Author Response:

Reviewer #3 (Public Review):

Murphy et al. further develop the linked selection model of Elyashiv et al. (2016) and apply it to human genetic variation data. This model is itself an extension of the McVicker et al. (2009) paper, which developed a statistical inference method around classic background selection (BGS) theory (Hudson and Kaplan, 1995, Nordborg et al., 1996). These methods fit a composite likelihood model to diversity data along the chromosome, where the level of diversity is reduced by a local factor from some initial "neutral" level π0 down to observed levels. The level of reduction is determined by a combination of both BGS and the expected reduction around substitutions due to a sweep (though the authors state that these models are robust to partial and soft sweeps). The expected reduction factor is a function of local recombination rates and genomic annotation (such as exonic and phylogenetically conserved sequences), as well as the selection parameters (i.e. mutation rates and selection coefficients for different annotation classes). Overall, this work is a nice addition to an important line of work using models of linked selection to differentiate selection processes. The authors find that positive selection around substitutions explains little of the variation in diversity levels across the genome, whereas a background selection model can explain up to 80% of the variance in diversity. Additionally, their model seems to have solved a mystery of the McVicker et al. (2009) paper: why the estimated deleterious mutation rate was unreasonably high. Throughout the paper, the authors are careful not only in their methodology but also in their interpretation of the results. For example, when interpreting the good fit of the BGS model, the authors correctly point out that stabilizing selection on a polygenic trait can also lead to BGS-like reductions.

Furthermore, the authors have carefully chosen their model's exogenous parameters to avoid circularity. The concern here is that if the input data into the model - in particular the recombination maps and segments liked to be conserved - are estimated or identified using signals in genetic variation, the model's good fit to diversity may be spurious. For example, often recombination maps are estimated from linkage disequilibrium (LD) data which is itself obtained from variation along the chromosome. Murphy et al. use a recombination map based on ancestry switches in African Americans which should prevent "information leakage" between the recombination map and the BGS model from leading to spuriously good fits. Likewise, the authors use phylogenetic conservation maps rather than those estimated from diversity reductions (such as McVicker et al.'s B maps) to avoid circularity between the conserved annotation track and diversity levels being modeled. Additionally, the authors have carefully assessed and modified the original McVicker et al. algorithm, reducing relative error (Figure A2).

One could raise the concern that non-equilibrium demography confounds their results, but the authors have a very nice analysis in Section 7 of the supplementary material showing that their estimates are remarkably stable when the model is fit separately in different human populations (Figure A35). Supporting previous work that emphasizes the dependence between BGS and demography, the authors find evidence of such an interaction with a clever decomposition of variance approach (Figure A37). The consistency of BGS estimates across populations (e.g. Figures A35 and A36) is an additional strong bit of evidence that BGS is indeed shaping patterns of diversity; readers would benefit if some of these results were discussed in the main text.

We appreciate the reviewer’s kind remarks. With regards to the results included in the main text vs the supplement, we attempted to strike a balance between having the main text remain communicative to a larger readership and providing experts with details they may find useful. We have, however, done our best for the supplementary analyses to be written clearly.

I have three major concerns about this work. First, it's unclear how accurate the selection coefficient estimates are given the non-equilibrium demography of humans (pre-Out of Africa split, and thus not addressed by the separate population analyses). The authors do not make a big point about the selection coefficient estimates in the main section of the paper, so I don't find this to be a big problem. Still, some mention of this issue might be helpful to readers trying to interpret the results presented in the supplementary text.

As the reviewer notes, we chose not to emphasize the inferred distributions of selection coefficients. Our main reason for this choice is the technical issue addressed in Appendix Section 1.5 (L561-564): “Second, thresholding potentially biases our estimates of the distribution of selection effects. While this bias is probably smaller than the bias without thresholding, its form and magnitude are not obvious. This is why we decided not to report the inferred distributions of selection effects in the Main Text.” We agree that if we were to focus on our estimates of the distribution of selection effects, the effects of demographic history would also need to be considered. This is, however, not the focus here.

Second, I'm curious whether the composite likelihood BGS model could overfit any variance along the chromosome - even neutral variance. At some level, the composite likelihood approach may behave like a sort of smoothing algorithm, albeit with a functional form and parameters of a BGS model. The fact that there is information sharing across different regions with the same annotation class should in principle prevent overfitting to local noise. Still, there are two ways I think to address this overfitting concern. First, a negative neutral control could help - how much variation in diversity along the chromosome can this model explain in a purely neutral simulation? I imagine very little, likely less than 5%, but I think this paper would be much stronger with the addition of a negative control like this. Second, I think the main text should include the R2 values from out-sample predictions, rather than just the R2 estimates from the model fit on the entire data. For example, one could fit the model on 20 chromosomes, use the estimated θΒ parameters to predict variation on the remaining two. The authors do a sort of leave-one-out validation at the window level (Figure A31); however, this may not be robust to linkage disequilibrium between adjacent windows in the way leaving out an entire chromosome would be.

The two requested analyses were done and their results are described above, in response to essential revisions (p. 2-3 here). In brief, there is no overfitting of neutral patterns or otherwise. We elaborate on why this finding is expected below.

Finally, I feel like this paper would be stronger with realistic forward simulations. The deterministic simulations described in the supplementary materials show the implementation of the model is correct, but it's an exact simulation under the model - and thus not testing the accuracy of the model itself against realistic forward simulations. However, this is a sizable task and efforts to add selection to projects like Standard PopSim are ongoing.

We agree that forward simulations would be a nice addition, but believe that it is a project in itself. Indeed, a major complication is that when, for computational tractability, purifying selection is simulated in small populations with realistic population-scaled parameters, the reduction in diversity due to selection at unlinked sites has a major effect on neutral diversity levels (see, e.g., Robertson 1961). We hope to address this issue in future work. Meanwhile, we note that the theory that we rely on has been tested against simulations in the past (e.g., Charlesworth et al., 1993; Hudson and Kaplan, 1995; Nordborg et al., 1996).

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Reply to the reviewers

Manuscript number: RC-2021-01158

Doi preprint: https://doi.org/10.1101/2021.11.16.468835

Corresponding author(s): Salah, MECHERI

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Reviewer #1 (Evidence, reproducibility and clarity (Required)):

__Whole sporozoite vaccines confer sterilizing protection against Plasmodium infection. However, further improvements of whole sporozoite vaccines is needed and requires a thorough understanding of the immune processes that mediate protection and the deployment of novel strategies further augment protective immunity while limiting the impact of factors that are detrimental to protection. Work from the Mecheri laboratory and others had previously established that IL-6 signaling plays a critical role in the immune response to a liver stage infection; engagement of IL-6 signaling promotes the initial control of a liver stage infection and enhances the protective adaptive immune response. Given this potent protective role for IL-6, Belhimeur and colleagues design a parasite strain in rodent malaria parasites that encodes and secrete murine IL-6 during liver stage infection. They show that upon infection of wildtype mice, these transgenic parasites i) are unable to transition to blood stage infection, ii) produce Il-6 and iii) induce a durable adaptive immune response that can protect against sporozoite challenge. This study is novel and intriguing. However, a superficial analysis of the transgenic parasite strain, an incomplete analysis of the immune response to infection and the lack of data regarding the possibility of IL-6 mediated immunopathology have dampened this reviewer's enthusiasm for the work.

**Major Concerns:** __

1)The data in Figure 3b-3d clearly indicate that the IL-6 encoding transgenic parasites exhibit a defect in parasite development within HepG2 cells that is maintained in vivo. The authors propose that an arrest of these parasites in the liver stage precludes their transition to blood stage infection and that this arrest is dependent on IL-6 signaling. To better support that claim the authors should:

a.Better characterize in vivo liver stage arrest using infected liver tissue analysis with immunofluorescence microscopy to determine when and how precisely IL-6 transgenic parasites are impacted in development.

Done. New data in figure 3B, C, D

b.Determine if arrested development of IL-6 transgenic parasites is truly dependent on IL-6 signaling using antibody blockade of IL-6 signaling and mice with genetic defects in IL-6 signaling.

Experiments were done using anti-IL-6 receptor blocking antibodies, but did not work. This was commented in the text and shown in Supplementary Fig 2 .

2)The authors claim that IL-6 production and secretion into the liver tissue augments the adaptive immune response to liver stage infection. This in turn results in a durable adaptive immune responses that protect against infection. However, the mechanistic underpinning of IL-6 signaling in the liver that is induced by their transgenic parasites and the impact on adaptive immune responses is poorly characterized:

a.There is no evidence that the protective adaptive immune response induced by IL-6 trangenic parasite infection is dependent on IL-6 signaling. Is superior protection and immunogenicity lost in IL-6 signaling deficient animals that are infected with IL-6 transgenic parasites?

Not addressed but the point is that IL-6 leads to attenuation.

b.What elements of the adaptive immune response are impacted? One can imagine that IL-6 mediated killing of infected hepatocytes might introduce more parasite antigen that can be acquired by antigen presenting cells, or that IL-6 mediated pro-inflammatory signaling might regulate the maturation of antigen presenting cells, increased differentiation of helper T cells, the downregulation of regulatory T cell function and frequency and/or the differentiation of effector CD8 T cells into long-lived hepatic memory CD8 T cells. The authors should conduct a more comprehensive analysis of how parasite-encoded IL-6 impacts adaptive immunity.

Done. An extensive analysis of CD4 and CD8 phenotype and status of activation is represented in Fig 9.

3)While IL-6 transgenic parasites induce a potent and durable adaptive immune response, the authors should show how this compares to published whole sporozoite immunizations. The authors should determine if immunization with IL-6 transgenic parasites is superior to for example immunization with radiation-attenuated sporozoites and generically attenuated sporozoites.

It not the point. The work presented here emphasizes the proof of concept that the proposed new strategy works. Follow up studies will compare this model to previous ones.

4) IL-6 signaling is a major player in inflammatory diseases and the induction of immunopathology. As such the authors should carefully examine the duration and magnitude of IL-6 protein production in the liver, and serum after IL-6 Tg parasite infection and determine if IL-6 signaling promotes liver immunopathology.

Not done but this point was discussed in the text. Also, we made it clear in the material and methods section that the way the construct was made, i.e the IL-6 production is time-frame restricted to the first 48h of liver infection, precisely because of the expression of IL-6 gene is under the control of LISP-2 promoter. Therefore there is no persistence of IL-6 production by liver stage parasites.

Reviewer #1 (Significance (Required)):

The paper is reporting a novel strategy to generate a whole sporozoite vaccine. Expression of IL6 in a transgenic parasite. This could be a significant contribution to the field if additional experiments as outlined in the critique are conducted.The work might also inform vaccine design for other pathogens.

Reviewer #2 (Evidence, reproducibility and clarity (Required)):

The manuscript describes the construction of a Plasmodium berghei that expresses murine interleukin-6 in exoerythrocytic (liver stage) parasites and the analysis of mice infected with sporozoites of this parasite line. They find that such parasites do not complete development in liver cells and therefore do not produce subsequent infection in red blood cells. The ability of prior infection with these parasites on the ability of the host to resist both wild type and heterologous species challenge is then examined.

The key assumption that underlies the study is that the observed phenotypes result from parasite expression of bioactive IL-6 that functions to modulate the immune system. Other explanations are not considered, for example the over-expression of secreted IL-6 may prevent the complete maturation of the intracellular parasite by clogging up the parasite secretory pathway. The authors use the 'wild type' parasite as the control but not only does the wild type not express IL-6 it also does not express the human DHFR gene used as a selection system. A much better control parasite would be one that expresses a non-bioactive IL-6 so that the potential effects on parasite maturation can be differentiated from those on the mouse immune system. Another control to be considered would be comparison with a genetically attenuated parasite with a block in late stage development, and which does not produce a host cytokine.

Interesting comment but key novel result is that co-infection studies show reversed phenotype of IL-6 transgenic parasites, likely due to counteracting Of IL-6 effect by Wild type parasites (Supplementary Fig 1)

Another assumption is that IL-6 is secreted from the infected liver cell and mediates its effects, presumably by binding to its cell surface receptor. The expectation of Il-6 secretion from the parasite is that it would accumulate in the parasitophorous vacuole - how would it get out of the infected host cell? While evidence is provided of IL-6 in the in vitro culture supernatant of infected cells - this might arise from damaged cells in rather artificial conditions. Have the authors considered doing the experiment of concurrent mouse infection with both wild type and recombinant parasites? If the mechanism of parasite killing in infected liver cells is as proposed, then a reduction of wild type parasites in the subsequent asexual blood stage would be expected.

Experiments done. We discussed both experiments: IL-6 receptor blocking antibody experiement (Suppl Fig 2), and mixed infection (Suppl Fig 1).

Figure 3 indicates that IL-6 TgPbA/LISP2 parasites are as efficient or better than wild type parasites at invading host cells but then they do not develop to maturity. What is the evidence that the key factor in their ability to immunize the host is expression of IL-6 rather than the effect of an attenuated parasite?

This is an interesting observation made by the reviewer. With the available data, we cannot really tell which of the two possibilities is operating in thin system. It could also be that the two option are interconnected.

In this model malaria infection, it looks like there are two lethal outcomes: one associated with experimental cerebral malaria at relatively low blood stage parasitemia (which I understand is a controversial model for human cerebral malaria) and the second associated with high blood stage parasitemia. Some of the protocols affect which outcome occurs (see for example Fig 6), but this observation is not properly discussed.

In many occasions, we did see in the past a discrepancy between anti-parasite immunity and anti-disease protection. In this particular experiment (Fig 6), we explored the dose effect of the IL-6 mutant. What is clear from this model is that at the high dose, 104 SPZ, we observe both anti-parasite and anti-disease protection and immunity, whereas at the lower doses, 103 and 102 SPZ, although there was no efficient anti-parasite immunity, mice did not die from cerebral malaria but much later from hyperparasitemia. We consider that the two low doses of IL-6 transgenic parasites did protect against disease expression.

For the data presented in Fig 7, why was there a challenge with WT PbA sporozoites before the heterologous Py challenge? If this step is excluded is there still an effect against P. yoelii? Why was the parasite chosen for the heterologous challenge Py17XNL? Since this parasite is largely restricted to reticulocytes in the blood stream would a different effect have been observed if the heterologous challenge parasite was, for example, P. chabaudi?

Out of scope.

Although the expectation is that IL-6 expression would not occur in the asexual blood stage, I think it would be important to demonstrate experimentally that this is the case.

Done. IL-6 transgenic parasite, when inoculated as infected erythrocytes have no development defect and grow normally in infected mice.

In Fig. 4A the y-axis is labelled IL-6 rRNA when it should be IL-6 mRNA.

Corrected

Reviewer #2 (Significance (Required)):

The significance of the report does depend on whether or not the experimental evidence is sufficient to support the claim that parasite expression of IL-6 is important in generating immunity. There has been a number of studies to show that infection with sporozoites that have been genetically attenuated to not complete subsequent development in the infected liver cell can provide immunity to subsequent infection; what is different about this study is that the authors specifically target the parasite to express a host protein that is likely to be important in acquisition of immunity. Therefore for the study to have high significance they have to show convincingly that it is the expression and activity of IL-6 that is important and I do not think this is the case with the experiments reported. If the authors are correct, then the idea of manipulating the host response by expression of host proteins by the parasite may be an attractive approach to dissect the key elements of immunity to sporozoite infection. At the moment, although there is a lot of focus on developing an attenuated whole sporozoite vaccine against malaria, and this study may provide proof of principle for including a host component in the parasite, there would still be long way to go before any practical application of this approach.

The key message was toned down. As the formal demonstration that the expression and activity of IL-6 is direcxtly involved in IL-6 transgenic parasites to confer protective immunity, we suggest to tone down the message by saying that IL-6 attenuates parasite virulence, the mechanism being likely through IL6 signaling detrimental effect on parasite development.

The audience would be those interested in parasite immunology.

__

Reviewer expertise: malaria parasite cell and molecular biology; host immunity.

**Referees cross commenting** __

__ I think all reviewers are of the opinion that there needs to be a better demonstration that the observed phenotype is mediated by expression and signaling of IL-6, for example by antibody blockade or using a mouse line with a genetic defect in IL-6 signaling. Looking at all the issues that have been raised by the reviewers and need to be addressed with further experimentation, my feeling is that this will take longer than 6 months.

__

Reviewer #3 (Evidence, reproducibility and clarity (Required)):

__ **Summary** This study explores the expression of murine IL-6 by rodent Plasmodium berghei as a means to generate transgenic parasites whose development in the liver is arrested, which may be used as a genetically attenuated pre-erythrocytic vaccine against malaria. The authors conclude that IUL-6-expressing Plasmodium parasites elicit CSD8+ T-cell mediated immune responses that protect against a subsequent challenge with infectious sporozoites.

**Major Comments** __

In Figure 3, the authors show the results of qRT-PCR analysis of mouse livers infected with WT or transgenic parasites. They then use HepG2 cells to assess hepatic parasite numbers and development. Why didn't the authors assess this also in vivo, in liver sections of infected mice?

Done. New data are presented in Fig 3B, C, D

Linked to the above, a more complete analysis of the parasite's behavior in HepG2 cells should be provided. The authors write in the discussion that "IL-6 transgenic parasites develop perfectly well in cultured hepatocytic cells". Does this mean that they develop to the production of infectious merozoites? This could be confirmed by allowing the infected cultures to progress for 60-70 hours and then collecting the supernatants of these cultures and injecting them into naïve mice, to understand whether or not infectious merozoites are formed in vitro.

New analysis demonstrate that IL-6 transgenic parasites actually display a developmental defect at the pre-erythrocytic stage in vivo.

Figure 3C: The authors mention this result almost in passing but fail to provide an explanation for this observation. Why is the number of transgenic parasite EEFs approximately double that of WT parasite EEFs?

A new figure 3 is provided and show that the EEF density (Fig 3B) was drastically reduced both at 24h and at 48h in mice infected with the IL-6 transgenic parasites as compared to those infected with WT PbA parasites, although the differences were not statistically significant. We also examined the size (Fig. 3C) of EEFs, and found the same tendency, namely a reduced size and diameter of IL-6 transgenic EEF as compared to those of WT PbA EEFs with a statistical difference only at 40h.

Figure 3D: The EEF area units (mm2) on the YY axis are certainly wrong. However, they cannot be um2 either, as 15-30 um2 would be far too small for EEFs at 48 hours post-infection. What is it then?

New data are now provided in a new Fig 3.

The authors write "... suggest that the failure of IL-6 Tg-PbANKA/LISP2 parasites to develop in the liver of infected mice is likely due to an active anti-parasite immune response mediated by parasite-encoded IL-6 in vivo". I have several issues with this statement. 1) as mentioned above, the in vitro data cannot be used to draw definitive conclusions about the parasites' behavior in vivo; 2) the transgenic parasites do not "fail to develop in the liver of infected mice". If anything, they develop less than their WT counterparts, which is different from "failing to develop". Clarifying how much they do develop would be important (see next comment).

We provide new in vivo data as to the development of IL-6 transgenic parasites. A new figure 3 is provided and show that the EEF density (Fig 3B) was drastically reduced both at 24h and at 48h in mice infected with the IL-6 transgenic parasites as compared to those infected with WT PbA parasites, although the differences were not statistically significant. We also examined the size (Fig. 3C) of EEFs, and found the same tendency, namely a reduced size and diameter of IL-6 transgenic EEF as compared to those of WT PbA EEFs with a statistical difference only at 40h. We replaced failure by a defect in development.

In connection with the above, I would like to know more about the time when the development of IL-6 Tg-PbANKA/LISP2 parasites is arrested in vivo, in the liver. Are these early- or late-arresting parasites? Is the liver stage of infection compromised during parasite development or at egress? To clarify this, the manuscript would benefit from a timecourse analysis of liver sections of mice infected with this parasite, including data on EEF numbers and sizes up to and beyond 48 h after sporozoite inoculation.

Done. See new figure 3.

Still linked to the issue of parasite arrest in vivo and the possibility of breakthroughs, the manuscript would benefit from an experiment where mice were injected with a high number of transgenic sporozoites and parasitemia is monitored thereafter, much like what was done in Figure 2D, but starting off with a larger inoculum of at least 5 x 10^5 sporozoites.

This was done and there was no breakthrough even with doses as high as 106 sporozoites

While the results shown to suggest that secreted IL-6 restricts the parasite's liver stage development in vivo, this could be more definitely demonstrated by performing an infection with the transgenic parasites in the context of blocking or absence of the host's IL-6 receptor. This experiment was done but unfortunately did not work (Suppl. Fig 2). That is, the treatment of mice infected with IL-6 transgenic parasites with anti-IL-6 receptor blocking antibodies did not reverse the infection phenotype. This was also discuss in the manuscript.

**Minor Comments**

__

The manuscript needs to be improved in terms of both language and format. Some examples, solely from the abstract, are listed below, but the manuscript needs to be appropriately revised in terms of language, grammar, punctuation and format throughout:__

-Space missing between "P." and "berghei"

Done

-Gene names should be italicized

Done

-Rephrase "Considering IL-6 as a critical proinflammatory signal..." to "Considering that IL-6 is a critical proinflammatory signal..."

Done

-"transgenic IL-6 sporozoites" should be "transgenic IL-6-expressing P. berghei sporozoites"

Done

-"impairs Plasmodium infection at the liver stage" should be "impairs the liver stage of Plasmodium infection"

Done

INTRODUCTION

The sentence "Among them, parasites lacking integrity of the parasitophorous vacuole, or late during development, and..." appears to be incomplete and needs rephrasing.

Done

The references used in sentence "During the last decade, in search of key mechanisms that determine the host inflammatory response, a set of host factors turned out to be critical for malaria parasite liver stage development (Mathieu et al., 2015); (Demarta-Gatsi et al., 2017; Demarta-Gatsi et al., 2016) (Grand et al., 2020)" do not all relate to the liver stage of infection. The authors need to select references that are relevant for their statement or else change the statement.

Rephrased

RESULTS

I suggest the authors change the title of Results section "Transgenic P. berghei parasites expressing IL-6 during the liver stage lose infectivity to mice" not only to improve the quality of the English language employed but also to better clarify the notion that they are talking about hepatic infectivity.

On the same section, please correct "timely specific timely".

Done

Transfectants are not "verified". If anything, the insertion of the gene in the parasite's genome is verified or, better still, confirmed.

Done

Sentence "The two lines behave similarly" is redundant.

Done

The legend of Figure 1 must include the definitions of all the acronyms in that figure.

Acronyms in the whole manuscript are defined elsewhere

"IL-6 transgenic sporozoites" is not an appropriate designation. If anything, they should be called IL-6-expressing P. berghei sporozoites".

Done

Figure 2 B: The YY axis should clarify that it refers to sporozoite numbers, as there are many other parasite stages in mosquitoes.

Done

Figure 2C: This scheme is hardly necessary. It would suffice to label the plots in D and E with the names of the parasite lines employed rather than "Group 1", "Group 2", "Group 3". The scheme is provided for more clarity and easy reading of the accompanying figures

Figure 2D, 2E: Why didn't the authors use the same scale on the XX axis of the two plots?

The qRT-PCR data per se do not substantiate the statement "Therefore, RT-qPCR analysis in the liver confirms that the loss of infectivity of IL-6 Tg-PbANKA/LISP2 SPZ is due to a defect in liver stage development in vivo", as a defect in invasion of hepatocytes cannot be excluded. The term "loss of infectivity" is also misleading. Do the authors mean loss of blood stage infectivity?

Yes

Sentence "... all parasites were able to invade and develop inside HepG2 cells." is misleading. The authors probably mean "parasites of both lines".

Changed

Figure 4: Why did the authors swap the order of the two experimental groups from one plot to the next? The same order should be used, to avoid confusion! Also, the authors should make the width of the bars in similar between the two plots.

Done

The authors should consider moving Figure 5 to the Supplementary materials.

Reviewer #3 (Significance (Required)):

*Nature and significance of the advance. Compare to existing published knowledge. Audience.*

This study extends our current knowledge on genetically attenuated malaria vaccine candidates and validates the concept of suicide parasites for immunization against malaria. This paper will be of interest to researchers working on malaria vaccination, as well as all those interested in transgenic Plasmodium parasites, and the biology and immunology of liver stage infection by malaria parasites.

*Your expertise.*

The co-reviewer and the reviewer are experts on the liver stage of Plasmodium infection and on pre-erythrocytic malaria vaccination.

**Referees cross commenting**

I agree with all of Reviewers 1 and 2's remarks and, upon consideration, I would like to revise my "Estimated time to Complete Revisions" to become between 3 and 6 months

Author Response

Reviewer #1 (Public Review):

The general idea of comparing response patterns to stress in the offspring generation is new and very interesting.

We thank Reviewer 1 for their time and thoughtful comments. We agree that these comparisons are new and very interesting and have added multiple revised analyses to the manuscript based on the reviewer comments that we think will further enhance the impact of and conclusions made in this study.

However, the data that are presented are in several ways preliminary. The phenotype comparisons are mostly convincing, although statistical treatments are partly unclear, given that each "replicate" includes itself many individuals.

The statistical treatments for groups of individuals are the same as in Burton et al., 2017, Burton et al., 2020, and Willis et al., 2021 which include the original reports of the intergenerational responses studied here. Replicates that include many individuals are relatively common when working with C. elegans and are usually compared using ANOVA or student’s t-tests (depending on the number of comparisons) to analyze the variation in batch effects as well as differences between populations of animals.

We believe this ability to assay hundreds or even thousands of animals, in total, for each comparison in this study makes our data substantially stronger and more reliable. However we are happy to perform any additional statistical tests the reviewer might want to see.

The transcriptomic data are minimal (only three replicates)

To address this comment we compared our original three replicates of RNA-seq from F1 animals from C. elegans parents exposed to P. vranovensis BIGb0446 to a second independent three replicates of F1 animals from C. elegans parents exposed to a second P. vranovensis isolate (BIGb0427 – the data for this second P. vranovensis isolate was already part of Fig. 4 of this manuscript).

By comparing these three new replicates to our previous findings from three original replicates we found that 515 of the 562 genes that exhibited a >2-fold change and were significant at padj <0.01 in the original three replicates were also changed at >2-fold and padj <0.01 in the new three replicates. We believe our findings that 91.6% of genes change >2-fold and remain significant at padj<0.01 even when the number of replicates is doubled (and a different isolate of P. vranovensis is used!) suggests that adding additional replicates would not substantially change the conclusions of this manuscript.

We would also like to highlight, as above, that because this analysis was done on populations of thousands of similarly staged animals, as opposed to individuals, that this further reduces the variability between replicates. In addition, much of our transcriptomic data from each species was then compared across species and genes were only analyzed for those that changed in multiple different species which themselves each represent a separate three additional replicates [ie genes that change in all 4 species analyzed have to exhibit significant (>2-fold, padj <0.01) changes across 12 total replicates].

Our new findings comparing six replicates did not substantially change the number of genes identified when compared to using three replicates, and the fact that for all of the main conclusions of this manuscript each set of triplicates from one species was then compared across 9 additional replicates from three other species from pools of thousands of animals makes us very confident that our results are robust and highly reproducible.

and lack comparison to the stress responses in the parental animals.

We agree with Reviewer 1 that comparisons to parental animals are interesting and important. Comparisons of F1 progeny gene expression patterns to parental animals were not included here because such comparisons were previously published in some of our original reports of these intergenerational effects (For example, see Burton et al., 2020). In summary, we found that most, but not all, of the effects on gene expression in F1 animals were also detected in parental animals. However, the transcriptional responses only turn on in F1 animals post gastrulation and do not appear to be due to the simple deposition of parental mRNAs into embryos (Burton et al., 2020).

We have updated the text to highlight these findings.

The analysis of the transcriptome data is limited to counting overlaps between significantly changed genes, without deeper discussion of the genes and pathways that are affected.

In the revised manuscript we have completely redone all of the transcriptomic analysis to use a stricter set of cutoffs for significance – both padj <0.01 and requiring a >2-fold change in expression based on the helpful comments of Reviewer 1 – which we agree with – see below.

As part of this new analysis we have now also included a deeper discussion of the genes that exhibited similar changes across species, including using g:Profiler to examine the genes that exhibited changes across all four species.

In addition, we have now paired our phenotypic and transcriptomic data across species to identify 19 new genes that we predict are highly likely to be involved in intergenerational responses to stress based on their expression patterns across species. These 19 genes come out of highly filtered analyses across species that identified a total of 23 genes that change only in species that adapt to P. vranovensis or osmotic stress and not in species that do not adapt.

Interestingly, this analysis identified nearly all of the previously known genes involved in intergenerational adaptations to these stresses including rhy-1, cysl-1, cysl-2 and gpdh-1. Thus, we predict the remaining 19 genes that came out of this analysis are highly likely to be involved in the responses to these stresses. Furthermore, in the revised text we highlight that our new list of 19 genes includes multiple conserved factors that are required for animal viability including genes involved in nuclear transport (imb-1 and xpo-2), the CDC25 phosphatase ortholog cdc-25.1, and the PTEN tumor suppressor ortholog daf-18. This new analysis will likely form the basis for future investigations into the mechanisms underlying these exciting intergenerational effects.

We believe this additional analysis greatly improves this manuscript. We are also happy to include any specific additional analysis the reviewer would like to see.

The top response genes that are directly tested have been discovered before. Hence, while interesting patterns are evident from the data, this work largely confirms prior work, including that described in Burton et al. 2020.

We have revised the text to highlight that the aims of this particular study were to determine if multigenerational responses to stress were evolutionarily conserved at any level, as well as to determine the potential costs of such effects and the specificity of the responses. Questions that were not addressed in any previous study of multigenerational effects, including Burton et al., 2020. Because of the aims of this study we believe it was critical to focus on genes that had an established role in these intergenerational responses in C. elegans and to compare and contrast the behavior and requirement of these genes in intergenerational responses in other species. (Although we note that this newly revised manuscript we have now also reported 19 new top response genes – see above).

In addition to our original goals, in this study we were able to determine the extent to which intergenerational transcriptional responses are conserved and the extent to which intergenerational transcriptional changes persist transgenerationally (which we find to be effectively not at all using our revised stricter analysis). We believe these findings are not only novel, but perhaps will be surprising to much of the intergenerational and transgenerational field and have a major impact on both how multigenerational studies are interpreted and how they are conducted in the future. This is especially the case for studies in C. elegans which is one of the leading model organisms to study the mechanisms underlying both intergenerational and transgenerational responses to stress.

For example, we note that several landmark studies of transgenerational effects (persisting into F3 or later generations) in C. elegans performed RNA-seq on F1 progeny (For example, Moore et al., Cell 2019 or Ma et al., Nature Cell Biology 2019). Our new findings reported here suggest that it is possible that none of the transcriptional effects detected in F1 animals will persist in F3 progeny. Furthermore, our studies demonstrate the importance of comparing C. elegans transcriptional effects to related Caenorhabditis species as we found that only a subset of the effects detected in C. elegans are conserved in any other Caenorhabditis species. (Such comparisons are important for determining if and to what extent observations of intergenerational and/or transgenerational effects observed in C. elegans represent conserved phenomena).

For all of these reasons we believe our data is highly exciting, will be of broad interest to the field, and represent novel and potentially unexpected findings that were not previously reported in any prior work including Burton et al., 2020.

Reviewer #2 (Public Review):

Transgenerational effects (TE) (usually defined as multigenerational effects lasting for at least three generations) generated a lot of interest in recent years but the adaptive value of such effects is unclear. In order to understand the scope for adaptive TE we need to understand i) whether such effects are common; ii) whether they are stress-specific; and iii) if there are trade-offs with respect to performance in different environments. The last point is particularly important because F1, F2 and F3 descendants may encounter very different environments. On the other hand, intergenerational effects (lasting for one or two generations) are relatively common and can play an important role in evolutionary processes. However, we do not know whether intergenerational and transgenerational effects have same underlying mechanisms.

This study makes a big step towards resolving these questions and strongly advances our understanding of both phenomena. Much of the previous work on mechanisms of multigenerational effects has been conducted in C. elegans and this works uses the same approach. They focus on bacterial infection, Microsporidia infection, larval starvation and osmotic stress. I did not quite understand why the authors chose to focus on P. vranovensis rather than P. aeruginosa P14 that has been used in previous studies of transgenerational effects in C. elegans. However, this is a minor point because I guess they were interested in broad transgenerational responses to bacterial infection rather than in strain-specific ones. The authors used different Caenorhabditis species, which is another strength of this study in addition to using multiple stresses.

We thank the reviewer for these comments. We’d like to briefly highlight that P. vranovensis was also shown to elicit the same transgenerational effects as P. aeruginosa in the bioRxiv version of the same papers that reported transgenerational effects for P. aeruginosa (Kaletsky et al., 2020 – GRb0427 is an isolate of P. vranovensis).

It is not clear to us why this result was not included in the final published version of this manuscript, but we in fact used P. vranovensis for these studies in part because of this bioRxiv paper and because we failed to detect any robust intergenerational effects using P. aeruginosa PA14 in any of our assays – including at the RNA-seq level (unpublished).

Nonetheless, we have since confirmed with Coleen Murphy’s lab that they do find P. vranovensis elicits the same transgenerational effect on behaviour as P. aeruginosa. We expect that future investigations into the conditions under which P. vranovensis elicits effects that are lost/erased after 1 generation and the conditions under which effects might be maintained for more than 3 generations will be highly interesting.

They found 279 genes that exhibited intergenerational changes in all C species tested, but most interestingly, they show that a reversal in gene expression corresponds to a reversal in response to bacterial infection (beneficial in two species and deleterious on one). This is very intriguing! This was further supported by similar observations of osmotic stress response.

We thank Reviewer 2 for their excitement and we agree that these findings were highly exciting.

They also report that intergenerational effects are stress-specific and there have deleterious effects in mismatched environments, and, importantly, when worms were subject to multiple stresses. It is quite likely that offspring will experience a range of environments and that several environmental stresses will be present simultaneously in nature. I really liked this aspect of this work as I think that tests in different environments, especially environments with multiple stresses, are often lacking, which limits the generality of the conclusions.

Another interesting piece of the puzzle is that beneficial and deleterious effects could be mediated by the same mechanisms. It would be interesting to explore this further. However, this is not a real criticism of this work. I think that the authors collected an impressive dataset already and every good study generates new research questions.

Given these findings, I was particularly keen to see what comes of transgenerational effects. The general answer was that there aren't many, and the authors conclude that all intergenerational effects that they studied are largely reversible and that intergenerational and transgenerational effects represent distinct phenomena. While I think that this is a very important finding, I am not sure whether we can conclude that intergenerational and transgenerational effects are not related.

In my view, an alternative interpretation is that intergenerational effects are common while transgenerational effects are rare. Because intergenerational effects are stress-specific, transgenerational effects could be stress-specific as well.

We agree with reviewer 2 that our findings suggest that intergenerational effects are common and transgenerational effects are either rare in comparison or only occur under specific conditions. We have updated the text to include this interpretation.

Perhaps different mechanisms regulate intergenerational responses to, say, different forms of starvation (e.g. compare opposing transgenerational responses to prolonged larval starvation (Rechavi et al. doi:10.1016/j.cell.2014.06.020) and rather short adulthood starvation (Ivimey-Cook et al. 2021 https://doi.org/10.1098/rspb.2021.0701). Perhaps some (most?) forms of starvation generate only intergenerational responses and do not generate transgenerational responses. But some do. Those forms of starvation that generate both intergenerational and transgenerational effects could do so via same mechanisms and represent the same phenomenon. I am by no means saying this is the case, but I am not sure that the absence of evidence of transgenerational effects in this study necessarily suggests that inter- and trans-generational effects are different phenomena.

We agree and, similar to above, have updated the text accordingly to state that it is also very possible that transgenerational effects only occur under certain conditions.

The only concern real concern was the lack of phenotypic data on F3 beyond gene expression. Ideally, I would like to see tests of pathogen avoidance and starvation resistance in F3. However, given the amount of work that went into this study, the lack of strong signature of potential transgenerational effects in gene expression, and the fact that most of these effects were shown previously to last only one generation, I do not think this is crucial.

We thank reviewer 2 for these comments and agree that phenotypic investigations of F3 effects are also very interesting.

We have previously investigated the phenotypic effects of all of the stresses used in this paper on F3 animals using the assays described here and consistent with our new gene expression findings we previously found that most of these stresses do not exert phenotypic effects in F3 animals (Burton et al. 2020, Willis et al 2021, Hibshman et al., 2016).

Separately, we have also attempted to investigate the effects of pathogen exposure on pathogen avoidance, as these effects have previously been reported to occur transgenerationally, but to date have been unable to consistently replicate these findings. We expect that this is likely due to what might be subtle differences in conditions between labs (differences in water used for the media prep, air humidity, potential differences in N2 wild-type strains etc….) because assays such as behavioral avoidance are known to be very sensitive to many different environmental inputs.

We currently believe that our experiences as they relate to intergenerational and transgenerational effects support the general conclusion of this manuscript that while intergenerational effects are common and easy to initiate across multiple labs (the intergenerational effects studied here have now been successfully reproduced in labs in the US, UK, and Canada), transgenerational effects might be more specific and/or only occur/be initiated under more stringent conditions – perhaps with the aim of avoiding the costs of such multigenerational effects.

Future studies of exactly when/under what conditions C. elegans initiates intergenerational vs transgenerational effects is likely to be very interesting.

It would be very interesting to compare gene expression and other phenotypic responses in F1 and F3 between P. vranovensis and PA14. Also, it would be interesting to test the adaptive value of intergenerational and transgenerational effects after exposure to both strains in different environments. This is would be very informative and help with understanding the evolutionary significance of transgenerational epigenetic inheritance of pathogen avoidance as reported previously. Why response to P. vranovensis is erased while response to PA14 is maintained for four generations? Are nematodes more likely to encounter one species than the other? Again, however, this is not something necessary for this study.

We completely agree with Reviewer 2 and have indeed attempted these experiments both in Burton et al., 2020 and in unpublished results.

With regards to the transgenerational F3 effects, as mentioned above, P. vranovensis has been reported to elicit the same transgenerational effect as P. aeruginosa PA14 – at least as reported in the Kaletsky et al., 2020 bioRxiv version of the manuscript from the same studies. (GRb0427 is an isolate of P. vranovensis).

To date, however, in our laboratory we have been unable to detect any transgenerational effects for either P. vranovensis or P. aeruginosa infection on gene expression data from RNA-seq experiments (data from this manuscript and unpublished data).

It is not yet clear why this is the case, but we note that the RNA-seq analysis from the transgenerational PA14 studies (published in Moore et al., Cell 2019) was performed on F1 animals and thus was looking at intergenerational effects – to our knowledge no RNA-seq on F3 progeny from animals exposed to PA14 has ever been published. Thus, as it stands there is no existing F3 gene expression studies done using PA14 for us to compare our results to, but it remains possible that PA14 does not elicit specific effects on F3 gene expression when analyzed by RNA-seq.

For F1 effects we have published a gene expression comparison for P. vranovensis and P. aeruginosa F1 effects in a previous manuscript (Burton et al 2020) and will add a mention of this to the text. Briefly, we detected very few F1 effects on gene expression when exposing adults to P. aeruginosa for 24 hours and parental infection by P. aeruginosa did not result in protection for offspring from P. vranovensis infection (Burton et al., 2020). We concluded that the intergenerational adaptation to P. vranovensis was not initiated by P. aeruginosa and was at least somewhat specific to P. vranovensis as well as the new species of Pseudomonas described in this manuscript which does cross protect.

The main strengths of this paper are i) use of multiple stresses; ii) use of multiple species; iii) tests in different environments; and iv) simultaneous evaluation of intergenerational and transgenerational responses. This study is first of a kind, and it provides several important answers, while highlighting clear paths for future work.

Excellent work and I think it will generate a lot of interest in the community, definitely want to see it published in eLife.

We agree with Reviewer 2 and thank them for their kind comments.

Reviewer #3 (Public Review):

In this manuscript, the authors address whether the mechanisms mediating intergenerational effects are conserved in evolution. This question is important not only to frame this phenomenon in an evolutionary context, but to address several interlinked questions: is there a mechanism in common between adaptive versus deleterious effects? What makes some effects last one instead of several generations? What is the ecological relevance for those mechanisms? Using Caenorhabditis elegans as a model of reference, they compare four types of intergenerational effects on additional three Caenorhabditis species.

The authors used previously characterized models of intergenerational inheritance, focusing on those that are likely to have adaptive significance. This is relevant, because the adaptive relevance of other published examples of inter- and transgenerational inheritance is not clear. They used functional studies to probe for conservation of mechanisms for bacterial infection and resistance to osmolarity stress, which is a major strength of this study. The data supports the claim of conservation in some types of intergenerational inheritance and divergence in others. One major question addressed in this manuscript is whether there is a potential overarching mechanism that confers stress-resistance across generations. Their experiments convincingly show that this is not the case, but that instead, there are stress-specific mechanisms responsible for intergenerational inheritance.

We agree and thank Reviewer 3 for their kind comments.

Author Response

Reviewer #1 (Public Review):

The relationship between genetic disease and adaptation is important for biomedical research as well as understanding human evolution. This topic has received considerable attention over the past several decades in human genetics research. The present manuscript provides a much more comprehensive and rigorous analysis of this topic. Specifically, the authors select a set of ~4000 human Mendelian disease genes and examine patterns of recent positive selection in these genes using the iHS and nSL tests (both haplotype test) for selection. They then compare the signals of sweeps to control genes. Importantly, they match the control set to the disease genes based upon many different genomic variables, such as recombination rate, amount of background selection, expression level, etc. The authors find that there is a deficit of selective sweeps in disease genes. They test several hypotheses for this deficit. They find that the deficit of sweeps is stronger in disease genes at low recombination rate and those that have more disease mutations. From this, the authors conclude that strongly deleterious mutations could be impeding selective sweeps.

Strengths

The manuscript includes a number of important strengths:

1) It tackles an important question in the field. The question of selection in disease genes has been very well-studied in the past, with conflicting viewpoints. The present study examines this topic in a rigorous way and finds a deficit of sweeps in disease genes.

2) The statistical analyses are rigorously done. The genome is a confusing place and there can often be many reasons why a certain set of genes could differ from another set of genes, unrelated to the variable of interest. Di et al. carefully match on these genomic confounders. Thus, they rigorously demonstrate that sweeps are depleted in disease genes relative to control genes. Further, the pipeline for ranking the genes and testing for significance is solid.

3) The Introduction of the manuscript nicely relates different evolutionary models and explanations to patterns that could be seen in the data. As such, the present manuscript isn't just merely an exploratory analysis of patterns of sweeps in disease genes. Rather, it tests specific evolutionary scenarios.

Weaknesses

1) The authors did not discuss or test a basic explanation for the deficit of sweeps in disease genes. Namely, certain types of genes, when mutated, give rise to strong Mendelian phenotypes. However, mutations in these genes do not result in variation that gives rise to a phenotype on which positive selection could occur. In other words, there are just different types of genes underlying disease and positive selection. I could think that such a pattern would be possible if humans are close to the fitness optimum and strong effect mutations (like those in Mendelian disease genes) result in moving further away from the fitness optimum. On the other hand, more weak effect mutations could be either weakly deleterious or beneficial and subject to positive selection. I'm not sure whether these patterns would necessarily be captured by the overall measures of constraint which the disease and non-disease genes were matched on.

We thank the reviewer for suggesting that alternative explanation. It is indeed important that we compare it with our own explanation. To rephrase the reviewer’s suggestion, it is possible that disease genes may just have a different distribution of fitness effects of new mutations. Specifically, mutations in disease genes might have such large effects that they will consistently overshoot the fitness optimum, and thus not get closer to this optimum. This would prevent them from being positively selected. Two predictions can be derived from this potential scenario. First, we can predict a sweep deficit at disease genes, which is what we report. Second, we can also predict that disease genes should exhibit a deficit of older adaptation, not just recent adaptation detected by sweep signals. Indeed, the decrease in adaptation due to (too) large effect mutations would be a generic, intrinsic feature of disease genes regardless of evolutionary time. This means that under this explanation, we expect a test of long-term adaptation such as the McDonald-Kreitman test to also show a deficit at disease genes.

This latter prediction differs from the prediction made by our favored explanation of interference between deleterious and advantageous variants. In this scenario, the sweep deficit at disease genes is caused by the presence of deleterious, and most importantly currently segregating disease variants. Because the presence of the segregating variants is transient during evolution, our explanation does not predict a deficit of long-term adaptation. We can therefore distinguish which explanation (the reviewer’s or ours) is the most likely based on the presence or absence of a long-term adaptation deficit at disease genes.

To test this, we now compare protein adaptation in disease and control genes with two versions of the MK test called ABC-MK and GRAPES (refs). ABC-MK estimates the overall rate of adaptation, and also the rates of weak and strong adaptation,and is based on Approximate Bayesian Computation. GRAPES is based on maximum likelihood. Both ABC-MK and GRPES have shown to provide robust estimates of the rate of protein adaptation thanks to evaluations with forward population simulations (refs). We find no difference in long-term adaptation between disease and control non-disease genes, as shown in new figure 4. This shows that the explanation put forward by the reviewer of an intrinsically different distribution of mutation effects at disease genes is less likely than an interference between currently segregating deleterious variants with recent, but not with older long-term adaptation. We even show in the new figure 4 that disease genes and their controls have more, not less strong long-term adaptation compared to the whole human genome baseline (new figure 4C). Also, disease genes in low recombination regions and with many disease variants have experienced more, not less strong long-term adaptation than their controls. Therefore, far from overshooting the fitness optimum due to stronger fitness effects of mutations, it looks like that these stronger fitness effects might in fact be more frequently positively selected in these disease genes.

We now provide these new results P15L418:<br /> “Disease genes do not experience constitutively less long-term adaptive mutations<br /> A deficit of strong recent adaptation (strong enough to affect iHS or 𝑛𝑆!) raises the question of what creates the sweep deficit at disease genes. As already discussed, purifying selection and other confounding factors are matched between disease genes and their controls, which excludes that these factors alone could possibly explain the sweep deficit. Purifying selection alone in particular cannot explain this result, since we find evidence that it is well matched between disease and control genes (Figures 2 and Figure 4-figure supplement 1). Furthermore, we find that the 1,000 genes in the genome with the highest density of conserved elements do not exhibit any sweep deficit (bootstrap test + block-randomized genomes FPR=0.18; Methods). Association with mendelian diseases, rather than a generally elevated level of selective constraint, is therefore what matters to observe a sweep deficit. What then might explain the sweep deficit at disease genes?

As mentioned in the introduction, it could be that mendelian disease genes experience constitutively less adaptive mutations. This could be the case for example because mendelian disease genes tend to be more pleiotropic (Otto, 2004), and/or because new mutations in mendelian are large effect mutations (Quintana-Murci, 2016) that tend to often overshoot the fitness optimum, and cannot be positively selected as a result. Regardless of the underlying processes, a constitutive tendency to experience less adaptive mutations predicts not only a deficit of recent adaptation, but also a deficit of more long-term adaptation during evolution. The iHS and nSL signals of recent adaptation we use to detect sweeps correspond to a time window of at most 50,000 years, since these statistics have very little statistical power to detect older adaptation (Sabeti et al., 2006). In contrast, approaches such as the McDonald-Kreitman test (MK test) (McDonald and Kreitman, 1991) capture the cumulative signals of adaptative events since humans and chimpanzee had a common ancestor, likely more than six million years ago. To test whether mendelian disease genes have also experienced less long-term adaptation, in addition to less recent adaptation, we use the MK tests ABC-MK (Uricchio et al., 2019) and GRAPES (Galtier, 2016) to compare the rate of protein adaptation (advantageous amino acid changes) in mendelian disease gene coding sequences, compared to confounding factors-matched non-disease controls (Methods). We find that overall, disease and control non-disease genes have experienced similar rates of protein adaptation during millions of years of human evolution, as shown by very similar estimated proportions of amino acid changes that were adaptive (Figure 5A,B,C,D,E). This result suggests that disease genes do not have constitutively less adaptive mutations. This implies that processes that are stable over evolutionary time such as pleiotropy, or a tendency to overshoot the fitness optimum, are unlikely to explain the sweep deficit at disease genes. If disease genes have not experienced less adaptive mutations during long-term evolution, then the process at work during more recent human evolution has to be transient, and has to has to have limited only recent adaptation. It is also noteworthy that both disease genes and their controls have experienced more coding adaptation than genes in the human genome overall (Figure 5A), especially more strong adaptation according to ABC-MK (Figure 5C). The fact that the baseline long-term coding adaptation is lower genome-wide, but similarly higher in disease and their control genes, also shows that the matched controls do play their intended role of accounting for confounding factors likely to affect adaptation. The fact that long-term protein adaptation is not lower at disease genes also excludes that purifying selection alone can explain the sweep deficit at disease genes, because purifying selection would then also have decreased long-term adaptation. A more transient evolutionary process is thus more likely to explain our results.”

Then P22L613: “More importantly, the fact that constitutively less adaptation at disease genes combined to more power to detect sweeps in low recombination regions does not explain our results, is made even clearer by the fact that disease genes in low recombination regions and with many disease variants have in fact experienced more, not less long-term adaptation according to an MK analysis using both ABC-MK and GRAPES (Figure 5F,G,H,I,J). ABC-MK in particular finds that there is a significant excess of long-term strong adaptation (Figure 4H, P<0.01) in disease genes with low recombination and with many disease variants, compared to controls, but similar amounts of weak adaptation (Figure 5G, P=0.16). It might be that disease genes with many disease variants are genes with more mutations with stronger effects that can generate stronger positive selection. The potentially higher supply of strongly advantageous variants at these disease genes makes it all the more notable that they have a very strong sweep deficit in recent evolutionary times. This further strengthens the evidence in favor of interference during recent human adaptation: the limiting factor does not seem to be the supply of strongly advantageous variants, but instead the ability of these variants to have generated sweeps recently by rising fast enough in frequency.”

2) While I think the authors did a superb job of controlling for genome differences between disease and non-disease genes, the analysis of separating regions by recombination rate and number of disease mutations does not seem as rigorous. Specifically, the authors tested for enrichment of sweeps in disease genes vs control and then stratified that comparison by recombination rate and/or number of disease mutations. While this nicely matches the disease genes to the control genes, it is not clear whether the high recombination rate genes differ in other important attributes from the low recombination rate genes. Thus, I worry whether there could be a confounder that makes it easier/harder to detect an enrichment/deficit of sweeps in regions of low/high recombination.

We thank the reviewer for emphasizing the need for more controls when comparing our results in low or high recombination regions. We have now compared the confounding factors between low recombination disease genes and high recombination disease genes, as classified in the manuscript. As shown in new supp table Figure 6 figure supplement 1, confounding factors do not differ substantially between low and high recombination disease genes, and are all within a range of +/- 25% of each other. It would take a larger difference for any confounding factor to explain the sharp sweep deficit difference observed between the low and high recombination disease genes. The only factor with a 35% difference between low and high recombination mendelian disease genes is McVicker’s B, but this is completely expected; B is expected to be lower in low recombination regions.

We now write P20L569: “Further note that only moderate differences in confounding factors between low and high recombination mendelian disease genes are unlikely to explain the sweep deficit difference (Figure 6-figure supplement 1).”

Regarding the potential confounding effect of statistical power to detect sweeps differing in low and high recombination regions, please see our earlier response to main point 2.

Reviewer #2 (Public Review):

This paper seeks to test the extent to which adaptation via selective sweeps has occurred at disease-associated genes vs genes that have not (yet) been associated with disease. While there is a debate regarding the rate at which selective sweeps have occurred in recent human history, it is clear that some genes have experienced very strong recent selective sweeps. Recent papers from this group have very nicely shown how important virus interacting proteins have been in recent human evolution, and other papers have demonstrated the few instances in which strong selection has occurred in recent human history to adapt to novel environments (e.g. migration to high altitude, skin pigmentation, and a few other hypothesized traits).

One challenge in reading the paper was that I did not realize the analysis was exclusively focused on Mendelian disease genes until much later (the first reference is not until the end of the introduction on pages 7-8 and then not at all again until the discussion, despite referring to "disease" many times in the abstract and throughout the paper). It would be preferred if the authors indicated that this study focused on Mendelian diseases (rather than a broader analysis that included complex or infectious diseases). This is important because there are many different types of diseases and disease genes. Infectious disease genes and complex disease genes may have quite different patterns (as the authors indicate at the end of the introduction).

We want to apologize profusely for this avoidable mistake. We have now made it clearer from the very start of the manuscript that we focus on mendelian non-infectious disease genes. We have modified the title and the abstract accordingly, specifying mendelian and non-infectious as required.

The abstract states "Understanding the relationship between disease and adaptation at the gene level in the human genome is severely hampered by the fact that we don't even know whether disease genes have experienced more, less, or as much adaptation as non-disease genes during recent human evolution." This seems to diminish a large body of work that has been done in this area. The authors acknowledge some of this literature in the introduction, but it would be worth toning down the abstract, which suggests there has been no work in this area. A review of this topic by Lluis Quintana-Murci1 was cited, but diminished many of the developments that have been made in the intersection of population genetics and human disease biology. Quintana-Murci says "Mendelian disorders are typically severe, compromising survival and reproduction, and are caused by highly penetrant, rare deleterious mutations. Mendelian disease genes should therefore fit the mutation-selection balance model, with an equilibrium between the rate of mutation and the rate of risk allele removal by purifying selection", and argues that positive selection signals should be rare among Mendelian disease genes. Several other examples come to mind. For example, comparing Mendelian disease genes, complex disease genes, and mouse essential genes was the major focus of a 2008 paper2, which pointed out that Mendelian disease genes exhibited much higher rates of purifying selection while complex disease genes exhibited a mixture of purifying and positive selection. This paper was cited, but only in regard to their findings of complex diseases. A similar analysis of McDonald-Kreitman tables3 was performed around Mendelian disease genes vs non-disease genes, and found "that disease genes have a higher mean probability of negative selection within candidate cis-regulatory regions as compared to non-disease genes, however this trend is only suggestive in EAs, the population where the majority of diseases have likely been characterized". Both of these studies focused on polymorphism and divergence data, which target older instances of selection than iHS and nSL statistics used in the present study (but should have substantial overlap since iHS is not sensitive to very recent selection like the SDS statistic). Regardless, the findings are largely consistent, and I believe warrant a more modest tone.

We thank the reviewer for their recommendation. We should have written more about what is currently well known or unknown about recent adaptation in disease genes, and in more nuanced terms. Instead of writing “Understanding the relationship between disease and adaptation at the gene level in the human genome is severely hampered by the fact that we don't even know whether disease genes have experienced more, less, or as much adaptation as non-disease genes during recent human evolution”, we now write in the new abstract:

“Despite our expanding knowledge of gene-disease associations, and despite the medical importance of disease genes, their recent evolution has not been thoroughly studied across diverse human populations. In particular, recent genomic adaptation at disease genes has not been characterized as well as long-term purifying selection and long-term adaptation. Understanding the relationship between disease and adaptation at the gene level in the human genome is hampered by the fact that we don’t know whether disease genes have experienced more, less, or as much adaptation as non-disease genes during the last ~50,000 years of recent human evolution.”

We also toned down the start of the introduction. We now write P3L74:

“Despite our expanding knowledge of mendelian disease gene associations, and despite the fact that multiple evolutionary processes might connect disease and genomic adaptation at the gene level, these connections are yet to be studied more thoroughly, especially in the case of recent genomic adaptation.”

Although we agree that others have made extensive efforts to characterize older adaptation or purifying selection at disease genes compared to non-disease genes, we still believe that our results are novel and more conclusive about recent positive selection. Our initial statement was however poorly phrased. To our knowledge, our study is the first to look at the issue using specifically sweep statistics that have been shown to be robust to background selection, while also controlling for confounding factors. These sweep statistics have sensitivity for selection events that occurred in the past 30,000 or at most 50,000 years of human evolution (Sabeti et al. 2006). This is a very different time scale compared to the millions of years of adaptation (since divergence between humans and chimpanzees) captured by MK approaches.

We also want to note that we did cite the Blekhman et al. paper for their result of stronger purifying selection in our initial manuscript. It is true however that we did not specify mendelian disease genes, which was confusing. We want to apologize again for it:

From the earlier manuscript: “Multiple recent studies comparing evolutionary patterns between human disease and non-disease genes have found that disease genes are more constrained and evolve more slowly (lower ratio of nonsynonymous to synonymous substitution rate, dN/dS, in disease genes) (Blekhman et al., 2008; Park et al., 2012; Spataro et al., 2017)”

“Among other confounding factors, it is particularly important to take into account evolutionary constraint, i.e the level of purifying selection experienced by different genes. A common intuition is that disease genes may exhibit less adaptation because they are more constrained (Blekhman et al., 2008)”

It is important to remember that, as we mention in the introduction, previous comparisons did not take potential confounding factors at all into account. It is therefore unclear whether their conclusions were specific to disease genes, or due to confounding factors. We have now made this point clearer in the introduction, as we believe that we have made a substantial effort to control for confounding factors, and that it is a substantial departure from previous efforts:

P7L201: “In contrast with previous studies, we systematically control for a large number of confounding factors when comparing recent adaptation in human mendelian disease and nondisease genes, including evolutionary constraint, mutation rate, recombination rate, the proportion of immune or virus-interacting genes, etc. (please refer to Methods for a full list of the confounding factors included).”.

P9L253: “These differences between disease and non-disease genes highlight the need to compare disease genes with control non-disease genes with similar levels of selective constraint. To do this and compare sweeps in mendelian disease genes and non-disease genes that are similar in ways other than being associated with mendelian disease (as described in the Results below, Less sweeps at mendelian disease genes), we use sets of control non-disease genes that are built by a bootstrap test to match the disease genes in terms of confounding factors (Methods)”.

Furthermore, we have now added a comparison of older adaptation in disease and non-disease genes using a recent version of the MK test called ABC-MK, that can take background selection and other biases such as segregating weakly advantageous variants into account. Also controlling for confounding factors, we find no difference in older adaptation between disease and non-disease genes (please see our response to main point 2).

Therefore, contrary to the reviewer’s claim that the sweep statistics and MK approaches should have substantial overlap, we now show that it is clearly not the case. We further show that the lack of overlap is expected under our explanation of our results based on interference between recessive deleterious and advantageous variants (see our responses to main point 1 and to reviewer 1 weakness 1).

Previous analyses were using much smaller mendelian disease gene datasets, less recent polymorphism datasets and, critically, did not control for confounding factors. We also note that reference 3 (Torgerson et al. Plos Genetics 2009) does not make any claim about recent positive selection in mendelian disease genes compared to other genes. Their dataset at the time also only included 666 mendelian disease genes, versus the ~4,000 currently known.

In short, we do think that we have a claim for novelty, but the reviewer is entirely right that we did a poor job of giving due credit to previous important work. These previous studies deserved much better credit than no credit at all. We want to thank the reviewer from avoiding us the embarrassment of not citing important work.

We now cite the papers referenced by the reviewer as appropriate in the introduction, based on the scope of their results:

P3L93: “Multiple recent studies comparing evolutionary patterns between human mendelian disease and non-disease genes have found that mendelian disease genes are more constrained and evolve more slowly (Blekhman et al., 2008; Quintana-Murci, 2016; Spataro et al., 2017; Torgerson et al., 2009). An older comparison by Smith and Eyre-Walker (Smith and Eyre-Walker, 2003) found that disease genes evolve faster than non-disease genes, but we note that the sample of disease genes used at the time was very limited.”

P5L134 “Among possible confounding factors, it is particularly important to take into account evolutionary constraint, i.e the level of purifying selection experienced by different genes. A common intuition is that mendelian disease genes may exhibit less adaptation because they are more constrained (Blekhman et al., 2008; Spataro et al., 2017; Torgerson et al., 2009),”

There are some aspects of the current study that I think are highly valuable. For example, the authors study most of the 1000 Genomes Project populations (though the text should be edited since the admixed and South Asian populations are not analyzed, so all 26 populations are not included, only the populations from Africa, East Asia, and Europe are analyzed; a total of 15 populations are included Figures 2-3). Comparing populations allows the authors to understand how signatures of selection might be shared vs population-specific. Unfortunately, the signals that the authors find regarding the depletion of positive selection at Mendelian disease genes is almost entirely restricted to African populations. The signal is not significant in East Asia or Europe (Figure 2 clearly shows this). It seems that the mean curve of the fold-enrichment as a function of rank threshold (Figure 3) trends downward in East Asian and European populations, but the sampling variance is so large that the bootstrap confidence intervals overlap 1). The paper should therefore revise the sentence "we find a strong depletion in sweep signals at disease genes, especially in Africa" to "only in Africa". This opens the question of why the authors find the particular pattern they find. The authors do point out that a majority of Mendelian disease genes are likely discovered in European populations, so is it that the genes' functions predate the Out-of-Africa split? They most certainly do. It is possible that the larger long-term effective population size of African populations resulted in stronger purifying selection at Mendelian disease genes compared to European and East Asian populations, where smaller effective population sizes due to the Out-of-Africa Bottleneck diminished the signal of most selective sweeps and hence there is little differentiation between categories of genes, "drift noise"). It is also surprising to note that the authors find selection signatures at all using iHS in African populations while a previous study using the same statistic could not differentiate signals of selection from neutral demographic simulations4.

We want to thank the reviewer profusely for putting us on the right track thanks to their insightful suggestion. As described in our response to reviewer 1 weakness 1, we have now shown with simulations that the interference of deleterious variants on advantageous variants is strongly decreased during a bottleneck of a magnitude similar to the Out of Africa bottlenecks experienced by East Asian and European populations. This decrease of interference is likely strong enough to not require any other explanation, even if other processes may also be at work, such as a decrease of the sweeps signals as suggested by the reviewer.

About the Granka et al. paper, the last author of the current manuscript has already shown in a previous paper (ref) that the type of approaches used to quantify recent adaptation is likely to be severely underpowered due to a number of confounding factors, notably including comparing genic and non-genic windows that are not sufficiently far from each other to not overlap the same sweep signals. Our result are also based on much more recent and less biased sets of SNPs used to measure the sweeps statistics.

The authors find that there is a remarkably (in my view) similar depletion across all but one MeSH disease classes. This suggests that "disease" is likely not the driving factor, but that Mendelian disease genes are a way of identifying where there are strongly selected deleterious variants recurrently arising and preventing positively selected variants. This is a fascinating hypothesis, and is corroborated by the finding that the depletion gets stronger in genes with more Mendelian disease variants. In this sense, the authors are using Mendelian disease genes as a proxy for identifying targets of strong purifying selection, and are therefore not actually studying Mendelian disease genes. The signal could be clearer if the test set is based on the factor that is actually driving the signal.

Based on the reviewer’s comment, we have now better explained why our results are unlikely to be a generic property of purifying selection alone. As we explain in our response to main point 3, our results cannot be explained by purifying selection alone, because we match purifying selection between disease genes and the controls. Indeed, we now show with additional MK analyses and GERP-based analyses that our controls for confounding factors already account for purifying selection. This is shown by the fact that disease genes and their controls have similar distributions of deleterious fitness effects.

In addition, we added a comparison that shows that purifying selection alone does not explain our results. Instead of comparing sweeps at disease and non-disease genes, we compared sweeps (in Africa) between the 1,000 genes with the highest density of conserved, constrained elements and other genes in the genome. If purifying selection is the factor that drives the sweep deficit at disease genes, then we should see a sweep deficit among the genes with the most conserved, constrained elements compared to other genes in the genome. However, we see no such sweep deficit at genes with a high density of conserved, selectively constrained elements (boostrap test + block randomization of genomes, FPR=0.18). See P15L424. Note that for this comparison we had to remove the matching of confounding factors corresponding to functional and purifying selection densities (new Methods P40L1131).

Again, our results are better explained not just by purifying selection alone, but more specifically by the presence of interfering, segregating deleterious variants. It is perfectly possible to have highly constrained parts of the genome without having many deleterious segregating variants at a given time in evolution.

The similarity across MeSH classes can be readily explained if what matters is interference with deleterious segregating variants. Because all types of diseases have deleterious segregating variants, then it is not surprising that different MeSH disease categories have a similar sweep deficit. We make that point clearer in the revised manuscript:

P26L707: “The sweep deficit is comparable across MeSH disease classes (Figure 8), suggesting that the evolutionary process at the origin of the sweep deficit is not diseasespecific. This is compatible with a non-disease specific explanation such as recessive deleterious variants interfering with adaptive variants, irrespective of the specific disease type.”.

One of the most important steps that the authors undertake is to control for possible confounding factors. The authors identify 22 possible confounding factors, and find that several confounding factors have different effects in Mendelian disease genes vs non-disease genes. The authors do a great job of implementing a block-bootstrap approach to control for each of these factors. The authors talk specifically about some of these (e.g. PPI), but not others that are just as strong (e.g. gene length). I am left wondering how interactions among other confounding factors could impact the findings of this paper. I was surprised to see a focus on disease variant number, but not a control for CDS length. As I understand it, gene length is defined as the entire genomic distance between the TSS and TES. Presumably genes with larger coding sequence have more potential for disease variants (though number of disease variants discovered is highly biased toward genes with high interest). CDS length would be helpful to correct for things that pS does not correct for, since pS is a rate (controlling for CDS length) and does not account for the coding footprint (hence pS is similar across gene categories).

Based on our response to the previous point, it is clear that a high density of coding sequences, or conserved constrained sequence in general are not enough to explain our results. Furthermore, we want to remind the reviewer that we already control for coding sequence length through controlling for coding density, since we use windows of constant sizes.

The authors point out that it is crucial to get the control set right. This group has spent a lot of time thinking about how to define a control set of genes in several previous papers. But it is not clear if complex disease genes and infectious disease genes are specifically excluded or not. Number of virus interactions was included as a confounding factor, so VIPs were presumably not excluded. It is clear that the control set includes genes not yet associated with Mendelian disease, but the focus is primarily on the distance away from known Mendelian disease genes.

We are sorry that we were not more explicit from the start of the manuscript. We now make it clearer what the set disease genes includes or not throughout the entire manuscript, by repeating that we focus specifically on mendelian, non-infectious disease genes. By noninfectious, we mean that we excluded genes with known infectious disease-associated variants. This does not exclude most virus-interacting genes since most of them are not associated at the genetic variant level with infectious diseases. It is also important to note that the effect of virus interactions is accounted for by matching the number of interacting viruses between mendelian disease genes and controls.

We write P29L818: “By non-infectious, we mean that we excluded genes with known infectious disease-associated variants. This does not exclude most VIPs since most of them are not associated at the genetic variant level with infectious diseases. It is important to note that the effect of virus interactions is accounted for by matching the number of interacting viruses between mendelian disease genes and controls.”

Minor comments:

On page 13, the authors say "This artifact is also very unlikely due to the fact that recombination rates are similar between disease and non-disease genes (Figure 1)." However, Figure 1 shows that "deCode recombination 50kb" is clearly higher in disease genes and comparable at 500kb. The increased recombination rate locally around disease genes seems to contradict the argument formulated in this paragraph.

We apologize for the lack of precision in this sentence. What we meant is that the recombination rates are not different enough that the mentioned hypothetical artifact would be able to explain our results. We also forgot to remind at this point in the manuscript that we match recombination between disease genes and controls. We now use more precise language:

P28L772 “The recombination rate at disease genes is also only slightly different from the recombination rate at non-disease genes (Figure 1), and we match the recombination rate between disease genes and controls.”.

Reviewer #3 (Public Review):

In this paper, the authors ask whether selective sweeps (as measured by the iHS and nSL statistics) are more or less likely to occur in or near genes associated with Mendelian diseases ("disease genes") than those that are not ("non-disease genes"). The main result put forward by the authors is that genes associated with Mendelian diseases are depleted for sweep signatures, as measured by the iHS and nSL statistics, relative to those which are not.

The evidence for this comes from an empirical randomization scheme to assess whether genes with signatures of a selective sweep are more likely to be Mendelian disease genes that not. The analysis relies on a somewhat complicated sliding threshold scheme that effectively acts to incorporate evidence from both genes with very large iHS/nSL values, as well as those with weaker signals, while upweighting the signal from those genes with the strongest iHS/nSL values. Although I think the anlaysis could be presented more clearly, it does seem like a better analysis than a simple outlier test, if for no other reason than that the sliding threshold scheme can be seen as a way of averaging over uncertainty in where one should set the threshold in an outlier test (along with some further averaging across the two different sweeps statistics, and the size of the window around disease associated genes that the sweep statistics are averaged over). That said, the particular approach to doing so is somewhat arbitrary, but it's not clear that there's a good way to avoid that.

In addition to reporting that extreme values of iHS/nSL are generally less likely at Mendelian disease genes, the authors also report that this depletion is strongest in genes from low recombination regions, or which have >5 specific variants associated with disease.

Drawing on this result, the authors read this evidence to imply that sweeps are generally impeded or slowed in the vicinity of genes associated with Mendelian diseases due to linkage to recessive deleterious variants, which hitchhike to high enough frequencies that the selection against homozygotes becomes an important form of interference. This phenomenon was theoretically characterized by Assaf et al 2015, who the authors point to for support. That such a phenomenon may be acting systematically to shape the process of adaptation is an interesting suggestions. It's a bit unclear to me why the authors specifically invoke recessive deleterious mutations as an explanation though. Presumably any form of interference could create the patterns they observe? This part of the paper is, as the authors acknowledge, speculative at this point.

We thank the reviewer for their comments. We are sorry that we did not provide a clear explanation of why only recessive deleterious mutations are expected to interfere more than other types of deleterious variants. This was shown by Assaf et al. (2015), and we should have stated it explicitly. The reason why recessive deleterious variants interfere more than additive or dominant ones is that they can hitchhike together with an adaptive variant to substantial frequencies before negative selection actually happens, when a significant number of homozygous individuals for the deleterious mutation start happening in the population. On the contrary dominant mutations do not make it to the same high frequencies linked to an adaptive variant, because they start being selected negatively as soon as they appear in the population.

We now write P18L496: “In diploid species including humans, recessive deleterious mutations specifically have been shown to have the ability to slow down, or even stop the frequency increase of advantageous mutations that they are linked with (Assaf et al., 2015). Dominant variants do not have the same interfering ability, because they do not increase in frequency in linkage with advantageous variants as much as recessive deleterious do, before the latter can be “seen” by purifying selection when enough homozygous individuals emerge in a population (Assaf et al., 2015).”

We have also confirmed with SLiM forward simulations that recessive deleterious variants interfere with adaptive variants much more than dominant ones (Table 1).

I'm also a bit concerned by the fact that the signal is only present in the African samples studied. The authors suggest that this is simply due to stronger drift in the history of European and Asian samples. This could be, but as a reader it's a bit frustrating to have to take this on faith.

We thank the reviewer for pointing out this issue with our manuscript. We have now shown, as detailed above in our response to main point 1, reviewer 1 weakness 1, that a weaker sweep deficit at disease genes in Europe and East Asia is an expected feature under the interference explanation, due to the weakened interference of recessive deleterious variants during bottlenecks of the magnitude observed in Europe and East Asia. We therefore believe that these new results strengthen our previous claim regarding the role interference between deleterious and advantageous variants. We want to thank the reviewer for forcing us to examine the difference between results in Africa and out of Africa, as the manuscript is now more consistent and our results substantially better explained.

There are other analyses that I don't find terribly convincing. For example, one of the anlayses shows that iHS signals are no less depleted at genes associated with >5 diseases than with 1 does little to convince me of anything. It's not particularly clear that # of associated disease for a given gene should predict the degree of pleiotropy experienced by a variant emerging in that gene with some kind of adaptive function. Failure to find any association here might just mean that this is not a particularly good measure of the relevant pleiotropy.

We agree with the reviewer that the number of associated disease may not be a good measure of pleiotropy. Unfortunately to our knowledge there is currently no good measure of gene pleiotropy in human genomes. Given that the evidence in favor of interference of deleterious variants is now strengthened, we have chosen to remove this analysis from the manuscript. As we now explain throughout the manuscript, pleiotropy is an unlikely explanation in the first place because of the fact that disease genes have not experienced less long-term adaptation (see the details on our new MK test results in the response to main point 2).

P16L447: “We find that overall, disease and control non-disease genes have experienced similar rates of protein adaptation during millions of years of human evolution, as shown by very similar estimated proportions of amino acid changes that were adaptive (Figure 5A,B,C,D,E). This result suggests that disease genes do not have constitutively less adaptive mutations. This implies that processes stable over evolutionary time such as pleiotropy, or a tendency to overshoot the fitness optimum, are unlikely to explain the sweep deficit at disease genes.”.

A last parting thought is that it's not clear to me that the authors have excluded the hypothesis that adaptive variants simply arise less often near genes associated with disease. The fact that the signal is strongest in regions of low recombination is meant to be evidence in favor of selective interference as the explanation, but it is also the regime in which sweeps should be easiest to detect, so it may be just that the analysis is best powered to detect a difference in sweep initiation, independent of possible interference dynamics, in that regime.

We thank the reviewer for stating these important alternative explanations that needed more attention in our manuscript. In our response to main point 2 above, we explain that higher statistical power in low recombination regions is unlikely to explain our results alone, because we also show that the sweep deficit is substantially present not only in low recombination regions, but also requires the presence of a higher number of disease variants. We also describe in our response to main point 2 how our new MK-test results on long-term adaptation make it very unlikely that mendelian disease genes experience constitutively less adaptation. We want to thank the reviewer again for pointing out this issue with our manuscript, since it was indeed an important missing piece.

Author Response

Reviewer #2 (Public Review):

(1) Much of the cited literature that is used to make the case for their hypothesis is very old and actually refers to active HIV infection and patient studies prior to ART. Also, the literature they cite regarding the role of H2S as an antimicrobial agent seem to be limited to tuberculosis infection.

We have revised the list of literature and included more relevant references post- ART era. Recently, the antimicrobial role of H2S is comprehensively examined in the context of tuberculosis. Given the close association of TB with HIV, we thought our study is very timely and essential. However, we would like to point out that the references showing the effect of H2S on infection caused by respiratory viruses are included in the manuscript (7-9). Further, recent findings showing the influence of H2S in the context of SARS-CoV2 infection are also included in the revised manuscript

(2) The choice of the latently infected model cell lines is rather unfortunate. There are much better defined models out there these days than J1.1 or U1 cells, such as the J-LAT cells from the Verdin lab or the various reporter cell lines generated by Levy and co-workers. In particularly, U1 cells should not be considered as latently infected, as the virus has a defect in the Tat/TAR axis and is mostly just transcriptionally attenuated. It is unclear why the authors only use J-LAT cells for one of the last experiments

As suggested by the reviewer, we have generated new data using J-LAT cells in the revised manuscript. First, we confirmed that PMA-mediated HIV-1 reactivation in J-LAT cells is associated with the down-regulation of cbs, cth, and mpst transcripts (Figure 1-figure supplement 1C-D in the revised manuscript). Additionally, we have performed several other mechanistic experiments in J-LAT cells to validate the data generated in U1 (see below response to # 3).

(3) It is further unclear why the authors perform most of the experiments using U1 cells, which are considered promonocytic, but in the end seek to demonstrate the influence of H2S on latent HIV-1 infection in CD4 T cells. Performing all experiments in J1.1 or better J-LAT cells would have seemed more intuitive.

The choice of U1 was based on our earlier studies showing that U1 cells uniformly recapitulate the association of redox-based mechanisms and mitochondrial bioenergetics with HIV-latency and reactivation (10-12). We have validated key findings of U1 cells in J1.1 and J-Lat cell lines. We genetically and chemically silenced the expression of CTH in J-Lat cells and examined the effect on HIV-1 reactivation. Consistent with U1 and J1.1, genetic silencing of CTH using CTH-specific shRNA (shCTH) reactivated HIV-1 in J-Lat (Figure 2-figure supplement 1F-G in the revised manuscript). Supporting this, pre-treatment of J-Lat with non-toxic concentrations of a well-established CTH inhibitor, propargylglycine (PAG) further stimulated PMA-induced HIV-1 reactivation (Figure 2-figure supplement 1H-I in the revised manuscript). Altogether, using various cell line models of HIV-1 latency, we confirmed that endogenous H2S biogenesis counteracts HIV-1 reactivation.

(4) The authors suggest that H2S production would control latent HIV-1 infection and reactivation. Regarding the idea that CBS, CTH or possibly MPST would control latent infection as a function of their ability to produce H2S from different sources, there are several questions. First, if H2S is the primary factor, why would the presence of e.g. MPST not compensate for the reduction of CTH? Second, why would J1.1 and U1 cells both host latent HIV-1 infection events, however, their CBS/CTH/MPST composition is completely different? Third, natural variations in CTH expression caused by culture over time are larger than variations caused by PMA activation.

These questions are important and complex. CBS, CTH, and MPST produce H2S in the sulfur network. CBS and CTH reside in the cytoplasm, whereas MPST is mainly involved in cysteine catabolism and is mitochondrial localized. The lack of compensation of CTH by MPST could be due to the compartmentalization of their activities. Furthermore, CTH and CBS activities are regulated by diverse metabolites, including heme, S-adenosyl methionine (SAM), and nitric oxide/carbon monoxide (NO/CO). In contrast, MPST activity responds to cysteine availability. How substrates/cofactors availability and enzyme choices are regulated in the cellular milieu of J1.1 and U1 is an interesting question for future experimentation.

Moreover, the tissue-specific expression/activity of CBS and CTH dictates their relative contributions in H2S biogenesis and cellular physiology (13). Some of these factors are likely responsible for differential expression of CBS, CTH, and MPST in J1.1 and U1 cells. Regardless of these concerns, viral reactivation uniformly reduces the expression of CTH in U1, J1.1, and J-Lat. While we cannot completely rule out natural variations in CTH expression over prolonged culturing, in our experimental setup CTH remained stably expressed and consistently showed down-regulation upon PMA treatment as compared to untreated conditions.

(5) Also, the statement that H2S production as exerted per loss of CTH would control reactivation is not supported by the kinetic data. In latently HIV-1 infected T cell lines or monocytic cell lines, PMA-mediated HIV-1 reactivation at the protein level is usually almost complete after 24 hours, but at this time point the difference between e.g. CTH levels only begins to appear in U1 cells. The data for J1.1. are even less convincing.

We have performed the kinetics of p24 production and CTH in U1 cells. We showed that the levels of p24 gradually increased from 6 h and kept on increasing till the last time point, i.e., 36 h post-PMA-treatment (Fig. 2D in the revised manuscript). The p24 ELISA detected a similar kinetics of p24 increase in the cell supernatant (Fig. 2E in the revised manuscript). The CTH levels show reduction at 24 h and 36 h. Based on these data, we report that HIV-1 reactivation is associated with diminished biogenesis of endogenous H2S. We have not made any claims that depletion of CTH precedes HIV reactivation. However, our CTH knockdown data clearly showed that diminished expression of CTH reactivates HIV-1 in the absence of PMA, which is consistent with our hypothesis that H2S production is likely to be a critical host component for maintaining viral latency.

(6) Figure 2F. PMA is known to induce an oxidative stress response, however, in the experiments the data suggest that PMA results in a downregulated oxidative stress response. Maybe the authors could explain this discrepancy with the literature. In fact, both shRNA transductions, scr and CTH-specific seem to result in a lower PMA response.

In our experiment, PMA treatment for 24 h results in down-regulation of oxidative stress genes. However, the effect of PMA on the oxidative stress responsive genes is time-dependent. In our earlier publication, we showed that 12 h PMA treatment induces oxidative stress responsive genes in U1 cells (12), whereas at 24 h, the expression of genes is down-regulated (10). Genetic silencing of CTH resulted in elevated mitochondrial ROS and GSH imbalance, which is in line with a further decrease in the expression of oxidative stress responsive genes as compared to PMA alone. As a consequence, PMA-treatment of U1-shCTH induced HIV-1 reactivation, which supersedes that stimulated by PMA or shCTH alone.

(7) Given that the others in subsequent experiments use GYY4137, which is supposed to mimic the increased release of H2S, the authors should have definitely included experiments in which they would overexpress CTH, e.g. by retroviral transduction. Specifically in U1 cells, which seemingly do not express CBS, overexpression of CBS should also result in a suppressed phenotype

We have explored the role of elevated H2S levels using GY44137. Treatment with GYY4137 suppressed HIV reactivation in multiple cell lines and primary CD4+ T cells. As suggested by the reviewer, overexpression of CTH could be another strategy to validate these findings. However, since the transsulfuration pathway and active methyl cycle are interconnected and share metabolic intermediates (e.g., homocysteine), overexpression of CTH could disturb this balance and may lead to metabolic paralysis. Owing to these potential limitations, we used a slow releasing H2S donor (GYY4137) to chemically complement CTH deficiency during HIV reactivation. We thank the reviewer for this comment.

(8) Figure 4F: The authors need to explain how they can measure a 4-fold gag RNA expression change in untreated cells. Also, according to Figure 4A, 300 µM GYY produces much less H2S than 5mM, yet the suppressive effect of 300 µM GYY is much higher?

The four-fold-expression in untreated cells is likely due to leaky control of viral transcription in J1.1 cells (14-16). However, to avoid confusion, we have replotted the results by normalizing the data generated upon PMA mediated HIV reactivation with the PMA untreated cells in the revised manuscript (Figure 4F in the revised manuscript). The suppressive effect of GYY4137 at the lower concentration is intriguing but consistent with the findings that high and low concentrations of H2S have profound and distinct effects on cellular physiology (3,17). One possibility is that the high concentration of H2S induces mitochondrial sulfide oxidation pathway to avert toxicity. This might modulate mitochondrial activity and ROS, resulting in the suppression of GYY4137 effect. Consistent with this, higher concentrations of H2S have been shown to cause pro-oxidant effects, DNA damage and genotoxicity (3,18). We have discussed these possibilities in the revised manuscript

(9) Initially, the authors argue "that the depletion of CTH could contribute to redox imbalance and mitochondrial dysfunction to promote HIV-1 reactivation"(p. 9). Less CTH would suggest less produced H2S. However, later on in the manuscript they demonstrate that addition of a H2S source (GYY4137) results in the suppression of HIV-1 replication and supposedly HIV-1 reactivation. This is somewhat confusing.

We show that depletion of endogenous H2S by diminished expression of CTH (U1-shCTH) resulted in higher mitochondrial ROS and GSH/GSSG imbalance. Both of these alterations are known to reactivate HIV-1 and promote replication (10,11,19). The addition of GYY4137 chemically compensated for the diminished expression of CTH, and prevented HIV-1 reactivation in U1-shCTH. These events are expected to suppress HIV-1 replication and reactivation. We have made this distinction clear in the revised manuscript.

(10) CTH, or for that matter CBS or MPST do not only produce H2S, however, they also are part of other metabolic pathways. It would have been interesting and important to study how these metabolic pathways were affected by the genetic manipulations and also how the increased presence of H2S (GYY4137) would affect the metabolic activity of these enzymes or their expression.

We fully agree with the reviewer. In fact, our NanoString data show that upon CTH knockdown (U1-shCTH), MPST levels were down-regulated and CBS remained undetectable (Fig. 2F in the revised manuscript). Additionally, GYY4137 treatment induced the expression of CTH but not MPST upon PMA addition (Fig. 5A in the revised manuscript). We have incorporated these findings in the revised manuscript. Given that CBS and CTH catalyzed at least eight H2S generating steps and two cysteine-producing reactions, the modulation of CTH by HIV is likely to have a widespread influence on transsulfuration pathway and active methyl cycle intermediates. Our future strategies are to generate a comprehensive understanding of sulfur metabolism underlying HIV latency and reactivation. These experiments require multiple biochemical and genetic technologies with appropriate controls. We hope that the reviewer would agree with our views that these experiments should be a part of future investigation. We thank the reviewer for this comment.

(11) H2S has been reported to cause NFkB inhibition by sulfhydration of p65; as such, the findings here are not particularly novel or surprising. Also, H2S induced sulfhydration is rather not targeted to a specific protein, let alone a HIV protein, making this approach a very unlikely alternative to current ART forms.

We believe that NF-kB inhibition is not the only mechanism by which H2S exerts its influence on HIV latency. Recent studies point towards the importance of the Nrf2-Keap1 axis in sustaining HIV-latency (20). Our data suggest an important role for Nrf2-Keap1 signaling in mediating the influence of H2S on HIV latency. Additionally, recruitment of an epigenetic silencer YY1 is also affected by H2S. Interestingly, YY1 activity is modulated by redox signaling (21), suggesting H2S could be an important regulator of YY1 activity in HIV-infected cells. We have so far, no evidence for viral proteins targeted by H2S. However, experiments to examine global S-persulfidation of host and HIV protein are ongoing in the laboratory to fill this knowledge gap. Lastly, our findings raise the possibility of exploring H2S donors with the current ART (not as an alternate to ART) for reducing virus reactivation. We have tone down the clinical relevance of our findings.

(12) The description of the primary T cell model used to generate the data in Figure 6 is slightly misleading. Also, the idea of this model was originally to demonstrate that "block and lock" by didehydro-cortistatin is possible. In this application, the authors did not investigate whether GYY4137 would actually induce a HIV "block and lock" over an extended period of time.

As suggested by the reviewer, we have cited the didehydro-cortistatin studies as the basis of our strategy. Our idea was to adapt the primary T cell model to begin understanding the role of H2S in blocking HIV rebound. Our results indicate the future possibility of investigating GYY4137 to lock HIV in deep latency for an extended period of time. However, comprehensive investigation would require long-term experiments and samples from multiple HIV subjects. In the current pandemic times with overburdened Indian clinical settings, we cannot plan these experiments. However, we hope our data form a solid foundation for HIV researchers to perform extended “block and lock” studies using H2S donors.

(13) However, the authors never provide evidence that endogenous H2S is altered in latently HIV-1 infected cells (which may actually be an impossible task). By the end of the manuscript, the authors have not provided clear evidence that the effects of e.g. CTH deletion would be mediated by the production of H2S, and not by another function of the enzyme. Similarly, the inability of stimuli to trigger efficient HIV-1 reactivation following the provision of unnaturally high levels of H2S is not surprising given reports on the effect of GYY4137 as anti-inflammatory agent and suppressor NF-kB activation. Unless the authors were to demonstrate a true "block and lock" effect by GYY4137 the data will likely have limited impact on the HIV cure field.

It's difficult to measure H2S levels in the latently infected primary cells due to the assay's sensitivity and the insufficient number of cells latently infected with HIV-1. However, in the revised manuscript we have clearly shown that cysteine levels are not affected by CTH depletion and cysteine deprivation does not reactivate HIV-1. These results indicate that the effects of CTH depletion are likely mediated by H2S. This is consistent with our data showing that GYY4137 specifically complement CTH deficiency and blocks HIV-1 reactivation in U1-shCTH. Further, we carried in-depth investigation to show that the effect of GYY4137 is not due to impaired activation of CD4+ T cells.

Lastly, since CTH catalyzed multiple reactions during H2S production, we cannot rule out the effect of other metabolites in this process. However, we think that this is outside the scope of the present study. Our study focuses on understanding of how H2S modulates redox, mitochondrial bioenergetics, and gene expression in the context of HIV latency. These understandings are likely to positively impact future studies exploring the role of H2S on HIV cure.

Author Response

Reviewer #1 (Public Review):

The authors sought to establish a standardized quantitative approach to categorize the activity patterns in a central pattern generator (specifically, the well-studied pyloric circuit in C. borealis). While it is easy to describe these patterns under "normal" conditions, this circuit displays a wide range of irregular behaviors under experimental perturbations. Characterizing and cataloguing these irregular behaviors is of interest to understand how the network avoids these dysfunctional patterns under "normal" circumstances.

The authors draw upon established machine learning tools to approach this problem. To do so, they must define a set of features that describe circuit activity at a moment in time. They use the distribution of inter-spike-intervals ISIs and spike phases of the LP and PD neuron as these features. As the authors mention in their Discussion section, these features are highly specialized and adapted to this particular circuit. This limits the applicability of their approach to other circuits with neurons that are unidentifiable or very large in number (the number of spike phase statistics grows quadratically with the number of neurons).

We agree with the reviewer that the size of the feature vectors as described grows quadratically with the number of neurons. The feature sets we describe are most suited for “identified” neurons – neurons whose identity and connectivity are known and can be reliably recorded from multiple animals. The method described here is best suited for systems with small numbers of identified neurons. For other systems, other feature vectors may be chosen, as we have suggested in the Discussion: Applicability to other systems.

The main results of the paper provide evidence that ISIs and spike phase statistics provide a reasonable descriptive starting point for understanding the diversity of pyloric circuit patterns. The authors rely heavily on t-distributed stochastic neighbor embedding (tSNE), a well-known nonlinear dimensionality reduction method, to visualize activity patterns in a low-dimensional, 2D space. While effective, the outputs of tSNE have to be interpreted with great care (Wattenberg, et al., "How to Use t-SNE Effectively", Distill, 2016. http://doi.org/10.23915/distill.00002). I think the conclusions of this paper would be strengthened if additional machine learning models were applied to the ISI and spike phase features, and if those additional models validated the qualitative results shown by tSNE. For example, tSNE itself is not a clustering method, so applying clustering methods directly to the high-dimensional data features would be a useful validation of the apparent low-dimensional clusters shown in the figures.

We thank the reviewer for these suggestions, and agree with the reviewer that t-SNE is not a clustering method, and directly clustering on t-SNE embeddings is rife with complexities. Instead we have used t-SNE to generate a visualization that allows domain experts to quickly label and cluster large quantities of data. This makes a previously intractable task feasible, and offers some basic guarantees on quality (e.g., no one data point can have two labels, because labels derive from position of data points in two dimensional space). In addition:

• We used uMAP, another dimensionality reduction algorithm, to perform the embedding step, and colored points by the original t-SNE embedding. (Figure 3—figure supplement 3). Large sections of the map are still strikingly colored in single colors, suggesting that the manual clustering did not depend on the details of the t-SNE algorithm, but is rather informed by the statistics of the data.

• We validated our method using synthetic data. We generated synthetic spike trains from different “classes” and embedded the resultant feature vectors using t-SNE. Data from different classes are not intermingled, and form tight “clusters” (Figure 2 -- figure supplement 4).

• Finally, we attempted to use hierarchical clustering to cluster the raw feature vectors, and were not able to find a reasonable portioning of the linkage tree that separated qualitatively different spike patterns (Figure at the top of this document). We speculate that this is because feature vectors may contain outliers that bias clustering algorithms that attempt to preserve global distance to lump the majority of the data into a single cluster, in order to differentiate outliers from the bulk of the data.

The authors do show that the algorithmically defined clusters agree with expert-defined clusters. (Or, at least, they show that one can come up with reasonable post-hoc explanations and interpretations of each cluster). The very large cluster of "regular" patterns -- shown typically in a shade of blue -- actually looks like an archipelago of smaller clusters that the authors have reasoned should be lumped together. Thus, while the approach is still a useful data-driven tool, a non-trivial amount of expert knowledge is baked into the results. A central challenge in this line of research is to understand how sensitive the outcomes are to these modeling choices, and there is unlikely to be a definitive answer.

