transfer events can be further explored by examining the changes in Em(QA) among the XFEL structures, considering the relatively slow electron-transfer step to QA that allows sufficient protein relaxation to occur (e.g., Dods et al. stated that “the electron-transfer step to QA has a single exponential decay time of 230 ± 30 ps, consistent with conventional Marcus theory” 8). That is, if the Em(QA) values are not higher in the 300-ps and 8-μs structures than in the other structures, it suggests that significant experimental errors exist, rendering the XFEL structures irrelevant to the electron transfer events. Consistent with this perspective, the present results demonstrate that the Em(QA) values in the 300-ps and 8-μs structures are not significantly higher than those in the other structures, including the dark state structure (Figure 8). Consequently, the lack of a clear relationship between the charge separated state and the changes in Em(QA) at 300 ps and 8-μs further strengthens the argument that the XFEL structures are irrelevant to the electron transfer events.”

Recommendations for the authors

“In addition to my main concerns, the following points should also be taken into consideration:

The authors presented from QM/MM calculations out-plane distortion of HL (and HM) induced upon the reduction using the dark structure for dataset a (Table 5). However, to compare with the XFEL structures corresponding to the charge-separated state [PLPM]+HL-, positive charge should be located at the special pair (or, either PL or PM). In the present work, it is noted that counter ions were added to neutralize the entire system (in Methods: p. 6, lines104-105), but the location(s) of the positive charge is unclear.”

We appreciate the valuable suggestion provided by the reviewer. To address this concern, we have calculated out-of-plane distortion of HL•– in the presence of PL•+. The results have been included in Table 5. Note that the results obtained in the presence of PL•+ are substantially the same as those obtained in PL0 (Table 5).

For clarity, we have rephrased the sentence referring to counter ions as follows:

“To neutralize the entire system, counter ions were added randomly around the protein using the Autoionize plugin in VMD 22.”

“In relation to the calculations, the authors showed the induced out-plane distortion of HM for dataset a; however, the results for HM seem not to be mentioned anywhere. Instead, the calculations for HL of the dark structure for dataset b should be useful, especially for comparing with the time-dependent changes in the dataset b XFEL structures as shown in Figure 7.”

We have made Table 6 to present the results for dataset b. The results are consistent with those for dataset a (Table 5).

Author Response

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

Reviewer #1 (Public Review):

This manuscript by Neininger-Castro and colleagues presents a novel automatic image analysis method for assessing sarcomeres, the basic units of myofibrils and validates this tool in a couple of experimental approaches that interfere with sarcomere assembly in iPSCcardiomyocytes (iPSC-CM).

Automatic quantification of sarcomeres is definitely something that is useful to the field. I am surprised that there is no reference in the manuscript to SarcTrack, published by Toepfer and colleagues in 2019 (PMID 30700234), which has exactly the same purpose. The advantage of the image analysis software presented in the current manuscript appears to me to be that it can cover both mature sarcomeres and nascent sarcomeres in premyofibrils effectively.

We whole-heartedly disagree that SarcTrack has the exact same purpose as sarcApp. sarcApp measures more than the frequency of actinin2 images, and can measure real-space quantifications of actinin, myomesin, and titin, which has not been done before in this way. However, SarcTrack is an interesting method that we hope many researchers find helpful in their research. SarcTrack is a particle tracker that outputs the dimensions of the objects found, but does not distinguish between Z-Lines and other actinin2-positive structures (Z-Bodies, adhesions). It also does not group these structures into higher order structures such as myofibrils and muscle stress fibers.

When going through the manuscript there were a few issues that should be addressed in a revised version of the manuscript:

1) I am a bit puzzled that they took 1.4 um length as a cutoff length for a mature A-band in their quantifications, since the consensus in the field for thick filament length seems to be 1.6 um?

We use 1.4 µm as a cutoff length for the length of a Z-Line rather than the A-Band. We believe the reviewer is referring to the width of the A-Band perpendicular to the Z-lines, which is indeed 1.6 µm. However, we are referring to the length of the Z-Lines, which can span anywhere from 1.4 µm to up to 10 or more µm. Thank you for allowing us to make the clarification.

2) When doing the knockdown for alpha and beta-myosin heavy chain, respectively, why did they not also do a Western blot for the "other" isoform as well (Figure 7)? We know that iPSCCM express a mixture, so the relatively mild phenotype that they observe in single knockdown experiments may well be due to concomitant upregulation of the expression of the other isoform. In my point of view this should be checked.

It is likely that in the single knockdown experiments the other isoform is upregulated, which is why we were careful in stating that neither muscle myosin alone is required for sarcomere formation. We do agree this would be an interesting experiment to check beyond the scope of this manuscript.

3) There seems to be a disconnect between the images for myomesin knockdown shown in Figure 8H and the quantification shown in Figure 8I, which makes me wonder whether the image shown in H middle (MYOM1 (1) KD), where the beta-myosin doublets do not seem to be much affected is really representative?

The image shown in the middle of H is representative of the mean length of beta-myosin doublets in MYOM1 (1) KD hiCMs. While the beta-myosin doublets are still present and organized, they are significantly shorter. In the zoomed out image, you can appreciate much shorter arrays of beta-myosin doublets that, while extending across the entire cell, are thinner than control cells.

Reviewer #2 (Public Review):

Neininger-Castro et al report on their original study entitled "Independent regulation of Z-lines and M-lines during sarcomere assembly in cardiac myocytes revealed by the automatic image analysis software sarcApp", In this study, the research team developed two software, yoU-Net and sarcApp, that provide new binarization and sarcomere quantification methods. The authors further utilized human induced pluripotent stem cell-derived cardiomyocytes (hiCMs) as their model to verify their software by staining multiple sarcomeric components with and without the treatment of Blebbistatin, a known myosin II activity inhibitor. With the treatment of different Blebbistatin concentrations, the morphology of sarcomeric proteins was disturbed. These disrupted sarcomeric structures were further quantified using sarcApp and the quantification data supported the phenotype. The authors further investigated the roles of muscle myosins in sarcomere assembly by knocking down MYH6, MYH7, or MYOM in hiCMs. The knockdown of these genes did not affect Z-line assembly yet the knockdown of MYOM affected M-line assembly. The authors demonstrated that different muscle myosins participate in sarcomere assembly in different manners.

Reviewer #3 (Public Review):

Neininger-Castro and colleagues developed software tools for the quantification of sarcomeres and sarcomere-precursor features in immunostained human induced pluripotent stem cellderived cardiac myocytes (hiCMs). In the first part they used a deep-learning- based model called a U-Net to construct and train a network for binarization of immunostained cardiomyocyte images. They also wrote graphical user interface (GUI) software that will assist other labs in using this approach and made it publicly available. They did not compare their approach to existing ones, but an example from one image suggests their binarization tool outperforms Otsu thresholding binarization.

In the second part they developed a software tool called sarcApp that classifies sarcomere structures in the binarized image as a Z-Line or Z-Body and assigns each to either a myofibril or to stress fibers. The tools can then automatically count and measure multiple features (33 per cell and 24 per myofibril) and report them on a per-cell, per-myofibril, and per- stress fiber basis.

To test the tools they used Blebbistatin to inhibit sarcomere assembly and showed that the sarcApp tool could capture changes in multiple features such as fewer myofibrils, fewer Z-Lines, decreased myofibril persistence, decreased Z-Line length and altered myofibril orientation in the Blebbistatin treated cells. With some changes the tool was also shown to quantify sarcomeres in titin and myomesin stained cardiomyocytes.

Finally they used sarcApp to quantify the changes in sarcomere assembly after siRNA mediated knockout of MYH7, MYH7, or MYOM. The analysis indicates that neither MYH6 nor MYH7 knockdown perturbed the assembly of Z- or M-lines, and that knockdown of MYOM perturbed the A-band/M-Line but not the Z-Line assembly according to features captured by the sarcApp tool.

Overall the authors developed and made publicly available an excellent software tool that will be very useful for labs that are interested in studying sarcomere assembly. Multiple features that are difficult to measure or count manually can be automatically measured by the software quickly and accurately.

There are however some remaining questions about these tools:

1) The binarization tool which is tailored to sarcomere image binarization appears promising but was not systematically compared with existing approaches.

We compared it with the existing approach we used previously in the lab, which was Otsu’s method for binarization. We are not aware of several other binarization approaches to compare to, other than using other machine learning techniques that are less advanced than a U-Net, the current standard in image-to-image translation.

2) How robust is the tool? The tool was tested on images from one type of cardiomyocytes (hiCMs) taken from one lab using Nikon Spinning Disk confocal microscope equipped with Apo TIRF Oil 100X 1.49 NA objective or instant Structured Illumination Microscopy (iSIM), using deconvolution (Microvolution software) and in a specific magnification. It remains to be seen whether the tool would be equally effective with images taken with other microscopy systems, with other cardiomyocytes (chick or neonatal rat), with different magnifications, live imaging, etc.

We tested the software with several magnifications, with live imaging, and with other tissues. We did not include the information in the manuscript because the data we tested the software with is for future manuscripts studying different aspects of sarcomere formation and maintenance. sarcApp reliably identifies Z-Lines and sarcomeres with deconvolved widefield fluorescence images of hiCMs and frozen human tissue, and are currently using it to measure zebrafish data for another study. Further, it works for live imaging with an actinin2-GFP (or similar) label. For the titin quantification, we would recommend using only 60-100X magnification, as the titin structures (doublets and rings) are not resolvable at lower magnifications.

3) The tool was developed for evaluation of sarcomere assembly. The authors show that for this application it can detect the perturbation by Blebbistatin, or knockdown of sarcomeric genes. It remains to be seen if this tool is also useful for assessment of sarcomere structure for other questions beside sarcomere assembly and in other sarcomere pathologies.

While this is beyond the scope of this specific methods paper, we welcome other researchers to use our software for other questions in other pathologies. We are currently doing the same for other manuscripts from our lab.

Reviewer #1 (Recommendations For The Authors):

1)"alpha-actinin..., which border the sarcomeric contractile machinery (thin and thick filaments); Z-lines do NOT border thick filaments in a relaxed sarcomere

We have removed “(thin and thick filaments)” from the text.

2) myomesin targeting siRNAs (gene name MYOM): there are actually three genes encoding for myomesin family members, specify, which one was targeted (I am assuming MYOM1).

Thank you for the clarification: we do target MYOM1

3) I am not surprised that they found not many mature Z-lines in the absence of both sarcomeric myosins; a similar codependence of assembly of mature Z-discs and the presence of functional thick filaments was previously shown by Geach and colleagues in 2015 (PMID 25845369)

Thank you for sharing this manuscript: we have added a reference to it in our study.

Reviewer #2 (Recommendations For The Authors):

This work offers the possibility to gain more insights into the process of sarcomere assembly through the advancement in sarcomeric or myofibril structure analyses. However, some clarifications are needed from the authors, please see below for the comments.

1) It is recommended that the authors include the time points for replating and harvesting hiCMs. After replating, the cardiomyocytes require at least three to four days for sarcomeric structures to reform. If the hiCMs were fixed before sarcomere assembly had completed, the staining of sarcomeric proteins including ACTN2 and titin could be compromised and it is difficult to tell if the phenotypes observed were consequences of drug treatments or knockdown of sarcomeric genes or simply because the replating hiCMs were fixed before their sarcomeric structures had fully regrown. It is also recommended that the authors replate hiCMs at a fixed time point to avoid discrepancies in the data.

Cardiomyocytes do not require three to four days for sarcomeric structures to re-form, and indeed only require 24 hours, with the first sarcomeres typically appearing at ~6 hours. We and others have published several studies demonstrating this (Fenix et al., eLIfe 2018, Taneja, Neininger and Burnette MBoC 2020, Chen et al. Nature Methods, 2022). While sarcomeres continue to develop and turn over after this time, our lab is interested in the beginning steps of sarcomerogenesis rather than the turnover of mature structures.

2) The sarcApp automatically identifies Z-lines and Z-bodies; however, is there an option for the users to set their own thresholds? Some users may select different criterions when quantifying sarcomeres. Moreover, the Z-lines and Z-bodies identified by the software are not always accurate. Can the users modify the list manually in an unbiased way. If this function is not available, the authors may consider adding this function to their software. sarcApp measures Zline and Z-bodies length but does not measure Z-line and Z-bodies width, but sometimes it is also necessary to measure the width.

Absolutely, users can modify the thresholds to identify Z-Lines and Z-Bodies. There is not a way for users to modify the list in an unbiased way per se, as editing the list of Z-Lines and Z-Bodies based on non-mathematical measurements is inherently biased, but the user is free to add in other Z-Lines and Z-Bodies as they wish. In this context, “manually” and “unbiased” is mutually exclusive.

3) It is recommended that the authors include the original images beside the sarcomeric structures identified by sarcApp (Figure 2A, 2C, 4C-F and more). It would be easier to compare the original Z-lines and Z-bodies with those identified by the software.

We have added these in Author response image 1.

Author response image 1.

Uncropped images and merges from Figures 2, 4 and 6, respectively.

4) The M-line length quantification data in Figure 3G, 5F, and 6H showed different colored-dots labeling n1 to n3, but the authors did not discuss the significance of these symbols.

We are not sure what the reviewer means by this statement: there is no significance of the different colored dots other than to mark the biological replicate shown. These graphs were created using SuperPlots, which was not stated in the original methods. It has now been added to the Statistical Analysis section.

5) Can the authors elaborate more on the reasons why they treated Blebbistatin at concentrations of 50µM and 100µM. Previous studies showed that 25µM of Blebbistatin was sufficient to delay the transformation of cardiomyocytes (PMID 27072942). Can the authors also comment on why they selected 6 hours, 12 hours, and 24 hours post replating for drug treatment. Moreover, the drug treatment at different time points was only done on ACTN2 but not titin or myomesin.

We selected 6, 12, and 24 hours for actinin2 to show the time course of sarcomere formation and to show that sarcomeres are developed by 24 hours, as also mentioned above. We are interested in future studies of the time course of titin and myomesin over time, and are working on it in the lab.

We chose 50 and 100 µM Blebbistatin as these completely blocked sarcomere assembly whereas treatment with 25 µM did not. This manuscript is a methods paper that aims to validate sarcApp and show how it could be used. We did not intend for it to be a comprehensive study of how different concentrations of blebbistatin affects sarcomere assembly.

We are also unsure what the reviewer means by “transformation of cardiomyocytes”. The manuscript with the PMID of 27072942 does not address this issue. The paper is a “review and analyze readmission data for patients who received a continuous flow left ventricular assist device (LVAD)”. We assume the reviewer is referring to differentiation. The model system we developed and published in eLife in 2018 does not use differentiating iPSC cardiac myocytes. The hiCMs we use are terminally differentiated but still immature, as they are more transcriptionally similar to primary fetal myocytes. As such, they do not maintain their sarcomeres when they removed from the 96 well and plated onto a glass coverslip for highresolution microscopy. These assemble sarcomeres within 24 hours with the sarcomeres forming close to the dorsal membrane and then rearrange overtime (e.g., moving from the top of the cell to the bottom) (Fenix et al., eLife 2018). With that said, we do agree with the reviewer that a study of sarcomere assembly in the context of cardiac myocyte differentiation would be a fascinating direction for future studies, and we think sarcApp could facilitate such studies.

6) The authors mentioned that the myofibrils of Z-line, titin, and M-line were randomly oriented after Blebbistatin treatments. The myofibrils were randomly oriented for titin and M-line. However, the orientation of Z-line after 50µM Blebbistatin treatment was not necessarily random, only the orientation after 100µM Blebbistatin treatment was randomized. The authors might consider changing bar graph to other types of charts if the orientation was really randomized after quantification.

We find that the bar chart is the most informative to us, but users can consider other types of charts in their analyses.

7) It is recommended that the authors include images staining ACTN2 at lower magnifications (Figure 1A, 1C). With current images, it is true that yoU-Net can separate Z-lines from Z-bodies yet it is difficult to tell if yoU-Net can still distinguish Z-lines from Z-bodies with larger images or it only applies to a small portion of the image.

The yoU-Net can distinguish Z-Lines from Z-Bodies with images of any size, as image size (height vs. width in pixels) does not affect how binarization occurs. During binarization, the only pixel requirement is that the width and height are divisible by 8 (for downsampling purposes). Usually this is not the case with raw images, so the image borders are slightly cropped to make them usable. In terms of resolution, we recommend using 60X-100X objectives on confocal or superresolution data for the clearest results. We have, however, successfully binarized deconvolved widefield images at 100X as well.

8) The authors mentioned that the knockdown of MYH7 did not affect Z-lines and M-lines; however, the structures of ACTN2, myomesin, and titin appeared more organized as compared to those in control.

We agree that the sarcomeres and myofibrils look slightly more organized, and did mean to state that the knockdown did not negatively affect Z-Lines and M-Lines and have updated the manuscript to be more accurate.

9) Please provide the merge images for Fig. 4D, 4E, 6B

The merge images for Fig. 4D, 4E, and 6B are included with the original images requested above (point 3)

10) In the text, they described" "antibodies to the titin I-band localize to both MSFs and sarcomeres in hiCMs (Figure 4A). Titin forms ring-like structures around the Z-Bodies of MSFs that are closer to the apparent sarcomere transition point (Figure 4A)" However, based on the antibody information they provided, it is not explicitly recognized for N-or C-terminus TITIN. Please provide TTN N-terminus or TTN-C terminus co-stainings with ACTN2 antibody to understand which part of TTN together with ACTN2 forms a Z-Body.

The TTN antibody is an N-terminal antibody localizing to the I-Band region of sarcomeres. We agree with the reviewer that a more thorough study of titin will be of interest and we are currently undertaking such a study. However, this is a methods paper presenting a tool. While some of the data we present does point to mechanistic hypotheses, it is beyond the scope of this study to fully characterize titin during sarcomere assembly.

11) TITIN doublet was used to indicate a sarcomere in Fig. 4C-D. Moreover, they also used another combination (myomesin and F-ACTIN) to label a sarcomere in Fig. 6D. Can they compare the difference between these two methods or by using these two methods (TITIN doublet) and (myomesin and F-ACTIN), how is the average length of sarcomere? Will the sarcomere length be the same?

We noted in the manuscript that due to the organization of titin doublets (wrapping around the ends of Z-Lines) that the average titin doublet will be approximately 0.3 um longer than the ZLine. We did not expect to see a difference in lengths of myomesin M-Lines and mature actinin2 Z-Lines and indeed do not see major differences in the average lengths (between 2.0 and 2.5 um in 24 hour control cells)

12) They used siRNA method to knockdown MYH6, MYH7 and MYOM and concluded that the knockdown of these genes did not affect the Z-line assembly. Even though they showed very nice knockdown efficiency of these proteins, they should (1) co-stain MYH6/TITIN/actinin2 and MYH6/ myomesin /actinin2 for Fig. 7C. (2) MYH7/TITIN/actinin2 and MYH7/ myomesin /actinin2 for Fig. 7I. (3) MYOM1/TITIN/actinin2 and MYOM2/TITIN/actinin2 for Fig. 8A. (4) MYH7/MYOM1 and MYH7/MYOM2 for Fig. 8H to make sure the cells they measured were truly knockdownpositive cells,

The antibodies for alpha and beta myosin are not very efficient for immunofluorescence, and work best for western blots. We decided also to choose a random subset of the cells on the dish to be sure to eliminate any risk of cherry-picking. While imaging cells on the dish, we looked only at the DAPI nuclear channel and selected 50 cells minimum per dish with only this channel, then imaged the other channels.

Minor comments:

1) Well-organized sarcomere structure on DMSO treated cells in Fig.5A and Fig. 6A, but it was disarray in Fig. S3M. Why?

Figure S3 shows hiCMs that have only been allowed to spread for 6 hours, which have not formed mature sarcomeres yet, hence the disarray.

2) Fig 1A, Fig2B: please label the name of the antibody, not the actin filament

We used phalloidin labelling here, which marks actin filaments. We have updated the figure legends to be more clear. Thank you!

3) Fig. 7I: actinin2 instead of actinin

Thank you for catching this! We have fixed it.

Reviewer #3 (Recommendations For The Authors):

Testing the app using images shot by other microscopy systems, magnifications, and cardiomyocytes from other species, as noted in the public review above, should make the app even more wildly useful.

A more formal head-to-head comparison with other approaches will be more convincing in showing the new tool is superior

I also think that a more detailed protocol for using the app will help other investigators.

The app counts and measures many features, but it is not always clear how and using what algorithm these are measured. Including these details in a protocol or even as comments in the code will be very helpful for others.

The protocol found on the public GitHub for the app will help other investigators to download, use, and understand the application. We have received contact from researchers who have been able to use the application without assistance from us, which is a good sign that the application is user-friendly and that the online protocol is sufficient.

Author Response

We thank the reviewers for their useful and constructive comments. In this provisional response, we will address a few of the major issues and plan to submit a detailed, point-by-point response along with the revised manuscript.

1. Robustness of activated combination of neurons (the ‘activated ensemble’).

The reviewers have asked for additional analyses and visualization of the group of neurons activated and a classification analysis to illustrate the point that the activated set of neurons would allow discrimination between different concentrations even after the spiking activity reduced significantly in the later trials. We relied on visualization using PCA (Manuscript Fig. 4) and quantification using correlation analysis (Manuscript Fig. 5a and Manuscript Supplementary Figure 2). But this point can be easily amplified further to support our conclusions and address a major concern raised by the reviewers.

Visualization of neural responses across trials and odorants: As recommended, we followed the procedures used in Stopfer et al., 2003 (Fig. 6c) and Miura et al., 2012(Fig. 3C) to image neural responses across recorded PNs as a matrix (Author response image 1).

Author response image 1.

Author response image 1: Spike counts averaged over the entire 4s odor presentation window across all recorded neurons are shown as a function of trial number (columns). The sorting is same across different panels. Note that there are 80 neurons whose response was monitored for hexanol and octanol responses (Dataset 1; first row of panels), and 81 neurons whose response was monitored for isoamyl acetate and benzaldehyde (Dataset 2; second row of panels). As can be noted, across the 25 trials the pattern of activation remains consistent. Also, the activated combination of neurons varied robustly with odor identity and intensity.

Classification analysis: To illustrate that there is enough information to recognize an odorant and discriminate between different intensities, we performed a leave-one-trial-out classification analysis. The left-out trial was assigned the class label of its nearest neighbor (using correlation distance metric). The results from this classification analysis are shown below in Author response image 2. As a control, we shuffled the odor class labels and repeated the leave-one-trial-out classification analysis.

Author response image 2.

Author response image 2: Results from classification analysis are shown for the two datasets: hexanol–octanol at different concentrations (dataset 1; 80 PNs), and isoamyl acetate and benzaldehyde (dataset 2; 81 PNs). We did a leave-onetrial-out validation. The true odor label is shown along the x-axis and the predicted odor label is shown along the yaxis. As can be noted, the class labels for every single trial were correctly predicted in both datasets. The result after class labels were shuffled is also shown for comparison. These results strongly support our conclusion that odor intensity information is preserved and odor concentration can be recognized independent of adaptation.

Correlation with the first trial:

We had shown the correlation across odorants and concentrations as a function of the trial (manuscript Figure 5A). To complement these analyses, here we focus on the correlations with the response evoked in the first trial of each odorant at high and low concentrations and plot this information as a function of trial number (Author response image 3, 4). As can be noted, the correlation across different trials of a given odorant at specific concentrations remains much higher than all other conditions.

Author response image 3.

Author response image 3: (top-left) Correlation between 80-dimensional neural responses (averaged over the entire 4s odor presentation window) with the first trial of hexanol at high intensity (hex-H; 1% v/v) is plotted as a function of trial number. (top-right) similar plots but correlation computed with neural responses evoked during the first trial of octanol at high intensity (oct-H; 1% v/v). (bottom-left) similar plots but correlation computed with neural responses evoked in the first trial of hexanol at low intensity (hex-L; 1% v/v). (bottom-right) similar plots but correlation computed with neural responses evoked in the first trial of octanol at low intensity (oct-L; 1% v/v).

Author response image 4.

Author response image 4: (top-left) Correlation between 81-dimensional neural responses (averaged over the entire 4s odor presentation window) with the first trial of isoamyl acetate at high intensity (iaa-H; 1% v/v) is plotted as a function of trial number. (top-right) similar plots but correlation computed with neural responses evoked in the first trial of benzaldehyde at a high intensity (bza-H; 1% v/v). (bottom-left) similar plots but correlation computed with neural responses evoked in the first trial of isoamyl acetate at low intensity (iaa-L; 1% v/v). (bottom-right) similar plots but correlation computed with neural responses evoked in the first trial of benzaldehyde at low intensity (bza-L; 1% v/v).

Behavioral significance and dynamics: The reviewers had wondered about the relevance of the behavior to the organism. The maxillary palps are sensory organs close to the mouth parts that are used to grab food and help with the feeding process. In our previous studies, we had shown that these palpopening responses are innately triggered by many ‘appetitive odorants.’ However, the probability of palp opening varied across different odorants (Chandak and Raman, 2023). Some odorants evoked higher palp-opening responses and others reduced the probability of palp-opening response (below the median value across odorants). Since all other parameters (such as the clicking sound of valves, and mechanical cues due to airflow during odor presentation), are the same across these different odorants, these observed differences in palp-opening response probability are attributed to the identity of the odorants presented.

Author response image 5.

Author response image 5: Preference indices were calculated for all odors tested and are shown as a bar plot (n = 26 locusts). Blue bars indicate odors classified as appetitive, gray bars indicate neutral odors and red bars indicate unappetitive odors. Locusts with a significant deviation from the median response (one-sided binomial test, P < 0.1, were classified as either being appetitive or unappetitive; P < 0.1, P < 0.05, **P < 0.01). Error bars indicate s.e.m. [Replotted Fig 1.c from Chandak and Raman, 2023].

We had also shown that we could train locusts to have stereo-typed palp-opening responses using the classical conditioning approach (odor – odor-conditioned stimulus and food reward – unconditioned stimulus; Video: https://static- content.springer.com/esm/art%3A10.1038%2Fncomms7953/MediaObjects/41467_2015_BFncomms7953 _MOESM483_ESM.mov; Saha et al., 2015). The dynamics of those conditioned palp-opening responses have been well characterized.

We will use similar tracking procedures to monitor and quantify the dynamics of innate palp-opening responses as well. We will add supplementary videos to fully capture this behavior.

Early vs. late neural responses:

Since behavioral responses are more likely to start as soon as the odorant is presented, the reviewers wondered whether there are differences in the observed findings if we focus only on the early neural activity (as it might be more important to triggering behavior). Note that the median response time for conditioned palp-opening responses is less than 750 ms (Saha et al., 2015, Chandak and Raman, 2023). Hence, we divided the neural dataset and analyzed the neural response patterns during these early (0-750 ms after onset) and late (2-4 s after odor onset) time windows. In both these epochs, we found that the total spike counts across neurons reduced as a function of trial number or repetition and the combination of neuron activated remained robust (Author response images 6-11). Hence, we conclude that while the neural responses in different time windows would be important for shaping other parameters of behavioral response dynamics, the overall behavioral response probability that we used in our analysis had a similar relationship with early, late, or total neural activity during the entire odor presentation (i.e. time-window of the neural response did not matter for the analyses presented in the manuscript).

Author response image 6.

Author response image 6: Total spike counts reduced as a function of trial number. This reduction was observed for the total spike counts during the entire odor presentation window and during both the early (0-750 ms) and late (2-4 s) response time windows. Dataset 1: 80 PNs, hexanol, and octanol odorants.

Author response image 7.

Author response image 7: Total spike counts reduced as a function of trial number. This reduction was observed for the total spike counts during the entire odor presentation window and during both the early (0-750 ms) and late (2-4 s) response time windows. Dataset 2: 81 PNs, isoamyl acetate, and benzaldehyde odorants.

Author response image 8.

Author response image 8: Similar plots as in Figures 3 and 4 but analyzing 80-dimensional spike count vectors calculated using only the first 750 ms of odor-evoked response. Note that the correlation with the odor evoked response in the first trial remains high across trials. But between different odorants or different intensities of the same odorant, the response correlation drops significantly. Dataset 1: 80 PNs, hexanol, and octanol odorants.

Author response image 9.

Author response image 9: Similar plots as in Figures 3 and 4 but analyzing 80-dimensional spike count vectors calculated using only the last 2 seconds of odor-evoked response. Note that the correlation with the odor evoked response in the first trial remains high across trials. But between different odorants or different intensities of the same odorant, the response correlation drops significantly. Dataset 1: 80 PNs, hexanol, and octanol odorants.

Author response image 10.

Author response image 10: Similar plots as in Figures 3 and 4 but analyzing 80-dimensional spike count vectors calculated using only the first 750 ms of odor-evoked response. Note that the correlation with the odor evoked response in the first trial remains high across trials. But between different odorants or different intensities of the same odorant, the response correlation drops significantly. Dataset 2: 81 PNs, isoamyl acetate, and benzaldehyde odorants.

Author response image 11.

Author response image 11: Similar plots as in Figures 3 and 4 but analyzing 80-dimensional spike count vectors calculated using only the last 2 seconds of odor-evoked response. Note that the correlation with the odor evoked response in the first trial remains high across trials. But between different odorants or different intensities of the same odorant, the response correlation drops significantly. Dataset 2: 81 PNs, isoamyl acetate, and benzaldehyde odorants.

Other Statistical Tests:

The reviewers felt that in many analyses, we did not include error bars to indicate the sample size, SEM, or SD. We will fix this by adding the sample size information to each panel and as appropriate. However, we would also like to point out that many of the analyses are done in a trial-by-trial fashion (e.g. Manuscript Figures 3 – 6). For these analyses, it would not be possible to add SEM or SD. One condition (hex -H or iaa-H) was repeated in each dataset, and we have added them in the results shown in this response letter to demonstrate repeatability. We will strive our best to add these statistics as would be appropriate, but this cannot be done for the trial-by-trial analyses.

References:

Stopfer M, Jayaraman V, Laurent G. Intensity versus identity coding in an olfactory system. Neuron. 2003 Sep 11;39(6):991-1004. doi: 10.1016/j.neuron.2003.08.011. PMID: 12971898.

Miura K, Mainen ZF, Uchida N. Odor representations in olfactory cortex: distributed rate coding and decorrelated population activity. Neuron. 2012 Jun 21;74(6):1087-98. doi: 10.1016/j.neuron.2012.04.021. PMID: 22726838; PMCID: PMC3383608.

Chandak, R., Raman, B. Neural manifolds for odor-driven innate and acquired appetitive preferences. Nat Commun 14, 4719 (2023). https://doi.org/10.1038/s41467-023-40443-2

Saha, D., Li, C., Peterson, S. et al. Behavioural correlates of combinatorial versus temporal features of odour codes. Nat Commun 6, 6953 (2015). https://doi.org/10.1038/ncomms7953

Author Response

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

Reviewer #1 (Public Review):

The cerebral cortex, or surface of the brain, is where humans do most of their conscious thinking. In humans, the grooves (sulci) and bumps (convolutions) have a particular pattern in a region of the frontal lobe called Broca's area, which is important for language. Specialists study features imprinted on the internal surfaces of braincases in early hominins by casting their interiors, which produces so-called endocasts. A major question about hominin brain evolution concerns when, where, and in which fossils a humanlike Broca's area first emerged, the answer to which may have implications for the emergence of language. The researchers used advanced imaging technology to study the endocast of a hominin (KNM-ER 3732) that lived about 1.9 million years ago (Ma) in Kenya to test a recently published hypothesis that Broca's remained primitive (apelike) prior to around 1.5 Ma. The results are consistent with the hypothesis and raise new questions about whether endocasts can be used to identify the genus and/or species of fossils.

We would like to thank Rev. 1 for their comments on our paper.

Reviewer #2 (Public Review):

The authors tried to support the hypothesis that early Homo still had a primitive condition of Broca's cap (the region in fossil endocasts corresponding to Broca's area in the brain), being more similar to the condition in chimpanzees than in humans. The evidence from the described individual points to this direction but there are some flaws in the argumentation.

We are grateful to Rev. 2 for their comments, although we partially agree with some of them.

First, we would like to rectify the statement of Rev. 2 that we “tried to support the hypothesis that early Homo still had a primitive condition of Broca's cap”, indeed, our aim was to test this hypothesis and not to try to validate it.

First, only one human and one chimpanzee were used for comparison, although we know that patterns of brain convolutions (and in addition how they leave imprints in the endocranial bones) are very variable.

We understand the point raised by Rev. 2 about the variation of brain convolutions in humans and chimpanzees. We used atlases published by Connolly (1950), Falk et al. (2018) and de Jager et al. (2019, 2022) to analyse the endocast of KNM-ER 3732 and compare it to the extant human and chimpanzee cerebral conditions. However, in Figure 2, for the sake of clarity only two Homo and Pan specimens were used to illustrate the comparison (as it has been done in other published papers, e.g., Carlson et al., 2011; Science, Gunz et al., 2020 Sci Adv). In the revised version, we modified the manuscript to explain further our approach (line 156) “We used brain and endocast atlases published in Connolly (1950), Falk et al. (2018) and de Jager et al. (2019, 2022; see also www.endomap.org) for comparing the pattern identified in KNM-ER 3732 to those described in extant humans and chimpanzees. To the best of our knowledge, these atlases are the most extensive atlases of extant human and chimpanzee brains/endocasts available to date and are widely used in the literature to explore variability in sulcal patterns. In Figure 2, the extant human and chimpanzee conditions are illustrated by one extant human (adult female) and one extant chimpanzee (adult female) specimens from the Pretoria Bone Collection at the University of Pretoria (South Africa) and in the Royal Museum for Central Africa in Tervuren (Belgium), respectively (Beaudet et al., 2018).”.

Second, the evidence from this fossil specimen adds to the evidence of previously describe individuals but still not yet fully prove the hypothesis.

We tempered our discussion by concluding that (line 116) “Overall, the present study not only demonstrates that Ponce de León et al.’s (2021) hypothesis of a primitive brain of early Homo cannot be rejected, but also adds information […]”.

Third, there is a vicious circle in using primitive and derived features to define a fossil species and then using (the same or different) features to argue that one feature is primitive or derived in a given species. In this case, we expect members of early Homo to be derived compared to their predecessors of the genus Australopithecus and that's why it seems intriguing and/or surprising to argue that early Homo has primitive features. However, we should expect that there is some kind of continuum or mosaic in a time in which a genus "evolves into" another genus. This discussion requires far more discussions about the concepts we use, maybe less discussion about what is different between the two groups but more discussion about the evolutionary processes behind them.

We fully agree with Rev. 2 on this aspect. We believe that identifying these differences/similarities between fossil and extant hominids constitute the first step of a better understanding of the evolutionary mechanisms. Our work suggests indeed a certain continuity between genera and raises questions on the genus concept and how to interpret the specimens currently attributed to early Homo. In the revised version of the manuscript we included a reference to this possible scenario (line 134): “[…] or to the absence of a definite threshold between the two genera based on the morphoarchitecture of their endocasts (Wood and Collard, 1999).”.

Fourth, the data of convolutional imprints presented are rather subjective when identifying which impressions represent which brain convolutions. Not seeing an impression does not necessarily mean that the corresponding brain feature did not exist. Interestingly, the manuscript does not mention and discuss at all the frontoorbital sulcus. This is a sulcus that usually runs from the orbital surface of the frontal lobe up to divide the inferior frontal gyrus in chimpanzees, a condition totally different than in humans who do not have a frontoorbital sulcus. Could such a sulcus be identified, this would provide a far more convincing argument for a primitive condition in this specimen. In Australopithecus sediba, e.g., the condition in this region seems to be a mosaic in which some aspects of the morphology seem to be more modern while one of the sulcual impressions can well be interpreted as a short frontoorbital sulcus. For this specimen, by the way, I would come back to my third point above: some experts in the field might argue that this specimen could belong to Homo rather than Australopithecus...

We agree that the presence of a fronto-orbital sulcus would be more conclusive. However, this sulcus has not been identified in KNM-ER3732 and the region in which we would expect to find it is not preserved. As demonstrated by Ponce de León et al. (2021), because of the topographic relationships between sulci (and cranial structures), it is possible to interpret imprints on endocasts and the evolutionary polarity of some traits even in the absence of landmarks such as the fronto-orbital sulcus. In Australopithecus sediba the main derived feature of the endocast corresponds to the ventrolateral bulge in the left inferior frontal gyrus, and not to the sulcal pattern itself (Carlson et al., 2011 Science). However, the discussion around the taxonomic status of this taxon confirms the urgent need for reconsidering specimens from that time period and clarifying the mosaic-like or concerted evolution of the derived Homo-like traits within our lineage. Regarding the subjective nature of this approach, we invite readers to examine the specimen on MorphoSource (https://www.morphosource.org/concern/media/000497752?locale=en) and to request access to the National Museums of Kenya to the physical or virtual specimen to falsify our hypothesis.

According to my arguments above, I think that this manuscript might revive interesting discussions about this topic but it is not likely to settle them because the data presented are not strong enough to fully support the hypothesis.

We would be more than happy to consider new/other specimens with similar chronological and geographical contexts and investigate further this hypothesis in the future.

Reviewer #3 (Public Review):

The authors provide a detailed analysis of the sulcal and sutural imprints preserved on the natural endocast and associated cranial vault fragments of the KNM-ER3732 early Homo specimen. The analyses indicate a primitive ape-like organization of this specimen's frontal cortex. Given the geological age of around 1.9 million years, this is the earliest well-documented evidence of a primitive brain organization in African Homo.

In the discussion, the authors re-assess one of the central questions regarding the evolution of early Homo: was there species diversity, and if yes, how can we ascertain it? The specimen KNM-ER1470 has assumed a central role in this debate because it purportedly shows a more advanced organization of the frontal cortex compared to other largely coeval specimens (Falk, 1983). However, as outlined in Ponce de León et al. 2021 (Supplementary Materials), the imprints on the ER1470 endocranium are unlikely to represent sulcal structures and are more likely to reflect taphonomic fracturing and distortion. Dean Falk, the author of the 1983 study, basically shares this view (personal communication). Overall, I agree with the authors that the hypothesis to be tested is the following: did early Homo populations with primitive versus derived frontal lobe organizations coexist in Africa, and did they represent distinct species?

I greatly appreciate that the authors make available the 3D surface data of this interesting endocast.

We are grateful to Rev. 3 for their comments and for contextualizing our finding. We would also like to point out that, although the 3D surface can be viewed on MorphoSource, permission from the National Museums of Kenya has to be requested for studying the specimen and getting access to the physical specimen and/or the 3D model.

Reviewer #1 (Recommendations For The Authors):

Holloway, Broadfield & Yuan (2004) estimate ER 3732 as having a cranial capacity of 750 cc, which is larger than chimps and australopiths and similar to ER 1470 (752 cc, same reference). (That for Dmanisi 2282 is somewhat smaller at around 650 cc.) Cranial capacities should be mentioned along with added discussion about possible allometric scaling of (increased) numbers of sulci with increasing brain size as well as possible shifts in locations of sulci relative to cranial sutures in larger-brained (including due to ontogenetic maturation) in individuals/species. Could these variables (especially brain size) be relevant for your discussion/conclusions?

We thank Rev. 1 for their suggestion. We included the estimate by Holloway et al. (2004) (line 95): “Holloway et al. (2004) estimated the endocranial volume as about 750-800 cc but insisted on the low reliability of their estimate.”. Additionally, we raised the possibility of potential allometric effect (line 149): “In parallel, the possibility of allometric scaling and influence of brain size on sulcal patterns in early Homo has to be further explored.” for future discussion.

From the two figures, it appears that the authors produced a virtual endocast from the cranial remains of ER 3732 and compared its features with those seen on a virtual reproduction of the corresponding natural endocast. If so, this needs to be clarified in the text, not just the figures.

We thank Rev. 1 for their suggestions that were integrated.

Reviewer #3 (Recommendations For The Authors):

While the sulcal imprints on the left hemisphere can be interpreted unambiguously, the anatomical assignment of those on the right side may need to be reconsidered, as they are more ambiguous. For example, the postcentral sulcus (pt) almost touches the middle frontal sulcus, which is an unlikely natural configuration.

We agree that the configuration on the right hemisphere is intriguing, especially when compared to the extant human and chimpanzee atlases. As such, we decided to change the label for what we think could be the inferior frontal sulcus and leave a question mark instead.

I encourage the authors to include:

• a posterior view in Figure 1, and mark the lambdoid suture, parts of which seem to be preserved especially on the left side. This will help the readership to better understand which parts of the endocranial morphology are preserved.

• a scale bar would be of great utility to appreciate the small size of this specimen. The distance from bregma to the Broca cap seems to be short, indicating an endocranial volume much smaller than the published estimate of 750 ccm. Perhaps the authors can provide a new estimate, which would provide further support for the arguments proposed in the discussion section, especially the question of any presence of Australopithecus at Koobi Fora.

We included a posterior view of the specimen in Figure 1 and scale bar and modified the legend accordingly. Unfortunately, we were not able to identify with certainty the feature that could correspond to the lambdoid suture. We might see the impression where the parietal bone meets the occipital bone, but there is a risk of misidentification (which is an issue frequently raised in the literature, see for example Gunz et al. 2020 Sci Adv). Concerning the endocranial volume, in the revised version of the manuscript we included the estimate by Holloway et al. (2004). Because the specimen only preserves the superior part, we are reluctant in providing an estimate of the total volume. However, we agree that this would be an interesting feature to integrate in the interpretation of this specimen.

Minor points

• This sentence needs to be clarified: «The superior temporal sulcus nearly intersects the lateral fissure on the right hemisphere».

• The terms «Broca's region» and «orbital cap» need some more context. Do the authors mean «Broca's cap» in either instance?

We clarified/modified when needed, thank you very much.

We included minor corrections in addition to those recommended by the reviewers:

-Lines 50, 74, 142, 149: “Broca’s area” instead of “Broca’s cap”

-Line 73: “in the pre-1.5 Ma Homo specimen” instead of “in pre-1.5 Ma Homo specimen”

-Line 100: we specified “in human brains and endocasts”

-Line 120: “sulcal pattern” instead of “sulcal patterns”

-Line 144: “behaviors” (plural)

Author Response:

Reviewer #1 (Public Review):

[...] Strengths:

The manuscript is well written and the experimental work well executed. It shows that major features of the classical two-component HipAB TA system have somehow been rerouted in the case of the tripartite HipBST. This includes the N-terminal domain of the HipA toxin, which now functions as bona fide antitoxin, and the partly relegated HipB antitoxin, which could only function as a transcription regulator. In addition, this work shows a new mode of inhibition of a kinase toxin and highlights the impact of the phosphorylation state of key toxin residues in controlling the activity of the antitoxin.

Weaknesses:

A major weakness of this work is the lack of data concerning the role of HipB, which likely does not act as an antitoxin. Does it act as a transcriptional regulator of the hipBST operon and to what extent both HipS and HipT contribute to such regulation? These are still open questions.

We thank the reviewer for their feedback and will include a supplementary figure (Figure 1 supplement 2) and accompanying text that shows the transcriptional role of HipB, and how HipS and HipT influence this regulatory effect.

In addition, there is no in-depth structural comparison between the structure of the HipBST solved in the work and the two recent structures of HipBST from Legionella. This is also a major weakness of this work.

A structural comparison to the recent structures from Legionella will be included in the discussion, including Figure 6 supplement 1.

Author Response:

This work presents valuable information about the specificity and promiscuity of toxic effector and immunity protein pairs. The evidence supporting the claims of the authors is currently incomplete, as there is concern about the methodology used to analyze protein interactions, which did not take potential differences in expression levels, protein folding, and/or transient interaction into account. Other methods to measure the strength of interactions and structural predictions would improve the study. The work will be of interest to microbiologists and biochemists working with toxin-antitoxin and effector-immunity proteins.

We thank the reviewers for considering this manuscript. We agree that this manuscript provides a valuable and cross-discipline introduction to new EI pair protein families where we focus on the EI pair’s flexibility and impacts on community structure. As such, we believe we have provided a solid foundation for future studies to examine non-cognate interactions and their possible effects on microbial communities. This, by definition, leaves some areas “incomplete” and, therefore, open for further investigations. While the methods we show do take into account potential differences in binding assays, we will more explicitly address how “expression, protein folding, and/or transient binding” may play into this expanded EI pair model upon revision and temper the discussion of the proposed model. We have responded to the reviewers’ public comments (italicized below).

Public Reviews:

Note: Reviewer 1, who appeared to focus on a subset of the manuscript rather than the whole, based their comments on several inaccuracies, which we discuss below. We found the tone in this reviewer's comments to be, at times, inappropriate, e.g., using "harsh" and "simply too drastic" to imply that common structure-function analyses were outside of the field-standard methods. We also note that the reviewer took a somewhat atypical step in reviewing this manuscript by running and analyzing the potential protein-complex data in AlphaFold2 but did not discuss areas of low confidence within that model that may contradict their conclusions. We are concerned their approach muddled valid scientific criticisms with problematic conclusions.

Reviewer #1 (Public Review):

In this manuscript, Knecht, Sirias et al describe toxin-immunity pair from Proteus mirabilis. Their observations suggest that the immunity protein could protect against non-cognate effectors from the same family. They analyze these proteins by dissecting them into domains and constructing chimeras which leads them to the conclusion that the immunity can be promiscuous and that the binding of immunity is insufficient for protective activity.

Strengths:

The manuscript is well written and the data are very well presented and could be potentially interesting. The phylogenetic analysis is well done, and provides some general insights.

Weaknesses:

1) Conclusions are mostly supported by harsh deletions and double hybrid assays. The later assays might show binding, but this method is not resolutive enough to report the binding strength. Proteins could still bind, but the binding might be weaker, transient, and out-competed by the target binding.

The phrasing of structure-function analyses as “harsh” is a bit unusual, as other research groups regularly use deletions and hybrid studies. Given the known caveats to deletion and domain substitutions, we included point-mutation analyses for both the effector and immunity proteins, as found on lines 105 - 113 and 255 - 261 in the current manuscript. These caveats are also why we coupled the in vitro binding analyses with in vivo protection experiments in two distinct experimental systems (E. coli and P. mirabilis). Based on this manuscript’s introductory analysis (where we define and characterize the genes, proteins, interactions, phylogenetics, and incidences in human microbiomes), the next apparent questions are beyond the scope of this study. Future approaches would include analyzing purified proteins from these effector (E) and immunity (I) protein families using biochemical assays, such as X-ray crystallography, circular dichroism spectroscopy, among others.

(Interestingly, most papers in the EI field do not measure EI protein affinity (Jana et al., 2019, Yadav et al., 2021). Notable exceptions are earlier colicin research (Wallis et al., 1995) and a new T6SS EI paper (Bosch et al., 2023) published as we submitted this manuscript.)

2) While the authors have modeled the structure of toxin and immunity, the toxin-immunity complex model is missing. Such a model allows alternative, more realistic interpretation of the presented data. Firstly, the immunity protein is predicted to bind contributing to the surface all over the sequence, except the last two alpha helices (very high confidence model, iPTM>0.8). The N terminus described by the authors contributes one of the toxin-binding surfaces, but this is not the sole binding site. Most importantly, other parts of the immunity protein are predicted to interact closer to the active site (D-E-K residues). Thus, based on the AlphaFold model, the predicted mechanism of immunization remains physically blocking the active site. However, removing the N terminal part, which contributes large interaction surface will directly impact the binding strength. Hence, the toxin-immunity co-folding model suggests that proper binding of immunity, contributed by different parts of the protein, is required to stabilize the toxin-immunity complex and to achieve complete neutralization. Alternative mechanisms of neutralization might not be necessary in this case and are difficult to imagine for a DNAse.

In response to the reviewer’s comment, we again reviewed the RdnE-RdnI AlphaFold2 complex predictions with the most updated version of ColabFold (1.5.2-patch with PDB100 and MMseq2) and have included them at the end of the responses [1].

However, the literature reports that computational predictions of E-I complexes often do not match experimental structural results (Hespanhol et al., 2022, Bosch et al., 2023). As such, we chose not to include the predicted cognate and non-cognate RdnE-I complexes from ColabFold (which uses AlphaFold2) and will not include this data in revised manuscripts. (It is notable that reviewer 1 found the proposed expanded model and research so interesting as to directly input and examine the AI-predicted RdnE-RdnI protein interactions in AlphaFold2.)

Discussion of the prevailing toxin-immunity complex model is in the introduction (lines 45-48) and Figure 5E. Further, there are various known mechanisms for neutralizing nucleases and other T6SS effectors, which we briefly state in the discussion (lines 359 - 361). More in-depth, these molecular mechanisms include active-site blocking (Benz et al., 2012), allosteric-site binding (Kleanthous et al., 1999 and Lu et al., 2014), enzymatic neutralization of the target (Ting et al., 2021), and structural disruption of both the active and binding sites (Bosch et al., 2023). Given this diversity of mechanisms, we did not presume to speculate on the as-of-yet unknown mechanism of RdnI protection.

3) Dissection of a toxin into two domains is also not justified from a structural point of view, it is probably based on initial sequence analyses. The N terminus (actually previously reported as Pone domain in ref 21) is actually not a separate domain, but an integral part of the protein that is encased from both sides by the C terminal part. These parts might indeed evolve faster since they are located further from the active site and the central core of the protein. I am happy to see that the chimeric toxins are active, but regarding the conservation and neutralization, I am not surprised, that the central core of the protein fold is highly conserved. However, "deletion 2" is quite irrelevant - it deletes the central core of the protein, which is simply too drastic to draw any conclusions from such a construct - it will not fold into anything similar to an original protein, if it will fold properly at all.

The reviewer’s comment highlights why we turned to the chimera proteins to dissect the regions of RdnE (formerly IdrD-CT), as the deletions could result in misfolded proteins. (We initially examined RdnE in the years before the launch of AlphaFold2.) However, the reviewer is incorrect regarding the N-terminus of RdnE. The PoNe domain, while also a subfamily of the PD-(D/E)XK superfamily, forms a distinct clade of effectors from the PD-(D/E)XK domain in RdnE (formally IdrD-CT) as seen in Hespanhol et al., 2022; this is true for other DNAse effectors as well. Many studies analyzing effectors within the PD-(D/E)XK superfamily only focus on the PD-(D/E)XK domain, removing just this domain from the context of the whole protein (Hespanhol et al., 2022; Jana et al., 2019). Of note, in RdnE, this region alone (containing the DNA-binding domain) is insufficient for DNAse activity (unlike in PoNe).

4) Regarding the "promiscuity" there is always a limit to how similar proteins are, hence when cross-neutralization is claimed authors should always provide sequence similarities. This similarity could also be further compared in terms of the predicted interaction surface between toxin and immunity.

Reviewer 1 points out a fundamental property of protein-protein interactions that has been isolated away from the impacts of such interactions on bacterial community structure. We have provided the whole protein alignments in supplemental figure 3, the summary images in Figure 3D, and the protein phylogenetic trees in Figure 3C. We encourage others to consider the protein alignments as percent amino acid sequence similarity is not necessarily a good gauge for protein function and interactions. RuBisCo is one example of how protein sequence similarity can be small while functions remain highly conserved. These data are publicly available on the OSF website associated with this manuscript https://osf.io/scb7z/, and we hope the community explores the data there.

In consideration of the enthusiasm to deeply dive into the primary research data, we have included the pairwise sequence identities across the entire proteins here: Proteus RdnI vs. Rothia RdnI: 23.6%; Proteus RdnI vs. Prevotella RdnI: 16.3%, Proteus RdnI vs. Pseudomonas RdnI: 14.6%; Rothia RdnI vs. Prevotella RdnI: 22.4%, Rothia RdnI vs. Pseudomonas RdnI: 17.6%; Prevotella RdnI vs. Pseudomonas RdnI: 19.5%. (As stated in response to reviewer 1 comment 2, we do not find it appropriate to make inferences based on AlphaFold2-predicted protein complexes.)

Overall, it looks more like a regular toxin-immunity couple, where some cross-reactions with homologues are possible, depending on how far the sequences have deviated. Nevertheless, taking all of the above into account, these results do not challenge toxin-immunity specificity dogma.

In this manuscript, we did not intend to dismiss the E-I specificity model but rather point out its limitations and propose an important expansion of that model that accounts for cross-protection and survival against attacks from other genera. We agree that it is commonly considered that deviations in amino acid sequence over time could result in cross-binding and protection (see lines 364-368). However, the impacts of such cross-binding on community structure, bacterial survival, and strain evolution have rarely been considered or addressed in prior literature, with exceptions such as in Zhang et al., 2013 and Bosch et al., 2023. One key insight we propose and show in this manuscript is that cross-binding can be a fitness benefit in mixed communities; therefore, it could be selected for evolutionarily (lines 378-380), even potentially in host microbiomes.

Reviewer #2 (Public Review):

Summary:

The manuscript by Knecht et al entitled "Non-cognate immunity proteins provide broader defenses against interbacterial effectors in microbial communities" aims at characterizing a new type VI secretion system (T6SS) effector immunity pair using genetic and biochemical studies primarily focused on Proteus mirabilis and metagenomic analysis of human-derived data focused on Rothia and Prevotella sequences. The authors provide evidence that RdnE and RdnI of Proteus constitute an E-I pair and that the effector likely degrades nucleic acids. Further, they provide evidence that expression of non-cognate immunity derived from diverse species can provide protection against RdnE intoxication. Overall, this general line of investigation is underdeveloped in the T6SS field and conceptually appropriate for a broad audience journal. The paper is well-written and, aside from a few cases, well-cited. As detailed below however, there are several aspects of this paper where the evidence provided is somewhat insufficient to support the claims. Further, there are now at least two examples in the literature of non-cognate immunity providing protection against intoxication, one of which is not cited here (Bosch et al PMID 37345922 - the other being Ting et al 2018). In general therefore I think that the motivating concept here in this paper of overturning the predominant model of interbacterial effector-immunity cognate interactions is oversold and should be dialed back.

We agree that analyses focusing on flexible non-cognate interactions and protection are underdeveloped within the T6SS field and are not fully explored within a community structure. These ideas are rapidly growing in the field, as evidenced by the references provided by the reviewer. As stated earlier, we did not intend to overturn the prevailing model but rather propose an expanded model that accounts for protection against attacks from foreign genera.

Strengths:

One of the major strengths of this paper is the combination of diverse techniques including competition assays, biochemistry, and metagenomics surveys. The metagenomic analysis in particular has great potential for understanding T6SS biology in natural communities. Finally, it is clear that much new biology remains to be discovered in the realm of T6SS effectors and immunity.

Weaknesses:

The authors have not formally shown that RdnE is delivered by the T6SS. Is it the case that there are not available genetics tools for gene deletion for the BB2000 strain? If there are genetic tools available, standard assays to demonstrate T6SS-dependency would be to interrogate function via inactivation of the T6SS (e.g. by deleting tssC).

Our research group showed that the T6SS secretes RdnE (previously IdrD) in Wenren et al., 2013 (cited in lines 71-73). We later confirmed T6SS-dependent secretion by LC-MS/MS (Saak et al., 2017).

For swarm cross-phyla competition assays (Figure 4), at what level compared to cognate immunity are the non-cognate immunity proteins being expressed? This is unclear from the methods and Figure 4 legend and should be elaborated upon. Presumably these non-cognate immunity proteins are being overexpressed. Expression level and effector-to-immunity protein stoichiometry likely matters for interpretation of function, both in vitro as well as in relevant settings in nature. It is important to assess if native expression levels of non-cognate cross-phyla immunity (e.g. Rothia and Prevotella) protect similarly as the endogenously produced cognate immunity. This experiment could be performed in several ways, for example by deleting the RdnE-I pair and complementing back the Rothia or Prevotella RdnI at the same chromosomal locus, then performing the swarm assay. Alternatively, if there are inducible expression systems available for Proteus, examination of protection under varying levels of immunity induction could be an alternate way to address this question. Western blot analysis comparing cognate to non-cognate immunity protein levels expressed in Proteus could also be important. If the authors were interested in deriving physical binding constants between E and various cognate and non-cognate I (e.g. through isothermal titration calorimetry) that would be a strong set of data to support the claims made. The co-IP data presented in supplemental Figure 6 are nice but are from E. coli cells overexpressing each protein and do not fully address the question of in vivo (in Proteus) native expression.

P. mirabilis strain ATCC29906 does not encode the rdnE and rdnI genes on the chromosome (NCBI BioSample: SAMN00001486) (line 151). Production of the RdnI proteins, including the cognate Proteus RdnI, comes from equivalent transgenic expression vectors. Specifically, the rdnI genes were expressed under the flaA promoter in P. mirabilis strain ATCC29906 (Table 1) for the swarm competition assays found in Figure 2C and Figure 4. This promoter results in constitutive expression in swarming cells (Belas et al., 1991; Jansen et al., 2003).

Lines 321-324, the authors infer differences between E and I in terms of read recruitment (greater abundance of I) to indicate the presence of orphan immunity genes in metagenomic samples (Figure 5A-D). It seems equally or perhaps more likely that there is substantial sequence divergence in E compared to the reference sequence. In fact, metagenomes analyzed were required only to have "half of the bases on reference E-I sequence receiving coverage". Variation in coverage again could reflect divergent sequence dipping below 90% identity cutoff. I recommend performing metagenomic assemblies on these samples to assess and curate the E-I sequences present in each sample and then recalculating coverage based on the exact inferred sequences from each sample.

This comment raises the challenges with metagenomic analyses. It was difficult to balance specificity to a particular species’ DNA sequence with the prevalence of any homologous sequence in the sample. Given the distinction in binding interactions among the examined four species, we opted to prioritize specificity, accepting that we were losing access to some rdnE and rdnI sequences in that decision. We chose a 90% identity cutoff, which, through several in silica controls, ensured that each sequence we identified was the rdnE or rdnI gene from that specific species. For the Version of Record, we will revisit this decision and consider trying to account for sequence divergence by lowering the identity cutoffs as suggested.

A description of gene-level read recruitment in the methods section relating to metagenomic analysis is lacking and should be provided.

Noted. We will also include the raw code and sequences on the OSF website associated with this manuscript https://osf.io/scb7z/.

Reviewer #3 (Public Review):

[...] Strengths:

The authors presented a strong rationale in the manuscript and characterized the molecular mechanism of the RdnE effector both in vitro and in the heterologous expression model. The utilization of the bacterial two-hybrid system, along with the competition assays, to study the protective action of RdnI immunity is informative. Furthermore, the authors conducted bioinformatic analyses throughout the manuscript, examining the primary sequence, predicted structural, and metagenomic levels, which significantly underscore the significance and importance of the EI pair.

Weaknesses:

1. The interaction between RdnI and RdnE appears to be complex and requires further investigation. The manuscript's data does not conclusively explain how RdnI provides a "promiscuous" immunity function, particularly concerning the RdnI mutant/chimera derivatives. The lack of protection observed in these cases might be attributed to other factors, such as a decrease in protein expression levels or misfolding of the proteins. Additionally, the transient nature of the binding interaction could be insufficient to offer effective defenses.

Yes, we agree with the reviewer and hope that grant reviewers’ share this colleague’s enthusiasm for understanding the detailed molecular mechanisms of RdnE-RdnI binding across genera. We will continue to emphasize such caveats as the next frontier is clearly understanding the molecular mechanisms for RdnI cognate or non-cognate protection. We address the concerns regarding expression levels in the response to reviewer 2, comment 2.

1. The results from the mixed population competition lack quantitative analysis. The swarm competition assays only yield binary outcomes (Yes or No), limiting the ability to obtain more detailed insights from the data.

The mixed swam assay is needed when studying T6SS effectors that are primarily secreted during Proteus’ swarming activity (Saak et al. 2017, Zepeda-Rivera et al. 2018). This limitation is one reason we utilize in vitro, in vivo, and bioinformatic analyses. Though the swarm competition assay yields a binary outcome, we are confident that the observed RdnI protection is due to interaction with a trans-cell RdnE via an active T6SS. By contrast, many manuscripts report co-expression of the EI pair (Yadev et al., 2021, Hespanhol et al., 2022) rather than secreted effectors, as we have achieved in this manuscript.

1. The discovery of cross-species protection is solely evident in the heterologous expression-competition model. It remains uncertain whether this is an isolated occurrence or a common characteristic of RdnI immunity proteins across various scenarios. Further investigations are necessary to determine the generality of this behavior.

We agree, which is why we submitted this paper as a launching point for further investigations into the generality of non-cognate interactions and their potential impact on community structure.

Comments from Reviewing Editor:

• In addition to the references provided by reviewer#2, the first manuscript to show non-cognate binding of immunity proteins was Russell et al 2012 (PMID: 22607806).
• IdrD was shown to form a subfamily of effectors in this manuscript by Hespanhol et al 2022 PMID: 36226828 that analyzed several T6SS effectors belonging to PDDExK, and it should be cited.

We appreciate that the reviewer and eLife staff pointed out missed citations. A revised manuscript will incorporate those studies and cite them appropriately.

[1] The Proteus RdnE in complex with either the Prevotella or Pseudomonas RdnI showed low confidence at the interface (pIDDT ~50-70%); this AI-predicted complex might support the lack of binding seen in the bacterial two-hybrid assay. On the other hand, the Proteus and Rothia RdnI N-terminal regions show higher confidence at the interface with RdnE. Despite this, the C-terminus of the Proteus RdnI shows especially low confidence (pIDDT ~50%) where it might interact near RdnE’s active site (as suggested by reviewer 1). Given this low confidence and the already stated inaccuracies of AI-generated complexes, we would rather wait for crystallization data to inform potential protection mechanisms of RdnI.

Author response image 1.

Author Response

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

We thank the reviewers for the constrictive and detailed feedback provided. We have adopted the proposed changes to improve the manuscript clarity and accessibility. The following revisions are included in the revised manuscript:

Reviewer #1 (Public Review):

The analytical framework is not sufficiently explained in the main text.

We think the reviewer is referring to the conceptual framework mentioned in introduction. In the previously submitted manuscript, we did not provide details because the framework is published elsewhere. However, we agree with the reviewer that a short explanation may be helpful, which we have included in the resubmitted manuscript.

The significance of findings in relation to functional changes is not clear. What are the consequences of enrichment of RNA transport or ribosome biogenesis pathways between pesticides and recovery stages, for example?

We thank the reviewer for this suggestion. In the previously submitted manuscript, we included an explanation of the central functions these pathways can alter (e.g. metabolism and infection response). These functions are self-explanatory. However, we have elaborated on the consequence that the disruption of these pathways can cause in the resubmitted manuscript.

The impact of individual biocides and climate variables, and their additive effects, are assessed but there is no information offered on non-additive interactions (e.g., synergistic, antagonistic).

This was a misunderstanding based on our use of the term synergistic in this context. The approach by which we define a synergistic or joint effect of two environmental variables on a taxonomic group is explained in the methods section. This analysis is based on climate variables and biocide types contributing the largest covariances in the correlation analysis explained in Supplementary Fig. 5; Step 4. The combined effect of two environmental variables on a taxon was considered to be significant if the biocide type and the climate variable were each significantly correlated with the taxon over the same time window, and their average Pearson correlation was > 0.5 with padj < 0.05 (SWC analysis with 10,000 permutations). The biocide type and the climate variable were interpreted to have a joint effect on a given taxon if the linear combination of the biocide type and the climate variable had a larger Pearson correlation coefficient than each of the correlations between the family and the biocide type and the family and the climate variable individually, in the same time interval with padj < 0.05 (with 10,000 permutations in the SWC analysis). We realise that the use of synergistic or additive was not correct in this context and have replaced the term synergistic with joint effect throughout the manuscript.

The level of confidence associated with results is not made explicit. The reader is given no information on the amount of variability involved in the observations, or the level of uncertainty associated with model estimates.

As we didn’t use traditional statistical approaches, confidence level estimation in the traditional sense is not possible. Instead, we used permutation tests and adjusted P-values to identify significant correlations in the data. These approaches are more robust than traditional statistics for integrating and discovering complex, group-wise patterns among high-dimensional datasets. While most forms of machine learning require large sample sizes, sCCA uses fewer observations to identify the most correlated components among data matrices and captures the multivariate variability of the most important features.

The major implications of the findings for regulatory ecological assessment are missed. Regulators may not be primarily interested in identifying past "ecosystem shifts". What they need are approaches which give greater confidence in monitoring outcomes by better reflecting the ecological impact of contemporary environmental change and ecosystem management. The real value of the work in this regard is that: (1) it shows that current approaches are inappropriate due to the relatively stable nature of the indicators used by regulators, despite large changes in pollutant inputs; (2) it presents some better alternatives, including both taxonomic and functional indicators; and (3) it provides a new reference (or baseline) for regulators by characterizing "semi-pristine" conditions.

We thank the reviewer for this suggestion, which we have included in the main text (L451461)

Reviewer #2 (Public Review):

Results - They are brief and should expand some more. Particularly, there are no results regarding metabarcoding data (number of reads, filtering etc.). These details are important to know the quality of the data which represents the bulk of the analyses. Even the supplementary material gives little information on the metabarcoding results (e.g. number of ASVs - whether every ASV of each family were pooled etc.).

We thank the reviewer for this suggestion. We have included a paragraph in results reporting read numbers and other statistics. The filtering criteria and handling of samples can be found in methods (L658-661; L670-675). As explained in methods the taxonomy was assigned using qiime feature-classifier classify-sklearn and used at family level where possible. When classification was not possible at family level because of incomplete/missing information in the online database or a poor match to reference database, the lowest classification possible was used.

The drivers of biodiversity change section could be restructured and include main text tables showing the families positively or negatively correlated with the different variables (akin to table S2 but simplified).

As there are over 180 unique families/taxonomic units correlated with at least one biocide or environmental variable, a simplified version of this table would be too large to include in the main text. Therefore, we prefer to keep this information in supplementary table 2 complete with correlation statistics.

We thank the reviewers for providing detailed feedback on the manuscript and respond to their suggestions as follows:

Reviewer #1 (Recommendations For The Authors):

Thank you for the opportunity to review your manuscript, which I found interesting and enjoyable to read. Here are some suggestions for improving it.

Remove spaces before citations in text.

Lines 51-53: "Community-level biodiversity reliably explained freshwater ecosystem shifts whereas traditional quality indices (e.g. Trophic Diatom Index) and physicochemical parameters proved to be poor metrics for these shifts." Seems to be the wrong way around / not clear???

Rephrased to clarify.

Line 54: Should be "...advocates the use of..." or "...demonstrates the advantages of..."

Done, thanks for the suggestion.

Line 62: Spell out numbers <10, i.e. "sixth mass extinction"

Done, thank you.

Lines 66-72: These sentences lack clarity. It's not clear that "experimental manipulation of biodiversity" hasn't involved investigation of "multi-trophic changes". By the third of these four sentences it is not clear what "they" is referring to. And in the fourth sentence, "these holistic studies" are not defined. Perhaps it would suffice to say that experiments have so far focused primarily on a single trophic level and largely neglected freshwater systems.

We have rephrased to improve clarity.

Line 81: Delete unnecessary bracket

Done, thank you.

Line 82: "a minority of freshwater ecosystems" sounds as if you're saying that few freshwater ecosystems are represented in BioTIME, which seems obvious and would also apply to terrestrial and marine systems. Do you mean that freshwater ecosystems re not well represented in the data?

We have clarified the sentence, thanks.

Line 106: Resolve issue with citation in text at the end of the sentence (repeated at line 109 and possibly other lines).

Done, thank you.

Line 116: By ">1999s" do you mean 1990s?

This was a typo. it was supposed to be >1999

Line 120: The reader would benefit greatly from a brief explanation of explainable network models and multimodal learning in the introduction. Why are these the right tools to use? How do they work in this context? Figure 1 helps to some extent but needs more commentary in the text.

We have included an explanation of the explainable network models and multimodal learning and how their use can be beneficial to the study of diverse data types.

Line 144: Here and throughout the text the language could be much more efficient and readable. "Alpha diversity" does not require a definite article. Furthermore, when referring to significance it is convention to state the p-value, test statistic and test used.

As there are different p-values for each barcode, we have included them in legend to Supplementary Fig. 1 to avoid crowding the main text. We prefer to leave the text unchanged for this reason.

