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    1. Evaluation Summary:

      This study is of interest to scientists working in the field of genetic control of glial cell differentiation, myelination and repair. The data are extensive, of high quality, support their main conclusions, and provide novel insights into regulation of genetic compensatory mechanisms. The presentation and interpretation of the data can be improved.

      (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. The reviewers remained anonymous to the authors.)

    2. Reviewer #1 (Public Review):

      In the present manuscript, the authors investigate regulatory roles of class IIa histone deacetylases (HDACs) in Schwann cells on developmental myelination, as well as on myelin repair after acute nerve injury. The study directly builds on previous observations (Gomis-Coloma et al., 2018) where the authors have shown that the primary HDACs of Schwann cells, HDAC4 and HDAC5, have redundant functions and cause only a mild delay in myelination in a double knock out (dKO), suggesting compensatory mechanisms by other HDACs. In the present study the authors indeed show compensatory upregulation of HDAC7 in HDAC4/5 dKO. They furthermore show by ablating all three HDACs that, next to a induction of HDAC9 expression, myelination is further delayed and the architecture of Remak bundles even permanently altered. The authors provide high quality data employing a broad spectrum of methodology, including conditional mutagenesis in mice, electrophysiology, immunofluorescence, electron microscopy, RNAseq, ChIP, cell culture, qPCR and Western blotting to justify their hypothesis of a regulatory and compensatory role of HDACs in Schwann cells during development and regeneration. The physiological relevance of this compensatory network, however, is not intuitive. Better discussion and elaboration of central findings in triple KOs in comparison to single KOs (and vice versa) would strongly improve the manuscript.

      In detail, the following points may improve the strength of the manuscript:

      1) With regard to the triple mutants (HDAC4,5 and 7) the authors present a data set from P2 to P21 and another at P60. Here, the manuscript would benefit from more comparable data sets for the respective timeline. E.g. the authors show an increased SC number at P21. What happens to these Schwann cells? Are they still present at P60? In line, the authors show that even in the triple mutants the expression of certain genes including cJun remains upregulated. How do the authors explain this upregulation? It would be helpful to know whether these genes remain upregulated in myelinating SC or whether persisting supernumerary SC are responsible for the expressio of cJun and others at later timepoints (e.g. by IHC)?

      2) An important point is the description of the Remak- SC phenotype, which, in contrast to the only transient myelination phenotype, seems to persist in triple mutants. The authors suggest a defect of axonal segregation independent of a sorting defect and link this to a ectopic expression of genes of the melanocytic lineage. Given the importance of the Remak phenotype, a more detailed elaboration of this aspect also in dKO and cKO would be a strong benefit for the manuscript. In addition, the proposed ectopic expression of the melanocytic lineage genes would profit from a more extensive discussion and description with regard to their potential (transient) expression in wildtype Schwann cells and their functional relevance in relation to the observed Remak SC pathology. Moreover, the EM image in figure 2E suggests not only an increased number but also size of axons in the Remak bundles of triple mutants, in contrast to the respective quantification. As this point is crucial with regard to a potential sorting defect, the authors should carefully reevaluate the discrepancy between the presented image and data.

      3) Regarding the expression changes of HDAC7 and HDAC9 in mutant mice: The authors only show HDAC7 expression at P60, while the proposed role of HDAC7 concerns early postnatal development. Could the authors comment on the expression of HDAC7 at earlier timepoints?<br> Furthermore, within the manuscript, the authors suggest a "de novo" expression of HDAC9 in triple mutants. However, the authors show a small, but significant upregulation of HDAC9 already in single cKO4 nerves (Fig S1A) as well as in single cKO7 mice (Fig. 9A), hence a more careful usage of the term "de novo" may be advisable.

      4) In general, the discussion of the single HDAC knockout mutants is sometimes too sparse. This applies especially to the description of the cKO4 mice, which show a number of, albeit subtle, important differences with regard e.g. to the number of unmyelinated axons at P2 and P8 as well as with regard to the number of Schwann cell nuclei. However, the authors conclude that the single KO does not show a prominent phenotype. Though, given the compensatory mechanisms between HDACs in SC and the fact that the double HDAC4 (in SC) and HDAC5 (global) knockout display a similar phenotype to single HDAC4 mutants, this point requires more discussion. This dKO dataset, however, is redundant to the previously published study by the authors (Gomis-Coloma et al., 2018).

      5) The authors then tested the mutants after injury. The presentation of data from these experiments, however, is a bit confusing as it is going back and forth between nerve crush and cut, different mutants (cKO4, KO5, dKO, tKO) and time points of analysis (10dpi, 20dpi, 21dpi, 30dpi). All mutants show a decreased remyelination after crush, the dKO and tKO further present increased c-Jun mRNA and protein at 10dpi and reduction of Krox20, Mbp, Mpz, Periaxin. The sequencing results are said to be obtained after nerve injury, however, it is not clear whether this was a cut or crush. Four days after nerve cut in tKO, the authors report increased expression of genes typical for repair Schwann cells, as well as a more rapid myelin debris clearance, although it is unclear how this was measured. Only by quantifying the number of still intact myelin profiles early after injury as in figure 5A? If the authors would like to stress the point of myelin clearance, additional information on degeneration profiles and autophagy (LC3bI-II, p62 Western blots) or data on macrophage abundance is needed and would gain meaningful insight.

      6) Mechanistically, the authors investigated the genes that respond to HDACs or to which HDACs bind. It is nicely shown that HDAC4 can bind the c-Jun promoter, thereby repressing its expression, but also to the TSS of Mcam, belonging to the melanocyte lineage. However, a potential role of this finding is not further clarified. In addition, the generalized conclusion that "class IIa HDACs bind to and repress the expression of melanocyte lineage genes and negative regulators of myelination allowing myelination and remyelination proceed in a timely fashion" may be revised, considering that only HDAC4 has been tested. On the other side, it is nicely shown that c-Jun can bind to the HDAC7 promoter, inducing its expression. This is well analyzed both in vitro and in vivo using conditional c-Jun gain and loss of function in SC development. Here, although ectopic c-Jun overexpression in mice artificially increases HDAC7 expression in development, adding a more (patho-)physiological relevant experiment using c-Jun cKO in a nerve injury paradigm would be an asset.

      7) The final hypothesis from the authors is, that upon lack of the functionally redundant HDAC4/5 and the concomitant de-repression of c-Jun, HDAC7 is upregulated upon binding of c-Jun to compensate for the loss and ensure myelination, although delayed. If HDAC7 is also lost, Mef2d expression increases and induces "de novo" expression of HDAC9. The data presented in the manuscript indeed provide evidence of a role for HDAC4, HDAC5 and HDAC7 in developmental myelination and nerve repair with compensatory potential for each other. However, the physiological relevance of this compensatory functions is, although interesting, not quite clear and the manuscript may profit from a discussion of this point.

    3. Reviewer #2 (Public Review):

      The classIIa Histone De-Acetylases (HDAC) play important roles in the transcriptional control of differentiation of a wide range of cell types. This class of HDACs is regulated by different signalling pathways and it involves the shuttling of the protein into the nucleus. Indeed, previous work from this lab has demonstrated that increased levels of cAMP shuttles HDAC4 into the nucleus of Schwann cells where it recruits NcoR1/HDAC3 to repress c-Jun expression and allows commencement of a myelin-related gene expression program. Thus, HDAC4 links cAMP signalling to repression of a 'repressor' to stimulate cell differentiation. However, genetic deletion of HDAC4 (or HDAC5 and HDAC4/HDAC5) does not have a significant effect on Schwann cell differentiation and myelination in vivo, suggesting that other compensatory mechanisms might exist.

      Building upon their previous work, Velasco-Aviles and colleagues now demonstrate the existence of a genetic compensatory mechanism that relies on functional redundancies among the ClassIIa HDACs and the transcription factors c-Jun and Mef2d.

      Using genetic ablation of multiple HDAC genes, extensive morphological analysis of developing and regenerating nerves combined with gene expression analysis, provide a description of the gene regulatory mechanisms that maintain adequate levels of ClassIIa HDACs required for peripheral nerve development and repair.<br> Their data are of high quality and support their major finding.

      One interesting finding is that in the tKO, in which myelination eventually appears to progress normally, Remak Schwann cells are deficient in segregating lower calibre axons into cytoplasmic cuffs (Figure 2E). The authors interpret this a segregation defect and not as a sorting defect (page 5). Now, it is difficult to see how these two cellular mechanisms can be distinguished or whether they are different mechanisms to begin with. Notably, the unsorted bundle of axons presented in Figure2E also contains larger calibre axons that should normally be myelinated. Therefore, a simpler interpretation is that tKO Schwann cells are moderately impaired in axonal segregation, which results in the failure to sort out the occasional larger calibre axons from bundles and ensheathment of the smaller calibre axons into mature Remak bundles. There is no justification for proposing a 'segregation' mechanism different from the 'sorting' mechanism. As the sorting process critically depends on the elaboration of a basement membrane, it would be of interest to have a closer look at the basement membrane in EM and by IF in nerve sections and maybe WB. Is there any evidence for reduced laminin/collagen (or their receptors) expression in tKO nerves?

      It is argued throughout the manuscript that classIIa HDACs are involved in the repression of repressors of myelination. It is stated that in injured nerves a strong upregulation of such negative regulators of developmental myelination is observed (page 17). Regulators such as c-Jun, Runx2, Sox2 etcetera. To avoid confusion, it is important to clearly distinguish between developmental and repair functions (exemplified by c-Jun) and in Schwann cells cultured in the absence of axonal contact. Confusingly and erroneously, it is also stated that the POU domain transcription factor Oct6 blocks the transition from promyelinating Schwann cell into myelinating cells. The quoted paper does not support this idea at all. On the contrary, it demonstrates that Oct6 expression is required for the progression of promyelinating cells into fully myelinating cells.

    1. Author Response:

      Reviewer #1 (Public Review):

      [...] While the study is addressing an interesting topic, I also felt this manuscript was limited in novel findings to take away. Certainly the study clearly shows that substitution saturation is achieved at synonymous CpG sites. However, subsequent main analyses do not really show anything new: the depletion of segregating sites in functional versus neutral categories (Fig 2) has been extensively shown in the literature and polymorphism saturation is not a necessary condition for observing this pattern.

      We agree with the reviewer that many of the points raised were appreciated previously and did not mean to convey another impression. Our aim was instead to highlight some unique opportunities provided by being at or very near saturation for mCpG transitions. In that regard, we note that although depletion of variation in functional categories is to be expected at any sample size, the selection strength that this depletion reflects is very different in samples that are far from saturated, where invariant sites span the entire spectrum from neutral to lethal. Consider the depletion per functional category relative to synonymous sites in the adjoining plot in a sample of 100k: ~40% of mCpG LOF sites do not have T mutations. From our Fig. 4 and b, it can be seen that these sites are associated with a much broader range of hs values than sites invariant at 780k, so that information about selection at an individual site is quite limited (indeed, in our p-value formulation, these sites would be assigned p≤0.35, see Fig. 1). Thus, only now can we really start to tease apart weakly deleterious mutations from strongly deleterious or even embryonic lethal mutations. This allows us to identify individual sites that are most likely to underlie pathogenic mutations and functional categories that harbor deleterious variation at the extreme end of the spectrum of possible selection coefficients. More generally, saturation is useful because it allows one to learn about selection with many fewer untested assumptions than previously feasible.

      Similarly, the diminishing returns on sampling new variable sites has been shown in previous studies, for example the first "large" human datasets ca. 2012 (e.g. Fig 2 in Nelson et al. 2012, Science) have similar depictions as Figure 3B although with smaller sample sizes and different approaches (projection vs simulation in this study).

      We agree completely: diminishing returns is expected on first principles from coalescent theory, which is why we cited a classic theory paper when making that point in the previous version of the manuscript. Nonetheless, the degree of saturation is an empirical question, since it depends on the unknown underlying demography of the recent past. In that regard, we note that Nelson et al. predict that at sample sizes of 400K chromosomes in Europeans, approximately 20% of all synonymous sites will be segregating at least one of three possible alleles, when the observed number is 29%. Regardless, not citing Nelson et al. 2012 was a clear oversight on our part, for which we apologize; we now cite it in that context and in mentioning the multiple merger coalescent.

      There are some simulations presented in Fig 4, but this is more of a hypothetical representation of the site-specific DFE under simulation conditions roughly approximating human demography than formal inference on single sites. Again, these all describe the state of the field quite well, but I was disappointed by the lack of a novel finding derived from exploiting the mutation saturation properties at methylated CpG sites.

      As noted above, in our view, the novelty of our results lies in their leveraging saturation in order to identify sites under extremely strong selection and make inferences about selection without the need to rely on strong, untested assumptions.

      However, we note that Fig 4 is not simply a hypothetical representation, in that it shows the inferred DFE for single mCpG sites for a fixed mutation rate and given a plausible demographic model, given data summarized in terms of three ranges of allele frequency (i.e., = 0, between 1 and 10 copies, or above 10 copies). One could estimate a DFE across all sites from those summaries of the data (i.e., from the proportion of mCpG sites in each of the three frequency categories), by weighting the three densities in Fig 4 by those proportions. That is, in fact, what is done in a recent preprint by Dukler et al. (2021, BioRxiv): they infer the DFE from two summaries of the allele frequency spectrum (in bins of sites), the proportion of invariant sites and the proportion of alleles at 1-70 copies, in a sample of 70K chromosomes.

      To illustrate how something similar could be done with Fig. 4 based on individual sites, we obtain an estimate of the DFE for LOF mutations (shown in Panel B and D for two different prior distributions on hs) by weighting the posterior densities in Panel A by the fraction of LOF mutations that are segregating (73% at 780K; 9% at 15K) and invariant (27% and 91% respectively); in panel C, we show the same for a different choice of prior. For the smaller sample size considered, the posterior distribution recapitulates the prior, because there is little information about selection in whether a site is observed to be segregating or invariant, and particularly about strong selection. In the sample of 780K, there is much more information about selection in a site being invariant and therefore, there is a shift towards stronger selection coefficients for LOF mutations regardless of the prior.

      Our goal was to highlight these points rather than infer a DFE using these two summaries, which throw out much of the information in the data (i.e., the allele frequency differences among segregating sites). In that regard, we note that the DFE inference would be improved by using the allele frequency at each of 1.1 million individual mCpG sites in the exome. We outline this next step in the Discussion but believe it is beyond the scope of our paper, as it is a project in itself – in particular it would require careful attention to robustness with regard to both the demographic model (and its impact on multiple hits), biased gene conversion and variability in mutation rates among mCpG sites. We now make these points explicitly in the Outlook.

      Similarly, I felt the authors posed a very important point about limitations of DFE inference methods in the Introduction but ended up not really providing any new insights into this problem. The authors argue (rightly so) that currently available DFE estimates are limited by both the sparsity of polymorphisms and limited flexibility in parametric forms of the DFE. However, the nonsynonymous human DFE estimates in the literature appear to be surprisingly robust to sample size: older estimates (Eyre-Walker et al. 2006 Genetics, Boyko et al. 2008 PLOS Genetics) seem to at least be somewhat consistent with newer estimates (assuming the same mutation rate) from samples that are orders of magnitude larger (Kim et al. 2017 Genetics).

      We are not quite sure what the reviewer has in mind by “somewhat consistent,” as Boyko et al. estimate that 35% of non-synonymous mutations have s>10^-2 while Kim et al. find that proportion to be “0.38–0.84 fold lower” than the Boyko et al. estimate (see, e.g., Fig. 4 in Kim et al., 2017). Moreover, the preprint by Dukler et al. mentioned above, which infers the DFE based on ~70K chromosomes, finds estimates inconsistent with those of Kim et al. (see SOM Table 2 and SOM Figure S5 in Dukler et al., 2021).

      More generally, given that even 70K chromosomes carry little information about much of the distribution of selection coefficients (see our Fig. 4), we expect that studies based on relatively sample sizes will basically recover something close to their prior; therefore, they should agree when they use the same or similar parametric forms for the distribution of selection coefficients and disagree otherwise. The dependence on that choice is nicely illustrated in Kim et al., who consider different choices and then perform inference on the same data set and with the same fixed mutation rate for exomes; depending on their choice anywhere between 5%-28% of non-synonymous changes are inferred to be under strong selection with s>=10^-2 (see their Table S4).

      Whether a DFE inferred under polymorphism saturation conditions with different methods is different, and how it is different, is an issue of broad and immediate relevance to all those conducting population genomic simulations involving purifying selection. The analyses presented as Fig 4A and 4B kind of show this, but they are more a demonstration of what information one might have at 1M+ sample sizes rather than an analysis of whether genome-wide nonsynonymous DFE estimates are accurate. In other words, this manuscript makes it clear that a problem exists, that it is a fundamental and important problem in population genetics, and that with modern datasets we are now poised to start addressing this problem with some types of sites, but all of this is already very well-appreciated except for perhaps the last point.

      At least a crude analysis to directly compare the nonsynonymous genome-wide DFE from smaller samples to the 780K sample would be helpful, but it should be noted that these kinds of analyses could be well beyond the scope of the current manuscript. For example, if methylated nonsynonymous CpG sites are under a different level of constraint than other nonsynonymous sites (Fig. S14) then comparing results to a genome-wide nonsynonymous DFE might not make sense and any new analysis would have to try and infer a DFE independently from synonymous/nonsynonymous methylated CpG sites.

      We are not sure what would be learned from this comparison, given that Figure 4 shows that, at least with an uninformative prior, there is little information about the true DFE in samples, even of tens of thousands of individuals. Thus, if some of the genome-wide nonsynonymous DFE estimates based on small sample sizes turn out to be accurate, it will be because the guess about the parametric shape of the DFE was an inspired one. In our view, that is certainly possible but not likely, given that the shape of the DFE is precisely what the field has been aiming to learn and, we would argue, what we are now finally in a position to do for CpG mutations in humans.

      Reviewer #2 (Public Review):

      This manuscript presents a simple and elegant argument that neutrally evolving CpG sites are now mutationally saturated, with each having a 99% probability of containing variation in modern datasets containing hundreds of thousands of exomes. The authors make a compelling argument that for CpG sites where mutations would create genic stop codons or impair DNA binding, about 20% of such mutations are strongly deleterious (likely impairing fitness by 5% or more). Although it is not especially novel to make such statements about the selective constraint acting on large classes of sites, the more novel aspect of this work is the strong site-by-site prediction it makes that most individual sites without variation in UK Biobank are likely to be under strong selection.

      The authors rightly point out that since 99% of neutrally evolving CpG sites contain variation in the data they are looking at, a CpG site without variation is likely evolving under constraint with a p value significance of 0.01. However, a weakness of their argument is that they do not discuss the associated multiple testing problem-in other words, how likely is it that a given non synonymous CpG site is devoid of variation but actually not under strong selection? Since one of the most novel and useful deliverables of this paper is single-base-pair-resolution predictions about which sites are under selection, such a multiple testing correction would provide important "error bars" for evaluating how likely it is that an individual CpG site is actually constrained, not just the proportion of constrained sites within a particular functional category.

      We thank the reviewer for pointing this out. One way to think about this problem might be in terms of false discovery rates, in which case the FDR would be 16% across all non-synonymous mCpG sites that are invariant in current samples, and ~4% for the subset of those sites where mutations lead to loss-of-function of genes.

      Another way to address this issue, which we had included but not emphasized previously, is by examining how one’s beliefs about selection should be updated after observing a site to be invariant (i.e., using Bayes odds). At current sample sizes and assuming our uninformative prior, for a non-synonymous mCpG site that does not have a C>T mutation, the Bayes odds are 15:1 in favor of hs>0.5x10^-3; thus the chance that such a site is not under strong selection is 1/16, given our prior and demographic model. These two approaches (FDR and Bayes odds) are based on somewhat distinct assumptions.

      We have now added and/or emphasized these two points in the main text.

      The paper provides a comparison of their functional predictions to CADD scores, an older machine-learning-based attempt at identifying site by site constraint at single base pair resolution. While this section is useful and informative, I would have liked to see a discussion of the degree to which the comparison might be circular due to CADD's reliance on information about which sites are and are not variable. I had trouble assessing this for myself given that CADD appears to have used genetic variation data available a few years ago, but obviously did not use the biobank scale datasets that were not available when that work was published.

      We apologize for the lack of clarity in the presentation. We meant to emphasize that de novo mutation rates vary across CADD deciles when considering all CpG sites (Fig. 2-figure supplement 5c), which confounds CADD precisely because it is based in part on which sites are variable. We have edited the manuscript to clarify this.

      Reading this paper left me excited about the possibility of examining individual invariant CpG sites and deducing how many of them are already associated with known disease phenotypes. I believe the paper does not mention how many of these invariant sites appear in Clinvar or in databases of patients with known developmental disorders, and I wondered how close to saturation disease gene databases might be given that individuals with developmental disorders are much more likely to have their exomes sequenced compared to healthy individuals. One could imagine some such analyses being relatively low hanging fruit that could strengthen the current paper, but the authors also make several reference to a companion paper in preparation that deals more directly with the problem of assessing clinical variant significance. This is a reasonable strategy, but it does give the discussion section of the paper somewhat of a "to be continued" feel.

      We apologize for the confusion that arose from our references to a second manuscript in prep. The companion paper is not a continuation of the current manuscript: it contains an analysis of fitness and pathogenic effects of loss-of-function variation in human exomes.

      Following the reviewer’s suggestion to address the clinical significance of our results, we have now examined the relationship of mCpG sites invariant in current samples with Clinvar variants. We find that of the approximately 59,000 non-synonymous mCpG sites that are invariant, only ~3.6% overlap with C>T variants associated with at least one disease and classified as likely pathogenic in Clinvar (~5.8% if we include those classified as uncertain or with conflicting evidence as pathogenic). Approximately 2% of invariant mCpGs have C>T mutations in what is, to our knowledge, the largest collection of de novo variants ascertained in ~35,000 individuals with developmental disorders (DDD, Kaplanis et al. 2020). At the level of genes, of the 10k genes that have at least one invariant non-synonymous mCpG, only 8% (11% including uncertain variants) have any non-synonymous hits in Clinvar, and ~8% in DDD. We think it highly unlikely that the large number of remaining invariant sites are not seen with mutations in these databases because such mutations are lethal; rather it seems to us to be the case that these disease databases are far from saturation as they contain variants from a relatively small number of individuals, are subject to various ascertainment biases both at the variant level and at the individual level, and only contain data for a small subset of existing severe diseases.

      With a view to assessing clinical relevance however, we can ask a related question, namely how informative being invariant in a sample of 780k is about pathogenicity in Clinvar. Although the relationship between selection and pathogenicity is far from straightforward, being an invariant non-synonymous mCpG in current samples not only substantially increases (15-10fold) the odds of hs > 0.5x10-3 (see Fig. 4b), it also increases the odds of being classified as pathogenic vs. benign in Clinvar 8-51 fold. In the DDD sample, we don’t know which variants are pathogenic; however, if we consider non-synonymous mutations that occur in consensus DDD genes as pathogenic (a standard diagnostic criterion), being invariant increases the odds of being classified as pathogenic 6-fold. We caution that both Clinvar classifications and the identification of consensus genes in DDD relies in part on whether a site is segregating in datasets like ExAC, so this exercise is somewhat circular. Nonetheless it illustrates that there is some information about clinical importance in mCpG sites that are invariant in current samples, and that the degree of enrichment (6 to 51-fold) is very roughly on par with the Bayes odds that we estimate of strong selection conditional on a site being invariant. We have added these findings to the main text and added the plot as Supplementary Figure 13.

      Reviewer #3 (Public Review):

      [...] The authors emphasize several times how important an accurate demographic model is. While we may be close to a solid demographic model for humans, this is certainly not the case for many other organisms. Yet we are not far off from sufficient sample sizes in a number of species to begin to reach saturation. I found myself wondering how different the results/inference would be under a different model of human demographic history. Though likely the results would be supplemental, it would be nice in the main text to be able to say something about whether results are qualitatively different under a somewhat different published model.

      We had previously examined the effect of a few demographic scenarios with large increases in population size towards the present on the average length of the genealogy of a sample (and hence the expected number of mutations at a site) in Figure 3-figure supplement 1b, but without quantifying the effect on our selection inference. Following this suggestion, we now consider a widely used model of human demography inferred from a relatively small sample, and therefore not powered to detect the huge increase in population size towards the present (Tennessen et al. 2012). Using this model, we find a poor fit to the proportion of segregating CpG sites (the observed fraction is 99% in 780k exomes, when the model predicts 49%). Also, as expected, inferences about selection depend on the accuracy of the demographic model (as can be seen by comparing panel B to Fig 4B in the main text).

      On a similar note, while a fixed hs simplifies much of the analysis, I wondered how results would differ for 1) completely recessive mutations and 2) under a distribution of dominance coefficients, especially one in which the most deleterious alleles were more recessive. Again, though I think it would strengthen the manuscript by no means do I feel this is a necessary addition, though some discussion of variation in dominance would be an easy and helpful add.

      There's some discussion of population structure, but I also found myself wondering about GxE. That is, another reason a variant might be segregating is that it's conditionally neutral in some populations and only deleterious in a subset. I think no analysis to be done here, but perhaps some discussion?

      We agree that our analysis ignores the possibilities of complete recessivity in fitness (h=0) as well as more complicated selection scenarios, such as spatially-varying selection (of the type that might be induced by GxE). We note however that so long as there are any fitness effects in heterozygotes, the allele dynamics will be primarily governed by hs; one might also imagine that under some conditions, the mean selection effect across environments would predict allele dynamics reasonably well even in the presence of GxE. Also worth exploring in our view is the standard assumption that hs remains fixed even as Ne changes dramatically. We now mention these points in the Outlook.

      Maybe I missed it, but I don't think the acronym DNM is explained anywhere. While it was fairly self-explanatory, I did have a moment of wondering whether it was methylation or mutation and can't hurt to be explicit.

      We apologize for the oversight and have updated the text accordingly.

    2. Evaluation Summary:

      Diminishing returns on sampling new variable sites with increasing samples sizes is a classic limitation of population genomics and one that limits the power of population genomic approaches to make site-specific inferences of natural selection. This timely study demonstrates that methylated CpG sites, which have a mutation rate an order of magnitude higher than other sites in the genome, are saturated with polymorphisms in modern human genomic datasets. They can thus serve as a starting point for understanding the effects of natural selection at the resolution of single nucleotide sites. The manuscript is a clearly written presentation of the state of the field and the claims are supported by a variety of thoughtful analyses. Additional work will be needed to take full advantage of the insights from this study.

      (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. The reviewers remained anonymous to the authors.)

    3. Reviewer #1 (Public Review):

      Agarwal and Przeworski have performed a very timely and interesting study of the distribution of fitness effects (DFE) of new mutations. This study is timely because modern human population genetic datasets have finally achieved sample sizes for which a certain class of nucleotide sites, i.e. methylated CpG sites, when neutrally evolving, should approach near complete polymorphism saturation. If every neutral site is expected to carry at least one variant, the classic problem of distinguishing sites that are monomorphic due to chance (no mutation) versus sites that are monomorphic due to selective constraint (removed mutations) is greatly simplified. The point at which population genomic datasets are saturated with polymorphisms should represent a major advance in understanding the DFE at individual sites and is what immediately piqued my interest.

      Overall, this manuscript is a thorough and thoughtful examination of this topic; to my enjoyment there were several times where a question came to mind that was addressed shortly later in the paper. I believe the authors have made a compelling case for why methylated CpG sites provide an entry point for understanding the site-specific DFE. I found the section "Interpreting monomorphic and polymorphic sites in current reference databases" particularly insightful as a guide to thinking about future datasets; similarly, I thought the comparison with CADD scores (Fig S9) provided important food for thought regarding confounders to maps of constraint generated from vast numbers of species using modern genomic datasets.

      While the study is addressing an interesting topic, I also felt this manuscript was limited in novel findings to take away. Certainly the study clearly shows that substitution saturation is achieved at synonymous CpG sites. However, subsequent main analyses do not really show anything new: the depletion of segregating sites in functional versus neutral categories (Fig 2) has been extensively shown in the literature and polymorphism saturation is not a necessary condition for observing this pattern. Similarly, the diminishing returns on sampling new variable sites has been shown in previous studies, for example the first "large" human datasets ca. 2012 (e.g. Fig 2 in Nelson et al. 2012, Science) have similar depictions as Figure 3B although with smaller sample sizes and different approaches (projection vs simulation in this study). There are some simulations presented in Fig 4, but this is more of a hypothetical representation of the site-specific DFE under simulation conditions roughly approximating human demography than formal inference on single sites. Again, these all describe the state of the field quite well, but I was disappointed by the lack of a novel finding derived from exploiting the mutation saturation properties at methylated CpG sites.

      Similarly, I felt the authors posed a very important point about limitations of DFE inference methods in the Introduction but ended up not really providing any new insights into this problem. The authors argue (rightly so) that currently available DFE estimates are limited by both the sparsity of polymorphisms and limited flexibility in parametric forms of the DFE. However, the nonsynonymous human DFE estimates in the literature appear to be surprisingly robust to sample size: older estimates (Eyre-Walker et al. 2006 Genetics, Boyko et al. 2008 PLOS Genetics) seem to at least be somewhat consistent with newer estimates (assuming the same mutation rate) from samples that are orders of magnitude larger (Kim et al. 2017 Genetics). Whether a DFE inferred under polymorphism saturation conditions with different methods is different, and how it is different, is an issue of broad and immediate relevance to all those conducting population genomic simulations involving purifying selection. The analyses presented as Fig 4A and 4B kind of show this, but they are more a demonstration of what information one might have at 1M+ sample sizes rather than an analysis of whether genome-wide nonsynonymous DFE estimates are accurate. In other words, this manuscript makes it clear that a problem exists, that it is a fundamental and important problem in population genetics, and that with modern datasets we are now poised to start addressing this problem with some types of sites, but all of this is already very well-appreciated except for perhaps the last point.

      At least a crude analysis to directly compare the nonsynonymous genome-wide DFE from smaller samples to the 780K sample would be helpful, but it should be noted that these kinds of analyses could be well beyond the scope of the current manuscript. For example, if methylated nonsynonymous CpG sites are under a different level of constraint than other nonsynonymous sites (Fig. S14) then comparing results to a genome-wide nonsynonymous DFE might not make sense and any new analysis would have to try and infer a DFE independently from synonymous/nonsynonymous methylated CpG sites.

    4. Reviewer #2 (Public Review):

      This manuscript presents a simple and elegant argument that neutrally evolving CpG sites are now mutationally saturated, with each having a 99% probability of containing variation in modern datasets containing hundreds of thousands of exomes. The authors make a compelling argument that for CpG sites where mutations would create genic stop codons or impair DNA binding, about 20% of such mutations are strongly deleterious (likely impairing fitness by 5% or more). Although it is not especially novel to make such statements about the selective constraint acting on large classes of sites, the more novel aspect of this work is the strong site-by-site prediction it makes that most individual sites without variation in UK Biobank are likely to be under strong selection.

      The authors rightly point out that since 99% of neutrally evolving CpG sites contain variation in the data they are looking at, a CpG site without variation is likely evolving under constraint with a p value significance of 0.01. However, a weakness of their argument is that they do not discuss the associated multiple testing problem-in other words, how likely is it that a given non synonymous CpG site is devoid of variation but actually not under strong selection? Since one of the most novel and useful deliverables of this paper is single-base-pair-resolution predictions about which sites are under selection, such a multiple testing correction would provide important "error bars" for evaluating how likely it is that an individual CpG site is actually constrained, not just the proportion of constrained sites within a particular functional category.

      The paper provides a comparison of their functional predictions to CADD scores, an older machine-learning-based attempt at identifying site by site constraint at single base pair resolution. While this section is useful and informative, I would have liked to see a discussion of the degree to which the comparison might be circular due to CADD's reliance on information about which sites are and are not variable. I had trouble assessing this for myself given that CADD appears to have used genetic variation data available a few years ago, but obviously did not use the biobank scale datasets that were not available when that work was published.

      Reading this paper left me excited about the possibility of examining individual invariant CpG sites and deducing how many of them are already associated with known disease phenotypes. I believe the paper does not mention how many of these invariant sites appear in Clinvar or in databases of patients with known developmental disorders, and I wondered how close to saturation disease gene databases might be given that individuals with developmental disorders are much more likely to have their exomes sequenced compared to healthy individuals. One could imagine some such analyses being relatively low hanging fruit that could strengthen the current paper, but the authors also make several reference to a companion paper in preparation that deals more directly with the problem of assessing clinical variant significance. This is a reasonable strategy, but it does give the discussion section of the paper somewhat of a "to be continued" feel.

    5. Reviewer #3 (Public Review):

      Agarwal et. al combine a few well-known ideas in population genetics - diminishing returns in sampling new alleles with increasing sample size and the enrichment of invariant sites for sites under strong purifying selection - and point out the exciting result that sample sizes of modern human data sets are sufficiently large that, for highly mutable sites, saturation mutation has been reached. This is my favorite kind of result - one that is strikingly obvious in retrospect but that I had never considered (and probably wouldn't have). The manuscript is well written, and a number of my concerns or questions while reading were resolved directly by the authors later on. I have no major concerns, but a few potential suggestions that might strengthen the presentation.

      The authors emphasize several times how important an accurate demographic model is. While we may be close to a solid demographic model for humans, this is certainly not the case for many other organisms. Yet we are not far off from sufficient sample sizes in a number of species to begin to reach saturation. I found myself wondering how different the results/inference would be under a different model of human demographic history. Though likely the results would be supplemental, it would be nice in the main text to be able to say something about whether results are qualitatively different under a somewhat different published model.

      On a similar note, while a fixed hs simplifies much of the analysis, I wondered how results would differ for 1) completely recessive mutations and 2) under a distribution of dominance coefficients, especially one in which the most deleterious alleles were more recessive. Again, though I think it would strengthen the manuscript by no means do I feel this is a necessary addition, though some discussion of variation in dominance would be an easy and helpful add.

      There's some discussion of population structure, but I also found myself wondering about GxE. That is, another reason a variant might be segregating is that it's conditionally neutral in some populations and only deleterious in a subset. I think no analysis to be done here, but perhaps some discussion?

      Maybe I missed it, but I don't think the acronym DNM is explained anywhere. While it was fairly self-explanatory, I did have a moment of wondering whether it was methylation or mutation and can't hurt to be explicit.

    1. Author Response:

      Reviewer #2 (Public Review):

      Tissue microarrays have become a mainstay in clinical and basic research, for both discovery and validation of biomarkers. The authors approach the possible sampling variation in a thoughtful way, not only quantifying the issue systematically, but working towards a solution.

      Major Comments:

      o The authors split the variation in to two co-existing explanations, either intratumoral heterogeneity or batch effect (likely a degree of both play a role). Batch correction inherently reduces noise (the latter) at the cost of reducing signal (the former). It would be useful to know what approaches have been employed to test for overfitting. The authors claim in the introduction the use of different methods for maintaining "biological" variation, but that analysis seems limited.

      We agree that overfitting is a potential concern for any model. The large number of tumor cores per each batch is less likely to give rise to overfitting if few parameters per batch are estimated. We consider overfitting of the adjustment models a separate problem from overadjustment, which would remove biological variation and which depends on balancing of batches with respect to biological factors. The results from our simulations (Fig. 5, Fig. 5–figure supplement 1) address the latter. “Biological variation” between TMAs was maintained in each simulated data set (Fig. 5–figure supplement 1). All mitigation approaches are more successful in recovering the true association (Fig. 5) compared to not addressing batch effects.

      o Were there considerations for the variability in Gleason scoring between members of the study team?

      We agree that this is an important consideration. Gleason scores in our study are from a centralized, standardized re-review of full tissue sections performed before constructing the TMAs. These use cores from the highest-density tumor regions. See Stark et al. (JCO 2009, referenced) on how variability was removed.

      o The manuscript involves the processing of a number of different cohorts in the field of prostate cancer. It would be important to know how would the performance of the batchma approach would change in tumors with greater heterogeneity.

      We do not have additional empirical data. We would to like to emphasize that there is substantial heterogeneity within the large prostate cancer case series that we analyzed, which was sampled from population-based cohorts. Moreover, in the last paragraph of the section, “Validation batch effect mitigation in plasmode simulation,” we tested the methods implemented in the batchtma package in simulations that involved scenarios with far greater heterogeneity than empirically observed (Figure 5– figure supplement 3; the actual data on biomarkers with high between-TMA ICCs corresponds to the setting “some confounding”).

    2. Evaluation Summary:

      Tissue microarrays (TMA) have become a mainstay in clinical and basic research, for both discovery and validation of biomarkers. This manuscript provides relevant methodologic considerations for cancer researchers investigating tissue-biomarkers using TMAs. A comprehensive investigation was conducted using a combination of analytic approaches using empirical data and simulated data to support key findings and conclusions. The authors approach the possible sampling variation in a thoughtful way, not only quantifying the issue systematically, but working towards a solution.

      (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. The reviewers remained anonymous to the authors.)

    3. Reviewer #1 (Public Review):

      Tissue microarrays (TMAs) are a critical tool for conducting tissue-biomarker research. In this report, the authors investigated whether technical aspects involved in TMA-based investigations contribute to the presence of batch-effects (e.g., differences in the values of biomarkers measured in tumor samples due to non-biological factors) and tested multiple ways to correct for the measurement error resulting from batch-effects.

      Using data generated from 20 prostate cancer biomarker investigations using 14 different TMAs that included tumor tissue from over 1400 men with prostate cancer, the investigators determined that tumor characteristics such as stage, grade, and date of diagnosis do not contribute to the batch-effects observed across the 14 TMAs. Though these findings may not be generalizable for all potential tissue-biomarkers investigated using TMAs, TMAs developed using different protocols for patient selection and tissue acquisition, preservation, and TMA construction as well as those with smaller sample size and for other cancer types.

      The authors then evaluated six different statistical methods to correct the measurement error due to batch-effects. The strengths and limitations of each method investigated are discussed. An overall strength of this study is the availability of empirical data generated from 20 biomarker investigations using the same TMAs to identify which statistical method leads to the most valid (e.g., true) biomarker estimates. Data simulations were used to determine how each method used to correct the biomarker measurement error due to batch-effects influenced the biomarker-cancer outcome relationship. This is another strength of the investigation which provide a robust assessment of different statistical approaches to overcoming the influence of batch-effects using both empirical and simulated data.

      The author's conclusion that bath-effects are not an error of an individual study, but a feature of this type of research utilizing TMAs is supported by the results reported. While the extent of potential bias introduced from batch-effects does vary between studies based on the data reported, the author's recommendations are well supported and will contribute to improving the validity of tissue-biomarker investigations using TMAs.

    4. Reviewer #2 (Public Review):

      Tissue microarrays have become a mainstay in clinical and basic research, for both discovery and validation of biomarkers. The authors approach the possible sampling variation in a thoughtful way, not only quantifying the issue systematically, but working towards a solution.

      Major Comments:<br> o The authors split the variation in to two co-existing explanations, either intratumoral heterogeneity or batch effect (likely a degree of both play a role). Batch correction inherently reduces noise (the latter) at the cost of reducing signal (the former). It would be useful to know what approaches have been employed to test for overfitting. The authors claim in the introduction the use of different methods for maintaining "biological" variation, but that analysis seems limited.<br> o Were there considerations for the variability in Gleason scoring between members of the study team?<br> o The manuscript involves the processing of a number of different cohorts in the field of prostate cancer. It would be important to know how would the performance of the batchma approach would change in tumors with greater heterogeneity.

    1. Author Response:

      Reviewer #2 (Public Review):

      In all vertebrate species investigated, cerebrospinal fluid contacting the cerebrospinal fluid express the channel PKD2L1 (in macaques, mice and zebrafish: Djenoune et al., Frontiers in Neuroanatomy 2014; in lamprey: Jalalvand et al., Current Biology 2016b; Jalalvand et al J Neurosci 2018). However, in all species investigated these cells fall into two functional types based on their axial sensitivity to detect spinal curvature (in vivo for zebrafish: Bohm et al., Nature Communications 2016; Hubbard et al., Current Biology 2016), expression of neuropeptides and neuromodulators (in lamprey;: Christenson et al., Neurosci Letter 1991; Schotland et al., JCN 1996; in zebrafish: Djenoune et al., Scientific Reports 2017) or their firing patterns (in mouse: Petracca et al J Neurosci 2016; Di Bella et al., Cell Reports 2019).

      While the microscopy techniques used here are outstanding and bring without a doubt important evidence on the location and density of DSVs, there are concerns to address regarding the consolidation and interpretation of the physiological recordings of the ciliated neurons and pharmacology based on evidence that only ASIC1 channel is expressed in lamprey (see phylogenic analysis: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3047259/), and that the lamprey ASIC1a is proton insensitive (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1464184/).

      In the article of Coric et al 2005 they had identified one clone of cDNA that corresponded to ASIC1 and when expressed in oocytes, no pH sensitivity was found under these conditions. They do not comment regarding the possible presence of ASIC3. The review of Grunder and Chen (2010) has a focus on ASIC1a and base their comment in lamprey on Coric et al 2005. Our evidence for the presence of ASIC3 in lamprey is that both the mechanical and pH response are blocked by APETx2, a selective antagonist of ASIC3, (Jalalvand et al 2016, Nature. com), strongly suggesting the presence of ASIC3 in the lamprey. ASIC3 is present in both the peripheral and central nervous system in mammals.

      Reviewer #3 (Public Review):

      This manuscript uses a variety of optical superresolution techniques to explore the structure and function of different cerebrospinal fluid contacting (CSF-c) neurons. First, Expansion Microscopy and Lightsheet Microscopy are combined to image large volumes of tissue from lamprey and to demonstrate the known organization of somatostatin-expressing and dopaminergic CSF-c neurons (Fig. 1). The authors then used STED to explore the subcellular location of somatostatin and dopamine in CSF-c neurons and demonstrate their presences in vesicle-like structures ranging between 60 and 200 nm (Fig. 2). Subsequently, the relation between GABA and somatostatin is examined in somatostatin CSF-c neurons. The authors show that there is no obvious colocalization between these molecules and that only somatostatin levels are altered in response to changes in the extracellular pH (Fig. 3). The authors furthermore demonstrate that dopamine levels with dopaminergic CSF-c neurons are also insensitive to pH changes (Fig. 4).