We agree with the reviewer entirely.

Nonetheless, the authors show results which suggest that this analysis framework may be useful for the community of researchers studying central pattern generators. They use their method to qualitatively characterize a variety of network perturbations -- temperature changes, pH changes, decentralization, etc.

In some cases it is difficult to understand the level of certainty in these qualitative observations. A first look at Figure 5a suggests that three different kinds of perturbations push the circuit activity into different dysfunctional cluster regions. However, the apparent spatial differences between these three groups of perturbations might be due to animal-level differences (i.e. each preparation produces multiple points in the low-D plot, so the number of effective statistical replicates is smaller than it appears at first glance). Similarly, in Figure 9, it is somewhat hard to understand how much the state occupancy plots would change if more animals were collected -- with the exception of proctolin, there are ~25 animals and 12 circuit activity clusters which may not be a favorable ratio. It would be useful if a principled method for computing "error bars" on these occupancy diagrams could be developed. Similar "error bars" on the state transition diagrams (e.g. Fig 6a) would also be useful.

We agree with the reviewer. Despite this paper containing data from hundreds of animals, the dataset may not be sufficiently large to perform some necessary statistical checks. We agree with the reviewer that a more rigorous error analysis would be useful, but is not trivially done.

Finally, one nagging concern that I have is that the ISIs and spike phase statistics aren't the ideal features one would use to classify pyloric circuit behaviors. Sub-threshold dynamics are incredibly important for this circuit (e.g. due to electrical coupling of many neurons). A deeper discussion about what is potentially lost by only having access to the spikes would be useful.

We agree with the reviewer that spike times aren’t the ideal feature to use to describe circuit dynamics. This is especially true in the STG, where synapses are graded, and coupling between cells can persist without spiking. However, the data required simply do not exist, as it requires intracellular recordings, which are substantially harder to perform (and maintain over challenging perturbations) than extracellular recordings.

Finally, the signal to the muscles – arguably the physiologically and functionally relevant signal – is the spike signal, suggesting that spike patterns from the pyloric circuit are a useful feature to measure. Nevertheless, this is an important point, and we thank the reviewer for raising it, and we have included it in the section titled Discussion: Technical considerations.

Overall, I think this work provides a useful starting point for large-scale quantitative analysis of CPG circuit behaviors, but there are many additional hurdles to be overcome.

Reviewer #2 (Public Review):

This manuscript uses the t-SNE dimensionality reduction technique to capture the rich dynamics of the pyloric circuit of the crab.

Strengths:

• The integration of a rich data-set of spiking data from the pyloric circuit

• Use of nonlinear dimension reduction (t-SNE) to visualise that data

• Use of clusters from that t-SNE visualisation to create subsets of data that are amenable to consistent analyses (such as using the "regular" cluster as a basis for surveying the types of dynamics possible in baseline conditions)

• Innovative use of the cluster types to describe transitions between dynamics within the baseline state and within perturbed states (whether by changes to exogenous variables, cutting nerves, or applying neuromodulators)

• Some interesting main results: o Baseline variability in the spiking patterns of the pyloric circuit is greater within than between animals

o Transitions to silent states often (always?) pass through the same intermediate state of the LP neuron skipping spikes

Weaknesses:

• t-SNE is not, in isolation, a clustering algorithm, yet here it is treated as such. How the clusters were identified is unclear: the manuscript mentions manual curation of randomly sampled points, implying that the clusters were extrapolations from these. This would seem to rather defeat the point of using unsupervised techniques to obtain an unbiased survey of the spiking dynamics, and raises the issue of how robust the clusters are

We have used t-SNE to visualize the circuit dynamics in a two-dimensional map. We have exploited t-SNE’s ability to preserve local structure to generate an embedding where a domain expert can efficiently manually identify and label stereotyped clusters of activity. As the author points out, this is a manual step, and we have emphasized this in the manuscript. The strength of our approach is to combine the power of a nonlinear dimensionality reduction technique such as t-SNE with human curation to make a task that was previously impossible (identifying and labelling very large datasets of neural activity) feasible.

To address the question of how robust the manually identified clusters are, we have:

1) used another dimensionality reduction technique, uMAP, to generate an embedding and colored points by the original t-SNE map (Figure 3 – figure supplement 3). To rough approximation, the coloring reveals that a similar clustering exists in this uMAP embedding.

2) We generated synthetic spike trains from pre-determined spike pattern classes and used the feature vector extraction and t-SNE embedding procedure as described in the paper. We found that this generated a map (Figure 2—figure supplement 4) where classes of spike patterns were well separated in the t-SNE space.

• the main purpose and contribution of the paper is unclear, as the results are descriptive, and mostly state that dynamics in some vary between different states of the circuit; while the collated dataset is a wonderful resource, and the map is no doubt useful for the lab to place in context what they are looking at, it is not clear what we learn about the pyloric circuit, or more widely about the dynamical repertoire of neural circuits

• in some places the contribution is noted as being the pipeline of analysis: unfortunately as the pipeline used here seems to rely in manual curation, it is of limited general use; moreover, there are already a number of previous works that use unsupervised machine-learning pipelines to characterise the complexity of spiking activity across a large data-set of neurons, using the same general approach here (quantify properties of spiking as a vector; map/cluster using dimension reduction), including Baden et al (2016, Nature), Bruno et al (2015, Neuron), Frady et al (2016, Neural Computation).

• Some key limitations are not considered:

o the omission of the PY neuron activity means that the map as given is incomplete: potentially there are many more states, and hence transitions, within or beyond those already found that correspond to changes in PY neuron activity

We agree with the reviewer that the omission of the PY neurons’ activity means that the map is incomplete. There are likely many more states, and hence many more transitions, than the ones we have identified. In addition, we note that there are other pyloric neurons whose activity is also missing (AB, IC, LPG, VD). However, measuring just LP and PD allows us to monitor the activity of the most important functional antagonists in the system (because they are effectively in a half-center oscillator because PD is electrically coupled to AB). In general, the more neurons one measures, the richer the description of the circuit dynamics will be. Collecting datasets at this scale (~500 animals) from all pyloric neurons is challenging, and we have revised the manuscript to make this important point (see Discussion: Technical considerations).

o The use of long, non-overlapping time segments (20s) - this means, for example, that the transitions are slow and discrete, whereas in reality they may be abrupt, or continuous.

We agree with the reviewer. There are tradeoffs in choosing a bin size in analyzing time series – choosing longer bins can increase the number of “states” and choosing shorter bins can increase the number of transitions. We chose 20s bins because it is long enough to include several cycles of the pyloric rhythm, even when decentralized, yet was short enough to resolve slow changes in spiking. We have included a statement clarifying this (see Discussion: Technical considerations).

o tSNE cannot capture hierarchical structure, nor has a null model to demonstrate that the underlying data contains some clustering structure. So, for example, distances measured on the map may not be strictly meaningful if the data is hierarchical.

We agree with the reviewer. t-SNE can manifest clusters when none exist (Section 4 of https://distill.pub/2016/misread-tsne/) and can obscure or merge true clusters. We have restricted analyses that rely on distances measured in the map to cases where there are qualitative differences in behavior (e.g., with decentralization, Fig 7) or have compared distances within subsets of data where a single parameter is changed (e.g., pH or temperature, Fig 5). The only conclusion we draw from these distance measures is that data are more (or less) spread out in the map, which we use as a proxy for variability. We have included a statement discussion limitations of using t-SNE (Discussion: Comparison with other methods).

• the Discussion does not include enough insight and contextualisation of the results.

We have completely rewritten the discussion to address this.

Reviewer #3 (Public Review):

Gorur-Shandilya et al. apply an unsupervised dimensionality reduction (t-SNE) to characterize neural spiking dynamics in the pyloric circuit in the stomatogastric ganglion of the crab. The application of unsupervised methods to characterize qualitatively distinct regimes of spiking neural circuits is very interesting and novel, and the manuscript provides a comprehensive demonstration of its utility by analyzing dynamical variability in function and dysfunction in an important rhythm-generating circuit. The system is highly tractable with small numbers of neurons, and the study here provides an important new characterization of the system that can be used to further understand the mapping between gene expression, circuit activity, and functional regimes. The explicit note about the importance of visualization and manual labeling was also nice, since this is often brushed under the rug in other studies.

Major concern:

While the specific analysis pipeline clearly identifies qualitatively distinct regimes of spike patterns in the LP/PD neurons, it is not clear how much of this is due to t-SNE itself vs the initial pre-processing and feature definition (ISI and spike phase percentiles). Analyses that would help clarify this would be to check whether the same clusters emerge after (1) applying ordinary PCA to the feature vectors and plotting the projections of the data along the first two PCs, or (2) defining input features as the concatenated binned spike rates over time of the LP & PD neurons (which would also yield a fixed-length vector per 20 s trial), and then passing these inputs to PCA or tSNE. As the significance of this work is largely motivated by using unsupervised vs ad hoc descriptors of circuit dynamics, it will be important to clarify how much of the results derive from the use of ISI and phase representation percentiles, etc. as input features, vs how much emerge from the dimensionality reduction.

We agree with the reviewer that is important to clarify how much of our results come from the data itself, and how we parameterize them using ISIs and phases, and how much comes from the choice of t-SNE as a dimensionality reduction algorithm. We have addressed this concern in the following ways:

1. We used principal components analysis on the feature vectors and measured triadic differences in features such as the period and duty cycle of the PD neuron. We found that triadic differences were lower in the t-SNE embedding than in the first two PCA features, or in shuffled t-SNE embeddings (Figure 2– Figure supplement 2), suggesting that the embedding is creating a useful representation that captures key features of the data.

2. We have used uMAP to reduce the dimensionality of the feature matrix to two dimensions and found that it too preserved the coarse features of the embedding that we observe with t-SNE. Coloring the uMAP embedding by the t-SNE labels revealed that the overall classification scheme was intact (Fig 3 – figure supplement 3).

3. We generated a synthetic dataset and applied the unsupervised part of our algorithm to it (conversion to ISIs, phases, etc., then t-SNE). We colored the points in the t-SNE embedding by the category in the synthetic dataset. We found that categories were well separated in the t-SNE plot, and each cluster tended to have a single color. This validates the overall power of our approach and shows that it can recover clustering information in large spike sets (Figure 2—figure supplement 4).

4. We have run k-means and hierarchical clustering on the feature vectors directly and shown that our method is superior to these naïve clustering algorithms running on the feature vectors. We speculate that this is because these clustering methods attempt to partition the full space using global distances, at the expense of distance along the manifold on which the data is located. Algorithms like t-SNE are biased towards local distances, and discount global distances between points outside a neighborhood, and are this better suited here.

Author Response

Reviewer 1

Panda and co-workers analyzed RS fMRI recordings from healthy patients and from two types of coma: UWS and MCS. They characterized the time-resolved functional connectivity in terms of metastability (time-variance of the Kuramoto order parameter), spatiotemporal patterns via non-negative tensor factorization, and its relationship to the eigenmodes of structural connectivity. Finding greater metastability and non-stationarity of the DMN network in healthy MCS patients, than in UWS patients, they found that the best discriminators to classify the different DoCs are the number of excursions (nonstability) from the DMN, salience and FPN networks extracted by the NNTF analysis. Interestingly, the data-driven NNTF yielded a novel sub-network comprising the FPN and some subcortical structures. The excursions and dwell times from this FPN subnetwork showed to be significantly lower in the UWS patients than in MCS. Surrogate data testing assures that the different methods and fits are effectively expressing the functional connectivity matrices measured.

Overall, I think that the results are correct and they advance in the characterization and understanding of the brain under DoC. However, some improvements can be made in the way the results, and the rationale behind them, are presented.

We thank Prof. Patricio Orio for his assessment.

While reading the Results section, it is easy to have the impression of a disconnected set of analyses that just happened to be together. In particular, the section about the structural eigenmodes and their relationship with the time-resolved FC seems to have little connection with the rest of the work, except for confirming (yet again) that DoC patients have a less dynamic FC. More elaboration about the relevance of these results, and what they say about DoC (that other dynamical FC analyses don't), is needed both in the introduction and discussion. Although a clear explanation is given in the introduction, the bottom line seems to be yet another measure of metastability. Perhaps, a better explanation of what underlies the 'modulation strength of eigenmodes expression' will be helpful for distinguishing this analysis from others. How novel is the connection that is being done with the structural connectivity and why is this important? Moreover, the eigenmodes analysis has little-to-none importance in the discrimination of patients done at the end; thus, its place within the big picture is hard to evaluate.

We understand the reviewer’s position. Part one of our work covers time-resolved FC and spatiotemporal networks in DoC. Part two covers the relationship between timeresolved FC and eigenmodes of the structural network. The rationale for including part two is the following: there is a lot of literature that shows that eigenmodes of the structural network can be considered as ‘building blocks’ or basis functions/vectors for spatiotemporal networks at the functional level (Aqil et al., 2021; Atasoy et al., 2016, 2018; Deslauriers-Gauthier et al., 2020; Gabay et al., 2018; Gabay and Robinson, 2017; Robinson et al., 2016; Robinson, 2021; Tewarie et al., 2019, 2020; Wang et al., 2017). Ideally to link part one and two, you would take this notion further by analysing if the magnitude eigenmode coefficients differed between UWS, MCS and healthy controls and how this would relate to dwell times or expression of spatiotemporal networks. However, this would lead to an immense multiple testing issue, which would be impossible to overcome with our sample size. An important link between part one and two of our work is the relationship between change in eigenmode expression and metastability. Our measure for metastability is only a proxy for metastability. Lack of change in eigenmode expressions seems to confirm this result of metastability.

To allow for better integration of part one and two of our work, we have added to the introduction:

“These eigenmodes can be considered as patterns of ‘hidden connectivity’ that come to expression at the level of functional networks. It has been postulated that eigenmodes form elementary building blocks for spatiotemporal dynamics (Aqil et al., 2021). There is evidence that the well-known resting state networks can be explained by activation of a small set of eigenmodes (Atasoy et al., 2018).”

We have also clarified in the result section:

“As resting-state network activity can be explained by activation of structural eigenmodes, we next analyse the role of fluctuations in eigenmode expression over time.”

Something that I find counter-intuitive and that may confuse some readers, is the (apparent) contradiction between the diminished metastability in the DoC conditions and the reduced dwell times (Figure S1; also "the inability to sequentially dwell for prolonged times in a different set of eigenmodes", as stated in the Discussion). Fewer excursions and shorter dwell times can only mean that some networks are just less visited and maybe this would be enough to distinguish between conditions. Further explaining this will help to understand better the implications of the work.

We understand the reviewer’s point, however we disagree that diminished metastability is in contradiction with the findings on dwell times. We show that dwell times are reduced in the posterior DMN, FPN and sub-FPTN networks, however, there is very long dwelling in the residual network in DoC. Hence, the brain resides in fewer network states in DoC, which is in agreement with reduced metastability. Our proxy for metastability is the standard deviation of the Kuramoto order parameter. Whenever there are more visits to network states, or switching between network states as is the case for healthy controls in our data, this would lead to phase uncoupling followed by phase synchronization, which would hence boost the standard deviation of the Kuramoto order parameter (a proxy for metastability).

We agree with the reviewer that the sentence starting “the inability to sequentially dwell for prolonged….” Is confusing. We have now removed this statement.

We have now added to the result section:

“These findings of very short dwell times in the posterior DMN, FPN and sub-FPTN and long dwell time in the residual network can be considered as a contraction of the functional network repertoire in DoC, which is in agreement with a loss in metastability in these patients.”

Finally, some comments about the connection(s) of these analyses with the commonly used FCD analysis (based on sliding windows of pair-wise correlations) will be useful, to put better this work into the big picture of time evolution of the functional connectivity.

We have now discussed sliding window-based analysis in the context of our work in the methodology section.

“Lastly, we have used a high temporal resolution method to estimate time-resolved connectivity at every time point instead of a sliding window-based method. Previous studies using sliding window approaches have provided novel insights into brain dynamics of loss of consciousness, such as the brain co-occurrence of functional connectivity patterns, which is known as brain states and its temporal (i.e., rate of pattern occurrence (probability) and between pattern transition probabilities) alteration in loss of consciousness in DoC patients (Demertzi et al., 2019) and anaesthesia induced loss of consciousness (Barttfeld et al., 2014a; Uhrig et al., 2018). However, sliding window approaches have limited sensitivity to non-stationarity in the fMRI BOLD signals (Hindriks et al., 2016) and lack to provide spatial alteration of classical brain functional network. The exploration of the spatiotemporal aspects of well-known resting state networks is an important step forwards for better understanding the relation between brain function and consciousness, in a way that is impossible to achieve at the whole brain level. In addition, recent work on time-resolved connectivity shows that brief periods of co-modulation in BOLD signals are an important driving factor for functional connectivity (Esfahlani et al., 2020; Hindriks et al., 2016).”

Reviewer 2

The study is of high significance, rigor, and novelty. Despite the many studies of repertoire, dynamic connectivity, etc., in the study of consciousness, there is (surprisingly, as I confirmed with a literature search) a dearth of application of these approaches to disorders of consciousness. The manuscript is well-written and transparent about its limitations. The author should consider the following recommendations:

We thank the reviewer for his/her assessment of our work.

1) There is frequent reference to "subcortical" and related networks, but I see no description in the text of which subcortical structures are involved. Panel N of figure 2 is helpful but I think that more explicit detail is important, especially given the specific predictions of mesocircuit theory.

We have provided details for the subcortical networks presented in the Panel N of Figure 2. In the manuscript we provide a textual description of the brain areas that are part of the network. To improve the clarity of the description of the network, we also now refer to it as “subcortical fronto-temporoparietal (Sub-FTPN)”.

In the result section, it read as: “This modulated subcortical fronto-temporoparietal network consist of the following brain regions: bilateral thalamus, caudate, right putamen, bilateral anterior and middle cingulate, inferior and middle frontal areas, supplementary motor cortex, middle and inferior temporal gyrus, right superior temporal, bilateral inferior parietal and supramarginal gyrus.”

2) Similarly, although the global neuronal workspace does posit a critical role for recurrent frontal-parietal networks, can the authors be more specific about the nodes of the proposed workspace and what they found empirically?

As above mentioned, we have provided more details about the regions part of the “subcortical fronto-temporoparietal”. As the reviewers rightfully noted, this network also shows some overlap with the Global Neuronal Workspace. We refer to that in more detail in the discussion, highlighting how our functional networks overlap and differ with the two networks (i.e., one feedforward only, one with recurrent activity), and with the predictions of the mesocircuit model. For more detail, please refer to the reply to point 1 of “Recommendations for the authors”.

3) The classification sensitivity/specificity did not, in my opinion, add much to the manuscript, especially since the number of patients is not remotely close to what would be required for a population-based diagnostic approach. If the authors chose to include this with any reference to diagnosis (highlighted in the introduction and elsewhere), I would encourage a comparison with similar data from other clinical or neuroimagingbased diagnostic approaches. However, I think the value of the study resides more with mechanistic understanding than diagnosis.

We agree with your suggestions that the primary aim of our work is to provide a mechanistic understanding of loss of consciousness. Therefore, we have removed the classification part from the paper and explain our findings focusing on mechanism of pathological unconsciousness rather than its potential as a clinical diagnostic tool. This change has required several textual edits throughout the manuscript.

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Author Response

Reviewer #1 (Public Review):

This manuscript investigates a role for YAP in replication. Previous work from this group has shown that Yap knock-down leads to accelerated S-phase and an abnormal progression of DNA replication in the frog eye. Here they extend this to show that YAP depletion accelerates S-phase and DNA replication in the frog embryo, and that YAP binds a DNA replication regulator called Rif1. Combing assays suggest that YAP acts on origin firing. This is an interesting new aspect of YAP function. I am not an expert on DNA replication, however, I feel that the manuscript would have been improved if more mechanistic insight was gained into how Rif1 and YAP interact, and how that interaction influences replication timing.

In the revised version of the manuscript, we have strengthened our conclusion that Yap regulates the dynamics of DNA replication. We now provide additional experiments in addition to DNA combing and nascent strand analysis by agarose gel electrophoresis: Rhodamine-dUTP incorporation/nucleus, 32P-dCTP incorporation, western blotting for replication fork proteins. All show that DNA synthesis and origin activation is increased after Yap depletion.

Moreover, in the revised manuscript we also directly compared the effects of YAP depletion to those of Rif1 depletion alone (page 7, New Figure 4). As for Yap depletion, we first quantified rhodaminedUTP incorporation after Rif1 depletion by direct fluorescence microscopy that demonstrated a clear increase of DNA synthesis, consistent with Alver et al. 2017. Second, we performed DNA combing experiments after Rif1 depletion in egg extracts that show a marked increase in DNA replication and fork density like those seen after Yap depletion, spanning from very early to mid S-phase. We therefore found that Rif1 depletion and Yap depletion qualitatively show the same main effects: an increase of DNA synthesis and fork density, that are more pronounced in early S-phase. We also noticed quantitative differences in the direct fluorescence after rhodamine incorporation of whole nuclei and fork density, with stronger effects after Rif1 depletion compared to Yap depletion. This suggests that there might be an additional mechanism for Rif1 in regulating origin activation.

The title of the manuscript is "A non-transcriptional function of YAP orchestrates the DNA replication program". It is not clear that YAP "orchestrates" DNA replication - for this to be true, it would have to be signal responsive. Since the authors did not reveal any links to YAP activity (such as YAP phosphorylation or nuclear/cytoplasmic distribution) it is not "orchestrating" DNA replication.

We have replaced “orchestrates” by “regulates”.

Figure 1 shows that YAP is recruited onto chromatin after MCM2 and MCM7 and at the same time as PCNA and the start of DNA synthesis. Addition of geminin, an inhibitor of Cdt and MCM loading inhibits YAP loading onto chromatin. YAP immuno depletion leads to premature DNA synthesis or replication. Fig 1 B is quite confusing- the labeling in Figure 1B is likely incorrect.

We apologize for this confusion. This has been corrected and the Figure 1B is now properly labelled.

Figure 2 investigates if YAP depletion affects origin firing or fork speed, using DNA combing. Fig 2A shows that there is increased activated replication origins and decreased distance between origins. The authors say that the increase of fork density is more pronounced than the decreased distance, suggesting YAP is regulating the activation of origins. The number of replicates is low. This is especially true for the conclusion that eye length is unaltered -it appears that there is a subset of eye length that is increased in 2F, which might reach significance if triplicates were performed.

As the referee points out, both the observed increase of fork density and decrease of origin distances argues that origin activation is increased after Yap depletion. The fact that the increase of the fork density seems more pronounced than the local decrease of neighbouring origins allows a more detailed interpretation, explicitly that whole clusters of origins are activated on top of origins inside already active clusters. This can be observed in the two independent experiments probing many fibers for eye distances and eyes numbers.

Concerning Figure 2F, the scatter plot makes it look like that the impression that there are more eyes with larger sizes after Yap depletion, but please note that there are also more EL measured as stated in the legend (Mock n=182 versus Yap n=311). To highlight this parameter, we added these numbers below the scatter plot in the revised Figure 2F, as we have done consistently for all of the experiments presented in the revised Figures. The means of the two EL distributions are numerically different but since both distributions are not Gaussian (tested by d'Agostino and Pearson test), only non-parametric tests can apply (Mann-Whitney or Kolmogorov Smirnow test). The results of the two non-parametric tests show that the distributions are not significantly different, as mentioned in the legend. However, we cannot rule out that after Yap depletion some larger eyes may arise from fusions of forks or from a higher fork speed, but again, the tests, applied to a high number of measurements, show no significant statistical differences.

The authors conducted AP-MS on egg extracts to identify proteins that co-IP with YAP. One of many proteins identified was RIF1 Figure 3 shows a co-IP with RIF1 and YAP. It is a very weak co-IP.

We agree that the Rif/Yap co-IP is weak, but it is reproducible in several independent experiments with different extracts. There could be many reasons for this. Co-IPs with a high molecular weight partner like Rif1 (250 kDa) are generally tedious (poor gel migrations and WB transfer). Further, Rif1 has been described as having a subnuclear localisation and to associate with the nuclear lamina and heterochromatin. These characteristics are known to make the proteins highly insoluble. These technical limitations have been reported for the mouse Rif1 for instance (Sukackaite R; et al. Sci Rep 2017 May 18;7(1):2119). In fact, similar “weak co-IPs” were also obtained between Rif1 and Nanog (Wang J. et al. Nature 2006 (444), 364–368 ) as well as with PPI (Hiraga S. et al. EMBO Rep. 2017 Mar;18(3):403-419). Finally, it could also be that this interaction is not permanent but dynamic, making it difficult to capture in a Co-IP. Taken together, these parameters mean that the identification of the interaction is in itself challenging. What we did manage to provide is a reciprocal co-IP using the endogenous proteins, which we believe best reflects native conditions.

Figure 4 shows that YAP levels increase during development and that depletion of YAP or RIF1 leads to increased cell division. The authors use Trim-away to deplete YAP and RIF1 and find that depletion of either leads to an increased number of small cells. The YAP depletion shown in Fig 4B is clear, as is the increased number of small cells in YAP depletion or RIF1 depletion.

Figure 4 supplement 1 is arguing that trim away and morpholino combined are more effective. Quantitation of the western blots in panel A is needed for this to be convincing.

The quantification is now presented in new Figure 5-figure supplement 1A. At the 2-cell stage, we observe some fluctuations in the amounts of Yap between samples, the origin of which we do not fully understand. At the 4-cell stage, a reduction in Yap is observed regardless of the depletion strategy used. It is from the 8-cell stage onwards that differential effects between the depletion methods can be appreciated. From this stage onwards, the quantifications confirm that the TRIM-Away and morpholino combined are more effective than taken separately.

Figure 5 shows that RIF1 is expressed in the eye in RSC and that loss of RIF1 leads to a small eye. Panel B shows that by western blot analysis RIF1 antibody is specific. However, antibodies can have very different abilities in western vs staining. The RIF1 and YAP antibodies should be validated in staining. Also, the staining in Fig5C is at low resolution for both YAP and RIF1 and the identification of foci is unclear.

This is indeed an important issue. To address this point, we performed immunostaining on retinal sections from embryos depleted with the target protein and compared the fluorescent signal obtained in control versus depleted samples. We show that upon depletion of Yap or Rif, the signal from the immunostaining is severely reduced for Yap or Rif1, respectively, which attests the specificity of the antibodies used in this study. We have added an additional supplementary Figure to show this control (Figure 6-figure supplement 1).

We agree with the reviewers that the quality of the images could be improved. We now provide confocal images with a better resolution (Figure 6C).

For Rif1, we observe a clear nuclear staining, rather non-homogenous which is consistent with data reported in the literature. Indeed, Rif1 localisation has been shown to be highly dynamic during the cell cycle and also during S-phase (Cornacchia D. et al. EMBO J. 2012). Some brighter foci could be observed at specific phases (such as G1-phase) but overall, the general pattern appears rather “granular” and restricted to the nucleus. This is what we are also observing. Interestingly, Rif1 does not appear to colocalize with the replication fork or with the replicative helicase MCM3 (Cornacchia D. et al. EMBO J. 2012). The replication foci observed in this study are therefore to be understood independently of the Rif1 localisation pattern.

For Yap, we do not detect any granular expression but observe rather homogeneous nuclear and cytoplasmic staining, which is also consistent with reported data showing YAP nucleo-cytoplasmic shuffling (see for instance Manning S.A. et al. Curr Biol. 2018). STED microscopy might be necessary for higher resolution.

It is difficult to see the points the authors wish to communicate in Figure 6. There is almost no Edu in the YAP-MO, which questions the ability to recognize the different patterns in this region of the eye.

Our observations show that there are fewer EdU positive cells in the Yap-MO but not “no EdU”. The fluorescence intensity in the green-labelled nuclei in Figure 7C after Yap MO does not appear different from that in the control-MO. Under these conditions, there is no reason to think that one pattern is more difficult to recognise than the other one.

Reviewer #2 (Public Review):

This paper is of potential interest within the field of DNA replication, as it identifies a novel role for YAP protein in DNA replication dynamics. However, the conclusions are not supported by properly controlled data. Several aspects of data analysis and representation need to be revised.

In this manuscript, the authors characterized YAP function in the control of DNA replication dynamics, taking advantage of the Xenopus laevis system.

They found that YAP is recruited to replicating-chromatin and showed that its chromatin enrichment depends on the assembly of pre-RC proteins. In addition, they show that the immuno-depletion of YAP leads to increased DNA synthesis and origin activation, revealing YAP's possible role in the regulation of replication dynamics.

The authors were also interested in finding YAP potential partners that could mediate its function. They identified Rif1, a major regulator of replication timing, as a novel YAP interactor during DNA replication.

As RIF1 expression in vivo is restricted to the stem cell compartment of the Xenopus retina, similar to YAP, the authors assessed whether Rif1 could regulate the spatial-temporal program of DNA replication in stem cells. They showed that depletion of Rif1 at early stages of Xenopus embryos development leads to alterations in replication foci of retinal stem cells, resembling the effect observed following YAP down-regulation.

Finally, they studied the impact of YAP and RIF1 down-regulation at early stages of development, showing that their absence results in the acceleration of cell division rate of Xenopus embryos, where RNA transcription is absent. Based on these results they concluded that YAP has a role in S-phase independent from transcription.

The higher rate of DNA synthesis observed in the absence of Yap in Figure 1D is not very evident from the gels in Figure 1, supplement 3B. The timing of the experiments is continuously changing throughout the figures. It is therefore difficult to compare them. Also, comparisons across different gels are difficult to interpret. Most importantly, relative quantification on gel images cannot support the claim of increased DNA synthesis in the absence of YAP. To accurately quantify the replication of DNA added to the extract, the total amount of DNA synthesized must be quantified.

Although we do not agree that relative quantification on gel images cannot support the claim of increased DNA synthesis in the absence of Yap, we thank the reviewer for his suggestion since we now provide additional data clearly strengthening our conclusion.

Many studies, published in high standards journals and coming from different Xenopus replication laboratories have quantified DNA synthesised after 32P-dCTP incorporation and separation by agarose gel electrophoresis (Shechter et al, 2004; Trenz et al, 2008; Guo et al, 2015; Walter & Newport, 1997; Suski et al, 2022, Nature). Nevertheless, as the referee suggested, we quantified the total amount of DNA synthesized in three new independent experiments. These new results, presented page 5, lines 34-39 and shown in Figure 1G, support our conclusion, as they also show that Yap depletion increases total DNA synthesis. Please note that the DNA combing results presented in Figure 2 also show that replication is increased after Yap depletion. Finally, we also added another set of experiments to Figure 1 to further confirm these findings. We used the incorporation of Rhodamine-dUTP followed by the quantification of the fluorescence intensity within nuclei. This nuclei-fluorescence based method is frequently used in proliferation assays to assess nucleotide incorporation resulting from the DNA replication process in other organisms. Our new results demonstrate that DNA synthesis is increased 1.5-fold in six biological replicates and represent a third independent method, in addition to DNA combing and 32P-dCTP incorporation, showing that DNA synthesis is increased upon YAP depletion. These new results are now presented page 5, lines 27-24 and shown in Figure 1D-F.

As explained in the MM section page 14 in the original manuscript, the replication extent (percent of replication) differs for a specific time point from one extract to another, because each egg extract prepared from one batch of eggs replicates nuclei with its own replication kinetics. To overcome this problem and to compare different independent experiments performed using different egg extracts, the data points of each sample were normalized to maximum incorporation value.

It is also necessary to analyze the dynamics and the abundance of chromatin-bound replication proteins associated with the active replication fork after Yap depletion using chromatin binding assays. This would further confirm the increase in the fork density observed by DNA combing experiments.

We thank the referee for this suggestion and we added a western blot of chromatin bound proteins after Yap depletion. This shows that two replication proteins associated with the active replication fork, namely Cdc45 and PCNA, are enriched after Yap depletion compared to the control at the beginning of S-phase. This observation further supports the DNA combing results showing that more forks are active after YAP depletion. This new data is now presented page 6 lines 25-32 and displayed in Figure 2H.

We would like to stress here that with these additional methods added to the revised version, five different methods in total (Rhodamine-dUTP incorporation/nucleus, 32P-dCTP incorporation - total synthesis, 32P-dCTP incorporation - nascent strand analysis, DNA combing, western blotting for replication fork proteins) show that DNA synthesis and origin activation is increased after Yap depletion.

The quantification of the amount of YAP in Figure 1B is confusing. The legend of the chart states "Control in light grey and presence of geminin in black", but the bar colors are of different shades of grey. It is not clear how to evaluate them.

We apologize for this confusion. This has been corrected and the Figure 1B is now properly labelled.

The efficiency of depletion for both Rif1 and YAP is different in Figure 4B and Figure 4A, supplement 1.

We agree with the referee that the efficiency of depletion is different in both figures. This is explained by the fact that the extent of the depletion varies from experiment to experiment. We work with different batches of in vitro fertilized embryos and extracts, so these differences simply reflect the technical/biological variability.

Moreover, the combined use of the TRIM-away approach with injections of MO led to a stronger and prolonged YAP depletion but also triggered toxicity in the tadpoles, which display severe abnormalities.

It is important to point out that abnormal development is not always attributable to a toxic effect. Many losses of gene function result in malformations without being ascribed to toxicity or unspecific effects. However, we agree with the reviewers on the need to present a rescue experiment, which is now shown in new Figure 5C and new Figure 5-figure supplement 1B. In addition, we also provide gain-of-function (GOF) data for YAP in early embryos. In brief, we find that the Yap GOF leads to opposite outcomes than those of its depletion with embryos at the same stage of development, having fewer and larger cells than the control. Furthermore, we show that the effects of Yap depletion, i.e. embryos with more and smaller cells than the control at the same developmental stage, are rescued by the injection of MO-resistant Yap mRNA to restore the protein level. This is true for both embryonic divisions (new Figure 5C) and development, as we obtained normal-looking neurula after Yap rescue (new Figure 5-figure supplement 1B). Overall, these data now clearly show that Yap is both sufficient and necessary to maintain the rate of embryonic divisions and that this phenotype is specific since it can be rescued by expressing Yap alone. These new data are presented page 8, lines 2-10.

Reviewer #3 (Public Review):

The article by Garcia et al clearly describes a set of experiments establishing Yap as a novel regulator of DNA replication dynamics. Its characterization as both a RIF1 interaction partner as well as playing its own role in replication initiation will likely have a significant impact on the field, as currently little is known about how DNA replication during early embryonic cell divisions is regulated.

The authors aim to identify a non-transcriptional function of YAP through the use of the Xenopus in vitro replication system and Yap depletion. Strengths of the paper include the particularly appropriate use of the Xenopus in vitro replication system, as well as the combined use of Trim-Away and morpholino oligonucleotides to deplete Yap and Rif1. Moreover, these experiments were elegantly complemented by single-molecule molecular combing and in vivo studies. Identifying Yap as a novel regulator of DNA replication dynamics, the authors achieved their aim. Through characterization of Yap as both playing a role in replication initiation and as a Rif1 interaction partner will likely have a significant impact on the field, as currently little is known about how DNA replication during early embryonic cell divisions is regulated. A weakness of the paper is that some of the representative data does not appear to be very representative of the entire data set.

We replaced representative data in Figure 2 A, which we think better reflects the main conclusions of the entire data set.

Reviewer #1 (Public Review):

1: The authors formulate competing hypotheses on the behavioral impact of alpha oscillations using signal detection theory (SDT) (Intro and Fig. 1). SDT is indeed well suited for this, as it is used to compute the orthogonal behavioral metrics d' (discriminability) and criterion (bias). However, soon the authors write:

"The higher d' for conservative trials may be due to the more skewed mapping between the false alarm (FA) rate to its Z-value in our d' computation. Specifically, when criterion (or the decision boundary) intersects the noise distribution at its right tail, small changes in FA rate are nonlinearly exaggerated after Z-transformation. As we did not observe a difference in accuracy between conservative and liberal trials, which is a more robust measure of perceptual discriminability when target presence rate equals 50%, we argue that the observed statistically significant d' difference is equivocal."

And also:

"For the binning analyses, we mainly focused on the percentage correct (i.e., accuracy),<br /> and hit and FA rates, because these metrics scale linearly (as opposed to d', which scales<br /> nonlinearly as the hit rate increases or FA rate decreases linearly) and are well defined for both<br /> behavioral data and MVPA outputs."

And indeed from Fig. 3 onwards they do not really use SDT anymore, which is confusing given the Introduction and Fig. 1. I think it's also problematic, as accuracy, hit-rate and fa-rate are not orthogonal and are therefore much less suited to arbitrate between their competing hypotheses. As a result, I'm not convinced the paper accomplishes what it sets out to do in the Introduction.

2: Related, if indeed the authors choose to deviate from SDT, they should put the metric "% yes-choices" on equal footing with accuracy. For example, in Fig. 3A, we can see that alpha oscillations predict a reduction of hit-rate as well as fa-rate; this suggest that the main effect is actually on choice bias (% yes-choices) rather than accuracy. If that's true, then the title of this manuscript is misleading.

3: Have the authors considered to test for non-monotonic effects of alpha oscillations and cortical computation and behavior?

4: The authors use challenging and sophisticated methods, but these are introduced very casually. For example:

"To obtain a more fine-grained picture of the alpha power modulation of behavior, we applied generalized linear mixed models (GLMMs; see Methods) to account for both between-subjects and within-subject trial-by-trial response variability, and to estimate the effects of alpha oscillatory power on d' and criterion simultaneously."

And:

"To evaluate the quality of visual information coding, we used multivariate pattern analysis (MVPA), operationalizing the quality of visual representation as the neural classifier's classification performance. We used the priming trials to train binary classifiers to classify target-present vs. absent trials in a time-resolved manner, [...]".

It would help a lot if the authors could unpack their rationale some more. For example, why did they consider between-subjects effects, and could they show some scatter plots with between-subjects correlations before turning to the GLMM? Also, what is the question the authors wanted to answer that required training the classifier in a time-resolved manner (which I like, on a personal note)?

5: Throughout, the label "liberal trials" is odd, given that group-average criterion > 0 on those trials (Fig. 2C).

6: It would be nice to explicitly bridge to the literature on (pupil-linked) arousal predicted shifts in decision-making, and to findings on the relationship between alpha oscillations and (pupil-linked) arousal.

On July 5, 1935, President Roosevelt created the Wagner Act, also known as the Nation Labor Relations Act. The Act included many things such as entitlement to wages and benefits, hour of work, overtime arrangements and overtime compensation, and leave for illness, maternity, vacation or holiday. Labor and working conditions. (n.d.). https://firstforsustainability.org/risk-management/understanding-environmental-and-social-risk/environmental-and-social-issues/labor-and-working-conditions/ National Labor Relations Act (1935). (2021, November 22). National Archives. https://www.archives.gov/milestone-documents/national-labor-relations-act

Sadly I think we're talking past each other somehow; I broadly agree with all of your original thread. Perhaps there's also some context collapse amidst our conversations across multiple platforms which doesn't help.

Maybe my error was in placing my comment on your original thread rather than a sub branch on one of the top several comments? I didn't want to target anyone in particular as the "invented by Luhmann myth" is incredibly wide spread and is unlikely to ever go away. It's obvious by some of the responses I've seen from your thread here in r/antinet that folks without the explicit context of the history default to the misconception that Luhmann invented it. This misconception tends to reinforce the idea that there's "one true way" (the often canonically presented "perfect" Luhmann zettelkasten, rather than the messier method that he obviously practiced in reality) when, instead, there are lots of methods, many of which share some general principles or building blocks, but which can have dramatically different uses and outcomes. My hope in highlighting the history was specifically to give your point more power, not take the opposite stance. Not having the direct evidence to the contrary, you'll noticed I hedged my statement with the word "seems" in the opening sentence. I apologize to you that I apparently wasn't more clear.

I love your comparison of LYT and zettelkasten by the way. It's reminiscent of the sort of comparison I'm hoping to bring forth in an upcoming review of Tiago Forte's recent book. His method—ostensibly a folder based digital commonplace book, which is similar to Milo's LYT—can be useful, but he doesn't seem to have the broader experience of history or the various use cases to be able to advise a general audience which method(s) they may want to try or for which ends. I worry that while he's got a useful method for potentially many people, too many may see it and his platform as a recipe they need to follow rather than having a set of choices for various outcomes they may wish to have. Too many "thought leaders" are trying to "own" portions of the space rather than presenting choices or comparisons the way you have. Elizabeth Butler is one of the few others I've seen taking a broader approach. A lot of these explorations also means there are multiple different words to describe each system's functionality, which I think only serves to muddy things up for potential users rather than make them clearer. (And doing this across multiple languages across time is even more confusing: is it zettelkasten, card index, or fichier boîte? Already the idea of zettelkasten (in English speaking areas) has taken on the semantic meaning "Luhmann's specific method of keeping a zettelkasten" rather than just a box with slips.)

Author Response

Reviewer 1

Strengths:

This manuscript combines experimental, exploratory, and observational methods to investigate the big question in innovation literature--why do some animals innovate over others, and how information about innovations spread. By combining a variety of methods, the manuscript tackles this question in a number of ways, and finds support for previous work showing that animals can learn about foods via social olfactory inspection (i.e., muzzle to muzzle contact), and also presents data intended to investigate the role of dispersing animals in innovation and information spread.

Using data from a previously-published experiment, the manuscript illustrates how investigators can numerous interesting questions while limiting the disturbances to wild animals. The manuscript's attempt at using exploratory analysis is also exciting, as exploratory analyses provide a useful tool for behavior research-indeed, Tinbergen insisted that behavior must first be described.

Weaknesses:

The manuscript's introduction is a bit unclear as to how the fact that dispersing males may be an important source of information ties to innovations in response to disruptions due to climate change, humans, or new predators, if at all. An introduction regarding the role of dispersed animals in introducing novel behaviors and social transmission would better prepare readers for the questions presented in the manuscript. As it stands now, the manuscript only provides one sentence discussing the theoretical relevance of investigating the role of dispersing animals in innovations.

We have added some information about this to the introduction (lines 66 – 69 and 121-123) and maintain our discussion of it in the discussion.

Additionally, while the manuscript attempts to use exploratory analysis, it does not provide enough theoretical background as to why certain questions were asked while the data were explored. While the discussion provides some background as to the role of dispersing males in innovation, the introduction provides little background, and thus does not properly frame the issue. It is unclear how dispersing males became of interest and why readers should be interested in them. As the manuscript reads now, it may be that dispersing males became interesting only as a result of the exploratory analysis-except that the predictions explicitly mentions dispersing males. Thus, manuscript at present makes it difficult to know if the questions surrounding immigrant males resulted from the exploratory analysis, or was a question the analyses were intended to answer from the beginning. If this question only came out after first reviewing the results, then this needs to be made clear in the introduction. I see no issue with reporting observations that were the result of investigations into earlier results, but it needs to be reported in a way that can be replicated in future research-I need to know the decision process that took place during the data exploration.

We hope this is clearer from our new research aims (lines 125-173)

The manuscript never clearly defines what counts as an immigrant male; presumably, in this species, all adult males in the group should be immigrants, as females are the philopatric sex. Sometimes, the manuscript uses "recently" to modify immigrant males, but doesn't define exactly what counts as recent, except to say that the males that innovated were in their respective groups for fewer than 3 months, but never explains why three months should be an important distinction in adult male tenure.

We realise how we wrote about this previously was not clear and perhaps misleading. We noticed that the males that innovated had been in the group for less than three months. We do not know if this is necessary for them to innovate or not. We also added to the discussion a description of the male in AK19 who had been in the group for four months and did no innovate – as he had many other traits which we would expect to exclude him from criteria for innovation (e.g. very old, post-prime, and inactive – died within months of the experiment).

Due to the above weaknesses, the provided predictions are a bit murky. It is not clear how variation between groups in accordance with who innovated, or initiated eating a novel food, or demographics is related to the central issue. The manuscript does contribute to the literature by looking at changing rates of muzzle contact over exposure to a novel food source, and provides a good extension of previous findings; that, if muzzle contacts help animals learn about new foods, then rates of muzzle contacts involving novel foods should decrease as animals become familiar with the food. However, this point isn't explicit in the manuscript.

This is now addressed in the new aims paragraph (lines 125-173)

Finally, it is also unclear as to why changing rates of muzzle contact AND whether certain individual level variables like knowledge, sex, age, and/or rank might influence muzzle contacts during opportunities to innovate.

We are not sure exactly what the reviewer means here, but hope that the substantial revisions we have made now address their concern.

As for the methods, the manuscript doesn't provide enough details as to why certain decisions were made. For example, no reason is given as to why only the first four sessions after an animal ate were considered, why the first three months of tenure (but not four, as seen on one group that didn't innovate) was considered to be a critical time for which immigrant males may innovate, why (including the theoretical reasons) the structure of models for one analysis was changed (dropping one variable, adding interactions), or even how the beginning and ending of a trial was decided, despite reporting that durations varied widely,-from 5 minutes to two hours.

Please see: above about the male with 4 month tenure; and top of document for description of our updated models.

The discussion contains results that are never elsewhere presented in the manuscript- (2a) Individual variation in uptake of a novel food according to who ate first).

It was just an error in the sub-title in the discussion – this is now amended. But all the other corresponding details were already there, in the list of research aims in the introduction and in the results as well.

Finally, the largest issue with the manuscript is that its results are not as convincing as the conclusions made. An issue with all the analyses is that some grouping variables in some analyses but not others despite the fact that all of the analyses contain multiple groups (necessitating group as a grouping variable) and multiple observations of the same individuals (i.e., immigrant males tested in multiple groups, necessitating animal identity as a random effect), and not accounting for individual exposure to the experiment when considering whether animals ate the food in the allotted period (an important consideration given the massive differences in trial times), making these results difficult to interpret in their current forms. As for the results regarding muzzle contact, the analyses has a number of issues that make it difficult to determine if the claims are supported. These issues include not explaining why rank calculated a year before the experiments took place was valid or if rank was calculated among all group members or within age and sex classes, not explaining how rank was normalized, and not conducting any kind of formal model comparisons before deciding the best model.

Mostly addressed at top of this document. Regarding rank calculations: rank was not calculated a year before the experiments, it was calculated using a year’s worth of data up to the beginning of the experiments – and ranks were calculated among all group members - we have made this clearer in the methods. We also explained our method of normalisation, and noted that it was an error to include non-normalised rank in one of the models – this has now been rectified

As for the results regarding immigrant males and innovation, little is done to help the fact that these results are from very few observations and no direct analyses. It is possible that something that occurs relatively often but in small sample sizes, like dispersing animals, could have immense power in influencing foraging traditions, and observation is a necessary step in understanding behavior. However, the manuscript doesn't consider any alternative hypotheses as to why it found what it found. No other possible difference between the groups was considered (for example, the groups that rapidly innovated appear to be quite smaller than the groups that did), making the claim that immigrant males were what allowed groups to innovate unconvincing. This is particularly true given that some groups in this study population have experimental histories (though this goes unmentioned in the current manuscript), which likely influenced neophobia-especially given work by the same research group showing that these animals are more curious compared to their unhabituated counterparts.

We have added more discussion of alternative hypotheses to the discussion (line numbers mentioned above).

Regarding the comment about rapid innovation in smaller groups – we are not sure what the reviewer means here – all groups except BD were similar sized. The second largest group, NH, had one of the quickest innovations and a smaller group (KB) innovated only at the third exposure. Unless the reviewer instead refers to the spread of the innovation here? This is also not quite what we see in the data – BD is the largest group and one of the fastest to spread, and KB is the smallest group and the slowest to spread. Regarding groups experimental histories, all the five studied groups have already been used in field experiments. The group (LT) with the least experimental history was the one having the greatest proportion of individuals eating the novel food at the first and over the four exposures (see Fig. 2) while one of the groups with the most experimental history (NH) was one having a smaller proportion of individuals eating the food across the experiment. This is discussed in the discussion (lines 370-380).

Reviewer 2

I have separated my issues with the manuscript into three sub-headings (Conceptual Clarity, Observational Detail and Analysis) below.

1) Conceptual clarity

There are a number of areas where it would greatly benefit the manuscript if the authors were to revisit the text and be more specific in their intentions. At present, the research questions are not always well-defined, making it difficult to determine what the data is intended to communicate. I am confident all of these issues could be fixed with relatively minor changes to the manuscript.

For example, Line 104: Question 1 is not really a question, the authors only state that they will "investigate innovation and extraction of eating the food", which could mean almost anything.

We re-wrote the research questions paragraph and results with this advice in mind – hope it is clearer now. We keep the innovation part just descriptive and hope this is less problematic now.

Question 2a (line 98) is also very vague in it's wording, and I'm left unclear as to what the authors were really interested in or why. This is not helped by Line 104 which refuses to make predictions about this research question because it is "exploratory". Empirical predictions are not simply placing a bet on what we think the results of the study will be, but rather laying out how the results could be for the benefit of the reader. For instance, if testing the effects of 10 different teaching methods on language acquisition-rate: Even if we have no a priori idea of which method will be most effective, we can nevertheless generate competing hypotheses and describe their corresponding predictions. This is a helpful way to justify and set expectations for the specific parameters that will be examined by the methods of the study. In fact, in the current paper, the authors in fact had some very clear a priori expectations going into this study that immigrant males would be vectors of behavioural transmission (clear that is from the rest of the introduction, and the parameters used in their analysis, which were not chosen at random).

We have now updated the whole research aims (lines 125-173).

The multiple references to 'long-lived' species in the abstract (line 16 and introduction (39, 56) is a bit confusing given the focus of this study. Although such categorisations are arbitrary by nature (a vervet is certainly long-lived compared to a dragonfly), I would not typically put vervet monkeys (or marmosets, line 62) in the same category as apes (references 8 and 9) or humans (line 62) in this regard.

When we use “long-lived” in the introduction, we explain that we mean animals with slow generational turnover for whom genetic adaptation is relatively slow – too slow to adapt to very rapid environmental change. Within the distinctions the reviewer makes here, we feel that vervets and marmosets are much more similar to apes than to dragonflies etc. in this respect… and we think making the comparisons that we do are valid in this context (though we do agree that for other reasons we would not find it appropriate). We have modified the sentence in the introduction (line 4042) and hope this is clearer now. The study in reference 9 is about crop-raiding, which is something vervets can learn to do within one generation too. In addition, reference 8 is used as it was one of the earlier and long-standing definitions of innovation which we are using here – we are not comparing vervets to apes directly, but we do not think a different definition of innovation is required.

This contributes a little towards the lack of overall conceptual focus for the manuscript: beginning in this fashion suggests the authors are building a "comparative evolutionary origins" story, hinting perhaps at the phylogenetic relevance of the work to understanding human behaviour, but the final paragraph of the study contextualises the findings only in terms of their relevance to feeding ecology and conservation efforts. I would recommend that the authors think carefully about their intended audience and tailor the text accordingly. This is not to say that readers interested in human evolution will not be interested in conservation efforts, but rather that each of these aspects should be represented in each stage of the manuscript (otherwise - conservationists may not read far into the Introduction, and cultural evolution fans will be left adrift in the Conclusion).

We agree that the line running through the whole paper needed to be clearer and have tried to improve this.

2) Observational detail

There are a number of areas of the manuscript which I found to be lacking in sufficient detail to accurately determine what occurred in these experimental sessions, making the data difficult to interpret overall. All of this additional information ought to be readily available from the methods used (the experiments were observed by 3-5 researchers with video cameras (line 341)) and is all of direct relevance to the research questions set out by the authors.

We added more details about the experiment in the method section.

While I appreciate that it will take quite a bit of work to extract this information, I am certain that it would greatly improve the robustness and explanatory power of this study to do so.

The data on who was first to innovate/demonstrate successful extraction of the food in each group (Question 1) and subsequent uptake (Question 2), as well as the actual mechanism by which that uptake occurred (the authors strongly imply social learning in their Discussion, but this is never directly examined) is difficult to interpret based on the information presented. Some key gaps in the story were:

We did not intend to claim that muzzle contact was the specific mechanism by which individuals learned to extract and eat peanuts – we rather use this experiment to evaluate the function of muzzle contact in the presence of a novel food.

We did not record observation networks in all groups during experiments and cannot obtain accurate ones from all our videos – we hope it is clearer in our text now. Our group’s previous study (Canteloup et al., 2021) already shows social transmission of the opening techniques using data of two of our groups (NH and KB).

• Which/how many individuals encountered the food and in what order? I.e., were migrants/innovators simply the first to notice the food?

No, and we have now added some info about other individuals approaching the box and inspecting the peanuts before innovation took place

• Did any individuals try and fail to extract the food before an "innovator" successfully demonstrated?
• How many tried and failed to extract the nuts before and after observing effective demonstrators?

We have added the number of individuals that inspected the peanuts (visually and with contact)

• Were individuals who observed others interact with the food more likely to approach and/or extract it themselves?
• Did group-members use the same methods of extraction as their 'innovators'?

Yes – this is the topic of Canteloup et al. 2021 – and these data are not presented again here. That study was on two of the groups presented here (KB and NH), and with up to 10 exposures in each of those groups and present a fine-grained analysis of peanuts opening techniques used by monkeys. We hope this is clearer now in the text where we refer to this paper.

• How many tried and succeeded without having directly observed another individual do so (i.e. 'reinvention' as per Tennie et al.)?

For this, and the above points: We did not record an observation network for the groups added in this study and are not able to answer this – it is not the focus of this study. For this reason, we do not make claims in this line in the present study, and are cautious with our social learning related language. Whilst we examine the role of muzzle contact in acquiring information about a novel food, we do not expect this behaviour to be a necessary prerequisite in being able to extract and eat this food – indeed many individuals who learned to eat did not perform muzzle contacts. This aspect of the study is about using this novel food situation to explore whether muzzle contact serves information acquisition – which our evidence suggests it does.

Moreover, the processing of this food is not complex and is similar to natural foods in their environment, and we do expect individuals to be capable of reinventing it easily (and this point with Tennie’s hypothesis is actually discussed in Canteloup et al. 2021 paper) – but the point here is that their natural tendency is to be neophobic to unknown food, and therefore they do not readily eat it until they see a conspecific doing so, after which they do. And we also used this opportunity, though in a very small sample size, to investigate which individuals would overcome that neophobia and be the first to eat successfully.

The connective tissue between the research questions set out by the authors is clearly social learning. In short: the thesis is that Migrants/Innovators bring a novel behaviour to the group, then there is 'uptake' (social learning), which may be influenced by demographic factors and muzzle-contact (biases + mechanisms). Given this focus (e.g. lines 224-264 of the Discussion), I would expect at least some of the details above to be addressed in order to provide robust support for these claims.

See above – the reason we talk about ‘uptake’ rather than social learning is that we really see this as a case of social disinhibition of neophobia, rather than more detailed social learning such as copying or imitation, as it would be in a tool-use setting, for example (though in Canteloup et al. 2021 paper, evidence is found that the specific methods to open peanuts are socially transmitted).

Question 2a (Lines 136-146): This data is hard to interpret without knowing how much of the group was present and visible during these exposures.

Please see response to reviewer 1 on this.

For example: 9% update in NH group does not sound impressive, but if only 10% of the total group were present while the rest were elsewhere, then this is 90% of all present individuals. Meanwhile if 100% of BD group were present and only experienced 31% uptake, then this is quite a striking difference between groups.

Experiments were done at sunrise at monkeys’ sleeping site in AK, LT, NH and KB where most of the group was present in the area; we added more precision on this point in the Method section (lines 615-619).

Of course, there is also an issue of how many individuals can physically engage with the novel food even if they want to - the presence of dominant individuals, steepness of hierarchy within that group, etc, will significantly influence this (and is all of interest with regards to the authors' research questions).

We discuss this with respect to the result showing that higher rank individuals were more likely to extract and eat the food at the first exposure and over all four exposures

Muzzle-contact behaviour: The authors use their data to implicate muzzle-contact in social learning, but this seems a leap from the data presented (some more on this in the Analysis section).

We hope our distinction between information acquisition and information use is clearer now.

For example: - What is the role of kinship in these events?

We did not analyse kinship here, but we see a lot of targeting towards adult males, and we do not have reliable kinship data for them. We also checked (see response to reviewer 3) the muzzle contacts initiated by knowledgeable adult females, and they are mostly towards adult males, not towards related juveniles (see new figure 4D and lines 497-500).

• Did they occur when the juvenile had free access to the food (i.e. not likely to be chased off by a feeding adult)?

We recorded muzzle contacts visible within 2m of the box, so individuals were not necessarily eating at the box at the time of engaging in muzzle contacts. However, the majority of muzzle contacts that we could record took place directly at the edge of the box – at the location where the food is accessed – so an individual would not likely be if they were not able to have access to the food. It is possible they could be there and not eating, but they would not have been chased off, otherwise they would not be able to engage in muzzle contacts there. But it is not entirely clear what the reviewer’s point is here.

• Did they primarily occur when adults had a mouthful of food? (i.e. could it simply be attempted pilfering/begging)

This is not typical of this species. Very few specific individuals remove food from others’ mouths, and they do it with their hands, usually beginning with grooming their face and cheekpouches, before prising their mouth open and removing food from the victim’s cheekpouches

• What proportion of PRESENT (not total) individuals were naïve and knowledgeable in each group for each trial (if 90% present were knowledgeable, then it is not surprising that they would be targeted more often)?

We agree somewhat with this statement, but given the multiple ways we show the effect of knowledge – both at the individual level and the group level (effect of exposure number i.e. overall group familiarity) – we feel we present enough evidence to establish the link between knowledge of the food and muzzle contacts. We find that the model showing the interaction between exposure number and number of monkeys eating on the overall rate of muzzle contacts actually addresses this issue, because we see that when many monkeys are eating during later exposures, when many were indeed knowledgeable, the rate of muzzle contacts is massively decreased. Moreover, if 90% of the individuals present are knowledgeable, then only 10% of the individuals present are naïve, and we show both that knowledgeable individuals are targeted, but also that naïve individuals are initiators.

• Did these events ever lead to food-sharing (In other words, how likely are they to simply be begging events)?

We do not observe food-sharing in vervets.

• Did muzzle-contact quantifiably LEAD to successful extraction of the food? If the authors wish to implicate muzzle-contact in social learning, it is not sufficient to show that naïve individuals were more likely to make muzzle-contact, they must also show that naïve individuals who made more muzzlecontact were more likely to learn the target behaviour.

We disagree here, because there is a distinction between information acquisition and information use - obtaining olfactory information about a novel resource that conspecifics are eating is not the same as learning a complex tool use behaviour for which detailed observation of a model is required. We are not claiming that that muzzle contact is THE mechanism by which the monkeys learn how to eat the food – but we do believe that the clear separation between naïve individuals initiating and knowledgeable individuals being target, and the decrease of the rate of this behaviour as groups’ familiarity with the food increases – is good evidence that this behaviour functions to acquire information about a novel food.

3) Analysis

There are a number of issues with the current analysis which I strongly recommend be addressed before publication. Some of these are likely to simply require additional details inserted to the manuscript, whereas others would require more substantial changes. I begin with two general points (A & B), before addressing specific sections of the manuscript.

A) My primary issue with each of the analyses in this manuscript is that the authors have fit complex statistical models for each of their analyses with no steps to ascertain whether these models are a good fit for the data. With a relatively small dataset and a very large number of fixed effects and interactions, there is a considerable risk of overfitting. This is likely to be especially problematic when predictor variables are likely to be intercorrelated (age, sex and rank in the case of this analysis).

We have now checked for overfitting in our models.

The most straightforward way to resolve this issue is to take a model-comparison approach. Fitting either a) a full suite of models (including a 'null' model) with each possible permutation of fixed effects and interactions (since the authors argue their analysis is exploratory) or b) a smaller set of models which the authors find plausible based on their a priori understanding of the study system. These models could then be compared using information criterion to determine which structure provides the best out-of-sample predictive fit for the data, and the outputs of this model interpreted. Alternatively, a model-averaging approach can be taken, where the effects of each individual predictor are averaged and weighted across all models in the set. Both of these approaches can be performed easily using the r package 'MuMIn'. There are also a number of tutorials that can be found online for understanding and carrying out these approaches.

Please see our answer at the beginning of the document, detailing how we have updated our models.

B) It does not seem that interobserver reliability testing was carried out on any of the data used in these analyses. This is a major oversight which should be addressed before publication (or indeed any re-analysis of the data).

We have added this now and mention it above already.

Line 444: Much more detail is needed here. What, precisely, was the outcome measure? Was collinearity of predictors assessed? (I would expect Age + Rank to be correlated, as well as Sex + Rank).

This is now addressed (please see details above) – we use VIFs to assess multicollinearity of predictors in our models and find they are all satisfactory (see R code).

Line 452. A few comments on this muzzle-contact analysis:

The comments below are a little confusing as some seem to refer to the muzzle-contact rate model (previously line number 452), and some seem to refer to the initiator/receiver model. We have tried to figure out which comments refer to which, and answer accordingly.

"We investigated muzzle contact behaviour in groups where large proportions of the groups started to extract and eat peanuts over the first four exposures"

What was the criteria for "a large proportion"?

All groups are now included in this analysis.

The text for this muzzle-contact analysis would indicate that this model was not fit with any random effects, which would be extremely concerning. However, having checked the R code which the authors provided, I see that Individual has been fit as a random effect. This should be mentioned in the manuscript. I would also strongly recommend fitting Group (it was an RE in the previous models, oddly) and potentially exposure number as well.

The model about muzzle contact rate never contained individual as a random effect because individuals are not relevant in this model – it is the number of muzzle contacts occurring during each exposure. However, the reviewer might refer here to the model that we forgot to provide the script for. Nonetheless, we have substantially revised this model, it now (Model 3) includes all groups, and has group as a random effect.

Following on from this, if the model was fit with individual as a random effect it becomes confusing that Figure 3 which represents this data seemingly does not control for repeated measures (it contains many more datapoints than the study's actual sample size of 164 individuals). This needs to be corrected for this figure to be meaningfully interpretable.

Figure 3 is not related to the model described in (original) line 452.

The numbers were referring to the number of muzzle contacts, and this was written in the figure caption. However, we no longer present these details on the new figure (see Fig 4).

Finally, would it make sense to somehow incorporate the number of individuals present for this analysis? Much like any other social or communicative behaviour, I would predict the frequency of occurrence to depend on how many opportunities (i.e. social partners) there are to engage in it.

We have included the number of monkeys eating in our muzzle contact rate model now (Model 3) as upon further thought, we found that this was the issue leading us to want to exclude exposures, and only include the groups where many monkeys were eating. We have resolved this now by including all groups and not dropping exposures, and rather we include an interaction between number of monkeys eating and exposure number. We feel this addresses our hypothesis here much more satisfactorily. We hope these updates also address the reviewers concerns adequately.

Line 460: "For BD and LT we excluded exposures 4 and 3, respectively, due to circumstances resulting in very small proportions of these groups present at these exposures"

What was the criterion for a satisfactory proportion? Why was this chosen

See above – this is now addressed.

Line 461: "We ran the same model including these outlier exposures and present these results in the supplementary material (SM3)."

The results of this supplemental analysis should be briefly stated. Do they support the original analysis or not?

We no longer present this like this. We revised the model examining muzzle contact rate substantially and actually included the number of individuals eating in the model rather than excluding groups where this number was low. The results of the new model show good support our hypothesis.

Line 465: "Due to very low numbers of infants ever being targets of muzzle contacts, we merged the infant and juvenile age categories for this analysis."

This strikes me as a rather large mistake. The research question being asked by the authors here is "How does age influence muzzle-contact behaviour?"

Then, when one age group (infants) is very unlikely to be a target of muzzle-contact, the authors have erased this finding by merging them with another age category (juveniles). This really does not make sense, and seriously confounds any interpretation of either age category.

Yes we agree with this issue, and no longer do that. Rather we remove the infant data from this model, which is now Model 6, because of the large amounts of error they introduced into the model due to the small sample size. We show the process in the R code, and we describe our reasons in the text (lines 713-719). Since we are now only comparing within age- and sex-categories (see below) we do not find this decision introduces any bias.

Lines 466-474: Why was rank removed for the second and third models? Why is Group no longer a random effect (as in the previous analysis)? The authors need to justify such steps to give the reader confidence in their approach.

This is now addressed and discussed in descriptions of our new models.

Furthermore - because of the way this model is designed, I do not think it can actually be used to infer that these groups are preferentially targeted, merely that adult female and adult males are LESS likely to target others than to be targeted themselves, which is a very different assertion.

Because the specific outcome measure was not described here, this only became apparent to me after inspecting Figure 3, where outcome measure is described as "Probability of (an individual) being a target rather than initiator" - so, it can tell us that adults are more often targeted rather than initiating, but does not tell us if they are targeted more frequently than juveniles (who may get targeted very often, but initiate so often that this ratio is offset).

We thank the reviewer for noticing this as we had indeed chosen an inappropriate model for what we were intending to measure – this has been addressed now with two additional models (Models 4 and 5; see details at the top of document). We nonetheless found the aspects of this model to still be highly interesting, so have re-framed it to focus on them.

Lines 467-473: "Our first simple model included individuals' knowledge of the novel food at the time of each muzzle contact (knowledgeable = previously succeeded to extract and eat peanuts; naïve = never previously succeeded to extract and eat peanuts) and age, sex and rank as fixed effects. Individual was included as a random effect. The second model was the same, but we removed rank and added interactions between: knowledge and age; and knowledge and sex. The third model was the same as the second, but we also added a three-way interaction between knowledge, age and sex."

This is a good example of some of the issues I describe above. What is the justification for each of these model-structures? The addition and subtraction of variables and interactions seems arbitrary to the reader.

For Model 6, we no longer include rank at all, because we had not hypothetical reason to (see lines 723-725). We now begin with the three-way interaction, and only remove this, because it is not significant, and the model had problems converging as well, due to its complexity. We show this in the R script. We retain only the two separate interactions, and we do not include group as a random effect in this model due to the complexity AND because we do not think there is a theoretical requirement for it to be included here (this is explained in lines 730-735- in the manuscript. We report the results of the 3-way interaction in the supplementary material – SM3 Table S2).

Reviewer 3

In this study, the authors introduce a novel food that requires handling time to five vervet monkey groups, some of which had previous experience with the food. Through the natural dispersal of males in the population, they show that dispersing individuals transmit behavioral innovations between groups and are often also innovators. They also examine muzzle contact initiations and targets within the groups as a way to determine who is seeking social information on the new food source and who is the target of information seeking. The authors show that knowledgeable adults are more often the target of muzzle contacts compared to young individuals and those that are not knowledgeable.

This is a very interesting study that provides some novel insights. The methods employed will be useful to others that are considering an experimental approach to their field research. The data set is good and analyzed appropriately and the conclusions are justified. However, there are several areas where the paper could be improved for readers in terms of its clarity.

1) It wasn't until the Discussion that it became clear to me that the actual physiological and personality traits of dispersers were being linked with innovation. From the Title, Abstract, and Introduction, it seemed as though the focus was on dispersing males bringing their experience with a novel food to a new group to pass it on. I think it needs to be made clear much earlier in the manuscript that the authors are investigating not only the transmission of behavioural adaptation but also how the traits of dispersers might may make them more likely to innovate.

We have now addressed this above.

2) Early in the paper on line 28, the authors state that continued initiation of muzzle contacts by adult females could have been an effort to seek social information. This is true but another interpretation is that females were imparting or giving social information. It seems important here and elsewhere (lines 322-323) to consider and report the target of these initiations. If these were directed at more knowledgeable individuals, it supports the idea that this was social information seeking. If muzzle contacts were directed to younger or unknowledgeable individuals, it would imply a form of teaching, which is possible but perhaps unlikely, so I think the authors need to be totally clear here.

We thank the reviewer for pointing this out We looked into our data and now present figure 4D, showing that almost all knowledgeable adult females’ muzzle contacts were targeted towards knowledgeable adult males and talk about it in the discussion (lines 499-500).

3) The argument made on lines 344-350 needs more fleshing out to be convincing or it should be deleted. The link between number of dispersers, social organization, and large geographic range seems a little muddled. There are many dispersing individuals in species that are not typically in large multi-male, multi-female social organizations. Indeed, in many species both sexes disperse. Think of pair living birds where both sexes disperse and geographic range can be enormous. There are also no data or references presented here to show that species in multi-male, multi-female social organizations do have larger geographic ranges than those that are not in these social organizations. It seems to me that, even if this is the case, niche is more important than social organization, for instance not being dependent on forests to constrain much of your range.

We have removed this section

Reviewer #2 (Public Review)

I have separated my issues with the manuscript into three sub-headings (Conceptual Clarity, Observational Detail and Analysis) below.

1) Conceptual clarity

There are a number of areas where it would greatly benefit the manuscript if the authors were to revisit the text and be more specific in their intentions. At present, the research questions are not always well-defined, making it difficult to determine what the data is intended to communicate. I am confident all of these issues could be fixed with relatively minor changes to the manuscript.

For example, Line 104: Question 1 is not really a question, the authors only state that they will "investigate innovation and extraction of eating the food", which could mean almost anything.

Question 2a (line 98) is also very vague in it's wording, and I'm left unclear as to what the authors were really interested in or why. This is not helped by Line 104 which refuses to make predictions about this research question because it is "exploratory". Empirical predictions are not simply placing a bet on what we think the results of the study will be, but rather laying out how the results could be for the benefit of the reader. For instance, if testing the effects of 10 different teaching methods on language acquisition-rate: Even if we have no a priori idea of which method will be most effective, we can nevertheless generate competing hypotheses and describe their corresponding predictions. This is a helpful way to justify and set expectations for the specific parameters that will be examined by the methods of the study. In fact, in the current paper, the authors in fact had some very clear a priori expectations going into this study that immigrant males would be vectors of behavioural transmission (clear that is from the rest of the introduction, and the parameters used in their analysis, which were not chosen at random).

The multiple references to 'long-lived' species in the abstract (line 16 and introduction (39, 56) is a bit confusing given the focus of this study. Although such categorisations are arbitrary by nature (a vervet is certainly long-lived compared to a dragonfly), I would not typically put vervet monkeys (or marmosets, line 62) in the same category as apes (references 8 and 9) or humans (line 62) in this regard. This contributes a little towards the lack of overall conceptual focus for the manuscript: beginning in this fashion suggests the authors are building a "comparative evolutionary origins" story, hinting perhaps at the phylogenetic relevance of the work to understanding human behaviour, but the final paragraph of the study contextualises the findings only in terms of their relevance to feeding ecology and conservation efforts. I would recommend that the authors think carefully about their intended audience and tailor the text accordingly. This is not to say that readers interested in human evolution will not be interested in conservation efforts, but rather that each of these aspects should be represented in each stage of the manuscript (otherwise - conservationists may not read far into the Introduction, and cultural evolution fans will be left adrift in the Conclusion).

2) Observational detail

There are a number of areas of the manuscript which I found to be lacking in sufficient detail to accurately determine what occurred in these experimental sessions, making the data difficult to interpret overall. All of this additional information ought to be readily available from the methods used (the experiments were observed by 3-5 researchers with video cameras (line 341)) and is all of direct relevance to the research questions set out by the authors.

While I appreciate that it will take quite a bit of work to extract this information, I am certain that it would greatly improve the robustness and explanatory power of this study to do so.

The data on who was first to innovate/demonstrate successful extraction of the food in each group (Question 1) and subsequent uptake (Question 2), as well as the actual mechanism by which that uptake occurred (the authors strongly imply social learning in their Discussion, but this is never directly examined) is difficult to interpret based on the information presented. Some key gaps in the story were:

- Which/how many individuals encountered the food and in what order? I.e., were migrants/innovators simply the first to notice the food?<br /> - Did any individuals try and fail to extract the food before an "innovator" successfully demonstrated?<br /> - How many tried and failed to extract the nuts before and after observing effective demonstrators?<br /> - Were individuals who observed others interact with the food more likely to approach and/or extract it themselves?<br /> - Did group-members use the same methods of extraction as their 'innovators'?<br /> - How many tried and succeeded without having directly observed another individual do so (i.e. 'reinvention' as per Tennie et al.)?

The connective tissue between the research questions set out by the authors is clearly social learning. In short: the thesis is that Migrants/Innovators bring a novel behaviour to the group, then there is 'uptake' (social learning), which may be influenced by demographic factors and muzzle-contact (biases + mechanisms). Given this focus (e.g. lines 224-264 of the Discussion), I would expect at least some of the details above to be addressed in order to provide robust support for these claims.

Question 2a (Lines 136-146): This data is hard to interpret without knowing how much of the group was present and visible during these exposures.

For example: 9% update in NH group does not sound impressive, but if only 10% of the total group were present while the rest were elsewhere, then this is 90% of all present individuals. Meanwhile if 100% of BD group were present and only experienced 31% uptake, then this is quite a striking difference between groups.

Of course, there is also an issue of how many individuals can physically engage with the novel food even if they want to - the presence of dominant individuals, steepness of hierarchy within that group, etc, will significantly influence this (and is all of interest with regards to the authors' research questions).

Muzzle-contact behaviour: The authors use their data to implicate muzzle-contact in social learning, but this seems a leap from the data presented (some more on this in the Analysis section).

For example:<br /> - What is the role of kinship in these events?<br /> - Did they occur when the juvenile had free access to the food (i.e. not likely to be chased off by a feeding adult)?<br /> - Did they primarily occur when adults had a mouthful of food? (i.e. could it simply be attempted pilfering/begging)<br /> - What proportion of PRESENT (not total) individuals were naïve and knowledgeable in each group for each trial (if 90% present were knowledgeable, then it is not surprising that they would be targeted more often)?<br /> - Did these events ever lead to food-sharing (In other words, how likely are they to simply be begging events)?<br /> - Did muzzle-contact quantifiably LEAD to successful extraction of the food? If the authors wish to implicate muzzle-contact in social learning, it is not sufficient to show that naïve individuals were more likely to make muzzle-contact, they must also show that naïve individuals who made more muzzle-contact were more likely to learn the target behaviour.

3) Analysis

There are a number of issues with the current analysis which I strongly recommend be addressed before publication. Some of these are likely to simply require additional details inserted to the manuscript, whereas others would require more substantial changes. I begin with two general points (A & B), before addressing specific sections of the manuscript.

A) My primary issue with each of the analyses in this manuscript is that the authors have fit complex statistical models for each of their analyses with no steps to ascertain whether these models are a good fit for the data. With a relatively small dataset and a very large number of fixed effects and interactions, there is a considerable risk of overfitting. This is likely to be especially problematic when predictor variables are likely to be intercorrelated (age, sex and rank in the case of this analysis).

The most straightforward way to resolve this issue is to take a model-comparison approach. Fitting either a) a full suite of models (including a 'null' model) with each possible permutation of fixed effects and interactions (since the authors argue their analysis is exploratory) or b) a smaller set of models which the authors find plausible based on their a priori understanding of the study system. These models could then be compared using information criterion to determine which structure provides the best out-of-sample predictive fit for the data, and the outputs of this model interpreted. Alternatively, a model-averaging approach can be taken, where the effects of each individual predictor are averaged and weighted across all models in the set. Both of these approaches can be performed easily using the r package 'MuMIn'. There are also a number of tutorials that can be found online for understanding and carrying out these approaches.

B) It does not seem that interobserver reliability testing was carried out on any of the data used in these analyses. This is a major oversight which should be addressed before publication (or indeed any re-analysis of the data).

Line 444: Much more detail is needed here. What, precisely, was the outcome measure? Was collinearity of predictors assessed? (I would expect Age + Rank to be correlated, as well as Sex + Rank).

Line 452. A few comments on this muzzle-contact analysis:

"We investigated muzzle contact behaviour in groups where large proportions of the<br /> groups started to extract and eat peanuts over the first four exposures"

What was the criteria for "a large proportion"?

The text for this muzzle-contact analysis would indicate that this model was not fit with any random effects, which would be extremely concerning. However, having checked the R code which the authors provided, I see that Individual has been fit as a random effect. This should be mentioned in the manuscript. I would also strongly recommend fitting Group (it was an RE in the previous models, oddly) and potentially exposure number as well.

Following on from this, if the model was fit with individual as a random effect it becomes confusing that Figure 3 which represents this data seemingly does not control for repeated measures (it contains many more datapoints than the study's actual sample size of 164 individuals). This needs to be corrected for this figure to be meaningfully interpretable.

Finally, would it make sense to somehow incorporate the number of individuals present for this analysis? Much like any other social or communicative behaviour, I would predict the frequency of occurrence to depend on how many opportunities (i.e. social partners) there are to engage in it.

Line 460: "For BD and LT we excluded exposures 4 and 3, respectively, due to circumstances resulting in very small proportions of these groups present at these exposures"

What was the criterion for a satisfactory proportion? Why was this chosen?

Line 461: "We ran the same model including these outlier exposures and present these results in the supplementary material (SM3)."

The results of this supplemental analysis should be briefly stated. Do they support the original analysis or not?

Line 465: "Due to very low numbers of infants ever being targets of muzzle contacts, we merged the infant and juvenile age categories for this analysis."

This strikes me as a rather large mistake. The research question being asked by the authors here is "How does age influence muzzle-contact behaviour?"<br /> Then, when one age group (infants) is very unlikely to be a target of muzzle-contact, the authors have erased this finding by merging them with another age category (juveniles). This really does not make sense, and seriously confounds any interpretation of either age category.

Lines 466-474: Why was rank removed for the second and third models? Why is Group no longer a random effect (as in the previous analysis)? The authors need to justify such steps to give the reader confidence in their approach.

Furthermore - because of the way this model is designed, I do not think it can actually be used to infer that these groups are preferentially targeted, merely that adult female and adult males are LESS likely to target others than to be targeted themselves, which is a very different assertion.

Because the specific outcome measure was not described here, this only became apparent to me after inspecting Figure 3, where outcome measure is described as "Probability of (an individual) being a target rather than initiator" - so, it can tell us that adults are more often targeted rather than initiating, but does not tell us if they are targeted more frequently than juveniles (who may get targeted very often, but initiate so often that this ratio is offset).

Lines 467-473: "Our first simple model included individuals' knowledge of the novel food at the time of each muzzle contact (knowledgeable = previously succeeded to extract and eat peanuts; naïve = never previously succeeded to extract and eat peanuts) and age, sex and rank as fixed effects. Individual was included as a random effect. The second model was the same, but we removed rank and added interactions between: knowledge and age; and knowledge and sex. The third model was the same as the second, but we also added a three-way interaction between knowledge, age and sex."

This is a good example of some of the issues I describe above. What is the justification for each of these model-structures? The addition and subtraction of variables and interactions seems arbitrary to the reader.

Author Response

Reviewer #1 (Public Review):

This is an extremely well-done study, revealing a fascinating phenotype of mes-4 mutant, which they show upregulates X-linked genes, leading to PGC death. These X-linked genes are mostly oogenesis genes, upregulation of which likely impedes normal proliferation of PGCs. The results are very concrete and supports their conclusion, and contribute significantly to the field. I do not have any major concerns except for a couple of conceptual issues. First, the title 'germline immortality' does not seem to be well aligned with the results. It is not wrong that PGCs die in mes-4 mutant, and thus the germline is 'mortal': however, the term 'germline immortality' implies multi-generational passages of germline, and the data in the present study, where mutant PGCs just die in the offspring, do not necessarily point to 'germline immortality' per se. So, I suggest to change the title to reflect the contents of the paper better.

Good point. We changed germline immortality to germline survival and/or development throughout the paper.

Second, although the authors speculate (in the discussion) why X activation is toxic to germ cells (discussing that upregulated X-linked genes are oogenesis genes, whose precocious activation is toxic to PGCs), there is not sufficient discussion as to why the effect is mostly limited to X chromosome, and why mes-4 is specifically involved in this. Is it because all oogenesis genes are concentrated on X chromosome? (likely not). Are autosomal genes that are upregulated in mes-4 mutant also oogenesis genes? Is this related to dosage compensation? I would like to see fuller discussions as to why X chromosome requires special regulation, also discussing the role of mes-4 in this context. I understand that the authors might have refrained from expanding discussions on matters that do not have any data, but without this discussion, I feel that many readers will be left wondering 'why?'.

As noted in Point #5 above, we added to Discussion whether up-regulation of X genes in mes-4 mutant PGCs and EGCs reflects a defect in dosage compensation or a defect in keeping the oogenesis program (which is enriched for X-linked genes) quiet in the nascent germline (see lines 604-630). Based on new analyses showing up-regulation of oogenesis genes (on the X and autosomes) in mes-4 and PRC2 nascent germlines and the points in Discussion, we favor the view that the essential function of MES-4 and PRC2 is to repress X-linked oogenesis genes in PGCs and EGCs (see Figures 6 and 7, associated figure supplements, and lines 389-417).

Reviewer #2 (Public Review):

This manuscript makes substantial progress in resolving a long-standing mystery regarding the precise role of the histone methyltransferase MES-4 in promoting germline development. MES-4 maintains the histone modification H3K36me3 and germ cell survival, but prior evidence was unable to distinguish among several possibilities for target pathways. This paper utilizes a transcriptional profiling approach at the critical time of germline development to definitively demonstrate that the essential function of MES-4 is to repress X gene expression in germ cells. This result is surprising because X repression is an indirect effect of MES-4 activity (MES-4 does not localize to the X), while the direct effect of maintaining germline gene expression is not essential. To buttress this finding, the authors also utilize a series of elegant genetic experiments to independently test whether expression from the X is sufficient to cause germ cell degeneration. They then go further to identify a single X-linked target, lin-15b, as a primary contributor to the inappropriate X-linked gene expression in mes-4 mutants, by showing that loss of lin-15b activity rescues both the germline degeneration and X mis-expression of mes-4 mutants. Finally, the authors demonstrate that PRC2, the H3K27me3 histone methyltransferase and MRG-1, a candidate H3K36me3 effector protein, are also involved in promoting X silencing through lin-15b.

The manuscript's strengths lie in the development or application of novel techniques, including the profiling of individual pairs of PGCs (a non-trivial advancement), as well as some very well-designed and conceptually innovative genetic assays. These were used to address specific and important gaps in knowledge regarding the phenotype of mes-4, which had been elusive despite having been studied for almost 30 years. Although specific to C. elegans in some ways, the findings are clearly relevant to conserved regulatory events, such as epigenetic memory mechanisms and establishment of opposing chromatin states. Thus, this work provides a substantial advance in the field overall.

One limitation of this study is the lack of clarity about the conclusions regarding the relationship between the two H3K36me3 histone methyltransferases mes-4 and met-1, and between X vs autosomal gene expression. The authors do not precisely state what genes (X or A) are affected in the met-1 and mes-4 mutants. Ultimately, this confusion muddles the final message of X chromosome upregulation being the critical contributor to the mes-4 germline degeneration phenotype. The experiment presented in figure 3B indicates that loss of mes-4 or met-1 is sufficient to prevent germline development even when the Xs are repressed, indicating that failure to activate autosomal gene expression is also an underlying cause of the degeneration. Perhaps this cannot be definitively concluded without directly assessing met-1 and met-1;mes-4 mutant PGCs (or EGCs) for gene expression changes. If technically possible, this would be a very valuable experiment to directly examine autosomal gene expression changes in the double mutant.

We profiled met-1 PGCs and observed very few mis-regulated genes (Figure 7 – supplemental figure 1). We tried to profile met-1; mes-4 double mutant PGCs, which completely lack both MET-1 and MES-4 and inherit chromosomes that lack H3K36me3. That was not feasible, due to the high level of embryonic lethality and rapid deterioration of PGCs dissected from met-1; mes-4 double mutant larvae. Notably, this demonstrates that germlines that lack both maternal K36me3 HMTs are sicker than those that lack just 1 of the HMTs. The high degree of embryo lethality suggests an essential function for MET-1 and MES-4 in the soma. As requested, we generated and included a list of X and autosomal genes mis-regulated in met-1, mes-4, and other mutant PGCs (see Figure 7—figure supplement 1).

The sterility of hermaphrodites with a met-1; mes-4 mutant XspXsp germline and lacking either maternal MES-4 or maternal MET-1 may be due to mis-regulation of autosomal genes, or it may reflect that the X chromosomes are not repressed in met-1; mes-4 XspXp germlines that lack H3K36me3. To test that, we would need to profile those XspXsp PGCs. It is not feasible to identify mutant F1 larvae with Xsp/Xsp PGCs immediately after hatching, which is required for transcript profiling. We think that the main message from analyzing met-1; mes-4 mutant XspXsp germlines -- that inherited H3K36me3 marking is not critical for germline development but re-establishment of marking is important and requires both enzymes – does not require our delving into the cause of sterility of mutant XspXsp germlines lacking MET-1.

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Autophagy of the endoplasmic reticulum (ER-phagy) is a fundamental process that is essential for maintaining cellular homeostasis and quality control. We recently identified a novel mechanism regulating ER-phagy in both plants and animals that is based on the ubiquitin-like protein modifiers ATG8 and UFM1, and the ER-associated protein, C53. Here, we use a combination of evolutionary, biochemical, and physiological experiments to investigate the evolution and regulation of this process. We reveal the dynamic evolution of UFM1 and the ubiquity of C53-mediated autophagy across eukaryotes. Leveraging these results, we then identify an ancestral molecular toggle switch, mediated by shuffled ATG8-interacting motifs (sAIMs), that controls C53-mediated autophagy through competitive binding between UFM1 and ATG8. These findings provide new insights into the evolution of UFM1, reveal a conserved mechanism for the regulation of ER-phagy, and raise new and exciting hypotheses about the diversity and function of the UFMylation pathway. We believe that this work will be of interest to those studying autophagy and cellular stress response but will also serve as an interesting example of the benefits of combining evolutionary analyses with biochemical and cellular experiments.

Our manuscript has been reviewed by three reviewers through ReviewCommons, whose comments, and our responses, can be found below. Two of the reviewers (Reviewer 1 and 3) were supportive of our work and its significance whereas Reviewer 2 questioned the novelty of our findings.

Each of the reviewers’ comments can be addressed through a few supporting experiments as well as an improved manuscript which clarifies the novelty and significance of our results. While being supportive of our work, Reviewer 1 requested minor additional experiments to support our mechanistic conclusions and Reviewer 3 suggested that we expand our characterizations of C53 function to additional eukaryotic supergroups. These experiments are straightforward to perform, the materials and protocols to accomplish them are already established, and our overall conclusions are robust to the resulting outcomes.

In contrast, Reviewer 2 did not suggest any additional experiments but rather challenged the novelty of our results as well as some of our interpretations. In particular, Reviewer 2 was uncertain of how our phylogenomic analyses built upon a previous study, published in 2014, which used comparative genomics to identify ubiquitin-related machinery across eukaryotes. Although it was an oversight to not reference this study (we cited a more recent article showing the same results), we were aware of their conclusions that UFMylation was present in the last eukaryotic common ancestor but absent in Fungi. We now clearly outline, both below and within the manuscript, our key phylogenomic results. These were acquired after implementing more advanced and comprehensive comparative genomic searches which allowed us to identify dynamic patterns in UFMylation evolution and permitted co-evolutionary analyses which were not only important for informing our experimental hypotheses but generated new functional questions. Our phylogenomic analyses are also linked to biochemical and physiological data, providing, for the first time, experimental support for our conclusions regarding UFMylation evolution. Similarly, Reviewer 2 suggested that our mechanistic results were an incremental extension of our previous work. Although our current work does of course build on our initial identification of C53-mediated autophagy, this manuscript provides novel insights into the importance and function of this process by revealing its ubiquity across eukaryotes and by characterizing the mechanistic details of its regulation. Ultimately, we disagree with Reviewer 2 but appreciate that this misunderstanding likely resulted from a lack of context and clarity in our manuscript which we have now resolved.

As outlined in detail below, we will address the reviewers concerns through additional experiments, analyses, and improvements to the text.

Thank you for considering our manuscript. We look forward to hearing from you.

Description of the planned revisions

We thank the reviewers for carefully evaluating our manuscript and for providing us with an opportunity to respond to their suggestions and criticisms. As you can see below in our pointby-point response, we address each of the points raised by the reviewers through the addition of supporting experiments, analyses, and an improved text. Altogether, we think these additional experiments and textual changes will significantly improve the manuscript. Therefore, we would like to thank all the reviewers and editors for their time and input.

Referee #1

Evidence, reproducibility and clarity

In this manuscript Picchianti et al. provide novel insights into the interaction of C53 with UFM1 and ATG8. Initially, the authors show that protein modification by UFM1 exists in the unicellular organism Chlamydomonas reinhardtii. To that end they demonstrated that pure Chlamydomonas UBA5, UFC1 and UFM1 proteins, can charge UFC1. Then, they showed that C53 interacts with ATG8 and UFM1. Specifically, they found that the sAIM are essential for the interaction with UFM1, while substituting this motif with canonical AIM prevents the binding of UFM1 but not of ATG8. Since binding of C53 to ATG8 recruits the autophagy machinery, the authors suggest that ufmylation of RPL26 releases UFM1 from C53 which allows the binding of ATG8. Overall, the authors demonstrate that C53 that forms a complex with UFL1 connects between protein ufmylation and autophagy by its ability to bind both UBLs. Here the authors revisited the assumption that only multicellular organisms have the UFM1 system. Using bioinformatic tools they show that it exists also in unicellular organism. Also, they show that in some organisms the E3 complex UFL1, UFBP1 and C53 exist but not UBA5, UFC1 or UFM1. This is a very interesting observation that suggests an additional role for this complex. In Fig 1C the authors show that in Chlamydomonas RPL26 undergoes ufmylation. Please use IP against RPL26 and then a blot with anti UFM1. From the current experiment it is not clear how the authors know that this is indeed RPL26 that undergoes ufmylation

RPL26 is highly conserved across eukaryotes, so by comparing our western blots with previous studies (Walczak et. al., 2019, Wang et al. 2020), we concluded that these bands corresponded to UFMylated RPL26. However, we agree with the reviewer that we need to confirm the identify of RPL26 with additional assays. Since the submission of the manuscript, we tested RPL26 antibodies in Chlamydomonas and showed that they work well. So, we will update our figure with the confirmation westerns.

In the second part of the manuscript the authors characterize the interaction of C53 with ATG8 and UFM1. This is a continuation of their previous published work (Stephani et al, 2020). Here the reviewer thinks that further data on the binding of these proteins to C53 is required. Specifically, defining the Kd of these interactions using ITC or other biophysical method can contribute to the study.

We agree with the reviewer. To obtain the KD values, we will perform ITC experiments with C53 wild type, a C53 sAIM mutant and a C53 cAIM variant titrated with ATG8 and UFM1.

Under normal condition the authors suggest that C53 binds UFM1 and this keeps it inactive. The reviewer thinks that this claim needs further support. Using IP (maybe with crosslinker) the author can show that C53, in normal conditions, bind more UFM1 than ATG8. Also, since the interaction of UFM1 to C53 is noncovalent, it will be nice to show how alternations in UFM1 expression levels can affect the activation of C53.