Line 155: "The primary producer's composition" is grammatically awkward and less suitable than "the composition of primary producers". This kind of awkwardness occurs again at line 285 ("diatom's") and possibly in other parts of the manuscript.

Thanks, corrected.

Line 169: The statement that this family was "relatively more abundant" needs a little more explanation. What is it relative to - other groups or to previous stages?

More abundant than in the other phases – the sentence has been modified.

Line 179: Nested brackets are unnecessary and affect readability. This could simply be a new sentence, i.e. "For example, Nitrospiraceae (nitrite oxidizers)..."

Done, thanks.

Line 215: "Functional biodiversity", which implies that some biodiversity is functional and some not, does not seem an appropriate term to describe the results you present in this section. Simply "functioning of the prokaryotic community" would suffice.

Thanks, done.

Line 214-233: This section may be inaccessible for many readers. For example, what are Kegg Orthologs and what role do they play in the functioning of a lake ecosystem? The explanation comes later in the paragraph but there needs to be a gentler introduction before diving into specific technical concepts.

We appreciate this comment and have included a short explanation of what KEGG and KO terms mean.

Supplementary Figure 3: It would be helpful to superimpose the lake stages here, as done in Figure 2.

The figure has been updated with coloured data points corresponding to each phase, as in supplementary figure 1.

Line 265: Should be "19 of which were identified..."

Done, thanks.

Line 284: "Predominantly" rather than "prominently"?

Done

Line 242-316: This section is good in that it identifies and ranks individual biocides and climate variables but there is no information on non-additive interactions (e.g., synergistic, antagonistic). Could the authors at least comment on why this was not done or not necessary, and what uncertainties this omission could introduce into the results?

This was a misunderstanding based on our use of the term synergistic in this context. the approach by which we define a synergistic or joint effect of two environmental variables on a taxonomic group is explained in the methods section. This analysis is based on climate variables and biocide types contributing the largest covariances in the correlation analysis explained in Supplementary Fig. 5; Step 4. The combined effect of two environmental variables on a taxon was considered to be significant if the biocide type and the climate variable were each significantly correlated with the taxon over the same time window, and their average Pearson correlation was > 0.5 with padj < 0.05 (SWC analysis with 10,000 permutations) – this is shown in Supplementary Fig. 5; Step 6. The biocide type and the climate variable were interpreted to have an additive effect on a given taxon if the linear combination of the biocide type and the climate variable had a larger Pearson correlation coefficient than each of the correlations between the family and the biocide type and the family and the climate variable individually, in the same time interval with padj < 0.05 (with 10,000 permutations in the SWC analysis). we have replace synergistic with joint effect to avoid confusion.

Figure 4: These 3-D plots are very hard to read. Without additional features (e.g. shadows on each plane, or lines connecting points to planes) it is impossible for the viewer to tell where the points are located on each axis.

We have created interactive 3D plots here: https://environmental-omicsgroup.github.io/Biodiversity_Monitoring/.

Figure 5: Legend entry should be "summer precipitation" not "precipitations". "Additive effect" rather than "joint effect" would be more consistent with the main text.

“Precipitations” has been updated to “precipitation” where relevant throughout. We left ‘joint effect’ and unified the main text, responding to a previous comment of this reviewer on the meaning of synergistic effects in our study.

Line 348: Doesn't your approach also require specialist skills? I often feel that the "traditional" versus "molecular" monitoring debate misses this point. Some comment on the training and development needs for those interested in applying the sedaDNA approach would be welcome. Otherwise it is an unfair comparison.

Whereas the application of high throughput sequencing technologies requires training, these technologies are well established with publicly available standard operating procedures. As compared to direct observations, high throughput sequencing provides replicable results regardless of the operator. Moreover, the application of metabarcoding to sedaDNA or more generally eDNA can be outsourced to established environmental services, removing the need for training if it is a limiting factor. The above has been included in discussion.

Line 391: "Significantly did" what? "Did significantly change over time" would be better.

Done, thanks.

Line 407: Should be "an indicator of..." and "did not significantly change over time..."

Done, thanks.

Line 408-410: Regulators are not necessarily interested in identifying past "ecosystem shifts", so this does not seem to be the best way to contrast the capabilities of the sedaDNA approach with those of LTDI2. The real value of this work, in my opinion, is threefold. First, it shows that the reliance on diatoms as indicators of ecological status is inappropriate due to the relatively stable nature of diatom communities in the face of large environmental changes. Second, it presents some better alternatives, including both taxonomic and functional indicators. And third, it provides a new reference point for regulators by characterising "semi-pristine" conditions.

Thanks for the insightful suggestion. We agree with the reviewer on the advantages and have spelled them out in the resubmitted manuscript.

Line 445: What are "housekeeping functions"? I checked the Cuenca-Cambronero paper cited but did not find the term there.

Housekeeping functions are essential basic cellular functions that are evolutionary conserved. They are more commonly present in public databases because they have been characterised in a number of model species (e.g. Drosophila, C. elegans and Mus musculus). Our reference it not to the Cuenca-Cambronero paper, but to Mi et al, describing the reference database PANTHER. We included the definition of housekeeping functions in the main text.

Line 449: Briefly state the main functional changes found here.

Examples have been included.

Lines 451-452: Whilst this statement may be found in the cited source, most readers I suspect would not identify with it. Indeed, one could argue that most of freshwater ecology has been dedicated to this very task (documenting chemical impacts on biodiversity)! A more balanced view is needed here.

The sentence the reviewer refers to includes also reference to climate change. Climate change and chemical pollution are the two most common causes of biodiversity loss, and not only in freshwater ecosystems.

Lines 463-466: These examples both point to non-additive (synergistic) effects, which were not assessed in the current study.

Please refer to our explanation above about the inappropriate use of synergistic and, here, additive. We have altered the text throughout to use joint effects as we do not investigate synergistic, antagonistic and additive effects as traditionally described in ecology.

Lines 472-474: This sentence is unclear. Do you mean that this approach surpasses others in terms of reliability? If so, I don't believe this has been demonstrated in the paper.

We apologise. The word ‘reliability’ should have not been in the text. We have improved the clarity of this sentence.

Lines 474-482: In these sentences it is unclear whether or not you are talking about your method or contrasting it with another method(s). If the latter, which method or methods are you referring to?

We have fixed this sentence to better reflect that our algorithm provides a high degree of confidence that surpasses state-of-the-art analysis, which predominantly identify patterns of co-occurrence of taxa within communities (e.g. Correlation-Centric Network).

Line 631: Should be "Physico-chemical variables". I have not extensively checked the rest of the methods for such errors.

Thank you, the text has been changed where present.

Reviewer #2 (Recommendations For The Authors):

Introduction Line 80 remove extra ')'

Done, thank you.

Line 81 rephrase e.g includes few freshwater ecosystems

We modified this sentence also following Reviewer #1

Line 83 although, instead of whereas?

Done, thanks.

Line 106 formatting reference issue

Line 109 same as above

Thank you, noted.

Results

Line 141 - 144 how was the sampling of the sediment performed over the 100 year core? Every year? Every 5 years? Or were they pooled to represent the (as of yet unlisted) phases?

The reviewer is correct that details are not provided here. They are in methods. We have added some text to explain the basic concepts of how the core was obtained and sliced and refer the reader to the method section for more details.

Line 154 the authors have not yet explicitly listed the lake phases, so it is difficult to refer to them now.

Noted, the addition of a short explanation at the beginning of the results section should take care of this issue.

Line 216 - may be worth briefly explaining KEGG orthologs and how these relate to functional biodiversity.

We thank the reviewer. Also responding to a similar comment from Reviewer #1, we included a description of KO terms and their links to functional biodiversity.

Lines 249 - 260 instead of a supplementary table, it could remain in the main text

Supplementary table 2 is a multi-tab table including information for each region amplified here. It is not possible to include this table in the main text.

Materials and Methods Due to the formatting of the manuscript (results & discussion before materials and methods), many of the results are not clearly understood without having to visit the M&M section. Particularly, how the biocide types were obtained (Historic records plus persistence of DDT in sediments). This could be resolved y including a few sentences on how the data was gathered in the results section. Overall, materials and methods are sufficient, however, it is not clear how many of the 37 metabarcoding samples correspond to which of the lake phases. Finally, I suggest a better organization of M&Ms by having subheadings for each section. For example, under Biodiversity fingerprinting across 100 years, one subheading could de DNA extraction and sequencing, another subheading could be bioinformatics.

We thank the reviewer for the suggestion. To alleviate the issues linked to the methods section coming after the results section, we have introduced a short explanation of the sediments core and the lake phases at the beginning of the results section. A description of the climate and chemical data has been included at the beginning of the section ‘Drivers of biodiversity change’ in results. Subheadings were introduced in methods as suggested.

Author Response

Reviewer #1 (Public Review):

.In the best genetically and biochemically understood model of eukaryotic DNA replication, the budding yeast, Saccharomyces cerevisiae, the genomic locations at which DNA replication initiates are determined by a specific sequence motif. These motifs, or ARS elements, are bound by the origin recognition complex (ORC). ORC is required for loading of the initially inactive MCM helicase during origin licensing in G1. In human cells, ORC does not have a specific sequence binding domain and origin specification is not specified by a defined motif. There have thus been great efforts over many years to try to understand the determinants of DNA replication initiation in human cells using a variety of approaches, which have gradually become more refined over time.

In this manuscript Tian et al. combine data from multiple previous studies using a range of techniques for identifying sites of replication initiation to identify conserved features of replication origins and to examine the relationship between origins and sites of ORC binding in the human genome. The authors identify a) conserved features of replication origins e.g. association with GC-rich sequences, open chromatin, promoters and CTCF binding sites. These associations have already been described in multiple earlier studies. They also examine the relationship of their determined origins and ORC binding sites and conclude that there is no relationship between sites of ORC binding and DNA replication initiation. While the conclusions concerning genomic features of origins are not novel, if true, a clear lack of colocalization of ORC and origins would be a striking finding.

Thank you. That is where the novelty of the paper lies.

However, the majority of the datasets used do not report replication origins, but rather broad zones in which replication origins fire. Rather than refining the localisation of origins, the approach of combining diverse methods that monitor different objects related to DNA replication leads to a base dataset that is highly flawed and cannot support the conclusions that are drawn, as explained in more detail below.

We are using the narrowly defined SNS-seq peaks as the gold standard origins and making sure to focus in on those that fall within the initiation zones defined by other methods. The objective is to make a list of the most reproducible origins. Unlike what the reviewer states, this actually refines the dataset to focus on the SNS origins that have also been reproduced by the other methods in multiple cell lines. We will change the last box of Fig. 1A to say: Identify reproducible SNS-seq origins that are contained in IZs defined by Repli-seq, OK-seq and Bubble-seq. These are the “shared origins”. This and the Fig. 2B (as it is) will make our strategy clearer.

Methods to determine sites at which DNA replication is initiated can be divided into two groups based on the genomic resolution at which they operate. Techniques such as bubble-seq, ok-seq can localise zones of replication initiation in the range ~50kb. Such zones may contain many replication origins. Conversely, techniques such as SNS-seq and ini-seq can localise replication origins down to less than 1kb. Indeed, the application of these different approaches has led to a degree of controversy in the field about whether human replication does indeed initiate at discrete sites (origins), or whether it initiates randomly in large zones with no recurrent sites being used. However, more recent work has shown that elements of both models are correct i.e. there are recurrent and efficient sites of replication initiation in the human genome, but these tend to be clustered and correspond to the demonstrated initiation zones (Guilbaud et al., 2022).

These different scales and methodologies are important when considering the approach of Tian et al. The premise that combining all available data from five techniques will increase accuracy and confidence in identifying the most important origins is flawed for two principal reasons. First, as noted above, of the different techniques combined in this manuscript, only SNS-seq can actually identify origins rather than initiation zones. It is the former that matters when comparing sites of ORC binding with replication origin sites if a conclusion is to be drawn that the two do not co-localise.

Exactly. So the reviewer should agree that our method of finding SNS-seq peaks that fall within initiation zones actually refines the origins to find the most reproducible origins. We are not losing the spatial precision of the SNS-seq peaks.

Second, the authors give equal weight to all datasets. Certainly, in the case of SNS-seq, this is not appropriate. The technique has evolved over the years and some earlier versions have significantly different technical designs that may impact the reliability and/or resolution of the results e.g. in Foulk et al. (Foulk et al., 2015), lambda exonuclease was added to single stranded DNA from a total genomic preparation rather than purified nascent strands), which may lead to significantly different digestion patterns (ie underdigestion). Curiously, the authors do not make the best use of the largest SNS-seq dataset (Akerman et al., 2020) by ignoring these authors separation of core and stochastic origins. By blending all data together any separation of signal and noise is lost. Further, I am surprised that the authors have chosen not to use data and analysis from a recent study that provides subsets of the most highly used and efficient origins in the human genome, at high resolution (Guilbaud et al., 2022).

1) We are using the data from Akerman et al., 2020: Dataset GSE128477 in Supplemental Table 1. We can examine the core origins defined by the authors to check its overlap with ORC binding.

2) To take into account the refinement of the SNS-seq methods through the years, we actually included in our study only those SNS-seq studies after 2018, well after the lambda exonuclease method was introduced. Indeed, all 66 of SNS-seq datasets we used were obtained after the lambda exonuclease digestion step. To reiterate, we recognize that there may be many false positives in the individual origin mapping datasets. Our focus is on the True positives, the SNS-seq peaks that have some support from multiple SNS-seq studies AND fall within the initiation zones defined by the independent means of origin mapping (described in Fig. 1A and 2B). These True positives are most likely to be real and reproducible origins and should be expected to be near ORC binding sites.

We will change the last box of Fig. 1A to say: Identify reproducible SNS-seq origins that are contained in IZs defined by Repli-seq, OK-seq and Bubble-seq. These are the “Shared origins”.

Ini-seq by Torsten Krude and co-workers (Guillbaud, 2022) does NOT use Lambda exonuclease digestion. So using Ini-seq defined origins is at odds with the suggestion above that we focus only on SNS-seq datasets that use Lambda exonuclease. However, Ini-seq identifies a much smaller subset of SNS-seq origins, so we will do the analysis with just that smaller set in the revision of the paper.

References:

Akerman I, Kasaai B, Bazarova A, Sang PB, Peiffer I, Artufel M, Derelle R, Smith G, Rodriguez-Martinez M, Romano M, Kinet S, Tino P, Theillet C, Taylor N, Ballester B, Méchali M (2020) A predictable conserved DNA base composition signature defines human core DNA replication origins. Nat Commun, 11: 4826

Foulk MS, Urban JM, Casella C, Gerbi SA (2015) Characterizing and controlling intrinsic biases of lambda exonuclease in nascent strand sequencing reveals phasing between nucleosomes and G-quadruplex motifs around a subset of human replication origins. Genome Res, 25: 725-735

Guilbaud G, Murat P, Wilkes HS, Lerner LK, Sale JE, Krude T (2022) Determination of human DNA replication origin position and efficiency reveals principles of initiation zone organisation. Nucleic Acids Res, 50: 7436-7450

Reviewer #2 (Public Review):

Tian et al. perform a meta-analysis of 113 genome-wide origin profile datasets in humans to assess the reproducibility of experimental techniques and shared genomics features of origins. Techniques to map DNA replication sites have quickly evolved over the last decade, yet little is known about how these methods fare against each other (pros and cons), nor how consistent their maps are. The authors show that high-confidence origins recapitulate several known features of origins (e.g., correspondence with open chromatin, overlap with transcriptional promoters, CTCF binding sites). However, surprisingly, they find little overlap between ORC/MCM binding sites and origin locations.

Overall, this meta-analysis provides the field with a good assessment of the current state of experimental techniques and their reproducibility, but I am worried about: (a) whether we've learned any new biology from this analysis; (b) how binding sites and origin locations can be so mismatched, in light of numerous studies that suggest otherwise; and (c) some methodological details described below.

Major comments:

Line 26: "0.27% were reproducibly detected by four techniques" -- what does this mean? Does the fragment need to be detected by ALL FOUR techniques to be deemed reproducible?

If the reproducible SNS-seq peaks are included in the reproducible initiation zones found by the other methods, then we consider it reproducible across datasets. The strategy is to focus our analysis on the most reproducible SNS-seq peaks that happen to be in reproducible initiation zones. It is the best way to confidently identify a very small set of true positive origins.

And what if the technique detected the fragment is only 1 of N experiments conducted; does that count as "detected"?

A reproducible SNS-seq origin has been reproduced above a statistical threshold of 20 reproductions. A threshold of reproduction in 20 datasets out of 66 SNS-seq datasets gives an FDR of <0.1. This is explained in Fig. 2a and Supplementary Fig. S2. For the initiation zones, we considered a Zone even if it appears in only 1 of N experiments, because N is usually small. This relaxed method for selecting the initiation zones gives the best chance of finding SNS-seq peaks that are reproduced by the other methods.

Later in Methods, the authors (line 512) say, "shared origins ... occur in sufficient number of samples" but what does sufficient mean?

Sufficient means that SNS-seq origin was reproducibly detected in ≥ 20 datasets and was included in any initiation zone defined by three other techniques.

Then on line 522, they use a threshold of "20" samples, which seems arbitrary to me. How are these parameters set, and how robust are the conclusions to these settings? An alternative to setting these (arbitrary) thresholds and discretizing the data is to analyze the data continuously; i.e., associate with each fragment a continuous confidence score.

We explained Fig. 2a and Supplementary Fig. S2 in the text as follows: The occupancy score of each origin defined by SNS-seq (Supplementary Fig. 2a) counts the frequency at which a given origin is detected in the datasets under consideration. For the random background, we assumed that the number of origins confirmed by increasing occupancy scores decreases exponentially (see Methods and Supplementary Table 2). Plotting the number of origins with various occupancy scores when all SNS-seq datasets published after 2018 are considered together (the union origins) shows that the experimental curve deviates from the random background at a given occupancy score (Fig. 2a). The threshold occupancy score of 20 is the point where the observed number of origins deviates from the expected background number (with an FDR < 0.1) (Fig. 2a). In the Methods: In other words, the number of observed origins with occupancy score greater than 20 is 10 times more than expected in the background model. This approach is statistically sound and described by us in (Fang et al. 2020).

Line 20: "50,000 origins" vs "7.5M 300bp chromosomal fragments" -- how do these two numbers relate? How many 300bp fragments would be expected given that there are ~50,000 origins? (i.e., how many fragments are there per origin, on average)? This is an important number to report because it gives some sense of how many of these fragments are likely nonsense/noise. The authors might consider eliminating those fragments significantly above the expected number, since their inclusion may muddle biological interpretation.

I think we confused the reviewer by the way we wrote the abstract. The 50,000 origins that are mentioned in the abstract is the hypothetical expected number of origins that have to fire to replicate the whole 6x10^9 base diploid genome based on the average inter-origin distance of 10^5 bases (as determined by molecular combing). The 7.5M 300 bp fragments are the genomic regions where the 7.5M union SNS-seq-defined origins are located. Clearly, that is a lot of noise, some because of technical noise and some due to the fact that origins fire stochastically. Which is why our paper focuses on a smaller number of reproducible origins, the 20,250 shared origins. Our analysis is on the 20,250 shared origins, and not on all 7.5M union origins. Thus, we are not including the excess of non-reproducible (stochastic?) origins in our analysis.

The revised abstract in the revised paper will say: “Based on experimentally determined average inter-origin distances of ~100 kb, DNA replication initiates from ~50,000 origins on human chromosomes in each cell-cycle. The origins are believed to be specified by binding of factors like the Origin Recognition Complex (ORC) or CTCF or other features like G-quadruplexes. We have performed an integrative analysis of 113 genome-wide human origin profiles (from five different techniques) and 5 ORC-binding site datasets to critically evaluate whether the most reproducible origins are specified by these features. Out of ~7.5 million union origins identified by 66 SNS-seq datasets, only 0.27% were reproducibly contained in initiation zones identified by three other techniques (20,250 shared origins), suggesting extensive variability in origin usage and identification in different circumstances.”

Line 143: I'm not terribly convinced by the PCA clustering analysis, since the variance explained by the first 2 PCs is only ~25%. A more robust analysis of whether origins cluster by cell type, year etc is to simply compute the distribution of pairwise correlations of origin profiles within the same group (cell type, year) vs the correlation distribution between groups. Relatedly, the authors should explain what an "origin profile" is (line 141). Is the matrix (to which PCA is applied) of size 7.5M x 113, with a "1" in the (i,j) position if the ith fragment was detected in the jth dataset?

The reviewer is correct about how we did the PCA and have now included the description in the Methods. We will also do the pairwise correlations the way the reviewer suggests (a) by techniques, (b) by cell types (SNS-seq), (c) by year of publication (SNS-seq).

It's not clear to me what new biology (genomic features) has been learned from this meta-analysis. All the major genomic features analyzed have already been found to be associated with origin sites. For example, the correspondence with TSS has been reported before:

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6320713/

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6547456/

So what new biology has been discovered from this meta-analysis?

The new biology can be summarized as: (a) We can identify a set of reproducible (in multiple datasets and in multiple cell lines) SNS-seq origins that also fall within initiation zones identified by completely independent methods. These may be the best origins to study in the midst of the noise created by stochastic origin firing. (b) The overlap of these True Positive origins with known ORC binding sites is tenuous. So either all the origin mapping data, or all the ORC binding data has to be discarded, or this is the new biological reality in mammalian cancer cells: on a genome-wide scale the most reproduced origins are not in close proximity to ORC binding sites, in contrast to the situation in yeast. (c) All the features that have been reported to define origins (CTCF binding sites, G quadruplexes etc.) could simply be from the fact that those features also define transcription start sites (TSS), and origins prefer to be near TSS because of the favorable chromatin state.

Line 250: The most surprising finding is that there is little overlap between ORC/MCM binding sites and origin locations. The authors speculate that the overlap between ORC1 and ORC2 could be low because they come from different cell types. Equally concerning is the lack of overlap with MCM. If true, these are potentially major discoveries that butts heads with numerous other studies that have suggested otherwise. More needs to be done to convince the reader that such a mis-match is true. Some ideas are below:

Idea 1) One explanation given is that the ORC1 and ORC2 data come from different cell types. But there must be a dataset where both are mapped in the same cell type. Can the authors check the overlap here? In Fig S4A, I would expect the circles to not only strongly overlap but to also be of roughly the same size, since both ORC's are required in the complex. So something seems off here.

We agree with the reviewer that there is something “off here”. Either the techniques that report these sites are all wrong, or the biology does not fit into the prevailing hypothesis. One secret in the ORC ChIP field that our lab has struggled with for quite some time is that the various ORC subunits do not necessarily ChiP-seq to the same sites. The poor overlap between the binding sites of subunits of the same complex either suggests that the subunits do not always bind to the chromatin as a six-subunit complex or that all the ChIP-seq data in the Literature is suspect. We provide in the supplementary figure S4A examples of true positive complexes (SMARCA4/ARID1A, SMC1A/SMC3, EZH2/SUZ12), whose subunits ChIP-seq to a large fraction of common sites. As shown in Supplementary Fig. S4C, we do not have ORC1 and ORC2 ChIP-seq data from the same cell-type. We have ORC1 ChIP-seq and SNS-seq data from HeLa cells and ORC2 ChIP seq and origins from K562 cells, and so will add the proximity/overlap of the binding sites to the origins in the same cell-type in the revision.

Idea 2) Another explanation given is that origins fire stochastically. One way to quantify the role of stochasticity is to quantify the overlap of origin locations performed by the same lab, in the same year, in the same experiment, in the same cell type -- i.e., across replicates -- and then compute the overlap of mapped origins. This would quantify how much mis-match is truly due to stochasticity, and how much may be due to other factors.

A given lab may have superior reproducibility compared to the entire field. But the notion of stochasticity is well accepted in the field because of this observation: the average inter-origin distance measured by single molecule techniques like molecular combing is ~100 kb, but the average inter-origin distance measure on a population of cells (same cell line) is ~30 kb. The only explanation is that in a population of cells many origins can fire, but in a given cell on a given allele, only one-third of those possible origins fire. This is why we did not worry about the lack of reproducibility between cell-lines, labs etc, but instead focused on those SNS-seq origins that are reproducible over multiple techniques and cell lines.

Idea 3) A third explanation is that MCMs are loaded further from origin sites in human than in yeast. Is there any evidence of this? How far away does the evidence suggest, and what if this distance is used to define proximity?

MCMs, of course, have to be loaded at an origin at the time the origin fires because MCMs provide the core of the helicase that starts unwinding the DNA at the origin. Thus, the lack of proximity of MCM binding sites with origins can be because the most detected MCM sites (where MCM spends the most time in a cell-population) does not correspond to where it is first active to initiate origin firing. This has been discussed. MCMs may be loaded far from origin site, but because of their ability to move along the chromatin, they have to move to the origin-site at some point to fire the origin.

Idea 4) How many individual datasets (i.e., those collected and published together) also demonstrate the feature that ORC/MCM binding locations do not correlate with origins? If there are few, then indeed, the integrative analysis performed here is consistent. But if there are many, then why would individual datasets reveal one thing, but integrative analysis reveal something else?

We apologize for this oversight. In the revised manuscript we will discuss PMC3530669, PMC7993996, PMC5389698, PMC10366126. None of them have addressed what we are addressing, which is whether the small subset of the most reproducible origins proximal to ORC or MCM binding sites, but the discussion is essential.

Idea 5) What if you were much more restrictive when defining "high-confidence" origins / binding sites. Does the overlap between origins and binding sites go up with increasing restriction?

We will make origins more restrictive by selecting those reproduced by 30-60 datasets. The number of origins will of course fall, but we will measure whether the proximity to ORC or MCM-binding sites increases/decreases in a statistically rigorous way.

Overall, I have the sense that these experimental techniques may be producing a lot of junk. If true, this would be useful for the field to know! But if not, and there are indeed "unexplored mechanisms of origin specification" that would be exciting. But I'm not convinced yet.

It would be nice in the Discussion for the authors to comment about the trade-offs of different techniques; what are their pros and cons, which should be used when, which should be avoided altogether, and why? This would be a valuable prescription for the field.

Thanks for the suggestion. We will do what the reviewer suggests: use cell type-specific data wherever origins have been defined by at least two methods in the same cell type, specifically reporting the percent of shared origins amongst the datasets to compare whether some methods correlate better with each other. ORC ChIP-seq and MCM ChIP-seq data do not define origins: they define the binding sites of these proteins. Thus we will discuss why the ChIP-seq sites of these protein complexes should not be used to define origins.

Reviewer #3 (Public Review):

Summary: The authors present a thought-provoking and comprehensive re-analysis of previously published human cell genomics data that seeks to understand the relationship between the sites where the Origin Recognition Complex (ORC) binds chromatin, where the replicative helicase (Mcm2-7) is situated on chromatin, and where DNA replication actually beings (origins). The view that these should coincide is influenced by studies in yeast where ORC binds site-specifically to dedicated nucleosome-free origins where Mcm2-7 can be loaded and remains stably positioned for subsequent replication initiation. However, this is most certainly not the case in metazoans where it has already been reported that chromatin bindings sites of ORC, Mcm2-7, and origins do not necessarily overlap, likely because ORC loads the helicase in transcriptionally active regions of the genome and, since Mcm2-7 retains linear mobility (i.e., it can slide), it is displaced from its original position by other chromatin-contextualized processes (for example, see Gros et al., 2015 Mol Cell, Powell et al., 2015 EMBO J, Miotto et al., 2016 PNAS, and Prioleau et al., 2016 G&D amongst others). This study reaches a very similar conclusion: in short, they find a high degree of discordance between ORC, Mcm2-7, and origin positions in human cells.

Strengths: The strength of this work is its comprehensive and unbiased analysis of all relevant genomics datasets. To my knowledge, this is the first attempt to integrate these observations and the analyses employed were suited for the questions under consideration.

Thank you for recognizing the comprehensive and unbiased nature of our analysis. The fact that the major weakness is that the comprehensive view fails to move the field forward, is actually a strength. It should be viewed in the light that we cannot even find evidence to support the primary hypothesis: that the most reproducible origins must be near ORC and MCM binding sites. This finding will prevent the unwise adoption of ORC or MCM binding sites as surrogate markers of origins and may perhaps stimulate the field to try and improve methods of identifying ORC or MCM binding until the binding sites are found to be proximal to the most reproducible origins. The last possibility is that there are ORC- or MCM-independent modes of defining origins, but we have no evidence of that.

Weaknesses: The major weakness of this paper is that this comprehensive view failed to move the field forward from what was already known. Further, a substantial body of relevant prior genomics literature on the subject was neither cited nor discussed. This omission is important given that this group reaches very similar conclusions as studies published a number of years ago. Further, their study seems to present a unique opportunity to evaluate and shape our confidence in the different genomics techniques compared in this study. This, however, was also not discussed.

We will do what the reviewer suggests: use cell type-specific data wherever origins have been defined by at least two methods in the same cell type, specifically reporting the percent of shared origins amongst the datasets to compare whether some methods correlate better with each other. Thanks for the suggestion. ORC ChIP-seq and MCM ChIP-seq data do not define origins: they define the binding sites of these proteins. Thus, we will discuss why the ChIP-seq sites of these protein complexes should not be used to define origins.

We do not cite the SNS-seq data before 2018 because of the concerns discussed above about the earlier techniques needing improvement. We will discuss other genomics data that we failed to discuss.

We will cite the papers the reviewer names:

Gros, Mol Cell 2015 and Powell, EMBO J. 2015 discuss the movement of MCM2-7 away from ORC in yeast and fliesand will be cited. MCM2-7 binding to sites away from ORC and being loaded in vast excess of ORC was reported earlier on Xenopus chromatin in PMC193934, and will also be cited.

Miotto, PNAS, 2016: publishes ORC2 ChIP-seq sites in HeLa (data we have used in our analysis), but do not measure ORC1 ChIP-seq sites. They say: “ORC1 and ORC2 recognize similar chromatin states and hence are likely to have similar binding profiles.” This is a conclusion based on the fact that the ChIP seq sites in the two studies are in areas with open chromatin, it is not a direct comparison of binding sites of the two proteins.

Prioleau, G&D, 2016: This is a review that compared different techniques of origin identification but has no primary data to say that ORC and MCM binding sites overlap with the most reproducible origins.

Author Response

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

Reviewer #1 (Public Review):

This study investigates the context-specificity of facial expressions in three species of macaques to test predictions for the 'social complexity hypothesis for communicative complexity'. This hypothesis has garnered much attention in recent years. A proper test of this hypothesis requires clear definitions of 'communicative complexity' and 'social complexity'. Importantly, these two facets of a society must not be derived from the same data because otherwise, any link between the two would be trivial. For instance, if social complexity is derived from the types of interactions individuals have, and different types of signals accompany these interactions, we would not learn anything from a correlation between social and communicative complexity, as both stem from the same data.

The authors of the present paper make a big step forward in operationalising communicative complexity. They used the Facial Action Coding System to code a large number of facial expressions in macaques. This system allows decomposing facial expressions into different action units, such as 'upper lid raiser', 'upper lip raiser' etc.; these units are closely linked to activating specific muscles or muscle groups. Based on these data, the authors calculated three measures derived from information theory: entropy, specificity and prediction error. These parts of the analysis will be useful for future studies.

The three species of macaque varied in these three dimensions. In terms of entropy, there were differences with regard to context (and if there are these context-specific differences, then why pool the data?). Barbary and Tonkean macaques showed lower specificity than rhesus macaques. Regarding predicting context from the facial signals, a random forest classifier yielded the highest prediction values for rhesus monkeys. These results align with an earlier study by Preuschoft and van Schaik (2000), who found that less despotic species have greater variability in facial expressions and usage.

Crucially, the three species under study are also known to vary in terms of their social tolerance. According to the highly influential framework proposed by Bernard Thierry, the members of the genus Macaca fall along a graded continuum from despotic (grade 1) to highly tolerant (grade 4). The three species chosen for the present study represent grade 1 (rhesus monkeys), grade 3 (Barbary macaques), and grade 4 (Tonkean macaques).

The authors of the present paper define social complexity as equivalent to social tolerance - but how is social tolerance defined? Thierry used aggression and conflict resolution patterns to classify the different macaque species, with the steepness of the rank hierarchy and the degree of nepotism (kin bias) being essential. However, aggression and conflict resolution are accompanied by facial gestures. Thus, the authors are looking at two sides of the same coin when investigating the link between social complexity (as defined by the authors) and communicative complexity. Therefore, I am not convinced that this study makes a significant advance in testing the social complexity for communicative complexity hypothesis. A further weakness is that - despite the careful analysis - only three species were considered; thus, the effective sample size is very small.

Social tolerance in macaques is defined by various covarying traits, among which rates of counter-aggression and conflict resolution are only two of many included (see Thierry 2021 for a recent discussion and review). We do not deviate from Thierry’s definition of social tolerance. We simply highlight that the constellation of behavioral traits in the most tolerant macaque species results in a social environment where the outcome of social interactions is more uncertain (see introduction lines 102-114). As we argue throughout the paper, higher uncertainty can be used as a proxy for higher complexity and thus we conclude that the most tolerant macaque species have the highest social complexity. While most social behavior in macaques is accompanied by some facial behavior, we were careful to define social contexts only from the body language/behavior (e.g., lunge for aggression, grooming for affiliation) of the individuals involved and ignored the facial behavior used (see method lines 371-381). Therefore, the facial behavior of macaques (communication signals) was not used in defining either social tolerance (and by extension complexity) or the social context in which it was used. We feel like this appropriately minimizes any elements of circularity in the analysis of social and communicative complexity.

Regarding the effective sample size of three species, we agree that it is small, and it is a limitation of this study. However, the methodology we used is applicable to any species for which FACS is available (including other non-human primates, dogs, and horses), and therefore, we hope that other datasets will complement ours in the future. Nevertheless, we now acknowledge this limitation in the discussion (lines 314317).

Reviewer #2 (Public Review):

This is a well-written manuscript about a strong comparative study of diversity of facial movements in three macaque species to test arguments about social complexity influencing communicative complexity. My major criticism has to do with the lack of any reporting of inter-observer reliability statistics - see comment below. Reporting high levels of inter-observer reliability is crucial for making clear the authors have minimized chances of possible observer biases in a study like this, where it is not possible to code the data blind with regard to comparison group. My other comments and questions follow by line number:

We agree that inter-observer coding reliability is an important piece of information. We now report in more detail the inter-observer reliability tests that we conducted on lines 384-392.

38-40. Whereas I am an advocate of this hypothesis and have tested it myself, the authors should probably comment here, or later in the discussion, about the reverse argument - greater communicative complexity (driven by other selection pressures) could make more complicated social structures possible. This latter view was the one advocated by McComb & Semple in their foundational 2005 Biology Letters comparative study of relationships between vocal repertoire size and typical group size in non-human primate species.

It is true that an increase in communicative complexity could allow/drive an increase in social complexity. Unfortunately our data is correlational in nature and we cannot determine the direction of causality. We added such a statement to the discussion (lines 311-314).

72-84 and 95-96. In the paragraph here, the authors outline an argument about increasing uncertainty / entropy mapping on to increasing complexity in a system (social or communicative). In lines 95-96, though, they fall back on the standard argument about complex systems having intermediate levels of uncertainty (complete uncertainty roughly = random and complete certainty roughly = simple). Various authors have put forward what I think are useful ways of thinking about complexity in groups - from the perspective of an insider (i.e., a group member, where greater randomness is, in fact, greater complexity) vs from the perspective of an outside (i.e., a researcher trying to quantify the complexity of the system where is it relatively easy to explain a completely predictable or completely random system but harder to do so for an intermediately ordered or random system). This sort of argument (Andrew Whiten had an early paper that made this argument) might be worth raising here or later in the discussion? (I'm also curious where the authors sentiments lie for this question - they seem to touch on it in lines 285-287, but I think it's worth unpacking a little more here!)

In this study we used three measures of uncertainty (entropy, context specificity, and prediction error) to approximate complexity. However, maximum entropy or uncertainty would be achieved in a system that is completely random (and thus be considered simple). Therefore, the species with the highest entropy values, or unpredictability, could be interpreted as having a simpler communication system than a species with a moderately high entropy/unpredictability value. Our argument is that animal communication systems cannot possibly be random, otherwise they would not have evolved as signals. In systems where we know the highest entropy (or unpredictability) will not be due to randomness, as is the case with animal social interactions and communication, we can conclude that the system with the highest uncertainty is the most complex. We have now expanded upon this point in the discussion (lines 286-294). See also response to reviewer 1 below.

115-129. See also:

Maestripieri, D. (2005). "Gestural communication in three species of macaques (Macaca mulatta, M. nemestrina, M. arctoides): use of signals in relation to dominance and social context." Gesture 5: 57-73.

Maestripieri, D. and K. Wallen (1997). "Affiliative and submissive communication in rhesus macaques." Primates 38(2): 127-138.

On that note, it is probably worth discussing in this paragraph and probably later in the discussion exactly how this study differs from these earlier studies of Maestripieri. I think the fact that machine learning approaches had the most difficulty assigning crested data to context is an important methodological advance for addressing these sorts of questions - there are probably other important differences between the authors' study here and these older publications that are worth bringing up.

Our study differs from these two studies in that the studies above classified facial behavior into discrete categories (e.g., bared-teeth, lip-smack), whereas we adopted a bottom-up approach and made no a priori assumptions about which movements are relevant. We broke down facial behavior down to their individual muscle movements (i.e., Action Units). Measuring facial behavior at the level of individual muscle movements allows for a more detailed and objective description of the complexity of facial behavior. This is a general point in advancing the study of facial behavior that is discussed in the introduction (lines 60-71) and discussion (lines 206-208). The reason we don’t draw a direct comparison with the studies above is because they had a slightly different focus. Our study was more focused on complexity of the (facial) communication system in general rather than comparing whether the different species use the same facial behavior in the same/different social contexts.

220-222. What is known about visual perception in these species? Recent arguments suggest that more socially complex species should have more sensitive perceptual processing abilities for other individuals' signals and cues (see Freeberg et al. 2019 Animal Behaviour). Are there any published empirical data to this effect, ideally from the visual domain but perhaps from any domain?

This is an interesting point. We are not aware of any studies showing differences in visual perceptions within the macaque genus. Both crested macaques and rhesus macaques are able to discriminate between individuals and facial expressions in match-to-sample tasks with comparable performances (Micheletta et al., 2015a, 2015b; Parr et al. 2008; Parr & Heinz, 2009). Similarly, several macaque species are sensitive to gaze shifts from conspecifics (Tomasello et al. 1998; Teufel et al. 2010; Micheletta & Waller, 2012).

274-277. I am not sure I follow this - could not different social and non-social contexts produce variation in different affective states such that "emotion"-based signals could be as flexible / uncertain as seemingly volitional / information-based / referential-like signals? This issue is probably too far away from the main points of this paper, but I suspect the authors' argument in this sentence is too simplified or overstated with regard to more affect-based signals.

Emotion-based signals could, in theory, also produce flexible signals and it is possible that some facial expressions reflect an emotional state. However, some previous studies have suggested that facial expressions are only used as a display of emotion, rather than such signals having evolved for a different function such as announcing future intentions. In our study we found that macaques used, in some cases, the same facial expressions (i.e. combination of Action Units) in at least two different social contexts that, presumably, differed in their emotional valence. Thus, it is unlikely that particular facial expressions are bound to a single emotion. We think that this is an important point to make even though it is slightly beyond the scope of our paper.

288 on. Given there are only three species in this study, the chances of one of the species being the 'most complex' in any measure is 0.33. Although I do not believe this argument I am making here, can the authors rule out the possibility that their findings related to crested macaques are all related to chance, statistically speaking?

We are not aware of a way to rule out this possibility. However, we believe that we are appropriately cautious throughout the paper and acknowledge that having only investigated three species is a limitation of this study in the discussion (lines 314-317, see also our response to reviewer 1 above).

329-330. The fact that only one male rhesus macaque was assessed here seems problematic, given the balance of sexes in the other two species. Can the authors comment more on this - are the gestures they are studying here identical across the sexes?

We agree it would have been preferable to collect data on more than one male rhesus macaque, but that was unfortunately not possible. We are not aware of any studies showing differences in the use of facial behavior between male and female rhesus macaques. If differences exist, most likely these would occur in a sexual/mating context. However, in our study we only considered affiliative (non-sexual), submissive, and aggressive contexts, where we have no a priori reason to believe that there are sex differences.

354-371. Inter-observer reliability statistics are required here - one of the authors who did not code the original data set, or a trained observer who is not an author, could easily code a subset of the video files to obtain inter-observer reliability data. This is important for ruling out potential unconscious observer biases in coding the data.

We agree this is an important piece of information. We now report in more detail the inter-observer reliability tests that we conducted on lines 384-392:

“An agreement rating of >0.7 was considered good [Ekman et al 2002] and was necessary for obtaining certification. To obtain a MaqFACS coding certification, AVR, CP, and PRC coded 23 video clips of rhesus macaques and the MaqFACS codes were compared to the data of other certified coders (https://animalfacs.com).

The mean agreement ratings obtained were 0.85, 0.73, 0.83 for AVR, CP, and PRC, respectively. In addition, AVR and CP coded 7 videos of Barbary macaques with a mean agreement rating of 0.79. AVR and PRC coded 10 videos of crested macaques with a mean agreement rating of 0.74.”

Reviewer #1 (Recommendations For The Authors):

Given the long debate on the concept of information exchange in animal communication, I would also recommend being more careful with the term 'exchanges of information' (line 271). Perhaps it's better to be agnostic in the context of this paper.

As suggested, we now changed the phrasing to focus on the behavior of the animals, rather than suggesting that information is being exchanged (lines 270-273),

Line 281: "This result confirms the assumption that facial behaviour in macaques is not used randomly": the authors are knocking down a straw man. Nobody who has ever studied animal communication would consider that signals occur randomly. Otherwise, they would not have evolved as signals.

Indeed, nobody claims that animal communication signals are used randomly. Although it may be taken for granted, we feel it is worthwhile to reiterate this point, given that we used relative entropy and prediction error as measures of complexity. For instance, maximum entropy or unpredictability would be achieved in a system that is completely random (and thus be considered simple). Therefore, the species with the highest entropy values, or lowest predictability, could be interpreted as having a simpler communication system than a species with a moderately high entropy value. But if we are working under the assumption that animal communication systems cannot possibly be random, then we can conclude that the species whose communication system has the highest entropy is in fact the most complex. We tried to make this justification clearer in the discussion (lines 285-294).

I did not follow why there is a higher reliance on facial signals when predation pressure is higher. Apart from the fact that the authors cannot address this question, they may want to reconsider this idea altogether.

We now expand on the logic of why predation pressure might affect the use of facial signals (see lines 308-309): “When predation pressure is higher, reliance on facial signals could be higher than, for example vocal signals, such as to not draw attention of predators to the signaller.”

Technical comments:

One methodological issue that requires clarification is what the units of analysis are. The authors write that each row in their analysis denoted an observation time of 500 ms. How many rows did the authors assemble? The authors mention a sample size of > 3000 social interactions in the abstract. How did they define social interactions? And how many 'time windows' of 500 ms were obtained? Did they take one window per interaction or several? If several, then how was this move accounted for in the analysis? The reporting needs to be more accurate here. Most likely, the bootstrapping took care of biases in the data, but still, this information needs to be provided.

We have now added some additional information to the method section. Social interactions for each context had the following definitions: “Social context was labeled from the point of view of the signaler based on their general behavior and body language (but not the facial behavior itself), during or immediately following the facial behavior. An aggressive context was considered when the signaler lunged or leaned forward with the body or head, charged, chased, or physically hit the interaction partner. A submissive context was considered when the signaler leaned back with the body or head, moved away, or fled from the interaction partner. An affiliative context was considered when the signaler approached another individual without aggression (as defined previously) and remained in proximity, in relaxed body contact, or groomed either during or immediately after the facial behavior. In cases where the behavior of the signaler did not match our context definitions, or displayed behaviors belonging to multiple contexts, we labeled the social context as unclear. Social context was determined from the video itself and/or from the matching focal behavioral data, if available.” (lines 371-382). The total duration of all social interactions per social context, and thus the number of 500ms windows/rows, have been added to Table 1 (lines 395-397). There were several 500ms windows per social interaction. All 500ms time blocks per interaction were used in the statistical analyses in order to retain all the variation and complexity of the facial behavior (Action Unit combinations) used by the macaques (lines 403-405). Indeed the bootstrapping procedure was used to account for any biases in the data.

Overall, I would recommend providing more information on the actual behaviour of the animals. The paper is strong in handling highly derived indices representing the behaviour, but the reader learns little about the animals' behaviour. Thus, it would be great if statements about the entropy ratio were translated into what these measures represent in real life. For context specificity, this is clear, but for entropy, not so much.

A high entropy ratio essentially suggests that a species uses a high variety of unique facial behavior/signals and all signals in the repertoire are used roughly equally often (rather than one facial behavior being used 90% of the time and others rarely used). We have tried our best to better explain this point in the introduction (lines 75-81) and discussion (lines 215-222). Discussing exactly what these signals are and what they mean was beyond the scope of this paper.

Line 106: nepotism, not kinship

Changed as suggested (line 106).

Line 113: I would avoid statements about how a monkey society is perceived by its members.

We think that noting how individuals may perceive their social environment is worthwhile when defining social complexity, so have retained this point but changed the phrasing to be more speculative (lines 112-113).

Line 329: I was very surprised that only one male was represented in the data for rhesus monkeys. The authors try to wriggle their way out of this issue in the supplementary material ("Therefore, we have no a priori reason to expect an overall difference in the diversity and complexity of facial behaviour between the sexes"), but I think this is a major shortcoming of the analysis. They should ascertain whether there are no sex differences in the other two species regarding their variables of interest. They could then make a very cautious case for there being no sex differences in rhesus either. But of course, they would not know for sure.

As with our response to reviewer 2 above, we agree that it would have been preferable to collect data on more than one male rhesus macaque, but that was unfortunately not possible. We are not aware of any studies showing differences in the use of facial behavior between male and female rhesus macaques. If differences exist, most likely these would occur in a sexual/mating context. However, in our study we only considered affiliative (non-sexual), submissive, and aggressive contexts, where we have no a priori reason to believe that there are sex differences. Looking at sex differences in the use of facial behavior would be a worthwhile study on its own, but it is outside the scope of this paper.

This paper would make a stronger contribution if it focussed on the comparative analysis of facial expressions and removed the attempt of testing the social complexity for communicative complexity hypothesis.

A comparative analysis of the contextual use of specific facial movements is important. But this paper is focused on making a more general comparison of the communication style and complexity across species. The social complexity hypothesis for communicative complexity is one of the key theoretical frameworks for such an investigation and allows us to frame our study in a broader context. We contribute important data on 3 species with methods that can be replicated and extended to others species. Therefore, we believe that it is a worthy contribution to investigations of the evolution of complex communication.

REFERENCES

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Author Response

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

We are grateful for the helpful comments of both reviewers and have revised our manuscript with them in mind.

One of the main issues raised was that readers may by default assume that our models are correct. We in fact made it very clear in our discussion that the models are merely hypotheses that will need testing by “wet” experiments and we do not therefore agree that even readers unfamiliar with AF would assume that the models must be correct. It was also suggested that readers could be reassured by including extensive confidence estimates such as PAE plots. As it happens, every single model described in the manuscript had reasonably high PAE scores and more crucially the entire collection of output files, including PAE data, are readily accessible on Figshare at https://doi.org/10.6084/m9.figshare.22567318.v2, a fact that the reviewers appear to have overlooked. The Figshare link is mentioned three times in the manuscript. Embedding these data within the manuscript itself would in our view add even more details and we have therefore not included them in our revised manuscript. Likewise, it is rather simple for any reader to work out which part of a PAE matrix corresponds to an interaction observed in the corresponding pdb prediction. Besides which, it is our view that the biological plausibility and explanatory power of models is just as important as AF metrics in judging whether they may be correct, as is indeed also the case for most experimental work.

Another important point was that the manuscript was too long and not readable. Yes, it is long and it could well be argued that we could have written a different type of manuscript, focusing entirely on what is possibly the simplest and most important finding, namely that our AF models suggest that in animal cells Wapl appears to form a quarternary complex with SA, Pds5, and Scc1 in a manner suggesting that a key function of Wapl’s conserved CTD is to sequester Scc1’s Nterminal domain after it has dissociated from Smc3. For right or for wrong, we decided that this story could not be presented on its own but also required 1) an explanation for how Scc1 is induced to dissociate from Smc3 in the first place and 2) how to explain that the quarternary complex predicted for animal cells was not initially predicted for fungi such as yeast. The yeast situation was an exception that clearly needed explaining if the theory was to have any generality and it turned out that delving into the intricate details of the genetics of releasing activity in yeast was eventually required and yielded valuable new insights. We also believe that our work on the recruitment of Eco/Esco acetyl transferases to cohesin and the finding that sororin binds to the Smc3/Scc1 interface also provided important insight into how releasing activity is regulated. We acknowledge that the paper is indeed long but do not think that it is badly written. It is above all a long and complex story that in our view reveals numerous novel insights into how cohesin’s association with chromosomes is regulated and have endeavoured to eliminate any excessive speculation. We feel it is not our fault that cohesin uses complex mechanisms.

Notwithstanding these considerations, we have in fact simplified a few sections and removed one or two others but acknowledge that we have not made substantial cuts.

It was pointed out that a key feature of our modelling, namely the predicted association of Wapl’s C-terminal domain with SA/Scc3’s CES is inconsistent with published biochemical data. The AF predictions for this interface are universally robust in all eukaryotic lineages and crucially fully consistent with published and unimpeachable genetic data. We note that any model that explains all findings is bound to be wrong for the very simple reason that some of these findings will prove to be incorrect. There is therefore an art in Science of judging which data must be explained and accommodated and which should be ignored. In this particular case, we chose to ignore the biochemistry. Time will tell whether our judgement proves correct.

Last but not least, it was suggested that we might provide some experimental support for our proposed SA/Scc3-Pds5-Scc1-WaplC quaternary complex. We are in fact working on this by introducing cysteine pairs (that can be crosslinked in cells) into the proposed interfaces but decided that such studies should be the topic of a subsequent publication. It would be impossible with the resources available to our labs to follow up all of the potential interactions and we therefore decided to exclude all such experiments.

We are grateful for the detailed comments provided by both reviewers, many of which were very helpful, and in many but not all cases have amended the manuscript accordingly.

With regard to the more specific comments:

Reviewer #1 (Recommendations For The Authors):

1) One concern is that observed interfaces/complexes arise because AF-multimer will aim to pack exposed, conserved and hydrophobic surfaces or regions that contain charge complementarity. The risk is that pairwise interaction screens can result in false positive & non-physiological interactions. It is therefore important to report the level of model confidence obtained for such AF calculations:

A) The authors should color the key models according to pLDDT scores obtained as reported by AF. This would allow the reader to judge the estimated accuracy of the backbone and side chain rotamers obtained. At least for the key models and interactions it would be important to know if the pLDDT score is >90 (Correct backbone and most rotamers) or >70 (only backbone is correct).

B) It would also be important to report the PAE plots to allow estimation of the expected position error for most of the important interactions. pLDDT coloring and PEA plots can be shown side-by-side as shown in other published data (e.g. https://pubmed.ncbi.nlm.nih.gov/35679397/ (Supplementary data)

C) The authors should include a Table showing the confidence of template modeling scores for the predicted protein interfaces as ipTM, ipTM+pTM as reported by AlphaFold-multimer. Ideally, they would also include DockQ scores but this may not be essential. Addition of such scores would help classification into Incorrect, Acceptable or of high quality. For example, line 1073 et seq the authors show a model of a SCC1SA and ESCO1 complex (Fig. 37). Are the modeling scores for these interfaces high? It does not help that the authors show cartoons without side chains? Can the authors provide a close-up view of the two interfaces? Are the amino acids are indeed packed in a manner expected for a protein interface? Can we exclude the possibility that the prediction is obtained merely because the sequence segments (e.g. in ESCO1 & ESCO2) are hydrophobic and conserved?

We do not agree that including this level of detail to the text/figures of the manuscript would be suitable. All the relevant data for those who may be sceptical about the models are readily available at https://doi.org/10.6084/m9.figshare.22567318.v2. In our view, the cartoon versions of the models are easier for a reader to navigate. Anyone interested in the molecular details can look at the models directly.

Importantly, no amount of statistical analysis can completely validate these models. What is required are further experiments, which will be the topic of further work from our and I dare from other laboratories.

D) When they predict an interaction between the SA2:SCC1 complex and Sororin's FGF motif, they find that only 1/5 models show an interaction and that the interaction is dissimilar to that seen of CTCF. Again, it would be helpful to know about modeling scores. Can they show a close-up view of the SORORIN FGF binding interface to see if a realistic binding mode is obtained? Can they indicate the relevant region on the PAE plot?

Given that AF greatly favours other interactions of Sororin’s FGF motif over its interaction with SA2-Scc1, we do not agree that dwelling on the latter would serve any purpose.

2) Line 996: AF predicts with high confidence an interaction between Eco1 & SMC3hd. What are the ipTM (& DockQ if available) scores. Would the interface score High, Medium or Acceptable?

As mentioned, see https://doi.org/10.6084/m9.figshare.22567318.v2.

3) Line 1034 et seq: Eco1/ESCO1/ESCO2 interaction with PDS5. Interface scores need to be shown to determine that the models shown are indeed likely to occur. If these interactions have low model confidence, Fig. 36 and discussion around potential relevance to PDS5-Eco1 orientation relative to the SMC3 head remains highly speculative and could be expunged.

See https://doi.org/10.6084/m9.figshare.22567318.v2. It should be clear that the predictions are very similar in fungi and animals. Crucially, we know that Pds5 is essential for acetylation in vivo, so the models appear plausible from a biological point of view.

4) Considering the relatively large interface between ECO1 and SMC3, would the author consider the possibility that in addition to acetylating SMC3's ATPase domain, ECO1 remains bound to cohesin-DNA complex, as proposed for ESCO1 by Rahman et al (10.1073/pnas.1505323112)?

This is certainly possible but we would not want to indulge in such speculation.

5) E.g. Line 875 but also throughout the text: As there is no labeling of the N- and C-termini in the Figures, is frequently unclear what the authors are referring to when they mention that AF models orient chains in a certain manner.

Good point. This has been amended. However, the positions of N- and C- is all available at https://doi.org/10.6084/m9.figshare.22567318.v2.

6) Fig19B: PAE plots: authors should indicate which chains correspond to A, B, C. Which segment corresponds to the TYxxxR[T/S]L motif? Can they highlight this section on the PAE plot?

Good point and amended in the revised manuscript.

Minor comments:

1) Line 440: the WAPL YSR motif is not shown in Fig. 14A

2) Line 691: Scc3 spelling error.

3) Line 931: Sentence ending '... SCC3 (SCC3N).' requires citation.

4) Line 1008: Figure reference seems wrong. It should read: Fig. 34A left and right. Fig. 34B does not contain SCC1.

Many thanks for spotting these. Hopefully, all corrected.

5) Fig. 41 can be removed as it shows the absence of the interaction of Sororin with SMC1:SCC1. Sufficient to mention in the text that Sororin does not appear to interact with SMC1:SCC1.

This is possible but we decided to leave this as is.

Reviewer #2 (Recommendations For The Authors):

Minor points

(1) Are there any predicted models in which one of the two dimer interfaces of the hinge is open when the coiled coils are folded back, as seen in the cryo-EM structure of human cohesin-NIPBL complex in the clamped state?

No AF runs ever predicted half opened hinges. It is possible that the introduction of mutations in one of the two interfaces might reveal a half-opened state and we ought to try this. However, it would not be appropriate for this manuscript, we believe.

(2) Structures of the SA-Scc1 CES bound to [Y/F]xF motifs from Sgo1 and CTCF have been reported, suggesting that a similar motif could interact with SA/Scc3. Surprisingly, AF did not predict an interaction between Scc3/SA and Wapl FGF motifs, which only bind to the Pds5 WEST region. On the other hand, AF predicted interactions of the Sororin FGF motif with both Pds5 WEST and SA CES. Can the authors comment on this Wapl FGF binding specificity? What will happen if a Wapl fragment lacking the CTD is used in the prediction?

This seems to be an academic point as the CTD is always present.

Author Response

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

Reviewer #1 (Recommendations For The Authors):

1) The authors need to validate that RAP1-HA still retains its essential function. As indicated above, if RAP1-HA still retains its essential functions, cells carrying one RAP1-HA allele and one deleted allele are expected to grow the same as WT cells. These cells should also have the WT VSG expression pattern, and RAP1-HA should still interact with TRF.

We demonstrated that C-terminally HA-tagged RAP1 co-localizes with telomeres by a combination of immunofluorescence and fluorescence in situ hybridization (Cestari and Stuart, 2015, PNAS), and co-immunoprecipitate telomeric and 70 bp repeats (Cestari et al. 2019 Mol Cell Biol). We also showed by immunoprecipitation and mass spectrometry that HA-tagged RAP1 interacts with nuclear and telomeric proteins, including PIP5Pase (Cestari et al. 2019). Others have also tagged T. brucei RAP1 with HA without disrupting its nuclear localization (Yang et al. 2009, Cell), all of which indicate that the HA-tag does not affect protein function. As for the suggested experiment, there is no guarantee that cells lacking one allele of RAP1 will behave as wildtype, i.e., normal growth and repression of VSGs genes. Also, less than 90% of T. brucei TRF was reported to interact with RAP1 (Yang et al. 2009, Cell), which might be indirect via their binding to telomeric repeats rather than direct protein-protein interactions.

2) The authors need to remove the His6 tag from the recombinant RAP1 fragments before the EMSA analysis. This is essential to avoid any artifacts generated by the His6-tagged proteins.

Our controls show that the His-tag is not interfering with RAP1-DNA binding. We show in Fig 3CG by EMSA and in Fig S5 by EMSA and microscale thermophoresis that His-tagged full-length rRAP1 does not bind to scrambled telomeric dsDNA sequences, which demonstrates that His-tagged rRAP1 does not bind unspecifically to DNA. Moreover, in Fig 3G and Fig S5, we show that His-tagged rRAP11-300 also does not bind to 70 bp or telomeric repeats. In contrast, the full-length His-tagged rRAP1, rRAP1301-560, or rRAP1561-855 bind to 70 bp or telomeric repeats (Fig 3C-G). Since all proteins were His-tagged, the His tag cannot be responsible for the DNA binding. We have worked with many different His-tagged proteins for nucleic acid binding and enzymatic assays without any interference from the tag (Cestari and Stuart, 2013; JBC; Cestari et al; 2013, Mol Cell Biol; Cestari and Stuart, 2015, PNAS; Cestari et al. 2016; Cell Chem Biol; Cestari et al. 2019 Mol Biol Cell).

3) More details need to be provided for ChIPseq and RNAseq analysis regarding the read numbers per sample, mapping quality, etc.

Table S3 includes information on sequencing throughput and read length. Mapping quality was included in the Methods section “Computational analysis of RNA-seq and ChIP-seq”, starting at line 499. In summary, we filtered reads to keep primary alignment (eliminate supplementary and secondary alignments). We also analyzed ChIP-seq with MAPQ ≥20 (99% probability of correct alignment) to distinguish RAP1 binding to specific ESs, including silent vs active ES (ChIP-seq). We included Fig S4 to show the effect of filtering alignments on the active vs silent ESs. We used MAPQ ≥30 to analyze RNA-seq mapping to VSG genes, including those in subtelomeric regions. Our scripts are available at https://github.com/cestari-lab/lab_scripts. We also included in the Methods, lines 522-524: “Scripts used for ChIP-seq, RNA-seq, and VSG-seq analysis are available at https://github.com/cestari-lab/lab_scripts. A specific pipeline was developed for clonal VSG-seq analysis, available at https://github.com/cestarilab/VSG-Bar-seq.”

4) The authors should revise the Discussion section to clearly state the authors' speculations and their working models (the latter of which need solid supporting evidence). Specifically, statements in lines 218 - 219 and lines 224-226 need to be revised.

The statement “likely due to RAP1 conformational changes” in line 228 discusses how binding of PI(3,4,5)3 could affect RAP1 Myb and MybL domains binding to DNA. We did not make a strong statement but discussed a possibility. We believe that it is beneficial to the reader to have the data discussed, and we do not feel this point is overly speculative. For lines 224-226 (now 234-235), the statement refers to the finding of RAP1 binding to centromeric regions by ChIP-seq, which is a new finding but not the focus of this work. To make it clear that it does not refer to telomeric ESs, we edited: “The finding of RAP1 binding to subtelomeric regions other than ESs, including centromeres, requires further validation.” Since RAP1 binding to centromeres is not the focus of the work, future studies are necessary to follow up, and we believe it is appropriate in the Discussion to be upfront and highlight this point to the readers.

Our model is based on the data presented here but also on scientific literature. We have reviewed the Discussion to prevent broad speculations. When discussing a model, we stated (line 245): “The scenario suggests a model in which …”, to state that this is a working model. Similarly, in Results (line 201) we included: “Our data suggest a model in which…”.

5) The authors should revise the title to reflect a more reasonable conclusion of the study.

We agree that the title should be changed to imply a direct role of PI(3,4,5)P3 regulation of RAP1, which is not captured in the original title. This will provide more specific information to the readers, especially those broadly interested in telomeric gene regulation and RAP1. The new title is: PI(3,4,5)P3 allosteric regulation of repressor activator protein 1 controls antigenic variation in trypanosomes

6) The authors are recommended to provide an estimation of the expression level of the V5-tagged PIP5pase from the tubulin array in reference to the endogenous protein level.

The relative mRNA levels of the exclusive expression of PIP5Pase mutant compared to the wildtype is available in the Data S1, RNA-seq. The Mut PIP5Pase allele’s relative expression level is 0.85fold to the WT allele (both from tubulin loci). We also showed by Western blot the WT and Mut PIP5Pase protein expression (Cestari et al. 2019, Mol Cell Biol). Concerning PIP5Pase endogenous alleles, we compared normalized RNA-seq counts per million from the conditional null PIP5Pase cells exclusively expressing WT or the Mut PIP5Pase alleles (Data S1, this work) to our previous RNA-seq of single-marker 427 strain (Cestari et al. 2019, Mol Cell Biol). We used the single-maker 427 because the conditional null cells were generated in this strain background. The PIP5Pase WT and Mut mRNAs expressed from tubulin loci are 1.6 and 1.3-fold the endogenous PIP5Pase levels in single-marker 427, respectively. We included a statement in the Methods, lines 275-278: “The WT or Mut PIP5Pase mRNAs exclusively expressed from tubulin loci are 1.6 and 1.3-fold the WT PIP5Pase mRNA levels expressed from endogenous alleles in the single marker 427 strain. The fold-changes were calculated from RNA-seq counts per million from this work (WT and Mut PIP5Pase, Data S1) and our previous RNA-seq from single marker 427 strain (24).”

7) The authors are recommended to provide more detailed EMSA conditions such as protein and substrate concentrations. Better quality EMSA gels are preferred.

All concentrations were already provided in the Methods section. See line 356, in topic Electrophoretic mobility shift assays: “100 nM of annealed DNA were mixed with 1 μg of recombinant protein…”. For microscale thermophoresis, also see lines 375-376 in topic Microscale thermophoresis binding kinetics: “1 μM rRAP1 was diluted in 16 two-fold serial dilutions in 250 mM HEPES pH 7.4, 25 mM MgCl2, 500 mM NaCl, and 0.25% (v/v) N P-40 and incubated with 20 nM telomeric or 70 bp repeats…”. Note that two different biochemical approaches, EMSA and microscale thermophoresis, were used to assess rRAP1-His binding to DNA. Both show agreeable results (Fig 3 and 5, and Fig S5. Microscale thermophoresis shows the binding kinetics, data available in Table 1). The EMSA images clearly show the binding of RAP1 to 70 bp or telomeric repeats but not to scramble telomeric repeat DNA.

Reviewer #2 (Recommendations For The Authors):

Major comments:

Figures

All figures should have their axes properly labeled and units should be indicated. For many of the ChIPseq datasets it is not clear whether the authors show a fold enrichment or RPM and whether they used all reads or only uniquely mapping reads. Especially the latter is a very important piece of information when analyzing expression sites and should always be reported. The authors write, that all RNA-seq and ChIP-seq experiments were performed in triplicate. What is shown in the figures, one of the replicates? Or the average?

ChIP-seq is shown as fold enrichment; we clarified this in the figures by including in the y-axis RAP1-HA ChIP/Input (log 2). We included in figure legends, see line 710: “Data show fold-change comparing ChIP vs Input.”. For quantitative graphs (Fig 2B, D, and E, and Fig 5F and G), data are shown as the mean of biological replicates. Graphs generated in the integrated genome viewer (IGV, qualitative graphs) is a representative data (Fig 2A, C, and F, and Fig 5D-E). All statistical analyses were calculated from the three biological replicates. Uniquely mapped reads were used. We also included ChIP-seq analysis with MAPQ ≥10 and 20 (90% and 99% probability of correct alignment, respectively) to distinguish RAP1 binding to ESs. Fig S4 shows the various mapping stringency and demonstrates the enrichment of RAP1-HA to silent vs active ES.

Figure 1 is very important for the main argument of the manuscript, but very difficult (impossible for me) to fully understand. It would be great if the author could make an effort to clarify the figure and improve the labels. Panel Fig 1E. Here it is impossible to read the names of the genes that are activated and therefore it is impossible to verify the statements made about the activation of VSGs and the switching.

We have edited Fig 1E to include the most abundant VSGs, which decreased the amount of information in the graph and increased the label font. We also re-labeled each VSG with chromosome or ES name and common VSG name when known (e.g., VSG2). We included Table S1 in the supplementary information with the data used to generate Fig 1E. In Table S1, the reader will be able to check the VSG gene IDs and evaluate the data in detail. We included in the legend, line 700: “See Table S1 for data and gene IDs of VSGs.”

Figure 1F: This panel is important and should be shown in more detail as it distinguishes VSG switching from a general VSG de-repression phenotype. VSG-seq is performed in a clonal manner here after PIP5Pase KD and re-expression. To show that proper switching has occurred place in the different clones, instead of a persistent VSG de-repression, the expression level of more VSGs should be shown (e.g. as in panel E) to show that there is really only one VSG detected per clone. For example, it is not clear what the authors 'called' the dominant VSG gene.

We showed in supplementary information Fig S1 B-C examples of reads mapping to the VSGs. Now we included a graph (Fig S1 D) that quantifies reads mapped to the VSG selected as expressed compared to other VSG genes considered not expressed). The data show an average of several clones analyzed. Other VSGs (not selected) are at the noise level (about 4 normalized counts) compared to >250 normalized counts to the selected as expressed VSGs.

As mentioned in the public comments, I don't see how the data from Fig 1E and 1F fit together. Based on Fig 1E VSG2 is the dominant VSG, based on Fig 1F VSG2 is almost never the dominant VSG, but the VSG from BES 12.

In Fig 1E, the VSG2 predominates in cells expressing WT PIP5Pase, however, in cells expressing Mut PIP5Pase, this is not the case anymore. Many other VSGs are detected, and other VSG mRNAs are more abundant than VSG2 (see color intensity in the heat map). The Mut cells may also have remaining VSG2 mRNAs (from before switching) rather than continuous VSG2 expression. This is the reason we performed the clonal analysis shown in Fig 1F, to be certain about the switching. While Fig 1F shows potential switchers in the population, Fig 1E confirms VSG switching in clones.

Many potential switchers were detected in the VSG-seq (Fig 1F, the whole cell population is over 107 parasites), but not all potential switchers were detected in the clonal analysis because we analyzed 212 clones total, a fraction of the over 107 cells analyzed by VSG-seq (Fig 1E). Also, it is possible that not all potential switchers are viable. A preference for switching to specific ESs has been observed in T. brucei (Morrison et al. 2005, Int J Parasitol; Cestari and Stuart, 2015, PNAS), which may explain several clones switching to BES12.