      The authors have previously shown that somatostatin CSF-c neurons are mechanosensitive and now also demonstrate this for dopaminergic CSF-c neurons (Ext Data Fig. 1). They also show that their mechanosensitivity is mediated differently (ie. Not through ASIC3). To further explore this difference in mechanosensitivity, the authors set out to explore ciliary structure of these ciliated neurons. By combining expansion and STED, the authors succeed in resolving ciliary ultrastructure and demonstrate that they can distinguish between between motile (9+2) and primary cilia (9+0) (Fig. 5). They find that the majority of cilia on somatostatin-expressing CSF-c neurons is primary, whereas all cilia on dopaminergic CSF-c neurons were motile (Fig. 6).

      Overall, this is an interesting imaging study that reports a number of technical steps that enable tissue-imaging with exquisite detail, such as discriminating between motile and primary cilia. It also nicely demonstrates what sort of new data can now be obtained in tissue, e.g. changes in vesicle numbers upon certain stimuli. However, as explained below, both the composition of the main text and the reproduction quality of the figures make it hard to judge the biological significance of this work.

      Comments:

      1/ Overall, I feel the paper needs a thorough rewrite. The introduction should give more insights into the underlying biology and also clarify which questions are being asked and why these are important. Currently the introduction is mostly a long summary of all results, but it doesn't help to understand the biology that underlies this work. Because of that the different experimental pieces currently feel a bit random and disconnected.

      We have rewritten part of the Introduction to better expose the underlying biological questions.

      2/ The reproduction quality of Figures 1, 2, 3 and 6 in the merged PDF is not great. In many cases, I cannot read the annotations or appreciate the content of the images. This make it rather impossible to judge the quality of the work. The data shown in Figure 5 is very impressive and I am sure the raw data for the other figures is equally great, but I can only judge what I see for myself.

      We provide Figures at high resolution.

      3/ Page 8: the conclusion that PKD2L1 is the mechanosensitive receptor for dopaminergic CSF-c neurons is only based on its presence in this cells. To really demonstrate this loss-of-function experiments would be needed.

      PKD2L1 has been found to be responsible for mechanosensitivity in Zebrafish (Böhm et al. 2016).

      We agree that to demonstrate a loss of function in lamprey a knock-out experiment would be needed. Transgenic techniques unfortunately cannot be used in lamprey since each generation lasts around 7 years, and there is no specific blocker for PKD2L1 to apply either. Therefore, we have modified the sentences.

    1. Evaluation Summary:

      This paper will be of interest to electrophysiologists, systems neuroscientists and neural engineers. The authors describe a framework for evaluating the comparison between LFP dynamics and spikes and perform this comparison for several datasets recorded from motor, premotor, and sensory areas of cortex in rhesus macaque monkeys. These results serve as an important benchmark for the information content of LFP recordings, which is relevant to data collection in neuroscientific investigations and to designing brain computer interfaces.

      (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. The reviewers remained anonymous to the authors.)

    2. Reviewer #1 (Public Review):

      Gallego-Carracedo and colleagues investigated the relationship between neural spiking activity and local field potentials (LFP) across three different sensorimotor areas (dorsal premotor (PMd), primary motor (M1), and area 2 of somatosensory cortex (S1)) during a well-trained motor behavior. In contrast to previous studies, where spiking-LFP relationships were studied at the level of single neurons, the authors explore whether and how mesoscopic signals like the LFP are related to population-level patterns in spiking activity (referred to as "latent dynamics"). This is a very interesting and potentially valuable revisiting of LFP-spiking relationships, since increasing evidence has shifted focus away from purely single-neuron-based analyses towards population perspectives. Insights into relationships between LFP and latent dynamics may also inform interpretations of these signals.

      The largest strength of this paper is the large amount of data. The paper includes analyses of datasets from 3 brain areas (7 implanted arrays total) in 4 animals. This reveals that LFP - latent dynamics relationships vary across brain areas and opens possibilities to fully examine relationships of all signals. The wealth of data allows them to clearly show that LFP-latent relationships are frequency-dependent and vary across brain areas.

      The primary weaknesses of the paper are that it skips some important preliminary analyses, does not fully describe/interpret the broad diversity of data they present, and their interpretation of "stable relationship" is somewhat unclear.

      1) Given the frequency-based analyses presented, more detailed characterization of the LFP spectra will greatly benefit the paper. A key question the authors should address is whether the frequency-dependence (and its variance across areas) is related to differences in power spectra across areas. They present an analysis suggesting their results are not simply explained by variance differences across bands, but there are no analyses to address power differences (and deviations from the 1/f "noise" spectrum).

      2) The data reveal some clear differences between subjects and across areas that are not fully elaborated on. The relationship between decoding performance and LFP-latent correlation appears to only be present in M1. The relationships in PMd and area 2 are not quantified or commented on in much detail. Similarly, across all areas there are notable differences in LFP-latent correlations in some frequency bands (primarily the lower frequencies) between animals that is not addressed.

      3) One of the manuscript's primary claims is that LFP-latent correlations are "stable" within areas while being different between areas. These claims are the main basis of their interpretation that these relationships reflect biophysical properties of the cortical networks (e.g. cytoarchitecture). The claim of stable relationships focus on comparing between motor planning and execution task epochs. These task epochs appear to include partially overlapping time windows based on their methodological description, which seems like a potential confound that should be addressed. The time windows used are also different durations, which should be controlled for. Moreover, their results also show that LFP-latent relationships change (mostly disappearing) in inter-trial intervals. If these correlations truly reflect properties of circuit structure, I am unclear on why they would be task-dependent. This interpretational point needs significant clarification.

    3. Reviewer #2 (Public Review):

      In this paper, Gallego-Carracedo, Perich, Chowdhury, Miller, and Gallego set up an important question: In much of systems neuroscience, researchers record spiking data from populations of single neurons or multi-unit channels to estimate neural population state. Applying dimensionality reduction algorithms like PCA to the high dimensional neural population state yields an estimate of the lower-dimensional latent dynamics, which are commonly understood to be a compact representation of the patterns of activity in the brain. Understanding the relationship between these latent dynamics and behaviors, sensory inputs, or cognition, represents a central goal of systems neuroscience.

      In most such experiments, local field potential (LFP), is often also recorded, as it is simple to do so and these complementary signals may also provide scientific utility. In many other studies, often including studies using human participants, only LFP recordings are possible due to constraints of the neural sensors or recording equipment. Understanding the relationships between the LFP signals and latent dynamics thus represents an important bridge for helping to contextualize studies relying on LFP alone. In addition, better understanding the link between the two recording modalities (i.e. understanding the relative information content contained of each), in principle, could help to elucidate the biophysical mechanisms by which LFP arises from the collective spiking activity of neural circuits.

      The authors outline four central hypotheses: 1) That there should be a robust relationship between LFP and latent dyamics, 2) that this relationship should be frequency dependent, 3) that these relationships are similar between preparation and movement (for data recorded in PMd, M1, and S1), and 4) that different areas should have different relationships between LFP and latent dynamics.

      The motivation for this work is strong, and the quality and breadth of the data sets collected and curated is impressive. With a narrow reading of these hypotheses, the analyses presented here support the authors conclusions. The author's explicit goal is to assess whether any relationship exists between LFP and latent dynamics. Their analysis reveals that for certain frequency bands, in certain brain areas, that information in LFP signals is also contained within the manifold of latent activity.

      While the stated goals, hypotheses, and overall presentation of this paper are all clear, the primary analysis method limits the broader interpretability of the results. The main analysis method that the authors use to assess the relationship between LFP and latent dynamics is the distribution of correlation coefficients derived from applying canonical correlations analysis (CCA) between the latent dynamics and individual channels of LFP, for individual frequency bands on those channels. As described, this method produces a metric for how well signals within a specific LFP frequency band on one channel is represented within the manifold of latent dynamics, allowing for a rotation of that manifold.

      This analysis, however, does not say anything, however regarding the information contained in the manifold of latent dynamics that is not present within LFP signals. An analysis capable of revealing these differences would provide a more actionable takeaway for contextualizing what information is lost when an experiment relies on LFP signals alone. For a concrete hypothetical example, if every individual LFP channel (within one frequency band) contained a signal that perfectly correlated with principal component 1 (or any other PC), then the metric would report a distribution clustered tightly around 1. While this metric cannot get any higher, suggesting a high degree of alignment between LFP and latent dynamics, it appears to ignore the fact that in this contrived scenario, no LFP channels have captured any information about PCs 2,3,4,...,n, and the metric tells us nothing about the information lost by only recording LFP. If we don't know what information is lost, it is difficult to know how to apply these results to contextualize other studies based on LFP recordings, which is one of the stated broader motivations for this paper.

      These limitations aside, the authors have carefully shown that there appears to be a frequency dependence between which LFP bands share similar information with the latent dynamics. In addition, they establish that LFP recordings in PMd, M1, and S1 show different relationships with the latent dynamics, and that the degree of LFP correlation with latent dynamics is stable between the movement preparation and execution. This paper is well written, with extraordinary attention to detail and clarity throughout.

    1. Evaluation Summary:

      Toxoplasma gondii is a widespread parasite of warm blooded animals, with estimates suggesting 2 billion people are currently and chronically infected with this pathogen. Many questions remain as to how humans control and eliminate T. gondii following infection. In this manuscript, Rinkenberger et al. reveal a previously unidentified and understudied host factor, RARRES3 that promotes cell autonomous control of T. gondii in human cells. The precise mechanism of control and its in vivo relevance remain areas for additional work.

      (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #2 agreed to share their name with the authors.)

    2. Author Response:

      Reviewer #1 (Public Review):

      Rinkenberger et al. take a forward genetics ORF overexpression approach to identify human interferon (IFN)-inducible gene (ISG) products driving host defense to the protozoan pathogen Toxoplasma. The screen encompassing approximately 500 ISG identifies 3 ISG candidates and is able to validate 2 of these 3, namely the transcription factor IRF1 and the retinoic acid receptor responsive gene RARRES3, which becomes the focus of the study. Using gain- and loss-of-function approaches the study demonstrates that RARRES3 promotes the reduction of parasitic burden in human cell lines. Importantly, the study provides evidence linking RARRES3 functionally to the previously reported interferon-inducible defense mechanism of host-mediated parasite extrusion. Overall, the discovery of RARRES3 as an anti-parasitic factor is potentially of broad interest to the field of innate immunity, parasitology and, more generally, microbial pathogenesis, although its physiologically importance or its role in host defense to pathogens other than Toxoplasma was not explored in this study.

      Strengths:

      The paper takes an unbiased genetics approach to identify novel human genes that execute cell-autonomous host defense against the parasite Toxoplasma

      The study is well controlled and convincingly demonstrates that RARRES3 limits parasitic burden in human cell lines, using both gain- and loss-of-function approaches.

      The study provides indirect evidence that RARRES3 mediates the expulsion of parasites from infected cells

      The study shows that some clonal lineages of Toxoplasma are resistant to RARRES3-mediated immunity suggesting that some Toxoplasma strains may have evolved mechanisms to counteract the host defense pathway(s) regulated by RARRES3.

      Weaknesses:

      The physiological relevance of RARRES3-mediated parasite egress during the course of Toxoplasma infections is unclear and not discussed.

      We added a section in the Discussion covering the potential physiological relevance of RARRES3.

      Regarding the failure to see an IDO phenotype (Fig. 1F), the authors may consider that there standard media and serum contains relatively high concentrations of tryptophan (Materials and Methods doesn't provide any information on the exact trp concentration used) and that IDO cannot catabolize the excess amount of tryptophan present in media + serum to achieve tryptophan starvation conditions. I believe previous studies demonstrating IDO-mediated nutritional immunity in cell culture used trp-limited culture conditions. Without any careful experiments using titrated concentrations of trp, the conclusion that IDO cannot restrict Toxo in A549 cells does not seem justified

      A brief discussion of this point has been added to the results discussing figure 1F and the conclusions have been toned down. We have specified in the text that our culture medium (DMEM containing 10% FBS) contains 16 ug/ml concentration of tryptophan. Although this might mask the effects of IDO, we are able to appreciate inhibition of parasite growth in response to INF-g, suggesting this pathway is not the most important in A549 cells. We agree with the importance and requirement of tryptophan for parasite growth, we just didn't observe the involvement of IDO1 in our experimental set up.

      The authors state that RARRES3 deficiency was complemented with RARRES3 ectopic expression. However, it is unclear from the data presentation whether complemented KOs are statistically different from controls (KO + FLUC) under IFNgamma primed conditions (Fig. 5B) and thus whether complementation was actually achieved.

      The complemented KO is not significantly different from WT demonstrating complementation. A “ns” comparison between these bars has been added to the figure for clarity.

      The paper lacks any direct evidence for RARRES3-mediated parasite egress.

      We conducted a live imaging experiment to directly observed parasite egress in RARRES3 and FLUC ectopically expressing A549s. The data is presented in figure 6C and videos 1-2.

    3. Reviewer #1 (Public Review):

      Rinkenberger et al. take a forward genetics ORF overexpression approach to identify human interferon (IFN)-inducible gene (ISG) products driving host defense to the protozoan pathogen Toxoplasma. The screen encompassing approximately 500 ISG identifies 3 ISG candidates and is able to validate 2 of these 3, namely the transcription factor IRF1 and the retinoic acid receptor responsive gene RARRES3, which becomes the focus of the study. Using gain- and loss-of-function approaches the study demonstrates that RARRES3 promotes the reduction of parasitic burden in human cell lines. Importantly, the study provides evidence linking RARRES3 functionally to the previously reported interferon-inducible defense mechanism of host-mediated parasite extrusion. Overall, the discovery of RARRES3 as an anti-parasitic factor is potentially of broad interest to the field of innate immunity, parasitology and, more generally, microbial pathogenesis, although its physiologically importance or its role in host defense to pathogens other than Toxoplasma was not explored in this study.

      Strengths:

      The paper takes an unbiased genetics approach to identify novel human genes that execute cell-autonomous host defense against the parasite Toxoplasma<br> The study is well controlled and convincingly demonstrates that RARRES3 limits parasitic burden in human cell lines, using both gain- and loss-of-function approaches.

      The study provides indirect evidence that RARRES3 mediates the expulsion of parasites from infected cells

      The study shows that some clonal lineages of Toxoplasma are resistant to RARRES3-mediated immunity suggesting that some Toxoplasma strains may have evolved mechanisms to counteract the host defense pathway(s) regulated by RARRES3.

      Weaknesses:

      The physiological relevance of RARRES3-mediated parasite egress during the course of Toxoplasma infections is unclear and not discussed.

      Regarding the failure to see an IDO phenotype (Fig. 1F), the authors may consider that there standard media and serum contains relatively high concentrations of tryptophan (Materials and Methods doesn't provide any information on the exact trp concentration used) and that IDO cannot catabolize the excess amount of tryptophan present in media + serum to achieve tryptophan starvation conditions. I believe previous studies demonstrating IDO-mediated nutritional immunity in cell culture used trp-limited culture conditions. Without any careful experiments using titrated concentrations of trp, the conclusion that IDO cannot restrict Toxo in A549 cells does not seem justified

      The authors state that RARRES3 deficiency was complemented with RARRES3 ectopic expression. However, it is unclear from the data presentation whether complemented KOs are statistically different from controls (KO + FLUC) under IFNgamma primed conditions (Fig. 5B) and thus whether complementation was actually achieved.

      The paper lacks any direct evidence for RARRES3-mediated parasite egress.

    4. Reviewer #2 (Public Review):

      The manuscript by Rinkenberger et al. titled "Over-expression Screen of Interferon-Stimulated Genes Identifies RARRES3 as a Restrictor of Toxoplasma gondii Infection" describes a series of experiments to investigate the role of IFNg-induced genes, or 'ISGs', in T. gondii restriction in human cells. In humans, mechanisms of Toxoplasma gondii restriction are both cell-type specific and diverse, not relying solely on the IRG system observed in mice. Hence there are many unanswered questions as to how humans control and ultimately clear this widespread parasite of warm-blooded animals. Importantly, the authors use an unbiased over-expression ISG library to understand what additional host genes and mechanisms are employed by human cells to control parasitic infection.

      The initial screen and experimental validation, using ectopical expression, reveal IRF1 and RARRES3 as important host factors capable to restrict T. gondii infection in human cells. Importantly, RARRES3 induces premature parasite egress which can be blocked by Compound 1, a parasite egress inhibitor. Moreover, RARRES3 acts independently of host cell death pathways and appears to work autonomously in several contexts suggesting a new mode of parasite restriction not yet described.

      The manuscript is well written. The methods employed to test the hypothesis and RARRES3 function are adequate and relevant. Little is known about RARRES3. The discussion is informative and addresses why so few ISGs were found to impact parasite restriction, whereas similar screens for viral pathogens appear to turn up many ISGs with anti-pathogen capabilities. Potential mechanisms are discussed.

    1. Evaluation Summary:

      The paper by Rai and colleagues examines the transcriptional response of Candida glabrata, a common human fungal pathogen, during interaction with macrophages. They use RNA PolII profiling to identify not just the total transcripts but instead focus on the actively transcribing genes. By examining the profile over time, they identify particular transcripts that are enriched at each time point, building a hierarchical model for how a transcription factor, CgXbp1, may regulate part of this response. While the authors have generated a large and potentially impactful dataset, along with several interesting observations, it is important to be cautious as the direct targets of CgXbp1 were characterized under one particular condition and the transcriptional analyses were obtained in another condition, one shown to be highly dynamic as during macrophage infection.

      (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. The reviewers remained anonymous to the authors.)

    2. Reviewer #1 (Public Review):

      The manuscript by Rai et al., presents a straightforward approach to identify key transcriptional changes occurring as Candida glabrata infects macrophages. This is based on the premise that the changes occurring early on, as part of the pathogen response, are key in determining the progression of the infection process. Candida species are important opportunistic fungal pathogens, posing a relevant problem among immunocompromised populations. While for decades C. albicans has been responsible for most candidiasis infections, in recent years reports have indicated an upsurge in infections caused by Candida glabrata. The capacity of the latter to survive and divide within immune cells, and its increased resistance to drugs like fluconazole makes of this pathogen an organism of interest. Therefore, new information that can help to molecularly dissect aspects related to its infectious process is relevant both from the clinical and scientific points of view.

      In this study, based on CHiP-seq assays directed to elongating polymerase the authors identified a series of DEGs displaying different expression profiles over a time course during macrophage infection. The authors identify several hundred of genes that show distinct profiles, from increased expression at early times, to ones becoming more active later on in the process. Based on GO analyses several correlations are drawn regarding key physiological changes that may be key for survival and virulence. Such chronological study of transcriptional changes (with a good resolution) over the first hours of macrophage infection represents an important dataset from where different testable hypotheses can emerge. The authors paid special attention to several transcription factors encoding genes which expression was high during early time points. Among them, they focused on a homolog of the S. cerevisiae transcriptional repressor Xbp1. Thus, they generated a KO of CgXBp1 and interrogated the resulting strain regarding its gene expression profile, through an equivalent time-course. The RNAPol II-Chipseq analysis showed a series of genes which expression was accelerated relative to WT, which can be interpreted as many of them being directly repressed by CgXBp1. To assess the latter, they attempt to conduct a Chip-seq of a tagged version of CgXBp1 as C. glabrata infects macrophages, nevertheless, the correlation between replicates was low and further analyses were not conducted (data not shown). Therefore, the conditions of the assay were changed and CgXbp1 Chip-seq was performed in quiescent cells, a condition where Xbp1 is known to play important roles in S. cerevisiae. This data indicated that among direct targets there are several genes encoding TFs, which suggest an important transcriptional cascade where CgXbp1 plays an important role. Such data are correlated with the RNAPolII data obtained early on in the study, and a mechanistic model is proposed. Importantly, CgXbp1 appears to recognize different types of cis-elements in the bound promoters: one similar to the reported one in yeast and another one displaying a quite different DNA logo. Additional analyses focus on determining the consequences on growth, virulence, and fluconazole resistance of the CgXbp1 (and complemented strain). Three aspects stand out: increased resistance to fluconazole, decreased proliferation in macrophages, and decreased virulence. Such phenotypes are not discussed in extenso, since most of that section focuses on the transcriptional aspects of the work.

      While the datasets are valuable and several observations are interesting, it is important to be cautious as the direct targets of CgXbp1 were characterized under one particular condition and the transcriptional analyses were obtained in another condition, one shown to be highly dynamic. Therefore, several inferred targets may or may not be under CgXbp1 control during macrophage infection. Most importantly, as it is, the study does not provide a clear parallel between one list of genes and the other one, to get a glimpse of such concepts. Since CgXbp1 shows to recognize distinct binding motifs, it becomes relevant to understand whether one group behaves differently from the other one in the absence of CgXbp1.

    3. Reviewer #2 (Public Review):

      This manuscript describes the temporal transcriptional response of Candida glabrata during macrophage infection and characterizes the role of the transcriptional repressor CgXbp1 the process. The manuscript is well written, the experiments were well conducted and the subject is very interesting.

      However, a few issues should be addressed to improve the quality of the manuscript. Particularly, it will be important to: 1) Either repeat the experiment or discuss further the unexpected failure to obtain reliable ChIP-seq results for Xbp1 within the macrophage microenvironment. during macrophage infection". The option for defined media makes it difficult to compare with the RNA PolII dataset; 2) Validate experimentaly the proposed consensus sequence recognized by Xbp1; 3) Use standard MIC determination, to have a clear notion on the impact of Xbp1 on fluconazole resistance.

      These extra experiments will provide a stronger basis for the author's claims and increase the foreseen impact of this work.

    4. Reviewer #3 (Public Review):

      The paper by Rai and colleagues examines the transcriptional response of Candida glabrata, a common human fungal pathogen, during interaction with macrophages. They use RNA PolII profiling to identify not just the total transcripts but instead focus on the actively transcribing genes. By examining the profile over time, they identify particular transcripts that are enriched at each timepoint, and build a hierarchical model for how a transcription factor, Xbp1, may regulate this response. Due to technical difficulties in identifying direct targets of Xbp1 during infection, the authors then turn to the targets of Xbp1 during cellular quiescence.

      The authors have generated a large and potentially impactful dataset, examining the responses of C. glabrata during an important host-pathogen interface. However, the conclusions that the authors make are not well supported by the data. The ChIP-seq is interesting, but the authors make conclusions about the biological processes that are differentially regulated without testing them experimentally. Because Candida glabrata has a significant percent of the genome without GO term annotation, the GO term enrichment analysis is less useful than in a model organism. To support these claims, the authors should test the specific phenotypes, and validate that the transcriptional signature is observed at the protein level.

      Additionally, the authors should also include images of the infections, along with measurements of phagocytosis, to show that the time points are the appropriate. At 30 minutes, are C. glabrata actually internalized or just associated? This may explain the difference in adherence genes at the early timepoint. For example, in Lines 123-132, the authors could measure the timing of ROS production by macrophages to determine when these attacks are deployed, instead of speculating based on the increased transcription of DNA damage response genes. Potentially, other factors could be influencing the expression of these proteins. At the late stage of infection, the authors should measure whether the C. glabrata cells are proliferating, or if they have escaped the macrophage, as other fungi can during infection. This may explain some of the increase in transcription of genes related to proliferation.

      An additional limitation to the interpretation of the data is that the authors should put their work in the context of the existing literature on C. albicans temporal adaptation to macrophages, including recent work from Munoz (doi: 10.1038/s41467-019-09599-8), Tucey (doi: 10.1016/j.cmet.2018.03.019), and Tierney (doi: 10.3389/fmicb.2012.00085), among others.

      When comparing the transcriptional profile between WT and xbp1 mutant, it is not clear whether the authors compared the strains under non-stress conditions. The authors should include an analysis of the wild-type to xbp1 mutants in the absence of macrophage stress, as the authors claims of precocious transcription may be a function of overall decreased transcriptional repression, even in the absence of the macrophage stress. The different cut-offs used to call peaks in the two strain backgrounds is also somewhat concerning-it is not clear to me whether that will obscure the transcriptional signature of each of the strains. Additionally, the authors go on to show that the xbp1 mutant has a significant proliferation defect in macrophages, so potentially this could confound the PolII binding sites if the cells are dying.

      In the section on hierarchical analysis of transcription factors, at least one epistasis experiment should have been performed to validate the functional interaction between Xbp1 and a particular transcription factor. If the authors propose a specific motif, they should test this experimentally through EMSA assays to fully test that the motif is functional.

      The jump from macrophages to quiescent culture is also not well justified. If the transcriptional program is so dynamic during a timecourse of macrophage infection, it is hard to translate the findings from a quiescent culture to this host environment.

      Overall, there is a strong beginning and the focus on active transcription in the macrophage is an exciting approach. However, the conclusions need additional experimental evidence.

    5. Reviewer #4 (Public Review):

      Macrophages are the first line of defense against invading pathogens. C. glabrata must interact with these cells as do all pathogens seeking to establish an infection. Here, a ChIP-seq approach is used to measure levels of RNA polymerase II levels across Cg genes in a macrophage infection assay. Differential gene expression is analyzed with increasing time of infection. These differentially expressed genes are compared at the promoter level to identify potential transcription factors that may be involved in their regulation. A factor called CgXbp1 on the basis of its similar with the S. cerevisiae Xbp1 protein is characterized. ChIP-seq is done on CgXbp1 using in vitro grown cells and a potential binding site identified. Evidence is provided that CgXbp1 affects virulence in a Galleria system and that this factor might impact azole resistance.

      As the authors point out, candidiasis associated with C. glabrata has dramatically increased in the recent past. Understanding the unique aspects of this Candida species would be a great value in trying to unravel the basis of the increasing fungal disease caused by C. glabrata. The use of ChIP-seq analysis to assess the time-dependent association of RNA polymerase II with Cg genes is a nice approach. Identification of CgXbp1 as a potential participant in the control of this gene expression program is also interesting. Unfortunately, this work suffers by comparison to a significant amount of previous effort that renders the progress detailed here incremental at best.

      I agree that their ChIP-seq time course of RNA polymerase II distribution across the Cg genome is both elegant and an improvement on previous microarray experiments. However, these microarray experiments were carried out 14 years ago and while the current work is certainly at higher resolution, little more can be gleaned from the current work. The authors argue that standard transcriptional analysis is compromised by transcript stability effects. I would suggest that, while no approach is without issues, quite a bit has been learned from approaches like RNA-seq and there are recent developments to this technique that allow for a focus on newly synthesized mRNA (thiouridine labeling).

      The CgXbp1 characterization relies heavily on work from S. cerevisiae. This is disappointing as conservation of functional links between C. glabrata and S. cerevisiae is not always predictable. The effects caused by loss of CgXBP1 on virulence (Figure 4) may be statistically significant but are modest. No comparison is shown for another gene that has already been accepted to have a role in virulence to allow determination of the biological importance of this effect. The phenotypic effects of the loss of XBP1 on azole resistance look rather odd (Figure 6). The appearance of fluconazole resistant colonies in the xbp1 null strain occurs at a very low frequency and seems to resemble the appearance of rho0 cells in the population. The vast majority of xbp1 null cells do not exhibit increased growth compared to wild-type in the presence of fluconazole. Irrespective of the precise explanation, more analysis should be performed to confirm that CgXbp1 is negatively regulating the genes suggested in Figure 6A to be responsible for the increased fluconazole resistance. Additionally, the entire analysis of CgXbp1 is based on ChIP-seq performed using cells grown under very different conditions that the RNA polymerase II study. Evidence should be provided that the presumptive CgXbp1 target genes actually impact the expression profiles established earlier.

    1. Reviewer #3 (Public Review):

      In this manuscript the authors describe the biogenesis and the mechanism of action of a pair of cis-encoded sRNAs: CJnc190 and CJnc180. Both RNAs are being processed by RNase III. 5' and 3' ends mapping together with in vitro and in vivo experiments using purified RNase III and rnc deletion mutant demonstrated that the processing of CJnc190 sRNA depended on the formation of an intramolecular duplex, while CJnc180 sRNA processing required the presence of the antisense CJnc190 sRNA. The mature CJnc190 and CJnc180 sRNA specious are 69 and 88 nt long respectively. They also show that mature CJnc190 sRNA represses translation of ptmG via base-pairing and CJnc180 sRNA antagonizes CJnc190 repression acting as a sponge, scavenging CJnc190 sRNA. In addition, they find that two promoters are responsible for the synthesis of CJnc190 sRNA and both transcripts are subject to RNase III processing.

      The study represents an enormous amount of work. The data are solid and generally support the overall conclusions. Having said that the manuscript is overwhelming, loaded with too many details which make the reading difficult and in the absence of a bigger picture many times uninspiring.

    2. Author Response:

      Reviewer #2 (Public Review):

      Campylobacter jejuni is serious food-borne pathogen and understanding how the various products necessary for pathogenesis are regulated is a key step in preventing its growth and/or treating disease. Here, Sharma and coworkers demonstrate the complex pathway that leads to the maturation of two complementary regulatory RNAs and how one of the RNAs antagonizes the other to relieve repression of a virulence-related gene. The work is detailed and convincing, and provides a reference point for the roles of regulatory RNAs in C. jejuni as well as other bacteria. Future work will be needed to better understand when each of these RNAs is best expressed and processed into active form, and to fully support the idea that one RNA acts as an antagonist for the other.

      We thank the reviewer for their positive feedback on our work. Additional experiments (Figure 7B) provide additional evidence that CJnc180 is an antagonist of CJnc190 and affects ptmG.

      Reviewer #3 (Public Review):

      In this manuscript the authors describe the biogenesis and the mechanism of action of a pair of cis-encoded sRNAs: CJnc190 and CJnc180. Both RNAs are being processed by RNase III. 5' and 3' ends mapping together with in vitro and in vivo experiments using purified RNase III and rnc deletion mutant demonstrated that the processing of CJnc190 sRNA depended on the formation of an intramolecular duplex, while CJnc180 sRNA processing required the presence of the antisense CJnc190 sRNA. The mature CJnc190 and CJnc180 sRNA specious are 69 and 88 nt long respectively. They also show that mature CJnc190 sRNA represses translation of ptmG via base-pairing and CJnc180 sRNA antagonizes CJnc190 repression acting as a sponge, scavenging CJnc190 sRNA. In addition, they find that two promoters are responsible for the synthesis of CJnc190 sRNA and both transcripts are subject to RNase III processing.

      The study represents an enormous amount of work. The data are solid and generally support the overall conclusions. Having said that the manuscript is overwhelming, loaded with too many details which make the reading difficult and in the absence of a bigger picture many times uninspiring.

      We thank this reviewer for the overall positive feedback. We agree that this is a very complex story. We have made several revisions to the text and Figures and have moved details to the Supplementary Information. We hope this facilitates reading of our manuscript.

    3. Evaluation Summary:

      Campylobacter jejuni is serious food-borne pathogen and understanding how the various products necessary for pathogenesis are regulated is a key step in preventing its growth and/or treating disease. Here, Sharma and coworkers describe the complex pathway that leads to the maturation of two complementary regulatory RNAs and how one of the RNAs antagonizes the other to relieve repression of a virulence-related gene.

      (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. The reviewers remained anonymous to the reviewers.)

    4. Reviewer #1 (Public Review):

      The manuscript describes the mechanisms of biogenesis of two antisense sRNAs by RNase III in C. jejuni, CJnc180 and CJnc190, as well as the specific post-transcriptional activity of CJnc190 on ptmG. The study provides thorough experimental support of (i) binding of CJnC190 to repress translational of ptmG, (ii) RNAse III processing to produce mature CJnc190 and CJnc180 transcripts, (iii) location and contribution of CJnc180/190 promoters and 3' ends, and (iv) mechanisms of RNase III cleavage of CJnc180 and CJnc190. Notably, this study proposes a novel cis-sRNA processing mechanism of CJnc180 in which base pairing with antisense sRNA CJnc190 facilitates proper cleavage by RNase III. Overall, this well constructed and informative study provides impactful knowledge that furthers the field of regulatory RNAs.

    5. Reviewer #2 (Public Review):

      Campylobacter jejuni is serious food-borne pathogen and understanding how the various products necessary for pathogenesis are regulated is a key step in preventing its growth and/or treating disease. Here, Sharma and coworkers demonstrate the complex pathway that leads to the maturation of two complementary regulatory RNAs and how one of the RNAs antagonizes the other to relieve repression of a virulence-related gene. The work is detailed and convincing, and provides a reference point for the roles of regulatory RNAs in C. jejuni as well as other bacteria. Future work will be needed to better understand when each of these RNAs is best expressed and processed into active form, and to fully support the idea that one RNA acts as an antagonist for the other.

    1. Evaluation Summary:

      This paper examines the effects of amylospheroids, highly neurotoxic assemblies of β-amyloid, on aortic function and on cultured cells. The authors propose that the interaction of amylospheroids with the sodium pump in endothelial cells induces production of reactive oxygen species to ultimately comprise nitric oxide generation. The study provides some new insight into mechanisms underlying brain blood vessel dysfunction and will be interesting neuroscientists who study neurovascular contribution to neurodegenerative diseases. The conclusions of the manuscript are supported by the data, but alternative approaches would make the study stronger.

      (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #2 agreed to share their name with the authors.)

    2. Reviewer #1 (Public Review):

      In this manuscript, the authors use extensive pharmacologic manipulations to examine a pathway by which oligomeric amyloid beta assemblies bind to a sodium potassium pump subunit and lead to an increased proportion of inactive endothelial nitric oxide synthetase. They speculate that this reduced eNOS activity in endothelial cells could underlie changes in cerebral perfusion in Alzheimer's disease. While the idea that eNOS activity is altered in Alzheimer's is not novel, having been described elsewhere in relation to amyloid beta, the authors clearly outline a new pathway involving NAKalpha3 providing mechanistic insight into eNOS changes. Further, their data uses immunofluorescence, western blotting, and qPCR to show rat aorta and cultured human brain microvessel endothelial cells express NAKalpha3--a protein previously believed to be a neuron-specific.

    3. Reviewer #2 (Public Review):

      The current study by Sasahara et al. examined the cerebrovascular effects of amylospheroids (ASPD), highly neurotoxic ~30-mer assemblies of β-amyloid (Aβ), which the author's group purified from human brains of AD patients and characterized in previous studies. The authors propose that the aberrant interaction of ASPD with NAKalpha3 in endothelial cells induces production of reactive oxygen species (ROS) from mitochondria and activates protein kinase C (PKC). In turn, PKC phosphorylates inactive form of eNOS, reduces NO production, and attenuates carbachol-induced vasorelaxation. These conclusions were based on ASPD immunostaining of brain sections from AD patients, the ASPD effects on carbachol (a muscarinic M3 receptor agonist)-induced vasorelaxation in rat aortic rings, and in vitro studies in primary human brain endothelial cells, including the effect of ASPD on carbachol-induced eNOS phosphorylation and NO production and ASPD-induced ROS production. These data add a new class of mechanisms by which Aβ impairs neurovascular regulation in the brain, and the manuscript could make an interesting contribution to the field of vascular contributions to cognitive impairment and dementia.

    1. Evaluation Summary:

      This paper will be of interest to oncologists and dermatologists and has high clinical relevance. It reveals a novel mechanism of EGFR inhibitor-induced rash which be may closely related to atrophy of dermal white adipose tissue (dWAT). A series of experimental manipulations dissect the mechanism with a murine model, supporting the major claims of the paper.

      (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. The reviewers remained anonymous to the authors.)

    2. Reviewer #1 (Public Review):

      The authors present an interesting concept for the mechanism of rash induction in EGFR inhibitor (EGFRi) treated rats. EGFRi causes production of pro-inflammatory factors in epidermal keratinocytes which may induce dedifferentiation and reduction of the dWAT compartment, presumably mediated via PPAR. Factors produced by dedifferentiated FB then recruit monocytes thereby inducing skin inflammation. This work is aiming to improve targeted cancer therapy efficiency and is therefore of potential clinical relevance.

      However, most of the conclusions drawn by the authors are based on correlations, e.g. between the amount of dWAT and rash intensity. Mechanistic data have been mainly generated in vitro. The exact order of events to formulate a definitive mechanistic proof in vivo for this hypothesis is missing. In particular, it is not clear which cells in the skin, apart from keratinocytes, are specifically targeted by EGFR inhibitors and/or by Rosiglitazone. The authors also do not show EGFR staining in adipocytes and its inhibition by Afa. The effects of Afa and Rosi on monocytes / macrophages are completely ignored by the authors. Additionally, some of the presented results are overinterpreted and not really supporting what is claimed.

      Most importantly, the whole study is based on inhibitor treatments. Afatinib for example is not only inhibiting EGFR but all other erbB family members and as such it represents a panErbB inhibitor and it is not clear whether the observed effects are induced by inhibition of EGFR of other erbB receptors which have been shown to have also effects in the skin. For further specification of the role of EGFR, other, more specific inhibitors should be used to confirm the basic concept along with genetic proof either in genetically engineered mice or by Crispr-mediated-deletion.

      To further support the hypotheses of the authors, the study needs to be further substantiated by mechanistic experiments and the clinical relevance should be strengthened by performing histologic analysis of skin samples of patients treated with EGFRi and respective analysis of rash and e.g. BMI etc.

    3. Reviewer #2 (Public Review):

      Leying Chen et al. investigated the mechanism of EGFR inhibitor-induced rash. They find that atrophy of dermal white adipose tissue (dWAT), a highly plastic adipose tissue with various skin-specific functions, correlates with rash occurrence and exacerbation in a murine model. The data indicate that EGFR inhibition induces the dedifferentiation of dWAT and lipolysis , finally lead to dWAT reduction which is a hallmark of the pathophysiology of rash. Notably, they demonstrate that stimulating dermal adipocyte expansion with a high-fat diet (HFD) or the pharmacological PPARγ agonist rosiglitazone (Rosi) ameliorated the severity of rash. Therefore, PPARγ agonists may represent a promising new therapeutic strategy in the treatment of EGFRI-related skin disorders pending to be confirmed in further study.

      The conclusions of this paper are mostly well supported by data, but some results need to be clarified and verified.

      1) PPAR signaling in the pathology of EGFRI-induced skin toxicity.<br> In figure 2 , the results show Rosi reversed the dedifferentiation of dermal adipocytes induced by Afa. This may due to PPARγ upregulation but not be confirmed in the results. The relative genes expression in dWAT after treated with Afa and ROSi were not demonstrated in the results.

      2) the effect of PPAR signaling on PDGFRA-PI3K-AKT pathway<br> The AKT pathway is a key downstream target of EGFR kinase, so it is reasonable to see p-AKT1 and p-AKT2 levels were decreased by Afa (figure 3C) However, addition of Rosi to Afa significantly activated both AKT1 and AKT2 . What is the underlying mechanism for the results and whether it is related to the PPAR signaling pathway.

      3) According to figure 3 F , 3G and 3H., authors draw a conclusion that " a lack of APs and mature dWAT impairs the maintenance of the host defense and hair growth in the skin" In my opinion, there are no results can directly prove this. According to figure 3H, the impairment of hair growth may be caused by EGFR inhibition of hair follicles.

      4) EGFRI stimulates keratinocytes (HaCaT cells) to produce lipolytic cytokines (IL-6) (Figure 4G). IL6 enhanced the lipolysis of differentiated dFB (Figure S4M) and C18 fatty acids were supposed to be released the cell matrix during lipolysis.<br> In figure 4H, HaCaTcells supernatants and dFB supernatants were collected. IL-6 was supposed to increase in HaCaTcells supernatants and was confirmed in Figure 4SK and S4L.However, C18 fatty acids were not showed to be in the dFB supernatants in the study directly.

    1. Author Response:

      Reviewer #2 (Public Review):

      The CRISP-Cas9 complex has revolutionized genomic editing techniques. The widespread application of this new molecular tool enables a precise and accurate DNA cleavage that has been impossible to achieve. Yet, in some cases, the system suffers from a lack of specificity. In this paper, the authors present a new study on the characterization of the allosteric communication within the CRISP-Cas9 complex. They identified three different mutations that disrupt the complex's internal allosteric communication, affecting the cleavage reaction's specificity to different extents. The authors argue that the various degrees of perturbation are correlated with the Cas9 specificity. Given the size of the complex, the authors utilize a divide and conquer approach to studying the structural-dynamic changes of the isolated HNH endonuclease using NMR spectroscopy. Then they used molecular dynamics simulations to relate the changes in the isolated enzyme to the entire complex. As marked by the authors, the effects of the selected mutations (K855A, K810A, and K848A) are minimal. The HSQC spectrum in Figure 2B shows only marginal chemical shift changes in the protein fingerprint. The latter is supported by the CD spectra that show no significant perturbations in the dichroic profiles. However, the lineshapes reveal substantial changes in the enzyme dynamics apparent from the broadening of several signals. The chemical shift perturbations, although small, show that K855A has the most pronounced spectroscopic changes followed by K810A and K848A. As expected, the most significant differences are revealed by relaxation studies. The authors performed T1, T2, and heteronuclear NOE experiments to characterize the fast dynamics of the protein in the NMR time scale, revealing the most significant differences in the K855A mutant.

      Additionally, they used CPMG dispersion experiments to analyze the dynamics in the micro-to-millisecond time scale. From these measurements, the authors conclude that the relaxation characteristics of the mutants do not change significantly, i.e., the mutants possess conformational flexibility similar to the wild type. To interpret the dynamic behaviors of the different HNH variants, the authors performed MD simulations and analyzed the allosteric network using community analysis. The computational work revealed the connections between the communities and how the mutants affect interdomain communication (figure 5).

      Overall the paper is exciting and shows how NMR and MD simulations can be used synergistically to dissect the intra- and inter-molecular allosteric communication in highly complex systems. However, there are a few shortcomings that the authors need to address. One significant concern is the lack of a direct comparison between the NMR studies and the MD simulations. Additionally, it is unclear how these dynamics or structural perturbations caused by these selected mutants are converted into the enzyme's increased or decreased specificity.

      Other technical concerns:

      A) The authors performed relaxation measurements for fast dynamics. However, they did not calculate the order parameters for the protein backbone. Usually, the order parameters from the protein backbone can be directly compared to the calculated values from MD trajectories. How do the S2 values from the two techniques compare?