We thank the reviewer for this suggestion. Since the submission of the manuscript, we have obtained UFM1 overexpression lines. We will pull on C53 using our C53 antibody and check for ATG8 levels in wild type and UFM1 overexpressing lines under normal and stress conditions. We think this will show how alterations in UFM1 levels can affect C53 activation.

Finally, the authors suggest that ufmylation of RPL26 allows binding of ATG8 to C53 and this, in turn, leads to C53 activation. Can the authors show that in cells lacking UBA5, under normal condition or with Tunicamycin treatment, ATG8 does not activate C53 due to the fact that UFM1 does not leave C53.

In Stephani et al., we showed that C53-mediated autophagy requires the UFMylation machinery. In ufl1 and ddrgk1 mutants, C53 becomes insensitive to ER stress. However, to supplement these results, we will perform autophagic flux assays using the native C53 antibody to test autophagic degradation of C53 in a uba5 and ufc1 mutant under normal and tunicamycin stress conditions. The uba5 mutant that we have is a knockdown, so that’s why we will include the ufc1 mutant in our experiments.

Significance

This manuscript advances our understanding of the connection of ufmylation to autophagy which is mediated by C53.

Thank you!

Referee #2

Evidence, reproducibility and clarity

The manuscript from Picchianti et al. seeks to define the role of CDK5RAP3 (hereinafter referred as C53) during autophagy and its interplay with UFMylation. Together with UFL1 and DDRGK1, C53 is a component of a trimeric UFM1 E3 ligase complex that modifies the 60S ribosomal protein RPL26 at the endoplasmic reticulum (ER) surface upon ribosomal stalling (among other proposed functions that are not addressed). Several previous studies have implicated the UFMylation pathway in autophagy or ER-phagy although a non-autophagic fate for UFM1- tagged ribosomal subunits has also been reported. A previous study from the same authors (PMID: 32851973) identified an intrinsically disorder region (IDR) in C53 that is necessary and sufficient for interaction between C53 and autophagy receptor, ATG8. They reported that this IDR comprises four non canonical ATG8 interacting motifs (AIM), named shuffled AIMs (sAIMs) and showed that combinatorial mutagenesis of sAIM1, sAIM2, and sAIM3 abrogates ATG8 binding. A similar effect was observed for plant C53, though an additional canonical AIM (cAIM) in the C53 IDR had to be mutated to completely abolish C53 and ATG8 interaction. The earlier study reported that C53 IDR also interacts with UFM1, and this interaction can be disrupted in vitro by adding increasing concentration of ATG8, suggesting that ATG8 and UFM1 may compete with one another for C53 binding. The present paper attempts to build on this previous work by using phylogenomics to infer a coevolutionary relationship between UFMylation machinery and sAIMs in C53, which the authors argue, constitutes further evidence of the primary importance of a role for UFMylation in ER homeostasis. The manuscript includes a lot of biochemical data using variations of in vitro and in vivo pull-down experiments to define the roles of individual AIMs in mediating the binding of C53 to ATG8 and to UFM1. They also use NMR spectroscopy in an attempt to define the structural basis of the UFM1 and ATG8 binding to C53, concluding that plant C53 interacts with UFM1 mainly through sAIM1, while interaction with ATG8 requires cAIM as well as sAIM1 and sAIM2. Finally, the authors attempt to contextualize these findings by conducting studies on Arabidopsis mutants, showing that replacing sAIMs with cAIMs causes increases sensitivity to ER stress and apparently increases formation of C53 intracellular puncta that may colocalize with ATG8. From these data the authors concluded that the dual-ATG8 and UFM1 binding of C53 IDR regulates C53 recruitment to autophagosomes in response to ER stress. Major Issues: 1) The phylogenomics analysis conclusion that UFM1 is common in unicellular lineages and did not evolve in multicellular eukaryotes is not novel, as another comprehensive analysis of UFM1 phylogeny, published eight years ago - in 2014 - by Grau-Bové et al. (PMID: 25525215), also reported that UFM1, UBA5, UFC1, UFL1 and UFSP2 were likely present in LECA and lost in Fungi. Although the phylogenomic analysis by Picchianti et al. is also extended to DDRGK1 and C53 proteins, and some parasitic and algal lineages, their findings are incremental. Their proposed coevolution of sAIM and UFM1 is based on presence-absence correlation observed within five species (i.e., Albugo candida, Albuco laibachii, Piromyces finnis, Neocallimastix californiae, Anaeromyces robustus). However, this coevolutionary relationship must be further investigated by substantially increasing the taxonomic sampling within the UFM1-lacking group.

We were aware that previous studies had investigated the distribution of UFMylation proteins across eukaryotes and that these analyses had predicted the presence of UFMylation in LECA and subsequent loss in Fungi. We included a more recent citation noting this (Tsaban et al. 2021) but apologise for not citing Grau-Bové et al. (2014), which we have now included. We must emphasize that our results are not incremental. Although we had made a point of emphasizing the presence of UFM1 in LECA, this was to counter a recent and highly cited paper in the field which claimed that UFMylation evolved in plants and animals (Walczak et al. 2019). Below we note the novel and important results from our phylogenomic analyses: 1. We used improved taxonomic sampling and more advanced comparative genomics methods to identify UFMylation components sensitively and specifically across eukaryotes. This involved the inclusion of additional eukaryotic genomes, phylogenetic annotation of orthologs, and genomic searches to complement proteome predictions. These methods are essential for accurately identifying UFMylation components and yield more robust results than using sequence similarity clustering (Tsaban et al. 2021) or un-curated Pfam HMMER search results (Grau-Bové et al. 2014). 2. By placing our UFMylation reconstructions in a modern phylogenetic context we were not only able to support previous observations which noted the presence of UFM1 in LECA and its loss in Fungi (Grau-Bové et al. 2014) and Plasmodium (Tsaban et al. 2021), but also to identify novel patterns in the evolution of UFMylation. This included the observation of recurrent losses in diverse but trophically-related lineages (such as algae and parasites) and revealed the retention of certain UFMylation components in the absence of UFM1. We identified the frequent coretention of UFL1 and DDRGK1 following UFM1 loss in multiple eukaryotic groups, including Fungi, which were previously thought to be devoid of UFMylation machinery. These previously uncharacterized patterns, suggest that these proteins could have alternative functions and may be functionally associated with life history. These results therefore expand on and add complexity to our understanding of the evolution of UFMylation. 3. By conducting a comprehensive and accurate survey of UFMylation components we were able to use our data to examine co-evolutionary trends between C53 and UFM1, which would have been incomplete and inaccurate using previously curated datasets. As the reviewer noted, only five species were identified that encoded C53 but lacked UFM1. This is not a reflection of insufficient taxon sampling, but rather the strong co-evolution between C53 and UFM1 (i.e., when UFM1 is lost, C53 is almost always lost as well). We attempted to identify additional cases by searching hundreds of fungal and oomycete genomes as well as those from other eukaryotes, but no other species were found. We agree with the reviewer that additional taxa would have made our analyses stronger, but importantly, we do not rely on genomic correlations to infer function. Rather, we use these correlations to generate functional hypotheses which we then tested experimentally. In this way, we do not rely on the strength of our correlations. We have now revised the manuscript to include additional context (including citations) and have improved the clarity of the text to better convey the novelty of our findings.

2) The manuscript presents an overwhelming amount of biochemical and structural data obtained from a variety of protein binding techniques (i.e., NMR spectroscopy, in vitro GSTpulldown, fluorescence microscopy-based on-bead binding assays, and native massspectrometry). The results are poorly explained and not organized in a logical manner. Moreover, no attempt was made to explain the rationale behind using one technique over the other or how one method complements another to build a stronger conclusion than any individual approach. Given that none of the methods employed report quantitative measurement of binding affinities between C53 IDR and UFM1 or ATG8, it is not clear how the data presented in this manuscript contribute to our understanding of the proposed competition model for UFM1 and ATG8 binding to C53 IDR. To conclude that an interaction is "stronger" or "weaker" it is necessary to measure equilibrium binding constants. Fortunately, there are suitable techniques, including surface plasmon resonance (SPR), microscale thermophoresis (MST), fluorescence anisotropy, or calorimetry that are available to dissect these complex competitive binding interactions and to build models.

We thank the reviewer for their suggestion. Although we attempted to describe the rationale behind each experiment (please see the line 135-137; on-bead binding assays, line154-157; NMR, 177-181), we agree that the volume of data and variety of techniques warrants additional explanation. We will revise the manuscript to further explain our rationale for using each of the different approaches. As we noted above in our response to reviewer 1, we will also perform relevant ITC binding assays to quantify the interaction between C53, ATG8, and UFM1.

3) The NMR studies have the potential to dissect the types of dynamic binding inherent in unstructured proteins. However, the abundant NMR data presented combined with the aforementioned binding studies, remarkably, do not seem to significantly advance our understanding of how the system is organized or even how UFM1 and ATG8 bind C53, beyond the rather vague and somewhat circular conclusion stated in the abstract: "...we confirmed the interaction of UFM1 with the C53 sAIMs and found that UFM1 and ATG8 bound the sAIMs in a different mode." Or on line 165 "Altogether these results suggested that ATG8 and UFM1 bind the sAIMs withn C54 IDR, albeit in a different manner".

We agree that NMR has the potential to dissect the complex binding interactions between UFM1, ATG8, and C53, but disagree with the reviewer’s interpretation that our NMR data fail to achieve this. To sum up, our NMR data: 1. Revealed the structural basis of the interaction of C53-IDR with ATG8 and UFM1 at atomic resolution by showing that UFM1 binds preferentially to sAIM1 in the fast-intermediate exchange [Fig.4 and Fig. S7B], instead ATG8 binds cAIM in the slow-intermediate exchange, and once cAIM is occupied, it binds sAIM1,2 with lower affinity in the fast-intermediate exchange (Fig.4 and Fig.S7D). 2. Determined conformational changes in C53 IDR upon binding of ATG8, but not UFM1 (Fig.S7E), which lead to increased dynamics in distinct regions in C53 IDR. These data could explain how binding of first ATG8 would trigger C53-dependent recruitment of the tripartite complex to autophagosomes. 3. Identified how UFM1 binds to atypical hydrophobic patch in C53 sAIM, similar to what was shown for the UBA5 LIR/UFIM. To sum up, our results shed light on how both UBLs interact with C53, being sAIM1 the highest affinity binding site for UFM1 while ATG8 binds cAIM preferentially before occupying sAIM1,2. To provide more detailed information on the atomic details of the interaction between C53 and the UBLs, we will perform molecular docking studies by using the restraints obtained from the experimental NMR data.

4) The functional assays performed in Arabidopsis do not support the competitive model between UFM1 and ATG8 for binding to C53 during C53-mediated autophagy. The fluorescence microscopy images do not provide convincing evidence of colocalization between C53 and ATG8. In fact, in contrast to the claims made in the text or the quantification, mCherry-C53 fluorescence does not seem to localize in discrete puncta and its signal does not seem to overlap with ATG8A.

We disagree with the reviewer’s interpretation of these results although we acknowledge that there is some subtlety in interpreting the co-localization data. Importantly, Arabidopsis has 9 ATG8 isoforms and C53 can bind to most of them with varying affinities (see Stephani et al). Because of this, we do not expect C53 puncta to fully colocalize with ATG8A puncta. Additionally, the C53 puncta are smaller and more subtle than ATG8 puncta, which label the entire autophagosome. To reconcile this, we will quantify the effect by performing colocalization analyses under normal and stress conditions. We will also upload all the raw images as supporting material, so that anyone can independently assess our images.

Minor Issues: 1. The authors might choose to avoid teleological arguments such as (line 135): "As the phylogenomic analysis suggested that eh sAIMs have been retained to mediate C53-UFM1 interaction..."

We thank the reviewer for this suggestion and will modify the text accordingly.

1. The authors refer on multiple occasions to C53 "autoactivation" without defining what they mean by this. Do they propose that C53 UFMylates itself?.

We refer to C53 activity as the ability to recruit the autophagy machinery and initiate cargo sequestration and degradation in the vacuole. We attempted to explain this in lines 57-61 but we will reword it more clearly, as suggested by the reviewer.

1. The paper might want to avoid preachy philosophical statements like "Our evolutionary analysis also highlights why we should move beyond yeast and metazoans and instead consider the whole tree of life when using evolutionary arguments to guide biological research." (333- 335). While this is indeed a laudable goal, given the rather limited insights from this study, it is unclear how this paper exemplifies the notion.

We added this statement as we were intrigued by our evolutionary analyses’ ability to link C53 to UFM1 (an association which took years to identify experimentally) and generate useful functional hypotheses about the interaction between C53 sAIMs and UFM1. As we mentioned above, we also wanted to highlight this point in reference to a recent prominent study in the field which drew conclusions after only considering animals, plants, and fungi (Walczak et al., 2019). We believe this point is important and underappreciated by some cell biologists, but we will modify the text to make it more generic: “This work highlights the utility of using evolutionary analyses and eukaryotic diversity to generate mechanistic hypotheses for cellular processes”.

Significance

Overall, while the manuscript contains an abundance of new data, the overall conclusion of the work, stated in the title: "Shuffled ATG8 interacting motifs form an ancestral bridge between UFMylation and C53-mediated autophagy" does not constitute a significant advance beyond other published phylogenomic analysis (below) and the two previous papers by the same authors, including the 2020 paper "A cross-kingdom conserved ER-phagy receptor maintains endoplasmic reticulum homeostasis during stress (PMID: 32851973)" and the 2021 paper "C53 is a cross-kingdom conserved reticulophagy receptor that bridges the gap between selective autophagy and ribosome stalling at the endoplasmic reticulum PMID: 33164651)". While a regulatory interaction between UFMylation and autophagy is of potential importance, the data in this manuscript do not constitute a major advance and fail to provide new mechanistic insight to explain the role of C53 IDR in autophagy and its interplay with UFMylation

We disagree with the reviewer’s suggestion that our work does not constitute a significant advance. We outlined above in detail the novel insights that were obtained from our phylogenomic analysis which involved using improved methods to reveal a much more dynamic and informative picture of UFMylation evolution than has been described previously. Likewise, this manuscript builds substantially on our previous mechanistic work. In our 2020 paper (which is summarized in the mentioned 2021 review article), we identified C53 as an ER-associated protein that binds ATG8 through sAIMs and interacts with the phagophore after RPL26 UFMylation. This work linked C53 activity to ER-phagy and highlighted its importance in plant and animal stress response. However, key questions remained unanswered prior to our current work such as whether this mechanism is conserved across eukaryotes, especially in unicellular species, how C53 activity is regulated, and how UFM1 and ATG8 interact with C53. Our current manuscript builds on this work with the following key results: 1. We use a combination of phylogenomic and experimental analyses to demonstrate that C53 function is conserved across eukaryotes. 2. We reveal a mechanism whereby UFM1 and ATG8 compete for binding at the sAIMs in the C53 IDR and characterize how each of these ubiquitin-like proteins interacts in an alternative way (see the NMR results described above). 3. We show how the sAIMs are required for the regulation of C53-mediated autophagy and reveal the importance of UFM1-ATG8 competition in preventing C53 autoactivation, which causes unnecessary autophagic degradation and impairs cellular stress responses.

These insights are fundamental for understanding the mechanisms regulating C53-mediated autophagy which were unknown before this work. We will therefore adjust our manuscript to more clearly and explicitly explain how our data build on previous observations so that the novelty and significance of our results are clearer.

Referee #3

Evidence, reproducibility and clarity

Picchianti and colleagues have investigated a conserved molecular framework that orchestrates ER homeostasis via autophagy. For this, they have carried out phylogenomics and large-scale gene family analyses across eukaryote diversity as well as a barrage of molecular lab work. The amount of work carried out as well as the overall quality of the study is impressive.

Thank you!

I have only a few comments that should be very easy to tackle. (1) Maybe I missed it, but please upload all alignments used for phylogenetics and phylogenomics for reproducibility to e.g. Zenodo, Figshare or other suitable OA databases.

We included the alignments in the supplementary data, but as suggested, we will upload all the source data including the scripts and the alignments to Zenodo.

(2) "Why these non-canonical motifs were selected during evolution, instead of canonical ATG8 interacting motifs remains unknown" --> Maybe there is no "why" and these were not selected at all. Could be random... drift, non-adaptive constructive neutral evolution. I am not saying that asking "why" in evolutionary biology is wrong. It, however, often does not yield satisfactory answers--or any answer at all.

The reviewer is completely right that “why” is not the right way to frame an evolutionary question. Thank you for pointing this out. We will revise the text and make sure that we remove these kinds of deterministic statements.

(3) The authors make a case for UFMylation in LECA and I am fully sympathetic with this. However, getting rid of misfoled/problematic proteins and subcellular entities is something that prokaryotes also to a certain degree must have (and still do) master. Are inclusion bodies or export their only answers (I don't know)? Of course, in eukaryotes with all their intracellular complexity this is likely more of an issue. Given the scope of this manuscript (i.e. shedding light on that ancient framework, deep evolutionary roots in eukaryote evolution etc. etc.) it would be very interesting to read the authors thoughts on this and also pinpoint the prokaryote/eukaryote divide in light of the machinery discussed here.

Thank you for this suggestion. We did indeed check whether any of the UFMylation machinery were present in prokaryotes and only found homologs of UFSP2. These results are consistent with Grau-Bové et al. (2014) who conducted an equivalent analysis and concluded that UFMylation machinery were derived during eukaryogenesis. We will make reference to this in the revised manuscript.

Significance

This study not only impresses with the volume of experiments and data, but also the courage to show conservation of a molecular framework by working with such a range of distantly-related eukaryotes. The results and conclusions from this study should be interesting to anyone working in the broad fields of cellular stress and/or autophagy--both extremely timely topics.

We thank the reviewer for understanding our take-home message and the advances made. We especially thank the reviewer for understanding the challenge of connecting in silico genomic data with in vivo and in vitro experiments.

CROSS-CONSULTATION COMMENTS

Referee #2 The challenge in providing a fair review of this manuscript is to clearly define what contributions are novel, significant advances. It is difficult to tell the way the manuscript is written, as it is unclear how the new data - which are voluminous- actually advance the model already put forth by the same authors in two previous publications. It is also unfortunate that the authors overlooked the 2004 phylogenomics paper. There clearly are some new pieces of information here, but the overall increment in knowledge is rather minimal. Response from Referee #3 I agree that the authors somehow steamroll the reader with a wealth of data. But I think this can be addressed by the authors by requesting a lot more justification and by giving them the opportunity to put the significant advances into their own words. This is, in my opinion, quite doable in course of a revision. Overall I have to say that I am very sympathetic with the crosseukaryote reactivity approach that the authors have taken. It is quite intriguing.

We thank the reviewers for this useful exchange. We agree that our manuscript was not clear enough to emphasize the novelty of our results which likely resulted from the volume and diversity of the experiments and analyses that were presented. We have now revised the manuscript to improve the context and rationale for the study, the intent and hypotheses behind each experiment, and the novel results and insights obtained in each section.

Response from Referee #2 I agree that the cross-eukaryote approach is intriguing. Shouldn't we be concerned that the 2004 publication already made two of their key points (ie present in LECA, loss in Fungi). What is the incremental insight from this paper? I'd appreciate an opinion from an evolutionary biologist as to how strongly one can conclude functional co-evolution from such correlative data, especially given the rather small number of supporting examples. Is it also necessary to consider counter-examples- ie species that have sAIMs but no UFM1 (I believe that they found a few such cases)?

Importantly, we do not conclude functional co-evolution from our correlative data. Instead, we used these correlations to generate hypotheses that we tested with various experiments in different model systems. For example, the apparent correlation between C53 sAIMs and UFM1 prompted us to test whether or not UFM1 and sAIMs interact. Regardless of sample size or statistical significance, phylogenomic analyses can never demonstrate functional links, only correlations, which is why we combined these two approaches. Although only a few species encoded C53 without UFM1, each of these contained C53 cAIMs and lacked sAIMs (Figure 2c). There are species with UFM1 that lack C53 but this makes sense as UFM1 is used in other processes besides ER-phagy. We have revised the text to make our approach and reliance on certain data clearer.

Response from Referee #3 Well with these deep evolutionary questions this is always a challenge. Where does one stop to sample more homologs for one's analyses (one from each supergroup [which are no longer recognised by the community])? In that sense, the authors are right to make the parsimonious base assumption that if X and Y interact in species A and B (no matter how distant they are related) then X and Y interacted in the last common ancestor of A and B. That being said, if I would have designed this study, I would have sampled more broadly for my in vitro crosseukaryote approach. But also this, I think, could be carried out by the authors in a reasonable timeframe. Specifically, they have now sampled from Amorphea and Archaeplastida, they should add one from TSAR, one Haptista, one Cryptista, and one CRuM. If they synthesised the proteins via a company, they could have the constructs in a few weeks for about 1K Euro - I do not think that this would be an unreasonable request.

We agree that testing C53 function in additional species would strengthen our understanding of the conservation of this pathway across eukaryotes, as it cannot be assumed that orthologous proteins will function in the same way across all species. To our knowledge there is no other work showing experimentally that the UFMylation pathway is working in a single-celled organism. We focussed our efforts on the unicellular green alga, Chlamydomonas due to its relative experimental tractability. However, testing this was not trivial as it required us to establish expression and purification protocols, isolate Chlamydomonas mutants, optimize physiological stress assays, and perform the experiments.

Nevertheless, we agree that we could expand our in vitro assays with C53 orthologs from additional species. As suggested by reviewer 3, we will now synthesize 6 more C53 isoforms from two TSAR representatives (the alveolate, Tetrahymena thermophila, and the stramenopile, Phytophthora sojae), as well as a representative from Haptista (Emiliania), Cryptista (Guillardia), Diplomonada (Trypanosoma), and CRuMs (Rigifila). We will test their interaction with human and plant ATG8 and UFM1 proteins. We have also added two species from CRuMs into our phylogenomic analysis.

The list of experiments that we can do to address the reviewer’s concerns: 1. Repeat experiment in Figure 1C probing with �-RPL26. 2. To calculate KD values, perform ITC experiments with C53 wild-type, C53 sAIM mutant and C53 cAIM variant titrated with ATG8 and UFM1. 3. Perform CoIP experiments using C53 antibody in wild type and UFM1 overexpressing lines and detect for ATG8 association, under normal and stress conditions. 4. We will test autophagic degradation of C53 in uba5 and ufc1 mutants under normal and tunicamycin stress conditions by performing autophagic flux assays using the native C53 antibody 5. Molecular docking studies to see C53’s structural rearrangements leading to ATG8 and UFM1 binding. 6. Figures from co-localization experiments in Figure 5G will be revisited and we will perform additional co-localization analyses such as Pearson coefficient under normal and stress conditions. We will also upload all the raw images as supporting material, so that anyone can independently assess our images. 7. We will upload all the source data for phylogenomic analyses, including scripts and alignments to Zenodo. 8. Test the interaction of 6 newly synthesised C53 isoforms from: (1) an alveolate (tsAr, Ciliate), (2) a stramenopile (tSar, Phaeodactylum), (3) a haptophyte (Emiliania), (4) a cryptophyte (Guillardia), (5) a diplomonad (Trypanosoma) and (6) a CrRuM with human and plant ATG8 and UFM1 proteins.

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Referee #2

Evidence, reproducibility and clarity

The manuscript from Picchianti et al. seeks to define the role of CDK5RAP3 (hereinafter referred as C53) during autophagy and its interplay with UFMylation. Together with UFL1 and DDRGK1, C53 is a component of a trimeric UFM1 E3 ligase complex that modifies the 60S ribosomal protein RPL26 at the endoplasmic reticulum (ER) surface upon ribosomal stalling (among other proposed functions that are not addressed). Several previous studies have implicated the UFMylation pathway in autophagy or ER-phagy although a non-autophagic fate for UFM1-tagged ribosomal subunits has also been reported.

A previous study from the same authors (PMID: 32851973) identified an intrinsically disorder region (IDR) in C53 that is necessary and sufficient for interaction between C53 and autophagy receptor, ATG8. They reported that this IDR comprises four non canonical ATG8 interacting motifs (AIM), named shuffled AIMs (sAIMs) and showed that combinatorial mutagenesis of sAIM1, sAIM2, and sAIM3 abrogates ATG8 binding. A similar effect was observed for plant C53, though an additional canonical AIM (cAIM) in the C53 IDR had to be mutated to completely abolish C53 and ATG8 interaction. The earlier study reported that C53 IDR also interacts with UFM1, and this interaction can be disrupted in vitro by adding increasing concentration of ATG8, suggesting that ATG8 and UFM1 may compete with one another for C53 binding.

The present paper attempts to build on this previous work by using phylogenomics to infer a co-evolutionary relationship between UFMylation machinery and sAIMs in C53, which the authors argue, constitutes further evidence of the primary importance of a role for UFMylation in ER homeostasis. The manuscript includes a lot of biochemical data using variations of in vitro and in vivo pull-down experiments to define the roles of individual AIMs in mediating the binding of C53 to ATG8 and to UFM1. They also use NMR spectroscopy in an attempt to define the structural basis of the UFM1 and ATG8 binding to C53, concluding that plant C53 interacts with UFM1 mainly through sAIM1, while interaction with ATG8 requires cAIM as well as sAIM1 and sAIM2. Finally, the authors attempt to contextualize these findings by conducting studies on Arabidopsis mutants, showing that replacing sAIMs with cAIMs causes increases sensitivity to ER stress and apparently increases formation of C53 intracellular puncta that may colocalize with ATG8.

From these data the authors concluded that the dual-ATG8 and UFM1 binding of C53 IDR regulates C53 recruitment to autophagosomes in response to ER stress.

Major Issues:

1. The phylogenomics analysis conclusion that UFM1 is common in unicellular lineages and did not evolve in multicellular eukaryotes is not novel, as another comprehensive analysis of UFM1 phylogeny, published eight years ago - in 2014 - by Grau-Bové et al. (PMID: 25525215), also reported that UFM1, UBA5, UFC1, UFL1 and UFSP2 were likely present in LECA and lost in Fungi. Although the phylogenomic analysis by Picchianti et al. is also extended to DDRGK1 and C53 proteins, and some parasitic and algal lineages, their findings are incremental. Their proposed coevolution of sAIM and UFM1 is based on presence-absence correlation observed within five species (i.e., Albugo candida, Albuco laibachii, Piromyces finnis, Neocallimastix californiae, Anaeromyces robustus). However, this coevolutionary relationship must be further investigated by substantially increasing the taxonomic sampling within the UFM1-lacking group.
2. The manuscript presents an overwhelming amount of biochemical and structural data obtained from a variety of protein binding techniques (i.e., NMR spectroscopy, in vitro GST-pulldown, fluorescence microscopy-based on-bead binding assays, and native mass-spectrometry). The results are poorly explained and not organized in a logical manner. Moreover, no attempt was made to explain the rationale behind using one technique over the other or how one method complements another to build a stronger conclusion than any individual approach. Given that none of the methods employed report quantitative measurement of binding affinities between C53 IDR and UFM1 or ATG8, it is not clear how the data presented in this manuscript contribute to our understanding of the proposed competition model for UFM1 and ATG8 binding to C53 IDR. To conclude that an interaction is "stronger" or "weaker" it is necessary to measure equilibrium binding constants. Fortunately, there are suitable techniques, including surface plasmon resonance (SPR), microscale thermophoresis (MST), fluorescence anisotropy, or calorimetry that are available to dissect these complex competitive binding interactions and to build models.
3. The NMR studies have the potential to dissect the types of dynamic binding inherent in unstructured proteins. However, the abundant NMR data presented combined with the aforementioned binding studies, remarkably, do not seem to significantly advance our understanding of how the system is organized or even how UFM1 and ATG8 bind C53, beyond the rather vague and somewhat circular conclusion stated in the abstract: "...we confirmed the interaction of UFM1 with the C53 sAIMs and found that UFM1 and ATG8 bound the sAIMs in a different mode." Or on line 165 "Altogether these results suggested that ATG8 and UFM1 bbind the sAIMs withn C54 IDR, albeit in a different manner".
4. The functional assays performed in Arabidopsis do not support the competitive model between UFM1 and ATG8 for binding to C53 during C53-mediated autophagy. The fluorescence microscopy images do not provide convincing evidence of colocalization between C53 and ATG8. In fact, in contrast to the claims made in the text or the quantification, mCherry-C53 fluorescence does not seem to localize in discrete puncta and its signal does not seem to overlap with ATG8A.

Minor Issues:

1. The authors might choose to avoid teleological arguments such as (line 135): "As the phylogenomic analysis suggested that eh sAIMs have been retained to mediate C53-UFM1 interaction..."
2. The authors refer on multiple occasions to C53 "autoactivation" without defining what they mean by this. Do they propose that C53 UFMylates itself?.
3. The paper might want to avoid preachy philosophical statements like "Our evolutionary analysis also highlights why we should move beyond yeast and metazoans and instead consider the whole tree of life when using evolutionary arguments to guide biological research." (333-335). While this is indeed a laudable goal, given the rather limited insights from this study, it is unclear how this paper exemplifies the notion.

Referees cross-commenting

Referee #2

The challenge in providing a fair review of this manuscript is to clearly define what contributions are novel, significant advances. It is difficult to tell the way the manuscript is written, as it is unclear how the new data - which are voluminous- actually advance the model already put forth by the same authors in two previous publications. It is also unfortunate that the authors overlooked the 2004 phylogenomics paper. There clearly are some new pieces of information here, but the overall increment in knowledge is rather minimal.

Response from Referee #3

I agree that the authors somehow steamroll the reader with a wealth of data. But I think this can be addressed by the authors by requesting a lot more justification and by giving them the opportunity to put the significant advances into their own words. This is, in my opinion, quite doable in course of a revision. Overall I have to say that I am very sympathetic with the cross-eukaryote reactivity approach that the authors have taken. It is quite intriguing.

Response from Referee #2

I agree that the cross-eukaryote approach is intriguing. Shouldn't we be concerned that the 2004 publication already made two of their key points (ie present in LECA, loss in Fungi). What is the incremental insight from this paper?

I'd appreciate an opinion from an evolutionary biologist as to how strongly one can conclude functional co-evolution from such correlative data, especially given the rather small number of supporting examples. Is it also necessary to consider counter-examples- ie species that have sAIMs but no UFM1 (I believe that they found a few such cases)?

Response from Referee #3

Well with these deep evolutionary questions this is always a challenge. Where does one stop to sample more homologs for one's analyses (one from each supergroup [which are no longer recognised by the community])? In that sense, the authors are right to make the parsimonious base assumption that if X and Y interact in species A and B (no matter how distant they are related) then X and Y interacted in the last common ancestor of A and B. That being said, if I would have designed this study, I would have sampled more broadly for my in vitro cross-eukaryote approach. But also this, I think, could be carried out by the authors in a reasonable timeframe. Specifically, they have now sampled from Amorphea and Archaeplastida, they should add one from TSAR, one Haptista, one Cryptista, and one CRuM. If they synthesised the proteins via a company, they could have the constructs in a few weeks for about 1K Euro - I do not think that this would be an unreasonable request.

Significance

Overall, while the manuscript contains an abundance of new data, the overall conclusion of the work, stated in the title: "Shuffled ATG8 interacting motifs form an ancestral bridge between UFMylation and C53-mediated autophagy" does not constitute a significant advance beyond other published phylogenomic analysis (below) and the two previous papers by the same authors, including the 2020 paper "A cross-kingdom conserved ER-phagy receptor maintains endoplasmic reticulum homeostasis during stress (PMID: 32851973)" and the 2021 paper "C53 is a cross-kingdom conserved reticulophagy receptor that bridges the gap between selective autophagy and ribosome stalling at the endoplasmic reticulum PMID: 33164651)". While a regulatory interaction between UFMylation and autophagy is of potential importance, the data in this manuscript do not constitute a major advance and fail to provide new mechanistic insight to explain the role of C53 IDR in autophagy and its interplay with UFMylation

Reviewer #2 (Public Review):

The authors utilize the publicly available dHCP dataset to ask an interesting question: how does postnatal experience and prenatal maturation influence the development of the visual system. The authors report that experience and prenatal maturation differentially contribute to different aspects of development. Namely, the authors quantify cortical thickness, myelination, and lateral symmetry of function as three different metrics of development. The homotopy and preterm infant analyses are strengths that, on their own, could have justified reporting. However, I have concerns about the analytic approaches that were used and the conclusions that were drawn. Below I list my major concerns with the manuscript.

PMA vs. GA vs. PT

1. The authors seek to understand the contribution of experience and prenatal development, yet I am unsure why the authors focused on the variables they did. There are three variables of interest used throughout this study: Gestational age at birth (GA), postnatal time (PT), and postmenstrual age at the time of scan (PMA). The last metric, PMA, is straightforwardly related to GA and PT since PMA = GA + PT. In most (but not all) of the manuscript, the authors use PMA and PT, with GA used without justification in some cases but not in others.

It is unclear why PMA is used at all: PMA is necessarily related to PT and GA, making these variables non-independent. Indeed, the authors show that PMA and PT are highly correlated. The authors even say that "the contribution of postnatal experience to the development was not clarified because PMA reflects both prenatal endogenous effect and postnatal experience." So, why not use GA at birth instead of PMA? Clearly, GA is appropriate in some cases (e.g., Figure S4 or in some of the ANOVA applications), and to me, it seems to isolate the effect the authors care about (i.e., duration of prenatal development). Perhaps there is some theoretical justification for using PMA, but if so, I am unaware.

That said, I expect that replacing all analyses involving PMA with GA will substantially change the results. I do not see this as a bad thing as I think it will make the conclusions stronger. As is, I am left unsure about what the key takeaways of this paper are.

2. Using GA instead of PMA will have several benefits: 1) It will be much simpler to think of these two variables since they contrast the duration of fetal maturation and time postnatally. 2) This will help the partial correlation analyses performed since the variance between the variables is more independent. It will also mean that the negative relationships observed between PT and cortical thickness when controlling for PMA (e.g., Figure 2h) might disappear (reversed signs for partial correlations are common when two covariates are correlated). 3) this will allow the authors to replace Figure 1a with a more informative plot. Namely, they could use a scatter of GA and PT, giving insight into the descriptive statistics of both dimensions.

3. I suspect that one motivation for the use of PMA over GA is for the analysis in Figure 6. In this analysis, the authors pick a group of term infants with a PMA equal to the preterm infants. Since PMA is the same, the only difference between the groups (according to the authors) is the amount of postnatal experience. However, this is not the only difference between the groups since they also vary in GA (and now PT and GA are negatively correlated almost perfectly). I don't know how to interpret this analysis since both the amount of prenatal maturation and postnatal experience vary between the groups.

Justification of conclusions and statistical considerations

I had concerns about some of the statistical tests and conclusions that the authors made. I refer to some of these in other sections (e.g., the homotopy analyses), but I raise several here.

4. I am not sure what evidence the authors are using to make this claim: "we found that the cortical myelination and overall functional connectivity of ventral cortex developed significantly with the PMA but was not directly influenced by postnatal time." Postnatal time is significantly correlated with cortical myelination, as shown in Figures 2g, 2h, 3b, 3c, and postnatal time is significantly correlated with functional connectivity, as shown in Figures 4h, 5c, 5d, and 5e. Hence, this general claim that "the development of CT was considerably modulated by the postnatal experience while the CM was heavily influenced by prenatal duration" doesn't seem to be supported: both myelination and thickness are affected by postnatal experience and prenatal duration (as measured by PMA). A similar sentiment is expressed in the abstract. Perhaps the authors suggest different patterns in the strength of change for PMA vs. PT across these metrics, but if so, then statistical tests need to support that conclusion, and the claims need to reflect that sentiment.

Interestingly, Figure S4 presents a compelling ANOVA that does support this conclusion. Still, this result is relegated to the supplement, and it also uses GA, rather than PMA, making it hard to reconcile with the other claims made in the main text. Moreover, it uses ANOVAs, which dichotomizes a continuous variable. Here and elsewhere in the manuscript (e.g., Figures 3d, 3e), the authors split the infants into quartiles and compare them with ANOVAs. Their use for visualization is helpful, but it is unclear what the statistical motivation for this is rather than treating these as continuous variables like is possible with linear mixed-effects models. Moreover, it is unclear why the authors excluded half the data from the study (i.e., quartiles 2 and 3) in this ANOVA when all four quartiles could be used as factors.

5. It is unclear what the evidence is to support the following claim: "Both CT and CM show higher correlation with PMA in the posterior than anterior region, and higher correlation in the medial than lateral part within the anatomical mask (Figure 2a and Figure S2b-c [sic])" From Figure 2 or Figure S2, I don't see a gradient. From Figure S3, there might be a trend in some plots, but it is hard to interpret since it is non-monotonic. More generally, is there a statistical test to support this claim?

6. "and the interaction [sic] was more prominent in CM (simple effect: t = 10.98, p < 10-9) that in than CT (t = 2.07, p < 0.05)." Does 'more prominent' mean it is 'significantly stronger'? If not, then the authors should adjust this claim

7. Are the authors Fisher Z transforming their correlations? In numerous places, correlation values seem to be added together or used as the input to other correlation analyses. It is unclear from the methods whether the authors are transforming their correlation values to make that use appropriate.

Homotopy analyses

The homotopy section is a strength of the paper, but I have doubts about the approach taken to analyze this data and some of the conclusions drawn. I don't expect any of my suggestions to change the takeaway of this section, but I do think they are essential criticisms to address.

8. I do not think that the non-homotopic control condition is appropriate. In Arcaro & Livingstone (2017), the authors had 3 categories for this analysis: homotopic pairs (e.g., left V1 vs. right V1), adjacent pairs (e.g., left V1 vs. right V2), and distal pairs (e.g., left V1 vs. right PHA1). In the homotopy analysis performed by Li and colleagues, they compare homotopic pairs with all other pairs. I don't think that is generous to the test since non-homotopic pairs include adjacent pairs that should be similar and distal pairs that shouldn't be similar. This may explain why some non-homotopic distribution overlaps with the homotopic distribution in Figure 4c.

9. Regardless of this decision, I think the authors should reconsider their statistical test. I think the authors are using a between samples t-test to compare the 34 homotopic pairs with the hundreds of non-homotopic pairs. This is statistically inappropriate since the items are not independent (i.e., left V1 vs. right V1 is not independent of left V1 vs. right V2, which is also not independent of left V3 vs. right V2). This means the actual degrees of freedom are much lower than what is used. Moreover, I am unsure how the authors do this analysis across participants since this test can be done within participants. The authors should clarify what they did for this analysis and justify its appropriateness.

10. Could the authors speculate on why the correlations in homotopic regions are so much lower than what Arcaro and Livingstone (2017) found. I can think of a few possibilities: higher motion in infants, less rfMRI data per participant, different sleep/wake states, and different parcellation strategies. Regarding the last explanation, I think this is a real possibility: the bilateral correlation may be reduced if the Glasser atlas combines functionally heterogeneous patches of the cortex. Hence, the authors should consider this and other possible explanations.

11. The authors assume that the homotopic analyses mean that there are lateral connections between hemispheres (e.g., "Furthermore, the connections among the ventral visual cortex have developed during this early stage. Specifically, the homotopic connections between bilateral V1 and between bilateral VOTC both increased with GA, indicating an increased degree of functional distinction"). While this might be true, it doesn't need to be. Functional connectivity can be observed between regions that lack anatomical connectivity. Instead, two regions could both be driven by another region. In this case, the thalamus might drive symmetrical activity in the visual cortex.

Miscellaneous

12. I am not sure what the motivation of this line is: "Moreover, those studies did not fully control the visual experience in the first few weeks of the subjects, thus cannot give a clear conclusion whether the innate functional connectivity is unrelated to postnatal visual experience." Arcaro, Schade, Vincent, Ponce, & Livingstone (2017) did control the visual experience of subjects. Moreover, the research here doesn't control infant experience in the way this sentence implies: it implies an experiment manipulation (i.e., fully control) rather than a statistical control that is done here. Consider rephrasing

13. I am not sure why this claim is made: "Area V1 was selected because this region is the most basic region for visual processing and probably is the most experience-dependent area during early development". Is there evidence supporting this claim? Plasticity is found throughout the visual cortex, and I think which region is most plastic depends on the definition of plasticity. For instance, most people have the same tuning properties to gabor gratings (e.g., a cardinality bias), but there is enormous variability in face tuning across cultures.

14. The abstract says 783 infants were included in this study, but far fewer are actually used. The authors should report the 407 number in the abstract if any number at all.

15. Any comparisons of preterms and terms ought to be given the caveat that the preterm environment can be very different than the term environment: whereas a term infant goes home and sees friends and family without restriction, the preterm environment can be heavily regulated if they are in a NICU. Authors should either provide details about the environments of the preterms in their study, or they should consider how differences in the richness of visual experience - regardless of quantity - may affect visual development.

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Reply to the reviewers

Reply to the reviewers

Referee #1

Evidence, reproducibility and clarity

1. This manuscript constructs a gene expression model with various factors. Specifically, the effect of cell size on gene expression is considered, which is often ignored by previous studies. One interesting finding is that the absolute number of the gene products and the concentration can have different distributions. Some predictions of the models are validated by experimental data on E. coli and yeast. This manuscript uses the mean-field approximation for cell volume, which has good accuracy when the number of stages is large. The usage of the power spectrum has a satisfactory effect on studying the concentration oscillation.

Response: Thank you for the positive comments.

1. Overall the paper was very difficult to follow and digest easily because of all the different factors and mechanisms invoked. It is mainly an issue of providing sufficient details for each of the factors and organizing them in a systematic and logical way. Although there is a supplementary appendix, it was hard to keep track of all the elements in the main manuscript. Perhaps something like Fig 1 of the Appendix can be presented in the main body to outline all the ingredients and how they affect each other.

Response: In the revised manuscript, we moved Supplementary Fig. S1 in the previous version into the main text to outline all the ingredients and how they affect each other (see page 8, Fig. 2). Moreover, we provided many details for each of the biological factors and tried to organize them in a more systematic and logical way (see pages 3-7).

1. It might be good to provide a more detailed description of the goal (studying gene product number and concentration under different parameters) after introducing the full and the reduced models. A table of symbols would also be helpful.

Response: In the revised manuscript, we added a table explaning the meaning of all model parameters (see page 4, Table 1). Moreover, we provided a detailed description of the goal of the present paper after introducing the full and reduced models (see page 7).

1. Some technical details in the Methods section are in fact helpful in understanding the conclusions. They can be moved to the Results section.

Response: In the revised manuscript, we moved many technical details in Methods and Supplementary Notes to the main text to help the readers better understand the conclusions (see pages 5-10).

1. One concern is that the central concept of this manuscript, “stage”, is not thoroughly discussed. This concept should have some significant biological meaning, not just be coined for mathematical convenience.

Response: In the revised manuscript, we explained in detail the biological meaning of the effective cell cycle stages (see page 4). Specifically, recent studies have revealed that in many cell types, the accumulation of some activator to a critical threshold is used to promote mitotic entry and trigger cell division, a strategy known as activator accumulation mechanism. In E. coli, the activator was shown to be FtsZ; in fission yeast, it is believed to be a protein upstream of Cdk1, the central mitotic regulator, such as Cdr2, Cdc25, and Cdc13. Biophysically, the N effective stages can be understood as different levels of the key activator. Moreover, we pointed out that the power law form for the rate of cell cycle progression may come from cooperativity of the key activator that triggers cell division.

1. Fig. 1(b) is a little strange. For the left panel, the x-axis (stage) is discrete, then the volume (y-axis) should be a step function, not a straight red line.

Response: In the revised manuscript, we added some red dots in the stage-volume plot to show the dependence of the mean cell volume vk on cell cycle stage k for the mean-field model (see page 3, Fig. 1). Moreover, we emphasized that the joining of these dots by a straight red line is simply a guide to the eye.

Significance

1. The main advance is a more complete model of gene expression under more realistic organism growth conditions.

Response: Thank you for acknowledging the results of the manuscript.

Referee #2

Evidence, reproducibility and clarity

1. Jia et al. introduce a modeling framework to represent stochastic gene expression, with an explicit representation of cell volume growth, cell cycle progression (and its dependency on cell volume) and gene dosage compensation. The model is very elegant and general in that it can represent a variety of situations, simply as a matter of parametrization. Under a simplifying assumption, the authors derive a number of metrics (include stationary distribution of gene product and power spectrum of gene product fluctuation dynamics), for both absolute number and concentration of gene product molecules. They use their model and derivations to examine under which conditions cell can achieve homeostasis in the concentration of the expressed gene product, despite changes in cell volume and gene copy number following replication. They also present and discuss the conditions giving rise to specific features (i.e. bimodality in stationary distribution, peak in power spectrum) and examine these features in experimental data to conclude to infer the underlying homeostasis strategies. The model is rather general and powerful. The simplifying assumption seems reasonable (and the authors investigate to some extent its limitations, i.e. Fig. 2). The conclusions are overall convincing.

Response: Thank you for the positive comments.

Major comments 1. My main concern is that the metrics that the authors use to assess concentration homeostasis (i.e. the γ parameter and the presence / absence of peak in power spectrum) do not seem quite appropriate to describe how much variability / fluctuations in concentration are driven by cell cycle effects. Indeed, the γ parameter measures how much the *average* concentration in each cell cycle stage varies throughout the cell cycle. However, this variability should be compared to the total variability due to both cell cycle effects and stochastic bursting dynamics. A given level of cell-cycle dependency (say γ = 0.2) could be very visible if gene expression is weakly noisy (e.g. B low and hni high) and completely invisible is gene expression is highly bursty (large B and small hni). In the latter situation, cell-cycle effects would be meaningless for the cell to minimize. In essence, reusing the authors notations, I think γ/φ1/2 , would be a more relevant metric to observe.

Response: In the revised manuscript, we showed that the total concentration noise φ can be decomposed as φ = φext + φint, where φext is the extrinsic noise which characterizes the fluctuations between different stages due to cell cycle effects and φint is the intrinsic noise which characterizes the fluctuations within each stage due to stochastic bursty synthesis and degradation of the gene product (see page 11). Based on the above decomposition, we introduced a new metric γ = φext/φ, which characterizes the accuracy of concentration homeostasis. Clearly, the new metric γ reflects the relation contribution of cell cycle effects in the total concentration variability. All discussions about concentration homeostasis are based on the new metric γ in the revised manuscript. Moreover, all figures have been updated by using this new metric.

1. Similarly, when inspecting the peak in the power spectrum, the weight of the Lorentzian function(s) creating the peak, should be compared to the stationary component (λN , uN in the authors’ notations).

Response: We cannot quite understand why the weights uk of the Lorentzian functions should be compared to the stationary component uN . In fact, all the weights uk except uN are actually complex numbers and we are not so sure about the meaning of uk/uN . However in the revised manuscript, we emphasized that the power spectrum G(ξ) is normalized so that G(0) = 1 throughout the paper (see page 13). To better understand concentration oscillations and its relation to homeostasis, we depicted both γ and H as a function of B and hni (see Supplementary Fig. S5). As expected, the off-zero peak becomes lower as B increases and as hni decreases since both of them correspond to an increase in concentration fluctuations which counteracts the regularity of oscillations; noise above a certain threshold can even completely destroy oscillations. Furthermore, we found that γ and H have similar dependence on B and hni. This again shows that the occurrence of concentration oscillations is intimately related to the visibility of cell cycle effects in concentration fluctuations.

A complementary analysis including these two points and a discussion the relative contribution of cell-cycle effects and bursting dynamics in the total variability/fluctuation of concentrations would be important to include.

Response: In the revised manuscript, we made some complementary analysis and discussion about the relative contribution of cell cycle effects and stochastic birth-death dynamics in the total variability of concentrations (see pages 11-14).

Minor comments 3. The dashed line on Fig. 3a is defined as κ = √ 2 1−β . First is this empirical or does it come from a derivation? Second, it seems incomplete since it should depend on w. Intuitively, this line should correspond to the value of κ that would best mimic balanced biosynthesis in the case where β 6= 1. In other words, κ should be so that hρB0 /V (t)iprereplication = hκρB0 /V (t)ipostreplication, which yields κ = 2w(1−β) ∗ (w − 1)/w ∗ [2w(β−1) − 1]/[2(1−w)(β−1) − 1]. This indeed simplifies into κ = √ 2 1−β when w = 0.5.

Response: Thank you for providing such a beautiful derivation. In the revised manuscript, we added this derivation into the main text (see pages 12-13). Moreover, we also made it clear that this relation can also be obtained from the perspective of power spectrum (see page 14).

1. η is used in the caption of Fig. 2, which is cited on page 4. But it is defined only 2 sections later, on page 6.

Response: In the revised manuscript, we gave the definition of η in both Table 1 and the caption of Fig. 3 (Fig. 2 in the old version). Please see page 4, Table 1 and page 9, Fig. 3.

1. w is used in the main text, but only defined in the caption of Fig. 3.

Response: In the revised manuscript, we gave the definition of w in both Table 1 (see page 4) and on page 7.

1. w is defined as “the proportion of cell cycle before replication”. Is this in terms of cell cycle stages (i.e. w = N0/N) or actual time?

Response: In the revised manuscript, we made it clear that w represents the proportion of cell cycle duration before replication, which should be distinguished from the proportion N0/N of cell cycle stages before replication (see page 7). This is because the transition rate between cell cycle stages is an increasing function of cell size, which means that earlier (later) stages have longer (shorter) durations.

1. Fig. 3 indicates that power spectra are normalized so that G(0) = 1, but G(0) = 10 on the first two graphs.

Response: Corrected as suggested (see page 12, Fig. 4). Thank you.

1. Page 11: “bimodality in the concentration distribution is significantly less apparent”. I would suggest rephrasing “bimodality in the concentration distribution is absent” since there should be no reference to “significance” and bimodality is either present or absent (binary), not less apparent.

Response: Corrected as suggested. Thank you.

Referees cross-commenting

1. Regarding the comment from reviewer 3 that ”a direct validity test should use data sets of at least two types (total, nascent RNA, etc)”. I almost made a related comment in my review, but then I held it off: This issue with using nascent RNA data is that their model does not allow an ON state. They assume that gene products are produced in instantaneous bursts, which is a fair assumption if the lifetime of gene products is large compared to the time the gene stays ON. This is ok if the considered ”gene products” are mRNA or proteins, but not nascent RNAs (for which the lifetime is the time to transcribe the gene). I did not make this comment in the end because I think the model is useful regardless. To comply with reviewer 3’s request, maybe the authors could use distributions of mRNA and protein products, but I’m not sure that such data exists (since they need cell-cycle-resolved data).

Response: It is not possible to validate our model with nascent mRNA data because the model in its present form cannot predict nascent mRNA fluctuations. This is because unlike mature mRNA, nascent mRNA cannot be assumed to decay via first-order kinetics. A detailed response is provided below to the original comment made by Referee 3. Regarding the comment on the use of cell-cycle-resolved data measuring mRNA and protein expression – while we agree it would make an excellent test of our model, we could not find such a dataset in the literature. We point out that our model, in its present form, is interesting as it is, as a detailed biological model of mature mRNA and protein number / concentration fluctuations in growing cells. Its predictions are yet to be fully confirmed and hence may stimulate the development of further experimental single-cell studies.

Significance

1. The advance of this paper is essentially technical. The authors present a model that incorporates and unifies previously studied effects (cell volume homeostasis, concentration homeostasis, bursting transcription). There is no major conceptual novelty, but the combination of these different aspects and the derivations that authors present are very valuable and might be applicable to interpret data in various species.

Response: Thank you for acknowledging the results of the manuscript.

Referee #3

Evidence, reproducibility and clarity

1. The manuscript analyses a phenomenological model of stochastic gene expression. The model couples bursty transcription with cell growth, division and DNA replication. The cell cycle is divided into a large number of stages whose exponential lifetimes depend on the cell volume. It is argued that concentrations of gene products are distributed according to mixed Gamma distributions, whereas the copy numbers follow mixed negative binomial distributions. The number of modes can be different for concentrations and copy numbers, for instance the copy numbers can be unimodal while concentrations are bimodal. The case when the mean concentration does not depend on the cell cycle stage is called perfect homeostasis. It is argued that perfect homeostasis leads to Gamma distribution of the gene product concentration and that deviations from a Gamma distributions result mainly from deviations of the concentration from perfect homeostasis. It is also proposed that concentration homeostasis is difficult to obtain. These qualitative predictions of the model are tested using two data sets, one for E.coli and another for fission yeast.

Response: Thank you for acknowledging the results of the manuscript.

Major comments 1. A huge number of states called “cell cycle stages” have exponential life times. On my opinion, this sequence of stages is just a technicality for keeping the model within a discrete Markovian framework. More natural choices are possible, such as piecewise deterministic Markov processes, age structured diffusions, etc. The biological significance (if there is any) of such states should be explained.

Response: In the revised manuscript, we explained in detail the biological meaning of the effective cell cycle stages (see page 4). Specifically, recent studies have revealed that in many cell types, the accumulation of some activator to a critical threshold is used to promote mitotic entry and trigger cell division, a strategy known as activator accumulation mechanism. In E. coli, the activator was shown to be FtsZ; in fission yeast, it is believed to be a protein upstream of Cdk1, the central mitotic regulator, such as Cdr2, Cdc25, and Cdc13. Biophysically, the N effective stages can be understood as different levels of the key activator. Moreover, we pointed out that the power law form for the rate of cell cycle progression may come from cooperativity of the key activator that triggers cell division.

1. The timescales of stochastic gene expression are not correctly taken into account. It is considered that during an exponential stage the bursting approximation describes gene expression in terms of Gamma distributions for concentrations and in terms of negative binomial distributions for copy numbers. This approximation is only valid if the lifetime of a stage is much larger than the time needed to generate a burst. For RNA, this condition cannot be fulfilled for a large number of states N and/or for two states promoters with a relatively long ON state. For the protein and/or in the case of translational bursting, the condition is even more difficult to fulfil. I agree with the Reviewer 2 that once the master equation accepted the results make sense. But my criticism is different and concerns the master equation itself. In this equation the burst is considered instantaneous, whereas it needs finite time in reality. Concerning nascent mRNA, ON/OFF etc. I disagree. The notion of instantaneous burst with well defined burst size and burst frequency on a stage has a meaning if the lifetime of this stage (which is not mRNA or protein lifetime) is short. The model validity should be clearly stated.

Response: Thank you for pointing out this important issue. When we talk about the validity of the model, we should stick to the full model, instead of the mean-field model. This is because once the full model makes sense, the mean-field model must work well when N ? 15, as we have shown in Fig. 3 and Supplementary Fig. S3. Hence our reply is based on the validity of the full model. We will reply to the above comments from the following three aspects. First, we agree with the referee that in our model, we assume that the gene product is produced in instantaneous bursts with the reaction scheme G ρpk (1−p) −−−−−−→ G + kM, k ≥ 1, M d −→ ∅, (1) where the mean burst size scales as V (t) β . Of course, in reality there is a finite time for the bursts to occur. A more general assumption is that within each cell cycle, the gene expression dynamics is characterized by the following three-stage model: G ρ −→ G ∗ , G∗ r −→ G, G∗ sV (t) β −−−−→ G ∗ + M, M u−→ M + P, M v −→ ∅, P d −→ ∅, (2) where the first two reactions describe the switching of the gene between an inactive state G and an active state G∗ the middle two reactions describe transcription and translation, and the last two reactions describe the degradation of the mRNA M and the protein P. Here the synthesis rate of mRNA depends on cell volume via a power law form with power β ∈ [0, 1]. Dosage compensation can be modeled by a decrease in the gene activation rate (for each gene copy) from ρ to κρ/2 upon replication. Previous studies have revealed that the bursting of mRNA and protein has different biophysical origins: transcriptional bursting is due to a gene that is mostly inactive, but transcribes a large number of mRNA when it is active (r ? ρ and s/r is finite), whereas translational bursting is due to rapid synthesis of protein from a single short-lived mRNA molecule (v ? d and u/v is finite). Under the above timescale separation assumptions, both mRNA and protein are produced in a bursty manner with the reaction scheme described by Eq. (1). The burst frequency for mRNA and protein are both ρ before replication and κρ after replication. The mean burst size for mRNA is (s/r)V (t) β and the mean burst size for protein is (su/rv)V (t) β , both of which have a power law dependence on cell volume (see pages 5-6). In Supplementary Figs. S1 and S2, we compare the mRNA and protein distributions for the bursty model with the reaction scheme given by Eq. (1) and the three-stage model with the reaction scheme given by Eq. (2), where both models under consideration have a cell cycle and cell volume description. It can be seen that the distributions for the two models are very close to each other under the above timescale separation assumptions with the bursty model being more accurate as r/ρ and v/d increase. Moreover, we find that the accuracy of the bursty model is insensitive to the value of the number of stages N. Here the values of N are chosen so that the ratio of the average time spent in each stage (T /N, where T ≈ (log 2)/g is the mean cell cycle duration) and the mean burst duration time (1/ρ) ranges from ∼ 0.5 − 2. This shows that the effectiveness of the bursty model does not require that the lifetime of a cell cycle stage is sufficient long. Due to mathematical complexity, we only focus on the bursty model in the present paper. The consistency between the gene product distributions for the two models justifies our bursty assumption. Second, while we assume bursty expression here, our model naturally covers non-bursty expression since the latter can be regarded as a limit of the former. Hence all the conclusions in the present paper are applicable to both bursty and non-bursty expression. In the revised manuscript, we emphasized this point (see page 4 for a detailed explanation). Last but not least, if the lifetime of the gene product is much shorter compared to the lifetime of each cell cycle stage, then the gene expression dynamics will rapidly relax to a quasi-steady state for each stage. In this case, the gene product fluctuations at each stage can be characterized by a gamma distribution in terms of concentrations and by a negative binomial distribution in terms of copy numbers, and hence the distribution of concentrations (copy numbers) for a population of cells is naturally a mixture of N gamma (negative binomial) distributions. However, the powerfulness of our analytical distribution (see page 10, Eq. (8)) is that it serves an accurate approximation when N ? 1 without making any timescale assumptions. The effectiveness of our analytical distributions is validated in Supplementary Fig. S3 for three different cases: (i) the degradation rate d of the gene product is much smaller than the cell cycle frequency f; (ii) d and f are comparable; (iii) d is much larger than f. In the revised manuscript, we also emphasized these points (see page 10).

1. DNA replication is a stochastic event and does not occur after a fixed number of exponential stages as it is considered in this model. Concerning replication: in the model this occurs after exactly N0 steps. In reality, replication occurs somewhere between the start of S and G2/M. N0 is in fact a random variable. Probably a new mean field assumption is needed here with some justification, but I have seen nothing in the paper.

Response: We agree with the referee that replication of the whole genome occurs in the S phase, which occupies a considerable portion of the cell cycle and thus cannot be assumed to occur after a fixed number of exponential stages. However, our model is for a single gene and since the replication time of a particular gene is much shorter than the total duration of the S phase, it is reasonable to consider it to be instantaneous. In addition, recent experiments have shown that the time elapsed from birth to replication for a particular gene occupies an approximately proportion of the cell cycle, which is called the stretched cell cycle model. This is also consistent with our assumption that replication of the gene of interest occurs after exactly N0 stages. While replication occurs after a fixed number of stages, nevertheless the time of replication is stochastic since each stage has a random lifetime. In the revised manuscript, we emphasized these points (see pages 4-5).

1. The results in the Methods were derived heuristically and their relation to the master equation (12) is not explicit (except for the part concerning moments and their power spectrum). Furthermore, one would like to have some estimates of the biases introduced by the mean field approximation. Concerning biases introduced by the mean field approximation: Figure 2 is a numerical simulation, some analytical estimates could be better. As Figure 2 looks rather convincing, I reclassify this as minor comment.

Response: We agree with the referee that the derivation of moments is rigorous, but the derivation of the analytical distribution given in Methods is not rigorous and cannot be directly obtained from the master equation. In the revised manuscript, we emphasized that the analytical distribution is not exact but it serves as a very good approximation (see pages 10 and 22). We showed that the analytical distribution agrees well with stochastic simulations when the number of cell cycle stages N ≥ 15 (see page 9, Fig. 3 and Supplementary Fig. S3). The logic behind our approximate distribution is that while the gene product may produce complex distribution of concentrations (copy numbers), when the number of cell cycle stages is large, the distribution must be relatively simple within each stage and thus can be well approximated by a simple gamma (negative binomial) distribution (see page 22). Due to the complexity of our model, it is very difficult to provide any analytical estimates on the bias introduced by the mean-field approximation. Often the bias of an approximation can be estimated when the approximation emerges from a systematic method such as van Kampen’s system-size expansion (see Ref. [21]). However, our mean-field model cannot be seen as the zero order term of some expansion and hence it is not possible to calculate the next-order correction which would be needed to estimate the error. However, we have tested very large swathes of parameter space and found that the mean-field approximation always works well when N ≥ 15 which is the physiologically relevant regime for most types of cells (see discussion on P. 7).

1. The model is not minimal and depends on a huge number of parameters. It is not clear how these parameters were found and if overfitting was avoided. One may have doubts about the identifiability of the parameter N. What difference is between N = 59 and N = 60 (the value of N for the cyanobacterium)?

Response: In the revised manuscript, we used synthetic data to show that all the model parameters involved in our model (except d and β which can be determined based on a priori knowledge) can be accurately estimated from cell-cycle resolved lineage data of cell volume and gene expression (see Supplementary Note 7). We provided details of the parameter inference method, compared the input parameters with the estimated ones and verify that they are identifiable (see Supplementary Table 1). We did not use real data to test our inference method because we could not find cell-cycle resolved lineage data for mRNA or proteins. As we noted, this is in principle possible via cell-cycle fluorescent markers. We also note that parameter inference for less detailed but similar models have been made in our previous papers — the parameters related to cell volume dynamics have been inferred in E. coli (see Ref. [51]) and fission yeast (see Ref. [52]) using the method of distribution matching, and the parameters related to gene expression dynamics have be estimated in E. coli (see Ref. [40]) using the method of power spectrum matching. Moreover, for our purpose, i.e. to investigate the effect of cell cycle and cell volume on gene expression, we do believe that our model is minimal. We captured cell growth with only one parameter g, the degree of balanced biosynthesis with one parameter β (β = 0 corresponds to the case where the synthesis rate is independent of cell volume and β = 1 corresponds to the case where the synthesis rate scales linearly with cell volume), the variability in cell cycle duration with only one parameter N, gene replication with only one parameter N0, gene dosage compensation with only one parameter κ (κ = 1 corresponds to perfect dosage compensation and κ = 2 corresponds to no dosage compensation), and the variation of size control strategy across the cell cycle with two parameters α0 and α1 (αi → 0 corresponds to timer, αi = 1 corresponds to adder, and αi → ∞ corresponds to sizer). The biological meaning of the cell cycle stages were clarified in the revised manuscript (see page 4). For our purpose, we believe that our model cannot be simpler.

1. The authors should make clear which cell biology aspects are important, which are less important, and which were neglected in the context of their problem. Thus, in their model, cell cycle acts on gene expression mainly by duplication of burst sources and thus by increase of burst frequency after replication. Another important source of gene expression variability during the cycle, the mitotic transcription repression, is neglected.

Response: In the revised manuscript, we clarified which cell biology aspects are important for gene expression dynamics (see page 17). Specifically, in our model, cell cycle and cell volume act on gene expression mainly by (i) the dependence of the burst size on cell volume; (ii) the increase in the burst frequency upon replication; (iii) the change in size control strategy upon replication; (iv) the partitioning of molecules at division. Point (iv) strongly affects copy number fluctuations, while it has little influence on concentration fluctuations. In addition, in the revised manuscript, we also elucidated the limitations of our model including mitotic transcription repression and others (see pages 19-20).

1. The validity test of the model is indirect. It was tested that the concentration distribution deviates from Gamma and that the deviation correlates positively to the lack of accuracy of the concentration homeostasis. However, many models can have this behaviour. A direct validity test should use data sets of at least two types (total, nascent RNA, etc.) allowing direct estimates of some model parameters (such as burst size and frequency using nascent RNA). Concerning parsimony, I think that the authors should test it. Are all the parameters identifiable? Is there any overfitting? They could use parameter uncertainty, comparison of training /testing errors, etc. Some details about the parameter fitting method should be provided.

Response: Regarding the parameter fitting and identifiability we have provided a detailed response to a previous comment above. However we emphasize that for the generation of Fig. 7, we did not need to estimate all model parameters from data. Hence in the previous version of the manuscript, no such estimation was done — we simply extracted the homeostasis accuracy γ, the height H of the off-zero peak of the power spectrum, and the Hellinger distance D of the concentration distribution from its gamma approximation directly from data. Finally, we point out that our model can be used to predict the dynamics of mature mRNAs, but it cannot be used to describe the dynamics of nascent mRNAs. This is because nascent mRNAs do not decay via a first-order reaction but their removal, i.e. their detachment from the gene which leads to mature mRNA, is better approximated by a reaction with a fixed decay time. This models the elongation time of nascent transcripts which does not suffer from much noise because the RNAP velocity is to a good approximation constant along the gene. See e.g. the following two papers for details: H. Xu, S. O. Skinner, A. M. Sokac, I. Golding, Stochastic kinetics of nascent RNA. Phys. Rev. Lett. 117, 128101 (2016). S. Braichenko, J. Holehouse, R. Grima. Distinguishing between models of mammalian gene expression: telegraph-like models versus mechanistic models. J. R. Soc. Interface 18, 20210510 (2021). Because of the fixed delay, the delay telegraph model (the telegraph model with a delayed degradation reaction) is non-Markovian and very different from the usual Markovian telegraph model which describes the dynamics of mature mRNA within each cell cycle. See e.g. the Supplementary Information of the following paper: X. Fu, et al. Accurate inference of stochastic gene expression from nascent transcript heterogeneity. bioRxiv (2021). Given the mathematical complexity introduced by a fixed delay, using it to describe the dynamics of nascent mRNA within each cell cycle leads to a non-Markovian model that is even more analytically intractable than the present one for mature mRNA. While an interesting research question, this is clearly far removed from the scope of our current manuscript.

Minor comments 8. The introduction could be more pedagogical. Right now it is just an accumulation of loosely related and sometimes abruptly introduced statements. For instance, we understand that the authors want to oppose their approach to other extant approaches. However, extant approaches should be better reviewed, some of them are aged structured and perfectly suited for analysing cell cycle data. It would be useful for the reader that an example of observation explained by their model and not explained by other models (age structured or not) is discussed in detail. The model of this work does not explain size control, it just assumes that this holds, and does not discuss cell population aspects. A more nuanced positioning of this approach with respect to the literature would be useful for judging its value.

Response: In the revised manuscript, we rewrote the introduction part to make it more pedagogical (see pages 1-2). In particular, we compared three popular models describing the cell size dynamics and the associated size homeostasis. The advantages and disadvantages of the three models were discussed.

1. The meaning of N should be discussed from the very start when the model is introduced.

Response: In the revised manuscript, we explained in detail the biological meaning of the effective cell cycle stages (see page 4). Specifically, recent studies have revealed that in many cell types, the accumulation of some activator to a critical threshold is used to promote mitotic entry and trigger cell division, a strategy known as activator accumulation mechanism. In E. coli, the activator was shown to be FtsZ; in fission yeast, it was believed to be a protein upstream of Cdk1, the central mitotic regulator, such as Cdr2, Cdc25, and Cdc13. Biophysically, the N effective stages can be understood as different levels of the key activator. Moreover, we pointed out that the power law form for the rate of cell cycle progression may come from cooperativity of the key activator that triggers cell division.

1. The authors call constitutive expression the situation when the mean copy number does not depend on the volume. This choice should be clarified as in general constitutive as opposed to specific, localised or transitory expression refers to non-regulated gene expression. It seems to me that in this context, expression is only partially constitutive (independent on the volume).

Response: In the present paper, constitutive expression means that the gene product is produced one at a time and is not produced in a bursty manner. It does not mean that the mean copy number does not depend on the volume. In the revised manuscript, we provided a more detailed discussion about how constitutive expression can be viewed as a limit of bursty expression (see page 4).

1. In figure 1b and for exponential growth the y axis should be log(volume) instead of volume. The mean field approximation is called both “of novel type” (Discussion) and “which has a long history of successful use in statistical physics” (p4). If something is novel, then one should clearly explain why.

Response: In fact, the y-axis in Fig. 1(b) should be volume instead of log(volume). This is because the x-axis represents the cell cycle stage instead of the real time. Note that for the adder strategy (α0 = α1 = 1), it follows from Eq. (3) on page 7 that the mean cell volume at stage k is vk = v1 + (k − 1)M0/N0, which linearly depends on k. This explains why the red curves in Fig. 1(b) are straight lines instead of exponential curves. In the revised manuscript, we also explained why the mean-field approximation used is novel (see page 7). Specifically, we pointed out that the mean-field approximation is not made for the whole cell cycle, rather we make the approximation for each stage and thus different stages have different mean cell volumes. This type of piecewise mean-field approximation, as far as we know, is novel and has not been used in the study of concentrating fluctuations before.

1. The word “cyclo-stationarity” is used with not much definition. If this means just stationary distribution of the gene products why not use just “stationarity” instead. What means “cyclo”? A number of properties were called “rare” but it is not clear on what grounds.

Response: In the revised manuscript, we removed the term “cyclo-stationarity” and simply assumed that the copy number and concentration distributions of the gene product at each cell cycle stage have reached the steady state (see page 8). In addition, for each property that was called “rare”, we explained the reasons in detail (see pages 14 and 17).

1. I did not find a proof that the copy number distribution has less modes than the concentration distribution.

Response: In fact, it is very difficult to prove that the concentration distribution has less modes than the copy number distribution. However, we have tested very large swathes of parameter space and found that the number of modes of the concentration distribution is always less than or equal to that of the copy number distribution. In the revised manuscript, we emphasized this point (see page 16).

Significance

1. The strength of this work is that it incorporates in a stochastic gene expression model a number of ideas on size control and dosage compensation that were discussed elsewhere from a cell population point of view. However, the proposed model is based on a number artificial choices that are difficult to justify biologically: a huge number of cell cycle discrete states and inappropriate handling of the timescales characterizing stochastic gene expression. Furthermore, the model is not minimal but depends instead on a huge number of parameters. I found the paper difficult to read and in the results presentation is not suitable for biologists that would need more details on the justification of the modelling choices and on the experimental validation of the model.

Response: All these points have been addressed in previous replies.

1. For mathematicians, the calculations are rather standard and may seem trivial.

Response: Our model is complex due to the coupling between gene expression dynamics, cell volume dynamics, and cell cycle events. It is far more complex than standard models of gene expression (see e.g. Refs. [2,84,85]) because of the large amount of biology encapsulated in it and we presented a first analytical- and simulation-based analysis of concentration fluctuations when concentration homeostasis is broken.

The computations of many quantities in the present paper are non-trivial. First, we showed that the generalized added volumes before and after replication both have an Erlang distribution. Using this property, we computed the mean cell volume in each cell cycle stage which is needed in the mean-field approximation. Furthermore, the computations of the power spectrum of concentration fluctuations are also highly non-trivial. The analytical expression of the power spectrum allows us to precisely determine the onset of concentration homeostasis. While the computations of moments of concentration fluctuations are standard, we used to the moments to construct an analytical concentration distribution which serves as an accurate approximation when N is large. Our concentration distribution is generally valid when concentration homeostasis is broken and goes far beyond recent models for growing cells which require concentration homeostasis and which do not take into account DNA replication, dosage compensation and size control mechanisms that vary with the cell cycle phase (e.g. Ref. [26] ).

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Referee #3

Evidence, reproducibility and clarity

The manuscript analyses a phenomenological model of stochastic gene expression. The model couples bursty transcription with cell growth, division and DNA replication. The cell cycle is divided into a large number of stages whose exponential lifetimes depend on the cell volume. It is argued that concentrations of gene products are distributed according to mixed Gamma distributions, whereas the copy numbers follow mixed negative binomial distributions. The number of modes can be different for concentrations and copy numbers, for instance the copy numbers can be unimodal while concentrations are bimodal. The case when the mean concentration does not depend on the cell cycle stage is called perfect homeostasis. It is argued that perfect homeostasis leads to Gamma distribution of the gene product concentration and that deviations from a Gamma distributions result mainly from deviations of the concentration from perfect homeostasis. It is also proposed that concentration homeostasis is difficult to obtain. These qualitative predictions of the model are tested using two datasets, one for E.coli and another for fission yeast.

Major comments:

The model encompasses a number of artificial choices:

• A huge number of states called "cell cycle stages" have exponential life times. On my opinion, this sequence of stages is just a technicality for keeping the model within a discrete Markovian framework. More natural choices are possible, such as piecewise deterministic Markov processes, age structured diffusions, etc. The biological significance (if there is any) of such states should be explained.
• The timescales of stochastic gene expression are not correctly taken into account. It is considered that during an exponential stage the bursting approximation describes gene expression in terms of Gamma distributions for concentrations and in terms of negative binomial distributions for copy numbers. This approximation is only valid if the lifetime of a stage is much larger than the time needed to generate a burst. For RNA, this condition cannot be fulfilled for a large number of states N and/or for two states promoters with a relatively long ON state. For the protein and/or in the case of translational bursting, the condition is even more difficult to fulfil.
• DNA replication is a stochastic event and does not occur after a fixed number of exponential stages as it is considered in this model. The results in the Methods were derived heuristically and their relation to the master equation (12) is not explicit (except for the part concerning moments and their power spectrum). Furthermore, one would like to have some estimates of the biases introduced by the mean field approximation. The model is not minimal and depends on a huge number of parameters. It is not clear how these parameters were found and if overfitting was avoided. One may have doubts about the identifiability of the parameter N. What difference is between N=59 and N=60 (the value of N for the cyanobacterium)? The authors should make clear which cell biology aspects are important, which are less important, and which were neglected in the context of their problem. Thus, in their model, cell cycle acts on gene expression mainly by duplication of burst sources and thus by increase of burst frequency after replication. Another important source of gene expression variability during the cycle, the mitotic transcription repression, is neglected.<br /> The validity test of the model is indirect. It was tested that the concentration distribution deviates from Gamma and that the deviation correlates positively to the lack of accuracy of the concentration homeostasis. However, many models can have this behaviour. A direct validity test should use datasets of at least two types (total, nascent RNA, etc.) allowing direct estimates of some model parameters (such as burst size and frequency using nascent RNA).

Minor comments:

The introduction could be more pedagogical. Right now it is just an accumulation of loosely related and sometimes abruptly introduced statements. For instance, we understand that the authors want to oppose their approach to other extant approaches. However, extant approaches should be better reviewed, some of them are aged structured and perfectly suited for analysing cell cycle data. It would be useful for the reader that an example of observation explained by their model and not explained by other models (age structured or not) is discussed in detail. The model of this work does not explain size control, it just assumes that this holds, and does not discuss cell population aspects. A more nuanced positioning of this approach with respect to the literature would be useful for judging its value.

The meaning of N should be discussed from the very start when the model is introduced.

The authors call constitutive expression the situation when the mean copy number does not depend on the volume. This choice should be clarified as in general constitutive as opposed to specific, localised or transitory expression refers to non-regulated gene expression. It seems to me that in this context, expression is only partially constitutive (independent on the volume).

In figure 1b and for exponential growth the y axis should be log(volume) instead of volume.

The mean field approximation is called both "of novel type" (Discussion) and "which has a long history of successful use in statistical physics" (p4). If something is novel, then one should clearly explain why.<br /> The word "cyclo-stationarity" is used with not much definition. If this means just stationary distribution of the gene products why not use just "stationarity" instead. What means "cyclo"?

A number of properties were called "rare" but it is not clear on what grounds.

I did not find a proof that the copy number distribution has less modes than the concentration distribution.

Referees cross-commenting

Part 1

I agree with the Reviewer 2 that once the master equation accepted the results make sense. But my criticism is different and concerns the master equation itself. In this equation the burst is considered instantaneous, whereas it needs finite time in reality.

Part 2 (response to Part 2 of Rev2)

• concerning replication: in the model this occurs after exactly N_o steps. In reality, replication occurs somewhere between the start of S and G2/M. N_o is in fact a random variable. Probably a new mean field assumption is needed here with some justification, but I have seen nothing in the paper
• concerning biases introduced by the mean field approximation: Figure 2 is a numerical simulation, some analytical estimates could be better. As Figure 2 looks rather convincing, I reclassify this as minor comment.
• concerning nascent mRNA, ON/OFF etc. I disagree. The notion of instantaneous burst with well defined burst size and burst frequency on a stage has a meaning if the lifetime of this stage (which is not mRNA or protein lifetime) is short. The model validity should be clearly stated.
• concerning parsimony, I think that the authors should test it. Are all the parameters identifiable? Is there any overfitting? They could use parameter uncertainty, comparison of training /testing errors, etc. Some details about the parameter fitting method should be provided.

Significance

The strength of this work is that it incorporates in a stochastic gene expression model a number of ideas on size control and dosage compensation that were discussed elsewhere from a cell population point of view. However, the proposed model is based on a number artificial choices that are difficult to justify biologically: a huge number of cell cycle discrete states and inappropriate handling of the timescales characterizing stochastic gene expression. Furthermore, the model is not minimal but depends instead on a huge number of parameters.

I found the paper difficult to read and in the results presentation is not suitable for biologists that would need more details on the justification of the modelling choices and on the experimental validation of the model. For mathematicians, the calculations are rather standard and may seem trivial. I am a systems biologist with a background in mathematics and theoretical physics.

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Referee #2

Evidence, reproducibility and clarity

Summary:

Jia et al. introduce a modeling framework to represent stochastic gene expression, with an explicit representation of cell volume growth, cell cycle progression (and its dependency on cell volume) and gene dosage compensation. The model is very elegant and general in that it can represent a variety of situations, simply as a matter of paramterization. Under a simplifying assumption, the authors derive a number of metrics (include stationary distribution of gene product and power spectrum of gene product fluctuation dynamics), for both absolute number and concentration of gene product molecules. They use their model and derivations to examine under which conditions cell can achieve homeostasis in the concentration of the expressed gene product, despite changes in cell volume and gene copy number following replication. They also present and discuss the conditions giving rise to specific features (i.e. bimodality in stationary distribution, peak in power spectrum) and examine these features in experimental data to conclude to infer the underlying homeostasis strategies.

Major comments:

The model is rather general and powerful. The simplifying assumption seems reasonable (and the authors investigate to some extent its limitations, i.e. Fig. 2). The conclusions are overall convincing.

1. My main concern is that the metrics that the authors use to assess concentration homeostasis (i.e. the γ parameter and the presence/absence of peak in power spectrum) do not seem quite appropriate to describe how much variability/fluctuations in concentration are driven by cell cycle effects. Indeed, the γ parameter measures how much the average concentration in each cell cycle stage varies throughout the cell cycle. However, this variability should be compared to the total variability due to both cell cycle effects and stochastic bursting dynamics. A given level of cell-cycle dependency (say γ=0.2) could be very visible if gene expression is weakly noisy (e.g. B low and <n> high) and completely invisible is gene expression is highly bursty (large B and small <n>). In the latter situation, cell-cycle effects would be meaningless for the cell to minimize. In essence, re-using the authors notations, I think γ / ϕ^1/2, would be a more relevant metric to observe.
2. Similarly, when inspecting the peak in the power spectrum, the weight of the Lorenztian function(s) creating the peak, should be compared to the stationary component (λ_N, u_N in thhe authors' notations).

A complementary analysis including these two points and a discussion the relative contribution of cell-cycle effects and bursting dynamics in the total variability/fluctuation of concentrations would be important to include.

Minor comments:

1. The dashed line on Fig. 3a is defined as κ = sqrt(2)^(1-β). First is this empirical or does it come from a derivation? Second, it seems incomplete since it should depend on ω. Intuitively, this line should correspond to the value of κ that would best mimic balanced biosynthesis in the case where β≠1. In other words, κ should be so that <ρB' / V(t)>_prereplication = <κρB' / V(t)>_postreplication which yields κ = 2^(ω(1-β)) * (ω-1)/ω * [2^(ω(β-1))-1]/[2^((1-ω)(β-1))-1] This indeed simplifies into κ = sqrt(2)^(1-β) when ω=0.5.
2. η is used in the caption of Fig. 2, which is cited on page 4. But it is defined only 2 sections later, on page 6.
3. ω is used in the main text, but only defined in the caption of Fig. 3.
4. ω is defined as "the proportion of cell cycle before replication". Is this in terms of cell cycle stages (i.e. ω=N_0/N) or actual time?
5. Fig. 3 indicates that power spectra are normalized so that G(0)=1, but G(0)=10 on the first two graphs.
6. Page 11: "bimodality in the concentration distribution is significantly less apparent". I would suggest rephrasing "bimodality in the concentration distribution is absent" since there should be no reference to "significance" and bimodality is either present or absent (binary), not less apparent.

Referees cross-commenting

Part 1.

I agree with reviewer 1 that a table of symbols would be helpful. On reviewer 3's second Major Comment, I don't think that the "the lifetime of a stage [has to be] much larger than the time needed to generate a burst". From how the authors write and solve the master equation, I don't think that such a separation of timescale is necessary. The authors should indeed clarify this and if reviewer 3 is correct, then that's indeed a major limitation. On reviewer 3's second Major Comment, I don't think that the "the lifetime of a stage [has to be] much larger than the time needed to generate a burst". From how the authors write and solve the master equation, I don't think that such a separation of timescale is necessary. The authors should indeed clarify this and if reviewer 3 is correct, then that's indeed a major limitation. On reviewer 3's comment "DNA replication [...] does not occur after a fixed number of exponential stages", I don't think I agree with this statement. Cell cycle progression relies on an ensemble of biochemical reactions. Representing this as a set of exponential waiting-time distributions with different means is probably amongst the most general and agnostic ways of representing this. Whether these exponential waiting-times only depend on cell volume is another question. This actually links back to reviewer 3's first Major comment and reviewer 1's comment that the concept of "stage" should be better discussed.

Regarding the need for "estimates of the biases introduced by the mean field approximation" (reviewer 3), I guess that's the goal of figure 2. Maybe reviewer 3 should make more explicit what she/he would like to see.

Regarding the comment from reviewer 3 that "a direct validity test should use datasets of at least two types (total, nascent RNA, etc)". I almost made a related comment in my review, but then I held it off: This issue with using nascent RNA data is that their model does not allow an ON state. They assume that gene products are produced in instantaneous bursts, which is a fair assumption if the lifetime of gene products is large compared to the time the gene stays ON. This is ok if the considered "gene products" are mRNA or proteins, but not nascent RNAs (for which the lifetime is the time to transcribe the gene). I did not make this comment in the end because I think the model is useful regardless. To comply with reviewer 3's request, maybe the authors could use distributions of mRNA and protein products, but I'm not sure that such data exists (since they need cell-cycle-resolved data).

I disagree with the statements that "the proposed model is based on a number artificial choices that are difficult to justify biologically" and that "the model is not minimal but depends instead on a huge number of parameters." In my opinion, the model is elegantly simple to capture the mechanisms under study (i.e. the effect of cell cycle and cell volume on stochastic gene expression). It is expressed so that the model captures a broad range of situations (i.e. it reduces to simpler models as a matter of choosing parameter values, e.g. \Beta=0 => transcription independent of cell cycle; \alpha => \infty cell cycle depends only on size ...). I do not think that a series of exponential distributions for cell cycle progression is inappropriate, it is the most agnostic and general way of representing an ensemble of biochemical reactions that would be meaningless to describe explicitly. Instead, only their dependency on cell volume is taken into account (and in a very general way, i.e. parameters 'a' and \alpha). It is fair to ask the authors to clarify the concept of "stage", but I see this model as being as simple as possible, but not simpler, for the authors' purpose.

Finally, I agree that the paper is probably "not suitable for biologists" but disagree that "for mathematicians, the calculations are rather standard and may seem trivial."

Part 2. Resp. to reviewer 3 on the master equation (Part 1 of Rev3):

Ok, I understand better your comment. What you mean by "the time needed to generate a burst" is the time that the gene produces RNAs, not the lifetime of the gene product (which is 1/d). That's true. It is essentially the same ifdea as what I write in my previous comment about nascent RNA data not being well captured by the model. Again, I think this is fine for "gene products" that are somewhat stable (not the case for nascent RNAs, but ok for mRNAs and proteins). This is fine by me as long as the authors explicit better this limitation of their model.

Part 3. Response to Reviewer 3 (Part 2 of Rev 3)

• concerning replication: Note that the mean field approximation is on cell volume, not on stage progression ("To simplify this model, [...] we ignore volume fluctuations at each stage but retain fluctuations in the time elapsed between two stages", p3). So the time at which replication occurs is already a random variable in the model. It is the sum of all the exponentially distributed random variables corresponding to stages 1 to N_0. The resulting distribution of replication time from the start of cell cycle is a random variable, which can be anything from very deterministic (N_0 very high) to very variable (N_0 very low).
• concerning nascent mRNA, ON/OFF etc. : I'm not sure I get your objection, but the best is probably to let the authors respond to your original comment.
• concerning parsimony: Ok, you're right. The authors should test it.

Significance

The advance of this paper is essentially technical. The authors present a model that incorporates and unifies previously studied effects (cell volume homeostasis, concentration homeostasis, bursting transcription). There is no major conceptual novelty, but the combination of these different aspects and the derivations that authors present are very valuable and might be applicable to interpret data in various species.

The paper is suitable for a physics/mathematics/computational audience. It is rather technical and would not be understood by readers with only a biology background.

Field of expertise of the reviewer: Gene regulation, single-molecule imaging, stochastic modeling.

I have a general idea of Amanda Knox's story but I had never heard any specific details about the story like names or places or how she was treated. I find that with most aspects of society, especially with online activities, the people do tend to go for the crazy and outlandish stories. Once most people make up their minds about a person or story then it can be hard to change their viewpoints. No matter how many times Amanda may want to show the proof that she is an innocent person caught in the wrong place at the wrong time those people who paint her in a certain light will never change their viewpoints. Another story I can think of that shares some similarities is the Gypsy Rose case. Now Gypsy was active in the crime whereas Amanda was not active in her alleged crime. The main similarities between the two stories is how the media grabbed hold of it and that there are shows, movies, characters, etc, that are based of these real life people and the real things that happened to them. There are plenty of people who want to hold Gypsy as accountable as her at the time boyfriend and others who think she was innocent but a product of her surroundings. The way this little girl, that we were told at the time of the crime, was painted as a monster is insane to think about. But if that can happen to a young woman then anything can be thought about a mid twenties adult woman in a foreign country. The way the public romanticizes or dehumanize a person for actions they may or may not take can be insane to think about. These people who get treated this way almost never get to go back as a normal everyday person.

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Reply to the reviewers

Reply to the reviewers

We wish to thank all three reviewers for their thorough examination of our manuscript and their constructive criticism that allowed us to increase its quality. You will see that, following their recommendations, we have included a good amount of new data in the manuscript. Specifically, we added a new figure with experiments proposed by the reviewers (now Fig. 4), as well as Figs. S3 and S4. In addition, we expanded one paragraph of our Discussion to comment on a very recent article published by Huang et al in Nature Structural and Molecular Biology with conclusions pertaining the interplay of Rpd3 and Gcn5 in PHO5 gene regulation. Below we include the point-by-point response (in blue) with the changes we have implemented to address their specific points. All the additions and changes in the manuscript are made in red.

Referee #1

Evidence, reproducibility and clarity

In this manuscript, Novačić et al., investigate into a mechanisms of the non-coding transcriptiondriven regulation of the phosphate-responsive PHO5 gene. The authors employ CRSPRi system to discern direct contribution of the antisense non-coding transcription (CUT025) expressed during phosphate -rich conditions to transcriptional repression of the yeast PHO5 gene and therefore challenging previous study from the Svejstrup's lab that proposed a positive role for non-coding transcription in control of PHO5 gene. They propose a model where non-coding transcription represses PHO5 by mediating recruitment of Rpd3 histone deacetylase leading to altered chromatin structure at PHO5 promoter due to reduced recruitment of the RSC chromatin remodelling complex. Overall, the data presented in the manuscript are of a good quality, experiments are well controlled and nicely presented. Manuscript is well written. My specific comments are below: 1. I am somewhat confused by the data presented in Figure 5. While there is similar impact on the chromatin structure seen in rrp6D and air1Dair2D strains (Fig 5C) that corresponds to more "closed" configuration of chromatin , it is not consistent with H3 ChIP data that show higher nucleosome occupancy across PHO5 UAS in rrp6D but loss of nucleosomes in the double mutant (or there is a mistake perhaps while plotting the data?)

We now realize that the data was plotted confusingly, and we apologize for it. While doing the H3 ChIP experiment we only prepared the +Pi samples for the air1Δ air2Δ double mutant. In the figure we only included this one data point for the double mutant, which could lead to the false conclusion that at other timepoints there are no histones at its PHO5 promoter region. We decide to remove this data point from the figure to avoid the confusion and only keep the air1Δ air2Δ data for the ClaI assay. We believe that this should not be an issue as this data point is not critical for the conclusions we are making.

1. To further explore direct link between nc transcription, Rpd3 and rrp6 mediated effect, I suggest to test the effect on PHO5 induction upon rpd3 and rrp6 deletions in CRISPRi CUT025 background.

We performed this experiment and now include it as Fig. S3 in the manuscript. As expected, expressing the CRISPRi system only made difference when Rpd3 was present.

1. It seems that most noticeable effect of blocking nc transcription by an elegant approach that utilizes CRISPRi system on the phosphatase activity is seen between 0-1.5h of induction. I suggest taking additional time points at 30-45 min.

We took additional timepoints and the results were incorporated as the new Fig. 5E. The CRISPRi effect resulting in higher acid phosphatase activity was still most noticeable after 1,5 h of induction. This was mostly in line with the fact that the difference in PHO5 mRNA levels was most pronounced after 30 min of induction (Fig. 5D), as the time needed to achieve measurable protein level after induction can lag significantly for secretory proteins, such as acid phosphatase. Secretory proteins are cotranslationally translocated into the ER, after which they traverse the secretory pathway and undergo modifications before being finally exported to the periplasm where their activity can be measured. Consequently, the increase in acid phosphatase activity upon induction is only measurable after at least an hour.

1. How do authors explain that the effect of the exosome mutations are reversed and phosphatase activity is increased at later time point (20 h, Fig 2A)? I suggest using more distinct colour for dis3 mutants.

That effect is indeed somewhat surprising. We hypothesize that the effects we are seeing after 20 h reflect the specific conditions of prolonged induction, i.e. keeping the chromatin open or semi-open for a very long period of time, which do not necessarily reflect the early gene induction period that we are using as a read-out of the effect of different mutations on acid phosphatase expression kinetics. We previously noticed a similar effect with chromatin remodeler-related mutants (e.g. rsc2Δ, unpublished result from S. Barbarić group), which speak in favour of the prolonged induction conditions resulting in a chromatin state with its own specialized cofactor requirements. We therefore consider the chromatin state after prolonged induction a topic for another study, however, we now comment on this effect in the manuscript. The dis3 mutants are now shown in more distinct colours.