Note that in Fig 1F, tet + cells did not switch VSGs at all; all 118 clones expressed VSG2. We relabeled Fig 1F for clarity and included the VSG names. We added gene IDs in the Figure legends, see line 702 “ BES1_VSG2 (Tb427_000016000), BES12_VSG (Tb427_000008000)…”

Statements in Introduction / Discussion

The statement in lines 82/83 is very strong and gives the impression that the PIP5Pase-Rap1 circuit has been proven to regulate antigenic variation in the host. However, I don't think this is the case. The paper shows that the pathway can indeed turn expression sites on and off, but there is no evidence (yet) that this is what happens in the host and regulates antigenic variation during infection. The same goes for lines 214/215 in the discussion.

We agree with the reviewer, and we edited these statements. The statement lines 82-83: “The data provide a molecular mechanism…” to “The data indicates a molecular mechanism…” For lines 224225: “and provides a mechanism to control…” to “and indicates a mechanism to control…”. We also included in lines 261-262: “It is unknown if a signaling system regulates antigenic variation in vivo.” Also edited lines 262-263: “…the data indicate that trypanosomes may have evolved a sophisticated mechanism to regulate antigenic variation...”.

New vs old data

In general, for Figures 1 - 4, it was a bit difficult to understand which panels showed new findings, and which panels confirmed previous findings (see below for specific examples). In the text and in the figure design, the new results should be clearly highlighted. Authors: All data presented is new, detailed below.

Figure 1: A similar RNA-seq after PIP5Pase deletion was performed in citation 24. Perhaps the focus of this figure should be more on the (clone-specific) VSG-seq experiment after PIP5Pase re-introduction.

This is the first time we show RNA-seq of T. brucei expressing catalytic inactive PIP5Pase, which establishes that the regulation of VSG expression and switching, and repression of subtelomeric regions, is dependent on PIP5Pase enzyme catalysis, i.e., PI(3,4,5)P3 dephosphorylation. Hence, the relevance and difference of the RNA-seq here vs the previous RNA-seq of PIP5Pase knockdown.

Figure 2: A similar ChIP-seq of RAP1 was performed in citation 24, with and without PIP5Pase deletion. Could new findings be highlighted more clearly?

Our and others’ previous work showed ChIP-qPCR, which analyses specific loci. Here we performed ChIP-seq, which shows genome-wide binding sites of RAP1, and new findings are shown here, including binding sites in the BES, MESs, and other genome loci such as centromeres. We also identified DNA sequence bias defining RAP1 binding sites (Fig 2A). We also show by ChIP-seq how RAP1-binding to these loci changes upon expression of catalytic inactive PIP5Pase. To improve clarity in the manuscript, we edited lines 129-130: “We showed that RAP1 binds telomeric or 70 bp repeats (24), but it is unknown if it binds to other ES sequences or genomic loci.”

Figure 4: Binding of Rap1 to PI(3,4,5)P3, but not to other similar molecules, was previously shown in citation 24. Could new findings be highlighted more clearly?

We published in reference 24 (Cestari et al. Mol Cell Biol) that RAP1-HA can bind agarose beadsconjugated synthetic PI(3,4,5)P3. Here, we were able to measure T. brucei endogenous PI(3,4,5)P3 associated with RAP1-HA (Fig 4F). Moreover, we showed that the endogenous RAP1-HA and PI(3,4,5)P3 binding is about 100-fold higher when PIP5Pase is catalytic inactive than WT PIP5Pase. The data establish that in vivo endogenous PI(3,4,5)P3 binds to RAP1-HA and how the binding changes in cells expressing mutant PIP5Pase; this data is new and relevant to our conclusions. To clarify, we edited the manuscript in lines 180-182: “To determine if RAP1 binds to PI(3,4,5)P3 in vivo, we in-situ HA-tagged RAP1 in cells that express the WT or Mut PIP5Pase and analyzed endogenous PI(3,4,5)P3 levels associated with immunoprecipitated RAP1-HA”.

Sequencing.<br /> I really appreciate the amount of detail the authors provide in the methods section. The authors do an excellent job of describing how different experiments were performed. However, it would be important that the authors also provide the basic statistics on the sequencing data. How many sequencing reads were generated per run (each replicate of the ChIP-seq and RNA-seq assays)? How long were the reads? How many reads could be aligned?

The sequencing metrics for RNA-seq and ChIP-seq for all biological replicates were included in Table S3 (supplementary information). The details of the analysis and sequencing quality were described in the Methods section “Computational analysis of RNA-seq and ChIP-seq”. To be clearer about the analysis, we also included in Methods, lines 522-524: “Scripts used for ChIP-seq, RNA-seq, and VSG-seq analysis are available at https://github.com/cestari-lab/lab_scripts. A specific pipeline was developed for clonal VSG-seq analysis, available at https://github.com/cestari-lab/VSG-Bar-seq.”.

Minor comments:

Figure 1B: I would recommend highlighting the non-ES VSGs and housekeeping genes with two more colors in the volcano plot, to show that it is mostly the antigen repertoire that is deregulated, and not the Pol ll transcribed housekeeping genes. This is not entirely clear from the panel as it is right now.

The suggestion was incorporated in Fig 1B. We color-coded the figure to include BES VSGs, MES VSGs, ESAGs, subtelomeric genes, core genes (typically Pol II and Pol III transcribed genes), and Unitig genes, those genes not assembled in the 427-2018 reference genome.

Were the reads in Figure 2a filtered in the same way as those in Figure 2C? To support the statements, only unique reads should be used.

Yes, we also added Fig S4 to make more clear the comparison between read mapping to silent vs active ES.

It would be good if the authors could add a supplementary figure showing the RAP1 ChIP-seq (WT and cells lacking a functional PIP5Pase) for all silent expression sites.

We had RAP1 ChIP-seq from cells expressing WT PIP5Pase already. We have it modified to include data from the Mutant PIP5Pase. See Fig S3 and S5.

In Figure 5D, after depletion of PIP5Pase, RAP1 binding appears to decrease across ESAGs, but ESAG expression appears to increase. How can this be explained with the model of RAP1 repressing transcription?

We included in the Results, lines 208-212: “The increased level of VSG and ESAG mRNAs detected in cells expressing Mut PIP5Pase (Fig 5D) may reflect increased Pol I transcription. It is possible that the low levels of RAP1-HA at the 50 bp repeats affect Pol I accessibility to the BES promoter; alternatively, RAP1 association to telomeric or 70 bp repeats may affect chromatin compaction or folding impairing VSG and ESAG genes transcription.”.

Reviewer #3 (Recommendations For The Authors):

Line 114 - typo? Procyclic instead of procyclics:

Fixed, thanks.

Line 233 - the phrasing here is confusing, may want to replace "whose" with "which" (if I am interpreting correctly):

Thanks, no changes were needed. I have had the sentence reviewed by a Ph.D.-level scientific writer.

Methods - there is no description of VSG-seq analysis in the methods. Is it done the same way as the RNA-seq analysis? Is the code for analysis/generating figures available online?

The procedure is similar. We included an explanation in Methods, lines 503-504: “RNA-seq and VSG-seq (including clonal VSG-seq) mapped reads were quantified…”. Also, in lines 522-54: “Scripts used for ChIP-seq, RNA-seq, and VSG-seq analysis are available at https://github.com/cestari-lab/lab_scripts. A specific pipeline was developed for clonal VSG-seq analysis, available at https://github.com/cestarilab/VSG-Bar-seq.”.

Fig 1H - Is this from RNA-seq or VSG-seq analysis of procyclics?

The procyclic forms VSG expression analysis was done by real-time PCR. To clarify it, we included it in the legend “Expression analysis of ES VSG genes after knockdown of PIP5Pase in procyclic forms by real-time PCR”. We also amended the Methods, under the topic RNA-seq and real-time PCR, line 402-407: “For procyclic forms, total RNAs were extracted from 5.0x108 T. brucei CN PIP5Pase growing in Tet + (0.5 µg/mL, no knockdown) or Tet – (knockdown) at 5h, 11h, 24h, 48h, and 72h using TRIzol (Thermo Fisher Scientific) according to manufacturer's instructions. The isolated mRNA samples were used to synthesize cDNA using ProtoScript II Reverse Transcriptase (New England Biolabs) according to the manufacturer's instructions. Real-time PCRs were performed using VSG primers as previously described (23).”

Fig 2 A - Where it says "downstream VSG genes" I assume "downstream of VSG genes" is meant? the regions described in this figure might be more clearly laid out in the text or the legend

Fixed, thanks. We included in the text in Results, line 140: “… and Ts and G/Ts rich sequences downstream of VSG genes”.

Fig 2E - what does "Flanking VSGs" mean in this context?

We added to line 705, figure legends: “Flanking VSGs, DNA sequences upstream or downstream of VSG genes in MESs. “

Fig 2H - Why is the PIP5Pase Mutant excluded from the Chr_1 core visualization?

We did not notice it. We included it now; thanks.

Author Response

We thank the reviewers for their rigorous and insightful comments, as well as their positive feedback on the manuscript. We agree with reviewer #1 that substantial additional work is needed for a complete mechanistic understanding of how NI circuitry works and we expect that the transgenic tools we generated will be valuable for such experiments. It is noteworthy that specific driver lines do not currently exist for IPN neurons, which limited our ability to perform optogenetic experiments activating the IPN to NI pathway. Reviewer 2 asks for additional clarification and analysis on various experiments, which we intend to address in a revised manuscript. We concur with reviewer #3 that, with the existing data, it is not possible to conclude with certainty that the IPN projections from gsc2 and rln3 NI neurons are solely axonal in nature. Additional experiments with axon- and dendrite- specific markers will be used to resolve this point in future work.

Author Response

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

eLife assessment

This important study was designed to examine the bypass of Ras/Erk signaling defects that enable limited regeneration in a mouse model of hepatic regeneration. This hepatocyte proliferation is associated with the expression by groups of cells of mRNA-loaded CD133+ intracellular vesicles that mediate an intercellular signaling pathway that supports proliferation. These are new observations, supported by convincing data, that have broad significance to the fields of regeneration and cancer.

First of all, we greatly appreciate the very positive take of this work by eLife editors and also thank the two reviewers for their constructive comments. We have provided point-by-point responses as follows.

Reviewer #1 (Public Review):

This study was designed to examine the bypass of Ras/Erk signaling defects that enable limited regeneration in a mouse model of hepatic regeneration. The authors show that this hepatocyte proliferation is marked by expression of CD133 by groups of cells. The CD133 appears to be located on intracellular vesicles associated with microtubules. These vesicles are loaded with mRNA. The authors conclude that the CD133 vesicles mediate an intercellular signaling pathway that supports cell proliferation. These are new observations that have broad significance to the fields of regeneration and cancer.

The primary observation is that the limited regeneration observed in livers with Ras/Erk signaling defects is associated with CD133 expression by groups of cells. The functional significance of CD133 was tested using Prom1 KO mice - the data presented are convincing.

The major weakness of the study is that some molecular mechanistic details are unclear - this is, in part, due to the extensive new biology that is described. Nevertheless, the data used to support some key points in this study are unclear:

We fully agree that some details of the molecular mechanisms are yet to be elucidated for the CD133+ vesicles (intercellsomes, as we named). This is the first report of a new direct cell-cell communication mechanism provoked in stress response to proliferative signal deficit.

Remarkably, many questions remain open for the molecular mechanisms for formation and functions of relatively well-characterized structures such as exosomes/EVs, despite a huge body of literature since their discoveries.

a) What is the evidence that the observed CD133 groups of cells are not due to clonal growth. Is this conclusion based on the time course (the groups appear more rapidly than proliferation) or is this based on the GFP clonal analysis?

This is indeed a very critical point for this study. Our initial thought and efforts were indeed on finding evidence that supports clonal expansion of progenitor cells. However, the experiments showed that the CD133+ cells were negative for all other stem/progenitor cell markers and that they are mature hepatocytes. CD133 expression was upregulated dramatically in regenerating livers and disappeared upon completion of liver regeneration. Furthermore, suppression of Ras-Erk signaling by Shp2 and Mek inhibitors robustly induced CD133 expression in a variety of cancer cell lines in culture in vitro.

At 2 days after PHx, we already observed big colonies, which were unlikely derived from a single initiating cell (Figure 1). The GFP clonal analysis unambiguously demonstrated the heterogenous origin of the clustered cells (Figure 3). We detected mixed GFP-positive and -negative cells within each colony, without a single colony consisting entirely of GFP-positive cells. The original colony sizes were estimated to be 10 cells or more (Figures 3G and Figure 3–figure supplement 1B). Thus, both the sizes and compositions in the GFP clonal analyses support the assertion that CD133+ cell clusters originated from multiple mature hepatocytes.

b) What is the evidence that the CD133 vesicles mediate intercellular communication. This is an exciting hypothesis, but what is the evidence that this happens? Is this inferred from IEG mRNA diversity? or some other data. Is there direct evidence of transfer - for example, the does the GFP clonal analysis show transfer of GFP that is not mediated by clonal proliferation? Moreover, since the hepatocytes are isogenic, what distinguishes the donor and recipient cells? Increased clarity concerning what is hypothesis and what is directly supported by data - would improve the presentation of this study.

Per the reviewer’s advice, we have clarified these points in the revised version. Our proposal that CD133 vesicles mediate intercellular communication was supported by these experimental results.

A). Data in Fig. 5 suggest direct trafficking of the vesicles, as CD133 existed on the filaments that bridge the tightly contacting cells. This was confirmed by two different CD133 antibodies in mouse and human. Of note, CD133+ vesicles are negative for CD9, CD63 or CD81, markers for exosomes/EVs. We could only isolate CD133+ vesicles from cell lysates in vitro and mouse tissue lysates, but not from cell supernatants from which exosomes/EVs are isolated.

B). More direct evidence of the transfer was presented in Fig. 6H, showing Myc-tagged CD133 molecules transferred from one cell to another. In response to reviewers’ comments, we now conducted correlative light and electron microscopy to characterize the exchange event around the cell-cell border at EM level (new Figure6-figure supplement 2).

C). Further experimental evidence was provided in the single and double gene KO experiments in Fig. 8E-G, suggesting the functional significance of CD133 in intercellular communication.

D). In addition to the data above, the IEG mRNA diversity analyses based on scRNA-seq support the mRNA exchange model. The isogenic CD133+ SKO hepatocytes were found to lack different IEG transcripts randomly. This is why we propose a mutually sharing model, rather than a donor and recipient model. Importantly, the mRNA diversity (entropy) model also illustrates the association of CD133 and “stemness", as described in the discussion.

In sum, we believe that a most reasonable interpretation of the current data set is a model of direct cell-cell communication via CD133+ vesicles. We take the reviewer’s point and have made changes to the text to better distinguish conclusion and hypothesis, which will be validated in future studies.

Reviewer #2 (Public Review):

The manuscript by Kaneko set out to understand the mechanisms underlying cell proliferation in hepatocytes lacking Shp2 signals. To do this, the authors focused on CD133 as the proliferating clusters of cells in the Shp2 knockout (SKO) livers are CD133 expressing. After excluding the contribution of progenitors that are CD133 to this cell population, the authors focused on the intrinsic regulation of CD133 by Met/Shp2 regulated Ras/Erk pathway and showed upregulation of CD133 to be a compensatory signal to overcome loss of Ras/Erk signal and suggested Wnt10a in the regulation of CD133 signal. The study then focused on the observed filament localization of CD133 in the CD133+ cluster of cells. The study went on to identify the CD133+ vesicles that contain primarily mRNA vs. microRNA like other EVs. Specifically, the authors identified several mRNA species that encode IEGs, indicating a potential role for these CD133+ vesicles in cell proliferation signal transmission to neighboring cells via delivery of the IEG mRNAs as cargos. Finally, they showed that the induction of CD133 (and by derivative, the CD133+ vesicles) are necessary for maintaining cell proliferation in the cell cluster with high proliferation capacities in the SKO livers; and in intestinal crypt organoids treated with Met inhibitors to block Ras/ERk signal.

1) The identification of CD133+ vesicles is largely based on staining and costainings. Though the experiments are very well done with many controls and approaches, the authors may want to perform one or two key experiments with EM to definitively demonstrate the colocalization. For example, the mCherry experiment in Fig6H and the colocalization experiments for CD133 and HuR in Fig 7.

Many thanks for the suggestion. We now completed the two suggested key experiments with new results added to the revised manuscript. For the mCherry experiment, we conducted correlative light and electron microscopy to characterize the exchange event between cells that stably express CD133-GFP fusion protein and mCherry+ cells (new Figure 6-figure supplement 2). The CD133-GFP was clearly found in the mCherry+ cells around the border, demonstrating the intercellular traffic. For the colocalization of CD133 and HuR, we performed double immunogold staining on the isolated vesicles, with the new results presented in the revised Figure7-figure supplement 1D.

2) Since CD133+ marks the 50nM intracellsome defined by the authors, it is unclear what the CD133- vesicles used as controls are. Are they regular EVs that are larger in size? This needs better clarification as they are used as a control for many experiments such as Fig 7A.

Per the advice, we added more explanation to the revised text. We used regular EVs as the control, since they are the well-studied intercellular communication vesicles. Since the EVs are highly heterogenous, we did not choose to select a specific subpopulation of EVs. We used the well-established polymer-based precipitation method to isolate the EV fraction from cell culture supernatant for RNA-seq analysis. We did detect the enrichment of micro-RNAs in the isolated EVs, consistent with reports in the literature. Strikingly, the CD133 vesicles isolated from cell lysates showed a completely distinct RNA profile, relative to the EVs.

Author Response:

We thank the reviewers for their constructive comments. Below we include a point by point response.

Reviewer #1 (Public Review):

[...] Elaborate on the Methodology: Provide an in-depth explanation of the two active learning batch selection methods, including algorithmic details, implementation considerations, and any specific assumptions made. This will enable readers to better comprehend and evaluate the proposed techniques.

We thank the reviewer for this suggestion. Following this comments we will extend the text in Methods (in Section: Batch selection via determinant maxi- mization and Section: Approximation of the posterior distribution) and in Supporting Methods (Section: Toy example). We will also include the pseudo code for the Batch optimization method.

Clarify Evaluation Metrics: Clearly specify the evaluation metrics employed in the study to measure the performance of the active learning methods. Additionally, conduct statistical tests to establish the significance of the improvements observed over existing batch selection methods.

Following this comment we will add to Table 1 details about the way we computed the cutoff times for the different methods. We will also provide more details on the statistics we performed to determine the significance of these differences.

Enhance Reproducibility: To facilitate the reproducibility of the study, consider sharing the code, data, and resources necessary for readers to replicate the experiments. This will allow researchers in the field to validate and build upon your work more effectively.

This is something we already included with the original submission. The code is publicly available. In fact, we provide a phyton library, ALIEN (Active Learning in data Exploration) which is published on the Sanofi Github (https://github.com/Sanofi-Public/Alien). We also provide details on the public data used and expect to provide the internal data as well. We included a small paragraph on code and data availability.

Reviewer #2 (Public Review):

[...] I would expect to see a comparison regarding other regression metrics and considering the applicability domain of models which are two essential topics for the drug design modelers community.

We want to thank the reviewer for these comments. We will provide a detailed response to their specific comments when we resubmit.

Author Response

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

Reviewer #1 (Recommendations For The Authors):

• Introduction: "In plants, IP7 and IP8 decrease upon Pi starvation and mutants lacking either of the enzymes necessary for their synthesis induce the phosphate starvation response, leaving it open whether IP7 , IP8 , or both, control the Pi starvation program (10, 13, 14)" In plants, ITPK enzymes catalyze the formation of 5-InsP7 from InP6 https://pubmed.ncbi.nlm.nih.gov/34274522/ and VIH enzymes the formation of 1,5-InsP8 from 5-InsP7 https://pubmed.ncbi.nlm.nih.gov/25901085/ . Loss-of-function of both ITPK1 https://pubmed.ncbi.nlm.nih.gov/34274522/ and of VIH1/2 https://pubmed.ncbi.nlm.nih.gov/31436531/ https://pubmed.ncbi.nlm.nih.gov/31419530/ results in constitutive Pi starvation responses. vih1/vih2 mutants lack 1,5-InsP8 and hyperaccumulate 5-InsP7 https://pubmed.ncbi.nlm.nih.gov/31436531/ and act directly upstream of the transcription factors PHR1/PHL1 https://pubmed.ncbi.nlm.nih.gov/31436531/ https://pubmed.ncbi.nlm.nih.gov/33452263/ https://pubmed.ncbi.nlm.nih.gov/34857773/ , the case for 1,5-InsP8 is settled in plants. I would suggest revising this statement and citing all relevant literature.

We don't see the case for 1,5-IP8 as settled in plants, and none of the papers mentioned above draws this strong conclusion. This may be due to several limitations in the available data. The mentioned studies do not allow to differentiate the effects of 1-IP7 and 1,5-IP8 and, where binding or competition experiments have been performed, e.g. on the transcription factors, the differences in the Kd values for IP7 and IP8 were minor. Furthermore,1,5-IP8 levels and Pi starvation response do not always correlate. IPTK1 mutants, for example, show Pi overaccumulation, and low 5-IP7, but normal 1,5-IP8 (Riemer et al., 2021). Finally, plants are complex organisms with multiple tissue types that serve for accumulating, exporting, transporting or finally consuming Pi. Therefore, correlating inositol pyrophosphate levels from whole-plant extracts with a Pi starvation response is problematic, except if these data could both be obtained from the same cell types or at least tissues.

The comment of the reviewer made us recognize that the complex situation in plants deserves a more detailed coverage and we have therefore adjusted the introduction accordingly.

Results: "We determined the corresponding lysines in Pho81 (Fig. S3), created a point mutation in the genomic PHO81 locus that substitutes one of them, K154, by alanine, and investigated the impact on the PHO pathway."

In my opinion, it would be important to test here in a quantitative in vitro binding assay if (i) the SPX domain of Pho81 can bind PP-InsPs including 1,5-InsP8, (ii) if the dissociation constant is in agreement with the cellular levels of 1,5-InsP8 in yeast (compare Fig. 2) and (iii) if the K154A mutation blocks or reduces the binding of 1,5-InsP8. Without such experimentation, I find the statement "this result underlines the efficiency of the K154A substitution in preventing PP-IP binding to the Pho81 SPX domain." to be overly speculative, as no binding experiment has been conducted.

We agree with the comment of the reviewer concerning the overstatement in the phrase. It has been deleted.

As mentioned already in our previous work (Wild et al., 2016), Pho81SPX counts among the SPX domains that we could not express recombinantly. Likewise, full-length Pho81, which would be the relevant object for correlating in vitro binding studies with the cellular concentrations, has not been accessible. Expression in yeast did not provide sufficient material for ITC or other quantitative techniques. Therefore, we refrained from pursuing binding studies. Nevertheless, given the high conservation of the positively charged patch on SPX domains and the fact that, in every case where it has been tested so far, SPX domains showed inositol polyphosphate binding activity, we find it a conservative assumption that the Pho81SPX binds them as well. This is supported by the effects of the binding site mutant, which mimics the effect of ablating IP8 synthesis.

Results: "Inositol pyrophosphate binding to the SPX domain labilizes the Pho81-Pho80 interaction." Again, in the absence of any protein - protein interaction assay I find this statement not to be supported by the experiments outlined in the manuscript. The best way to address this point would be to perform either co-IP or in vitro pull-down experiments between Pho81-SPX and Pho81-85, in the pre- and absence of 1,5-InsP8 and/or using the Pho81 point-mutants described in the text.

Since Pho81 could not be produced recombinantly, neither by us nor by others who worked on this protein previously, quantitative in vitro binding assays are not accessible for now. A simple IP suffers from the problem that Pho81 interacts with Pho85-Pho80 not only through the SPX domain but also through the minimum domain. The latter interaction may be constitutive. Since the main point of the manuscript is not to dissect the exact mechanisms of Pho85-Pho80 regulations, but only to address the point why the postulated inactivation of this kinase by an 1-IP7/minimum domain complex makes no sense, we prefer not to show a profound (and more complex) analysis of how the different Pho81 domains contribute to binding.

To test the potential of the SPX domain for binding Pho85/Pho80 in vivo, we have created a GFP-fusion of the SPX domain of Pho81. This fusion protein localizes mainly to the cytosol when cells are on high-Pi. Upon Pi starvation, it concentrates in the nucleus. This concentration is not observed in pho80 mutant background (New Fig. S7).

In line with this, I would suggest to move the molecular modelling/docking studies from the discussion into the results section and to use these models to design some interface mutations that could be tested in coIP and/or pull-down assays. Alternatively, the authors may choose to omit the discussion section starting with: "Even though the minimum domain is unlikely to function as a receptor for PP-IPs this does not ... and ending with . In sum, multiple lines of evidence support the view that the SPX domain exerts dominant, 1,5-IP8 mediated control over Pho81 activity in response to Pi availability."

We have now moved the modelling data to the Results section. The structure prediction of the interface is experimentally validated. Data on the effect of interface substitutions are already published, although these substitutions had not been recognized as affecting a common interface at the time. Substituting the interface residues either on the side of Pho80 or of Pho81 constitutively activates Pho85-Pho80 kinase and destabilizes its interaction with Pho81. This was shown by Co-IP experiments from cell extracts by Huang et al. We mention the respective substitutions in the manuscript and cite the paper in which their effect on PHO pathway activation had been described.

Reviewer #2 (Recommendations For The Authors):

Some points need additional attention by the authors:

• In general, it would be helpful to introduce abbreviations more thoroughly (certain enzyme names, PA, MD, ...)

We paid more attention to this.

• Also in general, the authors may want to think about the nomenclature of inositol pyrophosphates. Given the expansion of PP-IPs that are being detected in different organisms these days it may be a good time to convert to a more precise nomenclature, i.e. 5PP-IP5 instead of 5-IP7; and 1,5(PP)2-IP4, instead of 1,5-IP8. The latter could just be stated once, and then be abbreviated as IP8.

To our understanding the field has not yet come up with a unified nomenclature. Therefore, we prefer to stick with the more practical nomenclature that we have chosen, which also corresponds to what is commonly used in presentations and discussions among colleagues. We have now introduced a sentence making the link to the nomenclature that the reviewer has proposed.

• p. 1, Abstract: "negative bioenergetic impacts" - the phrasing seems really vague

Agreed, but we find it difficult to be more explicit and precise in the abstract while remaining concise and not distracting from the main message. This aspect is better explained in the introduction.

• p. 3, Significance statement: "... unified model across all eukaryotic kingdoms" While the intended meaning of this wording is better explained in the text later, the phrasing here suggests a more all-encompassing study at hand, instead of a conclusion that fits more closely with established reports from other organisms. Please rephrase.

We have adapted the phrase to avoid this impression.

• p. 4: "IPTKs" - are the ITPKs meant here?

Yes, that was a typo.

• p. 7, the introduction ends abruptly and could use a concluding sentence.

Done

• p.7, "enzymes diphosphorylation either the..."; I understand what the authors are trying to say with diphosphorylating, but the enzymes are phosphorylating a phosphorylated substrate.

Yes. We changed the phrase to "....adding phosphate groups at the 1- or 5-positions....".

• p. 7, subtitle "...concentrations and kinetics of..."; kinetics of what? Synthesis/turnover?

We corrected this subtitle

• p. 8, with regards to the recovery experiment: Was this recovery determined elsewhere (please cite)? Otherwise it would be beneficial to include an extra figure to illustrate these recoveries in the supplementary information. And do the authors suspect some hydrolysis of IP8 given the lower recovery?

We have now added the experiment testing recovery of IPPs as the new Fig. S1.

• p. 9: It is appreciated that the authors point out the concentration of IP6 in S. cerevisiae. I found that concentration rather low, and the authors could highlight this a bit more, given their ability to carry our absolute quantification.

This was a leftover from a previous version of the paper. Since the paper does not treat IP6 or lower inositol polyphosphates, we have deleted this phrase.

• p. 9, Fig 2: The exponential decay of 5-IP7 is very nicely shown in Figure 2c. But one of the most important discussion points is IP8 being the key controller of the PHO pathway - it would therefore be beneficial for the argument to also show the same kind of graph for IP8 and if possible, fit a function to the data points to better quantify and compare the decay processes (e.g. via "half-life time" of PP-IPs during starvation, in addition to the suggested "critical concentration" which was only discussed for 5-IP7 thus far).

Kinetic resolution is an issue here. The approach shown in Figs. 2 and 5 is not apt to determine a critical concentration of IP8 because the decline upon transfer to starvation conditions is too fast and difficult to relate to the equally rapid induction of the PHO pathway. We shall address this point in a more appropriate setup in a future study.

• p.9, Fig 2a: Where does the 5-IP7 come from in the kcs1Δ strain? In the text the authors state that 5-IP7 in kcs1Δ was not detected, but the figure suggests otherwise. Please explain.

Currently, we do not know where these residual signals stem from. One possibility is that they represent other isomers that exist in minor concentrations and that are not resolved from 5-IP7 in CE. We added a sentence to the figure legend to indicate this.

• p. 10: "IP8 was undetectable in kcs1Δ and decreased by 75% in vip1Δ. kcs1Δ mutants also showed a 2 to 3-fold decrease in 1-IP7, suggesting that the synthesisof 1-IP7 depends on 5-IP7. This might be explained by assuming that a significant source of 1-IP7 is synthesis of 1,5-IP8 through successive action of Kcs1 and Vip1, followed by dephosphorylation to 1-IP7." - Please specify this statement. Do the authors mean that 1,5-IP8 is only produced transiently below the detection capabilities of the method but that there still is a (reduced) flux from 5-IP7 to 1,5-IP8 to 1-IP7? Otherwise it would seem paradoxical to have a dependency on a non-existing metabolite in that cell line.

This was not clearly expressed. The revised version now says: " ... a 2 to 3-fold decrease in 1-IP7, suggesting that the synthesis of 1-IP7 depends on 5-IP7. This might be explained by assuming that, in the wildtype, most 1-IP7 stems from the conversion of 5-IP7 to 1,5-IP8, followed by dephosphorylation of 1,5-IP8 to 1-IP7.". We hope that this clarifies the matter.

• p. 10: "pulse-labeling approaches are not available for PP-IPs." While this statement is correct, a recent paper co-authored by Qui and Jessen showed nice pulse-labeling data for the lower Ips and could be cited here (PMID: 36589890)

Yes, indeed, we should have been more precise here. What we wanted to express was that rapid pulse-labeling methods for following phosphate group turnover were lacking, with a temporal resolution of minutes rather than hours. Existing pulse labeling approaches, including the study mentioned by the reviewer, do not provide that. We have changed the phrase accordingly.

• p. 10: continuation of caption of Fig 2: "were extracted [and] analyzed"

Corrected. Thank you.

• p. 12: How is 1-IP7 made in the vip1 kcs1 double mutant?

As explained above, we suspect that these may be side products of IPMKs, which accumulate in the absence of vip1 phosphatase.

• p. 13, caption to Figure 3: "XXX cells were analyzed" please replace the place holder XXX.

Done. Thank you.

• p. 13, Fig 3B, C, D and p. 50, Fig. S4: On screen the contrast between the different shades of grey of the bars are just visible enough, but not on paper, I suggest using a higher contrast/ different colouring scheme.

We enhanced the contrast.

• p. 24, 25, Fig 7.: I could not really appreciate the AlphaFold part, and found it unnecessary. No docking or molecular dynamics simulations were carried out here, and it was not clear to me what information should be gleaned from this part.

Following this comment, we have modified the respective part of the text. This part refers to a publication from the O'Shea lab (Nat. Chem Biol. 4,25) proposing the model that 1-IP7 and the Pho81 minimum domain bind competitively to the active site of Pho85 to inhibit its kinase activity. Modeling of complexes between Pho81, Pho80 and Pho85, which we present in the manuscript, rather suggests binding of the minimum domain to a groove in Pho80. This is important because it provides a viable alternative model for the action of the minimum domain. It suggests the minimum domain as a constitutive linker that attaches Pho80 to Pho85. Importantly, this model accounts perfectly for the results of previous random mutagenesis studies on Pho80 and on the minimum domain, which had independently identified both the Pho80 groove and the minimum domain residues that bind it in the prediction as critical residues for inhibition of Pho85, and for integrity of the Pho85/Pho80/Pho81 complex. We find this alternative explanation for Pho85-Pho80 regulation by Pho81, which we can derive by combining the predictions with already published experimental data, an important element to re-evaluate the relevance of 1-IP7 in PHO pathway regulation and resolve one of the existing discrepancies.

• p. 28: No experiments were carried out with plants or mammals. The relevance for plants or mammalian systems therefore seems to be overstated at this point in time.

We are not quite sure how to interpret this remark. We do not claim that our data support a role for IP8 in mammals and plants. But we refer to and cite studies providing the strongest evidence in favor of it in these systems. The relevance of our current study relies in refuting seemingly strong evidence from yeast, which had been diametrically opposed to the data obtained in plants and mammals. The revision of the situation in yeast now paves the way to drawing a coherent concept for fungi, plants and mammals. We feel that this is important and should be underlined.

• p. 31: "300 mL of 3% ammonium" - 300 µL?

Yes. Thank you.

• p. 45, CE-ESI-MS parameters: "1IP8"

Corrected.

• p. 47: Figure S1: Please include more experimental details in the caption and/or methods section. Was a similar analysis software used as e.g. Figure S2 (NIS Elements Software)? Please also include all the analysis software in the Methods section under "fluorescence microscopy". Unless these additional experimental details already clarify the following point: Can the authors briefly comment on why the morphological determination in S1 requires trypan blue staining while in later experiments the yeast cells are readily recognized by the software in "simple" brightfield images?

Trypan blue staining is not strictly required for this. It is just a simple method to fluorescently stain the cell wall. There are many other ways of delineating the cells. It could also have been done in a brightfield image.

We updated the figure legend to better describe how these measurements were done and deposited the script and training file on figshare.

• p. 48: "can be downloaded from **" please insert the link once the script is available online.

It has been deposited at Figshare under DOI 10.6084/m9.figshare.c.6700281

Reviewer #3 (Recommendations For The Authors):

1) Italicize the scientific names of the organisms; this was inconsistent throughout the manuscript. Also, gene names should be italicized; this was also inconsistent (e.g., p.12 "... did not induce the PHO84 and PHO5 [sic] promoters...).

Done

2) Summary of the Figure 2A data in the text (p.9) probably has swapped the determined concentrations for 1-IP7 and IP8 (0.3 µM or 0.5 µM) as compared with the data figure.

Yes, indeed. We have corrected this.

3) Figure 2A: which of the mutant PP-IP levels are significantly different from the WT control?

We have now added asterisks to indicate the significance for every mutant.

4) In the discussion on the data (Fig. 2A), I was tripped up by the verb tense in this phrase "5-IP7 has not been detected in the kcs1Δ mutant and 1-IP7 has been strongly reduced..."; I think you want to use the past tense "was" in both cases [as is used in the next sentence]. It made me wonder if there was a difference in the detection of 5-IP7 and IP8 in the kcs1Δ mutant, you could detect 5-IP7 but not IP8; if so, where did the 5-IP7 come from?

We have corrected the tense. Thank you for highlighting this. For the residual inositol pyrophosphate signal in kcs1Δ. We do not know its origin. One possibility, which we now mention in the text, is that it stems from IPMK side activity. It should be underlined that all signals disappear upon PI starvation.

Figure 2C, include the data points that the lines are built from (suggestion).

We refrained from that for the line graphs. For reasons of consistency, we should do this for every line graph. If we did that, Fig. 4B would become quite hard to read.

6) Figure 3B-D, please check that the stipples or hatches are in the figure - the printed copy lacked them although I could see them in the electronic version; this was also true for Figures 5 and 6 (I do not know if it is a printer issue, but other hatches were visible: e.g., not seen in S4 but seen in S5).

They are visible in our copies, also after printing. They may have been lost during file conversion at the journal.

7) The text description of the Pho4-yEGFP, Pho5-yEGFP and Pho84-yEGFP says that the kcs1Δ mutant "showed Pho4-yEGFP constitutively in the nucleus already ... and PHO5 and PHO84 were activated". However, the data is more complex than that: whereas the localization of Pho4-yEGFP is constitutively nuclear, there is a higher basal (repressed) expression of both Pho5 and Pho84 as well as increased expression of both proteins under -Pi conditions. What accounts for the increased expression when Pho4 is already nuclear? This is also seen in the vip1Δ kcs1Δ mutant.

We agree with the reviewer, but we cannot explain this effect with certainty. One possibility could be a wider dysregulation of Pi metabolism in kcs1 mutants. To name a few possibilities: Wildtype cells have polyphosphate reserves that are gradually mobilized during the first hours of P-starvation. kcs1 mutants don't have those and might fall into a "deeper" state of starvation faster. It should be kept in mind that the starvation response is also regulated at the level of chromatin structure, and by antisense transcripts. The influence of kcs1 on these processes is unclear.

8) Figure 9 legend: please add a definition of the MP region (in red) and include it more explicitly in the described model.

We now mention the relevant region also in the legend and have labeled the relevant regions in the images (Huang et al., 2001).

9) Figure S2 legend: information is missing (downloading link).

It has been deposited at Figshare under DOI 10.6084/m9.figshare.c.6700281

10) Figure S4 and S5, missing statistics.

They have been added to the new Fig. S6, which interprets differences between strains and conditions. Fig. S4 (now S3) shows timecourses of IPPs down to zero. Adding statistics for all pairwise differences between the timepoints would be almost an overkill.

Author Response

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

eLife assessment

It is very important to find practical and efficient means in order to increase agricultural productivity. Drawing on data from variable field environments, this study provides a useful theoretical framework to identify new factors that could increase agricultural production. There is solid evidence to support the authors' claims, though following the fate of candidate species after introduction into rice fields would have strengthened the study. Plant biologists and ecologists working in nature and fields will find the work interesting.

Thank you so much for your careful evaluation of our manuscript. We are very pleased to hear that you found our framework useful. We have revised our manuscript according to the "Recommendations for the Authors" to improve our manuscript.

Public Review

Reviewer #1 (Public Review):

This manuscript describes the identification of influential organisms on rice growth and an attempt of validation. The analysis of eDNA on rice pot and mimic field provides rice growth promoting organisms. This approach is novel for plant ecology field. However current results did not fully support whether eDNA analysis-based detection of influencing organism.

Thank you so much for evaluating our manuscript. We have carefully read and responded to your comments. We hope our responses resolve your concerns on our study.

The strength of this manuscript is to attempt application of eDNA analysis-based plant growth differentiation. The weakness is too preliminary data and experimental set-up to make any conclusion. The trials of authors experiments are ideal. However, the process of data analysis did not meet certain levels. For example, eDNA analysis of different time points on rice growth stages resulted in two influential organisms for rice growth. Then they cultivate two species and applied rice seedlings. Without understanding of fitness and robustness, how we can know the effect of the two species on rice growth.

We agree with your comments that we did not have the fitness data of the two species and/or rice seedlings. Thus, it is still difficult to obtain deep understanding of the mechanisms of our findings that the species introduced in the system would influence rice growth. Nonetheless, our study demonstrated the effectiveness of our research framework as we found evidence that the species that were discovered by the eDNA monitoring and time series analysis indeed cause changes in the system. We believe that the first step is to show that the framework is workable and that detailed understanding of the mechanisms or genetic pathway was not a focus of our study. To avoid misunderstanding, we have added several explanations regarding this point in L426–431 and L447. For example, in L426, we have added the following statement: "... the detailed dynamics of the two introduced species was unclear (i.e., the fate of the introduced species). This is particularly important for understanding how the introduced organisms affected rice performance...".

The authors did not check the fate of two species after introducing into rice. If this is true, it is difficult to link between the rice gene expression after treatments and the effectiveness of two species. I think the validation experiment in 2019 needs to be re-conducted.

We did not check the fate of the two species (except measuring the eDNA concentrations of the species), and it is true that we cannot show evidence of "how" these two species influence the rice gene expression. Understanding molecular mechanisms of the phenomenon that we found is important (especially from the viewpoint of molecular biology), but our primary objective was to demonstrate that our "eDNA x time series analysis" framework is feasible for detecting previously overlooked but influential organisms. To this end, we believe that we achieved our objective and repeating the validation experiment should be for a different purpose (i.e., for understanding molecular mechanisms). We have clarified these points in L426–431 and L447 as explained above.

Reviewer #2 (Public Review):

The manuscript "Detecting and validating influential organisms for rice growth: An ecological network approach" explores the influence of biotic and abiotic entities that are often neglected on rice growth. The study has a straightforward experimental design, and well thought hypothesis for explorations. Monitoring data is collected to infer relationships between species and the environment empirically. It is analyzed with an up-to-date statistical method. This allowed the manuscript to hypothesize and test the effects most influential entities in a controlled experiment.

Thank you so much for your careful evaluations. We are pleased to see that you evaluated our manuscript positively. We have further revised our manuscript according to your comments and hope the revision has resolved your concerns.

The manuscript is interesting and sets up a nice framework for future studies. In general, the manuscript can be improved significantly, when this workflow is smoothly connected and communicated how they follow each other more than the sequence and dates provided. It is valuable philosophical thinking, and the research community can benefit from this framework.

Thank you for your suggestions. In order to improve the logic flow and readability of our manuscript, we have revised the descriptions of workflow and clarified how the experimental and statistical steps were connected to each other. To do so, we have added brief explanations about what/how we did at the first sentence of Results subsections (some of these explanations were only in Materials and Methods in the original manuscript). Also, we have moved all of the Supplementary Materials and Methods to the main text. We have thoroughly revised the manuscript, and we hope that all the parts of our manuscript have been connected more smoothly than in the original manuscript.

I understand the length and format of the manuscript make it difficult to add more details, but I am sure it can refer to/clear some concepts/methods that might be new for the audience. How/why variables are selected as important parts of the system, a tiny bit of information about the nonlinear time series analysis in the early manuscript, and the biological reasoning behind these statistically driven decisions are some examples.

We have explained how/why variables are selected (in L125), added more information about the nonlinear time series analysis (in L129 and L175) , and added the biological reasoning behind the statistical decisions (L195).

Reviewer #3 (Public Review):

Most farming is done by subtracting or adding what people want based in nature. However, in nature, crops interact with various objects, and mostly we are unaware of their effects. In order to increase agricultural productivity, finding useful objects is very important. However, in an uncontrolled environment, it coexists with so many biological objects that it is very inefficient to verify them all experimentally. It is therefore necessary to develop an effective screening method to identify external environmental factors that can increase crop productivity. This study identified factors presumed to be important to crop growth based on metabarcoding analysis, field sampling, and non-linear analysis/information theory, and conducted a mesocosm experiment to verify them experimentally. In conclusion, the object proposed by the author did not increase rice yield, but rather rice growth rate.

Thank you so much for your evaluation of our manuscript. We have revised our manuscript based on your comments, and hope it has been improved compared with the original version.

Strength

In actual field data, since many variables are involved in a specific phenomenon, it is necessary to effectively eliminate false positives. Based on the metabarcoding technique, various variables that may affect rice growth were quantitatively measured, although not perfectly, and the causal relationship between these variables and rice growth was analyzed by using information transfer analysis. Using this method, two new players capable of manipulating rice growth were verified, despite their unknown functions until now. I found this process to be very logical, and I think it will be valuable in subsequent ecological studies.

We are very pleased to see that you found our framework is very logical and potentially beneficial for future ecological studies.

Weaknesses

CK treatment's effectiveness remains questionable. Rice's growth was clearly altered by CK treatment. The validation of the CK treatment itself is not clear compared to the GN treatment, and the transcriptome data analysis results do not show that DEG is not present. The possibility of a side effect caused by a variable that the author cannot control remains a possibility in this case. Even though this part is mentioned in Discussion, it is necessary to discuss various possibilities in more detail.

We agree that the effectiveness of the CK treatment was questionable. We have added some more discussion about this point in L376: "The unclear effects of the CK treatment relative to those of the GN treatment could be due to the relatively unstable removal method (i.e., C. kiiensis larvae were manually removed by a hand net) or incomplete removal of the larvae (some larvae might have remained after the removal treatment)."

Reviewer #1 (Recommendations For The Authors):

Comment #1-1 This manuscript describes identification of influential organisms on rice growth and an attempt of validation. The analysis of eDNA on rice pot and mimic field provides rice growth promoting organisms. This approach is novel for plant ecology field. However current results did not fully support whether eDNA analysis-based detection of influencing organism.

Thank you for your careful evaluations of our manuscript. We are pleased to see you found that our approach is novel. We have revised our manuscript in accordance with your comments, and we hope that the revision and responses resolved your concerns.

Comment #1-2 1. Experimental setting: Authors made up small scale pot system in 2017 and then expanded manipulative experiment. I do not understand how two influencing organism sequences were identified from the single treatment depending on different time points. How they can be convince the two organisms affect the rice growth rather than other biological and environmental factors.

In 2017, we performed an intensive monitoring of the experimental rice plots and obtained large time series data (122-day consecutive monitoring x 5 plots = 610 data points). The time series data were analyzed using the information-theoretic causal analysis. The analysis is critically different from correlational analyses and designed to identify causal relationships among variables. Although we understand that field manipulation experiments are a common and straightforward approach to identify causal relationships among organisms, we chose the "fieldmonitoring + time-series-based causal analysis" approach. This is because, as explained in the main text, there are numerous factors that could influence rice performance, and it is practically impossible to perform manipulative experiments for all the potential factors that could influence rice growth. On the other hand, our "field-monitoring + timeseries-based causal analysis" approach has a potential to identify multiple factors under field conditions, even by the single experimental treatment.

Nonetheless, we must admit that our time-series-based approach still has a chance to misidentify causal factors. Our framework relies on statistics, so the chance of false-positive detection of causality cannot be zero. This was exactly the reason why we performed the "validation" experiment in 2019. To complement the statistical results of the 2017 experiments, we performed another experiment in 2019.

Comment #1-3 2. eDNA technology: The eDNA analysis based on four universal primers 16s rRNA, 18s rRNA, ITS, and COI regions must not be enough to identify specific species. The resolution of species classification may not meet to confirm exact species. Thus, the accuracy of two species that they selected for further experiment is difficult to be confirmed. Authors also referred to "putative Globisporangium".

Your point is correct. The DNA barcoding regions we selected are short and it is often difficult to identify species. However, this limitation could not have been overcome even if we had chosen a different genetic marker. The long-read sequencing technology could partially solve the issue, but the number of sequence reads generated by the long-read technique is less than that by the short-read sequencing technology, and comprehensive detection of all species in an ecological community was still challenging. Our approach struck a balance among the identification resolution, comprehensiveness of the analysis, and sequencing costs. In addition, even though we could not identify most ASVs at the species level, some ASVs could be identified at the species level (52 ASVs among the 718 ASVs which had causal influences on rice growth), and we selected the two species (G. nunn and C. kiiensis) from the 52 species.

Further, the taxa assign algorithm we used here (i.e., Claident; Tanabe & Toju 2012 PLoS ONE 10.1371/journal.pone.0076910) adopted conservative criteria for species identification and has a low falsepositive probability.

More importantly, this is also the reason why we performed the "validation" experiment in 2019. The species identified in the 2017 experiment are still "potential" organisms that influence rice growth (i.e., the hypothesis-generating phase), and we tested the hypothesis in 2019.

Nonetheless, we must admit that clear description of potential limitations is important. Thus, we have discussed this in L418: "As for the second issue, short-read sequencing has dominated current eDNA studies, but it is often not sufficient for lower-level taxonomic identification. Using long-read sequencing techniques (e.g., Oxford Nanopore MinION) for eDNA studies is a promising approach to overcome the second issue".

Comment #1-4 3. Biological relevance 1: Authors identify two organisms as influencing organism for rice growth. As conducting the first experiment in 2017, the 2019 experiment was different from natural condition. The two experiments in 2017 and 2019 were conducted under different conditions. How do they compare the experiments? At least, the eDNA analyses in 2017 and 2019 should be very similar. I cannot find such data.

The experimental conditions were different between 2017 and 2019 because they were conducted in different years. Theoretically, it is ideal if the experimental conditions in 2019 are covered by the range of experimental conditions in 2017 (e.g,. rice variety, air temperature, rainfall, and solar radiation). If this condition were satisfied, the attractor (i.e., rice growth trajectory delineated in the state space) in 2019 would be within that in 2017, and our model prediction in 2017 would be used to predict dynamics in 2019 accurately. To fulfill the conditions, we made as much effort as possible: we used the same rice variety and soils in 2019 as those used in 2017, and started our experiment at the same timing in 2019 as that in 2017.

Although natural ecological dynamics cannot be precisely controlled, our monitoring revealed that the ecological dynamics in 2019 was qualitatively similar to that in 2017. To demonstrate that the experimental conditions and eDNA community data were similar between the two experiments, we have presented the climate and eDNA data in an inset figure in Figure 3a, Figure 1–figure supplement 2, Figure 3–figure supplement 2. We must admit that these dynamics are not identical, but we hope that this resolves your concern.

Comment #1-5 4. Lack of detail description: In the Materials and Methods, there are many parts which lack on detail description. For instance, authors must described the two species cultivation, application concentrations, and application methods.

We have moved Supplementary Materials and Methods to the main text and added more detailed descriptions in Materials and Methods. Also, to improve the logical flow and readability of our manuscript, we have added brief explanations about what/how we did at the first sentence of Results subsections (some of these explanations were only in Materials and Methods in the original manuscript). We have added the reference for how to cultivate G. nunn in L608 (Kobayashi et al., 2010; Tojo et al., 1993) (C. kiiensis was not cultivated but removed from the system as in Materials and Methods), and application concentrations. Application methods were described in Materials and Methods, the section Field manipulation experiments in 2019 in L596.

Comment #1-6 5. Validation: Application of one species clearly resulted to promote rice growth. They must include appropriate control treatment. If they pick same genus but different species that identified no specific effect on rice growth through eDNA analysis, no effect on growth can be provided. Generally application of large population of certain non-harmful organism confer plant growth promotion. It is not surprising result. Authors need to prove effectiveness of eDNA analysis. In addition, the field experiments required at least two years of consistent data for publication because environmental factors are so dynamic.

Thank you for pointing this out. We agree with your comment that species that were predicted to have no effect should not promote rice growth in a validation experiment. It was also one of our inititial experimental plans to include such species in our manipulation experiment in 2019, but we could not include them because of the limitation of time, labor, and money. More extensive validation of the statistical results of the 2017 data, including multi-year experiments, would further validate the effectiveness of our approach, which should be done as future studies. To clarify this point, we have added statements in the paragraph starting at L396.

Comment #1-7 In conclusion, I suggest that authors need more large data analysis and validate with more accurate and meaningful protocol.

As we explained in the revised manuscript and the Response to Comments #1-2 to #1-7, our study demonstrated a novel research framework to detect previously overlooked influential organisms under field conditions. We agree that larger data analysis would be ideal to further validate our approach, but whether and how to collect larger data is constrained by time, money, and labor. We believe that our study was designed carefully and could provide meaningful avenues for developing an ecological-network based, novel, and environment-friendly agriculture solutions.

Reviewer #2 (Recommendations For The Authors):

Comment #2-1 Lines 97-110: This is so cool. Modeling with empirical data is very powerful. But a rice field is an open system consisting of metacommunity dynamics. Maybe a tiny bit of biological and biogeochemical background here would be good.

Thank you for your comments. We have added a few examples of how and in which systems these methods were used to evaluate community dynamics and detect biological interactions in L109-L118.

Comment #2-2 Lines 111-126: I like the summary of the study here. I think the influential species concept can be a little more elevated. Paine's famous keystone species work has been cited but a couple more pieces of literature can help to enhance the ecological importance of this work.

We have explained the work by Paine (1966) a bit more and added one more paper that showed the effect of multiple predator species on the system dynamics at L88. We have also added a relevant sentence at L137 to emphasize the ecological/agricultural significance of our work.

Comment #2-3 Experimental design/Figure 1:

Is there any rationale behind choosing red individuals to measure the growth?

Is there any competition between the individuals in the pots?

Figure 1e: It is nice to show the ASVs in time. I wonder how the plot would look like when normalized by biomass/DNA content/coverage/rarefaction because of the seasonality.

As for the first question, we chose the four individuals to minimize the edge effects (i.e., effects of microclimates and neighboring rice would be different between the four rice individuals and those planted in the edge regions). We have mentioned this in the legend of Figure 1.

As for the second question, there might be competition among the individuals in the pot. However, we did not measure the effect of competition (e.g., by comparing the growth with/without other rice individuals).

As for the third question, we published detailed dynamics of ecological community in the Supplementary Figures in Ushio (2022) Proceedings B https://doi.org/10.6084/m9.figshare.c.5842766.v1. In addition, we have uploaded a video showing the temporal dynamics of some top (= most abundant) ASVs in https://doi.org/10.6084/m9.figshare.23514150.v2.

We have mentioned the supporting information in L153.

Comment #2-4 Line 146-147: Is this damage influence the inferences? Maybe it is better to justify.

While we occasionally observed physical damages, it is unlikely that they affected our causal inference because the changes in the rice heights due to the damages were smaller and less frequent than those due to growth. We have noted this at L151.

Comment #2-5 Line 161-162: Maybe refer readers to the methods section where you explain UIC analysis. It'd be easier to interpret the figures.

Mentioned.

Comment #2-6 Line 175-176: I believe very brief information in the intro about the organisms might help explain the hypothesis and interpret the results better.

We have included brief information of the two species at L197.

Comment #2-7 Figure 2: Species interaction strength: Are these proxies to the Jacobians? Is there a threshold for the influence we can consider strong/weak? For example, influential species compared to diagonal elements of the Jacobians (intraspecies interactions) could be shown as a mean vertical line in Figure 2b.

"Influences to rice growth" in Figure 2b is transfer entropy (TE) from a target ASV to rice growth. They are not proxies of the Jacobians, but they might positively correlate with the absolute value of the Jacobians. We have clarified this point in the legend (L953). More direct estimations of the Jacobian can be done using the MDR S-map method (Chang et al. 2021 DOI:10.1111/ele.13897), but we did not perform the MDR S-map in the present manuscript (see Ushio et al. 2023 https://doi.org/10.7554/eLife.85795 for the application of the MDR S-map). As for TE, there is no clear threshold to distinguish strong/weak interactions.

Comment #2-8 Figure 2: Looking at panels c and d, it looks like there is a negative frequency selection between two influential species. Is it a reasonable observation?

This is an interesting point. In this manuscript, we have not carefully examined the interspecific relationship between these two particular species. However, the interspecific interactions were examined in detail and reported in Ushio (2022) Proceedings of the Royal Society B DOI:10.1098/rspb.2021.2690). We re-checked the result in Ushio (2022); although there is a negative correlation between them, we did not find any (statistical) causal relationship between them.

Comment #2-9 Line 209: What is t-SNE analysis? Because of the manuscript's format, maybe methods should be shortly referred to in the relevant section or explained in brackets.

We have spelled out t-SNE.

Comment #2-10 Line 212-214: Maybe briefly explain what the hypotheses are for the alternative analysis, and what is the contribution of the results to the study.

We have added a brief explanation at L241: "Alternative statistical modeling that included the treatments (the control versus GN or CK treatments) and manipulation timing (i.e., before or after the manipulation), which simultaneously took the temporal changes of all the treatments into account, also showed qualitatively similar results (Supplementary file 4), further supporting the results."

Comment #2-11 Figure 3b/c: Maybe species names as panel titles could be helpful. d: Treatment names with initials in the legend could be also helpful to read the plots.

We have added species name as panel titles of Figure 3b,c. Treatment names were included in the legend of Figure 3.

Comment #2-12 Line 233: Maybe mention why the manuscript uses the word "clear".

We have mentioned this in L185.

Comment #2-13 Line 234-236: I think that these alternative tests should be explained somewhere.

We have revised the sentence so that it includes some explanations (L241). Also, we have referred to Materials and Methods.

Comment #2-14 Figure 4: The title says ecological community compositions, and panels show the growth rates and cumulative growth.

Thank you for pointing this out. This was a typo and we have corrected it.

Comment #2-15 Lines 246-269: Can these expression patterns be transient and relevant to the time point that the sample is taken?

Yes, these expression patterns were transient. We collected rice leaf samples for RNA-seq 1 day before the first manipulation and 1, 14, and 38 days after the third manipulation (see Supplementary file 3 for the sampling design). When we merged the pot locations, we observed no difference in the gene expression for samples 1 day before the first manipulation and 14 and 38 days after the third manipulation (except for two genes in samples 38 days after the manipulation), and thus, we consider the DEGs that appeared only in the short period after the manipulation. We have mentioned this in L278 and L383: "We found almost no DEGs for leaf samples taken one day before and 14 and 38 days after the third manipulation (the leaf sampling event 1, 3, and 4), suggesting that the influences of the treatments on the gene expression patterns were transient." (L278) and "These changes were observed relatively quickly and transient." (L383)

Comment #2-16 I wonder if a conceptual framework figure would help to generalize the workflow that can be used for other studies.

Thank you for your suggestion. Although we agree with your comment that such a figure would be helpful to generalize the workflow, we believe that our framework is clear and decided not to include it in the present manuscript. We might consider including such a figure (like Figure 1a in Ushio 2022) if we have an opportunity to write a review paper regarding this topic.

Comment #2-17 Lines 329-335: I feel this information is unclear in the early manuscript. Maybe it's necessary to clearly communicate in the beginning.

We have explained that we could not find any relevant information at least at the time we detected the ASVs in L189.

Comment #2-18 Lines 336-337: Can these species be identified in the previous data set from the ASV sequences?

Yes, these species were identified in the DNA data set obtained in 2017.

Comment #2-19 Lines 387-397: Are there any measurements such as total biomass, and statistical methods to help with the eDNA bias and data compositionality?

We have confirmed that our quantitative eDNA metabarcoding generates comparable results with the fluorescence-based method and quantitative PCR (e.g., see Supplementary Figures in Ushio 2022) (mentioned in L310 in the revised manuscript). However, at least in this study, we could not perform a direct comparison of the eDNA data with species abundance and/or biomass. This is partly because the number of our target species was too large (> 1,000 species). The accurate estimation of species abundance and/or biomass is one of our next goals.

Comment #2-20 Line 472: Maybe mention transfer entropy somewhere in the early manuscript.

We have mentioned this in L175.

Comment #2-21 Lines 494-503: Maybe a summary of this reasoning should be mentioned somewhere in the early manuscript too.

We have described a brief summary of the reasoning in L195.

Comment #2-22 Lines 29-33 If this sentence is simplified it might be easier to follow.

The sentence has been divided into two sentences in L28. Also, each sentence has been simplified.

Comment #2-23 Line 38 Maybe "macrobes" can be explicitly mentioned. Fungi, protozoa, etc.

Mentioned.

Comment #2-24 Line 139: I am not sure if the date should be in the title.

Similar monitoring was done in 2017 and 2019. Thus, we think the date is necessary in the section title.

Comment #2-25 Figure 1: There are 4 red individuals in the design but 5 measurements in the plots.

Heights and SPAD of the four individuals were measured for each plot and the averaged values were used as representative values for each plot. Therefore, 20 measurements (= 4 rice individuals 5 plots) were done every day, but each plot has one rice height for each day. We have clarified this in the legend of Figure 1: "the average values of the four individuals were regarded as representative values for each plot."

Comment #2-26 Figure 1b: Maybe use the same axis length for the temperature as the other plots?

Corrected.

Comment #2-27 Lines 259-261: Are there the names of the genes in databases?

Yes, these are gene names used in the rice databases (e.g., The Rice Annotation Project Database; https://rapdb.dna.affrc.go.jp/inde x.html).

Reviewer #3 (Recommendations For The Authors):

Comment #3-1 Additionally, RGR is not statistically significant, but statistical significance is observed only in cumulative growth because data presentation does not reflect plant characteristics. RGR changes according to the developmental stage of the plant. Therefore, if RGR data are shown separately according to the rice growing season, the cumulative growth pattern and the pattern will appear similar.

RGRs were calculated daily (i.e., cm/day) and they changed depending on the developmental stage of the rice (Figure 1 and Figure 4–figure supplement 1). Therefore, we might find similar RGR patterns if we focus on a specific period of the growing season. However, unfortunately, we performed the intensive (i.e., daily) monitoring in 2019 only during the field manipulation period (middle June to middle July 2019), and we cannot investigate the changes in cumulative growth throughout the growing season (this depends on how many days we add up RGR to calculate the cumulative growth, though). We agree that, if we had investigated the detailed pattern of RGR throughout the growing season in 2019, we could have found similar pattens between RGR and cumulative growth rate at a certain period in the growing season. In Figure 4, the cumulative growths were calculated based on the RGRs before the third manipulation or during 10 days after the third manipulation. We clarified this in the legend of Figure 4.

Author Response

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

Reviewer #1 (Public Review):

This paper describes the development and initial validation of an approach-avoidance task and its relationship to anxiety. The task is a two-armed bandit where one choice is 'safer' - has no probability of punishment, delivered as an aversive sound, but also lower probability of reward - and the other choice involves a reward-punishment conflict. The authors fit a computational model of reinforcement learning to this task and found that self-reported state anxiety during the task was related to a greater likelihood of choosing the safe stimulus when the other (conflict) stimulus had a higher likelihood of punishment. Computationally, this was represented by a smaller value for the ratio of reward to punishment sensitivity in people with higher task-induced anxiety. They replicated this finding, but not another finding that this behavior was related to a measure of psychopathology (experiential avoidance), in a second sample. They also tested test-retest reliability in a sub-sample tested twice, one week apart and found that some aspects of task behavior had acceptable levels of reliability. The introduction makes a strong appeal to back-translation and computational validity, but many aspects of the rationale for this task need to be strengthened or better explained. The task design is clever and most methods are solid - it is encouraging to see attempts to validate tasks as they are developed. There are a few methodological questions and interpretation issues, but they do not affect the overall findings. The lack of replicated effects with psychopathology may mean that this task is better suited to assess state anxiety, or to serve as a foundation for additional task development.

We thank the reviewer for their kind comments and constructive feedback. We agree that the approach taken in this paper appears better suited to state anxiety, and further work is needed to assess/improve its clinical relevance.

Reviewer #1 (Recommendations For The Authors):

1) For the introduction, the authors communicate well the appeal of tasks with translational potential, and setting up this translation through computational validity is a strong approach. However, I had some concerns about how the task was motivated in the introduction:

a) The authors state that current approach-avoidance tasks used in humans do not resemble those used in the non-human literature, but do not provide details on what exactly is missing from these tasks that makes translation difficult.

Our intention for the section that the reviewer refers to was to briefly convey that historically, approach-avoidance conflict would have been measured either using questionnaires or joystick-based tasks which have no direct non-human counterpart. However, we note that the phrasing was perhaps unfair to recent tasks that were explicitly designed to be translatable across species. Therefore, we have amended the text to the following:

In humans, on the other hand, approach-avoidance conflict has historically been measured using questionnaires such as the Behavioural Inhibition/Activation Scale (Carver & White, 1994), or cognitive tasks that rely on motor biases, for example by using joysticks to approach/move towards positive stimuli and avoid/move away from negative stimuli, which have no direct non-human counterparts (Guitart-Masip et al., 2012; Kirlic et al., 2017; Mkrtchian et al., 2017; Phaf et al., 2014).

b) Although back-translation to 'match' human paradigms to non-animal paradigms is useful for research, this isn't the end goal of task development. What really matters is how well these tasks, whether in humans or not, capture psychopathology-relevant behavior. Many animal paradigms were developed and brought into extensive use because they showed sensitivity to pharmacological compounds (e.g., benzodiazepines). The introduction accepts the validity of these paradigms at face value, and doesn't address whether developing human tests of psychopathology based on sensitivity to existing medication classes is the best way to generate new insights about psychopathology.

We agree that whilst paradigms with translational and computational validity have merits of their own for neuroscientific theory, clinical validity (i.e. how well the paradigm reflects a phenomenon relevant to psychopathology) is key in the context of clinical applications. While our findings of associations between task performance and self-reported (state) anxiety suggest that our approach is a step in the right direction, the lack of associations with clinical measures was disappointing. Although future work is needed to more directly test the sensitivity of the current approach to psychopathology, this may mean that it, and its non-human counterparts, do not measure behaviours relevant to pathological anxiety. Since our primary focus in this paper was on translational and computational validity, we have opted to discuss the author’s suggestion in the ‘Discussion’ section, as follows:

Further, it is worth noting that many animal paradigms were developed and widely adopted due to their sensitivity to anxiolytic medication (Cryan & Holmes, 2005). Given the lack of associations with clinical measures in our results, it is possible that current translational models of anxiety may not fully capture behaviours that are directly relevant to pathological anxiety. To develop translational paradigms of clinical utility, future research should place a stronger emphasis on assessing their clinical validity in humans.

c) The authors may want to bring in the literature on the description-experience gap (e.g., PMID: 19836292) when discussing existing decision tasks and their computational dissimilarity to non-human operant conditioning tasks.

We thank the reviewer for this useful addition to the introduction. We have now added the following to the 'Introduction’ section:

Moreover, evidence from economic decision-making suggests that explicit offers of probabilistic outcomes can impact decision-making differently compared to when probabilistic contingencies need to be learned from experience (referred to as the ‘description-experience gap’; Hertwig & Erev, 2009); this finding raises potential concerns regarding the use of offer-based tasks in humans as approximations of non-human tasks that do not involve explicit offers.

d) How does one evaluate how computationally similar human vs. non-human tasks are? What are the criteria for making this judgement? Specific to the current tasks, many animal learning tasks are not learning tasks in the same sense that human learning tasks are, in terms of the number of trials used and if the animals are choosing from a learned set of contingencies versus learning the contingencies during the testing.

The computational similarity of human and non-human strategies in a given translational task can be tested empirically. This can be done by fitting models to the data and assessing whether similar models explain choices, even if parameter distributions might vary across species due to, for example, physiological differences. Indeed, non-human animals require much more training to perform even uni-dimensional reinforcement learning, but once they are trained, it should be possible to model their responses. In fact, it should even be possible to take training data into account in some cases. For example, the training phase of the Vogel/Geller-Seifter preclinical tests require an animal to learn to emit a certain action (e.g. lever press) simply to obtain some reward. In the next phase, an aversive outcome is introduced as an additional outcome, but one could model both the training and test phase together – the winning model in our studies would be a suitable candidate to model behaviour here. As we also discuss predictive validity in the ‘Discussion’ section, we opted to add the following text there too:

… computational validity would also need to be assessed directly in non-human animals by fitting models to their behavioural data. This should be possible even in the face of different procedures across species such as number of trials or outcomes used (shock or aversive sound). We are encouraged by our finding that the winning computational model in our study relies on a relatively simple classical reinforcement learning strategy. There exist many studies showing that non-human animals rely on similar strategies during reward and punishment learning (Mobbs et al., 2020; Schultz, 2013); albeit to our knowledge this has never been modelled in non-human animals where rewards and punishment can occur simultaneously.

2) What do the authors make of the non-linear relationship between probability of punishment and probability of choosing the conflict stimulus (Fig 2d), especially in the high task-induced anxiety participants? Did this effect show up in the replication sample as well?

Figures 2c-e were created by binning the continuous predictors of outcome probabilities into discrete bins of equal interval. Since punishment probability varied according to Gaussian random walks, it was also distributed with more of its mass in the central region (~ 0.4), and so values at the extreme bins were estimated on fewer data and with greater variance. The non-linear relationships are likely thus an artefact of our task design and plotting procedure. The pattern was also evident in the replication sample, see Author response image 1:

Author response image 1.

However, since these effects were estimated as linear effects in the logistic regression models, and to avoid overfitting/interpretations of noise arising from our task design, we now plot logistic curves fitted to the raw data instead.

3) How correlated were learning rate and sensitivity parameters? The EM algorithm used here can sometimes result in high correlations among these sets of parameters.

As the reviewer suspects the parameters were strongly correlated, especially across the punishment-specific parameters. The Pearson’s r estimates for the untransformed parameter values were as follows:

Reward parameters: discovery sample r = -0.39; replication sample r = -0.78

Punishment parameters: discovery sample r = -0.91; replication sample r = -0.85

We have included the correlation matrices of the estimated parameters as Supplementary Figure 2 in the ‘Computational modelling’ section of the Supplement.