      The reviewer is absolutely correct and we have now included S^2 parameters for each K-to-A mutant and determined the difference from WT HNH (new Figure Supplement S7). We also added discussion of these data on page 9, lines 185-189. Briefly, S^2 parameters for each mutant are quite similar to those of WT HNH, evidenced by DeltaS^2 values (greater or equal to) 0.1 for the majority of residues. These data also mirror DeltaS^2 values determined from MD simulations. Further, we note agreement between S^2 and the 1^H-[15^N] NOE that show depressed values sporadically between residues 800-825, surrounding residue 850, and at the C-terminus.

      B) The authors state that the differences in the relaxation dispersion profiles are less than 1.5 Hz, indicating small changes in dynamics. Did the author compare all residues or a subset of residues?

      C) In the discussion, the authors refer to the synchronous motions that may be responsible for specificity. How did they deduce that the motions are synchronous? From MD simulations or the global fitting of the CPMG curves? Do motions need to be synchronous for effective allosteric communications?

      In the manuscript, we referred to “synchronous” when describing community network analysis (CNA), where groups of residues displaying highly synchronized dynamics are gathered in communities. This wording is indeed employed in several computational studies harnessing CNA (PNAS, 2017, 114, E3414-E3423). We therefore did not employ the word “synchronous” as mechanistically. We have now changed our phrasing in the manuscript to avoid any confusion.

      D) Finally, the authors claim that mutations can target sites identified in this study (hotspots) to improve CRISP-Cas9 function. Can the authors elaborate more on this point? How do they envision mutations to tune the function of the complex?

      We thank the reviewers for this insightful comment, which gives us the opportunity to suggest critical hotspots for mutational studies. Our computational analysis indicated that the three K–to–A mutations mainly disrupt the cross-talk between the A1 and A2 communities (Figure 4). This effect is observed for all mutants, and is confirmed by the analysis of the NMR relaxation data (Figure 5), suggesting that the A1-A2 communities are critical hotspots for the signal transmission. Building on this observation, mutational studies targeting residues of the A1-A2 communities could impact the allosteric communication and, in turn, modulate the function and specificity of the system. We have now included this discussion in the main text (page 16, lines 332-341 and page 18, lines 393-395) adding Figure 6. The Abstract was also amended including this information.

    1. Author Response:

      Reviewer #1 (Public Review):

      In their manuscript entitled "PBN-PVT projection modulates negative emotions in mice", Zhu et al. combine circuit mapping techniques with behavioral manipulations to interrogate the function of anatomical projections from the parabrachial nucleus (PBN) to the paraventricular nucleus of the thalamus (PVT). The study addresses an important scientific question, since the PVT and particularly the posterior PVT is known to be mostly sensitive to aversive signals, but the neural circuit mechanisms underlying this process remain unknown. Here the authors contribute important evidence that PBN inputs to the PVT may be critical for this process. Specifically, the authors identify that the PVT receives glutamatergic projections from the PBN that promote aversive behavioral responses but do not modulate nociception. The latter finding is intriguing considering that the PBN is an important node in pain processing and that the PVT has recently emerged as a modulator of pain. Overall, the study includes an impressive array of techniques and manipulations and offers insight to an important scientific question. The authors' conclusions will be significantly strengthened by the inclusion of some additional experiments and controls.

      It is in my view problematic that the authors used different genetic strategies to target the PBN-PVT pathway. For example, in Figure 1 the authors used Vglut2-cre mice for the anterograde tracings but later on in the same figure used constitutively expressed ChR2 in the PBN to assess functional connectivity with the PVT using ex-vivo patch-clamp electrophysiology. In Figure 2 the authors once again employed Vglut2-Cre mice to target PBN projections to the PVT and manipulate these projections optogenetically during behavioral tests. However, in the following figure (Fig. 3) the authors then use a retro-Cre approach and chemogenetics. The interchangeable use of these different manipulations is not warranted by data presented by the authors. For example it is unclear whether all PBN neurons projecting to the PVT are glutamatergic and express VGLUT2. When using the constitutively expensed ChR2 in the PBN to demonstrate glutamatergic projections to the PVT, the authors may be faced by potential contamination from adjacent brain stem structures like the LC and DRN, which project to the PVT and are known to contain glutamatergic neurons (vglut1 and vglut3, respectively). Another example, for figure 4 why did the authors not use Vglut2-cre mice and inhibited PBN terminals in the PVT as in Figure 2?

      We agree with the reviewer. Now we have reframed this manuscript. We first presented the slice recording results from wild-type mice (Figure 1). We recorded both the EPSCs and IPSCs. We found that light-induced EPSCs in 34 of 52 neurons and light-induced IPSCs in 4 of 52 neurons. Please see Page 5 Line 119 to Line 121. We carefully examined the ChR2 virus infection area. Please see the following Fig R1 showcase. We found that there were dense ChR2-mCherry+ neurons in the PBN. We also observed ChR2-mCherry+ neurons in the nearby ventrolateral periaqueductal gray (VLPAG), locus coeruleus (LC), cuneiform nucleus (CnF), and laterodorsal tegmental nucleus (LDTg). And the dorsal raphe nucleus (DR) was not infected. We agreed with the reviewer that there could be potential contamination from the LC, which releases dopamine and norepinephrine to the PVT by LC-PVT projection. We have discussed this on Page 13 Line 375 to Line 380.

      Figure R1. AAV-hSyn-ChR2-mCherry virus infection showcase. LPBN, lateral parabrachial nucleus. MPBN, medial parabrachial nucleus; VLPAG: ventrolateral periaqueductal gray; LC, locus coeruleus; CnF, cuneiform nucleus; LDTg, laterodorsal tegmental nucleus; DR, dorsal raphe nucleus; scp, superior cerebellar peduncle, scale bar: 200 μm.

      We performed tdTomato staining with VgluT2 mRNA in situ hybridization and found that about 94.4% of tdTomato+ neurons express VgluT2 mRNA. These results indicate that the majority of PVT-projecting PBN neurons are glutamatergic. These new results have been included in Figure 1R−U.

      Then we used VgluT2-ires-Cre mice to perform tracing (Figure1−figure supplement 2) and behavioral tests (optogenetic activation in Figure 2, optogenetic inhibition in Figure 4). We also performed the pharmacogenetic activation of PVT-projecting PBN neurons on wild-type mice (Figure 3). We observed that pharmacogenetic activation of the PVT-projecting PBN neurons reduced the center duration in the OFT, similar to the optogenetic activation OFT result. We also observed that pharmacogenetic activation of the PVT-projecting PBN neurons induced freezing behaviors. Our pharmacogenetic activation experiment supported the hypothesis that PBN-PVT projections modulate negative affective states.

      Now we have now performed the optogenetic inhibition of the PBN-PVT projections using VgluT2-ires-Cre mice. We found that inhibition of PBN-PVT projections reduces 2-MT-induced aversion-like behaviors and footshock-induced freezing behaviors. These new results have been included in Figure 4, Figure 4−figure supplement 1 and 2, and were described in the text. Please see the text Page 9 Line 254 to Page 10 Line 274.

      Related to the previous point, in the retrograde labeling experiment (Fig. 1) it would be useful if the authors determined what fraction of retrogradely label cells are indeed VGLUT2+. For behavioral experiments employing the retro-Cre approach the authors may be manipulating a heterogenous population of PBN neurons which could be influencing their behavioral observations. In general, the authors should ensure that a similar population of PBN-PVT neurons is been assessed throughout the study.

      We have now performed tdTomato staining with VgluT2 mRNA in situ hybridization and found that approximately 94.4% of tdTomato+ neurons expressed VgluT2 mRNA. These results indicated that the majority of PVT-projecting PBN neurons are glutamatergic. These new results have been included in Figure 1R−U and were described in the text. Please see Page 5 Line 129 to Line 132.

      The authors' grouping of the behavioral data into the first vs the last four minutes of light stimulation in the OF does not seem to be properly justified an appears rather arbitrary. Also related to data analysis, the unpaired t-test analysis in the fear conditioning experiment in Figure 4J seems inappropriate. ANOVA with group comparisons is more appropriate here.

      To provide a more detailed profile of the behaviors in the OFT, we further divided the laser ON period (5−10 minutes) into five one-minute periods and analyzed the velocity, non-moving time, travel distance, center time, and jumping. We found that the velocity and non-moving time were increased, and the center time was decreased in the ChR2 mice during most periods. Furthermore, we observed that the travel distance and jumping behaviors were increased only in the first one-minute period in ChR2 mice. These new results have been included in Figure 2−figure supplement 2 and were described in the text. Please see Page 7 Line 179 to Line 189. We also discussed this on Page 14 Line 396 to Line 403.

      We now performed the optogenetic inhibition of PBN-PVT projections in footshock-induced freezing behavior on Vglut2-ires-Cre mice (Figure 4J−K). And we revised the statistics (Unpaired student's t-test) and calculated the percentage of freezing behaviors in 10 minutes, which matched the constant optogenetic inhibition. Similar changes have been made in the Figure 4−figure supplement 3K.

      Considering the persistency of the effect in the OF following optogenetic stimulation of PBN-PVT afferents, the lack of such persistent effect in the RTPA is hard to reconcile. By performing additional experiments the authors attempt to settle this discrepancy by proposing that the PBN-PVT pathway promotes aversion but does not facilitate negative associations. I find this conclusion to be problematic. If the pathway is critical for conveying aversive signals to the PVT, one expects that at the very least it would be require for the formation of associate memories involving aversive stimuli. However, the authors do not show data to this effect. Instead they show that animals decrease their acute defensive reactions to aversive stimuli (2-MT and fear conditioning), but do not show whether associative memory related to this experience (e.g. fear memory retrieval) is impacted by manipulations of the PBN-PVT pathway.

      We have now performed several experiments to examine the effects of the PBN-PVT projections on aversion formation and memory retrieval.

      We first performed a prolonged conditioned place aversion that mimics drug-induced place aversion. And we found that optogenetic activation of PBN-PVT projections did not induce aversion in the postconditioning test on Day 4. These new results have been included in Figure 2−figure supplement 2H−I and described in the text. Please see Page 7 Line 196 to Line 199.

      Then, we performed the classical auditory fear conditioning test and found that optogenetic inhibition of PBN-PVT projections during footshock in the conditioning period did not affect freezing levels in contextual test or cue test (Laser OFF trials). And inhibition of PBN-PVT projections during contextual test or cue test (Laser On trials) did not affect freezing levels either. These data suggest that PBN-PVT projections are not crucial for associative fear memory formation or retrieval. These new results have been included in Figure 4−figure supplement 2 and described in the text. Please see Page 10 Line 268 to Page Line 274. We also discussed this on Page 15 Line 430 to Page 16 Line 473.

      A similar lack of connection between aversive signals within the PVT and the PBN pathway is found in the photometry data presented in Figure 5. While importantly the authors' observation of aversive modulation of the pPVT reproduces data from other recent studies, the question here is whether the increased activity of PVT neurons is mediated by input from the PBN. The cFos experiment included in this figure attempts to draw this connection, but empirical evidence is required.

      We have now performed the dual Fos staining experiment and the optoeletrode experiment.

      In the dual Fos staining experiment, we found that there was a broad overlap between optogenetic stimulation-activated neurons (expressing the Fos protein) and footshock-activated neurons (expressing the fos mRNA) (Figure 6−figure supplement 1B−E).

      In optoelectrode experiment, there was also a broad overlap between laser-activated and footshock-activated neurons. This result was consistent with the dual Fos staining result, suggesting that PVTPBN neurons were activated by aversive stimulation. Next, we analyzed the firing rates of PVT neurons during footshock with laser sweeps and footshock without laser sweeps. We found that the footshock stimulus with laser activated 30 of 40 neurons and increased the overall firing rates of 40 neurons compared with the footshock without laser result (Figure 6I). These results indicated that activation of PBN-PVT projections could enhance PVT neuronal responses to aversive stimulation.

      These new results have been included in Figure 6, Figure 6−figure supplement 1, and described in the text. Please see Page 10 Line 295 to Page 11 Line 317. We also discussed these results on Page 15 Line 422 to Line 429.

      Reviewer #2 (Public Review):

      Zhu et al. investigated the connectivity and functional role of the projections from the parabrachial nucleus (PBN) to the paraventricular nucleus of the thalamus (PVT). Using neural tracers and in vitro electrophysiological recordings, the authors showed the existence of monosynaptic glutamatergic connections between the PBN and PVT. Further behavioral tests using optogenetic and chemogenetic approaches demonstrated that activation of the PVT-PBN circuit induces aversive and anxiety-like behaviors, whereas optogenetic inhibition of PVT-projecting PBN neurons reduces fear and aversive responses elicited by footshock or the synthetic predator odor 2MT. Next, they characterized the anatomical targets of PVT neurons that receive direct innervation from the PBN (PVTPBN). The authors also showed that PVTPBN neurons are activated by aversive stimuli and chemogenetically exciting these cells is sufficient to induce anxiety-like behaviors. While the data mostly support their conclusions, alternative interpretations and potential caveats should be addressed in the discussion.

      Strength:

      The authors used different behavioral tests that collectively support a role for PBN-PVT projections in promoting fear- and anxiety-like behaviors, but not nociceptive or depressive-like responses. They also provided insights into the temporal participation of the PBN-PVT circuit by showing that this pathway regulates the expression of affective states without contributing for the formation of fear-associated memories. Because previous studies have shown that activation of projection-defined PVT neurons is sufficient to induce the formation of aversive memories, the differences between the present study and previous findings reinforce the idea of functional heterogeneity within the PVT. The authors further explored this functional heterogeneity in PVT by using an anterograde viral construct to selectively label PVT neurons that are targeted by PBN inputs. Together, these results connect two important brain regions (i.e., PBN and PVT) that were known to be involved in fear and aversive responses, and provide new information to help the field to elucidate the complex networks that control emotional behaviors.

      Weakness:

      The authors should avoid anthropomorphizing the behavioral interpretation of the findings and generalizing their conclusions. In addition, there is a series of potential caveats that could interfere with the interpretation of the results, all of which must be discussed in the article. For example, the long protocol duration of laser stimulation, the possibility of antidromic effects following photoactivation of PBN terminals in PVT, and the existence of collateral PBN projections that could also be contributing for the observed behavioral changes. Additional clarification about the exclusive glutamatergic nature of the PBN-PVT projection should be provided and the present findings should be reconciled with prior studies showing the existence of GABAergic PBN-PVT projections.

      We agree with the reviewer. Now we have revised the text carefully to avoid using subjective terms. We showed the light-induced EPSCs and IPSCs results in Figure 1, and we performed RNAscope experiments to clarify the glutamatergic nature of the PVT-projecting PBN neurons (Figure 1 and Figure1−figure supplement 1). We also added discussion about the laser stimulation protocol, the potential possibility of antidromic effects, and collateral projections. Please see Page 14 Line 413 to Page 15 Line 418, and Page 16 Line 449 to Line 457.

      We also added several experiments to dissect the effect of manipulation of the PBN-PVT projection in fear memory acquisition and retrieval. These new results have been included in Figure 4−figure supplement 2 and described in the text. Please see Page 10 Line 268 to Line 274. We also discussed this on Page 15 Line 430 to Page 16 Line 473.

      Reviewer #3 (Public Review):

      Zhu YB et al investigated the functional role of the parabrachial nucleus (PBN) to the thalamic paraventricular nucleus (PVT) in processing negative emotions. They found that PBN send excitatory projection to PVT. The activation of PBN-PVT projection induces anxiety-like and fear-like behaviors, while inhibition of this projection relieves fear and aversion.

      Strengths:

      The authors dissected anatomic and functional connection between the PBN and the PVT by using comprehensive modern neuroscience techniques including viral tracing, electrophysiology, optogenetics and pharmacogenetics. They clearly demonstrated the significant role of PBN-PVT projection in modulating negative emotions.

      Weaknesses:

      The PBN contains a variety of neuronal subtypes that expressed distinct molecular marker such as CGRP, Tac1, Pdyn, Nts et al. The PBN also send projections to multiple targets, including VMH, PAG, BNST, CEA and ILN that could mediate distinct function. What's the neuronal identity of PVT-projecting PBN neurons, how is the PVT projection and other projections organized, are they overlapping or relative independent pathway? Those important questions were not examined in this study, which make it hard to relate this finding to other existing literature.

      We have now performed the RNAscope experiments detecting VgluT2, Tac1, Tacr1, Pdyn mRNA, and fluorescent immunostaining detecting CGRP protein in the PBN. We found that about 94.4% of tdTomato+ neurons express VgluT2 mRNA. We also found that tdTomato+ neurons were only partially co-labeled with Tacr1, Tac1, or Pdyn mRNA, but not with CGRP. These results indicate that the majority of PVT-projecting PBN neurons are glutamatergic. These new results have been included in Figure 1, Figure 1−figure supplement 1, and were described in the text. Please see Page 5 Line 129 to Line 140.

      We also provided the collateral projections from PVT-projecting neurons in Figure 1−figure supplement 3, Page 6 Line 148 to Line 151, and discussed on Page 16 Line 449 to Line 457.

    2. Evaluation Summary:

      This study will interest neuroscientists, in particular those interested in the neurocircuitry of emotional behaviors. Using modern neuroscience techniques, the authors demonstrate that anatomical projections from a brain stem structure called the parabrachial nucleus to the paraventricular nucleus thalamus contribute to aversive states like fear and anxiety. Overall, the study offers important details of a previously uncharacterized brain circuit, although some additional experiments are required to fully substantiate the authors' claims.

      (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #1 and Reviewer #2 agreed to share their names with the authors.)

    3. Reviewer #1 (Public Review):

      In their manuscript entitled "PBN-PVT projection modulates negative emotions in mice", Zhu et al. combine circuit mapping techniques with behavioral manipulations to interrogate the function of anatomical projections from the parabrachial nucleus (PBN) to the paraventricular nucleus of the thalamus (PVT). The study addresses an important scientific question, since the PVT and particularly the posterior PVT is known to be mostly sensitive to aversive signals, but the neural circuit mechanisms underlying this process remain unknown. Here the authors contribute important evidence that PBN inputs to the PVT may be critical for this process. Specifically, the authors identify that the PVT receives glutamatergic projections from the PBN that promote aversive behavioral responses but do not modulate nociception. The latter finding is intriguing considering that the PBN is an important node in pain processing and that the PVT has recently emerged as a modulator of pain. Overall, the study includes an impressive array of techniques and manipulations and offers insight to an important scientific question. The authors' conclusions will be significantly strengthened by the inclusion of some additional experiments and controls.

      It is in my view problematic that the authors used different genetic strategies to target the PBN-PVT pathway. For example, in Figure 1 the authors used Vglut2-cre mice for the anterograde tracings but later on in the same figure used constitutively expressed ChR2 in the PBN to assess functional connectivity with the PVT using ex-vivo patch-clamp electrophysiology. In Figure 2 the authors once again employed Vglut2-Cre mice to target PBN projections to the PVT and manipulate these projections optogenetically during behavioral tests. However, in the following figure (Fig. 3) the authors then use a retro-Cre approach and chemogenetics. The interchangeable use of these different manipulations is not warranted by data presented by the authors. For example it is unclear whether all PBN neurons projecting to the PVT are glutamatergic and express VGLUT2. When using the constitutively expensed ChR2 in the PBN to demonstrate glutamatergic projections to the PVT, the authors may be faced by potential contamination from adjacent brain stem structures like the LC and DRN, which project to the PVT and are known to contain glutamatergic neurons (vglut1 and vglut3, respectively). Another example, for figure 4 why did the authors not use Vglut2-cre mice and inhibited PBN terminals in the PVT as in Figure 2?

      Related to the previous point, in the retrograde labeling experiment (Fig. 1) it would be useful if the authors determined what fraction of retrogradely label cells are indeed VGLUT2+. For behavioral experiments employing the retro-Cre approach the authors may be manipulating a heterogenous population of PBN neurons which could be influencing their behavioral observations. In general, the authors should ensure that a similar population of PBN-PVT neurons is been assessed throughout the study.

      The authors' grouping of the behavioral data into the first vs the last four minutes of light stimulation in the OF does not seem to be properly justified an appears rather arbitrary. Also related to data analysis, the unpaired t-test analysis in the fear conditioning experiment in Figure 4J seems inappropriate. ANOVA with group comparisons is more appropriate here.

      Considering the persistency of the effect in the OF following optogenetic stimulation of PBN-PVT afferents, the lack of such persistent effect in the RTPA is hard to reconcile. By performing additional experiments the authors attempt to settle this discrepancy by proposing that the PBN-PVT pathway promotes aversion but does not facilitate negative associations. I find this conclusion to be problematic. If the pathway is critical for conveying aversive signals to the PVT, one expects that at the very least it would be require for the formation of associate memories involving aversive stimuli. However, the authors do not show data to this effect. Instead they show that animals decrease their acute defensive reactions to aversive stimuli (2-MT and fear conditioning), but do not show whether associative memory related to this experience (e.g. fear memory retrieval) is impacted by manipulations of the PBN-PVT pathway.

      A similar lack of connection between aversive signals within the PVT and the PBN pathway is found in the photometry data presented in Figure 5. While importantly the authors' observation of aversive modulation of the pPVT reproduces data from other recent studies, the question here is whether the increased activity of PVT neurons is mediated by input from the PBN. The cFos experiment included in this figure attempts to draw this connection, but empirical evidence is required.

    4. Reviewer #2 (Public Review):

      Zhu et al. investigated the connectivity and functional role of the projections from the parabrachial nucleus (PBN) to the paraventricular nucleus of the thalamus (PVT). Using neural tracers and in vitro electrophysiological recordings, the authors showed the existence of monosynaptic glutamatergic connections between the PBN and PVT. Further behavioral tests using optogenetic and chemogenetic approaches demonstrated that activation of the PVT-PBN circuit induces aversive and anxiety-like behaviors, whereas optogenetic inhibition of PVT-projecting PBN neurons reduces fear and aversive responses elicited by footshock or the synthetic predator odor 2MT. Next, they characterized the anatomical targets of PVT neurons that receive direct innervation from the PBN (PVTPBN). The authors also showed that PVTPBN neurons are activated by aversive stimuli and chemogenetically exciting these cells is sufficient to induce anxiety-like behaviors. While the data mostly support their conclusions, alternative interpretations and potential caveats should be addressed in the discussion.

      Strength:

      The authors used different behavioral tests that collectively support a role for PBN-PVT projections in promoting fear- and anxiety-like behaviors, but not nociceptive or depressive-like responses. They also provided insights into the temporal participation of the PBN-PVT circuit by showing that this pathway regulates the expression of affective states without contributing for the formation of fear-associated memories. Because previous studies have shown that activation of projection-defined PVT neurons is sufficient to induce the formation of aversive memories, the differences between the present study and previous findings reinforce the idea of functional heterogeneity within the PVT. The authors further explored this functional heterogeneity in PVT by using an anterograde viral construct to selectively label PVT neurons that are targeted by PBN inputs. Together, these results connect two important brain regions (i.e., PBN and PVT) that were known to be involved in fear and aversive responses, and provide new information to help the field to elucidate the complex networks that control emotional behaviors.

      Weakness:

      The authors should avoid anthropomorphizing the behavioral interpretation of the findings and generalizing their conclusions. In addition, there is a series of potential caveats that could interfere with the interpretation of the results, all of which must be discussed in the article. For example, the long protocol duration of laser stimulation, the possibility of antidromic effects following photoactivation of PBN terminals in PVT, and the existence of collateral PBN projections that could also be contributing for the observed behavioral changes. Additional clarification about the exclusive glutamatergic nature of the PBN-PVT projection should be provided and the present findings should be reconciled with prior studies showing the existence of GABAergic PBN-PVT projections.

    5. Reviewer #3 (Public Review):

      Zhu YB et al investigated the functional role of the parabrachial nucleus (PBN) to the thalamic paraventricular nucleus (PVT) in processing negative emotions. They found that PBN send excitatory projection to PVT. The activation of PBN-PVT projection induces anxiety-like and fear-like behaviors, while inhibition of this projection relieves fear and aversion.

      Strengths:

      The authors dissected anatomic and functional connection between the PBN and the PVT by using comprehensive modern neuroscience techniques including viral tracing, electrophysiology, optogenetics and pharmacogenetics. They clearly demonstrated the significant role of PBN-PVT projection in modulating negative emotions.

      Weaknesses:

      The PBN contains a variety of neuronal subtypes that expressed distinct molecular marker such as CGRP, Tac1, Pdyn, Nts et al. The PBN also send projections to multiple targets, including VMH, PAG, BNST, CEA and ILN that could mediate distinct function. What's the neuronal identity of PVT-projecting PBN neurons, how is the PVT projection and other projections organized, are they overlapping or relative independent pathway? Those important questions were not examined in this study, which make it hard to relate this finding to other existing literature.

    1.  Evaluation Summary:

      The authors identify a novel compound called Dwn1 that suppresses the expression of Npas2, a key gene that delays wound healing. In doing so, they identify a novel treatment strategy for incisional surgical wounds that may have broader application to the treatment of scars in general.

      (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #1 and Reviewer #2 agreed to share their names with the authors.)

    2. Reviewer #1 (Public Review):

      Authors found that Dwn1 suppressed circadian Npas2 expression, and increased fibroblast cell migration, and decreased collagen synthesis in vitro. Then, they applied Dwn1 to full-thickness murine wounds and showed that Dwn1-treated dermal wounds healed faster and developed less granulation tissue than the controls. It was suggested that Dwn1 treatment might control the hypertrophic scaring .

      Authors showed the effectiveness in vito, but their in vivo skin defect model had too narrow skin defects to compare the scarring and the observation period seemed to be short.

    3. Reviewer #2 (Public Review):

      Building upon prior work in which the authors identified Npas2 as a key suppressor of wound healing, they perform a high-throughput drug screen to identify a compound called Dwn1 as a pharmacologic strategy to improve wound healing. A major strength of this paper is that it translates recent genetic work to a potential new therapy for wounds. The data support their conclusions that Dwn1 should be investigated further in as a new treatment. This paper also implicates peripheral circadian biology to the process of wound healing. Thus, this work has the potential to further unveil how circadian biology intersects with wound biology. They have also developed a unique way to assess linear incisional wounds which may be a useful technique for other investigators.

    1. Author Response:

      Reviewer #2 (Public Review):

      1. The novelty of the current observation of two types of links is overstated, for example, in the abstract: "Our data reveal the existence of two molecular connectors/spacers which likely contribute to the nanometer scale precise stacking of the ROS disks" (Line 25). In fact, both of these links have been shown before (Usukura and Yamada, 1981; Roof and Heuser, 1982; Corless and Schneider, 1987; Corless et al., 1987; Kajimura et al., 2000). These previous studies deserve to be recognized. Of special note is the paper by Usukura and Yamada whose images of the disc rim connectors are by no means less convincing than shown in the current manuscript. On the other hand, the novelty and impact of the data related to peripherin appears to be understated, particularly in the abstract.

      We changed the abstract line 27 to: “Our data confirm the existence of two previously observed molecular connectors …”, cite the recommended references in the introduction (lines 54-55), the results (lines 131-132), and the discussion (lines 282/285). To highlight the previous reports, we rephrased the sentence in lines 132-133, “In agreement with these previous findings, we observed structures that connect membranes of two adjacent disks …”; the discussion is rephrased in lines 280-281, “Similar connectors have been observed previously ...” and “… and their statistical analysis confirmed the existence of two distinct connector species.”, and in lines 291-292, “Based on previous studies combined with our quantitative analysis, we put forward a hypothesis for the molecular identity of the disk rim connector which agrees in part with recent models”.

      1. Notably, ROM-1 has not been found in peripherin oligomers larger than octamers (e.g. Loewen and Molday, 2000 and subsequent studies by Naash and colleagues). This should be discussed in the context of the current model.

      We agree that this is an important aspect. We pick subvolumes along all disk rims, and on average we obtain the ordered scaffold as shown in the manuscript. We expect heterogeneity in the data because of the different degrees of oligomerization and the exclusion of ROM1 from higher oligomers. Our analysis required substantial classification to achieve convergence to a stable average, indeed indicating heterogeneity in the rim structure. However, we could not resolve additional structures to sufficient quality. It might be that this heterogeneity is what ultimately limits our achievable resolution. We added these thoughts in the discussion starting in lines 377-378, “PRPH2-Rom1 oligomers isolated from native sources exhibit varying degrees of polymerization (Loewen and Molday, 2000), and ROM1 is excluded from larger oligomers (Milstein et al., 2020). We could not resolve this heterogeneity as additional structures to sufficient quality by subvolume averaging, but in combination with the inherent flexibility of the disk rim, this heterogeneity might be the reason for the restricted resolution of our averages.”

      1. The following statement should be reconsidered given the established role of cysteine-150 in peripherin oligomerization: "We hypothesize that the necessary cysteine residues are located in the head domain of the tetramers (Figure 5B), ..." It has been firmly established that only one cysteine (C150) located in the intradiscal loop is not engaged in intramolecular interactions and is essential for peripherin oligomerization.

      Thank you for this advice. We agree and rephrased our discussion in lines 368-371, “The intermolecular disulfide brides are exclusively formed by the PRPH2-C150 and ROM1-C153 cysteine residues, which are located in the luminal domain (Zulliger et al., 2018). We hypothesize that these disulfide bonds (Figure 5B), are responsible for the contacts across rows (Figure 3) ...”

      1. Line 340: "A model involving V-shaped tetramers for membrane curvature formation was proposed recently (Milstein et al., 2020), but it comprises two rows of tetramers which are linked in a head-tohead manner. Our analysis instead resolves three rows organized side-by side in situ (Figure 5A)." I am confused by this statement: doesn't your model also show long rows connected head-to-head? The real difference is that Milstein and colleagues proposed four tetramers per rim whereas the current data reveal three.

      Thank you for pointing out this imprecise description. The model proposed by Milstein and the model in the old version of our manuscript, both propose linkage between tetramers via their disk luminal domains. In our manuscript, we refer to the luminal domain as the head domain. However, to our understanding, the Milstein model suggests two rows of tetramers, where one tetramer in the first row is rotated 180° with respect to a tetramer in the second row (therefore head-to-head), while our data indicate that the V-shaped repeats which we originally hypothesized to be tetramers are only rotated ~63° with respect to one another and are therefore rather oriented side-by-side:

      Fig. 2: Comparison of models for the organization of the ROS disk rim as proposed in in Milstein et al., 2020 (top panel)

      and in our work (lower panel). We now rephrased lines 383-385, “Instead, our analysis in situ resolves three rows of repeats which are also linked by the luminal domain but are rather organized side-by-side (Figure 5A).”

      1. Line 347: "Our data indicate that the luminal domains of tetramers hold the disk rim scaffold together (Figure 3C), which is supported by the fact that most pathological mutations of PRPH2 affect its luminal domain (Boon et al., 2008; Goldberg et al., 2001). It is possible that these mutations impair the formation of tetramers, rows of tetramers, and their disulfide bond-stabilized oligomerization. These alterations could impede or completely prevent disk morphogenesis which, in turn, would disrupt the structural integrity of ROS, compromise the viability of the retina and ultimately lead to blindness." This is not an original idea, as many studies showed that disruptions in peripherin oligomerization lead to anatomical defects in disc formation and subsequent photoreceptor cell death.

      Thank you for pointing this out. Our data are indeed in good agreement with the results made by many groups and further expand on them. We rephrased the manuscript in several places to clarify this relationship: in the abstract lines 32-34, “Our Cryo-ET data provide novel quantitative and structural information on the molecular architecture in ROS and substantiate previous results on proposed mechanisms underlying pathologies of certain PRPH2 mutations leading to blindness.”; in the introduction lines 78-79, “… allowed us to obtain 3D molecular-resolution images of vitrified ROS in a close-to-native state providing further evidence for previously suggested mechanisms leading to ROS dysfunction”; and in the discussion lines 393-397, “In good agreement with previous work, it is possible that these mutations impair the formation of complexes, and their disulfide bond-stabilized oligomerization (Chang et al., 2002; Conley et al., 2019; Zulliger et al., 2018). Hence, these alterations could impede or completely prevent disk morphogenesis …”. Also, additional relevant publications are cited in line 395.

      1. In regards to the distance between disc rims and plasma membrane, the authors cite the data obtained with frogs (10 nm) but not a more relevant, previously reported measurement in mice (Gilliam et al, 2012). The value of 18 nm reported in that study is much closer to the currently reported value.

      We appreciate the reference to this excellent paper. We added it in lines 335-337, “This value was derived from amphibians (Roof and Heuser, 1982) and deviates considerably from recent results (18 nm, (Gilliam et al., 2012)) and from our current measurements in mice (~25 nm).” Our aim was to point out that a model for ROS organization that is often cited and is otherwise well-founded (BatraSafferling et al., 2006) makes a wrong assumption about distance in the context of the mammalian systems. 7. The authors are (correctly) being very careful in assigning the molecular identity of disc interior connectors to PDE6. However, they are more confident in assigning the disc rim connectors to GARP2, which is reflected in the labeling of these links in figure

      1. Their arguments are valid, but these links are not attached to peripherin (a protein considered to be the membrane binding partner for GARPs), which is not immediately consistent with this hypothesis. Perhaps it would be fair to re-label the corresponding links in figure 5 as "disc rim connectors".

      That is an excellent and fair suggestion. We changed Figure 5 accordingly.

      1. On a similar note, the disc rim connectors seem to be located where ABCA4 is presumed to be localized within the rim, which may not be just a coincidence. The authors already have tomograms obtained from ABCA4 knockout animals. Is it possible to analyze whether these links are preserved in these tomograms?

      We agree, this is an important question to address. Unfortunately, neither the biological preparation nor the tomograms of the ABCA4 knockout were as good in quality as for the WT. Still, we frequently see connectors at the disk rim, especially after denoising of the tomograms.

      Fig. 3: connectors at disk rims in WT (left) and ABCA4 knockout mice (right).

      Sometimes it appears the connectors between adjacent disks are linked via an intradisk densities, which was already observed in Corless et al., 1987. We thought that these densities could be ABCA4 and tried to find them with two approaches in our WT tomograms (data not shown). In the first approach using a segmentation similar to what we did for the connectors between disks, we found an order of magnitude fewer intradisk connectors than (inter)disk rim connectors. In the second approach, we used the positions of segmented (inter)disk rim connectors and classified rotational averages which focused on the disk luminal space next to the contact point of a connector with the disk membrane. Again, less than 10% of the disk rim connector subvolumes were assigned to classes with an additional luminal density. Both experiments indicate that disk rim connectors sometimes occur with an additional luminal density. In total, we found less than 100 of these intradisk densities, an observation which seems to be preserved in WT and ABCA4 KO. Based on this small number of positions/locations, however, we cannot draw any conclusion. Therefore, we did not add this point to the manuscript.

    1. Author Response:

      Reviewer #1 (Public Review):

      The introduction felt a bit short. I was hoping early on I think for a hint at what biotic and abiotic factors UV could be important for and how this might be important for adaptation. A bit more on previous work on the genetics of UV pigmentation could be added too. I think a bit more on sunflowers more generally (what petiolaris is, where natural pops are distributed, etc.) would be helpful. This seems more relevant than its status as an emoji, for example.

      We had opted to provide some of the relevant background in the corresponding sections of the manuscript, but agree that it would be beneficial to expand the introduction. In the revised version of the manuscript, we have modified the introduction and the first section of Results and Discussion to include more information about wild sunflowers, possible adaptive functions of floral UV patterns, and previous work on the genetic basis of floral UV patterning. More generally, we have strived to provide more background information throughout the manuscript.

      The authors present the % of Vp explained by the Chr15 SNP. Perhaps I missed it, but it might be nice to also present the narrow sense heritability and how much of Va is explained.

      Narrow sense heritability for LUVp is extremely high in our H. annuus GWAS population; four different software [EMMAX (Kang et al., Nat Genet 2010), GEMMA (Zhou and Stephens, Nat Genet. 2012), GCTA (Yang et al., Am J Hum Genet 2011) and BOLT_LMM (Loh et al., Nat Genet 2015)] provided h2 estimates of ~1. While it is possible that these estimates are somewhat inflated by the presence of a single locus of extremely large effect, all individuals in this populations were grown at the same time under the same conditions, and limited environmental effects would therefore be expected. The percentage of additive variance explained by HaMYB111 appears therefore to be equal to the percentage of phenotypic variance (~62%).

      We have included details in the Methods section – Genome-wide association mapping, and added this information to the relevant section of the main text:

      “The chromosome 15 SNP with the strongest association with ligule UV pigmentation patterns in H. annuus (henceforth “Chr15_LUVp SNP”) explained 62% of the observed phenotypic and additive variation (narrow-sense heritability for LUVp in this dataset is ~1).”

      A few lines of discussion about why the Chr15 allele might be observed at only low frequencies in petiolaris I think would be of interest - the authors appear to argue that the same abiotic factors may be at play in petiolaris, so why don't we see this allele at frequencies higher than 2%? Is it recent? Geographically localized?

      That is a very interesting observation, and we currently do not have enough data to provide a definitive answer to why that is. From GWAS, HaMYB111 does not seem to play a measurable role in controlling variation for LUVp in H. petiolaris; Even when we repeat the GWAS with MAF > 1%, so that the Chr15_LUVp SNP would be included in the analysis, there is no significant association between that SNP and LUVp (the significant association on chr. 15 seen in the Manhattan plot for H. petiolaris is ~20 Mbp downstream of HaMYB111). The rarity of the L allele in H. petiolaris could complicate detection of a GWAS signal; on the other hand, the few H. petiolaris individuals carrying the L allele have, on average, only marginally larger LUVp than the rest of the population (LL = 0.32 allele).

      The two most likely explanations for the low frequencies of the L allele in H. petiolaris are differences in alleles, or their effect, between H. annuus and H. petiolaris; or, as suggested by the reviewer, a recent introgression. In H. annuus, the Chr15_LUVp SNP is likely not the actual causal polymorphism affecting HaMYB111 activity, but is only in LD with it (or them); this association might be absent in H. petiolaris alleles. An alternative possibility is that downstream differences in the genetic network regulating flavonol glycosides biosynthesis mask the effect of different HaMYB111 alleles.

      H. annuus and H. petiolaris hybridize frequently across their range, so this could be a recent introgression that has not established itself; alternatively, physiological differences in H. petiolaris could make the L allele less advantageous, so the introgressed allele is simply being maintained by drift (or recurring hybridization). Further analysis of genetic and functional diversity at HaMYB111 in H. petiolaris will be required to differentiate between these possibilities.

      We have added a few sentences highlighting some of these possible explanations at the end the main text of the manuscript, which now reads:

      “Despite a more limited range of variation for LUVp, a similar trend (larger UV patterns in drier, colder environments) is present also in H. petiolaris (Figure 4 – figure supplement 4). Interestingly, while the L allele at Chr_15 LUVp SNP is present in H. petiolaris (Figure 1 – figure supplement 2), it is found only at a very low frequency, and does not seem to significantly affect floral UV patterns in this species (Figure 2a). This could represent a recent introgression, since H. annuus and H. petiolaris are known to hybridize in nature (Heiser, 1947, Yatabe et al., 2007). Alternatively, the Chr_15 LUVp SNP might not be associated with functional differences in HaMYB111 in H. petiolaris, or differences in genetic networks or physiology between H. annuus and H. petiolaris could mask the effect of this allele, or limit its adaptive advantage, in the latter species.“

      Page 14: It's unclear to me why there is any need to discretize the LUVp values for the analyses presented here. Seems like it makes sense to either 1) analyze by genotype of plant at the Chr15 SNP, if known, or 2) treat it as a continuous variable and analyze accordingly.

      We designed our experiment to be a comparison between three well-defined phenotypic classes, to reduce the experimental noise inherent to pollinator visitation trials. As a consequence, intermediate phenotypic classes (0.3 < LUVp < 0.5 and 0.8 < LUVp < 0.95) are not represented in the experiment, and therefore we believe that analyzing LUVp as a continuous variable would be less appropriate in this case. In the revised manuscript, we have provided a modified Figure 4 – figure supplement 1 in which individual data points are show (colour-coded by pollinator type), as well as a fitted lines showing the general trend across the data.

      The individuals in pollinator visitation experiments were not genotyped for the Chr15_LUVp SNP; while having that information might provide a more direct link between HaMYB111 and pollinator visitation rates, our main interest in this experiment was to test the possible adaptive effects of variation in floral UV pigmentation.

      Page 14: I'm not sure you can infer selection from the % of plants grown in the experiment unless the experiment was a true random sample from a larger metapopulation that is homogenous for pollinator preference. In addition, I thought one of the Ashman papers had actually argued for intermediate level UV abundance in the presence of UV?

      We have removed mentions of selection from the sentence - while the 110 populations included in our 2019 common garden experiment were selected to represent the whole range of H. annuus, we agree that the pattern we observe is at best suggestive. We have, however, kept a modified version of the sentence in the revised version of the manuscript, since we believe that is an interesting observation. The sentence now reads:

      “Pollination rates are known to be yield-limiting in sunflower (Greenleaf and Kremen, 2006), and a strong reduction in pollination could therefore have a negative effect on fitness; consistent with this plants with very small LUVp values were rare (~1.5% of individuals) in our common garden experiment, which was designed to provide a balanced representation of the natural range of H. annuus.”. (new lines 373-378)

      It is correct that Koski et al., Nature Plants 2015 found intermediate UV patterns to increase pollen viability in excised flowers of Argentina anserina exposed to artificial UV radiation. However, the authors also remark that larger UV patterns would probably be favoured in natural environments, in which UV radiation would be more than two times higher than in their experimental setting. Additionally, when using artificial flowers, they found that pollen viability increased linearly with the size of floral UV pattern.

      More generally, as we discuss later on in the manuscript, the pollen protection mechanism proposed in Koski et al., Nature Plants 2015 is unlikely to be as important in sunflower inflorescences, which are much flatter than the bowl- shaped flowers of A. anserina; consistent with this, and contrary to what was observed for A. anserina, we found no correlation between UV radiation and floral UV patterns in wild sunflowers (Figure 4c).

      I would reduce or remove the text around L316-321. If there's good a priori reason to believe flower heat isn't a big deal (L. 323) and the experimental data back that up, why add 5 lines talking up the hypothesis?