1. Figure 5A -label "H3 ChIP"

The label was added.

1. Error bars are quite high in Fig 1C, perhaps it is worth repeating the experiment

Since significant differences in PHO5 mRNA levels can be seen between wt and rrp6Δ mutant cells at 0,75 and 3 h of induction, we feel that the higher error bars at 5 h of induction are not worth repeating the experiment – especially since the values are bound to converge to a similar one after a longer induction period, as demonstrated in Fig. 1D.

Significance

significant of interest for general audience

Referee #2

Evidence, reproducibility and clarity

The authors study the PHO5 locus, which is known to a have antisense transcript and that has previously been shown the be important for activation of Pho5 sense transcription. The authors challenge the idea by an extensive analyses. They show the Pho5-AS represses sense transcription, and thus fits in the category as AS repressors instead of activators. They show a correlative data that when antisense goes down and sense goes up. They show that increase antisense levels leads to decrease sense levels. They use mutants of decay pathways to increase the levels antisense transcription. Moreover, they used crispri to repress the antisense transcript. Lastly, they show that histone deacetylation represses Pho5 sense. The data in the manuscript is convincing, and well presented. One thing that needs further clarification is the strategy to increase anti-sense levels by deletion mutants of decay or depletion of decay pathways. While it is clear that this stabilizes the pho5-AS and decrease pho5-sense, it is not clear that this causes an increase in transcription. Perhaps, it is possible that antisense transcript itself has a repressive effect. If one really wanted to increase antisense transcription than the antisense promoter should be increased in strength. On the other the CriprI experiment is very convincing. I am surprised how well the crisprI system works, it is thought to be not so efficient at blocking elongating polymerase and good at blocking initiation.

We thank the reviewer for this feedback. We performed additional experiments which you will find described below. Based on the results, we would like to keep the point about AS transcription causing the effect.

Major comments: - Are the key conclusions convincing? Perhaps, the conclusion that increased transcription leads to repression is not completely convincing. The authors use mutants in rrp6, exosome, and nrd1 to increase Pho5-AS transcription elongation. However, I am always under impression that these mutants stabilize the transcript. And the authors acknowledge this in their manuscript. So how do you discriminate between increased stability versus increased elongation? I support the conclusion that inhibition of Pho5-AS leads to increase Pho5-S. However, increase in elongation is not directly demonstrated. While still possible, it is equally possible that a more stable pho5-AS transcript has a repressive an effect on Pho5-AS. - Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether? See above. Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation. If the authors want to keep the message that increased transcription of Pho5-AS leads to more repression that may need to consider additional experiments. For example, increasing transcription from the antisense promoter.

We performed the proposed experiment and now include it in the manuscript as Fig. 4AB. Briefly, we inserted the strong constitutive TEF1 promoter in the antisense configuration downstream of the PHO5 gene ORF, so that it drives AS transcription. The results of this experiment very clearly show the inverse relationship between PHO5 mRNA and AS transcripts levels at +Pi conditions. Importantly, this strong constitutive AS transcription had an even more pronounced effect on PHO5 gene expression than deletion mutant backgrounds (in which, like in wt cells, the AS promoter is presumably weak), and did not allow for full level of PHO5 gene expression to be reached. To verify that the AS RNA itself does not have a regulatory role, but rather the act of its transcription represses the corresponding gene, we performed an additional experiment with appropriate diploid strains. The design of this experiment is standardly used to test whether an AS transcript can work in trans (for example see Nevers et al. 2018 NAR Fig. 6). This experiment is now included as Fig. 4C. Together, the results of these experiments paint a clear picture of AS transcription, and not AS level/stability itself, driving the repression of the PHO5 gene.

• Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated cost and time investment for substantial experiments. To me this is an optional experiment, but it would benefit the manuscript
• Are the data and the methods presented in such a way that they can be reproduced? yes - Are the experiments adequately replicated and statistical analysis adequate? yes

Minor comments: - Specific experimental issues that are easily addressable. - Are prior studies referenced appropriately? yes - Are the text and figures clear and accurate? Yes - Do you have suggestions that would help the authors improve the presentation of their data and conclusions? no

Significance

• Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field. The manuscript challenges previous work where it was claimed that Pho5-AS is important for activation of Pho5-S. As such, it is important work. In the field of noncoding the transcription the Pho5-AS fits in a class of AS transcript that has been well described.
• Place the work in the context of the existing literature (provide references, where appropriate). See above.
• State what audience might be interested in and influenced by the reported findings. In researchers in field of transcription, chromatin, and more specifically in yeast gene regulation.
• Define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate. Chromatin, transcription, yeast.

Referee #3

Evidence, reproducibility and clarity

Novačić et al present a manuscript entitled "Antisense non-coding transcription represses the PHO5 model gene via remodeling of promoter chromatin structure" which is a locus-specific follow up to previous studies from Soudet and Stutz groups on genome-wide analysis of transcription interference mediated by antisense transcripts in S cerevisiae. Critically, the authors here employ a CRISPRi approach to reduce antisense transcription from reaching the PHO5 promoter and in doing so show that kinetics of PHO5 induction are increased as would be predicted from their previous model. Additionally, they show predicted epistasis between rpd3 and rrp6 on PHO5 expression and gcn5 and rrp6 that are consistent with their model. Comments are relatively minor but should be addressed. Introduction p3. "This mechanism was subsequently explored genome-wide in yeast, which revealed a group of genes that in the absence of Rrp6 accumulate AS RNAs and are silenced in an HDACdependent manner (14)." This sentence appears awkward- perhaps move "in the absence of Rrp6" to after "AS RNAs"?

Corrected as proposed.

p3 "Under a high phosphate concentration Pho4 undergoes phosphorylation by the cyclindependent-kinase (Pho80-Pho85)" Since "the" is used, don't use parentheses around Pho80-Pho85

Corrected as proposed.

Methods Give amount/concentration of glycine used in quenching formaldehyde for ChIP. Give the exact wash conditions and buffers not "extensively"

All of those details are now provided in the manuscript. Figure 4C.

Describe schematic in legend

It is now described.

Figure 4D. Indicate time of induction in legend.

This was lacking for Figs. 4B-C (now 5B-C) so we added it there.

Figure 5A. air∆ data are missing from later time points?

Please see our first response to Reviewer 1. We removed the air1Δ air2Δ double mutant data, as we only had one data point for it in this assay.

Figure 6. Legend needs to indicate what Pi conditions are. Since PHO5 expressed, appears to be low Pi. An issue that needs to be discussed is that rpd3∆ appears to decrease expression of PHO5 AS. Is this simply because of increased PHO5 expression? Does rpd3∆ have any effects on AS in high Pi? This is important to interpret if effects of rrp6 and rpd3 are epistatic or additive.

We thank the Reviewer for bringing this to our attention. To explore the effect of rpd3Δ on PHO5 AS level, we quantified the PHO5 AS transcript by RT-qPCR with cells grown in (chemically defined) high Pi medium, which we now include in Fig. 7A. We find that rpd3Δ mutation has practically no effect on PHO5 AS transcript level both in the wt and the rrp6Δ mutant background. This result speaks in favor of rrp6Δ and rpd3Δ being epistatic rather than additive.

Figure 7. Sth1-CHEC data are hard to interpret. Some sort of quantification might be required as effects are not clear from the browser track nor is it clear from browser track that the results are reproducible. Examination of Sth1-AA effects in gcn5∆ background might be more compelling that the effect on RSC is via acetylation. Otherwise it is a bit hard to say as RSC could be functioning in parallel to the acetylation-dependent pathways implicated.

We agree that the presumption that histone acetylation recruits RSC to the PHO5 gene promoter had to be tested. We therefore include the experiment involving Sth1-AA depletion in the gcn5Δ background as Fig. 8A. This experiment was complicated by the fact that RSC is highly abundant (and at the same time essential for cell viability), but we resolved this by starting to deplete RSC two hours before gene induction. These results position RSC and Gcn5 in the same pathway. In contrast, more complete Sth1 depletion severely impaired viability of the rrp6Δ mutant, making it hard to interpret the effect, so we now include this result as Fig. S4.

To show the effect of AS transcription on RSC recruitment to the PHO5 promoter more quantitatively, we re-analyzed the Sth1-CHEC data (for two independent biological replicates) and now include the log2 values for the changes in Sth1 binding in the text of the manuscript.

Significance

The work is focused and narrower in impact but important because direct tests of locus-specific effects are performed, validating models from previous genomic analyses. **Referees cross-commenting**

I think the other reviews are very reasonable. I would just suggest to the authors that they think carefully about the reviews and decide what they think is most valuable to improving the work/presentation

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Reply to the reviewers

Revision Plan

1. General Statements

We really appreciate the positive comments and suggestions of the reviewers on our submitted manuscript. We think we will be able to solve the issues inquired by reviewers by adding new data and revising the phrases as detailed below.

2. Description of the planned revisions

Reviewer #1:

Major comments

Localization analysis of a transiently expressed MAP70 transgene with inactivating phosphosite mutations would be important to see whether the identified conserved phosphosites are relevant for MAP70 interaction with MTs. This experiment could be performed rapidly using transient expression in BY-2 cells.

We agree on the importance of this analysis. Therefore we are currently preparing fluorescent markers of Nt-MAP70-2-like and its phospho-blocked (Ala) version to coexpress with MT and nuclear markers in BY-2 cells. We estimate that we need three more months to complete this experimsnt.

The authors propose that PP2 blocks phragmoplast formation by preventing phosphorylation of class II Kinesin-12 proteins. In support, authors show that PP2 treatment correlates with a decrease in KIN12A phosphopeptide count (not fully abolished) and its failure to localize to emerging phragmoplasts in BY-2 cells and Physcomitrium. As class II Kinesis-12 proteins have been previously implicated in phragmoplast assembly this is a fairly reasonable hypothesis, but would benefit from the analysis of transgenic KIN12A variants carrying inactivating (A) or potentially activating (D/E) phosphosite mutations. Is loss of phosphorylation sufficient to prevent phragmoplast localization? Can an activated variant rescue PP2-induced KIN12A localization and cell division defects? As above, using transient expression in BY-2 cells would be a fast approach to tackle these questions.

We are currently preparing fluorescent markers of phospho-blocked (Ala) and phospho-mimic (Asp) versions of KIN12A (PAKRP1) to coexpress with MT and nuclear markers in BY-2 cells. We will check whether they localize to phragmoplast and also test PP2 effects. We would need three more months to complete these analyses.

Reviewer #2:

Major comments

• The manuscript would strongly benefit from being revised by a native english speaker. There are many unusual or awkward formulation, in particular in the abstract.

We apologize for unnatural sentences. After adding new data and correcting the manuscript, we will ask a native english speaker to revise it.

Reviewer #3:

Major comments

The major concern is lack of evidence to connect MAP70 and MT disruption upon treatment with PD-180970, in contrast to PP2, which was shown to affect localization of Kinesin-12. I wonder if authors could use taxol to stabilize MTs, then observe the localization of MAP70 with application of PD-180970?

As we responded to reviewer 1, we are preparing the fluorescent marker of Nt-MAP70-2-like to coexpress with MT and nuclear markers in BY-2 cells. By using this multi-color marker, we will test whether PD-180970 affects the localization of MAP70 on MTs, also using taxol. However, in our experiene, taxol is not a very effective inhibitor and may not work in our transient expression system in BY-2 cells. In that case, we will analyze whether phospho-mimic (Asp) version can prevent MT disruption in the presence of PD-180970 to assess the relation of PD-180970, MAP70 and MT disruption.

I have another concern on the action of PD-180970. PD-180970 appears to affect ubiquitously indispensable proteins for MTs. If PD-180970 disrupt MT by inhibiting phosphorylation of some MAPs, it must need time for turnover of proteins phosphorylated before PD-180970 was applied. In the proteomics experiment, author treated the cells with the compounds for 8-9 hr. On the other hand, in BY-2 cells, PD-18970 disrupted MTs only 30 min after application of PD-180970. I wonder if proteins were replaced during the 30 min. Could authors examine how long it takes to affect interphase MTs? If PD-180970 disrupts MTs in a 5-10 min like oryzalin, it is unlikely that inhibition of phosphorylation of proteins like MAP70 caused MT disruption. Rather, it may inhibit some proteins that have activity to disrupt microtubules but are usually inactivated by phosphorylation or inhibit something directly without phosphorylation.

We agree that there is no evidence that PD-180970 disrupts MTs by inhibiting phosphorylation of MAP70. In our live-imaging system, in which reagents are added to liquid cultivation medium, the time from the reagent application to the arrival to each cell varies. Therefore, in order to accurately measure the time required for the inhibitor to take effect, it is necessary to design a new assay system, such as using fluorescent dyes to monitor the reagent's diffusion. In addition, since some reactions mediated by protein phosphorylation occur rapidly, minute-order observations might not be sufficient. Therefore, as an alternative strategy to assess the direct involvement of MAP70 phosphorylation on MT stabilization, we will examine whether PD-180970 induces MT disruption using strains expressing the phospho-blocked (Ala) and phospho-mimic (Asp) versions of MAP70 described above.

3. Description of the revisions that have already been incorporated in the transferred manuscript

Reviewer #1:

Minor comments

The authors identified the analogs PD-166326 and PP1 as potent inhibitors of cell division. For completeness, it would be interesting to include a description of these arrest phenotypes and how they compare with that of PD180870 or PP2.

We have added the effects of all tested compounds on Arabidopsis embryos in Fig. S3C and Table S1. Based on this data and the results of tobacco BY-2 cells, we have compared the effects of PD-166326 and PD180870, and PP1 and PP2 in Results.

Although there are two more obvious candidates in the phosphoproteome datasets on which the authors focus on, there is very little discussion on whether the other top hits and whether they might be involved in cell division. On a related note, there is no discussion on the specificity of these compounds and the likelihood of phenotypes unrelated to cell division.

We have added the information of “Similar proteins in Arabidopsis” and “Description and putative functions” for all identified candidates for PD-180970 and PP2 in Table S2 and S3, respectively. With referring this information, we have added the sections to describe the possible contributions of these candidates on MT organization and phragmoplast formation in Results. In addition, we have described the specificity of these compounds and the phenotypes unrelated to cell division in the section for the results of Arabidopsis roots (Fig. S2A).

1st results section:

"...developed into the globular stage without causing morphological defects..."

Should omit the word "causing" or replace with "any/detectable"

We have omitted the word "causing".

Reviewer #2:

Even if the identification of the kinase(s) targeted by these two compounds is missing, the characterisation of at least two downstream effectors of these elusive kinase(s) inhibited by PD-180970 and PP2 is an important step forward. I would recommend to this point make very clear in the writing (e.g. already in the abstract). Upon a superficial reading, the reader could assume that MAP70s and PAKRP1s are the direct molecular targets of these compounds.

We appreciate the very positive comments. To clarify this point, in addition to the following responses to each suggestion, we have changed the last sentense of the abstract to “These properties make PD-180970 and PP2 useful tools for transiently controlling plant cell division at key manipulation nodes that are conserved in diverse plant species”.

Major comments

• I would modify the title to shift the emphasis from the methodology to the biological targets identified.

We have changed the title to “Identification of novel compounds inhibiting microtubule organization and phragmoplast formation in diverse plant species”.

• Concerning MAP70s the authors claim that there is little functional data about this family. Yet, a recent paper (https://www.science.org/doi/10.1126/sciadv.abm4974) identifies MAP70-5 as necessary for the proper organisation of CMTs in the endodermis and its ability to actively remodel to accommodate emergence of the lateral root primordium in Arabidopsis thaliana. This could provide a functional context to test several of the predictions that the authors list in the discussion.

We have referred this paper in Results and Discussion, as “MAP70-5 was reported to increase MT length in vitro and to reorganize cortical MTs to alter the endodermal cell shape for lateral root initiation, suggesting that MAP70-5 mediates dynamic change of MT arrays”.

Minor comments

• The narrative would be improved by moving the section "PD-180970 and PP2 do not irreversibly damage viability" before the phosphoproteomic section.

We have moved the “irreversibly” section to before the “phosphoproteomics” section.

Reviewer #3:

Minor comments

In supplemental data, authors show only 12 or 14 candidates of the target. It is interesting how other MAPs including homologues of MAP70 and Kiesnin-12 in BY-2 cells were scored in the phospho-proteomics assay. I suggest authors show longer lists of proteomics including other MAPs. It would be valuable information for the research community.

We apologize for not providing the complete dataset. We have added Dataset S1 of total protein sequences that we predicted from published RNA-sea data of BY-2 cells, and all identified proteins of phosphoproteomics assay for PD-180970 and PP2 in Datasets S2 and S3, respectively. We have moved the lists of top candidates to Tables S2 and S3.

In Abstract, authors should mention that the two compounds reduced phosphorylation level of diverse proteins including MAP70 and Kinesin-12. This is very important results and, otherwise, it may cause misunderstanding of the activity of the compounds. In addition to this, it is better to rephrase the following sentence. "presumably by inhibiting MT-associated proteins (MAP70)" with "presumably by inhibiting phosphorylation of MT-associated proteins (MAP70)."

To avoid such a misunderstanding, we have changed the descriptions in Abstract to “Phosphoproteomic analysis showed that these compounds reduced phosphorylation level of diverse proteins. In particular, PD-180970 inhibited phosphorylation of the conserved serine residues in MT-associated proteins (MAP70). PP2 significantly reduced the phosphorylation of class II Kinesin-12, and impaired its localization at the phragmoplast emerging site”. Due to this change, the suggested sentence was eliminated. Also in Discussion, we have mentioned the reduction of phosphorylation of various proteins by stating, "we found that PD-180970 and PP2 reduced the phosphorylation levels of diverse proteins. These parts may be further modified depending on the results of the phospho-blocked (Ala) and phospho-mimic (Asp) analyses.

Page7 line 1st. it would be better to insert "of MAP70 family" after "in the conserved MT-binding domain" because the MT binding domains are unique to the MAP70 family. I could not understand why this is " (2nd line) consistent with PD-18970 severely disrupting all the tested MT structure". At current stage, there is no evidence that dephosphorylation of MAP70 caused the microtubule disruption. I suggest authors remove the sentence (", which was~MT structures").

We agreed on both points and have corrected them as the reviewer suggested.

I think this is an interesting way to phrase this. Taken by itself, we may have taken Clack's narrative as the truth, but when examined beside the other narratives, her unreliability is exposed and her character traits (and flaws) become clear.

Author Response

Reviewer #1 (Public Review):

Viola et. al. compared the electron transfer efficiency of two types of oxygenic far-red photosystem II (PSII) with the "conventional" PSII and analyzed how these far-red PSII use the limited energy from infrared photons to proceed photosynthesis. Oxygenic photosynthesis is an energy-intensive process, and a large headroom is also needed for preventing harmful back-reactions from occurring, which can produce singlet oxygen. This research investigated how the far-rad PSII managed to do their work with limited energy.

The authors measured and compared the forward reactions of different kinds of PSII (Chl-a-PSII, Chl-d-PSII and Chl-f-PSII), including the flash-induced chlorophyll fluorescence decay and S-states turnover. These results led to a conclusion that the forward reaction quantum efficiency was not changed between "conventional" PSII and far-red PSII. However, the back-reactions of three types of PSII are different based on the measurements of the prompt fluorescence decay, delayed luminescence decay, and thermoluminescence band locations. The authors concluded that the two far-red PSII (Chl-d-PSII and Chl-f-PSII) have a different strategy for utilizing infrared light. Indeed, the authors showed that Chl-d-PSII containing cyanobacteria produced more singlet oxygen than other types, and this result was explained by the energy profile in the electron transfer chain.

The major strength of this research is the authors made a direct comparison of different far-red PSII under the same conditions. It's exciting to have a side-by-side comparison between two types of far-red PSII. In addition, the authors also measured the singlet oxygen produced from all types of PSII which clearly showed the differences in the routes of recombination.

We thank the reviewer for the interest demonstrated in our work and for the thoughtful comments, that we have addressed below.

However, there are some concerns:

1) The flash-induced fluorescence decay, thermoluminescence, delayed luminescence and S-states turnovers of the Chl-d-PSII and Chl-f-PSII have been characterized before (ref 5, 26, 39), but from intact cells compared to isolated membranes in this study, and similar conclusions have been achieved. The authors mentioned four reasons (lines 115-120, see the manuscript for the authors' arguments "i." to "iv.") why it's important to use isolated membranes. However, in my opinion, these reasons are not sufficiently strengthened:

i. The transmembrane potentials from cells can be collapsed by adding uncouplers;

ii. The authors mentioned the quinone pool in the cells is uncontrollable, but the authors didn't actually measure or manipulate the quinone pool in the membrane (e.g., the ratio of QB/QB-/empty-pocket in the samples);

iii. The phycobilisomes can be controlled by different conditions through state transitions;

iv. The isolation of membranes may not remove membrane-related quenching mechanisms (e.g., PSII quenching in State II, spillover, etc.).

We do not agree with the reviewer on this point. We consider the use of membranes (or isolated PSII) as being the best solution to limit the effects listed at the end of the Introduction and to provide consistency between the different measurements, some of which cannot be performed in intact cells (i.e., the UV absorption measurements). More specifically:

i) The effectiveness of uncouplers in dissipating the membrane potential is likely to vary between species (e.g., Chroococcidiopsis cells form aggregates incapsulated by a protective layer of excreted polymers) and should be assessed by directly measuring the membrane potential. ElectroChromic Shift-based measurements of the membrane potential in cyanobacteria have only been demonstrated in Synechocystis sp. PCC6803 and Synechococcus elongatus sp. PCC7942 (Viola et al. 2019, https://doi.org/10.1073/pnas.1913099116) and still need to be adapted to the far-red species used here. Additionally, commonly used uncouplers such as CCCP and FCCP are ADRY reagents, that interfere with PSII water splitting by directly reducing TyrZ (Ghanotakis et al. 1982, https://doi.org/10.1016/0005-2728(82)90115-3), and would affect all the measurements presented in this work.

ii) In the dark, the redox state of the PQ pool in cyanobacterial cells has been observed to be kept in a highly reduced state by respiration, with potential consequences on the QB/QB- ratio. This could well vary between species, based on their different physiologies and growth conditions. In isolated cyanobacterial membranes and PSII, the QB/QB- ratio is expected to be around 50% after a short dark adaptation. This seems to be the case in our samples, based on the flash-dependent oscillations of the S2QB- and S3QB- thermoluminescence shown in Appendix 2 compared to the literature (Rutherford et al. 1982, https://doi.org/10.1016/0005-2728(82)90061-5), assuming an initial ~75% S1 population, as confirmed by the flash-dependent oxygen evolution and UV absorption. This is now mentioned in Appendix 2.

iii) The control of state transitions requires specific illumination regimes incompatible with the conditions required for our experiments. Moreover, state transitions remain largely uncharacterised in the far-red species used in the present work. In some of these species, the situation is further complicated by the presence of both visible and far-red light-absorbing phycobilisomes that have a different spatial distribution in the cell (MacGregor-Chatwin et al. 2022, https://doi.org/10.1126/sciadv.abj4437).

iv) Non-photochemical energy quenching in cyanobacteria seems to occur in phycobilisomes, due to the action of the Orange Carotenoid Protein (OCP). Both OCP and the phycobilisomes, if present in cyanobacterial cells (and that depends on the strains), are removed when membranes are isolated. It’s been proposed that direct quenching of the PSII core occurs in Synechococcus elongatus 7942 cells in state II (Choubeh et al. 2018, https://doi.org/10.1016/j.bbabio.2018.06.008), but since the mechanism has not been elucidated, no conclusion can be made on whether this could occur in membranes. The same is true for spill-over. Additionally, neither of the two mechanisms could be better controlled in cells than in membranes, so there would be no advantage here from working in vivo.

In addition, the authors reached a conclusion that the Chl-f-PSII containing species should suffer from fluctuation light-induced membrane potential spikes, but don't actually measure this in physiologically relevant preparations. It will be more beneficial to use intact cells instead of an isolated membrane. I suggest the authors either restrict their conclusions to what the isolated membranes clearly show or make measurements in intact cells.

The proposal that the far-red forms of PSII (both Chl-d-PSII and Chl-f-PSII) should suffer from increased charge recombination induced by spikes of membrane potential in fluctuating light is not new (see for example Nürnberg et al. 2018, https://doi.org/10.1126/science.aar8313), and is based on the observations made in plant PSII (Davis et al. 2016, https://doi.org/10.7554/eLife.16921) and assumed to be universal in oxygenic photosynthesis. In PSII, the transfer of electrons from the primary donor chlorophyll to QA occurs vectorially in the membrane, against the trans-membrane electric field, thanks to these electron transfer steps being exergonic. Spikes in the electric field due to sudden intensity fluctuations increase the probability of backward electron transfer. If the overall drop in the energy of the electron from the primary donor to QA is smaller (in a long wavelength PSII), it should result in a higher probability of backward transfer for a given trans-membrane electric field, and therefore a greater susceptibility to spikes in the electric field. We did not measure these effects and we do not claim to have done so. As already mentioned in the answer to point i) above, doing so would require the development of ElectroChromic Shift-based measurements of the membrane potential in the cyanobacterial species containing far-red photosystems. This is a separate research project beyond the scope of the present work.

In conclusion, we believe that our statement justifying the use of isolated membranes at the end of the Introduction is valid.

1. The authors measured the fluorescence decays as part of the evidence to show the stability of S2QA-. I have several concerns about these measurements:

i. In figure 2B, the WL C. thermalis (blue) trace has a unique decay phase with a lifetime of about 0.2s, which the authors denoted as S2QA- recombination. Could the author elaborate on how this phase was assigned to this state?

All decay kinetics in presence of DCMU are bi-phasic (with an additional faster phase in the WL and FR C. thermalis samples, attributed to a small fraction of centres where DCMU did not bind). In the manuscript we did originally assign both phases as arising from S2QA- recombination, but it is true that the middle phase, that is slightly faster in WL C. thermalis, is too fast to originate from that. This phase can rather be ascribed to TyrZ•(H+)QA- recombination occurring in a fraction of intact PSII centres before the full stabilization of charge separation, as shown in Debus et al. 2000 (https://doi.org/10.1021/bi992749w), or in centres lacking a Mn-cluster. We have now modified the paragraph regarding the fluorescence decay in presence of DCMU accordingly (L. 142-145): “The shorter lifetime (~0.22-1 s) of the middle decay phase (amplitude 15-20%) was compatible with it originating from TyrZ•(H+)QA- recombination occurring either in centres lacking an intact Mn-cluster (24) or in intact centres before charge separation is fully stabilised, as proposed in (23).”.

A luminescence decay phase with a similar lifetime was initially ascribed, incorrectly, only to TyrZ•(H+)QA- recombination occurring in centres devoid of an intact Mn-cluster, in Appendix 5. This has now been rectified.

ii. In figure S1 (the full version of 2B), all the fluorescence traces seem to rise at the end of the measurements. Could the authors check whether the measuring light intensity was actinic?

This rise is significant only in the A. marina dataset (now Figure 2-figure supplement 1), and given the low signal to noise ratio in the last points of the fluorescence curve, we consider this small anomaly to be a measuring artefact. The rise is absent in the other traces in Figure 2- figure supplement 1 and in Figure 2B, except for the last point of the A. marina dataset in Fig. 2B. The corresponding Source data provided, shows that a rise in the last point of the measurements is only present in one of the three A. marina replicates (#2), while the non-decaying fluorescence is present in all A. marina samples and discussed in the text. Except for this last anomalous point, the decay curves of the A. marina replicate #2 do not differ significantly from the other two replicates. This clearly suggests an artefact, and is not consistent with the measuring light being actinic. A clarifying sentence has been added in the legend of Figure 2- figure supplement 1.

iii. In figure S2, it seems to me that the fluorescence decay of Synechocystis + DCMU (Green open squares) was slower than the WL C. thermalis and is similar to the FRL C. thermalis in figure 2B. If the Synechocystis + DCMU is indeed similar to FR C. thermalis, would that be consistent with the authors' conclusions?

When fitting the Synechocystis+DCMU fluorescence decay kinetics (in what is now Appendix 1-figure 1), we obtain two decay phases with, respectively: an amplitude of ~12% and lifetime of ~0.22 s, and an amplitude of ~81% and lifetime of ~7.9 s. These values are similar to those reported for WL C. thermalis in Table 1, with an overall fluorescence decay faster than in FR C. thermalis. Nonetheless, because of the limited number of Synechocystis biological replicates, we limit ourselves to a qualitative comparison. The luminescence decay kinetics are also faster in Synechocystis (as in WL C. thermalis) than in FR C. thermalis (now Figure 5- figure supplement 2).

These data are consistent with our conclusions: the energy gap between QA- and Phe in Chl-f-PSII is at least as large as in Chl-a-PSII, or could even be larger, as suggested by the slower S2QA- recombination measured by fluorescence (Figure 2) and luminescence (Figure 3) decay.

iv. It's known that DCMU will alter the redox potential of QA/QA- in plants. Would it have similar effects to the PSII studied in this research? If so, it will be meaningful to include these effects in the energy diagram in fig 7.

Yes, we do expect DCMU to change the QA/QA- redox potential in our samples, as it does in plants and other cyanobacteria, although the actual effect in different PSII types would need to be measured. The energy gap values in now Figure 8 are only estimates based on literature values and on the relative changes reported here, they are not calculated from any of our data and do not specifically refer to the experimental conditions we used, including the use of DCMU. For this reason, we think that adding the effects of DCMU in the diagram would not be particularly useful and could be confusing.

1. The authors didn't use WL C. thermalis for measuring oxygen evolution and the authors claimed that the PSII content in WL C. thermalis is too low. Is that a technical issue (e.g., cannot purify PSII enriched membranes) or a biological issue (i.e., white light condition produced less PSII)? In Fig S9C, the oxygen generated from WL C. thermalis is comparable to FR C. thermalis. Could the author explain how they reached the conclusion that PSII in WL C. thermalis was low? In addition, the author should also provide evidence showing that the samples of WL C. thermalis do not have significant PSII activity under far-red light.

We did measure the flash dependence of oxygen evolution in WL C. thermalis membranes, and we did observe oscillations with visible flashes (but not with far-red flashes, as expected). However, the data were not good enough to be able to perform any significant analysis. Unfortunately, in the case of WL C. thermalis, we have not been able to isolate O2-evolving cores, as stated in L. 194-195. The WL C. thermalis data have now been added in Figure 3- figure supplement 1, together with the non-normalised traces of all other samples (following the suggestion by reviewer #3), and the text has been modified accordingly. The data in Figure 3- figure supplement 1 also provide evidence that the samples of WL C. thermalis do not have significant PSII activity under far-red light (although this was already clearly demonstrated in Nürnberg et al. 2018).

We do have evidence that the PSII content per chlorophyll is lower in WL C. thermalis than in FR C. thermalis, based on fluorescence emission spectra, yield of isolated PSII and PSI from purification procedures, and O2 evolution per chlorophyll, as can be seen for example in Figure 3- figure supplement 1. The levels of PSII accumulation depend on the growth stage (among other factors) in model species such as Synechocystis. Since C. thermalis cells grow more slowly than other cyanobacteria species and their physiology has not been studied in detail yet, it is difficult to control the levels of PSII accumulation. This explains the inter-sample variability in the rates of O2 evolution per chlorophyll measured with the Clark electrode, that have now been added in Appendix 6-table 1.

1. The authors used an indirect method, which used chemical trap histidine and oxygen consumption, for measuring the production of singlet oxygen from different types of PSII. I have several concerns about this approach.

i. Why not use a probe that reacts directly with singlet oxygen probes like SOSG or EPR probes to unambiguously confirm the production of singlet oxygen? The difficulties of not using SOSG mentioned in Rehman et al (SI Ref#22) should be no longer problems when isolated membranes were used. The advantage would be a validation of the results and perhaps increased sensitivity.

Although SOSG or EPR probes could also be used to detect singlet oxygen production, these other methods seem to be significantly less sensitive than histidine trapping. For example, Fufezan et al. 2007 (https://doi.org/10.1074/jbc.M610951200) used the EPR spin trap TEMPO and needed 30 minutes of illumination. Extended illumination (up to 1 hour) has also been used to detect singlet oxygen using SOGE (Flors et al 2006, https://doi.org/10.1093/jxb/erj181).With the histidine trapping method used here, less than 2 minutes of illumination were required to measure the singlet oxygen production rates. This allowed potential problems of prolonged illumination (e.g. a loss of intact PSII centres due to photodamage) to be minimised, and allowed us to confirm the results obtained in isolated membranes with those obtained in intact cells.

As shown in now Figure 6- figure supplement 1E, the histidine-dependent oxygen consumption was suppressed by the singlet oxygen quencher sodium azide, as also shown in Rehman et al. 2013 (https://doi.org/10.1016/j.bbabio.2013.02.016). We also independently confirmed that the singlet oxygen generated by illumination of the dye Rose Bengal can be efficiently detected with the histidine trapping method and suppressed by the addition of sodium azide (Figure 6- figure supplement 1F). For these reasons, we are confident that what we measure with the histidine trapping method is singlet oxygen production.

ii. In Rehman et al (SI Ref#22), wild-type Synechocystis cells showed significant production of singlet oxygen in the presence of DCMU and His (Figure 3A in SI Ref#22), however, the amount of singlet oxygen measured from the membranes in this study seemed to be less (Fig S10E). Could the authors provide some explanations?

Fig. 3A in Rehman et al. showed that the production of singlet oxygen was about 10% with respect to the oxygen evolution activity in absence of additions (open squares). The light saturation curves in Fig. 4B of the same paper also show that at saturating light intensity the singlet oxygen production rate is about 10% compared to the O2 evolution rate. The traces we show in Figure 6-figure supplement 1 are only representative. The comparison should be made between the results in Rehman et al. and the averages of biological replicates that we show in Fig. 6 (membranes) and Appendix 6-figure 4A (cells). For WL and FR C. thermalis, we measure singlet oxygen production rates that are about 20% of the O2 evolution rates, slightly higher than those measured in Synechocystis in Rehman et al. Considering the variability between biological replicates, we consider our values in line with those in Rehman et al.

iii. Can the presented results distinguish the production of singlet oxygen from recombination or other sources (e.g., antenna, free chlorophyll)? Some key controls are needed to strengthen the authors' claims.

This is difficult to demonstrate unequivocally, but we have different lines of evidence that support the conclusion that the increase in singlet oxygen production in A. marina originates from differences in PSII charge recombination with respect to the other samples:

i) The high levels of singlet oxygen production are observed in intact cells as well as in membranes. In neither of these samples do we expect to have significant amounts of damaged PSII or free chlorophyll, so these seem highly unlikely as the main sources of the singlet oxygen in our measurements. This is now stated more explicitly in L. 305 and Appendix 6.

ii) According to the data in Appendix 6-figure 1B, singlet oxygen production in A. marina membranes shows a similar light saturation to that of maximal O2 evolution. This suggests that the singlet oxygen production we measure is related to PSII photochemistry. We have now stated this explicitly in L. 288-290.

iii) Our thermoluminescence and delayed luminescence results indicate that in Chl-d-PSII the energy gap between Phe and QA is smaller than in Chl-a-PSII, as already suggested in the literature, and Chl-f-PSII. Therefore, this indicates more charge recombination going via repopulation of Phe- in Chl-d-PSII, with a consequent increase of singlet oxygen production.

The antenna chlorophylls could form triplets under high light, by inter-system crossing, but in intact antennas the chlorophyll triplets are expected to be mostly quenched by nearby carotenoids (see https://www.jstor.org/stable/24030848 for a review on the subject). The generation of antenna triplet states in non-photoinhibitory conditions has been demonstrated in plant and algal thylakoids (Santabarbara et al 2002, 2007 doi: 10.1021/bi0201163, doi: 10.1016/j.bbabio.2006.10.007). Yet, these signals, which are attributed to a small population of damaged antennas, are small compared to those of triplets generated by charge recombination. Due to its apparently stochastic nature, the generation of antenna triplets by inter-system crossing is not expected to be significantly different between the different PSII complexes investigated in this study.

On the other hand, it is generally recognised that in the PSII reaction centre, the carotenoid on the D1 side is not close enough to ChlD1 to directly quench its triplet state, when formed (see Telfer et al. 1994, https://doi.org/10.1016/S0021-9258(17)36825-4). The singlet oxygen produced in the reaction centre could disrupt the coupling between chlorophylls and carotenoids in the antenna, resulting in singlet oxygen production also from the antenna, in a cascade effect. This can happen with prolonged strong illumination (Fufezan et al. 2002, https://doi.org/10.1016/S0014-5793(02)03724-9).

iv. I could not fully understand the singlet oxygen production experiments with tris-washed samples. In my opinion, the Mn-cluster depleted PSII should have accelerated charge recombination (100 ms between the YZ/QA, vs ~ 5 sec between the S2/QA), which should lead to an increase in singlet oxygen production. Correct me if I'm wrong about this, but if my reasoning is correct then how do the authors explain the discrepancy?

Our rationale for performing the tris-washing experiment was indeed to see if this would lead to an increase in singlet oxygen production, thus implying that the high production in the A. marina samples could arise from a higher fraction of PSII centres without the Mn-cluster, as explained both in the main text and in Appendix 6. The fact that the treatment did not increase the singlet oxygen production suggests that this does not specifically arise from PSII lacking the Mn-cluster.

The lack of singlet oxygen increase following tris-washing is not necessarily controversial, as the fact that TyrZ•QA- recombination is faster than S2QA- recombination does not necessarily imply that more of it occurs via backward electron transfer from QA- to Phe. The removal of the Mn-cluster could decrease the production of singlet oxygen by charge recombination, since it causes an increase in the redox potential of QA and, therefore, of the energy gap between Phe and QA, thus decreasing the probability of charge recombination going via the repopulation of Phe-. This is proposed to be a mechanism to protect PSII during photoactivation of the Mn-cluster (see Johnson et al 1995, https://doi.org/10.1016/0005-2728(95)00003-2).

Our data show that the singlet oxygen production in A. marina is not specifically related to PSII lacking the Mn-cluster and are not in conflict with what is expected based on our knowledge of PSII energetics.

v. The y-axes in Figure S10 should either contain "delta" (Δµmol O2 ml-1) or use the measured absolute oxygen concentration. I'd suggest the latter, since the reaction is oxygen consuming, it's good to show that all the samples started with similar amounts of dissolved oxygen. Low O2 levels could decrease 1O2 production, though this would be more of an issue with cells than membranes.

The y-axis labels in the figures (now Figure 6-supplementary figure 1 and Appendix 6-figures 1D and E, 2, 3 and 4A) have been changed to Δµmol O2 ml-1. We prefer to show the traces after subtraction of the baseline recorded in the dark (now explicitly indicated in the corresponding figure legends) for a better visual comparison. All samples were left to equilibrate with air (stirred) before starting the measurements, so all started with similar levels of dissolved oxygen. This is especially important when measuring PSI-dependent oxygen consumption (Appendix 6-figure 3), because the addition of ascorbate and TMPD leads to a transient drop in oxygen concentration in the sample, which leads to artefacts in absence of the equilibration step. This information has been added to the corresponding Materials and Methods section (4.5). Additionally, when using Rose Bengal to generate singlet oxygen, the histidine-dependent oxygen consumption was about 10 times higher than in any of the measurements done with biological samples, and still we did not observe saturation of the signal in the illumination time used (added panel F in Figure 6- figure supplement 1). Therefore, we are confident that the singlet oxygen measurements in membranes and cells were not skewed by limiting oxygen concentrations in the measuring chamber.

The y-axis labels of what is now Appendix 6-figure 1B and C have also been corrected (as ml-1 was used instead of h-1).

Reviewer #3 (Public Review):

In this manuscript, Viola and co-authors address the question of how far-red-light-adapted (FRL) Photosystem II (PSII) is able to bypass the "red limit", or the minimum photon energy/frequency for charge separation to proceed effectively. They attempt to do so primarily by measuring the consequence of failure to overcome the red limit: charge recombination. From this work they have concluded that FRL PSIIs are able to achieve similar efficiency of flash-induced water-oxidizing complex turnover to those adapted to standard visible light. However, they conclude that FRL PSII which uses chlorophyll-d is significantly more susceptible to charge recombination and singlet oxygen formation, leading to increased sensitivity to high-light conditions. FRL PSII which uses chlorophyll-f, however, is adapted to be more resistant to photodamage. These strategies are differentiated by the number and type of far-red chlorophyll used and tuning of redox potentials of cofactors in PSII.

The methods employed are well-chosen to present complementary evidence to address the questions posed. The authors have supported themselves using polarography, fluorescence decay, absorption, luminescence and thermoluminescence, and spectrometry, all of which are employed in a manner well-established in the quantification of processes in standard PSII preparations. The results, however, have some loss of data such as total yields which would be useful in interpretation as the authors have chosen to extensively normalize data for ease of visual comparison of certain features.

Overall, the authors have adequately achieved their aims and their conclusions are well-supported. The authors also clearly state their own expectations of the impact of their work at the end of the Discussion; thanks to these results, we can better understand the ecological niche of each type of FRL-PSII and how these significantly disparate systems may be used in future agricultural research and development.

We thank the reviewer for the positive evaluation of our work.

Following the reviewer’s suggestions, the total yields (on a chlorophyll basis) of the flash-dependent oxygen evolution have been provided in Figure 3- figure supplement 1. These include the flash-dependent oxygen evolution data measured in WL C. thermalis membranes, that were previously omitted because of the unsatisfactory quality, and are still omitted from Figure 3 (normalised data and fits) for the same reason. The S-state distributions calculated from the fits of the flash-dependent oxygen evolution have been added in Table 2.

Additionally, the non-normalised oxygen evolution and consumption rates used for Figure 6A and Appendix 6-figure 4 are now provided in Appendix 6-table 1.

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Reply to the reviewers

Answers to reviewers’ comments

(Reviewers comments are in italics. Text modifications in the manuscript file are in blue.)

Overall, we acknowledge referee’s careful reading of the paper and comments that we think have helped further improvement of the manuscript.

On the attached pages are our detailed point by point responses to the referees’ comments along with a description of how the manuscript was modified in accordance.

New data included:

In response to the comments and suggestions of both reviewers 1 and 3, we conducted new experiments to test genetic interactions between different actors of the BMP and activin pathways. These new results confirm and complement the analyses described in the original manuscript. Furthermore, as suggested by reviewer 2, we have further studied the phenotypes of hiPSC-CM, by analyzing gene expression profiles and by analyzing the morphological changes induced as a result of PAX9 knockdown.

NB: The title has been slightly modified, to highlight the conserved features of the genetic architecture of cardiac performance revealed in the study

__Former title: __Genetic architecture of natural variation of cardiac performance in flies.

__Novel title: __Genetic architecture of natural variation of cardiac performance: From flies to humans.

Reviewer 1

1. 1. The authors utilized the RNAi-mediated knockdown approach in their functional validation studies. It is not clear how each genetic variation (SNP) affects its associated genes. Could some of the SNPs activate the candidate gene expression? For the 4 candidate genes that failed to show cardiac defects, could the overexpression of these 4 genes alter cardiac performance? Answer 1- Of course, we cannot predict direction of the effect of the variants on the function of the genes. In this context, loss-of-function experiments are subjected to a risk of false negatives. It is indeed possible that in the case of a lack of effect of the loss of function, a gain of function could reveal an effect. But gain-of-function experiments are difficult to control, and often subjected to non-specific effects because it is complicated to control the level of over-expression compared to endogenous expression. This did not seem suitable for an extensive analysis of a large number of genes. We therefore chose to test only for loss of function.

In addition, our approach to testing heart-specific RNAi aims to assess the quality of the association results by comparing RNAi for genes identified by GWAS to randomly selected genes. It is not intended to describe precisely the involvement of each gene individually.

(See also answer to reviewer 2 comment n°2 and the modifications to the manuscript that have been made and which address these criticism)

* 2. babo is the type I activin receptor, not type 2. *

Answer 2- Thank you, we have corrected this error.

• The authors show BMP and activin pathway genetically interacts to affect cardiac performance. But it is interesting to find that these interactions are in a trait-dependent manner. For example, it seems that babo and dpp epistatically interact to regulate FS, while they additively regulate HP and DI. The authors need to discuss the complex genetic interaction further. *

Answer 3- See reply to reviewer 3, comment N°2 below.

4*. Both snoo and sog are identified from GWAS. How about babo and dpp? Are there any identified SNPs associated with babo and dpp? *

Answer 4- Considering GWAS for mean phenotypes, there is no variant in dpp that are within the 100 best ranked SNPs nor within the variants identified using fast epistasis. But given the size of the DGRP population we are far from being exhaustive, as we do not reach saturation. It is therefore difficult to comment on these ‘negative’ results. However, we do identify one variant in babo using fast epistasis (see figure 2B and Table S3).

5. It is unclear why the mad KD behaves oppositely to dpp mutant, although both proteins are involved in BMP pathway. In Figure S5, the mad KD shows reduced FS and HP, but dpp LOF mutant shows increased FS and HP (Figure S4). Can the authors perform RNAi to knockdown dpp specific in the heart to reexamine the role of dpp in the regulation of cardiac function. The whole body LOF mutant dpp-d14 might not target cardiac tissue directly to control heart performance like mad KD.

Answer 5- (see also answer to reviewer 3 comment n°2) We did perform heart specific dpp RNAi experiments together with other tests for interactions using new allelic combinations of activin and BMP pathways and therefore can compare heart specific knock down to heterozygotes for amorphic mutations for both dpp and mad.

Regarding dpp, congruent effects on HP, DI, SI, ESD and EDD were observed between mutant and RNAi, while RNAi had opposite effects on FS compared to heterozygotes dppd14 mutants (decreased and increased FS compared to control, respectively). In the case of mad, heterozygous mutants had no effect on FS, EDD and ESD, but similarly to dpp mutants it increased SI, DI and HP. mad RNAi uniquely decreased HP, DI and SI and increased AI. However, similarly to dpp RNAi, it induced a decrease of FS.

Thus, systemic versus heart specific knockdown of genes induce specific effects, suggesting cardiac non-autonomous interactions. This complex picture of TGFb involvement is now discussed in the result section (see below, Reviewer 3, major comment 2).

6*. The authors selected two novel genes to study the conversed regulation in both flies and human iPSC cells. Besides testing these novel genes, the authors should also verify whether the conserved pathways, like TGF-beta, regulate heart performance in human iPSC cells similar to the flies. *

Answer 6- We focused on poxm/Pax9 and sr/Egr2 because none of these TFs were known to have cardiac function in fly nor in mammals. Our paralleled analyses in fly and hiPS-CM illustrates how the description of the genetic architecture of cardiac traits in flies can accelerate discovery in mammals.

There is extensive literature describing the involvement of TGF B /BMP and Activin pathways in heart development and diseases in humans, hence the choice not to focus on these pathways in iPS-CM.

Reviewer 2:

1. • It will be interesting to compare this fly GWAS to human heart disease GWAS data (for example, cardiomyopathy, arrhythmia, heart failure) from patients. Such cross comparison could make the data set more valuable. * Answer 1- We actually did make this comparison (Table 2, Table S11) and we agree it significantly validates our approach. This identified a set of orthologous genes associated with cardiac traits both in Drosophila and humans, supporting the conservation of the genetic architecture of cardiac performance traits, from arthropods to mammals.

2. RNAi is the only experimental approach in this manuscript to validate the functional significance from data analyses. Authors may consider using genetic mutations such as deficiency lines or P-element lines to offer an alternative approach. This is simply a suggestion to improve the rigor and reproducibility, not absolutely required. *

Answer 2- In an attempt to provide a consistent analysis of loss of gene function, our strategy was to concentrate our analysis on the effects of heart specific knock down. This allows us to compare -in a global way- the effects of the knock down of genes identified by GWAS to those of randomly selected genes.

Our objective was to provide a global view of the heart specific effects of the identified genes, and not to characterize precisely the involvement of each of them, using a combination of mutant alleles, RNAi and gain of function. Given the experimental burden of analyzing cardiac function, such a strategy would have indeed required us to concentrate only a very small number of genes.

We however recognize that this strategy has limitations:

• Some variants may lead to gain-of-function effects of genes, and our strategy is not able to test for these effects.

• Some variants may come from non-cell-autonomous effects, which would not be replicated by our targeted RNAi strategy in the heart.

Therefore, the false negative rate of our experiments is difficult to estimate.

We have tried to put this into perspective and to highlight the limitations of our analysis in the results section describing RNAi validation of GWAS results.

“To assess in an extensive way whether mutations in genes harboring SNPs associated with variation in cardiac traits contributed to these phenotypes ….. (…)

…… These results therefore supported our association results. It is important to emphasize that our approach is limited to testing the effect of tissue-specific gene knock down. Since some of the variants may lead to increased gene function and/or expression, this can lead to a false negative rate that is difficult to estimate. In addition, some of the associated variants may influence heart function by non cell-autonomous mechanisms, which would not be replicated by cardiac specific RNAi knock down.”

*In order to validate the roles of predicted TF binding sites, the best approach would be introducing point mutations using CRISPR/Cas9 within the binding motif then testing out molecular and physiological outcomes. Rather authors chose to test indirectly to knock down those TFs. If so, authors need to at least acknowledge the potential caveats of such approach and the limitation in related data interpretation. *

Answer 3- The reviewer is right, the definitive proof of the involvement of a potential TF binding site on the regulation of a gene located in cis requires to mutate the binding site and to analyze the effect on the expression of the corresponding gene. But this may not be sufficient to definitely demonstrate that the potential TF is indeed a regulator of that gene (the binding motif may be target of yet another TF): definitive proof may require motifs/TF DNA binding domain swaps. This would have been out of the scope of the present study. In addition, the effects on heart performance of mutating one TFBS at a time (among several dozens) may be too weak to allow their characterization with available tools and approaches.

We acknowledge however that our approach provides an indirect validation of transcription factors binding sites predictions. This was, in our opinion, the most efficient way to evaluate the potential effect of predicted transcription factors.

We clarify this in the result section:

“We did not test individually the effects on cardiac performance of mutations in predicted TFBSs located near the SNPs because any individual effect would probably be too small to be detectable by the available methods. Rather, we tested the potential involvement of their cognate TFs by cardiac specific RNAi mediated KD”

• hiPSC-CM data is somewhat limited by only showing the HR and AP duration data. It is recommended to include some immunocytochemistry data to show the morphology, sarcomere structure of these hiPSC-CMs. Gene expression data generated by qPCR or RNA-seq in particular focusing CM structure and function genes would be helpful too.*

Answer 4- As suggested by referee 2, we have now performed gene expression analysis and immunostaining of PAX9 KD which gave the strongest phenotype in iPSC-CM (Figure 4 J-M). This unraveled increased expression of Na+ and K+ channels, which is in line with APD shortening phenotype, as well as down regulation of CASQ2, consistent with calcium transient shortening. Expression analysis also revealed increased sarcomeric genes and NPPA/B expression, which was consistent with increased CM size as quantified by the area of TNNT2 staining per nuclei.

These new data are described at the end of the result section:

“APD shortening for PAX9 KD was coincident with increased expression of Na+ and K+ ion channels (SCN5A, KCNH2 and KNCQ1) (Figure 4J), supporting the APD shortening phenotype. In this context, the AP kinetics also correlated with shorter calcium transient duration (Figure S8A-D and H-K), including faster upstroke and downstroke calcium kinetics and increased beat rate (peak frequency) (Figure S8E-G and L, M), consistent with decreased expression of Calsequestrin 2 isoform (CASQ2) associated with PAX9 KD (Figure 4J). Finally, assessment of the PAX9 KD effect on sarcomeric content revealed an increase in sarcomeric gene expression (Figure 4K), and an upregulation of genes associated with an hypertrophic response (NPPA, NPPB and NPR1 (Battistoni Et al Circulating biomarkers with preventive, diagnostic and prognostic implications in cardiovascular diseases, Int J Cardiol, 2012, vol. 157) which was coincident with increased CM size as quantified by the area of TNNT2 staining per cardiac nuclei (Figure 4 L, M).

Collectively, these data illustrate conserved functions for poxm/PAX9 and sr/EGR2 in setting the cardiac rhythm and identify PAX9 as a novel and key regulator of cardiac performance at the cellular level, via the integrated regulation of expression of genes controlling electrophysiology, calcium handling and sarcomeric functions in hiPSC-CMs.”

Reviewer 3

Major Comments:

1- There is an assumption in the use of RNAi knockdown to validate the genes identified in the quantitative analysis, and that is that natural variants are themselves hypomorphic. It is possible that among the variants identified some are hypermorphic, or among the transcription factor binding sites that variants lead to increased factor binding. While RNAi knockdown is an excellent choice to begin validation, I do not think the authors can rule out that a gene not functionally validated by their RNAi tests does not have a role in cardiac function.

Answer 1. Please see our answers to reviewer 1 comment n°1 and reviewer 2 comment n°2.

* 2- After performing RNAi knockdown to validate genes identified by GWAS the authors focus on the TGFbeta signaling pathway for downstream analysis. To do so they examine heterozygotes for sog, a repressor of BMP signaling, and snoo, an activator of Activin pathway. The data from the snoo/sog heterozygote is compelling in its disruption of heart phenotypes, and the authors conclude a "coordinated action of activin and BMP." snoo, however, also works as a transcriptional repressor in the BMP pathway, so it's possible that the effects the authors are seeing here could be confined to an increase in BMP signaling. Unlike snoo and sog, mutations in babo and dpp are both expected to have negative effects on Activin and BMP signaling, respectively. The babo/dpp interaction is not as quantitatively convincing as the snoo/sog data, despite the integral roles both babo and dpp play in their respective pathways. If both pathways are connected, why do snoo/sog heterozygotes affect SI phenotypes, while babo/dpp heterozygotes affect fractional shortening? I think the authors data suggest an interesting potential interaction between these pathways, which could be confirmed by examining further mutant combinations, knockdowns or increased expression transgenes, but falls short of a "confirmed synergystic genetic interaction." It does, however, underscore the value of the data in the paper for opening up new avenues for future study. *

Answer 2 (and reviewer 1 comments 3 and 5).

These comments led us to reconsider the analysis of the phenotypes associated with loss of function of the TGFb pathway, and to analyze other pathway components combinations.

We acknowledge reviewer 3 criticisms on snoo/sog experiments, which are difficult to interpret given the broad action snoo may have on both BMP and activin pathways. We have addressed this in the result section.

We have also analyzed other allelic combinations of BMP and activin pathways components, which strengthen the analysis performed on dpp/babo. Indeed, we tested babo/tkv heterozygotes (respectively specific activin and BMP receptors) and found significant genetic interactions for ESD and EDD. Albeit non-significant, babo/tkv double heterozygotes display a tendency to non-additive effects on FS (p= 0,054). mad/smox heterozygotes (respectively specific downstream TFs of BMP and activin pathways) display interactions (non-additive effects) on HP, SI, DI, ESD and EDD. These new results (Supplemental Figure 4) are thus supporting the hypothesis of genetic interactions between the pathways, but also reveal, as suggested by reviewer 3, a complex relationship between both pathways since interactions are revealed for specific traits in each of the mutant combinations analyzed.

The phenotypes related to the individual loss of function of each of the actors of these pathways (dpp, tkv and mad for BMP; babo and smox for activin) are however very similar. When they have an effect, heterozygous amorphic alleles of these genes display increased phenotypes related to rhythmicity (HP, DI, SI, AI) and FS, but decreased cardiac diameters (ESD and EDD).

Finally, as pointed out by reviewer 1, the picture is certainly even more complex since the phenotypes of RNAi mediated heart specific loss of function are not always similar to those of systemic loss of function. Indeed, mad RNAi causes a reduction of HP, DI, SI and FS (Figure S5) whereas heterozygotes for mad12 have either no or opposite effect on these phenotypes, and mad RNAi causes a significative increase in AI whereas mad12 has no effect (Figure S4). The discrepancy between tissue specific RNAi and heterozygous background was also found in the case of dpp, but specifically for the FS. Indeed, as suggested by reviewer 1 we have analyzed the loss of function of dpp by heart-specific RNAi. dpp RNAi results in a reduction of the FS (like mad RNAi) whereas the loss of function in the whole-body results in an increase of the FS.

We therefore re-wrote the whole corresponding section of the results and modified Figure S4 to include babo/tkv; smox/mad and dppRNAi data.

“We further focused on the TGFb pathway, since members of both BMP and activin pathways were identified in our analyses. We tested different members of the TGFb pathway for cardiac phenotypes using cardiac specific RNAi knockdown (Figure 2C), and confirmed the involvement of the activin agonist snoo (Ski orthologue) and the BMP antagonist sog (chordin orthologue). Notably, Activin and BMP pathways are usually antagonistic (Figure 2D). Their joint identification in our GWAS suggest that they act in a coordinated fashion to regulate heart function. Alternatively, it may simply reflect their involvement in different aspects of cardiac development and/or functional maturation. In order to discriminate between these two hypotheses, we tested if different components of these pathways interacted genetically. Single heterozygotes for loss of function alleles show dosage-dependent effects of snoo and sog on several phenotypes, providing an independent confirmation of their involvement in several cardiac traits (Figure S4). Importantly, compared to each single heterozygotes, snooBSC234/ sogU2 double heterozygotes flies showed non additive SI phenotypes (two-way ANOVA p val: 2,1 10-7) suggesting a genetic interaction (Figure 2E and Figure S4A). It is worth noting however that snoo is also a transcriptional repressor of the BMP pathway (PMID: 16951053). The effect observed in snooBSC234/ sogU2 double heterozygotes can therefore alternatively arise as a consequence of an increased BMP signaling without affecting the activin pathway. We thus tested other allelic combinations for loss of function alleles of BMP and activin pathways. babo/tkv heterozygotes (respectively activin and BMP type 1 receptors) displayed non additive ESD and EDD phenotypes (Figure S4C). Synergistic interaction of BMP and activin pathways was also suggested by the analysis of fractional shortening in loss of function mutants for babo and dpp, the BMP ligand (Figure S4B). Of note, babo/tkv double heterozygotes also displayed a tendency to non-additive effects on FS albeit non-significant (two-way anova p= 0,054). In addition, mad/smox heterozygotes (specifc downstream TFs of BMP and activin pathways) displayed non-additive effects on several traits, including phenotypes related to rhythmicity (HP, SI, DI) and contractility (ESD and EDD) (Figure S4D). Altogether, cardiac performance in response to allelic combinations of activin and BMP supported a coordinated action of both pathways in the establishment and/or maintenance of cardiac activity. This was further supported by the observation that simple heterozygotes for the tested loss of function alleles displayed similar trends with respect to cardiac performance, irrespective of the pathway considered (dpp, tkv and mad for BMP; babo and smox for activin). Indeed, they displayed either no effect or increased fractional shortening and rhythmicity phenotypes (HP, DI, SI, AI), and decreased cardiac diameters (ESD and EDD). This suggests coordinated activity of both pathways. Importantly, the genetic interactions were tested using amorphic alleles that lead to systemic loss of function. The observed phenotypes may thus not unravel cardiac specific effects of the pathways. In support of this, mad cardiac specific RNAi knock down was tested (see below, Figure S5) and lead to a decreased HP, DI, SI and FS whereas heterozygotes for mad12 have either no (FS) or opposite (HP, DI, SI) effect on these phenotypes (Figure S4D). Inversely, mad RNAi caused a significant increase in AI whereas mad12 had no effect. However, heart specific dpp RNAi knock down (Figure S4E) lead to similar phenotypic trends compared to dppd14 (increased HP, DI, SI, decreased EDD and ESD) with the notable exception of FS which was reduced following cardiac specific KD (Figure S4E), but increased in dppd14heterozygotes (Figure S4B). Taken together, these data point to a complex picture of TGFb pathway activity in regulating cardiac performance, involving both the activin and the BMP pathways as well as gene specific effects with both systemic and tissue-specific contributions.”

*Minor Comments: *

* There is an enormous amount of data in this paper, but there are places where things are summarized a little too briefly. For example, there are no definitions given at the beginning of the Results section for traits like "Heart Period" or "Systolic Interval," which would make this work significantly more accessible for other Drosophila researchers. (They do touch on this when they explain later in the paper that certain variants are "associated with quantitative traits linked to heart size and contractility" but more background earlier would be helpful.) When we consider heart performance traits, what is the baseline from known mutants? In other words, where is the line between variation and defect? *

Answers:

• We have detailed the description of the traits analyzed at the beginning of the result section. We hope this improves the ease of reading in the direction suggested by the reviewer. “7 cardiac traits were analyzed across the whole population (Dataset S1 and Table 1). As illustrated in Figure 1A, we analyzed phenotypes related to the rhythmicity of cardiac function: the systolic interval (SI) is the time elapsed between the beginning and the end of one contraction, the diastolic interval (DI) is the time elapsed between two contractions and the heart period (HP) is the duration of a total cycle (contraction + relaxation (DI+SI)). The arrhythmia index (AI, std-dev(HP)/mean (HP)) is used to evaluate the variability of the cardiac rhythm. In addition, 3 traits related to contractility were measured. The diameters of the heart in diastole (End Diastolic Diameter, EDD), in systole (End Systolic Diameter, ESD), and the Fractional Shortening (FS), which measures the contraction efficacy (EDD-ESD/EDD).“

• With respect to the baseline of cardiac performance, there is no simple answer. The baseline is influenced by the genetic background and the experimental conditions. This is the reason why any analysis of mutants or RNAi is conducted in comparison with its own control, analyzed at the same time. Concerning the DGRP lines, no baseline can be defined, since the objective is to measure the diversity of cardiac performance traits within a natural population.

Embrace the mystery, the sacred - accepting that one will be gone forevermore is a mighty task as our culture teaches us to seek recognition. The last thing we want to be is unrecognized, a nobody. And yet, when we are dead and dissipated back into the rest of the world, that is exactly what we will become.

But we have to accept that reality before we can build and think beyond it to a deeper possibility of meaning. Reality brought us forth to begin with. Every moment is already sacred.

Author Response

Reviewer #2 (Public Review):

1) “…it was important that the output response was intimately linked to the bound state of the receptor, in this case the TCR, with ligand unbinding rapidly reversing all proofreading steps. This means that dissociation of a single TCR should disrupt signaling, and implicitly assumes a direct physical connection between the bound receptor and the KP modifications. However, this mechanism becomes much harder to argue when the KP steps are physically uncoupled from bound TCR, such as in LAT microclusters or DAG production.”

We agree that signaling events in the kinetic proofreading chain must be linked to ligand unbinding. We have added discussion to the paragraphs beginning on page 20 line 440 of recent work from Yi et al. 2019 and Lo et al. 2018 suggesting a physical link between bound TCRs and LAT clusters. The full paragraphs are reproduced below.

“The kinetic proofreading model requires all intermediate steps to reset upon unbinding of the ligand (Fig. 1A). This means that information about the receptor’s binding state must be communicated to all proofreading steps. If kinetic proofreading steps exist beyond the T cell receptor, how is unbinding information conveyed to these effectors? Importantly, there is evidence of physical proximity of LAT with the receptor. While TCR/Zap-70 and LAT/PLCγ microclusters form spatially segregated domains, these domains remain adjacent to one another (Yi et al., 2019). Lo et al. demonstrated that the protein Lck binds Zap-70 with its SH2 domain and LAT with its SH3 domain, potentially bridging the two signaling domains together and propagating binding information (Lo et al., 2018).

An attractive reset mechanism is the segregation of CD45 away from bound receptors, creating spatial regions in which TCR and LAT associated activating events can occur (S. J. Davis & van der Merwe, 2006). Super-resolution microscopy by Razvag et al. measured TCR/CD45 segregated regions within seconds of antigen contact at the tips of T cell microvilli (Razvag et al., 2018). Upon unbinding, these regions of phosphatase exclusion collapse, allowing CD45 to dephosphorylate receptor ITAMs and LAT clusters. However, the rate of dephosphorylation for LAT and receptor ITAMs could differ. LAT clusters exclude CD45 in reconstituted bilayer systems, potentially limiting the dephosphorylation to LAT molecules at the edges of the cluster thus slowing reset (Su et al., 2016). The kinetics of multivalent protein-protein interactions within TCR and LAT clusters can also influence dephosphorylation and dissociation rates (Goyette et al., 2022).

A CD45-mediated reset mechanism would restrict proofreading to membrane-bound signaling events occurring within a CD45-depleted region. Downstream events that dissociate away from the membrane or diffuse out of the segregated region could not directly participate in the proofreading chain, as the collapse of a CD45 segregated region could not reset signaling entities released into the cytosol (e.g. release of IP3 in the cleavage of PIP2 to DAG).”

2) …The data clearly demonstrate a time delay between receptor binding and the measured outputs, but it is not so surprising that this lag would exist in propagating the signal through the intracellular network.

We apologize for this point of confusion in our methodology. We are unable to measure the time lag between receptor binding and signal propagation through the network because our system is terminated by blue light. Binding is stochastically initiated much like native ligand/receptor interactions. The time values reported in our dataset are the average ligand binding half-lives of the LOV2 ligand under various intensities of constant blue-light illumination, as measured by separate in vitro kinetic washout experiments. Our model is fit to the steady-state signaling output achieved after a 3 minute exposure of cells to LOV2 ligands of an average ligand binding half-life enforced by constant blue light illumination. We clarify this point by including the following paragraphs beginning on page 8 line 170.

“We are unable to control when binding events start since our optogenetic system is inhibited by blue-light, as opposed to being activated by blue-light. The initiation of binding after blue-light inhibition is a function of both the stochastic relaxation of inhibited LOV2 back into the binding-state as well as the diffusion of binding-state LOV2 from outside the previously illuminated area. Without temporal control over the start of binding, it is difficult to measure the time delay between ligand binding and a downstream signaling event (Yi et al., 2019). Such studies typically require careful single-molecule imaging of numerous stochastic binding events (Lin et al., 2019).

To overcome this technical limitation of our system, we chose instead to measure the steady-state output of the antigen signaling cascade achieved several minutes after ligand binding. Kinetic proofreading systems behave differently than non-proofreading systems at steady-state. A non-proofreading system’s steady-state output is set by the number of ligand-bound receptors and not the binding half-lives of those ligands (Fig. 3D, left). In contrast, a kinetic proofreading system can produce different steady-state outputs in response to ligands of different binding half-lives, even when ligand densities are adjusted to achieve equivalent occupancy (Daniels et al., 2006) (Fig. 3D, right). Signaling events take varying amounts of time to occur after ligand binding (Lin et al., 2019; Yi et al., 2019). However, the temporal delays between steps are on the order of tens of seconds. By imaging the cells after minutes of constant exposure to a set ligand binding half-life, we measure the steady state output achieved at a signaling event in the cascade on a longer timescale than these delays (Tischer & Weiner, 2019).”

3) The authors use a simple equation for KP to fit their datasets in Figure 4, equivalently to their previous work. However, no goodness-of-fit metric is provided for these fits, and by manual inspection it is hard to see the defining curves of their KP model in the datasets, especially not for LAT and DAG, where the datasets look much more like vertical bars. The estimated values of steps (n) may well be the best fit to the data, but they are not necessarily a 'good' fit.

To assist readers in assessing how well our models fit our datasets, we have included heatmaps of the residuals from each model fit (Fig 4S3) on page 52, along with discussion (reproduced below) of the residual plots of regions where our models imperfectly capture our dataset on page 13 line 283.

“To assess our model fits, we evaluated the residuals of each model subtracted from their respective dataset. For Zap70 recruitment, our model underestimates the degree of activation at moderate binding half-lives and receptor occupancies, as indicated by the positive region in the center of the heatmap. It is possible that Zap70 recruitment reaches saturation at shorter ligand binding half-lives than our model predicts (Fig. 4S3 A). For both LAT clustering and DAG generation, our models performed poorest in the region of lowest occupancy and shortest half-life (Fig. 4S3 B&C). In this region of our dataset, the fluorescent signal from bound LOV2 above the background fluorescence of unbound LOV2 is smallest. To compensate for fluorescence of unbound LOV2, we subtract off the local background fluorescence of unbound LOV2 around each cell. In doing so we may be underestimating the amount of LOV2 bound to each cell, leading to an underestimation of signaling output by the models. Future studies at LOV2 densities approaching single molecule would better capture this regime of receptor occupancy, but cell-to-cell variation in activation would be too high to be compatible with our current steady-state analysis (Lin et al., 2019).”

4) The values of n are also very high, which would imply that the kp rate constant might be very fast to compensate; no estimates of this value are presented. Recent data from the Dushek lab (Pettmann et al, eLife 2021) measured n to be ~3, which seems much more physically realistic. Furthermore, in their previous published work, Tischer & Weiner measured n to be 2.7 for DAG production but in the present study it is now n=11.3, using the same equation

We are unable estimate the kp rate constant, as our datasets are at steady state and do not provide temporal information. To assess the plausibility of our higher n value fits, we explored the steady-state model presented in Ganti et al. PNAS 2020, which defines a kp rate of 0.1 s-1. This model predicts the minimum number of signaling steps required to achieve a defined Hopfield error rate at defined cognate-ligand/self-ligand concentration and half-life ratios. Our exploration of this model is detailed in Fig. 4S4 on page 53 and detailed in discussion on page 14 line 299

“In our previous work our model fit fewer (N=2.7) steps to DAG generation. We now fit a higher number of steps (N=11.3) to DAG generation. This change could be due to the incorporation of ICAM into our current study, which has been shown to potentiate ligand discrimination (Pettmann et al., 2021). Furthermore, our previous antibody-based adhesion may have short-circuited some proofreading steps by irreversibly holding the cell membrane close to the supported lipid bilayer. To evaluate if our higher value fits are indeed the best fit values for our datasets, we fit our model to each dataset while holding the value of N constant in the range of zero to fourteen steps, and evaluated the average residual value for each model fit (Fig 4S3 D). For all signaling steps, the fit value of N was near the minima of average residual and had a lower average residual value than a model with 3 proofreading steps.

To assess the plausibility of a larger number of proofreading steps, we implemented the steady state kinetic proofreading model from Ganti et al. (Ganti et al., 2020). The model estimates the minimum number of proofreading steps required to discriminate between cognate-ligands and self-ligands with different binding half-lives present at a given concentration ratios at a given Hopfield error-rate (Hopfield, 1974). First, we evaluated what combinations of ligand half-lives and concentration ratios an 11-step kinetic proofreading network could discriminate at an error rate less than 10-3 (Fig 4S4 A). We chose the error rate of 10-3, as it is an order of magnitude less than the theorized 10-4 upper limit error rate of the native TCR (Ganti et al., 2020). At moderate half-life ratios, an 11-step network can discriminate cognate peptides present in small concentrations (e.g. 1 cognate-ligand per 1000 self-ligands at a half-life ratio of 6).

In our optogenetic system, the ratio of the average ligand binding half-life between the longest suppressive half-life and the shortest fully activated half-life is about 2. However, an 11-step network is insufficient to discriminate between ligands with a half-life ratio of 2, even at the high ligand ratio of 1 (equal concentrations of cognate- and self-ligand). This suggests our cells are unlikely to be detecting the average ligand binding half-life of each blue-light condition, but are more likely detecting longer-lived binding events from the underlying distribution of half-lives. Another possibility is that our in vitro washout measurements, which measure average ligand binding half-lives of soluble ligands diffusing in three dimensions, differ from the half-lives of ligand-receptor interactions between the cell’s plasma membrane and the supported lipid bilayer diffusing in two dimensions (J. Huang et al., 2010).

To better explore the kinetic proofreading model space, we generated heatmaps reporting the required number of steps to discriminate combinations of ligand and half-life ratios at an error rate of 10-3 (Fig 4S4 B). To discriminate between ligands with a half-life ratio of two, at least 14 steps are needed when the ligands are at equal concentrations, and more than 25 steps are needed if cognate-ligands are 1 per 1000 self-ligands. The required number of proofreading steps decreases rapidly as the half-life ratio increases, reaching a minimum of 8-steps needed for a concentration ratio of 1/1000 and a half-life ratio of 10, which is more in line with physiological half-life ratios between agonist and non-agonist peptides (M. M. Davis et al., 1998).

After comparing our results with the Ganti model, this analysis suggest that our number of fit proofreading steps may be somewhat inflated as a function of our use the average ligand binding half-lives of three dimensional washout experiments in place of the two dimensional single molecule information T cells use to make activation decisions. However, the higher fit N values are more consistent with the required number of steps to discriminate ligands under more physiological conditions than our previous measurements of ~3 steps, which would not be expected to discriminate ligands with half-life ratio of 10 even at a ligand ratio of 1 (Fig 4S4 B, right).”

5) If the fitted value of n provides no realistic insight into the KP mechanism, it should not be discussed as though it does.

The many assumptions of our simplistic model likely results in error in determining the absolute number of fit proofreading steps. We feel the strength of our model lies in capturing the relative increase in the strength of proofreading as signal propagates through the cascade, and not determining the absolute number of proofreading steps, though it is comforting that our values are broadly consistent with the expectations of Ganti et al. To highlight the point that relative values are the most important feature of our experiments, we are open to normalizing our n fit values by the fit n of Zap70 for all discussion of our results and the proofreading strength increase shown in Fig 4D if the reviewers think this will better highlight the relative increase in proofreading strength.

6) While it is good to confirm it, the result that downstream signaling complexes reset more slowly than distal ones is surely to be expected, given the increased number of steps over which ligand unbinding must traverse, as in their Erlang distribution. You would not expect ERK phosphorylation to decrease at the same rate as LAT cluster dissociation for this same reason. However, the fact that the lifetime of LAT clustering (14.2s) or ZAP70 (9.6s) is so different to LOV2 (3.3s) provides good evidence that it is not proofreading, as by definition the measured outputs should rapidly return to the 'unbound' state in line with ligand unbinding. At least for LAT, there must be a 'memory' from previous signalling lasting several seconds, which means the system has not reset, as required for true KP.

Slower resetting of downstream signaling events in a kinetic proofreading cascade is not a given, as it could be the case that all events reset at the same rate. One requirement for kinetic proofreading is that events in the chain be irreversible on the timescale of the ligand binding half-life. The steps are reset through an orthogonal pathway, opposed to traversing back down a chain of reversible reactions. Both the TCR and LAT are dephosphorylated by the phosphatase CD45, and it would be possible for CD45 to dephosphorylate both proteins at the same rate (or even dephosphorylate LAT faster than the TCR). To clarify this point, we have expanded discussion on possible reset mechanism on page 21 line 451 as reproduced below

“An attractive reset mechanism is the segregation of CD45 away from bound receptors, creating spatial regions in which TCR and LAT associated activating events can occur (S. J. Davis & van der Merwe, 2006). Super-resolution microscopy by Razvag et al. measured TCR/CD45 segregated regions within seconds of antigen contact at the tips of T cell microvilli (Razvag et al., 2018). Upon unbinding these regions of phosphatase exclusion collapse, allowing CD45 to dephosphorylate receptor ITAMs and LAT clusters. However, the rate of dephosphorylation for LAT and receptor ITAMs could differ. LAT clusters exclude CD45 in reconstituted bilayer systems, potentially limiting the dephosphorylation to LAT molecules at the edges of the cluster thus slowing reset (Su et al., 2016). The kinetics of multivalent protein-protein interactions within TCR and LAT clusters can also influence dephosphorylation and dissociation rates (Goyette et al., 2022).

A CD45-mediated reset mechanism would restrict proofreading to membrane-bound signaling events occurring within a CD45-depleted region. Downstream events that dissociate away from the membrane or diffuse out of the segregated region could not directly participate in the proofreading chain, as the collapse of a CD45 segregated region could not reset signaling entities released into the cytosol (e.g. release of IP3 in the cleavage of PIP2 to DAG).”

We also added discussion of recent work from Harris et al. quantifying the slower timescale of Ca++ and ERK reset upon TCR signal termination on Page 23 line 498 as reproduced below.

“Recently Harris et al. quantified the reset rate of the downstream signaling events Ca++ release and ERK phosphorylation upon signal inhibition to be 29 seconds and 3 minutes respectively (Harris et al., 2021). They showed both Ca++ and ERK levels can persist across short inhibitions of signaling. What makes LAT clusters different than these persistent downstream events? The dissolution of LAT clusters is directly triggered by the unbinding of ligand from the TCR, and both the TCR and LAT are de-phosphorylated by CD45. To our knowledge, the rate of ERK dephosphorylation or cytosolic Ca++ depletion are not accelerated by TCR unbinding, and are turned over through constant rather than agonist-gated degradation. A useful future line of inquiry would be to quantify the reset rate for signaling steps throughout the cascade upon ligand unbinding versus orthogonal signal inhibition (e.g. kinase inhibition).”

Author Response

Reviewer #1 (Public Review):

The paper presents a Bayesian model framework for estimating individual perceptual uncertainty from continuous tracking data, taking into account motor variability, action cost, and possible misestimation of the generative dynamics. While the contribution is mostly technical, the analyses are well done and clearly explained. The paper provides therefore a didactic resource for students wishing to implement similar models on continuous action data.

First off, the paper is lucidly written - which made it a very pleasant read, especially compared to many other modeling papers, and the authors are to be congratulated for this. As such, the paper provides a valuable resource for didactic purposes alone. While the employed methods are not necessarily individually novel, the assembly of various parts into a coherent framework appears nonetheless valuable.

Thank you for the positive evaluation!

I have two major concerns, though:

1). My main comment regards the model comparison using WAIC (Figure 4E) or cross-validation (Figure S4a): If we translate these numbers into Bayes factors, they are extraordinarily high. I assume that the p(x_i|\theta_s) in equation 7 are calculated assuming that the motor noise on u_{i,t} is independent? This would assume that motor processes act i.i.d with a timeframe of 60ms, which is probably not a very realistic assumption- given that much of the motor variability (as stated by the authors) comes likely from a central (i.e. planning) origin. Would the delta-WAIC not be much smaller if motor noise was assumed to be correlated across time points? Would this assumption change the \sigma estimates?

Thank you for posing this question. First, sequential models tend to have much larger differences in the likelihood of parameters given data because of the large number of individual data points within a single sequence. Thus, it is not uncommon for model comparison to show much more extreme differences between models for sequential data, as is the case in the present manuscript.

Second, since our computational framework is based on LQG control, the model indeed assumes that motor noise is independent across time steps. We agree that this assumption might not be realistic for time steps of 16ms duration. While this assumption is certainly a simplification, the assumption of independent noise across time steps is very common both in perceptual models as well as in models of motor control, and there is to our knowledge no computationally straightforward way around it in the LQG framework. It thus applies to all of the models considered in this paper, as they all assume temporally uncorrelated noise, both in perception and action. Therefore, the ranking between the models in the model comparison should hopefully not be affected in a systematic way favoring individual models disproportionately more than others, although the magnitudes of differences in WAIC might be smaller. Since the differences in WAIC are currently in the range of 1e4, we think that they will still be significant, even when accounting for correlated noise.

Third, we think that the simplifying assumption of independent noise does not invalidate the calculation of the WAIC, which assumes independence across trials. The p(x_i | theta_s) in equation (8) are the likelihoods of whole trials. To compute them, we assume independence of the motor noise across time steps.

We have added a short passage in the subsection ‘model comparison’:

“Note that the assumption of independent noise across time steps might lead to WAIC values that are larger than those obtained under a more realistic noise model involving correlations across time. However, this should not necessarily affect the ranking between models in a systematic way, i.e. favoring individual models disproportionately more than others.”

and a passage in the discussion that points out that modeling the noise as being independent across time points is a simplifying assumption:

“Finally, assuming independent noise across time steps at the experimental sampling rate of (60Hz) is certainly a simplifying assumption. Nevertheless, the assumption of independent noise across time steps is very common both in models of perceptual inference as well as in models of motor control, and there is to our knowledge no computationally straightforward way around it in the LQG framework.”

2). While the results in Figure 4a are interesting, the deviation of the \sigma estimates from the standard psychophysical estimates for the most difficult condition remains unexplained. What are the limits of this method in estimating perceptual acuity near the perceptual threshold? Is there a problem that subjects just "give up" and the motor cost becomes overwhelming? Would this not invalidate the method for threshold detection?

We fully agree that for the most difficult conditions at the lowest contrasts all sequential models we considered are biased with respect to the uncertainties obtained with the 2AFC experiment, which is supposed to be equivalent. Interestingly, when considering synthetic data, we did not see such a discrepancy. Thus, the observed bias points towards an additional mechanism such as a computational cost or computational uncertainty, that is not captured by the current models at very low contrast.

For the results in Fig. 4, we assumed a constant behavioral cost across all conditions. The assumption that the cost is independent of perceptual uncertainty might not hold in reality, exactly in line with your hypothesis that subjects might just "give up". There are other possible explanations, though, that could potentially be relevant here. For example, the visual system is known to integrate visual signals over longer times, when contrast is lower. This may introduce additional non-linearities in the integration, which could affect the sensitivity, as already pointed out in the study by Bonnen et al. (2015).

We have added the following passage in the discussion section:

“In the lowest contrast conditions, all models we considered show a large and systematic deviation in the estimated perceptual uncertainty compared to the equivalent 2AFC task. Note that when considering synthetic data, we did not see such a discrepancy. Thus, the observed bias points towards additional mechanisms such as a computational cost or computational uncertainty, that are not captured by the current models at very low contrast. One reason for this could be that the assumption of constant behavioral costs across different contrast conditions might not hold at very low contrasts, because subjects might simply give up tracking the target although they can still perceive its location. Another possible explanation is that the visual system is known to integrate visual signals over longer times at lower contrasts [Dean & Tolhurst, 1986; Bair & Movshon, 2004], which could affect not only sensitivity in a nonlinear fashion but could also lead to nonlinear control actions extending across a longer time horizon. Further research will be required to isolate the specific reasons.“

Reviewer #2 (Public Review):

This manuscript develops and describes a framework for the analysis of data from so-called continuous psychophysics experiments, a relatively recent approach that leverages continuous behavioral tracking in response to dynamic stimuli (e.g. targets following a position random walk). Continuous psychophysics has the potential to dramatically improve the pace of data collection without sacrificing the ability to accurately estimate parameters of psychophysical interest. The manuscript applies ideas from optimal control theory to enrich the analysis of such data. They develop a nested set of data-analytic models: Model 1: the Kalman filter (KF), Model 2: the optimal actor (which is a special case of a linear quadratic regulator appropriate for linear dynamics and Gaussian variability), Model 3: the bounded actor w. behavioral costs, and Model 4: the bounded actor w. behavioral costs and subjective beliefs. Each successive model incorporates parameters that the previous model did not. Each parameter is of potential importance in any serious attempt to human model visuomotor behavior. They advertise that their methods improve the accuracy the inferred values of certain parameters relative to previous methods. And they advertise that their methods enable the estimation of certain parameters that previous analyses did not.

What were the parameters? In this context, the Kalman filter model has one free parameter: perceptual uncertainty of target position (\sigma). The optimal actor (Model 2) incorporates perceptual uncertainty of cursor position (\sigma_p) and motor variability (\sigma_m), in addition to perceptual uncertainty of target position (\sigma) that is included in the Kalman filter (Model 1). The bounded actor with behavioral costs (Model 3) incorporates a control cost parameter (c) that penalizes effort ('movement energy'). And the bounded actor with behavioral costs and subjective beliefs (Model 4) further incorporates the human observer possibly mistaken 'beliefs' about target dynamics (i.e. how the human's internal model of target motion differs from the true generative model. Model allows for the true target dynamics (position-random-walk with drift = \sigma_rw) to be mistakenly believed to be governed by a position-random-walk with drift = \sigma_s plus a velocity-random-walk with drift = \sigma_v).

The authors develop each of these models, show on simulated data that true model parameters can be accurately inferred, and then analyze previously collected data from three papers that helped to introduce the continuous psychophysics approach (Bonnen et al. 2015, 2017 & Knoll et al. 2018). They report that, of the considered models, the most sophisticated model (Model 4) provides the best accounting of previously collected data. This model more faithfully approximates the cross-correlograms relating target and human tracking velocities than the Kalman filter model, and is favored by the widely applicable information criterion (WAIC).

The manuscript makes clear and timely contributions. Methods that are capable of accurately estimating the parameters described above from continuous psychophysics experiments have obvious value to the community. The manuscript tackles a difficult problem and seems to have made important progress.<br /> Some topics of central importance were not discussed with sufficient detail to satisfy an interested reader, so I believe that additional discussion and/or analyses are required. But the work appears to be well-executed and poised to make a nice contribution to the field.

The manuscript, however, was an uneven read. Parts of it were very nicely written, and clearly explained the issues of interest. Other parts seemed organized around debatable logic, making inappropriate comparisons to--and misleading characterizations of--previous work. Other parts still were weakened by poor editing, typos, and grammatical mistakes.

Overall, it is a nice piece of work. But the authors should provide substantially more discussion so that readers will develop a better intuition and how and why the inference routines enable accurate estimation, and how the values of certain parameters trade off with one another. Most especially, the authors should be very careful to accurately describe and appropriately use the previous literature.

Thanks for the generous overall assessment and the thorough review! We hope that we can address the points you raised in our revised manuscript with the answers to your specific comments below.

To summarize, we have substantially revised the discussion section to clarify our reasoning and avoid potential misinterpretations of parts of our manuscript as a misrepresentation of previous work. We have also extended the introduction and the exposition of our models in the results section to help readers develop an intuition about the models and inference routines.

Author Response

Reviewer #1 (Public Review):

This paper describes a systematic biochemical analysis of UBX proteins in facilitating protein unfolding by the p97-UFD1-NPL4 (referred to here is the p97 complex). The p97 complex binds Ub and unfolds it to allow the ubiquitylated protein to be translocated into the p97 ATPase pore for unfolding. This paper demonstrates that UBX proteins are able to reduce the necessary ubiquitin chain length in order to support unfolding by p97. They explore this using ubiquitylated CMG helicase as a substrate. Removal of CMG helicase from replicated DNA is required for completion of DNA synthesis.

First the authors demonstrate that the p97 complex only only unfolds CMG with very long Ub chains. The then show that the high threshold for Ub is reduced when UBXN7, FAF1 or FAF2 are added. These proteins bind to both the p97 complex and Ub in substrates. This is then followed up in cells by demonstrating that removal of UBXN7 and FAF1 reduces CMG disassembly and is synthetic with reduced CMG ubiquitin ligase activity.

The conclusion that human p97 requires UBX proteins to support unfolding/segregase activity when Ub chains are short would be strengthened by more precise characterization of the length of ubiquitin chains being studied, as the methods do not precisely determine the chain lengths and how this is overlapping with the number and location of primary ubiquitylation sites on Mcm7.

Please see our reply above to essential revision point 2 (data in Figure 1-figure supplement 1 and Figure 2-figure supplement 3)

The in cellulo results, while consistent with a contributing role for FAF1 and UBXN7 in disassembly of the CMG by p97, indicate that either other factors are required in cells or that p97 can disassemble CMG with relative short chains in cells without the need for the UBX proteins. This needs to be reconciled with the proposed model.

We now discuss on lines 444-450 that CMG disassembly in the absence of UBXN7 and FAF1 might be promoted by additional UBX proteins not characterised in this study, or else be due to extensive CMG-MCM7 ubiquitylation that bypasses the requirement for UBX proteins (as predicted by our data in Figure 1). Note that short ubiquitin chains on CMG-MCM7 in cells treated with p97 inhibitor need to be interpreted with caution, as it is likely that p97 inhibition lowers the pool of free ubiquitin in cells. This point is discussed on lines 444-445 of the revised manuscript.

Reviewer #3 (Public Review):

The ATPase p97 (Cdc48 in yeast) unfolds ubiquitinated substrates with the help of its heterodimeric cofactor UFD1-NPL4 (U-N). Using the previously established CMG helicase complex as model substrate in a fully reconstituted biochemical assay, Fujisawa and Labib show that p97-U-N can efficiently disassemble the helicase complex only when it is modified with multiple, long ubiquitin chains. This is in contrast to the yeast Cdc48-U-N complex, which disassembles helicase complexes carrying long or short (6-10 ubiquitin moieties) chains with similar efficiency. The authors demonstrate that the requirement of p97-U-N for long chains can be overcome by the presence of p97 cofactors of the UBA-UBX type, including UBXN7, FAF1, FAF2 and (much less so) UBXN1. They show that this reduction in the 'ubiquitin threshold' of p97-U-N by UBXN7, FAF1 and FAF2 requires their UBX domain mediating p97 binding. They further show that the UBA and UIM domains of UBXN7 contribute to its activity in the assay, whereas the UBA domain of FAF1 and FAF2 is dispensable. Instead, a coiled-coil domain preceding the UBX domain of FAF1 and FAF2 is required for their activity, and both the coiled-coil-UBX domain organization and its activity are conserved in the worm homologue UBXN-3. Using UBXN7 and FAF1 knockout cells, Fujisawa and Labib then demonstrate that UBXN7 is required for efficient CMG helicase disassembly during S phase, with a minor contribution of FAF1, whereas both cofactors possess redundant roles in mitotic CMG helicase disassembly. Finally, the authors show that UBXN7 and FAF1 double knockout cells are hypersensitive to the NEDDylation inhibitor MLN4924 and suggest that this reflects their importance for p97-U-N unfoldase activity under conditions of restricted ubiquitination activity.

This manuscript describes the intriguing observation that the yeast and mammalian Cdc48/p97-U-N complexes have distinct requirements, at least in the in vitro assay used, with respect to the substrate´s ubiquitination state and to the presence of additional cofactors. While the concept of UBA-UBX cofactors assisting/stimulating Cdc48/p97-U-N activity is well-established, their link to ubiquitin chain length is novel and unexpected. The experiments are performed to a high technical standard, and the conclusions are mostly supported by the data. However, a shortcoming of the paper is that it remains entirely descriptive regarding the effect of the UBX proteins on the ubiquitin threshold, without providing mechanistic insights into their function or the molecular basis underlying the distinct thresholds.

1) It remains unclear if the failure of p97-U-N to disassemble the helicase complex carrying short ubiquitin chains reflects impaired binding, priming or translocation of the substrate. It should be straightforward to test if the UBA-UBX cofactors simply stabilize the p97-U-N-substrate complex.

As shown in previous studies, human UFD1-NPL4 bind stably to p97 in the absence of UBX proteins (our new data in Figure 3-figure supplement 2D illustrate this).

The distinct domain requirements for UBXN7 (UBA, UIM, UBX) and FAF1/FAF2 (coiled-coil-UBX) suggest different mechanisms of stimulation, which should be discussed in more detail.

We discuss further the roles of UBXN7 and FAF1/FAF2 on lines 533-548.

The additive defects of the UBXN7 and FAF1 double knockout cells could indicate either redundant functions (as the authors propose) or synergistic function of both cofactors. To that end, the authors could test if UBXN7 and FAF1 can bind simultaneously to the same p97-U-N-substrate complex and if they act synergistically in helicase disassembly, e.g. at limiting cofactor concentrations.