We have now also re-fitted the winning model using variational Bayesian inference (VBI) via Stan, and found that the cross-parameter correlations were much lower than when the data were fitted using EM. We also ran a sensitivity analysis assessing whether using VBI changed the main findings of our studies. This showed that the correlation between task-induced anxiety and the reward-punishment sensitivity index was robust to fitting method, as was the mediating effect of reward-punishment sensitivity index on anxiety’s effect on choice. This indicates that overall our key findings are robust to different methods of parameter-fitting.

We now direct readers to these analyses from the new ‘Sensitivity analyses’ section in the manuscript, as follows:

As our procedure for estimating model parameters (the expectation-maximisation algorithm, see ‘Methods’) produced high inter-parameter correlations in our data (Supplementary Figure 2), we also re-estimated the parameters using Stan’s variational Bayesian inference algorithm (Stan Development Team, 2023) – this resulted in lower inter-parameter correlations, but our primary computational finding, that the effect of anxiety on choice is mediated by relative sensitivity to reward/punishment was consistent across algorithms (see Supplement section 9.8 for details).

We have included the relevant analyses comparing EM and VBI in the Supplement, as follows:

[9.8 Sensitivity analysis: estimating parameters via expectation maximisation and variational Bayesian inference algorithms]

Given that the expectation maximisation (EM) algorithm produced high inter-parameter correlations, we ran a sensitivity analysis by assessing the robustness of our computational findings to an alternative method of parameter estimation – (mean-field) variational Bayesian inference (VBI) via Stan (Stan Development Team, 2023). Since, unlike EM, the results of VBI are very sensitive to initial values, we fitted the data 10 times with different initial values.

Inter-parameter correlations

The VBI produced lower inter-parameter correlations than the EM algorithm (Supplementary Figure 8).

Sensitivity analysis

Since multicollinearity in the VBI-estimated parameters was lower than for EM, indicating less trade-off in the estimation, we re-tested our computational findings from the manuscript as part of a sensitivity analysis. We first assessed whether we observed the same correlations between task-induced anxiety and punishment learning, and reward-punishment sensitivity index (Supplementary Figure 9a). Punishment learning rate was not significantly associated with task-induced anxiety in any of the 10 VBI iterations in the discovery sample, although it was in 9/10 in the replication sample. On the other hand, the reward-punishment sensitivity index was significantly associated with task-induced anxiety in 9/10 VBI iterations in the discovery sample and all iterations in the replication sample. This suggests that the correlation of anxiety and sensitivity index is robust to these two fitting approaches.

We also re-estimated the mediation models, where in the EM-estimated parameters, we found that the reward-punishment sensitivity index mediated the relationship between task-induced anxiety and task choice proportions (Supplementary Figure 9b). Again, we found that the reward-punishment sensitivity index was a significant mediator in 9/10 VBI iterations in the discovery sample and all iterations in the replication sample. Punishment learning rate was also a significant mediator in 9/10 iterations in the replication sample, although it was not in the discovery sample for all iterations, and this was not observed for the EM-estimated parameters.

Overall, we found that our key results, that anxiety is associated with greater sensitivity to punishment over reward, and this mediates the relationship between anxiety and approach-avoidance behaviour, were robust across both fitting methods.

As an aside, we were unable to run the model fitting using Markov chain Monte Carlo sampling approaches due to the computational power and time required for a sample of this size (Pike & Robinson, 2022, JAMA Psychiatry).

4) What is the split-half reliability of the task parameters?

We thank the reviewer for this query. We have now included a brief section on the (good-to-excellent) split-half reliability of the task in the manuscript:

We assessed the split-half reliability of the task by correlating the overall proportion of conflict option choices and model parameters from the winning model across the first and second half of trials. For overall choice proportion, reliability was simply calculated via Pearson’s correlations. For the model parameters, we calculated model-derived estimates of Pearson’s r values from the parameter covariance matrix when first- and second-half parameters were estimated within a single model, following a previous approach recently shown to accurately estimate parameter reliability (Waltmann et al., 2022). We interpreted indices of reliability based on conventional values of < 0.40 as poor, 0.4 - 0.6 as fair, 0.6 - 0.75 as good, and > 0.75 as excellent reliability (Fleiss, 1986). Overall choice proportion showed good reliability (discovery sample r = 0.63; replication sample r = 0.63; Supplementary Figure 5). The model parameters showed good-to-excellent reliability (model-derived r values ranging from 0.61 to 0.85 [0.76 to 0.92 after Spearman-Brown correction]; Supplementary Figure 5).

5) The authors do a good job of avoiding causal language when setting up the cross-sectional mediation analysis, but depart from this in the discussion (line 335). Without longitudinal data, they cannot claim that "mediation analyses revealed a mechanism of how anxiety induces avoidance".

Thank you for spotting this, we have now amended the text to:

… mediation analyses suggested a potential mechanism of how anxiety may induce avoidance.

Reviewer #2 (Public Review):

Summary:

The authors develop a computational approach-avoidance-conflict (AAC) task, designed to overcome limitations of existing offer based AAC tasks. The task incorporated likelihoods of receiving rewards/ punishments that would be learned by the participants to ensure computational validity and estimated model parameters related to reward/punishment and task induced anxiety. Two independent samples of online participants were tested. In both samples participants who experienced greater task induced anxiety avoided choices associated with greater probability of punishment. Computational modelling revealed that this effect was explained by greater individual sensitivities to punishment relative to rewards.

Strengths:

Large internet-based samples, with discovery sample (n = 369), pre-registered replication sample (n = 629) and test-retest sub group (n = 57). Extensive compliance measures (e.g. audio checks) seek to improve adherence.

There is a great need for RL tasks that model threatening outcomes rather than simply loss of reward. The main model parameters show strong effects and the additional indices with task based anxiety are a useful extension. Associations were broadly replicated across samples. Fair to excellent reliability of model parameters is encouraging and badly needed for behavioral tasks of threat sensitivity.

We thank the reviewer for their comments and constructive feedback.

The task seems to have lower approach bias than some other AAC tasks in the literature. Although this was inferred by looking at Fig 2 (it doesn't seem to drop below 46%) and Fig 3d seems to show quite a strong approach bias when using a reward/punishment sensitivity index. It would be good to confirm some overall stats on % of trials approached/avoided overall.

The range of choice proportions is indeed an interesting statistic that we have now included in the manuscript:

Across individuals, there was considerable variability in overall choice proportions (discovery sample: mean = 0.52, SD = 0.14, min/max = [0.03, 0.96]; replication sample: mean = 0.52, SD = 0.14, min/max = [0.01, 0.99]).

Weaknesses:

The negative reliability of punishment learning rate is concerning as this is an important outcome.

We agree that this is a concerning finding. As reviewer 3 notes, this may have been due to participants having control over the volume used to play the aversive sounds in the task (see below for our response to this point). Future work with better controlled experimental settings will be needed to determine the reliability of this parameter more accurately.

This may also have been due to the asymmetric nature of the task, as only one option could produce the punishment. This means that there were fewer trials on which to estimate learning about the occurrence of a punishment. Future work using continuous outcomes, as the reviewer suggests below, whilst keeping the asymmetric relationship between the options, could help in this regard.

We have included the following comment on this issue in the manuscript:

Alternatively, as participants self-determined the loudness of the punishments, differences in volume settings across sessions may have impacted the reliability of this parameter (and indeed punishment sensitivity). Further, the asymmetric nature of the task may have impacted our ability to estimate the punishment learning rate, as there were fewer occurrences of the punishment compared to the reward.

The Kendall's tau values underlying task induced anxiety and safety reference/ various indices are very weak (all < 0.1), as are the mediation effects (all beta < 0.01). This should be highlighted as a limitation, although the interaction with P(punishment|conflict) does explain some of this.

We now include references to the effect sizes to emphasise this limitation. We also note, as the reviewer suggests, that this may be due to crudeness of overall choice proportion as a measure of approach/avoidance, as it is contaminated with variables such as P(punishment|conflict).

One potentially important limitation of our findings is the small effect size observed in the correlation between task-induced anxiety and avoidance (Kendall's tau values < 0.1, mediation betas < 0.01). This may be attributed to the simplicity of using overall choice proportion as a measure of approach/avoidance, as the effect of anxiety on choice was also influenced by punishment probability.

The inclusion of only one level of reward (and punishment) limits the ecological validity of the sensitivity indices.

We agree that using multi-level outcomes will be an important question for future work and now explicitly note this in the manuscript, as below:

Using multi-level or continuous outcomes would also improve the ecological validity of the present approach and interpretation of the sensitivity parameters.

Appraisal and impact:

Overall this is a very strong paper, describing a novel task that could help move the field of RL forward to take account of threat processing more fully. The large sample size with discovery, replication and test-retest gives confidence in the findings. The task has good ecological validity and associations with task-based anxiety and clinical self-report demonstrate clinical relevance. The authors could give further context but test-retest of the punishment learning parameter is the only real concern. Overall this task provides an exciting new probe of reward/threat that could be used in mechanistic disease models.

We thank the reviewer again for helping us to improve our analyses and manuscript.

Reviewer #2 (Recommendations For The Authors):

Additional context:

In the introduction "cognitive tasks that bear little semblance to those used in the non-human literature" seems a little unfair. One study that is already cited (Ironside et al, 2020) used a task that was adapted from non-human primates for use in humans. It has almost identical visual stimuli (different levels of simultaneous reward and aversive outcome/punishment) and response selection processes (joystick) between species and some overlapping brain regions were activated across species for conflict and aversiveness. The later point that non-human animals must be trained on the association between action and outcome is well taken from the point of view of computational validity but perhaps not sufficient to justify the previous statement.

Our intention for this section was to briefly convey that historically, approach-avoidance conflict would have been measured either using questionnaires or joystick-based tasks which have no direct non-human counterpart. However, we agree that this phrasing is unfair to recent studies such as those by Ironside and colleagues. Therefore, we have amended the text to the following:

In humans, on the other hand, approach-avoidance conflict has historically been measured using questionnaires such as the Behavioural Inhibition/Activation Scale (Carver & White, 1994), or cognitive tasks that rely on motor biases to approach/move towards positive stimuli and avoid/move away from negative stimuli which have no direct non-human counterparts (Guitart-Masip et al., 2012; Kirlic et al., 2017; Mkrtchian et al., 2017; Phaf et al., 2014).

It would be good to speculate on why task induced anxiety made participants slower to update their estimates of punishment probability.

Although a meta-analysis of reinforcement learning studies using reward and punishment outcomes suggests a positive association between punishment learning rate and anxiety symptoms (and depressed mood), we paradoxically found the opposite effect. However, previous work has suggested that distinct forms of anxiety associate differently with anxiety (Wise & Dolan, 2020, Nat. Commun.), where somatic anxiety was negatively correlated with punishment learning rate whereas cognitive anxiety showed the opposite effect. We have now added the following to the manuscript, and noted that future work is needed to understand the potentially complex relationship between anxiety and learning from punishments:

Notably, although a recent computational meta-analysis of reinforcement learning studies showed that symptoms of anxiety and depression are associated with elevated punishment learning rates (Pike & Robinson, 2022), we did not observe this pattern in our data. Indeed, we even found the contrary effect in relation to task-induced anxiety, specifically that anxiety was associated with lower rates of learning from punishment. However, other work has suggested that the direction of this effect can depend on the form of anxiety, where cognitive anxiety may be associated with elevated learning rates, but somatic anxiety may show the opposite pattern (Wise & Dolan, 2020) and this may explain the discrepancy in findings. Additionally, parameter values are highly dependent on task design (Eckstein et al., 2022), and study designs to date may be more optimised in detecting differences in learning rate (Pike & Robinson, 2022) – future work is needed to better understand the potentially complex association between anxiety and punishment learning rate. Lastly, as punishment learning rate was severely unreliable in the test-retest analyses, and the associations between punishment learning rate and state anxiety were not robust to an alternative method of parameter estimation (variational Bayesian inference), the negative correlation observed in our study should be treated with caution.

Were those with more task-based anxiety more inflexible in general?

The lack of associations across reward learning rate and task-induced anxiety suggest that this was not a general inflexibility effect. To test the reviewer’s hypothesis more directly, we conducted a sensitivity analysis by examining the model with a general learning rate – this did not support a general inflexibility effect. Please see the new section in the Supplement below:

[9.10 Sensitivity analysis: anxiety and inflexibility]

As anxious participants were slower to update their estimates of punishment probability, we determined whether this was due to greater general inflexibility by examining the model including two sensitivity parameters, but one general learning rate (i.e. not split by outcome). The correlation between this general learning rate and task-induced anxiety was not significant in either samples (discovery: tau = -0.02, p = 0.504; replication: tau = -0.01, p = 0.625), suggesting that the effect is specific to punishment.

Was the 16% versus 20% of the two samples with clinically relevant anxiety symptoms significantly different? What about other demographics in the two samples?

The difference in proportions were not significantly different (χ2 = 2.33, p = 0.127). The discovery sample included more females and was older on average compared to the replication sample – information which we now report in the manuscript:

The discovery sample consisted of a significantly greater proportion of female participants than the replication sample (59% vs 52%, χ2 = 4.64, p = 0.031). The average age was significantly different across samples (discovery sample mean = 37.7, SD = 10.3, replication sample mean = 34.3, SD = 10.4; t785.5 = 5.06, p < 0.001). The differences in self-reported psychiatric symptoms across samples did not reach significance (p > 0.086).

It would be interesting to know how many participants failed the audio attention checks.

We have now included information about what proportion of participants fail each of the task exclusion criteria in the manuscript:

Firstly, we excluded participants who missed a response to more than one auditory attention check (see above; 8% in both discovery and replication samples) – as these occurred infrequently and the stimuli used for the checks were played at relatively low volume, we allowed for incorrect responses so long as a response was made. Secondly, we excluded those who responded with the same response key on 20 or more consecutive trials (> 10% of all trials; 4/6% in discovery and replication samples, respectively). Lastly, we excluded those who did not respond on 20 or more trials (1/2% in discovery and replication samples, respectively). Overall, we excluded 51 out of 423 (12%) in the discovery sample, and 98 out of 725 (14%) in the replication sample.

There doesn't appear to be a model with only learning from punishment (i.e. no reward learning) included in the model comparison. It would be interesting to see how it compared.

We have fitted the suggested model and found that it is the least parsimonious of the models. Since participants were monetarily incentivised based on the rewards only, this was to be expected. We have now added this ‘punishment learning only’ model and its variant including a lapse term into the model comparison. The two lowest bars on the y-axis in Author response image 2 represent these models.

Author response image 2.

Were sex effects examined as these have been commonly found in AAC tasks. How about other covariates such as age?

We have now tested the effects of sex and age on behaviour and on parameter values. There were indeed some significant effects, albeit with some inconsistencies across the two samples, which for completeness we have included in the manuscript, as follows:

While sex was significantly associated with choice in the discovery sample (β = 0.16 ± 0.07, p = 0.028) with males being more likely to choose the conflict option, this pattern was not evident in the replication sample (β = 0.08 ± 0.06, p = 0.173), and age was not associated with choice in either sample (p > 0.2).

Comparing parameters across sexes via Welch’s t-tests revealed significant differences in reward sensitivity (t289 = -2.87, p = 0.004, d = 0.34; lower in females) and consequently reward-punishment sensitivity index (t336 = -2.03, p = 0.043, d = 0.22; lower in females i.e. more avoidance-driven). In the replication sample, we observed the same effect on reward-punishment sensitivity index (t626 = -2.79, p = 0.005, d = 0.22; lower in females). However, the sex difference in reward sensitivity did not replicate (p = 0.441), although we did observe a significant sex difference in punishment sensitivity in the replication sample (t626 = 2.26, p = 0.024, d = 0.18).

Minor: Still a few placeholders (Supplementary Table X/ Table X) in the methods

We thank the reviewer for spotting these errors. We have now corrected these references.

Reviewer #3 (Public Review):

This study investigated cognitive mechanisms underlying approach-avoidance behavior using a novel reinforcement learning task and computational modelling. Participants could select a risky "conflict" option (latent, fluctuating probabilities of monetary reward and/or unpleasant sound [punishment]) or a safe option (separate, generally lower probability of reward). Overall, participant choices were skewed towards more rewarded options, but were also repelled by increasing probability of punishment. Individual patterns of behavior were well-captured by a reinforcement learning model that included parameters for reward and punishment sensitivity, and learning rates for reward and punishment. This is a nice replication of existing findings suggesting reward and punishment have opposing effects on behavior through dissociated sensitivity to reward versus punishment.

Interestingly, avoidance of the conflict option was predicted by self-reported task-induced anxiety. This effect of anxiety was mediated by the difference in modelled sensitivity to reward versus punishment (relative sensitivity). Importantly, when a subset of participants were retested over 1 week later, most behavioral tendencies and model parameters were recapitulated, suggesting the task may capture stable traits relevant to approach-avoidance decision-making.

We thank the reviewer for their useful analysis of our study. Indeed, it was reassuring to see that performance indices were reliable across time.

However, interpretation of these findings are severely undermined by the fact that the aversiveness of the auditory punisher was largely determined by participants, with the far-reaching impacts of this not being accounted for in any of the analyses. The manipulation check to confirm participants did not mute their sound is highly commendable, but the thresholding of punisher volume to "loud but comfortable" at the outset of the task leaves substantial scope for variability in the punisher delivered to participants. Indeed, participants' ratings of the unpleasantness of the punishment was moderate and highly variable (M = 31.7 out of 50, SD = 12.8 [distribution unreported]). Despite having this rating, it is not incorporated into analyses. It is possible that the key finding of relationships between task-induced anxiety, reward-punishment sensitivity and avoidance are driven by differences in the punisher experienced; a louder punisher is more unpleasant, driving greater task-induced anxiety, model-derived punishment sensitivity, and avoidance (and vice versa). This issue can also explain the counterintuitive findings from re-tested participants; lower/negatively correlated task-induced anxiety and punishment-related cognitive parameters may have been due to participants adjusting their sound settings to make the task less aversive (retest punisher rating not reported). It can therefore be argued that the task may not actually capture meaningful cognitive/motivational traits and their effects on decision-making, but instead spurious differences in punisher intensity.

We thank the reviewer for raising this important potential limitation of our study. We agree that how participants self-adjusted their sound volume may important consequences for our interpretations of the data. Unfortunately, despite the scalability of online data collection, this highlights one of its major weaknesses in the lack of controllability over experimental parameters. The previous paper from which we obtained our aversive sounds (Seow & Hauser, 2021, Behav Res, doi.org/10.3758/s13428-021-01643-0) contains useful analyses with regards to this discussion. When comparing the unpleasantness of the sounds played at 50% vs 100% volume, the authors indeed found that the lower volumes lead to lower unpleasantness ratings. However, the magnitude of this effect did not appear to be substantial (Fig. 4 from the paper), and even at 50% volume, the scream sounds we used were rated in the top quartile for unpleasantness, on average. This implies that the sounds have sufficient inherent unpleasantness, even when played at half intensity. We find this reassuring, in the sense that any self-imposed volume effects may not be large. Of note, our instructions to participants to adjust the volume to a ‘loud but comfortable’ level was based on the same phrasing used in this study.

To the reviewers point on how this might affect the reliability of the task, we have included the following in the ‘Discussion’ section:

Alternatively, as participants self-determined the loudness of the punishments, differences in volume settings across sessions may have impacted the reliability of this parameter (and indeed other measures).

Please see below for analyses accounting for punishment unpleasantness ratings.

This undercuts the proposed significance of this task as a translational tool for understanding anxiety and avoidance. More information about ratings of punisher unpleasantness and its relationship to task behavior, anxiety and cognitive parameters would be valuable for interpreting findings. It would also be of interest whether the same results were observed if the aversiveness of the punisher was titrated prior to the task.

As suggested, we have now included sensitivity analyses using the unpleasantness ratings that show their effect is minimal on our primary inference. We report relevant results below in the ‘Recommendations For The Authors’ section. At the same time, we think it is important to acknowledge that unpleasantness is a combination of both the inherent unpleasantness of the sound and the volume it is presented at, where only the latter is controlled by the participant. Therefore, these analyses are not a perfect indicator of the effect of participant control. For convenience, we reproduce the key findings from this sensitivity analysis here:

Approach-avoidance hierarchical logistic regression model

We assessed whether approach and avoidance responses, and their relationships with state anxiety, were impacted by punishment unpleasantness, by including unpleasantness ratings as a covariate into the hierarchical logistic regression model. Whilst unpleasantness was a significant predictor of choice (positively predicting safe option choices), all significant predictors and interaction effects from the model without unpleasantness survived (Supplementary Figure 11). Critically, this suggests that punishment unpleasantness does not account for all of the variance in the relationship between anxiety and avoidance.

Mediation model

When unpleasantness ratings were included in the mediation models, the mediating effect of the reward-punishment sensitivity index did not survive (discovery sample: standardised β = 0.003 ± 0.003, p = 0.416; replication sample: standardised β = 0.004 ± 0.003, p = 0.100; Supplementary Figure 12). Pooling the samples resulted in an effect that narrowly missed the significance threshold (standardised β = 0.004 ± 0.002, p = 0.068).

More generally, whether or not to titrate the punishments (and indeed the rewards) is an interesting experimental decision, which we think should be guided by the research question. In our case, we were interested in individual differences in reward/punishment learning and sensitivity and their relation to anxiety, so variation in how aversive the sounds affected approach-avoidance decisions was an important aspect of our design. In studies where the aim is to understand more general processes of how humans act under approach-avoidance conflict, it may be better to tightly control the salience of reinforcers.

Ultimately, the best test of the causal role of anxiety on avoidance, and against the hypothesis that our results were driven by spurious volume control effects, would be to run within-subjects anxiety interventions, where these volume effects are naturally accounted for. This will be an important direction for future studies using similar measures. We have added a paragraph in the ‘Discussion’ section on this point:

Relatedly, participants had some control over the intensity at which the punishments were presented, which may have driven our findings relating to anxiety and putative mechanisms of anxiety-related avoidance. Sensitivity analyses showed that our finding that anxiety is positively associated with avoidance in the task was robust to individual differences in self-reported punishment unpleasantness, whilst the mediation effects were not. Future work imposing better control over the stimuli presented, and/or using within-subjects designs will be needed to validate the role of reward/punishment sensitivities in anxiety-related avoidance.

Although the procedure and findings reported here remain valuable to the field, claims of novelty including its translational potential are perhaps overstated. This study complements and sits within a much broader literature that investigates roles for aversion and cognitive traits in approach-avoidance decisions. This includes numerous studies that apply reinforcement learning models to behavior in two-choice tasks with latent probabilities of reward and punishment (e.g., see doi: 10.1001/jamapsychiatry.2022.0051), as well as other translationally-relevant paradigms (e.g., doi: 10.3389/fpsyg.2014.00203, 10.7554/eLife.69594, etc).

We agree with the reviewer that our approach builds on previous work in reinforcement learning, approach-avoidance conflict and translational measures of anxiety. Whilst there are by now many studies using two-choice learning tasks with latent reward and punishment probabilities, our main, and which we refer to as ‘novel’, aim was to bring these fields together in such a way so as to model anxiety-related behaviour.

We note that we do not make strong statements about whether these effects speak to traits per se, and as Reviewer 1 notes, the evidence from our study suggests that the present measure may be better suited to assessing state anxiety. While computational model parameters can and are certainly often interpreted as constituting stable individual traits, a more simple interpretation of our findings may be that state anxiety is associated with a momentary preference for punishment avoidance over reward pursuit. This can still be informative for the study of anxiety, especially given the notion of a continuous relationship between adaptive/state anxiety and maladaptive/persistent anxiety.

Having said that, we agree with the underlying premise of the reviewer’s point that how the measure relates to trait-level avoidance/inhibition measures will be an interesting question for future work. We appreciate the importance of using tasks such as ours and those highlighted by the reviewer as trait-level measures, especially in computational psychiatry. We have now included a discussion on the potential roles of cognitive/motivational traits, in line with the reviewer’s recommendation – briefly, we have included the suggested references by the reviewer, discussed the measure’s potential relevance to cognitive/motivational traits, and direct interested readers to the broader literature. Please see below for details.

Reviewer #3 (Recommendations For The Authors):

As stated in the public review, punisher unpleasantness and its relationship to key findings (including for retest) should be reported and discussed.

We signpost readers to our new analyses, incorporating unpleasantness ratings into the statistical models, from the main manuscript as follows:

Since participants self-determined the volume of the punishments in the task, and therefore (at least in part) their aversiveness, we conducted sensitivity analyses by accounting for self-reported unpleasantness ratings of the punishment (see the Supplement). Our finding that anxiety impacts approach-avoidance behaviour was robust to this sensitivity analysis (p < 0.001), however the mediating effect of the reward-sensitivity sensitivity index was not (p > 0.1; see Supplement section 9.9 for details).

We reproduce the relevant section from the Supplement below. Overall, we found that the effect of anxiety on choices (via its interaction with punishment probability) remained significant after accounting for unpleasantness, however the mediating effect of reward-punishment sensitivity was no longer significant when unpleasantness ratings were included in the model. As noted above, unpleasantness ratings are not a perfect measure of self-imposed sound volume, and indeed punishment sensitivity is essentially a computationally-derived measure of unpleasantness, which makes it difficult to interpret the mediation model which contains both of these measures. However, since we found that anxiety affected choice over and above and effects of self-imposed sound volume (using unpleasantness ratings as a proxy measure), we argue that the task still holds value as a model of anxiety-related avoidance.

[Supplement Section 9.9: Sensitivity analyses of punishment unpleasantness]

Distribution of unpleasantness

The punishments were rated as unpleasant by the participants, on average (discovery sample: mean rating = 31.1 [scored between 0 and 50], SD = 13.1; replication sample: mean rating = 32.1, SD = 12.7; Supplementary Figure 10).

Approach-avoidance hierarchical logistic regression model

We assessed whether approach and avoidance responses, and their relationships with state anxiety, were impacted by punishment unpleasantness, by including unpleasantness ratings as a covariate into the hierarchical logistic regression model. Whilst unpleasantness was a significant predictor of choice (positively predicting safe option choices), all significant predictors and interaction effects from the model without unpleasantness ratings survived (Supplementary Figure 11). Critically, this suggests that punishment unpleasantness does not account for all of the variance in the relationship between anxiety and avoidance.

Mediation model

When unpleasantness ratings were included in the mediation models, the mediating effect of the reward-punishment sensitivity index did not survive (discovery sample: standardised β = 0.003 ± 0.003, p = 0.416; replication sample: standardised β = 0.004 ± 0.003, p = 0.100; Supplementary Figure 12). Pooling the samples resulted in an effect that narrowly missed the significance threshold (standardised β = 0.004 ± 0.002, p = 0.068).

Test-retest reliability of unpleasantness

The test-retest reliability of unpleasantness ratings was excellent (ICC(3,1) = 0.75), although participants gave significantly lower ratings in the second session (t56 = 2.7, p = 0.008, d = 0.37; mean difference of 3.12, SD = 8.63).

Reliability of other measures with/out unpleasantness

To assess the effect of accounting for unpleasantness ratings on reliability estimates of task performance, we extracted variance components from linear mixed models, following a standard approach (Nakagawa et al., 2017) – note that this was not the method used to estimate reliability values in the main analyses, but we used this specific approach to compare the reliability values with and without the covariate of unpleasantness ratings. The results indicated that unpleasantness ratings did not have a material effect on reliability (Supplementary Figure 14).

We discuss the findings of these sensitivity analyses in the ‘Discussion’ section, as follows:

Relatedly, participants had some control over the intensity at which the punishments were presented, which may have driven our findings relating to anxiety and putative mechanisms of anxiety-related avoidance. Sensitivity analyses showed that our finding that anxiety is positively associated with avoidance in the task was robust to individual differences in self-reported punishment unpleasantness, whilst the mediation effects were not. Future work imposing better control over the stimuli presented, and/or using within-subjects designs will be needed to validate the role of reward/punishment sensitivities in anxiety-related avoidance.

Introduction and discussion should spend more time relating the task and current findings to existing procedures and findings examining individual differences in avoidance and cognitive/motivational correlates.

We thank the reviewer for the opportunity to expand on the literature. Whilst there are numerous behavioural paradigms in both the human and non-human literature that involve learning about rewards and punishments, our starting point for the introduction was the state-of-the-art in translational models of approach-avoidance conflict models of anxiety. Therefore, for the sake of brevity and logical flow of our introduction, we have opted to bring in the discussion on other procedures primarily in the ‘Discussion’ section of the manuscript.

We have now included the reviewer’s suggested citations from their ‘Public Review’ as follows:

Since we developed our task with the primary focus on translational validity, its design diverges from other reinforcement learning tasks that involve reward and punishment outcomes (Pike & Robinson, 2022). One important difference is that we used distinct reinforcers as our reward and punishment outcomes, compared to many studies which use monetary outcomes for both (e.g. earning and losing £1 constitute the reward and punishment, respectively; Aylward et al., 2019; Jean-Richard-Dit-Bressel et al., 2021; Pizzagalli et al., 2005; Sharp et al., 2022). Other tasks have been used that induce a conflict between value and motor biases, relying on prepotent biases to approach/move towards rewards and withdraw from punishments, which makes it difficult to approach punishments and withdraw from rewards (Guitart-Masip et al., 2012; Mkrtchian et al., 2017). However, since translational operant conflict tasks typically induce a conflict between different types of outcome (e.g. food and shocks/sugar and quinine pellets; Oberrauch et al., 2019; van den Bos et al., 2014), we felt it was important to implement this feature. One study used monetary rewards and shock-based punishments, but also included four options for participants to choose from on each trial, with rewards and punishments associated with all four options (Seymour et al., 2012). This effectively requires participants to maintain eight probability estimates (i.e. reward and punishment at each of the four options) to solve the task, which may be too difficult for non-human animals to learn efficiently.

We have also included a discussion on the measure’s potential relevance to cognitive/motivational traits as follows:

Finally, whilst there is a broad literature on the roles of behavioural inhibition and avoidance tendency traits on decision-making and behaviour (Carver & White, 1994; Corr, 2004; Gray, 1982), we did not replicate the correlation of experiential avoidance and avoidance responses or the reward-punishment sensitivity index. Since there were also no significant correlations across task performance indices and clinical symptom measures, our findings suggest that the measure may be more sensitive to behaviours relating to state anxiety, rather more stable traits. Nevertheless, how performance in the present task relates to other traits such as behavioural approach/inhibition tendencies (Carver & White, 1994), as has been found in previous studies on reward/punishment learning (Sharp et al., 2022; Wise & Dolan, 2020) and approach-avoidance conflict (Aupperle et al., 2011), will be an important question for future work.

We also now direct readers to a recent, comprehensive review on applying computational methods to approach-avoidance behaviours in the ‘Introduction’ section:

A fundamental premise of this approach is that the brain acts as an information-processing organ that performs computations responsible for observable behaviours, including approach and avoidance (for a recent review on the application of computational methods to approach-avoidance conflict, see Letkiewicz et al., 2023).

I am curious why participants were excluded if they made the same response on 20+ consecutive trials. How does this represent a cut-off between valid versus invalid behavioral profiles?

We apologise for the lack of clarity on this point in our original submission – this exclusion criterion was specifically if participants used the same response key (e.g. the left arrow button) on 20 or more consecutive trials, indicating inattention. Since the left-right positions of the stimuli were randomised across trials, this did not exclude participants who repeatedly chose the same option frequently. However, as we show in the Supplement, this, along with the other exclusion criteria, did not affect our main findings.

We have now clarified this as follows:

… we excluded those who responded with the same response key on 20 or more consecutive trials (> 10% of all trials; 4%/6% in discovery and replication samples, respectively) – note that as the options randomly switched sides on the screen across trials, this did not exclude participants who frequently and consecutively chose a certain option.

Author Response

The following is the authors’ response to the previous reviews

Reviewer # 1 (Public Review)

Specific comments

1) For all cell-based assays using shRNA to knock down CRB3, it would be desirable to perform rescue experiments to ensure that the observed phenotype of CRB3 depleted cells is specific and not due to off-target effects of the shRNA.

Thank you for your comments. Based on your suggestions, we performed the rescue experiments to observe any alterations in the primary cilia of CRB3-depleted MCF10A cells with overexpressed CRB3. The revised parts can be found in lines 186-188 and the new Supplementary Figure 3A-C has been added.

2) Figure 3G: it is very difficult to see that the red stained structures are primary cilia.

Yes, the staining structure of primary cilia in mammary ductal lumen are less clear than that of individual cells and in renal tubule in Figure 3G. We used recognized acetylated tubulin and γ-tubulin to stain the primary cilia, which were clearly labeled in individual cells. However, the labeled primary cilia in renal tubule were longer length and demonstrated a more pronounced structure than those in the mammary ductal lumen. In the mammary ductal lumen of the 10 mice we analyzed, the primary cilia showed shorter length and staining structure than the others shown in Figure 3G. This difference may be due to the distinct characteristics of primary cilia in different tissues.

3) Figure 5A: it is unfortunate the authors chose not to show the original dataset (Excel file) used for generating this figure; this makes it difficult to interpret the data. It is general policy of the journal to make source data accessible to the scientific community.

In accordance with the journal policy, we have provided the original dataset (Excel file) for Figure 5A, as detailed in “Figure 5–Source Data 1”.

4) The authors have a tendency to overinterpret their data, and not all claims put forth by the authors are fully supported by the data provided.

We have carefully read through the whole text and have revised the overinterpretation parts. These parts can be found in lines 48-50, lines 93-95, and lines 260-261.

Reviewer # 2 (Public Review)

Thank you for recognizing and supporting our research for this manuscript.  

Reviewer # 1 (Recommendations For The Authors)

1) Abstract line 48-51: data overinterpretation. The authors cannot claim this based on the data they are presenting. Please modify the statement/temper the claims.

Thanks for your comments. We have revised this sentence in the abstract, as well as lines 48-50 for details.

2) There are several grammatical errors throughout the manuscript. In particular, the following sentences/statements are either wrong, confusing or non-sensical: lines 55-56; lines 87-90; lines 93-95; lines 385-387; lines 409-410.

Thanks for your positive comments. We have modified lines 55-56 to become new lines 54-55. These sentences in lines 87-90 and lines 93-95 are difficult to understand and logically problematic, so we have carefully revised this paragraph (new lines 85-90). Lines 385-387 have been deleted as they are non-sensical. Lines 409-410 contain misrepresentations. We have revised them in new lines 408-409.

3) Lines 257-259: this is data over-interpretation. It is not correct to state CRB3 is highly dynamic without having done any live cell imaging.

Thank you for your comments. We have revised this sentence, see revised lines 260-261 for details.

4) Figure 8E: if cells do not make cilia when CRB3 is lost (Figure 3), how is it possible to analyze SMO localization to cilia in these cells?

Thank you for your comments. We used immunofluorescence techniques, with acetylated tubulin and SMO co-staining, to analyze the localization of SMO to cilia. The results of immunofluorescent staining of primary cilium and statistical analysis in Figure 3 showed that the proportion of cells with primary cilium was significantly lower in the CRB3 knockdown group, but cells with primary cilium were still present. We used laser confocal microscopy micrographs to identify cells with primary cilium by staining acetylated tubulin, then analyzed the co-localization under the SMO channel, and finally analyzed the proportion of SMO-positive cilia. Several publications (J Cell Biol. 2020;219(6):e201904107; Science. 2008;320(5884):1777-81; Proc Natl Acad Sci U S A. 2012;109(34):13644-9.) have demonstrated that knocking down genes can affect primary cilium formation, and this method has also been used to examine the localization of SMO-related signaling pathway molecules on primary cilium.

5) Lines 366-366: based on the relative low magnification of the images in Figure 8H it is difficult to assess the subcellular localization of GLI1 and whether there is a difference between wild type and the Crb3 mutant cells. For example, it is not clear if GLI1 is localizing to the centrosome-cilium axis. Please modify the text accordingly.

Thank you for your good suggestions. As you mentioned, IHC cannot observe the subcellular localization of GLI1 on the centrosome-cilium axis. However, since GLI1 is a transcriptional effector at the terminal end of the Hh signaling pathway, we may not have made it clear that what we observed in the IHC results was the localization of GLI1 in the nucleus. Therefore, we have revised the description accordingly, as described in line 368 and lines 520-521.

6) Figure 7D, E: the zoomed-in images look pixelated.

Thank you for your positive comments. We have replaced these images in the new Figure 7D and E.

7) Figure 8B: Acetylacte-tub is misspelled.

Thank you for your comments. We have revised and standardized the acetylated tubulin stain to "Ace-tubulin" in all immunofluorescent images throughout the manuscript.

Reviewer # 2 (Recommendations For The Authors)

1) 1) CRB3 is present in mammals as 2 isoforms, A and B, originating from an alternative splicing. In this study, the authors never mention this fact and when using approaches to KO or KD CRB3A/B they are likely to deplete both isoforms which have been shown to have different C-terminal domains and functions (Fan et al., 2007). This is also important for the CRB3 antibodies used in the study since according to the material and methods section they are either against the extracellular domain common to both isoforms or the intracellular domain which is only similar in the domain close to transmembrane between the 2 isoforms. Since the antibodies used in each figure are not detailed it is impossible to know if the authors are detecting CRB3A or B or both. Please provide the information and correct for the actual isoform detected in the data and conclusions.

From the revised version we know now that CRB3B is used for exogenous expression. It has been shown that each isoform has a different role and localization in cells so why focus only on CRB3B for this study?

Thank you for your positive comments. First, previous literature has reported that CRB3b localizes in the primary cilium of MDCK cells. We have corrected the Introduction to specify CRB3b (line 81). Secondly, in the methodology section, we show that the CDs sequence of CRB3b was PCR-amplified from RNA extracted from MCF10A cells. We also designed primers specific to CRB3a but were unable to amplify them, indicating that CRB3b is significantly more expressed in epithelial cells than CRB3a. Finally, according to the company recommended by Genecards website for purchasing CRB3 cloning products, the only CRB3 sequence available in the CRB3 cDNA ORF Clone in Cloning Vector, Human (Cat: HG14324-G) from Sino Biological is CRB3b.

2) 3) The authors use GFP-CRB3A/B, it is not stated which isoform, over-expression to localize CRB3A/B in MCF10A cells (figure 4A). The levels of expression appear to be very high in the GFP panel and it is likely that the secretory pathway of the cells is clogged with GFP-CRB3A/B in transit from the ER to the plasma membrane. Thus, the colocalization with pericentrin might be due to the accumulation of ER and Golgi around the centrosome. This colocalization should be done with the endogenous CRB3A/B and with a better resolution.

The authors do not answer about the potential mislocalization of overexpressed exogenous protein.

We acknowledge the reviewer's perspective. The large amount of exogenous protein overexpression in the cell could potentially obstruct the protein secretion pathway, resulting in the accumulation of the exogenous protein at the ER and Golgi. Such accumulation could create the false impression of co-localization between CRB3b and the centrosome. To provide additional details (lines 215-217 and lines 426-433), we re-expressed the results exogenously and subsequently used staining of endogenous CRB3 and γ-tubulin in Fig. 4C to confirm the co-localization of CRB3 and the centrosome.

3) 4) The staining for CRB3A/B in Figure 4C (red) is striking with a very strong accumulation in an undefined intracellular structure and the authors do not provide any explanation for such a difference with the GFP-CRB3A/B just above.

The authors explain that two different photonic techniques are used (classical versus confocal) but in a cell biology manuscript confocal microscopy is now the standard technique.

Thank you for your comments. We have included a discussion on the partial concordance between CRB3's endogenous staining and exogenous expression results in the "Discussion" section, specifically in lines 420-435.

4) 7) In addition, the authors claim (Line 251/252) that Rab11 is necessary for the transport of CRB3A/B but they should KD Rab11 to show this.

The author's answer is that blocking endocytosis with dynasore is as good as knocking down Rab11 to show its interaction and role in CRB3A/B transport which is not the case.

Thank you for your comments. As requested by the reviewers, we have conducted experiments to knockdown Rab11 and detect CRB3 intracellular trafficking, as shown in the new Supplementary Figure 5B and added lines 258-260. These results provide additional support for our conclusions.

5) 8) The domain of CRB3A/B that is necessary for the interaction with Rab11 is the N-terminal part of the extracellular domain. This domain is thus inside the transport vesicles and not accessible from the cytoplasm. Given that Rab11 is a cytoplasmic protein, how the 2 proteins could interact across the membrane? The authors do not even discuss this essential point for their hypothesis. Comment on the revised version: the authors still do not understand the basic of cell biology since they claim that the extracellular domain of CRB3 can be in contact with Rab11 after endocytosis. Even after endocytosis the extracellular domain of CRB3A/B is inside the lumen of the endosome and not in contact with the cytosol where Rab11 is located. Lines 420-421 of the revised manuscript still claim this interaction between the two proteins without providing the link between the cytosol where Rab11 is and the endosome lumen where the extracellular domain of CRB3A/B is. Please correct.

Thank you for your positive comments. After carefully studying the relevant knowledge, we strongly agree with the reviewer's point of view. We have toned down our claim and removed the description regarding the binding of Rab11 endosomes to specific structural domains of intracellular CRB3 that we were unable to confirm (see lines 443-444 and lines 465-466).

Author Response

Reviewer #1 (Public Review):

The authors report a new bioinformatics pipeline ("SPICE") to predict pairwise cooperative binding-sites based on input ChIP-seq data for transcription factor (TF)-of-interest, analyzed against DNA-binding sites (DNA motifs) in a database (HOCOMOCO). The pipeline also predicts the optimal distance between the paired binding sites. The pipeline correctly predicted known/reported transcription factor cooperations, and also predicted new cooperations, not yet reported in literature. The authors choose to follow up on the predicted interaction between Ikaros and Jun. Using ChIP-seq in mouse B cells, they show extensive overlap in binding regions between Ikaros and Jun in LPS+IL21 stimulated cells. In a human B-lineage cell line (MINO) they show that anti-Ikaros Ab can co-immunoprecipitate Jun protein, and that the MINO cell extracts contain protein(s) that can bind to the CNS9 probe (conserved region upstream of IL10 gene), and that binding is lost upon mutation of two basepairs in the AP1 binding motif, and reduced upon mutation of two basepairs in the non-canonical Ikaros binding motif. Part of this protein complex is super-shifted with an anti-Jun antibody, and more DNA is shifted with addition of an anti-Ikaros antibody.

The authors perform EMSA showing that recombinant Jun can bind to the tested DNA-region (IL10 CNS9) and that addition of recombinant Ikaros (or anti-Ikaros antibody in Fig 3E) can enhance binding (increase amount of DNA shifted). The authors lastly show that the IL10 CNS9 DNA region can enhance transcription in B- and T-cells with a luciferase reporter assay, and that 2 bp mutation of the Ikaros or Jun DNA motifs greatly reduce or abolish this activity.

This is interesting work, with two main contributions: The SPICE pipeline (if made available to the scientific community), and the report of interaction between Ikaros and Jun. However, the distinction between DNA motifs, and the proteins actually binding and having a biological function, should be made clear consistently throughout the manuscript. The same DNA motifs can be bound by multiple factors, for instance within transcription factor families with highly homology in the DNA-binding regions of the proteins.

The reviewer has correctly assessed the content of our manuscript.

Some specific points:

SPICE: It is unclear if this is uploaded somewhere to be available to the scientific community.

Thanks for this comment. We will upload the SPICE pipeline and its associated scripts (R and shell) via GitHub.

It was unclear if Ikaros-Jun interaction was initially found from primary Jun ChIP-seq (and secondary Ikaros motif from HOCOMOCO) or from primary Ikaros CHIP-seq (and secondary Jun motif from HOCOMOCO). And - what were the two DNA motifs (primary and secondary, and their distance) from the SPICE analysis?

The IKZF1-JUN interaction was found from primary JUN ChIP-seq data and searching for secondary IKZF1 motifs identified in the HOCOMOCO database. We will provide the primary and secondary motifs in our revised manuscript.

Authors have mostly careful considerations and statements. One additional comment is that binding does not equal function (Fig 2D), and that opening of chromatin (by any other factor(s)) can give DNA-binding factors (like Ikaros and Jun) the opportunity to bind, without functional consequence for the biological process studied.

We appreciate that the reviewer believes our considerations and statement are careful. We agree that opening of chromatin can give the opportunity of factors to bind, and we now make this point in the manuscript.

Figure 2E: Ikaros is reported to be expressed at baseline in murine B cells, yet the Ikaros ChIP-seq in unstimulated cells had what looks to be no significant or low peaks. LPS stimulation induced strong Ikaros ChIP-seq signal. A western blot showing the Ikaros protein levels in the 3 conditions could help understand if the binding pattern is due to protein expression level induction. Similar for Jun (western in the 3 conditions), which seemed to mainly bind in the LPS+IL21 condition. Furthermore, as also suggested below, tracks showing Ikaros and Jun binding from all conditions (unstimulated, LPS only and LPS+IL21 stimulated cells), at select genomic loci, would be helpful in illustrating this difference in signal between the different cell conditions. This is relevant in regards to the point of cooperativity of binding.

The main point of the paper was showing functional cooperation and proximity of binding. However, the use of purified JUN and Ikaros protein suggest cooperative binding. Exhaustive evaluation of the JUN-Ikaros association is left for future studies.

ChIP-seq in mouse B cells showed that Ikaros bound strongly in LPS stimulated cells, in the (relative) absence of Jun binding (Fig. 2C). However, in EMSA (Fig 3C), there is no binding when the AP1 site is mutated, and the authors describe this as Ikaros binding site. What does the Ikaros binding look like at this genomic location in LPS (only) stimulated cells? The authors could show the same figure as in Fig 2F but show Ikaros and Jun ChIP-seq tracks at IL10 CNS9 locus from all conditions to compare binding in unstimulated, LPS and LPS+IL21 cells.

As requested, we now show Ikaros and Jun ChIP-seq tracks from unstimulated, LPS-treated, and LPS + IL21-treated cells. Both Ikaros and cJUN were bound to the Il10 upstream CNS9 region with LPS treatment of cells (see Author response image 1, highlighted in red box), but binding was weaker than that observed with LPS + IL21.

Author response image 1.

Also: How does this reconcile with the luciferase assay in Fig 4E, where LPS (only) stimulation is used, which in Fig 2E only/mainly induced Ikaros, and not Jun ChIP-seq signal (while EMSA indicate Ikaros cannot bind the site alone, but can enhance Jun-dependent binding).

As shown above, in the LPS (only) condition, both IKZF1 (Ikaros) and cJUN bind to Il10 CNS9 locus. Thus, this is not in conflict with our luciferase assay data in Fig. 4E, which showed Ikaros is dependent on AP-1 binding. Moreover, the AP-1 site in Fig. 4D and 4E can be bound by other AP-1 factors as well, such as JUND, JUNB, BATF, etc. These points can be made in the manuscript. These factors potentially can compete with cJUN binding and their roles remain to be explored.

Comment on statements in results section: The luciferase assays in B and T cells do not demonstrate the role of the proteins Ikaros or Jun directly (page 10, lines 208 and surrounding text). The assay measures an effect of the DNA sequences (implying binding of some transcription factor(s)), but does not identify which protein factors bind there.

We agree with the reviewer. It is reasonable and even likely that different family members may be partially redundant. This point is now made on our revised manuscript.

Lastly, the authors only discuss Ikaros (using the term IKZF1 which is the gene symbol for the Ikaros protein). There are other Ikaros family members that have high homology and that are reported to bind similar DNA sequences (for instance Aiolos and Helios), which are expressed in B-cells and T-cells. A discussion of this is of relevance, as these are different proteins, although belonging to the same family (the Ikaros-family) of transcription factors. For instance, western for Aiolos and Helios will likely detect Aiolos in the B cells used, and Helios in the T cells used.

We agree with the reviewer. As requested, we now discuss the possibility that Aiolos or Helios may also contribute.

Reviewer #2 (Public Review):

The study is performed with old tool Spamo (12 year ago), source data from Encode (2010-2012), even peak caller tool version MACS is old ~ 2013. De novo motif search tool is old too (new one STREME is not mentioned). Any composite element search tool published for the recent 12 years are not cited, there are some issues in data analysis in presentation. Almost all references are from about 8-10 year ago (the most recent date is 2019)

The title is misleading

Instead of “A new pipeline SPICE identifies novel JUN-IKZF1 composite elements”

It should be written as “Application of SpaMo tool identifies novel JUN-IKZF1 composite elements”

It reflects the pipeline better but honestly shows that the novelty is missed.

Regarding the above two points, we respectfully disagree with the reviewer. Although SpaMo was used, the pipeline we developed is new and our findings are distinctive. The pipeline can systematically screen and predict novel protein-protein binding complex, and our discovery related to IKZF1-JUN composite element is new and the biological findings and validation are distinctive. This point is now made in the revised manuscript. As requested, we have added some additional references.

The study was performed on too old data from ENCODE, authors mentioned 343 Encode ChIP-Seq libraries, but authors even did not care even about to set for each library the name of target TF (Figure 1E, Figure S2, Table 2).

Although we used ENCODE data, which was in part when we initially developed the algorithm, those data are valid and using them allowed us to demonstrate the functionality of SPICE, which is versatile and can be used on datasets of one’s choice as well. As requested, in the revised manuscript we have added the names of the TFs in Figs, Fig. S2, and Table 1.

Reviewer #3 (Public Review):

The authors of this study have designed a novel screening pipeline to detect DNA motif spacing preferences between TF partners using publicly available data. They were able to recapitulate previously known composite elements, such as the AP-1/IRF4 composite elements (AICE) and predict many composite elements that are expected to be very useful to the community of researchers interested in dissecting the regulatory logic of mammalian enhancers and promoters. The authors then focus on a novel, SPICE predicted interaction between JUN and IKZF1, and show that under LPS and IL-21 treatment, JUN and IKZF1 in B cells have significant overlap in their genomic localization. Next, to know whether the two TFs physically interact, a co-immunoprecipitation experiment was performed. While JUN immunoprecipitated with an anti-IKZF1 antibody, curiously IKZF1 did not immunoprecipitate with an anti-JUN antibody. Finally, EMSA and luciferase experiments were performed to show that the two TFs bind cooperatively at an IL20 upstream probe.

The reviewer has described the basic results of the study.

Major strengths:

1) SPICE was able to recapitulate previously known composite elements, such as the AP-1/IRF4 composite elements (AICE).

2) Under LPS and IL-21 treatment, JUN and IKZF1 in B cells have significant overlap in their genomic localization. This is very good supporting evidence for the efficacy of SPICE in detecting TF partners.

We are glad that the reviewer believes that SPICE is effective in detecting TF partners.

Major weaknesses:

1) The authors fail to convincingly show that IKZF1 and Jun physically interact. A quantitative measurement of their interaction strength would have been ideal.

We agree that it is not conclusive that the factors interact directly as opposed to binding to nearby sites on DNA, which is what SPICE was intended to detect. We never intended to claim that we established a definite physical interaction. The coIP worked in one direction, but not reliably in the other, even though we have tried a total of four different antibodies. We now mention in the revised manuscript that we have tried the additional anti-JUN antibodies, cJun (60A8, CST) and JunD (D17G2, CST).

2) The super-shift experiment to show that the proteins bound to their EMSA probe were indeed IKZF1 and JUN are not very convincing and would benefit from efforts to quantify the shift (Figure 3E). Nuclear extracts from cells with single or double CRISPR knock outs of the two TFs would have been ideal.

We agree that using single or double knockouts would be helpful, but other Ikaros family or Jun family members could be involved, so such studies might not be definitive. That is why we used purified proteins to show apparent cooperative binding (Figure 4C).

3) There is a second band beneath the more prominent band in the EMSA experiment with recombinant IKZF1 and JUN (Figure 4C). This second band is most probably bound by IKZF1 because it becomes weaker when the IKZF1 site is mutated and is completely absent when only JUN is added. This is completely ignored by the authors. Therefore, experiments with EMSA fail to convincingly show that IKZF1 and Jun bind cooperatively. They could just as well bind independently to the two sites.

The second band has a faster mobility and might relate to IKZF1, although this is difficult to know. We comment on this band on revised manuscript. As noted above, the purified protein experiments do suggest cooperativity. However, our overall intent was to identify factors binding in proximity, which SPICE has successfully done, even if the binding was “independent”.

Author Response

Reviewer #1 (Public Review):

In this manuscript, the authors explored the benefits of intermittent fasting on the cardiac physiology through a multi-omics approach and compared different fasting times (IF12; IF 16 and EOD) for a duration of 6 months. Combining the RNA-sequencing, proteomics and phosphor-proteomics analysis, the authors have made an interesting observation that different fasting times would lead to different changes that could be important for the cardiac physiology. Moreover, the changes observed at transcriptional level are different from protein level, suggesting a post-transcriptional regulation mechanism. Using western blot, the authors have confirmed the key signaling pathways, including AMPK, IRS pathway to be significantly altered upon intermittent fasting for 16hrs. Lastly, as a proof of concept for better cardiac function, the animals were challenged with dobutamine and echocardiography was performed to show the mice subjected to intermittent fasting have better cardiac systolic function.

The impact of intermittent fasting on cardiovascular health has been well characterized in several studies. This report appears to be the first one utilizing a multi-omics approach and provided an interesting dataset at transcriptome, proteome and phosphor-proteome levels, and would serve as a valuable data resource for the field. I have the following concerns:

Major concerns:

1) The rationale for choosing the intermittent fasting pattern and timing While the 16:8 intermittent fasting is relatively standard, what is the rationale to test IF 12 hours? As a 4-hour fasting difference might not cause dramatic changes in transcriptome and proteome. Also, what is the rationale to perform 6 months study? The dobutamine stress test is not a terminal procedure, have the authors examined the cardiac function prior to 6 months to see whether there is a difference?

We sincerely thank the reviewer for providing insightful comments and feedback on our study. The aim of our research is to gain a comprehensive understanding of molecular reprogramming in the heart during intermittent fasting using multi-omics techniques. We acknowledge the reviewer's concern regarding the selection of three different time points for intermittent fasting. Our rationale for choosing these time points was to align with the practices commonly used by researchers in the field. By doing so, we intended to explore and compare the effects of different intermittent fasting regimens on the heart. Through our study, we found that a longer fasting period resulted in the most significant changes in the proteome abundance. Though we agree that 4-hour fasting difference may not significantly alter transcriptome and proteome in terms of expressions, remarkable changes of post-translational modifications such as phosphorylation can occur during shorter time periods and this is evident based on the analyses of the modulated phosphoproteins. Hence, we included 12 hours time point also to our analysis. In fact, we would like to emphasize that all three fasting regimens had notable effects on pathways regulating cellular carbohydrates, lipid and protein metabolism, cell-cell interactions, and myocardial cell contractility. Regarding the duration of our study, we opted for a 6-month duration of intermittent fasting to investigate the impact of chronic intermittent fasting on heart transcriptome and proteome changes. While shorter-term (2-3 months) intermittent fasting studies in animals also have shown beneficial effects, we wanted to delve deeper into the molecular alterations induced by long-term intermittent fasting. We acknowledge the reviewer's observation about the dobutamine stress test not being a terminal procedure. In our manuscript, we aimed to present extensive resource data offering molecular insights into intermittent fasting-induced structural and signaling changes in the heart, focusing on various intermittent fasting time intervals. Additionally, we included the effect of cardiac function in response to intermittent fasting, specifically examining the intermittent fasting 16 hours (IF16) group, and highlighted key pathway modulations at this time point as supporting evidence. We appreciate the reviewer’s concern about examining cardiac function prior to 6-month. Although we did not perform this analysis in the current study, we fully agree that such comparison is required for a better understating of the temporal effects of molecular pathways in relation to heart functions during the course of intermittent fasting.

2) Lack of validation study. One interesting observation from this study is the changes of transcriptome does not reflect all the changes at protein level and there is a differential gene expression pattern in IF12, IF16 and EOD. If this is the case, the authors should select a few important targets and provide both mRNA and protein level analysis, as a proof of concept for the bioinformatics analysis accuracy.

We appreciate the reviewer's attention to the comparison of proteome and transcriptome data across different intermittent fasting regimens, as well as their interest in understanding any specific deviations in dietary regimens or sets of proteins. Indeed, it is well-established that post-transcriptional regulation can lead to discrepancies between mRNA and protein levels, primarily due to translational control or protein degradation mechanisms. Posttranscriptional buffering of proteins, particularly enzymes and kinases, is a plausible explanation, given their regulation through post-translational modifications, such as phosphorylations or allosteric regulations. Despite observing a modest correlation between the proteome and transcriptome data, which is generally common, we did identify certain enzymes, such as HMGC2, PDK4ACOT, CLPX, and RNASE4, with a high level of concordance between protein and mRNA abundances. These instances of agreement between the two data types suggest a coordinated regulation of these enzymes at the transcriptional and translational levels during intermittent fasting. To facilitate a clearer understanding of the correlation between proteome and transcriptome data, we have included correlation levels next to the scatter plots in our manuscript. These annotations aim to provide additional insights and aid readers in assessing the relationship between the two datasets.

3) Poor western blot image quality. The quality of the western blot has several issues: a. the change of pAMPK/AMPK appears to be a decrease of total AMPK instead of change at p-AMPK level. Same with GSK3a/b. There appears to be an increase of total GSK3a/b. The AKT should also be blotted and quantified at phosphorylation level. The western blot should be clearly labeled, for the ones with double bands, including GSK3a/b, the author should clearly label which is GSK3a and which is GSK3b. For the IRS with non-specific band, the author should point out IRS-1 band itself.

We appreciate the reviewer's careful evaluation of our study and acknowledge the concerns raised regarding the quality of the western blot images. Despite revising these experiments multiple times, we acknowledge that the immunoblot images may not meet the highest quality standards. We have included the original immunoblots in the supplementary section to ensure transparency and provide additional data for reference.

Reviewer #2 (Public Review):

This study provides an unbiased characterization of the cardiac proteome in the setting of intermittent fasting. The findings constitute a resource of quantitative proteomic data that sheds light on changes in cardiac function due to diet and that may be used in the future by other investigators. There are a number of key missing details that limit interpretation or present opportunities to strengthen the study.

1) For example, the authors find that apolipoproteins are altered with fasting but it is not clear whether this is a contribution of myocardial tissue changes or systemic effects spilling into blood in cardiac tissues.

We appreciate the reviewer's consideration of the potential effect of spilling blood on our study results. While we agree that such an effect is possible, we would like to emphasize that the observed overall changes in the proteome profile, particularly in pathways regulating metabolism and other cardiac remodeling-associated processes, suggest that the alterations we observed are more likely attributed to changes within the myocardial tissues themselves. We would like to highlight that blood microparticles or extracellular proteins were not enriched in our proteome data and hence the impact of blood spilling is not a concern. In fact, the biological processes we observed were majorly associated with ECM receptor interaction, focal adhesion and signaling pathways, which are not typical for secreted or extracellular proteome encompassing blood leakage.

2) Some statements in the text like "Approximately one-third of the differentially expressed proteins in IF groups compared to AL were enzymes with catalytic activity involved in energy homeostasis pathways" do not appear to be supported by data.

The enzymes among all the differentially expressed proteins in the intermittent fasting (IF) groups compared to the ad libitum (AL) control group are indicated in Supplementary Table S2. This constitutes one-third of the total number of differentially expressed proteins and several of these are involved in metabolic and energy homeostasis pathways.

3) It is not clear how the list of Kinases were generated for Figure 1B.

For the kinases indicated in Figure 1B, all the kinases from the proteins that were differentially expressed among the different dietary regimens compared to the control ad libitum (AL) group were first identified (listed in Supplementary Table S2), followed by enrichment analysis ((FDR ≤ 0.05) of the identified kinases across different pathways identified from KEGG pathways derived from DAVID bioinformatics resources.

4) Changes in chromatin or gene expression are not measured so the conclusion that EOD led to 'epigenetic changes' relative to IF16 is not well supported.

We appreciate the reviewer's feedback. Our statement in the manuscript referred specifically to the changes observed in Figure 2, where we presented increased proteomic abundance in pathways related to chromatin remodeling, chromatin organization, gene expression regulation, and histone modification in the EOD (Every Other Day Fasting) group compared to the IF 16 (Intermittent Fasting for 16 hours) group based on functional process and pathway enrichment analysis. Our comprehensive bioinformatics analysis, depicted in Figure 2, provides intriguing insights into these pathways. We acknowledge that further validation and in-depth studies through additional experiments and functional assays are essential to strengthen the conclusion from such observations, which is beyond the scope of the current study. We thank the reviewer for such valuable suggestions that are very useful for our ongoing studies, where we aim to obtain a more robust and thorough understanding of the impact of intermittent fasting on chromatin-related processes.

5) There are also a number of areas where the text is vague. For example, it is not clear what is meant by 'trend shift' when discussing EOD results and Figure 3 generally could use additional information to better understands the figures.

We would like to clarify that the term 'trend shift' refers to the change in the direction of protein and transcript level alterations. Based on the 2-D enrichment analyses that revealed correlated and non-correlated functional processes at the proteome and transcriptome levels, it was evident that during the early intermittent fasting 12 hours (IF12) regimen, the abundance changes of the proteins and transcripts involved in these processes were altered in the same direction (Supplementary Fig. 4b). Nevertheless, with increased fasting hours, mainly in the Every Other Day Fasting (EOD) group, we observed that the levels of proteins and transcripts involved in several of the functional processes appeared to be non-correlated as compared to the IF12 group (Fig. 2d). In Figure 3, we summarize the overall altered protein networks associated with the different intermittent fasting regimens, highlighting densely connected clusters of proteins along with their associated biological processes and pathways. Additionally, we unravel the impact of intermittent fasting on transcriptional rewiring and highlight regimen-specific alterations of specific transcriptional factors, several of which were found to have metabolic implications.

6) An interesting finding is that the IF16 groups showed cardiac hypertrophy (SFig 11b). This is potentially a novel finding and the text should elaborate more on this phenomenon.

We sincerely thank the reviewer for bringing attention to this intriguing aspect of our study. The data you have highlighted warrants further investigation, and we are committed to delving deeper into this area in our future research.

Author Response

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

Reviewer #1 (Public Review):

The manuscript focused on roles of a key fatty-acid synthesis enzyme, acetyl-coA-carboxylase 1 (ACC1), in the metabolism, gene regulation and homeostasis of invariant natural killer T (NKT_ cells and impact on these T cells' roles during asthma pathogenesis. The authors presented data showing that the acetyl-coA-carboxylase 1 enzyme regulates the expression of PPARg then the function of NKT cells including the secretion of Th2-type cytokines to impact on asthma pathogenesis. The results are clearcut and data were logically presented.

Major concerns:

1) This study heavily relied on the CD4-CreACC1fl/fl mice. While using of a-GalCer stimulation and Ja18KO mice mitigated the concern, it is still a major concern that at least some of the phenotype were due to the effect on conventional CD4 T cells. For example, the deletion of ACC1 gene seems also decreased the numbers of conventional CD4 T cells (Fig. 2D, Fig. S1D). Previously there were reports showing ACC1 gene in conventional CD4 T cells also plays a role in lung inflammation (Nakajima et al., J. Exp. Med. 218, 2021). If the authors believe the phenotype observed was mainly due to iNKT cells, rather than conventional CD4 T cells, a compare/contrast of the two studies should be discussed to explain or reconcile the results.

As the reviewer pointed out, although we have experimentally demonstrated the critical role of ACC1 in iNKT cells in the regulation of allergic asthma, use of Cd4-CreAcc1fl/fl mice inevitably brings the role of conventional CD4+ T-cells in question.

The study conducted by Nakajima et al, which reported that the absence of ACC1 in CD4+ T-cells resulted in reduced numbers and functional impairment of memory CD4+ T-cells, leading to less airway inflammation further suggests possibility of involvement of conventional CD4+ T-cells in regulation of allergic asthma. The direct compare/contrast of two studies seems difficult since Nakajima et al have focused on the role of ACC1 in memory CD4+ T cells while we have focused on iNKT cells.

However, based on our experimental results, we believe that iNKT cells more contribute to the regulation of allergic asthma for the following reasons - (i) while the number of iNKT cells were significantly reduced in Cd4-CreAcc1fl/fl mice, the number of conventional CD4+ T cells were only slightly reduced, (ii) Cd4-CreAcc1fl/fl mice were dramatically decreased in their AHR in α-GalCer induced iNKT cell dependent allergic asthma model, and (iii) Jα18 KO mice that lack iNKT cells almost completely restore their AHR when adoptively transferred with WT iNKT cells but not ACC1-deficient iNKT cells. These results indicate that ACC1-mediated regulation of AHR is significantly dependent on iNKT cells, which might contribute to AHR in the study conducted by Nakajima et al. as well. From these, we believe that while ACC1 is a critical regulator of both conventional CD4+ T cells and iNKT cells in regulation of allergic asthma, iNKT cells may contribute more to regulation of allergic asthma compared to CD4+ T cells. We have summarized the above-mentioned contents in LINES: 421-441 with the reference you have mentioned:

"It should be noted that Cd4-CreAcc1fl/fl mice lack ACC1 expression in both conventional CD4+ T cells and iNKT cells. It should be noted that Cd4-CreAcc1fl/fl mice lack ACC1 expression in both conventional CD4+ T cells and iNKT cells. While the use of iNKT cell- specific Cre system would demonstrate critical role of ACC1 in iNKT cells regarding allergic asthma, there is no iNKT cell-specific Cre system available yet. In addition, the study conducted by Nakajima et al, which reported that the absence of ACC1 in CD4+ T cells resulted in reduced numbers and functional impairment of memory CD4+ T cells, leading to less airway inflammation further suggests possibility of involvement of conventional CD4+ T cells in regulation of allergic asthma. However, based on our experimental results, we believe that iNKT cells more contribute to the regulation of allergic asthma for the following reasons - (i) while the number of iNKT cells were significantly reduced in Cd4-CreAcc1fl/fl mice, the number of conventional CD4+ T cells were only slightly reduced, (ii) Cd4-CreAcc1fl/fl mice were dramatically decreased in their AHR in α-GalCer induced allergic asthma model, and (iii) Jα18 KO mice that lack iNKT cells almost completely restore their AHR when adoptively transferred with WT iNKT cells but not ACC1-deficient iNKT cells. These results indicate that ACC1-mediated regulation of AHR is significantly dependent on iNKT cells, which might contribute to AHR in the study conducted by Nakajima et al. as well. From these, we believe that while ACC1 is a critical regulator of both conventional CD4+ T cells and iNKT cells in regulation of allergic asthma, iNKT cells may contribute more to regulation of allergic asthma compared to CD4+ T cells."

2) The overall significance of the manuscript is related to the potential clinical suppression of ACC1 in human asthma patients. However, the authors only showed the elevated ACC1 genes in these patients, not even in vitro data demonstrating that suppression of ACC1 genes in the iNKT cells from patients could have potential therapeutic effect or suppression of the relevant cytokines.

We would like to appreciate reviewer’s critical comment here. Due to paucity of iNKT cells in human PBMCs, it is extremely difficult to experimentally manipulate expression level of ACC1 in human iNKT cells. Alternatively, to address reviewer’s comment, we compared the cytokine expression of ACC1high iNKT cells from human allergic asthma patients to ACC1low iNKT cells from healthy individuals or non-allergic asthma patients. Our results show that iNKT cells from allergic asthma patients express higher levels of IL4 and IL13 than those from healthy individuals or non-allergic asthma patients, suggesting that the level of ACC1 is most likely involved in functionality of human iNKT cells as well. The results are newly shown in supplementary Fig. 5C with explanation in LINES 376-378 and 382-384:

LINES 376-378: Lastly, the expression levels of IL4 and IL13 were significantly higher in iNKT cells from the allergic asthma patients compared to those from healthy controls and nonallergic asthma patients (Fig. S5C).

LINES 382-384: Thus, iNKT cells from allergic asthma patients express higher ACC1, FASN and PPARG levels and lower levels of a glycolysis which is accompanied with higher levels of IL4 and IL13 than iNKT cells from healthy controls and nonallergic asthma patients.