      We had fairly strong reasons to believe temperature might play an important role in floral UV pattern diversity: a link between flower temperature and UV patterns has been proposed before (Koski et al., Current Biol 2020); a very strong correlation exists between temperature and LUVp in our dataset; and, perhaps more importantly, inflorescence temperature is known to have a major effect on pollinator attraction (Atamian et al., Science 2016; Creux et al., New Phytol 2021). While it is known that UV radiation is not particularly energetic, we didn’t mean line 323 to imply that we were sure a priori that there wouldn’t be any effect of UV patterns of inflorescence temperature.

      In the revised manuscript, we have re-organized that section and provided the information reported in line 323 (UV radiation accounts for only 3-7% of the total radiation at earth level) before the experimental results, to clarify what our thought process was in designing those experiments. The paragraph now reads:

      “By absorbing more radiation, larger UV bullseyes could therefore contribute to increasing temperature of the sunflower inflorescences, and their attractiveness to pollinators, in cold climates. However, UV wavelengths represents only a small fraction (3-7%) of the solar radiation reaching the Earth surface (compared to >50% for visible wavelengths), and might therefore not provide sufficient energy to significantly warm up the ligules (Nunez et al., 1994). In line with this observation, different levels of UV pigmentation had no effect on the temperature of inflorescences or individual ligules exposed to sunlight (Figure 4e-g; Figure 4 – figure supplement 3).”

      Page 17: The discussion of flower size is interesting. Is there any phenotypic or genetic correlation between LUVP and flower size?

      This is a really interesting question! There is no obvious genetic correlation between LUVp and flower size – in GWAS, HaMYB111 is not associated to any of the floral characteristics we measured (flowerhead diameter; disk diameter; ligule length; ligule width; relative ligule size; see Todesco et al., Nature 2020). There is also no significant association between ligule length and LUVp (R^2 = 0.0024, P = 0.1282), and only a very weak positive association between inflorescence size and LUVp (R^2 = 0.0243, P = 0.00013; see attached figure). There is, however, a stronger positive correlation between LUVp and disk size (the disk being the central part of the sunflower inflorescence, composed of the fertile florets; R^2 = 0.1478. P = 2.78 × 10-21), and as a consequence a negative correlation between LUVp and relative ligule size (that is, the length of the ligule relative to the diameter of the whole inflorescence; R^2 = 0.1216, P = 1.46 × 10-17). This means that, given an inflorescence of the same size, plants with large LUVp values will tend to have smaller ligules and larger discs. Since the disk of sunflower inflorescences is uniformly UV- absorbing, this would further increase the size of UV-absorbing region in these inflorescences.

      While it is tempting to speculate that this might be connected with regulation of transpiration (meaning that plants with larger LUVp further reduce transpiration from ligules by having smaller ligules - relative ligule size is also positively correlated with summer humidity; R^2 = 0.2536, P = 2.86 × 10_-5), there are many other fitness-related factors that could determine inflorescence size, and disk size in particular (seed size, florets/seed number...). Additionally, in common garden experiments, flowerhead size (and plant size in general) is affected by flowering time, which is also one of the reason why we use LUVp to measure floral UV patterns instead of absolute measurements of bullseye size; in a previous work from our group in Helianthus argophyllus, size measurements for inflorescence and UV bullseye mapped to the same locus as flowering time, while genetic regulation of LUVp was independent of flowering time (Moyers et al., Ann Bot 2017). Flowering time in H. annuus is known to be strongly affected by photoperiod (Blackman et al., Mol Ecol 2011), meaning that the flowering time we measured in Vancouver might not reflect the exact flowering time in the populations of origin of those plants – with consequences on inflorescence size.

      In summary, there is an interesting pattern of concordance between floral UV pattern and some aspects of inflorescence morphology, but we think it would be premature to draw any inference from them. Measurements of inflorescence parameters in natural populations would be much more informative in this respect.

      Reviewer #2 (Public Review):

      The genetic analysis is rigorously conducted with multiple Helianthus species and accessions of H. annuus. The same QTL was inputed in two Helianthus species, and fine mapped to promotor regions of HaMyb111.

      While there is a significant association at the beginning of chr. 15 in the GWAS for H. petiolaris petiolaris, we should clarify that that peak is unfortunately ~20 Mbp away from HaMYB111. While it is not impossible that the difference is due to reference biases in mapping H. petiolaris reads to the cultivated H. annuus genome, the most conservative explanation is that those two QTL are unrelated. We have clarified this in the legend to Fig. 2 in the revised manuscript.

      The allelic variation of the TF was carefully mapped in many populations and accessions. Flavonol glycosides were found to correlate spatially and developmentally in ligules and correlate with Myb111 transcript abundances, and a downstream flavonoid biosynthetic gene. Heterologous expression in Arabidopsis in Atmyb12 mutants, showed that HaMyb111 to be able to regulate flavonol glycoside accumulations, albeit with different molecules than those that accumulate in Helianthus. Several lines of evidence are consistent with transcriptional regulation of myb111 accounting for the variation in bullseye size.

      Functional analysis examined three possible functional roles, in pollinator attraction, thermal regulation of flowers, and water loss in excised flowers (ligules?), providing support for the first and last, but not the second possible functions, confirming the results of previous studies on the pollinator attraction and water loss functions for flavonol glycosides. The thermal imaging work of dawn exposed flower heads provided an elegant falsification of the temperature regulation hypothesis. Biogeographic clines in bullseye size correlated with temperature and humidity clines, providing a confirmation of the hypothesis posed by Koski and Ashmann about the patterns being consistent with Gloger's rule, and historical trends from herbaria collections over climate change and ozone depletion scenarios. The work hence represents a major advance from Moyers et al. 2017's genetic analysis of bullseyes in sunflowers, and confirms the role established in Petunia for this Myb TF for flavonoid glycoside accumulations, in a new tissue, the ligule.

      Thank you. We have specified in the legend of Fig. 4i of the revised manuscript that desiccation was measured in individual detached ligules, and added further details about the experiment in the Methods section.

      While there is a correlation between pigmentation and temperature/humidity in our dataset, it goes in the opposite direction to what would be expected under Gloger’s rule – that is, we see stronger pigmentation in drier/colder environments, contrary to what is generally observed in animals. This is also contrary to what observed in Koski and Ashman, Nature Plants 2015, where the authors found that floral UV pigmentation increased at lower latitudes and higher levels of UV radiation. While possibly rarer, such “anti-Gloger” patterns have been observed in plants before (Lev-Yadun, Plant Signal Behav 2016).

      Weakness: The authors were not able to confirm their inferences about myb111 function through direct manipulations of the locus in sunflower.

      That is unfortunately correct. Reliable and efficient transformation of cultivated sunflower (much less of wild sunflower species) has eluded the sunflower community (including our laboratories) so far – see for example discussion on the topic in Lewi et al. Agrobacterium protocols 2016, and Sujatha et al. PCTOC 2012. We had therefore to rely on heterologous complementation in Arabidopsis; while this approach has limitations, we believe that its results, given also the similarity in expression patterns between HaMYB111 and AtMYB111, and in combination with the other experiments reported in our manuscript, make a convincing case that HaMYB111 regulates flavonol glycosides accumulation in sunflower ligules.

      Given that that the flavonol glycosides that accumulate in Helianthus are different from those regulated when the gene is heterologously expressed in Arabidopsis, the biochemical function of Hamyb111, while quite reasonable, is not completely watertight. The flavonol glycosides are not fully characterized (only Ms/Ms data are provided) and named only with cryptic abbreviations in the main figures.

      We believe that the fact that expression of HaMYB111 in the Arabidopsis myb111 mutant reproduces the very same pattern of flavonol glycosides accumulation found in wild type Col-0 is proof that its biochemical function is the same as that of the endogenous AtMYB111 gene – that is, HaMYB111 induces expression of the same genes involved in flavonol glycosides biosynthesis in Arabidopsis. Differences in function between HaMYB11 and AtMYB111 would have resulted in different flavonol profiles between wild type Col-0 and 35S::HaMYB111 myb111 lines. It should be noted that the known direct targets of AtMYB111 in Arabidopsis are genes involved in the production of the basic flavonol aglycone (Strake et al., Plant J 2007). Differences in flavonol glycoside profiles between the two species are likely due to broader differences between the genetic networks regulating flavonol biosynthesis: additional layers of regulation of the genes targeted by MYB111, or differential regulation (or presence/absence variation) of genes controlling downstream flavonol glycosylation and conversion between different flavonols.

      In the revised manuscript, we have added the full names of all identified peaks to the legend of Figures 3a,b,e.

      This and the differences in metabolite accumulations between Arabidopsis and Helianthus becomes a bit problematic for the functional interpretations. And here the authors may want to re-read Gronquist et al. 2002: PNAS as a cautionary tale about inferring function from the spatial location of metabolites. In this study, the Eisner/Meinwald team discovered that imbedded in the UV-absorbing floral nectar guides amongst the expected array of flavonoid glycosides, were isoprenilated phloroglucinols, which have both UV-absorbing and herbivore defensive properties. Hence the authors may want to re-examine some of the other unidentified metabolites in the tissues of the bullseyes, including the caffeoyl quinic acids, for alternative functional hypotheses for their observed variation in bullseye size (eg. herbivore defense of ligules).

      This is a good point, and we have included a mention of a more explicit mention possible role of caffeoyl quinic acid (CQA) as a UV pigment in the main text, as well as highlighted at the end of the manuscript other possible factors that could contribute to variation for floral UV patterns in wild sunflowers.

      We should note, however, that CQA plays a considerably smaller role than flavonols in explaining UV absorbance in UV-absorbing (parts of) sunflower ligules, and the difference in abundance with respect to UV-reflecting (parts of) ligules is much less obvious than for flavonols (height of the absorbance peak is reduced only 2-3 times in UV- reflecting tissues for CQA, vs. 7-70 fold reductions for individual quercetin glycosides). Therefore, flavonols are clearly the main pigment responsible for UV patterning in ligules. This is in contrast with the situation for Hypericum calycinum reported in Gronquist et al., PNAS 2002, were dearomatized isoprenylated phloroglucinols (DIPs) are much more abundant than flavonols in most floral tissue, including petals. The localization of DIPs accumulation, in reproductive organs and on the abaxial (“lower”) side of the petals (so that they would be exposed when the flower is closed), is also more consistent with a role in prevention of herbivory; no UV pigmentation is found on the adaxial (“upper”) part of petals in this species, which would be consistent with a role in pollinator attraction.

      The hypotheses regarding a role for the flavonoid glycosides regulated by Myb111 expression in transpirational mitigation and hence conferring a selective advantage under high temperatures and low and high humidities, are not strongly supported by the data provided. The water loss data from excised flowers (or ligules-can't tell from the methods descriptions) is not equivalent to measures of transpiration rates (the stomatal controlled release of water), which are better performed with intact flowers by porometry or other forms of gas-exchange measures. Excised tissues tend to have uncontrolled stomatal function, and elevated cuticular water loss at damaged sites. The putative fitness benefits of variable bullseye size under different humidity regimes, proposed to explain the observed geographical clines in bullseye size remain untested.

      We have clarified in the text and methods section that the desiccation experiments were performed on detached ligules. We agree that the results of this experiments do not constitute a direct proof that UV patterns/flavonol levels have an impact on plant fitness under different humidities in the wild – our aim was simply to provide a plausible physiological explanation for the correlation we observe between floral UV patterns and relative humidity. However, we do believe they are strongly suggestive of a role for floral flavonol/UV patterns in regulating transpiration, which is consistent with previous observations that flowers are a major source of transpiration in plants (Galen et al., Am Nat 2000, and other references in the manuscript). As suggested also by other reviewers, we have softened our interpretation of these result to clarify that they are suggestive, but not proof, of a connection between floral UV patterns, ligule transpiration and environmental humidity levels.

      “While desiccation rates are only a proxy for transpiration in field conditions (Duursma et al. 2019, Hygen et al. 1951), and other factors might affect ligule transpiration in this set of lines, this evidence (strong correlation between LUVp and summer relative humidity; known role of flavonol glycosides in regulating transpiration; and correlation between extent of ligule UV pigmentation and desiccation rates) suggests that variation in floral UV pigmentation in sunflowers is driven by the role of flavonol glycosides in reducing water loss from ligules, with larger floral UV patterns helping prevent drought stress in drier environments.” (new lines 462-469)

      Detached ligules were chosen to avoid confounding the results should differences in the physiology of the rest of the inflorescence/plant between lines also affect rates of water loss. Desiccation/water loss measurements were performed for consistency with the experiments reported in Nakabayashi et al Plant J. 2014, in which the effects of flavonol accumulation (through overexpression of AtMYB12) on water loss/drought resistance were first reported. It should also be noted that the use of detached organs to study the effect of desiccation on transpiration, water loss and drought responses is common in literature (see for example Hygen, Physiol Plant 1951; Aguilar et al., J Exp Bot 2000; Chen et al., PNAS 2011; Egea et al., Sci Rep 2018; Duursma et al., New Phytol 2019, among others). While removing the ligules create a more stressful/artificial situation, mechanical factors are likely to affect all ligules and leaves in the same way, and we can see no obvious reason why that would affect the small LUVp group more than the large LUVp group (individuals in the two groups were selected to represent several geographically unrelated populations).

      We have included some of the aforementioned references to the main text and Methods sections in the revised manuscript to support our use of this experimental setup.

      Alternative functional hypotheses for the observed variation in bullseye size in herbivore resistance or floral volatile release could also be mentioned in the Discussion. Are the large ligules involved in floral scent release?

      We have added sentences in the Results and Discussion, and Conclusions section in the revised manuscript to explore possible additional factors that could influence patterns of UV pigmentation across sunflower populations, including resistance to herbivory and floral volatiles. While some work has been done to characterize floral volatiles in sunflower (e.g. Etievant et al. J. Agric. Food Chem; Pham-Delegue et al. J. Chem. Ecol. 1989), to our knowledge the role of ligules in their production has not been investigates.

      In the revised manuscript, the section “A dual role for floral UV pigmentation” now includes the sentences:

      “Although pollinator preferences in this experiment could still affected by other unmeasured factors (nectar content, floral volatiles), these results are consistent with previous results showing that floral UV patterns play a major role in pollinator attraction (Horth et al., 2014, Koski ad Ashman, 2014, Rae and Vamosi, 2013, Sheehan et al., 2016).” (new lines 378-381)

      And the Conclusions sections includes the sentence:

      “It should be noted that, while we have examined some of the most likely factors explaining the distribution of variation for floral UV patterns in wild H. annuus across North America, other abiotic factors could play a role, as well as biotic ones (e.g. the aforementioned differences in pollinator assemblages, or a role of UV pigments in protection from herbivory (Gronquist et al., 2001)).” (new lines 540-544)

      Reviewer #3 (Public Review):

      Todesco et al undertake an ambitious study to understand UV-absorbing variation in sunflower inflorescences, which often, but not always display a "bullseye" pattern of UV-absorbance generated by ligules of the ray flowers. [...] I think this manuscript has high potential impact on science on both of these fronts.

      Thank you! We are aware that our experiments do not provide a direct link between UV patterns and fitness in natural populations (although we think they are strongly suggestive) and that, as pointed out also by other reviewers, there are other possible (unmeasured) factors that could explain or contribute to explain the patterns we observed. In the revised manuscript we have better characterized the aims and interpretation of our desiccation experiment, and modified the main text to acknowledge other possible factors affecting pollination preferences (nectar production, floral volatiles) and variation for floral UV patterns in H. annuus (pollinator assemblages, resistance to herbivory).

    1. Evaluation Summary:

      Since the inception of comparative genomics, mining phyletic patterns has been a powerful approach for the discovery of previously unknown biological interactions. The authors use a combination of singular value decomposition of the phyletic pattern matrix and random forests classification method to uncover potential protein-protein interactions. The work illustrates the utility of such methods, which are finding increasing application in addressing various computational biological problems, such as predicting protein-protein interactions from genomic information.

      (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. The reviewers remained anonymous to the authors.)

    2. Reviewer #1 (Public Review):

      The manuscript of Zaydman et al. proposes a spectral analysis of phylogenetic profiles, which allows to identify signals of protein-protein interaction or association at different scales, from direct PPI over pathways to phenotypes and finally to phylogenetic relationships.

      The paper reports some potentially very interesting results:

      - Different scales are related to different (even if overlapping) windows in the spectrum of the phylogenetic profiles, with the most global scale (phylogeny) related to the largest singular values, and the most local scale (physical PPI) to much smaller singular values.

      - Using this observation, and the correlation of proteins (projections of groups of orthologs to the SVD) across windows in the spectrum, the authors are able to extract a hierarchy of protein networks, which get refined from some general phenotype (bacterial mobility in the paper) to several pathways and complexes (e.g. chemotaxis, flagellum).

      - This allows to associate proteins of unknown function to some pathways or complexes; the paper shows a case of experimental validation for one new association.

      - Using a supplementary layer of supervised machine learning (interacting and non-interacting proteins), they claim to have more precise results than some recent PPI networks reconstructed using amino-acid coevolution (Cons et al.).

      While these results seem to be highly interesting and, in some cases, potentially spectacular, the paper is very hard to read and to understand. It is written in a semi-technical jargon mixing spectral analysis, machine learning and information theory. Even having expertise in these fields, I had to continuously jump between the main text, the methods and the figure (including the supplementary figures - a total of 86 pages) to follow the argumentation of the paper. The authors should make a serious effort to ensure that the main messages become more accessible.

    3. Reviewer #2 (Public Review):

      From its inception comparative genomics has held the promise of predicting protein-protein interactions using the phyletic patterns of proteins. The current work represents another iteration in the long series of such attempts, which aims to use the increasingly popular applications of machine learning to this classic problem. The authors start by using the phyletic pattern matrix for orthologous proteins and perform singular value decomposition on it to obtain the successive SVD components. They observed that the higher ranked SVD components were dominated by information from the phylogenetic relationships between organisms. However, there was a large unaccounted variance contained in the lower components, which they sought to further query for potential biologically relevant information such as indirect interactions and direct interactions, such as PPIs. They assembled benchmarks using known biological databases for assessing the inferred interactions which were derived from the "spectral correlation" which they obtained from row correlations in the U and V matrices of the decomposition of their ortholog phyletic pattern matrix. Given that the correlations can be a mix of all kinds of signals, including phylogenetic, indirect and direct interactions, they used a gold-standard set of well characterized E. coli K12 protein pairs to train random forest models for learning direct PPIs.

      The attractive aspects of this work include: 1) the use of a comprehensive phyletic pattern matrix for orthologs; 2) A reliable training set for the random forest method; 3) the assembly of multiple benchmarking sets with thorough benchmarking of the method. 4) Recovery of subsystems of bacterial flagellar motility and other systems.

      Weaknesses: 1) Bacteria tree is not uniformly sequenced. There is an overrepresentation of certain lineages, e.g., of gammaproteobacteria and terrabacteria (Bacillus group) in the starting matrix. This could potentially bias the quality of the correlations that are obtained in the ``mid-range' SVD components; 2) The actual biological inferences drawn for the role of the tested gene in twitching mobility might be over-interpreted. Briefly, the authors recover 4 uncharacterized proteins (Q9I5G6, Q9I5R2, Q9I0G2, Q9I0G1) as part of their T4 pilus sub-graph and infer a general function for them in the twitching mobility. They chose Q9I5G6 because it was the only one with a supposed domain of unknown function (DUF4845). However, it should be noted that Q9I5R2 also contains another such domain DUF805 along with a Zn-ribbon domain. Further, Q9I0G2 is a T2SS secretion platform protein and Q9I0G1i is the ATPase engine for the pilus. Genomic neighborhood analysis by this referee revealed that DUF4845 likely functions with the signal peptidase in secretion. Thus, given the role of the pilus in secretion and mobility, the best one could infer is a role for DUF4845 in pilus function perhaps with a greater intersection with secretion. This could even indirectly affect the mobility function which the authors' experiments are said to support. However, the authors state right in the abstract they have uncovered a twitching mobility effector. At best they could say they have uncovered a potential component that might be functionally linked to the T4 pilus which might affect secretion or twitching mobility. Indeed, the phyletic pattern of DUF4845 does not immediately suggest that all organisms with it also possess definitive twitching mobility.

      While methods of this kind have the promise to serve biological functional inference, the actual example provided does not appear to be the strongest. That said, I do think the work presents a method that might have utility in computational inferences of function, especially if combined with other forms of information from comparative genomics.

    4. Reviewer #3 (Public Review):

      The authors describe a computational prediction framework aimed at connecting individual genes into progressively larger units of function: from protein complexes to higher-order pathways. The framework is based on the tracking of the presence and absence of orthologous genes across a large number of genomes; the authors' method is demonstrated to work well, albeit only for prokaryotic organisms. The basic evolutionary signal used by the authors has been described previously, and has been used previously to predict protein-protein associations, but the authors take it a step further by carefully deconstructing the signal into multiple components: a phylogenetic component, a direct protein-protein interaction component, and a more indirect association component. They then construct a hierarchical model of functional linkages, for any prokaryotic genome of interest. Finally, they use this to predict and experimentally verify the function of a previously uncharacterized protein in Pseudomonas aeruginosa.

      This is a well-written and carefully executed study, taking a known prediction technique to a new level. It has broad applicability, and should be of interest to a wide readership.

    1. Reviewer #2 (Public Review):

      The manuscript of Kunze et al. aimed at finding how different kinds of fluctuations in temperature affect the disease outcome. The authors used Daphnia magna - Ordospora colligate host - parasite system exposed to a range of temperatures which were either stable, regularly fluctuating, or included a single heat wave, and measured fitness of the host (as reproductive output) and the parasite (infection rate and spore burden). The experiment is very well designed, and the methods of data analysis are sound and well suited to address the questions stated by the authors.

      The authors found that the unstable thermal conditions change the fitness of the host and the parasite. Temperature fluctuations narrowed thermal breadth for infection and spore burden of the parasite, whereas the heat wave caused shift in thermal optimum and a strong increase of maximal spore burden of the parasite. Both thermal variation treatments resulted in shifts in thermal optimum and maximal performance of the host. The most interesting (and surprising) result was the spectacular increase in spore burden of the parasite exposed to heat wave in comparison to fluctuating temperature treatment and stable temperature treatment, obtained in 16°C.

      Authors rightfully conclude that the outcome of infection could be strongly altered by variations in thermal regime. This context dependency might to some extent explain the limited accuracy of disease spread models. This is critical especially in the face of climate change, which is expected to result in more frequent and more rapid thermal variation events. Moreover, the narrowed thermal performance curve of the parasite (especially in the high temperatures range) under fluctuating temperature regime indicates, that the thermal tolerance of some organisms to warming might be overestimated, when tested under (less realistic) stable thermal conditions.

      I think the paper of Kunze et al. is a very strong contribution to the field of disease ecology, and I find no major weaknesses. The Introduction and Discussion sections are well written and provide some extensive overview of the relevant literature. The study design and results are described clearly and the conclusions are well supported. I have no major criticism to this manuscript.

    2. Author Response:

      Reviewer #1 (Public Review):

      Kunze et al. provide an interesting experiment aimed to understand the effects of variable temperature regimes in host-pathogen interactions. This is one of the most complete experiments to date, that goes beyond exploring increasing but constant temperature regimes. The experimental setup is strong, exposing Daphnia magna to the natural range of temperature variability and realistic fluctuating (+-3C) and extreme (6C pulse) regimes. Daphnia exposure to Odospora colligata pathogens was also rightly tested against a placebo control. Aided by their experimental approach Kunze et al. explore their results with clear figures and fine text, getting deep into our understanding of the thermal performance of important host and pathogen life history traits (such as reproductive output) and setting them in the larger picture of global warming. In short, I am impressed by the quality of the new information provided by this ms.

      Thank you for these positive comments on our manuscript.

      Reviewer #2 (Public Review):

      The manuscript of Kunze et al. aimed at finding how different kinds of fluctuations in temperature affect the disease outcome. The authors used Daphnia magna - Ordospora colligate host - parasite system exposed to a range of temperatures which were either stable, regularly fluctuating, or included a single heat wave, and measured fitness of the host (as reproductive output) and the parasite (infection rate and spore burden). The experiment is very well designed, and the methods of data analysis are sound and well suited to address the questions stated by the authors. The authors found that the unstable thermal conditions change the fitness of the host and the parasite. Temperature fluctuations narrowed thermal breadth for infection and spore burden of the parasite, whereas the heat wave caused shift in thermal optimum and a strong increase of maximal spore burden of the parasite. Both thermal variation treatments resulted in shifts in thermal optimum and maximal performance of the host. The most interesting (and surprising) result was the spectacular increase in spore burden of the parasite exposed to heat wave in comparison to fluctuating temperature treatment and stable temperature treatment, obtained in 16{degree sign}C. Authors rightfully conclude that the outcome of infection could be strongly altered by variations in thermal regime. This context dependency might to some extent explain the limited accuracy of disease spread models. This is critical especially in the face of climate change, which is expected to result in more frequent and more rapid thermal variation events. Moreover, the narrowed thermal performance curve of the parasite (especially in the high temperatures range) under fluctuating temperature regime indicates, that the thermal tolerance of some organisms to warming might be overestimated, when tested under (less realistic) stable thermal conditions. I think the paper of Kunze et al. is a very strong contribution to the field of disease ecology, and I find no major weaknesses. The Introduction and Discussion sections are well written and provide some extensive overview of the relevant literature. The study design and results are described clearly and the conclusions are well supported. I have no major criticism to this manuscript.

      We thank the reviewer for these positive comments on our manuscript.

    3. Evaluation Summary:

      Kunze et al. provide a fine experiment to show that both increases in mean temperature and (extreme) variability in temperature regimes have important consequences in host-pathogen interactions. The results presented in this manuscript shed a light on why disease spread models fed by experimental data (commonly obtained in stable environmental conditions) are frequently inaccurate. These results lead us to more realistic understanding of the impacts of climate change in biological species but also identify the need of mechanisms behind species interaction in fluctuating environments/temperatures. This manuscript thus comes timely as the planet is warming, and disease ecologists, limnologists, epidemiologists and physiologists are interested in the consequences.

      (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. The reviewers remained anonymous to the authors).

    4. Reviewer #1 (Public Review):

      Kunze et al. provide an interesting experiment aimed to understand the effects of variable temperature regimes in host-pathogen interactions. This is one of the most complete experiments to date, that goes beyond exploring increasing but constant temperature regimes. The experimental setup is strong, exposing Daphnia magna to the natural range of temperature variability and realistic fluctuating (+-3C) and extreme (6C pulse) regimes. Daphnia exposure to Odospora colligata pathogens was also rightly tested against a placebo control. Aided by their experimental approach Kunze et al. explore their results with clear figures and fine text, getting deep into our understanding of the thermal performance of important host and pathogen life history traits (such as reproductive output) and setting them in the larger picture of global warming. In short, I am impressed by the quality of the new information provided by this ms.

    1. Joint Public Review:

      Gupta et al. investigate the mechanism of Mcm2-7 helicase loading using an in vitro reconstituted S. cerevisiae system by single molecule colocalization spectroscopy and sm-FRET. Previous biochemical and single-molecule studies have led to contrasting models as to whether one or two ORCs are involved in recruiting and loading both Mcm2-7 hexamers in a double hexamer. How the transitions between OCCM and MO loading intermediates are coordinated was likewise unknown. Using COSMOS and sm-FRET, the authors convincingly show that a) a single ORC recruits both the first and second Mcm2-7 hexamer in the majority of observed loading events, b) ORC recruits the first and second Mcm2-7 hexamer using similar ORC-MCM interactions, c) ORC is retained at the origin by the formation of an MO complex and these interactions stabilize the first Mcm2-7 hexamer on DNA and d) Cdt1 release coordinates the transition between OCCM and MO complexes. These data are consistent with the proposed ORC-flip model, which posits that ORC is released from the original DNA site and rebinds on the opposite side of the first loaded Mcm2-7 for loading of the second Mcm2-7. This work provides important new insights into understanding the mechanisms of bidirectional replicative helicase loading.

      The paper is an important contribution and the reviewers asked for a number of clarifications and explanations about the data.

    2. Evaluation Summary:

      The paper describes single-molecule experiments that address the assembly of a double hexamer of the Mcm2-7 complex that is required to license all origins of DNA replication in eukaryotic cells by formation of a pre-Replicative Complex (pre-RC). The observations show that one Origin Recognition Complex, an ATP-dependent DNA binding protein, can load both Mcm2-7 hexamers in opposite orientation. The results nicely complement prior data on the mechanism of pre-RC assembly.

      (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #1 agreed to share their name with the authors.)

    1. Evaluation Summary:

      This manuscript will be of interest for scientists interested in cell cycle, DNA repair, and genome stability reporting the unexpected discovery that the DNA-dependent protein kinase (DNA-PK) is required for DSB resection in G0 cells, whereas it is known and confirmed here that it inhibits resection in G1 and G2 cells. This finding has important implications for the clinical application of DNA-PK-targeted inhibitors. The data are of high quality and derive from two independent cell lines, genetic requirements were mostly established by gene knockouts, and the latest genome-wide sequencing techniques were applied to measure resection tracts. The key claims of the manuscript are supported by the data presented by the authors; however, further validations are needed to strengthen the quality and impact of the paper. 

      (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #1 agreed to share their name with the authors.)

    2. Joint Public Review:

      Fowler et al. report on hits of a CRISPR-Cas9 FACS-based screen for chromatin associated RPA in quiescent murine pre-B cells that lack DNA ligase 4 (to prevent NHEJ) identifying component required for DSB resection or inhibiting DSB resection in G0 cells. The screen is well validated by previously published results (Chen et al. 2021 eLife) and controls reported in this manuscript. Unexpectedly, the authors identify all components of the DNA-dependent protein kinase complex, Ku70, Ku80 and DNA-PK catalytic subunit, as being required for DSB resection in G0 cells. This was surprising as DNA PK inhibits DSB resection in G1 and G2 cells, which was confirmed in this work. The results are verified by END-seq, showing strand specificity, and processing is dependent on MRE1 and CtIP, assuring that the RPA signal reports on DSB resection. Independent confirmation is derived from results with FBXL12, an DNA-PK-specific ubiquitin E3 ligase, which leads to DNA-PK turnover and counteracts DSB resection is G0 cells. The genetic dependencies were established by gene knockouts, and key results confirmed in a human cell line (MCF-10A). The specificity of the effect for DNA-PK was confirmed using inhibitors against ATM, which showed no effect on DSB resection, whereas DNA-PK inhibitors mimicked the genetic dependency. 

      The manuscript is well structured and describes an interesting finding for the DNA repair community, speculating that DSBs repair in quiescent cells functions differently than in cycling cells. This has implications on how non-cycling cells in the body, such as neurons, could handle DNA damage, but remains to be validated in the corresponding model. In addition, more experiments need to be performed to adequately support the key conclusions of the manuscript with respect to the applicability in human cells and with regards to the distinction between resection in G0 and G1 cells. Some data are lacking a precise description of the methods which need to be extended.

    1. Evaluation Summary: 

      Homeostatic plasticity helps to confine neural network activity within limits. In this study, the authors show that loss of PAR bZIP family of transcription factors leads to overcompensation of excitatory synaptic transmission and average network activity upon sustained activity deprivation. The work identifies an endogenous transcriptional program that constrains upward homeostatic response and whose activity is implicated in preventing aberrant network activity associated with epilepsy and other brain disorders. These are exciting results that address the question of broad importance. While most arguments are supported by data of high quality, further experiments would strengthen the claims about the relative contribution of excitatory and inhibitory mechanisms and clarify the nature of compensation.

      (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #1 agreed to share their name with the authors.)

    2. Reviewer #1 (Public Review): 

      Homeostatic plasticity is a process that helps to confine neural network activity within limits. While our understanding of the expression mechanisms of homeostatic plasticity have considerably advanced, very little is known of how the bounds of permissive activity levels are set and kept in check. In this study, the authors present data indicating that activation of PAR bZIP family of transcription factors help confine the extent of homeostatic synaptic plasticity, illustrating the existence of a negative regulator of homeostatic plasticity. 

      The study addresses a timely topic of general interest and the key findings are important. The data as presented leave some questions concerning the conclusion that Par bZIP proteins act as negative regulators of homeostatic synaptic plasticity. Although TTX treatment substantially increases the expression of both HLF and TEF, HLF is dramatically upregulated in PV+ neurons compared to pyramidal cells. In addition, when whole cortical lysates are examined, the kinetics of upregulation of HLF and TEF appear to differ. Given that slice cultures from mice lacking HLF, TEF and DBP show a strong reduction in mIPSC amplitude, which is otherwise compensated presumably via mechanisms independent of Par bZIP transcription factors, additional characterization of the expression properties and the respective roles of HLF and TEF in pyramidal and PV+ neurons might help provide a clearer view of when and how HLF and TEF are engaged to regulate network activity.

    3. Reviewer #2 (Public Review): 

      Valakh et al. report increased transcript abundance of PAR bZIP transcription factors after treating organotypic cortical mouse brain slice cultures with TTX for five days. Triple-knockout neurons lacking the transcription factors Hlf, Dbp and Tef displayed a more pronounced increase in calcium spike frequency and mEPSC frequency upon TTX treatment than controls, suggesting that these transcription factors limit homeostatic compensation. The study addresses a very interesting and almost completely unresolved question - the mechanisms that may constrain homeostatic plasticity. In principle, this paper presents highly relevant data for the field of synaptic transmission and synaptic plasticity.

    4. Reviewer #3 (Public Review): 

      The manuscript by Valakh et al. discovered transcription factors (TFs) belonging to the PAR bZIP family that normally limit upward homeostatic response without affecting baseline activity levels. The authors show that long-term blockade of spikes by TTX activates Hlf, Tef, and Dbp TFs in both excitatory and inhibitory neurons. The authors demonstrate that chronic silencing in TKO slice cultures that lack all 3 TFs causes over-compensation at the presynaptic level, reflected by larger increase in mEPSC frequency, but not at the postsynaptic level or at the level of intrinsic excitability in excitatory neurons. In addition, homeostatic plasticity of inhibitory synapses at excitatory neurons was disturbed by TKO. At the network level, over-compensation of average activity level was observed in TKO following prolonged network silencing. In contrast, no deficits in downward homeostasis from hyperactive state were detected. 

      These are exciting results that demonstrate a novel transcriptional program that normally restricts upward homeostatic plasticity and prevents over-compensation. While previous studies revealed transcriptional regulation that enables downward firing rate homeostasis by REST (Pozzi et al., EMBO 2013), this work is the first one to identify transcriptional regulation that restricts upward firing rate homeostasis. Hlf, Tef, and Dbp TFs are regulated by circadian clock and may be implicated in many types of physiological regulations across light-dark phases. The knockout mice lacking all 3 TFs show epilepsy phenotype and short lifespan that can be related to a novel mechanism discovered by the authors. The paper is of high significance for both basic neuroscience and neuropathology related to homeostatic deficits, such as epilepsy, neuropsychiatric disorders and many more.

    1. Author Response:

      Reviewer #3 (Public Review):

      The Schepartz lab have previously shown that the binding of growth factors results in the formation of two distinct coiled coil dimers within the juxtamembrane (JM) segment. These two isomeric coiled coil structures are also allosterically preferred by point mutations within transmembrane (TM) helix. In this manuscript, authors demonstrate that the JM coiled coil is a binary switch, governing the trafficking status of EGFR, either towards degradative or recycling pathway.

      They design novel variants of EGFR (E661R and KRAA) that mimic the two distinct coiled coil types, EGF-type and TGF-α-type. These variants are further validated using bipartite tetracysteine- ReAsH system. In order to assess the trafficking of these variants, authors use confocal imaging to measure colocalization with respective organelle markers. In addition, authors also use variants with point mutations at TM segment that controls the JM coiled coil state to demonstrate that the trafficking is dependent on JM segment and not growth factor identity. EGFR signaling is of prime importance in cancer biology and trafficking plays a major role, where the degradative pathway decreases the signaling, in contrast to recycling pathway that sustains the signaling. The authors clearly demonstrate this switch in EGFR lifetime using relevant variants and show how well-known tyrosine kinase inhibitors regulate this in a drug resistant non-small cell lung cancer model.

      The model proposed by the authors is mostly well supported by data, but few points require clarification.

      i) The authors need to address why the switch is incomplete when JM mutants are used but appears complete with TM mutants. A) Does this mean recycling requires other criteria in addition to JM segment? B) Is it possible that TM mutants cause other changes in addition to controlling JM segment? C) Would it be better if organelle transmembrane markers were used (Tf, Lamp1, NPC1 etc.).

      The revised manuscript now includes a discussion of why the localization switch is less complete for the JM mutants than for the TM mutants. Whether these differences mean that the direction of trafficking requires direct interactions with the JM segment, or alternatively that the TM mutants cause other relevant changes in EGFR is currently under investigation.

      ii) It would be helpful to represent data as a distribution or scatter points instead of bar plot. Did authors observe any expression level dependence on their colocalization and lifetime assays?

      Figures 2 and 3 have been changed to illustrate both bars and individual points. We did not evaluate the effect of expression level on the extent of colocalization or EGFR lifetime.

      iii) Did authors investigate the lifetime of JM variants? Like it was shown with TM variants in Fig 4.

    2. Evaluation Summary:

      This manuscript investigates the cellular role of the juxtamembrane region in the EGF receptor, a poorly understood portion of the EGFR cytosolic domain that connects the transmembrane segment to the kinase domain. Through a series of well-designed experiments, the work shows that the endocytic trafficking route of EGFR following its activation is determined by the juxtamembrane coiled-coil conformation in a model cell line. This finding is important for three reasons. It identifies a critical role for the juxtamembrane region; it resolves the discrepancy that TGF-beta dissociation from EGFR is supposed to occur at higher pH, yet the EGFR-TGF-beta complex continues to signal from endosomes; and it pinpoints the mechanism of EGFR inhibition by a new class of tyrosine kinase inhibitors.

      (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. All reviewers agreed to share their names with the authors.)

    3. Reviewer #1 (Public Review):

      In the paper "Coiled coil control of growth factor and inhibitor-dependent EGFR trafficking and Degradation", Mozumdar et al investigate the cellular role of the juxtamembrane region in the EGF receptor. The juxtamembrane segment is a poorly understood motif in the EGFR cytosolic domain that physically connects the transmembrane segment to the kinase domain. It can form two kinds of coiled-coil dimers depending on whether the ligand it is bound to is TGF-a or EGF. EGFR traffics either down the endolysosomal pathway, or via the recycling pathway depending on the ligand it binds, and previous studies linked the trafficking route it adopted to the stability of the ligand-EGFR complex, or EGFR dimer strength. Through a series of well-designed experiments, this paper shows that the endocytic trafficking route of EGFR following its activation is determined by the juxtamembrane coiled coil conformation in a model cell line.

      This finding is important for three reasons. First, it identifies a critical role for the ill-understood juxtamembrane region. Second, it resolves the discrepancy that TGF-α is thought to dissociate from EGFR before EGF yet the EGFR-TGFα complex continues to signal from endosomes. Finally, it pinpoints the mechanism of EGFR inhibition by a new class of tyrosine kinase inhibitors, which downregulate EGFR activity by funnelling the EGFR-inhibitor complex for lysosomal degradation.

    4. Reviewer #2 (Public Review):

      The premise of this study is that a juxtamembrane coiled-coil structure in EGFR exists in two isomeric orientations depending on ligand occupancy, mutations or tyrosine kinase inhibitors. The authors show a plausible correlation between the orientation of this coiled-coil domain and receptor fate which could be important in driving tumor phenotypes.

      In this study, the authors use three sets of molecular tools to manipulate the conformation of the coiled-coil domain and capture receptor trafficking and fate. First, they design mutations in the coiled coil helices that favor either the EGF or TGF type conformations, that feature either a Leu rich or charged interface, respectively. The effect of the mutations on conformation is convincingly validated using a Cys binding fluorescence reporter and recombinant EGFR. However, the sorting of mutant receptors in response to either EGF or TGF, although shifted in the predicted direction, is not perfectly correlated for clear conclusions to be made. For example, E661R receptor gains the ability to associate with Rab7 endosomes in response to TGF binding, however, it also loses much of the original association with Rab11 (ideally, this should not have changed). The KRAA mutant appears to be non-selective for ligand in association with Rab11 although it results in poor association with Rab7 endosomes for both ligands. In any case, these experiments are incomplete without evaluation of receptor fate in lysosomal degradation and inconclusive as presented.

      In contrast, the next set of tools consisting of mutations in the GXXG motif (previously validated in Sinclair et al., 2018) yield results that are much easier to interpret. Mutation G628F sends the receptor to Rab7 endosomes and on to lysosomal degradation in response to TGF. Conversely, mutation G628V sends the receptor to Rab11 endosomes where it escapes degradation in response to EGF. In each case, there is a significant and convincing gain of function phenotype that correlates a shift in endosomal localization to receptor fate.

      The last set of tools are tyrosine kinase inhibitors used in conjunction with constitutively active and endocytosed EGFR. Here, the authors make a nice case for endosome association and receptor fate that is uncoupled from the inhibition of phosphorylation. Again, there is good correlation between Rab7 protein association and receptor degradation, irrespective of the kinase inhibitor activity.

      Overall, the authors make a convincing case that sorting of receptor to Rab7 endosomes results in effective lysosomal degradation. However, the argument that conformation of the coiled-coil motif drives endosomal sorting and fate is not well supported. Mutations in the coiled-coil domain had confusing outcomes, and no information on coiled-coil conformation was presented for the tyrosine kinase inhibitors. Only the G628 mutants present the complete set of correlations, although not all in this manuscript (some of the pertinent experiments are already published).

    5. Reviewer #3 (Public Review):

      The Schepartz lab have previously shown that the binding of growth factors results in the formation of two distinct coiled coil dimers within the juxtamembrane (JM) segment. These two isomeric coiled coil structures are also allosterically preferred by point mutations within transmembrane (TM) helix. In this manuscript, authors demonstrate that the JM coiled coil is a binary switch, governing the trafficking status of EGFR, either towards degradative or recycling pathway.