Previous studies have found that UBXN7 binds to p97 and UFD1-NPL4 with a 1:6:1 ratio and the same is true for FAF1, without any evidence of both UBXN7 and FAF1 binding to the same p97-UFD1-NPL4 complexes (Hanzelmann et al., 2011). Correspondingly, we did not observe any synergistic effect of FAF1 with UBXN7 upon the disassembly of ubiquitylated CMG by p97-UFD1-NPL4, when comparing reactions with a single UBX protein or reactions with both (our unpublished data).

2) Having all purified proteins at hand, the authors should test which component of the system causes the elevated ubiquitin threshold of mammalian p97-U-N, by combining yeast Cdc48 with mammalian U-N and vice versa, etc.

We thank the reviewer for this very interesting suggestion. The data are presented in Figure 3, showing that human UFD1-NPL4 and yeast Ufd1-Npl4 set the ubiquitin threshold for their cognate unfoldase enzymes.

Can yeast Ubx5, which is a clear homologue of UBXN7, substitute for the mammalian UBA-UBX cofactors?

This was also an interesting suggesting – we tested Ubx5 and didn’t see any stimulation. We didn’t include the data as we lack a positive control for Ubx5 activity.

3) The authors emphasize that mammalian p97-U-N in the absence of UBA-UBX cofactors requires long ubiquitin chains for activity. However, they should consider the possibility that the critical property is chain topology, rather than chain length. There is evidence that p97-U-N prefers substrates with branched chains (see PMIDs 28512218, 29033132), and multiple ubiquitin chains on the helicase substrate may mimic those.

We thank the reviewer for raising this important point and we now cite the two papers mentioned above, on lines 171 and 177.

In the revised version of the manuscript, we characterise carefully the ubiquitin chains that are formed under the various conditions used (Figure 1-figure supplement 1). Importantly, we also show that human p97-UFD1-NPL4 can disassemble highly ubiquitylated CMG, regardless of whether there are several or just one ubiquitin chains attached to CMG-Mcm7 (Figure 1-figure supplement A+C; Figure 2-figure supplement 3A).

Moreover, we also show that human p97-UFD1-NPL4 is comparable to yeast Cdc48-Ufd1-Npl4 in being able to disassemble CMG that is highly ubiquitylated with ‘K48-only’ ubiquitin that cannot form mixed chain linkages (Figure 2-figure supplement 3B).

These data indicate that p97-UFD1-NPL4 can disassemble heavily ubiquitylated CMG complexes with long K48-linked ubiquitin chains on CMG-Mcm7, regardless of the number of chains and regardless of the presence of other chain linkages (in addition to K48-linked chains).

It appears that worm CDC48-U-N in the absence of UBXN-3 cannot efficiently disassemble substrate carrying even long chains (Fig. 3 - supplement 2). The authors should discuss this finding in the context of their ubiquitin threshold model.

This is an interesting point, suggesting that the threshold of C. elegans CDC-48_UFD-1_NPL-4 is even higher than human p97-UFD1-NPL4, in the absence of UBX proteins. However, we think that this issue is beyond the scope of our manuscript and likely requires structural biology to provide a definitive explanation. Our manuscript just uses the C. elegans enzymes to make one simple and clear point – namely that the essential role of the coiled coil domain of human FAF1 is conserved in its worm orthologue UBXN-3.

Author Response

Reviewer #1 (Public Review):

This work by Wei-Jia Luo and colleagues elegantly employs in vitro and in vivo models to demonstrate that within the mouse liver, macrophages respond to lipopolysaccharide (LPS) by releasing active IL-12 (IL-12p70), which is a heterodimer of IL-12p35 and IL-12p40. They observed that the availability of "free" IL-12p35 to this heterodimerization process is governed by the molecular chaperone HLJ1. In response to LPS, HLJ1 separates homodimerized IL-12p35 into monomers, which then can heterodimerize with IL-12p40 to form active IL-12p70. This active IL-12 is released from macrophages in the liver, which then act on neighboring natural killer T cells to release interferon gamma. This interferon gamma circulates systemically and is responsible for mortality in a mouse model of endotoxic shock.

Overall, this work is mechanistically compelling and demonstrates a novel multicellular inflammatory pathway that contributes to death in a murine model of endotoxic shock. However, it is unclear if the observed pathway is limited to this highly reductionist model, or if it applies to models that better approximate the complexity of human sepsis. Indeed, the long-standing concept of "cytokine storm" as the major mediator of sepsis has largely failed to yield benefits in clinical trials. These numerous and repeated translational failures cast doubt on the translational validity of reductionist in vivo animal models of sepsis.

Thank the reviewer’s affirmation. One of the major aims of our work is to identify a novel multicellular inflammatory pathway mediated by HLJ1 that contributes to endotoxic shock. We agree that although our understanding of cytokine storm as the major mediator of sepsis had made dramatic progress over the past decade, these findings could not translate yet into effective treatments. As the reviewer mentioned, almost all clinical trials targeting cytokine effects failed, especially in the context of sepsis. We also know that among several explanations, the appropriateness of in vivo animal models should be concerned (Chousterman et al., 2017). Some approaches to treat cytokine storm were aimed to target the direct tissue consequences of inflammation cascade such as the blood vessel (London et al., 2010). Another possible strategy to treat cytokine storm was to target signaling that promotes cytokine synthesis and secretion (Maceyka et al., 2012). It may be feasible to quell the cytokine storm after infection by targeting upstream signaling, and reducing cytokine synthesis as well as secretion is a valid alternative to direct cytokine antagonism (Chousterman et al., 2017). Furthermore, in this study we found Hlj1−/− mice showed reduced IFN-g and improved survival when treated with daily systemic antibiotics after CLP surgery (Figure 6), indicating that targeting cytokine storm in combination with antibiotics provides a promising therapeutic strategy to treat sepsis. Combined, we think HLJ1-targeting strategy might be a potential therapy to treat cytokine storm-associated sepsis. We emphasized and discussed the concept in the Discussion of our revised manuscript (Page 19, line 441-453).

We highly appreciated the reviewer #1 and other reviewers raised the same issue. We worked hard and attentively to response comments point-by-point below.  

This raises several specific concerns with regard to the model used by the investigators:

(1) The authors use a massive dose of LPS that rapidly leads to the death of mice in 24 hours. This massive and rapid mortality is not consistent with human sepsis, which is a more crescendo course with a mortality of ~30%. Indeed, when the authors used a more clinically-relevant model of mild endotoxemia, HLJ1 appeared to have no impact on mortality (Figure 1A).

Thank for the comment. Indeed, since we observed HLJ1 knockout mice could survive from high dose of LPS, we use 20 mg/kg LPS to perform the subsequent experiments based on these obvious and significant phenomena. We also recognized the importance of administration of low dosages of LPS. To address this issue, we performed additional experiments and made some revisions point-by-point.

i. Because 4 mg/kg is a common non-lethal dosage to induce TLR4 and IFN-γ signaling (Kunze et al., 2019; Malgorzata-Miller et al., 2016), we performed additional experiment with 4 mg/kg LPS according to the editor’s suggestion. As a result, Hlj1−/− mice showed lower serum levels of BUN, creatinine and ALT and thus less severe organ damage than Hlj1+/+ mice after 4mg/kg LPS treatment. The data are showed in Figure 1C and D of revised Figure 1 (Figure 1).

ii. We also performed ELISA test and found that serum levels of IFN-γ were lower in Hlj1−/− mice than in Hlj1+/+ mice after 4 mg/kg LPS injection. The result is in Figure 2C of revised Figure 2 (Figure 2).

iii. Combined, this result indicated the effect of HLJ1 deletion on reducing IFN-γ and alleviating organ injury can also be found during moderate endotoxemia. We described and discussed the result in the revised manuscript (Page 6, line 134-141; Page 18, line 423-437)

(2) LPS is a model of endotoxemia, not a model of sepsis. Accordingly, it is unclear if the protective benefit of blocking IL-12 will similarly be seen as a live-infection model of sepsis, in which inflammatory signaling may be necessary for pathogen clearance.

Thank the reviewer for raising these critical issues and providing valuable suggestions. This issue was also mentioned by other reviewers. Although the LPS-induced endotoxemia is a simple model with higher reproducibility and reliability comparing to other sepsis models, it indeed cannot represent actual sepsis and is based on the notion that it is the host’s response to bacteria but not the pathogen itself, that leads to mortality and organ failure (Deitch, 2005). Therefore, according to the reviewers’ suggestion, we performed additional live-infection model of sepsis including cecal ligation and puncture (CLP) which resembles clinical disease and septic shock (Deitch, 2005) to reassure the importance of HLJ1 on sepsis. As a consequence, we found IFN-γ expression was lower in liver and spleen of Hlj1−/− mice comparing to Hlj1+/+ mice (Figure 6A and B). We analyzed serum markers of organ dysfunction and Hlj1−/− mice showed lower serum levels of BUN, creatinine and AST (Figure 6C). H&E staining showed kidney injury at the histology level after CLP surgery, while Hlj1−/− mice showed less severe kidney injury than Hlj1+/+ (Figure 6D). We further found Hlj1−/− mice showed significantly improved survival compared to Hlj1+/+ mice when mice were treated with systemic antibiotics (Figure 6E). Combined, we demonstrated the effect of HLJ1 deletion on attenuation of CLP-induced sepsis with down-regulated IFN-γ, and concluded that the benefit of blocking IL-12 and HLJ1 can similarly be seen as a live-infection model of sepsis. The result is showed as below (revised Figure 6). The corresponding result was also added in the revised manuscript (Page 11-12, line 268-286). Please check it as well as the above responses to other reviewers.

Page 11-12, line 268-286 "HLJ1 deletion protect mice from CLP-induced organ dysfunction and septic death To address the question whether HLJ1 also regulates IFN-γ-dependent septic shock in live infection model, we performed CLP (cecal ligation and puncture) surgery which more resembles clinical disease and human sepsis. CLP significantly induced transcriptional levels of IFN-γ in the liver of Hlj1+/+ mice comparing to mice receiving sham surgery while Hlj1−/− mice showed significantly lower IFN-γ mRNA than Hlj1+/+ mice (Figure 6A). This phenomenon was not restricted to the liver since lower expression of splenic IFN-γ was also found in Hlj1−/− mice (Figure 6B). The CLP surgery resulted in serious renal and liver damage while Hlj1−/− mice showed alleviated organ dysfunction with significantly lower serum levels of BUN, creatinine and AST (Figure 6C). H&E staining showed kidney injury at the histology level after CLP, while Hlj1−/− mice showed less severe kidney injury than Hlj1+/+ mice (Figure 6D). However, there was no significant difference in survival when comparing Hlj1+/+ and Hlj1−/− mice (Figure 6E). We hypothesized that severe bacteremia contributed to mortality in mice that did not receive any treatment, so we treat mice with systemic antibiotics. As a result, Hlj1−/− mice displayed significantly improved survival compared with Hlj1+/+ mice when mice received daily systemic antibiotics after CLP (Figure 6E). These results implied the agent responsible for bacteria clearance can be combined with immune modulation such as HLJ1 targeting to improve the outcome of sepsis."

(3) Finally, it is unclear if the findings are only relevant to mice, or if they also have relevance to humans.

We admit human studies is important, while there are some objective difficulties need to be overcame; for example, cohort identification, individual variation, and clinical considerations. This is our limitation since our findings were only based on animal models and human cell lines. We further performed CLP experiments which is more relevant to human sepsis, while it is not a true human study. That had been added as Figure 6 of our revised manuscript (Figure 6). Actually, based on the present result, we plan to initiate some specific clinical human studies. For example, we plan to collect blood monocytes from critically ill patients from ICU to see whether HLJ1 expression levels in monocytes is higher in patients with sepsis than in patients without sepsis. On the other hand, we also want to know whether HLJ1 expression levels in monocytes or in serum are correlated to inflammatory markers such as C-reactive protein, procalcitonin, and lactate in sepsis patients, because we found serum levels of HLJ1 correlated to IL-12 in mouse. In our unpublished preliminary result, HLJ1 can be detected in serum of patients with sepsis. This inspires us to investigate whether HLJ1 can be a diagnostic or prognostic marker in the future. We anticipate these results can be in our future publications. Thank you very much for your understanding.  

Reviewer #2 (Public Review):

The authors show that HLJ1 converts misfolded IL-12p35 homodimers to monomers, which maintains bioactive IL-12p70 heterodimerization and secretion. In turn, this contributes to increased IL-12 activity, leading to enhanced IFN-gamma production and lethality in mice challenged with LPS to model sepsis.

Strengths:

• Huge and diverse dataset (e.g. in vivo, in vitro, single cell RNAseq, adoptive transfer etc.) with interesting findings that could be of relevance to the field.

We deeply thank the reviewer for the affirmation. We hope our comprehensive dataset can provide a novel insight of relevance to the field. With this information, we also keep investigating the underlying molecular alteration resulting from endotoxin-induced immune responses. Thank you very much. At the mention of our weaknesses raised by the reviewer, we totally agreed on it and take it very seriously and revised point-by-point. Thank you very much.

Weaknesses:

• The flow/narrative of the paper is very hard to follow. This may result from the fact that the order of presented results is a bit puzzling. Normally, one would add-in the cytokine results (now figure 3), after the survival curves in Figure 1. Furthermore, the flow cytometry data presented in Figure 4 is more or less a validation of the scRNAseq data presented in Figure 2 in another organ. Likewise, Figure 5 is sort of a validation of Figure 3 in another organ. The authors seem to jump from organ to organ, from in vivo to in vitro and vice-versa all the time which makes the paper extremely difficult to follow.

Thank the reviewer for the valuable suggestion. Actually, we were also hesitant to this arrangement in our first submission. We rearranged our results so that the flow/narrative of the paper can be easier to follow:

1. We moved the result of figure 3 to become figure 2 so that the cytokine array results would after the survival curve results.

2. The flow cytometry result presented in Figure 4 was moved to Figure 5 so that it would after the result of sc-RNA sequencing.

3. The qPCR result of pro-proinflammatory cytokines presented in figure 5 was moved to Figure 2-figure supplement 1 so that it would be a validation of cytokine array in another organ.

In addition, along with other suggestions from reviewers, we have rewritten the introduction and the discussion sections and reorganized whole manuscript so that we can focus more on important issues. All the modification and rearrangement can be checked in the revised manuscript with changes tracked. Please check our revised manuscript. Thank you for your kind suggestions.

• Use of extremely high dosages of LPS.

Thank for the comment. This issue had been raised by several reviewers and the editor. Indeed, since we observed HLJ1 knockout mice could survive from high dose of LPS, we use 20 mg/kg LPS to perform the subsequent experiments based on this obvious and significant phenomenon. We also recognized the importance of administration of low dosages of LPS. To address this issue, we performed additional experiments and made some revisions point-by-point.

i. Because 4 mg/kg is a common non-lethal dosage to induce TLR4 and IFN-γ signaling (Kunze et al., 2019; Malgorzata-Miller et al., 2016), we performed additional experiment with 4 mg/kg LPS according to the editor’s suggestion. As a result, Hlj1−/− mice showed lower serum levels of BUN, Creatinine and ALT and thus less severe organ damage than Hlj1+/+ mice after 4mg/kg LPS injection (Figure 1C). H&E staining showed kidney injury at the histology level after LPS treatment, while Hlj1−/− mice showed less severe kidney injury than Hlj1+/+ mice (Figure 1D). The data are showed in Figure 1C and D (in below) of revised Figure 1 (Figure 1).

ii. We also performed ELISA test and found that serum levels of IFN-γ were lower in Hlj1−/− mice than in Hlj1+/+ mice after 4 mg/kg LPS injection. The result is in Figure 2C (in below) of revised Figure 2 (Figure 2).

iii. Combined, this result indicated the effect of HLJ1 deletion on reducing IFN-γ and alleviating organ injury can also be found during moderate endotoxemia. We described and discussed the result in the revised manuscript (Page 6, line 134-141; Page 18, line 423-437)

• Much of the presented data is replication of previous work. For instance, neutralization of IFN-γ (e.g. Billiau et al., Eur. J. Immunol. 1987; Car et al. J. Exp. Med. 1994) and anti-IL-12 (e.g. Zisman et al., Shock 1997) has been shown to lower mortality in LPS models in mice.

Thank reviewer for the reminding. We apologized for our unclear description leading to misunderstanding. To carefully and firstly identify the novel role of HLJ1 in sepsis, we actually investigated it on several well-known bases. Indeed, the role of IFN-γ and IL-12 has been recognized in previous studies and their neutralization attenuating LPS-induced endotoxic shock have been reported. However, our study focused on the effect of HLJ1 deletion on IL-12/IFN-γ-axis and septic death. Firstly, we observed IFN-γ and IL-12 decreased after HLJ1 deletion during sepsis. On the one hand, we use IL-12/IFN-γ neutralization and found it could improve survival in wild-type mice rather than in Hlj1 knockout mice, suggesting the importance of HLJ1 in IL-12/IFN-γ-mediated mortality. On the other hand, if the difference of mortality rate across genotypes could become no difference after IL-12 or IFN-γ neutralization, then we can infer that HLJ1 contributes to mortality mainly through IL-12 and IFN-γ signaling. These ideals came from a previous study published in Cell (Ponzetta et al., 2019). The authors elegantly proved the role of Csf3r in IL-12/IFN-γ-axis and subsequent tumor incidence by showing that IFN-γ neutralization can alter the phenotype in wildtype mice rather than in knockout mice. This rationale inspired and prompted us to perform the similar neutralization experiment for understanding the precise role of HLJ1 in sepsis.

• No true sepsis model is used, only LPS. This is important, as for instance neutralization of IFN-γ and IL-12 has been shown to improve outcome in endotoxemia before (see above), but had no effect on survival in more relevant sepsis models such as cecal ligation and puncture (e.g. see Romero et al., Journal of Leukocyte Biology 2010; Zisman et al., Shock 1997). Furthermore, IFN-γ is even proposed (and used on a small scale) as therapy in sepsis patients to reverse immunosuppression.

Thank the reviewer raised these critical issues and provided valuable suggestions. It was also mentioned by other reviewers. Although the LPS-induced endotoxemia is a simple model with higher reproducibility and reliability comparing to other sepsis models, it indeed cannot represent actual sepsis and is based on the notion that it is the host’s response to bacteria but not the pathogen itself, that leads to mortality and organ failure (Deitch, 2005). Therefore, we performed additional model including cecal ligation and puncture (CLP) which resembles clinical disease and septic shock (Deitch, 2005) to reassure the importance of HLJ1 to human sepsis. Please see our revised Figure 6 (Figure 6) and responses to other reviewers above.

In accordance with the previous result from Romero et al showing that IFN-γ neutralization did not improve survival rate, we observed similar survival rate between Hlj1+/+ and Hlj1−/− mice after CLP. However, when they treated mice with systemic antibiotics, IFN-γ knockout mice survived significantly better than wild-type mice (Romero et al., 2010). In CLP model, it is possible that severe bacteremia contributed to mortality in mice that did not receive antibiotics in an IFN-γ-independent manner, so we treated mice with systemic antibiotics immediately after CLP. As a result, we further found Hlj1−/− mice showed significantly improved survival compared to Hlj1+/+ mice when mice were treated with systemic antibiotics after CLP surgery (Figure 6E), indicating that targeting cytokine storm in combination with antibiotics provides a promising therapeutic strategy to treat sepsis. The result is showed in Figure 6E (in below) of revised Figure 6 (Figure 6). This suggests that HLJ1-targeting strategy can be combined with antibiotics to become combined therapy for future clinical applications. We emphasized and discussed the concept in the Discussion of the revised manuscript (Page 18-19, line 441-453).

Author Response

Reviewer #1 (Public Review):

In their manuscript "CompoundRay: An open-source tool for high-speed and high-fidelity rendering of compound eyes", the authors describe their software package to simulate vision in 3D environments as perceived through a compound eye of arbitrary geometry. The software uses hardware accelerated ray casting using NVIDIA Optix to generate simulations at very high framerates of ~5000 FPS on recent NVIDIA graphics hardware. The software is released under the permissive MIT license, publicly available at https://github.com/ManganLab/eye-renderer, and well documented. CompoundRay can be extraordinarily useful for computational neuroscience experiments exploring insect vision and robotics with insect like vision devices.

The manuscript describes the target of the work: realistic simulating vision as perceived by compound eyes in arthropods and thoroughly reviews the state of the art. The software CompoundRay is then presented to address the shortcomings of existing solutions which are either oversimplifying the geometry of compound eyes (e.g. assuming shared focal points), using an unrealistic rendering model (e.g. local geometry projection) or being slower than real-time.

The manuscript then details implementation choices and the conceptual design and components of the software. The effect of compound eye geometries is discussed using some examples. The speed of the simulator depending on SNR is assessed and shown for three physiological compound eye geometries.

I find the described open source compound eye vision simulation software extraordinarily useful and important. The manuscript reviews the state of the art well. The figures are well made and easy to understand. The description of the method and software, in my opinion, needs work to make it more succinct and easier to understand (details below). In general, I found relevant concepts and ideas buried in overly complicated meandering descriptions, and important details missing. Some editorial work could help a lot here.

Thank you for the very positive feedback.

Major:

1) The transfer of the scene seen by an arbitrary geometry compound eye into a display image lacks information and discussion about the focal center/ choice of projection. I believe that only the orientation of ommatidia is used to generate this projection which leads to the overlap/ non-coverage in Fig. 5c. Correct? It would be great if, for such scenarios, a semi-orthogonal+cylindrical projection could be added? Also, please explain better.

For clarification, CompoundRay allows for a number of projection modes from any 3D sampling surface to visualised 2D projections. This has now been made clearer with an updated Methods section “From single ommatidia to full compound eye” (lines 171-188), and also a more clarified explanation of the display pipeline within the “CompoundRay Software Pipeline” section (lines 245-247).

We note that Fig 5 is simply intended as an example of the extreme differences in information that can be provided by nodel (the current state of the art) and non-nodal imagers (as in biological systems). A user could indeed produce custom projections (as now noted in the future work section of the Discussion), such as semi-orthgonal+cylindrical projections by modifying the projection shaders but we do not feel that this adds substantially to the desired message of Fig 5 as currently all view images are generated using the same projection method allowing them to be compared. Further to this, a semi-orthogonal+cylindrical projection would only serve to display these types of eyes and not be of significant use outside of this category of design. Rather, the utility of CompoundRay for research is now demonstrated by the inclusion of an entirely new example experiment (lines 394-467) (Fig 10) which compares artificial and realistic compound eye models in a visual tracking task.

In additional we note that specific references to the “orientation-wise spherical mapping” of images have been added to appropriate image captions (Fig 5 & 6).

Finally, we have attempted to be more explicit about about the way that 2D projection systems work within CompoundRay (182-185)

2) It is clear that CompoundRay is fast and addresses complex compound eyegeometries. It remains unclear, why global illumination models are discussed while the implementation uses ray casting to sample textures without illumination which is equivalent to projection rendering which runs fast on much simpler hardware. If the argument is speed and simplicity of writing the code, that's great, write it so. If it is an intrinsic advantage of the ray-casting method, then comparison with the 'many-cameras' approach sketched below should be done:

In your model, each ommatidium is an independent pin-hole camera. Instead of sampling this camera by ray-casting, you could use projection rendering to generate a small image per ommatidium-camera, then average over the intensities with an appropriate foveation function (Gaussian in your scenario, but could be other kernels). The resolution of the per-camera image defines the number of samples for anti-aliasing, randomizing will be harder than with ray-casting ;). What else is better when using ray-casting? Fewer samples? Hardware support? Possible to increase recursion depth and do more global things than local illumination and shadows? Easier to parallelize on specific hardware and with specific software libraries? Don't you think it would make sense to explain the entire procedure like that? That would make the choice to use ray-casting much easier to understand for naive readers like me.

Thanks for this feedback, and can see that it was misleading to include this in our previous Methods section. We have now reduced and moved discussion of global illumination models to the future work section at the end of the Discussion. We have also added a clarification to the end of this document that summarises this point as it was raised by multiple reviewers (see Changes Relating to Colour and Light Sampling)

3) CompoundRay, as far as I understand, currently renders RGB images at 8-bitprecision. This may not be sufficient to simulate the vision of arthropod eyes that are sensitive to other wavelengths and at variable sensitivity.

Thanks for pointing out this easy-to-miss implementation detail. Indeed, you are correct that the native output is at 8-bit level as is standard to match display equipment. However, we note that the underlying on-GPU implementation operates at a 32-bit depth, so exposing this to the higher-level Python API should be possible, which could then be used as you suggest. We view adding enhanced lighting properties including shadows, illumination and higher bit depths so as to better support increased-bandwidth visual sensor simulation as future updates which we have now outlined in the Discussion (line 549-553).

Reviewer #2 (Public Review):

In this paper, the authors describe a new software tool which simulates the spatial geometry of insect compound eyes. This new tool improves on existing tools by taking advantage of recent advances in computer graphics hardware which supports high performance real-time ray tracing to enable simulation of insect eyes with greater fidelity than previously. For example, this tool allows the simulation of eyes in which the optical axes of the ommatidia do not converge to a single point and takes advantage of ray tracing as a rendering modality to directly sample the scene with simulated light rays. The paper states these aims clearly and convincingly demonstrates that the software meets these aims. I think the availability of a high-quality, open-source software tool to simulate the geometry of compound eyes will be generally useful to researchers studying vision and visual behavior in insects and roboticists working on bio-inspired visual systems, and I am optimistic that the describe tool could fill that role well.

Thankyou for the positive feedback.

As far as weaknesses of the paper, the most major issue for me is that I could not find any example of why the additional modeling fidelity or speed is useful in understanding a biological phenomenon. While the work is technically impressive, I think such a demonstration would increase its impact substantially.

An example experiment has been added as requested.

I can identify a few more, relatively minor, weaknesses: the software tool is not particularly easy to install but I think this is due primarily to the usage of advanced graphics hardware and software libraries and hence not something the authors can easily correct. In fact, the authors provide substantial documentation to help with installation.

Indeed, we have tried to ease installation as much as possible by provided detailed documentation. This has been updated since initial submission and proven sufficient for multiple users. We have looked into dockerising the code but as correctly identified by the reviewer there are significant challenges due to proprietory hardware and their drivers.

Another weakness of the tool, which the authors might like to address in the paper, is that there are some aspects of insect vision and optics which are not directly addressed. For example, the wavelength and polarization properties of light rays are hardly addressed despite extensive research into the sensation of these properties. Furthermore, the optical model employed here is purely ray based and would not allow investigating the wave nature of light which is important for propagation from the corneal surface to the photoreceptors in many species.

Indeed, it is correct that the current implementation does not allow such advanced light modellign features but as our initial aim was to allow arbitrary surface shapes this was considered beyond the scope of this work. However, we have added a short description of extensions that the method would allow without significant architectural changes which include many of those listed by the reviewer. As the renderer simulates light as it reaches the lens surface, it is hoped that further works will be able to use this natural boundary between the eye surface and it’s internals to build further computational models that use the data generated in CompoundRay as a starting point to then simulate inside-eye light transport.

Kerzin differentiates between existence and intrinsic existence. Intrinsic existence is what the Buddha and what Nagarjuna is trying to negate.

Rovelli makes a good point about a prevalent attitude that science offers a truer perspective than common sense, while Nagarjuna is pointing out that even the scientific explanation is not the final one. For one thing, it implicitly depends on the existence of a reified self who is the ultimate solidified existing agent and final authority, which Nagarjuna negates with his tetralemma.

Very interesting the way it describes different professions. Although there have been some approaches to the memex none of them have been this universally usable.

Even with current Zettlkasten technology like Logseq, a way to create a trail, and send off a particular trail to a friend is not present. I wonder what the copyright laws would look like when it comes to sharing excerpts as part of annotated trails like this. Would it be covered under Fair Use? What would a file format or a renderer for this look like?

We have gotten away from written annotations for digital work and I'm not entirely sure it's a good thing. I want to think through the trade-offs of this.

Author Response

Reviewer #2 (Public Review):

In this manuscript, Villalta, Schmitt, Estrozi and colleagues report their results on genome compaction in one of the most complex known viruses, the Mimivirus. This work will be of interest to a broad readership, and particularly to virologists and structural biologists. The authors describe a novel mechanism used by mimivirus to compact and package its 1.2 Mb dsDNA genome. In particular, the mimivirus genome is shown to be packed into magnificent cylinder-like assemblies composed of GMC-type oxidoreductases, presenting yet another remarkable case of enzyme exaptation. By using cryo-electron microscopy (cryo-EM) and cryo-electron tomography (cryo-ET), the authors determined the structures of such fibers in several relaxation states, which presumably represent different stages of nucleoprotein unpacking upon delivery into host cytoplasm. The authors also suggest (although do not directly visualize) that the lumen of the genomic fibers contains several viral enzymes, most notably, DNA-dependent RNA polymerase, which is necessary for cytoplasmic replication of the mimivirus. Overall, this is an important discovery, which further expands our appreciation of the "inventiveness" of viruses.

We thank this reviewer for the positive and constructive comments. We provide now some additional data corresponding to unpublished follow up studies, we hope will help all reviewers assessing the quality and reliability of our work.

I am not an expert on helical reconstructions and cannot evaluate the validity of the models. Thus, my specific comments will focus on aspects of the work with which I am more familiar.

1) In light of the presented results, it is reasonable to assume that GMC-type oxidoreductases of the mimivirus are very important for the formation of functional virions. However, in a previous study (PMID: 21646533), it has been shown that the genes encoding GMC-type oxidoreductases can be deleted from the virus genome (M4 mutant) without the loss of infectivity. The M4 virions were devoid of the external fibers decorating the icosahedral capsid, but the genome was still packaged. How do the authors reconcile these results with those presented in the present manuscript? This should be addressed in the Discussion section.

In fact, like the reviewers, we initially assumed that the GMC-oxidoreductases were essential. Now, we believe it might be premature to assume that GMC-type oxidoreductases are the only type of proteins that can be involved in the scaffolding of the Mimiviridae genomic fibers. We managed to extract the genomic fiber of M4 (the isolate without GMC oxidoreductases). The fiber also has a rod-shaped structure but protein composition analysis of the purified fiber shows that different proteins are involved in its assembly.

We hope the reviewers will accept to reserve our finding for a following publication.

2) The authors state that mimivirus encodes two GMC-type oxidoreductases (qu_946 and qu_143) and that both could be fitted into the electron densities. However, I could not understand whether the authors think that the fibers are heteroassemblies of both oxidoreductases or different fibers are composed of different proteins, or only one is used for fiber formation. Please clarify. In case you are not able to distinguish between the two homologs (e.g., due to limited resolution), state so explicitly.

We cannot discriminate between the two GMC-oxidoreductases due to their close identity (69% identity, 81% similarity) and the resolution of the map. Yet we think that in most cells the qu_946 GMC-oxidoreductase is the most abundant at the time of genome packaging (from our proteomic study, between 2 and 9 times). Yet, in some cells the second GMCoxidoreductase could become the most abundant and, in that case, the genomic fiber is built using qu_143.

3) I am slightly puzzled by the observed "ball of yarn". It is hard for me to imagine that a cylindrical container/fiber containing a continuous dsDNA genome could be bent or fragmented into bundles because this would break the protein-protein interactions holding the fiber together. In Figures 1C and S1, are these parts of the same fiber or multiple fibers coming out of one capsid? Related to this question - is there evidence (e.g., from qPCR) that Mimivirus carries a single copy of genomic dsDNA per capsid?

We believe this reviewer should think in terms of packaging. The folded genome is packaged through two lipid membranes (the one lining the capsid interior and the one in the nucleoid) concomitantly with its wrapping by the protein shell ribbon. Thus, there is plenty of space in the nucleoid at the beginning of the packaging and the genomic fiber is gently folded inside. But as more genome needs to be packaged, this compresses the flexible fiber into the nucleoid until it is totally encased in the nucleoid. That also defines the size of the nucleoid in the icosahedral capsid. This tight packaging is exemplified in Fig 1A for instance or the AFM images of the nucleoid enclosed in P3 of this file.

We provide a more general answer in the answers requested by the editor.

We think that the entire genome can only be packaged in the capsid through its assembly within the protein shell. We also think the genomic fiber is progressively built on the genomic DNA while it progresses into the capsid, most likely by an energy driven packaging machinery. This process can be compared to bacterial pili assembly, except that pili are built on the surface of the cell, while the genomic fiber is built into a compartment, the nucleoid, forcing it to fold in this compartment, which is only possible due to the high flexibility of the genomic fiber. Thus, the entire genome corresponds to ~40 µm of genomic fiber, which when folded as a ball can entirely fit into the nucleoid. The organization of the genome in a large “tubular structure” and its folding inside the nucleoid compartment has been previously reported by AFM studies of the mimivirus particles (Kuznetsov, Y. G. et al. Virology 2010; Kuznetsov YG et al. J. Virol. 2013, Fig 15), which the authors refer to as “highly condensed nucleoprotein masses about 350 nm in diameter within the inner membrane sacs of virions”, with the presence of tubular structure they refer to as “thick cables of the nucleic acid” (image P3 herein).

4) The authors describe the interactions between the monomers in the dimer of qu_946 as well as between qu_946 and DNA. I would also like to see a brief description of protein-protein interactions between subunits within the same helical strand as well as between helical strands, which hold the whole assembly together (i.e., what are the contacts between green subunits as well as between green and yellow subunits shown in Fig 2C). The authors suggest that the shell "would guide the folding of the dsDNA strands into the structure" (L310). To support this statement, the authors could show the lumen of the fiber rendered by electrostatic potential.

We thank this reviewer for these suggestions. An additional supplementary Table (Table S4) is now provided listing the various contacting residues in each genomic fiber map and for each GMC-oxidoreductase. The number of contacts obviously decrease in the relaxed structure, but even in the compact forms, we noticed there are relatively few contacts intra and inter-strands, which may also explain the flexibility of the structure. We now provide a new figure 3 in which the lumen of the fiber is rendered by electrostatic potential for the Cl1a map and each of the two GMC-oxidoreductases.

5) Please provide some background information on the distribution of GMC-type oxidoreductases in other families of giant viruses, so that it is clearer whether the described packaging mechanism is specific to mimiviruses or is more widespread.

This is a central point, also linked to the question about M4. In fact, like the reviewers, we initially assumed that the GMC-oxidoreductases were essential. Now, we believe it might be premature to assume that GMC-type oxidoreductases are the only type of proteins that can be involved in the scaffolding of the Mimiviridae genomic fibers.

If this reviewer still thinks this is essential to this manuscript we can provide a multiple alignment of the GMC-oxidoreductases of members of each clade upon request.

Reviewer #3 (Public Review):

Since it was presented to the scientific community as a viral entity, mimivirus has the unlimited capacity to cause surprise and admiration. In this manuscript, Villalta, Schmitt, Estrozi, et al. and Abergel present how the mimivirus gigantic genome is organized into the virion. The authors succeeded in developing a protocol to trigger virus genome uncoating followed by genome-associated proteins purification. The presented data indicates that a helical shield composed of two GMC-type oxidoreductases is associated with the mimivirus genome, named genomic fiber. By cryo-EM, and cryo-tomography different forms and stages of the genomic fiber were detailed described, indicating the dynamics of fibers conformational changes, likely related to genome packing and uncoating during the virus replication cycle. In-depth analysis of a substantial number of individual virus fibers revealed that the mimivirus genome is folded and organized inside the aforementioned helical shield, which seems to be novel among giant icosahedral viruses. Proteomics in association with image analysis indicates that mimivirus packed genome forms a channel, which accommodates key enzymes related to early phases of the replication cycle, especially RNA polymerase subunits.

I must disclose that I am not an expert on structural virology and proteomic analysis. Therefore, I don't feel I can contribute to the improvement of this kind of analysis. That said, I congratulate the authors for their efforts to make the manuscript story understandable to nonexperts.

We are grateful to this reviewer for these positive comments.

I have a few suggestions and comments:

1) Please consider the "nucleocapsid" concept during genomic fiber presentation. I believe it fits in;

We fully agree and this was why we referred to APBV-1. Obviously, it was not clear and we now explicitly use the word “nucleocapsid” in the text.

2) The "ball of yarn" analogy is nice, but fig 1C shows several fibers unconnected (free) in one of their ends. I am wondering if it means that the genomic fiber is not a long-single structure covering the whole genome, but a bunch of several independent helical structures covering the whole genome and attached in such "ball of yarn". Like several threads connected. Could the authors clarify that please?

In the “ball of yarn” structures, there are clearly breaks that give the impression of multiple fibers. Yet, these breaks are due to the multiple steps of the extraction, enrichment and purification treatment. The genomic fiber is built as a long (~40 µm) single structure folded in the nucleoid while it is loaded. As a result, it is tightly packed into the nucleoid and broken into fragments upon release due to the fragilizing treatment. As exemplified in the CryoEM image provided above (P9) on freshly opened capsids, these breaks appear to depend on the treatment. This reviewer could also look at the answer we provided to Reviewer 2 point 3 as this could help clarify how it is possible to package the genomic fiber and subsequently fold it into the nucleoid to the point where it is tightly packed and under pressure.

3) Considering previously published data on proteomics of viral factories and transcriptomics of mimivirus: is there any temporal association between GMC-type oxidoreductases' peak of expression and genome replication during the viral cycle? what about RNA pol subunits? Are all those proteins highly expressed during the late cycle? or do they reach the peak concomitantly with genome replication? This information can support the discussion on the genome-fibers assembly during the cycle.

We thank this reviewer for these suggestions. We now added time of expression of the proteins involved in the genomic fiber composition along the manuscript. We added explicit sentences in the main text both for the GMC-oxidoreductases and RNA polymerase subunits. The RNA polymerase as well as proteins involved in mRNA maturation are in the virion (Table S2 B) and studies by others demonstrate early transcription takes place in the nucleoid once transferred in the host cytoplasm (Reference 24). We also provided a link to the reviewers where to find the expression data for the different mimivirus genes. http://www.igs.cnrs-mrs.fr/mimivirus/

4) Taken together, data seem convincing to demonstrate that the virus genome is located inside the helical shield. However, I believe that the authors could better explain why we only see 20 kb fragments in the gel, including in the control (in Fig S2).

We hope our answers to this comment will convince this reviewer.

Fig S2 corresponds to a regular 1% agarose gel and not to a PFGE gel. This gel was simply to show there is DNA associated with the genomic fiber and not to show the size of the DNA as the genomic fiber has been broken into pieces and we thus do not expect to have very high molecular weight. I must point out that when extracting the DNA form Mimivirus capsids using standard kits and pipetting, it also migrates at the top of the gel (Lane 1 in Fig. S2) while it would likely appear as a smear above 20 kb on a PFGE. By contrast when the viral particles are put into plugs prior lysis, the genomic DNA migrates at the proper size, as shown in the publication from Boyer et al. 2011 (reference 31), showing the genome of Mimivirus is a linear genome migrating around 1.37 Mb (Fig 1, Panel B, Lane M1). In P9 of this letter, an image of a long (> 6 µm) and flexible fiber is presented.

Reviewer #4 (Public Review):

In the manuscript "The giant Mimivirus 1.2 Mb genome is elegantly organized into a 30 nm helical protein shield", the authors show that, when subjected to low pH stress, the Mimivirus particle releases 30nm-diameter filamentous assemblies. These filaments consist of a protein shell that envelopes the Mimivirus genomic DNA. The protein shell is composed of two GMC-oxidoreductases, the same protein that forms the long fibers emanating from the capsid of the Mimivirus.

Overall, despite being interested in the subject, this scientist was left confused about several aspects of the paper described below. The presentation of the material is also confusing.

We hope the answers and images we provide to all Reviewers in page 2 to 12 herein will clarify the various points raised by this reviewer.

1) The presented data do not allow the estimation of the amount of mimivirus genome organized into 30 nm diameter filaments. Hence, the title of the paper is misleading.

The entire genome should be packaged in the genomic fiber. That was already observed by other and we now provide an image of the nucleoid imaged by AFM that was published. The image was extracted from Kuznetsov et al. J. Virol. 2013. See p9 of this letter.

2)The filamentous structures are a result of extremely harsh treatment of the virus particle, which starts with a 1.5 hour-long incubation at pH 2. Do the filaments actually exist inside the virus particle as the title of the paper implies?

The 1 h incubation at 30°C and pH 2 was only applied to recover the nucleoids (see material and method section “Nucleoid extraction”) presented in Fig S1A. Acidic treatment was never applied to produce the genomic fiber as we noticed it is sensitive to both temperature and acidic treatment. All steps of the extraction protocol were performed at pH 7.5 (section: “Extraction and purification of the mimivirus genomic fiber”). We must emphasize that the release of the genomic fiber can be seen at the very first step of the extraction protocol (protease treatment). The sample was also controlled at each step of the protocol by negative staining TEM to assess the status of the genomic fiber. We had to optimize the protocol as using a too soft proteolytic treatment led to too few opened particles but with mostly a compact genomic fiber released, if it was too harsh, all particles were opened but the genomic fiber was mostly in the ribbon state. We had to compromise to get a decent amount of compact and relaxing structures to be able to perform the present work. We would like to stress out that we could reproducibly obtain the genomic fiber from many preparations and that we could observe them with different virions (including M4), even using different protocols (only the one with the better yield is reported in the manuscript).

In the Figure 1B the genomic fiber can be seen inside a virion and is still encased in the membrane compartment. These structures were not reported in previous cryo-EM analyses of the virions. As said above, they were only reported by AFM studies of the mimivirus particles (Kuznetsov, Y. G. et al. Virology 2010; Kuznetsov YG et al. J. Virol. 2013, Fig 15). See p9.

Or [might] these filaments [form during] host take over?

Or [perhaps] these filaments [result from a harsh in vitro treatment] and have nothing to do with either?"

The first two questions can be answered with the help of cryoFIB tomography, which might be beyond the scope of a "paper revision". However, the properties of the two GMCoxidoreductases in the presence and in the absence of genomic DNA must be examined in greater detail. Can these proteins, by themselves, form similar hollow filaments (or any filaments) when subjected to the same treatment as the virus?

I personally have difficulties to imagine that such a complex structure could be the result of an artefact due to the treatment for several reasons: - It is unlikely that by simply putting the GMC-oxidoreductases with DNA would result in a helical structure where the DNA is folded 5 times and internally lining the protein shell (extended data video1 of one tomogram). It would be like crystallizing the proteins (in a heterogeneous sample) onto the folded DNA to form a helix with a hollow lumen. The crystallographic data obtained by others by on the mimivirus GMC-oxidoreductase did not produce tubular structures either and they reported 3 crystal forms. They overexpressed the proteins in E. coli and did not report such structures bound to DNA either.

• Given the presence of compact and relaxed forms, once relaxed the helix cannot go back to a compact state passively by simply rewinding suggesting the relaxed forms are the result of decompaction of a constrained structure. This is also supported by the loss of DNA in the relaxed state Cl3. Last steps of unfolding correspond to the loss of one ribbon strand after the other.

• The contacts between chains intra and inter strand are also scarce supporting an active assembly of the structure. We now provide an additional supplementary Table S4 with the different contacts for the different states of the genomic fiber.

  3) Although the assignment of the qu_946 oxidoreductase to the corresponding cryo-EM density is correct (as the resolution is high enough), I am confused about the other oxidoreductase (qu_143). Where does it fit to? Which structure does it form?

We cannot discriminate between the two GMC-oxidoreductases due to their close identity (69% identity, 81% similarity) and the resolution of the map. Yet we think that in most cells the qu_946 GMC-oxidoreductase is the most abundant at the time of genome packaging (from our proteomic study, between 2 and 9 times). Yet, in some cells the second GMCoxidoreductase could become the most abundant and, in that case, the genomic fiber is built using qu_143.

Equally important, what is going on with the N-terminal 50-residue domain of qu_946? Is there a space for it in the cryoEM map? Is it disordered?

The N-terminal domain is only present in the fibrils decorating the capsids. As illustrated in Fig S12, when analyzed by MS-based proteomics, the comparison of the peptide coverage of the GMC-oxidoreductases whether they compose the fibrils or the genomic fiber is not the same. The N-terminal domain is clearly covered when the fibrils (data not shown) or intact virions are analyzed and not covered when the analysis is performed on the genomic fiber. That is why we propose this N-terminal domain could be an addressing signal (see main text) and that a protease could be cleaving it in the case of the genomic fiber assembly.

Main text: The proteomic analyses provided different sequence coverages for the GMCoxidoreductases depending on whether samples were virions or the purified genomic fiber preparations, with substantial under-representation of the N-terminal domain in the genomic fiber (Fig. S12). Accordingly, the maturation of the GMC-oxidoreductases involved in genome packaging must be mediated by one of the many proteases encoded by the virus or the host cell.

Indeed, there is no space to accommodate this domain as it would prevent the interaction between the protein shell and the DNA or/and induce an increase of the genomic fiber diameter that would be too big to be accommodated into the nucleoid.

4) The bubblegram analysis is not very convincing. The bubbles appear to correlate with the length or thickness of the structure - the long or overlapped structures form bubbles. The bubbles may not be due to the presence of DNA.

The point is, as demonstrated by our structural studies, that the relaxed structure lost the DNA. This is why bubble cannot be seen in the relaxed broken fibers. On long fibers still in compact form, the DNA is visible in the structure and bubble can be seen. Yet the evidence for the presence of DNA in the structure is also provided by the agarose gel of the purified genomic fiber and the cryo-EM structures. Bubblegrams are just one additional analysis which was provided.

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Reply to the reviewers

We are sincerely grateful to the reviewers for several key comments that led us to correct some mistakes and better appreciate how to put our findings in the context of recently published data. These changes undoubtedly improved the manuscript.

Many other reviewer comments seem to equate chaperone binding with a functional chaperone role in de novo folding. These are not the same. Cytosolic chaperones presumably “sample” nearly every protein that is synthesized by cytoplasmic ribosomes. This does not mean that every such protein would misfold if even one of those chaperones failed to bind it. If we want to understand what chaperone mutations might cause human disease due to septin misfolding, for example, it will not be enough to catalog all the chaperones that bind septins. We have already done that. What will help is to understand which chaperones make functional contributions to septin folding and complex assembly. Our study is the first to experimentally address chaperone roles in de novo septin folding, period. We take responsibility for not being sufficiently clear about the goals of our work, and, to emphasize these points, we added one sentence to the Introduction and revised another.

Another consistent criticism was that the use of the E. coli system, both in vivo and in vitro, limited our ability to gain insight into the folding of septins in eukaryotic cells and led to a “tessellated view”. For example, reviewers claimed that our model about translation elongation rates for Cdc12 were “based mainly on the E. coli system and bioinformatics analysis”. We disagree with this interpretation. Key evidence in support of our model come from published data in yeast, specifically the much higher density of ribosomes on Cdc12 and the accumulation of ribosomes on the Pro-rich cluster near the Cdc12 N terminus. These are precisely the kinds of “more stringent analysis” in “authentic yeast” (to use Reviewers’ language) that we would have wanted to do to test our model, had they not already been done by others. Without specific suggestions, we struggle to imagine what other kinds of experiments the Reviewers have in mind, apart from a eukaryotic version of a reconstituted cell-free translation system, which Reviewer #1 admits “would be substantially difficult” and “time consuming”. While we are intrigued by the reconstituted eukaryotic cell-free translation system that was published last year (which we mentioned on lines 994-995) and look forward to exploring it in future studies, it is not commercially available and we agree that the amount of effort required to prepare it ourselves is unrealistic for the current study. Most importantly, we do not find in the critiques provided any specific reason why our E. coli-based systems experiments are intrinsically less “stringent” or “rigorous”.

Accordingly, we think that, together with the results of multiple new experiments (detailed below), the extensive re-writing and re-ordering that we have done in the revised manuscript will be enough to better emphasize the importance and rigor of our findings and thus to address all of the Reviewers’ specific concerns.

Reviewer 1 thought that our manuscript “does not even provide new information, since the involvement of CCT and the Hsp70 system is not novel” and thought that “the key finding of this manuscript is how chaperones are involved in the de novo folding of septins, which is not conceptually new because of previous findings, including those of the authors”. Reviewer #3 also stated that “the function of Tric/CCT in septin folding and assembly is well documented”.

We were quite surprised at this reaction, since we dedicated a significant portion of the original manuscript (lines 68-76 and 319-322) to explicitly discussing the only other paper in the literature that specifically addresses the question of whether or not CCT is required for de novo septin folding. As a reminder, that paper explicitly stated that “it is unlikely that CCT is required to fold septins de novo” and “septins probably do not need CCT for biogenesis or folding”. With regard to involvement of the Hsp70 system, the only existing evidence in the literature on this subject is the aggregation of some septins in ssb1∆ ssb2∆ cells. Like the CCT study, that study did not distinguish whether this was a result of problems during septin synthesis and before septin complex assembly, or, alternatively, whether pre-folded and assembled septins were subject to disassembly, misfolding, and aggregation. Our experiments specifically test the fate of newly-synthesized septins prior to assembly in living cells. Our previous findings documented physical interactions between wild-type septins and multiple chaperones but did not address whether these interactions had any functional relevance. We previously reported functional effects of interactions between chaperones and MUTANT septins but, again, these studies did not address functional chaperone requirements for WILD-TYPE septins. While we did our best to highlight these points in the original document without devoting excessive amounts of text, we accept responsibility for not making these points sufficiently clear and to address this issue we added additional text, including the text quoted above, to the Introduction.

While Reviewer #3 commented that the manuscript “is overall well presented”, Reviewer 1 thought that the manuscript was “complicated to read” with “no logical connections, just a list of many results” and mentioned that part of the difficulty was “that it contains many negative results”.

In addition to reorganizing the manuscript, as suggested by the reviewers, we added more text at the beginning and end of nearly every section to even more explicitly state the logical connections between results. In our opinion, negative results of properly controlled experiments are valuable to the research community, and we do not understand what it is about negative results that makes them difficult to read about. Many of the extra experiments we performed were in anticipation of being asked to perform them by reviewers, some of which generated negative results. We are reluctant to remove negative results unless there is a more compelling reason. For example, to address another reviewer concern, we did remove the negative results with the Ydj1–Ssa2 compensatory mutants.

Reviewer #2: “4) Figure 2: The labeling on the protein structure makes it seem like the exact region for Ydj1 and Hsp70 was experimentally identified, when it hasn’t.”

We acknowledge that the first sentence of the figure legend (“the colored ribbon follows the color scheme in the sequences at right for overlapping β-aggregation, Ydj1 and Hsp70-binding sites”) could be misinterpreted, since only in the second sentence does it say “Sequence alignments show predicted binding sites”. We corrected this mistake, and added the text “Predicted chaperone binding sites” as the first words in the legend to this figure.

Reviewer #2: “8) The authors confusingly jump back and forth between different Septins and different chaperone (Ssa1-4, Ydj1, Sis1, Hsp104). We would ask the authors to re-arrange the manuscript, collating all the yeast work in one section and bacterial work in another.”

We re-arranged the manuscript and put all the yeast work in one section and all the bacterial work in another, with the exception of the studies of individually purified Cdc3 and Cdc12, which we put in between the yeast studies of the kinetics of de novo assembly and the yeast studies of post-translational assembly. Our reasoning is that the studies with the purified proteins demonstrate challenges with maintaining native conformations in the absence of chaperones and other septins, which flows naturally into the yeast studies asking about the ability of “excess” septins to maintain oligomerization-competent conformations in the absence of other septins and when we experimentally eliminate specific chaperones. All of the work actually manipulating E. coli genes/proteins is now together.

Reviewer #3: “1. The co-translational binding of CCT to nascent polypeptide chains has been studied (Stein et al., Mol Cell 2019). While the authors indicate that septin subunits are engaged co-translationally, they do not comment which ones are interacting with CCT and at which state of translation. This information is crucial and should also be mentioned in the discussion section.”

We are grateful to the Reviewer for bringing up this point, which we had overlooked. We hadn’t noticed that, in the end, only Cdc3 met the CCT confidence threshold to be included in the supplemental data of the Stein et al. paper. All septins co-purified with CCT in an earlier Dekker et al proteomic study, so we strongly suspect that the failure of the other septins to meet the confidence threshold in the Stein et al paper reflects the sensitivity of that assay, rather than a significant difference in how septin GTPase domains interact with CCT. We also hadn’t appreciated that according to that study, the main sites in the Cdc3 GTPase domain bound by CCT and Ssb are the same. Hence our statement that Ssb bound to septins “earlier” during translation, and CCT bound “later” was wrong. Instead, the overlapping Ssb and CCT site in Cdc3 turns out to be remarkably consistent with a conclusion from Stein et al paper, that CCT binds Rossmann-fold proteins like septins at sites where “early” beta strands have been translated and expose a chaperone-binding surface that later becomes buried by an alpha helix. We corrected our mistake in the text and in our model figure and added: (1) a new supplemental figure with predicted septin structures and a sequence alignment indicating where CCT and Ssb bound; and (2) text discussing the confidence thresholds for “calling” septin-CCT interaction, the Rossmann-fold binding, and how we interpret Ssb and CCT binding to the same site.

Reviewer #3 “3. Figure 3: It is recommended to also follow Cdc10-GFP and Cdc12-GFP fluorescence. This will on the one hand generalize the presented findings and provide a direct link to other parts of the study (e.g. crosslinking analysis of Cdc10).

We carried out the requested experiment for Cdc12, using Cdc12-mCherry rather than Cdc12-GFP because of the formation of non-native foci that we observed with Cdc12-GFP. We also attempted to analyze Cdc10, using an existing GAL1/10-promoter-driven Cdc10-mCherry plasmid that we’d made a few years ago, but it did not behave as expected, with high expression even in the absence of galactose (not shown), which prevented us from performing the requested experiment. We have a Cdc10-GFP plasmid with the inducible MET15 promoter, but this promoter does not provide sufficiently low levels of expression in repressive conditions, so there would be too much expression at the beginning of the experiment for us to accurately follow accumulation thereafter. Instead, we tried the only other plasmid we had with the GAL1/10-promoter controlling a tagged septin: Cdc11-GFP. Above a certain threshold of expression, Cdc11-GFP formed unexpected cortical foci, but we were still able to perform the analysis and found a clear delay in septin ring signal in cct4 cells, providing the requested generalization to other septins, if not Cdc10.

Reviewer #3 “5. Figure 4C: The finding that only ssb1 but not ssb2 knockouts have an effect on joining of free Cdc12-mCherry subunits into septin rings is puzzling. Similarly, Ssb1 largely acts co-translationally, while in this assay post-translational septin ring assembly is monitored. The authors need to comment on these two points.”

We did not examine ssb2 knockouts, so we do not know to what the Reviewer is referring in the first point. If the Reviewer means that they are puzzled by the fact that we saw a phenotype in cells in which only SSB1 was deleted and SSB2 remained, we offer two explanations. As can be seen in the Saccharomyces Genome Database entry for SSB1 (https://yeastgenome.org/locus/S000002388/phenotype), there are at least a dozen known phenotypes associated with deletion of SSB1 in cells with wild-type SSB2. We even showed a very clear septin misfolding/mislocalization phenotype in Supplemental Figure 4D. Thus while our findings are new and provide novel insights into Ssb function, they are not unprecedented. The Reviewer is correct that most Ssb is ribosome-bound and thus Ssb1 “largely acts co-translationally” but ~25% of Ssb is not ribosome-associated (PMID: 1394434). Furthermore, the lack of a strong phenotype for ssb1∆ cells in our new kinetics-of-folding experiment (see below), plus the realization that Ssb and CCT both bind the same site in Cdc3, leads us to a new model: Ssb acts both co- and post-translationally in septin folding, but only the post-translational function is associated with a phenotype in ssb1∆ cells, because in that assay we drastically overexpress a tagged septin and thereby exceed the Ssb chaperone capacity that remains when we delete SSB1. This logic also explains the first ssb1∆ phenotype we saw, when overexpressing Cdc10(D182N)-GFP. In the kinetics-of-folding assay, on the other hand, tagged septin expression is much lower and reducing the amount of total Ssb by ~50% (via SSB1 deletion) likely does not compromise Ssb function in folding the tagged septin. We therefore removed our statement that “Ssb dysfunction leaves nascent septins in non-native conformations that are aggregation-prone and unrecognizable to CCT”, revised our model figure accordingly, and added new text and citations to explain our new model.

Reviewer #3 “Additionally, they should test whether the appearance of septin ring fluorescence is slowed down in ssb1 mutants (as shown for cct4-1 mutant cells in Figure 3B).”

We agree that slower septin folding in ssb1∆ cells is a prediction of our model, and we performed the requested experiment and include the results in our revised manuscript. The new data show that the appearance of septin ring fluorescence is not delayed in ssb1∆ mutants, which is easily explained by the ability of Ssb2 to chaperone the folding of the low levels of tagged septin that we express in these kinds of experiments (see above).

Reviewer #3: “7. Figure 5G: The data is not convincing. This reviewer cannot detect a specific Cdc12 band accumulating in presence of GroEL/ES.”

We re-ran the reactions again with fresh reagents and this time ran the gel longer to reduce excess signal from free fluorescent puromycin and the bright Cdc10 bands. We now see a very clear band for full-length Cdc12 in the reaction with added GroEL/ES, fully consistent with our mass spectrometry results. We updated the figure with the new results.

Reviewer #3: “Furthermore, the activity tests done for the chaperonin system are confusing (Supplemental Figure 7). The ATPase rate (slope!) of GroEL/GroES seems higher as compared to GroEL but according to the authors it should be opposite.”

In our assays, the ATPase activity is so fast that for our “time 0” timepoint, much of it has already occurred by the time the reaction can be physically stopped and measured. In other words, the handling time is such that we can’t visualize what happened in the earliest stages of the reaction, where the rates could accurately be estimated as slopes. This is obvious from the fact that at time 0, the absorbance for the “GroEL alone” reaction is already more than twice the absorbance for GroEL+ES. We added clarifying text to the figure legend.

Reviewer #3: “The refolding assay using Rhodanese as substrate is also confusing: What is the activity of native Rhodanese? The aggregated Rhodanese sample seems to have substantial activity that is not too different from a GroEL/ES-treated one. From the presented data it is not clear to the reviewer to which extend GroEL/ES prevents aggregation and supports folding of denatured Rhodanese.”

We thank the Reviewer for bringing this to our attention, because made we mistakenly left out the values for native Rhodanese with the reporter. With regard to the aggregated Rhodanese, we failed to note that this sample contains urea. When the urea absorbance is subtracted, it is clear that the GroEL/ES-treated sample has higher activity. Furthermore, some native enzyme is likely still active within the aggregated sample, explaining the “substantial activity” that the Reviewer correctly notes. We corrected the figure and added clarifying text to the figure legend.

Reviewer #3: “the study goes astray following aspects that does not seem relevant to this reviewer (e.g. the role of N-terminal proline residues for Cdc12 translation, Fig. 5E/F).”

We acknowledge that we did a poor job of introducing the N-terminal Pro-rich cluster in Cdc12 with relation to our model of slow Cdc12 translation. Instead, we have revised and reorganized the manuscript to set up these experiments as a direct test of our model: if ribosome collisions on the body of the ORF drive mRNA decay, then decreasing the spacing of those ribosomes should exacerbate the problem, and eliminating the Pro-rich cluster (where published yeast data already show ribosomes accumulate) is the most logical way to test the prediction. Far from being irrelevant, the results fit the prediction perfectly and thus support the model. We expect that this change will highlight the importance of these experiments for the reader.

Reviewer #2: “1) Fig. 1 Is the folding of Cdc3 being measured in cells lacking chaperones mentioned towards the end of the paper or are the authors referring to the lack of yeast proteins?”

We are unclear as to what the Reviewer is asking here. The title of Figure 1 states that these are “purified yeast septins” and the figure legend further emphasizes this fact. Additionally, the Coomassie-stained gel in Figure 1A shows a single band, corresponding to purified 6xHis-Cdc3. The proteins were purified from wild-type E. coli cells, so all E. coli chaperones were present when Cdc3 initially folded, but chaperones and all other proteins were removed during the purification and prior to the analysis. We do not know what change to make.

Reviewer #2 asked “How do the authors account for the septin defect in Ssa4 delete cells in unstressed conditions where Ssa4 would be very low already? According to the authors previous work, Ssa2 and 3 should be able to compensate.”

We explicitly addressed this point in the original manuscript (lines 893-898). Again, we think here the Reviewer is equating chaperone binding with chaperone function. According to our previous work, Ssa2 and Ssa3 are able to bind septins, but this does not mean that they can fold septins the same way as Ssa4. We cite several papers that discuss the distinct functional roles for the different Ssa proteins. We do not think that additional clarification of this point would strengthen the manuscript.

Reviewer #3: “6. Figure 5B: It is unclear why Cdc3 is observed in the pulldown of His-tagged Cdc12 (37˚C), although no Cdc12 was isolated under these conditions. How is that possible?”

That is not possible. As we indicate in the figure legend and with the red asterisk, the only band appearing in that lane is a non-specific band that cross-reacts with the anti-Cdc3 and/or anti-Cdc11 antibodies. This is why it is also present in the “No septins” control lanes. We made the asterisk larger to help accentuate this point.

Reviewer #3: “Furthermore, the authors observe a specific effect on Cdc12-Cdc11 assembly in the E. coli groEL mutant. How do they rationalize this specific effect as Cdc12-Cdc3 assembly remained unchanged? This observation also seems in conflict with the suggestion of the authors that Cdc12 preferentially recruits Cdc11 before interacting with Cdc3 (page 45, lane 1024).”

Cdc11 was not expressed in the groEL mutants because no Cdc11 gene was present in those cells, as explained in the body text and indicated in the labeling above the lanes in Figure 5A. The band near the size of Cdc11 is a non-septin protein that bound to the beads in the groEL-mutant cells, as is shown in the immunoblot using anti-Cdc11 antibodies in Figure 5B. Thus there is no conflict to rationalize.

Reviewer #1: “The only evidence that CCT binds to septin is the list of LC-MS/MS. Western blotting would provide more solid data.” and “2) The cross-linking experiments appears not to have been successful. Why are the Ssas, Ydjs etc not detected here? “

First, CCT subunits are relatively low-abundance, expressed at 5- to 50-fold lower levels than other chaperone families in the yeast cytosol (see PMID: 23420633). To the Reviewer’s second point, we did in fact detect other chaperones in our crosslinking mass spectrometry experiments, including Ydj1, multiple Ssa and Ssb chaperones, Hsp104, etc., as can be seen in Table S1. However, they were also detected in negative control experiments. This is not surprising, given that these chaperones are among the most common “contaminants” of affinity-based purification schemes (see the CRAPome database at https://reprint-apms.org/). It was for this reason we had to perform so many negative control experiments, which likely produced some false negative results, as some “real” interactions were likely discarded when the same chaperone showed up in our controls. We added a figure panel with a Venn diagram of overlap between experimental and control samples, and text pointing out this caveat of our approach.

Second, in this experiment we attempted to identify proteins that transiently interact with a specific region of Cdc10 that will later become buried in a septin-septin oligomerization interface. Due to the transient nature of the interaction, we do not expect to detect high levels of crosslinked chaperones. Mass spectrometry is significantly more sensitive than immunoblotting, so there is no guarantee that we would be able to detect a band even if the crosslinking works as desired. Indeed, the crosslinked bands we saw by immunoblot for GroEL were quite faint (see Figure 2F), despite the fact that GroEL and the T7-promoter-driven Cdc10 were among the most abundant proteins in those E. coli cells.

Third, there is no commercially available, verified antibody recognizing yeast Cct3 for which to perform the requested immunoblot experiment. Since both the N and C termini of CCT subunits project into the folding chamber, it is unwise to use a standard epitope tagging approach, as the tags may compromise function. Indeed, for purification purposes others inserted an affinity tag in an internal loop in Cct3 (PMID: 16762366). We have a yeast strain with Cct6 tagged in an analogous way, but to perform the requested immunoblot experiment with Cct3 would require creating or obtaining the Cct3-tagged strain, deleting NAM1/UPF1, and introducing our Bpa tRNA/synthetase and GST-6xHis-Cdc10 plasmids. Given the sensitivity of detection concerns stated above, we doubt this would help.

In summary, we prefer not to attempt the requested immunoblot experiments.

Reviewer #1: “-Fig. 3B ant related Figures: The experiment to see if GFP-tagged septin accumulates in the bud neck is important, but only the graphs after the analysis are shown. The authors should provide the readers with representative examples from imaging data.”

We are confused, because the images at the bottom of Figure 3A already show what the Reviewer requests. As stated in the figure legend, these are representative examples of the imaging data from a middle timepoint of one of the experiments. It would be nearly impossible (for space reasons) to provide representative images for all of the timepoints for all of the genotypes for all of the experiments. Since in our new experiments we introduce new tagged septins (Cdc11-GFP and Cdc12-mCherry), we also now include representative images of cells expressing these proteins, as well.

Reviewer #2: “3) If the authors had evidence of chaperone interaction from their previous study, why did they not simply do IPs with fragments of the septins/chaperones?”

We are unclear why the Reviewer is suggesting IPs after referring to our previous study. IPs are a poor choice for transient interactions, which is why we mostly avoided them in previous studies, and instead used a novel approach (BiFC) to “trap” chaperone–septin interactions. Moreover, we seek to identify chaperones that bind wild-type septins at future septin-septin interfaces on the path towards the native conformation. Fragments of septin proteins would likely misfold and would therefore likely attract chaperones that wouldn’t normally bind the full-length septin. Indeed, our previous studies demonstrated that even a single non-conservative amino acid substitution was sufficient to alter chaperone-septin binding. Thus IPs with fragments of septins or chaperones would be highly unlikely to yield informative results for the questions we seek to answer. We strongly prefer not to attempt these suggested experiments.

Reviewer #2: “5) While differences between Ssa paralogs are highly interesting, using deletions of Ssas is not useful, given that yeast compensate by overexpressing other paralogs. The yeast GFP Septin assays should be repeated in yeast lacking all Ssas and expressing one paralog on a constitutive promoter (See numerous papers by Sharma and Masison).”

We disagree that ssa deletions are “not useful”, since if the overexpressed paralogs cannot fulfill the same function as the deleted SSA, then we will see a phenotype. Which we do. Furthermore, we had already obtained and thoroughly tested a strain like the ones mentioned by the reviewer (ECY487, a.k.a. JN516, from Betty Craig’s lab, with ssa2∆ ssa3∆ ssa4∆ and SSA1, which is constitutively expressed, PMID: 8754838), but we found that, as published, it divides slightly more slowly even under the most permissive of conditions. The requested strain cannot be analyzed using our method, because slow accumulation of ring fluorescence could be attributed to other defects unrelated to septin folding. Thus we strongly prefer not to attempt the suggested experiments.

Reviewer #2: “7) The authors need to clarify the experiment with the Ydj1 D36N and Ssa2 R169H. In Reidy et al, they never fully biochemically test this system and it was never examined for Ssa2-Ydj1. The authors would need to do some fundamental experiments to demonstrate the validity and functionality of this double mutant in yeast.”

Given that this experiment was unable to generate meaningful data, since the mutations affected the kinetics of induction of the GAL1/10 promoter, we do not think the requested biochemical experiments would add any value to the study. Instead, we removed these studies from the manuscript.

Reviewer #3: “4. Figure 3B: The difference between wt and cct4-1 cells in appearance of septin ring fluorescence is observed at one timepoint. Since this experiment is considered highly relevant, the authors are asked to include another timepoint to bolster the conclusion that Cdc3-GFP folding and thus septin ring assembly is delayed in the CCT mutant.”

We carried out new experiments with cct4-1 cells using Cdc12-mCherry and Cdc11-GFP with more timepoints than in our original cct4-1 experiments with Cdc3-GFP. Since these experiments provide the same kinds of results, but at multiple timepoints, we do not see the value in repeating the Cdc3-GFP experiment.

Reviewer #3: “If Ssb1 functions to maintain Cdc12 in an assembly competent state preventing misfolding, one would expect either enhanced degradation or aggregation of Cdc12-mCherry in ssb1 mutant cells. Did the authors check for such scenario? Septin aggregation has been shown in a ssb1 ssb2 double deletion strain (Willmund et al., 2013), yet the data shown here predict that aggregation might already occur in single ssb1 mutants.”

We already examined septin aggregation in single ssb1 mutants and showed these data (Supplementary Figure 4D). Indeed, this phenotype was the rationale for testing post-translational septin assembly in ssb1 single mutants. We have seen no evidence of septin degradation in any context (as we mentioned on line 889), so we would not expect it here. While we added new text and a very new citation showing that many “misfolded” conformations of wild-type E. coli proteins avoid aggregation and degradation, we do not think that the suggested experiments would add enough value to the current study to justify the effort, time and expense.

Reviewer #3: “Fig. 3C: The figure showing septin ring fluorescence does not include error bars. This is crucial, also because the difference between wt and ssa4 mutant cells is not large.”

There are, in fact, error bars included in the figure, as can be most clearly seen for the final timepoint for the ssa4∆ cells. For most of the other timepoints the error bars are smaller than the data point symbols (the circles and squares). We do not think that adjusting the size or opacity of the symbols to better show the error bars will be sufficiently valuable to justify the effort.

Many people treat their blogging practice as an experimental thought space. They try out new ideas, explore a small space, attempt to come to understanding, connect new ideas to their existing ideas.

Ton Zylstra coins/uses the phrase "metablogging" to think about his blogging practice as an evolving thought space.

How can we better distill down these sorts of longer ideas and use them to create more collisions between ideas to create new an innovative ideas? What forms might this take?

The personal zettelkasten is a more concentrated form of this and blogging is certainly within the space as are the somewhat more nascent digital gardens. What would some intermediary "idea crucible" between these forms look like in public that has a simple but compelling interface. How much storytelling and contextualization is needed or not needed to make such points?

Is there a better space for progressive summarization here so that an idea can be more fully laid out and explored? Then once the actual structure is built, the scaffolding can be pulled down and only the idea remains.

Reminiscences of scaffolding can be helpful for creating context.

Consider the pyramids of Giza and the need to reverse engineer how they were built. Once the scaffolding has been taken down and history forgets the methods, it's not always obvious what the original context for objects were, how they were made, what they were used for. Progressive summarization may potentially fall prey to these effects as well.

How might we create a "contextual medium" which is more permanently attached to ideas or objects to help prevent context collapse?

How would this be applied in reverse to better understand sites like Stonehenge or the hundreds of other stone circles, wood circles, and standing stones we see throughout history.

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

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Reply to the reviewers

General response to the reviewer

We thank all reviewers for their constructive comments on our manuscript. We were very pleased to see that the reviewers found our study ‘…represent new insight in the field’ (rev#1) and ‘…contains important and exciting novel findings’ (rev#2), and ‘…gives a more detailed perspective on how Src proteins (Src42A in Drosophila) control epithelial stability and the contraction of specific surfaces of epithelial cells’ (rev#3). The reviewers raised a number of specific points that we partially addressed already in a preliminary revision of the manuscript. Some more points will require some additional experiments that we will incorporate in a fully revised version of the manuscript.

Reviewer #1

(Evidence, reproducibility and clarity (Required)): Highest priority: 1) The Src42A knockdown and germline clone experiments both cause defects in cellularization (Fig. 2B and 9A), which could result in differences in the state of the blastoderm epithelium (cell size, cell number, structural integrity, organization, etc.) between the experimental and control conditions. In addition, Src42A knockdown appears to affect the size and shape of the egg (Fig. 9A and 9C). The manuscript would be strengthened if the authors included data to demonstrate that the initial structure of the epithelium is mostly normal (quantifications of cell size, number, etc.) in the Src42A RNAi condition, as this would bolster the argument that germband extension, rather than due to indirect effects resulting from the cellularization defects. The authors may have relevant data to do this on-hand, for example using data associated with figures 1, 3, 6, and 9.

Response:

The cellularization phenotype of src42A knockdown embryos has a penetrance of about 50% and exhibits a variable expressivity. We attempted to characterize this phenotype in detail, but failed to identify any dramatic differences in cellularization of the src42A knockdown embryos compared to wild type. The localization of E-cadherin, in turn is not affected, but occasionally, nuclei are dropping out of the blastoderm before cellularization is accomplished. This can result in patches of irregular cellularization, but the blastoderm epithelium in stage 6 embryos did not display major defects in overall structure. We will present additional data on the cellularization phenotypes in the fully revised manuscript. As the referee suggested, we will analyze our data to determine potential effects on the cell size, cell number and overall organization of the blastoderm before germband extension. We plan to present these data as an additional Suppl. Mat. Figure in the full revision.

Lower priority:

5) Figure 8 - in my opinion, using a FRAP or photoconversion approach would be a more convincing demonstration of differences in E-cadherin residency times / turnover rate than time-lapse imaging of E-cadherin:GFP alone. Authors should decide whether this improvement is worth the investment.

Response:

We thank the reviewer for this comment. While we believe that the data presented in Fig. 8 demonstrates a significant difference in the E-cadherin residence time based on E-cadherin-GFP fluorescence intensity, we agree with the referee that FRAP analyses would provide additional evidence to support our conclusion. For the full revision, we will therefore attempt to perform FRAP-experiments on src42A knockdown embryos expressing E-cadherin-GFP and compare the recovery time to the wild type.

Reviewer #1 (Significance (Required)):

The manuscript by Backer et al. examines the function of Src42A in germband extension during Drosophila gastrulation. Prior studies in the field have shown that Src family kinases play an important role in the early embryo, including cellularization (Thomas and Wieschaus 2004), anterior midgut differentiation (Desprat et al. 2008), and germband extension (Sun et al. 2017; Tamada et al. 2021). In this study, the authors showed that Src42A was enriched at adherens junctions and was moderately enriched along junctions with myosin-II. They then showed that maternal Src42A depletion exhibits phenotypes, starting with cellularization and including a defect in germband extension. The authors focus on defects in germband extension and found that Src42A was required for timely rearrangement of junctions and that the Src42A RNAi phenotype is enhanced by Abl RNAi. Finally the authors show that E-cadherin turnover is affect by Src42A depletion.

Overall, this study provided a higher resolution description of how Src42A regulates the behavior of junctions during germband extension. I thought the authors conclusions were well supported by the data and represent new insight in the field.

Reviewer #2 (Evidence, reproducibility and clarity (Required)):

Summary: Chandran et al. investigate the role of Src42A in axis elongation during Drosophila gastrulation. Using maternal RNAi and CRISPR/Cas9-induced germline mosaics, they revealed that Src42A is required to contract junctions at anterior/posterior cell interfaces during cell intercalations. Using time-lapse imaging and image analysis, they further revealed the role of Src42A in E-Cad dynamics at cell junctions during this process.

By analyzing double knockdown embryos for Src42A and Abl, they further showed that Src42A might act in parallel to Abl kinase in regulating cell intercalations. The authors proposed that Src42A is involved in two processes, one affecting tension generated by myosin II and the other acting as a signaling factor at tricellular junctions in controlling E-Cad residence time. Overall, the data are clear and nicely quantified. However, some data do not convincingly support the conclusion, and statistical analyses are missing for an experiment or two. Methods for several quantifications also need improvement in writing. Also, several figures (Figures 6-8) do not match the citation in the text and need to be corrected.

Page and line numbers were not indicated in the manuscript. For my comments, I numbered pages starting from the title page (Title, page 1; Abstract, page 2, Introduction, pages 3-6; Results, pages 7-14; Discussion, pages 15-18; M&M, 19-23; Figure legends, 28-30) and restarted line numbers for each page. For Figures 6-8 that do not match the citation in the text, I still managed to look at the potentially right panels. All the figure numbers I mention here are as cited in the text. My detailed comments are listed below.

Response:

We apologize for the lack of organization of the manuscript and the figure numbering. In the revised version we have added page numbers, line numbers and we corrected the figure numbers.

Major comments: 1. b-Cat/E-Cad signals at the D/V and A/P junctions in Src42Ai (Figs. 5-6). These data are critical for their major conclusion and should be demonstrated more convincingly.

In Fig. 5A, the authors said, "When the AP border was cut, the detached tAJs moved slower in Src42Ai embryos compared to control (Fig. 5A)". However, even control tAJs do not seem to move that much in the top panels, and I found the images not very convincing.

Response:

We thank the referee for commenting on the lack of clarity in the presentation of the data. The overall movement within the first 10 seconds after the laser cut (determined by movement of adjacent D/V tAJs from each other) was about 2 µm in the wildtype, while in the mutant it was 1 µm. Despite this 50% difference, it may be difficult to appreciate this difference from looking at Fig. 5A in our original submission. The yellow lines in Fig 5A only showed the region of the cut, but did not indicate the movement of the tAJ from each other, which may have led to a distraction from the actual movement. We will change the annotation and the marks within the figure to visualize the movement much more clearly in the full revision. In the fully revised manuscript, we will also add movies from the experiments including marks of the tricellular junctions to follow the displacement as part of the Supplemental Material.

Based on the genetic interaction between Src42A and Abl using RNAi (Fig. 7), the authors argue that Src42A and Abl may act in parallel. However, the efficiency of Abl RNAi has not been tested. It can be done by RT-PCR or Abl antibody staining. Also, the effect of Abl RNAi alone on germband extension should be tested and compared with Src42A & Abl double RNAi embryos. I expect the experiments can be done within a few weeks without difficulty.

Response:

We agree with the referee that it is important to determine the level of depletion in Abl RNAi embryos in order to interpret the genetic relationship between Abl and Src42A. In the full revision of the manuscript, we will follow the advice of the referee and analyze the knockdown, preferably by antibody labeling with an anti-Abl antibody. We will also generate single knockdowns of abl in embryos and determine their effect on germband extension compared to wildtype and src42/abl double knockdown.

Minor comments:

Fig. 2 - Fig. 2B: Higher magnification images of the defective cytoplasm can be shown as insets.

Response:

We will add some higher magnification images of the cellularization phenotype in the full revision of the manuscript. In addition, as mentioned in the response to reviewer #1, we will provide a more detailed analysis of the cellularization in src42Ai embryos in the fully revised manuscript.

• Fig. 2E: A simple quantification of the penetrance of cuticle defects in Src42A mutants and RNAi will be helpful, as shown in Fig. S3.

Response:

In the full revision, we will add the quantification of the occurrence of the different classes of cuticle phenotypes.

Fig. 9 - Fig. 9A: Magnified views of the cytoplasmic clearing can be added as insets.

Response: As described in our response to the comments made by referee #1, we will add a more detailed analysis of the cellularization phenotype in the full revision.

Page 14, lines 9-10: More explicit description of the phenotype rather than just "stronger compared to Src42Ai" will be helpful.

Response:

In the full revision, we will add a more detailed description of the phenotype and re-analyze and present data on the hatching rate, stage of lethality and cuticle phenotypes.

Reviewer #2 (Significance (Required)): This work revealed the role of Src42A in regulating germband extension. A previous study suggested the roles of Src42A and Src64 in this developmental process using a partial loss of both proteins (Tamada et al., 2021). Using different approaches, the authors demonstrated a role of Src42A in regulating E-Cad dynamic at cell junctions during Drosophila axis elongation. Most of the analyses were done with maternal knockdown using RNAi, but they successfully generated germline clones for the first time and confirmed the RNAi phenotypes. Overall, this work contains important and exciting novel findings. This work will be of general interest to cell and developmental biologists, particularly researchers studying epithelial morphogenesis and junctional dynamics. I have expertise in Drosophila genetics, epithelial morphogenesis, imaging, and quantitative image analysis.

Reviewer #3 (Evidence, reproducibility and clarity (Required)):

Chandran et al. report on the function of Src42A during cell intercalation in the early Drosophila gastrula. They create a Src42A-specific antibody (there are two Src genes in the fly genome) and examine the localization of Src42A and observe a planar-polarized distribution at cell interfaces. They then measure cell-contractile dynamics and show that T1 contraction is slower after Src42A disruption. The authors then argue that Src42A functions in a parallel pathway to the Abl protein, and that E-cadherin dynamics (turnover) is altered in Src42A disrupted embryos. Src function at these stages has been studied previously (though not to the degree that this study does), and in some respects the manuscript feels a little preliminary (please label figures with figure number!), but after editing this should be a polished study that merits publication in a developmentally-focused journal.

1) Does the argument that Src42A has two functions fully make sense? Myosin II function is known to affect E-cadherin stability (and vice versa), so it seems that Src42A could affect both MyoII and Ecad by either decreasing Myosin II function/engagement at junctions or by destabilizing Ecad.

Response:

We thank the referee for raising an important point that we may not have discussed appropriately in our initial submission. We agree that the reciprocal relationship between actomyosin and E-cadherin might not be reflected equivocally in our manuscript. As the referee points out, Src42A could affect both MyoII planar localization and E-cadherin dynamics through the same pathway. Previous studies showed that Src is involved in translating the planar polarized distribution of the Toll-2 receptor by recruiting Pi3-Kinase activity to the Toll-2 receptor complex resulting in planar polarized distribution of MyoII at the A/P interfaces. These data, however do not address the possibility that a well-known Src target, the E-cadherin/ß-Catenin complex, which is extensively remodeled in germband extension contributes to the delay in germband extension. The observed defects in both studies can be attributed to both a defect in abnormal planar polarization of MyoII and the abnormal dynamics of the E-cadherin/ß-catenin complex. In either of these cases, we suggest that Src42A phosphorylates distinct substrates, the Toll-2 intracellular domain in the MyoII planar polarity pathway and the E-cad/ß-Cat complex controlling E-cad dynamics. Given the relationship between MyoII and E-cadherin, however, it is not possible to decide whether these two effects are independent functions of Src42A or are consequences of each other. Since we cannot resolve a possible epistatic relationship between these potential two activities of Src42A, we decided to extend the discussion on this topic by taking both possible scenarios into account and discussing them appropriately. We will add this discussion in the full revision of the manuscript.

) One obvious question that arises is the nature of cleavage defects that are mentioned that happen previously to intercalation. For example, is E-cad normal prior to intercalation initiating? How specific are the observed defects to GBE?

Response:

please see response to referee #1

3) Pg. 10, "the shrinking junction along the AP axis strongly reduces its length with an average of 1.25 minute" - what is this measurement? How much is "strongly"?

Response:

We thank the referee for pointing out our inappropriate qualitative statement of the experimental data, which was indeed misleading. The measurement of the shrinking junction was based upon the time it takes for the AP interface junction between two adjacent vertices on the DV axis to shrink into a single 4-cell vertex. The time for this contraction was on average 1 minute 25 seconds. The data in Fig.4 A’,C show that after 2 minutes in the control embryo 100% of the observed AP junctions have collapsed and the extension of the new DV junction along AP axis has begun. At the same timepoint of 2 minutes in the src42A knockdown, we show in Fig. 4B’,D that the shrinking of the AP junction interface has still not been completed in 60% of the cases.

In the full revision, we will remove the qualitative statement and replace it with a correct description of the measurements taken and will refer to the data described in Fig. 4 A-D.

4) Also pg. 10, "the AP junction was not markedly reduced after 1 minute" - what is the criteria for this statement? X%? 1 minute is very specific, it feels like how much of a reduction/non-reduction should also be specific.

Response:

please see response to point 3.

Reviewer #3 (Significance (Required)):

This study gives a more detailed perspective on how Src proteins (Src42A in Drosophila) control epithelial stability and the contraction of specific surfaces of epithelial cells.

Description of the revisions that have already been incorporated in the transferred manuscript

Reviewer #2 and #3 noted that the manuscript was somewhat unorganized with regard to lacking the numbering of pages, lines and figures. We also noted that in the submission process the figures were not presented in the correct order. In the preliminary revision of the manuscript, we fixed these problems to facilitate the evaluation of our transferred manuscript by editorial boards.

In addition, we also addressed issues that the referees mentioned by editing the text according to their comments. We also addressed problems regarding the presentation of the figures and statistical analyses of the data. The following changes were made:

1. We added page numbers and line numbers.
2. We added figure numbers to the figure panels.
3. We corrected ordering of figures in the transferred manuscript.
4. We addressed the following comments by statistical analyses, editing the text and the figures:

   Regarding comments from Reviewer #1:

Highest Priority:

2) There is a discrepancy in the staging of embryos used between some of the analyses, which make it hard to interpret some of the data. For example, characterization of the knockdowns in Fig. 1A and B are based on stages 10 and 15, whereas the majority of the paper is focused on earlier stages 6 - 8 during germband extension (e.g., Fig. 1D). The analysis for Fig. 1B would be more meaningful if it was done on the same stages used for subsequent phenotypic analysis so they can be directly compared.

Response:

We thank the referee for pointing out an apparent misunderstanding caused by the description of Fig. 1A,B. The data presented in Fig.1A and 1B do not show RNAi knockdown experiments, but show a comparison between embryos that are heterozygous or homozygous for the loss-of-function allele src42A26-1. These data were intended to demonstrate that zygotic mutants still maintain levels of maternal Src42A protein up until late stages of development. Data for embryos at an earlier stage (stage 5) were shown in the Supplementary Fig. S1E, where no difference in protein levels of Src42A can be observed between heterozygous and homozygous zygotic src42A26-1 embryos.

At the beginning of the results sections 1 and 2 of the preliminary revised manuscript, we added a sentence to address the referee’s concern that earlier stages exhibit no difference in protein levels and will refer to Fig. S1E. We also more explicitly spelled that out that the experiment (referring to Fig.1A,B and S1) was intended to look at zygotic mutants and to demonstrate that our novel Src42A antibody was able to detect the reduction of maternal Src42A protein in mid- to late-stage homozygous zygotic embryos.

3) There is incongruence between figures in terms of which junctional pools (bAJs vs. tAJs) of beta-catenin and E-cadherin are quantified that makes it difficult to draw comparisons between analyses. For example, pTyr levels are examined for both bAJs and tAJs in Figure 3, however, only tAJs are considered in Fig. 8. Similarly, in some cases planar cell polarity is considered (e.g., comparison of levels at AP vs DV bAJs in Fig. 6 and 9), and in other cases (e.g. Fig. 8) it is not.

Response:

We thank the referee for commenting on the different readouts for different pools of cell junctions in our experiments. In our study we considered effects on src42A on both, bAJs and tAJs by RNAi knockdown of src42A. We decided to present the data for bAJ and tAJ in separate figures for clarity and structure. For example, the data for the effect of src42A knockdown on the planar polarized distribution on bAJs of E-cadherin were presented in Fig.6, while the effect on E-cadherin residence time in tAJs were presented in Fig.8. The analysis pTyr levels considered both pools in order to determine whether src42A knockdown leads to an overall reduction of pTyr levels or to a reduction in a specific junctional pool. From our data we conclude that pTyr levels show a similar reduction in both, the bAJ and the tAJ junctions.

In order to address the reviewer’s comment, we have linked the figures more stringently with the results text of the preliminary revision. We only referred to the reduction in PTyr levels in Fig. 3 to point out that both junctional pools are affected by reduced PTyr in src42i embryos. Furthermore, we referred to the individual figure panels when addressing junctional pools and explain the rationale to focus on particular pools (bAJs or tAJ) in the experiments in detail. For Fig. 6 we point out in the preliminary revised manuscript that we focus the analyses on the known planar polarized distribution of beta-catenin and E-Cadherin.

Lower priority: 1) Introduction, 2nd paragraph - The modes of cell behaviors described to drive cell intercalation leaves out another clear example in the literature - Sun et al., 2017 - which describes a basolateral cell protrusion-based mechanism. While the authors cite this paper later, leaving it out when summarizing the state of the field misrepresents the current knowledge of the range of mechanisms responsible.

Response:

We thank the referee for this remark. In the preliminary revision, we have added to the introduction that the cell behaviors associated with germband elongation include apical and basolateral rearrangements of the cells indicating that basolateral protrusions also contribute to the set of mechanisms that drive germ band elongation.

2) 'defective cytoplasm' - this term is confusing, and could perhaps be replaced with 'cellularization defect', or something similar.

Response:

We agree that the term we applied for the cellularization defect may be misleading. The observation, we intended to describe with the term was a defect in the cytoplasmic clearing which occurs in the last syncytial division and the beginning of the cell formation process. We changed the description of this observation according now refer to the defect in the preliminary revised manuscript as ‘cytoplasmic clearing defect’.

3) Tests of statistical significance are not uniformly applied across the figures. For instance, Figures 3G + H indicate statistical significance, but Fig. 3D + E do not. Performing statistical tests throughout the paper, or clearly articulating a rationale when they are not used, would strengthen the manuscript. Specifically, the authors should consider this for Fig. 3D + E, and Fig. 7D + E, to support their arguments that rates of germband extension are different between conditions.

Response:

We agree with the reviewer and have provided statistical analysis for the data displayed in Fig. 3D,E and Fig. 7D,E in the preliminary revision of the manuscript.

4) Page 12 - "We found that Src42A showed a distinct localization at the tAJs (Fig. 1B)": Figure 1B shows a quantification of levels at bAJs, not tAJs.

Response:

In the preliminary version of the revised manuscript, we added a quantification of the localization of Src42A at the tAJs as a part of Suppl Fig. S4. In Fig. S4A-C we show that Src42A is enriched in comparison to the bAJs.

Regarding comments from reviewer #2:

Major Comments:

In Fig. 6A, b-Cat signals look fuzzier and dispersed and have more background signals in the control, compared to the Src42Ai background. Also, b-Cat signals in the control image do not seem to show enrichment at the D/V border, as shown in Tamada et al., 2012.

Response:

We agree with the referee that the image in Fig. 6A for the control is fuzzier and looks dispersed. This is due to the fixation method that we used. In this experiment we did not apply heat fixation, but used formaldehyde fixation in which b-catenin protein, in addition to the junctional pool, is also maintained in the cytoplasm creating the fuzzy cytoplasmic staining. We chose to do this in order to be able to co-immunolabel the embryos with b-catenin and E-cadherin antibodies; the latter staining is not working with the heat fixation applied in the Tamada et al. 2012 paper. Despite the slightly lower quality of the staining, the quantification of the data clearly indicated an effect of src42A knockdown on the planar polarized distribution of E-cad/b-cat complex does show an enrichment. In the preliminary revision added a note to the figure legend to indicate the fact that the fixation procedure was not optimized for b-catenin junctional staining. In the preliminary revision we also added a quantification of live imaging data recording E-cadherin-GFP in wild-type and src42Ai embryos. We present these additional data in Fig. S5 in the preliminary revision of the manuscript. These data are consistent with the results in Fig. 6 from the immunolabeling and support our conclusion that E-cadherin AP/DV ratio is increased in Src42A knockdown embryos.

In Fig. 6B, C, it is not clear how the intensity was measured and how normalization was done. Was the same method used for these quantifications as "Protein levels at bicellular and tricellular AJs" on pages 21-22? Methods should be written more explicitly with enough details.

Response:

We thank the referee for pointing out the lack of detail in explaining how the quantification was done. In the preliminary revision of the manuscript, we extended a paragraph entitled ‘Protein levels at bicellular and tricellular junctions’ in the methods section that will serve this purpose and describe the methods that were applied for each quantification and the method as to how the data were normalized.

Does each sample (experimental repeat) for the D/V border in Fig. 6B match the one right below for the A/P border in Fig. 6C? It should be clearly mentioned in the figure legend. The ratio of the DV intensity to AP intensity will better show the compromised planar polarity of the b-Cat/E-Cad complex.