3) The authors report that a-GalCer administration can induce the AHR, however, in the cited paper (Hachem et al., Eur J. Immunol. 35, 2793, 2005), iNKT cell activation seems to have the opposite effect to inhibit AHR. Did the authors mean to cite different papers?

We apologize for the confusion. We have replaced the inaccurate reference with the reference below in LINES 863-865:

1. Glycolipid activation of invariant T cell receptor+ iNKT cells is sufficient to induce airway hyperreactivity independent of conventional CD4+ T cells, Proc Natl Acad Sci USA, 103 pp, 2782-2787 (2006),

Reviewer #2 (Public Review):

In this study the authors sought to investigate how the metabolic state of iNKT cells impacts their potential pathological role in allergic asthma. The authors used two mouse models, OVA and HDM-induced asthma, and assessed genes in glycolysis, TCA, B-oxidation and FAS. They found that acetyl-coA-carboxylase 1 (ACC1) was highly expressed by lung iNKT cells and that ACC1 deficient mice failed to develop OVA-induced and HDM-induced asthma. Importantly, when they performed bone marrow chimera studies, when mice that lacked iNKT cells were given ACC1 deficient iNKT cells, the mice did not develop asthma, in contrast to mice given wildtype NKT cells. In addition, these observed effects were specific to NKT cells, not classic CD4 T cells. Mechanistically, iNKT cell that lack AAC1 had decreased expression of fatty acid-binding proteins (FABPs) and peroxisome proliferator-activated receptor (PPAR)γ, but increased glycolytic capacity and increased cell death. Moreover, the authors were able to reverse the phenotype with the addition of a PPARg agonist. When the authors examined iNKT cells in patient samples, they observed higher levels of ACC1 and PPARG levels, compared to healthy donors and non-allergic-asthma patients.

We are very grateful for your kind appreciation of our work.

Reviewer #1 (Recommendations For The Authors):

1) Related to major concern I, an iNKT cell-specific knockout of ACC1 in iNKT cells is highly desirable and should be used to directly address the question.

As the reviewer suggested, iNKT cell-specific deletion of ACC1 will provide invaluable information to our study. Unfortunately, Cre-Loxp system that specifically targets iNKT cells has not be developed. Thus, we opted to use CD4-Cre system, which is the gold standard Cre system for the study of iNKT cells. In addition, to highlight the role of ACC1 in iNKT cells in relation to regulation of allergic asthma, we performed iNKT cell-dependent experiment models and conducted adoptive transfer of iNKT cells into iNKT cell-deficient mice (Jα18 KO). These have been discussed in the section of Discussion in LINES:421-441:

"It should be noted that Cd4-CreAcc1fl/fl mice lack ACC1 expression in both conventional CD4+ T cells and iNKT cells. While the use of iNKT cell- specific Cre system would demonstrate critical role of ACC1 in iNKT cells regarding allergic asthma, there is no iNKT cell-specific Cre system available yet. In addition, the study conducted by Nakajima et al, which reported that the absence of ACC1 in CD4+ T cells resulted in reduced numbers and functional impairment of memory CD4+ T cells, leading to less airway inflammation further suggests possibility of involvement of conventional CD4+ T cells in regulation of allergic asthma. However, based on our experimental results, we believe that iNKT cells more contribute to the regulation of allergic asthma for the following reasons - (i) while the number of iNKT cells were significantly reduced in Cd4-CreAcc1fl/fl mice, the number of conventional CD4+ T cells were only slightly reduced, (ii) Cd4-CreAcc1fl/fl mice were dramatically decreased in their AHR in α-GalCer induced allergic asthma model, and (iii) Jα18 KO mice that lack iNKT cells almost completely restore their AHR when adoptively transferred with WT iNKT cells but not ACC1-deficient iNKT cells. These results indicate that ACC1-mediated regulation of AHR is significantly dependent on iNKT cells, which might contribute to AHR in the study conducted by Nakajima et al. as well. From these, we believe that while ACC1 is a critical regulator of both conventional CD4+ T cells and iNKT cells in regulation of allergic asthma, iNKT cells may contribute more to regulation of allergic asthma compared to CD4+ T cells."

2) For Fig. 5A, RT-PCR verification of PPARg gene expression level change is needed.

As suggested, we have verified the level of Pparg expression of ACC1-deficient iNKT cells through real time PCR and have added the results to Figure 5A.

3) Verifying at least the cytokine secretion can be regulated by manipulating ACC1 expression in human asthma patient samples will make the paper much stronger.

We would like to appreciate reviewer’s critical comment here. Due to paucity of iNKT cells in human PBMCs, it is extremely difficult to experimentally manipulate expression level of ACC1 in human iNKT cells. Alternatively, to address reviewer’s comment, we compared the cytokine expression of ACC1high iNKT cells from human allergic asthma patients to ACC1low iNKT cells from healthy individuals or non-allergic asthma patients. Our results show that iNKT cells from allergic asthma patients express higher levels of IL4 and IL13 than those from healthy individuals or non-allergic asthma patients, suggesting that the level of ACC1 is most likely involved in functionality of human iNKT cells as well. The results are newly shown in supplementary Fig. 5C with explanation in LINES 376-378 and 382-384:

LINES 376-378: Lastly, the expression levels of IL4 and IL13 were significantly higher in iNKT cells from the allergic asthma patients compared to those from healthy controls and nonallergic asthma patients (Fig. S5C).

Minor points:

1) What are the cells being stained in Fig. S2C? Are they iNKT cells? If yes, why there is a tetramer-negative population?

The density plot on the left panel of Fig. S2C represents magnetically enriched thymic iNKT cells. Due to their scarcity, thymic iNKT cells were enriched using CD1d tetramer via magnetic activated cell sorting (MACS)-based enrichment technique. After enrichment, we re-stained enriched cells with CD1d tetramers and gated out CD3 and CD1d tetramer double positive cells via flow cytometry to specifically identify iNKT cells. Due to the imperfect purity of magnetic cell separation technique, a small proportion of CD1d tetramer-negative population is seen in the left panel of Fig. S2C.

A brief mention of this methodology has been added to the “Preparation and activation of murine T and iNKT cells” section under Materials and Methods in LINES 560-566:

"Alternatively, thymic and liver mononuclear cells were labeled with APC-conjugated ɑ-GalCer/CD1d tetramers, bound to anti-APC magnetic beads, and enriched on a MACS separator (Miltenyi Biotec, Auburn, CA, USA; purity 89%). To analyze the development of thymic iNKTs cells, we re-stained enriched cells with CD1d tetramer and gated out CD3 and CD1d tetramer double positive cells via flow cytometry to identify thymic iNKT cells, which were used for further analysis."

2) Where are the adoptive transferred iNKT cells purified/sorted from? Are they from lungs of Acc1fl/fl or CD4-cre/Acc1fl/fl mice, asthma-induced already? As there are very few iNKT cells in healthy and untreated mice. There is little described or explained in Methods and Materials.

The adoptively transferred iNKT cells were purified and pooled from the lungs of at least 10 mice per group. Briefly, mouse lungs were finely chopped into small pieces using razor blades and enzymatically digested using type IV collagenase. iNKT cells from the lungs were sorted via FACS using CD1d tetramers. Approximately, 6.0 × 105 of iNKT cells were obtained from the lungs at least of 10 mice. A brief mention of this methodology was added to the “Adoptive transfer of iNKT cells in allergic asthma models” section in Materials and Methods in LINES 568-574: iNKT cells were obtained from the lungs of at least 10 Acc1fl/fl or Cd4-CreAcc1fl/fl mice. Mouse lungs were finely chopped into small pieces using razor blades and were enzymatically digested using type IV collagenase. iNKT cells from the lungs were sorted via FACS using CD1d tetramers. Approximately, 6.0 × 105 of iNKT cells were obtained from at least 10 mice and were adoptively transferred into individual recipient mouse via the intratracheal route.

3) The use of 2-NBDG was not explained in multiple locations, particularly in Fig.5H. How is its fluorescence used to track iNKT cells? No description in Materials and methods.

2-NBDG, a fluorescence tagged glucose analog is a indicator for measurement of glucose uptake in cells. The fluorescence intensity in 2-NBDG-treated cells represents the degree of glucose uptake in cells, which can be measured using flow cytometry. Thus, in the experiments where we treated 2-NBDG, we described the results as "glucose uptake". A brief explanation of this methodology was added to the main text in LINES 253-254. In addition, we have provided the detailed use of 2-NBDG in ‘Measurement of glucose uptake capacity’ under the section of Materials and methods in LINES 599-607: Measurement of glucose uptake capacity using 2-NBDG assay. After treating 2-NBDG, the fluorescence intensity of cells were measured using flow cytometry and represented the degree of glucose uptake in cells.

4) Fig. 3A legends: it should be "Ja18 KO"?

We would like to appreciate your comment on our mistake here. We have corrected this in the legend of figure 3A.

5) There are two different mechanisms for explaining the less severe asthma/AHR phenotype in ACC1-KO iNKT cells. One is lower number of iNKT cells due to cell death, the other decreased cytokine secretions. It is not clear to the reviewer, what are the relationship between two mechanisms. Are they both contributing to the asthma phenotype or cooperative?

As you mentioned, ACC1-deficient iNKT cells showed increase in intrinsic pathway of apoptosis as well as decrease in their cytokine secretion simultaneously. Thus, we believe that increase in cell death and decrease in cytokine expression of ACC1-deficient iNKT cells cooperatively contributed to the asthma phenotype. The above-mentioned point was discussed in LINES 453-458: Furthermore, the apoptotic tendency of the ACC1-deficient iNKT cells was accompanied by their functional impairment. The ACC1-deficient iNKT cells exhibited impaired viability and functionality. Treatment of glycolysis inhibitor in ACC1-deficient iNKT cells not only restored cellular survival but also their functionalities. From these results, we speculate that ACC1-mediated regulation of both cellular homeostasis and cytokine production cooperatively contributed to the asthma phenotype.

Reviewer #2 (Recommendations For The Authors):

Overall, this is a very strong study with few concerns.

1) Are there tissue specific differences in the iNKT cell populations? The authors examined lung iNKT cells in the Figs 1-3, and used liver NKT cells for the mechanistic studies in Fig 4-5. The studies shown in Fig S2 suggest that ACC1 deficient iNKT cells have developmental defects and impaired homeostatic proliferative capacity. Does ACC1 impact lung and liver iNKT cells similarly and is the lack of allergic asthma in ACC1 deficient iNKT cells due to defective iNKT cell trafficking to the lungs or a failure to survive after transfer (Fig 3)?

In absence of ACC1, the number of iNKT cells from both lungs and livers decreased and showed consistent features (i.e: metabolic parameters), suggesting that there was no tissue specific role of ACC1 in INKT cells.

In the adoptive transfer experiments, we transferred equal number of WT and ACC1-deficient iNKT cells directly into mouse lungs via intratracheal route. Thus, decreased numbers of adoptively transferred ACC1-deficient iNKT cells is more likely from their intrinsically impaired homeostatic proliferative capacity, not due to defective trafficking to the lungs.

2) Similarly, are chemokine receptor expression patterns similar between WT and ACC1 deficient iNKTs (Fig 4)?

We compared chemokine receptor expression of WT and ACC1-deficient iNKT cells using our RNA-seq and verified their expression levels via real time q-PCR. The expression levels of these chemokine receptors were comparable between the two groups of iNKT cells. The results are newly shown in supplementary Fig. 4I with explanation in LINES 351-357:

Meanwhile, chemokine receptor signaling is also implicated in regulating homeostasis of iNKT cell in the periphery. In particular, Meyer et al. suggested that iNKT cells require CCR4 to localize to the airways and to induce AHR. Thus, we examined the expression of several chemokine receptors, including CCR4. We found that WT and ACC1-deficient iNKT cells did not differ in their chemokine receptor expressions, suggesting that the chemokine signaling may not be critical for ACC1-mediated regulation in AHR.

3) The authors data suggest that Tregs are not playing a major role in the regulation of asthma induction in their ACC1 deficient mice, based on FoxP3 expression. Did the authors perform suppressor assays to show that the Tregs function similarly in WT and ACC1 deficient mice?

We would like to appreciate reviewer’s reasonable comment. However, we did not experimentally compare the suppressive capacity of WT and ACC1-deficient Tregs under the asthmatic conditions, due to minimal differences in their Foxp3 expression (Foxp3 expression is a critical determinant of suppressive function of Tregs- (Immunity. 2019 Feb 19;50(2):302-316.; Nat Immunol 2003; 4: 330–336; Cell Mol Immunol. 2015 Sep;12(5):558-65.)). Thus, we speculate that the suppressive capacity between WT and ACC1-deficient Tregs might be similar. Nevertheless, since the suppressive capacity of Tregs can also be regulated by other soluble factors and surface molecules, we cannot completely rule out the possibility that ACC1-deficient Tregs might differ in their suppressive capacity to WT Tregs in asthma. In short, while there are clear limitations to our interpretation here, we believe it is unlikely that Tregs from WT and ACC1 deficient mice show difference in their suppressive capacity during asthma. We have included above-mentioned points in the section of Discussion in LINES 415-419: In this regard, Tregs may also play a major role in asthma. However, the expression level of Foxp3 was comparable between WT and ACC1-deficient Tregs. The level of Foxp3 to some extent, serves as a critical determinant of suppressive function of Tregs. Thus, we speculate that they might not critically contribute to the development of asthma, although we cannot completely rule out the contribution of Tregs to our studies.

Author Response

We would like to thank the reviewers for their positive and constructive comments on the manuscript.

We are planning the following revisions to both DGRPool and the corresponding manuscript to address the reviewers’ comments:

1) We agree with reviewer #1 that normalizing the data could potentially improve the GWAS results. Thus, we plan to explore the implementation of this option and assess its impact on the overall results. We will also investigate replacing the ANOVA test with a KRUSKAL test. Instead of upfront data normalization, we will consider using the PLINK –pheno-quantile-normalize option. Both options will be compared on a set of phenotypes where we can analyze the output (i.e., for phenotypes where we expect to find specific variants), to determine whether these strategies enhance the detection power.

2) We also agree with both reviewers that gene expression information is of interest. However, we recognize that incorporating such information would entail substantial work (as elaborated in our response to comments below). We feel that this extensive work is beyond the current scope of this paper, which primarily focuses on phenotypes and genotype-phenotype associations. Nonetheless, we are committed to enhancing user experience by including more gene-level outlinks to Flybase. Additionally, we will link variants and gene results to Flybase's online genome browser, JBrowse. By following the reviewers' suggestions, we aim to guide DGRPool users to potentially informative genes.

3) In agreement with reviewer #2, we acknowledge that additional tools could enhance DGRPool's functionality and facilitate meta-analyses for users. Therefore, we are in the process of developing a gene-centric tool that will allow users to query the database based on gene names. Moreover, we intend to integrate ortholog databases into the GWAS results. This feature will enable users to extend Drosophila gene associations to other species if necessary.

4) Finally, we also concur with both reviewers about making minor edits to the manuscript to address their feedback.

Reviewer #1 (Public Review):

This is a technically sound paper focused on a useful resource around the DRGP phenotypes which the authors have curated, pooled, and provided a user-friendly website. This is aimed to be a crowd-sourced resource for this in the future.

The authors should make sure they coordinate as well as possible with the NC datasets and community and broader fly community. It looks reasonable to me but I am not from that community.

We thank the reviewer for the positive comments. We are relatively well-connected to the D. melanogaster community and aim to leverage this connection to render the resource as valuable as possible. DGRPool in fact already reflects the input of many potential users and was also inspired by key tools on the DGRP2 website. Furthermore, it also rationalizes why we are often bridging our results with other resources, such as linking out to Flybase, which is the main resource for the Drosophila community at large.

I have only one major concern which in a more traditional review setting I would be flagging to the editor to insist the authors did on resubmission. I also have some scene setting and coordination suggestions and some minor textual / analysis considerations.

The major concern is that the authors do not comment on the distribution of the phenotypes; it is assumed it is a continuous metric and well-behaved - broad gaussian. This is likely to be more true of means and medians per line than individual measurements, but not guaranteed, and there could easily be categorical data in the future. The application of ANOVA tests (of the "covariates") is for example fragile for this.

The simplest recommendation is in the interface to ensure there is an inverse normalisation (rank and then project on a gaussian) function, and also to comment on this for the existing phenotypes in the analysis (presumably the authors are happy). An alternative is to offer a kruskal test (almost the same thing) on covariates, but note PLINK will also work most robustly on a normalised dataset.

We thank the reviewer for raising this interesting point. Indeed, we did not comment on the distribution of individual phenotypes due to the underlying variability from one phenotype to another, as suggested by the reviewer. Some distributions appear normal, while others are clearly not normally distributed. This information is 'visible' to users by clicking on any phenotype; DGRPool automatically displays its global distribution if the values are continuous/quantitative. We acknowledge the reviewer's concerns regarding the use of ANOVA tests. However, we consider it acceptable to perform linear regression (including ANOVA tests) on non-normally distributed data, as only the prediction errors need to follow a normal distribution.

Furthermore, the ANOVA test is solely conducted to assess whether any of the potential covariates (such as well-established inversions and symbiont infection status) are associated with the phenotype of interest. PLINK2 automatically corrects for the effects of these covariates during GWAS by considering them as part of the regression model.

Nevertheless, we concur with the reviewer that normalizing the data could potentially enhance GWAS results. Consequently, we commit to exploring the impact of data normalization on the overall outcomes. Additionally, we will consider replacing the ANOVA test with a KRUSKAL test, and using the PLINK –pheno-quantile-normalize option. We intend to compare both approaches using a set of phenotypes where we can compare the output (i.e., where specific variants are expected to be identified). This comparison will help us determine if either method enhances the detection power.

Minor points:

On the introduction, I think the authors would find the extensive set of human GWAS/PheWAS resources useful; widespread examples include the GWAS Catalog, Open Targets PheWAS, MR-base, and the FinnGen portal. The GWAS Catalog also has summary statistics submission guidelines, and I think where possible meta-data harmonisation should be similar (not a big thing). Of course, DRGP has a very different structure (line and individuals) and of course, raw data can be freely shown, so this is not a one-to-one mapping.

Thank you for the suggestion. We will cite these resources in the Introduction and check the GWAS catalog submission guidelines to compare to the ones we are proposing in this paper.

For some authors coming from a human genetics background, they will be interpreting correlations of phenotypes more in the genetic variant space (eg LD score regression), rather than a more straightforward correlation between DRGP lines of different individuals. I would encourage explaining this difference somewhere.

We appreciate this potential issue and we will make this distinction clearer in the manuscript to avoid any confusion.

This leads to an interesting point that the inbred nature of the DRGP allows for both traditional genetic approaches and leveraging the inbred replication; there is something about looking at phenotype correlations through both these lenses, but this is for another paper I suspect that this harmonised pool of data can help.

We agree with the reviewer and hope that more meta-analyses will be made possible by leveraging the harmonized data that are made available through DGRPool.

I was surprised the authors did not crunch the number of transcript/gene expression phenotypes and have them in. Is this because this was better done in other datasets? Or too big and annoying on normalisation? I'd explain the rationale to leave these out.

This is a very good point raised by the reviewer, and this is in fact something that we initially wanted to do. However, to render the analysis fair and robust, it would require processing all datasets in the same way. This implies cataloging all existing datasets and processing them through the same pipeline. Then, it also requires adding a “cell type” or “tissue” layer, because gene expression data from whole flies is obviously not directly comparable to gene expression data from specific tissues or even specific conditions. This would be key information as phenotypes are often tissue-dependent. So, as implied by the reviewer, we deemed this too big of a challenge beyond the scope of the current paper. Nevertheless, we plan to continue investigating this avenue, especially given the strong transcriptomics background of our lab, in a potential follow-up paper.

I think 25% FDR is dangerously close to "random chance of being wrong". I'd just redo this section at a higher FDR, even if it makes the results less 'exciting'. This is not the point of the paper anyway.

We agree with the reviewer that this threshold implies a higher risk of false positive results. However, this is not an uncommonly used threshold (Li et al., PLoS biology, 2008; Bevers et al., Nature Metabolism, 2019; Hwangbo et al, Elife, 2023), and one that seems robust enough in our analysis since similar phenotypes are significant in different studies. Nevertheless, we will revisit these results and explore how a more stringent threshold may impact the results.

I didn't buy the extreme line piece as being informative. Something has to be on the top and bottom of the ranks; the phenotypes are an opportunity for collection and probably have known (as you show) and cryptic correlations. I think you don't need this section at all for the paper and worry it gives an idea of "super normals" or "true wild types" which ... I just don't think is helpful.

This section of the paper was intended to investigate anecdotal evidence suggesting that certain DGRP lines consistently rank at the top or bottom when examining fitness-related traits. If accurate, this observation could imply that inbreeding might have made these lines generally weaker, potentially introducing bias into studies aimed at uncovering the genetic basis of complex traits. However, as per the analyses presented, we did not discover support for this phenomenon. Nevertheless, we consider this message important to convey. In response to the reviewer's feedback, we intend to provide a clearer explanation of the reasoning behind this section of the paper and its main conclusion.

I'd say "well-established inversion genotypes and symbiot levels" rather than generic covariates. Covariates could mean anything. You have specific "covariates" which might actually be the causal thing.

Thank you. We will update the manuscript accordingly.

I wouldn't use the adjective tedious about curation. It's a bit of a value judgement and probably places the role of curation in the wrong way. Time-consuming due to lack of standards and best practice?

Thank you. We will update the manuscript accordingly.

Reviewer #2 (Public Review):

Summary:

In the present study, Gardeux et al provide a web-based tool for curated association mapping results from DRP studies. The tool lets users view association results for phenotypes and compare mean phenotype ~ phenotype correlations between studies. In the manuscript, the authors provide several example utilities associated with this new resource, including pan-study summary statistics for sex, traits, and loci. They highlight cross-trait correlations by comparing studies focused on longevity with phenotypes such as oxphos and activity.

Strengths:

-Considerable efforts were dedicated toward curating the many DRG studies provided.

-Available tools to query large DRP studies are sparse and so new tools present appeal

Weaknesses:

The creation of a tool to query these studies for a more detailed understanding of physiologic outcomes seems underdeveloped. These could be improved by enabling usages such as more comprehensive queries of meta-analyses, molecular information to investigate given genes or pathways, and links to other information such as in mouse rat or human associations.

We appreciate the reviewer's kind comments.

Regarding the tools, we concur with the reviewer that incorporating additional tools could enhance DGRPool and facilitate users in conducting meta-analyses. Therefore, we intend to introduce a gene-centric tool that enables users to query the database based on gene names. Additionally, we will establish links to ortholog databases within the GWAS results, thereby allowing users to extend fly gene associations to other species, if required.

Furthermore, we have plans to link out to a 'genome browser-like' view (Flybase’s JBrowse tool) of the GWAS results centered around the affected variants/genes. We are considering integrating this feature into the new gene-centric tool as well.

Another potential downstream analysis we are considering is gene-set enrichment. This analysis would involve assessing the enrichment of genes in Gene Ontology or other pathway databases directly from the GWAS results page.

Author Response

We would like to thank reviewers and editors for their thoughtful and constructive review of our manuscript. Below we have provided responses to specific points in the reviewers’ comments and eLIFE assessment, highlighting areas of the manuscript that will be edited for clarity and where efforts will be made to provide data to address reviewer concerns upon a future resubmission.

eLife assesment:

The authors report that Dbp5 functions in parallel with Los1 in tRNA export, in a manner dependent on Gle1 and requiring the ATPase cycle of Dbp5, but independent of Mex67, Dbp5's partner in mRNA export. The evidence for this conclusion is still incomplete, as is the biochemical evidence that Dbp5 interacts directly with tRNA in vitro with Gle1 and co-factor InsP6 triggering Dbp5 ATPase activity in the Dbp5-tRNA complex. The evidence that Dbp5 interacts with tRNA in cells independently of Los1, Msn5 and Mex67 is, however, solid.

We intend to edit the text to make clear our conclusions and accommodate clarifications on a few details of this assessment.

(1) We would clarify that our data supports a model in which Dbp5 recruitment to tRNA is independent of Mex67 as an adapter in cells; however, this does not mean that Mex67 and Dbp5 do not still co-function in tRNA export. For example, it is possible Dbp5 and Mex67 could still co-function in the same pathway, but instead of Dbp5 working down stream of Mex67, Dbp5 may in fact work upstream as an adapter for Mex67. Edits to the text will be made to ensure this distinction is clear and highlight the possibility for future investigation to elucidate this relationship.

(2) We would like to highlight that based on structural and biochemical data detailing synergistic activation of Dbp5 ATPase cycle by Gle1/InsP6 and single stranded RNA, it is difficult to imagine a scenario where the apparent synergistic activation of Dbp5 ATPase cycle by tRNA and Gle1/InsP6 (Figure 5) is achieved independent of direct RNA binding. For this reason, we still support the claim that the observed synergistic activation, in combination with other in-vivo and in-vitro data provided in the manuscript, support a model where Dbp5 directly binds tRNA. However, we intend to edit the text to highlight this nuance and potential alternative conclusions based on reviewer feedback.

Reviewer #1 (Public Review):

“At least one result suggests that the idea of these pathways in parallel may be too simplistic as deletion of the LOS1 gene, which is not essential decreases the interaction of tRNA export substrate with Dbp5 (Figure 2A). If the two pathways were working in parallel, one might have expected removing one pathway to lead to an increase in the use of the other pathway and hence the interaction with a receptor in that pathway…. The obvious missing experiment here with respect to genetics is the test of whether deletion of the MSN5 gene in the cells, which combines deletion of LOS1 and the dbp5_R423A allele, shown in Figure 1D would be lethal…. The authors provide evidence of a model where the helicase Dbp5 plays a role in tRNA export from the nucleus. Further evidence is required to determine whether Dbp5 could function in the same pathway as the previously defined tRNA export receptors, Los1 and Msn5. There are genetic tests that could be performed to explore this question. Some of the biochemistry presented would show when Los1 is absent that the interaction of Dbp5 with tRNA decreases, which could support a model where Dbp5 plays a role in coordination with Los1”

We agree that this is an important point that should be made clear and discussed in the text. We also agree that further experiments would be needed to be to confirm Dbp5 functions broadly in tRNA export in parallel to both Msn5 and Los1. We will aim to address these points in resubmission and discuss possible alternative conclusions of the presented results.

Reviewer #1 (Public Review):

“While some of the binding assays show rather modest band shifts (Figure 4B for example), the data in Figure 4A showing that there is no binding detected unless a non-hydrolyzable ATP analogue is employed, argues for specificity in nucleic acid binding. The question that does arise is whether the binding is specific for tRNA.”

The specificity of the in-vitro interactions of Dbp5 are an important point of discussion. We will work to expand the topic of specificity of the in-vitro experiments during resubmission.

Reviewer #1 (Public Review):

“With the exception of the binding studies, which also employ a mixture of yeast tRNAs, this study relies primarily on a single tRNA species to come to the conclusions drawn. Many other studies have used multiple tRNAs to explore whether pathways characterized are generalizable to other tRNAs.“

It was previously shown that Dbp5 functions to support the export of multiple tRNA species (https://doi.org/10.7554/eLife.48410). As such, we agree that additional tRNAs should be tested to explore whether phenotypes reported here are also generalizable to other tRNAs. We will add data targeting additional tRNAs during resubmission.

Reviewer #2 (Public Review):

“there are some pieces of data that are misinterpreted. (Figure 1A and B look the same; in Fig 1E, the DAPI staining is abnormal; in Fig 4 the bands can't be seen.)”

Figure 1A and B represent separate experiments, showing that deletion of Los1 does not alter Dbp5 localization and conversely loss of Dbp5 does not alter Los1 localization. As such localization patterns under loss-of-function conditions look the same as wild-type localization for each protein respectively as noted. We believe that we have come to the same conclusion as the reviewer on Figure 1A and B (and this data is not misinterpreted), but also understand this panel will need to be adjusted for clarity and readability. We will make efforts to edit this figure and accompanying text make the data and conclusions clearer, including addressing the EMSAs in figure 4 and associated text for clarity.

Author Response

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

We greatly appreciate the positive feedback of the reviewers and have modified the manuscript to address their comments, including changes to the text, figures, and methods. We believe that these revisions have strengthened and improved the manuscript. Reviewers’ comments in blue and detailed responses in black are below.

Reviewer #1 Weaknesses:

• Is "function" of the ISNs to balance "nutrient need" or osmolarity? Balancing hemolymph osmolarity for physiological homeostasis is conceptually different from balancing thirst and hunger.

We have added the following text to the introduction to address this: “Thus, the ISNs sense both AKH and hemolymph osmolality, arguing that they balance internal osmolality fluctuations and nutrient need (Jourjine, Mullaney et al., 2016).” (ln 80-82).

• The final schematic nicely sums up how the different peptidergic pathways might work together, but it is unclear which connections are empirically-validated or speculative. It would be informative to show which parts of the model are speculative versus validated. For example, does FAFB volume synapse = functional connectivity and not just anatomical proximity? A bulk of the current manuscript relies on "synapses of relatively high confidence" (according to Materials and methods: line 522). I recommend distinguishing empirically tested & predicted connections in the final schematic, and maybe reword/clarify throughout the manuscript as "predicted synaptic partners"

We modified the schematic to clarify EM based connections versus functionally validated connections. We also clarified the EM predicted synaptic partners, using “predicted synaptic partners” throughout the manuscript.

Reviewer #2 Areas for further development:

• Does BIT inhibit all of the IPCs or some of them? I think it is critical to indicate the ROIs used for each neuron in the methods. Which part of the neuron is used for imaging experiments? Dendrites, cell bodies, or synaptic terminals?

ROIs used for quantification are described in the figure legends: “ArcLight response of BiT soma…” (Fig 2, Fig S2), “Calcium responses of CCHa2R-RA neurites in SEZ…” (Fig 4), “Calcium response of CCHa2R-RA SEZ neurites…” (Fig S4), “Calcium response of CCAP neurites…” (Fig 5, Fig S5), “Calcium response of all IPC somas…” (Fig S3). We have added ROIs used for quantification to the ‘In vivo calcium imaging’ and the ‘In vivo voltage imaging’ methods sections (ln 493-494).

• The discussion section is not giving big picture explanation of how these neurons work together to regulate sugar and water ingestion. Silencing and activation experiments are good, but without showing the innate activity of these neural groups during ingestion, it is not clear what their functions are in terms of regulating fly behavior.

We agree that how these peptidergic neurons coordinately regulate feeding is unclear. As peptide signals may act at a distance and may cause long-lasting neural activity state changes, studying their integration over space and time is challenging. Acute imaging during feeding would only in part address this challenge, as cumulative changes in nutrient need signals may impart circuit changes that are not apparent by monitoring the acute activity of peptidergic neurons. We modified a paragraph in the discussion to address this (ln 434-443).

“Overall, our work sheds light on neural circuit mechanisms that translate internal nutrient abundance cues into the coordinated regulation of sugar and water ingestion. We show that the hunger and thirst signals detected by the ISNs influence a network of peptidergic neurons that act in concert to prioritize ingestion of specific nutrients based on internal needs. We hypothesize that multiple internal state signals are integrated in higher brain regions such that combinations of peptides and their actions signify specific needs to drive ingestion of appropriate nutrients. As peptide signals may act at a distance and may cause long-lasting neural activity state changes, studying their integration over space and time is a future challenge to further illuminate homeostatic feeding regulation.”

Reviewer #1 (Recommendations For The Authors):

• For the final schematic figure, it may be informative to include nanchung and AKHR in the schematic.

We now include this (Fig 6).

• For the ingestion duration with optogenetic activation, I don't think the right way to represent the data is by normalizing them to the no LED control. I think it should show raw ingestion time. I understand that the normalized data make the figure "cleaner" (no need to show +/- LED separately) but I think visualization of the raw data is important.

We now include this in a new Supplemental Figure (Fig S6).

• Methods for ingestion with optogenetic activation should be detailed in the Methods section.

We expanded upon this in the ‘Temporal consumption assay (TCA)’ methods section. (ln 461-466).

Reviewer #2 (Recommendations For The Authors):

1) I think the authors are not following the recommendations of the Flywire community which recommends that people who contributed to the tracing of neurons are offered authorship in the published papers. I see the authors are thanking other lab members who have done tracing for the neurons described in this study, but I would like them to clarify whether they are following the guidelines provided by Flywire.

We followed the Flywire guidelines and contacted all Flywire users contributing more that 10% to neuron edits for permission to publish with acknowledgements. (see Flywire guidelines https://docs.google.com/document/d/1bUkOB5JnT3u__JDvAoVDHJ3zr5NXQtV_63yx2w6Tcc/edit).

2) The method section for voltage imaging is missing.

We now include a section on voltage imaging (ln 496-498).

3) ROIs for imaging are not indicated in the methods or in the figures. It is hard to judge what is the origin of neural activity plotted in the figures; are they imaging cell bodies, dendrites, or axons?

ROIs used for quantification are described in the figure legends: “ArcLight response of BiT soma…” (Fig 2, Fig S2), “Calcium responses of CCHa2R-RA neurites in SEZ…” (Fig 4), “Calcium response of CCHa2R-RA SEZ neurites…” (Fig S4), “Calcium response of CCAP neurites…” (Fig 5, Fig S5), “Calcium response of all IPC somas…” (Fig S3). We have added ROIs used for quantification to the ‘In vivo calcium imaging’ and the ‘In vivo voltage imaging’ methods sections (ln 493-494).

Author Response

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

Reviewer #1 (Public Review):

Thank you very much for your advices and comments. We took your suggestion into consideration and decided to modify it as you suggested. We will add more data and analysis on this topic in the article to make the exposition fuller.

1) There are different cells in liver tissue, in which BATF protein is expressed most.

Based on the analysis of single-cell public data (GEO accession: GSE129516), BATF is expressed in every cell cluster in the liver, with the highest expression in T cells and the least in cholangiocytes (Author response image 1).

Author response image 1.

2) The statistical data should be provided to support the liver specific over-expression of BATF.

The results of WB in figure2 (C & E) have been quantified and relevant content has been corrected.

3) For in vivo study, food intake is key data to exclude the change of energy intake.

Feed intake related result plots have been added to figureS2A.

4) For Fig.6 Since PD1 are also highly expressed in heart and spleen, how to exclude the effect of PD1 antibody on these tissues?

According to the images of the heart (Author response image 2 left) and spleen (Author response image 2 right) during mouse dissection, the morphology and size of the two organs were similar in HFD-CN and HFD-PD1 group. Moreover, relevant literature indicated that PD-1 blockade had little impact on the number and function of transferred T cells within the spleen(Peng et al.)，and anti-PD-1 had no effect on mouse splenic cell proliferation (Shindo et al.).Du et al. showed in their study that single use of PD-1 antibody (10 mg/kg, once every three days, for 4 weeks) did not affect mouse heart (Du et al.). Both our results and related literature indicated that PD 1 antibody should not have adverse effects on the heart and spleen.

Author response image 2.

Reviewer #2 (Public Review):

Thank you very much for your advices and comments. We have seriously considered your suggestion and will focus on it in our future research.

Weakness

1) BATF protein is also abundantly expressed in control hepatocyte, but the knockdown of BATF had no effect on lipid accumulation. Besides, the expression of BATF was elevated by high fat diets. So it will be interesting to investigate its role in the liver by using its hepatic conditional knockout mice.

We appreciate the reviewers' suggestion to investigate other functions of BATF in the liver besides its protective role in a high-fat environment. However, we did not use BATF knockout mice in this study because our data indicated that BATF knockdown had no effect on lipid accumulation. We will pursue further research and validation in future studies.

2) The data for the direct regulation of BATF on PD1 and IL-27 is not enough, it is better to carry out CHIP experiment to further confirm it.

Thank you for your valuable comments. The article by Kevin Man et al. found that, upregulation of transcription factor BATF regulates PD1 expression and repairs impaired cellular metabolism (Man et al.). This confirms that BATF has a regulatory effect on PD1. And in our manuscript, the dual luciferase reporter assay of BATF and PD1 confirmed that BATF can regulate the expression of PD1(Fig 5G). This confirms that BATF has a regulatory effect on PD1. We do not have conclusive evidence for a direct interaction between BATF and IL-27 yet, but there are some relevant studies that support their connection. For instance, BATF and IRF1 were found to be transcription factors induced early by IL-27 treatment, and essential for Tr1 cell differentiation and function, both in vitro and in vivo (Karwacz et al.). Moreover, Zhang et al. identified BATF as one of the transcription factors regulating IL-27 expression by transcription factor prediction and RNA sequencing analysis (Zhang et al.). These results lay the foundation for elucidating the regulation of PD1 and IL-27 by BATF.

Reviewer #2 (Recommendations For The Authors):

1. In Figure 3D, which subunit of AMPK was tested, alpha, beta or gamma?

Thank you for your valuable comments. We detected the expression level of AMPKα1, We have modified the relevant names in the figure and manuscript.

Reference：

Du, Shisuo, et al. "Pd-1 Modulates Radiation-Induced Cardiac Toxicity through Cytotoxic T Lymphocytes." 13.4 (2018): 510-20. Print.

Karwacz, Katarzyna, et al. "Critical Role of Irf1 and Batf in Forming Chromatin Landscape During Type 1 Regulatory Cell Differentiation." 18.4 (2017): 412-21. Print.

Man, Kevin, et al. "Transcription Factor Irf4 Promotes Cd8+ T Cell Exhaustion and Limits the Development of Memory-Like T Cells During Chronic Infection." 47.6 (2017): 1129-41. e5. Print.

Peng, Weiyi, et al. "Pd-1 Blockade Enhances T-Cell Migration to Tumors by Elevating Ifn-Γ Inducible Chemokinespd-1 Blockade Improves the Effectiveness of Act for Cancer." 72.20 (2012): 5209-18. Print.

Shindo, Yuichiro, et al. "Interleukin 7 and Anti-Programmed Cell Death 1 Antibody Have Differing Effects to Reverse Sepsis-Induced Immunosuppression." 43.4 (2015): 334. Print.

Zhang, Huiyuan, et al. "An Il-27-Driven Transcriptional Network Identifies Regulators of Il-10 Expression across T Helper Cell Subsets." 33.8 (2020): 108433. Print.

Author Response

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

We would first like to thank the reviewers and the editor for their insightful comments and suggestions. We are particularly glad to read that our so<ware package constitutes a set of “well-written analysis routines” which have “the potential to become very valuable and foundational tools for the analysis of neurophysiological data”. We have updated the manuscript to address their remarks where appropriate.

Additionally, we would like to stress that this kind of tools is in continual development. As such, the manuscript offered a snapshot of the package at one point during this process, which in this case was several months ago at initial submission. Since then, several improvements were implemented. The manuscript has been further updated to reflect these more recent changes.

From the Reviewing Editor:

The reviewers identified a number of fundamental weaknesses in the paper.

1) For a paper demonstrating a toolbox, it seems that some example analyses showing the value of the approach (and potentially the advantage in simplification, etc over previous or other approaches) are really important to demonstrate.

As noted by the first reviewer, the online repository (i.e. GitHub page) conveys a better sense of the toolboxes’ contribution to the field than the present manuscript. This is a fair remark but at the same time, it is unclear how to illustrate this in a journal article without dedicating a great deal of page space to presenting raw code, while online tools offer an easier and clearer way to do this. As a work-around, our strategy was to illustrate some examples of data analysis in Figures 4&5 by comparing each illustrated processing step to the corresponding command line used by the Pynapple package. Each step requires a single line of code, meaning that one only needs to write three lines of code to decode a feature from population activity using a Bayesian decoder (Fig. 4a), compute a cross-correlograms of two neurons during specific stimulus presentation (Fig. 4b) or compute the average firing rate of two neurons around a specific time of the experimental task (Fig. 4c). We believe that these visual aides make it unnecessary to add code in the main text of this manuscript. However, to aid reader understanding, we now provide clear references to online Jupyter notebooks which show how each figure was generated in figure legends as well as in the “Code Availability” section.

https://github.com/pynapple-org/pynapple-paper-2023

Furthermore, we have opted-in for the “Executable Research Articles” feature at eLife, which will make it possible to include live scripts and figures in the manuscript once it is accepted for publication. We do not know at this stage what it entails exactly, but we hope that Figures 4&5 will become live with this feature. The readers will have the possibility to see and edit the code directly within the online version of the manuscript.

2) The manuscript's claims about not having dependencies seem confusing.

We agree that this claim was somewhat unfounded. There are virtually no Python packages that do not have dependencies. Our intention was to say that the package had no dependencies outside the most common ones, which are Numpy, Scipy, and Pandas. Too many packages in the field tend to have long list of dependencies making long-term back-compatibility quite challenging. By keeping depencies minimal, we hope to maximise the package’'s long term back-compatibility. We have rephrased this statement in the manuscript in the following sections:

Figure 1, legend.

“These methods depend only on a few, commonly used, external packages.”

Section Foundational data processing: “they are for the most part built-in and only depend on a few widely-used external packages. This ensures that the package can be used in a near stand-alone fashion, without relying on packages that are at risk of not being maintained or of not being compatible in the near future.”

3) Given its significant relevance, it seems important to cite the FMATool and describe connections between it (or analyses based on it) and the presented work.

Indeed, although we had already cited other toolboxes (including a review covering the topic comprehensively), we should have included this one in the original manuscript. Unfortunately, to the best of our knowledge, this toolbox is not citable (there is no companion paper). We have added a reference to it in plain text.

4) Some discussion of integration between Pynapple and the rest of a full experimental data pipeline should be discussed with regard to reproducibility.

This is an interesting point, and the third paragraph of the discussion somewhat broached this issue. Pynapple was not originally designed to pre-process data. However, it can, in theory, load any type of data streams a<er the necessary pre-processing steps. Overall, modularity is a key aspect of the Pynapple framework, and this is also the case for the integration with data pre-processing pipelines, for example spike sorting in electrophysiology and detection of region of interest in calcium imaging. We do not think there should be an integrated solution to the problem but, instead, to make it possible that any piece of code can be used for data irrespective of their origin. This is why we focused on making data loading straightforward and easy to adapt to any particular situation. To expand on this point and make it clear that Pynapple is not meant to pre-process data but can, in theory, load any type of data streams a<er the necessary pre-processing steps, we have added the following sentences to the aforementioned paragraph:

“Data in neuroscience vary widely in their structure, size, and need for pre-processing. Pynapple is built around the idea that raw data have already been pre-processed (for example, spike sorting and detection of ROIs).”

5) Relatedly, a description of how data are stored a<er processing (i.e., how precisely are processed data stored in NWB format).

We agree that this is a critical issue. NWB is not necessarily the best option as it is not possible to overwrite in a NWB file. This would require the creation of a new NWB file each time, which is computationally expensive and time consuming. It also further increases the odds of writing error. Theoretically, users who needs to store intermediate results in a flexible way could use any methods they prefer, writing their own data files and wrappers to reload these data into Pynapple objects. Indeed, it is not easy to properly store data in an object-specific manner. This is a long-standing issue and one we are currently working to resolve.

To do so, we are developing I/O methods for each Pynapple core objects. We aim to provide an output format that is simple to read and backward compatible in future Pynapple releases. This feature will be available in the coming weeks. To note, while NWB may not be the central data format of Pynapple in future releases, it has become a central node in the neuroscience ecosystem of so<ware. Therefore, we aim to facilitate the interaction of users with reading and writing for this format by developing a set of simple standalone functions.

Reviewer #1 (Public Review):

A typical path from preprocessed data to findings in systems neuroscience o<en includes a set of analyses that o<en share common components. For example, an investigator might want to generate plots that relate one time series (e.g., a set of spike times) to another (measurements of a behavioral parameter such as pupil diameter or running speed). In most cases, each individual scientist writes their own code to carry out these analyses, and thus the same basic analysis is coded repeatedly. This is problematic for several reasons, including the waste of time, the potential for errors, and the greater difficulty inherent in sharing highly customized code.

This paper presents Pynapple, a python package that aims to address those problems.

Strengths:

The authors have identified a key need in the community - well-written analysis routines that carry out a core set of functions and can import data from multiple formats. In addition, they recognized that there are some common elements of many analyses, particularly those involving timeseries, and their object- oriented architecture takes advantage of those commonalities to simplify the overall analysis process.

The package is separated into a core set of applications and another with more advanced applications, with the goal of both providing a streamlined base for analyses and allowing for implementations/inclusion of more experimental approaches.

Weaknesses:

There are two main weaknesses of the paper in its present form.

First, the claims relating to the value of the library in everyday use are not demonstrated clearly. There are no comparisons of, for example, the number of lines of code required to carry out a specific analysis with and without Pynapple or Pynacollada. Similarly, the paper does not give the reader a good sense of how analyses are carried out and how the object-oriented architecture provides a simplified user interaction experience. This contrasts with their GitHub page and associated notebooks which do a better job of showing the package in action.

As noted in the response to the Reviewing Editor and response to the reviewer’s recommendation to the authors below, we have now included links to Jupyter notebooks that highlight how panels of Figures 4 and 5 were generated (https://github.com/pynapple-org/pynapple-paper-2023). However, we believe that including more code in the manuscript than what is currently shown (I.e. abbreviated call to methods on top of panels in Figs 4&5) would decrease the readability of the manuscript.

Second, the paper makes several claims about the values of object-oriented programming and the overall design strategy that are not entirely accurate. For example, object-oriented programming does not inherently reduce coding errors, although it can be part of good so<ware engineering. Similarly, there is a claim that the design strategy "ensures stability" when it would be much more accurate to say that these strategies make it easier to maintain the stability of the code. And the authors state that the package has no dependencies, which is not true in the codebase. These and other claims are made without a clear definition of the properties that good scientific analysis so<ware should have (e.g., stability, extensibility, testing infrastructure, etc.).

Following thFMAe reviewer’s comment, we have rephrased and clarified these claims. We provide detailed response to these remarks in the recommendations to authors below.

There is also a minor issue - these packages address an important need for high-level analysis tools but do not provide associated tools for preprocessing (e.g., spike sorting) or for creating reproducible pipelines for these analyses. This is entirely reasonable, in that no one package can be expected to do everything, but a bit deeper account of the process that takes raw data and produces scientific results would be helpful. In addition, some discussion of how this package could be combined with other tools (e.g., DataJoint, Code Ocean) would help provide context for where Pynapple and Pynacollada could fit into a robust and reliable data analysis ecosystem.

We agree the better explaining how Pynapple is integrated within data preprocessing pipelines is essential. We have clarified this aspect in the manuscript and provide more details below.

Reviewer #1 (Recommendations For The Authors):

Page 1

• Title

The authors should note that the application name- "Pynapple" could be confused with something from Apple. Users may search for "Pyapple" as many python applications contain "py" like "Numpy". "Pyapple" indeed is a Python Apple that works with Apple products. They could consider "NeuroFrame", "NeuroSeries" or "NeuroPandas" to help users realize this is not an apple product.

We thank the referee for this interesting comment. However, we are not willing to make such change at this point. The community of users has been growing in the last year and it seems too late to change the name. To note, it is the first time such comment is made to us and it does not seem that users and collaborators are confused with any Apple products.

• Abstract

The authors mentioned that the Pynapple is "fully open source". It may be better to simply say it is "open source".

We agree, corrected.

Assuming the authors keep the name, it would be helpful if the full meaning of Pynapple - Python Neural Analysis Package was presented as early as possible.

Corrected in the abstract.

• Highlight

An application being lightweight and standalone does not imply nor ensure backward compatibility. In general, it would be useful if the authors identified a set of desirable code characteristics, defined them clearly in the introduction, and then describe their so<ware in terms of those characteristics.

Thank you for your comment. We agree that being lightweight and standalone does not necessarily imply backward compatibility. Our intention was to emphasize that Pynapple is designed to be as simple and flexible as possible, with a focus on providing a consistent interface for users across different versions. However, we understand that this may not be enough to ensure long-term stability, which is why we are committed to regular updates and maintenance to ensure that the code remains functional as the underlying code base (Python versions, etc.) changes.

Regarding your suggestion to identify a set of desirable code characteristics, we believe this is an excellent idea. In the introduction, we briefly touch upon some of the core principles that guided our development of Pynapple: a lightweight, stable, and simple package. However, we acknowledge that providing a more detailed discussion of these characteristics and how they relate to the design of our so<ware would be useful for readers. We have added this paragraph in the discussion:

“Pynapple was developed to be lightweight, stable, and simple. As simplicity does not necessarily imply backward compatibility (i.e. long-term stability of the code), Pynapple main objects and their properties will remain the same for the foreseeable future, even if the code in the backend may eventually change (e.g. not relying on Pandas in future version). The small number of external dependencies also decrease the need to adapt the code to new versions of external packages. This approach favors long-term backward compatibility.”

Page 2

• The authors wrote -

"Despite this rapid progress, data analysis o<en relies on custom-made, lab-specific code, which is susceptible to error and can be difficult to compare across research groups."

It would be helpful to add that custom-made, lab-specific code can lead to a violation of FAIR principles (https://en.wikipedia.org/wiki/FAIR_datadata). More generally, any package can have errors, so it would be helpful to explain any testing regiments or other approach the authors have taken to ensure that their code is error-free.

We understand the importance of the FAIR principles for data sharing. However, Pynapple was not designed to handle data through their pre-processing. The only aspect that is somehow covered by the FAIR principles is the interoperability, but again, it is a requirement for the data to interoperate with different storage and analysis pipelines, not of the analysis framework itself. Unlike custom-made code, Pynapple will make interoperability easier, as, in theory, once the required data loaders are available, any analysis could be run on any dataset. We have added the following sentence to the discussion:

“Data in neuroscience vary widely in their structure, size, and need for pre-processing. Pynapple is built around the idea that raw data has already been pre-processed (for example, spike sorting and ROI detection). According to the FAIR principles, pre-processed data should interoperate across different analysis pipelines. Pynapple makes this interoperability possible as, once the data are loaded in the Pynapple framework, the same code can be used to analyze different datasets”

• The authors wrote -

"While several toolboxes are available to perform neuronal data analysis ti–11,2ti (see ref. 29 for review), most of these programs focus on producing high-level analysis from specified types of data and do not offer the versatility required for rapidly-changing analytical methods and experimental methods."

Here it would be helpful if the authors could give a more specific example or explain why this is problematic enough to be a concern. Users may not see a problem with high-level analysis or using specific data types.

Again, we apologize for not fully elaborating upon our goals here. Our intention was to point out that toolboxes o<en focus on one particular case of high-level analysis. In many cases, such packages lack low level analysis features or the flexibility to derive new analysis pipelines quickly and effortlessly. Users can decide to use low-level packages such as Pandas, but in that case, the learning curve can be steep for users with low, if any, computational background. The simplicity of Pynapple, and the set of examples and notebooks, make it possible for individuals who start coding to be quickly able to analyze their data.

As we do not want to be too specific at this point of the manuscript (second paragraph of the intro) and as we have clarified many of the aspects of the toolbox in the new revised version, we have only added the following sentence to the paragraph:

“Users can decide to use low-level data manipulation packages such as Pandas, but in that case, the learning curve can be steep for users with low, if any, computational background.”

• The authors wrote -

"To meet these needs, a general toolbox for data analysis must be designed with a few principles in mind"

Toolboxes based on many different principles can solve problems. It is likely more accurate to say that the authors designed their toolbox with a particular set of principles in mind. A clear description of those principles (as mentioned in the comment above) would help the reader understand why the specific choices made are beneficial.

We agree that these are not “universal” principles and clearly more the principles we had in mind when we designed the package. We have clarified these principles and made clear that these are personal point of views.

We have rephrased the following paragraph:

“To meet these needs, we designed Pynapple, a general toolbox for data analysis in systems Neuroscience with a few principles in mind.“

• The authors wrote -

"The first property of such a toolbox is that it should be object-oriented, organizing so<ware around data."

What facts make this true? For example, React is a web development library. A common approach to using this library is to use Hooks (essentially a collection of functions). This is becoming more popular than the previous approach of using Components (a collection of classes). This is an example of how Object-oriented programming is not always the best solution. In some cases, for example, object- oriented coding can cause problems (e.g. it can be hard to find the place where a given function is defined and to figure out which version is being used given complex inheritance structures.)

In general, key selling points of object-oriented programming are extension, inheritance, and encapsulation. If the authors want to retain this text (which would be entirely reasonable), it would be helpful if they explained clearly how an object-oriented approach enables these functions and why they are critical for this application in particular.

The referee makes a particularly important point. We are aware of the limits of OOP, especially when these objects become over-complex, and that the inheritance become unclear.

We have clarified our goal here. We believe that in our case, OOP is powerful and, overall, is less error- prone that a collection of functions. The reasons are the following:

An object-oriented approach facilitates better interactions between objects. By encapsulating data and behavior within objects, object-oriented programming promotes clear and well-defined interfaces between objects. This results in more structured and manageable code, as objects communicate with each other through these well-defined interfaces. Such improved interactions lead to increased code reliability.

Inheritance, a key concept in object-oriented programming, allows for the inheritance of properties. One important example of how inheritance is crucial in the Pynapple framework is the time support of Pynapple objects. It determines the valid epoch on which the object is defined. This property needs to be carried over during different manipulations of the object. Without OOP, this property could easily be forgotten, resulting in erroneous conclusions for many types of analysis. The simplest case is the average rate of a TS object: the rate must be computed on the time support ( a property of TS objects), not the beginning to the end of the recording (or of a specific epoch, independent of the TS). Finally, it is easier to access and manipulate the meta information of a Pynapple object than without using objects.

• The authors wrote -

"drastically diminishing the odds of a coding error"

This seems a bit strong here. Perhaps "reducing the odds" would be more accurate.

We agree. Now changed.

Page 3

• The authors wrote -

". Another property of an efficient toolbox is that as much data as possible should be captured by only a small number of objects This ensures that the same code can be used for various datasets and eliminates the need of adapting the structure"

It may be better to write something like - "Objects have a collection of preset variables/values that are well suited for general use and are very flexible." Capturing "as much data as possible" may be confusing, because it's not the amount that this helps with but rather the variety.

We thank the referee for this remark. We have rephrased this sentence as follows:

“Another property of an efficient toolbox is that a small number of objects could virtually represents all possible data streams in neuroscience, instead of objects made for specific physiological processes (e.g. spike trains).”

• The authors wrote -

"The properties listed above ensure the long-term stability of a toolbox, a crucial aspect for maintaining the code repository. Toolboxes built around these principles will be maximally flexible and will have the most general application"

There are two issues with this statement. First, ensuring long-term stability is only possible with a long- term commitment of time and resources to ensure that that code remains functional as the underlying code base (python versions, etc.) changes. If that is something you are commisng to, it would be great to make that clear. If not, these statements need to be less firm.

Second, it is not clear how these properties were arrived at in the first place. There are things like the FAIR Principles which could provide an organizing framework, ideally when combined with good so<ware engineering practices, and if some more systematic discussion of these properties and their justification could be added, it would help the field think about this issue more clearly.

The referee makes a valid point that ensuring long-term stability requires a long-term commitment of time and resources to maintain the code as the underlying technology evolves. While we cannot make guarantees about the future of Pynapple, we believe that one of the best ways to ensure long-term stability is by fostering a strong community of users and contributors who can provide ongoing support and development. By promoting open-source collaboration and encouraging community involvement, we hope to create a sustainable ecosystem around Pynapple that can adapt to changes in technology and scientific practices over time. Ultimately, the longevity of any scientific tool depends on its adoption and use by the research community, and we hope that Pynapple can provide value to neuroscience researchers and continue to evolve and improve as the field progresses.

It is noteworthy that the first author, and main developer of the package, has now been hired as a data scientist at the Center for Computational Neuroscience, Flatiron Institute, to explicitly continue the development of the tool and build a community of users and contributors.

• The authors wrote -

"each with a limited number of methods..."

This may give the impression that the functionality is limited, so rephrasing may be helpful.

Indeed! We have now rephrased this sentence:

“The core of Pynapple is five versatile timeseries objects, whose methods make it possible to intuitively manipulate and analyze the data.”

• The authors wrote that object-oriented coding

"limits the chances of coding error"

This is not always the case, but if it is the case here, it would be helpful if the authors explain exactly how it helps to use object-oriented approaches for this package.

We agree with the referee that it is not always the case. As we explained above, we believe it is less error-prone that a collection of functions. Quite o<en, it also makes it easier to debug. We have changed this sentence with the following one:

“Because objects are designed to be self-contained and interact with each other through well-defined methods, users are less likely to make errors when using them. This is because objects can enforce their own internal consistency, reducing the chances of data inconsistencies or unexpected behavior. Overall, OOP is a powerful tool for managing complexity and reducing errors in scientific programming.”

• Fig 1

In object-oriented programming, a class is a blueprint for the classes that inherit it. Instantiating that<br /> class creates an object. An object contains any or all of these - data, methods, and events. The figure could be improved if it maintained these organizational principles as figure properties.

We agree with the referee’s remark regarding the logic of objects instantiation but how this could be incorporated in Fig. 1 without making it too complex is unclear. Here, objects are instantiated from the first to the second column. We have not provided details about the parent objects, as we believe these details are not important for reader comprehension. In its present form, the objects are inherited from Pandas objects, but it is possible that a future version is based on something else. For the users, this will be transparent as the toolbox is designed in such a way that only the methods that are specific to Pynapple are needed to do most computation, while only expert programmers may be interested in using Pandas functionalities.

• The authors wrote that Pynapple does -

"not depend on any external package"

As mentioned above, this is not true. It depends on Numpy and likely other packages, and this should be explained. It is perfectly reasonable to say that it depends on only a few other packages.

As said above, we have now clarified this claim.

Page 5.

• The authors wrote -

"represent arrays of Ts and Tsd"

For a knowledgeable reader's reference, it would be helpful to refer to these either as Numpy arrays (at least at first when they are defined) or as lists if they are native python objects.

Indeed, using the word “arrays” here could be confusing because of Numpy arrays. We have changed this term with “groups”.

• The authors wrote -

"Pynapple is built with objects from the Pandas library ... Pynapple objects inherit the computational stability and flexibility"

Here a definition of stability would be useful. Is it the case that by stability you mean "does not change o<en"? Or is some other meaning of stability implied?

Yes, this is exactly what we meant when referring to the stability of Pandas. We have added the following precision:

“As such, Pynapple objects inherit the long-term consistency of the code and the computational flexibility computational stability and flexibility from this widely used package.”

Page 6

• Fig 2

In Fig 2 A and B, the illustrations are good. It would also be very helpful to use toy code examples to illustrate how Pynapple will be used to carry out on a sample analysis-problem so that potential users can see what would need to be done.

We appreciate the kind works. Regarding the toy code, this is what we tried to do in Fig. 4. Instead of including the code directly in the paper, which does not seem a modern way of doing this, we now refer to the online notebooks that reproduce all panels of Figure 4.

• The authors wrote -

"While these objects and methods are relatively few"

In object-oriented programming, objects contain methods. If a method is not in an object, it is not technically a method but a function. It would be helpful if the authors made sure their terminology is accurate, perhaps by saying something like "While there are relatively few objects, and while each object has relatively few methods ... "

We agree with the referee, we have changed the sentence accordingly.

• The authors wrote -

"if not implemented correctly, they can be both computationally intensive and highly susceptible to user error"

Here the authors are using "correctly" to refer to two things - "accuracy" - gesng the right answer, and "efficiency" - gesng to that answer with relatively less computation. It would be clearer if they split out those two concepts in the phrasing.

Indeed, we used the term to cover both aspects of the problem, leading to the two possible issues cited in the second part of the sentence. We have changed the sentence following the referee’s advice:

“While there are relatively few objects, and while each object has relatively few methods, they are the foundation of almost any analysis in systems neuroscience. However, if not implemented efficiently, they can be computationally intensive and if not implemented accurately, they are highly susceptible to user error.”

• In the next sentence the authors wrote -

"Pynapple addresses this concern."

This statement would benefit from just additional text explaining how the concern is addressed.

We thank the referee for the suggestion. We have changed the sentence to this one: “The implementation of core features in Pynapple addresses the concerns of efficiency and accuracy”

Page 9

• The authors wrote -

This is implemented via a set of specialized object subclasses of the BaseLoader class. To avoid code redundancy, these I/O classes inherit the properties of the BaseLoader class. "

From a programming perspective, the point of a base class is to avoid redundancy, so it might be better to just mention that this avoids the need to redefine I/O operations in each class.

We have rephrased the sentence as follows:

“This is implemented via a set of specialized object subclasses of the BaseLoader class, avoiding the need to redefine I/O operations in each subclass"

• The authors wrote -

"classes are unique and independent from each other, ensuring stability"

How do classes being unique and independent ensure stability? Perhaps here again the misunderstanding is due to the lack of a definition of stability.

We thank the referee for the remark. We first changed “stability” for “long-term backward compatibility”. We further added the following sentence to clarify this claim. “For instance, if the spike sorting tool Phy changes its output in the future, this would not affect the “Neurosuite” IO class as they are independent of each other. This allows each tool to be updated or modified independently, without requiring changes to the other tool or the overall data format.”

• The authors wrote -

"Using preexisting code to load data in a specific manner instead of rewriting already existing functions avoids preprocessing errors"

Here it might be helpful to use the lingo of Object-oriented programming. (e.g. inheritance and polymorphism). Defining these terms for a neuroscience audience would be useful as well.

We do not think it is necessary to use too much technical term in this manuscript. However, this sentence was indeed confusing. We have now simplified it:

“[…], users can develop their own custom I/O using available template classes. Pynapple already includes several of such templates and we expect this collection to grow in the future.”

Page 10

• The authors wrote -

"These analyses are powerful because they are able to describe the relationships between time series objects while requiring the fewest number of parameters to be set by the user."

It is not clear that this makes for a powerful analysis as opposed to an easy-to-use analysis.

We have changed “powerful” with “easy to use".

Page 12

"they are built-in and thus do not have any external dependencies"

If the authors want to retain this, it would be helpful to explain (perhaps in the introduction) why having fewer external dependencies is useful. And is it true that these functions use only base python classes?

We have rephrased this sentence as follows:

“they are for the most part built-in and only depend on a few common external packages, ensuring that they can be used stand-alone without relying on packages that are at risk of not being maintained or of not being compatible in the near future.”

Other comments:

• It would be helpful, as mentioned in the public review, to frame this work in the broader context of what is needed to go from data to scientific results so that people understand what this package does and does not provide.

We have added the following sentence to the discussion to make sure readers understand:

“The path from data collection to reliable results involves a number of critical steps: exploratory data analysis, development of an analysis pipeline that can involve custom-made developed processing steps, and ideally the use of that pipeline and others to replicate the results. Pynapple provides a platform for these steps.”

• It would also be helpful to describe the Pynapple so<ware ecosystem as something that readers could contribute to. Note here that GNU may not be a good license. Technically, GNU requires any changes users make to Pynapple for their internal needs to be offered back to the Pynapple team. Some labs may find that burdensome or unacceptable. A workaround would be to have GNU and MIT licenses.

The main restriction of the GPL license is that if the code is changed by others and released, a similar license should be used, so that it cannot become proprietary. We therefore stick to this choice of license.

We would be more than happy to receive contributions from the community. To note, several users outside the lab have already contributed. We have added the following sentence in the introduction:

“As all users are also invited to contribute to the Pynapple ecosystem, this framework also provides a foundation upon which novel analyses can be shared and collectively built by the neuroscience community.”

• This so<ware shares some similarities with the nelpy package, and some mention of that package would be appropriate.

While we acknowledge the reviewer's observation that Nelpy is a similar package to Pynapple, there are several important differences between the two.

First, Nelpy includes predefined objects such as SpikeTrain, BinnedSpikeTrain, and AnalogSignal, whereas Pynapple would use only Ts and Tsd for those. This design choice was made to provide greater flexibility and allow users to define their own data structures as needed.

Second, Nelpy is primarily focused on electrophysiology data, whereas Pynapple is designed to handle a wider range of data types, including calcium imaging and behavioral data. This reflects our belief that the NWB format should be able to accommodate diverse experimental paradigms and modalities.

Finally, while Nelpy offers visualization and high-level analysis tools tailored to electrophysiology, Pynapple takes a more general-purpose approach. We believe that users should be free to choose their own visualization and analysis tools based on their specific needs and preferences.

The package has now been cited.

Reviewer #2 (Public Review):

Pynapple and Pynacollada have the potential to become very valuable and foundational tools for the analysis of neurophysiological data. NWB still has a steep learning curve and Pynapple offers a user- friendly toolset that can also serve as a wrapper for NWB.

The scope of the manuscript is not clear to me, and the authors could help clarify if Pynacollada and other toolsets in the making become a future aspect of this paper (and Pynapple), or are the authors planning on building these as separate publications.

The author writes that Pynapple can be used without the I/O layer, but the author should clarify how or if Pynapple may work outside NWB.

Absolutely. Pynapple can be used for generic data analysis, with no requirement of specific inputs nor NWB data. For example, the lab is currently using it for a computational project in which the data are loaded from simple files (and not from full I/O functions as provided in the toolbox) for further analysis and figure generation.

This was already noted in the manuscript, last paragraph of the section “Importing data from common and custom pipelines”

“Third, users can still use Pynapple without using the I/O layer of Pynapple.”.

We have added the following sentence in the discussion

“To note, Pynapple can be used without the I/O layer and independent of NWB for generic, on-the-fly analysis of data.”

This brings us to an important fundamental question. What are the advantages of the current approach, where data is imported into the Ts objects, compared to doing the data import into NWB files directly, and then making Pynapple secondary objects loaded from the NWB file? Does NWB natively have the ability to store the 5 object types or are they initialized on every load call?

NWB and Pynapple are complimentary but not interdependent. NWB is meant to ensure long-term storage of data and as such contains a as much information as possible to describe the experiment. Pynapple does not use NWB to directly store the objects, however it can read from NWB to organize the data in Pynapple objects. Since the original version of this manuscript was submitted, new methods address this. Specifically, in the current beta version, each object now has a “save” method. Obviously, we are developing functions to load these objects as well. This does not depend on NWB but on npz, a Numpy specific file format. However, we believe it is a bit too premature to include these recent developments in the manuscript and prefer not to discuss this for now.

Many of these functions and objects have a long history in MATLAB - which documents their usefulness, and I believe it would be fisng to put further stress on this aspect - what aspects already existed in MATLAB and what is completely novel. A widely used MATLAB toolset, the FMA toolbox (the Freely moving animal toolbox) has not been cited, which I believe is a mistake.

We agree that the FMA toolbox should have been cited. This ha now been corrected.

Pynapple was first developed in Matlab (it was then called TSToolbox). The first advantage is of course that Python is more accessible than Matlab. It has also been adopted by a large community of developers in data analysis and signal processing, which has become without a doubt much larger than the Matlab community, making it possible to find solutions online for virtually any problem one can have. Furthermore, in our experience, trainees are now unwilling to get training in Matlab.

Yet, Python has drawbacks, which we are fully aware of. Matlab can be very computationally efficient, and old code can usually run without any change, even many years later.

A limitation in using NWB files is its standardization with limited built-in options for derived data and additional metadata. How are derived data stored in the NWB files?

NWB has predetermined a certain number of data containers, which are most common in systems neuroscience. It is theoretically possible to store any kind of data and associated metadata in NWB but this is difficult for a non-expert user. In addition, NWB does not allow data replacement, making is necessary to rewrite a whole new NWB file each time derived data are changed and stored. Therefore, we are currently addressing this issue as described above. Derived data and metadata will soon be easy to store and read.

How is Pynapple handling an existing NWB dataset, where spikes, behavioral traces, and other data types have already been imported?

This is an interesting point. In theory, Pynapple should be able to open a NWB file automatically, without providing much information. In fact, it is challenging to open a NWB file without knowing what to look for exactly and how the data were preprocessed. This would require adapting a I/O function for a specific NWB file. Unfortunately, we do not believe there is a universal solution to this problem. There are solutions being developed by others, for example NWB Widgets (NWB Widgets). We will keep an eye on this and see whether this could be adapted to create a universal NWB loader for Pynapple.