      They design novel variants of EGFR (E661R and KRAA) that mimic the two distinct coiled coil types, EGF-type and TGF-α-type. These variants are further validated using bipartite tetracysteine- ReAsH system. In order to assess the trafficking of these variants, authors use confocal imaging to measure colocalization with respective organelle markers. In addition, authors also use variants with point mutations at TM segment that controls the JM coiled coil state to demonstrate that the trafficking is dependent on JM segment and not growth factor identity. EGFR signaling is of prime importance in cancer biology and trafficking plays a major role, where the degradative pathway decreases the signaling, in contrast to recycling pathway that sustains the signaling. The authors clearly demonstrate this switch in EGFR lifetime using relevant variants and show how well-known tyrosine kinase inhibitors regulate this in a drug resistant non-small cell lung cancer model.

      The model proposed by the authors is mostly well supported by data, but few points require clarification.<br> i) The authors need to address why the switch is incomplete when JM mutants are used but appears complete with TM mutants. A) Does this mean recycling requires other criteria in addition to JM segment? B) Is it possible that TM mutants cause other changes in addition to controlling JM segment? C) Would it be better if organelle transmembrane markers were used (Tf, Lamp1, NPC1 etc.).<br> ii) It would be helpful to represent data as a distribution or scatter points instead of bar plot. Did authors observe any expression level dependence on their colocalization and lifetime assays?<br> iii) Did authors investigate the lifetime of JM variants? Like it was shown with TM variants in Fig 4.

    1. Evaluation Summary:

      This paper represents multiple milestones in our understanding of the evolution and extinction of Pleistocene equids, including revising the timing of extinction and clarifying the evolutionary history of Equus (Sussemionus) ovodovi. The discovery of the late persistence of non-caballine equid taxa in northern China until deep into the late Holocene is particularly important. This finding will be of broad interest to the paleontology, paleoecology, archaeology, paleogenomic communities and should stimulate important future research into equid extinction processes.

      (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. The reviewers remained anonymous to the authors.)

    2. Reviewer #1 (Public Review):

      In this manuscript, Cai and authors offer a new and important discovery demonstrating the persistence of a clade on non-caballine equids, Sussemionus, well into the later millennia of the Holocene in northern China. My expertise does not lie with the genomics analysis, so I will not offer detailed comment - but as an outsider, the arguments seemed well-supported and convincing.

      The primary weakness of the article lies in the omission of detailed archaeological context, and in the failure to consider implications for and from human societies. All specimens were taken directly from archaeological sites, but no information is given about the archaeological sites and cultures the specimens were derived from. In early China, ca. 3500 BP, the persistence of wild equid taxa is a very significant finding. This time period was a very dynamic period across northern East Asia, with the first introduction of domestic horses and the first spread of other livestock pastoralism (see Brunson et al, https://www.sciencedirect.com/science/article/abs/pii/S2352409X20300535). And, as summarized in Yuan and Flad (2006), many of the earliest sites speculatively linked with domestic horses that predate the final Shang Dynasty are isolated equid bones from archaeological sites, without definitive archaeological data to determine domestic or wild status. Therefore, the archaeological context of these finds is really important - how were each of the bones originally identified in archaeological reports? Is there associated evidence that the equids were hunted and eaten? The authors must add a section describing the archaeological context in greater detail, and considering the possible implications of the finds. For example, the persistence of sussemione equids through the 2nd millennium BCE implies that researchers must be exceedingly careful in zooarchaeological identifications prior to this period. Moreover, the result might also warrant a discussion about the role of pastoral cultures, or the introduction of domestic horses, in the final extinction of the sussemiones. Without such a summary, it is incomplete to suggest that their final extinction is a result of inbreeding and reduced genetic diversity.

    3. Reviewer #2 (Public Review):

      Dawei Cai and colleagues present a series of firsts and new discoveries including (1) the first high coverage genome from an equid that is unequivocally an extinct species and (2) demonstrating that Equus (Sussemionus) ovodovi survived into the late Holocene, belonged to a lineage sister to all extant non-caballine equids, and underwent extensive admixture soon after its divergence from non-caballine equids.

      The manuscript is clearly laid out and well written. The analyses are conducted logically and to a high standard, which includes testing the impacts of reference genome choice and DNA misincorporations in nearly all analyses. The conclusions are mostly supported by the data but some methodological clarifications and discussion of conflicting results are required.

      Strengths/weaknesses of the five main findings:

      (1) Sussemiones survived into the late Holocene.<br> Strengths: It is remarkable that Sussemiones survived so late into the Holocene, but the authors present radiocarbon evidence from multiple skeletal elements and sites supporting the late survival hypothesis. Combined with the genomic evidence, there is very strong support for this assertion.<br> Weaknesses: The manuscript does not describe the radiocarbon methods, such as which laboratory these analyses were conducted in and whether samples were ultrafiltered or not. A description of the calibration methods and curve version used is also lacking.

      (2) Equus (Sussemionus) ovodovi is a sister lineage to all extant non-caballine equids.<br> Strengths: The authors construct both exome and candidate neutral loci phylogenies from across the nuclear genome, including testing the impact of two different reference genomes. All analyses support the same placement of E. ovodovi with 100% bootstrap support. The assertion is therefore strongly supported.<br> Weaknesses: No weaknesses identified.

      (3) The early evolution of the lineages leading to the E. ovodovi and the three main extant equid groups was characterised by extensive admixture.<br> Strengths: The authors use three different methods to infer the presence, extent, and/or direction of admixture.<br> Weaknesses: A major weakness here is the incongruence between the TreeMix models and the D-statistics and G-PhoCS analyses (the latter two give a coherent story). Given the large admixture events determined by G-PhoCS, it seems concerning that these events are not recovered as migration edges in the TreeMix analyses.

      (4) Population size of E. ovodovi over the past 2 Myr.<br> Strengths: The authors correct for differences in genome coverage to allow for the PSMC profiles between four equid taxa to be comparable, allowing for comparison of population size trajectories.<br> Weaknesses: In Figure 4, the presented PSMC profiles are a mix of those with or without transitions (comparing profiles to Figure - 4 figure supplement 1). Given that the exclusion of transitions impacts the PSMC profiles, these should be standardized in Figure 4 to give a fair comparison.

      (5) Inbreeding was a contributing factor to the extinction of E. ovodovi.<br> Strengths: The authors determine heterozygosity and runs-of-homozygosity in E. ovodovi and compare these to all living equids, and find that E. ovodovi had low heterozygosity although not excessive runs-of-homozygosity.<br> Weaknesses: The authors should be more cautious with their interpretation/phrasing on L383-384, given that inbreeding and/or reduced genetic diversity has not been demonstrated as the extinction driver.

    1. Evaluation Summary:

      This study fuses images from cardiac magnetic resonance imaging and T1-mapping to reconstruct 3D anatomical models of the heart from hypertrophic cardiomyopathy patients. Using the model, they investigated potential contributions of diffuse fibrosis to arrhythmogenesis of the heart model in response to focal stimulation. While not perfect, the computer model significantly outperforms other risk predictors, and highlights diffuse fibrosis as a possible underlying cause. This study will be of interest to clinicians and basic scientists involved in heart rhythm research.

      (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #3 agreed to share their name with the authors.)

    2. Reviewer #1 (Public Review):

      This study fused images from CMR and T1 mapping to reconstruct 3D anatomical models of the heart for HCM patients. Using the model, they investigated potential contributions of diffusive fibrosis to arrhythmogenesis of the heart model in response to focal stimulus. They found that the diffusive fibrosis contributed to increased incidence of ventricular arrhythmias.

      The study is of some interest. However, there are some concerns regarding its publication in its present form.

      1) Details are unclear about how the imaging segmentation and alignment were conducted. Especially when CMR and T1-mapping data were fused together, how the slice images were aligned as mismatch is of a challenge and can affect the simulation results and conclusion.

      2) It is unclear what is the spatial resolution of the CMR, and how the spatial resolution of about 330 micrometre was achieved for the finite element model.

      3) It is unclear how the incorporation of fibre structures was done and validated. Given that fact that at different stages of HCM and individual differences, the fibre structures are different in different subjects. Without consideration of this, conclusions based on the diffusive fibrosis are non-conclusive.

      4) It is also unclear how the physiological model for the HCM was developed and validated for the patient-specific model.

    3. Reviewer #2 (Public Review):

      The overall aims of this work are to use computer models of electrical activation to (i) understand how remodelling of structure and function in hypertrophic cardiomyopathy promotes ventricular arrhythmias, and (ii) to assess whether a model-based approach could be used to predict the risk of arrhythmias in specific patients.

      The approach taken by the authors builds on previous work by this group, where a personalized mesh representing the ventricles is constructed from automated analysis of cardiac MRI. Models of human electrophysiology are then solved on this mesh with simulated pacing, to identify vulnerability to arrhythmias.

      The major strength of this approach is that it presents an environment within which an investigation that may be technically difficult, time-consuming, or unethical in a patient can be undertaken to guide treatment or assess risk. It is very promising.

      However, although the methodology used is sound, there are important assumptions that underpin this approach and limit the extent to which the outcomes are trustworthy. These include:

      1. MRI physics. The MR signal is produced from a finite volume of tissue, which is about an order of magnitude larger than the size of finite elements used in the computer model. Thus, the personalised mesh may not capture small scale features that could be important for initiation of arrhythmias.

      2. Cardiac mechanics. The mesh used to solve the computer model is static, whereas the heart contracts with every beat. Mechanical contraction not only changes the shape of the heart and the thickness of the ventricular wall, but also feeds back into electrical activity.

      3. Population variability. The electrical model used in this study is a standard representation for the human ventricles. This is adjusted to capture some features of electrical activity in fibrotic regions, but these are not well characterised so assumptions are made. The patterns of electrical activation and recovery in the human heart vary from place to place within the human ventricles, with time within the same patient in response to external effects including autonomic activity, and from one patient to another. Hypertrophic cardiomyopathy is usually a progressive disease, so patterns of fibrosis may change over time.

      Nevertheless, this study has found evidence that diffuse fibrosis plays a role in the vulnerability to arrhythmias in hypertrophic cardiomyopathy, and found that, in this group of 26 patients, a model-based approach can provide a more accurate risk stratification than other methods based on patient clinical data.

    4. Reviewer #3 (Public Review):

      In their paper the authors set out to develop a novel ventricular arrhythmia risk assessment in the setting of hypertrophic cardiomyopathy.

      The authors combine contrast enhanced MRI with T1 mapping data to construct computer models of HCM patient hearts that include patient specific distribution of fibrosis. They then use these personalized hearts to assess the propensity for ventricular arrhythmias.

      The others demonstrate using a computational approach that diffuse fibrosis increases vulnerability to ventricular arrhythmia. It's an important finding especially because diffuse fibrosis is not a parameter that is typically tracked in HCM patients.

      The potential impact of the work described in the study is high. By accounting for diffuse fibrosis as a risk factor for ventricular arrhythmias in HCM patients, the authors demonstrate improved sensitivity, specificity and accuracy compared to other risk predictive parameters. It is feasible that as a result of the study that diffuse fibrosis may be tracked in these patients as an indicator of propensity to deadly arrhythmias.

    1. Evaluation Summary:

      Multiple inherited mutations in the epithelial CFTR anion-permeable channel cause cystic fibrosis through different molecular mechanisms that can be targeted by different types of drugs to treat the disease. Drawing from available structural information and double-mutant cycle analysis of patch-clamp recordings, Simon and Csanády find that one of the most common CFTR disease-causing mutations, R117H, disrupts an interaction between the R117 side-chain and a main-chain carbonyl that selectively stabilizes the open state of the channel. These findings may open new paths of exploration for treating patients carrying this mutation, and provide important mechanistic constraints towards understanding the gating mechanism of CFTR proteins.

      (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #1 and Reviewer #2 agreed to share their names with the authors.)

    2. Reviewer #1 (Public Review):

      Understanding the effects of cystic fibrosis-causing mutations in CFTR channel function, stability or expression is important because this determines the choice of treatment for the disease. The negative effects on gating of a common disease-causing mutation, R117H, were puzzling because it is located at an extracellular loop, away from the sites that control ATP-dependent gating in the channel. In the present manuscript, Simon and Csanády identify a hydrogen bond between the side chain of R117 and the main-chain carbonyl of E1124 that is present in a structure that is thought to closely represent the open state and absent in a structure representative of the closed state of the protein. The authors perform molecular modeling to identify that a residue deletion at E1124 is predicted to disrupt this interaction, and show that CFTR channels with the deletion behave very similar to those carrying the single R117H mutation in regards to channel closure kinetics in a mutant background lacking ATP hydrolysis, consistent with the proposed interaction found in the structures. Using two different mutant backgrounds to disrupt ATP hydrolysis, and channels carrying either the R117H mutation or the E1224 deletion, or both perturbations, the authors measure the rates of channel opening and closure to both the resting state and a short-lived flickering closed state that occurs within open bursts of ATP-bound channels. From their measurements, the authors perform mutant cycle analysis and find that the two perturbations have non-additive effects consistent with a disruption of a stabilizing interaction that occurs only in the open state but not in the deactivated state or the short-lived closed state that occurs within open bursts. By comparing the predictions from kinetic models of channel function, the authors find that the energetics of disrupting the open state-stabilizing interaction can fully explain the major effects of the R117H mutation in the background channels utilized in the study, and suggest that a similar mechanism operates in WT CFTR channels carrying the R117H mutation. The data is of high quality, the analysis is carefully done, and the conclusions are well supported by the evidence that is provided, and are of both clinical and mechanistic relevance. Importantly, the finding the interaction established by R117H occurs only in the open state provides a relevant constraint for associating structures to specific functional states of the channel.

      Although the conformational changes associated with formation or disruption of the interaction involving R117 are evident in the published structural models, it would be important to confirm whether these are supported by the experimental maps. Few details and data are provided in relation to the molecular dynamics simulations/molecular modeling that were carried out, which precludes evaluating the robustness of the calculations. The authors utilize the measurements in the D1370N background (Figure 3A) to calculate gating parameters from the kinetic models, but the burst-length in the R117H, E1124Δ, and R117H+E1124Δ appear to short in the recordings, raising concerns about the robustness of the parameters associated with the intra-burst transitions. Also regarding these intra-burst transitions, whereas the observed effects for the gating equilibrium constant are consistent with the authors' interpretation, the effects of the structural perturbations on the associated rate constants are intriguing: if the interaction occurs only in the open state, then the transition from the Cf state to the open state should not be affected by any of the perturbations, but this rate seems to also become altered, perhaps suggesting some degree of stabilization by the interaction in the Cf state or a destabilization of the transition state.

    3. Reviewer #2 (Public Review):

      Cystic Fibrosis (CF) is the most common fatal genetic disease in Caucasian populations. Disease-associated mutations of the CFTR gene often result in defects in opening/closing (or gating) of the CFTR channel. Recent breakthroughs in the development of drugs that target the CFTR protein itself pave the way for structure-based drug design, the success of which depends on our comprehensive understanding of how mutations cause functional abnormalities and how pharmaceutical reagents may act on CFTR channel folding and gating dynamics. Current studies by Simon and Csanady were meant to address the former by focusing on one mutation R117H commonly found in CF patients with less severe symptoms.

      Major strengths of the manuscript include diligent utilization of the mutant cycle analysis, high-quality single-channel recordings and detailed data interpretations in the context of gating energetics.

      This reviewer is more concerned with authors' structural interpretations of the data as there is no direct evidence for the assumed mutation-induced disruption of the hydrogen bond (e.g., E1124Δ) because it is the backbone carbonyl, not the side-chain, at position 1124 that is involved in hydrogen bond formation. Some molecular dynamic simulations were carried out to support this assumption, but the reported change of the hydrogen-bond distance by E1124Δ seems quite small. It is questionable if this change is adequate to explain quantitatively the reported 2.6 kT enthalpy change. Moreover, despite the fact that the hydrogen bond is found in the phosphorylated, ATP-bound structure of human CFTR, it is noted that this structure does not show a patent anion conduction pathway. Thus, some precautions are warranted when this structure is taken literally as the "open" channel conformation. Indeed, there are major discrepancies regarding pore-lining residues shown in this structure and those based on functional studies, suggesting that additional conformational changes in the transmembrane segments likely take place for the channel to sojourn to the true open state.

      A few minor discrepancies between the current report and previous publications, although not necessarily affect their conclusions, may need clarification.

    4. Reviewer #3 (Public Review):

      The cystic fibrosis transmembrane conductance regulator (CFTR) is an anion channel crucial for salt and water transport across epithelial cells. CFTR mutations causes its dysfunction, and the dysfunction causes cystic fibrosis.

      R117H is one of the most common mutations in cystic fibrosis. It was known that the R117H mutation affect ion channel gating and reduce conductance of the channel, but the molecular mechanism underlying is unclear. In this paper, the authors produced high-quality data through a very robust electrophysiology and thermodynamic approaches, and the data showed that a hydrogen bond between the arginine 117 side chain and the glutamate 1124 main chain carbonyl group on the extracellular side of CFTR stabilizes the open state of the ion channel. Therefore, the R117H mutation lowers the conductivity of the ion channel by breaking the hydrogen bond and induces a malfunction of CFTR.

      There are five classes of cystic fibrosis mutations. By elucidating the molecular mechanism of these mutations, we can consider their application in therapeutics. Since the R117H mutation is a representative of Class IV CFTR mutations, which induce malfunction of ion conductance through the channel, researches on it, like presented in this paper, will guide the development of therapeutics targeting Class IV mutation.

    1. Author Response:

      Reviewer #1 (Public Review):

      1) It seems like this model treats chromosome gains and losses equivalently. Is this appropriate? Chromosome loss events are much more toxic than chromosome gain events - as evidenced by the fact that haploinsufficiency is widespread, and all autosomal monosomies are embryonically-lethal while many trisomies are compatible with birth and development. Can the authors consider a model in which losses exert a more significant fitness penalty that chromosome gains?

      While we agree that monosomies are more detrimental than trisomies in non-cancerous tissue, this is not necessarily the case in tumors in which monosomy is often observed (see PMID: 32054838). Nevertheless, to address this critique we have now added a model variant with an additional condition in which cells experience extreme fitness penalties (90% reduction) if any chromosome is haploid. We apply this condition to all selection models and find this attenuates a ploidy increase over time in diploid cells in most selection models (see Figure 3 ‘haploid penalty’).

      2) Chromosomes do not missegregate at the same rate (PMID: 29898405). This point would need to be discussed, and, if feasible, incorporated into the authors' models.

      While this may be true in some contexts, the limited data on this topic (namely Worral et al. Cell Rep. 2018 and Dumont et al. EMBO J. 2020) do not agree on which chromosomes are mis-segregated more often. Worral suggested chromosomes 1-2 are particularly mis-segregated, whereas Dumont finds chromosome 3, 6, X are the highest. These differences may be explained by a context-dependent effects that depend on the model and mechanism of mis-segregation. Worral uses nocodazole washout to generate merotelics whereas Dumont gets mis-segregation through depleting CENP-A. It is unknown which if these mechanisms, if either, is representative of the mechanisms at play in human tumors so we decided to take a general approach assuming equivalent mis-segregation rates. However, we appreciate that this will be a question for other readers and we have now added this to the discussion.

      3) It would be helpful if the authors could clarify their use of live cell imaging (e.g., in Fig 6G). Certain apparent errors that are visible by live-cell imaging (like a lagging chromosome) can be resolved correctly and result in proper segregation. It is not clear whether it is appropriate to directly infer missegregation rates as is done in this paper.

      We did not perform this live cell imaging experiment. We cite these data as being kindly offered by the Kops laboratory and they correspond to the scDNAseq data for normal colon and CRC organoids from Bolhaqueiro et al. Nat Gen. 2019. We agree that chromosome mis-segregation rates cannot be directly inferred by imaging. As you say, lagging chromosomes may resolve and segregate to the correct daughter cell. The fundamental assumption is that, although not all lagging chromosomes mis-segregate, that specimens with higher rate of lagging chromosomes have higher rates of mis-segreation. Because there is no gold-standard measure of CIN in the literature to date, we feel it is necessary to show the correlation between the two and how the data from that study relates to the inferred rates in this study. We have made this clearer in the text.

      4) The authors would need to discuss in greater detail earlier mathematical models of CIN, including PMID: 26212324, 30204765, and 12446840 and explain how their approach improves on this prior work.

      We now provide a more detailed discussion on prior mathematical models, incorporating these and others.

      Reviewer #2 (Public Review):

      Weakness of the framework include: (1) Most notably, the presented framework is lacking expanded characterization and validation of selection models that are biologically relevant.

      We have taken this critique to heart. To address this, we have greatly expanded the models and their characterization. We now explicitly include a neutral model throughout, tested various modifications of the model (Figure 3C-E), and use ABC to enable model selection (see Table 3).

      The current framework simply applies a scalar exponent to already published fitness models for selection. It is unclear what this exponent mirrors biologically, beyond amplifying the selection pressures already explored in existing gene abundance and driver density models.

      We implemented cellular fitness as the sum of normalized chromosome scores such that the fitness of euploid cells is 1 and the probability of division = 0.5. In this framework, within the ‘abundance’ model, a cell with triploidy of chromosome arm 1p would have a fitness of 0.98. With no additional selection, the probability that this cell divides is 0.98 x 0.5 = 0.49. The published fitness models for karyotype selection do not experimentally determine how fitness relates to the probability of division within a given time. For example, there is no clear reason why (or evidence indicating) an extra copy of chromosome arm 1p would reduce the probability of division from 0.5 to precisely 0.49 for a given period. The proposed model of karyotype selection that our ‘abundance’ model is based on only stipulates that aneuploidy of larger chromosomes is more detrimental than small chromosomes. Thus, these fitness values behave as arbitrary units and, therefore, we believe that adjusting and fitting an arbitrary scaling factor to the biological data is appropriate. For example, with an additional selection of S=10, the same cell with trisomy of chromosome arm 1p would divide with a probability of F^S x 0.5 = 0.98^10 x 0.5 = 0.41.

      We could have implemented a multiplicative framework where fitness (F_mult) is defined as the total deviation from euploid fitness (1) multiplied by a scaling factor S (F_mult = S(1 - F)). For the trisomy 1p example, the same fitness value (F^S=0.9810) can be achieved multiplicatively as exponentially via 1 – (9.14 x (1 - 0.98)) ~ 0.98^10. Thus, the same fitness values can be achieved through arbitrary scaling. We regret that this may have been misinterpreted because it was implemented exponentially vs multiplicatively.

      To further address this critique, we have now better fitted the S values with a flat prior probability across all values, shown how it relates to P_misseg in posterior probabilities (e.gs, Figure 6C, Table 3) and performed the separate analysis requested in critique #5 below.

      (2) Towards this, how is the CIN ON-OFF model in which CIN is turned off after so many cell divisions relevant biologically? Typically CIN is a considered a trait that evolves later in cancer progression, that once tolerated, is ongoing and facilitates development of metastasis and drug resistance. A more relevant model to explore would be that of the effect of a whole genome duplication (WGD) event on population evolution, which is thought to facilitate tolerance of ensuing missegregation events (because reduce risk of nullisomy).

      We agree that the CIN ON-OFF model had limited biologic relevance and removed this. To improve on this, we have changed our approach to use constant CIN for a much longer period of time (3000 time steps). We agree that WGD is a relevant phenomenon. However, others have already explicitly modeled this (see PMID: 26212324 and 32139907), so we avoid doing the same. Instead, we show that tetraploid founding cells tolerate high mis-segregation rates better than diploid founding cells.

      (3) The authors utilize two models of karyotype fitness - a gene abundance model and driver density model - to evaluate impact of specific karyotypes on cellular fitness. They also include a hybrid model whose fitness effects are simply the average of these two models, which adds little value as only a weighted average.

      To date we do not have an experimentally-defined human selection model. The gene abundance model is limited in that it considers all genes equally which inadequately considers disease function and essentiality. By contrast the driver density model weights tumor suppressors and oncogenes which may not operate in all context and ignores the essential functions of most other protein-coding genes. We believe the hybrid model can compensate for these mutual defects, but acknowledge the importance an experimentally derived models to adjudicate which is best.

      In silico results shows inferred missegregation rates are extremely disparate across the two primary models. And while a description of these differences is provided, the presented analyses do not make clear the most important question - which of these models is more clinically relevant? Toward this, in Figure 2F, the authors claim the three models approach a triploid state - which is unsupported by the in silico results. Clearly the driver model approaches a triploid state, as previously reported. But the abundance model does not and hybrid only slightly so, given that it is simply a weighted average of these two approaches. Because the authors have developed a Bayesian strategy for inferring which model parameters best fit observed data, it would be very useful to see which model best recapitulates karyotypes observed in cancer cell lines or patient materials.

      We agree that the abundance and hybrid models are unable to approach a triploid state, in earnest, as does the driver and have made that clearer in the text and improved the figure panel in question for clarity. To address your latter point on which model best fits observed data, we have implemented a model selection scheme to do this (see Table 3). This indicates the gene abundance model as the most biologically relevant and provides evidence for stabilizing selection as the primary mode of selection occurring in the organoid and biopsy data we analyzed.

      (4) Topological features of phylogenetic trees, while discriminatory, are largely dependent on accurate phylogenetic tree reconstruction. The latter requires more careful consideration of cell linkages beyond computing pairwise Euclidean distances and performing complete-linkage clustering. For example, a WGD event, would appear very far from its nearest cell ancestor in Euclidean space.

      While more granular cell linkage data would certainly improve phylogenetic reconstruction, low-coverage scDNA- sequencing (0.01-0.05x) is unable to reliably recover SNPs that would enable this approach. Clustering on copy number similarity remains the standard approach at this point (see PMID: 33762732). We have added this to the discussion.

      (5) Finally, experimental validation of the added selection exponential factor is imperative. Works have already shown models of karyotypic evolution without additional selection exponential coefficient can accurately recover rates of missegregation observed in human cell lines and cancers by fluorescent microscopy. Incorporation of this additional weight on selection pressure has not been demonstrated or validated experimentally. This would require experimental sampling of karyotypes longitudinally and is a critical piece of this manuscript's novelty.

      As described in #1 above, the selection values of F are in arbitrary units and so we believe a selection scaling factor is important to include in the model. For example, without additional selection, a hypothetical aneuploid cell with a trisomy resulting in F = 0.95 would be 5% less likely to divide than a euploid cell with F = 1. The exponent scales the selection such that when S = 2, the fitness of the trisomic cell is F ~ 0.9, or 10% less likely to divide. This scaling is necessary to enable both positive and negative selection in a system fitness is decided as the sum of chromosome scores. To further validate the additional weight on selection pressure we did the following:

      1. We constrained the prior distribution of simulated data for our model selection to S=1 giving only the base fitness values without additional scaling. We, again, performed model selection on the data from Bolhaqueiro et al., 2019 and Navin et al., 2011 and found that, with this constrained prior dataset, we inferred mis- segregation rates (see Table 4) that were far below rates seen in cancer cell lines (see Figure 6E).

      2. Given the initial clarification that reviewers were looking for longitudinal analysis, we leveraged data provided by the authors of Bolhaqueiro et al., 2019 where they sequenced single cells from 3 clones from organoid line 16T at 3 weeks and 21 weeks after seeding. We inferred mis-segregation rates and selective pressures in these clones at the 3-week timepoint. We did so under the Abundance model using the same prior distribution of steps given that the diversity of populations under the Abundance model rapidly reach a steady state. When we simulated additional populations using these inferred characteristics we found that the karyotype composition of the simulated populations most closely resembled the biological population than did populations simulated with the unmodified selection values (see Figure 6 — figure supplement 4). This lends credence to the biological relevance of scaled selective pressure vs. unmodified selective pressure.

      Reviewer #3 (Public Review):

      1) Given the importance of the selection paradigm in determining the observed karyotypic heterogeneity, a significant weakness of the work is that there is no attempt to learn the selection paradigm from the observed data. This is important because there is an interrelationship between selection, the chromosomal alteration rate, and the observed data and so the accuracy of the inferred alteration rate is likely to be compromised if an inappropriate model of selection is used.

      We have implemented a model selection strategy to address this critique. Accordingly, we infer mis-segregation rate under each model and take the result of the best-fit model to be the inferred rate. In this case, stabilizing selection under

      2) Somewhat relatedly, how the population of cells grows (e.g. exponential growth vs constant population size) also effects the observed karyotype heterogeneity, but the modelling only allows for exponential growth which may be an inappropriate of the public datasets analysed.

      We have now concurrently modeled chromosomal instability with a constant population size by approximating constant- population Wright Fisher dynamics (see Materials and Methods). We find these models produce similar results at the karyotype level, addressing concerns about the effects of growth patterns on karyotype evolution in this model.

      3) There are some technical concerns about the approximate Bayesian computation analysis (choice of prior distributions, testing for convergence, matching of the growth model to cell growth patterns in the data, and temporal effects) which need to be addressed to ensure this part of the analysis is robust.

      To address these concerns, we improved and more clearly detailed the prior distributions for each inference within the figure legends, we tested for karyotype convergence in each model (see Figure 3), and we demonstrate that inference under the Abundance model is robust to changes in the number of time steps included in the prior data (see Figure 6 — figure supplement 1).

    1. Author Response:

      Reviewer #3 (Public Review):

      1) The two algorithms presented are essentially a low-pass and high-pass filter on binarized odor. As such, it may not be so surprising that there is a tradeoff between which algorithm works better depending on the frequency content of different environments. The low-pass filter (algorithm 1) works better in environments with mostly low-frequency fluctuations (boundary layer plume, low wind-speed, high diffusivity) while the high-pass filter (algorithm 2) works better in environments with mostly high-frequency fluctuations (high windspeed, low diffusivity). To understand what is essential in these algorithms I think it would be useful to (1) compare the two algorithms to a "null" algorithm that drives upwind orientation whenever odor is present (i.e. include thresholding and binarization but no filtering), (2) compare navigation success metrics directly to the frequency content of different environments, (3) examine how navigation success depends on the filtering cutoff of the two algorithms (tau_on and tau_w). Comparing to the null algorithm with no filtering I think is important to determine whether there is actually a tradeoff to be made, or whether a system that can approximate a flat transfer function (or at least capture all relevant frequencies in the environment) is ideal and must be approximated with biological parts.

      For (1) and (3), we have now added simulations of the models for a range of different timescales, including an integrator with an infinitely fast timescale corresponding to the “null” model the reviewer describes (Results lines 376-380, Figure 4—figure supplement 2 and Materials and methods lines 1008-1025). We find that changing the timescale of the intermittency filter largely leaves performance unchanged whereas changing the timescale of the frequency filter is akin to changing the gain on the frequency filter, as predicted by Equations 24 and 29. Since we do find a local maximum in the frequency filter timescale, we conclude that there are benefits to filtering in time. For (2), many plumes we simulate in Fig. 5 span a wide range of frequencies and intermittencies; we chose to plot performance as a function of diffusivity / windspeed to emphasize how performance depends on environment parameters that shape the statistics of the plume (flow and odor dynamics). Note that we renamed 𝜏! to 𝜏".

      2) While the two algorithms presented here present a nice conceptual division, biological filtering algorithms are likely to incorporate elements of both. For example, the adaptive compression algorithm of Alvarez-Salvado (which is eliminated in the simplification used here) provides some sensitivity to odor onsets and is based on well-described adaptation at the olfactory periphery. Synaptic depression algorithms likewise provide sensitivity to derivatives as well as integration over time, and synaptic depression with multiple timescales has been described in detail at various stages of the olfactory system. A productive extension of the work done here would be to explore the utility of biophysically-motivated filtering algorithms for navigation in different environments.

      Thank you for this suggestion, which led us to extend our work in that interesting direction. We have now generalized our model to respond to odor intensity (rather than its binarized version) by implementing an adaptive compression taken from prior modeling efforts (Alvarez-Salvado et al, eLife 2018) (added to Fig. 3; also see additional Fig. 3 Supplement 1). Moreover, we now also consider navigators that respond to odor signals using a biophysical model of odor transduction, ORN firing, and PN firing, in addition to synaptic depression within the ORN-PN synapse, which combines modeling efforts from prior works (Gorur-Shandilya, Demir, et al, eLife 2017; Nagel & Wilson, Nat. Neurosci. 2015; Fox & Nagel, “Synaptic control of temporal processing in the Drosophila olfactory system” arXiv 2021). This realistic circuit model produced exciting results that indicate that the natural ORN-PN circuitry can, to some degree, satisfy the dual demands of intermittency and frequency sensing. These results are shown in the new Fig. 6.

      3) It would be helpful in the Discussion to present a clearer picture of what the frequency content of natural environments is likely to be. For example, flies stop walking at windspeeds above ~70cm/s (Yorozu 2009). In contrast, flies in flight are likely to encounter much sparser and high frequency plume encounters, as they are moving through the air at much faster speeds and because odors encountered here would be away from the boundary layer. Therefore the best test of the tradeoff hypothesis would likely be to compare temporal filtering of odor plumes by neural circuitry in flying vs walking flies. This would connect to the literature in motion detection as well, where octopamine release during flight causes a speeding of the motion detection algorithm.

      We have added lines 47-48 to the introduction describing the natural frequency content of plumes and lines 574-578 discussing how one might see evidence of this tradeoff when comparing between walking and flying flies.

    2. Reviewer #3 (Public Review):

      1) The two algorithms presented are essentially a low-pass and high-pass filter on binarized odor. As such, it may not be so surprising that there is a tradeoff between which algorithm works better depending on the frequency content of different environments. The low-pass filter (algorithm 1) works better in environments with mostly low-frequency fluctuations (boundary layer plume, low wind-speed, high diffusivity) while the high-pass filter (algorithm 2) works better in environments with mostly high-frequency fluctuations (high windspeed, low diffusivity). To understand what is essential in these algorithms I think it would be useful to (1) compare the two algorithms to a "null" algorithm that drives upwind orientation whenever odor is present (i.e. include thresholding and binarization but no filtering), (2) compare navigation success metrics directly to the frequency content of different environments, (3) examine how navigation success depends on the filtering cutoff of the two algorithms (tau_on and tau_w). Comparing to the null algorithm with no filtering I think is important to determine whether there is actually a tradeoff to be made, or whether a system that can approximate a flat transfer function (or at least capture all relevant frequencies in the environment) is ideal and must be approximated with biological parts.

      2) While the two algorithms presented here present a nice conceptual division, biological filtering algorithms are likely to incorporate elements of both. For example, the adaptive compression algorithm of Alvarez-Salvado (which is eliminated in the simplification used here) provides some sensitivity to odor onsets and is based on well-described adaptation at the olfactory periphery. Synaptic depression algorithms likewise provide sensitivity to derivatives as well as integration over time, and synaptic depression with multiple timescales has been described in detail at various stages of the olfactory system. A productive extension of the work done here would be to explore the utility of biophysically-motivated filtering algorithms for navigation in different environments.

      3) It would be helpful in the Discussion to present a clearer picture of what the frequency content of natural environments is likely to be. For example, flies stop walking at windspeeds above ~70cm/s (Yorozu 2009). In contrast, flies in flight are likely to encounter much sparser and high frequency plume encounters, as they are moving through the air at much faster speeds and because odors encountered here would be away from the boundary layer. Therefore the best test of the tradeoff hypothesis would likely be to compare temporal filtering of odor plumes by neural circuitry in flying vs walking flies. This would connect to the literature in motion detection as well, where octopamine release during flight causes a speeding of the motion detection algorithm.

    1. Evaluation Summary:

      Smith et al. describes the propagation patterns of electrical activity in the brains of human epileptic patients. The authors demonstrate that interictal spikes, commonly observed electrical events in epileptic patients, propagate in a similar manner to seizures. This suggests that interictal spikes could be used in surgical planning, which would be of great interest to neurosurgeons and neurologists treating patients with medication refractory epilepsy.

      (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #1 agreed to share their name with the authors.)

    2. Reviewer #1 (Public Review):

      The authors used microelectrode recordings in patients with drug-resistant epilepsy, automatically detected interictal and ictal epleptiform discharges, and measured the directions of travel of both. They found that most interictal discharges are traveling waves with two opposite-facing predominant directions of travel. Furthermore, they found that the direction of travel for interictal traveling waves was similar to that for ictal discharges. They conclude that studying interictal discharge propagation can reveal information about seizure propagation. This is an elegant approach to answering the important question of whether the spatiotemporal propagation of IEDs can elucidate seizure propagation.

      The strengths of this paper are that it addresses an important question and uses elegant quantitative techniques to try to answer it in human subjects. It is relevant to epilepsy clinical care as well as our understanding of how information spreads in the brain.

      The authors' aims are largely met, but there are some questions about the methods and results that would be important to address to be sure that their conclusions are supported. These are:<br> 1. To be sure to demonstrate validation of their discharge detection methods. It would be important to report on positive predictive values for a random subset of detections, particularly on a testing set of data not used for training?

      2. The authors write in the abstract and discussion that interictal discharges (IEDs) traverse the same path as ictal discharges (SDs), but the angle between the SDs and IEDs was 24 degrees, and the IEDs weren't exactly opposite the ictal wavefront (150 degrees). This raises questions as to whether these two classes of abnormal activity really follow the same path.<br> It would be important to account for the sizable angle between their observed paths. In some ways this seems to refute more than support the main conclusions.

      3. The influence of sampling error and method of recording are important to discuss, and how they might alter the brain and its conduction of abnormal activity. It would be important to be clear about what effects if any these factors have on the conclusions and information recorded.

      4. It would be important to explain how seizures were defined and the rationale for this definition. This is an elusive topic and one in great debate, so this would be helpful to understand your thinking and also to assess the paper's relevance to clinical epileptic events.

      Overall this is a very interesting study on an important topic, and one that is relevant to both basic science research and the clinical evaluation and care of patients with epilepsy.

    3. Reviewer #2 (Public Review):

      In this manuscript, Smith et al analyze a dataset comprising multi-day microelectrode recordings acquired for the purpose of surgical planning in human epilepsy patients. The authors evaluate the propagation of 3 activity modes: 1) interictal epileptiform discharges (IEDS); 2) the ictal wavefront (IW), which is a slowly expanding wave of tonic neural firing; and 3) Seizure discharges (SD), which are rapidly travelling waves of activity that follow the IW. Specifically, the key findings are: 1) IED propagation direction is non-uniform and most commonly bimodal, with the two modes being antipodal. 2) SDs and IEDs propagate in approximately the same direction, which is approximately antipodal from the IW 3) there is a strong relationship between the predominant IED direction and recruited SD direction, also between the auxillary IED direction and pre-recruited SD direction. These findings support the potential utility of interictal spikes in surgical planning for refractory epilepsy. The ability to use IEDs would be particularly beneficial as they are considerably more frequent than seizures and typically occur without withdrawal of patients from their medication. This work is interesting and potentially important, however some additional analysis is necessary to support the potential translational utility of this approach as well as revision to improve readability.

    4. Reviewer #3 (Public Review):

      Interictal epileptiform discharges, known to occur between seizures in patients with epilepsy are not thought to provoke an ictal event. Pre-ictal epileptiform or seizure discharges on the other hand, occur prior to a seizure. However, it is unclear if these two pathological events are connected spatiotemporally or at single- or multi-unit level. This question is fundamental to our understanding of epileptogenesis, because there exists a subgroup of patients who have interictal epileptiform discharges on their scalp electroencephalogram (EEG) and seldom have a seizure. At the same time, there are patients who have completely normal EEGs but have frequent unprovoked epileptic seizures.

      Smith et al., using complex computational methods were able to model epileptiform discharges in the seizure core versus away from the core. They showed that interictal discharges are predominantly bidirectional, whether in the seizure core or away from it. The technique to model MUA is difficult because the units can be heterogenous in the seizure core. The process to sort through MUAs and IED waveforms from multiple days of data, is labor intensive and requires intense dedication. Their strengths are using a technique that they have showed to be reproducible from multiple groups. In this paper, the authors employ their traveling waveform modeling from their prior well published work to interictal discharges that were collected over multiple days with or without seizures. The strengths of being able to study traveling ictal wavefront and compare seizure discharges to interictal discharges can only be achieved on a Utah array. This is also their weakness. The Utah array is implanted on the neocortex and close to the seizure zone, it is hard not to imagine the bias that exists when they model the traveling wavefront of the IEDs to that of their seizure discharges. It is unclear if this bidirectional IED hypothesis applies to mesial temporal lobe seizures where the ictal onset is far from the neocortex.

    1. Evaluation Summary:

      This paper will be of broad interest to many fields, including drug discovery, cancer biology, and structure biology. It makes a significant advance in understanding the mechanism of action of hormone therapies for breast cancer, and how resistance driving mutations alter drug responses. The structural biology data has clear potential for strong impact though some additional analysis might be needed.

      (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #2 agreed to share their name with the authors.)

    2. Reviewer #1 (Public Review):

      This is an impressive structural and molecular mechanistic study in the modes of action of a series of SERMs / SERDs. Such large scale comparative studies on ligands binding to ER (or proteins in general) and of great value to the field as these studies greatly surpass studies on isolated singular examples in terms of impact and relevance. It is in the comparison of a large set of compounds that trends and design principles can be extracted.

      Also of high relevance is the study of these SERMs/SERDs in the context of the resistance acquiring ER537/538 mutations. Molecular understanding of the underlying mechanisms is lacking here and the authors make a valuable contribution.<br> The data on the pyrrolidine methyl-substituted Lasofoxifenes are fascinating.

    3. Reviewer #2 (Public Review):

      Fanning and Greene present an important addition to the idea that one of the two major classes of ER antagonists, selective estrogen receptor degraders (SERDs), do not require degradation for efficacy. They used a chemical biology approach to develop derivatives of lasofoxifene analogs that have a single methyl group added and identified isomers that either stabilize or destabilize the receptor. They profiled these compounds and a panel of clinical and preclinical antagonists for effects on receptor levels, post-translational modifications, coactivator recruitment, transcriptional activity and proliferation. They generated two major conclusions: degradation does not correlate with efficacy; and that the patterns of pharmacology across the panel of ligands are markedly different in the WT ER or in two constitutive mutants that drive metastatic disease and treatment resistance. They presented a series of crystal structures with 8 ligands bound to the WT or Y537S ER. A more careful interpretation of these structures might be needed to make conclusions regarding the structural basis of efficacy, but the structures are of high interest in revealing how the Y537S mutant changes how the ligands interact with the receptor to drive differences in pharmacology from the WT receptor.