Response:

We thank the reviewer for pointing out a lack of clarity in our presentation. The experimental repeats for each measurement do indeed match, i.e. the measurement of the DV border matches the same adjacent 4-cell pair in the same embryo and in total 5 distinct embryos were analyzed for each experiment. In the preliminary revision of the manuscript, we explain this detail of the experimental design in the figure legend. In the preliminary revision, we also determined the ratios of DV/AP cell interfaces for b-Cat and E-Cad and added this quantification as panel 6C and 6E for a clearer presentation of the data.

Minor notes: Page 4, missing comma after "For example"

Response: The text was edited accordingly.

Page 4, "inevitable" does not make sense in this context

Response: We eliminated ‘inevitable’ and replaced it with ‘critical’ to better indicate the importance of Canoe protein for germband elongation.

Page 7, lines 6-7 - The localization of Src42A in control should be described in more detail and more clearly here.

Response: In the preliminary revised manuscript, we extended our description of the distribution of Src42A in more detail pointing out its dynamics and differential distribution at distinct plasma membrane domains.

Supplemental Fig S1 - Fig. S1D: Based on the head structure and the segmental grooves, the embryo shown here is close to late stage 13/early stage 14, not stage 15. - Fig S1E: It will be helpful if the predicted protein band and non-specific bands are indicated by arrows/arrowheads in the figure.

Response:

We thank the referee for their careful observation of the embryonic stage. We agree that the embryo was actually a younger stage. In the preliminary revision, we replaced the images with an example of an older stage. We will also add clear annotations as arrows to clearly mark the specific protein bands in Fig. S1E.

Page 7, lines 21-22 - "Src42A was slightly enriched at the AP interface" - To argue that, quantification should be provided.

Response:

We thank the referee for pointing out a qualitative statement that we made with regard to the distribution of Src42A at the AP cell interfaces. In the preliminary revision of the manuscript, we present an additional quantification of the imaging data of Src42A immunolabeling. In Figure S4A-C, we now present a quantification of the enrichment of Src42A at the tricellular junctions. In addition, the new Fig. S4D,E shows a quantification of the planar polarized distribution of Src42A at the AP cell interfaces.

Figure 1 - Fig. 1B: Src42A levels should be compared between control (Src42A/+) and Src42A/Src42A for each stage. It currently shows a comparison between Src42A/Src42A of stages 10 and 15.

Response:

We thank the referee for the comment. As indicated in our response to referee #1, the point of this analysis was to (1) provide evidence for the specificity of our new anti-Src42A antibody and (2) to demonstrate the presence of substantial material contribution of Src42A protein in zygotic mutant. We do not see the advantage to provide a detailed developmental Western-blot analysis, but we provide data in Suppl. Mat Fig S1E showing that the level of Src42A is unimpaired in stage 6 zygotic src42A[26-1] homozygous mutant embryos.

• Fig. 1B: The figure legend says, "dotted line represents mean value and error bars," but there are no dotted lines shown in the figure. Also, what p-value is for ****? It should be mentioned in the figure legend. It also says Src42A levels were normalized against E-Cad intensity here (stages 10 and 15). They have shown that E-Cad levels are affected in Src42A RNAi during gastrulation (Fig. 6). Is E-Cad not affected in Src42A26-1 zygotic mutants at stages 10 and 15?

Response:

We thank the referee for pointing out inaccuracies in the presentation and the description of Fig.1B. In the preliminary revision, we emphasized the marks on the graph and provide p-values throughout. Regarding the E-Cadherin levels: E-cadherin levels were altered in src42A RNAi knockdown embryos, but not in zygotic mutants, even at later developmental stages.

Page 8, line 14 - "Embryos expressing TRiP04138 showed reduced hatching rates with variable penetrance and expressivity depending on the maternal Gal4 driver used (Fig. 2B)" - Fig. 2B doesn't seem to be a right citation for this sentence.

Response:

We agree with the referee and in the preliminary revised manuscript we corrected the reference to the conclusion drawn from Figure 2A’, which does show the relationship of hatching rate to the various maternal Gal4 drivers.

• Fig. 2C: It will be helpful to indicate two other non-specific bands in the figure with arrows/arrowheads with a description in the figure legend.

Response:

In the preliminary revision, we added an arrow to mark the band specific for Src42A and asterisks to mark unspecific bands in Fig 2C.

Page 9, line 9 - This is the first time that the fast and the slow phases of germband extension are mentioned. As these two phases are used to compare the Src42A and Src42A Abl double RNAi phenotypes, they should be introduced and explained better earlier, perhaps in Introduction.

Response:

We thank the referee for pointing out that the two phases of germband extension were not introduced. We added a sentence to introduce and define the distinct phases of extension movements in the preliminary revision.

Fig. 3 - Fig. 3A: It will be helpful to mark the starting and the ending points of germband elongation with different markers (arrows vs. arrowheads or filled vs. empty arrowheads).

Response:

In the preliminary revision, we added distinct markers to indicate the start and endpoints of germband elongation to make this figure easier to read.

• Fig. 3C figure legend: R2 is wrongly mentioned in Fig. 3D, E. Also, R2 (coefficient of determination) needs to be defined either in the figure legend or Materials & Methods.

Response:

We thank the referee for pointing this misleading reference to us. In the preliminary revision we corrected the reference to R2 in Fig,3D,E and will describe the definition of R2 in the figure legend.

• Fig. 3D, E: statistical analysis is missing.

Response:

In the preliminary revision, we included a statistical analysis of the data (see ref #1). We changed the figure to indicate the data sets that were analyzed and added the p-values to the figure legend.

• Fig. 3G and H should be cited in the text.

Response:

In the preliminary revision, we added references to Fig 3G,H in the result section to the annotation of Fig.3F).

• Fig. 3F: It should be mentioned that the heat map is shown for pY20 signals in the figure legend, with an intensity scale bar in the figure.

Response:

In the preliminary revision, we added an intensity scale bar to the figure panel and mentioned the relationship to the PY20 signal.

Fig. 7A: Arrows can be added to mark the delayed germband extension.

Response:

In the preliminary revision, we added arrows to mark the anterior and posterior extent of the germband.

Fig. 8A: It should be mentioned that the heat map is shown for E-Cad signals in the figure legend, with an intensity scale bar in the figure.

Response:

In the preliminary revision, we added an intensity scale to the heat map and mention the relationship to the E-cadherin signal in the figure legend.

Fig. S3G: An arrowhead can be added to the gel image to indicate the band described in the legend.

Response:

In the preliminary revision, we added an arrow to help annotating the Src42A-specific bands on the Western blot.

• Fig. 9B: Arrow/arrowheads can be added to show the absence of the signals in the nurse cells.

Response:

In the preliminary revision, we added markers to help recognizing the reduced signal in the nurse cells and the oocyte.

• Fig. 9C: Indicate the ending point of the germband extension by arrows.

Response: In the preliminary revision, we added arrows to mark the anterior and posterior extent of the germband.

Regarding comments from reviewer #3:

Minor notes: Page 4, missing comma after "For example"

Response: The text was edited accordingly.

Page 4, "inevitable" does not make sense in this context Response:

In the preliminary revision, we eliminated ‘inevitable’ and replaced it with ‘critical’ to better indicate the importance of Canoe protein for germband elongation.

Description of analyses that authors prefer not to carry out

Referee #1 point2 and Referee#2 minor comment figure 1. Both referees suggest that figure 1 AB should include earlier developmental stages according to the stages looked at in the RNAi knockdown experiment.

Response:

The referees’ comments are likely based on a misunderstanding. The data that the reviewer are referring to present analyses of the zygotic phenotype of embryos homozygous for the src42A26-1 loss of function allele. They are not related to the maternal RNAi knockdown experiments, but were meant to demonstrate the existence and extent of a maternal pool of Src42A protein, that persists even to late stages in development. The maternal knockdown mutants are analyzed in detail at the appropriate stages in Fig. 2.

As described in our response above, we don’t feel that a detailed developmental stage Western analysis of wildtype and src42A26-1 embryos would provide significant additional insights. As mentioned in our response above, data for an earlier developmental stage (before germband elongation, as requested by the referees, were provided in Suppl. Fig. S1E.

Referee #1 Point 6) Figure 8E - showing images of multiple tAJs, rather than z-slices of a single vertex, would better support the claim here, as the assertion is that Src42a levels are different between control and sdk RNAi conditions, and not that it varies in the z-dimension.

Response:

The image series of Fig. 8E shows one representative example of multiple tAJs that have been imaged for this experiment (n=6 for wild type and n=10 for sdk RNAi). We think that the presentation of Z-slices for this experiment is important as the protein distribution needs to be considered for a larger area along the apical-lateral cell interface. In addition the quantification of the data for multiple tAJs was presented in Fig. 8F,G as a graph. We would therefore rather not change this figure in the revised manuscript.

Referee #3 suggests that anti MyoII staining should accompany the analysis of tension measurements in the germband.

As this analysis has already been performed by Tamada et al. 2021, we decided not to reproduce these data, but rather extend the analysis towards tension measurements, which support the findings by Tamada et al. 2021 on a functional level. We do not see the added value of adding MyoII labeling.

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

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Reply to the reviewers

Manuscript number: RC-2021-01016

Corresponding author(s): Dennis Klug

1. General Statements [optional]

Dear editor, dear reviewers,

thank you very much for the quick review of our manuscript as well as for the constructive criticism and the interesting discussion of our results. Reading the comments, we realized that we may have put too much emphasis on the in vivo microscopy of sporozoites and their interaction with the salivary gland. We believe that the generated mosquito lines can be used to address different scientific questions, the in vivo microscopy of host-pathogen interactions being only one of them. Because of this imbalance, and to address some of the reviewers' comments, we have partially rewritten the manuscript (particularly the introduction). At the same time, we have implemented additional data on the inducibility of the promoters used, as well as on the functionality of hGrx1-roGFP2 in the salivary glands. Furthermore, we created an additional figure to better present the expression patterns of trio and saglin promoters within the median lobe, and we expanded the section on in vivo microscopy of sporozoites. We hope that these results further highlight the significance of our study. Accordingly, we have also changed the title of the manuscript to „A toolbox of engineered mosquito lines to study salivary gland biology and malaria transmission” to indicate the broad applicability of the generated mosquito lines and we have included an additional co-author, Raquel Mela-Lopez, who conducted the redox analysis. We hope that these changes will adequately answer the questions of the reviewers and address any concerns they may have had. We look forward to hearing from you.

With our kind regards,

Dennis Klug

Katharina Arnold

Raquel Mela-Lopez

Eric Marois

Stéphanie Blandin

2. Point-by-point description of the revisions

Reviewer #1 (Evidence, reproducibility and clarity (Required)):

**Summary**

This manuscript reports the generation and characterization of transgenic lines in the African malaria mosquito Anopheles coluzzii that express fluorescent proteins in the salivary glands, and their potential use for in vivo imaging of Plasmodium sporozoites. The authors tested three salivary gland-specific promoters from the genes encoding anopheline antiplatelet protein (AAPP), the triple functional domain protein (TRIO) and saglin (SAG), to drive expression of DsRed and roGFP2 fluorescent reporters. The authors also generated a SAG knockout line where SAG open reading frame was replaced by GFP. The reporter expression pattern revealed lobe-specific activity of the promoters within the salivary glands, restricted either to the distal lobes (aapp) or the middle lobe (trio and sag). One of the lines, expressing hGrx1-roGFP2 under control of aapp promoter, displayed abnormal morphology of the salivary glands, while other lines looked normal. The data show that expression of fluorescent reporters does not impair Plasmodium berghei development in the mosquito, with oocyst densities and salivary gland sporozoite numbers not different from wild type mosquitoes. Salivary gland reporter lines were crossed with a pigmentation deficient yellow(-) mosquito line to provide proof of concept of in vivo imaging of GFP-expressing P. berghei sporozoites in live infected mosquitoes.

**Major comments**

Overall the manuscript is very well written with a clear narrative. The data are very well presented. The generation of the transgenic mosquito lines is elegant and state-of-the art, and the new reporter lines are thoroughly characterized.

This is a nice piece of work that is suitable for publication, although the in vivo imaging of sporozoites is somewhat preliminary and would benefit from additional experiments to increase the study impact.

We would like to thank the reviewer for his/her appreciation of our manuscript. In the revised version, we have included additional experiments on in vivo imaging of sporozoites, which allowed us to quantify moving and non-moving sporozoites imaged under the cuticle of live mosquitoes. Although this is still a proof of concept, we believe that these new data provide novel interesting data and will better illustrate potential applications.

The reporter mosquito lines express fluorescent salivary gland lobes, yet the authors only provide imaging of parasites outside the glands. It would be relevant to provide images of the parasite inside the fluorescent glands.

We have now included images showing sporozoites inside the salivary glands in vivo in Figure 8C and discuss possible ways to further improve resolution and efficiency of the imaging procedure in lines 563-586.

The advantage of the pigmentation-deficient line over simple reporter lines is not clear, essentially due to the background GFP fluorescent in figure 5C. Imaging of GFP-expressing parasites should be performed in mosquitoes after excision of the GFP cassette under control of the 3xP3 promoter. This would probably allow to document the value of the reporter lines more convincingly.

Indeed, by incorporating two Lox sites in the transgenesis cassette, we designed the yellow(-)KI line to permit removal of the fluorescent cassette and completely exclude expression of the transgenesis reporter EGFP. Still, EGFP expression in the yellow(-)KI adults is restricted to the eye and ovary, as we show now in Figure 7 supplement 1D. In contrast, no EGFP fluorescence was observed in the thorax area (Figure 7 supplement 1D). Therefore, we believe that the benefit of removing the fluorescence cassette for this study is limited. Moreover, the generation of such a line would take at least 3-4 months before experiments could be performed. Nevertheless, we agree with the reviewer that removal of the fluorescence cassette would be instrumental for follow-up studies. To draw the reader's attention to this issue, we now discuss background fluorescence in lines 378-387.

Along the same line, it is unclear if the DsRed spillover signal in the GFP channel is inherent to the high expression level or to a non-optimal microscope setting. This is a limitation for the use of the reporter lines to image GFP-expressing parasites.

We have discussed this problem with the head of the imaging platform at our institute, and we believe that it is not a problem that occurs due to incorrect settings. Rather, it seems to be due to the significant expression differences of the two fluorescence reporters used. We agree with the reviewer that this is a limitation and discuss the problem now in lines 416-412 and 565-567.

The authors should fully exploit the SAG(-) line, which is knockout for saglin and provides a unique opportunity to determine the role of this protein during invasion of the salivary glands. This would considerably augment the impact of the study. In this regard, line 131 and Fig S3E: why is there persistence of a PCR band for non-excised in the sag(-)EX DNA?

We definitely share the reviewer's enthusiasm about saglin and its role in parasite development in mosquitoes. We have thoroughly characterized the phenotype of sag(-) lines with respect to fitness and Plasmodium infection. These results are described in a spearate manuscript currently in peer review and available as a preprint on bioRxiv (https://doi.org/10.1101/2022.04.25.489337). Furthermore, in the revised manuscript, we have included additional data on the transcriptional activity of the saglin promoter with respect to the onset of expression and blood meal inducibility (Figure 2). In addition, we have included a completely new Figure 3 to highlight the spatial differences in transcriptional activity of the saglin promoter compared with the trio promoter. These new data are commented in lines 206-276.

There might be a misunderstanding in the interpretation of the genotyping PCR. The PCR shown in Figure 1 – figure supplement 3, displays PCR products for different genomic DNAs (sag(-)EX, sag(-)KI and wild type) using the same primer pair. „Excised“ refers to sag(-)EX while „non excised“ refers to sag(-)KI and „control“ to wild type. Primers were chosen in a way to yield a PCR product as long as the transgene has integrated, only the shift in size between „excised“ and „non excised“ indicates the loss of the 3xP3-lox fragment. We have now changed the labeling of the respective gel in Figure 1 – figure supplement 3 to make this clearer.

Did the authors search for alternative integration of the construct to explain the trioDsRed variability?

We validated trio-DsRed cassette insertion in the X1 locus by PCR. The only way to rule out an additional integration of the transgene would be whole genome sequencing, which we did not perform. Still, we believe that the observed expression patterns are due to locus-specific effects of the X1 locus. Indeed, several lines of evidence point in this direction: (1) transgenesis was realized using the phage Φ31 integrase that promotes site-specific integration (attP is 38bp long and very unlikely to occur as such in the mosquito genome) and for which we never detected insertion in other sites in the genome for other constructs inserted in X1 and other docking lines; (2) additional unlinked insertions would have been easily detected during the first backcrosses to WT mosquitoes we perform in order to isolate the transgenic line and homozygotise it; (3) we have often observed variegated expression patterns for other transgenes located in the X1 locus in the past, leading us to believe that this locus is subjected to variegation influencing the expression of the inserted promoters. Usually, the variation we observe is simpler (e.g. strong and weak expression of the fluorescent reporter placed under the control of the 3xP3 promoter in the same tissues where it is normally expressed), but some promoters are more sensitive to nearby genomic environment than others, which we believe is the case for trio. Finally, should there be additional insertions of the transgenesis cassette in the genome, they should all be linked to the X1 locus as we would otherwise have detected them in the first crosses as mentioned above, which is unlikely. Thus, although very unlikely, we cannot exclude a single additional and linked insertion possibly explaining the high/low DsRed patterns, but variegation would still be required to explain other patterns. We have mentioned this alternative explanation in the manuscript in lines 522-524.

Line 254-255. Does the abnormal morphology of SG from aapp-hGrx1-roGFP2 result in reduced sporozoite transmission?

This is an interesting question. For future experiments, it could indeed be important to test if the transmission of sporozoites by the generated salivary gland reporter lines is not impaired. However, the quantification of the number of sporozoites in aapp-hGrx1-roGFP2 expressing salivary glands did not reveal any significant differences from the wild type (Figure 5 – figure supplement 1B) and would definitely be sufficient to infect mice. As we have no evidence for reduced invasion of sporozoites in the salivary glands of aapp-hGrx1-roGFP2 and of the DsRed reporter lines, no good reason to believe that the expression of fluorescent proteins would interfere with parasite transmission, and as we produced these lines as tools to follow sporozoite interaction with salivary glands, we have not performed transmission experiments.

Of note, we have now included images of highly infected salivary glands of all reporter lines in Figure 5 – figure supplement 1D to confirm that expression of the respective fluorescence reporter does not interfere with sporozoite invasion. Also we have not observed that sporozoites do not invade salivary gland areas displaying high levels of hGrx1-roGFP2.

**Minor comments**

-Line 51: sporogony rather than schizogony

Schizogony was replaced with sporogony.

-Line 56: sporozoites are not really deformable as they keep their shape during motility

This sentence was removed.

-In the result section, it is not clearly explained where constructs were integrated.

We have now included the sentence „...with an attP site on chromosome 2L...“ (line 173) and the respective reference (PMID: 25869647) to give more information about the integration site.

Line 106 and 434-435: for the non-expert reader, it is not clear what X1 refers to, strain or locus for integration?

X1 refers to both, the locus and the docking line. We have rephrased the beginning of the result section (previously line 106) to give more information about the integration site as mentioned above.

-Line 112-115: the rational of integrating GFP instead of SAG is not clearly explained here, but become clearer in the discussion (line

We have slightly rephrased the sentence to better explain the reasoning for this procedure (lines 182-184).

-Line 140: FigS2A instead of S3A

This mistake was corrected in the revised manuscript.

-Perhaps mention that GFP reporters (SG) might be useful to image RFP-expressing parasites.

We have now included an image of the aapp-hGrx1-roGFP2 line infected with a mCherry expressing P. berghei strain in Fig. 7D.

-Line 236: the authors cannot exclude integration of an additional copy (as mentioned in the discussion line 367-368).

As discussed above, we removed „..as a single copy...“ and introduced the possibility of an additional integration linked to X1 (lines 522-524).

-Line 257-258. The title of this section should be modified as SG invasion was not captured.

The title was rephrased. It reads now „Salivary gland reporter lines as a tool to investigate sporozoite interactions with salivary glands” (line 356-357).

-Line 287: remove "considerable number" since there is no quantification.

This was removed. In addition, we included new data in this section of the manuscript and rephrased the results accordingly (lines 406-427).

-Line 400-402: Klug and Frischknecht have shown that motility precedes egress from oocysts (PMID 28115054), so the statement should be modified.

Thank you for this suggestion. The passage was modified accordingly.

-Line 404: remove "significant number" since there is no quantification.

This section was rephrased and the phrase "significant number" was removed (lines 406-427).

-Line 497: typo "transgenesis"

The typo was correct in the revised manuscript.

-FigS1: add sag-DsRed in the title

Thank you for spotting this inconsistency, we corrected this mistake (line 1134).

-Stats: Mann Whitney is adequate for analysis in fig 2C but not 2B, where ANOVA should be used (more than 2 groups).

We have performed now an one-way-ANOVA test and adapted figure and figure legend accordingly.

Reviewer #1 (Significance (Required)):

This work describes a technical advance that will mainly benefit researchers interested in vector-Plasmodium interactions. Invasion of salivary glands by Plasmodium sporozoites is an essential step for transmission of the malaria parasite, yet remains poorly understood as it is not easily accessible to experimentation. The development of transgenic mosquitoes expressing fluorescent salivary glands and with decreased pigmentation provides novel tools to allow for the first time in vivo imaging in live mosquitos of the interactions between sporozoites and salivary glands.

Reviewer's expertise: malaria, Plasmodium berghei, genetic manipulation, host-parasite interactions

Reviewer #2 (Evidence, reproducibility and clarity (Required)):

The first achievements of the Klug et al. study are the (i) genetical engineering of the Anopheles coluzzii mosquitoes reared in insectarium, that stably express distinct fluorescent reporters (DsRed and hGrx1-roGFP2 and EGFP) under the putative "promoters" of genes reported to encode proteins expressed differentially in the pluri-lobal salivary glands(Sg) of anthropophilic blood-feeding adult females, (ii) the analysis of the promoter activity - based on the selected fluorescent reporter - with a primary focus on the salivary gland/Sg (including at the Sg lobe level) of the adult female but also considering the preimaginal developmental time with larvae and pupa samples. Of note, some data confirm the already reported time-dependent and blood meal-dependent promoter activity for the related Anopheles species. The last part presents preliminary dataset on live imaging of Plasmodium berghei sporozoites with the aim of highlighting the usefulness of these A. coluzzii transgenic

lines to better understand how the rodent Plasmodium sporozoites first colonize and then settle as packed cells in Sg acinar host cells.

**Major comments**

The two first objectives presented by the authors have been convincingly achieved with (i) the challenging production of four different lines expressing different single or double reporters chosen by the authors (and appropriately presented in the result text and figure sections), (ii) the careful analysis of the spatiotemporal expression of the DsRed reporter under two "promoters" studied and with regards to the blood feeding event parameter. However, if the reason why the authors have put so much effort in the production of their transgenic mosquitoes is (and as mentioned) to provide a significant improved setting enabling the behavioral analysis of sporozoites upon colonization and survival in the Sg, it seems this part is kind of limited. Likely in relation with this perception is the fact I found the introductory section often confusing and not enough direct to the points: in particular distinguishing the rationale from the necessity to produce appropriate models, and clarifying what is/are the added value(s) offered by these new transgenic lines models when compared to what exist (in Anopheles stephensi) with specific evidence that argue for this knowledge gain. At this stage, it is unfortunately not clear to me, what is the bonus of imaging the Plasmodium fluorescent sporozoites in hosts with fluorescent salivary gland lobes if one can not monitor key events of the Sg-sporozoite interaction that were not reachable without the fluorescent mosquito lines. Furthermore, it should be better explained why the rodent Plasmodium species has been chosen rather Plasmodium falciparum (or other human species) for which A. coluzzii is a natural host; may be just mentioning that this study would serve as a proof of concept but bringing real biological insights would be fine.

We would like to thank the reviewer for his/her evaluation of our manuscript, which has helped us clarify our manuscript on several points. Our goal here was a proof of concept demonstrating potential applications for the fluorescent salivary gland reporter lines and for the low pigmented yellow(-) line we generated. In vivo imaging of sporozoites in salivary glands is one possible application that we intended to use as proof-of-concept, but we tailored the manuscript too restrictively with this aim in mind and neglected other applications as well as characterization of the biology of salivary glands in general. To improve this, we have included further data on the blood inducibility of the promoters tested (Figure 2), the functionality of roGFP2 in the salivary glands (Figure 5), and the use of the generated lines in the examination and definition of expression patterns of salivary gland proteins in vivo (Figure 6). Accordingly, we have adjusted the entire manuscript to adequately describe all the results presented. We have also rephrased major parts of the abstract and the introduction to better describe the impact of salivary gland biology on the transmission of pathogens, and to explain the anatomy of salivary glands in more detail.

We agree with the reviewer that it would be desirable to show direct salivary gland-sporozoite interactions in vivo. Still we believe that having mosquito lines expressing a fluorescent marker in the salivary gland as well as weakly pigmented mosquitoes are a first step to make this visualization possible, although we cannot provide a lot of quantitative data about this interaction yet.

1- The three genes and gene products selected by the authors should definitively be more systematically explained, which means for example the authors need to introduce the different mosquito species and the parasite-mosquito host pairs they are then referring to for the promoter/encoded proteins of their interest. In the same vein, I did not find any information as to the choice of the mosquito species (A. Coluzzii) for the current work. I was curious to know what is the advantage since better knowledge was available with Anopheles stephensi with respect to (i) Saglin and its promotor activity, (ii) aap driven dsRed expression (lines already existing) and (iii) sporozoite-gland interaction.

We have largely reworded the introduction to clarify the rationale for selecting these three promoters while providing a better understanding of salivary gland biology in general.

The choice of the mosquito species depends, in our opinion, strongly on the perspective and on the experiments to be performed. We agree with the reviewer that the malaria mosquito A. stephensi is a widely used model, based on its robustness in breeding and its high susceptibility to P. berghei and P. falciparum infections. However, in these cases, both vector-parasite pairs are to some extend artificial. Indeed, although it is also a vector of P. falciparum in some regions, A. stephensi mostly transmits P. vivax that cannot be cultured in vitro. Thus research efforts on this vector-parasite pair is limited. Also, due to the emerging number of observed differences between Anopheles species and their susceptibility to Plasmodium infection and transmission, more research has recently been conducted on African mosquito species. This effect is also reinforced by the fact that P. falciparum, unlike all other Plasmodium species infecting humans, causes the most deaths, making control strategies for species from the A. gambiae complex such as A. coluzzii particularly important. As a result, the number of available genetic tools in A. coluzzi/gambiae has overpaced A. stephensi. These include mosquito lines with germline-specific expression of Cas9 for site-directed transgenesis, lines expressing Cre for lox-mediated recombination, and several docking lines. Such tools are, as far as we know, not available in A. stephensi and were essential in reaching our objectives. Docking lines are of particular interest because they allow reliable integration into a characterized locus, which is an advantage over random transposon-mediated integration. Random insertion sites have generally not been characterized in the past, which can cause problems since integrations regularly occur in coding sequences. Docking lines also enable comparison of different transgenes as they are all integrated in the same genetic environment, which does not ensure some expression variation as illustrated in our manuscript. For all these reasons, we have thus chosen to work with A. coluzzii.

Concerning the use of the murine malaria parasite P. berghei instead of the human one P. falciparum, there are two reasons that motivated our choice. (1) For in vivo imaging of sporozoites, we needed a parasite line that is strongly fluorescent at this stage, and there is no such line existing for P. falciparum. Actually, there is no fluorescent P. falciparum line able to efficiently infect A. coluzzii reported thus far, as reporter genes have all been inserted in the Pfs47 locus that is required by P. falciparum for A. coluzzii colonization. (2) Imaging P. falciparum infected mosquitoes, especially with sporozoites in their salivary glands, requires to have access to a confocal microscope in a biosafety level 3 laboratory. Hence our objective here was indeed to provide a proof of principle of in vivo imaging of sporozoites in the vicinity or inside salivary glands using our engineered mosquitoes, and to provide a first analysis of this process using P. berghei as a model of infection. Nevertheless, we agree with the reviewer that the goal should be to work as close as possible to the human pathogen.

Despite the wide range of topics that this study touches on, we want to try and keep the manuscript as concise as possible. Therefore, we have not discussed the advantages and disadvantages of the different vector-parasite pairs and ask the reviewer to indulge us in this.

2- To help clarifying the added value of the present study, introducing the species names of the mosquito and the Plasmodium that serve as a model would be appreciated.

We have included now the name of the used Plasmodium species in line 361. At this position we also give now more details about the transgene this line is carrying. We mention the used mosquito species A. coluzzii now at different positions in the manuscript (e.g. lines 52, 162 and 177).

3- Since a focus is the salivary gland of the blood feeding female Anopheles sp., a rapid description of the glands with different lobes and subdomains the results and figure 1 nicely refer to, would help in the introduction.

We explain now the anatomy of female and male mosquito salivary glands in the introduction (lines 119-123). The different lobes are now also indicated in the salivary gland images shown in several figures including Figure 1.

4- That description could logically introduce the few proteins actually identified with lobe specific or cell domain specific expression (apical versus basal side, intracellular or surface expose, vacuole, duct...) profiles. The context with regards to sporozoite biology would then easily validate the "promoter choice". As a minor remark, I miss the reason why the authors wrote " the astonishing degree of order of the structures (referring to the packing of sporozoites within the Sg acinars) raise the question whether sporozoite can recognize each other". Please clarify since packing/accumulation can be passive due to cell mechanical constraints and explain what this point has to see with the question and experimental work proposed here?)

We thank you for this suggestion. We have reworded key parts of the introduction to make the reasons for using the three selected promoters clearer. We also mention now other proteins expressed in the salivary glands which have been characterized in more detail because of their effect on blood homeostasis (e.g. anticoagulants) (lines 136-139).

The mention of stack formation of salivary gland sporozoites served only to clarify that almost nothing is known about the behavior of sporozoites within the salivary glands in vivo to explain why new methods are needed to make these processes visible. We have now reworded this passage to make this clearer, and we also mention that stack formation could also occur due to mechanical constraints, as suggested by the reviewer (lines 101-102, 106-110).

5- The selection of hGrx1-roGFP2 is quite interesting and justified but there is then no use of this reporter property in the preliminary characterization of the Sg and Sg-sporozoite interaction. Could the authors provide such characterization?

We have now implemented data testing the functionality of hGrx1-roGFP2 in the salivary glands. We also show qualitatively that the redox state of glutathione does not change upon infection with P. berghei sporozoites (Figure 6). We now describe and discuss these new data in lines 337-354.

6- Figure 1: it would be nice to add in the legend at what time the dissection/imaging has been made (age, blood feeding timing?). I would also omit the double mutant trio-Dsred/aapDsred in the main figure (may be supplemental) since the two single mutants Dsred separately together with the double mutant (with different fluorescence) already provide the information. I would suggest to regroup the phenotypic presentation of the transgenic line made in the KI mosquitoes (current figure 5) in the main figure 1.

We have now added the missing information about the age of dissected mosquitoes and their feeding status in the legend of Figure 1. We also thank the reviewer for the suggestion to replace one image displaying aapp and trio promoter activity in trans-heterozygous mosquitoes with an image of the pigment deficient mutant yellow(-)KI. Still, due to the changes made to the manuscript based on the reviewers comments in general, we have now implemented new data highlighting the functionality of the generated salivary gland reporter lines investigating the redox state of glutathione as well as the expression pattern of the saglin and trio promoters at the single cell level (see Figure 3 and 6). Therefore it would no longer seem logical to introduce the yellow(-)KI mutant in Figure 1 while further data on this mutant are provided in the last two figures of the manuscript and discussed later in the manuscript (Figure 7 and 8). In addition we believe that co-expression of different transgenes (carrying fluorescent reporters) in the median and the distal lobes could potentially be interesting for certain applications. We believe that readers who might actually be interested in combining both transgenes in a cross would like to see the outcome to better evaluate the usefulness before experiments are planned and performed. This is especially true because localization as well as expression strength may differ between different fluorescence reporters while using the same promoter (e.g. the hGrx1-roGFP2 construct appears less bright and more localized to the apex of the distal-lateral lobes than dsRed, while expression of both reporters is driven by the aapp promoter in aapp-hGrx1-roGFP2 and aapp-DsRed, respectively).

7- Figure 2:

1. a) Is there anything known on the Sgs' size change overtime. It seems that between day 1 and 2 there is an increase of size and volume as much as I can evaluate the volume (Fig S4). Could that mean that there is increase in cell number in the lobes and therefore more cells expressing the transgene which would account for the signal intensity increase rather than more transcripts per cell? Thank you for this interesting question. The changes in the morphology of the salivary glands in Anopheles gambiae following eclosion have been studied in detail by Wells et al., 2017 (PMID: 28377572) which we cite now in the introduction (line 122-123). According to this reference, cell counts of the salivary gland are not changing upon emergence of the adult mosquito. However, we agree with the reviewer that the glands appear smaller and differ in morphology directly after eclosion. We noted that glands of freshly emerged females are more „fragile“ during dissections and lack secretory cavities, as reported by Wells et al., 2017. We believe that the increase in size occurs through the formation and filling of the secretory cavities which has been reported to take place within the first 4 days after emergence (Wells et al., 2017). This observation is in accordance with our observations that the promoters of the saliva proteins AAPP and Saglin display only weak activity after hatching, or, in the case of TRIO are not yet active directly after emergence. The timing of the formation of the secretory cavities is also in agreement with our time course experiment (Figure 2) which shows a strong increase in fluorescence intensity in dissected glands within the first 4 days after emergence.

2. b) why choosing 24h after the blood meal to assess promoter activity in the Sgs? Do we have any information on how the blood meal impact on the Sgs'development. At this time anyway the sporozoites are far from being made. Yosshida and Watanabe 2006 mentioned at significant decrease of Sg proteins post-blood feeding. Could the authors detail their rationale based on what the questions they wish to address Thank you for this question. Unfortunately, the data available in the literature on this topic are very sparse, so we could only refer to few previous publications. The decision to quantify the fluorescence signals as early as 24 hours after blood feeding was based on Yoshida et al, Insect Mol. Biol, 2006, PMID: 16907827. The authors of this study generated the first salivary gland reporter line in A. stephensi by using the aapp promoter sequence to drive DsRed expression, and showed by qRT-PCR that DsRed transcripts increase 1-2 days after blood feeding compared to controls. Consistent with this observation and because we were concerned that putative changes in protein levels would only be visible for a short period of time, we began quantification one day after feeding. Since we observed significant changes in fluorescence intensity for the aapp-DsRed and sag(-)KI lines 24 hours after blood feeding, we retained the experimental setup and did not change it further. Nevertheless, we agree with the reviewer that different time points could help determine how long the effect lasts, and whether trio expression might also be regulated by blood feeding, but at a later time point. Still, our main objective here was to validate that the ectopic expression of DsRed driven by the aapp promoter in the aapp-DsRed line was indeed induced upon blood feeding as previously reported (PMID: 16907827). This experiment allowed us to confirm the inducibility of aapp in a different way and to show for the first time that saglin, but not trio, is induced one day after blood feeding. Our transgenic lines could be used for follow-up studies investigating the inducibility of salivary gland-specific promoters by different stimuli, or after infection with Plasmodium sporozoites. For example, for trio, transcription has been shown to increase after infection of the salivary gland by Plasmodium (PMID: 29649443).

8- Figure 3: The figure is quite informative in terms of subcellular localization. Concerning the section "Natural variation of DsRed expression in trio-DsRed mosquitoes", I think it could be shortened because because it is a bit out of the focus the study.

We agree with the reviewer that this part of the manuscript sticks a bit out and is not perfectly in line with the remaining results because it doesn’t deal with the salivary gland. Still, we would like to emphasise that in this work, we particularly want to show possible applications of the generated mosquito lines to address unanswered questions in host-parasite interactions and salivary gland biology. As a result, this manuscript establishes potentially important tools. For this reason, we feel it is important to mention the natural variation in DsRed expression, as this natural variation can have a significant impact on crossing schemes (especially with lines inheriting other DsRed-marked transgenes) and experiments (e.g. visualizing DsRed expression by western blot in larval and pupal stages). Furthermore, it is important for the use of the line to show that the transgene is inserted only once, at the expected location, which we try to emphasize with figure 4 – figure supplement 1 and figure 4 – figure supplement 2.

We would also like to note that transgenesis in Anopheles is a relatively young field of research and altered expression patterns of ectopically used promoters have rarely been described so far, although this could have major implications e.g. in the case of gene drives. Therefore, we hope that the data shown will bring this previously neglected observation more into focus and highlight the importance of accurate characterization of generated transgenic mosquito lines.

9- In contrast the last section of live imaging of P. berghei sporozoites in the vicinity and within salivary gland should be expanded. The 2 sentences summarizing the data are quite frustrating "We also observed single sporozoites moving actively through tissues in a back and forth gliding manner (Fig. 6B, Movie 3) or making contact with the salivary gland although no invasion event could be monitored"

We have now implemented new data and extended Figure 8 showing the results of the in vivo imaging in a qualitative manner. We have rephrased the result and discussion section accordingly.

10- I am aware of the technical difficulties to perform live imaging of sporozoite on whole mosquitoes, even when the salivary gland lobe under observation is closely apposed to the cuticle but that seems to be the final aim of the authors. I looked very carefully to the three movies and I am sorry but at this stage I could not make meaningful analysis out of them, and could not agree with the conclusions: for instances, the authors specify that sporozoites were undergoing back and forth movements (movie 3) but I do not see that and do not see the Sg contours in the available movies? The authors should also add bar and time scales to their movies. Having an in-depth description with regards to the sub-domain marked by a relevant reporter would strengthen the study, even if images are not collected in the whole mosquito to get higher resolution.

We thank the reviewer for this comment. We have to admit that parasite imaging in fluorescent salivary glands in vivo is an ambitious goal given the complex biological system we are working with. We believe that the system presented in our manuscript is a first and important step to enable the analysis of the interaction of sporozoites with salivary glands, although in-depth analysis will require further optimization and considerable time, especially to generate quantitative data. Therefore, we now downstate the significance of our results in this respect and changed the title accordingly. Still, we also provide a more detailed analysis of the data we have already collected (Figure 8 and lines 406-427). Because we focus on the analysis of sporozoites in the thorax area in the revised manuscript, the outlines of the salivary gland are not necessarily visible in the images.

I am not sure I understand the relevance of this quite condensed sentence in the text. Could the authors rephrase and expand if they wish to keep the issues they refer to. "The sporozoites' distinctive cell polarization and crescent shape, in combination with high motility, allows them to „drill" through tissues". I would stress more on the main unknown in terms of sporozoite-Sg interactions and the need to get right models for applying informative approaches (i.e. here, imaging).

We thank you for this suggestion. The sentence mentioned has been removed in its entirety. We have also adjusted the text accordingly and reworded most of the introduction to make the narrative clearer (lines 91-119).

Of note, it could help to point that the "Sgs is a niche in which the sporozoites which egress from the oocyst could mature and be fully competent when co-deposited with the saliva into the dermis of their intermediary hosts"

We have now implemented a similar sentence in the introduction (lines 93-98).

Reviewer #2 (Significance (Required)):

1- Clear technical significance with the challenging molecular genetics achieved in the mosquito A. coluzzii.

2- More limited biological significance: fair analysis and gain of knowledge of spatio-temporal of reporter expression under the selected promoter but limited significance of the final goal analysis which concerns the Plasmodium sporozoite biology once egressed from oocysts

As stated above, we changed the title to place the focus on the engineered mosquito lines.

3- Previous reports cited by the authors have used the DsRed reporter and the aap promoter in another Anopheles (i.e. A. stephensi, Yoshida and Watanabe, Insect Mol Biol, 2006; Wells and Andrew, 2019) which is also a natural host and vector for human Plasmodium spp.) with significantly more resolutive 3D visualization of GFP-fluorescent P. berghei but in dissected salivary glands and not in whole mosquitoes. The Wells and Andrew publication entitled "Salivary gland cellular architecture in the Asian malaria vector mosquito Anopheles stephensi" in Parasite Vectors, 2015 would deserve to be reference and described.

Thank you very much for this suggestion. We considered citing Wells and Andrews (PMID: 26627194). However, this reference focuses very specifically on the subcellular localization of AAPP and shows only highly magnified sections of immunostained dissected and fixed salivary glands. Working only with the AAPP promoter, we felt it important to refer to the previously observed expression pattern along the entire salivary gland, as shown in Yoshida and Watanabe (PMID: 16907827). Nevertheless, we have cited two other publications by Wells and Andrews (PMID: 31387905 and 28377572) at various points in the manuscript.

4- Audience: I would say that this work should be of interest of mostly scientists investigating Plasmodium biology (basic and field research) or in entomology of Diptera.

5- To describe my fields of expertise, I can refer to my extensive initial training in entomology including at one point in the genetic basis of mosquito-virus interaction. I have also been working for more than 20 years in the field of Apicomplexa biology (Plasmodium and Toxoplasma) and I have long-standing interest in live and static high-resolution imaging.

Reviewer #3 (Evidence, reproducibility and clarity (Required)):

Klug et al. generated salivary gland reporter lines in the African malaria mosquito Anopheles coluzzii using salivary gland-specific promoters of three genes. Lobe-specific reporter activity from these promoters was observed within the salivary glands, restricted either to the distal lobes or the medial lobe. They characterized localization, expression strength and onset of expression in four mosquito lines. They also investigated the possibility of influences of the expressed fluorescent reporters on infection with Plasmodium berghei and salivary gland morphology. Using crosses with a pigmentation deficient mosquito line, they demonstrated that their salivary gland reporter lines represent a valuable tool to study the process of salivary gland colonization by Plasmodium parasites in live mosquitoes. SG positioning close to the cuticle in 20% of females in this strain is another key finding of this study.

The key findings from this study are largely quite convincing. The authors have created a suite of SG reporter strains using modern genetic techniques that aid in vivo imaging of Plasmodium sporozoites.

Vesicular staining within salivary acinar cells should be stated as "vesicle-like" staining unless a co-stain experiment in fixed SGs is conducted using antisera against the marker protein(s) and antisera against a known vesicular marker (e.g. Rab11). It may also be possible to achieve this in vivo using perfusion of a lipid dye (e.g. Nile Red), but this is not necessary. As is, in Fig. 3A, there are images in which it appears that the vesicle-like staining is located both within acinar cells' cytoplasm and in the secretory cavities (e.g. Fig. 3A: aapp-DsRed bottom and middle), and this is fine, but should be more inclusively stated. Fixed staining of the reporter strain SGs would allow for clarification of this point. In previous work, other groups have observed vesicle-like structures in both locations (e.g. PMID: 33305876).

Thank you very much for this suggestion. Indeed, when we observed the vesicle-like localization, we had similar ideas and considered investigating the identity of the observed particles in more detail. Ultimately, however, we concluded that the localization of DsRed does not play a critical role in the use of the lines as such and believe that a more detailed investigation of the trafficking of the fluorescent protein DsRed is beyond the scope of this study.

We have thus followed the suggestion of the reviewer and now use the phrase „vesicle-like“ throughout the manuscript. In addition, we extended the discussion on the different localizations observed and presented some explanations that might have led to this observation. We also included a new reference that investigated the localization of AAPP using immunofluorescence (PMID: 28377572).

Morphological variation is extensive among individual mosquito SGs, thought to impact infectivity, and well documented in the literature. The manuscript should be edited to make it much clearer (e.g. n = ?) exactly how many SGs, especially in microscopy experiments, were imaged before a "representative" image was selected from each data point and in any additional experiment types where this information is not already presented. Figure S8 is an example where this was done well. Figure 3A-B is an example where this was not well done. All substantial variation (e.g. "we detected a strangulation..." - line 189) across individual SGs within a data point should be noted in the Results. Because of the genetics and labor involved, acceptable sample sizes for minor conclusions may be small (5-10), but should be larger for major conclusions when possible.

Thank you for this comment. We have improved this point by specifying precisely the number of samples and of repetitions in the respective figure legends. For example, we have now quantified the proportion of moving sporozoites and report both the number of sporozoites evaluated and the number of microscopy sessions required (see Figure 8).

Thank you for this comment. We have improved this point by specifying precisely the number of samples and of repetitions in the respective figure legends. For example, we have now quantified the proportion of moving sporozoites and report both the number of sporozoites evaluated and the number of microscopy sessions required (see Figure 8). Regarding Figure 3, fluorescence expression and localization in salivary gland reporter lines was actually very uniform in each line. We added the following sentence in the legend of revised figures 3 and 5: “Between 54 and 71 images were acquired for each line in ≥3 independent preparation and imaging sessions. Representative images presented here were all acquired in the same session”.

Sporozoite number within SGs has been shown to be quite variable across the infection timeline, by mosquito species, by parasite strain, in the wild vs. in the lab, and according to additional study conditions. The authors mention that the levels they observed are consistent with their prior studies and experience, but they did not utilize the reporter strains and in vivo imaging to support these conclusions, instead relying on dissected glands and a cell counter. It is important for these researchers to attempt to leverage their in vivo imaging of SG sporozoites for direct quantification, likely using the "Analyze Particles" function in Fiji. The added time investment for this additional analysis would be around two weeks for one person experienced in the use of the imaging software.

Thank you for this interesting suggestion. Indeed, it would be beneficial to use an imaging based approach to quantify the sporozoite load inside the salivary glands. We already used „watershed segmentation“ in combination with the „Analyze Particles“ function in Fiji on images of infected midguts to determine oocyst numbers. Still, we believe this analysis cannot be applied to images of infected salivary glands mainly because of differences in shape and location of the oocyst and sporozoite stages. Sporozoites inside salivary glands form dense, often multi-layered stacks. Because of this close proximity, watershedding cannot resolve them as single particles which could subsequently be counted. This creates an unnecessary error by counting accumulations of sporozoites as one, likely leading to an underestimation of actual parasite numbers. Furthermore, given that the proximity issue could be resolved e.g. by performing infections yielding lower sporozoite densities, another problem would be that infected salivary glands prepared for imaging are often slightly damaged leading to a leak of sporozoites from the gland into the surrounding. These leaked sporozoites are likely not included on images which would then be used for analysis, potentially leading again to an underestimation of counts. Since these issues are circumvented by the use of a cell counter, we believe that this method is still the method of choice in acquiring sporozoite numbers.

Nevertheless, we can understand the reviewer's concern that counts performed with a hemocytometer do not reflect the variability in the sporozoite load of individual mosquitoes. To highlight that all generated reporter lines can have high sporozoite counts, we have now included images of highly infected salivary glands for each line in Figure 7D.

This manuscript is presented thoughtfully and such that the data and methods could likely be well-replicated, if desired, by other researchers with similar expertise.

The statistical analysis is appropriate for the experiments conducted. It is currently unclear if some experiments were adequately replicated. That information should be added to the paper throughout where it is missing.

We do appreciate your comments on our efforts to give all required information for other laboratories to replicate our experiments. We have added the missing information about the number of independent experiments in the respective figure legends wherever appropriate.

Studies from multiple groups should be more thoroughly referenced when the authors are describing the "vesicle-like" staining patterns observed in SGs from reporter strains (e.g. Fig. 3A). Is this similar to the SG vesicle-like structures observed previously (e.g. PMIDs: 28377572, 33305876, and others)?

Thank you for this comment. We did not discuss this observation in detail in the first version of our manuscript because the observed localization was rather unexpected, as DsRed was not fused to the AAPP leader/signal peptide. The observed localization is therefore difficult to explain, however, we have expanded the discussion on this (lines 465-482) and now cite one of the proposed references (PMID: 28377572, lines 468-469).

There are minor grammar issues in the manuscript text (e.g. "Up to date" should be "To date"). The figures are primarily presented very clearly and accurately. One minor suggestion: In cases such as Fig. S2A images 3 and 6, where some of the staining labels are very difficult to read, please move all labels for the figure to boxes located directly above the image.

We are sorry for the grammatical errors we have missed in the first version of our manuscript. We have now performed a grammar check over the whole manuscript. We have also increased the font size of the captions in the above figures and tried to make them better readable by moving the captions over the images.

The data and conclusions are presented well.

Reviewer #3 (Significance (Required)):

This report represents a significant technical advance (improved in vivo reporter strain and sporozoite imaging), and a minor conceptual advance (active sporozoite active motility), for the field.

This work builds off of previous SG live imaging studies involving Plasmodium-infected mosquitoes (e.g. Sinnis lab, Frischneckt lab, etc.), addressing one of the major challenges from these studies (reliable in vivo imaging inside mosquito SGs).

This work will appeal to a relatively small audience of vector biology researchers with an interest in SGs. Many in the field still see the SGs as intractable, instead choosing to focus on the midgut due to ease of manipulation. Perhaps work like this will spark new interest in tangential research areas.

I have sufficient expertise to evaluate the entirety of this manuscript. Some descriptors of my perspective include: bioinformatics, SG molecular biology, mosquito salivary glands, microscopy, RNA interference, SG infection, and SG cell biology.

Reviewer #4 (Evidence, reproducibility and clarity (Required)):

Klug et al generated transgenic mosquito lines expressing fluorescent reporters regulated by salivary gland specific promoters and characterized fluorescent reporter expression level over the time, subcellular localization of fluorescent reporters, and impact on P. berghei oocyst and salivary gland sporozoite generation. In addition, by crossing one of the lines (aapp-DsRed) with yellow(-) KI mosquitoes, they open up the possibility to perform in vivo visualization of salivary glands and sporozoites.

Overall the generation and characterization of these transgenic lines is well-done and will be helpful to the field. However, there are several concerns with the in vivo imaging data shown in Figure 6, which does not convincingly show fluorescent sporozoites in the lobe or secretory cavity of a fluorescent salivary gland lobe. This needs to be addressed. Points related to this concern are outlined below:

(1) Although the authors mention that the DsRed signal was strong enough to see with GFP channel, it would be more appropriate to show that the DsRed signal from salivary glands and GFP channel image co-localize.

We now show a merge of the GFP and DsRed signal in Figure 7 – figure supplement 2 The yellow appearance of the salivary gland in the merge likely indicates the spillover of the DsRed signal into the GFP channel. In addition we discuss the issue in lines 416-412 and 565-567.

(2) Mosquitoes were pre-sorted using the GFP fluorescence of the sporozoites on day 17-21. From figure 4B, median salivary gland sporozoite number was about 10,000 sporozoites/mosquito on day 17-18. However, in Figure 6A there are no sporozoites in the secretory cavities. They should be able to see sporozoites in the cavities at this time. Can the authors confirm that they can visualize sporozoites in secretory cavities in vivo and perhaps show a picture of this.

This is entirely correct. We also examined mosquitoes for the presence of sporozoites in the salivary glands and wing joints prior to imaging, as shown in Figure 7B and Figure 7 – figure supplement 2A, to increase the probability that sporozoites could be observed. Nevertheless, the area of the salivary gland that comes to the surface is often small and limited to a few cells that can be imaged with good resolution. Unfortunately, these same cells were often not infected although other regions of the salivary glands must have been very well infected based on the previously observed GFP screening (Figure 7B). In addition, with the confocal microscope available to us, we struggled to achieve the necessary depth to image sporozoites in the cavities of the salivary gland cells. For this reason, we were often able to detect a strong GFP signal in the background, but not always to resolve the sporozoites sufficiently well. Still, we have now included an image showing sporozoites in salivary glands (Figure 8C). However, we believe that the method can be further improved to be more efficient and provide better resolution. We discuss possible ways to further improve the imaging in lines 563-586.

(3) There is no mention of the number of experiments performed (reproducibility) and no quantification of the imaging data. In the results (line 287-288), the authors state that sporozoites are present in tissue close to the gland and sometimes perform active movement. How can this be? Do they believe these sporozoites are on route to entering? More relevant to this study would be a demonstration that they can see sporozoites in the secretory cavities of the salivary gland epithelial cells, this should shown. If they have already performed a number of experiments, I would suggest to do quantification of the number of sporozoites observed in defined regions . The mention that sporozoites are moving is confounded by the flow of hemolymph. How do they know that the sporozoites are motile versus being carried by the hemolymph. Perhaps it's premature to jump to sporozoite motility in the mosquito when they haven't even shown sporozoite presence in the salivary glands.

Thank you very much for this comment. We have followed the suggestions of the reviewer and have now quantified the behavior of sporozoites in the thorax area of the mosquito. For the analysis, we only considered sporozoites that could be observed for at least 5 minutes. This analysis revealed that 26% of persistent sporozoites performed active movements, which in most cases resembled patch gliding previously described in vitro. We adjusted the results section accordingly. In addition, we have changed the figure legend to accurately indicate the number of experiments performed. Likewise, we now also provide an image of sporozoites that we assume are located in the salivary gland (Figure 8C). Although we have not yet been able to image and quantify vector-sporozoite interactions extensively (further improvements would be required, as mentioned previously), we believe these results illustrate the potential of the transgenic lines.

(4) In vivo imaging has been performed with the mosquito' sideways. Was this the best orientation? Have you tried other orientations like from the front (Figure 5B orientation).

It is true that in the abdominal view as shown in Figure 7B the fluorescence in the salivary glands is very well visible. This is mainly due to the fact that in this area the cuticle is almost transparent and therefore serves as a kind of "window". Nevertheless, the salivary glands are not close to the cuticle in this position, which makes good confocal imaging impossible. Imaging always worked best where the salivary gland was very close to the cuticle, and this was always laterally. However, there were differences in the position of the salivary glands in individual mosquitoes, which also led to slight differences in the imaging angle.

Overall, the text is easy to follow and I have only few suggestions.

Thank you for this comment.

In the result section, the authors describe the DsRed expression during development of mosquito (line 194-236) after they describe subcellular localization of fluorescent reporters. I felt the flow was disrupted. Thus, this part (line 194-236) could summarize and move to line 135. In this way, the result section flow according to the main figures.

Thank you very much for this suggestion. We have considered your idea, but based on the changes we have made in response to reviewer comments and new data implemented in the form of two new figures, we believe the current order in the results section is more appropriate. The rationale was primarily to first characterize the expression of fluorescent reporters in the salivary glands of all lines before going into more detail on expression in other tissues of a single line. We then finish with potential applications like in vivo imaging of sporozoite interactions with salivary glands.

Also, and as mentioned previously (reviewer 2, point 8), we believe it is important to describe the variability of ectopic promoter expression at a given locus with sufficient details, as this has not been characterized thus far despite its importance.

In the result section, text line 186-190, the authors describe the morphological alternation of salivary gland in aapp-hGrx1-roGFP2. I would suggest to mention that this observation was only in one of lateral lobe. (I saw that it was mentioned in the figure legend but not in the main text.)

We believe there has been a misunderstanding. The morphological alteration in salivary glands expressing aapp-hGrx1-roGFP2 was observed in all distal-lateral lobes to varying degrees (quantification in Figure 6E). To include as many salivary glands as possible in the quantification and because in some images only one distal-lateral lobe was in focus, only the diameter of one lobe per salivary gland was measured and evaluated. We have now revised the legend to prevent further misunderstandings.

In the discussion section, author discuss localization of fluorescent reporters (line 322-331). When I looked at aapp-DsRed localization pattern (Figure 3A), the pattern looked similar to the previous publication by Wells et al 2017 (https://www.nature.com/articles/s41598-017-00672-0). This publication used AAPP antibody and stain together with other markers (Figure 4-7). This publication could be worth referring in the discussion section.

Thank you for this suggestion. According to the information available through Vectorbase, we did not fuse DsRed with any coding sequence of AAPP that could potentially encode a trafficking signal. Therefore, it is rather unlikely that the observed DsRed localization in our aapp-DsRed line and the localization observed by AAPP immunofluorescence staining in WT mosquitoes match. This is further exemplified by the cytoplasmic localization of hGrx1-roGFP2 in the aapp-hGrx1-roGFP2 line, where the reporter gene was cloned under the control of the same promoter. For this reason, we had not mentioned this reference in the first version of the manuscript. In the revised manuscript, we have included now the suggested reference (lines: 475-476) and extended the discussion on possible reasons which led to the observed localization pattern.

In the text, authors describe salivary gland lobes as distal lobes and middle lobe. It would be more accurate to refer to the lobes as the lateral and medial lobes. The lateral lobes can then be sub-divided into proximal and distal portions. I would suggest to use distal lateral lobes, proximal lateral lobes and median lobe as other references use (Wells M.B and Andrew D.J, 2019).

Thank you for this suggestion. We have corrected the nomenclature for the description of the salivary gland anatomy as suggested throughout the manuscript.

Overall, the figures are easy to understand and I have following suggestions and questions.

Figure 1C) It is hard to see WT salivary gland median lobe. If authors have better image, please replace it so that it would be easier to compare WT and transgenic lines.

We have replaced the wild-type images of salivary glands in this figure and labeled the median and distal-lateral lobes accordingly (see Figure 1).

Figure 2) While it was interesting to observe the significant expression differences between day 3 and day 4, have you checked if this expression maintained over time or declines or increases (especially on day 17-21 when author perform in vivo imaging)?

Thank you for this interesting question. We have not quantified fluorescence intensities in mosquitoes of higher age. Nevertheless, we regularly observed spillover of DsRed signaling to the GFP channel during sporozoite imaging, suggesting that expression levels, at least in aapp-DsRed expressing mosquitoes, remain high even in mosquitoes >20 days of age (see Figure 8A). We also confirmed this observation by dissecting salivary glands from old mosquitoes, whose distal lateral lobes always showed a strong pink coloration even in normal transmission light (data not shown).

Figure 3A) There is no description of "Nuc" in figure legend. If "nuc" refers to nucleus, have you stained with nucleus staining dye (example, DAPI)?

Thank you for spotting this missing information in the legend. Initial images shown in this figure were not stained with a nuclear dye. To test whether the observed GFP expression pattern really colocalizes with DNA, we performed further experiments in which salivary glands from both aapp-hGrx1-roGFP2 and sag(-)KI mosquitoes were stained with Hoechst. We have now included these new data in Figure 3 - figure supplement 1. It appears that GFP is concentrated around the nuclei of the acinar cells, which makes the nuclei clearly visible even without DNA staining.

Figure 4B) The number of biological replicates in the figure and the legend do not match (In the figure, there are 3-5 data points and, in the legend, text says 3 biological replicates.)

Thank you for spotting this inconsistency. The number of biological replicates refers to the number of mosquito generations used for experiments. The difference is due to the fact that sometimes two experiments were performed with the same generation of mosquitoes using two different infected mice. We have clarified the legend accordingly to avoid misunderstandings.

Figure 4C) The number of data points from (B) is 5. However, in (C) only 4 data points are presented.

We have corrected this mistake. In the previous version, the results of two technical replicates were inadvertently plotted separately in (B) instead of the mean.

Figure 5) I would suggest to have thorax image of P. berghei infected mosquito to show both salivary glands and parasites.

Thank you for this suggestion. Images in Figure 7B (previously Figure 5) were replaced with an infected specimen to show salivary glands (DsRed) and sporozoites (GFP) together.

Reviewer #4 (Significance (Required)):

The transgenic lines that authors created have potential for in vivo imaging of salivary gland and sporozoite interactions. Since the aapp and trio lines have distinct fluorescence expression, they could help elucidate why sporozoites are more likely to invade distal lateral lobes compare to median lobe.

My areas of expertise are confocal microscope imaging, mosquito salivary gland and Plasmodium infection and sporozoite motility.

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Referee #2

Evidence, reproducibility and clarity

The first achievements of the Klug et al. study are the (i) genetical engineering of the Anopheles coluzzii mosquitoes reared in insectarium, that stably express distinct fluorescent reporters (DsRed and hGrx1-roGFP2 and EGFP) under the putative "promoters" of genes reported to encode proteins expressed differentially in the pluri-lobal salivary glands(Sg) of anthropophilic blood-feeding adult females, (ii) the analysis of the promoter activity - based on the selected fluorescent reporter - with a primary focus on the salivary gland/Sg (including at the Sg lobe level) of the adult female but also considering the preimaginal developmental time with larvae and pupa samples. Of note, some data confirm the already reported time-dependent and blood meal-dependent promoter activity for the related Anopheles species. The last part presents preliminary dataset on live imaging of Plasmodium berghei sporozoites with the aim of highlighting the usefulness of these A. coluzzii transgenic lines to better understand how the rodent Plasmodium sporozoites first colonize and then settle as packed cells in Sg acinar host cells.

Major comments

The two first objectives presented by the authors have been convincingly achieved with (i) the challenging production of four different lines expressing different single or double reporters chosen by the authors (and appropriately presented in the result text and figure sections), (ii) the careful analysis of the spatiotemporal expression of the DsRed reporter under two "promoters" studied and with regards to the blood feeding event parameter. However, if the reason why the authors have put so much effort in the production of their transgenic mosquitoes is (and as mentioned) to provide a significant improved setting enabling the behavioral analysis of sporozoites upon colonization and survival in the Sg, it seems this part is kind of limited. Likely in relation with this perception is the fact I found the introductory section often confusing and not enough direct to the points: in particular distinguishing the rationale from the necessity to produce appropriate models, and clarifying what is/are the added value(s) offered by these new transgenic lines models when compared to what exist (in Anopheles stephensi) with specific evidence that argue for this knowledge gain. At this stage, it is unfortunately not clear to me, what is the bonus of imaging the Plasmodium fluorescent sporozoites in hosts with fluorescent salivary gland lobes if one can not monitor key events of the Sg-sporozoite interaction that were not reachable without the fluorescent mosquito lines. Furthermore, it should be better explained why the rodent Plasmodium species has been chosen rather Plasmodium falciparum (or other human species) for which A. coluzzii is a natural host; may be just mentioning that this study would serve as a proof of concept but bringing real biological insights would be fine.

1- The three genes and gene products selected by the authors should definitively be more systematically explained, which means for example the authors need to introduce the different mosquito species and the parasite-mosquito host pairs they are then referring to for the promoter/encoded proteins of their interest. In the same vein, I did not find any information as to the choice of the mosquito specie (A. Coluzzii) for the current work. I was curious to know what is the advantage since better knowledge was available with Anopheles stephensi with respect to (i) Saglin and its promotor activity, (ii) aap driven dsRed expression (lines already existing) and (iii) sporozoite-gland interaction.

2- To help clarifying the added value of the present study, introducing the species names of the mosquito and the Plasmodium that serve as a model would be appreciated.

3- Since a focus is the salivary gland of the blood feeding female Anopheles sp., a rapid description of the glands with different lobes and subdomains the results and figure 1 nicely refer to, would help in the introduction.

4- That description could logically introduce the few proteins actually identified with lobe specific or cell domain specific expression (apical versus basal side, intracellular or surface expose, vacuole, duct...) profiles. The context with regards to sporozoite biology would then easily validate the "promoter choice". As a minor remark, I miss the reason why the authors wrote " the astonishing degree of order of the structures (referring to the packing of sporozoites within the Sg acinars) raise the question whether sporozoite can recognize each other". Please clarify since packing/accumulation can be passive due to cell mechanical constraints and explain what this point has to see with the question and experimental work proposed here?)

5- The selection of hGrx1-roGFP2 is quite interesting and justified but there is then no use of this reporter property in the preliminary characterization of the Sg and Sg-sporozoite interaction. Could the authors provide such characterization?

6- Figure 1: it would be nice to add in the legend at what time the dissection/imaging has been made (age, blood feeding timing?). I would also omit the double mutant trio-Dsred/aapDsred in the main figure (may be supplemental) since the two single mutants Dsred separately together with the double mutant (with different fluorescence) already provide the information. I would suggest to regroup the phenotypic presentation of the transgenic line made in the KI mosquitoes (current figure 5) in the main figure 1.

7- Figure 2:

a) Is there anything known on the Sgs' size change overtime. It seems that between day 1 and 2 there is an increase of size and volume as much as I can evaluate the volume (Fig S4). Could that mean that there is increase in cell number in the lobes and therefore more cells expressing the transgene which would account for the signal intensity increase rather than more transcripts per cell?

b) why choosing 24h after the blood meal to assess promoter activity in the Sgs? Do we have any information on how the blood meal impact on the Sgs'development. At this time anyway the sporozoites are far from being made. Yosshida and Watanabe 2006 mentioned at significant decrease of Sg proteins post-blood feeding. Could the authors detail their rationale based on what the questions they wish to address

8- Figure 3: The figure is quite informative in terms of subcellular localization. Concerning the section "Natural variation of DsRed expression in trio-DsRed mosquitoes", I think it could be shortened because because it is a bit out of the focus the study.

9- In contrast the last section of live imaging of P. berghei sporozoites in the vicinity and within salivary gland should be expanded. The 2 sentences summarizing the data are quite frustrating "We also observed single sporozoites moving actively through tissues in a back and forth gliding manner (Fig. 6B, Movie 3) or making contact with the salivary gland although no invasion event could be monitored"

10- I am aware of the technical difficulties to perform live imaging of sporozoite on whole mosquitoes, even when the salivary gland lobe under observation is closely apposed to the cuticle but that seems to be the final aim of the authors. I looked very carefully to the three movies and I am sorry but at this stage I could not make meaningful analysis out of them, and could not agree with the conclusions: for instances, the authors specify that sporozoites were undergoing back and forth movements (movie 3) but I do not see that and do not see the Sg contours in the available movies? The authors should also add bar and time scales to their movies. Having an in-depth description with regards to the sub-domain marked by a relevant reporter would strengthen the study, even if images are not collected in the whole mosquito to get higher resolution.

I am not sure I understand the relevance of this quite condensed sentence in the text. Could the authors rephrase and expand if they wish to keep the issues they refer to. "The sporozoites' distinctive cell polarization and crescent shape, in combination with high motility, allows them to „drill" through tissues". I would stress more on the main unknown in terms of sporozoite-Sg interactions and the need to get right models for applying informative approaches (i.e. here, imaging).

Of note, it could help to point that the "Sgs is a niche in which the sporozoites which egress from the oocyst could mature and be fully competent when co-deposited with the saliva into the dermis of their intermediary hosts"

Significance

1- Clear technical significance with the challenging molecular genetics achieved in the mosquito A. coluzzii.

2- More limited biological significance: fair analysis and gain of knowledge of spatio-temporal of reporter expression under the selected promoter but limited significance of the final goal analysis which concerns the Plasmodium sporozoite biology once egressed from oocysts

3- Previous reports cited by the authors have used the DsRed reporter and the aap promoter in another Anopheles (i.e. A. stephensi, Yoshida and Watanabe, Insect Mol Biol, 2006; Wells and Andrew, 2019) which is also a natural host and vector for human Plasmodium spp.) with significantly more resolutive 3D visualization of GFP-fluorescent P. berghei but in dissected salivary glands and not in whole mosquitoes. The Wells and Andrew publication entitled "Salivary gland cellular architecture in the Asian malaria vector mosquito Anopheles stephensi" in Parasite Vectors, 2015 would deserve to be reference and described.

4- Audience: I would say that this work should be of interest of mostly scientists investigating Plasmodium biology (basic and field research) or in entomology of Diptera.

5- To describe my fields of expertise, I can refer to my extensive initial training in entomology including at one point in the genetic basis of mosquito-virus interaction. I have also been working for more than 20 years in the field of Apicomplexa biology (Plasmodium and Toxoplasma) and I have long-standing interest in live and static high-resolution imaging.

The human, or ANY multicellular animal or plant body is a prime example of cooperation....billions of cells in cooperation with each other to regulate the body system.

The body of any multi-cellular organism, whether flora or fauna is an example of exquisite cellular and microbial cooperation. A multi-cellular organism is itself a superorganism in this sense. And social organisms then constitute an additional layer of superorganismic behavior.

Reviewer #2 (Public Review):

The research paper presents a modeling approach aimed at disentangling mother's genetic effects on their offspring in two components: prenatal environment and postnatal environment. Specifically, the authors use SEM on adopted and non-adopted individuals from the UK Biobank and leverage the variation in genetic similarities from different family structures. Because the UK Biobank is not created as an adoption study, they build seven different family structures to include all possible family combinations that can provide information regarding the two parameters of interest: those representing prenatal and postnatal environment respectively. The model is used on two phenotypes (birthweight and education attainment) to illustrate it.

The results indicate an 'expected pattern of maternal genetic effect on offspring birthweight' and 'unexpectedly large prenatal (intrauterine) maternal genetic effects on offspring education attainment. The authors mention this result can likely be explained by adopted offspring being raised by biological relatives. They then show simulations supporting this hypothesis.

We praise the authors for the complex analyses executed and the work done to create the model and make the scripts available to the research community. The models can be a valuable addition to the behavior genetics literature and to researcher's toolkit. We do however have a few concerns regarding 1. the meaning of the results, 2. model building decisions and the choice of sample and 3. the way some limitations are addressed. We go into more details for each of these points.

1. Interest to study mothers' genetic effects as acting via the prenatal environment or the postnatal environment and the meaning of the parameters tested by the model

I think this is an interesting question and a useful distinction for a number of phenotypes and the authors use the adoption design in an innovative way to define and estimate parameters that correspond to this distinction. However, I would suggest that the expressions of prenatal environmental effect and postnatal environmental effect (as distinct pathways for mother's gene to be expressed) seem to be an overstatement.

The definition of mother genetic effects (effects of mother genotype on their child phenotype, over and above any genetic transmission) is citing Wolf & Wade 2009 (line 56) which mention the more general notion of 'maternal effect' that are defined as effect of genotype, phenotype (or both) on their offspring. I would argue that postnatal maternal genetic effects (as currently defined in the paper) are likely environmental effect and not only 'genetic effects'.

These environmental effects are indeed partly influenced by mother's genes, but also strongly affected by other variables such as culture, generation, SES, education. It is not possible to disentangle these effects in the design(s) used here.

This consideration can affect the authors definition of the covariance between an adopted individual's genotype and phenotype as a function of prenatal (but not postnatal) maternal genetic effects (line 93-94). The authors current assumption does not consider the potential for environmental modulation of the effect of adopted mothers' genes (which are not zero for several phenotypes). Postnatal maternal genetic effects are thus also likely to capture and represent environmental differences.

2. Model building decisions specific to the UK biobank

One of the main issues is that the method is tested on a sample that is not built as an adoption design. This forced the authors to make decision to circumvent this problem and lead to important limitations that are not inherent to their method, but to the specific sample they applied it to.

a. Having adoptive parents partly genetically related to the child is breaking the logic of the adopted design. Thus, it brings back the genetic confound (passive gene-environment correlation) problem of usual family-based design. In their case, it alters their ability to differentiate between prenatal and postnatal environment.

b. In section starting on line 426, the authors have included simulations to show how this issue could be addressed. However, it does not help the fact that in their model applied to the UK biobank, the information regarding the degree of genetic similarity between adopting parents and biological parents and the child is unknown.

c. To address this problem in their analyses of UK biobank, authors used (Lines 302 & 417) information regarding whether children were breastfed or not (on the basis that this knowledge would be more common if the child was raised by a biological family relative) to identify adopted singletons raised by biological relatives. However, this is, at best, a mediocre index of genetic relatedness. I can see other reasons for participants to have knowledge of if they have been breastfed: because they were adopted at an older age, because they are still (or have been) in contact with their biological mother. It is also possible, albeit rare, that adoptive parents may breastfeed a child via the use of drugs to stimulate milk production. Line 420: the fact that the prenatal maternal estimate became non-significant after removing participants that were breastfed do provide results more in-line with what would be expected. But we can't use expected results as a basis to evaluate the validity of the approach. The absence of GxE and rGE are two other strong assumptions of the model that could also produce this kind unexpected results.

d. I would suggest discussing the issue of genetic relatedness between adopting parents and offspring in terms of passive rGE which is a common problem for the estimation of parental effects in every familial design.<br /> e. Line 291: why use an unweighted PRS for EY3 (Lee, 2018), while the usual way of computing PRS (as a weighted sum of risk alleles) was used for birthweight?

3. Limitations<br /> Assess other limitations of their method.

a. limitation of the availability of birth father information,

b. prenatal events uncorrelated with birthmother's genes (disease or accidents),

c. Inferring prenatal environment effect from higher birth mother correlation compared to birthfather is subject to bias from measurement differences between the two (Loehlin, 2016).

d. age at which the child is adopted (if the child has been partly raised by birth parents before adoption, it would bias (raise) the estimates of prenatal effects).

e. evocative rGE not mentioned. It has been shown that parents partly react to children's behaviors. Thus, the estimate of maternal genetic postnatal effects could be biased (lowered) by evocative gene-environment correlation. In other words, the model also assumes no evocative gene-environment correlation.

Final thoughts:

1. I would like a better case made for why it is important to distinguish genetic effects into prenatal and postnatal effect.

2. I would suggest the author make a clear distinction between the limits inherent to their sample (UK biobank) from those inherent to their methodological approach. I see important usefulness is plague by limits inherent to the sample used. At the same time, I am not aware of the availability of a big enough sample of adopted children with genotypic information available to compute PRS.

Author Response

Reviewer #2 (Public Review):

Summary

The research paper presents a modeling approach aimed at disentangling mother's genetic effects on their offspring in two components: prenatal environment and postnatal environment. Specifically, the authors use SEM on adopted and non-adopted individuals from the UK Biobank and leverage the variation in genetic similarities from different family structures. Because the UK Biobank is not created as an adoption study, they build seven different family structures to include all possible family combinations that can provide information regarding the two parameters of interest: those representing prenatal and postnatal environment respectively. The model is used on two phenotypes (birthweight and education attainment) to illustrate it.

The results indicate an 'expected pattern of maternal genetic effect on offspring birthweight' and 'unexpectedly large prenatal (intrauterine) maternal genetic effects on offspring education attainment. The authors mention this result can likely be explained by adopted offspring being raised by biological relatives. They then show simulations supporting this hypothesis.

We praise the authors for the complex analyses executed and the work done to create the model and make the scripts available to the research community. The models can be a valuable addition to the behavior genetics literature and to researcher's toolkit. We do however have a few concerns regarding 1. the meaning of the results, 2. model building decisions and the choice of sample and 3. the way some limitations are addressed. We go into more details for each of these points.

1) Interest to study mothers' genetic effects as acting via the prenatal environment or the postnatal environment and the meaning of the parameters tested by the model .

I think this is an interesting question and a useful distinction for a number of phenotypes and the authors use the adoption design in an innovative way to define and estimate parameters that correspond to this distinction. However, I would suggest that the expressions of prenatal environmental effect and postnatal environmental effect (as distinct pathways for mother's gene to be expressed) seem to be an overstatement.

The definition of mother genetic effects (effects of mother genotype on their child phenotype, over and above any genetic transmission) is citing Wolf & Wade 2009 (line 56) which mention the more general notion of 'maternal effect' that are defined as effect of genotype, phenotype (or both) on their offspring. I would argue that postnatal maternal genetic effects (as currently defined in the paper) are likely environmental effect and not only 'genetic effects'. These environmental effects are indeed partly influenced by mother's genes, but also strongly affected by other variables such as culture, generation, SES, education. It is not possible to disentangle these effects in the design(s) used here.

Although we have referred to the maternal effects estimated in our manuscript as “prenatal maternal genetic effects” and “postnatal maternal genetic effects”- all of these effects on the offspring are mediated through maternal phenotypes (which as the reviewer correctly notes, will be influenced by both genes and the environment). In other words, the maternal PRS used in our study proxies some maternal phenotype/s that then forms part of the offspring’s prenatal and/or postnatal environment which then affects the offspring’s phenotype. We have referred to these effects as maternal genetic effects rather than just maternal effects to emphasize the causal link with the maternal genotype and the fact that we are only proxying that part of the maternal phenotype that is explained by the relevant genetic variation (NB. This is consistent with the Wolf & Wade 2009 definition of maternal effects i.e. “…the causal influence of maternal genotypes on offspring phenotypes…”). We agree with the reviewer that our model is not attempting to disentangle proportions of variance due to genetic and environmental factors (which is not its purpose).

This consideration can affect the authors definition of the covariance between an adopted individual's genotype and phenotype as a function of prenatal (but not postnatal) maternal genetic effects (line 93-94). The authors current assumption does not consider the potential for environmental modulation of the effect of adopted mothers' genes (which are not zero for several phenotypes). Postnatal maternal genetic effects are thus also likely to capture and represent environmental differences.

Assuming that adopted offspring are not biologically related to their adoptive mothers, then adopted individuals’ PRS should not be correlated with adoptive mothers’ PRS. The corollary is that adoptive mothers’ PRS should not influence the covariance between adopted individuals’ PRS and phenotype (i.e. regardless of whether there is environmental modulation of the effect of adopted mothers’ genes on offspring phenotype). It is true, however, that we do not consider genotype by environment interaction effects in our model, and that this is a limitation of our model. We allude to this important point several times in the Discussion:

“Those assumptions explicitly encoded in Figure 1 include that the total maternal genetic effect can be decomposed into the sum of prenatal and postnatal components, that genetic effects are homogenous across biological and adoptive families, the absence of genotype x environment interaction…”

And

“In contrast, in our design it is more important that genetic effect sizes are homogenous across adopted and non-adopted individuals (i.e. no genotype by environment interaction)…”.

At the request of the reviewer, we now include additional discussion of GxE and other assumptions of our model in further detail in Supplementary File 17.

2) Model building decisions specific to the UK biobank. One of the main issues is that the method is tested on a sample that is not built as an adoption design. This forced the authors to make decision to circumvent this problem and lead to important limitations that are not inherent to their method, but to the specific sample they applied it to.

a) Having adoptive parents partly genetically related to the child is breaking the logic of the adopted design. Thus, it brings back the genetic confound (passive gene-environment correlation) problem of usual family-based design. In their case, it alters their ability to differentiate between prenatal and postnatal environment.

We agree that the UK Biobank was never designed for this purpose, and that data from it regarding adoption is less than perfect. Nevertheless, we think that an important conclusion of our paper is that large-scale biobanks (which because of their size) contain many hundreds/thousands of adopted individuals can be used to partition maternal genetic effects into prenatal and postnatal components, provided good quality data on the adoption process has been gathered and/or genetic information on their adoptive parents.

To help address the reviewer’s concerns we have created a Supplementary Table (Supplementary File 17) that summarizes some of the main limitations/assumptions of our model, whether they are specific to the UK Biobank dataset or intrinsic to our method, their consequences on model parameters, and possible options for addressing them.

b) In section starting on line 426, the authors have included simulations to show how this issue could be addressed. However, it does not help the fact that in their model applied to the UK biobank, the information regarding the degree of genetic similarity between adopting parents and biological parents and the child is unknown.

We agree- but we feel it is important to demonstrate (a) that cryptic biological relatedness between adopted individuals and their adoptive parents is a potential issue not only for our study, but for other studies attempting to utilize this information in the UK Biobank, and (b) that cryptic relatedness can be dealt with effectively through appropriate modelling in our SEM framework (i.e. even if it is not possible with the current data from UK Biobank). The corollary is that we recommend that the UK Biobank (and other large-scale biobanks) attempt to acquire information on adopted individuals and their parents through e.g. questionnaire.

c) To address this problem in their analyses of UK biobank, authors used (Lines 302 & 417) information regarding whether children were breastfed or not (on the basis that this knowledge would be more common if the child was raised by a biological family relative) to identify adopted singletons raised by biological relatives. However, this is, at best, a mediocre index of genetic relatedness. I can see other reasons for participants to have knowledge of if they have been breastfed: because they were adopted at an older age, because they are still (or have been) in contact with their biological mother. It is also possible, albeit rare, that adoptive parents may breastfeed a child via the use of drugs to stimulate milk production. Line 420: the fact that the prenatal maternal estimate became non-significant after removing participants that were breastfed do provide results more in-line with what would be expected. But we can't use expected results as a basis to evaluate the validity of the approach. The absence of GxE and rGE are two other strong assumptions of the model that could also produce this kind unexpected results.

We agree that (a) the inclusion of adopted individuals whose adoptive parents are biologically related to them is only one possible reason for unexpectedly strong prenatal maternal genetic effect estimates, (b) attempting to remove these individuals from the analysis using a proxy like breastfeeding information is less than perfect. As indicated above, we now discuss in detail alternative explanations for our results including violations of assumptions regarding the absence of GxE and rGE, and other explanations (assortative mating, stratification etc) (see new text in the Discussion and Supplementary File 17).

d) I would suggest discussing the issue of genetic relatedness between adopting parents and offspring in terms of passive rGE which is a common problem for the estimation of parental effects in every familial design.

We now include mention of passive rGE in the Discussion:

“Rather we hypothesize it is possible that our model could have been misspecified in that substantial numbers of adopted individuals in the UK Biobank may have in fact been raised by their biological relatives. This can be thought of as (unintentional) reintroduction of passive gene-environment correlation into the study. In other words, adopted children are brought up by their genetic relatives, who in turn provide the environment in which they are raised. This induces a correlation between adopted individuals’ PRS and their environment.”

e) Line 291: why use an unweighted PRS for EY3 (Lee, 2018), while the usual way of computing PRS (as a weighted sum of risk alleles) was used for birthweight?

We thank the reviewer for pointing this inconsistency out. We have now rerun the analyses using weighted and unweighted PRS for both birth weight and educational attainment. The reason for running both sets of analyses is that the GWAS on which the SNPs are selected (i.e. the weights are based), contains UK Biobank individuals. This may inflate the overall strength of association between the PRS and outcome through winner’s curse (although not differentially between individuals from adoptive and biological families). In contrast, unweighted scores should be much more robust to this inflation, and so are a useful sanity check on the results.

3) Limitations

As our Discussion is already very long, we have created a Supplementary Table (Supplementary File 17) that summarizes some of the main limitations/assumptions of our model, their consequences on model parameters, and possible options for addressing them. We also discuss specific concerns raised by the referee below.

Assess other limitations of their method.

a) limitation of the availability of birth father information,

Our model does not require information on adopted individual’s birth fathers (although it does require PRS on non-adopted individuals’ birth fathers- which is typically readily available). It does, however, make the assumption that fathers do not contribute prenatally to offspring traits- which we think is a reasonable assumption for the majority of offspring phenotypes. If PRS for adopted individuals’ biological fathers were available, then prenatal paternal genetic effects could be estimated as part of the model. To accommodate the reviewer’s request, we have included and discussed this limitation/assumption in more detail in Supplementary File 17.

b) prenatal events uncorrelated with birthmother's genes (disease or accidents),

We agree that our model assumes that maternal genotype is uncorrelated with prenatal environmental factors. We now discuss this assumption/limitation further in Supplementary File 17.

c) Inferring prenatal environment effect from higher birth mother correlation compared to birthfather is subject to bias from measurement differences between the two (Loehlin, 2016).

Whilst this is a limitation of adoption designs that estimate prenatal effects using the difference between maternal and paternal correlations with offspring phenotypes, this is not actually a limitation of our model. In our model we do not use (phenotypic) mother-child and father-child correlations (we use PRS-phenotype correlations). Also, in our model, information on the size of the prenatal (and postnatal) maternal genetic effects primarily comes from the difference between the PRS-phenotype covariance in adopted singletons compared to the PRS-phenotype covariance non-adopted individuals (i.e. not from the difference between maternal and paternal correlations with offspring phenotypes). We state this in the Introduction and Methods e.g.:

“Thus, the difference between the genotype-phenotype covariance in adopted and non-adopted singleton individuals provides important information on the likely size of postnatal genetic effects.”

It is also worth noting, that in our model, the size of the paternal PRS-offspring association does not factor into the estimation of maternal genetic effects (nor does the difference between the maternal PRS-offspring phenotype association and the paternal PRS-offspring phenotype association). Also, our model takes into account if there are differences in the amount of (random) measurement error in adoptive and non-adoptive families.

d) age at which the child is adopted (if the child has been partly raised by birth parents before adoption, it would bias (raise) the estimates of prenatal effects).

We agree and now discuss this limitation further in Supplementary File 17.

e) evocative rGE not mentioned. It has been shown that parents partly react to children's behaviors. Thus, the estimate of maternal genetic postnatal effects could be biased (lowered) by evocative gene-environment correlation. In other words, the model also assumes no evocative gene-environment correlation.

We agree and now discuss this limitation in Supplementary File 17 (although we note that the effect that evocative rGE will have on the SEM parameters will depend on the direction of the gene-environment correlation).

Final thoughts

1) I would like a better case made for why it is important to distinguish genetic effects into prenatal and postnatal effect.

We have included the following text in the Introduction:

“Given the increasing number of variants identified in GWAS that exhibit robust maternal genetic effects, a natural question to ask is whether these loci exert their effects on offspring phenotypes through intrauterine mechanisms, the postnatal environment, or both. Indeed, resolving maternal effects into prenatal and postnatal sources of variation could be a valuable first step in eventually elucidating the underlying mechanisms behind these associations (Armstrong-Carter et al. 2020), directing investigators to where they should focus their attention, and in the case of disease-related phenotypes, yielding potentially important information regarding the optimal timing of interventions. For example, the demonstration of maternal prenatal effects on offspring IQ/educational attainment, suggests that if the mediating factors that were responsible could be identified, then improvements in the prenatal care of mothers and their unborn babies which target these factors, could yield useful increases in offspring IQ/educational attainment.”

2) I would suggest the author make a clear distinction between the limits inherent to their sample (UK biobank) from those inherent to their methodological approach. I see important usefulness is plague by limits inherent to the sample used. At the same time, I am not aware of the availability of a big enough sample of adopted children with genotypic information available to compute PRS.

One of the main limitations inherent to our sample (UK Biobank) is the fact that currently we cannot be certain that adopted individuals are not biologically related to their adoptive parents. As we demonstrate, this limitation could be addressed if information were gathered regarding the relationships, which at least in principle could be done relatively easily in the UK Biobank (e.g. by questionnaire, or even better, by genotyping adoptive parents where possible). The SEMs could then be adjusted to take these relationships into account. We discuss this limitation, and many others, in Supplementary File 17, and divide the table according to whether the limitation is primarily a consequence of the dataset (UK Biobank) or the method more broadly.

We agree with the reviewer that the size of adoption studies is currently limited (e.g. Texas Adoption Project; Colorado Adoption Study etc). Nevertheless, it is likely that the number of adopted individuals available in large-scale Biobanks will increase over time, in which case models like the one espoused in this manuscript will become increasingly useful. Importantly, our method does not require adoptive families in order to partition maternal effects, merely adopted singleton individuals, and reliable information on the biological relatedness (or lack thereof) of their adoptive parents. We feel therefore that it is important that this sort of information be gathered so that the adopted individuals within these large-scale resources can be leveraged to examine interesting questions like the ones discussed in our manuscript.

We have added these points to the Discussion:

“We argue that of greater consequence for the validity of our model is that any genetic relationship between adoptive and biological parents is accurately modelled and included in the SEM. Through simulation, we have shown that the consequences of model misspecification depend upon which biological and adoptive parents are related, the nature of this relationship, and the proportion of adopted individuals in the sample who have had their relationship misspecified. Our simulations also showed that correctly modelling this relationship returns asymptotically unbiased effect estimates and correct type I error rates. Clearly, knowing these cryptic relationships in the UK Biobank would allow us to properly model them and better estimate prenatal and postnatal maternal genetic effects using this resource. We emphasize that accurately modelling these relationships does not require that actual genotypes for adoptive and/or biological parents be obtained (although this would be advantageous in terms of statistical power) as our SEM allows us to model these relationships in terms of latent variables. Indeed, as large-scale resources like the UK Biobank become more common, we expect that the number of adopted individuals who have GWAS will also increase, and consequently models like the one espoused in this manuscript will become increasingly useful. High quality phenotypic information on these adopted individuals and their adoptive parents including whether they share any biological relationship will be critical to making the most of these resources.”

MIT/Brown Vannevar Bush 研讨会于 1995 年 10 月 12 日至 13 日在麻省理工学院举行，以庆祝1945 年 7 月在大西洋月刊上发表的万尼瓦尔·布什（Vannevar Bush）的开创性文章《诚如所思》（As We May Think）发表50 周年。活动视频分五部分，这是第五部分，内容主要是：

1.Lee Sproull发表的演讲：“信息是不够的：计算机对生产性工作的支持”（Information Is Not Enough: Computer Support for Productive Work）。内容介绍：对一项新技术的任何设想都意味着对人类及其行为的设想。在这次演讲中，我描述了与个人计算的最有影响力的技术愿景相关的人类行为愿景，其缩影是万尼瓦尔·布什（Vannevar Bush）的Memex--孤独的思想者和问题解决者的愿景。我将这一愿景与关于人类生产性行为如何实际发生的另一种观点进行对比--在相互依赖的社会关系中。我回顾了目前计算机对社会行动者的支持状况，并提出了另一种观点，即信息处理从属于关系管理。

2.艾伦·凯（Alan Kay）发表的演讲：“Simex：布什的愿景中被忽视的部分”（Simex: the neglected part of Bush's Vision）。内容介绍：布什的愿景是在一张桌子上建立一个超链接的10000卷图书馆，它对个人计算的发展产生了巨大的影响，而且今天也有可能实现（甚至可以通过互联网超越它）。然而，尽管布什在30年代就从事（模拟）计算机模拟工作，但很可能他从他的工作或新建的Eniac中都看不到Memex的任何模拟作用。布什的设想中缺少什么，今天能不能发明出来？

3.第 2 天小组讨论。

MIT/Brown Vannevar Bush 研讨会于 1995 年 10 月 12 日至 13 日在麻省理工学院举行，以庆祝1945 年 7 月在大西洋月刊上发表的万尼瓦尔·布什（Vannevar Bush）的开创性文章《诚如所思》（As We May Think）发表50 周年。活动视频分五部分，这是第四部分，内容主要是：1.拉吉·瑞迪（Raj Reddy）：重新审视布什的智能系统（Bush's Intelligent Systems Revisited）。内容介绍：在他著名的论文 《诚如我思》中，万尼瓦尔·布什（Vannevar Bush）为创造能够解释图片、听写、理解语言、使用超链接和从数字图书馆进行关联检索的机器提供了一个愿景。在这次演讲中，我们将回顾50年来在这些预测方面所取得的进展。

Walter and Julia are examining a glass paperweight in George Orwell's 1984 without having context of what it is or for what it was used.

This is the same sort of context collapse caused by distance in time and memory that archaeologists face when examining found objects.

How does one pull out the meaning from such distant objects in an exegetical way? How can we more reliably rebuild or recreate lost contexts?

Link to: - Stonehenge is a mnemonic device - mnemonic devices in archaeological contexts (Neolithic carved stone balls

Some forms of orality-based methods and practices can be viewed as a method of "reading" physical objects.

Ideograms are an evolution on the spectrum from orality to literacy.

It seems odd to be pulling these sorts of insight out my prior experiences and reading while reading something so wholly "other". But isn't this just what "myths" in oral cultures actually accomplish? We link particular ideas to pieces of story, song, art, and dance so that they may be remembered. In this case Orwell's glass paperweight has now become a sort of "talking rock" for me. Certainly it isn't done in any sort of sense that Orwell would have expected, presumed, or even intended.