Reviewer #2 (Recommendations For The Authors):

Other tools and solutions are being developed by the NWB community. How will you make sure that these tools can take advantage of Pynapple and vice versa?

We recognize the importance of collaboration within the NWB community and are committed to making sure that our tools can integrate seamlessly with other tools and solutions developed by the community.

Regarding Pynapple specifically, we are designing it to be modular and flexible, with clear APIs and documentation, so that other tools can easily interface with it. One important thing is that we want to make sure Pynapple is not too dependent of another package or file format such as NWB. Ideally, Pynapple should be designed so that it is independent of the underlying data storage pipeline.

Most of the tools that have been developed in the NWB community so far were designed for data visualisation and data conversion, something that Pynapple does not currently address. Multiple packages for behavioral analysis and exploration of electro/optophysiological datasets are compatible with the NWB format but do not provide additional solutions per se. They are complementary to Pynapple.

Author Response

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

We would like to thank you for your thorough review of the manuscript. We have taken all comments into account in the revised version of the manuscript. Please find below our detailed responses to your comments.

eLife assessment

This study reports useful information on the limits of the organotypic culture of neonatal mouse testes, which has been regarded as an experimental strategy that can be extended to humans in the clinical setting for the conservation and subsequent re-use of testicular tissue. The evidence that the culture of testicular fragments of 6.5-day-old mouse testes does not allow optimal differentiation of steroidogenic cells is compelling and would be useful to the scientific community in the field for further optimizations.

Thank you for this assessment. We have carefully considered all comments and made the requested revisions to improve the manuscript.

Reviewer #1 (Public Review):

In this manuscript, the authors aimed to compare, from testis tissues at different ages from mice in vivo and after culture, multiple aspects of Leydig cells. These aspects included mRNA levels, proliferation, apoptosis, steroid levels, protein levels, etc. A lot of work was put into this manuscript in terms of experiments, systems, and approaches. However, as written the manuscript is incredibly difficult to follow. The Introduction and Results sections contain rather loosely organized lists of information that were altogether confusing. At the end of reading these sections, it was unclear what advance was provided by this work. The technical aspects of this work may be of interest to labs working on the specific topics of in vitro spermatogenesis for fertility preservation but fail to appeal to a broader readership. This may be best exemplified by the statements at the end of both the Abstract and Discussion which state that more work needs to be done to improve this system.

As suggested, we have reworked the manuscript to make it clearer, more meaningful and more precise. We believe that this work may be of interest to a broader readership. Indeed, the development of a model of in vitro spermatogenesis could be of interest for labs working on the specific period of puberty initiation, on germ and somatic cell maturation and on steroidogenesis under physiological and pathological conditions, and could also be useful for testing the toxicity of cancer therapies, drugs, chemicals and environmental agents (e.g. endocrine disruptors) on the developing testis.

There is a crucial unmet need to optimize the culture conditions for in vitro spermatogenesis. It is important to identify the deregulated molecular mechanisms leading to a decreased in vitro spermatogenic yield. Such results will be of great help to improve organotypic culture conditions. In the present study, we not only uncovered for the first time a failure in adult Leydig cell development, but also an alteration in the expression of several steroidogenic and steroid-metabolizing genes, which could explain the accumulation of progesterone and estradiol and the deficiency of androstenedione in cultured tissues. This hyperestrogenic and hypoandrogenic environment could explain, at least in part, the low efficiency of in vitro spermatogenesis. Furthermore, we show that the addition of hCG (LH homolog) is not sufficient to facilitate Leydig cell differentiation, restore steroidogenesis and improve sperm yield. These data provide valuable information for improving culture conditions. More fundamentally, this culture system could be a useful tool for identifying factors that are essential for the differentiation and functionality of adult Leydig cells during puberty initiation.

Recommendations For The Authors:

This reviewer appreciates that a lot of work was put into this manuscript in terms of experiments, systems, and approaches. However, the manuscript needs significant revision, and in this reviewer's opinion is not appropriate for a broader readership journal. The results seem rather incremental, and the topic is too specialized in its current format.

The manuscript was significantly revised taking into account the reviewer’s comments. In addition, as mentioned above, the development of a model of in vitro spermatogenesis could have wider applications and be of interest to a broader audience.

Comments for improvement, roughly in order of appearance:

1) Abstract - would recommend condensing to hit the main points of the manuscript.

The abstract has been condensed as suggested.

2) Introduction, overall - this is a rather loosely organized list of information that is not synthesized or communicated in a meaningful way. It contains overstatements and lumps together findings from both mice and primates and thus several statements for the actions of these steroid hormones are inaccurate. The authors rely much too heavily upon reviews and need to replace those with a more scholarly approach of carefully reading and citing primary literature.

The Introduction has been reorganized to make it clearer, more synthetic, more meaningful and more accurate. Only findings from rodents are presented. We carefully read the literature and replaced most of reviews by primary literature.

3) Results - this section was extremely difficult to read and comprehend, as it's essentially a laundry list of measurements of mRNAs, steroids, cholesterols, and proteins that go up or down or don't change at multiple ages, both in vitro and in vivo. The section would be improved greatly by an organization with rationale and concluding statements to prepare the reader for the factoid-style data that are presented.

As suggested, the Results section has been improved by an organization with rationale and concluding statements to make it easier to read and comprehend.

4) 47 - is this approach going to both "preserve and restore"? Sounds more like it will allow for the production of offspring, but the other goals are not going to happen from the approach listed in the latter part of that sentence - so not really "fertility restoration" but more of an insurance program that sperm can be produced for ART

Freezing of prepubertal testicular tissue, which contains spermatogonia, is a fertility preservation option proposed to prepubertal boys with cancer prior to highly gonadotoxic treatments. Several fertility restoration strategies, which aim to allow the production of spermatozoa from cryopreserved spermatogonia, are being developed, including in vitro spermatogenesis. This sentence has been rewritten.

5) 62 - specify whether this "decreased expression" is mRNA or protein, and is this because of a loss of Sertoli cells?

“Decreased expression” was replaced by “decreased mRNA levels”. The results we obtained in the cited study (Rondanino et al., 2017) suggest that the decrease in Rhox5 mRNA levels is not the consequence of a change in the proportion of Sertoli cells but reflects an alteration in Rhox5 gene expression. In Figure 6U of the present study, we show indeed that there is no loss of Sertoli cells in organotypic cultures.

6) 66 - what is "the first wave of mouse in vitro spermatogenesis"? Are these cultures from the first wave of mouse in vivo spermatogenesis, or is there a second wave of in vitro spermatogenesis? Please specify

In the mouse, the first entry into meiosis occurs around 8-10 dpp and the first spermatozoa are produced at around 35 dpp: this is the first wave of spermatogenesis which takes place at the onset of puberty. By culturing 6 dpp-old testes for 30 days, our aim is to reproduce in vitro all the stages of this first wave of spermatogenesis, i.e. entry into meiosis, completion of meiosis and spermiogenesis.

In the cited study (Pence et al., 2019), the authors cultured 5 dpp testes for 35 to 49 days and observed a decline in intratesticular testosterone levels in the cultured tissues, i.e. after the end of the first spermatogenic wave, compared to in vivo controls. Our sentence has been rewritten to make it clearer.

7) 78 - is there a difference in T production by Fetal vs Adult LCs? It is this reviewer's understanding that the levels of T around birth in mice (and then a few months after birth in humans) are quite high, similar to adults. So, what are the authors suggesting here by providing the list of expressed genes in these two LC populations?

As mentioned in the Introduction section, 17β-HSD3 – the enzyme responsible for the conversion of androstenedione to T – is not expressed in fetal Leydig cells but is expressed in adult Leydig cells. Therefore, unlike adult Leydig cells, fetal Leydig cells are not capable of synthesizing T.

In the present study, we investigated steroidogenesis but also wondered which types of Leydig cells could be detected under in vitro conditions. It is therefore important to explain to the reader which steroidogenic proteins are expressed by the different Leydig cell populations.

As described in O’Shaughnessy et al., 2002, levels of intratesticular T decline after birth, being very low between 10 and 20 dpp. Then, T levels increase. At 25 dpp, T levels are close to those observed at 1 dpp. T levels increase more than 16-fold between 25 and 30 dpp and then double between 30 dpp and adulthood. Therefore, intratesticular T levels around birth in mice are not as high as in adults, but are about 36-fold lower after birth than in adulthood. It has been shown that in the fetal testis, the conversion of androstenedione produced by fetal Leydig cells is achieved by the adjacent fetal Sertoli cells that express 17β-HSD3 (O’Shaughnessy et al., 2000; Shima et al., 2013). During postnatal development however, Sertoli cells lose the expression of 17β-HSD3 (O’Shaughnessy et al., 2000).

8) 79 -99 - can the authors revise this long list of information to provide a summary of what they are trying to communicate to the reader? What is the intention of this information?

This paragraph has been modified to make it clearer and more synthetic. As different Leydig cell markers are presented in the Results section, it is important to introduce the reader to the different types of Leydig cells, the proteins expressed by these cells and the factors involved in their proliferation and differentiation.

9) 101-2 - replace "involved in" with a more meaningful word - and it is this reviewer's understanding that T has not been shown convincingly to have much of a role in spermatogonial development, at least in mice - that statement is likely true in primates, but not mice; provide primary literature citations to be more precise, rather than a broad review that covers multiple species

“involved in” was replaced by “is essential for many aspects of spermatogenesis, including”. Moreover, we removed “spermatogonial proliferation and differentiation” and provide primary literature citations to be more precise.

10) 105-7 - similar concern for E as for T, above - KO mouse models for ERalpha and beta did not show defects in spermatogenesis as described - not sure what evidence the authors are specifically referring to here - cite primary literature rather than a review on Vitamin D + estrogen

We agree that the question of whether estrogens play a direct role in spermatogenesis was unanswered by the ER null mice. However, estrogens have been shown to be important for the long-term maintenance of spermatogenesis in the ArKO mouse (Robertson et al., 1999) and for the progression of normal germ cell development in the ENERKI mouse (Sinkevicius et al., 2009). This sentence has been reworded and primary literature is cited to be more precise.

11) 113-4 - there is no convincing evidence this reviewer is aware of that the AR is expressed in male germ cells, and therefore T actions on germ cells are indirect, through Sertoli cells and perhaps PTMs; if there is some, this sentence needs a citation showing that

We agree that there is no evidence that AR is expressed in male germ cells and that T acts indirectly on germ cells. This sentence has been rewritten.

12) 114-6 - this is untrue - nowhere in that paper was testosterone or androgen even mentioned!

This reference has been removed. We apologize for this mistake.

13) 116-7 - again, E actions through the ERs are thought to be indirect in the testis, not acting on germ cells; if this is incorrect, please add supportive citations and explain; replace "involved" with a more meaningful word; Rhox5 has a very minor role in spermatogenesis

In contrast to androgen receptors, which are localized in somatic cells, estrogen receptors have been found in most testicular cells, including germ cells. The studies reporting the expression of estrogen receptors in germ cells are cited in the Introduction section. The word “involved” was replaced by “promotes”.

Rhox5 (also known as Pem) has not a very minor role in spermatogenesis. On the contrary, its expression is crucial for normal spermatogenesis and sperm maturation, as loss of Rhox5 in male mice leads to reduced fertility, increased germ cell apoptosis, decreased sperm count and decreased sperm motility (MacLean et al., 2005).

14) 117 - Ref 29 does not support the statement about Rhox5's role in spermatogenesis

The reference (MacLean et al., 2005), supporting the statement about Rhox5’s role in spermatogenesis, was added in the manuscript.

15) 120 - Does FAAH have a protective role in that it is anti-apoptotic? Or just required for some other Sertoli cell function? Should re-word to be more specific.

FAAH (fatty acid amide hydrolase), whose expression is stimulated by estrogens, has been shown to have a crucial role in promoting survival of Sertoli cells by degrading anandamide (N-arachidonoylethanolamine), an endocannabinoid which has a pro-apoptotic activity (Rossi et al., 2007).

The sentence has been reworded to be more specific.

16) 127 - should complete the Introduction with a sentence summarizing what was done and found, for reader clarity

The Introduction has been completed for reader clarity.

17) 136 - misspelled the procedure

Orchidectomy was replaced by orchiectomy.

18) Mice - why use half-day nomenclature for postpartum mice? This is not standard in the literature.

Half-day nomenclature was used due to the uncertainty of the time of birth, which mostly takes place during the night. Since this is not standard in the literature, half-day nomenclature was removed in the entire manuscript.

19) 172-3 - the half-life of RA is very short (<1 hr), and it is light-sensitive. This addition every 8 days means that retinoids are present for a very minimal window of time - are the authors sure retinoids have no requirement elsewhere during spermatogenesis? And in the literature, the measured pulse of RA in the mouse lasts >40 hours (stages VII-IX)...

RA is mandatory for proper spermatogenesis and is needed many times during spermatogenesis (for review, see Schleif et al., 2022): RA is involved in spermatogonial differentiation, pre-meiotic activation and meiotic completion, establishment of the blood-testis barrier and spermiation. In our study, we did not add RA in the culture medium but retinol, the precursor of RA. Indeed, our previous studies have shown beneficial effects of retinol on in vitro spermatogenesis, including an increased production of spermatids with less nuclear alterations and DNA damage (Arkoun et al., 2015; Dumont et al., 2016).

The reason we added retinol (and not RA, which has a very short half-life) in this study and in our previous studies is that it can be oxidized into RA but also be stored in Sertoli cells in the form of retinyl esters for later use. As retinol is photosensitive, handling and storage were performed in tubes covered with aluminum foil, which protects from direct light exposure.

20) 362 - Start the Results section with a broader statement(s) that prepares the readers rather than jumping into specific experiments; it would be helpful for readers to have concluding sentences included as well for readers to navigate the Results section.

As suggested, the Results section has been improved by an organization with rationale and concluding sentences to facilitate reading.

21) 364 - KI67 is a marker of.

Ki67 is widely used as a cell proliferation marker.

22) 367 - replace "involved".

“involved” was replaced by “necessary for”.

23) What intensity thresholds were used to define a cell as positive or negative for a given marker? And there seemed to be no mention of controls - especially no primary antibody controls. This is a significant oversight if these were not done in parallel with every single immunostaining experiment.

We did not apply intensity thresholds. Cells presenting detectable labeling were defined as positive, while unlabeled cells were defined as negative.

Negative controls, performed by omitting the primary antibodies, were of course done in parallel to each immunostaining and are presented in Figure 1A, Figure 2J and Figure 5C. The mention of negative controls has been added in the Materials and methods section.

24) 388 - INSL3 - is this referring to mRNA or protein? Protein nomenclature is used...

INSL3 is here referring to the protein, whose concentrations were measured by radioimmunoassay.

25) 402 - typo.

“expect” was replaced by “except”.

26) 409 - do mRNA levels really "determine the testicular steroidogenic potential"??

This sentence has been reworded: “determine the testicular steroidogenic potential” was replaced by “highlight a potential deregulation of their expression”.

27) 410 - western should not be capitalized.

Western Blot was replaced by western blot in the entire manuscript.

28) 405-28 - this reviewer is underwhelmed by qRT-PCR results for a handful of markers - what is the purpose? The results do not prove anything about the function of the system.

As the differentiation of Leydig cells is not fully completed in organotypic cultures, we wanted to know which actors of the steroidogenic pathway show deregulated expression in vitro in comparison to physiological conditions, and thus which steps of the steroid hormone biosynthesis pathway may be impaired. We found that the expression of several genes encoding steroidogenic enzymes was decreased in vitro, notably that of Cyp17a1, necessary for the conversion of progesterone to androstenedione. Transcript levels of Hsd17b2, encoding an enzyme that converts estradiol to estrone and testosterone to androstenedione, were also decreased at D30.

Our data therefore show that the expression of several steroidogenic genes and steroid metabolizing genes is deregulated in organotypic cultures but we agree that these results do not prove anything about the function of the system.

We then found an accumulation of estradiol and progesterone, a decrease in androstenedione and unchanged testosterone levels in cultured tissues. The elevation in progesterone and the reduction in androstenedione in in vitro matured tissues could arise from the reduced expression of Cyp17a1. In addition, reduced Hsd17b2 transcript levels may explain why estradiol levels remain elevated in cultures while testosterone levels are similar to controls and androstenedione levels are low.

29) How do the authors interpret data gleaned from tissues containing a variably-sized necrotic core?

In the present study, the central necrotic area was consistent between all samples and variables: it represents on average 16-27% of the explants.

As in our previous publications and recent RNA-seq analyses (Rondanino et al., 2017; Oblette et al., 2019; Dumont et al., 2023), the central necrotic area was removed so that transcript and protein levels in the healthy part of the samples (i.e. where in vitro spermatogenesis occurs) could be measured and compared with in vivo controls. In order to be able to compare the healthy part of the in vitro matured tissues with in vivo controls, transcript levels were normalized to housekeeping genes (Gapdh and Actb) or to the Leydig cell-specific gene Hsd3b1 while protein levels were normalized to ACTB or to 3β-HSD.

30) 520 - after reading to this point, this reviewer was left confused and wondering why any of this is important to the reader unless that reader specifically works on this topic. The way the data were presented makes it nearly impossible for the reader to keep any of the data in their mind as they read. It's a seemingly endless list of ups and downs of many things under many conditions. What is the point of all of this? How will it advance our understanding of spermatogenesis? Or improve in vitro culture? Or help prepubertal cancer patients? Presumably, that will be explained in the Discussion, but at this point, this reviewer honestly has no idea what this all means. Why is this important??

We have modified the Results section by including rationale and concluding statements to make it easier to read and follow for all readers, not necessarily for those working on this topic.

As mentioned above, the identification of the molecular mechanisms that are deregulated in vitro will give us important insights for the optimization of the culture system. The development of an optimized model of in vitro spermatogenesis could lead to several applications, including improving our knowledge of the regulation of spermatogenesis during pubertal development.

In this study, our main findings are that the differentiation of the adult Leydig cell lineage, steroid biosynthesis, metabolism and signaling are altered in organotypic cultures, leading to a hyperestrogenic and hypoandrogenic environment. In addition, we show that the presence of an LH homolog, known to be critical to adult Leydig cell differentiation and to stimulate steroidogenesis, does not rescue the expression of adult Leydig cell markers and of several steroidogenic genes, steroid metabolizing genes and steroid target genes. Other factors required for Leydig cell maturation and functionality will have to be tested in the future on cultured testicular tissues. Improvements to this in vitro maturation procedure in animal models may be useful for future cultures of human testicular biopsies, although we are aware that more work needs to be done before prepubertal cancer patients can benefit from this in vitro maturation approach.

31) 619-20 - this sort of summarizes this reviewer's overall opinion of the manuscript. Not much seems to have been learned here that would justify publication in a broad readership journal like eLife. More work needs to be done to provide that sort of meaningful advance. The current work, with considerable re-writing to improve accuracy and clarity, is much better suited to a specialty journal where others who are working on this specific topic will appreciate its value.

We have carefully considered the reviewer’s comments and modified the manuscript to improve accuracy and clarity. We understand the reviewer’s point of view, but we believe that this work may be of interest not only to labs working on fertility preservation and restoration, but also to those working on puberty initiation, germ and somatic cell maturation, steroidogenesis under physiological and pathological conditions, and on the effect of cancer therapies, drugs, chemicals and environmental agents (e.g. endocrine disruptors) on the developing testis.

As mentioned above, we not only uncovered for the first time a failure in adult Leydig cell development, but also an alteration in the expression of several steroidogenic and steroid-metabolizing genes, which could explain the accumulation of progesterone and estradiol and the deficiency of androstenedione in cultured tissues. This hyperestrogenic and hypoandrogenic environment could explain, at least in part, the low efficiency of in vitro spermatogenesis. Furthermore, we show that the addition of hCG (LH homolog) is not sufficient to facilitate Leydig cell differentiation, restore steroidogenesis and improve sperm yield. These data provide valuable information for improving culture conditions. More fundamentally, this culture system could be a useful tool for identifying factors that are essential for the differentiation and functionality of adult Leydig cells during puberty initiation.

32) Why are the figures repeated at the end of the manuscript?

During the submission process, our bioRxiv preprint (which contains the figures) was merged with the same but higher quality figures.

Reviewer #2 (Public Review):

Preserving and restoring the fertility of prepubertal patients undergoing gonadotoxic treatments involves freezing testicular fragments and waking them up in a culture in the context of medically assisted procreation. This implies that spermatogenesis must be fully reproduced ex vivo. The parameters of this type of culture must be validated using non-human models. In this article, the authors make an extensive study of the quality of the organotypic culture of neonatal mouse testes, paying particular attention to the differentiation and endocrine function of Leydig cells. They show that fetal Leydig cells present at the start of culture fail to complete the differentiation process into adult Leydig cells, which has an impact on the nature of the steroids produced and even on the signaling of these hormones.

The authors make an extensive study of the different populations of Leydig cells which are supposed to succeed each other during the first month of life of the mouse to end up with a population of adult and fully functional cells. The authors combine quantitative in situ studies with more global analyzes (RT-QtPCR Western blot, hormonal assays), which range from gene to hormone. This study is well written and illustrated, the description of the methods is honest, the analyses systematic, and are accompanied by multiple relevant control conditions.

Since the aim of the study was to study Leydig cell differentiation in neonatal mouse testis cultures, the study is well conceived, the results answer the initial question and are not over-interpreted.

My main concern is to understand why the authors have undertaken so much work when they mention RNA extractions and western blot, that the necrotic central part had to be carefully removed. There is no information on how this parameter was considered for immunohistochemistry and steroid measurements. The authors describe the initial material as a quarter testis, but they don't mention the resulting size of the fragment. A brief review of the literature shows that if often the culture medium is crucial for the quality of the culture (and in particular the supplementations as discussed by the authors here), the size of the fragments is also a determining factor, especially for long cultures. The main limitation of the study is therefore that the authors cannot exclude that central necrosis can have harmful effects on the survival and/or the growth and/or the differentiation of the testis in culture. In this sense, the general interpretation that the authors make of their work is correct, the culture conditions are not optimized.

When using the organotypic culture system at a gas-liquid interphase, the central part of the testicular tissue becomes necrotic. As previously reported (Komeya et al., 2016), the central region receives insufficient nutrients and oxygen. In vitro spermatogenesis therefore only occurs in the seminiferous tubules present in the peripheral region. As in our previous publications and recent RNA-seq analyses (Rondanino et al., 2017; Oblette et al., 2019; Dumont et al., 2023), the central necrotic area was removed so that transcript and protein levels in the healthy part of the samples (i.e. where in vitro spermatogenesis occurs) could be measured and compared with in vivo controls. For histological and immunohistochemical analyses, only seminiferous tubules located at the periphery of the cultured fragments (outside of the necrotic region) were analyzed. Steroid measurements were performed on the entire fragments.

The initial material was indeed a quarter testis, which represents approximately 0.75 mm3. No growth of the fragments was observed during the organotypic culture period (Figure 8-figure supplement 1). We agree with the reviewer that the composition of the culture medium is not the only parameter to be considered for the quality of the culture and that the size of the fragments is also a determining factor. We previously determined that 0.75 mm3 was the most appropriate size for mouse in vitro spermatogenesis (Dumont et al., 2016). We do not exclude at all that central necrosis can have harmful effects on the survival and/or the growth and/or the differentiation of the testis in culture. Optimization of the culture medium and culture design (so that the tissue center receives sufficient nutrients and oxygen) will be necessary to increase the yield of in vitro spermatogenesis.

Organotypic culture is currently trying to cross the doors of academic research laboratories to become a clinical tool, but it requires many adjustments and many quality controls. This study shows a perfect example of the pitfall often associated with this approach. The road is still long, but every piece of information is useful.

Reviewer #3 (Public Review):

Moutard, Laura, et al. investigated the gene expression and functional aspects of Leydig cells in a cryopreservation/long-term culture system. The authors found that critical genetic markers for Leydig cells were diminished when compared to the in-vivo testis. The testis also showed less androgen production and androgen responsiveness. Although they did not produce normal testosterone concentrations in basal media conditions, the cultured testis still remained highly responsive to gonadotrophin exposure, exhibiting a large increase in androgen production. Even after the hCG-dependent increase in testosterone, genetic markers of Leydig cells remained low, which means there is still a missing factor in the culture media that facilitates proper Leydig cell differentiation. Optimizing this testis culture protocol to help maintain proper Leydig cell differentiation could be useful for future human testis biopsy cultures, which will help preserve fertility and child cancer patients.

Methods: In line 226, there is mention that the central necrotic area was carefully removed before RNA extraction. This is particularly problematic for the inference of these results, especially for the RT-qPCR data. Was the central necrotic area consistent between all samples and variables (16 and 30FT)? How big was the area? This makes the in-vivo testis not a proper control for all comparisons. Leydig cells are not evenly distributed throughout the testis. A lot of Leydig cells can be found toward the center of the gonad, so the results might be driven by the loss of this region of the testis.

When using the organotypic culture system at a gas-liquid interphase, the central part of the testicular tissue becomes necrotic. As previously reported (Komeya et al., 2016), the central region receives insufficient nutrients and oxygen. In vitro spermatogenesis therefore only occurs in the seminiferous tubules present in the peripheral region. As in our previous publications and recent RNA-seq analyses (Rondanino et al., 2017; Oblette et al., 2019; Dumont et al., 2023), the central necrotic area was removed so that transcript levels in the healthy part of the samples (i.e. where in vitro spermatogenesis occurs) could be measured and compared with in vivo controls. In order to be able to compare the healthy part of the in vitro matured tissues with in vivo controls, transcript levels of the selected genes were normalized to housekeeping genes (Gapdh and Actb) or to the Leydig cell-specific gene Hsd3b1.

The central necrotic area was consistent between all samples and variables: it represents on average 16-27% of the explants.

Moreover, we would like to point out that the gonads were cut into four fragments before in vitro cultures. It is therefore the central part of the cultured explants that was removed and not the central part of the gonads. The central part of the gonads was thus included in our analyses.

What did the morphology of the testis look like after culturing for 16 and 30 days? These images will help confirm that the culturing method is like the Nature paper Sato et al. 2011 and also give a sense of how big the necrotic region was and how it varied with culturing time.

Images showing mouse testicular tissues cultured for 16 and 30 days are presented in Figure 8-figure supplement 1. The cultured tissues resemble those shown by Sato et al., 2011. As mentioned above, the central necrotic area represents on average 16-27% of the explants. No significant difference in the area of the necrotic region was found between the two culture time points.

There are multiple comparisons being made. Bonferroni corrections on p-value should be done.

Bonferroni corrections are used when multiple comparisons are conducted. As mentioned in the Materials and methods section, multiple comparisons were not made in this study. Indeed, the non-parametric Mann-Whitney test was used to compare two conditions: in vitro vs in vivo (D16 FT vs 22 dpp, D16 CSF vs 22 dpp, D30 FT vs 36 dpp, D30 CSF vs 36 dpp, D30 FT + hCG vs 36 dpp, D30 CSF + hCG vs 36 dpp), cultures of fresh vs frozen tissues (6 dpp vs 6 dpp CSF, D16 FT vs D16 CSF, D30 FT vs D30 CSF, D30 FT + hCG vs D30 CSF + hCG) and cultures with vs without hCG (D30 FT + hCG vs D30 FT, D30 CSF + hCG vs D30 CSF). These comparisons were added in the Materials and methods section.

Results: In the discussion, it is mentioned that IGF1 may be a missing factor in the media that could help Leydig cell differentiation. Have the authors tried this experiment? Improving this existing culturing method will be highly valuable.

The decreased Igf1 mRNA levels found in the present study are in line with the RNA-seq data of Yao et al., 2017. As mentioned in the Discussion section, the addition of IGF1 in the culture medium led to a modest increase in the percentages of round and elongated spermatids in cultured mouse testicular fragments (Yao et al., 2017). However, the effect of IGF1 supplementation on Leydig cell differentiation was not investigated. The supplementation of organotypic culture medium with IGF1 is currently being tested in our research team.

Add p-values and SEM for qPCR data. This was done for hormones, should be the same way for other results.

p-values and SEM are shown for both qPCR and hormone data.

Regarding all RT-qPCR data-There is a switch between 3bHSD and Actb/Gapdh as housekeeping genes. There does not seem to be as some have 3bHSD and others do not. Why do Igf1 and Dhh not use 3bHSD for housekeeping? If this is the method to be used, then 3bHSD should be used as housekeeping for the protein data, instead of ACTB. Also, based on Figure 1B and Figure 2A (Hsd3b1) there does not seem to be a strong correlation between Leydig cell # and the gene expression of Hsd3b1. If Hsd3b1 is to be used as a housekeeper and a proxy for Leydig cell number a correlation between these two measurements is necessary. If there is no correlation a housekeeping gene that is stable among all samples should be used. Sorting Leydig cells and then conducting qPCR would be optimal for these experiments.

Hsd3b1 was used as a housekeeping gene only to normalize the mRNA levels of Leydig cell-specific genes. Therefore, Igf1 and Dhh transcript levels were not normalized with Hsd3b1 since Igf1 is expressed by several cell types in the testis (Leydig cells, Sertoli cells, peritubular myoid cells) and Dhh is expressed by Sertoli cells.

Regarding western blots, the expression of AR, CYP19 and FAAH could not be normalized with 3-HSD since AR is expressed by Leydig cells, Sertoli cells and peritubular myoid cells, CYP19 is expressed by Leydig cells and germ cells and FAAH is expressed by Sertoli cells. For CYP17A1 however, 3B-HSD was used as housekeeping instead of ACTB (Figure 2G).

No correlation was found between the number of Leydig cells per cm2 of testicular tissue shown in Figure 1 and Hsd3b1 mRNA levels presented in Figure 2. However, this result was expected since on the one hand the number of Leydig cells per cm2 was determined in the peripheral region of one tissue section whereas on the other hand Hsd3b1 transcript levels were measured in the entire peripheral region of the cultured fragments. The correction factor used for the analysis of genes expressed in Leydig cells present in the healthy part of the cultured tissues was therefore the Leydig cell selective marker Hsd3b1, as previously described (Cacciola et al., 2013).

Figure 2A (CYP17a1): It is surprising that the CYP17a1 gene and protein expression is very different between D30FT and 36.5dpp, however, the immunostaining looks identical between all groups. Why is this? A lower magnification image of the testis might make it easier to see the differences in Cyp17a1 expression. Leydig cells commonly have autofluorescence and need a background quencher (TrueBlack) to visualize the true signal in Leydig cells. This might reveal the true differences in Cyp17a1.

RT-qPCR and western blot analyses show that both Cyp17a1 mRNA levels and CYP17A1 protein levels are decreased in organotypic cultures at D30. However, we agree that such a decrease is not visible in immunostaining. No autofluorescence of Leydig cells could be observed in the negative controls (Figure 2J).

Figure 3D: there are large differences in estradiol concentration in the testis. Could it be that the testis is becoming more female-like? Leydig and Sertoli cells with more granulosa and theca cell features? Were any female markers investigated?

We show in the present study that the expression levels of the Sertoli cell-specific gene Dhh are not reduced in organotypic cultures. We also previously found that the expression levels of the Sertoli cell-specific gene Amh were not reduced in in vitro matured testicular tissues (Rondanino et al., 2017). Moreover, we have recently shown that Sox9, encoding a testis-specific transcription factor, is expressed in organotypic cultures (Dumont et al., 2023). Our recent transcriptomic analysis also revealed that the transcript levels of the pro-male sexual differentiation marker Sry and of the Sertoli cell-specific gene Dmrt1 remained unchanged in organotypic cultures compared to in vivo controls (Dumont et al., 2023). In addition, no increase in the mRNA levels of the female sex-determining genes Foxl2 and Rspo1 was found in vitro (Dumont et al., 2023). However, we cannot rule out that in vitro cultured testes are becoming more female-like as the expression of Hsd17b3, encoding an androgenic enzyme, is reduced (this study) while the expression of the feminizing gene Wnt4 is upregulated (Dumont et al., 2023).

Figure 3D and Figure 5A: It is hard to imagine that intratesticular estradiol is maintained for 16-30 days without sufficient CYP19 activity or substrate (testosterone). 6.5 dpp was the last day with abundant CYP19 expression, so is most of the estrogen synthesized on this first day and it sticks around? Are there differences in estradiol metabolizing enzymes? Is there an alternative mechanism for E production?

In the present study, abundant CYP19 expression was indeed found at 6 dpp. However, the expression of this enzyme was not measured between 6 dpp and D16. Therefore, we cannot be sure that 6 dpp is the last day with abundant CYP19 expression. We assume that the estradiol synthesized before D16 may then accumulate within the cultured tissues. In our study, we quantified the transcript levels of Sult1e1, encoding an estradiol metabolizing enzyme. SULT1E1 is thought to play a physiological role in protecting Leydig cells from estrogen-induced biochemical lesions (Tong et al., 2004). A reduction in Sult1e1 mRNA levels was found at D30 in comparison to in vivo controls, but this may occur earlier during organotypic culture. In addition, decreased transcript levels of Hsd17b2, which encodes an estrogen metabolizing enzyme that converts estradiol to estrone, were found at D30 in this study. We suggest in the Discussion section that elevated estradiol levels in cultured tissues could be a consequence of low Sult1e1 and Hsd17b2 expression. Our recent transcriptomic analyses show that the levels of Cyp1a1, Cyp1b1 and Comt, encoding other estrogen metabolizing enzymes, are unchanged in organotypic cultures (Dumont et al., 2023). To our knowledge, there is no alternative mechanism for estradiol production.

Recommendations For The Authors:

1) The acronyms, PLC, SLC, ILC, ALC, and FLC, become hard to follow. It is recommended to spell out the names.

PLC was replaced by progenitor Leydig cells, SLC by stem Leydig cells, ILC by immature Leydig cells, ALC by adult Leydig cells and FLC by fetal Leydig cells in the entire manuscript.

2) All Figures: Use letters for each bar graph. Difficult to make a connection from text to figure.

A letter was added to each bar graph.

3) Supplemental figure 1: Change "Changement du milieu" to English.

These words were replaced by “Medium change”.

4) Catalog numbers for antibodies are necessary.

The catalog numbers of the antibodies used in this study are presented in Supplementary Table 1.

Author Response

Reviewer #1 (Public Review)

The authors present a scRNAseq study describing the transcriptomes of the tendon enthesis during postnatal development. This is an important topic that has major implication for the care of common clinical problems such as rotator cuff repair. The results are a valuable addition to the literature, providing a descriptive data set reinforcing other, more comprehensive studies. There are weaknesses, however, in the scRNAseq analyses.

1)The authors should provide additional rationale for the PCA analysis shown in Fig 1d. It is uncommon to use PCA for histomorphologic parameters. These results do not convincingly demonstrate that P7 is as a critical developmental timepoint.

2) According to the methods, it appears that the entire humeral head-supraspinatus tendon was used for cell isolation for scRNAseq. This results in the inclusion of cells from a variety of tissues, including bone, growth plate, enthesis and tendon. As such, only a very small percentage of cells in the analysis came from the enthesis. Inclusion of such a wide range of cells makes interpretation of enthesis cells difficult.

3) The differentiationpseudotime analysis described in Fig 3 is difficult to follow. This map includes cell transcriptomes from vastly different tissues. Presumably, embedded in these maps are trajectories for osteoblast differentiation, chondrocyte differentiation, tenocyte differentiation, etc. With so many layers of overlapping information, it is difficult to (algorithmically) deduce a differentiation path of a particular cell type.

4) The authors uses the term function throughout the paper (e.g., functional definition of fibrocartilage subpopulations). However, this is a descriptive scRNAseq study, and function can therefore only theoretically be inferred from the algorithms used to analyze the data. A functional role for any of the identified pathways or processes can only be defined with gain- andor loss-of-function studies.

5) C2 highly expressed biomineralization-related genes (Clec3a, Tnn, Acan). The three example genes are not related to biomineralization.

6) The functional characterization of the three enthesis cell clusters is not convincing. For example, activation of metabolism-related processes can mean a lot of things (including changes in differentiation), yet the authors interpret it very specifically as role in postnatal fibrochondrocyte formation and growth.

7) The pseudotime analysis of the enthesis cell clusters is not convincing. The three clusters are quite close and overlapping on the UMAP. Furthermore, the authors focus on Tnn as a novel and unique gene, yet the expression pattern shown in Fig 5g implies even expression of this gene across all three clusters.

8) The TC1 markers (Ly6a, Dlk3, Clec3b) imply a non-tendon-specific cell population. Perhaps a tendon progenitor pool or an endothelial cell phenotype is more appropriate.

9) Pseudotime analyses assume that your data set includes cells from progenitor through mature cell populations. It is unclear that the timepoints studied here included cells from early progenitor states.

10) The CellChat analysis is difficult to follow, as the authors included 18 cell types. The number of possible interactions among so many cell types is enormous, and deducing valid connections between any two cell types in this case should be justified. Is the algorithm robust to so many possible interactions

Thank you very much for your comments and suggestions. According to your suggestions, we carefully revised the paper. We integrated our dataset with open source GSE182997 datasets and re-performed the downstream analysis. On the other hand, we added immunofluorescence tests to validate the results came from single-cell datasets. And we hope all the mentioned issues in prior version to be well addressed.

Reviewer #2 (Public Review)

To reveals cellular and molecular heterogeneity in enthesis, the authors established a single-cell temporal atlas during development. This study provides a transcriptional resource for further investigation of fibrocartilage development.

Thank you very much for your kind suggestions. According to your suggestions, we integrated our dataset with open source GSE182997 datasets and re-performed the downstream analysis. On the other hand, we added immunofluorescence tests to validate the results came from sinlge-cell datasets. And we hope the mentioned issues in prior version to be well addressed.

Author Response

eLife assesssment:

This paper conducts human and rodent experiments of non-invasive diffusion MRI estimates of axon diameter with the aim to establish whether these estimates provide biologically specific markers of axonal degeneration in MS. It will be of interest to researchers developing quantitative MRI methods and scientists studying neurodegeneration. The experiments provide evidence for the sensitivity of these markers, but do not directly validate axon diameter and do not reflect common pathological mechanisms across rodents and humans.

We thank the Editor for the appreciation of our work. Thanks to the addition of an extensive electron microscopy paradigm, we now include a direct validation of axonal damage and expand on the common pathological mechanisms across the two species. The new results are detailed in the manuscript and summarized in Fig. 3 in the manuscript

Reviewer #1 (Public Review):

1.1 My primary concern relates to how meaningful the human-rodent comparisons are, and whether these comparisons really advance our understanding of AxCaliber estimates in MS. I applaud the aim to conduct "matched" experiments in both rodent models and human disease. It is a strength that the experiments are aligned with respect to the MRI measurements (although there are some caveats to this mentioned below). But beyond that, the overlap is not what one might hope for: the pathology would seem to be very distinct in humans and rodents, and the histological validation is not specific to what the MRI measurements claim to estimate. To summarize the main findings: (i) in a rat model of general axonal degeneration, axon calibre estimates correlate with neurofilaments; (ii) in MS in humans, axon calibre estimates correlate with demyelinating lesions. This gives a picture of AxCalibre estimates correlating with neuropathology, but is this something that has not already been established in the literature? If the aim is to validate AxCaliber, then there is a logic in using a rodent model that isolates alterations to axonal radius, but what then does this add to the existing literature in that space? If the aim is to study MS (for which AxCaliber results have been previously reported in Huang et al), then why not use a rodent model of MS?

We thank the reviewer for their very insightful comments. Indeed, multiple sclerosis (MS) is a chronic neuroinflammatory and neurodegenerative disease of unknown etiology. An enormous effort has been made to obtain animal models that simulate the pathogenesis of this disease. However, while several models exist recapitulating distinct aspects of the disease (mostly related to demyelination), MS fundamentally remains a disease that only affects humans. This does not mean that EAE or lysolecithin models do not provide information on specific aspects and are therefore valuable. In fact, we believe that trying to replicate the pathological mechanisms of this disease in an animal model goes beyond the scope of the present work. In this work, our intention is to validate a biomarker of axonal damage preclinically, and for this, we use a model of axonal degeneration. We do not claim that this model should be valid to capture the complex clinical and pathological manifestation of MS, but we do think that it is a necessary step to ensure MRI sensitivity to axonal pathology. Why necessary? Because all the available (very limited) MRI literature which provides some form of validation: i) only focuses on healthy tissue, and ii) has an n of 1. Our preclinical paradigm gives conclusive evidence that the MRI axonal diameter proxy detects axonal damage as an increase in the mean diameter. This is now detailed in the discussion.

After this necessary preclinical validation, we then apply the same framework to a human disease like MS that, among other manifestations, is believed to also cause axonal pathology. The improvements with respect to the one published work about axonal diameter in MS are: i) the whole brain analysis, which allowed us to characterize the extent of these early alterations outside the demyelinated lesions; and ii) the larger sample size, which allowed us to uncover an association with disease duration, strengthening our hypothesis about increased axonal diameter being a marker of early disease (new Fig. 5).

Regarding the nonspecificity of histological validation, we thank the reviewer for this insightful comment, which triggered an additional analysis that we believe has added further value to the paper. Using electron microscopy, we found that in our model of neurodegeneration, axonal damage is indeed reflected as an increase in axon diameter (new Fig. 3). These recent findings strongly support the validation of our noninvasive diffusion MRI estimates of axon diameter alterations as an early-stage hallmark of normal-appearing tissue in MS.

Coming back to the comparison between pathology in humans and in rodents, the EM data also support our choice of preclinical model, showing axonal swelling, the same phenomenon reported and characterized in recent postmortem histological data in the normal-appearing white matter of MS patients (Luchicchi et al., Ann Neurol 2021) and in lesions (Fisher et al. Ann Neurol 2007).

All in all, we are confident that the new data supports the validity of this translational approach, and shed new light into the degenerating aspect of MS.

Changes in the manuscript

• Discussion, pag.12: It is important to stress that the aim of this work is not to propose a new animal model of MS, a disease that only affects humans, but rather to validate axonal damage detection (independently from the pathology that has induced it) through noninvasive MRI and apply the framework to characterize axonal pathology in MS.

1.2 I appreciate that both rodent and patient studies are time intensive, major endeavors. Neverthless, the number of subjects is very low in both rodent (n=9) and human (MS=10, control=6) studies. At the very least, this should be more openly acknowledged. But I'm concerned that this is a major weakness of the paper. Related to this, I find it hard to tell how carefully multiple comparison correction was performed throughout. It seems reasonably clear for the TBSS analyses, but then other analyses were performed in ROIs. Are these multiple comparisons corrected as well? Similarly, in Methods, I am confused by the statement that: "post hoc t tests corrected for multiple comparisons whenever a significant effect was detected". What does this mean?

We thank the reviewer for this comment. We agree that a small sample size was a weakness of the previous version of the paper, and therefore, in the new version, we have substantially increased the n for both animal and human experiments (from n=9 to 19 in animals, from 16 to 21 in humans). We removed the ROI analysis in the new version, and thus the confusing statement, and clarified the strategy for multiple comparisons.

Changes in the manuscript

• Data analysis, pag. 18: Lesion masks were excluded from the statistical analysis, and multiple comparisons across clusters were controlled for by using threshold-free cluster enhancement.

1.3 While I do not think the text is in any sense deliberately misleading, I think the authors would do well to either tone down their claims or consider more carefully the implications of the text in many places. Some that stuck out for me are:

Throughout, language in the paper (e.g., "Paired t tests were used to assess differences in the axonal diameter") presumes that the AxCaliber estimates specifically reflect axon diameter. I think the jury is out over whether this is true, particularly for measurements conducted with limited hardware specs. At the very least, I would encourage the author to refer to these measurements throughout as "estimates" of axon diameter.

Thank you for this clarification. We have indeed changed the notation, and now consistently refer to the estimates of axon diameter through MRI as the “MRI axonal diameter proxy”.

1.4 The authors suggest that their results provide "new tools for patient stratification" based on differences in lesion type, but it isn't clear what new information these markers would confer given that the lesions are differentiated based on T1w hypo/hyperintensities. In other words, these lesions are by definition already differentiable from a much simpler MRI marker.

Thank you for this insightful comment. The reviewer is right, and following the general reviewers’ assessment we have decided to not include the lesion analysis in the new version of the manuscript.

1.5 The authors note in the Discussion that: "sensitive to early stages of axonal degeneration, even before alterations in the myelin sheet are detected". Whether intentional or not, the implication in the context of this study is that this would hold for MS (that these markers would detect axonal degeneration preceding demyelination). While there is some discussion of alterations to axonal diameter in MS, the authors do not discuss whether these are the same mechanisms thought to occur in the IBO intervention used here.

Thank you for this comment. Indeed, the scope of the paper is not to assess whether axonal swelling precedes or not myelin alterations, so we agree with the reviewer that this sentence might be misleading and have removed it in the text. While we do not claim that ibotenic acid injections are able to replicate the complex clinical and pathological manifestation of MS (and now we made it clear in the revised manuscript, see comment 1), the electron microscopy paradigm indicates the presence of axonal swelling in the damaged fimbria, which is indeed the same pathological manifestation found in MS post-mortem data (see e.g. Fisher et al. Ann Neurol 2007).

1.6 In the Discussion, the authors note the lack of evidence for a relationship with disability or disease duration, but nevertheless, go on to interpret the "trends" they do observe. I would advise strongly against this: the authors acknowledge that their numbers are low, so I would avoid the temptation to speculate here.

The reviewer is 100% correct. We should have refrained from speculating. In the new version of the paper, however, thanks to the larger human cohort, we were able to find significant associations with disease duration in voxelwise analysis of the white matter skeleton in standard space and in the whole white matter in single subject space (new Figure 5).

1.7 In the Discussion state that "the use of neurofilaments has also been well validated in MS". Well validated for what? MS is a complex disease with a broad range of pathology, so this statement could be read to mean "neurofilaments are known to be altered in MS". However, in the context of this paragraph, the implication would seem to be that neurofilaments are a wellestablished proxy for axonal diameter. Is that the implication, and if so what general evidence is there for this?

We thank the reviewer for this insightful comment. Indeed, altered neurofilaments are not conclusive evidence of increased axonal diameter. In this context, the addition of electron microscopy data in the new manuscript version supports the claim.

Reviewer #2 (Public Review):

Diffusion MRI is sensitive to the brain microstructure, and it has been used to assess the integrity of white matter for nearly 3 decades. Its main limitation is the limited specificity, which makes it difficult to link changes in diffusion parameters to a given pathological substrate. Recently methods based on diffusion MRI that enable the estimation of axonal diameter, non invasively, have become available. This paper aims at validating one of such methods using an experimental model of neurodegeneration. The authors found a significant correlation between axonal diameter estimated by MRI and an histological marker of neurodegeneration. Although this is of great interest, as it demonstrates that this method is sensitive to neurodegeneration, a direct validation would require a measurement of axonal diameter using electron or confocal microscopy, rather than a correlation with a measure of axonal degeneration not directly related to axonal diameter. So, although these data are compelling, they do not prove that the increase in axonal diameter suggested by diffusion MRI corresponds to actual axonal swelling. The Authors also apply the same method to compare the white matter of patients with multiple sclerosis (MS) and healthy controls, showing widespread increases in axonal diameter in the patients. These data are compelling, but again, not conclusive. Other factors such as gloss could bias the MRI measurement and lead to an apparent increase in axonal diameter.

We would like to thank the reviewer for the positive assessment of our work and for the valuable suggestion. We are confident that the new version of the manuscript, by including an extensive validation based on electron microscopy, has addressed the reviewer´s criticisms.

Reviewer #3 (Public Review):

3.1 In this paper, Toschi et al. performed dMRI to in vivo estimate axon diameter in the brain and demonstrated that multi-compartmental modeling (AxCaliber) is sensitive to microstructural axonal damage in rats and axon caliber increase in demyelinating lesions in MS patients, suggesting that axon diameter mapping provides a potential biomarker to bridge the gap between medical imaging contrasts and biological microstructure. In particular, authors injected ibotenic acid (IBO) and saline in the left and right rat hippocampus, respectively, and compared in vivo estimated axon diameter and ex vivo neurofilament staining in left and right fimbria. The axon size estimation was larger in the fimbria of IBO injection side, where the neurofilament intensity is higher. Correlation of axon size estimation and neurofilament intensity was observed in both injection sides. Further, higher axon diameter estimation was observed in normal appearing white matter (NAWM) of MS patients, compared with the healthy subjects. The axon size estimation increased in hypointense lesions of T1 weighted contrast, but not in isointense lesions. Through the comparison of dMRI-estimated axon size and histology-based fluorescence intensity, authors indirectly validated the sensitivity of axon diameter mapping to the tissue microstructure in the rat brain, and further explored the axon size change in the brain of MS patients. However, the dMRI protocol and biophysical modeling in this study were not fully optimized to maximize the sensitivity to axon size estimation, and the dMRI-estimated axon size (4.4-5.4 micron) was much larger than values reported in previous histological studies (0.5-3 micron) [Barazany et al., Brain 2009]. Finally, although the modified AxCaliber model incorporated two fiber bundles in different directions, the fiber dispersion in each bundle was not considered (c.f. fiber dispersion ~20-30 degree in corpus callosum), potentially leading to overestimated axon diameter.

We thank the reviewer for their appreciation of our work, which we believe is substantially improved in this revised version through the inclusion of an electron microscopy paradigm. Below, the point-by-point response to the specific points raised.

3.2 The conclusions in this study are supported by experimental results. However, the dMRI protocol and biophysical model could be further optimized and validated: 1. To in vivo estimate the axon diameter ~1 micron using dMRI, strong diffusion weighting (b-value) should be applied to maximize the signal decay due to intra-axonal restricted diffusion and minimize the signal contribution of extra-cellular hindered diffusion. However, authors only applied maximal b-value = 4000 s/mm2, much smaller than values ~15,00020,000 s/mm2 in previous studies [Assaf et al., MRM 2008; Huang et al., BSAF 2020, 225:1277]. The use of low diffusion weighting in this study leads to a lower bound ~4-6 micron for accurate diameter estimation, the so-called resolution limit in [Nilsson et al., NMR Biomed 2017, 30:e3711]. In other words, the estimated axon diameter is potentially overestimated and related with the imaging protocol and image quality, confounding the biological interpretation.

We thank the reviewer for this insightful comment. Indeed, while the resolution limit is a concern, the chosen b-value has been a compromise between sensitivity to small structure and SNR, as indicated by recent animal (Crater et al., 2022) and human (Jensen et al., 2016; McKinnon et al., 2017; Moss et al., 2019) work, pointing at 3000-4000 s/mm2 as the b-value for which the intra-axonal water signal is dominant. In addition, a paper from the laboratory that first developed the Axcaliber method recently came out (Gast et al., 2023, DOI: 10.1007/s12021-023-09630-w) demonstrating that an MRI protocol with a maximum b-value between 3000 and 4000 s/mm2 (and even lower) is sufficient to capture, in vivo and in humans, various well-known aspects of axonal morphometry (e.g., the corpus callosum axon diameter variation) as well as other aspects that are less explored (e.g., axon diameter-based separation of the superior longitudinal fasciculus into segments). The same paper contains resources and further bibliography supporting the fact that experimental evidence suggests that the contribution of intra-axonal water to restricted diffusion signals dominates other factors (see Online Resource 1, section A of the same paper). To challenge this recent evidence from a neurobiology perspective, we include in the supplementary material a subset of experiments in animals with lower maximum b-value (2500 s/mm2, Fig. S1), where we are able to detect the same effect of increased MRI axonal diameter proxy in the injected hemisphere compared to control.

We would like to add that while extremely valuable and informative, simulation studies such as the excellent study by Veraart et al., 2020, are inevitably valid under certain assumptions. Among them, some critical ones are i) the need to neglect nonaxonal cells such as glia, ii) assuming that the bulk diffusivity of water in cerebral tissue would be the same as that of free water, and iii) impermeable barriers. All these assumptions are expected to play a role in the estimated resolution limit, a role difficult to quantify but likely substantial.

For this reason, we believe that our approach, which is 100% focused on neurobiology and measurements performed in real tissue, can offer a different perspective and fuel the ongoing debate on axonal diameter measurement feasibility. We acknowledge the value of the reviewer comment and discuss the issue of b-value in the discussion (see also comment 1.8).

Changes in the manuscript

• Discussion, pag. 12:<br /> Despite some inevitable minor differences due to different brain sizes and magnet features, the human protocol was built to match the main characteristics of the preclinical diffusion sequence, such as the b-value and diffusion time range. The chosen b-value has been a compromise between sensitivity to small structures and the signalto-noise ratio (SNR), as indicated by recent animal (Crater et al., 2022) and human (Gast et al., 2023; Jensen et al., 2016; McKinnon et al., 2017; Moss et al., 2019) work, pointing at 4000 s/mm2 as the b-value for which the intra-axonal water signal is dominant. However, following recent work supporting sensitivity of diffusion-weighted MRI to axonal diameter even at lower b-values (Gast et al., 2023), we tested a protocol with a lower b-value in a subset of animals, with the aim of facilitating future clinical AxCaliber studies. We found no qualitative differences in the outcome (MRI axonal diameter proxy was increased following fimbria damage). Further work and perhaps more realistic simulations, considering real cell composition and morphology, are needed to clarify this issue.

3.3 In this study, the positive correlation of dMRI-estimated axon size and neurofilament fluorescence intensity is indeed an encouraging result, and yet this validation is indirect since it relies on the positive correlation between neurofilament intensity and axon diameter in histology.

The reviewer correctly points out a severe limitation of the previous manuscript version, which is now addressed by including an extensive electron microscopy evaluation, recapitulated in new Fig. 3.

3.4 Authors did not consider the fiber dispersion in the proposed dMRI model. This can lead to overestimated axon diameter, even in the highly aligned WM, such as corpus callosum with ~20-30 degree dispersion in histology [Ronen et al., BSAF 2014, 219:1773; Leergaard et all, PLoS One 2010, 5(1), e8595] and MRI [Dhital et al., NeuroImage 2019, 189, 543; Novikov et al., NeuroImage 2018, 174:518].

The reviewer is correctly pointing out an important characteristic of while matter microstructure as is fibre dispersion. However, we would like to point out that the use of a second fiber population is expected to mitigate this effect by absorbing some axonal directional dispersion in areas of a single fiber. To support this, we quantified dispersion as the angle between the two main fiber orientations captured by the AxCaliber fit, as showed in Author response image 1 for two representative subjects (one control, upper line, and one MS, lower line; the “dispersion” maps are masked by a white matter probability mask, and superimposed to a T2w). Indeed, the angle between the two main fibres in the corpus callosum is around 20 degrees or lower, compatible with the bibliography cited by the reviewer, and higher in other white matter areas known to be characterized by fiber crossing and dispersion.

Author response image 1.

Angle in radians between the two main fiber orientations captured by the AxCaliber fit, as showed below for two representative subjects (one control, upper line, and one MS, lower line). The dispersion maps are masked by a white matter probability mask (P>=0.95), and superimposed to a T2-weighted image.

Author Response

Reviewer #1 (Public Review):

This study examines the factors underlying the assembly of MreB, an actin family member involved in mediating longitudinal cell wall synthesis in rod-shaped bacteria. Required for maintaining rod shape and essential for growth in model bacteria, single molecule work indicates that MreB forms treadmilling polymers that guide the synthesis of new peptidoglycan along the longitudinal cell wall. MreB has proven difficult to work with and the field is littered with artifacts. In vitro analysis of MreB assembly dynamics has not fared much better as helpfully detailed in the introduction to this study. In contrast to its distant relative actin, MreB is difficult to purify and requires very specific conditions to polymerize that differ between groups of bacteria. Currently, in vitro analysis of MreB and related proteins has been mostly limited to MreBs from Gram-negative bacteria which have different properties and behaviors from related proteins in Gram-positive organisms.

Here, Mao and colleagues use a range of techniques to purify MreB from the Gram-positive organism Geobacillus stearothermophilus, identify factors required for its assembly, and analyze the structure of MreB polymers. Notably, they identify two short hydrophobic sequences-located near one another on the 3-D structure-which are required to mediate membrane anchoring.

With regard to assembly dynamics, the authors find that Geobacillus MreB assembly requires both interactions with membrane lipids and nucleotide binding. Nucleotide hydrolysis is required for interaction with the membrane and interaction with lipids triggers polymerization. These experiments appear to be conducted in a rigorous manner, although the salt concentration of the buffer (500mM KCl) is quite high relative to that used for in vitro analysis of MreBs from other organisms. The authors should elaborate on their decision to use such a high salt buffer, and ideally, provide insight into how it might impact their findings relative to previous work.

Response 1.1. MreB proteins are notoriously difficult to maintain in a soluble form. Some labs deleted the N-terminal amphipathic or hydrophobic sequences to increase solubility, while other labs used full-length protein but high KCl concentration (300 mM KCl) (Harne et al, 2020; Pande et al., 2022; Popp et al, 2010; Szatmari et al, 2020). Early in the project, we tested many conditions and noticed that high KCl helped keeping a slightly better solubility of full length MreBGs, without the need for deleting a part of the protein. In addition, concentrations of salt > 100 mM would better mimic the conditions met by the protein in vivo. While 50-100 mM KCl is traditionally used in actin polymerization assays, physiological salt concentrations are around 100-150 mM KCl in invertebrates and vertebrates (Schmidt-Nielsen, 1975), around 50-250 in fungal and plant cells (Rodriguez-Navarro, 2000) and 200-300 mM in the budding yeast (Arino et al, 2010). However, cytoplasmic K+ concentration varies greatly (up to 800 mM) depending on the osmolality of the medium in both E. coli (Cayley et al, 1991; Epstein & Schultz, 1965; Rhoads et al, 1976), and B. subtilis, in which the basal intracellular concentration of KCl was estimated to be ~ 350 mM (Eisenstadt, 1972; Whatmore et al, 1990). 500 mM KCl can therefore be considered as physiological as 100 mM KCl for bacterial cells. Since we observed plenty of pairs of protofilaments at 500 mM KCl and this condition helped to avoid aggregation, we kept this high concentration as a standard for most of our experiments. Nonetheless, we had also performed TEM polymerization assays at 100 mM in line with most of MreB and F-actin in vitro literature, and found no difference in the polymerization (or absence of polymerization) conditions. This was indicated in the initial submission (e.g. M&M section L540 and footnote of Table S2) but since two reviewers bring it up as a main point, it is evident we failed at communicating it clearly, for which we apologize. This has been clarified in the revised version of the manuscript. We have also almost systematically added the 100 mM KCl concentration too as per reviewer #2 request and to conciliate our salt conditions with those used for some in vitro analysis of MreBs from other organisms (see also response to reviewer #2 comments 1A and 1B = Responses 2.1A, 2.1B below). We then decided to refer to the 100 mM KCl concentration as our “standard condition” in the revised version of the manuscript, but we compile and compare the results obtained at 500 mM too, as both concentrations are within the physiological range in Bacillus.

Additionally, this study, like many others on MreB, makes much of MreB's relationship to actin. This leads to confusion and the use of unhelpful comparisons. For example, MreB filaments are not actin-like (line 58) any more than any polymer is "actin-like." As evidenced by the very beautiful images in this manuscript, MreB forms straight protofilaments that assemble into parallel arrays, not the paired-twisted polymers that are characteristic of F-actin. Generally, I would argue that work on MreB has been hindered by rather than benefitted from its relationship to actin (E.g early FP fusion data interpreted as evidence for an MreB endoskeleton supporting cell shape or depletion experiments implicating MreB in chromosome segregation) and thus such comparisons should be avoided unless absolutely necessary.

Response 1.2. We completely agree with reviewer #1 regarding unhelpful comparisons of actin and MreB, and that work on MreB has been traditionally hindered from its relationship to eukaryotic actin. MreB is nonetheless a structural homolog of actin, with a close structural fold and common properties (polymerization into pairs of protofilaments, ATPase activity…). It still makes sense to refer to a protein with common features, common ancestry and widely studied as long as we don’t enclose our mind into a conceptual framework. This said, actin and MreB diverged very early in evolution, which may account for differences in their biochemical properties and cellular functions. Current data on MreB filaments confirm that they display F-actin-like and F-actin-unlike properties. We thank the reviewer for this insightful comment. We have revised the text to remove any inaccurate or unhelpful comparison to actin (in particular the ‘actin-like filaments’ statement, previously used once)

Reviewer #2 (Public Review):

The paper "Polymerization cycle of actin homolog MreB from a Gram-positive bacterium" by Mao et al. provides the second biochemical study of a gram-positive MreB, but importantly, the first study examines how gram-positive MreB filaments bind to membranes. They also show the first crystal structure of a MreB from a Gram-positive bacterium - in two nucleotide-bound forms, finally solving structures that have been missing for too long. They also elucidate what residues in Geobacillus MreB are required for membrane associations. Also, the QCM-D approach to monitoring MreB membrane associations is a direct and elegant assay.

While the above findings are novel and important, this paper also makes a series of conclusions that run counter to multiple in vitro studies of MreBs from different organisms and other polymers with the actin fold. Overall, they propose that Geobacillus MreB contains biochemical properties that are quite different than not only the other MreBs examined so far but also eukaryotic actin and every actin homolog that has been characterized in vitro. As the conclusions proposed here would place the biochemical properties of Geobacillus MreB as the sole exception to all other actin fold polymers, further supporting experiments are needed to bolster these contrasting conclusions and their overall model.

Response 2.0. We are grateful to reviewer #2 for stressing out the novelty and importance of our results. Most of our conclusions were in line with previous in vitro studies of MreBs (formation of pairs of straight filaments on a lipid layer, both ATP and GTP binding and hydrolysis, distortion of liposomes…), to the exception of the claimed requirement of NTP hydrolysis for membrane binding prior to polymerization based on the absence of pairs of filaments in free solution or in the presence of AMP-PNP in our experimental conditions (which we agree was not sufficient to make such a bold claim, see below). Thanks to the reviewer’s comments, we have performed many controls and additional experiments that lead us to refine our results and largely conciliate them with the literature. Please see the answer to the global review comments - our conclusions have been revised on the basis of our new data.

1. (Difference 1) - The predominant concern about the in vitro studies that makes it difficult to evaluate many of their results (much less compare them to other MreB/s and actin homologs) is the use of a highly unconventional polymerization buffer containing 500(!) mM KCL. As has been demonstrated with actin and other polymers, the high KCl concentration used here (500mM) is certain to affect the polymerization equilibria, as increasing salt increases the hydrophobic effect and inhibits salt bridges, and therefore will affect the affinity between monomers and filaments. For example, past work has shown that high salt greatly changes actin polymerization, causing: a decreased critical concentration, increased bundling, and a greatly increased filament stiffness (Kang et al., 2013, 2012). Similarly, with AlfA, increased salt concentrations have been shown to increase the critical concentration, decrease the polymerization kinetics, and inhibit the bundling of AlfA filaments (Polka et al., 2009).

A more closely related example comes from the previous observation that increasing salt concentrations increasingly slow the polymerization kinetics of B. subtilis MreB (Mayer and Amann, 2009). Lastly, These high salt concentrations might also change the interactions of MreB(Gs) with the membrane by screening charges and/or increasing the hydrophobic effect. Given that 500mM KCl was used throughout this paper, many (if not all) of the key experiments should be repeated in more standard salt concentration (~100mM), similar to those used in most previous in vitro studies of polymers.

Response 2.1A. As per reviewer #2 request, we have done at 100 mM KCl too most experiments (TEM, cryo-EM, QCMD and ATPase assays) initially performed at 500 mM KCl only. The KCl concentration affects both membrane binding and filament stiffness as anticipated by the reviewer but the main conclusions are the same. The revised version of the manuscript compiles and compares the results obtained at both high and low [KCl], both concentrations being within the physiological range in Bacillus. Please see point 1 of the response to the global review comments and the first response to reviewer 1 (Response 1.1) for further elaboration.

Please note that in Mayer & Amann, 2009 (B. subtilis MreB), light scattering in free solution was inversely proportional to the KCl concentration, with the higher light scattering signal at 0 mM KCl (!), a > 2-fold reduction below 30 mM KCl and no scatter at all at 250 mM, suggesting a “salting in” phenomenon (see also the “Other Points to address” answers 1A and 2, below) (Mayer & Amann, 2009). Since no effective polymer formation (e.g. polymers shown by EM) was demonstrated in these experiments, it cannot be excluded that KCl was simply preventing aggregation of B. subtilis MreB in solution, as we observe. For all their other light scattering experiments, the ‘standard polymerization condition’ used by Mayer & Amann was 0.2 mM ATP, 5 mM MgCl2, 1 mM EGTA and 10 mM imidazole pH 7.0, to which MreB (in 5 mM Tris pH 8.0) was added. No KCl was present in their ‘standard’ polymerization conditions.

This would test if the many divergent properties of MreB(Gs) reported here arise from some difference in MreB(Gs) relative to other MreBs (and actin homologs), or if they arise from the 400mM difference in salt concentration between the studies. Critically, it would also allow direct comparisons to be made relative to previous studies of MreB (and other actin homologs) that used much lower salt, thereby allowing them to definitively demonstrate whether MreB(Gs) is indeed an outlier relative to other MreB and actin homologs. I would suggest using 100mM KCL, as historically, all polymerization assays of actin and numerous actin homologs have used 50-100mM KCL: 50mM KCl (for actin in F buffer) or 100mM KCl for multiple prokaryotic actin homologs and MreB (Deng et al., 2016; Ent et al., 2014; Esue et al., 2006, 2005; Garner et al., 2004 ; Polka et al., 2009 ; Rivera et al., 2011 ; Salje et al., 2011). Likewise, similar salt concentrations are standard for tubulin (80 mM K-Pipes) and FtsZ (100 mM KCl or 100mM KAc in HMK100 buffer).

Response 2.1B. We appreciate the reviewer’s feedback on this point. Please note that, although actin polymerization assays are historically performed at 50-100 mM KCl and thus 100 mM KCl was used for other bacterial actin homologs (MamK, ParM and AlfA), MreB polymerization assays have previously been reported at 300 mM KCl too (Harne et al., 2020; Pande et al., 2022; Popp et al., 2010; Szatmari et al., 2020), which is closer to the physiological salt concentration in bacterial cells (see Response 1.1), but also in the absence of KCl (see above). As a matter of fact, we originally wanted to use a “standard polymerization condition” based on the literature on MreB, before realizing there was none: only half used KCl (the other half used NaCl, or no monovalent salt at all) and among these, KCl concentrations varied (out of 8 publications, 2 used 20 mM KCl, 2 used 50 mM KCl and 4 used 300 mM KCl).

1. (Difference 2) - One of the most important differences claimed in this paper is that MreB(Gs) filaments are straight, a result that runs counter to the curved T. Maritima and C. crescentus filaments detailed by the Löwe group (Ent et al., 2014; Salje et al., 2011). Importantly, this difference could also arise from the difference in salt concentrations used in each study (500mM here vs. 100mM in the Löwe studies), and thus one cannot currently draw any direct comparisons between the two studies.

One example of how high salt could be causing differences in filament geometry: high salts are known to greatly increase the bending stiffness of actin filaments, making them more rigid (Kang et al., 2013). Likewise, increasing salt is known to change the rigidity of membranes. As the ability of filaments to A) bend the membrane or B) Deform to the membrane depends on the stiffness of filaments relative to the stiffness of the membrane, the observed difference in the "straight vs. curved" conformation of MreB filaments might simply arise from different salt concentrations. Thus, in order to draw several direct comparisons between their findings and those of other MreB orthologs (as done here), the studies of MreB(GS) confirmations on lipids should be repeated at the same buffer conditions as used in the Löwe papers, then allowing them to be directly compared.

Response 2.2. We fully agreed with reviewer #2 that the salts could be affecting the assay and did cryo-EM experiments also in the presence of 100 mM KCl as requested. The results unambiguously showed countless curved liposomes on the contact areas with MreB (Fig. 2F-G and Fig. 2-S5), very similar to what was reported for Thermotoga and Caulobacter MreBs by the Lowe group. Our results therefore confirm the previous findings that MreBs can bend lipids, and suggest that, indeed, high salt may increase filament stiffness as it has been shown for actin filaments. We are very grateful to reviewer #2 for his suggestion and for drawing our attention to the work of Kang et al, 2013. The different bending observed when varying the salt concentration raise relevant questions regarding the in vivo behavior of MreB, since KCl was shown to vary greatly depending on the medium composition. The manuscript has been updated accordingly in the Results (from L243) and Discussion sections (L585-595).