    1. Evaluation Summary:

      This manuscript focusses on a little studied, but highly interesting presumptive mechanosensory cell type in cnidarians, the 'hair cell'. The work shows that the POU-IV transcription factor is required for the maturation of this cell type in the sea anemone Nematostella vectensis. Because POU-IV transcription factors also play essential roles in the differentiation of mechanoreceptors in many bilaterian phyla, this suggests an evolutionarily ancient role of POU-IV in regulating mechanosensory identity. This study will hence be of great interest to developmental biologists and evolutionary biologists who are interested in the developmental evolution of neuronal cell types.

      (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #1, Reviewer #2 and Reviewer #3 agreed to share their name with the authors.)

    2. Reviewer #1 (Public review)

      The paper demonstrates the role of Pou domains for various sensory cells. Using CRISPR to delete the gene, the authors show an incomplete deletion of sensory cells.  Further evidence shows problems with the formation of mechanosensory cells.<br> Overall, the presentation is clear but can be expanded by adding the role of bHLH genes (Atoh1 is upstream of Pou4f3).  If possible, I suggest expanding the role of TMC as it is the main receptor in mammalian hair cells that connects to the stereocilia.  Please note that the cnidarian organization is a central kinocilium surrounded by microvilli, comparable to choanoflagellates. This paper is a great original presentation but it could provide a broader perspective by expanding on the evolution of Pou IV and by adding a discussion of the evolution of bHLH, Myc and TMC in order to provide this broader perspective.

    3. Reviewer #2 (Public Review):

      Whereas the role of POU-IV for the differentiation of cnidocytes and other neurons of Nematostella has been previously characterized (Tourniere et al., 2020), the present study extends previous reports by specifically addressing the role of POU-IV for the so-called "hair cells" of Nematostella (not to be confused with the hair cells of the vertebrate inner ear and lateral line). These presumably mechanosensory hair cells are identified here as postmitotic neurons, which are ciliated and carry a collar of stereovilli - actin-filled microvilli with a long actin-rich rootlet. Using CRISPR/Cas9 based gene editing, the study shows that transgenic animals, in which the POU-IV gene has been disrupted, become touch insensitive. While hair cells can still be identified in these POU-IV mutants, they lack the stereovillar rootlets suggesting that POU-IV is required for proper hair cells maturation, but dispensable for early steps of hair cell specification and differentiation. The study then uses ChIP-Seq to identify direct target genes of POU-IV in Nematostella and to characterize a POU-IV binding motif, which turned to be evolutionarly highly conserved with POU-IV binding motifs in bilaterians. Comparison of the ChIP-Seq data with published bulk and single-cell transcriptome data indicated that POU-IV activates substantially different sets of effector genes (but no regulatory genes) in hair cells and cnidocytes, and identified polycystin1 as a hair cell-specific direct target of POU-IV. Taken together, this suggests that POU-IV had an evolutionary ancient role as a terminal selector gene for mechanosensory neurons, which predated the split between cnidarian and bilaterian lineages but that its function diverged (e.g. by the acquisition of new target genes) during the evolution of cnidocytes as a novel cell type in cnidarians.

      Combining gene editing with sequencing and with careful morphological and behavioral characterisation of cellular phenotypes, the study provides valuable new insights into the evolution of sensory neurons. POU-IV class transcription factors have previously been implicated in the specification of mechano- and chemosensory neurons in bilaterians. The present study together with the previous study of Tourniere et al. (2020) now suggests an even deeper evolutionary origin of this cell type in the last common ancestor of eumetazoans. The paper is very well written and the results are beautifully documented. The authors are overall cautious and conservative in the conclusions drawn from their findings. However, two points deserve a more critical discussion, first, the question of which sensory modality is mediated by the hair cells (are these dedicated mechanoreceptors or possibly multimodal cells?), and second, the question whether POU-IV serves as transcriptional activator or repressor in cnidocytes.

    4. Reviewer #3 (Public Review):

      In this manuscript, Ozmet et al. investigated the developmental genetics of mechanoreceptor cells (hair cells) in the cnidarian model N. vectensis. They used CRISPR-Cas9-mediated mutagenesis to showed that POU-IV homeodomain transcription factor regulates the differentiation of hair cells in this organism. The authors applied behavior assay, EM observations, and various types of fluorescence labeling to show that pou-iv -/- polyps exhibit defects in touch-sensitive behavior, likely due to the failure of forming the complete stereocilliary rootlet structure near the apical side of the hair cells in those mutant polyps. The authors went on to apply ChIP-seq in N. vectensis and showed that the POU-IV-binding motifs are conserved across Cnidaria and Bilateria. They also used this ChIP-seq dataset to screen for possible POU-IV downstream targets and identified one of the candidate genes, PKD1, as a conserved effector gene that has been shown playing important functions in hair cells across different bilaterian animals. Furthermore, by cross-checking their results with the newly published single-cell transcriptome data from N. vectensis adults, the authors identified the putative cell cluster (c79) of mechanosensory hair cells and confirmed that pou-iv and PKD1 are indeed co-expressed in this cell type. This approach also enabled the identification of additional candidate POU-IV downstream targets, and based on the GO term analysis, it appears that many of these genes are involved in ion transport and sensory perception functions. In summary, the authors provide strong evidence to support that POU-IV likely functions as a terminal selector factor of hair cell development in the sea anemone N. vectensis. Comparing their findings with other animals, the authors suggested that POU-IV factor plays a conserved role in regulating mechanoreceptor differentiation across Cnidarians and Bilaterians and that this regulatory mechanism may represent an ancestral trait dated back to their common ancestor.

      This is a detailed study on the role of POU-IV factor during cnidarian mechanoreceptor cell development. In general, the manuscript is well written, most of the data presented are of great quality, and the conclusions of the paper are supported by the data. This study is a significant advancement to our understanding on the evolutionary origin of sensory neurons and the possible genetic mechanisms underlying the diversification of neuronal cell types in animals.

      Strengths:<br> The authors applied multiple approaches to examine the developmental process of hair cells in N. vectensis and analyze the molecular genetic functions of POU-IV factor during this process. The generation of gene-specific KO animals with CRISPR-Cas9 mediated mutagenesis in N. vectensis and the characterization of the sensory ability of those mutant animals with behavior assays provide compelling data to show that POU-IV factor is involved in the final maturation of mechanoreceptor hair cells. The ChIP-seq data generated by this study further enabled the authors to analyze the POU-IV factor binding sequences across animals, and the data also help to identify candidate downstream targets of POU-IV factor in N. vectensis system. Because POU-IV factor is likely involved in the development of multiple cell types in N. vectensis (as shown by previous publications and this study), this dataset would be highly valuable in the future for analyzing the differentiation process of different neuronal cell types in N. vectensis. In fact, by comparing with the recently available scRNA-seq resources, the authors have demonstrated the usefulness of this dataset and pointed out several interesting future research directions. Because N. vectensis is one of the few experimentally tractable systems within Cnidarian, which represents the sister group of bilatarian animals, experimental data from N. vectensis would give important mechanistic insights to infer the possible developmental characters in the common ancestor of Cnidarians and Bilaterians.

      Weakness:<br> 1. The specificity of the POU-IV antibody staining. It appears that the signals of the POU-IV immune-staining are distributed quite extensively, especially near the basal part of the epithelia ectoderm (Figure 2A-L). And in their Western blot, the authors also noticed an extra band that might represent another protein in the N. vectensis sample that cross-reacted with their anti-POU-IV antibody. Although the authors provided controlled experiment showing that the immunostaining signals disappeared after they pre-absorbed the antibody with the POU-IV antigen (Figure 2 - supplement 2), this result can only demonstrate that indeed their antibody reacts specifically with this antigen. This cannot rule out the possibility that other N. vectensis protein(s) may possess peptide motifs similar to this antigen region and can be recognized by their antibody. Therefore, it would be nice if the authors can do double staining using in situ hybridization with pou-iv anti-sense riboprobe and immunostaining with their POU-IV antibody, to examine whether these two different methods would give overlapping results, so that they can be more confident about the specificity of their POU-IV antibody staining.<br> 2. The electron microscopy data (Figure 5J-L) are not as clear as one would expect showing the differences in rootlet structure between the wildtype and mutant polyps, given that the phalloidin staining results (Figure 5B, E, H) show quite noticeable reduction in the mutant polyp tip. It is very hard to see the stereovillar rootlets (rlst) in Figure 5J, and thus it is very difficult to assess whether these structures are indeed affected in Figure 5K and 5L. In addition, the rootlet structure of the apical cilium in Figure 5K and L (presumably underneath "ci") appears to be less prominent compared to that shown in Figure 1I (labeled as "rlci"). I am not sure whether this is due to differences of the section angle, or whether it really reflects some differences between the wildtype and mutant.

    1. Evaluation Summary:

      Kröger et al use 2-photon FLIM tomography to perform correlative imaging on in vitro, ex vivo and in vivo blood and skin cells to determine characteristic NADPH fluorescence lifetimes for M1 and M2 ends of macrophage spectrum. Interestingly, M1 and M2 macrophages, and all other tissue cells, had distinctive lifetime features leading to robust prediction of phenotypes, with ground trust defined by cytokine staining. They generate a decision tree that has ~90% accuracy in identifying M1 and M2 based on FLIM parameters and additional information. The ability to use two photon fluorescence lifetime tomography of NADPH fluorescence to identify macrophages and their inflammatory status in human tissues should open opportunities in experimental medicine and eventually medical diagnosis.

      (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #1 agreed to share their name with the authors.)

    2. Public Review:

      In "Label-free imaging of macrophage phenotypes and phagocytic activity in the human dermis in vivo using two-photon excited FLIM", Kroger et al attempt to visualize macrophages and distinguish their phenotypes using FLIM within the skin of live animals. This study provides data characterizing the fluorescence lifetime signatures of macrophages derived from peripheral blood mononuclear cells or dermal macrophages stimulated with IFNg or IL4, to polarize them towards more M1-like and M2-like phenotypes, respectively. The FLIM signatures are compared to macrophages in other conditions or other cell types, including macrophages ex vivo in skin cryosections, macrophages in forearm of healthy human individuals, as well as mast cells, dendritic cells, fibroblasts, and neutrophils in vitro. The authors then use immunohistochemistry to correlate the FLIM signatures with phenotype markers, CD68 and CD163. Finally, the authors visualize phagocytosis through morphological changes and identify FLIM signatures of phagocytic macrophages.

      Strengths:

      Using optical methods to non-invasively detect cells has significant interest for clinical and basic studies, and the impact of this study is considered high.

      The authors have identified different FLIM signatures for macrophages polarized towards different phenotypes in vitro, and were able to compare these signatures to those of other cell types and macrophages in the skin.

      They identified a few cells in the skin that expressed markers associated with macrophage polarization, and also exhibited the FLIM signatures that were established from the in vitro polarization studies.

      Weaknesses:

      There are some significant technical concerns given that macrophages are a highly heterogeneous population of cells, particularly in vivo during an activation event such as injury. The few cells analyzed in Figure 3 are not sufficient given the heterogeneity of macrophages in vivo. Mixed phenotypes are common in vivo, and it is unclear how the FLIM signatures would correlate to such mixed signatures.

      Visualizing a single phagocytosing cell in Figure 5 is also not sufficient to conclude that the method is capable of detecting phagocytosis events.

      Finally, the reporting of lifetime alone does not offer insights into the function of the macrophages. The study would be strengthened with further analysis that correlates FLIM signatures with metabolic state (free vs. bound NADPH).

    1. Author Response:

      Reviewer #2:

      What the authors attempt to achieve, and their approaches:

      The author attempt to establish by which mechanisms cholesterol influences the function of the GPCR A_{2A}R, an adenosine receptor. The role of cholesterol on GPCRs has been reported in a number of studies, primarily in cellular experiments, and the authors set out here to clarify the molecular mechanisms.

      To this end, they build upon their recent achievements to produce this protein and reconstitute it in nanodiscs, i.e. discoidal objects comprised of the membrane protein (here: A_{2A}R), lipids (here: POPC, POPG and cholesterol) and a membrane-scaffold protein (MSP) which wraps around this disc of protein+lipid. Nanodiscs allow studying proteins in solution, and are thought to be much more native-like than e.g. detergent micelles.

      The authors first use GTP hydrolysis experiments to quantify the basal activity and agonist potency at cholesterol concentrations from 0 to 13%. The cholesterol effects are weak but detectable. Then they use a single 19F label that reports on the protein's conformation (active, inactive) to show that the protein populates slightly more active states with cholesterol. (again, weak effects). Then they investigate G-protein binding to A_{2A}R in the nanodisc, and find (very!) weak enhancement at 13% cholesterol. These data point to weak positive allosteric modulation by cholesterol. They then use molecular dynamics simulations to probe the allosteric communication, using a recently proposed framework (Rigidity-transmission allostery). Doing these simulations in the presence of cholesterol (postions of cholesterol from X-ray structure) and absence. This analysis shows again only very weak effects of cholesterol, and this time the effect is opposite, i.e. negative allosteric modulation by cholesterol. Then they use 19F-labeled cholesterol analogues to probe by NMR the state of cholesterol (bound to protein?). Lastly, they use Laurdan fluorescence experiments and pressure NMR to establish that (i) the lipids become more ordered when cholesterol is present, and (ii) if one achieves such ordering even without cholesterol - namely by pressure - one may achieve similar effects as those that cholesterol has.

      Collectively, these data lead them to conclude that cholesterol has a (weak) positive allosteric effect on this receptor, and this effect is not a direct one, but goes via modulation of the membrane properties.

      We thank the reviewer for his comments and critique. A lot of his comments have to do with the nanodisc as a model system. We have therefore included an additional paragraph as discussed above, highlighting the advantages and disadvantages of the nanodisc. We’ve also included references to papers that have characterized nanodiscs or membrane proteins in nanodiscs. In our hands, 31P NMR spectra of POPC/POPG nanodiscs and their melt behavior is very similar to liposomes. We’ve tried to add to the discussion on nanodiscs without distracting too much from the focus in the paper.

      Major strengths and weaknesses of methods and results:

      The study addresses an important question, which inherently is difficult to answer: the effect of cholesterol is poorly understood and such studies require to work in an actual membrane. The authors do a careful combination of different methods to achieve their goal of identifying the mechanisms.

      Despite combining several methods, several of them have their inherent problems:

      (i) the nanodisc is too small to properly mimic the membrane environment, and it does not allow reaching relevant cholesterol concentrations. Moreover, it is not clear (to me) if one can exclude e.g. interactions of the protein with the surrounding MSP, or of cholesterol with MSP (see (iii) below).

      We agree. In principle, we should worry about MSP. On the other hand, this is a constant in all of the samples and we focus instead on the cholesterol-dependent effects. These nanodiscs are unarguably small. We’ve commented on this in the paper now. However, we’d expect that the confinement would if anything emphasize the cholesterol bound state. Yet, the NMR studies of F-cholesterol interactions at best identified transient bound states.

      (ii) the state of the protein (inactive, active) is probed with a single NMR-active site. The effects are small and I am not convinced that one shall interpret changes as small as the ones in Figures 3 and 4. In particular, how does this single probe behave at high pressure? Does it reflect an active state at 2000 bar pressure - where possibly other effects (unfolding?) may occur?

      Here we can be quite confident. The spectra are predicated on a recent paper (Huang, et al, 2021) published in Cell in the spring of this year. Each state was carefully correlated with specific functional assays and conditions in a self-consistent way. The labeling site used on TM6 was strategically chosen based on earlier crystallographic studies of inactive and active A2AR. We have other labeling sites (TM7 and TM5) but the point was to use the chemical shift signatures to talk about cholesterol-induced changes to the conformational ensemble assigned in the Cell paper. The differences are small, but the fact that PAM effects are observed across conditions (apo, inverse agonist-bound, agonist-bound, and G protein-bound) reassures us that the spectral differences between low and high cholesterol samples are real. Unfolding by 19F NMR is in this case easy to see – the effects become irreversible and independent of ligand and the chemical shift ends up as one upfield peak. We also see a stabilization of the A1 (active) state, and a slight downfield shift of the active ensemble with increased pressure, consistent with reduced exchange dynamics (and coalescence) associated with the active state. We’ve commented on this in the revised version while trying not to distract from the flow of the paper.

      (iii) the data in Figure 6 (19F of cholesterol analogs) are hard to interpret. Is cholesterol bound to the protein? Does the 19F shift reflect binding to the protein? or interactions within the confined space of the disc? or with MSP? The two analogs do not tell a coherent story.

      It is confusing. We agree. We were fully expecting to see a clear A2AR bound state of cholesterol either through a concentration-dependent shift or a new peak. We also looked for “hidden” bound states through 19F NMR CEST experiments. We never identified a bound state in the presence of a range of cholesterol concentrations, as a function of receptor drug. We did observe small shifts although often these effects were as prominent with inverse agonist as agonist, possibly pointing to the existence of multiple weak binding sites. We’ve added some of this to the conversation. It’s also certainly possible that cholesterol exhibits some interaction with MSP, although again MSP is a constant presence in all the samples while we are focusing on cholesterol-dependent effects. In any case, we never detected a bound signature characteristic of slow exchange. That’s significant to the study despite the ambiguity of the measurements.

      (iv) the pressure NMR study (Fig 7D) has weaknesses. The authors implicitly assume that pressure acts on the membrane, leading to more ordering. (They do recognize the possibility that pressure may have an effect on the protein directly, but consider that this direct effect on the protein is minor.) I think that their arguments are possibly incorrect: they apply here pressure onto a sample of nanodiscs, but all studies they cite to justify the use of pressure on membranes dealt with extended lipid bilayers (liposomes). To me it is not clear what is the lateral effect of pressure onto a nanodisc. Can water laterally enter into the bilayer and thus modify the lipid structure? I also note that previous pressure-NMR studies on a GPCR in micelles (rather than nanodiscs) showed a shift toward the active state. As a micelle is a very different thing than a nanodisc, this suggests that the pressure effect is, at least in part or predominantly, on the protein itself.

      On top of the weakness of the pressure NMR experiment to identify what actually happens to the disc, it is not clear either how to interpret the 19F shift at very high pressure (Fig 7D). Given that there is only a single NMR probe, far out in an artificial side chain, it is difficult to assess the state of the protein.

      These are good questions. Firstly, lipid bilayers (be it in liposomes, bicelles, or nanodiscs) are super soft and compressible systems – all known to change in hydrophobic thickness to pressure much more readily than proteins – be they membrane embedded or soluble. Secondly, the 19F NMR spectra are well-known to be representative of fully functional receptor as discussed above. Thirdly, even detergent micelles are susceptible to pressure (much more so than the receptor itself) See J. Phys. Chem. B 2014, 118, 5698−5706 (now referenced in the paper). Pressure will enhance hydrophobic thickness, even in a detergent host, by ordering the acyl chains. The lower specific volume states, selected by higher pressure, have a larger hydrophobic dimension. Thus, the effects seen earlier are equally an effect of environment. In the revised version, we simply make the point that the protein isn’t unfolded and that both cholesterol or pressure give rise to enhanced hydrophobic thickness and corresponding shifts in equilibria to the active states.

    2. Evaluation Summary: 

      Cholesterol has long been known to have significant effects on G protein-coupled receptor (GPCR) ligand binding properties and stability, and cholesterol/GPCR interactions have frequently been observed in high-resolution structures. However, relatively limited biophysical work has been done to investigate the mechanistic basis for cholesterol's effects. This manuscript describes the use of a sensitive 19F NMR probe to monitor conformational equilibria in a prototypical GPCR, the A2a adenosine receptor. These experiments, together with data from other NMR experiments, computational analysis, and G protein assays, show that the subtle effects of cholesterol derive in large part from modulation of membrane biophysical properties, in contrast to conventional allosteric modulators that exert their effects through direct long-lived receptor binding. 

      (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #2 agreed to share their name with the authors.)

    3. Reviewer #1 (Public Review): 

      Although the authors make persuasive arguments regarding the effect of cholesterol being indirect, much is based on comparison of what to expect for a classic allosteric modulator, where the binding affinity is typically far higher. By comparison, the local concentration of cholesterol in the bilayer is extremely high, and may even involve an interplay between different low-affinity sites that have been identified by prior structural studies. 

      One argument used by the authors that if cholesterol is an allosteric modulator, its binding to A2AR should depend on whether A2AR is in the active or inactive state (Fig. S4). The observation that the effect is more pronounced at low F7-chol may point to a not-insignificant effect, in particular when noting that the effect decreases at higher F7-chol. The observation that the chemical shift change is in the same direction for agonist and inverse agonist would be incompatible with a single binding site, but perhaps not with the existence of two binding sites. The fact that there is any effect at all on the F7-chol NMR spectrum suggests that F7-chol senses the state of A2AR and therefore must involve transient binding. It appears likely that, as the authors point out, "subtle direct interaction with cholesterol" are in play at the same time as "indirect effects through the membrane".

    4. Reviewer #2 (Public Review): 

      What the authors attempt to achieve, and their approaches: 

      The author attempt to establish by which mechanisms cholesterol influences the function of the GPCR A_{2A}R, an adenosine receptor. The role of cholesterol on GPCRs has been reported in a number of studies, primarily in cellular experiments, and the authors set out here to clarify the molecular mechanisms. 

      To this end, they build upon their recent achievements to produce this protein and reconstitute it in nanodiscs, i.e. discoidal objects comprised of the membrane protein (here: A_{2A}R), lipids (here: POPC, POPG and cholesterol) and a membrane-scaffold protein (MSP) which wraps around this disc of protein+lipid. Nanodiscs allow studying proteins in solution, and are thought to be much more native-like than e.g. detergent micelles. 

      The authors first use GTP hydrolysis experiments to quantify the basal activity and agonist potency at cholesterol concentrations from 0 to 13%. The cholesterol effects are weak but detectable. Then they use a single 19F label that reports on the protein's conformation (active, inactive) to show that the protein populates slightly more active states with cholesterol. (again, weak effects). Then they investigate G-protein binding to A_{2A}R in the nanodisc, and find (very!) weak enhancement at 13% cholesterol. These data point to weak positive allosteric modulation by cholesterol. <br> They then use molecular dynamics simulations to probe the allosteric communication, using a recently proposed framework (Rigidity-transmission allostery). Doing these simulations in the presence of cholesterol (postions of cholesterol from X-ray structure) and absence. This analysis shows again only very weak effects of cholesterol, and this time the effect is opposite, i.e. negative allosteric modulation by cholesterol. Then they use 19F-labeled cholesterol analogues to probe by NMR the state of cholesterol (bound to protein?). Lastly, they use Laurdan fluorescence experiments and pressure NMR to establish that (i) the lipids become more ordered when cholesterol is present, and (ii) if one achieves such ordering even without cholesterol - namely by pressure - one may achieve similar effects as those that cholesterol has. 

      Collectively, these data lead them to conclude that cholesterol has a (weak) positive allosteric effect on this receptor, and this effect is not a direct one, but goes via modulation of the membrane properties. 

      Major strengths and weaknesses of methods and results: 

      The study addresses an important question, which inherently is difficult to answer: the effect of cholesterol is poorly understood and such studies require to work in an actual membrane. The authors do a careful combination of different methods to achieve their goal of identifying the mechanisms. 

      Despite combining several methods, several of them have their inherent problems: 

      (i) the nanodisc is too small to properly mimic the membrane environment, and it does not allow reaching relevant cholesterol concentrations. Moreover, it is not clear (to me) if one can exclude e.g. interactions of the protein with the surrounding MSP, or of cholesterol with MSP (see (iii) below). 

      (ii) the state of the protein (inactive, active) is probed with a single NMR-active site. The effects are small and I am not convinced that one shall interpret changes as small as the ones in Figures 3 and 4. In particular, how does this single probe behave at high pressure? Does it reflect an active state at 2000 bar pressure - where possibly other effects (unfolding?) may occur? 

      (iii) the data in Figure 6 (19F of cholesterol analogs) are hard to interpret. Is cholesterol bound to the protein? Does the 19F shift reflect binding to the protein? or interactions within the confined space of the disc? or with MSP? The two analogs do not tell a coherent story. 

      (iv) the pressure NMR study (Fig 7D) has weaknesses. The authors implicitly assume that pressure acts on the membrane, leading to more ordering. (They do recognize the possibility that pressure may have an effect on the protein directly, but consider that this direct effect on the protein is minor.) I think that their arguments are possibly incorrect: they apply here pressure onto a sample of nanodiscs, but all studies they cite to justify the use of pressure on membranes dealt with extended lipid bilayers (liposomes). To me it is not clear what is the lateral effect of pressure onto a nanodisc. Can water laterally enter into the bilayer and thus modify the lipid structure? I also note that previous pressure-NMR studies on a GPCR in micelles (rather than nanodiscs) showed a shift toward the active state. As a micelle is a very different thing than a nanodisc, this suggests that the pressure effect is, at least in part or predominantly, on the protein itself. 

      On top of the weakness of the pressure NMR experiment to identify what actually happens to the disc, it is not clear either how to interpret the 19F shift at very high pressure (Fig 7D). Given that there is only a single NMR probe, far out in an artificial side chain, it is difficult to assess the state of the protein. 

      Appraisal of whether the authors achieved their aims, and whether the results support their conclusions:

      The manuscript presents a number of observations which can be interpreted in the way that is proposed here, but as stated above, several experiments have their own problems: from the small disc with little cholesterol to questions in interpreting 19F of cholesterol analogs and high-pressure NMR data. Collectively, the interpretations are somewhat tentative in my view. 

      Likely impact of the work on the field, and the utility of the methods and data to the community: 

      In my view, the conclusions of this manuscript are fairly tentative (see above). Nonetheless, given the difficulty of studying these effects by any experimental method, this work may have an impact in the GPCR field, and more broadly in the membrane-protein field. I hope that reviewers from those fields can clarify this question. From my NMR perspective I would say that the paper is a nice combination of methods, I feel that the authors carefully assembled the methods and thought about pitfalls -- but a number of issues remain, as listed above. They make it hard to reach final conclusions. Some of the data appear contradictory (e.g. negative allosteric modulation seen in MD, contradicts the positive allostery seen by other methods; the two cholesterol analogs are not consistent).

    5. Reviewer #3 (Public Review): 

      There has been much interest in whether cholesterol modulates the function of G protein-coupled receptors and, if so, how. Here Huang, Prosser and colleagues examine this question for the Adenosine A2 receptor using purified receptor, biophysical measurements in nanodiscs of well-defined composition, and a variety of functional measurements. They convincingly show that cholesterol does modulate A2AR function, but not via direct binding. Instead cholesterol exerts its impact by altering the thickness and dynamics of the membrane milieu. 

      This paper is extremely well written and seems to be typo free. State-of-the-art methods are used and this paper is thorough and rigourous. While there have been many papers on whether and how cholesterol interacts with and modulates GPCR function, this paper distinguishes itself in it thoroughness and the use of NMR spectroscopy not only to quantitate the functional states of the receptor under various conditions, but also to look directly at fluorinated cholesterol analogs as they are titrated into the protein, an approach that revealed no evidence for long lifetime binding of these analogs to the receptor. I found this paper to be mostly compelling in its conclusions but recommend that the authors address concerns regarding the cholesterol analog data.

    1. Author Response:

      Reviewer #1:

      The paper details a whole genome re-sequencing of 310 accessions of quinoa. This provides a good glimpse of diversity in this orphan crop, plus the GWAS studies are able to help provide the foundations for identifying key genes in quinoa variation. This will certainly advance our knowledge of this increasingly important orphan crop.

      1) One issue that permeates the entire paper is that the analysis is fairly basic and the authors do not make full use of the data. The analysis of population diversity is restricted to PCA, ADMIXTURE and phylogenetic analysis. It would probably broaden the impact of the paper if they can do deeper analysis of quinoa diversity, maybe looking at demographic history, looking at selection of highland vs. lowland, etc.

      Thanks for this suggestion. We performed a local PCA analysis by dividing the genome into 50 kb windows, and the results of the analysis are presented in Fig. S9. The results are added to the text, lines 189-209 and 556-562. Moreover, for a better understanding of the demographic history of quinoa, another study is underway with a very large set of additional genome sequences and additional outgroups.

      2) There is a focus on the rapid LD decay, which the authors attribute to the short breeding history and low selection. That seems like a stretch to make this conclusion based solely on LD decay. As they point out, many other factors could account for this, and the authors should provide other lines of evidence to draw this conclusion.

      The evidence of short breeding history in quinoa is also provided through admixtures analysis (Fig. S6) and genetic diversity analysis (Fig. S7 and S8).

      3) The GWAS analysis is good and does provide a good foundation for quinoa genetics. The authors discuss possible candidate genes is these GWAS regions. For the thousand seed weight, the relative small span of the GWAS peaks allows for localization of just a few genes in the GWAS region (CqPP2C5 and the CqRING). The GWAS associated with flowering time is larger - 1 Mb with 605 genes - but the authors focus on the GLX2-1 gene. This is again a stretch, as the large region precludes narrowing the candidate list unless there was a compelling mutation (for example a deletion or insertion of a major flowering time gene).

      Altogether, 605 genes are found in the 50kb flanking regions of the PCA-associated SNPs. This region is not 1 Mb, but 0.1 Mb in size. It was a typing error in the text corrected as 8.05-8.15 Mb (modified in the text line 284). In this region, we found 5 genes, and 3 of them were without any known annotation. The strongest association was found in the GLX2-1 gene and this association was also ‘consistent’ between years for all four traits. We modified the text line 285-286 and 287-290.

      Reviewer #2:

      A key genomic study on emerging, nutritious, alternative grain crop.

      Deep genomic data on hundreds of land races/accessions.

      Population structure analysis, could be enhanced.

      Agronomic growth and yield traits are correlated and environmentally sensitive.

      Genomic dissection via GWAS to multigenic loci with candidate genes add genomic prediction and selection.

      Inference on domestication.

      To improve population structure analysis, we performed a local PCA analysis by dividing the genome in 50 Kb windows, and the results of the analysis are presented in Fig. S9. The results are added to the text lines 189-209 and 556-562.

      We agree that the growing conditions typical of lowland (longer seasons) can prevent many accessions from reaching maturity. However, we observed that all accessions flowered and produced seeds. Nonetheless, GWAS with PCA (CP) has been shown to be effective in multiple studies (mentioned below) for genetically correlated traits. Therefore, we believe our analysis could address the bias that might occur due to maturity differences. We also discuss this in line 386-390 and 413-417.

      • Miao, C., Xu, Y., Liu, S., Schnable, P. S., & Schnable, J. C. (2020). Increased power and accuracy of causal locus identification in time series genome-wide association in sorghum. Plant physiology, 183(4), 1898-1909.

      • Yano, K., Morinaka, Y., Wang, F., Huang, P., Takehara, S., Hirai, T., ... & Matsuoka, M. (2019). GWAS with principal component analysis identifies a gene comprehensively controlling rice architecture. Proceedings of the National Academy of Sciences, 116(42), 21262-21267.

      • Aschard, H., Vilhjálmsson, B. J., Greliche, N., Morange, P. E., Trégouët, D. A., & Kraft, P. (2014). Maximizing the power of principal-component analysis of correlated phenotypes in genome-wide association studies. The American Journal of Human Genetics, 94(5), 662-676.

      Genomic selection and prediction are interesting points. We believe that our study marks an important first step on the way to genomic selection. We agree that in many breeding programs, using marker-assisted selection for polygenic traits failed. However, markers from QTL explaining a large proportion of the phenotypic variance can be useful for marker-assisted selection, as for instance, the markers from our QTL regions on Cq2A. The next step will be to provide a database for genomic selection. This requires a more extensive set of breeding lines (training population) which should be grown under different environments.

      Reviewer #3:

      The authors have re-sequenced 310 quinoa accessions and carried out field phenotyping of the same set of accessions for two years in order to characterize genetic diversity and analyze the genetic basis of agronomically important traits.

      The main strength of the manuscript is that the authors have carefully characterized more than 300 quinoa accessions, achieving a sufficiently large population size for GWAS analysis with good statistical power. It is especially promising that the phenotypes all show high heritability. This indicates that the field phenotyping was of high quality and provides a good starting point for discovering relevant marker-trait associations. In addition, the authors provide convincing evidence for distinct population characteristics of highland and lowland quinoa, adding additional information compared to previous work (Maughan, 2012).

      The weak points are related to the genotype data and the conclusions drawn based on the GWAS analysis.

      1) An important issue is related to the relatively low depth of coverage (4-10x) that was used for re-sequencing. Across the accessions, there is a pronounced negative correlation between the mean sequencing depth and the heterozygosity level, indicating that heterozygotes are overcalled in individuals with low coverage. This also results in heterozygosity levels that are generally higher than expected for what is assumed to be mainly homozygous inbred lines.

      We addressed your concern by providing the scatter plot as requested. We also calculated correlations between coverage and heterozygosity (Fig. S3b). However, correlations were not significant, and therefore we believe that the coverage was sufficient enough to achieve accurate SNP-calling (lines 106-108).

      2) Another potential issue concerns SNPs called in repetitive regions. Among the significant GWAS SNPs identified, a very large proportion appears to be found in intergenic regions. While this does not rule out that some of them are genuinely important associations, it does suggest a potentially high level of noise in the GWAS results. In addition to the filtering already imposed, which includes a filter for mapping quality, the SNPs called in intergenic regions with unusually high coverage could be more closely examined to determine the extent of the issue. Masking repetitive genomic regions using RepeatMasker or similar programs could be useful.

      Thank you for this suggestion; we understand the problem could occur due to the poor/incorrect mapping in the intergenic regions. Therefore, we applied stringent filtering to remove SNPs with more than 50% missing genotype data, minimum mean depth less than five, and minor allele frequency less than 5% for the GWAS analysis. SNP densities in intergenic regions are generally higher than in the genic regions. In this table, there are 511 (47% of all association) intergenic SNPs and 300 upstream or downstream (28%) that are associated with traits. Therefore, we do not think that we have an overwhelming majority of intergenic SNPs. Also, we believe that SNPs within repetitive regions are also important. For instance, repetitive elements can have a function in controlling gene expression. Moreover, since our SNP calling and filtering criteria were very stringent, the probability of having false positives in our SNP data set is very low. Therefore, we would not remove them from the GWAS analysis at this stage.

      3) When the authors discuss their GWAS results, they frequently focus on cherry-picked candidate genes, although, in several cases, the top SNPs in the region in question are not found within these candidates. A more broad focus on all genes within the LD blocks, while still mentioning the candidate genes, would be more informative.

      We obtained candidate genes based on whole-genome LD average (50 kb) and we provided LD heatmaps to show that Saponin genes and GLX2-1 are in LD with the strongest associated SNPs Modified line 259-260, 398. For thousand seed weight, we showed that the SNPs with significant p-values are located within both CqRING and CqPP2C genes. We also modified the text accordingly (Lines 24,81,249,251,254-255,274,275,285-286,287-290,300-302,391,396,397,398,405-406,409-410,413-418,420-422).

      4) The manuscript includes statements that a particular genotype "results in" some phenotypic outcome, although no causal relationship has been demonstrated. In general, there is a tendency to draw too strong conclusions based on the GWAS results.

      We modified the text based on the reviewer’s comment. Rephrased into “associated with”.

      5) As this is primarily a resource paper, the authors should make the complete genotype and phenotype data as well as the layout of the field trials available. It would not be possible to reproduce the GWAS analysis based on the data included with the current version. They should also clarify how the quinoa accessions described will be made accessible to the community and provide all scripts used for data analysis through GitHub or a similar repository.

      Most of the accessions are available from the IPK Gatersleben and the USDA genebanks. Materials that are not available from the genebanks can be obtained from the authors with a Standard Material Transfer Agreement (SMTA). Genomic data (Ready to use vcf files) and phenotypic data are made available through the Dryad repository https://doi.org/10.5061/dryad.zgmsbcc9m. Raw sequencing data are available from NCBI SRA. Also, detailed descriptions of the germplasm, phenotyping methods, and phenotypes are posted at https://quinoa.kaust.edu.sa/#/ and published in Stanschewski et al., 2021 (see lines 603-607).

  2. Dec 2021
    1. Reviewer #1 (Public Review):

      The model proposed here is the first large-scale model that actually performs a cognitive task, which in this case is working memory but could easily extend to decision making in general as is acknowledged by the authors. Briefly, each of the 30 areas are simulated as a rate, Wong-Wang circuit (i.e. two excitatory pools inhibit each other through a third, inhibitory population). The authors use previously collected anatomical data to constrain the model and show qualitatively match with the data, in particular how mnemonic activity emerges somewhat abruptly along the brain hierarchy.

      Strengths:

      Previous models have focused on neural dynamics during the so-called "resting state", in which subjects are not performing any cognitive task - thus, resting. This study is therefore an important improvement in the field of large-scale modelling and will certainly become an influential reference for future modelling efforts. As typically done in large-scale modelling, some anatomical data is used to constrain the model. The model shows several interesting characteristics, in particular how distributed working memory is more resilient to distractors and how the global attractors can be turned off by inhibition of only top areas.

      Weaknesses:

      Some of these results are not clear how they emerge, and some "biological constraints" do not seem to constrain. Moreover, some claims are slightly exaggerated, in particular how the model matches the data in the literature (which in some cases it does not) or how somatosensory working memory can be simulated by simply stimulating the "somatosensory cortex".

      This paper has two different models, one being a simplified version of the main model. However, it is not very clear what the simplified model adds the main findings, if not to show that the empirical anatomical connectivity does not constrain the full model.

  3. Nov 2021
    1. Author Response:

      Reviewer #1 (Public Review):

      Hickey et al. studied chromatin landscape changes in early Zebrafish embryos at three distinct stages: preZGA, ZGA and postZGA. Using ChIP-seq on these time-course samples, they examined developmental genes at their regulatory elements, including promoters and enhancers, that carry nucleosomes enriched with histone variant H2A.Z, as well as post-translational modifications H3K4me1 and H3K27ac, but with low DNA methylation, in early-stage embryos prior to turning on zygotic gene expression. During embryogenesis, this group of elements recruit a Polycomb Repressive Complex 1 (PRC1) component Rnf2 to "write" the ubiquitinated H2A or H2A.Z. The mono-ubH2A/Z then recruits a PRC2 component Aebp2 to further "write" the H3K27me3 repressive mark to silent these developmentally regulated genes in later stage embryos. Using a small molecule to inhibit Rnf2 abolishes H3K27me3 and leads to ectopic gene expression.

      Most of the data for the first half of this manuscript are presented in a clear and logic manner. The conclusions based on these correlation assays are quite obvious and well supported (except a few minor points raised below for clarifications, #2-#3). The major concern is for the second half of the manuscript where a drug is used to draw causal relationships (see point #1 below).

      1. Using small molecule could have secondary effects. It also seems that the drug-induced defects cannot be reversed after being washed away. Furthermore, this drug treatment eliminates almost all H3K27me3 genome-wide, regardless of their occupancy status with mono-ubH2A/Z, making it difficult to make the causal connection between the prerequisite mono-ubH2A/Z occupancy and the subsequent de novo H3K27me3. I think it is important for the authors to address this point more directly as this is the main conclusion of this work. Could the authors perform genetic analyses to confirm the specificity of the phenotypes?

      2. Page 8, line 160-163: "Curiously, enhancer cluster 5 (Figure 2A) was unique - displaying high H3K4me1, very high H3K27ac, and open chromatin (via ATAC-seq analysis; Figure 2 - figure supplement C, D) - but bore DNA methylation - an unusual combination given the typical strong correlation between high H3K4me1 and DNA hypomethylation." I suspect that the authors are talking about the chromatin state at pre-ZGA stage as this is the only stage DNA methylation pattern was included, but it is hard to tell that this cluster displays high H3K4me1 at all.

      We now see the confusion, and are happy to clarify this. We were intending to refer to to the histone marking at postZGA, and the DNAme at postZGA (for cluster #5) – as postZGA is the time when H3K4me1 is high, H3K27ac is very high, and DNAme remains high. The reviewer is right that we do not show the DNAme pattern at post ZGA, only preZGA. However, the DNAme pattern stays almost constant between preZGA (2.5 hpf) and postZGA (4.3 hpf) – a result we published previously in Potok et al., 2010 (note: the maternal genome shows DNA reprogramming prior to 2.5hr, and is then constant through ZGA). We did not include DNAme at every stage simply to save space in Panel A, which was getting crowded. However, to avoid the reader misunderstanding our point, we have taken care to make this clear in the revised manuscript. We thank the reviewer for raising this point.

      1. Page 10, line 206-207: "PRT4165 treatment also conferred limited new/ectopic Aebp2 peaks (Figure 4C, clusters 4, 6, 7,8)", it seems that clusters 4, 6, 7, 8 together are not "limited" compared to clusters 1, 3, and 5, and could be even more abundant.

      Thank you for this comment - we agree with the reviewer and have clarified this in the text and Figure 4. In the initial version, the section where we mention ‘limited’ additional sites was intend to refer to promoters, and although as only a modest fraction of the ectopic sites are at promoters, but we did not provide that context in the text. Indeed, if one looks at all sites in the genome, there are a large number of ectopic sites after PRT4165 treatment. This is shown clearly in the revised Figure 4 (which shows all genomic sites) and we have clarified this in the text.

      We were curious whether there is any feature that helps us understand what might unify the ectopic binding, and therefore underlie the mechanism(s). First, we tested whether binding sites for particular transcription factors might be enriched; however, we did not find a class of binding sites that represented more than 3% of the total sites. We note that others have reported some affinity of mammalian Aebp2 for DNA and some limited sequence specificity (Kim et al., NAR 2009), and in the absence of a high-affinity H2AUb target, that shadow DNA binding function may become more apparent. Furthermore, we did not observe chromatin marks that showed a highly significant degree of overlaps. Thus, although intriguing, there does not appear to yet be a logic to the ectopic binding observed.

      1. In the context of studying the chromatin state of developmental genes in early vertebrate embryos, there are two recent publications in mouse embryos which also investigated the crosstalk between mono-ubH2A and H3K27me3 at the ZGA transition in mouse (https://doi.org/10.1038/s41588-021-00821-2 and https://doi.org/10.1038/s41588-021-00820-3). It would be informative to add some discussion for comparisons between these two vertebrate organisms.