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

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Reply to the reviewers

Response to the reviewers

Manuscript number: RC-2022-01407

Corresponding author(s): Ivana, Nikić-Spiegel

1. General Statements

We would like to thank the reviewers for careful reading of our manuscript and for their insightful and useful comments. We are happy to see that the reviewers find these results to be of interest and significance. The way we understand reviewers’ reports, their main concerns can be roughly divided in following categories: 1) providing more quantitative data 2) interpretation of the Annexin V/PI assay 3) additional evidence for calpain involvement. We intend to address these experimentally or by modifying the text, as outlined below.

2. Description of the planned revisions

Reviewer #1

Fig1A/B o SYTO 16 staining suggests slight reshaping of nucleus upon spermine NONOate, showing less blurry punctae. From the SYTO 16 profile, this should be quantifiable.

By looking at the shown examples and the entire dataset, it appears to us as if neuronal nuclei are shrinking upon spermine NONOate treatment resulting in their less blurry appearance. We are not sure if this is what the reviewer is referring to, but this can also be quantified by measuring changes in neuronal nuclear size. We already have this data from the measurements shown in Fig4 and we intend to show it in the revised version of the manuscript. Line profile measurements are also possible, but the nuclear size quantification might be more suitable for this purpose.

o There is a subset of neuron nuclei that are SYTO 16 positive. Please quantify the ratio

We will use our existing dataset to quantify the ratio of NFL positive and SYTO16 positive nuclei.

FigS1A o Show NeuN with Anti-NFL merged figures

We will show merged NeuN and anti-NFL images, which might require rearrangement of the existing figures and figure panels. We will do this in the revised manuscript.

FigS1C o Show quantification and timeline. I want to know whether there is also a plateau reached here.

As the data shown in the FigS1C do not include NeuN staining, we will do additional experiments and perform proposed quantifications.

FigS2A-F o Though the statements might be true, selecting one nucleus for a line profile as a statement for the whole dataset seems problematic. Average a larger number of unbiased selected nuclei profiles across multiple cultures to make a stronger statement, or a percentage of positive nuclei as in FigS1b.

Corresponding images and line profiles are representative of the entire dataset. However, we agree with the reviewer that this is not obvious from the current manuscript version. Thus, to strengthen our findings, we intend to quantify the percentage of positive nuclei as in FigS1b. The only difference will be that instead of NeuN, we will use SYTO16 as a nuclear marker. The reason being that the existing datasets contain images of NFL and SYTO16 and not NeuN.

FigS3 • There are no fluorescence profiles, no quantification

As the reviewer suggests, we will quantify the ratio of NFL positive and SYTO16 positive nuclei, and include the quantifications in the revised manuscript.

General statement: There do seem to be punctated patterns of non-nucleus accumulating NFL fragments. Can they be localized to any specific structure?

We assume that the reviewer is referring to neuronal/axonal debris. They are present after injury but they do not colocalize with nuclear stains. We will address this in the revised manuscript.

Fig1C-F • I find it too simplistic to categorize c+f and d+e together. There is a huge difference in the examples of nuclear localization between d and e. To not comment on their distinction (if that is consistent) is problematic. Also, since we don't see a merge with either NeuN or SYTO 16, reader quantification is difficult.

We thank the reviewer for bringing this up. We will carefully check our entire dataset and we will update the figures and the text accordingly. We will also show the corresponding SYTO16 images, as the reviewer suggested.

Would the microfluidic device construction allow for time to transport any axonally damaged fragments to the soma?

Yes, the construction of the microfluidic devices allows the transport of axonal proteins back to the soma. Based on our experiments, it seems that damaged NFL from the axonal compartment could be contributing to the accumulation of NFL fragments in the nuclei. However, this contribution seems to be minimal as we cannot detect nuclear NFL upon the injury of axons alone. Alternatively, it could be that the processing of axonal NFL fragments proceeds differently if neuronal bodies are not injured and that this is the reason we don’t detect the NFL nuclear accumulation upon injury of axons alone. We will discuss this in the revised manuscript.

Fig2C+D • The statement ".... no annexin V was detected on the cell membrane" needs to be shown more clearly

We will modify figures to address this comment.

• Please provide merged AnnexinV/PI images

We will modify figures to address this comment.

• The conclusion about 2D, that nuclear accumulated NFL overlaps with PI is not supported by the example image shown. There are plenty of PI positive spots that are not NFL positive and even several NFL positive ones that do not have a clear PI staining. Please quantify and then show a very clear result in order to be able to suggest necrosis as the underlying process.

We are not sure if we understand the reviewer’s concern correctly. We will try to clarify it here and in the revised text. If necessary, we will tone down our conclusion, but the reason why not all of PI positive spots are NFL positive is most likely due to the fact that not all injured nuclei are NFL positive. We quantified in FigS1 that up to 60% of nuclei under injury conditions show NFL accumulations. That is why we are not surprised to see some PI positive/NFL negative nuclei. And the fact that there are some NFL positive nuclei which appear to be PI negative is most likely related to the fact that the PI binding is affected. In addition, upon closer inspection of NFL and PI panels in Fig2d it can be observed that NFL positive nuclei are also PI positive, albeit with a lower PI fluorescence intensity. We will modify the figure to show this clearly in the revised manuscript.

FigS5 C+D • If the case is made that nitric oxide damage induces necrosis, then why is it that the AnnexinV example of Staurosporine exposure (which induces apoptosis) looks similar to that of nitric oxide damage in Fig2d and necrosis induction with Saponin looks very different?

We thank the reviewer for bringing this up. We will try to clarify this in the revised manuscript. Regarding the specific questions, the most likely explanation why staurosporine treated neurons look similar to the ones treated with spermine NONOate is that in the late stages of apoptosis cell membrane ruptures and allows for the PI to label nuclei. This is probably the case here as illustrated by the nucleus in the middle of the image (FigS5c) that shows the fragmentation characteristic for the apoptosis. This is not happening in early apoptotic cells due to the presence of an intact plasma membrane. On the other hand, the reason why saponin treated cultures look different compared to spermine NONOate is that membranes are destroyed by saponin so that the PI can enter the cell. For that reason, there could have not been any AnnexinV binding to the membrane which would correspond to the AnnexinV signal of spermine NONOate treated neurons. As we will discuss below, we did not try to mimic spermine NONOate-induced injury with saponin treatment. Instead this was a control condition for PI labeling and imaging. We also used a rather high concentration of saponin which probably destroyed all the membranes which was not the case with spermine NONOate treatment. We intend to do additional control experiments to address this.

• Additionally, does necrosis induction with Saponin also cause NFL fragment accumulation in the nucleus? Please show a co-staining of them. Also, the authors want to make a claim about reduce PI binding in NFL accumulated necrotic cells. In these examples, the intensity of the nuclear stain of PI with Saponin looks dimmer than with Staurosporine. Are the color scalings similar? It might be that the necrotic process itself causes reducing binding of PI and is not related to the presence of NFL.

With regards to this question, it is important to note that Annexin V and PI imaging was done in living cells. To obtain the corresponding anti-NFL signal as shown in Fig 2c,d we had to fix the neurons, perform immunocytochemistry and identify the same field of view. We tried to do the same procedure after saponin treatment (Supplementary Figure 5d) but the correlative imaging was very difficult due to the detachment of neurons from the coverslip after the saponin treatment. For this reason, we could not identify the same field of view co-stained with NFL. However, other fields of view did not show NFL fragment accumulation. This could also be the consequence of the high saponin concentration that we used as we discuss above. We have also noticed the reduced intensity of PI binding in the nuclei of saponin-treated neurons. However, if the necrotic process itself reduces the binding of PI to the DNA, then all of the neurons treated with spermine NONOate would have an equally low PI signal. In our experiments, only the nuclei which contained NFL accumulations had a low PI signal, while the signal of NFL-negative nuclei was higher (as shown in Fig2d). We would also like to point out again that the saponin treatment was our control of the PI’s ability to penetrate cells and bind the DNA, as well as our imaging conditions, and not the control of the necrotic process itself. This is the reason why we didn’t go into details about neuronal morphology and NFL localization upon saponin treatment. We thank the reviewer for pointing this out since it prompted us to reevaluate what we wrote in the corresponding paragraph of the manuscript. We realized that the confusion might stem from our explanation of the AnnexinV/PI assay controls in the lines 196-198 (“Additional control experiments in which neurons were treated with 10 μM staurosporine (a positive control for induction of apoptosis) or with 0.1% saponin (a positive control for induction of necrosis) confirmed the efficiency of the annexin V/PI assay (Supplementary Fig. 5c,d).”). We will modify this portion of the text to clearly state that staurosporine and saponin treatments were controls of the AnnexinV and PI binding to their respective targets and not of the apoptosis/necrosis process. When it comes to the saponin treatment, our intention was only to permeabilize the membranes in order to allow PI penetration and DNA binding and not to induce necrosis or to mimic the effect of the spermine NONOate. We also intend to perform experiments with lower concentration of saponin to try to address this experimentally in addition to the text modifications.

Fig3d • Please show similarly scaled images from controls for proper comparison

We will show similarly scaled images of the control neurons so that they can be properly compared. They were initially not scaled the same for visualization purposes, but we will modify this in the revised manuscript.

• How do the authors scale the degree and kinetics of induced damage between application of hydrogen peroxide/CCCP and glutamate toxicity? Does glutamate toxicity take longer to affect the cell, not allowing enough time to accumulate NFL fragments in the nucleus?

It is challenging to scale the degree and kinetics of induced damage with different stressors. That is why we did not intend to do this. Instead we set different injury conditions based on the published literature. That is why can only speculate when it comes to this. In this regard, it can be that the glutamate toxicity takes “longer” to affect the cells even though it is very difficult to compare them on a timescale, especially when considering different mechanisms of action. We will discuss this limitation in the revised manuscript.

Fig4B • Some groups (like NO and NO + emricasan) have much larger numbers of close to 0 intensity, compared to the control group. Why?

We were wondering the same when we analyzed the data. The fact that our nuclear fluorescence intensity analysis picked up NFL signal in control neurons which had no nuclear NFL accumulation made us realize that the intensity measured in the nuclei of control group comes entirely from the out of focus fluorescence – from neurofilaments in cell bodies, dendrites and axons (an example can be seen in the FigS6). That is why we presented the corresponding data with a cut-off value based on the control signal (as mentioned in lines 238-240). Since the oxidative injury causes NFL degradation (not only in neuronal soma, but also neuronal processes), the overall fluorescence intensity of the NFL immunocytochemical staining is reduced in injured neurons. We can see that in all of our images. Consequently, there is no contribution of out of focus fluorescent signal to the measured fluorescence intensity in the majority of nuclei. Due to that, the nuclei without NFL accumulation (at least 40% of injured nuclei) will appear to have a close to 0 intensity of the fluorescent signal. We will discuss and clarify this additionally in the revised manuscript.

• Please add the ratio of above/below threshold (50/50 obviously in controls)

We will update the figure in the revised manuscript.

• The description of the CTCF value calculation seems a little... muddled? Several parameters are described whereas "integrated density" is not even used. Why not simply mean intensity of nuclear ROI-mean intensity of background ROI?

We included the integrated density in the description since it is measured together with the raw integrated density and can also be used for the CTCF value calculation. However, since we didn’t use it for the CTCF calculation, we will remove it from the corresponding section of the manuscript. We calculated the CTCF value instead of calculating mean intensity of the nuclear ROI - mean intensity of the background ROI, since the CTCF value also takes into account the area of the ROI and not just the mean intensity.

• Also, please tell me if the areas for nuclear ROIs change, as I noted for Fig1A/B

We will include this information in the revised manuscript.

• To make sure that one of the 3 experimental repeats didn't skew the results, please show the median fluorescence intensity for each individual experiment to clarify that the supposed effect is repeated across experiments.

We have already noticed that in the earliest of the three experiments overall fluorescence intensity was higher, but this was consistent across all the experimental groups and did not skew the results or affect the overall conclusion. However, we will double-check this and revise the figure.

• From the text "...and due to the NFL degradation during injury...": this seems to contradict the process? Either the NFL fragment accumulates in the nucleus or it is degraded during injury. And isn't the degradation through calpain what supposedly allows this fragment of NFL to go to the nucleus in the first place? I reckon that the authors are possibly trying to reconcile why there are many close-to-0 intensity nuclei in the NO and NO + emricasan groups, but I don't feel the explanation given here fits.

As we tried to explain in our response above, we think that the overall degradation of neurofilaments in neurons affects the fluorescence intensity originating from the out of focus neurofilaments. Therefore, the nuclei without NFL accumulation in injured conditions have a close to 0 fluorescence intensity. Additionally, we think that this is not an either/or situation, but that both degradation and nuclear accumulation of NFL happen simultaneously. We also think that degradation of axonal NFL and the transport of its tail domain to the soma will at least partially contribute to the accumulation in the nucleus. In any case, degradation and nuclear accumulation seem to be differentially regulated in individual neurons, as some of them show nuclear NFL accumulation and some not. Furthermore, calpain and other mechanisms could also cause NFL degradation up to the point at which these fragments can no longer be recognized by the anti-NFL antibody leading to the loss of signal. We will try to clarify this in the revised version of the manuscript.

Fig5 • Does the distribution of this GFP in B match any of the various antibody stainings of different NFL fragments? Perhaps this is still a valid fragment of NFL, just not picked up by any AB?

The GFP signal in B appears rather homogenous and it does not match any of the various antibody stainings of different NFL fragments. As the reviewer points out, this could also be a valid fragment of NFL fused to GFP that none of our antibodies is recognizing. We will clarify this in the revised manuscript.

• "... and was indistinguishable from the full277 length NFL-GFP." Based on what parameters?

We will clarify this in the revised text, but we meant in terms of overall neurofilament network and cell appearance, which is commonly used to test the effect of NFL mutations.

• The authors claim that b is different from d, but I am not convinced. I would like to see a time dependent curve from multiple cells showing a differential change in nuclear and cytosolic GFP signal.

As we also wrote in the manuscript, in the majority of neurons that were monitored during injury we were not able to detect an increase in the GFP fluorescence intensity in the nucleus. This is what prompted further experiments with NFL(ΔA461–D543)-FLAG. We will clarify this additionally in the revised manuscript and perform line profile intensity measurements to show the difference in nuclear and cytosolic GFP signal.

• Secondly, the somatic GFP intensity for NFL increases for full length NFL-GFP. How is this explained, if it is only a separation of NFL and GFP? If anything, GFP should float away. And if the answer is that NFL is recruited to the nucleus, you showed that inhibition of calpain activity partially prevents that. So, if calpain activity is necessary for the transport of NFL to the nucleus, then wouldn't it also cut the GFP from NFL before it reaches the nucleus?

We thank the reviewer for bringing this up and we apologize for the confusion. This can be explained by the fact that the images were scaled in a way that the GFP signal over time could still be seen easily (i.e. differently across different time points which we unfortunately forgot to mention in the figure legend). In the revised manuscript, we will either scale the images the same or we will alternatively show the displayed grey values in individual panels.

Fig6 • It is recommended to overlap the transfected cells with a stain for endogenous NFL to show that despite the absence of the FLAG-tag, there is still NFL.

We did not overlap the anti-NFL with anti-FLAG and SYTO16 staining, due to the space constraint and the intent to clearly show the overlap of FLAG and SYTO16 signals in the merged images above the graphs. However, the line profile intensity measurements were done in all three channels and show that despite the absence of FLAG, there is still NFL in the nucleus (Fig6b), or that both FLAG and NFL are present in the nucleus (Fig6d, NFL signal shown in gray). However, as this is not obvious and can easily be overlooked, we will show the endogenous NFL staining overlap in the revised version of the manuscript.

Fig7 • „ ...all disrupted neurofilament assembly...": this sounds like the staining for native NFL supposedly shows a distortion due to a dominant negative effect of the expression of these constructs? Please clarify.

Yes, we were referring to the disruption of neurofilament assembly due to a dominant negative effect of the expression of NFL domains. We will clarify this in the revised version of the manuscript.

Discussion: • The authors show that after overepression of the head domain only, it possibly passively diffuses into the nucleus even in the absence of oxidative injury. However, it seems to be suggested as well that the head domain would not be freely floating around if it wouldn't be for increased calpain activity as a result of oxidative injury in the first place. Therefore, a head domain fragment localized in the nucleus would still more prominently happen upon oxidative injury and interact with DNA through prior identified putative DNA interaction sites from Wang et al. Please comment.

That is correct. Upon injury and calpain cleavage, it is conceivable that a fragment containing the NFL head domain would also be present in the cell and could potentially diffuse to the nucleus and interact with the DNA. However, by staining injured neurons with an antibody that recognizes amino acids 6-25 of the NFL head domain, we were not able to detect an NFL signal in the nucleus (FigS2a,b). It could be that either the NFL head domain does not localize in the nuclei upon injury, or that the fragment localizing in the nucleus does not contain amino acids 6-25 of the NFL head domain. As the putative DNA-binding sites described by Wang et al involve 7 amino acids located in the first 25 residues of the NFL head domain, we would expect to detect it with the aforementioned antibody. However, as that was not the case we speculated that the interaction of NFL and DNA occurs differently in living cells, as opposed to the test tube conditions utilized by Wang et al. We will comment and clarify this in the revised version of the manuscript.

• Reviewer #2*

• Major Comments:

• The initial data presented in the paper is good, does response of oxidative damage with proper controls, testing the antibodies to NF-L and etc. (Fig. 1-Fig. 4). *

We thank the reviewer for their positive feedback.

1. The evidence for calpain involvement in NF-L cleavage during oxidative damage is missing. Provide the evidence for full length NF-L construct and deletion mutants transfected into cells by immunoblot for cleavage of NF-L, perform nuclear and cytoplasmic extract preparations and show that enrichment of the tagged cleaved NF-L fragment in nuclear fraction.

We thank the reviewer for their comments and suggestions. Since we saw in our microscopy experiments that calpain inhibition reduced the accumulation of NFL in the nucleus, and since it is known that NFL is a calpain substrate (Schlaepfer et al., 1985; Kunz et al., 2004 and others), we did not perform additional experiments to confirm the involvement of calpain in NFL degradation during injury. However, to strengthen our findings, we intend to perform the suggested experiments and include the results in the revised manuscript.

1. Show calpain activation during oxidative damage by performing alpha-Spectrin immunoblots identify calpain specific 150-kda Spectrin and caspase specific 120-kDa fragment generation in these cells. Also, calpain activation can be measured by MAP2 level alteration and p35 to p25 conversion. Without this evidence it's very hard to believe if the calpain activity is increased or decreased during oxidative damage and these markers are altered by using calpain inhibitors.

To confirm the calpain activation, we intend to perform anti-alpha spectrin and/or anti-MAP2 blots in lysates of control and injured neurons and include the results in the revised manuscript.

1. The premise that NF proteins are absent in cell bodies and present only in axons is not correct. It has been demonstrated by multiple investigators that NFs are present in the perikaryon and dendrites of many types of neurons (Dahl, 1983, Experimental Cell Research)., Dr. Ron Liem's group showed NF protein expression in cell bodies of dorsal root ganglion cells (Adebola et ., 2015, Human Mol Genetics) and also showed N-terminal antibodies for NF-L, NF-M and NF-H stain rat cerebellar neuronal cell bodies and dendrites (Kaplan et al., 1991, Journal of Neuroscience Research) when NFs are less phosphorylated. (Schlaepfer et al., 1981, Brain Research) show staining of cell bodies of cortex and dorsal root ganglion cell bodies with NF antibody Ab150, and Yuan et al., 2009 in mouse cortical neurons with GFP tagged NF-L.

We are not sure what the reviewer is referring to since we cannot find a corresponding section in which we claim that NF proteins are absent in cell bodies. We wrote the following “Anti-NFL antibody staining of neurons treated with the control compound showed the expected neurofilament morphology, that is, a strong fluorescence intensity in axons and lower intensity in cell bodies and dendrites (Fig. 1a)” in our results section (lines 119-121), but the claim we were trying to make there was that NF proteins are particularly abundant in axons. We will clarify this in the revised manuscript.

1. Quantifying NF-L signal or tagged NF-L fragment signals in the cell body by ICC has many problems and making conclusions. It's extremely difficult to have control over levels of proteins in transfected overexpression models and comparing two or three different constructs with each other by ICC. Not every cell expresses same levels of protein in transfected cells and quantifying it by ICC again has a major problem. This can be addressed if there are stable lines that express equal levels of protein in all cells that comparisons can be made. Under thesese circumstances validation of the hypothesis presented in the study has no strong direct evidence to demonstrate that calpain is activated and NF-L fragment translocate to the nucleus.

We agree that the results from overexpression-based experiments should be interpreted with caution as levels of expression vary between the cells. We intend to discuss this in the revised manuscript. However, we find it difficult to experimentally address this comment since we are not sure which specific experiments the reviewer is referring to. With regards to this, we would like to emphasize that most of the initial experiments in which we observed NFL accumulation in the nuclei of injured neurons were based on the ICC labeling of endogenous NFL and didn’t involve its overexpression. This includes labeling of endogenous NFL in various types of neurons, comparing the effects of different types of oxidative injury, as well as testing the effects of calpain inhibition on the observed nuclear accumulation (Figures 1-4; Supplementary Figures 1-6). We later resorted to the overexpression experiments in primary neurons (Figures 5-7; Supplementary Figure 7, 10) to gain more information about the identity of NFL fragment which was detected in the nucleus. Due to the low transfection efficiency of primary neurons, we performed an additional set of overexpression experiments in neuroblastoma ND7/23 cells (Figure 8; Supplementary Figures 8,9) and obtained similar results in a higher number of cells. We agree that having stable cell lines which e.g. express same levels of NFL domains would be a more elegant approach and we intend to make them for our follow-up studies, however the generation of said stable cell lines might be beyond the scope of this revision. Furthermore, looking at our data with overexpression of NFL domains in ND7/23 cells (Supplementary Figure 8,9), it appears to us as if different domains are rather homogenously expressed in different cells. While the expression levels might vary, it seems that they all show the same trend when it comes to their localization (which was the main point of those experiments).

1. The interpretation that NF-L preventing DNA labeling cells is misinterpretation. NFs have very long half-life compared to other proteins. Due to oxidative damage, DNA is degraded in the cells but NFs that have very long half-life you see as NFs rings in the dead cells. So, NFs do not prevent DNA labeling, but DNA or chromatin is degraded in dead cells.

We thank the reviewer for their useful insight. DNA degradation could certainly be the reason why we observe a lower fluorescence intensity of the propidium iodide fluorescence in the nuclei of injured neurons. We intend to discuss this in the revised manuscript. However, if the DNA degradation is the only reason for the lower PI fluorescence intensity, then the PI fluorescence intensity would be the same in all injured nuclei. In our experiments, we saw the reduced PI fluorescence intensity in nuclei that contained NFL accumulations and not in other nuclei. Additionally, we observed a reduction of SYTO16 fluorescent labeling of nuclei which contained accumulations of the NFL tail domain, even in the absence of oxidative injury. Due to these reasons we speculated that NFL accumulation in the nucleus might hinder nuclear dyes from interacting with the DNA. But this is only a speculation and we will try to clarify this further in the revised manuscript including alternative explanations.

Minor comments: 1. In the introduction on page 4 reference is missing for NF transport, aggregation and perikaryal accumulation (on line 93).

We will add a reference to the revised manuscript.

1. The statement in discussion on page 14 line 454 for Zhu et al., 1997 study is not accurate. It should be modified to sciatic nerve crush not spinal cord injury.

We will correct this mistake in the revised manuscript.

1. What is the size of the calpain cleaved NF-L tail domain? If you perform immunoblots on cell extracts treated with oxidative agents one would know it.

We will perform immunoblots on cell lysates and incorporate the corresponding results in the revised manuscript.

1. Authors could make their conclusions clear. This is particularly true for the experiments in Figure 4 panels c and d. It is very difficult to understand the conclusions of the experiments. First state the expectation and then described whether the expectation is true or different.

We will do as the reviewer suggested in the revised manuscript.

1. The ICC images are at extremely low magnification. They should be shown at 100x or 120x so that details of the cell body and the nucleus can be seen.

Our intention was to show larger fields of view and wherever appropriate insets, but we will try to improve this in the revised manuscript by either zooming in, cropping or adding additional insets with individual cell bodies and nuclei. In general, images were taken with an optimal resolution/pixel size in mind for any of the used objectives (60x/1.4 NA or 100x/1.49 NA) and we can easily modify our figure panels to show more details.

1. Oxidative damage leads to beaded accumulation of NF-L in neurites and axons. Authors should address this issue.

We will discuss this in the revised manuscript.

1. The combination treatment of the inhibitors (last 3 sets of the Fig. 4 b) has no statistical significance should be removed.

Actually, these differences were statistically significant (Supplementary Table 1). For clarity and as described in the figure legend (line 516: “The most relevant significant differences are indicated with an asterisk”) we showed only a subset of them on the graph, but we will change this in the revised manuscript.

1. Why only two antibodies recognize cleaved NF-L? If the antibodies at directed at tail region, they should recognize it unless the phosphorylated tail at Ser473 may inibit the antibody binding. In that case NF-L Ser473 specific antibody (EMD Millipore: MABN2431) may be used to test this idea.

This is a very good point that we also wonder about. Even if all antibodies are directed at tail region, exact epitopes are not described for all of them. That makes it also difficult for us to understand and speculate on this. However, we have already ordered the new antibody as suggested by the reviewer and we will experimentally test it.

**Referees cross-commenting**

I agree with the reviewer#1 about presenting the quantification data for the indicated figures to make conclusions strong and see how much of variation is there among sampled cells.

As discussed in our response to reviewer #1, we will provide additional quantifications.

3. Description of the revisions that have already been incorporated in the transferred manuscript

4. Description of analyses that authors prefer not to carry out

Reviewer #2, major comment 7. Authors could do chromatin immunoprecipitation (chip) analysis to identify NF-L binding sites on chromatin and perform gel shift assays to show NF-L tail domain binding to specific consensus DNA sequences.

We thank the reviewer for their suggestion. We are very interested in performing additional experiments and identifying the NFL binding sites on the DNA (either by chromatin immunoprecipitation or DamID-seq) and we intend to perform these experiments as soon as possible. Unfortunately, at the moment we do not have the expertise to perform such experiments in our lab. Instead, this type of follow-up project requires establishing a collaboration which is beyond the scope of this revision.

Author Response

Reviewer #1 (Public Review):

This paper examines EEG responses time-locked to (or "entrained" by) musical features and how these depend on tempo and feature identity. Results revealed stronger entrainment to "spectral flux" than to other, more commonly tested features such as amplitude envelope. Entrainment was also strongest for lowest rates tested (1-2 Hz).

The paper is well written, its structure is easy to follow and the research topic is explained in a way that makes it accessible to readers outside of the field. Results will advance the scientific field and give us further insights into neural processes underlying auditory and music perception. Nevertheless, there are a few points that I believe need to be clarified or discussed to rule out alternative explanations or to better understand the acquired data.

We thank the Reviewer for taking the time to evaluate our manuscript and for the positive response. We have now conducted further analyses to strengthen our conclusion that neural synchronization was strongest at slower musical tempi and to rule out an alternative explanation that neural synchronization was strongest for music presented near its own original or “natural” tempo. We also added some points to the Discussion in response to your comments; revised text is reproduced as part of our point-by-point responses below for your convenience. The page and line numbers correspond to the manuscript file without track changes.

1) Results reveal spectral flux as the musical feature producing strongest entrainment. However, entrainment can only be compared across features in an unbiased way if these features are all equally present in the stimulus. I wonder whether entrainment to spectral flux is only most pronounced because the latter is the most prominent feature in music. Can the authors rule out such an explanation?

Respectfully, it is not fully clear to us based on the literature that entrainment can only be compared across features fairly when those features are equally presented in the stimulus. Previous work in the speech domain has compared entrainment to amplitude envelope vs. spectrogram, vs. a symbolic representation of the time of occurrence of different phonemes (Di Liberto et al., 2015). Work in the music domain has compared entrainment to amplitude envelope (and its derivative) vs. features quantifying melodic expectation (surprise and entropy, quantified using a hidden Markov-model trained on a corpus of Western music; Di Liberto et al., 2020). In these papers, there was no quantification of the degree to which each feature was present in the stimulus material, and when comparing such qualitatively different features, it is not clear to us how one would do so. Nonetheless, these studies used the resulting TRF-based dependent measures to evaluate which feature best predicted the neural response. Here, although we do not know what acoustic feature might be most present / strongest in music, we believe that we can investigate the degree to which each feature predicts the neural response. In fact, we might argue the sort of reverse of the logic in your comment – that the TRF results actually tell us which feature is perceptually or psychologically the most important in terms of driving brain responses, which may not be fully predictable from the acoustics of those features.

From a data analysis perspective, we have independently normalized (z-scored) each feature as well as the neural data, as prescribed in Crosse et al., 2021, to try to level the playing field for the musical features we are comparing. Moreover, we made changes in the discussion to acknowledge your concern. The text is reproduced here for your convenience.

p. 26, l. 489-497: “One hurdle to performing any analysis of the coupling between neural activity and a stimulus time course is knowing ahead of time the feature or set of features that will well characterize the stimulus on a particular time scale given the nature of the research question. Indeed, there is no necessity that the feature that best drives neural synchronization will be the most obvious or prominent stimulus feature. Here, we treated feature comparison as an empirical question (Di Liberto et al., 2015), and found that spectral flux is a better predictor of neural activity than the amplitude envelope of music. Beyond this comparison though, the issue of feature selection also has important implications for comparisons of neural synchronization across, for example, different modalities.”

2) Spectral analyses of neural data often yield the strongest power at lowest frequencies. Measures of entrainment can be biased by the amount of power present, where entrainment increases with power. Can the authors rule out that the advantage for lower frequencies is a reflection of such an effect?

Thank you for this insightful comment. In response to your comment and the comments of Reviewer 3, we normalized the TRF correlations, stimulus–response correlations, and stimulus–response coherences by surrogate distributions that were calculated separately for each musical feature and – importantly – for every tempo condition. Following Zuk et al., 2021, we formed surrogate distributions by shifting the relevant neural data time course relative to the stimulus-feature time courses by a random amount. We did this 50 times, and for each shift re-calculated all dependent measures. We then normalized our dependent measures calculated from the intact time series relative to these surrogate distributions by subtracting the mean and dividing by the standard deviation of the surrogate distribution (“z-scoring”). Since the approach of shifting the neural data leaves the neural time series intact, the power spectrum of the data is preserved, but only its relationship to the stimulus is destroyed. After normalization, the plots obviously look a little different, but the main results – a higher level of neural synchronization to slower stimulation tempi and in response to the spectral flux – remain.

The changes can be found throughout the manuscript, but especially on p. 11, l. 210-218, Figures 2-3 and a more detailed explanation in the Methods section.

p. 39, l. 821-829: “In order to control for any frequency-specific differences in the overall power of the neural data that could have led to artificially inflated observed neural synchronization at lower frequencies, the SRCorr and SRCoh values were z-scored based on a surrogate distribution (Zuk et al., 2021). Each surrogate distribution was generated by shifting the neural time course by a random amount relative to the musical feature time courses, keeping the time courses of the neural data and musical features intact. For each of 50 iterations, a surrogate distribution was created for each stimulation subgroup and tempo condition. The z-scoring was calculated by subtracting the mean and dividing by the standard deviation of the surrogate distribution.”

A related point, what was the dominant rate of spectral flux in the original set of stimuli, before tempo was manipulated? Could it be that the slow tempo was preferred because in this case participants listened to a most "natural" stimulus?

This is a good point, thank you. We did two things to attempt to address this (see also comment Reviewer 3). First, the original tempo for each song can be found in Supplementary Table 1. To make the table more readable and more comparable with the main manuscript, we have updated the table and now state the original tempi in BPM and Hz. Second, we added histograms of the original tempi across all songs as well as the maximum amount by which all songs were tempo-shifted (i.e., the maximum tempo difference between the slowest (or fastest) version of each song segment compared to the original tempo). These histograms have been added to Figure 1 – figure supplement 2, and are paraphrased here for your convenience (p. 13 l. 265-273): The original tempo of the set of musical stimuli ranges between 1-2.75 Hz. This indeed overlaps with the tempo range that revealed strongest neural synchronization. When songs were tempo-shifted to be played at a slower tempo than the original, they were shifted by ~0.25-1.25 Hz. In contrast, shifting a song to have a faster tempo typically involved a larger shift of ~1-2.25 Hz. Thus, it is definitely possible that tempo, degree of tempo shift, and proximity to “natural” tempo were not completely independent values.

For that reason, to investigate the effects of the amount of tempo manipulation on neural synchronization, we conducted an additional analysis. We compared TRF correlations for a) songs that were shifted very little relative to their original tempi to b) songs that were shifted a lot relative to their original tempi. We did not have enough song stimuli to do this for every stimulation tempo, but we were able to do the TRF correlation comparison for two illustrative stimulation tempo conditions (at 2.25 Hz and 1.5 Hz). In those tempo conditions, we took the TRF correlations for up to three trials per participant when the original tempo was around the manipulated tempo (1.25-1.6 Hz for 1.5 Hz or 2.01-2.35 Hz for 2.25 Hz) and compared it to those trials where the original tempo was around 0.75¬–1 Hz faster or slower than the manipulated tempo at which the participants heard the songs (Figure 3 – figure supplement 2). This analysis revealed that there was no significant effect of the original music tempi on the neural response (please see Material and Methods, p. 40, l. 855-861 and Results p. 13, l. 265-273). In response to your and Reviewer’s 3 comments, we also added this additional point to the discussion.

p. 23-24 l. 427-436: “The tempo range within which we observed strongest synchronization partially coincides with the original tempo range of the music stimuli (Figure 1 – figure supplement 2). A control analysis revealed that the amount of tempo manipulation (difference between original music tempo and tempo the music segment was presented to the participant) did not affect TRF correlations. Thus, we interpret our data as reflecting a neural preference for specific musical tempi rather than an effect of naturalness or the amount that we had to tempo shift the stimuli. However, since our experiment was not designed to answer this question, we were only able to conduct this analysis for two tempi, 2.25 Hz and 1.5 Hz (Figure 3 – figure supplement 3), and thus are not able to rule out the influence of the magnitude of tempo manipulation on other tempo conditions.”

3) The authors have a clear hypothesis about the frequency of the entrained EEG response: The one that corresponds to the musical tempo (or harmonics). It seemed to me that analyses do not sufficiently take that hypothesis into account and often include all possible frequencies. Restricting the analysis pipeline to frequencies that are expected to be involved might reduce the number of comparisons needed and therefore increase statistical power.

Although we manipulated tempo, and so had an a priori hypothesis about the frequency at which the beat would be felt, natural music is a complex stimulus composed of different instruments playing different lines at different time scales, many or most of which are nonisochronous. Thus, we analyzed the data in two different ways – 1) based on TRFs and 2) based on stimulus–response correlation and coherence. Stimulus–response coherence is a frequency-domain measure, and so it was possible to do exactly as you suggest here and consider coherence only at the stimulation tempo and first harmonic, which we did (Figure 2E-J). However, for the TRF analyses, we followed previous literature (e.g., Ding et al., 2014; Di Liberto et al., 2020; Teng et al., 2021), and considered broader-band EEG activity (bandpass filtered at 0.5-30 Hz). Previous work has shown that the beat in music evokes a neural response at harmonics up to at least 4 times the beat rate (Kaneshiro et al., 2020), so we wanted to leave a broad frequency range intact in the neural data. Despite being based on differently filtered data, we found that the dependent measures from the two analysis approaches were correlated, which suggests to us that neural tracking at the stimulation tempo itself was probably the largest contributor to the results we observed here.

Related to your comment, we added two points to our discussion, which we reproduce here for your convenience.

p. 24-25, l. 453-461: “Regardless of the reason, since frequency-domain analyses separate the neural response into individual frequency-specific peaks, it is easy to interpret neural synchronization (SRCoh) or stimulus spectral amplitude at the beat rate and the note rate – or at the beat rate and its harmonics – as independent (Keitel et al., 2021). However, music is characterized by a nested, hierarchical rhythmic structure, and it is unlikely that neural synchronization at different metrical levels goes on independently and in parallel. One potential advantage of TRF-based analyses is that they operate on relatively wide-band data compared to Fourier-based approaches, and as such are more likely to preserve nested neural activity and perhaps less likely to lead to over- or misinterpretation of frequency-specific effects.”

p. 29 l. 564-577: “Despite their differences, we found strong correspondence between the dependent variables from the two types of analyses. Specifically, TRF correlations were strongly correlated with stimulation-tempo SRCoh, and this correlation was higher than for SRCoh at the first harmonic of the stimulation tempo for the amplitude envelope, derivative and beat onsets (Figure 4 - figure supplement 1). Thus, despite being computed on a relatively broad range of frequencies, the TRF seems to be correlated with frequency-specific measures at the stimulation tempo. The strong correspondence between the two analysis approaches has implications for how users interpret their results. Although certainly not universally true, we have noticed a tendency for TRF users to interpret their results in terms of a convolution of an impulse response with a stimulus, whereas users of stimulus–response correlation or coherence tend to speak of entrainment of ongoing neural oscillations. The current results demonstrate that the two approaches produce similar results, even though the logic behind the techniques differs. Thus, whatever the underlying neural mechanism, using one or the other does not necessarily allow us privileged access to a specific mechanism.”

Reviewer #2 (Public Review):

Kristin Weineck and coauthors investigated the neural entertainment to different features of music, specifically the amplitude envelope, its derivative, the beats and the spectral flux (which describes how fast are spectral changes) and its dependence on the tempo of the music and self-reports of enjoyment, familiarity and ease of beat perception.

They use and compare analysis approaches typically used when working with naturalistic stimuli: temporal response functions (TRFs) or reliable components analysis (RCA) to correlate the stimulus with its neural response (in this case, the EEG). The spectral flux seems the best music descriptor among the tested ones with both analyses. They find a stronger neural response to stimuli with slower beat rates and predictable stimuli, namely familiar music with an easy-to-perceive beat. Interestingly, the analysis does not show a statistically significant difference between musicians and non-musicians.

The authors provide an extensive analysis of the data, but some aspects need to be clarified and extended.

We thank the Reviewer for taking the time to evaluate and summarize our manuscript and for the great comments. We addressed the concerns and made changes throughout the manuscript, but especially in the introduction and discussion sections about the terminology (neural entrainment and neural measures), musical features of the stimuli, and musical experience of the participants. Below you can find the alterations described in more detail. The page and line numbers correspond to the manuscript file without track changes.

1) It would be helpful to clarify better the concepts of neural entertainment, synchronization and neural tracking and their meaning in this specific context. Those terms are often used interchangeably, and it can be hard for the reader to follow the rest of the paper if they are not explicitly defined and related to each other in the introduction. Note that this is fundamental to understanding the primary goal of the paper. The authors clarify this point only at the end of the discussion (lines 570-576). I suggest moving this part in the introduction. Still, it is unclear why the authors use the TRF model and then say they want to be agnostic about the physiological mechanisms underlying entertainment. The choice of the TRF (as well as the stimulus representation) automatically implies a hypothesis about a physiological mechanism, i.e., the EEG reflects convolution of the stimulus properties with an impulse response. Please could you clarify this point? I might have missed it.

Thank you for this valuable comment. We agree that it is fundamental to define and uniformly use terminology, and have made changes throughout the manuscript along these lines. First of all, we have changed all instances of “neural entrainment” or “neural tracking” to “neural synchronization”, as we think this term avoids evoking a specific theoretical background or strong mechanistic assumptions. Second, we have moved the Discussion paragraph you mention to the Introduction and expanded it. Specifically, we take the opportunity to address the association between specific analysis approaches (TRFs vs. stimulus–response correlation or coherence) and specific mechanistic assumptions (convolution of stimulus properties with an impulse response vs. entrainment of an ongoing oscillation, respectively). This allowed us to clarify what we mean when we say we prefer to stay agnostic to specific mechanistic interpretations. We are happy to have had the chance to strengthen this discussion, and think it benefits the manuscript a lot.

We reproduce the new Introduction paragraph here for your convenience.

p. 5-6, l. 101-123: “The current study investigated neural synchronization to natural music by using two different analysis approaches: Reliable Components Analysis (RCA) (Kaneshiro et al., 2020) and temporal response functions (TRFs) (Di Liberto et al., 2020). A theoretically important distinction here is whether neural synchronization observed using these techniques reflects phase-locked, unidirectional coupling between a stimulus rhythm and activity generated by a neural oscillator (Lakatos et al., 2019) versus the convolution of a stimulus with the neural activity evoked by that stimulus (Zuk et al., 2021). TRF analyses involve modeling neural activity as a linear convolution between a stimulus and relatively broad-band neural activity (e.g., 1–15 Hz or 1–30 Hz; (Crosse et al., 2016, Crosse et al., 2021); as such, there is a natural tendency for papers applying TRFs to interpret neural synchronization through the lens of convolution (though there are plenty of exceptions to this e.g., (Crosse et al., 2015, Di Liberto et al., 2015)). RCA-based analyses usually calculate correlation or coherence between a stimulus and relatively narrow-band activity, and in turn interpret neural synchronization as reflecting entrainment of a narrow-band neural oscillation to a stimulus rhythm (Doelling and Poeppel, 2015, Assaneo et al., 2019). Ultimately, understanding under what circumstances and using what techniques the neural synchronization we observe arises from either of these physiological mechanisms is an important scientific question (Doelling et al., 2019, Doelling and Assaneo, 2021, van Bree et al., 2022). However, doing so is not within the scope of the present study, and we prefer to remain agnostic to the potential generator of synchronized neural activity. Here, we refer to and discuss “entrainment in the broad sense” (Obleser and Kayser, 2019) without making assumptions about how neural synchronization arises, and we will moreover show that these two classes of analyses techniques strongly agree with each other.”

2) Interestingly, the neural response to music seems stronger for familiar music. Can the authors clarify how this is not in contrast with previous works that show that violated expectations evoke stronger neural responses ([Di Liberto et al., 2020] using TRFs and [Kaneshiro et al., 2020] using RCA])? [Di Liberto et al., 2020] showed that the neural response of musicians is stronger than non-musicians as they have a stronger expectation (see point 2). However, in the present manuscript, the analysis does not show a statistically significant difference between musicians and non-musicians. The authors state that they had different degrees of musical training in their dataset, and therefore it is hard to see a clear difference. Still, in the "Materials and Methods" section, they divided the participants into these two groups, confusing the reader.

Our findings are consistent with previous studies showing stronger inter-subject correlation in response music in a familiar style vs. music in an unfamiliar style (Madsen et al., 2019) and stronger phase coherence in response to familiar relative to unfamiliar sung utterances (Vanden Bosch der Nederlanden et al., 2022). We actually don’t think our results (stronger neural synchronization for familiar music) or these previous results are incompatible with work showing that violations of expectations evoke stronger neural responses. This work either manipulated music so it violated expectations (Kaneshiro et al., 2020) or explicitly modeled “surprisal” as a feature (Di Liberto et al., 2020). Thus, we could think of those stronger neural responses to expectancy violations as reflecting something like “prediction error”. Our music stimuli did not contain any violations, and we were unable to model responses to surprisal given the nature of our music stimuli, as we better explain below (p. 27 l. 514-529). Thus, neural synchronization was stronger to familiar music, and we would argue that listeners were able to form stronger expectations about music they already knew. We would predict that expectancy violations in familiar music would evoke stronger neural responses to those in unfamiliar music, though we did not test that here. We now include a paragraph in the Discussion reconciling our findings with the papers you have cited.

p. 27 l. 514-529: “We found that the strength of neural synchronization depended on the familiarity of music and the ease with which a beat could be perceived (Figure 5). This is in line with previous studies showing stronger neural synchronization to familiar music (Madsen et al., 2019) and familiar sung utterances (Vanden Bosch der Nederlanden et al., 2022). Moreover, stronger synchronization for musicians than for nonmusicians has been interpreted as reflecting musicians’ stronger expectations about musical structure. On the surface, these findings might appear to contradict work showing stronger responses to music that violated expectations in some way (Kaneshiro et al., 2020, Di Liberto et al., 2020). However, we believe these findings are compatible: familiar music would give rise to stronger expectations and stronger neural synchronization, and stronger expectations would give rise to stronger “prediction error” when violated. In the current study, the musical stimuli never contained violations of any expectations, and so we observed stronger neural synchronization to familiar compared to unfamiliar music. There was also higher neural synchronization to music with subjectively “easy-to-tap-to” beats. Overall, we interpret our results as indicating that stronger neural synchronization is evoked in response to music that is more predictable: familiar music and with easy-to-track beat structure.”

Your other question was why we did not see effects of musical training / sophistication on neural synchronization to music, when other studies have. There are a few possible reasons for this. One is that previous studies aiming to explicitly test the effects of musical training recruited either professional musicians or individuals with a high degree of musical training for their “musician” sample. In contrast, we did not target individuals with any degree of musical training, but attempted this analysis in a post-hoc way. For this reason, our musicians and nonmusicians were not as different from each other in terms of musical training as in previous work. Given this, we have opted to remove the artificial split into musician and nonmusician groups, and now only include a correlation with musical sophistication (as you suggest in your next comment), which was also nonsignificant (Figure 5 – figure supplement 2).

3) Musical expertise was also assessed using the Goldsmith Music Sophistication Index, which could be an alternative to the two-group comparison between musicians and non-musicians. Does this mean that in Figure 5, we should see a regression line (the higher the Gold-MSI, the higher should be the TRF correlation)? Since we do not see any significant effect, might this be due to the choice of the audio descriptor? The spectral flux is not a high-level descriptor; maybe it is worth testing some high-level descriptors such as entropy and surprise. The choice of the stimulus features defines linear models such as the TRF as they determine the hierarchical level of auditory processing, and for testing the musical expertise, we might need more than acoustic features. The authors should elaborate more on this point.

It is true that the Goldsmith Music Sophistication Index serves as an alternative way of investigating the effects of musical expertise on neural synchronization to natural music, and we now include this approach exclusively instead of dividing our sample (see response to the previous comment). Indeed, if musical sophistication would have an effect on the TRF correlations in this study, we would see a regression line in Figure 5 – figure supplement 2. Based on our experiment it is difficult to assess whether the lack of a correlation between neural measures and musical expertise is based on our choice of stimulus features. That is because our experiment was designed to investigate the effects of fundamental acoustic features of music, and it was not possible to calculate high-level descriptors, such as the entropy or surprisal, for the music stimuli we chose to work with – the stimuli were polyphonic, and moreover were purchased in a .wav format, so we do not have access to the individual MIDI versions or sheet music of each song that would have been necessary to apply, for example, the IDyOM (Information Dynamics of Music) model. As we cannot rule out that the (lack of) effects of varying levels of musical expertise on TRF correlations is due to our choice of stimulus features, we added this to the discussion.

p. 28 l. 541-546: “Another potential reason for the lack of difference between musicians and non-musicians in the current study could originate from the choice of utilizing pure acoustic audio-descriptors as opposed to “higher order” musical features. However, “higher order” features such as surprise or entropy that have been shown to be influenced by musical expertise (Di Liberto et al., 2020), are difficult to compute for natural, polyphonic music.”

4) Regarding the stimulus representation, I have a few points. The authors say that the amplitude envelope is a too limited representation for music stimuli. However, before testing the spectral flux, why not test the spectrogram as in previous studies? Moreover, the authors tested the TRF on combining all features, but it was not clear how they combined the features.

One of the main reasons that we did not use the spectrogram as a feature was that it wouldn’t be possible to use a two-dimensional representation for the RCA-based measures, SRCorr and SRCoh, so we would not have been able to compare across analysis approaches. However, spectral flux is calculated directly from the spectrogram, and so is a useful one-dimensional measure that captures the spectro-temporal fluctuations present in the spectrogram (https://musicinformationretrieval.com/novelty_functions.html). Thank you for making this important point, we added this explanation to the Materials and Methods section (p. 35 l. 726-727).

Sorry for not explaining the multivariate TRF approach better. Instead of using only one stimulus feature, e. g. the amplitude envelope, several stimulus features can be concatenated into a matrix (with the dimensions: time T x 4 musical features M at different time lags), which is then used as an input for the mTRFcrossval, mTRFtrain and mTRFpredict of the mTRF Matlab Toolbox (Crosse et al., 2016) – actually this is exactly how using a 2D feature like the spectrogram would work. The multivariate TRF is calculated by extending the stimulus lag matrix (time course of one musical feature at different time lags, T × τwindow) by an additional dimension (time course of several musical features at different time lags, T × M x τwindow). We added an explanation to the Methods section of the manuscript and hope that it is this way better understandable:

p. 39 l. 840-842: “For the multivariate TRF approach, the stimulus features were combined by replacing the single time-lag vector by several time-lag vectors for every musical feature (Time x 4 musical features at different time lags).”

Reviewer #3 (Public Review):

Subjects listened to various excerpts from music recordings that were designed to cover musical tempi ranging from 1-4 Hz, and EEG was recorded as subjects listened to these excerpts. The main and novel findings of the study were: 1) spectral flux, measuring sudden changes in frequency, were tracked better in the EEG than other measures of fluctuations in amplitude, 2) neural tracking seemed to be best for the slowest tempi, 3) measures of neural tracking were higher when subject's rated an excerpt as high for ease-of-tapping and familiarity, and 4) their measure of the mapping between stimulus feature and response could predict whether a subject tapped at the expected tempo or at 2x the expected tempo after listening to the musical excerpt.

One of the key strengths of this study is the use of novel methodologies. The authors in this study used natural and digitally manipulated music covering a wide range of tempi, which is unique to studies of musical beat tracking. They also included both measures of stimulus-response correlation and phase coherence along with a method of linear modeling (the temporal response function, or TRF) in order to quantify the strength of tracking, showing that they produce correlated results. Lastly, and perhaps most importantly, they also had subjects tap along with the music after listening to the full excerpt. While having a measure of tapping rate itself is not new, combined with their other measures they were able to demonstrate that neural data predicted the hierarchical level of tapping rate, opening up opportunities to study the relationship between neural tracking, musical features, and a subject's inferred metrical level of the musical beat.

Additionally, the finding that spectral flux produced the best correlations with the EEG data is an important one. Many studies have focused primarily on the envelope (amplitude fluctuations) when quantifying neural tracking of continuous sounds, but this study shows that, for music at least, spectral flux may add information that is tracked by the EEG. However, given that it is also highly correlated with the envelope, what additional features spectral flux contributes to measuring EEG tracking is not clear from the current results and worth further study.

All four of their main findings are important for research into the neural coding of musical rhythm. I have some concerns, however, that two of these findings could be a consequence of the methods used, and one could be explained by related correlations to acoustic features:

We thank the Reviewer for the very helpful review, the summary, and the great suggestions. We addressed the comments and performed additional analysis. We made changes throughout the manuscript, but especially 1) concerning the potential advantage of the neural response to slower music, 2) the effects of the amount of tempo manipulation on neural synchronization, 3) the SVM-related analysis and 4) the relation between stimulus features and behavioral ratings. The implemented modifications can be found below in more detail. The page and line numbers correspond to the manuscript file without track changes.

The authors found that their measures of neural tracking were highest for the lowest musical tempos. This is interesting, but it is also possible that this is a consequence of lower frequencies producing a large spread of correlations. Imagine two signals that are fluctuating in time with a similar pattern of fluctuation. When they are correctly-aligned they are correlated with each other, but if you shift one of the signals in time those fluctuations are mismatched and you can end up with zero or negative correlations. Now imagine making those fluctuations much slower. If you use the same time shifts as before, the signals will still be fairly correlated, because the rates of signal change are much longer. As a result, the span of null correlations also increases. This can be corrected by normalizing the true correlations and prediction accuracies with a null distribution at each tempo. But with this in mind, it is hard to conclude if the greater correlations found for lower musical tempos in their current form are a true effect.

Thank you for this great suggestion. We followed your lead (Zuk et al., 2021), and normalized all measures of neural synchronization (TRF correlation, SRCorr, SRCoh) relative to a surrogate distribution. The surrogate distribution was calculated by randomly and circularly shifting the neural data relative to the musical features for each of 50 iterations. This was done separately for every musical feature and stimulation tempo condition (Figures 2 and 3). After normalization, the results look qualitatively similar and the main results – spectral flux and slow stimulation tempi resulting in highest levels of neural synchronization – persist.

The changes in the manuscript based on your comment (and the comment of Reviewer 1) can be found throughout the manuscript, but especially on p. 11, l. 210-218, Figures 2-3 and a more detailed explanation in the Methods section:

p. 39, l. 821-829: “In order to control for any frequency-specific differences in the overall power of the neural data that could have led to artificially inflated observed neural synchronization at lower frequencies, the SRCorr and SRCoh values were z-scored based on a surrogate distribution (Zuk et al., 2021). Each surrogate distribution was generated by shifting the neural time course by a random amount relative to the musical feature time courses, keeping the time courses of the neural data and musical features intact. For each of 50 iterations, a surrogate distribution was created for each stimulation subgroup and tempo condition. The z-scoring was calculated by subtracting the mean and dividing by the standard deviation of the surrogate distribution.”

If the strength of neural tracking at low tempos is a true effect, it is worth noting that the original tempi for the music clips span 1 - 2.5 Hz (Supplementary Table 1), roughly the range of tempi exhibiting the largest prediction accuracies and correlations. All tempos above this range are produced by digitally manipulating the music. It is possible that the neural tracking measures are higher for music without any digital manipulations rather than reflecting the strength of tracking at various tempi. This could also be related to the author's finding that neural tracking was better for more familiar excerpts. This alternative interpretation should be acknowledged and mentioned in the discussion.

Thank you for these important suggestions (see also comment #2 (part 2) from Reviewer 1). First up, it is important to say that all music stimuli were tempo manipulated: even if the tempo of an original music segment was e. g. 2 Hz and the same song was presented at 2 Hz, it was still converted via the MAX patch to 2 Hz again (to make it comparable to the other musical stimuli). Second, it is true that we cannot fully exclude the possibility that the amount of tempo manipulation could have an effect on neural synchronization to music – meaning that less tempo manipulated music segments (so a stimulation tempo close to the original tempo) could result in higher neural synchronization. However, we have now conducted an additional analysis to address this as best we could.

We compared TRF correlations for a) songs that were shifted very little relative to their original tempi to b) songs that were shifted a lot relative to their original tempi. We did not have enough song stimuli to do this for every stimulation tempo, but we were able to do the TRF correlation comparison for two illustrative stimulation tempo conditions (at 2.25 Hz and 1.5 Hz). In those tempo conditions, we took the TRF correlations for up to three trials per participant when the original tempo was around the manipulation tempo (1.25-1.6 Hz for 1.5 Hz or 2.01-2.35 Hz for 2.25 Hz) and compared it to those trials where the original tempo was around 0.75¬–1 Hz faster or slower than the manipulated tempo at which the participants heard the songs (Figure 3 – figure supplement 2). This analysis revealed that there was no significant effect of the original music tempi on the neural response (please see Material and Methods, p. 40, l. 855-861 and Results p. 13, l. 265-273). In response to your and Reviewer’s 1 comments, we also added it to the discussion.

p. 23-24 l. 427-436: “The tempo range within which we observed strongest synchronization partially coincides with the original tempi of the music stimuli (Figure 1 – figure supplement 2). A control analysis revealed that the amount of tempo manipulation (difference between original music tempo and tempo the music segment was presented to the participant) did not affect TRF correlations. Thus, we interpret our data as reflecting a neural preference for specific musical tempi rather than an effect of naturalness or the amount that we had to tempo shift the stimuli. However, since our experiment was not designed to answer this question, we were only able to conduct this analysis for two tempi, 2.25 Hz and 1.5 Hz (Figure 3 – figure supplement 3), and thus are not able to rule out the influence of tempo manipulation on other tempo conditions.”

We also provide more information to the reader about the amount of tempo shift that each stimulus underwent. We added two plots to the manuscript that show 1) the distribution of original tempi of the music stimuli and 2) the distribution of the amount of tempo manipulation across all stimuli (Figure 1 – figure supplement 2).

Their last finding regarding predicting tapping rates is novel and important, and the model they use to make those predictions does well. But I am concerned by how well it performs (Figure 6), since it is not clear what features of the TRF are being used to produce this discrimination. Are the effects producing discriminable tapping rates and stimulation tempi apparent in the TRF? I noticed, though, that these results came from two stages of modeling: TRFs were first fit to groups of excerpts with different tapping rates or stimulation tempo separately, then a support vector machine (SVM) was used to discriminate between the two groups. So, another way to think about this pipeline is that two response models (TRFs) were generated for the separate groups, and the SVM finds a way of differentiating between them. There is no indication about what features of the TRFs the SVM is using, and it is possible this is overfitting. Firstly, I think it needs to be clearer how the TRFs are being computed from individual trials. Secondly, the authors construct surrogate data by shuffling labels (before training) but it is not clear at which training stage this is performed. They can correct for possible issues of overfitting by comparing to surrogate data where shuffling happens before the TRF computation, if this wasn't done already.

Thank you for noticing this important point. You are absolutely right – when re-analyzing that part of the results based on your comment, we noticed that we had an error in our understanding of the analysis pipeline. Indeed, we first calculated two TRF models for the separate groups (e. g. stimulation tempo = tapping tempo vs. stimulation tempo = 2* tapping tempo) based on all trials of each group apart from the left-out-trial. Next, the resulting TRFs were fed into the SVM which was used to predict the group. The shuffling of the surrogate data occurred at the SVM training step.

Based on your comment, we tried several approaches to solve this problem. First, we calculated TRFs on a single-trial basis (instead of using the two-group TRFs as before, only one trial was used to calculate the TRFs) and submitted the resulting TRFs to the SVM. The resulting SVM accuracy was compared to a “surrogate SVM accuracy” which was calculated based on shuffling the labels when training the SVM classifier. Second, we shuffled, as you suggest, the labels not at the SVM training step, but instead prior to the TRF calculation. This way we could compare our “original” SVM accuracies (based on the two-group TRFs) to a fairer surrogate dataset. However, in both cases the resulting SVM accuracies did not perform better than the surrogate data. Therefore, we felt that it is the fairest to remove this part from the manuscript. We are aware that this was one of the main results of the paper and we are sorry that we had to remove it. However, we feel that our paper is still strong and offers a variety of different results that are important for the auditory neuroscience community.

Lastly, they show that their measures of neural tracking are larger for music with high familiarity and high ease-of-tapping. I expect these qualitative ratings could be a consequence of acoustic features that produce better EEG correlations and prediction accuracies, especially ease-of-tapping. For example, music with acoustically-salient events are probably easier to tap to and would produce better EEG correlations and prediction accuracies, hence why ease-of-tapping is correlated with the measures of neural tracking. To understand this better, it would be useful to see how the stimulus features correlate with each of these behavioral ratings.

We agree that our rating-based results could be influenced by acoustic stimulus features (at least for ease of tapping, it’s actually not clear to us why familiarity would be related to acoustics). As it is difficult to correlate stimulus features (time-domain, and one time course per song) with behavioral ratings (one single value per song per participant), we conducted frequency-domain analysis on the musical features to arrive at a single value quantifying the strength of spectral flux at the stimulation frequency and its first harmonic. We calculated single-trial FFTs on the spectral flux (which was used for the main Figure 5) for the 15 highest- and 15 lowest-rated trials per behavioral category (enjoyment, familiarity, ease to tap the beat) and participant. We compared the z-scored FFT peaks at the stimulation tempo and first harmonic for the top- and bottom-rated stimuli. We did observe significant acoustic differences between top- and bottom-rated stimuli in each category, but the differences were not in the direction that would be expected based on acoustically more salient events leading to better TRF correlations, with the exception of ease of tapping. Easy-to-tap music did indeed have stronger spectral flux than difficult-to-tap music, which is intuitive. However, spectral flux was stronger for more enjoyed music (we did not see any significant differences between TRF correlations of more vs. less enjoyed music; Figure 5C) and for less familiar music (this is the opposite of what we saw for the TRF measures). Overall, given the inconsistent relationship between acoustics, behavioral ratings, and TRF measures, we would argue that acoustic features alone cannot solely explain our results (Figure 5 – figure supplement 1, p. 21 l. 381 – 387).

MIT/Brown Vannevar Bush 研讨会于 1995 年 10 月 12 日至 13 日在麻省理工学院举行，以庆祝1945 年 7 月在大西洋月刊上发表的万尼瓦尔·布什（Vannevar Bush）的开创性文章《诚如所思》（As We May Think）发表50 周年。活动视频分五部分，这是第一部分，内容主要是：

1.Paul Penfield的开幕致辞。

2.Andy van Dam介绍万尼瓦尔·布什及其经历。

3.Paul Kahn（Memex专家）：布什作品的视觉之旅（Visual tour of Bush's work）。

4.道格拉斯·恩格尔巴特（Douglas Engelbart）：对集体智慧的战略追求（The Strategic Pursuit of Collective IQ）。内容介绍：对我来说，布什在《诚如我思》中留下的遗产直接关系到提高人类组织所代表的社会有机体的集体智慧的非常真实和重要的潜力。最认真和有效地追求这种潜力的公司、机构--实际上是国家--显然会有强大的成功/生存优势。除此之外，整个人类能否在一个健康和 "人性化 "的社会、政治、经济和生态环境中生存，很可能取决于我们如何尽快和有效地明确追求这一潜力。

认真的追求将涉及到我们思考方式的许多变化，与 "我们工作方式"的许多同步变化相协调--以及我们可以合作、分享、扮演新的角色、行使新的/不同的技能和方法集，等等。简而言之，这将涉及到将人类的基本感觉、运动、精神和学习能力与集体开发、整合和应用知识的任务相结合的根本性新方法。

有效的追求将需要一种战略方法，其接受程度肯定会涉及到一些普遍存在的范式的关键转变。我想描述一下它们，以及它们在追求大规模集体智商显著提高的候选"引导"战略中的相对作用。

技术只是该战略中的一个重要因素，在这个因素中，关键是要加快开放的超文件系统的发展，要有适当的通用功能、应用领域、互操作性和可扩展性的目标。WWW/HTML的激动人心的出现提供了一个极其重要的推动力；我想描述一下下一阶段向OHS目标演变的一些候选者。

5.泰德·尼尔森（Theodor Holm Nelson）：小路通向何方（Where the Trail Leads）。内容介绍：像任何简洁的预言作品一样，《诚如我思》支持许多解释，并导致推断的问题。我们今天聚集在一起表示敬意，并争论谁的想法最忠实地表达了最初所说的内容。

布什预见到了一个可公开访问的、快速访问的连接性文献，这将允许人们发表已经存在的材料之间的连接。但他所预见的结构，即他所称的"线索"，与今天的意大利面条式的超文本相当不同；布什的结构是基于转包（transclusion）而不是链接。它值得详细研究。

经过适当的推断和打磨，我相信这个想法会导致跨平行媒体（连接的对象与它们的连接一起被看到），以及设计一个广泛的版权安排，以便不受约束地重新使用。

MIT/Brown Vannevar Bush 研讨会于 1995 年 10 月 12 日至 13 日在麻省理工学院举行，以庆祝1945 年 7 月在大西洋月刊上发表的万尼瓦尔·布什（Vannevar Bush）的开创性文章《诚如所思》（As We May Think）发表50 周年。活动视频分五部分，这是第三部分，内容主要是：1.迈克·莱斯克（Michael Lesk）发表的演讲：“信息检索的七个时代”（The Seven Ages of Information Retrieval）。内容介绍：万尼瓦尔·布什（Vannevar Bush）在1945年的文章中提出了一个快速获取世界图书馆内容的目标，看起来它将在65年后的2010年实现。因此，它的历史堪比一个人的历史。信息检索在20世纪50年代和60年代初有其学生时代的研究阶段；然后在20世纪70年代努力争取采用，但在20世纪80年代和90年代，随着自由文本检索系统的常规使用，它已被接受。例如，我的公司不再用纸印刷其公司电话簿。现在，它正在继续前进，开展声音和图像检索项目，同时以电子方式提供现在图书馆中的大部分内容。我们可以期待着布什的梦想在一个生命周期内完成。2.第 1 天小组讨论。

MIT/Brown Vannevar Bush 研讨会于 1995 年 10 月 12 日至 13 日在麻省理工学院举行，以庆祝1945 年 7 月在大西洋月刊上发表的万尼瓦尔·布什（Vannevar Bush）的开创性文章《诚如所思》（As We May Think）发表50 周年。活动视频分五部分，这是第二部分，内容主要是：

1.罗伯特·卡恩（Robert Kahn）发表的演讲：“用数字技术增强布什的愿景”（Augmenting Bush's Vision with Digital Technology）。内容介绍：尽管万尼瓦尔·布什（Vannevar Bush）在他的经典论文《诚如所思》（As We May Think）中描述了信息共享的重要性，但他的视野必然受到当时技术的限制。特别是，我们现在认为理所当然的数字计算和通信技术甚至还没有进入他的参考框架。本讲座将探讨计算机和通信基础设施的可能演变，以及架构、技术和智能在该系统中的作用。连通性以及几乎无限的数字对象、通用服务和应用将刺激网络中的思想共享、各种联合活动、虚拟实体和团队工作。在分布式任务执行的背景下，网络内和网络外的软件代理的作用将被考虑。最后，将对智能分布式系统的前景进行探讨。

2.蒂姆·伯纳斯-李（Tim Berners-Lee）发表的演讲：“超文本和我们的集体命运”（Hypertext and Our Collective Destiny）。内容介绍：布什考虑到研究人员被无法获取的信息所淹没的困境。他提出了MEMEX，一种可以快速访问并允许信息片段之间随机链接的机器。此后，网络和计算机使我们在速度和便利性方面超过了这个带有远见的设想。然而，我们在解决政治问题、管理大型组织或放大我们的团体直觉的能力方面没有看到巨大的进步。 我们必须做得更多，而不是赋予个人权力。我们必须让一起互动的人和机器以新的方式作为一个群体来行事。现在，我们可以通过我们的信息制造线索，我们必须创造一个基质，在这个基质中，这些线索将成长为一个越来越有意义的整体，而不是一个纠结的群体。我们和我们的文件能够作为一个大型机器一起运作，但不是作为一个大型的头脑。各种规模的团体都必须获得直觉、关联和发明的天赋，这些天赋我们通常与人而不是机器联系在一起，然后我们才能迎接布什对人类的挑战，"在种族经验的智慧中成长"，而不是 "在冲突中灭亡"。

Author Response

Reviewer #1 (Public Review):

The authors set out to consider more the role of the predator in predator-prey interactions, particularly from a collective locomotion aspect. This is an aspect which at times has been overlooked, with many theories, experiments and models focusing largely on the prey response, independent of how the predator behaves. The major strengths are the (1) excellent writing, (2) quality of the figures, (3) quantity of data, and (4) question tackled. The major weaknesses are (1) the volume of information (as a reader, it is quite hard to distil key points from the sheer volume of what has been presented), (2) the confined captive environment making it difficult to draw comparisons with a wild-type scenario, and (3) lack of clarity about the wider implications of the work outside of the immediate field.

We thank the reviewer for their thoughtful review and positive comments. To address the weaknesses highlighted by the reviewer, we have revised our manuscript throughout.

Reviewer #2 (Public Review):

The manuscript describes a laboratory-based predator-prey experiment in which pike hunt shiner fish as a way to gain insight into the selective pressures driving the evolution of collective behavior. Unlike the predictions of classical theoretical work in which prey on the edge of social groups are considered to be at highest risk of predation, the fish in the center of the school were primarily targeted by the pike. This is because the pike uses a hunting behavior in which it slowly moves to the center of the school, seemingly undetected, until it rapidly attacks prey directly in front of its snout. This study also differs from previous studies in that both the predator and prey motion are examined, and the success of predation attempts was precisely determined. While the study demonstrates why shiners would be under selective pressure to avoid the center of a school, I am not convinced that the results explain why shiners evolved to have schooling behavior.

The reviewer indeed highlights one of the main findings of our study, that fish closer to the group center are more at risk of being attacked by pike. They also give a proper account of its possible explanation, and highlight some of the main ways in which our study differs from previous work. The reviewer states that our results do not explain why shiners evolved to school. We agree and note that we also don’t claim this anywhere in the manuscript. Rather, we state our study provides important new insights about differential predation risk in groups of prey and highlight the important role of predator attack strategy and decision-making and prey response, with potential repercussions for the costs and benefits of grouping.

We have considerably revised our introduction to better explain the importance of understanding differential predation risk in animal groups (lines 36-50): A key challenge in the life of most animals is to avoid being eaten. Via effects such as enhanced predator detection (Lima, 1995; Magurran et al., 1985), predator confusion (Landeau and Terborgh, 1986), and risk dilution effects (Foster and Treherne, 1981; Turner and Pitcher, 1986), individuals living and moving in groups can reduce their risk of predation (Ioannou et al., 2012; Krause and Ruxton, 2002; Pitcher and Parrish, 1993; Ward and Webster, 2016). This helps explain why strong predation pressure is known to drive the formation of larger and more cohesive groups (Beauchamp, 2004; Krause and Ruxton, 2002; B. Seghers, 1974). However, the costs and benefits of grouping are not shared equally among individuals within groups, and besides differential food intake and costs of locomotion, group members themselves may experience widely varying risks of predation (Handegard et al., 2012; Krause, 1994; Krause and Ruxton, 2002). Where and who predators attack within groups not only has major implications for the selection of individual phenotypes, and thereby the emergence of collective behaviour and the functioning of animal groups (Farine et al., 2015; Jolles et al., 2020; Ward and Webster, 2016), but also shapes the social behaviour of prey and the properties and structure of prey groups. Hence, a better understanding of the factors that influence predation risk within animal groups is of fundamental importance.

And in the discussion now better explain the potential evolutionary consequences of the findings of our work (lines 456-466): Predation is seen as one of the main factors to shape the collective properties of animal groups (Herbert-Read et al., 2017) and has so far generally been seen as to drive the formation of larger, more cohesive groups that exhibit collective, coordinated motion (see e.g. Beauchamp, 2004; Ioannou et al., 2012; B. H. Seghers, 1974). Our finding that central individuals are more at risk of being predated could actually have the opposite effect, with schooling having a selective disadvantage and over time result in weaker collective behaviour and less cohesive schools. However, we do not deem this likely as selection is likely to be group-size dependent, as discussed above. Furthermore, our multi-model inference approach revealed that, despite more central individuals experiencing higher predation risk, being close to others inside the school was still associated with a lower risk of being targeted. As most prey experience many types of predators, including sit-and-wait predators and active predators that hunt for prey, the extent and direction of such selection effects will depend on the broader predation landscape in which prey find themselves.

Major strengths of the paper include the precise recording of the location and orientation of all fish at all times during the experiments. This indeed provides a rich dataset that can be used to search for the factors that predict the likelihood of attack and escape with higher statistical power.

The major concern I have about the manuscript is that the results somewhat contradict the aim of the paper as expressed in the introduction and discussion: that predator-prey interactions explain the emergent evolution of collective behavior. Figure 2C shows that fish in smaller clusters or those that were totally isolated experienced lower rates of predation and were not included in any subsequent analyses. This would suggest that shiners experiencing predation from pike would be under strong selection to avoid schooling behavior altogether. Can you compare the likelihood of predation for individuals in non-central school locations compared to individuals outside of schools altogether? It might be helpful to investigate whether other predators of shiners use predation strategies that target prey on the edge of the school to help explain why schooling could be useful. Did the likelihood of schooling decrease throughout the trials?

The reviewer makes a good point regarding the observation that pike tended to mainly attack individuals in the main school, questioning if this would result in a selective disadvantage for schooling. We would like to point out that this result is regarding the likelihood to attack an individual, not the likelihood for a successful attack. If we look at the later we find 5 out of 8 attacks away from the main school were successful, a ratio that is actually similar to that of the main school. More importantly, when wanting to understand how predation risk is linked to group size one needs to look at the per capita risk. If we do that for the group size we used in our study, despite a moderately elevated risk of being predated in a large group, the shiners in the main school still had considerably lower individual risk to be killed than those that occurred in small sub-groups or were alone. We would like to note that in our study the shiners did not really show proper fission-fusion behaviour and by far the majority of the time the shiners were in one large cohesive school. Therefore, we feel our dataset is not suitable for a proper investigation about the role of group size in predation risk.

We now clarify these points in the discussion (lines 467-471): While the finding that pike were more likely to attack the main school may also appear to indicate a selective disadvantage to school, calculating the per-capita-risk for each individual would actually reveal it is still safest to be part of the main school. Nevertheless, as the shiners in our study rarely exhibited fission-fusion dynamics we feel our dataset is not appropriate to make proper inferences about how predation risk is linked to group size.

We have also slightly extended the relevant sentences in the results to further clarify the clustering results (lines 144-150): We found that, by and large, the shiners were organised in one large, cohesive school at the time of attack and rarely showed fission-fusion behaviour (merging and splitting of schools) during the trials. Only occasionally there were one or two singletons besides the main school (25 attacks) or multiple clusters of more than two fish (12 attacks Figure 2C), which tended to exist relatively briefly (mean school size: 36.5 ± 0.8). In more than 80% of these cases, pike still targeted an individual in the main cluster (Figure 2C).

We now also provide more discussion about other predator types being likely to attack central prey (lines 343-354): That predators may actually enter groups and strike at central individuals is not often considered (Hirsch and Morrell, 2011), possibly because it contrasts with the long-standing idea that predation risk is higher on the edge of animal groups (Duffield and Ioannou, 2017; Krause, 1994; Krause and Ruxton, 2002; Stankowich, 2003). However, our finding is in line with the predictions of theoretical work that suggest that the extent of marginal predation may depend on attack strategy and declines with the distance from which the predator attacks (Hirsch and Morrell, 2011). Furthermore, increased risk of individuals near the centre of groups may be more widespread than currently thought. Predators not only exhibit stealthy behavioural tactics that enable them to approach and attack central individuals, as we show here, but may also do so by attacking groups from above (Brunton, 1997) or below (Clua and Grosvalet, 2001; Hobson, 1963; but see Romey et al., 2008), and by rushing into the main body of the group (Handegard et al., 2012; Hobson, 1963; Parrish et al., 1989).

We furthermore discuss the potential role of group size on the observed effects (lines 441-455): In particular, while group size is not expected to effect much whether ambush predators are likely to attack internal individuals, the specific risk of central individuals could both be hypothesized to decrease with group size, such as if the predator is more likely to attack when surrounded by prey, or to not be affected by it, such as if the predator actively targets central individuals. Whatever the process, the observed findings are likely for prey that move in groups of somewhat intermediate size; for very large groups, such as the huge schools encountered in the pelagic, ambush predators may simply not be able to attack the group centre due to spatial constraints. More generally, the tendency for predators to attack the centre of moving groups may depend on the medium in which the predator-prey interactions occur. As in the air there is potential for (fatal) collisions, and on land it is physically difficult for predators to enter groups and predators’ size advantage tends to be more limited, predators may be less likely to go for the group centre as compared to in aquatic or mixed (e.g. aerial predator hunting aquatic prey) systems. Hence, the important interplay we highlight between predator attack strategy and prey response may have different implications across different predator prey systems and warrants concerted further research effort.

Finally, in response to the reviewer’s question if the likelihood to school decreased through the trials, we did not see a change in packing faction (median nearest-neighbour distance) with repeated exposure to the pike, but shiners increasingly avoided the area directly in front of the pike’s head (lines 182-186): While the shiners did not show a change in their packing fraction (median nearest-neighbour distance) with repeated exposure to the pike (F1,52 = 1.81, p = 0.185), they increasingly avoided the area directly in front of the pike’s head (Appendix 2 – Figure 1A) resulting in the pike attacking from increasingly further away (target distance: F1,52 = 45.52, p < 0.001, see Appendix 2 – Figure 1B,C). See also further Appendix 2.

I am also curious whether tank size affects the behavior of the fish, both of the shiners and the pike. The pike seem to be approximately 1/3 the shortest length of the tank, and 6 inches of depth have constrained the movement to be mostly in the 2D plane. A lack of open space might limit the pike's ability to hunt in any way other than this stealthy strategy. Has this stealthy hunting strategy been described in other experiments in larger or more naturalistic conditions? Does open space affect the shiners' propensity to school? Although the manuscript describes that shiners tend to school near the surface of water, does the shallow depth affect the pike's behavior? The manuscript states that some pike never attacked -- were these the largest in the study?

While the tank is small relative to the real world, we actually decided on this size of ~2m2 based on previous experimental work on predator-prey dynamics. As we stated in the methods of the original manuscript (lines 543-545) we expect that if a much larger space would have been used, pike would actually still show the same approach and attack behaviour linked to their stealthy attack strategy. The stealthy hunting behaviour of pike and similar predators and their ability to thereby get very close to their prey has been described elsewhere (see e.g. references on lines 332-344 of the original manuscript).

We now better explain the potential limitation of the arena size in the discussion (lines 472-480): Laboratory studies on predator-prey dynamics like ours do, of course, have their limitations. Although the size of the arena we used (~2m2) is in line with behavioural studies with large schools of fish (e.g. Sosna et al., 2019; Strandburg-Peshkin et al., 2013) and experiments with live predators attacking schooling prey (Bumann et al., 1997; Magurran and Pitcher, 1987; Neill and Cullen, 1974; Romenskyy et al., 2020; Theodorakis, 1989), compared to conditions in the wild the prey and predator had limited space to move. However, as pike are ambush predators they tend to move relatively little to search for prey and rather rely on prey movement for encounters (Nilsson and Eklöv, 2008). Increasing tank size would have made effective tracking extremely difficult, or impossible, and while a much larger tank is expected to considerably increase latency to attack, we expect it to have relatively little effect on the observed findings.

We agree that the shallow depth of the tank is a limitation of our study and may have somewhat restricted the pikes’ natural behaviour, although pilot experiments showed that the pike exhibited normal movements and attack behaviours. Fish were tested in very shallow water to be able to acquire detailed individual-based tracking of the schools as well as compute features related to the visual field of the fish. We would also like to note that both shiners and pike can often be found in the littoral zone and come in very shallow water of only a few 10s of cm (see e.g. Krause et al., 2000b; Pierce et al., 2013; Skov et al., 2018), with some experimental work furthermore showing that pike may actually prefer shallow water (Hawkins et al., 2005). We don’t think that increasing the depth of the tank would have considerably changed the predatory behaviour of the pike, as the pike would be expected to still use their stealthy approach to get close to their prey even if the prey school would be more three-dimensional.

We now provide a much more extensive discussion of the limited depth used in the discussion (lines 480-494): In terms of water depth, fish were tested in relatively very shallow water. This was primarily done to be able to keep track of individual identities and compute features related to the visual field of the fish. Shiners naturally school in very shallow water conditions as well as near the surface in deeper water in the wild (Hall et al., 1979; Krause et al., 2000b; Stone et al., 2016) and also pike primarily occur in the shallow littoral zone, sometimes only a few of tens of cm deep (Pierce et al., 2013; Skov et al., 2018). Furthermore, pilot experiment showed the pike did exhibit normal swimming and attack behaviour with attack speeds and acceleration comparable to previous work (Domenici and Blake, 1997; Walker et al., 2005). Recent other work on predator-prey dynamics did not find a considerable impact of adding the third dimension to their analyses (Romenskyy et al., 2020). Still, the water depth used is a limiting factor of our study and in the future this type of work should be extended to deeper water while still keeping track of individual identities over time. We expect that adding the third dimension would not change the stealthy attack behaviour of the pike and therefore still put more central individuals most at risk, but possibly attack success would be reduced because of increased predator visibility and prey escape potential in the vertical plane, which remains to be tested.

We did not observe a relationship between pike size and tendency to attack.

Reviewer #3 (Public Review):

While it has long been clear that animals in groups (e.g., fish schools) benefit in terms of safety in numbers, there has also been a keen interest in which animals in the group are at higher versus lower risk (e.g., those in front, or along the edges) and how that might depend on the predator's attack strategy. This study addresses these important predator-prey details using a common predatory fish (northern Pike) attacking schools of prey fish (golden shiners). A strength of the study is that it uses cutting-edge video tracking and computational/statistical methods that allow it to quantify and follow each fish's (1 predator and 40 prey in a group) spatial position, relative spacing, orientation and even each individual's visual field and movement throughout each of 125 attacks. Most (70%) of these attacks were successful, but many were not. The variation in attack success allowed the investigators to do statistical analyses to identify key predator and prey behaviors that are associated with successful vs. unsuccessful attacks.

The study yielded numerous interesting insights. While conventional wisdom pictures predators initiating an attack from outside of the group thus putting individuals at the group's edge at greatest risk, this study found that pike typically approached the school of prey headon both in terms of the group's orientation and direction of movement, and often stealthily moved within the group before initiating an attack. To understand which prey individual was targeted by the predator, the highly quantitative video analyses examined 11 measures of each individual prey's position and orientation at the time that the pike initiated its attack. Of course, pike showed a strong tendency to target one of the 3 closest prey, particularly prey that were more or less directly in front of the pike. However, contrary to conventional wisdom, the analysis showed that targeted prey were closer to the center than the edge, and that an individual's position and orientation relative to other nearby prey also played an important role in whether it might be targeted by the predator. Not surprisingly, analyses showed that targeted prey were more likely to escape if they were further from the predator's head and if they exhibited higher maximum acceleration. Interestingly, during the actual strike, on average, the predator accelerated to a speed about 50% faster than the velocity of the targeted prey.

A limitation of the study (that the authors describe and discuss) is that it was conducted in a tank with no spatial refuges whereas in nature, pike are often found in areas with vegetation, and schools of prey can often potentially respond to the presence of a predator by moving towards refuge (e.g., vegetation). Also, the study was done in very shallow water (6 cm) -- likely shallower than many, if not most, natural predator-prey interactions for these species. In deeper water, the predator-prey interaction might be better analyzed in three dimensions (i.e., also accounting for variation in vertical height in the water), though the authors argue that this conventional idea is not necessarily true.

Overall, this study provides an impressive example of the use of modern technology and statistical analyses allows us to better describe and understand the fine-scale behaviors that affect an interaction of high importance for ecology and evolution.

We thank the reviewer for the care and attention put in their review and their detailed objective assessment of our study.

Regarding refuge use, it is true that in the wild pike are often found in areas with vegetation, but it is actually predominantly younger pike seeking refuge among vegetation from predators themselves, including from cannibalism by larger pike (see Skov & Lucas, 2018 Chapter 5). Vegetation is also used by pike as background camouflage rather than a refuge per se, but due to their elongated body and narrow frontal body pike are able to approach and ambush prey when no vegetation is available, as we show in our study. During pilot experiments we did provide pike with refuges, but as they never used them, and it would provide a hiding place for hiding, which would have considerably impacted our ability to investigate predation risk within the schools, no refuges were provided during the experiment.

We now added an explanation about not using refuges in the discussion (lines 495502): For our experiments we used a testing arena without any internal structures such as refuges. This was a strategic decision as providing a more complex environment would have impacted the ability of the shiners to school in large groups and would have led fish to hide under cover. Although studying predator-prey dynamics in more complex environments would be interesting in its own regard, it would not have allowed us to study the questions we are interested in about the predation risk of free-schooling prey. Furthermore, pilot experiments indicated that the pike never used refuges (consistent with previous work, see Turesson and Brönmark, 2004), so they were not further provided during the actual experiment.

Regarding the shallow depth of the tank, we now better acknowledge this limitation and explain our reasoning (lines 480-482): In terms of water depth, fish were tested in relatively very shallow water. This was primarily done to be able to keep track of individual identities and compute features related to the visual field of the fish. We would also like to note that both shiners and pike spent a lot of their life in the littoral zone and occur in very shallow water of only a few 10s of cm (see e.g. Krause et al., 2000b; Pierce et al., 2013; Skov et al., 2018). Although the limited vertical space may have restricted the pikes’ natural behaviour to some extent, they did exhibit normal swimming and attack behaviour with attack speeds and acceleration comparable to previous work (Domenici and Blake, 1997; Walker et al., 2005). We now better discuss the limitation of the shallow depth used in the discussion on lines 477-494 (see also our responses above).

Author Response

Reviewer #1 (Public Review):

In this study, He and collaborators analyse eight samples from six patients with acral melanoma through single-cell RNA sequencing. They describe the tumour microenvironment in these tumours, including descriptions of interactions among distinct cell types and potential biomarkers. I believe the work is thoroughly done, but I have identified a few concerns in their depiction and interpretation of their results.

Strengths:

1) One of the few available single-cell studies of acral melanoma, including a non-European cohort of patients.

2) Data will be very useful to study the immune landscape of these rare tumours.

3) Data include adjacent tissue, primary tumours and a metastatic sample, covering all disease stages.

4) Analyses seem to be carefully done.

Things to improve:

1) Figures need much more description to be understandable, in particular, axes should be clearly labeled and the colour code should be specified

Thank you for your generous comments and suggestions. We have improved the integrity of some figures and added some figure legends. I believe this will further improve the quality of our manuscript.

2) In some places, I would recommend the authors soften their interpretation of their analyses (for example, when they suggest targeting TNFRSF9+ T cells as a novel therapy), as these are nearly all bioinformatic in a small number of samples

As for the conclusions of TNFRSF9, we indeed provided a possibility that TNFRSF9 may serve as a novel therapy. We made some changes to soften the statement. In addition, we have added instructions and explanations in the Discussion section.

3) I don't think the experiments add much to the literature, as these test already known oncogenes on a common, non-acral melanoma cell line. Thanks for your comments regarding the experiments included in our study. We have pointed out this deficiency in the Discussion section, and made some experimental changes. For example, we have removed the TWIST1-related experiments from the main Results section and shown them only as non-focus work in the Supplementary Figure.

It is difficult for us to obtain AM cell lines. No commercial AM cell lines can be purchased in ATCC or ECACC. AM cell lines are more difficult to establish and there are few reports on methods for establishing primary acral melanoma cell cultures (PMID: 22578220, PMID: 17488338). Some Japanese and Chinese researchers have isolated the primary generation of AM cells (e.g., PMID: 17488338, PMID: 22578220, PMID: 34097822), but due to the customs policy and the COVID-19 epidemic, we could not receive them within a short period. Moreover, these studies also stated their limitations; namely, that the stability during serial passaging had not been evaluated. Therefore, it may be very time-consuming to obtain operable AM cell lines for functional assays. However, our research group would like to have the opportunity to separate and culture primary cells in subsequent studies, and improve relevant experiments according to your valuable suggestions. Man thanks again for your comments.

Reviewer #2 (Public Review):

The study presented by Zan He et al dissects the main interactions between malignant and stromal cells present in acral melanoma samples and in adjacent tissues using single cell RNA sequencing. The study describes factors that allow communication between the different cell types, with a special focus on macrophages, lymphocytes and fibroblasts, along with malignant cells. Factors playing a role in cell-cell communication are identified and suggested to be relevant prognostic makers and/or attractive therapeutic targets.

Historically, the study of acral melanomas has been neglected due to the low incidence among Europeandescents and this formed an important gap of knowledge in the field and hindered the development of effective therapies to control the disease. Therefore, studies that address this unmet need in melanoma research are very important and should be motivated. This includes singlecell sequencing studies that allow one to study the complexity of tumours, including microenvironment features that influence the development and effectiveness of certain types of treatment. The present study contributes information on how cells interact in the acral melanoma microenvironment and this could be a first step toward better understanding how these interactions influence acral melanoma development, progression, and therapy response.

However, there are a few points that should be carefully considered. The authors use 3 adjacent tissues (which in theory is composed of normal skin next to a cancer lesion), 4 primary tumor samples, and one lymph node metastasis as a model to study tumor progression. Adjacent tissue is not considered a stage of tumour progression and the sample size is too small to rule out sample-dependent effects. The study is descriptive in nature and could better contextualize the findings regarding what is known for other subtypes of melanomas or other tumours. This is especially important to help readers understand why it would be relevant to study cutaneous melanomas located in acral skin. It would be helpful to explain how different it is from nonacral cutaneous melanoma, and what this study adds compared to other single-cell studies from cutaneous acral and non-acral melanomas.

Thank you for your generous comments. It is not accurate to represent the adjacent tissue samples as ‘tumour progression’, and our study did not want to focus on the tumour developmental process. We have revised related description in the text. Tumour adjacent tissues (ATs) have always been the focus of research on TMEs. Some studies believe that there are a lot of mutations and clone amplification in normal tissues adjacent to cancer, which may be in a pre-cancerous state (PMID: 33004515), and many single-cell studies of tumours have also sampled and paired para-cancer tissues (e.g., PMID: 29988129; PMID: 35303421).

The problem of sample size limits the generality of the results, as we pointed out in the Discussion section. Most acral melanoma (AM) patients opt for surgical resection at an early stage to avoid the possibility of metastasis. Hence, we rarely encounter patients with lymph gland (LG) metastases. We only collected one metastatic sample, because it is very rare in clinic. However, the sample has a high quality, such as a high cell activity of single cell suspension after dissociation (95.30%), and a rich amount of tumour cells and other stroma cells. Therefore, we added its sequencing data into the overall analyses, hoping to contribute to the comprehensiveness of resources and research.

It is important to link this study with the findings regarding what is known for other subtypes of melanomas. We have already supplied the comparison of AMs with non-acral skin cutaneous melanomas (CMs), using the published data. Your comments and advices are entirely helpful to us, and we believe that the current manuscript is more comprehensive and complete.

Author Response

Reviewer #1 (Public Review):

In this paper, the authors estimate growth curves ('nomograms') for hippocampal volume (HV) using Gaussian process regression applied to UK Biobank data and evaluate the influence of polygenic scores for HV on the estimated centile curves. By taking this into account, the centile scores are shifted up or down accordingly. The authors then apply this to the ADNI cohort and show that subjects with dementia mostly lie in the lower centiles, but this does not improve the prediction of transition from mild cognitive impairment to dementia.

This paper is reasonably well written and the finding that centile curves for different phenotypes are sensitive to genetic features will be of interest to many in the field, albeit perhaps somewhat unsurprising given the polygenic score evaluated here is for the same phenotype under investigation (i.e. HV). I think using centiles derived from nomograms/normative models for precisely assessing both current staging and progression of neurological disorders is a highly promising direction. Regarding this manuscript, I have a few comments about the methodology and interpretation of results, which I will outline below.

• My most significant concern is that It appears that the assumption of Gaussian residuals is violated by the HV phenotypes that the authors fit their GP to. For example, in figure 2, the distribution is clearly skewed, and the lower centiles -in particular- are poorly fit to the data. First, please provide additional metrics to assess the fit and calibration of these models quantitatively (the latter can be done e.g. via Q-Q plots).

Thanks for pointing this out. We are sorry for causing this confusion. The skew in the figure appears because the scatter plot overlayed with the GP-generated nomogram is showing ADNI samples of all diagnoses – not the UKB training data used for the GP. The lower centiles are mainly occupied by the participants with AD or MCI (see the new plots in Figure 5). In addition, the healthy subjects from ADNI do indeed fit the model reasonably well. We have added a supplementary figure to show just the healthy subject and have made the following edits in the text to address the confusion:

Lines 143-149: “Nomograms of healthy subjects generated using the SWA and GPR method displayed similar trends (Figure 2; Supplementary Figure S8). … This extension allowed 86% of all diagnostic groups from the ADNI to be evaluated versus 56% in the SWA Nomograms (Figure 2; Figure 2 – Figure Supplement 2).”