1. (Difference 3) - The next important difference between MreB(Gs) and other MreBs is the claim that MreB polymers do not form in the absence of membranes.

A) This is surprising relative to other MreBs, as MreBs from 1) T. maritime (multiple studies), E.coli (Nurse and Marians, 2013), and C. crescentus (Ent et al., 2014) have been shown to form polymers in solution (without lipids) with electron microscopy, light scattering, and time-resolved multi-angle light scattering. Notably, the Esue work was able to observe the first phase of polymer formation and a subsequent phase of polymer bundling (Esue et al., 2006) of MreB in solution. 2) Similarly, (Mayer and Amann, 2009) demonstrated B. subtilis MreB forms polymers in the absence of membranes using light scattering.

Response 2.3A. The literature does convincingly show that Thermotoga MreB forms polymers in solution, without lipids (note that for Caulobacter MreB filaments were only reported in the presence of lipids, (van den Ent et al, 2014)). Assemblies reported in solution are bundles or sheets (included in at the earlier time points in the time-resolved EM experiments reported by Esue et al. 2006 mentioned by the reviewer – ‘2 minutes after adding ATP, EM revealed that MreB formed short filamentous bundles’) (Esue et al, 2006). However, and as discussed above (Response 2.1A), the light scattering experiments in Mayer et Amann, 2009 do not conclusively demonstrate the presence of polymers of B. subtilis MreB in solution (Mayer & Amann, 2009). We performed many light scattering experiments of B. subtilis MreB in solution in the past (before finding out that filaments were only forming in the presence of lipids), and got similar scattering curves (see two examples of DLS experiments in Author response image 1) in conditions in which NO polymers could ever been observed by EM while plenty of aggregates were present.

Author response image 1.

We did not consider these results publishable in the absence of true polymers observed by TEM. As pointed out on the interesting study from Nurse et al. (on E. coli MreB) (Nurse & Marians, 2013), one cannot rely only on light scattering only because non-specific aggregates would show similar patterns than polymers. Over the last two decades, about 15 publications showed polymers of MreB from several Gram-negative species, while none (despite the efforts of many) showed a single convincing MreB polymer from a Gram-positive bacterium by EM. A simple hypothesis is that a critical parameter was missing, and we present convincing evidence that lipids are critical for Geobacillus MreB to form pairs of filaments in the conditions tested. However, in solution too we do occasionally see pairs of filaments (Fig 2-S2), and also sheet-like structures among aggregates when the concentration of MreB is increased (Fig. 2-S2 and Fig. 3-S2). Thus, we agree with the reviewer that it cannot be claimed that Geobacillus MreB is unable to polymerize in the absence of lipids, but rather that lipids strongly stimulate its polymerization, condition depending.

B) The results shown in figure 5A also go against this conclusion, as there is only a 2-fold increase in the phosphate release from MreB(Gs) in the presence of membranes relative to the absence of membranes. Thus, if their model is correct, and MreB(Gs) polymers form only on membranes, this would require the unpolymerized MreB monomers to hydrolyze ATP at 1/2 the rate of MreB in filaments. This high relative rate of hydrolysis of monomers compared to filaments is unprecedented. For all polymers examined so far, the rate of monomer hydrolysis is several orders of magnitude less than that of the filament. For example, actin monomers are known to hydrolyze ATP 430,000X slower than the monomers inside filaments (Blanchoin and Pollard, 2002; Rould et al., 2006).

Response 2.3B. We agree with the reviewer. We have now found conditions where sheets of MreB form in solution (at high MreB concentration) in the presence of ADP and AMP-PNP. However, we have now added several controls that exclude efficient formation of polymers in solution in the presence of ATP at low concentrations of MreBGs (≤ 1.5 µM), the condition used for the malachite green assays. At these MreB concentrations, pairs of filaments are observed in the presence of lipids, but very unfrequently in solution, and sheets are not observed in solution either (Fig. 2-S2A, B). Yet, albeit puzzling, in these conditions Pi release is reproducibly observed in solution, reduced only ~ 2 to 3-fold relative to Pi release in the presence of lipids (Fig. 5A and Fig. 5-S1). A reinforcing observation is when the ATPase assays is performed at 100 mM KCl (Fig. 5A). In this condition MreB binding to lipids is increased relative to 500 mM KCl (Fig. 4-S4C), and the stimulation of the ATPase activity by the presence of lipids is also stronger that at 500 mM (Fig. 5-S1A). Further work is needed to characterize in detail the ATPase activity of MreB proteins, for which data in the literature is very scarce. We can’t exclude that MreB could nucleate in solution or form very unstable filaments that cannot be seen in our EM assay but consume ATP in the process. At the moment, the significance of the Pi released in solution is unknown and will require further investigation.

C) Thus, there is a strong possibility that MreB(Gs) polymers are indeed forming in solution in addition to those on the membrane, and these "solution polymers" may not be captured by their electron microscopy assay. For example, high salt could be interfering with the absorption of filaments to glow discharged lacking lipids.

Response 2.3C. We appreciate the reviewer’s insight about this critical point. Polymers presented in the original Fig. 2A were obtained at 500 mM KCl but we had tested the polymerization of MreB at 100 mM KCl as well, without noticing differences. We have nonetheless redone this quantitatively and used these data for the revised Fig. 2A, as we are now using 100 mM KCl as our standard polymerization condition throughout the revised manuscript. We also followed the other suggestion of the reviewer and tested glow discharged grids (a more classic preparation for soluble proteins) vs non-glow discharged EM grids, as well as a higher concentration of MreB. Grids are generally glow-discharged to make them hydrophilic in order to adsorb soluble proteins, but the properties of MreB (soluble but obviously presenting hydrophobic domains) made difficult to predict what support putative soluble polymers would preferentially interact with. Septins for example bind much better to hydrophobic grids despite their soluble properties (I. Adriaans, personal communication). Virtually no double filaments were observed in solution at either low or high [MreB]. The fact that in some conditions (high [MreB], other nucleotides) we were able to detect sheet-like structures excluded a technical issue that would prevent the detection of existing but “invisible” polymers here. We have added these new data in Fig. 2-S2.

As indicated above, the reviewer’s comments made us realize that we could not state or imply that MreB cannot polymerize in the absence of lipids. As a matter of fact, we always saw some random filaments in the EM fields, both in solution and in the presence of non-hydrolysable analogues, at very low frequency (Fig. 2A). And we do see now sheets at high MreB concentration (Fig. 2-S2B). We could be just missing the optimal conditions for polymerisation in solution, while our phrasing gave the impression that no polymers could ever form in the absence of ATP or lipids. Therefore, we have:

1) analyzed all TEM data to present it as semi-quantitative TEM, using our methodology originally implemented for the analysis of the mutants

2) reworked the text to remove any issuing statements and to indicate that MreBGs was only found to bind to a lipid monolayer as a double protofilament in the presence of ATP/GTP but that this does not exclude that filaments may also form in other conditions.

In order to definitively prove that MreB(Gs) does not have polymers in solution, the authors should:

i) conduct orthogonal experiments to test for polymers in solution. The simplest test of polymerization might be conducting pelleting assays of MreB(Gs) with and without lipids, sweeping through the concentration range as done in 2B and 5a.

Response 2.3Ci. Following reviewer #2 suggestion, we conducted a series of sedimentation assays in the presence and in the absence of lipids, at low (100 mM) and high (500 mM) salt, for both the wild-type protein and the three membrane-anchoring mutants (all at 1.3 µM). Sedimentation experiments in salt conditions preventing aggregation in solution (500 mM KCl) fitted with our TEM results: MreB wild-type pelleting increased in the presence of both ATP and lipids (Fig. R1). The sedimentation was further increased at 100 mM KCl, which would fit our other results indicating an increased interaction of MreB with the membrane. However, in addition to be poorly reproducible (in our hands), the approach does not discriminate between polymers and aggregates (or monomers bound to liposomes) and since MreB has a strong tendency to aggregate, we believe that the technique is ill-suited to reliably address MreB polymerization and prefer not to include sedimentation data in our manuscript. The recent work from Pande et al. (2022) illustrates well this issue since no sedimentation of MreB (at 2 µM) was observed in solution in conditions supporting polymerization (at 300 mM KCl): ‘the protein does not pellet on its own in the absence of liposome, irrespective of its polymerization state’, implying that sedimentation does not allow to detect MreB5 filaments in solution (Pande et al., 2022).

ii) They also could examine if they see MreB filaments in the absence of lipids at 100mM salt (as was seen in both Löwe studies), as the high salt used here might block the charges on glow discharged grids, making it difficult for the polymer to adhere.

See above, Response 2.3C

iii) Likewise, the claim that MreB lacking the amino-terminus and the α2β7 hydrophobic loop "is required for polymerization" is questionable as if deleting these resides blocks membrane binding, the lack of polymers on the membrane on the grid is not unexpected, as these filaments that cannot bind the membrane would not be observable. Given these mutants cannot bind the membrane, mutant polymers could still indeed exist in solution, and thus pelleting assays should be used to test if non-membrane associated filaments composed of these mutants do or do not exist.

Response 2.3Ciii. This is a fair point, we thank the reviewer for this remark. We did not mean to state or imply that the hydrophobic loop was required for polymerization per se, but that polymerization into double filaments only efficiently occurs upon membrane binding, which is mediated by the two hydrophobic sequences. We tested all three mutants by sedimentation as suggested by reviewer #2. In the salt condition that limits aggregation (500 mM KCl) the mutants did not pellet while the wild-type protein did (in the presence of lipids) (Fig. R2 below), in agreement with our EM data. We tested the absence of lipids on the mutant bearing the 2 deletions and observed that the (partial) sedimentation observed at low KCl concentration was ATP and lipid dependent (Fig. R3).

Given our concerns about MreB sedimentation assays (see above, Response 2.3Ci), we prefer not to include these sedimentation data in our manuscript. Instead, we tested by TEM the possible polymerization of the mutants in solution (we only tested them in the presence of lipids in the initial submission). No filaments were detected in solution for any of the mutants (Fig. 4-S3A).

A final note, the results shown in "Figure 1 - figure supplement 2, panel C" appear to directly refute the claim that MreB(Gs) requires lipids to polymerize. As currently written, it appears they can observe MreB(Gs) filaments on EM grids without lipids. If these experiments were done in the presence of lipids, the figure legend should be updated to indicate that. If these experiments were done in the absence of lipids, the claim that membrane association is required for MreB polymerizations should be revised.

The TEM experiments show were indeed performed in the presence of lipids. We apologize for this was not clearly stated in the legend. To prevent all confusion, we have nevertheless removed these images in this figure since the polymerization conditions and lipid requirement are not yet presented when this figure is referred to in the text. We have instead added a panel with the calibration curve for the size exclusion profiles as per request of reviewer #3. The main point of this figure is to show the tendency of MreBGs to aggregate: analytical size-exclusion chromatography shows a single peak corresponding to the monomeric MreBGs, molecular weight ~ 37 KDa, in our purification conditions, but it can readily shift to a peak corresponding to high MW aggregates, depending on the protein concentration and/or storage conditions.

1. (Difference 4) - The next difference between this study and previous studies of MreB and actin homologs is the conclusion that MreB(Gs) must hydrolyze ATP in order to polymerize. This conclusion is surprising, given the fact that both T. Maritima (Salje · 2011, Bean 2008) and B. subtilis MreB (Mayer 2009) have been shown to polymerize in the presence of ATP as well as AMP-PNP.

Likewise, MreB polymerization has been shown to lag ATP hydrolysis in not only T. maritima MreB (Esue 2005), eukaryotic actin, and all other prokaryotic actin homologs whose polymerization and phosphate release have been directly compared: MamK (Deng et al., 2016), AlfA (Polka et al., 2009), and two divergent ParM homologs (Garner et al., 2004; Rivera et al., 2011). Currently, the only piece of evidence supporting the idea that MreB(Gs) must hydrolyze ATP in order to polymerize comes from 2 observations: 1) using electron microscopy, they cannot see filaments of MreB(Gs) on membranes in the presence of AMP-PNP or ApCpp, and 2) no appreciable signal increase appears testing AMPPNP- MreB(Gs) using QCM-D. This evidence is by no means conclusive enough to support this bold claim: While their competition experiment does indicate AMPPNP binds to MreB(Gs), it is possible that MreB(Gs) cannot polymerize when bound to AMPPNP.

For example, it has been shown that different actin homologs respond differently to different non-hydrolysable analogs: Some, like actin, can hydrolyze one ATP analog but not the other, while others are able to bind to many different ATP analogs but only polymerize with some of one of them.

Response 2.4. We agree with the reviewer, it is uncertain what analogs bind because they are quite different to ATP and some proteins just do not like them, they can change conditions such that filaments stop forming as well and be (theoretically) misleading. This is why we had tested ApCpp in addition to AMP-PNP as non-hydrolysable analog (Fig. 3A). As indicated above, our new complementary experiments (Fig. 3-S1B-D) now show that some rare (i.e. unfrequently and in limited amount) dual polymers are detected in the presence of ApCpp (Fig. 3A) and at high MreB concentration only in the presence of AMP-PNP (Fig. 3-S1B-D), suggesting different critical concentrations in the presence of alternative nucleotides. We have dampened our conclusions, in the light of our new data, and modified the discussion accordingly.

Thus, to further verify their "hydrolysis is needed for polymerization" conclusion, they should:

A. Test if a hydrolysis deficient MreB(Gs) mutant (such as D158A) is also unable to polymerize by EM.

Response 2.4A. We thank the reviewer for this suggestion. As this conclusion has been reviewed on the basis of our new data (see previous response), testing putative ATPase deficient mutants is no longer required here. The study of ATPase mutants is planned for future studies (see Response 3.10 to reviewer #3).

B. They also should conduct an orthogonal assay of MreB polymerization aside from EM (pelleting assays might be the easiest). They should test if polymers of ATP, AMP-PNP, and MreB(Gs)(D158A) form in solution (without membranes) by conducting pelleting assays. These could also be conducted with and without lipids, thereby also addressing the points noted above in point 3.

Response 2.4B. Please see Response 2.3Ci above.

C. Polymers may indeed form with ATP-gamma-S, and this non-hydrolysable ATP analog should be tested.

Response 2.4C. It is fairly possible that ATP-γ-S supports polymerization since it is known to be partially hydrolysable by actin giving a mild phenotype (Mannherz et al, 1975). This molecule can even be a bona fide substrate for some ATPases (e.g. (Peck & Herschlag, 2003). Thus, we decided to exclude this “non-hydrolysable” analog and tested instead AMP-PNP and ApCpp. We know that ATP-γ-S has been and it is still frequently used, but we preferred to avoid it for the moment for the above-indicated reasons. We chose AMPPNP and AMPPCP instead because (1) they were shown to be completely non-hydrolysable by actin, in contrast to ATP-γ-S; (2) they are widely used (the most commonly used for structural studies; (Lacabanne et al, 2020), (3) AMPPNP was previously used in several publications on MreB (Bean & Amann, 2008; Nurse & Marians, 2013; Pande et al., 2022; Popp et al., 2010; Salje et al, 2011; van den Ent et al., 2014)and thus would allow direct comparison. AMPPCP was added to confirm the finding with AMP-PNP. There are many other analogs that we are planning to explore in future studies (see next Response, 2.4D).

D. They could also test how the ADP-Phosphate bound MreB(Gs) polymerizes in bulk and on membranes, using beryllium phosphate to trap MreB in the ADP-Pi state. This might allow them to further refine their model.

Response 2.4D. We plan to address the question of the transition state in depth in following-up work, using a series of analogs and mutants presumably affected in ATPase activity, both predicted and identified in a genetic screen. As indicated above, it is uncertain what analogs bind because they are quite different to ATP and some may bind but prevent filament formation. Thus, we anticipate that trying just one may not be sufficient, they can change conditions and be (theoretically) misleading and thus a thorough analysis is needed to address this question. Since our model and conclusions have been revised on the basis of our new data, we believe that these experiments are beyond the scope of the current manuscript.

E. Importantly, the Mayer study of B. subtilis MreB found the same results in regard to nucleotides, "In polymerization buffer, MreB produced phosphate in the presence of ATP and GTP, but not in ADP, AMP, GDP or AMP-PNP, or without the readdition of any nucleotide". Thus this paper should be referenced and discussed

Response 2.4E. We agree that Pi release was detected previously. We have added the reference (L121)

1. (Difference 5) - The introduction states (lines 128-130) "However, the need for nucleotide binding and hydrolysis in polymerization remains unclear due to conflicting results, in vivo and in vitro, including the ability of MreB to polymerize or not in the presence of ADP or the non-hydrolysable ATP analog AMP-PNP."

A) While this is a great way to introduce the problem, the statement is a bit vague and should be clarified, detaining the conflicting results and appropriate references. For example, what conflicting in vivo results are they referring to? Regarding "MreB polymerization in AMP-PNP", multiple groups have shown the polymerization of MreB(Tm) in the presence of AMP-PNP, but it is not clear what papers found opposing results.

Response 2.5A. Thanks for the comment. We originally did not detail these ‘conflicting results’ in the Introduction because we were doing it later in the text, with the appropriate references, in particular in the Discussion (former L433-442). We have now removed this from the Discussion section and added a sentence in the introduction too (L123-130) quickly detailing the discrepancies and giving the references.

• For more clarity, we have removed the “in vivo” (which referred to the distinct results reported for the presumed ATPase mutants by the Garner and Graumann groups) and focus on the in vitro discrepancies only.

• These discrepancies are the following: while some studies showed indeed polymerization (as assessed by EM) of MreBTm in the presence of AMPPNP, the studies from Popp et al and Esue et al on T. maritima MreB, and of Nurse et al on E. coli MreB reported aggregation in the presence of AMP-PNP (Esue et al., 2006; Popp et al., 2010) or ADP (Nurse & Marians, 2013), or no assembly in the presence of ADP (Esue et al., 2006). As for the studies reporting polymerization in the presence of AMP-PNP by light scattering only (Bean & Amann, 2008; Gaballah et al, 2011; Mayer & Amann, 2009; Nurse & Marians, 2013), they could not differentiate between aggregates or true polymers and thus cannot be considered conclusive.

B) The statement "However, the need for nucleotide binding and hydrolysis in polymerization remains unclear due to conflicting results, in vivo and in vitro, including the ability of MreB to polymerize or not in the presence of ADP or the non-hydrolyzable ATP analog AMP-PNP" is technically incorrect and should be rephrased or further tested.

i. For all actin (or tubulin) family proteins, it is not that a given filament "cannot polymerize" in the presence of ADP but rather that the ADP-bound form has a higher critical concentration for polymer formation relative to the ATP-bound form. This means that the ADP polymers can indeed polymerize, but only when the total protein exceeds the ADP critical concentration. For example, many actin-family proteins do indeed polymerize in ADP: ADP actin has a 10-fold higher critical concentration than ATP actin, (Pollard, 1984) and the ADP critical concentrations of AlfA and ParM are 5X and 50X fold higher (respectively) than their ATP-bound forms(Garner et al., 2004; Polka et al., 2009)

Response 2.5Bi. Absolutely correct. We apologize for the lack of accuracy of our phrasing and have corrected it (L123).

ii. Likewise, (Mayer and Amann, 2009) have already demonstrated that B. subtilis MreB can polymerize in the presence of ADP, with a slightly higher critical concentration relative to the ATP-bound form.

Response 2.5Bii. In Mayer and Amann, 2009, the same light scattering signal (interpreted as polymerization) occurred regardless of the nucleotide, and also in the absence of nucleotide (their Fig. 10) and ATP-, ADP- and AMP-PNP-MreB ‘displayed nearly indistinguishable critical concentrations’. They concluded that MreB polymerization is nucleotide-independent. Please see below (responses to ’Other points to address’) our extensive answer to the Mayer & Amann recurring point of reviewer #2

Thus, to prove that MreB(Gs) polymers do not form in the presence of ADP would require one to test a large concentration range of ADP-bound MreB(Gs). They should test if ADP- MreB(Gs) polymerizes at the highest MreB(Gs) concentrations that can be assayed. Even if this fails, it may be the MreB(Gs) ADP polymerizes at higher concentrations than is possible with their protein preps (13uM). An even more simple fix would be to simply state MreB(Gs)-ADP filaments do not form beneath a given MreB(Gs) concentration.

We agree with the reviewer. Our wording was overstating our conclusions. Based on our new quantifications (Fig. 3-S1B, D), we have rephrased the results section and now indicate that pairs of filaments are occasionally observed in the presence of ADP in our conditions across the range of MreB concentration that could be tested, suggesting a higher critical concentration for MreB-ADP (L310-312). Only at the highest MreB concentration, sheet- and ribbon-like structures were observed in the presence of ADP (Fig. 3-S2B).

Other Points to address:

1) There are several points in this paper where the work by Mayer and Amann is ignored, not cited, or readily dismissed as "hampered by aggregation" without any explanation or supporting evidence of that fact.

We have cited the Mayer study where appropriate. However, we cannot cite it as proof of polymerization in such or such condition since their approach does not show that polymers were obtained in their conditions. Again, they based all their conclusions solely on light scattering experiments, which cannot differentiate between polymers and aggregates.

A) Lines 100-101 - While the irregular 3-D formations seen formed by MreB in the Dersch 2020 paper could be interpreted as aggregates, stating that the results from specifically the Gaballah and Meyer papers (and not others) were "hampered by aggregation" is currently an arbitrary statement, with no evidence or backing provided. Overall, these lines (and others in the paper) dismiss these two works without giving any evidence to that point. Thus, they should provide evidence for why they believe all these papers are aggregation, or remove these (and other) dismissive statements.

We apologize if our statements about these reports seemed dismissive or disrespectful, it was definitely not our intention. Light scattering shows an increase of size of particles over time, but there is no way to tell if the scattering is due to organized (polymers) or disorganized (aggregation) assemblies. Thus, it cannot be considered a conclusive evidence of polymerization without the proof that true filaments are formed by the protein in the conditions tested, as confirmed by EM for example. MreB is known to easily aggregate (see our size exclusion chromatography profiles and ones from Dersch 2020 (Dersch et al, 2020), and note that no chromatography profiles were shown in the Mayer report) and, as indicated above, we had similar light scattering results for MreB for years, while only aggregates could be observed by TEM (see above Response 2.3A). Several observations also suggest that aggregation instead of polymerization might be at play in the Mayer study, for example ‘polymerization’ occurring in salt-less buffer but ‘inhibited’ with as low as 100 mM KCl, which should rather be “salting in” (see below). We did not intend to be dismissive, but it seemed wrong to report their conclusions as conclusive evidence. We thought that we had cited these papers where appropriate but then explained that they show no conclusive proof of polymerization and why, but it is evident that we failed at communicating it clearly. We have reworked the text to remove any issuing and arbitrary statement about our concerns regarding these reports (e.g. L93 & L126).

One important note - There are 2 points indicating that dismissing the Meyer and Amann work as aggregation is incorrect:

1) the Meyer work on B. subtilis MreB shows both an ATP and a slightly higher ADP critical concentration. As the emergence of a critical concentration is a steady-state phenomenon arising from the association/dissociation of monomers (and a kinetically limiting nucleation barrier), an emergent critical concentration cannot arise from protein aggregation, critical concentrations only arise from a dynamic equilibrium between monomer and polymer.

• Critical concentration for ATP, ADP or AMPPNP were described in Mayer & Amann (Mayer & Amann, 2009) as “nearly indistinguishable” (see Response 2.5Bii)
• Protein aggregation depends on the solution (pH and ions), protein concentration and temperature. And above a certain concentration, proteins can become instable, thus a critical concentration for aggregation can emerge.

2) Furthermore, Meyer observed that increased salt slowed and reduced B. subtilis MreB light scattering, the opposite of what one would expect if their "polymerization signal" was only protein aggregation, as higher salts should increase the rate of aggregation by increasing the hydrophobic effect.

It is true that at high salt concentration proteins can precipitate, a phenomenon described as “salting out”. However, it is also true that salts help to solubilize proteins (“salting in”), and that proteins tend to precipitate in the absence of salt. Considering that the starting point of the Mayer and Amann experiment (Mayer & Amann, 2009) is the absence of salt (where they observed the highest scattering) and that they gradually reduce this scattering by increasing KCl (the scattering is almost abolished below 100 mM only!) it is plausible that a salting-in phenomenon might be at play, due to increased solubility of MreB by salt. In any case, this cannot be taken as a proof that polymerization rather than aggregation occurred.

B) Lines 113-137 -The authors reference many different studies of MreB, including both MreB on membranes and MreB polymerized in solution (which formed bundles). However, they again neglect to mention or reference the findings of Meyer and Amann (Mayer and Amann, 2009), as it was dismissed as "aggregation". As B. subtilis is also a gram-positive organism, the Meyer results should be discussed.

We did cite the Mayer and Amann paper but, as explained above, we cannot cite this study as an example of proven polymerization. We avoided as much as possible to polemicize in the text and cited this paper when possible. Again, we have reworked the text to avoid any issuing or dismissive statement. Also, we forgot mentioned this study at L121 as an example of reported ATPase activity, and this has now been corrected.

2) Lines 387-391 state the rates of phosphate release relative to past MreB findings: "These rates of Pi release upon ATP hydrolysis (~ 1 Pi/MreB in 6 min at 53{degree sign}C) are comparable to those observed for MreBTm and MreB(Ec) in vitro". While the measurements of Pi release AND ATP hydrolysis have indeed been measured for actin, this statement does not apply to MreB and should be corrected: All MreB papers thus far have only measured Pi release alone, not ATP hydrolysis at the same time. Thus, it is inaccurate to state "rates of Pi release upon ATP hydrolysis" for any MreB study, as to accurately determine the rate of Pi release, one must measure: 1. The rate of polymer over time, 2) the rate of ATP hydrolysis, and 3) the rate of phosphate release. For MreB, no one has, so far, even measured the rates of ATP hydrolysis and phosphate release with the same sample.

We completely agree with the reviewer, we apologize if our formulation was inaccurate. We have corrected the sentence (L479). Thank you for pointing out this mistake.

3) The interpretation of the interactions between monomers in the MreB crystal should be more carefully stated to avoid confusion. While likely not their intention, the discussions of the crystal packing contacts of MreB can appear to assume that the monomer-monomer contacts they see in crystals represent the contacts within actual protofilaments. One cannot automatically assume the observations of monomer-monomer contacts within a crystal reflect those that arise in the actual filament (or protofilament).

We agree, we thank the reviewer for his comments. We have revamped the corresponding paragraph.

A) They state, "the apo form of MreBGs forms less stable protofilaments than its G- homologs ." Given filaments of the Apo form of MreB(GS) or b. subtilis have never been observed in solution, this statement is not accurate: while the contacts in the crystal may change with and without nucleotide, if the protein does not form polymers in solution in the apo state, then there are no "real" apo protofilaments, and any statements about their stability become moot. Thus this statement should be rephrased or appropriately qualified.

see above.

B) Another example: while they may see that in the apo MreB crystal, the loop of domain IB makes a single salt bridge with IIA and none with IIB. This contrasts with every actin, MreB, and actin homolog studied so far, where domain IB interacts with IIB. This might reflect the real contacts of MreB(Gs) in the solution, or it may be simply a crystal-packing artifact. Thus, the authors should be careful in their claims, making it clear to the reader that the contacts in the crystal may not necessarily be present in polymerized filaments.

Again, we agree with the reviewer, we cannot draw general conclusions about the interactions between monomers from the apo form. We have rephrased this paragraph.

4) lines 201-202 - "Polymers were only observed at a concentration of MreB above 0.55 μM (0.02 mg/mL)". Given this concentration dependence of filament formation, which appears the same throughout the paper, the authors could state that 0.55 μM is the critical concentration of MreB on membranes under their buffer conditions. Given the lack of critical concentration measurement in most of the MreB literature, this could be an important point to make in the field.

Following reviewer’s #2 suggestion, we have now estimated the critical concentration (Cc=0.4485 µM) and reported it in the text. (L218).

5) Both mg/ml and uM are used in the text and figures to refer to protein concentration. They should stick to one convention, preferably uM, as is standard in the polymer field.

Sorry for the confusion. We have homogenized to MreB concentrations to µM throughout the text and figures.

6) Lines 77-78 - (Teeffelen et al., 2011) should be referenced as well in regard to cell wall synthesis driving MreB motion.

This has been corrected, sorry for omitting this reference.

7) Line 90 - "Do they exhibit turnover (treadmill) like actin filaments?". This phrase should be modified, as turnover and treadmilling are two very different things. Turnover is the lifetime of monomers in filaments, while treadmilling entails monomer addition at one end and loss at the other. While treadmilling filaments cause turnover, there are also numerous examples of non-treadmilling filaments undergoing turnover: microtubules, intermediate filaments, and ParM. Likewise, an antiparallel filament cannot directionally treadmill, as there is no difference between the two filament ends to confer directional polarity.

This is absolutely true, we apologize for our mistake. The sentence has been corrected (L82).

8) Throughout the paper, the term aggregation is used occasionally to describe the polymerization shown in many previous MreB studies, almost all of which very clearly showed "bundled" filaments, very distinct entities from aggregates, as a bundle of polymers cannot form without the filaments first polymerizing on their own. Evidence to this point, polymerization has been shown to precede the bundling of MreB(Tm) by (Esue et al., 2005).

We agree with reviewer #2 about polymers preceding bundles and “sheets”. However, we respectfully disagree that we used the word aggregation “throughout the paper” to describe structures that clearly showed polymers or sheets of filaments. A search (Ctrl-F: “aggreg”) reveals only 6 matches, 3 describing our own observations (L152, 163/5, and 1023/28), one referring to (Salje et al., 2011) (L107) but citing her claim that they observed aggregation (due to the N-terminus), and the last two (L100, L440) refer (again) to the Gaballah/Mayer/Dersch publications to say that aggregation could not be excluded in these reports as discussed above (Dersch et al., 2020; Gaballah et al., 2011; Mayer & Amann, 2009).

9) lines 106-108 mention that "The N-terminal amphipathic helix of E. coli MreB (MreBEc) was found to be necessary for membrane binding. " This is not accurate, as Salje observed that one single helix could not cause MreB to mind to the membrane, but rather, multiple amphipathic helices were required for membrane association (Salje et al., 2011).

Salje et al showed that in vivo the deletion of the helix abolishes the association of MreB to the membrane. This publication also shows that in vitro, addition of the helix to GFP (not to MreB) prompts binding to lipid vesicles, and that this was increased if there are 2 copies of the helix, but they could not test this directly in vitro with MreB (which is insoluble when expressed with its N-terminus). This prompted them to speculate that multiple MreBs could bind better to the membrane than monomers. However, this remained to be demonstrated. Additional hydrophobic regions in MreB such as the hydrophobic loop could participate to membrane anchoring but are absent in their in vitro assays with GFP.

The Salje results imply that dimers (or further assemblies) of MreB drive membrane association, a point that should be discussed in regard to the question "What prompts the assembly of MreB on the inner leaflet of the cytoplasmic membrane?" posed on lines 86-87.

We agree that this is an interesting point. As it is consistent with our results, we have incorporated it to our model (Fig. 6) and we are addressing it in the discussion L573-575.

10) On lines 414-415, it is stated, "The requirement of the membrane for polymerization is consistent with the observation that MreB polymeric assemblies in vivo are membrane-associated only." While I agree with this hypothesis, it must be noted that the presence or absence of MreB polymers in the cytoplasm has not been directly tested, as short filaments in the cytoplasm would diffuse very quickly, requiring very short exposures (<5ms) to resolve them relative to their rate of diffusion. Thus, cytoplasmic polymers might still exist but have not been tested.

This is also an interesting point. Indeed if a nucleated form, or very short (unbundled) polymers exist in the cytoplasm, they have not been tested by fluorescence microscopy. However, the polymers that localize at the membrane (~ 200 nm), if soluble, would have been detected in the cytoplasm by the work of reviewer #2, us or others.

11) lines 429-431 state, "but polymerization in the presence of ADP was in most cases concluded from light scattering experiments alone, so the possibility that aggregation rather than ordered polymerization occurred in the process cannot be excluded."

A) If an increased light scattering signal is initiated by the addition of ADP (or any nucleotide), that signal must come from polymerization or multimerization. What the authors imply is that there must be some ADP-dependent "aggregation" of MreB, which has not been seen thus far for any polymer. Furthermore, why would the addition of ADP initiate aggregation?

We did not mean that ADP itself would prompt aggregation, but that the protein would aggregate in the buffer regardless of the presence of ADP or other nucleotides. The Mayer & Amann study claims that MreB “polymerization” is nucleotide-independent, as they got identical curves with ATP, ADP, AMPPNP and even with no nucleotides at all (Fig. 10 in their paper, pasted here) (Mayer & Amann, 2009).

Their experiments with KCl are also remarkable as when they lowered the salt they got faster and faster “polymerization”, with the strongest light scattering signal in the absence of any salt. The high KCl concentration in which they got almost no more “polymers” was 75 mM KCl, and ‘polymerization was almost entirely inhibited at 100 mM’ (Fig. 7, pasted below). Yet the intracellular level of KCl in bacteria is estimated to be ~300 mM (see Response 1.1)

B) Likewise, the statement "Differences in the purity of the nucleotide stocks used in these studies could also explain some of the discrepancies" is unexplained and confusing. How could an impurity in a nucleotide stock affect the past MreB results, and what is the precedent for this claim?

We meant that the presence of ATP in the ADP stocks might have affected the outcome of some assays, generating the conflicting results existing in the literature. We agree this sentence was confusing, we have removed it.

12) lines 467-469 state, "Thus, for both MreB and actin, despite hydrolyzing ATP before and after polymerization, respectively, the ADP-Pi-MreB intermediate would be the long-lived intermediate state within the filaments."

A) For MreB, this statement is extremely speculative and unbiased, as no one has measured 1) polymerization, 2) ATP hydrolysis, and 3) phosphate release. For example, it could be that ATP hydrolysis is slow, while phosphate release is fast, as is seen in the actin from Saccharomyces cerevisiae.

We agree that this was too speculative. This has been removed from the (extensively) modified Discussion section. Thanks for the comment.

B) For actin, the statement of hydrolysis of ATP of monomer occurring "before polymerization" is functionally irrelevant, as the rate of ATP hydrolysis of actin monomers is 430,000 times slower than that of actin monomers inside filaments (Blanchoin and Pollard, 2002; Rould et al., 2006).

We agree that the difference of hydrolysis rate between G-actin and F-actin implies that ATP hydrolysis occurs after polymerization. We are afraid that we do not follow the reviewer’s point here, we did not say or imply that ATP hydrolysis by actin monomers was functionally relevant.

13) Lines 442-444. "On the basis of our data and the existing literature, we propose that the requirement for ATP (or GTP) hydrolysis for polymerization may be conserved for most MreBs." Again, this statement both here (and in the prior text) is an extremely bold claim, one that runs contrary to a large amount of past work on not just MreB, but also eukaryotic actin and every actin homolog studied so far. They come to this model based on 1) one piece of suggestive data (the behavior of MreB(GS) bound to 2 non-hydrolysable ATP analogs in 500mM KCL), and 2) the dismissal (throughout the paper) of many peer-reviewed MreB papers that run counter to their model as "aggregation" or "contaminated ATP stocks ." If they want to make this bold claim that their finding invalidates the work of many labs, they must back it up with further validating experiments.

We respectfully disagree that our model was based on “one piece of suggestive data” and backed-up by dismissing most past work in the field. We only wanted to raise awareness about the conflicting data between some reports (listed in response 2.5a), and that the claims made by some publications are to be taken with caution because they only rely on light scattering or, when TEM was performed, showed only disorganized structures.

This said, we clearly failed in proposing our model and we are sorry to see that we really annoyed the reviewer with our suspicion that the work by Mayer & Amann reports aggregation. As indicated above, we have amended our manuscript relative to this point. We also agree that our suggestion to generalize our findings to most MreBs was unsupported, and overstated considering how confusing some result from the literature are. We have refined our model and reworked the text to take on board the reviewer’s remarks as well as the new data generated during the revision process.

We would like to thank reviewer #2 for his in-depth review of our manuscript.  

Reviewer #3 (Public Review):

The major claim from the paper is the dependence of two factors that determine the polymerization of MreB from a Gram-positive, thermophilic bacteria 1) The role of nucleotide hydrolysis in driving the polymerization. 2) Lipid bilayer as a facilitator/scaffold that is required for hydrolysis-dependent polymerization. These two conclusions are contrasting with what has been known until now for the MreB proteins that have been characterized in vitro. The experiments performed in the paper do not completely justify these claims as elaborated below.

We understand the reviewer’ concerns in view of the existing literature on actin and Gram-negative MreBs. We may just be missing the optimal conditions for polymerisation in solution, while our phrasing gave the impression that polymers could never form in the absence of ATP or lipids. Our new data actually shows that MreBGs at higher concentration can assemble into bundle- and sheet-like structures in solution and in the presence of ADP/AMP-PNP. Pairs of filaments are however only observed in the presence of lipids for all conditions tested. As indicated in the answers to the global review comments, we have included our new data in the manuscript, revised our conclusions and claims about the lipid requirement and expanded on these points in the Discussion.

Major comments:

1) No observation of filaments in the absence of lipid monolayer can also be accounted due to the higher critical concentration of polymerization for MreBGS in that condition. It is seen that all the negative staining without lipid monolayer condition has been performed at a concentration of 0.05 mg/mL. It is important to check for polymerization of the MreBGS at higher concentration ranges as well, in order to conclusively state the requirement of lipids for polymerization.

Response 3.1. 0.05 mg/ml (1.3µM) is our standard condition, and our leeway was limited by the rapid aggregation observed at higher MreB concentrations, as indicated in the text. We have now tested as well 0.25 mg/ml (6.5 µM - the maximum concentration possible before major aggregation occurs in our experimental conditions). At this higher concentration, we see some sheet-like structures in solution, confirming a requirement of a higher concentration of MreB for polymerization in these conditions (see the answers to the global review comments for more details)

We thank the reviewer for pushing us to address this point. We have revised our conclusions accordingly.

2) The absence of filaments for the non-hydrolysable conditions in the lipid layer could also be because the filaments that might have formed are not binding to the planar lipid layer, and not necessarily because of their inability to polymerize.

Response 3.2. This is a fair point. To test the possibility that polymers would form but would not bind to the lipid layer we have now added additional semi-quantitative EM controls (for both the non-hydrolysable ATP analogs and the three ‘membrane binding’ deletion mutants) testing polymerization in solution (without lipids) and also using plasma-treated grids. These showed that in our standard polymerization conditions, virtually no polymers form in solution (Fig. 3-S1B and Fig. 4-S4A). Albeit at very low frequency, some dual protofilaments were however detected in the presence of ADP or AMP-PNP at the high MreB concentration (Fig. 3-S1D). At this high MreB concentration, the sheet-like structures occasionally observed in solution in the presence of ATP were frequent in the presence of ADP and very frequent in the presence of AMP-PNP (Fig. 3-S2B). We have revised our conclusions on the basis of these new data: MreBGs can form polymeric assemblies in solution and in the absence of ATP hydrolysis at a higher critical concentration than in the presence of ATP and lipids.

See the answers to the global review comments (point 2) and Response 2.3C to reviewer #2 for more details.

3) Given the ATPase activity measurements, it is not very convincing that ATP rather than ADP will be present in the structure. The ATP should have been hydrolysed to ADP within the structure. The structure is now suggestive that MreB is not capable of hydrolysis, which is contradictory to the ATP hydrolysis data.

Response 3.3. We thank the reviewer for her insightful remarks about the MreB-ATP crystal structure. The electron density map clearly demonstrates the presence of 3 phosphates. However, as suggested by the reviewer, the density which was attributed to a Mg2+ ion was to be interpreted as a water molecule. The absence of Mg2+ in the crystal could thus explain why the ATP had not been hydrolyzed.

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Author Response

Reviewer #2 (Public Review):

The authors present findings on a designed peptide, PITCR, and its role in inhibiting TCR activation through an extensive series of experiments. These include the measurement of phosphorylation in the TCR zeta chain and a number of associated signaling proteins such as Zap70, LAT, PLCg1, and SLP76. In addition, the authors measure the impact of PITCR on the TCR intracellular calcium response and examine the peptide-induced inhibition of TCR activation by antigen-presenting cells. They also present data indicating that the fluorescently labeled PITCR co-localizes with TCR in Jurkat cells and with ligand-bound TCR in primary murine cells. Overall the experiments provide useful insights into the mechanism of T cell activation and generally support an allosteric model of activation, while not necessarily excluding alternative models.

However, some aspects of the study do need clarification.

1) The authors do not provide a clear structural basis for their peptide design, which makes it difficult to understand the rationale for choosing this particular peptide. The use of a structural model based on the TCR zeta domain, for example, and how it becomes modified to generate PITCR would provide some clarity on what types of putative interactions are being engineered.

We thank the reviewer for giving us a chance to elaborate. We have expanded the results section to provide more information on the peptide design, where we now point out that the acidic residues in the TCR TM allow peptide design. We have also applied the artificial intelligence program AlphaFold-Multimer (AlFoM) to generate a structural model of the docking site of PITCR in the TCR (Figure 9), which informs on new mechanistic insights, as we describe in the updated results section and discuss below.

2) The inhibitory effects of PITCR are not large. Measurement of dose dependence might improve confidence in the results.

As the reviewer points out, we have performed an extensive set of experiments to assess the inhibitory effect of PITCR. We have demonstrated that PITCR inhibits TCR phosphorylation. We have also tested all proximal signaling proteins: Zap70, LAT, SLP76, and PLC gamma. Critically, in all cases a statistically significant inhibition is observed. Furthermore, inhibition was additionally seen when TCR was activated by peptide presentation in antigen-presenting cells. Interaction between PITCR and the receptor is supported by co-localization, co-IP and the new AlphaFold-Multimer prediction. We are therefore confident in the results presented and that the inhibitory effect indeed exists. As we responded to reviewer 1 above, we discuss that inconsistent results were obtained with lower PITCR concentrations, suggesting that the use of a high peptide concentration is required for robust inhibition.

3) Use of control peptides is not uniform. Control peptides similar to PITCR in Figure 1 and Figure 2 studies, for example, could strengthen the authors' arguments.

The original version of the manuscript contained two negative control peptides, the G41P mutant of PITCR, and pHLIP, another pH-responsive peptide which behaves as a conditional transmembrane peptide. However, for feasibility reasons we did not use all the negative controls in all different experiments, as we were satisfied when a negative control peptide acted as such in an experiment. However, because we agree that increased use of negative control peptides will strengthen the manuscript, we have expanded the use of negative control peptides. Specifically, the updated version of the manuscript contains a new section where AlFoM is used to predict the binding pose of PITCR and the structural consequences of interaction (see Figure 9 and the four new supplementary figures). AlFoM showed that PITCR binds with a large interaction interface, and peptide binding causes a large rearrangement of the two zeta chains in TCR. Importantly, neither of the two original negative control peptides (PITCRG41P or pHLIP) impacts the zeta chains. When we used a new negative control, the conditional transmembrane peptide TYPE7 developed by us, AlFoM did not predict it to bind to TCR, as expected, strengthening our argument.

Reviewer #3 (Public Review):

The use of pH-responsive TM-targeting peptides, which the authors previously developed, is a novel aspect of this study. Those peptides can be quite powerful for understanding molecular mechanisms of receptor signaling, such as the allosteric activation model as tested in this study. The manuscript contains several interesting approaches and observations, but there are concerns about the experimental design and interpretation of the results. More importantly, the authors' primary conclusion that the allosteric changes in the TM bundles determine TCR activation is not fully supported by the data presented. For example:

1) The authors provided confocal fluorescence images showing the colocalization of fluorescently labeled peptides and TCR subunits. Based on the data, they concluded that "PITCR is able to bind to TCR". This is misleading, because given the spatial resolution of the imaging technique, "colocalization" does not indicate binding or interaction between molecules. Because the peptide binding to the TM region is the pillar of the primary finding of this study, direct evidence supporting the peptide-TM binding or interaction is essential.

We have to disagree that our statement is misleading: the section of the manuscript that the reviewer referred to, said “suggesting that PITCR is able to bind to TCR before it is activated by OKT3“. Therefore, we were not making a conclusion, just a mere suggestion, that we consider is justified, particularly as it is supported by data presented later. Nevertheless, we certainly agree with the reviewer that co-localization experiments fundamentally cannot indicate binding. We have modified the results (page 11) to follow the suggestion of the reviewer and indicate that co-localization data are not proof of interaction. In addition, we provide new AlphaFold multimer data, which supports that transmembrane binding indeed occurs.

2) In calcium response experiments, the authors compared calcium influx (indicated by Indo-1 ratio) under different cell activation conditions (Figure 2). There are some concerns about how the authors interpreted the data: (1) The calcium plots from OKT3 activation in A-C panels are inconsistent. The plot in (A) showed a calcium peak after activation, which is not present in the plots shown in (B) and (C). There is no explanation or discussion on this inconsistency. (2) What is more concerning is that this prominent calcium peak in (A) was used to draw the conclusion that the designer peptide inhibitor effectively reduces calcium response. However, inconsistent with that conclusion, the calcium plots are indistinguishable for the three conditions: with PITCR (peptide inhibitor), with PITCRG41P (negative control that should not affect TCR activation), or no peptide. All three plots have similar magnetite and fluctuations. This does not support the authors' conclusion that the PITCR (peptide inhibitor) reduces calcium response in T cells.

We thank the reviewer for this comment. We have updated figure 3, which now contains a different replicate of the calcium assay, which we think it is more straightforward to analyze, and more clearly shows the calcium inhibition, as quantified in panel D of the figure.

3) Different types of T cells were used for separate measurements: E6-1 Jurkat T cells were used for calcium influx experiments, J. OT.hCD8+ Jurkat cells were used for CD69 measurements, and primary murine CD4+ T cells were used for colocalization imaging experiments. Rationales for the choices of cells in different measurements are also unclear. This is different from the common practice where different cell types are used in repeated experiments to test the generality of a finding. Here, they were used for different experiments, and findings were lumped together as "T cells", without further evidence/discussion on how translatable the findings from different cell types are.

As the reviewer suggests, we have updated the manuscript to include discussion on the particularities of the use of the different T cells in pages 18 and 19. We envisioned this work as a proof of principle for the design of a peptide that can eventually be modified to be used for pre-clinical applications, and this paper is a first step. With this idea in mind, we wanted to test if this peptide can work in different types of TCR since: (1) TCR populations are diverse; and (2) our design is based on the transmembrane domain of CD3zeta chain, which is largely conserved among species. Using different types of T cells met this goal since they have different types of TCR, but the transmembrane domain of CD3zeta is conserved. In our paper, we used human Jurkat-TCR, OT1-TCR coupled with hCD8, and murine CD4-TCR. In addition, we not only used one activation marker to test the peptide’s inhibitory effect, we used three: phosphorylation, calcium influx, and CD69 activation. For the co-localization experiment, we not only use murine CD4 T cells, but we also tested it in Jurkat T cells with/without OKT3 stimulation as well.

We selected these T cells because they were particularly suited for the breath of different measurements that this manuscript contains, based on published reports. In our opinion this approach broadens the relevance of the work.

4) The authors set out to test the model that TCR activation by pMHC occurs through allosteric changes in the TM region, but in most experiments, they activated Jurkat T cells by anti-CD3 antibody, not by antigen peptides. The anti-CD3 antibody activates TCR signaling through clustering. It is unclear whether TCR activation by anti-CD3 leads to the same allosteric changes in the TM region as activation by pMHC. As such, the main claim of the paper, namely that the designer peptide affects TCR signaling by disrupting the allosteric changes in the TM region, remains insufficiently supported by the data presented.

Figure 8 shows that the levels of co-IP in the presence of detergent are altered by OKT3 activation of TCR. It has recently been established (PMID: 34260912) that this assay allows the investigation of allosteric changes that contribute to activation of TCR. This evidence is supportive of allosterism in TCR activation. Additionally, the TCR proximal signaling is conserved between the Jurkat T cells activated by OKT3 and TCR activated by pMHC. We can reasonably argue that the peptide acts similarly in both conditions, since the peptide also exerts an inhibitory effect in T cells activated by antigen-presenting cells (Figure 4). The newly presented AlFoM model (Figure 9) predicts that PITCR binding displaces a zeta chain in TCR. This new result provides a plausible molecular rationale for the results in Figure 8, where we observe that PITCR changes transmembrane compactness, which has been linked to allosteric activation (Lanz et al., 2021; Prakaash et al., 2021).

Author Response

eLife assessment

This useful paper examines changes (or lack thereof) in birds' fear response to humans as a result of COVID-19 lockdowns. The evidence supporting the primary conclusion is currently inadequate, because the model used does not properly account for many potentially confounding factors that could influence the study's outcomes. If the analytic approach were improved, the findings would be of interest to urban ecologists, behavioral biologists and ecologists, and researchers interested in understanding the effects of COVID-19 lockdowns on animals.

Many thanks for these supportive words. We did our best to improve our manuscript according to the reviewers and editor comments. Importantly, we regret being unclear in the Methods, as our models already controlled for most of the confounds (see below) discussed by the reviewers.

For example, given that a single observer collected the data at most sites, site as a random intercept in the models controls also for the observer effects (which is one of the reasons why site is in the model). We added details to Methods (L352-356, see also “Statistical analyses” in the main text).

The first reviewer asked us to use “some measure of urbanity (e.g. Human Footprint Index) that varies across the cities included here”. Our main results are now based on country-specific models and hence, the use of a single value predictor for each city is not appropriate. Please, see also below.

The second reviewer is concerned about multicollinearity in our models because of the 0.95 correlation between Period and Stringency Index. However, these are key predictor variables of interest that have never been used within the same model as predictors. We now clearly explain this in the Methods (L458-538, 548-550) and within legend of Figure S2.

The third reviewer suggested that our models would benefit from controlling for day in the species-specific breeding cycle. Although we don’t have precise city-specific information on the timing of breeding stages in the sampled populations of birds, we partly control for these effects by including a random intercept of day within each year and species. This random factor explained most of the variance (see Table S1-S2) – something that could have been expected. In other words, we do control for what the third reviewer asked for. Similarly, we account for habitat features that may influence escape distance by including site in the models. Site usually refers to a specific park (we assume that within-park heterogeneity is lower than between park variation) and hence partly addresses the reviewer’s concern. Again, we highlight this within the Methods (L466-476).

Reviewer #1 (Public Review):

This paper uses a series of flight initiation "challenges" conducted both prior to and during COVID-19-related restrictions on human movement to estimate the degree to which avian escape responses to humans changed during the "anthropause". This technique is suitable for understanding avian behavioral responses with a high degree of repeatability. The study collects an impressive dataset over multiple years across five cities on two continents. Overall the study finds no effect of lockdown on avian escape distance (the distance at which the "target" individual flees the approaching observer). The study considers the variable of interest as both binary (during lockdown or prior to lockdown) and continuous, using the Oxford Stringency Index (with neither apparently affecting escape distance). Overall this paper presents interesting results which may suggest that behavioral responses to humans are rather inflexible over "short" (~2 year) timespans. The anthropause represents a unique opportunity to disentangle the mechanistic drivers of myriad hypothesized impacts humans have on the behavior, distribution, and abundance of animals. Indeed, this finding would provide important context to the larger body of literature aimed at these ends.

Thank you very much for your positive feedback.

However, the paper could do more to carefully fit this finding into the broader literature and, in so doing, be a bit more careful about the conclusions they are able to draw given the study design and the measures used. Taking some of these points (in no particular order):

Thank you. We did our best in addressing your comments (see below and updated Methods, Results and Discussion sections).

1) Oxford Stringency Index is a useful measure of governmental responses to the pandemic and it's true that in some scenarios (including the (Geng et al. 2021) study cited by this paper) it can correlate with human mobility. However, it is far from a direct measure of human mobility (even in the Geng study, to my reading, the index only explained a minority of the variation). Moreover, particular sub-components of the index are wholly unrelated to human mobility (e.g. would changes to a country's public information campaign lead to concomitant changes in urban human mobility?). Finally, compliance with government restrictions can vary geographically and over time (i.e. we might expect lower compliance in 2021 than in 2020) and the index is calculated at the scale of entire countries and may not be very reflective of local conditions. Overall this paper could do more to address the potential shortcomings of the Oxford Stringency Index as a measure of human mobility including attempting to validate the effect on human mobility using other datasets (e.g. the google dataset and/or those discussed in (Noi et al. 2022). This is of critical importance since the fundamental logic of the experimental design relies on the assumption that stringency ~ mobility.

Thank you for this comment. First, Oxford Stringency Index seemed to us as the best available index for our purposes, i.e to estimate people's mobility during the shutdown because restrictions surely influenced the possibility that people would be outside, and because the index is a country-specific estimate. However, in addition, we now checked all indices mentioned in Noi et al. 2022 and found useful only the Google Mobility Reports, which we now use, because (a) it is publicly available, (b) it is available also for territories outside US, and (c) provides data for each city included in our dataset as well as for urban parks where most of our data were collected. Note that some platforms are no longer providing their mobility data (e.g. Apple).

However, Google Mobility provides day-to-day variation in human mobility, whereas we are interested in overall increase/decrease in human mobility. Nevertheless, we correlated the Google mobility index with the Stringency index and found that human mobility generally decreases with the strength of the anti-pandemic measures adopted in sampled countries (albeit the effect for some countries, e.g. Poland, is small; Fig. 5).

Moreover, we also added analysis using # of humans collected directly in the field during escape trials (e.g. Fig. 6 and S6) and found that the link between # of humans and Stringency index or Google Mobility was weak and noise, 95%CIs widely crossing zero (Fig. 6).

Importantly, if we use Google Mobility and # of humans, respectively, as predictors of escape distance, the results are qualitatively very similar to results based on Oxford Stringency Index (Fig. S6), or Period, with tiny effect sizes for both (95%CIs for Google Mobility -0.3 – 0.06, Table S5, for # of humans -0.12 – 0.02, Table S6) supporting our previous conclusions.

Note that Google Mobility and the number of humans have their limitations (see our comment to the editor and the Methods section in the main manuscript, e.g. L418-433). The lack of Google Mobility data for years before the COVID-19 pandemic does not allow us to fully explore whether overall human activity decreased during COVID-19 or not (our test for period prior and during COVID-19). If the year 2022 reflects a return to “normal” (which is to be disputed due to COVID-19-driven rise in home office use) the 2020 and 2021 had on average lower levels of human activity (Fig. 4). Whether such a difference is biologically meaningful to birds is unclear given the immense day-to-day change in human mobility and presence (Fig 4). Moreover, the number of humans capture within- and between-day variation rather than long-term changes in human presence.

We added details on the new analysis into the method and results sections (e.g. Fig. 4-6; L142-165, 418-438, 495-535) and Supplementary Information (Figs. S5-S9 and associated Tables) and discuss the problematic accordingly. Moreover, to enhance clarity about country specific effect (or their lack), we also add country specific estimates to the Results (Fig. 1 and Fig. S6 and respective Tables). Finally, our statistical design and random structure of the model allowed us to control for spatial and temporal variation in compliance with government restrictions.

2) The interpretation of the primary finding (that behavioral responses to humans are inflexible) could use a bit more contextualization within the literature. Specifically, the study offers three potential explanations for the observed invariance in escape response: 1) these behaviors are consistent within individuals and this study provides evidence that there was no population turnover as a result of lockdowns; 2) escape response is linked to other urban adaptations such that to be an urban-dwelling species dictates escape response; and/or 3) these populations already exhibit maximum habituation and the reduction in human mobility would only have increased that habituation but that trait is already at a boundary condition. Some comments on each of these respectively:

Thank for these comments. We incorporated them in the main text (L293-329). Your point 1) corresponds to our point (i): “Most urban bird species in our sample may be relatively inflexible in their escape responses because the species may be already adapted to human presence” (L293-306); your point 2) to our point (ii): “Urban environment might filter for bold individuals (Carrete and Tella, 2013, 2010; Sprau and Dingemanse, 2017). Thus, the lack of consistent change in escape behaviour of urban birds during the COVID-19 shutdowns may indicate an absence (or low influx) of generally shy, less tolerant individuals and species from rural or less disturbed areas into the cities…” (L307-314); your point 3) to our point (iii): “Urban birds might have been already habituated to or tolerant of variation in human presence, irrespective of the potential changes in human activity patterns” (L315-329). To distinguish between (ii) and (iii) or the two from (i), individually-marked birds and comprehensive genetic analyses are needed, which we now note in the Discussion (L330-348). Importantly, we also discuss that the lack of response might be due to relatively small changes in human activity (L253-292), which we unfortunately could not fully quantify.

a) Even had these populations turned over as a result of a massive rural-to-urban dispersal event, it's not clear that the escape distance in those individuals would be different because this paper does not establish that these hypothetical rural birds have a different behavioral response which would be constant following dispersal. Thus the evidence gathered here is insufficient to tell us about possible relocations of the focal species.

Thank you for this point. We address this point in the Introduction and Discussion (L92-101, 307-314). Rural bird populations/individuals are on average less tolerant of humans than urban birds (e.g. Díaz et al. 2013, PloS One 8:e64634; Tryjanowski et al. 2020, J Tropic Ecol 36:1-5; Mikula et al. 2023, Nat Commun 14:2146) and at the same time, bird individuals seem consistent in their escape responses (Carrete & Tella 2010, Biol Lett 23:167–170; Carrete & Tella 2013, Sci Rep 3:1–7).

Additionally, the paper cites several papers that found no changes in abundance or movements of animals in response to lockdowns but ignore others that do. For example: (Wilmers et al. 2021), (Warrington et al. 2022) (though this may have been published after this was submitted...), and (Schrimpf et al. 2021).

We added the papers (L89-91). Thank you!

There is a missed opportunity to consider the drivers of some of these results - the findings in this paper are interesting in light of studies that did observe changes in space use or abundance - i.e. changes in space use could arise precisely because responses to humans are non-plastic but the distribution and activities of humans changed.

Thank you. Indeed, we now address this in the Discussion (L303-306): “However, some studies reported changes in the space use by wildlife (Schrimpf et al., 2021; Warrington et al., 2022; Wilmers et al., 2021). and these could arise, as our results indicate, from fixed and non-plastic animal responses to humans who changed their activities”.

To wit, the primary finding here would imply that the reaction norm to human presence is apparently fixed over such timescales - however, and critically, the putative reduction in human activity/mobility combined with fixed responses at the individual level might then imply changes in avian abundance/movement/etc.

Unfortunately, we have not measured changes in avian abundance or movements. But, please, note that the change in human mobility in sampled cities might be not as dramatic as initially thought and we consider this scenario to be most plausible in explaining no significant differences in avian escape responses before and during the COVID-19 shutdowns (see Fig. 4). Nevertheless, we add your point into the Discussion: If our findings imply that in birds the reaction norm to human presence is fixed over the studied temporal scale, the putative changes in human presence might then imply changes in avian abundance or movement (L293 and text below it).

b) If this were the case, wouldn't this be then measurable as a function of some measure of urbanity (e.g. Human Footprint Index) that varies across the cities included here? Site accounted for ~15% of the total variation in escape distance but was treated as a random effect - perhaps controlling for the nature of the urban environment using some e.g. remotely sensed variable would provide additional context here.

Urbanity mirrors the long-term level of human presence in cities whereas we were interested mainly in the rather short-term effects of potential changes of human presence on bird behaviour. Thus, we are not sure how adding such variable will help elucidating the current results. Please, also note that we added the country-specific analysis. Site indeed accounted for considerable amount the total variance in escape distance and that is why it was included as random intercept, which controls for non-independents of data points from each city. This could partly help us to control for difference in habitat type (e.g. urbanization level) within cities.

c) Because it's not clear the extent to which the populations tested had turned over between years, the paper could do with a bit more caution in interpreting these results as behavioral. This study spans several years so any response (or non-response) is not necessarily a measure of behavioral change because the sample at each time point could (likely does) represent different individuals. In fact, there may be an opportunity here to leverage the one site where pre-pandemic measures were taken several years prior to the pandemic. How much variance in the change in escape distance is observed when the gap between time points far exceeds the lifetime of the focal taxa versus measures taken close in time?

We believe the initial Fig S4, now Figure 2, addresses this point. The between years temporal variation in FIDs exceeds the variation due to lockdowns. This is true both for measures taken in consecutive years, as well as for measures taken far apart.

d) Finally, I think there are a few other potential explanations not sufficiently accounted for here:

i) These behaviors might indeed be plastic, but not over the timescales observed here.

We agree and have added this point (L301-303). Thank you.

ii) Time of year - this study took place during the breeding season. The focal behavior here varies with the time of year, for example, escape distance for many of these species could be tied up in nest defense behaviors, tradeoffs between self-preservation and e.g. nest provisioning, etc.

Please, note that we controlled for the date in our analyses. Date was used as a proxy for the progress in the breeding season (L463-464 and Fig. 1 caption). Note that we collected data only from foraging or resting individuals, and data were neither collected near the nest sites nor from individuals showing warning behaviours, which we now note (L400-401).

iii) Escape behaviors from humans are adaptively evolved, strongly heritable, and not context dependent - thus we would only expect these behaviors to change on evolutionary timescales.

We discussed this at L307-308 and 381-383. Escape behaviors from humans are highly consistent for individuals, populations, and species (Carrete & Tella 2010, Biol Lett 23:167–170; Díaz et al. 2013, PloS One 8:e64634; Mikula et al. 2023, Nat Commun 14:2146). Whether such behavior is consistent across contexts is less clear (e.g. Diamant et al. 2023, Proc Royal Soc B, in press; but see, e.g. Radkovic et al. 2019, J Ecotourism 18:100-106; Gnanapragasam et al. 2021, Am Nat 198:653-659). Escape distance is often not measured simultaneously, for example, with human presence. In other words, whereas general level of human presence may have no effect on escape distance, the day-to-day or hour-to-hour variations might. We need studies on fine temporal scales (day-to-day or hour-to-hour) using marked individual to elucidate this phenomenon.

iv) See point one above - it's possible that the lockdown didn't modify human activity sufficiently to trigger a behavioral response or that the reaction norm to human behavior is non-linear (e.g. a threshold effect).

We agree, now use also Google Mobility Reports and # of humans data to elucidated this phenomenon and have added such interpretations to L253-292 and, e.g. Fig. 4.

LITERATURE CITED Geng DC, Innes J, Wu W, Wang G. 2021. Impacts of COVID-19 pandemic on urban park visitation: a global analysis. J For Res 32:553-567. doi:10.1007/s11676-020-01249-w

Noi E, Rudolph A, Dodge S. 2022. Assessing COVID-induced changes in spatiotemporal structure of mobility in the United States in 2020: a multi-source analytical framework. Int J Geogr Inf Sci.

Schrimpf MB, Des Brisay PG, Johnston A, Smith AC, Sánchez-Jasso J, Robinson BG, Warrington MH, Mahony NA, Horn AG, Strimas-Mackey M, Fahrig L, Koper N. 2021. Reduced human activity during COVID-19 alters avian land use across North America. Sci Adv 7:eabf5073. doi:10.1126/sciadv.abf5073

Warrington MH, Schrimpf MB, Des Brisay P, Taylor ME, Koper N. 2022. Avian behaviour changes in response to human activity during the COVID-19 lockdown in the United Kingdom. Proc Biol Sci 289:20212740. doi:10.1098/rspb.2021.2740

Wilmers CC, Nisi AC, Ranc N. 2021. COVID-19 suppression of human mobility releases mountain lions from a landscape of fear. Curr Biol 31:3952-3955.e3. doi:10.1016/j.cub.2021.06.050

Reviewer #2 (Public Review):

Mikula et al. have a large experience studying the escape distances of birds as a proxy of behavioral adaptation to urban environments. They profited from the exceptional conditions of social distance and reduced mobility during the covid-19 pandemic to continue sampling urban populations of birds under exceptional circumstances of low human disturbance. Their aim was to compare these new data with data from previous "normal" years and check whether bird behavior shifted or not as a consequence of people's lockdown. Therefore, this study would add to the growing body of literature assessing the effect of the covid-19 shutdown on animals. In this sense, this is not a novel study. However, the authors provide an interesting conclusion: birds have not changed their behavior during the pandemic shutdown. This lack of effects disagrees with most of the previously published studies on the topic. I think that the authors cannot claim that urban birds were unaffected by the covid-19 shutdown. I think that the authors should claim that they did not find evidence of covid-19-shutdown effects. This point of view is based on some concerns about data collection and analyses, as well as on evolutionary and ecological rationale used by the authors both in their hypotheses and results interpretation. I will explain my criticisms point by point:

We are grateful for your positive appraisal of our manuscript, as well as for your helpful critical comments. We toned down the discussion to claim, as suggested by you, that we did not find evidence for effects of covid-19-shutdowns on escape behaviour of birds in urban settings (see Results and Discussion sections). In general, we attempted to provide a more nuanced discussion and reporting of our findings. We also changed the manuscript title to “Urban birds' tolerance towards humans was largely unaffected by the COVID-19 shutdowns” and added validation using Google Mobility Reports (Fig. 5 & S6, Table S3a and S5) and the actual number of humans (Fig. 6 and S6; Table S3b-e and S6). Note however that there is only a single robust study on the topic of shutdown and animal escape distances (Diamant et al. 2023, Proc Royal Soc B, in press), i.e. the topic is largely unexplored (e.g. L99-101), whereas we discuss our finding in light of shutdown influences on other behaviours (L293-329).

1) The authors used ambivalent, sometimes contradictory, reasoning in their predictions and results interpretation. Some examples:

We tried to clarify our reasoning and increased consistency in our claims in the Introduction. Please, note that we simplified the Introduction and now provide one main expectation: FIDs of urban birds should increase with decreased human presence. This pattern is robustly empirically documented, regardless of the mechanism involved (e.g. Díaz et al. 2013, PloS One 8:e64634; Tryjanowski et al. 2020, J Tropic Ecol 36:1-5; Mikula et al. 2023, Nat Commun 14:2146). Please, see our revised Discussion for a more comprehensive discussion of mechanisms which could explain the patterns described in our study.

1.1) The authors claimed that urban birds perceive humans as harmless (L224), but birds actually escape from us, when we approach them... Furthermore, they escape usually 5 to 20 m away. This is more distance that would be necessary just to be not trampled.

We agree and have deleted mentions that humans are perceived as harmless.

1.2) If we are harmless, why birds should spend time monitoring us as a potential threat (L102)? Indeed, I disagree with the second prediction of the authors. I could argue that reduced human activity should increase animal vigilance because real bird predators (e.g. raptors) may increase their occurrence or activity in empty cities. If birds should increase their vigilance because the invisible shield of human fear of their predators is no longer available, then I would expect longer escape distances.

Thank you for this comment. We deleted this prediction and largely rewrote Introduction based on your comments and comments from the other reviewers.

1.3) To justify the same escape behavior shown by birds in pre- and pandemic conditions from an adaptive point of view, the authors argued a lack of plasticity and a strong genetic determination of such behavior. This contravenes the plasticity proposed in the previous point or the expected effect of the stringency index (L112).

We now attempted to write this more clearly while incorporating your suggestions. In the Discussion, we now propose various hypothesis that can, but need not be mutually exclusive. Please, note that we simplified the Introduction and now provide one main hypothesis: FIDs of urban birds should increase with decreased human presence.

In my opinion, some degree of plasticity in the escape behavior would be really favorable for individuals from an adaptive perspective, as they may face quite different fear landscapes during their lives. Looking at the figures, one can see notable differences in the escape distance of the same species between sites in the same city. As I can hardly imagine great genetic differences between birds sampled in a park or a cemetery in Rovaniemi, for instance, I would expect a major role of plasticity to explain the observed variability. Furthermore, if escape behavior would not be plastic, I would not expect date or hour effects. By including them in their models, the authors are accepting implicitly some degree of plasticity.