      Reviewer #2 (Public Review):

      One model for polycomb domain establishment suggests that PRC2 adds H3K27me3 first, and then recruits PRC1 for silencing. The key evidence for this model is the H3K27me3-binding module CBX proteins in canonical PRC1 complexes. This model has been revised by recent studies, and it is now well recognized that the polycomb domains can be de novo established in a different order. In other scenarios, including X inactivation, a non-canonical PRC1 complex that lacks CBX proteins catalyzes ubH2A first, and PRC2 complex is subsequently recruited through recognizing ubH2A modification by its Jarid2 and Aebp2 subunits.

      In this manuscript, Hickey and co-workers analyzed the temporal change of various epigenetic marks around ZGA stages during zebrafish early embryo development. Based on their experimental data and bioinformatic analysis, they suggest that polycomb establishment in zebrafish embryo is following the 'non-canonical' order, in which H3K27me3 establishment is dependent on ubH2A pre-deposition and the following recruitment of Aebp2-PRC2 complex. Moreover, they suggest that polycomb-silenced developmental genes are solely repressed by ubH2A, independent of H3K27me3. Overall, the functional analysis (RNF2 inhibitor experiments) conducted in the current study highlights the critical function of PRC1 and ubH2A in silencing developmental genes during early embryo development. Moreover, this study provides clues that could reconcile with the earlier observations that H3K27me3 seems largely dispensable for silencing developmental genes in zebrafish early embryo (e.g. PMID: 31488564).

      The main concern is two similar studies have just been published in Nature Genetics using mouse early embryos, and the observation of this manuscript largely agree with the two mouse studies, rendering the novelty of this study.

      In addition, certain conclusions in the manuscript requires further experimental support:

      1. While the authors claim that H3K27me3 is established after ZGA, it is quite surprising to me that they did NOT analyzed the H3K27me3 pattern before ZGA. While IF staining suggests a minimal level of H3K27me3 before ZGA (Fig1 S2B), previous ChIP-seq analysis demonstrate that H3K27me3 are present (e.g. PMID: 22137762).

      Briefly, in our own work, we do not detect H3K27me3 by IF prior to ZGA, and we could not detect H3K27me3 peaks by ChIP during preZGA (also mentioned as ‘data not shown’ in Murphy et al., 2018).

      1. While the RNF2 inhibitor experiment clearly demonstrates that PRC1 is required for the deposition of both ubH2A and H3K27me3, that does not necessarily mean that PRC1-mediated ubH2A deposition precedes H3K27me3. The establishment and maintenance of polycomb domain usually requires the crosstalk and reinforcement between polycomb complexes. Therefore, the deficiency in either PRC1 or PRC2 complex may lead to the decreased level of both marks. To clarify a hierarchical order of the polycomb domain establishment, a phenotypic analysis of PRC2 deficiency is also necessary.

      Here, we emphasize that prior to performing the inhibitor experiment, we addressed the temporal order of addition in Figure 1 and in Figure 1 – figure supplement 1. H2Aub1 is added extensively to thousands of developmental genes during preZGA, well before H3K27me3 is detected. We interpret this as evidence that H2Aub1 temporally precedes H3K27me3 during embryonic development. We will also mention (described in the Discussion) that maternal zygotic loss of Ezh2, which eliminates all H3K27me3 in the genome at all embryo stages does not result in the activation of developmental genes.

      1. Parental difference. As shown in Fig.1B, ubH2A level varies greatly in sperm and egg, which suggests that the reprogramming process of ubH2A (and perhaps H3K27me3) distribution could be significantly different for the two parental alleles. It would be interesting to analyze the ubH2A and H3K27me3 distribution in germ cells before fertilization.

      We appreciate the reviewer’s comment and agree that this would be an interesting line of inquiry. However, this would require genomics analyses from reciprocal crosses of highly polymorphic fish strains. This would involve very considerable additional work. Therefore, we will consider this in our future studies.

      1. The role of Aebp2 subunit. Given the well-characterized function of Aebp2 in recognizing ubH2A, an involvement of Aebp2-PRC2 complex in establishing H3K27me3 on PRC1 pre-deposited regions is not unexpected. Indeed, Aebp2 co-localized well with ubH2A marked regions (Fig.3). However, an issue not clarified in the manuscript is whether Aebp2 is the sole subunit for the recruitment of PRC2 to ubH2A marked regions. Paralleled analysis of the changes for Aebp2 and H3K27me3 upon RNF2 inhibitor treatment is necessary, and Aebp2-dependent and -independent regions should be separately classified for analysis.

      2. Role of PRC1 on the temporal regulation of gene expression during early development. The authors only analyzed the RNA-seq results for RNF2i treated embryos post ZGA. Therefore, it is currently not clear if the role of PRC1 in transcriptional repression is restricted to post-ZGA stages. RNA-seq analysis of RNF2i treated embryos on those stages are also warranted.

    2. Evaluation Summary:

      This manuscript is of broad interest to developmental biologists and those studying transcriptional/epigenetic regulation of cell-specific and housekeeping gene programs. The work demonstrates that Polycomb complexes coordinate the regulation of distinct groups of genes during early embryogenesis, which offers interesting insights into how very early embryos differentially control housekeeping versus specific developmental gene promoters/enhancers. The data are of high quality, and the conclusions are insightful yet measured.

      (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #3 agreed to share their name with the authors.)

    3. Reviewer #1 (Public Review):

      Hickey et al. studied chromatin landscape changes in early Zebrafish embryos at three distinct stages: preZGA, ZGA and postZGA. Using ChIP-seq on these time-course samples, they examined developmental genes at their regulatory elements, including promoters and enhancers, that carry nucleosomes enriched with histone variant H2A.Z, as well as post-translational modifications H3K4me1 and H3K27ac, but with low DNA methylation, in early-stage embryos prior to turning on zygotic gene expression. During embryogenesis, this group of elements recruit a Polycomb Repressive Complex 1 (PRC1) component Rnf2 to "write" the ubiquitinated H2A or H2A.Z. The mono-ubH2A/Z then recruits a PRC2 component Aebp2 to further "write" the H3K27me3 repressive mark to silent these developmentally regulated genes in later stage embryos. Using a small molecule to inhibit Rnf2 abolishes H3K27me3 and leads to ectopic gene expression.

      Most of the data for the first half of this manuscript are presented in a clear and logic manner. The conclusions based on these correlation assays are quite obvious and well supported (except a few minor points raised below for clarifications, #2-#3). The major concern is for the second half of the manuscript where a drug is used to draw causal relationships (see point #1 below).

      1. Using small molecule could have secondary effects. It also seems that the drug-induced defects cannot be reversed after being washed away. Furthermore, this drug treatment eliminates almost all H3K27me3 genome-wide, regardless of their occupancy status with mono-ubH2A/Z, making it difficult to make the causal connection between the prerequisite mono-ubH2A/Z occupancy and the subsequent de novo H3K27me3. I think it is important for the authors to address this point more directly as this is the main conclusion of this work. Could the authors perform genetic analyses to confirm the specificity of the phenotypes?

      2. Page 8, line 160-163: "Curiously, enhancer cluster 5 (Figure 2A) was unique - displaying high H3K4me1, very high H3K27ac, and open chromatin (via ATAC-seq analysis; Figure 2 - figure supplement C, D) - but bore DNA methylation - an unusual combination given the typical strong correlation between high H3K4me1 and DNA hypomethylation." I suspect that the authors are talking about the chromatin state at pre-ZGA stage as this is the only stage DNA methylation pattern was included, but it is hard to tell that this cluster displays high H3K4me1 at all.

      3. Page 10, line 206-207: "PRT4165 treatment also conferred limited new/ectopic<br> Aebp2 peaks (Figure 4C, clusters 4, 6, 7,8)", it seems that clusters 4, 6, 7, 8 together are not "limited" compared to clusters 1, 3, and 5, and could be even more abundant.

      4. In the context of studying the chromatin state of developmental genes in early vertebrate embryos, there are two recent publications in mouse embryos which also investigated the crosstalk between mono-ubH2A and H3K27me3 at the ZGA transition in mouse (https://doi.org/10.1038/s41588-021-00821-2 and https://doi.org/10.1038/s41588-021-00820-3). It would be informative to add some discussion for comparisons between these two vertebrate organisms.

    4. Reviewer #2 (Public Review):

      One model for polycomb domain establishment suggests that PRC2 adds H3K27me3 first, and then recruits PRC1 for silencing. The key evidence for this model is the H3K27me3-binding module CBX proteins in canonical PRC1 complexes. This model has been revised by recent studies, and it is now well recognized that the polycomb domains can be de novo established in a different order. In other scenarios, including X inactivation, a non-canonical PRC1 complex that lacks CBX proteins catalyzes ubH2A first, and PRC2 complex is subsequently recruited through recognizing ubH2A modification by its Jarid2 and Aebp2 subunits.

      In this manuscript, Hickey and co-workers analyzed the temporal change of various epigenetic marks around ZGA stages during zebrafish early embryo development. Based on their experimental data and bioinformatic analysis, they suggest that polycomb establishment in zebrafish embryo is following the 'non-canonical' order, in which H3K27me3 establishment is dependent on ubH2A pre-deposition and the following recruitment of Aebp2-PRC2 complex. Moreover, they suggest that polycomb-silenced developmental genes are solely repressed by ubH2A, independent of H3K27me3. Overall, the functional analysis (RNF2 inhibitor experiments) conducted in the current study highlights the critical function of PRC1 and ubH2A in silencing developmental genes during early embryo development. Moreover, this study provides clues that could reconcile with the earlier observations that H3K27me3 seems largely dispensable for silencing developmental genes in zebrafish early embryo (e.g. PMID: 31488564).

      The main concern is two similar studies have just been published in Nature Genetics using mouse early embryos, and the observation of this manuscript largely agree with the two mouse studies, rendering the novelty of this study.

      In addition, certain conclusions in the manuscript requires further experimental support:

      1. While the authors claim that H3K27me3 is established after ZGA, it is quite surprising to me that they did NOT analyzed the H3K27me3 pattern before ZGA. While IF staining suggests a minimal level of H3K27me3 before ZGA (Fig1 S2B), previous ChIP-seq analysis demonstrate that H3K27me3 are present (e.g. PMID: 22137762).<br> 2. While the RNF2 inhibitor experiment clearly demonstrates that PRC1 is required for the deposition of both ubH2A and H3K27me3, that does not necessarily mean that PRC1-mediated ubH2A deposition precedes H3K27me3. The establishment and maintenance of polycomb domain usually requires the crosstalk and reinforcement between polycomb complexes. Therefore, the deficiency in either PRC1 or PRC2 complex may lead to the decreased level of both marks. To clarify a hierarchical order of the polycomb domain establishment, a phenotypic analysis of PRC2 deficiency is also necessary.<br> 3. Parental difference. As shown in Fig.1B, ubH2A level varies greatly in sperm and egg, which suggests that the reprogramming process of ubH2A (and perhaps H3K27me3) distribution could be significantly different for the two parental alleles. It would be interesting to analyze the ubH2A and H3K27me3 distribution in germ cells before fertilization.<br> 4. The role of Aebp2 subunit. Given the well-characterized function of Aebp2 in recognizing ubH2A, an involvement of Aebp2-PRC2 complex in establishing H3K27me3 on PRC1 pre-deposited regions is not unexpected. Indeed, Aebp2 co-localized well with ubH2A marked regions (Fig.3). However, an issue not clarified in the manuscript is whether Aebp2 is the sole subunit for the recruitment of PRC2 to ubH2A marked regions. Paralleled analysis of the changes for Aebp2 and H3K27me3 upon RNF2 inhibitor treatment is necessary, and Aebp2-dependent and -independent regions should be separately classified for analysis.<br> 5. Role of PRC1 on the temporal regulation of gene expression during early development. The authors only analyzed the RNA-seq results for RNF2i treated embryos post ZGA. Therefore, it is currently not clear if the role of PRC1 in transcriptional repression is restricted to post-ZGA stages. RNA-seq analysis of RNF2i treated embryos on those stages are also warranted.

    5. Reviewer #3 (Public Review):

      Hickey et al. continue their groups investigation of how vertebrate embryos establish the proper chromatin regulatory context around the time of zygotic genomic activation for both housekeeping genes, which are spatially and temporally widely transcribed, and for developmental genes, which must remain off but must remain available for later lineage-restricted expression. Employing an array of ChIP-Seq analyses of chromatin marks and specific chromatin modifiers (e.g. Rnf2) on pre ZGA, ZGA, and post ZGA zebrafish embryos, they find increased deposition of H2Aub1 by Rnf2-PRC1 at Placeholder nucleosomes (enriched at developmental genes) which, in their model, serves to recruit Aebp2-PRC2 to lay down H3K27me3 marks to repress these developmental genes until their appropriate time of expression later in development post ZGA. Overall, the conclusions are largely well-supported by the data, the proposed models provide a thoughtful interpretation and roadmap forward for future work. Overall, the study also opens a number of interesting questions including how tissue specific transcription factors fit into the overall process as well.

      Strengths of the paper include the quality of the epigenetic profiling across multiple informative chromatin marks with chromatin accessibility and DNAme, with multiple highly concordant replicates for the profiling experiments. The resulting data is necessarily very dense and feature-rich, and the authors are largely successful in conveying the meaning of the many "metagene" displays and browser tracks and developing a coherent story of how these show the transcriptional regulatory events captured around ZGA. The high quality of this data will also be of great value for reanalysis across the field. The 'non-canonical' order of PRC component recruitment further broadens our understanding of the multiple mechanisms by which epigenetic regulators can function.

      Perhaps the main weakness, and one which is not in an obvious way technically surmountable for the present study but is worth considering going forward, is the reliance on the single drug PRT4165 to block Rnf2 activity and prevent H2Aub1 deposition. An orthologous genetic tool producing similar results would strengthen this mechanistic insight, but seems challenging given the likely wide-ranging effects of loss-of-function in chromatin modifiers (as noted in the Discussion with death at 3 dpf with Rnf2 LOF) and narrow window of time around the ZGA over just a few hours making inducible (e.g. Cre/lox or CRISPR) approaches likely challenging. The use of MZ mutants for Rnf2 might allow for further understanding of the precise temporal requirements for Rnf2 activity by removing maternal contribution that might function even earlier.

    1. Reviewer #3 (Public Review):

      Todesco et al undertake an ambitious study to understand UV-absorbing variation in sunflower inflorescences, which often, but not always display a "bullseye" pattern of UV-absorbance generated by ligules of the ray flowers.

      The authors first characterize the extensive variation across the range of two Helianthus species to set the stage for their questions. One of their main goals was to then identify genetic mechanisms of UV-absorbance variation. This portion of the paper is strong, and combines many different methods to arrive at a full picture of what is appears to be the primary genetic mechanism for UV-absorbance variation.

      Specifically, the authors grow GWAS panels for two species in BC, Canada. In H. annuus, the GWAS identified a region where genotype was very strongly associated with phenotype, and further analysis in F2-populations confirmed the association. Most variation was in the promoter near a gene that is expected to regulate flavonol production, and the authors found that expression patterns of this gene indeed match those of a known downstream flavonol pathway gene. The authors also verified this function by moving a sunflower copy into an Arabidopsis thaliana line that is null-mutant for the homolog. The sunflower copy restored normal flavonol production. In sunflowers, expression of this flavonol regulator was greater in UV-absorbing regions at the stage when UV-absorbance develops, and it was higher in plants with greater UV-absorbance. Further sequencing revealed that while little coding sequence variation correlates with phenotypes, upstream variation in the promoter region both covaries with alleles at the SNP highlighted by the GWAS and the phenotypes - clearly identifying cis-regulatory variation of this gene as an important driver of phenotypic variation.

      Next, the authors focus on what processes might maintain this phenotypic variation, and genetic variation at this locus, across the range of H. annuus. This portion of the work is not as conclusive, but does develop likely explanations that are consistent with the evidence.

      Specifically, plants with intermediate and large absorbing UV-phenotypes received more pollinator visits in a field trial. This is suggestive, but not conclusive evidence of fitness consequences via the pollinator pathway for several reasons: visits by pollinators may not translate directly to fitness (pollen limitation is not measured), relative preference for plants with larger UV-absorbance could be due to other phenotypes that also vary among populations (i.e. due to population structure, the effect was not tested within populations or F2 panels), and may or may not hold true in the in their local sites (where pollinator genotype, species composition, or background abiotic conditions could alter preferences). The authors also find ligules that are highly UV-absorbing retain water better, which they argue could be beneficial in stressfully dry sites, or costly in sites that are very hot and humid, where heat-dissipating effects of transpiration would be beneficial. With the current analysis and data, it is unclear if this difference in transpiration is in fact driven by the UV-absorbing pigments (it could be due to i.e. any other phenotype that co-varies due to population structure such as ligule stomata density) though the authors' explanation seems most likely. It also isn't clear how much ligule transpiration increases inflorescence transpiration (the authors may be able to elaborate), or whether ligule transpiration influences fitness in dry environments (though again, I agree with the authors that this seems likely).

      The potential effect of UV-absorbance on transpiration fits with the results of phenotypic-environment correlations, which are very tight for average temperature and relative humidity. It also fits with genotypic-environment correlations (for the region identified by GWAS): associations between temperature or relative humidity and variants in the region identified by GWAS are stronger than those between the environmental variables and putatively neutral genomic variation. These results tantalizingly suggest that abiotic environmental variation may select on UV-absorbing phenotypes, though as yet no conclusive link has been made between fitness and genotype, or between fitness and UV phenotype.

      In sum, Todesco et al identify the primary genetic mechanism underlying UV-absorbance variation across the range of H. annuus, and provide insight into mechanisms that most likely maintain this phenotypic and genotypic variation across the range of H. annuus and possibly in Helianthus generally. Not only do Todesco et al provide a nearly complete understanding of an interesting and potentially agronomically or horticulturally important phenotype, they also provide a great model of highly collaborative, creative science that combines expertise across fields. I think this manuscript has high potential impact on science on both of these fronts.

    2. Evaluation Summary:

      This work identifies the primary genetic mechanism underlying UV-absorbance variation across the geographic range of sunflower (Helianthus annuus) and provides evidence that suggests that abiotic variables, rather than pollinators, may maintain this variation in H. annuus and perhaps Helianthus broadly. While claims about direct links to fitness in natural population remain untested, the authors synthesize an ambitious amount of work from an impressive breadth of methods (from transgenics to ecology) that will be of high interest to ecologists and evolutionary biologists.

      (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #2 agreed to share their name with the authors.)

    3. Reviewer #1 (Public Review):

      Todesco et al. investigate the genetic causes of variation in UV pigmentation in sunflowers as well as the possible biotic and abiotic factors that play a role in natural variation for the trait among populations. Overall I am very enthusiastic about this manuscript as it does an elegant job of going from phenotype to a key locus and then presenting a solid foray into the factors causing variation. I have only a fe relatively minor comments.

      The introduction felt a bit short. I was hoping early on I think for a hint at what biotic and abiotic factors UV could be important for and how this might be important for adaptation. A bit more on previous work on the genetics of UV pigmentation could be added too. I think a bit more on sunflowers more generally (what petiolaris is, where natural pops are distributed, etc.) would be helpful. This seems more relevant than its status as an emoji, for example.

      The authors present the % of Vp explained by the Chr15 SNP. Perhaps I missed it, but it might be nice to also present the narrow sense heritability and how much of Va is explained.

      A few lines of discussion about why the Chr15 allele might be observed at only low frequencies in petiolaris I think would be of interest - the authors appear to argue that the same abiotic factors may be at play in petiolaris, so why don't we see this allele at frequencies higher than 2%? Is it recent? Geographically localized?

      Page 14: It's unclear to me why there is any need to discretize the LUVp values for the analyses presented here. Seems like it makes sense to either 1) analyze by genotype of plant at the Chr15 SNP, if known, or 2) treat it as a continuous variable and analyze accordingly.

      Page 14: I'm not sure you can infer selection from the % of plants grown in the experiment unless the experiment was a true random sample from a larger metapopulation that is homogenous for pollinator preference. In addition, I thought one of the Ashman papers had actually argued for intermediate level UV abundance in the presence of UV?

      I would reduce or remove the text around L316-321. If there's good a priori reason to believe flower heat isn't a big deal (L. 323) and the experimental data back that up, why add 5 lines talking up the hypothesis?

      Page 17: The discussion of flower size is interesting. Is there any phenotypic or genetic correlation between LUVP and flower size?

    4. Reviewer #2 (Public Review):

      The works seeks to understand the genetic basis and functional significance of variation in bullseye sizes in accessions and near relatives of Helianthus annuus.

      Strengths:

      The manuscript is very well written and referenced.

      The genetic analysis is rigorously conducted with multiple Helianthus species and accessions of H. annuus. The same QTL was inputed in two Helianthus species, and fine mapped to promotor regions of HaMyb111. The allelic variation of the TF was carefully mapped in many populations and accessions. Flavonol glycosides were found to correlate spatially and developmentally in ligules and correlate with Myb111 transcript abundances, and a downstream flavonoid biosynthetic gene. Heterologous expression in Arabidopsis in Atmyb12 mutants, showed that HaMyb111 to be able to regulate flavonol glycoside accumulations, albeit with different molecules than those that accumulate in Helianthus. Several lines of evidence are consistent with transcriptional regulation of myb111 accounting for the variation in bullseye size.

      Functional analysis examined three possible functional roles, in pollinator attraction, thermal regulation of flowers, and water loss in excised flowers (ligules?), providing support for the first and last, but not the second possible functions, confirming the results of previous studies on the pollinator attraction and water loss functions for flavonol glycosides. The thermal imaging work of dawn exposed flower heads provided an elegant falsification of the temperature regulation hypothesis. Biogeographic clines in bullseye size correlated with temperature and humidity clines, providing a confirmation of the hypothesis posed by Koski and Ashmann about the patterns being consistent with Gloger's rule, and historical trends from herbaria collections over climate change and ozone depletion scenarios. The work hence represents a major advance from Moyers et al. 2017's genetic analysis of bullseyes in sunflowers, and confirms the role established in Petunia for this Myb TF for flavonoid glycoside accumulations, in a new tissue, the ligule.

      Weakness:<br> The authors were not able to confirm their inferences about myb111 function through direct manipulations of the locus in sunflower.

      Given that that the flavonol glycosides that accumulate in Helianthus are different from those regulated when the gene is heterologously expressed in Arabidopsis, the biochemical function of Hamyb111, while quite reasonable, is not completely watertight. The flavonol glycosides are not fully characterized (only Ms/Ms data are provided) and named only with cryptic abbreviations in the main figures. This and the differences in metabolite accumulations between Arabidopsis and Helianthus becomes a bit problematic for the functional interpretations. And here the authors may want to re-read Gronquist et al. 2002: PNAS as a cautionary tale about inferring function from the spatial location of metabolites. In this study, the Eisner/Meinwald team discovered that imbedded in the UV-absorbing floral nectar guides amongst the expected array of flavonoid glycosides, were isoprenilated phloroglucinols, which have both UV-absorbing and herbivore defensive properties. Hence the authors may want to re-examine some of the other unidentified metabolites in the tissues of the bullseyes, including the caffeoyl quinic acids, for alternative functional hypotheses for their observed variation in bullseye size (eg. herbivore defense of ligules).

      The hypotheses regarding a role for the flavonoid glycosides regulated by Myb111 expression in transpirational mitigation and hence conferring a selective advantage under high temperatures and low and high humidities, are not strongly supported by the data provided. The water loss data from excised flowers (or ligules-can't tell from the methods descriptions) is not equivalent to measures of transpiration rates (the stomatal controlled release of water), which are better performed with intact flowers by porometry or other forms of gas-exchange measures. Excised tissues tend to have uncontrolled stomatal function, and elevated cuticular water loss at damaged sites.

      The putative fitness benefits of variable bullseye size under different humidity regimes, proposed to explain the observed geographical clines in bullseye size remain untested.

      Alternative functional hypotheses for the observed variation in bullseye size in herbivore resistance or floral volatile release could also be mentioned in the Discussion. Are the large ligules involved in floral scent release?

    1. Reviewer #2 (Public Review):

      Kluger and colleagues investigated the influence of respiration on visual sensory perception in a near-threshold task and argue that the detected correlation between respiration phase and detection precision is liked to alpha power, which in turn is modulated by the phase of respiration. The experiments involved detecting a low-contrast visual stimulus to the left or right of a fixation point with contrast settings adjusted via an adaptive staircase approach to reach a desired 60% hit rate, resulting in an observed hit rate of 54%. The main findings are that mutual information between the discrete outcome of hit-or- miss and the continuous contrast variable is significantly increased when respiration phase is considered as well. Furthermore, results show that neuronal alpha oscillation power is modulated in phase with respiration and that perception accuracy is correlated with alpha power. Time resolved correlation analysis aligned on respiration phase shows that this correlation peaks during inspiration around the same phase where the psychometric function for the visual detection task reaches a minimum.<br> The experimental design and data analysis seem solid but there are several concerns regarding the novelty of the findings and the interpretation of the results.

      Major concerns:<br> The finding that visual perception is modulated by the respiration cycle is not new (see e.g. Flexman et al. 1974 or Zelano et al. 2016).

      There are multiple studies going back decades that show alpha oscillation power to be modulated by breathing (e.g. Stancák et al., 1993, Bing-Canar et al. 2016). Also, as the authors acknowledge, it is well-established that alpha power correlates with neuronal excitability and perception threshold. What seems to be new in this study is the use of a linear mixed effect model to analyze the relationship between alpha power, respiration phase and perception accuracy. However, the results mostly seem to confirm previous findings.

      Magnetoencephalography captures broad band neuronal activity including gamma frequencies. As the authors show (Fig. 4) and other studies have shown, the power of neuronal oscillations across multiple frequency bands is modulated by respiration phase. Gamma and beta oscillations have been implicated in sensory processing as well. Support for the author's hypothesis that the perception threshold modulation with respiration is due to alpha power modulation would be strengthened if they could show that the power of oscillations in other frequency bands are not or only weakly linked to perception accuracy.

      In the discussion the authors speculate that respiration locked modulation of alpha power and associated neuronal excitability could be based on the modulation of blood CO2 levels. Most recent studies of respiratory modulation of brain activity have demonstrated significant differences between nasal and oral breathing, with nasal breathing (through activation of the olfactory bulb) typically resulting in a stronger influence of respiration on neuronal activity and behavioral performance than oral breathing. The authors only tested nasal breathing. If blood CO2 fluctuations are indeed responsible for the observed effect, there should be no difference in outcome between nasal and oral breathing. Comparing the two conditions would thus provide interesting additional information about the possible underlying mechanisms.

      Minor concerns:<br> Figures 1, 3 and 4: label fonts are too small on some panels.

      Supplementary figure 3: labels are illegible.

    2. Author Response:

      Reviewer #1 (Public Review):

      The main finding - that the moment-to-moment relationship between excitability and perception is coupled to the body's slower respiratory oscillation - is novel, interesting, and important for advancing our understanding of how the brain-body system works as a whole. The experiment is simple and elegant, and the authors strike the right level of making the most of the data without doing too much and obscuring the main findings. The primary weakness, in my opinion, is the inability to distinguish between the possibility that respiration modulates excitability and the possibility that respiration modulates something boring like signal-to-noise ratio. In terms of conclusions, I thought the authors stuck pretty well to the data. The one place where the conclusions felt a little bold was in terms of the respiration <> alpha <> behavior relationship, where it felt the authors had already made up their minds re: causality. I agree that it probably makes more sense for respiration to influence something about the brain than vice versa, and the background presented in the Intro/Discussion supports this. However, the analysis only tells us that the behavioral performance was modulated by both alpha and respiration (and their interaction, but this is no way causal). Overall, it will be necessary to differentiate the current interpretation from the possibility that breathing and alpha are two unrelated time courses that influence behavior at the same time (and even interact in how they influence behavior, but just not interact with each other), and I do not believe the phase-amplitude coupling analysis is sufficient for this.

      We thank the reviewer for their positive and constructive evaluation of our work.

      Reviewer #2 (Public Review):

      Kluger and colleagues investigated the influence of respiration on visual sensory perception in a near-threshold task and argue that the detected correlation between respiration phase and detection precision is liked to alpha power, which in turn is modulated by the phase of respiration. The experiments involved detecting a low-contrast visual stimulus to the left or right of a fixation point with contrast settings adjusted via an adaptive staircase approach to reach a desired 60% hit rate, resulting in an observed hit rate of 54%. The main findings are that mutual information between the discrete outcome of hit-or- miss and the continuous contrast variable is significantly increased when respiration phase is considered as well. Furthermore, results show that neuronal alpha oscillation power is modulated in phase with respiration and that perception accuracy is correlated with alpha power. Time resolved correlation analysis aligned on respiration phase shows that this correlation peaks during inspiration around the same phase where the psychometric function for the visual detection task reaches a minimum. The experimental design and data analysis seem solid but there are several concerns regarding the novelty of the findings and the interpretation of the results.

      Major concerns: The finding that visual perception is modulated by the respiration cycle is not new (see e.g. Flexman et al. 1974 or Zelano et al. 2016).

      There are multiple studies going back decades that show alpha oscillation power to be modulated by breathing (e.g. Stancák et al., 1993, Bing-Canar et al. 2016). Also, as the authors acknowledge, it is well-established that alpha power correlates with neuronal excitability and perception threshold. What seems to be new in this study is the use of a linear mixed effect model to analyze the relationship between alpha power, respiration phase and perception accuracy. However, the results mostly seem to confirm previous findings.

      Thank you for giving us the opportunity to clarify our approach and the conceptual novelty it provides. First, not at all do we claim that our study is the first to demonstrate respiration-related alpha changes. Not only do we prominently cite the work by Zelano and colleagues (JNeuro, 2016) in the Introduction and Discussion sections, we also have previous work from our own lab demonstrating these effects (see Kluger & Gross, PLoS Biol 2021). Second, the reviewer’s comment that ‘the results mostly seem to confirm previous findings’ unfortunately appears to frame a critical proof-of-concept as a lack of novelty: In order for us to claim a triadic relationship between respiration, excitability, and behaviour, it is paramount to first demonstrate that assumptions about pairwise relations (such as respiration <> alpha power and alpha power <> behaviour) are supported, which of course means replicating known results in our data. Third, in order to evaluate the novelty of our present study, it is crucial to consider its core aim, which was to characterise how automatic respiration is related to lowest-level perception by means of respiration-induced modulation of neural oscillations. At this point, we respectfully disagree with the reviewer’s assessment of our results being mostly replicative, as the references they provide differ from our approach in various key aspects: The classic study by Flexman and colleagues (1974) merely differentiates between inspiration and expiration, critically without accounting for the asymmetry between the two respiratory phases. Zelano and colleagues (2016) did not investigate visual perception at all, but instead asked participants to categorise emotional face stimuli (termed ‘emotion recognition task’). Stancák and colleagues (1993) did not investigate automatic, but paced breathing, which involves continuous, conscious top-down control of one’s breathing rhythm - a demand that is not comparable to automatic, natural breathing we investigate here. The same is true for any kind of respiratory intervention or training like the ‘mindfulness-of-breathing exercise’ employed in the study by Bing-Canar and colleagues (2016). Once again, the oscillatory changes reported by the authors are not induced by automatic breathing, but instead reflect the outcome of a conscious manipulation of the breathing rhythm. In highlighting the key differences between previous studies and our approach, we do hope to have dispelled the reviewer’s initial concern regarding the novelty of our findings.

      Magnetoencephalography captures broad band neuronal activity including gamma frequencies. As the authors show (Fig. 4) and other studies have shown, the power of neuronal oscillations across multiple frequency bands is modulated by respiration phase. Gamma and beta oscillations have been implicated in sensory processing as well. Support for the author's hypothesis that the perception threshold modulation with respiration is due to alpha power modulation would be strengthened if they could show that the power of oscillations in other frequency bands are not or only weakly linked to perception accuracy.

      We thank the reviewer for their well-justified suggestion to extend the spectral scope of our analyses to include other frequency bands. In response to their comment, we have recomputed our analysis pipeline for the frequency range between 2 - 70Hz. While the whole analysis and results are described in a new Supplementary Text and Supplementary Figures (see below), we outline key findings here.

      In keeping with the structure of our main analyses, we first computed cluster-corrected whole-scalp topographies for delta, theta, alpha, beta, and gamma bands for hits vs misses over time intervals 1s prior to stimulus presentation:

      *Fig. S4 | Band-specific topographies over time. Whole-scalp topographic distribution of normalised pre- and peristimulus power differences between hits and misses, separately for each frequency band. Channels with significant differences in the respective band are marked (cluster-corrected within the respective time frame). Related to Fig. 3.*

      Compared to the clear parieto-occipital topography of prestimulus alpha modulations, delta and theta effects were prominently shifted to anterior sensors, which renders their involvement in low-level visual processing highly unlikely. No significant effects were observed in the gamma range. In contrast, beta-band modulations were closest to the alpha effects in their topography, covering parietal as well as occipital sites. Although the size of normalised effects were markedly smaller in the beta band (compared to alpha frequencies, cf. colour scaling), the topographic distribution of prestimulus modulations as well as the spectral proximity of the two bands prompted further investigation of beta involvement. To this end, we computed the instantaneous correlation between individual beta power (over the respiration cycle) and respiratory phase, analogous to our main analysis shown in Fig. 4c. Consistent with the TFR analysis shown above, no significant correlation between oscillatory power and respiration time courses were found for delta, theta, and gamma bands. For the beta band, however, we found a significant correlation during the inspiratory phase, similar to the alpha correlation described in the main text (and shown for comparison in the new Supplementary Fig. S5):

      *Fig. S5 | Instantaneous correlation of beta power and perceptual sensitivity. Group-level correlation between individual beta and PsychF threshold courses (averaged between 14 - 30 Hz) with significant phase vector (length of seven time points) marked by dark grey dots (cluster-corrected). Correlation time course of the alpha band (see Fig. 4c) shown for reference in light grey. Related to Fig. 4.*

      While both alpha and beta power were correlated to the breathing signal during the inspiratory phase, the correlation time courses suggested that there might be differential effects in both frequency bands, as indicated by the phase shift visible in Supplementary Fig S5. Therefore, we finally recomputed the LMEM visualised in Fig. 4 with an additional factor for beta power. In this extended model, significant effects were found for both alpha (t(1790) = 3.27, p < .001) and beta power (t(1790) = 4.83, p < .001). Beta showed significant interactions with the sine of the respiratory signal (t(1790) = -3.52, p < .001) as well as with alpha power (t(1790) = -4.63, p < .001). Comparing the LMEM to the previous model which only contained alpha power (along with respiratory sine and cosine) confirmed the significant contribution of beta power in explaining PsychF threshold variation by means of a theoretical likelihood ratio test (χ²(4) = 60.43, p < .001). Overall, we thus found beta power to be i) significantly modulated by respiration (see Fig 1), ii) significantly suppressed over parieto-occipital sensors for hits vs misses (see Fig. S4), and iii) significantly contribute to variations in PsychF threshold (see Fig S5). Collectively, these findings suggest differential roles of alpha and beta power, which we discuss in the main text as well as in the Supplementary Text:

      “Whole-scalp control analyses across all frequency bands demonstrated that this topographical pattern was unique to alpha and beta prestimulus power (see Supplementary Text 1 and Fig. S4).”

      “Control analyses across all frequency bands yielded a significant instantaneous correlation between PsychF threshold and beta power as well, albeit at a slightly later phase (see Fig. S5). No significant correlations were found for the remaining frequency bands.”

      “Accordingly, one recent study proposed that the alpha rhythm shapes the strength of neural stimulus representations by modulating excitability (Iemi et al., 2021). Previous work by Michalareas and colleagues (2016) as well as our own data (see Supplementary Material) point towards an interactions between alpha and beta bands, as beta oscillations have very recently been implicated in mediating top-down signals from the frontal eye field (FEF) that modulate excitability in the visual cortex during spatial attention (Veniero et al., 2021). Our findings suggest that this top-down signalling is modulated across the respiration cycle in a way that changes behavioural performance.”

      In the discussion the authors speculate that respiration locked modulation of alpha power and associated neuronal excitability could be based on the modulation of blood CO2 levels. Most recent studies of respiratory modulation of brain activity have demonstrated significant differences between nasal and oral breathing, with nasal breathing (through activation of the olfactory bulb) typically resulting in a stronger influence of respiration on neuronal activity and behavioral performance than oral breathing. The authors only tested nasal breathing. If blood CO2 fluctuations are indeed responsible for the observed effect, there should be no difference in outcome between nasal and oral breathing. Comparing the two conditions would thus provide interesting additional information about the possible underlying mechanisms.

      We appreciate the reviewer’s well-justified remarks regarding the differential effects for nasal and oral breathing and their implications on underlying mechanisms such as CO2. In revising the present as well as other manuscripts, it has become evident that fluctuations of CO2 alone (and, as we previously discussed, related changes in pH) cannot possibly explain the effects we and others are observing. Therefore, the revised manuscript no longer discusses CO2 as a potential mechanism. We have removed the corresponding paragraph and instead refer to the distinction between nasal and oral breathing to strengthen the argument for OB-induced cross-frequency coupling:

      “As outlined in the introduction, there is broad consensus that cross-frequency coupling (Canolty and Knight, 2010; Jensen and Colgin, 2007) plays a central role in translating respiratory to neural rhythms: Respiration entrains neural activity within the olfactory tract via mechanoreceptors, after which the phase of this infraslow rhythm is coupled to the amplitude of faster oscillations (see Fontanini and Bower, 2006; Ito et al., 2014). While this mechanism is difficult to investigate directly in humans, converging evidence for the importance of bulbar rhythms comes from animal bulbectomy studies (Ito et al., 2014) and the fact that respiration-related changes in both oscillatory power and behaviour dissipate during oral breathing (Zelano et al., 2016; Perl et al., 2019). Thus, rhythmic nasal respiration conceivably aligns rhythmic brain activity across the brain, which in turn influences behaviour. In our present paradigm, transient phases of heightened excitability would then be explained by decreased inhibitory influence on neural signalling within the visual cortex, leading to increased postsynaptic gain and higher detection rates. Given that the breathing act is under voluntary control, the question then becomes to what extent respiration may be actively used to synchronise information sampling with phasic states of heightened excitability.”

      Reviewer #3 (Public Review):

      The topic is timely, the study is well-designed, and the work has been performed in a highly competent manner. The authors relate three variables: respiration, alpha power and perceptual performance, constituting a link between somatic and neuronal physiology and cognition. A particular strength is the temporal resolution of respiration effects on cognition (continuous analysis of the respiration cycle). Furthermore, results are well contextualized by very comprehensively written introduction and discussion sections (which, nevertheless, could be slightly shortened).

      We do appreciate the reviewer’s positive evaluation of our manuscript and are thankful for their constructive remarks. We respond to their comments in detail below and have shortened the Discussion section in response to one of the reviewer’s remarks (kindly see points 1.1 and 2 below).

      I have three points of criticism, all meant in a constructive way:

      1. I wonder whether the authors could have gone one step further in the analysis of causal mechanisms, rather than correlations. The analysis of timing (Fig. 4d) and the last sentence of the abstract suggest that they imagine a causal role of respiratory feedback on cognitive performance, mediated via coordination of brain activity (in the specific case, by increasing excitability in visual areas). This could be made more explicit by appropriate experiments and data analysis:

      1.1. Manipulating the input signal: former studies suggest that nasal respiration is crucial for effects on brain oscillations and/or performance (e.g. Yanovsky et al., 2014; Zelano et al., 2016). Thus, the causal inference could be easily checked by comparing nasal versus oral respiration, without changing gas- and pH-parameters of activity of brainstem centers. >Admittedly, this experiment may add significant work to the present data which, by themselves, are already very strong.

      We thank the reviewer for their insightful comment regarding the question of causality. We acknowledge that our interpretation should have been phrased a little more cautiously. Therefore, we have rephrased corresponding paragraphs at various instances throughout the manuscript (kindly see below). Particular under current circumstances, we further appreciate the reviewer’s concern regarding the acquisition of additional data for a direct comparison of nasal vs oral breathing. Their comment is of course entirely valid and we were eager to address it, especially since it relates to CO2- and/or pH-related mechanisms of RMBOs we previously discussed. In light of the reviewer’s comments (also see their related comment #2 below) and convincing evidence from both animal and human studies that already compared nasal and oral breathing, we no longer feel that changes in CO2 provide a reasonable explanation for respiration-related oscillatory and behavioural effects we observed here. Consequently, we have removed the corresponding paragraph from the Discussion section which now reads as follows:

      “As outlined in the introduction, there is broad consensus that cross-frequency coupling (Canolty and Knight, 2010; Jensen and Colgin, 2007) plays a central role in translating respiratory to neural rhythms: Respiration entrains neural activity within the olfactory tract via mechanoreceptors, after which the phase of this infraslow rhythm is coupled to the amplitude of faster oscillations (see Fontanini and Bower, 2006; Ito et al., 2014). While this mechanism is difficult to investigate directly in humans, converging evidence for the importance of bulbar rhythms comes from animal bulbectomy studies (Ito et al., 2014) and the fact that respiration-related changes in both oscillatory power and behaviour dissipate during oral breathing (Zelano et al., 2016; Perl et al., 2019). Thus, rhythmic nasal respiration conceivably aligns rhythmic brain activity across the brain, which in turn influences behaviour. In our present paradigm, transient phases of heightened excitability would then be explained by decreased inhibitory influence on neural signalling within the visual cortex, leading to increased postsynaptic gain and higher detection rates. Given that the breathing 17 act is under voluntary control, the question then becomes to what extent respiration may be actively used to synchronise information sampling with phasic states of heightened excitability.”

      1.2. Temporal relations: The authors show that respiration-induced alpha modulation precedes behavioral modulation (Fig. 4d and related results text). Again, this finding suggests a causal influence of respiration on performance, mediated by alpha suppression (see results, lines 318-320). Could the data be directly tested for causality (e.g. by applying Granger causality, dynamic causal modelling or other methods)? If this is difficult, the question of causality should at least be discussed more explicitly.