Lines 159-170 (description of figure 2): “Figure 2: Comparing Nomogram Generation Methods. Nomograms produced from healthy UKB subjects using the sliding window approach (SWA) (red lines) and gaussian process regression (GPR) method (grey lines) … The benefits of this extension can be seen with scatter plots of ADNI subjects of all diagnoses overlayed (E, F… A similar figure with only the Cognitively Normal ADNI subjects can be found in Figure 2 – Figure Supplement 2

Second, I think if the authors wish to make precise inferences about the centile distribution for the reference model, then the deviation from Gaussianity ought to be accommodated in some manner. There are several options for this, including different noise models (e.g. Gamma, inverse Gamma, SHASH, etc), variable transformation, or quantile regression. One option that could be useful in the context of Gaussian process regression is the use of likelihood warping (see e.g. Fraza et al 2021 Neuroimage and references therein) which was originally developed for GP models. I would recommend the authors pursue one of these routes and provide metrics to properly gauge the fit.

This is an excellent point. However, we believe that given that the training data indeed follows a Gaussian distribution (see new Figure 4 – Figure Supplement 3; reproduced below) across the relevant strata (sex, PGS) and across age groups, such modifications are not required.

• Related to the above, it is likely that the selection of subjects with high/low polygenic scores for HV changes the shape of the distribution. It is currently impossible to assess this because no data points are shown in these cases. Please also add this information, along with comparable quantitative metrics to those for the models above.

Thank you for bringing this up. We have now added a new supplementary figure with the shape of these distributions along with the Shapiro-Wilkens test results for each of them. As can be seen, the Shapiro-Wilkens tests detects mild deviation from Normality in some cases. However, given the size of the strata N>2000 this is not surprising. Moreover, would multiple testing be applied here across the 48 comparisons, then none of the tests would be significant at the corrected threshold (P<0.001).

• How did the authors handle site effects? There appears to be no adjustment for the fact that the ADNI data are acquired from different sites that were not used during the estimation of the normative models. I would expect to see this dealt with properly (e.g. via fixed or random effects included in the modelling) or at the very least a convincing demonstration that site effects are not clearly biasing the results.

We agree that site effects are a major issue; we have rerun the application experiments after adjusting the ADNI volumes with NeuroCombat. The results did not change significantly, but we have changed all the reported results with the updated results. In addition, we noted this in the methods section:

Lines 442-445: Finally, we used NeuroCombat 1 to adjust across ADNI sites and harmonize the volumes with the UKB Dataset. To do this we modelled 58 batches (UKB data as one batch and 57 ADNI sites as separate batches) and added ICV, sex, and diagnosis (assigning all UKB as Healthy and using the diagnosis columns in ADNI) to retain biological variation.

• How do the authors interpret the finding that the relationship between the polygenic scores and HV is different in the cohorts they consider (i.e. bimodal in UKB and unimodal in ADNI)? Does this call into question the appropriateness of the subsampled model for the clinical cohort?

While we do see a bimodal distribution in UKB the effect is not very strong as the other reviewers commented. Therefore, we have de-emphasized this aspect. One reason may be that we detect the slightly bimodal aspect in UKB because of greater statistical power due to the large sample size (one order of magnitude). One further aspect is the used SNP data, i.e., differences in genotyping platform and imputation. This is also the reason why integrating PGS directly into the predictive model comes with additional challenges. We have addressed this topic briefly in our discussion: Lines 390-392: “Lastly, a recent study of PGS uncertainty revealed large variance in PGS estimates63, which may undermine PGS based stratification; hence a more sophisticated method of building PGS or stratification may improve results further.”

• Perhaps the authors can comment on (or better, evaluate) how this genetic shift could be accommodated in normative models (e.g. the possibility of including polygenic risk scores as predictor variables in the normative model). This would remove the need for post hoc adjustment and would allow more precise control over the adjustment than just taking the upper/lower xxx % of the PGS distribution as is done in the current manuscript.

We agree that integration of the genetics directly into the normative models is a great idea. And this will be the direction we will be exploring in future work. However, PGS themselves are prone to show ‘site’ effects that depend on the genotyping method that was used as well as of the quality of genotyping and imputation. As a consequence, using the ‘raw’ PGS scores in predictive models brings its own challenges. Therefore, we feel that the current framework is simpler at this point and illustrates the potential of PGS when combined with normative models.

• Related to my point above, it is perhaps unsurprising that the polygenic score for the HV phenotype influences the centile distribution. I think the paper would benefit considerably by also evaluating other polygenic scores (e.g., APOE4 as in some of the prior cited references). it would be interesting to compare the magnitude and shape differences for these adjustments. The authors can consider this an optional suggestion.

Our rationale for focusing on HV PGS was that we sought to improve the accuracy of the normative model. The genetics influences HV and this is a first attempt to adjust for this in the normative modeling framework. Indeed, APOE-e4 has a sizable effect on HV. However, this is most likely mediated by nascent accelerated neurodegeneration, i.e., Alzheimer’s disease. Thus, in our view focusing on APOE-e4 would mean to focus on a disease effect. We address this issue briefly in the discussion (Lines 326-334). For sensitivity analysis, we did indeed test other PGS, such as AD and Whole-Brain-Volume, and found that these do not affect the normative models for HV.

Reviewer #3 (Public Review):

Given the large variation in and high heritability of hippocampus volume in the population, taking out known variation in the healthy population is a nice way of reducing heterogeneity, and a step forward towards using normative models in clinical practice. The dataset the nomograms are based on is large enough to do so even when stratified by polygenic scores for hippocampal volume, and these provide interesting information on the role of genetics in hippocampus volume.

There are however several concerns regarding the applicability of the models to the ADNI dataset. First, the lack of overlap in the age range between the dataset the model is trained on and the application to subjects that are outside that age range is questionable. The authors prefer Gaussian process regression (GPR) over a sliding window-based approach using the argument that the former allows for predictions in a larger age range but extrapolation beyond the reach of the data is usually not valid. The claim that Supplementary Figure 6 shows accurate extension beyond these limits is in my opinion not justified. If anything, we can be rather certain that the extensive growth of the hippocampus up to age 48 is not realistic (see e.g. Dima et al., 2022).

As mentioned already in response to reviewer #1, this was a miscommunication on our side. We only used the ADNI samples that were within the age range of the models they were being plotted against. The GPR model did not require smoothing at the edges of the age-range and thus can support a wider age range than the SWA. This is why we stated that the extension of the nomograms enabled more of the ADNI dataset to be used, i.e., because otherwise these samples were outside the range of the model and could not be used.

We have changed the following lines in the manuscript to make this idea explicit:

Lines 477-478 (end of GPR methods section): “For both SWM and GPR models, we only tested the ADNI samples that lay within the age range of each model respectively.”

Regarding the accurate extension claim, we have edited the line (411-412) in the discussion so that it now reads:

Lines 347-348 “In fact, our GPR model can potentially be extended a few years beyond those limits”

Thank you for pointing out the discrepancy in the hippocampal growth around 48 with the results by Dima et al. 2022. Although sample sizes between the two studies are similar. The data availability in UKB for ages 45-50 is rather sparse (N<100; see new Figure 4 – Figure Supplement 3). Thus, the observed growth is likely due to under sampling. The growth effect has been observed in other studies using UKB data7,8. We have noted this in the discussion:

Lines 354-356:” However, there is a possibility that our results suffer from edge effects. For example, we suspect that the peak noted in the male nomogram is likely due to under-sampling in the younger participants.”

Second, the drop in mean 'percentile' difference between high and low polygenic scoring individuals that if one uses genetically adjusted nomograms seems nice, but this difference is currently just a number and the reader cannot see whether this difference is significant, or clinically relevant.

We have now provided a new figure (Figure 5) that shows the boxplots behind those numbers. The MCI-to-AD conversion analyses in the ADNI explored the clinical benefit of genetically adjusted nomograms. However, adjusted, and un-adjusted percentiles performed equally well. In the discussion we argue that the MCI stage is already too late and earlier stages may benefit from the increased precision:

Lines 373-378: “However, despite this sizable effect, genetically adjusted nomograms did not provide additional insight into distinguishing MCI subjects that remained stable or converted to AD. Nonetheless, the added precision may prove more useful in early detection of deviation among CN subjects, for instance in detecting subtle hippocampal volume loss in individuals with presymptomatic neurodegeneration.”

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Referee #2

Evidence, reproducibility and clarity

In this paper, the authors show that the turnover of centriole components is necessary for proper centriole maintenance within Drosophila cultured cells (during prologued cell cycle arrest) and within Drosophila oocytes, where centrioles are normally degraded prior to fertilisation. They highlight Ana1 as an important player in centriole maintenance. The authors begin with a candidate screen to identify core centriole proteins that are required to properly maintain centrioles. They then focus on Ana1, given that its depletion had the strongest effect, and show that its depletion leads to a reduction in the levels of centriole components in Drosophila oocytes. They show that the previously observed ability of centriole-targeted Polo to counteract centriole loss depends at least in part on Ana1 and that targeting Ana1 to centrioles also counteracts centriole loss. The authors conclude that Ana1 is a component of the PCM-promoted centriole integrity pathway.

Major comments

1. The authors say that Plk4 depletion does not lead to centriole loss, but there are significant differences in centriole number between the control and Plk4 depletion cells in Fig 1F and S1D. Please comment.
2. One of the main results is that depletion of centriole components leads to a reduction in centrosome numbers when measured 8 days after S-phase arrest. I wonder whether a restriction of centriole duplication could add to this effect? Any cells that were in G2 or M phase when the drugs were added would presumably progress into the following S-phase and duplicate their inherited centrioles, but not if centriole duplication proteins had been depleted. It's true that Plk4 depletion leads to a relatively mild centriole loss phenotype, but can the authors be sure that this is not due to variations in the efficiency of different RNAi constructs? Perhaps the authors can show that Plk4 depletion efficiently prevents centriole duplication under otherwise normal conditions.
3. The authors show that Ana1 depletion has the strongest effect, but this could in theory be due to differences in RNAi efficiency. I don't expect the authors to show the efficiency of all RNAi constructs, but they could state in the text that this is a caveat e.g. "...although we cannot rule out the possibility that differences in RNAi efficiency lead to the observed differences in severity of phenotype..."
4. A key conclusion is that core centriole components turnover to some extent and that the incorporation of new molecules is necessary for centriole maintenance. This is a very interesting and important point and so it would be nice to have more direct data to support it. This could be done in different ways, including transfecting fluorescently tagged centriole components after S-phase arrest and showing that some molecules become incorporated into the centrioles, or by performing FRAP experiments. Of course, it is possible that the turnover is so low that the incorporated fluorescent molecules cannot be detected...
5. The authors show that depletion of Ana1 from oocytes leads to a reduction in the intensity of centriole markers. They do not measure centrosome numbers, as the centrosomes cluster too tightly. The authors therefore can't be certain that Ana1 depletion leads to a reduction in centrosome numbers. The authors could show this by inhibiting centrosome clustering while depleting Ana1. There is a recent BioRxiv paper showing that centrosome clustering can be inhibited by depletion of Kinesin-1.
6. In Figure 3B the authors show that expression of GFP-Polo-PACT partially rescues the effect of "all PCM" depletion, but this seems strange given that Polo's role is presumably to recruit PCM (which has been depleted). Can the authors comment? Also, it would make sense to test whether GFP-Polo-PACT can rescue centriole loss after the depletion of Ana1 alone (not Ana1 and all PCM). If Ana1 has a role in recruiting Polo (either directly or indirectly), which has been shown previously in mitotic cells, then there should be a rescue to some extent.
7. In Fig4A,C, the authors say that γ-tubulin levels at centrosomes increase when GFP-Polo is forced onto the centrosomes - the graph seems to show a big increase, but the pictures do not...? Are the authors measuring total levels at all centrosomes? If so, I think they should be measuring the average at individual centrosomes. Also, why is the level of GFP alone not much higher when expressed with GFPnanoPACT (Fig 1B)? Presumably GFP should be recruited to the centrosomes by GFPnanoPACT.
8. The authors show that tethering Ana1-GFP to the centrioles counteracts centriole loss in oocytes (Fig4G). They say that the centrosomes are most likely inactive because they don't recruit PCM, but they have only looked at γ-tubulin, which is a downstream component of the PCM. I think it is important to check whether Polo is recruited, given that tethering Polo to centrioles also counteracts centriole loss and that a recent paper showed that Ana1 has a role in recruiting Polo to centrosomes (Alvarez-Rodigo et al., 2021). The authors also say that these centrosomes do not organise microtubules but do not show the data.
9. The authors propose that Ana1 is downstream of the PCM, and so over-expressing Ana1 should at least partially rescue centriole loss after PCM depletion. But I don't really agree with this. If Ana1 relies on the PCM then how would its overexpression manage to rescue the phenotype in the absence of the PCM? The finding that over-expressing Ana1 partially rescues centriole loss may instead suggest that Ana1 is either upstream of the PCM or part of an independent pathway. Indeed, the authors show that depletion of both the PCM and Ana1 has a stronger effect than either depletions individually - this is indicative of two independent pathways.

Minor comments

1. When the authors say that the centriole wall and cartwheel components are "dynamic" I think that they need to make it clear that this "dynamicity" is not very fast. Using the term dynamic tends to suggest rapid turnover (like in the PCM). Perhaps the authors could use the term "slow exchange" or something similar.
2. The authors currently use a 0 or 1 centriole categorisation - it would be nice to see the breakdown of what percentage of cells have 0, 1, 2, or >2 centrioles, perhaps in a supplementary excel file.

Significance

How centrioles are eliminated in certain cells is an interesting question and the data presented is also relevant to understanding centriole biology in general, because it seems that some apparently very stable structural proteins actually turnover. It is widely known that PCM proteins turnover relatively quickly, but core centriole proteins are considered to be stably incorporated. The data will therefore raise interest in the centrosome field. I do, however, feel that for the authors to make this point more strongly it would be good to show this more directly. Overall, this is a very interesting paper that is well written. The data is well presented and supports the conclusions that centriole components turnover and that Ana1 is involved in maintaining centriole integrity.

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Referee #1

Evidence, reproducibility and clarity

Summary:

In this manuscript Pimenta-Marques build on their previous work addressing how centrioles are stabilized and maintained or destabilized and disassembled, depending on the cell type and developmental context. Using Drosophila cell culture and oogenesis as an in vivo model for centriole destabilization, they identify the centriole wall protein Ana1 as a central player in centriole stability. Its presence is required for the maintenance even of mature centrioles, suggesting that there is continued turnover of centriole structural components.

Major comments:

1. The experiments and results are very well described and most of the conclusions are supported by the data. One aspect needs clarification though. It is not clear to this reviewer how the authors envision the regulation and mechanism by which Ana1 functions in centriole stability. The data suggest that it can stabilize centrioles independent of PCM (Fig. 3B, 5B), yet the authors claim in the results and discussion that it functions downstream of PCM. As presented, this does not make sense. I would argue the opposite, it may function upstream or in parallel to the PCM. Related to the above, the last sentence of the intro states: "Finally, we found that both Polo and the PCM require ANA1 to promote centriole structural integrity." This is shown for Polo, but where is the data showing that PCM requires ANA1 for promoting centriole stability?
2. I have a concern regarding the number n used for statistics in the quantifications. In many cases it seems that the number n of cells etc. was used (e.g. n>100 cells) rather than the number of experiments (e.g. n=3). The statistics should measure variability between experimental repetitions, not between cells etc. If statistics were indeed not done on experiments and would have to be changed, some of the observed effects may not be statistically significant and would require additional experimental replicates, which would increase the time needed for revision.

Minor comments:

1. I would advice the authors to improve the presentation of the figures. In particular the labels are in many cases very small and difficult to read. Readability is also reduced by the use of bold font in the labels and a mix of various font sizes within single figure panels.
2. The result section could be shortened/become more readable by moving several paragraphs to the intro or discussion.
3. The introduction is quite long and some parts read more like an introduction of a review on the topic.

Significance

This is a nice, focused study on the requirements underlying centriole stability and maintenance. The first part identifies the cartwheel, the centriole wall, and the PCM as important for centriole maintenance. The remaining parts identify and focus on the essential role of ANA1 in this process. This is an important finding, since the mechanisms underlying centriole stability and maintenance are poorly understood, yet highly relevant. Some cell types inactivate and/or disassemble centrioles during differentiation and this is likely important to their function. Providing more mechanistic insight, for example, regarding the relationship between ANA1 and PCM recruitment or the regulation of ANA1's centriole function by Polo, would have further strengthened the study. The audience interested in this work will be cell and developmental biologists. My expertise is in centrosome biology and microtubule organization.

Referees cross-commenting

I agree with the additional points raised by the other reviewers. I still think that overall the paper is fine and most things could be addressed in a reasonable time frame. The work does not provide much mechanism though. In this regard, the confusing placement of ANA1 downstream of PCM, would be the only mechanistic aspect, and it seems the authors got it wrong, at least based on the provided data. Here, additional experiments could elucidate these relationships further, but if this is not the goal, text changes could also address this and it would remain a smaller, more focused study.

Peer review report

Reviewer: Yulia Karmanova

Institution: Research Centre Kairos

email: yulia.karmanova@gmail.com

General assessment

In my honest opinion the topic of intercultural competence (ICC) should be of great interest not only to researchers involved in linguistics and pedagogics but to a general reader as well. By developing ICC, that represents a set of skills needed when encountering people from various backgrounds, one can learn valuable communication skills, flexibility in behaviour and become more aware of a lack of one’s tact and tolerance.

The manuscript is well written in an engaging and lively style, it provides excellent context about linguistic cues of ICC that will help educators steer and stimulate the ICC development of their students.

The manuscript cites relevant and sufficient literature that provides a very useful resource for current practitioners.

I do not identify fundamental flaws in the manuscript, there is nothing illogical or irrational, although I have a few suggestions for minor improvements. Please see my comments below for further details.

Essential revisions that are required to verify the manuscript

No essential revisions. The manuscript clearly describes the research methods of data collection and analysis as well as other meaningful parameters. Section number 3 (Research Method and Results) is recipe-like, the study can be reproduced.

The data collected for the research is impressive: 1,635 blogs (on average 400 words each) written by 672 students majoring in Hotel Management.

The data and analysis provided in the manuscript are not deprived of clarity and logic. No additional experiments are needed to validate the results presented in the manuscript.

Discussion and conclusion section aligns with objectives stated in the first section.

The authors of the manuscript made a valuable contribution by identifying linguistic markers for ICC in the language use of students blogging about intercultural experiences: I-perspective lexemes, insight verbs and quantifiers. These language cues make ICC more «tangible» and as a result provide teachers with concrete tools for giving students more targeted ICC assessments in their reflective writing tasks. By giving certain linguistic prompts to students, educators may form a more thoughtful and personalised approach in describing their intercultural experience.

Other suggestions to improve the manuscript

The content of the manuscript is scientifically sound but has minor shortcomings that could be improved by further revisions.

I do agree with the limitations of the research mentioned by the authors, especially with the lack of the explanatory value of a significant difference in frequency of use of the linguistic markers which I think can be resolved in future studies of this topic.

I suggest that the authors should involve more assessors in their future research. Two lecturer-researchers and three senior students were involved in the process which I assume is not enough for such large-scale research like this. A bigger team of professional assessors could make valuable contribution when analysing the data and resolving emerging research questions.

I would also recommend providing the manuscript with brief comments on the meanings of the parameters in column 4 (Table 3, 4, 5, 6) for readers’ clarity. What do t, p and n.s. stand for?

I believe that the manuscript would benefit from correcting minor inaccuracies. I would recommend to:
 replace «his» with gender neutral «their», page 6: In these blogs, the language use of students serves as a vehicle of information on the students’ development of ICC, offering the reader concrete cues – henceforth referred to as linguistic markers – of his reflective learning process.

• add a space between that and are, page 19: In order to bring more focus to our research, we initially focused on word categories thatare characteristic of properties that can be linked to ICC and cultural sensitivity, such as openness, self- relativity, curiosity and reflection or analytical thinking.

• add missing parentheses, page 22; Deardorff, D. 2006. Identification and Assessment of Intercultural Competence as a Student Outcome of Internationalisation. Journal of Studies in International Education, 10 (3), 241-266.

All in all, I find the topic of the manuscript fascinating and the research question relevant and essential to the field.

Decision

Verified manuscript: The content is scientifically sound, only minor amendments (if any) are suggested.

Author Response

Reviewer #1 (Public Review):

This manuscript by O'Herron et al. describes an all-optical method combining optogenetic stimulation and 2-photon microscopy imaging to simultaneously manipulate and monitor brain microvasculature contractility in three dimensions. The method itself, which represents a microvasculature-targeted variation on a theme previously elaborated for simultaneous stimulation and monitoring of ensembles of neurons, employs a spatial light modulator (SLM) to create three-dimensional activation patterns in the brains of cranial window-model transgenic mice expressing the excitatory opsin, ReaChR, in mural cells (smooth muscle cells and pericytes) under control of the PDGFRβ promoter. The authors demonstrated that, by splitting a single 1040-nm stimulating beam into multiple beamlets using an SLM, this system is capable of optogenetically activating ReaChR at discrete depths in the neocortex, depolarizing mural cells and producing highly localized constrictions in targeted, individual microvessels. Using this system to investigate the kinetics of optogenetic-induced contraction and sensory-evoked dilation, the authors found that the onset of optogenetically evoked contraction was much more rapid than that of sensory-evoked dilation, concluding that the observed lag between sensory stimulation and vascular response does not reflect intrinsic limitations of mural cell contractile mechanisms but is instead attributable to the time course of neurovascular coupling mechanisms. They further found that by titrating the stimulation duration they could completely negate the vasodilatory response to a concurrent sensory stimulus.

1) The red-shifted opsin, ReaChR, represents an improvement over opsins used in previously described 3D neuronal activation/monitoring systems. In particular, brief single-photon stimulation (100 ms) of ReaChR led to rapid, robust arteriole constrictions throughout the activation volume, whereas a previous generation ChR2 opsin required stimulation for seconds to achieve slowly appearing constrictions.

Thank you for pointing out this key takeaway from our manuscript. In Figure 9 of the revised manuscript, we provide a comparison of ReaChR-induced vasoconstriction, with data previously collected across microvascular zones using line-scanning in ChR2-expressing mice. These data show how ReaChR produces faster and more potent vasoconstriction in alpha-SMA expressing SMCs and ensheathing pericytes, but has similar effects on the slow contraction with capillary pericytes.

2) Single-photon stimulation was capable of completing stopping blood flow in a "first order pre-capillary branch". (Not clear what is meant by the phrase "pre-capillary branch"; anatomically, penetrating arterioles feed capillary branches.) While this speaks to the effectiveness of the method, it also highlights potential supraphysiological effects of stimulation and the importance of titrating stimulus intensity/duration to achieve physiologically meaningful responses.

We have removed the term “pre-capillary” to avoid causing confusion, and now use the term arteriole-capillary transition to denote the alpha-SMA positive segment that lies between the penetrating arteriole (0th order) and the alpha-SMA low/negative capillaries (>4th order). The rationale for this terminology is provided in our new review (PMID: 34672718), which explains why the transitional zone should be considered a separate vessel type that is not arteriole and not capillary.

We agree with the reviewer that titration of stimulation power/duration will be important and will depend on the application. We addressed this point by performing measurements of arteriole diameter with graded laser powers (Figures 5 & 7). There are many parameters to explore, but for the purposes of this manuscript, we clarify that the effect is titratable and that users should define physiological ranges in their specific circumstances, which may differ based on the experimental goals, age of mice, arteriolar size and vascular zone, and other factors.

We also note that some applications may want to mimic pathophysiological levels of constriction, for example to mimic the effects of arterial vasospasm after subarachnoid hemorrhage, or ensheathing pericyte contraction with MCAo stroke (PMID: 26119027), or to examine the neural consequences of transient small vessel occlusion.

3) In assessing effects of laser power, the authors assert that "increasing the laser power only slightly expanded the range of constriction". This seems a bit of an overstatement, given that increasing power (30-fold) had a greater effect on the spread (3x) than the magnitude (2x) of the response.

Thank you for pointing this out. We have re-worded this section to avoid the overstatement and to emphasize the results more clearly on the spatial spread of constriction relative to laser power.

The difference images in Figures 4B-C, G-H demonstrated that there was very limited spread of the constriction beyond the stimulation spots. We tested the effect of laser power on the spatial spread of constriction by stimulating with a broad range of power levels. We found that increasing the laser power led to a small increase in the spread of constriction. For example, a 30-fold increase in power (from 5 mW to 150 mW total power) led to ~3-fold increase in the spread of constriction (from ~25 µm to ~75 µm) (Figure 5A-H).

4) The suggestion that penetrating brain arterioles possess a mechanism for upstream conduction of constrictive responses is intriguing (although this intrigue is tempered by the lack of experimental support for the operation of such a mechanism in the brain microvasculature).

We are also intrigued by this hypothesis, which was supported by some evidence from a recent study of retinal vasculature. Kovacs-Oller et al. showed using neurocytin tracer injections into capillary pericytes, that they are linked through gap junctions and there is upstream directional diffusion of tracer. Further, they showed that electrical stimulation of a pericyte could lead to directional constriction from capillaries back to the arteriole in the retina (PMID: 32566247). The planar orientation of retinal vasculature makes this phenomenon easier to see. However, the 3D architecture of cortical vasculature is more challenging to study, particularly since the propagation along arterioles occurs along the Z axis, where spatiotemporal resolution of imaging is limited.

Given our new data on the effects of laser power on axial spread (see reply to points 10-13 below) and the difficulty in separating active propagation from out-of-focus activation, we think there is not sufficient evidence to claim that penetrating arterioles are propagating the signal through some active process. Further experiments, including studies of the mechanisms involved, will be needed to address this hypothesis. Therefore, we have removed any discussion of potential propagation of the signal, and instead focus on the relationship between laser power and axial resolution of activation.

5) The authors' premise for comparing contractile kinetics with sensory-evoked kinetics is flawed. In attempting to use the kinetics of optogenetic-induced constriction to infer something about the kinetics of sensory-evoked dilation, they are implicitly assuming that the kinetics of contraction and dilation processes intrinsic to mural cells are the same. This is highlighted by their use of the phrase "kinetics of the vasculature", which elides the possibility that dilation and contraction kinetics intrinsic to mural cells are different. Support for this latter possibility is provided by a previous report on renal afferent arterioles showing that the kinetics of myogenic constriction in arterioles are "substantially faster" than those of dilation (PMID: 24173354). Thus, their data do not rule out the possibility that the delay between sensory stimulation and vascular response reflects a slower intrinsic dilatory response rather than the time course of neurovascular coupling mechanisms. Furthermore, arterioles have an internal elastic lamina (IEL), which also determines the rates and degree of constriction and dilation. The IEL ends with the arterioles, and vessels with ensheathing contractile pericytes (and downstream) lack the constraints of the IEL.

We thank the reviewer for this constructive critique. We agree that there are many issues in comparing kinetics between sensory evoked dilation and our optogenetic constriction. We have re-worded this section to avoid any mechanistic implications in the discussion of the kinetics of the different processes. However, we wish to still incorporate the details about the rapid kinetics of constriction to highlight the utility of the approach to intervene/perturb sensory-evoked responses, given that contraction can be titrated and precisely timed. We discuss the utility of this approach further below.

6) It's not at all clear how overriding sensory-evoked dilation with optogenetically generated constriction provides a means for distinguishing neural activity from vascular responses. In particular, it is not clear how performing this maneuver while monitoring neuronal activity can provide the suggested insight into "aspects" of functional hyperemia that are essential to neuronal function beyond the relatively trivial observation that there is a point at which blood flow is too low to support continued neuronal activity.

Thank you for raising this point. We have added more detail to our thoughts on why over-riding functional hyperemia could provide insight into the dependence of neural activity on the blood flow increase. Neural circuits are extremely complex with many different sub-types of neurons playing different roles. These subtypes have been shown to have different metabolic sensitivities and thus, may be differentially affected by blocking functional hyperemia (PMID: 26284893). This could lead to altered circuit activity which could have profound consequences for neural processing. Additionally, the energy budgets of different cellular functions within neurons are quite different (PMID: 22434069) and reducing available energy by blocking functional hyperemia could lead to differing degrees of dysfunction across important cellular processes (e.g. re-establishing the membrane potential, recycling neurotransmitters) which could again have important consequences for neural coding. Furthermore, it has been shown that there is a steep gradient of oxygen moving away from penetrating arterioles, and so neurons at greater distances from vessels may be differentially affected by blocking the hyperemic response (PMID: 21940458).

7) With the exception of vasculo-neural coupling, where it would be the method of choice, the technology described leaves the impression of a capability in search of an application. That said, the ability to control blood flow to the point of completely stopping it may ultimately have applications in pathological settings.

In addition to our response above on the utility of over-riding arteriole dilation during functional hyperemia, we have added to the discussion more potential uses of the technique. These include: (1) To be able to manipulate blood flow without using pharmacology or having to induce neural activity could be useful for a variety of studies involving intrinsic reactivity and compliance of vessels in both health and disease. (2) The different microvascular zones have distinct contractile kinetics. There are details that remain unstudied, such as the kinetics of different sized vessels, their location in the network, their identity as collateral arterioles or pial arterioles. Vascular optogenetics can dissect the contractile characteristics of different vessel types, similar to probing a circuit board. (3) Studies of the physiological significance of vasomotion, with respect to brain clearance of metabolic waste products. Being able to directly drive vasomotion and alter its amplitude and frequency will be an important tool for studies in this field. (4) Functional hyperemia is also impaired in many diseases, but this dysfunction could arise from impaired activity of neurons, astrocytes, or vessels. Therefore, a method to disentangle specific changes to blood vessels in vivo could be useful for understanding the vascular contributions to such diseases.

Reviewer #2 (Public Review):

The manuscript by O'Herron et al. describes a new technique for all-optical interrogation of the vasculature in vivo. They expressed optogenetic actuator ReaChR in vascular smooth muscle. They activated ReaChR using single-photon or 2-photon absorption. In both cases, they observed rapid and reversible constriction (presumably, due to Ca increase). Single-photon activation produced widespread constriction; two-photon activation allowed targeting of individual vessels. Using a commercial 2-photon system with a spatial light modulator on the photoactivation 1040-nm beam, they demonstrated localized constriction at multiple points along the small and large cerebral arterioles at once targeted by individual beamlets. Overall, this is a very interesting paper that clearly lays out the methodology and experimental design and carefully considers a number of potential limitations and pitfalls. This paper will serve as a valuable recourse for a large community of eLife readers interested in cerebrovascular physiology in health and disease as well as in neurovascular coupling and interpretation of noninvasive imaging.

Given the chronic nature of the optical window, it is not clear why imaging was done under anesthesia. This point requires explanation. There is a concern that targeting of the vessel wall not possible in awake animals due to brain motion. If yes, that would be a serious limitation of the methodology.

To ensure that our method is compatible with awake experiments, we have added awake data to the manuscript (Figure 10). We show that individual vessels can be independently targeted in the awake animal and the outcomes are not profoundly different than in the anesthetized state. As with all awake experiments, due diligence must be taken to ensure the preparation is as stable as possible, and the occasional trial may have to be removed if motion artifacts are too large.

Reviewer #3 (Public Review):

Strengths: In the vascular field, previous implementation of optogenetics to constrict and dilate blood vessels, has used either single photon full field and fiber illumination, or alternatively confocal and 2-photon scanning of individual vascular segments with raster scanning. The former is limited in spatial precision, activating multiple vessels over a large area, whereas raster scanning is not ideal for accumulating currents and often results in slow temporal precision. Spatial light modulator (SLM) generated diffraction patterns to achieve patterned illumination have become increasingly used in neuroscience to achieve reliable 2-photon activation of targeted neuron populations. Here the authors use this technology to depolarize and constrict smooth muscle cells in vivo. By imaging and stimulating with 2 laser lines and different optical paths they are able to stimulate opsin expressing cells and image simultaneously, which is advantageous. By using the Red-shifted opsin ReaChR for their experiments, it is possible to combine this approach (cautiously) with imaging many of the classically used 2-photon fluorophores and genetic indicators, with excitation spectrums <1040nm. Future work using variations of the technique is likely to gain valuable insight into neurovascular biology.

Weaknesses: A major limitation of the current study is that although the authors achieve high spatial precision of ReaChR activation in the xy plane, the axial precision appears extremely poor compared to what would have been expected. For example, in Fig. 5-1 (using a 0.8NA, 16x objective), the authors achieve equivalent levels of surface arteriole constriction even when the SLM is focused 200um above the brain, and even larger constrictions as they initially move the focus away from the imaging plane. Although the axial spatial resolution appears better with the 1.1NA - 25X objective, such a large point spread function largely limits the utility of the technique, as there will always be a concern as whether the effects are spatially specific and not due to activation of vascular cells above and/or below the site of interest. This experiment that the authors have presented on axial precision is extremely important as it outlines a very important limitation of the technique (which is likely power dependent), but it remains to be completely characterized and understood. One possibility is that the power levels used by the authors are already above saturation, a problem raised by Rickgauer and Tank (2009)- PMID: 19706471, and therefore they may be able to refine the axial precision by using lower power. Further controls would be valuable to understand the precise cause of this large axial spread as it doesn't quite add up with the diameter of the bleach spot shown in figure 5-1D (some suggestions outlined in recommendations to the authors).

We agree with the reviewers on this point. We conducted several new experiments to help elucidate the limits of axial resolution. First, we have dropped the comparison between objectives with different NA’s. This leads to unnecessary confusion, and it is common knowledge that lower NA objectives will have poorer resolution in the axial plane. We now mention this as a factor to consider, but have removed it from the figures. Second, we have shown, as the reviewer suggests below, that the stimulation power used has a dramatic effect on the axial spread of constriction (Figure 6E and Figure 7). Low powers indeed show a more narrow axial spread. However, we typically use higher powers (near or above 100 mW) to generate large constrictions in penetrating arteries, and we also include these levels to show the greater axial spread they cause. In summary, we confirm with lower powers the 3D precision of the two-photon optogenetic technique, and we show that higher powers can be used to broadly constrict penetrating arterioles for studies seeking to modulate blood flow in columns of cortical tissue supplied by penetrating arterioles.

Regarding the stated inconsistency with the bleached spots, we think this mostly has to do with the difference between photo-bleaching fluorescent material (requiring lots of laser power) and photo-activating opsin channels (which can be done with much less power for very sensitive opsins). Additionally, the slide we bleached is optimally activated at ~800nm and so our 1040 nm stimulation required enormous power to burn the spot.

The current version of the paper also lacks adequate quantification of the results as it is composed primarily of representative examples, which limits a proper assessment of reproducibility and variability of the effects.

We agree that showing population averages will be more informative to the field. In the original submission, we showed mostly examples because the large parameter space (size and number of spots, position on vessels, duration and intensity of stimulation; if a stimulation train, the duration, number, and inter-pulse interval of stimulation) was explored in the early data rather than picking one set of conditions. However, we have now collected new data where parameters were typically the same and included population average plots in the figures that previously had only individual examples (Figures 2G,I, 4I,M, 4-1C, 5I, 6E,F, 7, 11-2 ) as well as the new data (Figures 8, 9, 10).

Author Response

Reviewer #1 (Public Review):

LaRue, Linder and colleagues present an automation (GLO-Bot) and analysis pipeline building on the previously developed GLO-Roots, which makes use of a constitutively expressed luciferase gene to image plant roots in thin soil containers (rhizotrons). After validation of the system using a set of 6 accessions, the authors then take advantage of the increased throughput to phenotype root system architecture (RSA) of 93 natural Arabidopsis accessions and perform genome-wide association to identify polymorphic genomic regions that are associated with specific RSA traits. I appreciate that the authors made all data available via zenodo.

The authors succeeded in automating the GLO-Root system. Overall, the GLO-Bot appears to be a nice platform to collect time-lapse images of root growth in soil-substrate using rhizotrons. The automation of the GLO-Roots system using the GLO-Bot is well described, although not in sufficient detail to be rebuilt by interested researchers, e.g. the software controlling the robot is not described or made available, precluding wide adoption of the method. The image processing pipeline is clearly described in the methods and in Figure 2. The pipeline open source and available for use and appears to work well overall, although in some cases the vector representation of the root system appears to be incomplete.

We thank reviewer #1 for raising these concerns. We have now made the general code for the software available (GitHub: https://github.com/rhizolab/rhizo-server). In addition, we uploaded the rhizotron laser cutting files (Zenodo DOI: https://doi.org/10.5281/zenodo.6694558) that would facilitate rebuilding the robot.

We understand the concerns about the vector representations of the root system.

These root system structures visible on the GLO-Bot images are indeed disconnected in many locations, due to variability in the reporter’s intensity and obstruction of the light path by soil particles. For traits like root angle, the disconnected nature of the root system is much less impactful as this method naturally uses “segments” of the root as individual elements for angle measurements.

The authors then present a quantitative analysis of RSA using a set of 93 accessions, with 6 replicates per accession, generating a large dataset on the diversity of RSA in Arabidopsis. Using average angle per day, the authors identify SNPs that significantly associated with angle at 28 days after sowing, and they describe a correlation between this trait and the mean diurnal temperature range at the site where the accession was originally collected. The main weakness of the manuscript in its current form are some details of the quantitative genetic analysis. In my opinion the quantitative genetic analysis would benefit from additional quality control as there are peculiarities in the dataset that was used as the basis for GWAS.

We understand the concerns from reviewer #1 about the quantitative genetic analysis. Ultimately, we performed the analyses in the way we explained in the paper with careful consideration. We have added in additional descriptions of the rationale for chosing certain methods that hopefully elucidate why we did the analyses in the way we did. We hope this paper serves as a resource for others to pursue additional studies on traits relevant to their research.

Reviewer #2 (Public Review):

Therese LaRue and colleagues have developed a second generation of the GLO-Roots system that had been developed in their lab and published in 2015. Importantly, the new system (GLO-Bot) and the analysis of the resulting images has now been largely automated and therefore provides a throughput allowing for genetic studies. In an impressive endeavor the authors have transformed more than 100 diverse accessions that had been selected using sensible criteria with the luciferase construct, which then allowed the RSA of these accessions to be measured using the GLO-Bot system. On a set of 6 diverse accessions, the authors carefully identify meaningful RSA traits that they then quantified in the accessions of a larger panel of almost 100 accessions. They also benchmarked the new imaging processing tools against gold-standard manual tools. Overall, they show that the data acquisition and analysis is reproducible and reasonably accurate. They then proceeded to conduct GWAS using the RSA traits and identified several significantly associated candidate SNPs. Finally, they correlated the RSA with environmental variables and found interesting correlations that are consistent with prior studies.

Strengths:

The manuscript presents interesting root phenotyping technology, a comprehensive atlas of RSA under rhizotron lab conditions in Arabidopsis, candidate genes potentially underlying RSA traits, and interesting associations of RSA and climate variables. This will be inspiring and useful to many other researchers and has the potential to be explored further in future studies.

We thank the reviewer for the encouraging feedback.

Weaknesses:

Some aspects of the data analyses are not well described and should be described more. The trait data is heavily processed to "breeding values" and it is a bit unclear when unprocessed and processed trait data is used and why. Also, limitations and caveats are not discussed sufficiently. For instance, presenting and discussing the issues and caveats of measuring RSA that was generated in thin and not very wide soil sheets using the GLO-Bot system when natural growth in soil is usually largely unconstrained. Moreover, the analysis of potential candidate genes from the GWAS is not very well developed. Finally, the trait data was not available with the manuscript and a major impact of a resource like this will come from the data being fully available to the community.

We appreciate the broad comments on the manuscript and have tried to address them through the specific responses below. Overall we believe the approaches we used are effective but with specific caveats and have used the revision as a means of better communicating the limitations of the approaches chosen.

Reviewer #3 (Public Review):

The authors provide a thorough description of a method to transform plants to be bioluminescent upon applications of the require substrate such that roots are visible on the windows of rhizoboxes. They have expanded on previous work by automatic the imaging process with a robot that moves rhizoboxes to an imager where images are captured. They have improved the image analysis pipeline to be mostly automated with a user presumably needed to run various scripts in batch mode on directories of images. One novel aspect of the image analysis pipeline is in using image subtraction to subtract the previous time root system from the current in order to identify new growth.

We thank the reviewer for highlighting the strengths of the manuscript.

Overall, I think the authors provide a great amount of detail in parts needed and the methods, but some recommendations to increase reproducibility are more information about actual root traits measured. For example, one concern would be if root length is only summing pixels without considering diagonal pixels having a length of square-root of two, sqrt(2).

This is a valid concern, rather than just summing the pixels, the length of the segments is actually calculated using the “Feret Diameter” (or caliper length) function in imageJ which does take diagonals into consideration

While the methodological aspects of the paper are compelling, the authors have furthered the significance through a biological application for genetic analysis among accessions of Arabidopsis and correlating root traits to climatic 'envirotypes' or data from the origin site of the respective accession. This genetic analysis would be furthered by greater consideration of time series analysis and multi-trait analysis, which is possible in GEMMA. The authors could consider genetic analysis of the PCA traits as well. Given the novelty of this type of time-series, multi-trait data - the authors can reach further here.

Absolutely, PCA approaches to disentangle the phenotype space would be highly interesting to further investigate, which we started in the Supplemental Figure 8. This figure decomposes all the data points including replicates and temporal values of the same replicate. The PC1 therefore mostly captures how plants change over time, while PC2 seems to capture the main trade-off of wide/horizontal vs deep/vertical root architectures that we describe throughout the text. We could make use of this PC space to quantify the average value per genotype in PC2 and utilize this value for GWA, although it is not obvious how replicated and temporal measurements behave in PCA and what would be its consequences when computing a genotype value. There will definitely be interesting work that we aim to pursue in this direction in the future.

Regarding the additional capabilities of GEMMA. We are not aware of a subtool that is able to analyze time series directly in GEMMA, but we will look into it. The multi-trait analysis in GEMMA is also interesting. We have utilized the multi-trait feature in the past, but this is limited to very few traits. We have 8 time points, thus 8 traits. For reference, when we have run multi-trait LMM with 2 traits, we have typically seen runtimes of ~9 days in large clusters. New tools continue to emerge in the field of quantitative genetics, such as the use of summary statistics of multiple GWAs to gain new insights, which we will pursue in the future. We have added possible future directions to the discussion section (page 14).

As far as the general structure of the manuscript, I struggled with the results mixing in the methods such that I was never sure if the lack of detail in methods there would be addressed later, along with the mixture of discussions. Perhaps these are personal choices, but the methods were also after supplemental. I simply ask the authors to consider the reader here by being honest with my own experience reading this manuscript.

We appreciate this comment of reviewer #3. Since this is a “Tools and Resources” article, we believe that a substantial part of the results section should include the methods that were applied. The methodology mentioned in the results section should always help the reader to understand the illustrated results in the figures. If readers would like to apply certain methods, however, more details can be found in the materials and methods section. We apologize if this was not always successful and led to confusion. In the final formatted version, all supplemental figures would be linked to the main figures so that the materials and methods section would follow the discussion.

Overall, I believe this manuscript advanced root phenotyping by providing relatively high-throughput (imaging is slow due to the long exposure times) data and doing the time-series, multi-trait genetic mapping. The authors mention imaging shoots but no data is presented - presumably, it would be interesting to tie that in but they may be reasons to not. The authors could also discuss more the advantages of this approach relative to color imaging that has also advanced significantly since the original GLO-Root paper was released. Last, I am not sure the description of the 6 accessions study adds much value to the paper, and probably many other preliminary studies were done to prototype. Overall, this is fantastic and substantial work presented in a compelling way.

Unfortunately, the shoot images that were taken did not have sufficient quality for further analysis and due to technical problems, the set of shoot images is not complete. We removed the part of shoot imaging from the text. It now reads:”Inside the imaging system, the rhizotrons were rotated using a Lambda 10-3 Optical Filter Changer (Sutter Instrument®, Novato, CA). If it was the first imaging day or a designated luciferin day (every six days), GLO-Bot added 50 mL of 300 μM D-luciferin (Biosynth International Inc., Itasca, IL) to the top of each rhizotron immediately before loading the rhizotron into the imager.”

The advantages of the GLO-Roots method over color imaging is clearly that the GLO-Roots method can capture a more complete image of root systems with finer roots (like Arabidopsis). We have added the possibility of using RGB imaging for bigger root systems to the discussion section (page 13).

I think in many ways it has become a privilege. In an age when practical skills and ability to repair are relatively rare, and when it is often cheaper (in money and time) to buy new, I think maintenance is a privilege. Can we share it, teach people to fish so to speak? Perhaps knowing how to maintain simply isn't enough; slim margins of personal time may not be best spent maintaining things (as opposed to maintaining oneself).

Reviewer #1 (Public Review):

The key question that Huang et al. are addressing is which approach, paratransgenesis, transgenesis, or the combination of both, is the most promising to combat malaria, killing parasites without affecting the mosquito host. They explored this question by generating a transgenic mosquito line secreting two effector molecules in the midgut and salivary glands, and infecting mosquitoes with Serratia bacteria expressing effector molecules. Their major finding is that a combination of both strategies has the highest inhibition of parasite development compared to transgenesis or paratransgenesis alone. This is further confirmed by mouse infections with a rodent malaria model showing that a combination of both strategies inhibits transmission to naïve mice.

This study is comprehensive and provides significant information on the possible use of these approaches for malaria control. The effects on parasite development are clear and convincingly confirm that these strategies have the potential for reducing malaria transmission. It cannot be ruled out, however, that the more pronounced effects on parasite development of the combined approaches may be due to differences in the fitness of these mosquitoes rather than a true additive or synergistic action between transgenesis and paratransgenesis. Another limitation is that the authors do not show when parasites are killed and do not provide direct evidence of the role of the bacterial-expressed factors in the killing mechanism.

The authors show very convincingly that transgenic mosquitoes (all possible combinations) have comparable fitness to wild types. However, these fitness studies are lacking in Serratia-infected mosquitoes, and in the transgenic-paratransgenic combination. Are those mosquitoes as fit as WT? Fitness costs could negatively affect parasite development indirectly, rendering the comparison between the treatments impossible (and negatively impacting this possible strategy). These are key controls that need to be added to the manuscript in order to support the finding that the combination is the best approach.

It is surprising that the Sg/E line inhibits oocyst development given it uses a salivary gland promoter. The authors hypothesize that this is most likely explained by mosquitoes ingesting saliva with the blood meal. This hypothesis is interesting but needs to be tested by determining the presence of Scorpine and MP2 protein in the blood bolus. Also, at what stage are parasites killed?

While the authors test the expression levels of Scorpine and MP2 by qRT-PCR and western blot in transgenic mosquitoes, they did not test levels in paratransgenic ones. In which tissues are these factors produced in Serratia-infected mosquitoes? Are Scorpine and MP2 produced in the midguts and/or salivary glands? And at what level? A quantitative comparison of scorpine and MP2 protein levels in transgenic and paratransgenic mosquitoes is important to determine whether levels are correlated to the effects on parasite development.

Related to this, the engineered Serratia bacteria appear to express 5 effector molecules rather than just MP2 and Scorpine. This obviously can affect the results and also makes a direct comparison less meaningful, but we couldn't find any information on the other effectors, or on whether they are expressed and potentially responsible for the observed anti-parasitic activity.

More information about the experimental setup is needed. The authors used a piggybac approach that has led to multiple insertions in some of the mosquito lines. Which lines did they use for the experiments? This is not clear in the manuscript. If multiple insertions were used, this should be stated and the feasibility of maintaining them (and efficacy) over different generations should be discussed.

Oocyst and sporozoite data are not normally distributed, and therefore presenting the median instead of the mean is more informative. Furthermore, the statistical analyses done do not appear to be appropriate for this data. The authors need to either FDR-correct for multiple comparisons or do a Kruskal-Wallis test with post hoc testing. It would also be important to do statistical analyses on the prevalence.

When discussing the ethical consequences of this approach, the authors should also discuss the possible effects of QF2, scorpine, and MP2 secretions in humans upon a blood feed.

The authors show Serratia vertical transmission over three generations, but as the CFUs decrease over multiple generations, they should discuss whether low levels of Serratia can still block parasite development. In general, the manuscript lacks a thorough discussion of the limitations of this study.

The discussion around line 280 should be more nuanced. I don't think the word 'protected' can be used as mice were not immunized but were simply not infected.

Reviewer #1 (Public Review):

The authors look at a few different nematode species to compare the dynamics of anaphase. They find that in some species the spindle oscillates transversely in anaphase, and in other species it does not. They ask what accounts for this different behavior. To address this question, they use ablation of the central spindle, and conclude from the result, correctly, that after the ablation the centrosomes are pulled to the opposite poles of the cell in all species. However, the magnitude, half-time and initial velocity of the recoil differ.

To understand what accounts for the quantitative difference, the authors

1) use a simple viscoelastic model of a constant force, F, pulling against a spring (with constant stiffness k), while the object moves through the viscous medium.

2) estimate the cytoplasmic viscosity from tracking yolk granules,

3) estimate parameters F and k from fitting the exponential recoil curves. They find that the greatest correlation between having transverse oscillation or not is with lower or higher viscosity, not with magnitude of the force or stiffness of the spring.

Two major problems with this study can be identified:

1) Meaning and significance: It is not clear if the transverse oscillation have a functional significance. In fact, they are more likely than not simply a byproduct of complex nonlinear mechanics of the mitotic spindle. It is important to understand what we can learn about the spindle mechanics from these oscillations, but there may be no evolutionary significance here. If the authors were asking - how, in many different species, the spindle scales with the cell size in the same way (as was done in Farhadifar et al 2020, which the authors do not to cite) despite large parameter variations - that would be a different story. But asking which parameter change is responsible for the behavior change is less meaningful.

2) The study is not convincing, mainly because the model used for the fit is overly simplistic. The force is not constant, the spring stiffness is not constant, the mechanics is not, etc. There are a few different, very complex models, of the anaphase spindle with transverse oscillations - comparing to simulations of these models would be more convincing. Also, I am not quite sure whether the volume fraction of yolk is a useful parameter. Does not measuring MSD give us the diffusion coefficient and viscosity directly? I think using the factor depending on the volume fraction artificially inflates the viscosity differences. Lastly, I do not understand the theoretical argument based on comparison with Nedelec's model: in that model, increasing viscosity only slowed the oscillations down, not abolished them.

In short, much more thorough investigation would be needed to understand which differences between the species account for the presence or absence of the oscillations, and one may question whether the answer would have a deep impact on our understanding of spindle mechanics.

Reviewer #2 (Public Review):

A summary of what the authors were trying to achieve:

The authors have developed an approach to prediction of T cell receptor:peptide-MHC (TCR:pMHC) interactions that relies on 3D model building (with published tools) followed by feature extraction and machine learning. The goal is to use structural and energetic features extracted from 3D models to discriminate binding from non-binding TCR:pMHC pairs. They are not the first to make such an attempt (e.g., Lanzarotti, Marcotili, Nielsen, Mol. Imm. 2018), but they provide a detailed critical evaluation of the approach that sets the stage for future attempts. The hope is that structure-based approaches may have better power to generalize from limited training data and/or to model unseen pMHCs.

An account of the major strengths and weaknesses of the methods and results:

The authors first report (section 4.1) that their structural and energetic features contain information on binding mode, highlighting complexes with reversed binding polarity, for example, and partly discriminating MHC class I from MHC class II structures. This is encouraging but not terribly surprising. Also, with regard to MHC I vs II discrimination, it is not clear how the class II peptides are registered with respect to one another. This needs to be done by alignment on MHC and mapping of structurally-corresponding peptide positions, since the extent of N- and C-terminal peptide overhangs varies between structures and is largely irrelevant to the docking mode. Interactions between the TCR and MHC are ignored in the feature extraction process; it's possible that including these interactions could improve performance. The authors state: "To be noted that not all structures could be successfully modelled by TCRpMHC models, and so we could not submit them to the feature extraction pipeline." It's unclear what effect this could have on the results: if the modeling failures are cases of structures for which no good CDR templates could be identified, then perhaps this could bias the results.

Section 4.2 reports a negative result: unsupervised learning applied to the extracted features is unable to discriminate binding from non-binding complexes. This suggests that there is not likely to be a simple energetic feature, such as overall binding energy, that reliably discriminates the true binders. In Section 4.3, the authors turn to supervised learning, in which training examples inform prediction by a classifier. One finding is that the pure-sequence approach using Atchley-factor encoding of the TCR:pMHC outperforms the structure-based approaches, though not by much. A combined model incorporating Atchley factors and structural features does slightly better. These results are a little hard to interpret because we don't know how challenging the 10-fold internal cross-validation is. It doesn't sound like there is any attempt to avoid testing on TCR:pMHCs that are nearly identical to TCR:pMHCs in the training sets, and the structural database is highly redundant, containing many slight variants of well-studied systems. It's also not clear how overlap between the template database used for 3D modeling and the testing set was handled; my guess is that since the model building is an external tool this was not controlled. Together, these factors may explain why the results on independent test sets are, for the most part, significantly worse than the cross-validation results. Another take-home message from the independent validation is that the sequence-only method seems to outperform the sequence+structure or structure-only methods. Although these are described as "out-of-sample validation", it's not clear how different these independent TCR:pMHC examples are from the structure dataset on which the model was trained.

Sections 4.4 and 4.5 report that prediction accuracy varies significantly across epitopes, and this is in part determined by sequence similarity to the structural database (which provides templates for modeling and also constitutes the training set for the model). In section 4.6, the authors determine that the model does not appear to be able to predict binding affinity (as opposed to the binary decision, binding versus non-binding). Finally, in section 4.7 the authors benchmark the predictor against two publicly available, sequence-based predictors. When predicting for epitopes present in their training sets, all methods do reasonably well, with the edge going to the sequence-based ERGO method. When predicting for epitopes not present in their training sets, none of the methods perform very well. The authors state that "these results suggest that the structure-based models developed in this study perform as well as the state-of-the-art sequence-based models in predicting binding to novel pMHC, despite learning from a much smaller training set." This may be true, but the predictions themselves are not much better than random guessing (AUROCs around 0.5-0.6).

An appraisal of whether the authors achieved their aims, and whether the results support their conclusions:

I'm doubtful that the proposed methods will form the basis of a practical prediction algorithm. In the absence of ability to generalize to unseen epitopes, simpler sequence-based approaches that leverage the ever-growing dataset of TCR:pMHC interactions seem preferable. I still think the study has value as a template and roadmap for future efforts, and a baseline for comparison. For me, a key unanswered question is whether the model-derived structural features are just a different, slightly noisier way of memorizing sequence, or actually contain orthogonal information that can enhance predictions. It might be possible to gain insight into this question by looking more carefully at the impact of model-building accuracy on performance (the authors use sequence similarity as a proxy, but this is confounded by overlap between the training set and the template set used for modeling). If model-building really adds something, it seems plausible that it does so by accurately capturing physical features of the true binding mode.

A discussion of the likely impact of the work on the field, and the utility of the methods and data to the community:

As state above, I think the present work will have a positive impact on the field of TCR:pMHC prediction by critically evaluating the structure-based approach (and also by testing two previously published methods on independent data). I am less convinced of the utility of the specific methods than of the overall conceptual framework, evaluation procedures, and training/testing sets.

Any additional context you think would help readers interpret or understand the significance of the work:

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Reply to the reviewers

Reviewer #1 (Evidence, reproducibility and clarity (Required)): **Summary:** Techniques to probe the local environment of membrane proteins are sparse, although the influence of lipids on the membrane protein's function are known since many years. Therefore, the paper by Umebayashi et al. is important. The environment-sensitive dye Nile red (NR) coupled to a membrane protein is an appropriate sensor for monitoring the local membrane fluidity. Linking of Nile red to the receptor via a flexible tether was achieved with the acyl carrier protein (ACP)-tag method. Experiments showed that depending on the ACP site a certain linker length is required to have NR inserted in the membrane and thus be an effective sensor for lipid disorder. This technology could be of general usability to study the environment of membrane proteins in the context of their function. As an example, the technique allowed insulin induced membrane disorder in the close insulin receptor vicinity to be observed. Further, results suggested that tyrosine activity is required for this disorder to happen. The experimental results appear to be complete and controls were made.

**Major comments:** 1) Sometimes technical terms are used without explanation: What is the GP value? What is ACP-IR? The spectrum was measured in number of rois? The reader can find those abbreveations out, but it would be nice to have them defined.

We have made a list of abbreviations.

2) Fig. 1d) is confusing. The ACP-IR labelling is evident in 3 panels, but there is no difference in the color (emission spectra of 1992-ACP-IR vs 2031-ACP-IR should be visible??). The DAPI staining is very different. When doing the latter, how difficult is it to get the staining equal?

The differences in spectra cannot be seen because we used pseudo colors for display of the DAPI and CoA-PEG-NR staining. The reviewer’s comments about the unequal DAPI staining is correct. The reason for this is most likely that the cell membrane is unequally permeabilized by PFA treatment. As the point of this figure is just to show that the plasma membrane is labeled, dependent upon the expression of the ACP-tagged insulin receptor, we don’t think that the variable intensities of the DAPI staining is important. DAPI is simply used to indicate the position of the cells.

3) How can one interpret Fig. 4: a) Control goes over 4 frames, at 240" insulin is added, and 10 frames should show a fluctuation difference?

We showed 4 frames after control treatment that showed no significant change was observed by control treatment. We expected that clear changes would be invoked by insulin treatment in GP images, however these changes, while visible in the GP images, are difficult to see for the untrained observer. This is the reason why we used the ZNCC method in the subsequent figures to better visualize the changes.

1. b) A color shift from blue to green is visible after insulin addition. But it is faint - difficult to assess from the pseudo color scheme. What does 1000 pixel top/1000 pixel bottom mean in c). Is it an attempt to better visualize the fluctuation? It is difficult to recognize a difference before and after adding insulin. d) It seems that the kymograph set should show this. What is the color scale? Why is 3 so untypical, i.e., no change? Box 6 is also peculiar: the left side does not show a strong change upon insulin administration, the right side does. Why? We appreciate the helpful comments for improving our manuscript.

As pointed out, the change of GP value is extremely small before and after insulin addition, so it is difficult to fully visualize the change with normal pseudo-color expression. To deal with this, we adopted the following two methods to visualize minute changes.

1) Visualization of local changes of the statistical GP value showed by ZNCC throughout the time-lapse images (Fig. 6 and Fig. S2B).

2) Visualization of the top/bottom 1000 pixels of the sorting ZNCC value in each image (Fig. 7 and Fig. S2C). The top 1000 pixels are the ones that showed the largest changes. The bottom 1000 pixels are the ones that showed the smallest changes.

Owing to these expressions, we found out that the level of the response against the insulin signal was spatially and temporally heterogeneous in the membrane.

As for the color scale, in order to clarify the meaning of the difference of color, we have added the description about the relationship between the color and the ZNCC value in the results section.

4) How is the kymogram calculated? The legend says 'The horizontal dimension represents the averaged ZNCC inside the rectangular area, and the vertical dimension represents time'. The averaged ZNCC is a single value, so it is not clear why the kymogram shows a variation from left to right. May it be the ZNCC was averaged just vertically?

We apologize that we did not provide information regarding making the kymograph.

In the yellow rectangular area (Fig. 6B), the ZNCC values of the pixels with the same x coordinate value were vertically averaged, which were represented as the horizontal direction of the kymograph. That is, one horizontal line of the kymograph holds the spatial distribution of the ZNCC value along the horizontal direction of the membrane, and the vertical direction shows their time changes. To make it easier to understand, we refined the description about the kymograph in the legend of Fig. 6.

5) When calculating cross-correlation values on images, they need to be aligned. What fraction of the total image does the selected 19x19 box represent? As described, I imagine that a rolling CC over 19x19 pixels is calculated over an image from the time lapse series comparing it with the reference Iave(x,y). Compared to the 3x3 median filtered CP image, the ZNCC image should then be much more blurred??

Below we provide more information regarding the calculation of ZNCC.

Each local window for ZNCC calculation is set to a 19x19 pixels centered on every single pixel excluding the edges of an image. The ZNCC value calculated in that window is set to a center pixel of that area. After that, a new window centered on the adjacent pixel is set and calculate the new ZNCC. That is, the calculation window is slid throughout the image. Also, the calculated ZNCC value is not set to all the pixels of the window, but is set to only the center pixel of the window, so there is no blur effect like median filtering.

The figure below shows a schematic view of our ZNCC calculation.

Schematic view of our ZNCC calculation

**Minor comment:** On page 16 supplementary is not spelled properly.

corrected

Reviewer #1 (Significance (Required)):

The key point of this paper is convincing and the new technology appears to have a lot of potential. It can be applied to study membrane protein function in the context of its environment, the lipid bilayer.

Membrane fluidity measurements have been developed (e.g., using fluorescent probes like laurdan). However, the trick to link a probe like nile red by ACP technology to the insulin receptor and to observe its activity is quite new.

A most recent description of such a technology is in TrAC Trends in Analytical Chemistry Volume 133, December 2020, 116092.

This is an interesting review, but not directly impacting on our work.

**Referees cross-commenting**

All comments are constructive and important. The paper is important but needs to be amended as proposed.

Reviewer #2 (Evidence, reproducibility and clarity (Required)): **Summary:** In this manuscript, authors generated an ACP-attached Nile Red probe in order to specifically label Insulin receptor in the membrane. Owing to this specificity, one can measure the lipid membrane properties around a specific protein in the membrane. **Major comments:**

For the conclusions in the manuscript to be convincing, in my opinion, these additional data need to be added. Some of these are new experiments, and some are detailed analysis of existing data. The new experiments are not for new line of investigation, instead it is to confirm their statements and conclusions. The major point is the reliability of spectral shift. In usual environment sensitive probes, it is certain that they are in the membrane whatever is done to the membrane. However, when the probe is attached to a protein, it is not trivial to have the same confidence that the probe is always inside the membrane, and it is in the same plane of the membrane. 1992-ACP-IR is a good example; authors state that it binds to the protein outside the membrane, but when there is cholesterol addition and -maybe more interestingly- cholesterol removal, the dye still reacts and changes its emission (even PreCT changes its emission quite a bit at the 570 nm region). This is a clear indication of a change in localization of the probe upon some changes in the membrane. This implies that observed spectral shifts may not be due to lipid packing differences, but due to localization of the probes. For this reason, it is crucial to know where any environment sensitive probe localize in the membrane with respect to membrane normal, and this knowledge is more important for this probe. Related to this, the spectral difference upon insulin treatment and activation of insulin receptor could be due to changes in probe's localization in the membrane. Especially because authors show in Fig1e, the spectra can change depending on the probe localization. Relatedly, quantum yield of NR should be significantly different when it is inside vs outside membrane. Authors should show QY for 1992-ACP-NR and 2031-ACP-NR with different PEG lengths and upon insulin treatment.

We understand the logic of the request to measure the QY, since the QY of Nile red is much higher in organic solvents than in aqueous solutions, so it might be predicted that the QY of Nile red is higher in a lipid bilayer than when covalently bound to the protein in an aqueous environment. However, this argument depends upon the mechanism for the increase in quantum yield when going from aqueous to a non-polar solution. One possible explanation is based on the intrinsic properties of the dye under the two conditions. The alternative explanation would be that the dye would aggregate (be insoluble) in aqueous solution and therefore either not fluoresce or self-quench. In this case, we believe that the latter is the explanation because we and others have previously shown the turn-on properties of the probe when binding to proteins (SNAP-tag and others). It is not simple to measure QY in the cell under a microscope, but we have done something similar shown in supplementary figure 4. We labeled the three ACP-receptor complexes with PEG11-Nile red and co-stained with antibody to the Insulin Receptor. We then calculated a relative quantum yield. There were very little differences at all between the relative quantum yields, so we conclude that it is not the environment of the probe, which affects the quantum yield under these conditions, but the fact that it is covalently attached to a protein and incapable of forming aggregates. What distinguishes these constructs is the emission spectrum, not the quantum yield. In supplementary Table 2 we also did QY measurements in vitro and we could reproduce the increase of quantum yield by association with liposomes or in organic solvents. We tested whether non-covalent association with a protein would increase the QY by incubation with the lipid binding protein, BSA, in PBS. This was not the case, strongly pointing to the conclusion that it is the covalent association with the protein that increases the QY, not association with a protein. We believe that our demonstration of changes in fluorescent spectra with changes in cholesterol, large changes in fluorescent spectra with linker length for the 1992 construct and voltage sensitivity using patch-clamp prove that the Nile red is reporting on the membrane environment under the conditions we propose.

**Minor comments:** - Fig 1d requires quantification We do not agree on this. This is simply to show that the labeling is dependent upon expression of the relevant ACP-IR constructs. There is no detectable labeling of the control.

• Voltage sensitivity of different PEG length of 2031-ACP probe should be added. We have added this data in figure 2 panel E.

• Fig 3a graph should show all data points, not only bar graphs. Also, the band in 3a for +CoA-PEG-NR is dimmer than other bands, is it specific to this particular gel since quantification does not show any difference?

There is no significant difference- Fig 4d, colour code is needed.

Done

• Fig 5b and Fig3d are basically the same experiments in terms of control measurement, why is the difference in 3b is 0.04 GP unit while it is 0.007 GP unit?

We explain in the MS, but have improved the title of Y-axis in Fig.5 b graph so that the difference in what is plotted is clear. - Why is inhibitor data so noisy? We should discuss.

We don’t know the exact reason why inhibitor data is noisy, but we speculate that the actin cytoskeleton and phosphoinositide-dependent signaling could affect the membrane stability, and the membrane environment would be fluctuated in the presence of latrunculin B or PI3K inhibitor.

Reviewer #2 (Significance (Required)): Overall, this is a very useful approach, and this line of research will yield very useful tools to shed light on how lipids surrounding proteins can change their function. Major advance of the paper is the new chemical biology tool. There is also biological data on how insulin can change the insulin receptor's membrane environment which is contradictory to some old literature claiming that InsR becomes more "rafty" upon insulin treatment (e.g., PMID: 11751579).

If this type of tagging proves robust and reproducible (limitations and concerns listed above and below), it could be used by other researchers to tag their protein of interest and investigate the lipid environment around those proteins.

The downside of this method is that the probe requires ACP tag, a relatively less used tag than others in biology, therefore researchers interested in using this probe should have their proteins with ACP tag. Moreover, the linker length and ACP-tag position are quite crucial parameters (and probably should be optimized for each protein). Longer PEG lengths cannot report on changes efficiently (Fig3b), while shorter lengths are prone to artefacts as they can go out of membrane (Fig1 and Fig2). This might limit its widespread use.

The reason for using the ACP tag is that neither the SNAP tap nor the HALO tag working. The tethered Nile Red preferred to bind to the tqg rather than inserting into the membrane.

**Referees cross-commenting** I agree with all comments and concerns of other reviewers. I see the usability and potential of this new technology along with its limitations as all three reviewers pointed out.

Reviewer #3 (Evidence, reproducibility and clarity (Required)): See below. No concerns on any of these issues.

Reviewer #3 (Significance (Required)): **Critique:** This MS reports a proof-of-principle for using site-directed environmentally sensitive probe technology to assess the local membrane environment of a receptor tyrosine kinase (IR) upon activation. This technology addresses a major gap in our arsenal of tools to study the mechanisms of membrane signaling as the parameters of interest are biophysical parameters rather than purely biochemical ones. How to do this with spatial and temporal resolution is a major challenge. This study builds on previous work by the Riezman group that develops an extrinsic labeling system to tether Nile Red to specific sites on the ectodomain of a signaling receptor and then probe local membrane environments as a function of receptor activity. This is a carefully done study is well-controlled, is clever in design and is well-described. Although the major issues to which such a general technology could contribute involve intracellular (and not extracellular) event, the advances described will be of general interest -- particularly that local membrane order decreases when IR becomes activated. Specific comments for the authors' consideration follow:

**Specific Comments:** (i) As a general comment, the authors are measuring extracellular plasma membrane leaflet properties that may or may not translate to what is happening in the local inner leaflet environment. A general reader may well miss the significance of this. This point needs to be more explicitly emphasized in the Discussion.

This has been discussed in the revised version.

(ii) Why not treat cells with a PLC inhibitor to block PIP2 hydrolysis and ask if that inhibits membrane disorder. It is PIP2 hydrolysis/resynthesis that regulates the actin cytoskeleton at signaling receptors and this seems an attractive candidate for study.

There is a long list of attractive post-signaling events of the insulin receptor and how this works in different cell types that could be tested. We believe that this is beyond the scope of this study and we encourage others to do this.

(iii) The data acquisition time is at least 4 min which is long enough for activated receptors to be recruited to sites of endocytosis. Can the authors exclude the possibility that what they are measuring isn't reflective of such spatial reorganization? Does a clathrin inhibitor block the observed change in local membrane order for activated IR? We determined localization to AP2 adaptor containing clathrin coated pits at the cell surface and showed that during the time-course of the experiment that there is no significant change in co-localization or evidence for endocytosis (new figure 9). Therefore, we decided not to do the clathrin inhibitor blocking experiment because we believe that it could only lead to indirect effects.

(iv) Receptor activation is accompanied by other transitions such as dimerization, etc. Can the authors exclude the possibility that what they are measuring is related to changes in depth of insertion of the NR probe into the plasma membrane outer leaflet that is a consequence of IR conformational transitions associated with activation? This is highly unlikely given the fact that fluidification of the membrane environment is found with all length linkers. Given the intervals in increases in linker length on the 2031 construct, which is the closest to the membrane, it is very difficult to conceive that any of the ones larger than 5 PEGs restrict significantly the membrane insertion of the dye. **Referees cross-commenting**

I think we have a consensus opinion

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Referee #3

Evidence, reproducibility and clarity

See below. No concerns on any of these issues.

Significance

Critique:

This MS reports a proof-of-principle for using site-directed environmentally sensitive probe technology to assess the local membrane environment of a receptor tyrosine kinase (IR) upon activation. This technology addresses a major gap in our arsenal of tools to study the mechanisms of membrane signaling as the parameters of interest are biophysical parameters rather than purely biochemical ones. How to do this with spatial and temporal resolution is a major challenge. This study builds on previous work by the Riezman group that develops an extrinsic labeling system to tether Nile Red to specific sites on the ectodomain of a signaling receptor and then probe local membrane environments as a function of receptor activity.

This is a carefully done study is well-controlled, is clever in design and is well-described. Although the major issues to which such a general technology could contribute involve intracellular (and not extracellular) event, the advances described will be of general interest -- particularly that local membrane order decreases when IR becomes activated. Specific comments for the authors' consideration follow:

Specific Comments:

(i) As a general comment, the authors are measuring extracellular plasma membrane leaflet properties that may or may not translate to what is happening in the local inner leaflet environment. A general reader may well miss the significance of this. This point needs to be more explicitly emphasized in the Discussion.

(ii) Why not treat cells with a PLC inhibitor to block PIP2 hydrolysis and ask if that inhibits membrane disorder. It is PIP2 hydrolysis/resynthesis that regulates the actin cytoskeleton at signaling receptors and this seems an attractive candidate for study.

(iii) The data acquisition time is at least 4 min which is long enough for activated receptors to be recruited to sites of endocytosis. Can the authors exclude the possibility that what they are measuring isn't reflective of such spatial reorganization? Does a clathrin inhibitor block the observed change in local membrane order for activated IR?

(iv) Receptor activation is accompanied by other transitions such as dimerization, etc. Can the authors exclude the possibility that what they are measuring is related to changes in depth of insertion of the NR probe into the plasma membrane outer leaflet that is a consequence of IR conformational transitions associated with activation?

Referees cross-commenting

I think we have a consensus opinion

Author Response

Reviewer #1 (Public Review):

This study addresses the important question of understanding the cellular physiology of cholinergic interneurons in the striatum. These interneurons play a key role in learning and performance of motivated behaviors, and are central to movement disorders, psychiatric disease, and addiction. Their unique physiology, which includes tonic pacemaking activity and active conductances that shape integration of dendritic inputs, is critical to their function but is still incompletely understood. The authors cleverly integrate a series of innovative electrophysiological and optical approaches to gain insight into dendritic physiology of these neurons. Their creative approach yields some interesting and novel findings. However, there are technical and conceptual concerns that need to be addressed before these results can be readily interpreted. Some refinement of analysis and presentation, and potentially some additional experiments, will therefore be required to strengthen the conclusions and facilitate interpretation of the results.

We believe that with several new sets of experiments and simulations, we have successfully refined the analysis and addressed the technical and conceptual problems. Indeed, we strengthened the conclusion with a novel pharmacological experiment that provided model-independent evidence of proximal-only boosting.

Major concerns:

1) This manuscript focuses on differential physiology of proximal and distal dendrites contribute to physiological activity and integration of inputs in cholinergic interneurons, suggesting that NaP and HCN currents act in concert to selectively boost inputs onto proximal dendrites (from thalamus), relative to inputs onto distal dendrites (from cortex). The results presented in Figures 1-4 are consistent with a distinct physiology of proximal-vs-distal dendrites based on purely electrical properties. Indeed, Figure 5 initially appears consistent with this model as well, since thalamic inputs (onto proximal dendrites) are boosted by an NaP conductance, while cortical inputs (onto distal dendrites) are not. This raises a key conceptual question: why are cortical inputs onto distal dendrites not boosted? Any depolarization of distal dendrites must pass through proximal dendrites before reaching the recording electrode at the soma. Shouldn't this signal be subject to the same active and passive conductances, and consequently the same boosting that shapes thalamic inputs onto proximal dendrites?

You are absolutely right in the case of a linear model (passive or quasi-linear). However, for a nonlinear system, there can be preferential boosting of proximal inputs. The new Appendix 2, addresses this point with computer simulations.

2) The quasi-linear approach to characterizing active and passive membrane properties is promising, and the choice of a cable-based model is well supported. However, the model itself is rather opaque, which limits confidence in the interpretation of the results. Additional analysis and description should be presented to alleviate concerns about whether the experimental data, which has a limited number of measurable values, may be over-fit by a model with too many free parameters. For example, why is the radius of the dendrite a free parameter that is allowed to vary in the full field vs proximal experiment (Lines 253-256) - and isn't it a serious red flag that the value returned for proximal dendrites is smaller than for the full field? Additional tables (e.g. fixed and free parameters and how they were determined), and figures (plots of how those parameters influence the fits, and how the parameters interact with one another) would considerably strengthen confidence in the conclusions drawn by the authors.

Thank you very much for this comment. We have added in the new ms a table with all the parameters fit in the various figures, and have discussed the possible pitfalls of overfitting. Most importantly, we have provided a new appendix (#1) to the manuscript that explains the effects of the various model parameters in a systematic fashion, beginning with a passive dendrites, followed by the effects of boosting and then the effect of restorative currents that give rise to resonances. This appendix addresses the questions raised by the reviewer regarding how the various parameters influence the fits.

We apologize, if we created a confusion, with respect to the meaning of the parameter r. It does not represent the radius of the dendrites (which is not explicitly represented at all, only implicitly through the space constant) but rather the electrotonic range of illumination. We indeed find that the fits consistently estimate a value of r for the proximal illumination which is smaller than that estimated for the full-field illumination, as it should.

Finally, our new pharmacological demonstration of differential boosting in the case of proximal vs. fullfield illumination (see above) is entirely independent of the quasi-linear model fit. So for the main thrust of the ms, which is to demonstrate a proximal localization of nonlinearities and its correspondence to the spatial localization of excitatory afferent inputs, this is now achieved, at least vis-à-vis the NaP current, independently of the qausilinear model. However, we still find the model useful as it is used to estimate the distribution of HCN currents and provides a framework to think about how to manipulate dendritic nonlinearities experimentally.

3) Technically, the use of ChR2 to modulate dendritic currents is creative. While the authors rightly acknowledge that activation/deactivation kinetics of the ChR2 channel will contribute to filtering, this important point should be expanded with additional analysis and potentially with new experiments. Of particular concern is the transition of ChR2 channels to an inactivated state over the comparatively long oscillating light pulse in Figure 3 Inactivation of ChR2 is prominent over this timescale and would precisely co-vary with the shift in oscillation frequency. To address this, the authors should present a direct measurement of this inactivation and account for it in their analysis of the chirp data. Alternatively, the chirp stimulus could be presented backwards (starting at high frequency), so that comparison of forwards-vs-backwards chirp recordings could disentangle this artefact. Either one or both of these additional experiments would be critical for interpreting the roll-off in photocurrent responses at high frequencies reported in Figure 3.

Touché! You were spot on with this critique and we were wrong. We have now conducted several new experiments (that appear in the main text and in Figure 3 and all its supplements) that show that including ChR2 kinetics explicitly in the model fits actually makes the fits more self-consistent and removes some of the glaring differences between the results from the somatic voltage perturbations (Figures 1–2) and the optogenetic illumination (Figure 3). So as per your request, we have now presented a direct measurement of the deactivation (Figure 3–figure supplement 1) and we have played the “chirp” backwards (Appendix 1–figure 2) to address the issue of inactivation.

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Reply to the reviewers

First we would like to express our deep gratitude to the reviewers for thoroughly and fairly reviewing our work.

Reviewer #1:

Major Concerns

1. A major concern I have is with the use of DAPT to modulate Notch signaling, and investigate the impact on integrins, Yap, cadherins, etc. Gamma-secretase, the target of DAPT, cleaves not only Notch receptors, but also IntegrinB1, Nectins, Cadherins, Ephrins and more. This recent review lists 149 substrates (Guner & Lichtenthaler Seminars in Cell & Developmental Biology 2020). The risk that some of the results reflect DAPT impact on IntegrinB1, Cadherins etc themselves is significant. The authors should validate their findings with more specific modulation of Notch activity, for example with a Notch blocking antibody, with siRNA, or with SAHM1. We agree with the reviewer´s comment and will add additional key experiments using SAHM1 as alternative inhibitor of Notch activity.

Furthermore, EGTA was used to "acutely destabilize VE-Cadherin". But EGTA chelates Calcium, which is essential for Notch structure, and EGTA is thus a well-known activator of Notch signaling (see eg Rand MD et al. (2000) Calcium depletion dissociates and activates heterodimeric notch receptors. Mol Cell Biol). The authors rightfully describe and cite this paper, but the use of EGTA nonetheless confounds interpretation. The authors check for NICD levels (at what timepoint?) but the staining is cytoplasmic (also not labelled in the figure per se, but described in the figure legend? - please label the staining in the panel). And in any case, NICD is very short-lived and nuclear staining cannot be taken as a hallmark of signaling activity. In particular if staining is performed at a time point at which the receptor and NICD may have been exhausted/depleted. The authors should validate these observations/conclusions with the Notch reporter to conclusively demonstrate whether EGTA does not activate Notch in their system.

To test whether transient treatment with EGTA causes Notch activation we will repeat this experiment with Notch reporter activity as readout.

Trans-endocytosis of NECD on different substrates: the authors suggest that trans-endocytosis of NECD by Dll4 increases on softer substrates. But the authors also show that soft substrates lead to spreading out of cells, which could confound interpretation (is overlapping membranes, not internalization). The authors could validate trans-endocytosis by FACS: check if red Dll4+ cells contain more NECD. It is also not clear to me in this experiment whether the authors are looking at green NECD, or Notch1 full length, since they write "overlap of Notch1 and Dll4", which would not reflect trans-endocytosis but interactions at the cell surface for both cells. Please also define "overlay intensity", or explain further.

We will validate the trans-endocytosis by flow cytometry. In addition, we describe the procedure for microscopic analysis more clearly (methods section, p 4; results section, p 17-19)

The authors conclude their introduction with a statement that mechanosensitivity of Notch is linked to endocytosis, but their conclusion from Fig 6C was that Notch stiffness-dependence was independent of endocytosis, using the rhDll4..?

We have now rephrased this sentence.

• *

Minor concerns

1. In the introduction, the authors describe Dll3 as a Notch ligand that activates Notch signaling in trans. To my knowledge, Dll3 has only been described as a cis-inhibitor of Notch signaling. (I think this may have arisen during repeated edits of the manuscript!) This has now been corrected in the current version.

In the introduction, the authors state that Notch1, Dll4 and Jag1 control angiogenesis, but then they only describe what Notch1/Dll4 do in the next few sentences. Perhaps one sentence to describe the role of Jag1 would help avoid the feeling of being "left hanging".

This has now been corrected in the current version.

Data presentation: please show all bar graphs with the individual replicates (dotplots).

We have now changed all bar graphs into scatter plots.

Data analysis/normalization: many graphs represent normalization of values in multiple steps which are not described in the methods/legends/results. For example, Notch reporter gene activity (Fig 1A) is Firefly divided by Renilla, and presumably normalized to the control condition at 1 (or an average of 1 for the three controls?). This is not explained. Also, it is not clear whether the data reported for the Control condition are Huvec on rhDll4 compared (normalized) to Huvec on control substrate (and similar for each other condition). What controls are included in this experiment? Please provide the full data to provide insight into the magnitude of activation by Dll4 itself. Perhaps "Control" is without rhDll4? But the bar underneath A/B implies this rhDll4 was used in all conditions.

We have edited our manuscript accordingly to avoid these ambiguities.

Statistics: data should be presented as means +/- standard deviation, not standard error of the mean (see for example Barde & Barde Perspect Clin Res. 2012): "SEM quantifies uncertainty in estimate of the mean whereas SD indicates dispersion of the data from mean. As readers are generally interested in knowing the variability within sample, descriptive data should be precisely summarized with SD."

We now use SD instead of SEM.

Statistics: In the Methods section, the authors state that one-way ANOVA was followed by Dunnett's multiple comparison test, and two-way ANOVA was followed by Tukey's multiple comparison test. Dunnett is used to compare every mean to a control mean, while Tukey is used to compare every mean with every other mean. Fig 1 describes using Dunnett for Fig 1B, but the end of the legend days Tukey was used. However Fig 1A,C show internal pairwise comparisons to plastic. Please be sure to explain which statistics were used where, and why, and if plastic was set as the comparator, please be explicit about this. Fig 3 uses "Sidak's corrected two-way ANOVA" and "Sidak's multiple comparison test"? I think Sidak is a method to correct alpha or p for multiple comparisons, as stated in the first instance, but it is described why this was used here, and not in other analyses, and whether the authors then applied Tukey's post-hoc test as described in the methods section? Similar comments for Fig 6. It is counter-intuitive that the plastic -1.5kPa PDMS difference with no error-bar overlap in 1A would be 1-star significance, while the plastic-70kPa difference with almost overlapping error bars in 1B would be 4-star significance. Please check/show values. In Fig 1B Figure legend, the authors write "Data is presented in a bar plot and compared with the integrin β____1 intensities without DAPT treatment", but this is not the statistical comparison presented. Fig 3B shows a very minor difference with overlapping error bars as 3-star significance? Is this correct?

We have checked all statistical issues and corrected where necessary. Since the sample size and variance were homogenous in all comparisons we now uniformly use ANOVA and Tukey´s multiple comparison test as post hoc to keep things simple.

How much nuclear NICD (NICD intensity) is there in control conditions? (Control missing from Fig 1B, D).

We will repeat the experiment and compare the NICD levels with those in non-activated cells on plastic.

A DAPI counterstaining for 1B/D right panels would facilitate evaluation of whether NICD nuclear intensity is increased. The same applies for nuclear YAP assessment in Fig 3B. I assume a nuclear counter-stain was done for quantification of nuclear NICD intensity, and nuclear YAP intensity, but this is not described in the Materials and Methods, please add a description of how intensity was quantified, and provide nuclear counterstain images. (Also, what is the unit on the y-axis of "intensity" graphs? Arbitrary units (a.u.)?

The counterstaining method with Hoechst as well as the use of the nuclear staining for quantitative analysis of images are now described in the Methods section and where needed in the figure legends. The y-axis of the intensity graphs now has a dimension (a.u.). We decided against overlay of the nuclear staining with the NICD or YAP images for graphical reasons (visibility of the respective staining).

How much "overall" integrin B1 is there in DAPT-treated conditions in Fig 2C? (related to the concept that DAPT could be cleaving integrin B1, it could be depleted at 24 hours..?)

We will additionally add this experiment and validate the effect of Noch inhibition on the overall intergrin level by the alternative inhibitor SAHM1

More details regarding the analysis procedure need to be added to the Methods Section. Were cells segmented and then mean intensity estimated for the whole cell? Was this done by means of Intensity Ratio Nuclei Cytoplasm Tool plugin for Fiji alone? Were images background corrected, corrected for inhomogeneous illumination, normalized? In the case of Integrin beta 1 active, the expression seems to be patterned, was intensity expressed as mean intensity of every pixel corresponding to cytoplasm? For VE Cadherin staining, how was intensity estimated (only pixels corresponding to membrane were considered or every pixel of the cell)? Many figures are originated from a confocal microscope: were z-stacks acquired and then maximum projections done? Were z-stacks acquired and then fluorescence quantified in 3D images? Was a single plane acquired or analyzed, and if that is the case, how was this plane chosen?

The requested information has now been inserted in the respective results and method sections.

In Fig 4A, how is VE-Cadherin intensity quantified? As an average per field of view? Or per cell? And if per cell, how was each cell delineated? And if not per cell, how were equal cell numbers ensured? In FRAP experiment, how was intensity quantified? Was it per cell, per field of view or per region? Was each bleached region analyzed separately, or each cell? The datapoints should be either added to Figure 4C or as supplementary to assess the fitting. How many bleached regions per cell were done and how many cells were analyzed? In FRAP experiment, was bleaching done with an increased pixel dwell time? Was laser intensity increased? Do you have an estimation of laser power (not percentage) or flux?

These issues are now described in more detail in the respective figure legend.

Figure S2 is not referenced in the manuscript - I think a reference to "Figure S3" in the NECD transendocytosis section (no page numbers or line numbering) should be to Fig S2 instead?

Sorry for this mistake! We corrected this now.

In Figure 5A NICD nuclear intensity normalized somehow (normalization not explained), and stiffness no longer appears to regulate NICD levels as shown in Figure 1B.

We have now described the normalization better in the figure legend. The difference to the results in Fig. 1B is that in Fig. 5A the cells were not activated by Dll4 sender cells or rhDll4 (endogenous Notch activity). This is now stated more clearly.

Fig 6B: From the immuno at right there is a clear stiffness-dependent difference in Transferrin uptake. How were "single cell uptake" and "number of particles" quantified? (How were cell bodies identified?) Uptake could also be verified with FACS.

In this point, we disagree with the reviewer: we really do not see a systematic difference in intensities between the different substrates. The process of image analysis is now better described in the figure legend. The result was so clear that we did not use FACS as complementary approach.

Fig 6C: there appear to be very different numbers of cells in the brightfield image at right. Are the 70, 1.5, and 0.5 kPa Notch reporter activities different from one another or only different from plastic? Might these results reflect cell density/increased Notch signaling due to more cell-cell contacts?

Unfortunately, with decreasing stiffness the PDMS gels become optically more and more cloudy, giving the false impression of a higher cell number. We tried to circumvent this by changing contrast and brightness of the images, but to no satisfying effect. We now mention this issue in the figure legend.

How was the Dll4 coating of the different substrates done?

The coating of the substrates is now described under a specific subheading in the Methods section.

It would be helpful to describe the composition of Collagen G (Collagen I) in the text (it is a risk to expect vendor information to remain available indefinitely).

The role and composition of the Collagen G coatings was included in the text (p 7). Further information on the manufacturer of the product used is included in the methods section.

Please list catalog numbers for all reagents, and dilutions used for antibodies.

We have added this information wherever possible.

Instead of using red and green for images, maybe cyan, yellow and/or magenta could be used to help the reader see what is being shown (especially if the reader might be color blind).

We will of course adhere to the respective policy of the publishing journal, once the manuscript is accepted.

Packages and tools such as Intensity Ratio Nuclei Cytoplasm Tool plugin for FIJI should be referenced.

We have now referenced respective tools.

Reviewer #2:

*Major comments: *

Is there difference on a growth rate of cells on softer vrs stiffer gels that could affect cell morphology/signaling pathways?

This is an important point and we will perform additional respective experiments.

Nuclear localization of NICD and YAP would be good to validate with western blot.

Quantification of Western Blots (especially after nuclear isolation) is – at least in our hands – much less sensitive and reliable then quantitative imaging. We do not think that this experiment would strengthen our study.

In Figure 3 and Figure 5, siRNA experiments would strengthen the data. DAPT is not only an inhibitor of Notch but affects to other proteins as well. This should be stated.

A similar point was raised by Reviewer#1 with the suggestion to use SAHM1 as an alternative to DAPT. As suggested we will add these experiments.

How was the mean VE-cadherin branch length determined? This term often refers to angiogenesis assay/sprout formation and maybe another one should be considered here to describe VE-cadherin junction morphology.

Add to all figure texts how many cells were used for the analyses*. *

The cell number is now added wherever appropriate.

In Fig. 6C the cell morphology of HUVECs look abnormal in comparison to other images and should be re-done.

In contrast to all other experiments the cells where not confluent in this case. The different morphology is a sign of the lack of neighbours, not of some problem with the cells.

Was all the data normally distributed and thus ANOVA was used? Please add more details on the statistics part. Did you remove outliers?

Like also suggested by Reviewer #1 we have added more information on statistics and streamlined this. The data are normally distributed, outliers wer not removed.

MTT assay of DAPT would need to be presented as it can be cytotoxic. Cells are not well visible in Fig 2C with DAPT. DAPI and F-actin staining would help to see the cell morphology.

We will add respective data on cell viability after DAPT (and SAHM1) treatment in a revised version of the manuscript.

Minor comments:

Please clarify how coating with rhDDL4 is done as this was unclear at least for this reviewer.

The coating of the substrates is now described under a specific subheading in the Methods section.

HUVECs are known to be hard to transfect. Please provide data on transfection efficiencies of all transiently transfected cells.

We did not systematically monitor transfection efficiencies in this context, since there was always an internal control (e.g. co-reporter in the reporter gene assay) or the data were obtained on a single cell based quantification. Generally, we yield transfection efficiencies around 30% with HUVECs.

Reviewer #3:

Major comments:

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1) The authors use recombinant Dll4 or Dll4-expressing ("sender") cells to activate Notch in co-cultured cells. This is per se fine however, one might over-estimate all other observed downstream effects as endogenous Notch activity is lower. It would be important to see how naïve HUVEC or other primary endothelial cells respond to changes in stiffness. qPCR of Notch target genes such as Hey1, Hey2, Hes5, Dll4 is frequently used as a readout of Notch activity in this context. Also. the Notch transcriptional reporter assay might be a suitable read-out-

In Fig.5A we show data on endogenous Notch activity (- EGTA) on substrates with different stiffness. In this case NICD levels in the nucleus do not differ. It will definitely be interesting to repeat this experiment based on the reporter gene assay.