We regret being unclear. We do accept some degree of plasticity. Yet, our study design prohibits the assessment of the degree of individual plasticity because sampled birds were not individually marked and approached repeatedly. We tried to soften the statements in our Discussion to not fully dismiss a possibility that urban birds have some degree of plasticity in their antipredator behaviour (L293-329). Note however, that while our data collection was not designed to test how hour-to-hour changes in human numbers influence escape distance, the effect of the number of humans (i.e. hour-to-hour variation in human numbers) in our sample was tiny.

The date and hour effect simply control for the particularities of the given day and hour (e.g. warm vs cold times or the time until sunset). In other words, the within species differences (even from the same park) may have little to do with individual plasticity, but instead may reflect between individual differences. We now add this issue to Methods (L471-476): “This approach enabled us to control for spatial and temporal heterogeneity and specificity in escape behaviour of birds (e.g. species-specific responses, changes in escape distances with the progress in the breeding season, spatial and temporal variation in compliance with government restrictions or particularities of the given day and hour)....”

2) Looking at the figures I do not see the immense stochasticity (L156, Fig. S3, S5) claimed by the authors. Instead, I can see that some species showed an obvious behavioral change during the shutdown. For instance, Motacilla alba, Larus ridibundus, or Passer domesticus clearly reduced their escape distances, while others like the Dendrocopos major, Passer montanus, or Turdus merula tended to increase it.

At L138-141 and 327-329 we discussed the within and between genera and cross-country variation and stochasticity in response to the shutdowns (Fig. 2). The reference to species-specific plots was perhaps a little bit misleading. We think that the essential figure, that we now reference at this point, is Figure 2 that shows the temporal trends and/or stochasticity that seem to have little in common with lockdowns. Please, also look at Figure 3 and S3-S4. These show that in all selected genera/species, the trends did not significantly deviate from central regression line which indicates no change in FID before and during the COVID-19 shutdowns.

On the other hand, birds in Poland tended to have larger escape distances during the shutdown for most species, while in Rovaniemi there was an apparent reduction of escape distances in most cases. The multispecies and multisite approach is a strength of this study, but it is an Achilles' heel at the same time. The huge heterogeneity in bird responses among species and sites counterbalanced and as a result, there was an apparent lack of shutdown effects overall. Furthermore, as most data comes from a few (European) species (i.e. Columba, Passer, Parus, Pica, Turdus, Motacilla) I would say that the overall results are heavily influenced (or biased) by them. The authors realize that results are often area- or species-specific (L203), therefore, does a whole approach make sense?

We are grateful for this valuable comment. We believe the general approach makes sense as there is a general expectation about how birds should respond to changes in human presence. That is why we control for non-independence of data points in our sample. Thus, although lots of data come from a few European species, this is corrected for by the model. Note that given the sheer number of sampled species, some site- or species-specific trends may have occurred by chance. Importantly, we believe that Figure 2, with species-site specific temporal trends, reveals that the between year stochasticity in escape distances seems greater that any effects of lockdowns. Nevertheless, we have further dealt with this issue in the revised manuscript by running country-specific models which again clearly showed no significant effect of Period on escape behaviour of birds (including, no effects in Poland and Finland).

3) The previous point is worsened by the heterogeneity of cities and periods sampled. For instance:

3.1) I can hardly imagine any common feature between a small city in northern Finland (Rovaniemi) and a megacity in Australia (Melbourne). Thus, I would not be surprised to find different results between them.

3.2) Prague baseline data was for 2014 and 2018, while for the rest of the study sites were for 2018 and 2019. If study sites used a different starting point, you cannot compare differences at the final point.

We are slightly confused by these comments.

3.1) The cities are expected to be different but (i) the difference may be smaller than imagined (e.g. park structures, managed grass cover, few shrubs and deciduous-dominated tree species) and (ii) we expect the effects of lockdowns to be similar across cities. Whether we have no people in Rovaniemi parks (which despite Rovaniemi’s small size are usually extremely well-visited) or no people in Melbourne parks should not make a difference in principle. Note however, that to avoid overconfident conclusions, we allow for different reaction norms within cities. Please, also note that we are now providing country-specific results which should identify whether shutdowns lead to different reaction in sampled countries. We found no strong effect of shutdowns in any of sampled countries/cities.

3.2) Because of the possible between site differences at the starting point, we use study site as random intercept and control for the between site reaction norms by including the random slope of the period. In other words, such possible differences do not influence outcomes of our models. Regardless, our a priori expectation is that the human activity levels in a given park was similar prior to covid and hence in 2014, 2018, and 2019. Again, we are now providing country-specific results which identify whether shutdowns led to different reactions in sampled countries, which they mostly did not

3.3) Due to the obvious seasonal differences between the northern and southern hemispheres, data collection in Australia began five months later than in the rest of the sites (Aug vs Mar 2020). There, urban birds faced already too many months of reduced human disturbances, while European birds were sampled just at the beginning of the lockdown.

We agree that each city or even park within the city has its specific environmental conditions (here including the time point of lockdown). That is why we control for city and park location in the random structure of the model (see Method section). We now add results per country that shows no clear differences (e.g. Fig. 1).

However, the aim of our study was to test for general, global effects of lockdowns, which are minimal. Note that we now specifically test for country-specific effects in separate models on each country (e.g. Fig. 1, Fig S6) but all country-specific effects are small and still centre around zero.

3.4) Some cities were sampled by a single observer, while others by many of them. Even if all of them are skilled birders, they represent different observers from a statistical point of view and consequently, observer identity was an extra source of noise in your data that you did not account for.

We agree. In Finland and Hungary, data were collected by two closely cooperating observers. In Poland, all data were collected by a single observer. In the Czech Republic and Australia, a single observer (P.M. and M.W., respectively) sampled 46 sites out of 56 and 32 sites out of 37, respectively. Each site was sampled by the same observer both before and during the shutdowns. We now clearly state it in the Methods (L352-356). In other words, our models already largely control for the possible observer confound by having site as a random intercept. Moreover, previous study showed that FID estimates do not vary significantly between trained observers (Guay et al. 2013, Wildlife Research, 40, 289-293).

4) Although I liked the stringency index as a variable, I am not sure if it captured effectively the actual human activity every day. Even if restrictive measures were similar between countries, their actual accomplishment greatly depended on people's commitment and authorities' control and sanctions. I would suggest using a more realistic measure of human activity, such as google mobility reports.

Thank you for this comment. We now validate the use of the stringency index with the Google Mobility Reports, showing that human mobility generally (albeit in some countries relatively weakly) decreases with the strength of governmental antipandemic measures. Please, note that our main research question is related to the general change in human outdoor activity and not to week-to-week, day-to-day or hour-to-hour changes captured by stringency index, Google Mobility or the number of humans during an escape trial data. Nevertheless, using Google Mobility and the number of humans as predictors led to the similar results as for stringency index and Period (Fig. 1 and S6). Please, see extended discussion on this topic in our manuscript (L270-292).

5) The authors used escape trials from birds on the ground and perched birds. I think that they are not comparable, as birds on the ground probably perceive a greater risk than those placed some meters above the ground, i.e. I would expect shorter escape distances for perched birds. As this can be strongly dependent on the species preferences or sampling site (i.e, more or less available perches), I wonder how this mixture of observations from birds on the ground and perched birds could be affecting the results.

We now added information that most birds were sampled when on the ground (79%). Importantly, previous studies have found that perch height has a minimum effect on FIDs (e.g. Bjørvik et al. 2015. J Ornithol 156:239–246; Kalb et al. 2019, Ethology 125:430-438; Ncube & Tarakini 2022, Afr J Ecol 60:533– 543; Sreekar et al. 2015,. Tropic Conserv Sci 8:505-512). We added this information to the Method section (L394-395).

6) The authors did not sample the same location in the same breeding season to avoid repeated sampling of the same individuals (L331). This precaution may help, but it does not guarantee a lack of pseudoreplication. Birds are highly mobile organisms and the same individuals may be found in different places in the same city. This pseudoreplication seems particularly plausible for Rovaniemi, where sampling points must be necessarily close due to the modest size of this city.

We appreciate your concern. We cannot fully exclude the possibility of sampling some individuals twice. However, we sampled during the breeding season within which most birds are territorial, active in the areas around the nests and hence an individual switching parks is unlikely. Also, most sampled birds in our study are passerines which have small territories (typically few hundred square meters). Some larger birds may have larger territories and move larger distance to forage (e.g. kestrels which often forage outside cities) but these birds represent a minority of our records and we have not sampled outside the cities.

7) An intriguing result was that the authors collected data for 135 species during the shutdown, while they collected data only for 68 species before the pandemic. Such a two-fold increase in bird richness would not be expected with a 36% increase in sampling effort during 2020-21. I wonder if this could be reflecting an actual increase in bird richness in urban areas as a positive result of the shutdown and reduced human presence.

There were 141 unique day-years during before COVID and 161 during COVID. So, the sampling effort as calculated by days does not explain the difference in species numbers. Whether the actual effort, which was 381 vs 463 h of sampling, explains the difference is unclear, which we now note in the Methods (L476-483). If not, your proposition is possible, but we would like to avoid any speculations on this topic in the manuscript as it is difficult to infer species diversity from FID sampling.

8) The authors dismissed the multicollinearity problem of explanatory variables unjustifiably (L383). However, looking at fig. S1, I can see strong correlations between some of them. For instance, period and stringency index were virtually identical (r=0.95), while temperature and date were also strongly correlated.

We are confused by this comment and think this reflects a misunderstanding. Period and stringency index are explanatory variables of interest that were never included in the same model and hence their correlation does not contribute to the within a model multicollinearity. To avoid further confusion, we note this within (Fig. S2) legend. However, we must be cautious when interpreting the results from the models on period, Google Mobility, # of humans and stringency index, as the four measure are similar.

We discuss multicollinearity of explanatory variables within the manuscript (L458-538, 548-550) and noted that, with the exception of temperature and day within the breeding season (r = 0.48), the correlations among explanatory variables were minimal. We thus used only temperature as an explanatory variable (i.e. fixed factor; also because temperature reflects both season and variation in temperature across a season) whereas the day was included as a random intercept to control for pseudoreplication within day. Collinearity between all other predictors was low (|r| <0.36).

9) The random structure of the models is a key element of the statistical analyses but those random factors are poorly explained and justified. I needed to look up the supplementary tables to fully understand the complex architecture of the random part of the models. To the best of my knowledge, random variables aim to account for undesirable correlations in the covariance matrix, which is expected in hierarchical designs, such as the present one. However, the theoretical violation of data independence may happen or not. As the random structure is usually of little interest, you should keep it as simple as necessary, otherwise random factors may be catching part of data variability that you would like to explain by fixed variables. I think that this is what is happening (at least, in part) here, as the authors included a too-complex random structure. For instance, if you include the year as a random factor, I think that you are leaving little room for the period effect. The authors simplified the random structure of the models (L387), but they did not explain how. Nevertheless, this model selection was not important at all, as the authors showed the results for several models. I assume, consequently, that the authors are considering all these models equally valid. This approach seems quite contradictory.

The random structure of the model controls for possible pseudoreplication in the data, that is for the cases where we have multiple data points that may not be independent and hence technically represent one. Apart from that, random structure tells us about where the variance in the data lies. This is often of interest and your previous questions about city, site or species specificities can be answered with the random part of the model. To follow up on your example, year is included in the model because data from a single year are not independent (for example because of delayed breeding season in one year vs. in another).

We regret being unclear about the model specification and have attempted to clarify the methods (L466-476). We first specified a model with an ideal random structure that necessarily was complex (perhaps too complex). We then showed that using models with simpler random structures did not influence the outcomes. We now use a simpler model within the main text, but do keep the alternative models to show that the results are not dependent on the random structure of the model (Fig. S1 and Table S2).

Reviewer #3 (Public Review):

This study examined the changes in fear response, as measured by the flight initiation distances (FID), of birds living in urban areas. The authors examined the FIDs of birds during the pandemic (COVID-19 lockdown restrictions) compared to FIDs measured before the pandemic (mostly in 2018 & 2019). The main study justification was that human presence changed drastically during the pandemic lockdowns and the change in human presence might have influenced the fear response of birds as a result of changing the "landscape of fear". Human presence was quantified using a 'stringency' index (government-mandated restrictions). Urban areas were selected from within five different cities, which included four European cities (Czech Republic - Prague, Finland - Rovaniemi, Hungary - Budapest, Poland - Poznan), and one city in the global south (Australia - Melbourne). Using 6369 flight initiation distances across 147 different bird species, the authors found that FIDs were not significantly different before the pandemic versus during the pandemic, nor was the variation in FID explained by the level of 'stringency'.

Major strengths: There are several strengths to this study that allows for understanding the variety of factors that influence a bird's response to fear (measured as flight initiation distances). This study also demonstrates that FIDs are highly variable between species and regions.

Specifically,

1) One of the major strengths of this paper is the focus on birds living in urban areas, a habitat type that is hypothesized to have changed drastically in the 'landscape of fear' experienced by animals during the pandemic lockdown restrictions (due to the presumed decrease in human presence and densities). Maintaining the focus on urban birds allowed for a deeper examination of the effect of human behaviour changes on bird behaviour in urban habitats, which are at the interface of human-wildlife interactions.

2) This study accounted for several variables that are predicted to influence flight initiation distances in birds including species, genus, region (country), variability between years, pandemic year (pre- versus during), the strictness of government-mandated lockdown measures, and ecological factors such as the human observer starting distance, flock size, species-specific body size, ambient air temperature (also a proxy of the timing during the breeding season), time of day, date of data collection (timing within the regional [Europe or Australia] breeding season), and categorization of urban site type (e.g. park, cemetery, city centre).

3) This study examined FIDs in two years previous to the pandemic (mostly 2018 and 2019, one site was 2014) which would account for some of the within- and between-year FID variation exhibited prior to the pandemic.

4) This study uses strong statistical approaches (mixed effect models) which allows for repeat sampling, and a post hoc analysis testing for a phylogenetic signal.

Thank you for your supportive and positive comments.

Major weaknesses: The authors used government 'stringency' as a proxy for human presence and densities, however, this may not have been an accurate measure of actual human presence at the study sites and during measurements of FIDs. Furthermore, although the authors accounted for many factors that are predicted to influence fear response and FIDs in birds, there are several other factors that may have contributed to the high level of variation and patterns in FIDS observed during this study, thus resulting in the authors' conclusion that FIDs did not vary between pre- and during pandemic years.

Thank you for your suggestions. We agree. To capture the general human presence in parks, we now incorporated an analysis using Google Mobility Reports (Fig S6b) that directly measures human mobility in each of sampled cities and specifically in urban parks where most our data were collected, and also address your further concerns that you detail below. Albeit not the main interest of our study, we now also incorporated an analysis using actual # of humans during an escape trial (Fig. S6c).

Moreover, we think that including further possible confounds should not influence our conclusions. In other words, including further confounds will decrease the variance that can be explained by shutdowns and thus such shutdown effects (if any) would be tiny and hence likely not biologically meaningful.

Specifically,

1) The authors used "government stringency" as a measure of change in human activity, which makes the assumption that the higher the level of 'stringency', the fewer humans in urban areas where birds are living. However, the association between "stringency" and actual human presence at the study sites was not measured, nor was 'stringency' compared to other measures of human presence such as human mobility.

Thank you for this essential comment. Initially, we viewed Oxford Stringency Index as the best available index for our purposes. However, we now further acknowledge its limitations (L) and validate the Oxford Stringency Index with the Google Mobility Reports data, showing that both indices are generally negatively (albeit sometimes weakly) correlated across sampled cities (i.e. human mobility decreases with the increasing stringency index). Although other human presence indices were used in the past, e.g. Cuebiq, Descartes Labs and Maryland Uni index, Apple (see Noi et al. 2022, Int J Geograph Info Sci, 36, 585-616), we used only the Google Mobility index because (a) it is publicly available, (b) is available also for territories outside US, and (c) provides data for urban parks within each city included in our dataset. Note however that Google Mobility data are inappropriate to answer our primary question, i.e. whether changes in human presence outdoors due to the COVID-19 shutdowns had any effect on avian tolerance towards humans. First, Google Mobility was available only for 2020-22, i.e. the baseline pre-COVID-19 data for 2018-2019 were unavailable. Thus, there was no way to check whether the human activity levels really changed during the COVID-19 years. Second, Google Mobility data are calculated as a change from 2020 January–February baseline for each day of the week for each city and its location (here we used parks). In other words, the data are not comparable between days and cities, albeit we attempted to correct for this within the random structure of the mixed model. Also, the data may be influenced by extreme events within the 2020 Jan–Feb baseline period (see here). Third, the Google Mobility varies greatly between days and across season (see Fig 4 & S5 or the first figure in these responses), likely more than the possible change due to shutdowns. Nevertheless, we found that results based on Google Mobility are qualitatively very similar to results based on stringency index. Moreover, we showed that the relationships between # of humans and both Google Mobility or Stringency index (Figure 6) are weak and noise with 95%CIs widely overlapping zero (Table S3b-e). Also, similarly to other predictors of human presence, # of humans only poorly predicted changes in avian escape distances. We added details on the new analysis into the Methods and Results and Supplement (L134-165 and associated figures and tables, L415-535).

2) There was considerable variation in FID measurements, which can be seen in the figures, indicating that most of the variation in FID was not accounted for in the authors' models.

We are confused by this statement. The fact that the FIDs varied does not translate directly to that our models did not account for the variation. Nevertheless, we do control for most of the discussed confounds (see further answers below). Importantly, it is unclear how including further possible confounds should influence our conclusions, unless the lockdowns effects are tiny, in which case those might not be biologically meaningful.

Factors that may have contributed to variation in FIDs that were not accounted for in this study are as follows:

a. The authors accounted for the date of data collection using the 'day' since the start of the general region's breeding season (Europe: Day 1 = 1 April; Australia: Day 1 = 15 August). Using 'day' since the breeding season started probably was an attempt to quantify the effect of the breeding stage (e.g. territory establishment, nest young, fledgling) on FIDs. However, breeding stages vary both within- and between species, as well as between sub-regions (e.g. Finland vs. Hungary). As different species respond to predation or human presence differently depending on the stage during their breeding cycle, more specificity in the breeding cycle stage may allow for explaining the observed variation and patterns in FID.

We agree. Although we don’t have a precise city-specific information on the timing of breeding stages in sampled populations of birds, we partly control for these effects by including a random intercept of day within each year and species. This random factor explained relatively high portion of the variance in our data (see Table S1 and S2) - perhaps something you expected.

b. Variation in species-specific FIDs may also vary with habitat features within urban sites, such as the proximity of trees and other protective structures (e.g. perches and cover), the openness of the area, and the level of stressors present (e.g. noise pollution, distance to roads). Perhaps accounting for this habitat heterogeneity would account for the FID variation measured in this study.

We agree. We don’t have such fine-scale data, but we included site identity (typically within a particular park or cemetery) which should account for the habitat heterogeneity among localities. Depending on the model, site explained relatively little variance (1-6%), indicating low heterogeneity between localities in these undescribed characteristics. Also note that park structure may be quite similar both within and between cities, i.e. managed green grass areas, with only a few shrubs and deciduous trees. Therefore, the possible minor habitat heterogeneity should not have any great impacts on our results.

c. The authors accounted for species and genus within their models, however, FIDs may vary with other species-specific (or even specific populations of a species) characteristics such as whether the species/population is neophobic versus neophilic, precocial versus altricial, and the level of behavioural plasticity exhibited. These variables were not accounted for in the analysis.

We agree that FIDs can be correlated with many possible factors. Here, we were interested in general patterns, while controlling for FID differences between species, as well as for possible species-specific reaction norms to lockdowns. Whether neophobic vs neophilic population or precocial versus altricial species react differently to lockdowns might be of interest, but it is beyond the scope of this study. However, that population and population specific reaction norms explain little variation (Table S2a, 0-6% of variation) so such a confound should not substantially influence our conclusion much. We do not have fine-scale data on the level of neophobia, but the effects of lockdowns seem similar for precocial (see Anas, Larus, Cygnus) and altricial (the remaining, mostly passerine) species in our dataset (see Fig. 3 and S3-S4). Please, note that we sampled mainly adults (L386). Moreover, the effects for clades, which may differ in their cognitive skills, are also similar (e.g. Corvids vs. Anas or Cygnus; Fig. 3).

d. Three different methods of measuring the distances between flight and the observer location were used, and FIDs were only measured once per bird, such that there were no measures of repeatability for a test subject. Thus, variation surrounding the measurement of FIDs would have contributed to the variation in FIDs seen during this study.

While all observers were trained, the three methods may add some noise to the FID estimates. However, the FID estimates from a single method may still slightly differ between observers (so do well standardized morphology measurements; Wang, et al. 2019, PLoS Biology, 17, e3000156). Importantly, FID estimates are highly replicable among skilled observers (Guay et al. 2013, Wildlife Research 40:289-293), and we previously validated this approach and showed that distance measured by counting steps did not differ from distance measured by a rangefinder (Mikula 2014, Ardea 102:53-60), which we now explicitly state (L391-394). Importantly, we control for observer bias by specifying locality as a random intercept (see further details in our response to the Editor). Moreover, each site was sampled by the same observer both before and during the shutdowns.

3) The sample design of this study may have influenced the FID variability associated with specific species, and specific populations of species. A different number of species were sampled across the time periods of interest; 68 species were sampled before the pandemic versus 135 species after the pandemic. However, the authors do not appear to have directly compared the FIDs for the same species before the pandemic compared to during the pandemic (e.g. the FIDs of Eurasian blackbirds before the pandemic versus during the pandemic). Furthermore, within the same country-city, it is unclear whether the species observed before the pandemic were observed at the same location (e.g. same habitat type such as the same park) during the pandemic. As a species' FID response may be influenced by population characteristics and features specific to each site (e.g. habitat openness), these factors may have influenced the variability in FID measurements in this study.

We regret being unclear in our methods. Our full model uses all data, but alternative models (see e.g. Fig. S1) used data with ≥5 as well as ≥10 observations before and during lockdowns for a given species. Importantly, Figure 2 and 3 depict data for species sampled at specific sites. We now clarify this within the Methods (L460-483) and the Results (L125-133 and associated figures) and in the figure legends (Fig. S1).

4) The models in this study accounted for many factors predicted to affect FIDs (see the section on major strengths), however, the number of fixed and random factors are large in number compared to the total sample size (N =6369), such that models may have been over-extended.

The number of predictors and random effects is well within the limits for the given sample size (Korner-Nievergelt et al. 2015. Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and Stan). Importantly, simpler models give similar results as the more complex ones (Fig. S1) and the visual (model free) representations of our raw and aggregated data confirm our model results. This, we suggest, makes our findings robust and convincing.

Overarching main conclusion

Overall, this study examines factors influencing FIDs in a variety of bird species and concludes that FIDs did not differ during the pandemic lockdowns compared to before the pandemic (2019 and earlier). Furthermore, FIDs were not influenced by the strictness of government-mandated restrictions. Although the authors accounted for many factors influencing the measurement of FIDs in birds, the authors did not achieve their aim of disentangling the effects of pandemic-specific ecological effects from ecological effects unrelated to the pandemic (such as habitat heterogeneity).

We find this statement confusing. We accounted for most relevant confounding factors and found little evidence for the strong effect of pandemic. Moreover, we now added country-specific analyses that confirm the lack of evidence, highlight the Figure 3 that shows no clear shutdown effect and also explore how levels of human presence changed over and within the years. Adding more possible confounds (albeit note that not many are left to add) might only further reduce the variation that could be explained by pandemic and hence such hypothetical effects of pandemic will be if anything small and thus likely not biologically meaningful.

Their findings indicate that FIDs are highly variable both within- and between- species, but do not strongly support the conclusion that FIDs did not change in urban species during the pandemic lockdown. Therefore, this study is of limited impact on our understanding of how a drastic change in human behaviour may impact bird behaviour in urban habitats.

It is unclear why you think our study lacks support for the conclusion that FIDs changed little during pandemic, if all results show no such effects. However, we toned down our Discussion and highlighted also potential issues linked to our approach (e.g. that sampled individuals were not marked and hence we cannot distinguish between various mechanisms that might explain the described pattern (L293-329) or that human presence may not have changed (L253-269). For further details see our previous response.

Overall, the study demonstrates the challenges in using FIDs as a general fear response in birds, even during a pandemic lockdown when fewer humans are presumably present, and this study illustrates the large degree of variation in FIDs in response to a human observer.

We appreciate and agree that our study demonstrates the challenges in quantifying human activity to understand bird escape distance and we added a paragraph on this topic to the discussion (L270-292).

Nevertheless, we hope that our above responses clarify and address most of the issues you had with our manuscript. We tried to show that (a) most of your proposed controls are indeed included in our study design, models, and visualisations, and that (b) multiple evidence (from models and visualisation of raw and aggregated data) support the no overall effect conclusion. We further emphasize the temporal and between- and within-species variability in FIDs in the Results and now specifically indicate that lockdowns did not influenced FIDs above such variability (Fig. 2-3, Fig. S3). In other words, the natural (e.g. temporal) variation in FIDs seems far greater that potential effects of lockdowns (Fig. 2). We believe that even if lockdowns would have tiny effects that could have been detected with more. stringent experimental design (e.g. individually tagged birds) or even more complex models, such effects would be far from being biologically meaningful.

Author Response

Reviewer #1 (Public Review):

The authors managed to show the broad botanical landscape and not only the main crops. This unique achievement is based on decades of establishing an excellent collection of a full comparative seed collection of the current flora. This allows the identification of species that usually are not identifiable. The authors were able to compare the crops that were grown there and identify the contribution of the Roman period with that of the Arab one. This excellent study is a landmark in how such studies should be done. The list of identified species will be used for many other studies on this subject.

We are very grateful to Reviewer #1 for this generous assessment.

Reviewer #2 (Public Review):

Fuks et al. provide extensive paleobotanical data from several sites in the Negev desert to address hypotheses regarding the relative importance of the Roman Agricultural Diffusion (RAD) and the Islamic Green Revolution (IGR) in the dispersal of crops across Eurasia.

While the overall claims from the authors are convincing, I found the presentation of the data somewhat difficult to follow.

Graphical visualization of the data with respect to the proposed hypotheses would go a long way towards making the argument clearer for a non-specialist audience.

The authors apply appropriate caveats in the discussion about their ability to assess IGR given their timeline only incorporates the first few hundred years and some IGR plants may not leave macrobotanical remains. Yet I think more could be done to explain how the data they do find provides positive evidence for RAD. Many of their findings are inferred to be RAD introductions not because of the timing in their sites, but because of previous evidence of introductions at other sites. It would thus be helpful to be more explicit about what additional evidence these findings provide beyond previously published data of introductions of many of these crops into the Levant.

We thank Reviewer #2 for the positive assessment and helpful comments. We have moved several tables out of the main text to the supplementary tables. We also added a new schematic of the main findings regarding 1st millennium CE introductions to the southern Levant and their significance in the Negev Highlands crop assemblage (Figure 4). We have also added explanatory text to clarify the point about taphonomy vs. period of diffusion.

Author Response

eLife assessment

This paper is of interest to researchers and policy makers involved in cervical cancer prevention. The paper provides insight into how the Covid19 pandemic accelerated changes in organized cervical cancer screening. The claim that self-sampling led to a major improvement of test coverage seems somewhat exaggerated and alternative hypotheses to those provided by the authors on the population who chose self-sampling are possible. Nonetheless, this is a valuable piece of work given the scope of the intervention(s) and the precedent it sets i.e. a crisis can in fact accelerate positive changes in screening that have been academic possibilities rather than practical realities.

Thank you for this supportive summary. We have included exact data on exactly how much of the population test coverage that was attributable to self-samples. We have furthermore decided to focus on the population test coverage that is caused by organised testing (either taken by a clinician at a time and place that the woman was invited to by the organised program or taken by the woman herself using a sampling kit mailed to her by the organised program). These 2 improved analyses are intended to facilitate interpretation of how much of the improved test coverage that is attributable to the mailing of self-sampling kits.

Reviewer #1 (Public Review):

During the Covid19 pandemic, most cervical cancer screening programs were temporarily put on hold. The authors describe how Swedish health authorities dealt with this situation by implementing primary self-sampling and by launching a campaign with concomitant vaccination and screening. Besides, they show that the coverage of the screening program was one year after the start of the pandemic at pre-pandemic levels.

Strengths of the paper are the clear presentation of the steps taken by the Swedish health authorities and the high quality of the presented screening coverage data which could be obtained directly from the screening registry. However, the paper would benefit from more in-depth analyses because the presented data raise questions. The number of invitations was >30 percent lower in the first year of the pandemic (Figure 1), but the screening coverage was only 4-5 percent lower. In the second year of the pandemic (year 2021), coverage was back at pre-pandemic levels, but the role of primary self-sampling in restoring screening coverage is a bit unclear. It is obvious that primary self-sampling made it possible to invite women again for screening during the pandemic, but there is no data on acceptance of primary self-sampling. Besides, the increase in coverage in year 2021 was only 4% and it is not clear whether such a modest increase could also have been achieved without primary self-sampling. In addition to self-sampling, the authors describe the launch of a concomitant vaccination and screening campaign. This is an interesting initiative but the authors do not show data on the coverage of this campaign in the target age range.

We are now explaining that population test coverage is calculated over a whole screening interval. For example, if the screening interval is 3 years and improved attendance would only fully impact the population test coverage after 3 years. Furthermore, we are now presenting the exact data on how much of the test coverage is indeed attributable to the mailing of self-sampling kits.

Reviewer #2 (Public Review):

The manuscript by Elfstrom et al describes the impact of implementing self-sampling as the primary screening test in Sweden to address decreases in coverage following the COVID pandemic. The authors have a very rich dataset including all records of invitations to screen and screening results in the Stockholm area. A limitation is that there is no individual record linkage to allow investigation of the profile of the individuals who chose to screen using the self-sample.

The conclusions are generally well supported by the authors with the following exceptions:

1) There was not enough evidence presented in the manuscript to conclude that "The most likely explanation for the large increase in population coverage seen is that the sending of self-sampling kits resulted in improved attendance in particular among previously non-attending women."

2) The authors state there is no evidence that delays in screening have impacted cervical cancer rates however they present no data to this effect in the manuscript.

Although all screening and invitation data is indeed collected to the national screening registry, linking this data is not allowed without a permission from the Swedish National Ethical Review Board. We did apply for such a permission, which was granted on 2023-02-01, and a full set of registry linkage analyses to investigate the point raised by the reviewer is now included.

The mention in discussion on stable cervical cancer rates was referring to public data from the national Cancer Registry. The source is now referenced.

Reviewer #3 (Public Review):

The authors report on the nature of interventions that were applied to aid and improve engagement in cervical screening, brought about by the SARS CoV Pandemic in Sweden.

I appreciate that the impact of these interventions, given that they are recent, will take some time to quantify but the description (and reach) of the policy changes that occurred in a short amount of time is of significant interest to the screening community. The piece on HPV Even Faster is particularly novel; I am not aware of another example of where this has been enacted within a routine programme.

Thank you for this supportive statement.

The authors make reference to (15) where the reader can find greater details relating to the population who received the offer of self sampling (and the nature of the device). However I was a little confused (in this stand alone piece) as to who the self sampling group constituted exactly. Did this group not include pregnant women, women invited for first screen or women on non routine recall?

This is correct, self-sampling kits were mailed to all women due for screening in the ages 26-70. Women due for screening aged 23-25 were invited for mid-wife-based sampling. Pregnant women were advised to come in for mid-wife-based screening, to save time. Women under follow-up from previous screens are not due for screening. This is now elaborated more clearly in the paper.

The authors state that "the most likely explanation for the large increase in population coverage seen is that the sending of self-sampling kits resulted in improved attendance in particular among previously non-attending women" - why is this written as speculation at this stage (?) is it not possible to attribute directly the contribution made by self sampling, or is this in hand?

See response to reviewer 2 above: Although all the data is indeed collected, we are not allowed to perform registry linkages without ethical permission. This has now been obtained and the requested analyses made.

While self sampling is certainly an option that can support uptake and enfranchisement in cervical screening - its overall performance is fundamentally contingent on the number of women who then comply with follow up should the HPV test be positive; it is not simply about who returns the sample. It would have been of interest to see the proportion of women who did comply with follow up.

The paper is not about follow-up strategies. Follow-up strategies are different in different settings and reporting is not standardized. They have also changed during the time of the study (e.g. cytology follow-up abandoned). A more detailed analysis of this would require a whole new paper.

Author Response

Reviewer #2 (Public Review):

1) It could benefit from fleshing out concepts instead of using parentheses, particularly in the abstract.

We agree and have amended the abstract and methods (please refer to responses provided to the editor’s comments 1a-1e)

2) There is space to expand on the results presented in Table 1, including an explanation of Affected cohorts 2008 vs Affected cohorts 2008-2009. It may also be useful to explain this analysis in the methods section.

Please refer to response provided to editor on the same question (comment 5).

3) Given that Australia is a best-case scenario and other countries have not had the same success in HPV vaccination coverage, in the discussion would it be possible to give a comparison of how these three scenarios would look different in a population with school-based vaccination but lower coverage volume, such that readers could understand how much of the success / failures of each of the three catch-up scenarios? It would be particularly helpful for readers who are not familiar with the modelling tool used in this analysis.

We have added some commentary in the discussion in response to the reviewer’s comment. In future, further similar work in countries with lower base coverage would be informative.

“Australia is a relatively high HPV vaccination coverage setting. Outcomes may be less favourable in a lower coverage setting, as there would be less protection from herd effects; however, the impact of disruptions might also be smaller in a setting with lower coverage, since a lower coverage program would be less effective. Nevertheless, the finding that if catch-up is performed expeditiously then it mitigates much of the effect from vaccination delays, is likely to hold in other settings. In a previous study (Simms et al, Lancet Public Health. 2020 Apr;5(4):e223-e234) modelling the health impacts of HPV vaccination hesitancy in Japan from 2013 to 2019 and the potential effects of restoring coverage to 70% with catch-up vaccination in 2020 is informative as it demonstrates that multi-age HPV catch-up vaccination, after catastrophic falls in coverage in Japan, would be effective in mitigating the effects. “

Author Response

Reviewer #1 (Public Review):

Ghosh and colleagues report on their multidisciplinary effort to improve cervical cancer screening attendance in the East Boston Neighborhood Health Center (March-August 2021). Specifically, the authors 1) identified using electronic medical records overdue follow-up visits, 2) scheduled screening appointments during regular clinic hours and weekends/evenings, and 3) surveyed patients on their experience. These objectives were clearly defined (although not consistently so throughout the manuscript) and data analyses/presentation were simple and straightforward, appropriate to the study design and methodology used.

Thank you for this comment. We have clarified the objectives in the revised manuscript.

Overall, it is unclear to what extent the overdue appointments were backlogs created by the COVID-19 pandemic or due to pre-pandemic factors that could have been exacerbated by the pandemic. In order to contextualize the current study and its findings, an elaboration is needed on whether the pandemic created the delays in cervical cancer screening or simply compounded the problem. For example, the authors report on page 8, lines 196-197 that in 30% of encounters (not clear how many of the 118 reviewed charts were overdue appointments) the healthcare provider did note the overdue appointments.

We have Figure 2 (now Figure 4) and added Figures 2and 5 to address this comment. In 2019, prior to the COVID-19 pandemic, approximately 70% of patients were up-to-date with cervical cancer screening, corresponding to 8467 patients overdue for screening. In 2020, the up-to-date percentage dropped to 63.5% and the overdue number increased to 8812. Figure 2 is a flowchart of the project which clarifies the “30%” mentioned in the reviewer comment

In addition, a brief description of the cervical cancer screening program in place would be informative.

We have added this in the “setting” section of the methods on page 4-5, lines 107-128)

Table 1 provides an effort versus value summary; however, these constructs are ill-defined, with few inconsistencies with what is reported in the text.

This table is intended to help inform clinics that are considering implementing quality improvement programs about the effort required and value obtained for different aspects of our program. These are based in part on proprietary cost analyses so certain details are not able to be included. We have amended the text/table to eliminate inconsistencies.

Comments specific to Aim 1:

The methodology is missing information on key elements, mainly relating to the decision-making process of establishing and defining the "validated" patient chart list (1375 overdue patients out of 6126 reviewed charts). A chart of the 1375 approached study population is also warranted (459 patients were screened, 622 could not be reached, and 203 cancelled/missed their appointments, what about the remaining 91 patients). A description of the characteristics of the study population and a comparison of the different groups (screened, not reached, cancelled/missed appointment) along these characteristics are missing.

We have added a flowchart with this information to the results section. See Figure 2.

Comments specific to Aim 2:

About 63% of the 459 scheduled screenings were done during the evening/weekend clinics, which represents a substantial gain and clearly indicates a window of opportunity to increase screening rates by pinpointing the importance of offering a convenient time to women attend screening visits. In general, and as expected, offering additional screening clinics was effective in addressing the backlog of patients, although with significant investment and resources as mentioned by the authors. How significant is significant?

We are not able to share these data publicly. We have added the following sentence: “The cost data is proprietary/not shareable but analysis by clinical leadership indicated the program was not cost-effective/sustainable.” Page 22, lines 678-80

Comments specific to Aim 3:

A more structured and detailed presentation/description of the survey instrument, its administration, response rate, and significance of results are warranted in the manuscript, albeit the joint reporting of this in the appended material.

We have added additional detail about the survey method (page 9, lines 225-6, 228-31) and results ( Page 14-5, lines 518-22, 530-3) . We also inserted the survey used in the clinics. (Figure 1).

Reviewer #2 (Public Review):

The purpose of this study is unclear from the introduction. Additionally, the methods are incomplete and did not describe how data was collected and analyzed. The results do not describe the sample. Once these are described more clearly, further comments can be made about what the authors were trying to achieve and the impact of the work on the field.

We have clarified the study purpose in the introduction: “The purpose of the project was to examine the impact of a Quality Improvement intervention on improving cervical cancer screening, as well as to evaluate the effectiveness and sustainability of different methods for addressing overdue screening.” (page 3, lines 87-90)We have also clarified the methods and results to describe data extraction more completely from electronic medical records and statistical analysis using descriptive statistics.

Author Response

Reviewer #2 (Public Review):

This work attempts to connect the diet of a mother to the physiology and feeding behaviors of multiple generations of her offspring. Using genetic and molecular biology approaches in the fruit fly model, the authors argue that this Lamarckian inheritance is mediated by germline-inherited chromatin and is regulated by the general activity of a histone methylase. However, many of the measured effects are small and variable, the statistical tests to prove their significance are missing or poorly described, and some experiments are inadequately described and lack important controls.

1) The authors claim that the diet of a mother can influence the physiology of her progeny for several generations. However, the observed effects of maternal diet on later generations were small and variable for most assays (see Fig1C, S1.1A, B, D). Additionally, the effect size between F0 HSD to ND was often larger than the effect size between the progeny of F0 parents and ND. To put it another way, if the authors were to compare the F1, F2, etc. to the F0 HSD flies, they would conclude that the majority of the response to diet is not maternally transmitted, and is directly controlled by the diet of the individual being measured.

We agree with the reviewer that the effect size of acute HSD exposure (in HSD-F0 flies) was stronger than that of transgenerational inheritance (in HSD-F1/2/3/4 flies). Similar observations were also made in other studies, see Klosin et al., Science, 2017, Bozler et al., eLife, 2019. We would argue this difference in effect size was as expected and with clear biological relevance.

For all living organisms, acute environmental changes (diet change included) have direct and profound influences on their survival and reproduction, and therefore need robust and immediate responses. In comparison, ancestral environmental changes may only provide some vague and indirect indications of the current living environment of the offspring. Such information may be beneficial for the survival and reproduction of the offspring, but the effect size is expected to be much smaller, or at least smaller than that of acute environmental changes.

Studies on Dutch Famine offers a good example. Human individuals who were prenatally exposed to famine were found to be associated with greater risk in metabolic diseases (Ravelli et al., NEJM, 1976). But nevertheless, direct high-fat diet exposure was still the much stronger risk factor for obesity and metabolic disorders (Bray et al., Am J Clin Nutr, 1998, Jéquier et al., Int J Obes Relat Metab Disord, 2002).

We have added additional discussions in the manuscript for clarification.

Furthermore, since our current study aimed to investigate the mechanism of behavioral transgenerational inheritance, we focused on the comparison between HSD-F1 flies (and their progeny) vs. ND-fed flies. As the ancestors of HSD-F1/2/3/4 flies were exposed to HSD, whereas HSD-F1/2/3/4 flies themselves were never exposed to HSD, any difference we observed between the two groups could be solely attributed to transgenerational inheritance of ancestral HSD exposure. With that saying, to better distinguish the effects of acute HSD exposure vs. transgenerational inheritance upon ancestral HSD exposure, we re-analysed and presented the comparisons among ND, HSD-F0, and HSD-F1 data in the manuscript (Figure 1. B-E, Figure 1-figure supplement 1. A-E, Figure 1-figure supplement 2. A-D, Figure 3. D-E, Figure 3-figure supplement 1. B-D, Figure 3-figure supplement 2 and 3. A-B).

2) The authors chose to study PER, which had the largest average effect sizes between conditions. However, PER was highly variable in the averaged data, with some individuals showing large effects and others having no effects. A better characterization of transgenerational PER may increase the robustness of this assay and confidence in its results. For example, the authors could measure PER in lineages derived from individual flies to determine when transgenerational effects on PER decline or disappear. This form of data collection could help to explain the high variation in the averaged data presented in the paper.

We acknowledged that PER in general was quite a variable behavioural trait (probably as to most if not all behavioural measures). It was not surprising since animal behaviours, as complex traits, could be influenced by numerous intrinsic and extrinsic factors, such as genetic background, developmental environment, diet, population density, environmental conditions, etc. Numerous PER studies have exhibited similar variability (Masek et al., PNAS, 2010, Marella et al., Neuron, 2012, Charlu et al., Nature Communication, 2013, Wang et al., Cell Metabolism, 2016, Wang et al., Cell Reports, 2020).

Nevertheless, in our current study we were able to identify statistically significant behavioural difference between ND-fed flies and HSD-F1/2/3 flies, demonstrating that ancestral HSD exposure imposed transgenerational inheritance on sweet sensitivity. To further increase the robustness of the study as suggested by the reviewer, we have conducted additional repetitions of many PER experiments and further confirmed the phenotype with less variability and more statistical power (Figure 1. G-I, Figure 3. D-E, Figure 3-figure supplement 1. B-D, Figure 3-figure supplement 2 and 3. A-B). The reviewer also suggested the use of isogenic flies, which might help to minimize the variations of genetic background. However, we think that demonstrating the behavioural difference in genetically diverse fly populations is a more credible way to show that such transgenerational inheritance is a reliable and generalizable phenomenon.

3) What do the error bars represent on any figure? There are many examples where the data is highly variable and lies completely outside of the error bars. What is the statistical test for significance that is carried out in each figure? The brief comment about statistics in the methods section is inadequate. The authors should also supply the raw data used to generate the figures so that readers can perform their own statistical tests.

Data in the manuscript were represented as means ± SEM (standard error of the mean) in all of our figures, which is a standard practice in the field (Masek et al., PNAS, 2010, Charlu et al., Nature Comm, 2013, Wang et al., Cell Metabolism, 2016). We have provided detailed explanations of the statistical tests in the manuscript. We have also prepared raw data files as suggested by the reviewer.

The model that global H3K27me3 is regulated by ancestral diet is unconvincing without further experimental validation and explanation. Points 4-10 address specific issues.

4) The authors performed ChIP on cycle 11 embryos. This stage is extremely short (11 min) and contains roughly 10 times less chromatin than embryos only 30 minutes older. These features make it very difficult to collect large numbers of precisely staged embryos without significant contamination. It is also debatable whether early cell cycles (including and preceding cycle 11) are slow enough to deposit and propagate histone marks in the presence of new histone incorporation. See the opposing arguments in Zenk et al 2017 and Li et al 2014. The authors could perform ChIP on older embryos to avoid this controversy.

We thank the reviewer for the clarification. Our embryo collection protocol involved allowing flies to lay eggs freely in a cage for 30 minutes followed by 50 minutes of incubation on a juice plate, and then completing the embryo sorting within 30 minutes. Therefore, to describe it in a more stringent way, our embryos should be in the stage between cycle 10-12. We have corrected this information in the manuscript (Figure 2. A).

Since all the embryos were sorted using the same morphological criteria within the same time frame, their developmental stages should be comparable (i.e. all from cycle 10-12). In several references we consulted, a broader range (cycle 9-13) was used for ChIP-seq sequencing analysis (for example, see Zenk et al., Science, 2017).

Surely any maternally inherited information will also be present in cycle 14 or 15 embryos if it is to influence the development or physiology of the brain. The observed differences in global H3K27me3 levels in F1 vs ND flies could be explained by slightly different aged embryo collections or technical variations in the ChIP protocol. The authors could strengthen their conclusion by performing more ChIP replicates. Alternatively, the authors could use orthogonal approaches like antibody staining or western blots to measure global H3K27me3 levels in precisely staged embryos.

We chose to use cycle 10-12 embryos because we aimed to identify epigenetic modulations directly transmitted through the maternal germline. Embryos in cycle 14-15 might reveal more profound changes, but since embryos in that stage had entered the zygotic phase and started the remodeling of histone modifications, we think it might mask the maternally transmitted changes we sought to identify.

In addition, we conducted two biological replicates for each group for the ChIP-seq analysis, which was a standard in the field (Zenk et al., Nature, 2021, Ing-Simmon et al., Nature Genetics, 2021). In the current study we further verified the genes identified in the ChIP-seq analysis in RNA-seq and qPCR analysis.

We further verified the ChIP-seq results by using western blot, which showed a ~2 folds increase in H3K27me3 modification in HSD-F1 early embryos vs. ND-fed embryos, in line with the ChIP-seq data (Figure 2-figure supplement 1. B). We have also provided immunofluorescence results for embryos at cycle 13 and cycle 14, which clearly showed a significant increase in H3K27me3 modifications in HSD-F1 embryos (Figure 2-figure supplement 1. C).

5) The authors measure PRC2 subunit mRNA levels in adult fly heads to attempt to explain the observed differences in inherited H3K27me3 levels in fly embryos. The authors should examine PRC2 components in germ cells and early embryos to understand how germ cells and early embryos generate H3K27me3 patterns.

We have now shown that Pcl and E(z) mRNA expression in HSD-F0 flies were not significantly changed vs. ND-fed flies (Figure 2-figure supplement 2. D-G). Meanwhile, H3K27me3 demethylase UTX and H3K27ac acetyltransferase Cbp showed significant decrease (Figure 2-figure supplement 2. H). Therefore, HSD exposure imposed complex epigenetic modifications in HSD-F0 flies, which then led to transmission of epigenetic marks to their progeny. Given the main scope of this study was to understand which epigenetic program mediated the behavioral transgenerational inheritance upon ancestral HSD exposure (but not that mediated acute HSD exposure), we focused our effect on H3K27me3 which was significantly changed between HSD-F1 flies vs. ND-fed flies.

6) The RNAi experiment targeting PRC2 components in embryos is uninterpretable without appropriate controls and an explanation of the genotypes used in the experimental paradigm. Are the authors crossing nosNGT mothers to UAS-RNAi fathers and assaying the progeny? What is the genotype of the F1 flies and how does it compare to the genotype of the ND flies? The authors should also note that the Gal4 drivers they use are not necessarily restricted to the ovary, and could directly affect other tissues controlling PER like neurons and muscle. Additionally, the authors should supply the appropriate controls to verify that their experimental paradigm has the intended effect. PRC2 proteins are presumably loaded into embryos and would be immune to zygotic-expressed RNAi. The authors could validate when PRC2 RNAi is effective by staining embryos for H3K27me3.

We have now added schematic diagrams and detailed explanations in our revised manuscript to better explain the RNAi experiments (Figure 3-figure supplement 1. A). As shown in the diagram, we compared each RNAi treatment group to appropriate genetic controls. We have also noted in the manuscript that the GAL4 drivers we used were not restricted to the ovary.

We have now verified the effect of PRC2 knockdown to reduce H3K27me3 in female germline by both western blot and immunofluorescence staining (Figure 3. B-C).

7) Although the authors do not note this, nosNGT>RNAi affects the PER of ND flies (compare Gal4>RNAi to just RNAi or just Gal4 in ND columns in Fig3A-D). This could be due to RNAi expression in neurons or muscles or some other indirect effect. Regardless of the mechanism, this result makes it difficult to interpret how RNAi treatments affect the transgenerational inheritance of PER if there is an equivalently strong nontransgenerational effect.

Although nosNGT>RNAi appeared to slightly affect PER response of ND-fed flies, there was no statistically significant difference (Figure 3-figure supplement 1. B and D, Figure 3-figure supplement 2. A-B). Rather, the effect of E(z) knockdown was evident in HSD-F1 flies (Figure 3-figure supplement 1. B), further confirming the involvement of H3K27me3 in transgenerational inheritance of PER reduction.

8) The matalpha gal4 experiment is inadequately explained in the text or methods. Are the authors expressing RNAi in the ovaries of the F0 flies that are fed an HSD? Does the ovary influence their PER somehow? Similar to point 8, there appears to be a nontransgenerational component to the RNAi phenotype that clouds the interpretation of the transgenerational effect (compare F0 in S3.1A-C).

We have now added a schematic diagram and detailed explanations in our revised manuscript to better explain the RNAi experiments (Figure 3. A). As shown in the diagram, we compared each RNAi treatment group to appropriate genetic controls.

Similar to point 7, although Mat-tub-GAL4>RNAi might seem to affect PER responses of ND-fed flies, there was no statistically significant difference (Figure 3. D-E). Rather, the effect of E(z) knockdown was evident in HSD-F1 flies (Figure 3. D), further confirming the involvement of H3K27me3 in transgenerational inheritance of PER reduction.

9) For the EED inhibitor experiments (both PER and calcium imaging), it is unclear whether the authors fed the mothers or their adult progeny the EED inhibitor. If adult progeny were fed, what tissues were affected? The authors should stain various tissues with an H3K27me3 antibody to verify the effectiveness of their inhibitor. Finally, the effect of the EED inhibitor on calcium imaging was not convincing because the variation was so large.

We have added a new schematic diagram and provided more detailed explanations in the manuscript for pharmacological interventions (Figure 4. A-D). To verify the effect of the drug treatment, we showed that compared to the control group fed with DMSO, flies fed with the inhibitor showed a significant decrease in H3K27me3 levels, demonstrating the effectiveness of the inhibitor (Figure 4-figure supplement 1. A).

We acknowledged the unsatisfactory quality of our calcium imaging experiments in our initial submission. We have now improved our experimental procedures to reach better data quality, while the conclusions remained consistent (Figure 4. E).

10) In all of the PRC2 RNAi and inhibitor experiments, are there any other phenotypes that would suggest that the treatments are working? There are many published PRC2 loss-offunction phenotypes (molecular and developmental) in different tissues. The authors could assure the reader that their treatments are working as expected by doing these controls.

As discussed above, we have now used western blot and immunofluorescence staining to validate the efficiency of PRC2 RNAi in female germline (Figure 3. B-C).

11) The authors propose that a transgenerationally inherited state of the caudal gene is responsible for the transgenerationally inherited PER. However, the experiments investigating the methylation state and expression level of caudal are unconvincing. Cad mRNA abundance varied immensely in the ND RNAseq samples. When the authors compared cad levels across generations, the effect size was small. A single outlier in the ND sample in both the RNAseq and the RTPCR experiments appears to drive up its mean and effect size. The H3K27me3 ChIP on cad is very similar in the F1 and ND samples and the acetylation peak on its promoter appears unchanged. The authors could vastly improve the caudal experiments in this paper by simply using cad antibodies to stain the relevant tissues that contribute to PER. For example, the authors could stain GR5a neurons for cad expression in different generations that inherit (or don't inherit) maternal PER to more accurately determine if cad levels are indeed transgenerationally regulated. The authors could also perform more ChIP experiments at a less variable stage to convincingly correlate epigenetic marks on cad with its expression level.

As discussed above, we conducted two biological replicates for each condition of the ChIP-seq analysis, which was a standard in the field (Zenk et al., Nature, 2021, IngSimmon et al., Nature Genetics, 2021). We have also performed western blot and immunofluorescence for H3K27me3 in ND vs. HSD-F1 embryos to further validate our ChIP-seq data (Figure 2-figure supplement 1. B-C).

As for Cad gene, H3K27m3 signals showed a statistically significant difference between ND-fed and HSD-F1 flies (Figure 5. D). We have also conducted additional qPCR experiments to verify the gene expression changes of the Cad gene (Figure 5. F, right), which was in line with the ChIP-seq data and further supported its validity.

It was worth noting that during the developmental time window of our ChIP-seq analysis, the acetylation signals in the promoter region of cad were very low (Figure 5. D), making it impossible to make a comparison.

Reviewer #3 (Public Review):

Jie Yang et al. investigated the transgenerational behavioral modification of a high-sugar diet (HSD) in Drosophila and revealed the underlying molecular and neural mechanisms. It has been reported that HSD exposure decreases sweet sensitivity in gustatory sensory neurons, resulting in reduced sugar response (Proboscis extension reflex, PER) in flies. The current study reports that this effect can be transmitted across generations through the maternal germline. Furthermore, the authors show that H3K27me3 modification is enhanced in the first-generation progenies of HSD-treated flies (F1), and genetical or pharmacological disruption of PCL-PRC2 complex blocks the behavioral change and restores the sweet sensitivity in the Gr5a+ sweet sensory neurons. The authors further analyze the differentially expressed genes in the F1 flies. Among H3K27me3 hypermethylated regions, they focus on homeobox genes and find a transcription factor Caudal (Cad), which shows decreased expression in the F1 flies. Knocking down Cad in Gr5a+ neurons results in decreased PER response to sucrose.

Transgenerational changes in physiology and metabolism have been broadly studied, while inherited changes at the behavioral level are much less investigated. This work provides convincing evidence for transgenerational modification of feeding behavior and digs out the underlying molecular and neural mechanisms. However, there still are several concerns that need to be clarified.

1) The epigenetic regulator PCR2 has been found to play an essential role in the 7d-HSDinduced modification of the PER response. In this study, it's important to clarify for the transgenerational change, whether epigenetic modification is required in the flies exposed to HSD (F0), the progenies (F1), or both. It would be very helpful for better interpretation if the procedures of HSD treatment in RNAi experiments and the drug treatments were stated in more detail. In addition, the F0 flies should be examined as the control.

In this current study our main scope was to understand the transgenerational influence of HSD exposure on the progeny. To this aim, we chose to study the physiological and behavioral differences between ND-fed flies vs. HSD-F1 flies (and their progeny on ND). HSD-F1 flies (and their progeny) were not exposed to HSD in their whole life cycle and therefore the physiological and behavioral changes we observed vs. ND-fed flies could be solely attributed to epigenetic modifications transmitted via germline cells from HSD-F0 flies. Therefore ND-fed flies were used as the main control.

As for HSD-F0 flies, the acute effects of HSD exposure could be more complex. Epigenetic factor was likely involved, as evident in Figure 3-figure supplement 1. C, Figure 3-figure supplement 3. A-B and Figure 4. C. In addition, HSD exposure might also directly affect gene expression and multiple signaling pathways in HSD-F0 flies (see Chen et al., Science China Life Sciences, 2020). Therefore, we did not aim to investigate how HSD exposure affected HSD-F0 flies in this current study. We have added additional discussions in the manuscript for clarification.

With that saying, we still added more HSD-F0 flies as controls when needed (Figure 2-figure supplement 2. D-G, Figure 3-figure supplement 1. C, Figure 4. C, Figure 5. F， left).

We have also modified the schematic diagrams and added more detailed explanations in the manuscript, in order to provide a clearer illustration of the experimental procedures (Figure 3. A, Figure 3-figure supplement 1. A, Figure 4. A, B and D). Specifically, we employed two different RNAi approaches. Firstly, we used genetic methods to obtain homozygous Mat-tub-gal4>UAS-gene X RNAi fly lines on chromosomes Ⅱ and Ⅲ for germline-specific knockdown (Figure 3, Figure 3-figure supplement 3). Secondly, we used heterozygous nosNGT-gal4>UAS-gene X RNAi flies for embryo-specific knockdown (Figure 3-figure supplement 1 and 2). Our drug experiments involved both treating the flies and measuring their PER (Figure 4. A-C) and treating the parental flies and measuring the PER of their progeny (Figure 4. D).

2) The information on the drug treatment period is also missing for imaging experiments (Fig.4C). Moreover, the response curve is very different from those recorded in the same neurons in previous studies. What’s the reason? Please also provide a representative image showing which part of the Gr5a neurons is recorded.

The experimental procedures of drug treatments were shown in Figure 4. A now. We fed adult flies with specific compounds for five days after eclosion, then measuring the calcium signals of Gr5a+ neurons when flies were fed with sucrose.

As suggested by the reviewer, we have now conducted calcium imaging experiments more carefully and thoroughly. We have now added the new data into the revised manuscript and the conclusions remained consistent (Figure 4. E). We recorded the calcium signal in the axons of Gr5a+ neurons in the SEZ.

3) It's unclear whether the decreased Cad expression upon HSD treatment specifically occurred in Gr5a+ neurons or a lot of cells. If the change in gene expression is significant in the qPCR test, it should occur in a large number of cells, most likely including different types of gustatory sensory neurons. If lower cad expression led to lower neural response and thereby lower behavioral response, how to specifically decrease the PER response to sucrose but not to other tastes? -whether HSD-induced desensitization is specific to sucrose in the offspring?

We agree that Cad expression might decrease in a lot of cells including Gr5a+ neurons in the proboscis. In order to investigate whether taste perception other than sweet sensing was also affected, we conducted PER experiments with fatty acids, which was another type of appetitive taste cues like sugars. Perception of fatty acids is mediated by ionotropic receptors such as ir25a, ir76b, and ir56b (Ahn, et al., eLife, 2017, Brown., et al, eLife, 2021).

Our results indicate that PER of fatty acid in HSD-F0 and HSD-F1 was not significantly reduced compared to the ND-fed controls (Figure 1-figure supplement 2. E-F). This suggests that the impact of Cad on gustatory sensory neurons might be specific to sweet sensitivity of Gr5a+ neurons.

4) In Fig.2D, data are sorted for genomic regions showing an up-regulated modification of H3K27me. It's unclear whether similar sorting was performed in panel C. This needs to be clarified.

The analysis shown in Figure 2C and 2D were linked. As for 2C, we identified genomic loci with enriched H3K27me3, H3K9me3, and H3K27ac peaks, and found that H3K27me3 peaks showed the most robust changes between ND-fed and HSD-F1 flies. Therefore we concentrated on these loci where H3K27me3 modifications were significantly changed between the two groups, and further analyzed their difference. As shown in Figure 2D, within these loci, H3K27ac modifications, which was functionally antagonizing to H3K27me3, were significantly reduced; whereas H3K9me3 signals within these loci remained unchanged. Such results confirmed that ancestral HSD exposure induced robust H3K27me3 modifications in certain genomic loci.

Author Response

Reviewer #1 (Public Review):

The paper proposes a novel approach, named ModCRE, which utilizes structure-based learning to predict the DNA binding preferences of transcription factors (TFs). The authors integrate both experimental knowledge of the structures of TF-DNA complexes and large amounts of high-throughput TF-DNA interaction data. Additionally, the authors have developed a server that automatically produces these characteristics for other TFs and their complexes with co-factors.

Strengths: The paper's integration of experimental knowledge and highthroughput data to develop statistical knowledge-based potentials to score the binding capability of TFs in cis-regulatory elements is a powerful strategy. The proposed approach can be applied to more than 80% of TF sequences, making it a general method for characterizing binding preferences.

Weaknesses: The paper is difficult to follow, as it contains many technical details and implementation details. The method applied is not always clear, and the paper focuses on implementation rather than the message. The results indicate that the nearest neighbors approach in Figure 4 outperforms the proposed method in many cases, and the proposed method seems to perform better only when similarity with the target is low. The same applies in Fig. 5 when using normalized ranked scores.

It appears that the authors have successfully developed a structure-based learning approach for predicting DNA binding preferences of transcription factors. However, the paper's technical language and implementation focus make it challenging to follow at times.

It seems the authors have successfully achieved most of their aims in improving predictions for TF-DNA interaction, and the results support their conclusions.

This work has the potential to significantly impact the field of TF-DNA binding and gene regulation, particularly for those interested in predicting PWMs for TFs with limited or unreliable experimental data.

General comment: We wish to thank the reviewer for his/her comments helping us to facilitate the reading, clarify the ideas and certainly improve the manuscript. We also thank his/her comments on the strengths. In the current revision we have tried to solve the faults and improve the weaknesses. Certainly, the results section contained many explanations of the method and its implementation rather than its use and application. Referred to figures 4 and 5, the reviewer is right too: Our approach can help to predict the binding motif of a transcription factor on difficult cases, when the PWMs of closest homologs are unknown, but the structure of its complex with DNA can be provided. Otherwise, when information of binding is available for close homologs, traditional state-of-the-art approaches are better than our approach and we recommend them.

Reviewer #2 (Public Review):

This work describes the development of a new structure-based learning approach to predict transcription binding specificity and its application in the modeling of regulatory complexes in cis-regulatory modules. The development of accurate computer tools to model protein-DNA complexes and to predict DNA binding specificity is a very relevant research topic with significant impact in many areas.

This article highlights the importance of transcriptional regulatory elements in gene expression regulation and the challenges in understanding their mechanisms. Traditional definitions of activating regulatory elements, such as promoters and enhancers, are becoming unclear, suggesting an updated model based on DNA accessibility and enhancer/promoter potential. Experimental techniques can assess the sequence preferences of transcription factors (TFs) for binding sites. Recent models propose a cooperative model in which regulatory elements work together to increase the local concentrations of TFs, RNA polymerase II, and other co-factors. Co-operative binding can be mediated through protein-protein or DNA interactions. The authors developed a structurebased learning approach to predict TF binding features and model the regulatory complex(es) in cis-regulatory modules, integrating experimental knowledge of structures of TF-DNA complexes and high-throughput TF-DNA interactions. They developed a server to characterize and model the binding specificity of a TF sequence or its structure, which was applied to the examples of interferon-β enhanceosome and the complex of factors SOX11/SOX2 and OCT4 with the nucleosome. The models highlight the co-operativity of TFs and suggest a potential role for nucleosome opening.

The results presented by the authors have a large variability in performance upon the different TF families tested. Therefore, it would be ideal if the performance/accuracy of the method is tested in some simple predictions and validated with prospective experimental data before applying it to model difficult scenarios such as those described here: SOX11/SOX2/OCT4 and nucleosome or interferon beta and enhanceosome. This will give more support to the models generated and thus the validity of the conclusions and hypothesis derived from them.

General comment: We wish to thank the reviewer for his/her comments, we really appreciate them and the opportunity to have new tests with our approach. Some of his/her comments coincide with those of reviewer 1. When this is the case, we will refer to our previous answers and modifications in the manuscript. In this revision we have included new tests to validate the approach using available and published experiments different than the ones used in the original submission. We hope the new information is sufficient to support our approach.

Author Response

Reviewer #1 (Public Review):

Davies et al. examined the role of the malaria parasite's FIKK4.1 protein kinase in trafficking and host membrane insertion of key proteins that are exported by the intracellular P. falciparum parasite. FIKK4.1 is one of 18 FIKK serine/threonine kinases exported into the host erythrocyte; these kinases phosphorylate both host proteins and exported parasite proteins. FIKK4.1 has previously been implicated in rigidification of the erythrocyte cytoskeleton. It is also known to affect trafficking and insertion of PfEMP1, the parasite's primary cytoadherence ligand, on the host cell surface. In the present studies, the authors perform sophisticated gene-editing experiments that combine conditional knockout of FIKK4.1 with tagging of two kinase targets with the TurboID proximity biotin-labeling enzyme to explore phosphorylation-dependent changes in target protein localization, structure, or protein-protein interactions. Using conditional knockout of each exported FIKK kinase, they determine that FIKK4.1 is the only kinase that regulates PfEMP1 surface exposure and that it does not appear to modulate surface translocation of RIFINs, a family of parasite antigens involved in immune evasion. The combination of gene-editing, proximity labeling and mass spectrometry, and biochemical studies in the paper is to be lauded. These findings identify key targets of exported kinases and will guide future studies of host cell remodeling.

Key limitations of the study:

1) TurboID tagging of FIKK4.1 followed by proximity labeling and mass spectrometry of biotinylated proteins revealed parasite-stage dependent labeling of 101 parasite proteins and 39 human proteins that come in contact with FIKK4.1. Although TurboID is a more efficient biotin ligase produced through directed evolution, nonspecific biotinylation of proteins that do not form biologically relevant interactions remains an issue. Biotin addition for 4 hours, as used here and in most studies using this ligase, allows for labeling of proteins that undergo random collisions with the TurboID-tagged protein. While there was clear enrichment of exported proteins in the FIKK4.1-tagged parasite at mature schizont stages when FIKK4.1 is in the host cytosol, only 66% of the proteins labeled were exported, consistent with labeling and recovery of irrelevant proteins. As the authors performed appropriate controls and interpreted their findings cautiously, this limitation results primarily from finite efficiency of TurboID, trace levels of endogenous biotin within cells, and other complexities associated with the technology.

We agree with the reviewer that there are limitations to TurboID and the mere presence of a protein in a dataset does not imply functional relevance (which is also true for IP data). However, it is highly complementary to data obtained through other methods (in our case previous cytoadhesion data and phosphoproteome data) and as we show here, can give high resolution information on the local protein environment of a protein. This is illustrated by highly significant protein-specific interaction datasets for PTP4 and KAHRP obtained from biological triplicate experiments. The site-specific protocol we use later in the paper allows us to eliminate unbiotinylated proteins non-specifically binding to beads which is a major advantage, evidenced by the much higher ratio of exported proteins observed in the PTP4 and KAHRP-turboID datasets.

2) The production of dual-edited parasites carrying conditional knockout of FIKK4.1 and TurboID tagging of either KAHRP or PTP4 permitted examination of changes in localization of exported proteins upon their phosphorylation by FIKK4.1. KAHRP and PTP4 are excellent choices for these experiments because they are established targets of the kinase and good candidates for effectors involved in PfEMP1 membrane insertion. Some 30-40 proteins exhibited significant changes in biotinylation by these TurboID-tagged proteins, suggesting altered localization or structure upon loss of FIKK4.1 kinase activity. PfEMP1 trafficking proteins (PTPs), Maurer's cleft proteins, exported heat shock proteins, and components of PSAC, a parasite-associated nutrient uptake channel, all exhibited changes. Although FIKK4.1 is not essential for in vitro parasite propagation, altered localization could result either directly from changes in phosphorylation status of the protein itself or could reflect indirect effects on the cell from loss of FIKK4.1.

The reviewer is correct in that we cannot exclude that it is not only loss of FIKK4.1 mediated phosphorylation sites that leads to the observed changes, but that the loss of the FIKK4.1 kinase domain affects the localisation of other proteins. Conditional inactivation of the FIKK4.1 kinase domain while retaining the overall protein would have been a more elegant approach. However, we do not predict the kinase domain of FIKK4.1 to be a strong structural component given that kinase domains often evolved to have low affinity interactions with their multiple targets and are less likely to act as scaffolding parts. As the reviewer points out, because we observed no growth defect upon deletion of FIKK4.1. Therefore we can be quite certain that the observed changes are not due to indirect effects caused by differences in growth but are a direct effect by the loss of the kinase domain and FIKK4.1’s enzymatic activity.

3) As a consequence of these two limitations, these experiments could not conclusively implicate either KAHRP or a specific PTP in PfEMP1 surface translocation. Whether specific Maurer's cleft proteins or the nutrient channel components contribute to PfEMP1 surface translocation could also not be addressed. The authors' Discussion section is appropriately cautious in interpreting changes in biotinylation upon FIKK4.1 disruption. Although a large amount of data has been generated in this sophisticated study, the precise mechanism of PfEPM1 trafficking and membrane insertion remains elusive.