      We appreciate the reviewer’s constructive criticism and their suggestion to employ causal analyses. While we agree that the overall pattern of results strongly suggests a causal cascade of respiration -> excitability -> perception, our interpretation with regard to a dynamic mechanism was probably overly strong. Unfortunately, it is indeed difficult to use directional analyses like Granger causality or DCM on the current data, since these methods quantify the relationship between two time series. They would not allow us to investigate the triad of respiration, alpha power, and behaviour, as we have discrete responses (i.e., single events) instead of a continuous behavioural measure. In fact, we are currently preparing a directional analysis of respiration-brain coupling (in resting-state data without a behavioural component) for an upcoming manuscript. In response to the reviewer’s remarks, we have toned down our interpretation throughout the manuscript and explicitly discuss the question of causality in the Discussion section of the revised manuscript:

      “The bootstrapping procedure yielded a confidence interval of [-33.17 -29.25] degrees for the peak effect of alpha power. While these results strongly suggest that respiration-alpha coupling temporally precedes behavioural consequences, they do not provide sufficient evidence for a strict causal interpretation (see Discussion)”

      “Rigorous future work is needed to investigate potentially causal effects of respiration-brain coupling on behaviour, e.g. by means of directed connectivity within task-related networks. A second promising line of research considers top-down respiratory modulation as a function of stimulus characteristics (such as predictability). This would grant fundamental insights into whether respiration is actively adapted to optimise sensory sampling in different contexts, as suggested by the animal literature.”

      1. At various instances, the authors suggest that respiration-induced changes in pH may be responsible for the changes in cortical excitability which, in turn, affect behavioral performance. In the discussion, they quote respective literature (lines 406-418). I glanced through the quoted papers by Feldman, Chesler, Lee, Dulla and Gourine - as far as I could see none of them suggests that the cyclic process of respiration induces significant cyclic shifts of pH in the brain parenchyma (if at all, this may occur in specialized chemosensory neurons in the brainstem). Moreover, recent real-time measurements by Zhang et al. (Chem. Sci 12:7369-7376) do also not reveal such cyclic changes in the cortex. Finally, translating oscillatory extracellular pH changes (if existent) into changes in inhibitory efficacy would require some time, potentially inducing delays and variance onto the cyclic changes at the network level. I feel that the evidence for the proposed mechanism is not sufficient, notwithstanding that it is a valid hypothesis. Please check and correct the interpretation of the cited literature if necessary.

      We acknowledge the reviewer’s caution regarding our suggestion of pH involvement, which is closely related to their previous comment (kindly see 1.1 above). As the reviewer mentions themselves, there are several studies demonstrating an absence of both neural and behavioural modulations for oral (vs nasal) breathing. These reports provide direct evidence against a mechanism driven by changes in CO2 and/or pH, which would be identical for nasal and oral breathing. Moreover, a second valid criticism is the uncertain temporal delay introduced by the (hypothetical) translation of pH changes into neural signals, which would most likely be incompatible with the ‘online’ (i.e., within-cycle) effects we report here. Therefore, as outlined in our response above, we have removed the pH-related suggestions from the Discussion section.

      1. Finally, some illustrations should be presented in a clearer way for those not familiar with the specifics of MEG analysis.

      We appreciate the reviewer’s suggestions regarding the clarity of our manuscript.

    3. Evaluation Summary:

      Kluger and colleagues investigated the influence of respiration on visual sensory perception in a near-threshold task and argue that the detected correlation between respiration phase and detection precision is liked to alpha power, which in turn is modulated by the phase of respiration. The main finding - that the moment-to-moment relationship between excitability and perception is coupled to the body's slower respiratory oscillation - poses a potentially important advance for advancing our understanding of how the brain-body system works as a whole.

      (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #3 agreed to share their name with the authors.)

    4. Reviewer #1 (Public Review):

      The main finding - that the moment-to-moment relationship between excitability and perception is coupled to the body's slower respiratory oscillation - is novel, interesting, and important for advancing our understanding of how the brain-body system works as a whole. The experiment is simple and elegant, and the authors strike the right level of making the most of the data without doing too much and obscuring the main findings. The primary weakness, in my opinion, is the inability to distinguish between the possibility that respiration modulates excitability and the possibility that respiration modulates something boring like signal-to-noise ratio. In terms of conclusions, I thought the authors stuck pretty well to the data. The one place where the conclusions felt a little bold was in terms of the respiration <> alpha <> behavior relationship, where it felt the authors had already made up their minds re: causality. I agree that it probably makes more sense for respiration to influence something about the brain than vice versa, and the background presented in the Intro/Discussion supports this. However, the analysis only tells us that the behavioral performance was modulated by both alpha and respiration (and their interaction, but this is no way causal). Overall, it will be necessary to differentiate the current interpretation from the possibility that breathing and alpha are two unrelated time courses that influence behavior at the same time (and even interact in how they influence behavior, but just not interact with each other), and I do not believe the phase-amplitude coupling analysis is sufficient for this.

    5. Reviewer #3 (Public Review):

      The topic is timely, the study is well-designed, and the work has been performed in a highly competent manner. The authors relate three variables: respiration, alpha power and perceptual performance, constituting a link between somatic and neuronal physiology and cognition. A particular strength is the temporal resolution of respiration effects on cognition (continuous analysis of the respiration cycle). Furthermore, results are well contextualized by very comprehensively written introduction and discussion sections (which, nevertheless, could be slightly shortened).

      I have three points of criticism, all meant in a constructive way:

      1. I wonder whether the authors could have gone one step further in the analysis of causal mechanisms, rather than correlations. The analysis of timing (Fig. 4d) and the last sentence of the abstract suggest that they imagine a causal role of respiratory feedback on cognitive performance, mediated via coordination of brain activity (in the specific case, by increasing excitability in visual areas). This could be made more explicit by appropriate experiments and data analysis:

      1.1. Manipulating the input signal: former studies suggest that nasal respiration is crucial for effects on brain oscillations and/or performance (e.g. Yanovsky et al., 2014; Zelano et al., 2016). Thus, the causal inference could be easily checked by comparing nasal versus oral respiration, without changing gas- and pH-parameters of activity of brainstem centers. Admittedly, this experiment may add significant work to the present data which, by themselves, are already very strong.

      1.2. Temporal relations: The authors show that respiration-induced alpha modulation precedes behavioral modulation (Fig. 4d and related results text). Again, this finding suggests a causal influence of respiration on performance, mediated by alpha suppression (see results, lines 318-320). Could the data be directly tested for causality (e.g. by applying Granger causality, dynamic causal modelling or other methods)? If this is difficult, the question of causality should at least be discussed more explicitly.

      2. At various instances, the authors suggest that respiration-induced changes in pH may be responsible for the changes in cortical excitability which, in turn, affect behavioral performance. In the discussion, they quote respective literature (lines 406-418). I glanced through the quoted papers by Feldman, Chesler, Lee, Dulla and Gourine - as far as I could see none of them suggests that the cyclic process of respiration induces significant cyclic shifts of pH in the brain parenchyma (if at all, this may occur in specialized chemosensory neurons in the brainstem). Moreover, recent real-time measurements by Zhang et al. (Chem. Sci 12:7369-7376) do also not reveal such cyclic changes in the cortex. Finally, translating oscillatory extracellular pH changes (if existent) into changes in inhibitory efficacy would require some time, potentially inducing delays and variance onto the cyclic changes at the network level. I feel that the evidence for the proposed mechanism is not sufficient, notwithstanding that it is a valid hypothesis. Please check and correct the interpretation of the cited literature if necessary.

      3. Finally, some illustrations should be presented in a clearer way for those not familiar with the specifics of MEG analysis. I add some specific suggestions below.

    1. Author Response:

      Reviewer #1 (Public Review):

      The model proposed here is the first large-scale model that actually performs a cognitive task, which in this case is working memory but could easily extend to decision making in general as is acknowledged by the authors. Briefly, each of the 30 areas are simulated as a rate, Wong-Wang circuit (i.e. two excitatory pools inhibit each other through a third, inhibitory population). The authors use previously collected anatomical data to constrain the model and show qualitatively match with the data, in particular how mnemonic activity emerges somewhat abruptly along the brain hierarchy.

      Strengths Previous models have focused on neural dynamics during the so-called "resting state", in which subjects are not performing any cognitive task - thus, resting. This study is therefore an important improvement in the field of large-scale modelling and will certainly become an influential reference for future modelling efforts. As typically done in large-scale modelling, some anatomical data is used to constrain the model. The model shows several interesting characteristics, in particular how distributed working memory is more resilient to distractors and how the global attractors can be turned off by inhibition of only top areas.

      Weaknesses Some of these results are not clear how they emerge, and some "biological constraints" do not seem to constrain. Moreover, some claims are slightly exaggerated, in particular how the model matches the data in the literature (which in some cases it does not) or how somatosensory working memory can be simulated by simply stimulating the "somatosensory cortex".

      This paper has two different models, one being a simplified version of the main model. However, it is not very clear what the simplified model adds the main findings, if not to show that the empirical anatomical connectivity does not constrain the full model.

      We thank the reviewer for this evaluation, and for appreciating the innovative character of our study in implementing a cognitive function in a data-constrained large-scale brain model. We hope that it will be useful for future studies planning to add cognitive functions to their large-scale models, and also for experimentalists who might benefit from this insight.

      In response to the detailed comments of the reviewer, and to address the weaknesses identified above, we have rewritten parts of the text, clarified important concepts and included a new simulations. Briefly:

      -We have clarified the nature and effects of the ‘biological constraints’ that we use. The full model that we use is indeed data-constrained, in the sense that we use real data to determine the values of many parameters. Having a data-constrained model, however, does not mean that all the results will be equally constrained. Some model results will critically depend on (some) data used to constrain the model, while other results will be more robust to changes in these parameters. We have highlighted this point and we also added explanations for each of the results presented.

      -We have corrected several claims along the text to make it more in line with experimental evidence, and included the new references suggested by the reviewer to this effect. For example, for the case of somatosensory WM mentioned by the reviewer, we have indicated that the existence of a ‘gating’ mechanism (explored in a supplementary figure) is important for achieving an accurate match with the experimentally observed effects of somatosensory stimulation.

      -Finally, we have highlighted the complementary benefits of the full and simplified models, and improved our motivation for the latter. Briefly, the simplified model allows us to identify the key ingredients needed for distributed WM (useful to generalize to other animal models), while the full model ensures that the main findings are still present when more realistic assumptions are made. A good example is the counterstream inhibitory bias, which is in principle not necessary for a simplified model but becomes a crucial factor to implement the distributed WM mechanism in our macaque model.

      Reviewer #2 (Public Review):

      There is a lot to like about this manuscript. It provides a large-scale model of a well-known phenomenon, the "delay activity" underlying working memory, our oldest and most enduring model of a cognitive function. The authors correctly state that despite the ubiquity of delay activity, there is little known about the macro and micro circuitry that produces it. The authors offer a computational model with testable hypotheses that is rooted in biology. I think this will be of interest to a wide variety of researchers just as delay activity is studied across a variety of animal models, brain systems, and behavior. It is also well-written.

      My main concern is the authors may be self-handicapping the impact of their model by not taking into account newer observations about delay activity. For a number of years now, evidence has been building that working memory is more complicated than "persistent activity" alone. Stokes, Pasternak, Dehaene, Miller and others have been mounting considerable evidence for more complex dynamics and for "activity-silent" mechanisms where memories are briefly held in latent (non-active) forms between bouts of spiking. There is also mounting evidence that the thalamus plays a key role in working memory (and attention). In particular, higher thalamic nuclei are critical for regulating cortical feedback. Cortical feedback plays a central role in the model presented here. The model presented in this manuscript just deals with persistent attractor states and the cortex alone.

      This is not to say that this manuscript does not have good value as is. No one disputes that some form of elevated, sustained, activity underlies working memory. This work adds insights into how that activity gets sustained and the role of, and interactions between, different cortical areas. The observation that the prefrontal and parietal cortex are more critical than other areas, that there are "hidden" attractor states, and "counterstream inhibitory bias" are important insights (and, importantly, testable). They will likely remain relevant even as the field is moving beyond persistent attractor states alone as the model for working memory. The new developments do not argue against the importance of delay activity in working memory. They show that it is more to the story, as inevitably happens in brain science.

      The authors do include a paragraph in the Discussion referencing the newer developments. Kudos to them for that. However, it presented as "new stuff to address in the future". Well, that future is now. These "newer" developments have been mounting over the past 10 years. The worry here is that by relying so heavily on the older persistent attractor dynamics model and presenting it as the only model, the authors are putting an early expiration date on their work, at least in terms of how it will be received and disseminated.

      We thank the reviewer for a careful and positive evaluation of our work. We consider that the main point raised here is indeed crucial: classical explanations of WM based on elevated and constant firing are an important part of the story, however other alternative or complementary approaches developed in the past years also deserve attention. These approaches include, to name a few, activitysilent mechanisms (Mongillo et al. 2008, Trübutschek et al. 2017), dynamic hidden states (Wolff et al. 2017), persistent activity without feedback (Goldman 2009), and paradigms relying on gamma bursts (Miller et al. 2018).

      It’s important to highlight, however, that our approach is “attractor network theory” not “persistent activity theory”, and an attractor does not have to be a steady state (tonic firing) but may display complex spatiotemporal patterns (fluid turbulence with tremendously rich temporal dynamics and eddies on many spatial scales is an attractor). We now have largely eliminated the use of “persistent” in the manuscript. On the other hand, for lack of a better word it’s fine to still use that term, if it is understood in a more general sense, which also includes stable representations in which the activity of individual neurons varies along the delay period (Goldman, 2009; Murray et al. 2017) or rhythmic activity which persists over time (Miller et al. 2018). The attractor network theory should be contrasted conceptually with mechanisms based on intrinsically transient memory traces (see Wang TINS 2021 for a more elaborated discussion on this).

      Our proposal for distributed WM has a general aim and it’s not restricted to the classical ‘elevated constant firing’ scenario. Following the reviewer’s suggestion, we have rewritten the text to make sure that multiple mechanisms of WM are acknowledged in different parts of the text, not only on a paragraph in the discussion. We have also acknowledged the importance of thalamocortical interactions and cited previous relevant studies in this sense (such as Guo et al. 2017), also as a response to comments from Reviewer 1.

      In addition, we have attempted to go beyond a simple rewriting and, using a variation of our simplified model, we now show that distributed WM representations can also happen in the context of activitysilent models (Figure 3 –figure supplement 1). In particular, we use a simplified network model with reduced local and long-range connectivity strength and incorporate short-term synaptic facilitation in synaptic projections. Our model results show that, while activity-silent memory traces can’t be maintained when areas are isolated from each other, inter-areal projections reinforce the synaptic efficacy levels and lead to a distributed representation via activity-silent mechanisms.

      We hope that this result serves to prove the generality of our distributed WM framework, and opens the door to subsequent studies focusing not only on distributed activity-silent mechanisms, but in distributed frameworks relying on other WM mechanisms as well.

    2. Evaluation Summary:

      Mejias and Wang propose here the first large-scale model of the brain that actually performs a cognitive task. Previous models have focused on neural dynamics during the so-called "resting state", in which subjects are not performing any cognitive task - thus, resting. This study is therefore an important improvement in the field of large-scale modelling and will certainly become an influential reference for future modelling efforts.

      (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. The reviewers remained anonymous to the authors.)

    3. Reviewer #2 (Public Review):

      There is a lot to like about this manuscript. It provides a large-scale model of a well-known phenomenon, the "delay activity" underlying working memory, our oldest and most enduring model of a cognitive function. The authors correctly state that despite the ubiquity of delay activity, there is little known about the macro and micro circuitry that produces it. The authors offer a computational model with testable hypotheses that is rooted in biology. I think this will be of interest to a wide variety of researchers just as delay activity is studied across a variety of animal models, brain systems, and behavior. It is also well-written.

      My main concern is the authors may be self-handicapping the impact of their model by not taking into account newer observations about delay activity. For a number of years now, evidence has been building that working memory is more complicated than "persistent activity" alone. Stokes, Pasternak, Dehaene, Miller and others have been mounting considerable evidence for more complex dynamics and for "activity-silent" mechanisms where memories are briefly held in latent (non-active) forms between bouts of spiking. There is also mounting evidence that the thalamus plays a key role in working memory (and attention). In particular, higher thalamic nuclei are critical for regulating cortical feedback. Cortical feedback plays a central role in the model presented here. The model presented in this manuscript just deals with persistent attractor states and the cortex alone.

      This is not to say that this manuscript does not have good value as is. No one disputes that some form of elevated, sustained, activity underlies working memory. This work adds insights into how that activity gets sustained and the role of, and interactions between, different cortical areas. The observation that the prefrontal and parietal cortex are more critical than other areas, that there are "hidden" attractor states, and "counterstream inhibitory bias" are important insights (and, importantly, testable). They will likely remain relevant even as the field is moving beyond persistent attractor states alone as the model for working memory. The new developments do not argue against the importance of delay activity in working memory. They show that it is more to the story, as inevitably happens in brain science.

      The authors do include a paragraph in the Discussion referencing the newer developments. Kudos to them for that. However, it presented as "new stuff to address in the future". Well, that future is now. These "newer" developments have been mounting over the past 10 years. The worry here is that by relying so heavily on the older persistent attractor dynamics model and presenting it as the only model, the authors are putting an early expiration date on their work, at least in terms of how it will be received and disseminated.

    1. Author Response:

      Reviewer #3 (Public Review):

      The paper contains a substantial amount of novel experimental work, the experiments appear well done, and the analysis of the data makes sense. Raw data and analysis scripts have been made fully available.

      I have two specific comments:

      • While the paper talks extensively about deep mutational scanning, I don't think this is a deep mutational scanning study. In deep mutational scanning, we usually make every possible single-point mutation in a protein. This is not what was done here, as far as I can tell.

      In the revised manuscript, we have avoided using deep mutational scanning to describe our experimental design. Instead, we described our approach as “a high-throughput experimental approach that coupled combinatorial mutagenesis and next-generation sequencing”

      • For the analysis of epistasis vs distance (Fig 4d, e, f), it would be better to look at side-chain distances rather than C_alpha distances. In covariation analyses, it can be seen that C_alpha distances are not a good predictor of pairwise interactions. Similar patterns may be observable here.

      See e.g.: A. J. Hockenberry, C. O. Wilke (2019). Evolutionary couplings detect side-chain interactions. PeerJ 7:e7280.

      Thank you for the suggestion. In the revised manuscript, we replaced the Cα analysis by a side-chain analysis according to Hockenberry and Wilke (see response to Essential Revisions above).

    1. Evaluation Summary:

      This paper is of interest not only for immunologists studying the inflammation, but also for biomedical researchers studying various biological processes using C57BL/6 mice. The data in this paper indicate that genetic differences between C57BL/6 substrains can affect reproducibility and generalizability in a broad range of biological studies with mouse models reported to date.

      (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. The reviewers remained anonymous to the authors.)

    2. Reviewer #1 (Public Review):

      Rawle, Le et al. investigated the influence of genetic background of murine models of granzyme A absence or inactivity on the arthritic foot swelling phenotype induced by chikungunya virus. By using CRISPR/Cas knockouts, the authors show that the reduced foot swelling previously attributed to the absence of granzyme A (GzmA-/-), was in truth due to the presence of intact nicotinamide nucleotide transhydrogenase in GzmA-/-, since the experimental controls used in previous studies bear a truncated Nnt, which results in the lack of activity of the corresponding enzyme. It is worth noting that these results challenge the previously stated role of granzyme A in the arthritic foot swelling phenotype induced by chikungunya virus infection. Moreover, an interesting analysis of previous literature reveals a significant number of studies comparing mice with truncated Nnt to mice with full length Nnt. Concerns over Nnt genotype in experimental settings are not new and have been raised in several studies since the description of the truncated Nnt in C57BL/6J by Toye et al. (PMID: 15729571). However, this is the first study to analyze Sequence Read Archive (SRA- NCBI) data in order to demonstrate that inappropriate comparisons regarding the Nnt genotype also occur due to listing errors and inadequate backcrossing in knock-out mice. Thus, this study emphasizes the importance of characterizing genetic backgrounds when conducting experiments on mice.

      Strengths

      By using CRISPR/Cas knockouts, the authors provide reliable data that strongly support the conclusions. Experimental controls and comparisons are well-conceived and suitable for the aim of the study.

      Weakness

      Despite the presence of a section dedicated to the re-investigation of the physiological role of granzyme A, the authors did not reach a conclusion on this subject. Experimental settings carried out to investigate the role of granzyme A were poorly explored. Moreover, the possible role of granzyme A in other chikungunya virus-induced phenotypes was not investigated, as acknowledged by the authors. Additionally, the analysis of SRA-NCBI data did not comprise the presence of heterozygous carriers of the truncated Nnt allele, which could also generate a distinct phenotype due to gene-dose effects.

    3. Reviewer #2 (Public Review):

      In a previous paper, the authors demonstrated that the chikungunya virus (CHIKV)-induced arthritis (foot swelling) was attenuated in Gzma gene knock-out (C57BL/6J-Gzma-/-) mice compared to C57BL/6J (B6J) mice. In this paper the authors created C57BL/6J-GzmaS211A knock-in mice that have a mutation at GzmA active site to verify the role of GzmA in CHIKV-induced arthritis. The authors observed that foot swelling following CHIKV infection was not different between B6J-GzmaS211A and B6J. The authors then conducted whole-genome sequencing of B6-Gzma-/-and found that this mouse has a mixed (mosaic) genetic background of C57BL/6J and C57BL/6N (B6N). Of note, B6J uniquely has a loss-of-function deletion mutation of exons 8-12 of the nicotinamide nucleotide transhydrogenase (Nnt) gene that plays a critical role in scavenging mitochondrially generated ROS, while both B6-Gzma-/-and B6N have an intact (functional) Nnt gene. The authors then created B6N-NntΔexon8-12 mice and demonstrated that CHIKV arthritic foot swelling was ameliorated in the mice, reinforcing the contention that the amelioration of foot swelling in B6-Gzma-/-mice was rather due to functional Nnt gene introgressed into the strain from B6N. The authors also conducted RNA-seq analysis of the CHIKV foot and revealed that signatures of CHIKV arthritis in B6-Gzma-/-mice are reduced ROS and more importantly reduced cell (leukocyte) migration. The authors then re-evaluated the physiological role of GzmA by administration of polyinosinic:polycytidylic acid (poly(I:C)) to C57BL/6J-GzmaS211A knock-in mice and concluded that one of the roles of circulating GzmA is to activate monocyte/macrophages. Finally, the authors undertook a k-mer mining approach to transcriptome data deposited in the public database to find that ~27% of NCBI Sequence Read Archive (SRA) Run accessions and ~38% of BioProjects are labeled erroneously as collected from C57BL/6J. Based on these data the authors concluded that widespread discrepancy in Nnt genotypes complicates granzyme A and other knockout mouse studies.

      This is a well-written paper containing interesting results. The conclusions of this paper are well supported by ample data obtained from an appropriately and carefully designed methodology.

      This reviewer agrees with the authors' contention that "the C57BL/6J-GzmaS211A knock-in mice should allow assessment of the physiological function of GzmA without the confounding influence of differences in Nnt or other genes associated with the mixed genetic background" (Line 516). However, this reviewer considers that the more appropriate model would be C57BL/6N-GzmaS211A knock-in mice with the functional Nnt gene, given that most humans have a functional NNT gene (Line 547).<br> Also, the authors may be able to describe how C57BL/6J-GzmaS211A (or C57BL/6N-GzmaS211A) knock-in mice can be used to resolve the critical issues concerning granzyme A, such as target(s) of the extracellular target of the enzyme and molecular mechanisms of the enzyme on activation of monocyte/macrophage for the benefit of the reader.

    1. Author Response:

      Reviewer #1 (Public Review):

      This manuscript describes single molecule measurements of rotation of the C10 ring of E. coli ATP synthase in intact complexes embedded in lipid nanodiscs. The major point of the work is to identify the mechanisms by which protonation/deprotonation steps produce torque between the a-subunit and the C10 ring, which is subsequently conveyed to F1 to couple to ATP synthesis. The work explores the pH-dependent of the "transient dwell" (TD) phenomenon of rotation motion to identify likely intermediates, showing a likely step of 11o in the "clockwise" (ATP synthase-related) direction. The results are then interpreted in the context of detailed structural information from previous cryo-EM and X-ray crystallographic reports, to arrive at a more detailed model for the partial steps for coupling of proton translocation to motion. The effects of site-specific mutations in the c-subunits appears to support the overall model.

      While the detailed structural arguments seem, at least to this reader, to be plausible, the text is not structured for any hypothesis testing, and one might imagine that alternative models are possible. No alternative models were presented, so it is not clear to what extent the 11o rotation step rules out such possibilities. This leaves the reader with the feeling that a lot of speculation occurs in the Discussion, but it is very difficult to figure out which parts are solid and which parts are speculation.

      We have now presented the results in terms of hypothesis testing specifying alternative hypotheses that exist in the literature. We then specify results presented in the manuscript that discriminate between alternate hypotheses.

      The Discussion also tries to pack in too many concepts, going well beyond the advances enabled by the TD results themselves. For example, the proton "funnel" concept is quite interesting, but it is not easy to see how the TD leads up to it. This overpacking makes it difficult to pinpoint the real advances, and dilutes the message sets the reader up to ask for more support for such extensive modeling. Do the mechanistic details set up good testable hypothesis for future experimental tests?

      It is clear that the pKa values we determined are the result of multiple residues involved in the proton transfer process. It is currently not possible to determine where the input channel starts. The recent structures now show that the residues that were thought to define the input channel are far from the surface and must communicate with the periplasm via the funnel. Residues in the funnel likely impact the pKa values that we have measured. The proton transfer-dependent 11 degree step that we measured must also depend upon the funnel. Our results clearly show that this 11 degree step depends upon the correct protonation states of both the input and output channels, and that this depends on the differences in the high and low pKa values. The possibility also exists that this funnel that is absent in the output channel may provide a proton reservoir to supply the input channel, which promotes the ability of input and output channels to drive the 11 degree synthase steps. We have now included this information in the manuscript, and for these reasons, we decided that discussion of the funnel must remain.

      Overall, the text would be far more impactful if it focused more tightly on the implications of the TD results themselves, testing specific sets of models, and taking more care to guide the readers through the interpretation.

      We have extensively rewritten the entire manuscript to address these issues. To help guide the readers, we added more background information to the introduction and pose alternate hypotheses. In the results, we now guide the readers by restating how the experiments can test a given hypothesis, and include brief conclusions that explain why a hypothesis is eliminated or favored based on the results. We shortened the Discussion to make it more focused, with the exception that we provided additional information that has been requested by the reviewers. We also tie each point in the discussion to the results presented. Of course, a good discussion is meant to put the results and conclusions of the manuscript into the context of results and conclusions from other laboratories, which we have done.

      Reviewer #2 (Public Review):

      This brilliant, beautiful and important study provides the essential kinetic framework for the recent, static high-resolution cryo-EM structures of F1FO ATPases from bacteria, chloroplasts and mitochondria. The elegantly conducted single-molecule work is necessarily complex, and its analysis is difficult to follow, even for someone who is intimately familiar with F1FO ATPases. Some more background and better explanations would help.

      We added additional background information to the Introduction, and we now periodically explain the reasoning and conclusions in the Results to help guide the readers.

      For F1FO ATPases, CCW rotation has little if any biological relevance, whereas CW rotation is centrally important. Evidently, the CW ATP synthesis mode is not accessible to the approach taken in this manuscript, since the ATP synthase is reconstituted into lipid nanodiscs rather than liposomes. This critical fact should be stated more clearly in the introduction.

      We now state explicitly that net rotation was observed as the result of F1-ATPase activity as requested. We also note that E. coli does sometimes use F1Fo as an ATPase-dependent proton pump to maintain a pmf across the membrane depending upon metabolic conditions.

      The central concepts of "transient dwells", "dwell times" and "power strokes" need to be introduced more fully for a general, non-expert audience.

      We added this information to the Introduction as requested.

      The manuscript describes the power stroke and dwell times in CCW ATP hydrolysis mode in unprecedented detail. Presumably the dwell times and power strokes apply equally to the physiologically relevant CW ATP synthesis mode, but are they actually the exact reverse? Is there evidence for transient dwells and 36{degree sign} power strokes divided into 11{degree sign}+25{degree sign} substeps during ATP synthesis?

      The 36° subunit-c stepping that contain 11° synthase-direction steps is a novel observation first reported in this study. To date, single-molecule studies of rotation during net ATP synthesis have been carried out using single-molecule FRET that have been able to observe only 3 or 4 consecutive synthase steps for a given F1Fo molecule (Dietz et al. ((2004) Proton-powered subunit rotation in single membrane-bound FoF1-ATP synthase. Nature Struct & Mol Bio). The FRET measurements do not have the time resolution to resolve sub-steps. Whether or not continuous rotation in the synthesis direction is the exact reverse of ATPase-dependent rotation is an important question that remains to be answered.

      The meaning of low, medium and high efficiency of transient dwell formation (Figure legend 2; lines 189/190; Figure 3; line 365) is not obvious and not well explained. How are these efficiencies defined? Why are they important? What would be 100% efficiency? And what would be 0%?

      Background information concerning the three efficiencies of transient dwell formation has been added to the Introduction, and we now explain their importance. We also now explain what 100% and 0% efficiency is in the Results.

      Why is it important whether transition dwells do or do not contain a synthase step? Is this purely stochastic? If not, what does it depend on?

      We added a paragraph to the discussion to explain that their formation depends on the kinetics of the rate of formation of the interaction between subunit-a and the c-ring versus the velocity of ATPase-depending rotation in the opposite direction, and that it depends on the energy that can drive the synthase-direction step relative to the energy that drives the ATPase direction power stroke. More work is required to define the energetic parameters of these opposing rotations that is beyond the scope of the work presented here.

      The formation of a salt bridge between aR210 of subunit-a and cD61 of the c-ring rotor would seem to be counter-productive for unhindered rotary catalysis. What is the evidence for such a salt bridge from the cryo-EM structures or molecular dynamics simulations?

      This is an excellent question, especially since the distances between aR210 and cD61 are more consistent with intervening water molecules. We revised the paragraph in the Discussion describing this point and have been more explicit about the importance of the aqueous vestibule between the output channel and aR210 must play during rotation, which includes the impact of the dielectric constant inside the vestibule. As a direct answer to the reviewer’s question, in the absence of water, a salt bridge between aR210 and cD61 in such a hydrophobic environment would be so strong that the energy of a proton from the input channel would never be able to dislodge them.

      Reviewer #3 (Public Review):

      Yanagisawa and Frasch utilise a gold nanorod single molecule method to probe the pH dependency of F1FO rotation. The experimental setup has been previously used to investigate both F1-ATPase and FO function in multiple studies. In this study, clockwise rotations are observed in transient dwells which may correlate to synthesis sub-steps. Mutations along the proposed proton path modify the pH dependency of the transient dwells.

      The strength of this manuscript can be seen in the rigorous way in which the problem has been explored. Testing the pH dependence of mutants along the proposed proton path and linking this to potential sub-steps using the known atomic structure.

      In my view, the main weakness of this study is the experimental design (shown in Fig. 1C). Strictly, the measurements show rotation of the c-ring relative to subunit-Beta rather than relative to subunit-a. Recent structures of E. coli F1FO ATP synthase inhibited by ADP (doi: 10.1038/s41467-020-16387-2) have shown that the peripheral stalk is flexible and can accommodate movements of the c-ring relative to the F1 (AlphaBeta)3-subunit ring. For example, comparison of PDB entries 6PQV and 6OQS shows that FO (the c-ring and subunit-a) can rotate 10 degrees as a rigid body relative to the F1 (AlphaBeta)3-subunit ring - with no relative rotation between the c-ring and subunit-a, or rotation of subunit-gamma. The authors discuss structures from this study related by a 25 degree rotation of the c-ring relative to subunit-a, but I do not believe they have ruled out the possibility that their observations show rotation of the FO as a rigid body. A preprint investigating E. coli F1FO ATP synthase in the presence of ATP has proposed that the complex becomes more flexible during ATP hydrolysis (doi: 10.1101/2020.09.30.320408), with the central stalk twisting by up to 65 degrees. The small CW movements seen in the transient dwells in this study could be attributed to 36 degree FO sub steps, facilitated by central stalk flexibility, with counter rotation facilitated by peripheral stalk flexibility.

      The data clearly show that the mutations of subunit-a residues in the input or output channels significantly change the pKa values of TD formation (Figs 2B and 2C), and can dramatically change the occurrence of the synthase-direction steps (see Figs 4D and 4E). These results clearly indicate that the rotational events observed in this study do not result from rotation of subunit-a and the c-ring as a unit.

      With regard to the recent structures that the reviewer refers to, we report differences in the efficiency of TD formation that are consistent with torsion induced by rotation of the c-ring relative to the beta subunit, which we reported previously (Yanagisawa and Frasch, JBC 2017), and which has been confirmed by independent single-single molecule studies by the Junge lab and by the Boersch lab using different approaches to our own (Sielaff et al., Molecules 2019). Both papers are cited in the manuscript. We have now expanded the introduction to include these results describing the impact of central stalk flexibility on the ability to form synthase-direction steps, and how these results are consistent with E. coli cryo-EM structures similar to those referred to (27).

      It is also unclear what causes the stochastic nature of transient dwells. Are these related to inhibition of F1-ATPase? Could increased drag in FO increase the likelihood of F1-ATPase inhibition?

      We now include background information from our prior publications that characterizes the kinetic component that affects the ability to form a transient dwell. Ishmukhametov et al. EMBO J (2010), reported an increase in TDs upon increasing the drag on the nanorod that slowed the power stroke angular velocity. We decribed the kinetics of TD formation in that paper. In the Discussion, we also now provide information concerning how the bioenergetics can impact the probability of TD formation.

    2. Evaluation Summary:

      This paper is of outstanding interest to the broad community of scientists interested in biological energy conversion in general and rotary ATPases in particular. The authors show that the 36{degree sign} power stroke in ATP synthesis is subdivided into two steps of 11{degree sign} and 25{degree sign} in the E. coli enzyme, which serves as a comparatively simple model of the fundamental and universally important process of ATP production in mitochondria and chloroplasts. By combining precise and sophisticated single-molecule studies with directed mutagenesis, this work provides the much-needed functional context for recent high-resolution cryo-EM structures of rotary ATPases.

      (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #2 agreed to share their name with the authors.)

    3. Reviewer #1 (Public Review):

      This manuscript describes single molecule measurements of rotation of the C10 ring of E. coli ATP synthase in intact complexes embedded in lipid nanodiscs. The major point of the work is to identify the mechanisms by which protonation/deprotonation steps produce torque between the a-subunit and the C10 ring, which is subsequently conveyed to F1 to couple to ATP synthesis. The work explores the pH-dependent of the "transient dwell" (TD) phenomenon of rotation motion to identify likely intermediates, showing a likely step of 11o in the "clockwise" (ATP synthase-related) direction. The results are then interpreted in the context of detailed structural information from previous cryo-EM and X-ray crystallographic reports, to arrive at a more detailed model for the partial steps for coupling of proton translocation to motion. The effects of site-specific mutations in the c-subunits appears to support the overall model.

      While the detailed structural arguments seem, at least to this reader, to be plausible, the text is not structured for any hypothesis testing, and one might imagine that alternative models are possible. No alternative models were presented, so it is not clear to what extent the 11o rotation step rules out such possibilities. This leaves the reader with the feeling that a lot of speculation occurs in the Discussion, but it is very difficult to figure out which parts are solid and which parts are speculation.

      The Discussion also tries to pack in too many concepts, going well beyond the advances enabled by the TD results themselves. For example, the proton "funnel" concept is quite interesting, but it is not easy to see how the TD leads up to it. This overpacking makes it difficult to pinpoint the real advances, and dilutes the message sets the reader up to ask for more support for such extensive modeling. Do the mechanistic details set up good testable hypothesis for future experimental tests?

      Overall, the text would be far more impactful if it focused more tightly on the implications of the TD results themselves, testing specific sets of models, and taking more care to guide the readers through the interpretation.

    4. Reviewer #2 (Public Review):

      This brilliant, beautiful and important study provides the essential kinetic framework for the recent, static high-resolution cryo-EM structures of F1FO ATPases from bacteria, chloroplasts and mitochondria. The elegantly conducted single-molecule work is necessarily complex, and its analysis is difficult to follow, even for someone who is intimately familiar with F1FO ATPases. Some more background and better explanations would help.

      For F1FO ATPases, CCW rotation has little if any biological relevance, whereas CW rotation is centrally important. Evidently, the CW ATP synthesis mode is not accessible to the approach taken in this manuscript, since the ATP synthase is reconstituted into lipid nanodiscs rather than liposomes. This critical fact should be stated more clearly in the introduction.

      The central concepts of "transient dwells", "dwell times" and "power strokes" need to be introduced more fully for a general, non-expert audience.

      The manuscript describes the power stroke and dwell times in CCW ATP hydrolysis mode in unprecedented detail. Presumably the dwell times and power strokes apply equally to the physiologically relevant CW ATP synthesis mode, but are they actually the exact reverse? Is there evidence for transient dwells and 36{degree sign} power strokes divided into 11{degree sign}+25{degree sign} substeps during ATP synthesis?

      The meaning of low, medium and high efficiency of transient dwell formation (Figure legend 2; lines 189/190; Figure 3; line 365) is not obvious and not well explained. How are these efficiencies defined? Why are they important? What would be 100% efficiency? And what would be 0%?

      Why is it important whether transition dwells do or do not contain a synthase step? Is this purely stochastic? If not, what does it depend on?

      The formation of a salt bridge between aR210 of subunit-a and cD61 of the c-ring rotor would seem to be counter-productive for unhindered rotary catalysis. What is the evidence for such a salt bridge from the cryo-EM structures or molecular dynamics simulations?

    5. Reviewer #3 (Public Review):

      Yanagisawa and Frasch utilise a gold nanorod single molecule method to probe the pH dependency of F1FO rotation. The experimental setup has been previously used to investigate both F1-ATPase and FO function in multiple studies. In this study, clockwise rotations are observed in transient dwells which may correlate to synthesis sub-steps. Mutations along the proposed proton path modify the pH dependency of the transient dwells.

      The strength of this manuscript can be seen in the rigorous way in which the problem has been explored. Testing the pH dependence of mutants along the proposed proton path and linking this to potential sub-steps using the known atomic structure.

      In my view, the main weakness of this study is the experimental design (shown in Fig. 1C). Strictly, the measurements show rotation of the c-ring relative to subunit-Beta rather than relative to subunit-a. Recent structures of E. coli F1FO ATP synthase inhibited by ADP (doi: 10.1038/s41467-020-16387-2) have shown that the peripheral stalk is flexible and can accommodate movements of the c-ring relative to the F1 (AlphaBeta)3-subunit ring. For example, comparison of PDB entries 6PQV and 6OQS shows that FO (the c-ring and subunit-a) can rotate 10 degrees as a rigid body relative to the F1 (AlphaBeta)3-subunit ring - with no relative rotation between the c-ring and subunit-a, or rotation of subunit-gamma. The authors discuss structures from this study related by a 25 degree rotation of the c-ring relative to subunit-a, but I do not believe they have ruled out the possibility that their observations show rotation of the FO as a rigid body. A preprint investigating E. coli F1FO ATP synthase in the presence of ATP has proposed that the complex becomes more flexible during ATP hydrolysis (doi: 10.1101/2020.09.30.320408), with the central stalk twisting by up to 65 degrees. The small CW movements seen in the transient dwells in this study could be attributed to 36 degree FO sub steps, facilitated by central stalk flexibility, with counter rotation facilitated by peripheral stalk flexibility.

      It is also unclear what causes the stochastic nature of transient dwells. Are these related to inhibition of F1-ATPase? Could increased drag in FO increase the likelihood of F1-ATPase inhibition?

    1. Reviewer #1 (Public Review): 

      Summary:

      Moody et al. presented a comprehensive investigation into the choice of marker genes and its impact on the reconstruction of the early evolution of life, especially regarding the length of the branch that separates domains Bacteria and Archaea in the phylogenetic tree. Specifically, this work attempts to resolve a debate raised by a previous work: Zhu et al. Nat Commun. 2019, that the evolutionary distance between the two domains is short as estimated using an expanded set of marker genes, in contrast to conventional strategies which involve a small number of "core" genes and indicate a long branch. 

      Through a series of analyses on 1000 genomes, Moody et al. defended the use of core genes, and reinforced the conventional notion that the inter-domain branch (the AB branch) is long, as inferred by the core gene set. They proposed that with the 381 marker genes (the "expanded" set) used by Zhu et al., the observed short branch length is an artifact due to inter-domain gene transfer and hidden paralogy. Through topology tests, they ranked the markers by "verticality", and showed that it is positively correlated with the AB branch length. They also conducted divergence time estimation and showed that even the most vertical genes led to an implausible estimate of the origin of life. 

      In parallel, Moody et al. surveyed the best marker genes using a set of 700 genomes. They recovered 54 markers, and demonstrated that ribosomal markers do not indicate a longer AB branch than non-ribosomal markers do. With the better half (27) of these marker genes, they conducted further phylogenetic analyses, which shows that potential substitutional saturation and the use of site-homogeneous models could contribute to the underestimation of the AB branch. Using this taxon set and marker set, they reconstructed the prokaryotic tree of life, which revealed a long AB branch, a basal placement of DPANN in Archaea, and a derived placement of CPR in Bacteria. 

      Prokaryotic tree of life:

      The scope(s) of the manuscript is somehow split. First, it is posed as a point-to-point rebuttal to the Zhu et al. paper, on the long vs. short AB branch question. Second, it introduces a new phylogeny of prokaryotes using 27 "good" marker genes, and demonstrates that DPANN is basal to Archaea, and CRP is derived within Bacteria. 

      The second scope has inadequate novelty. A recent paper (Coleman et al. Science. 2021), which was from a partially overlapping group of authors, was dedicated to the topic of CPR placement, and indicated the same conclusion (CPR being derived and sister to Chloroflexi) as the current work does, albeit using more sophisticated approaches. The paper also addressed the debate of CPR placement (including citing the Zhu et al. paper). Additionally, the basal placement of DPANN has also been suggested by previous works (such as Castelle and Banfield. Cell. 2018). Therefore, re-addressing these two topics using a largely well-established and repeatedly adopted method on a relatively small taxon set does not constitute a significant extension of current knowledge. 

      The debate:

      The first scope appears to be the more important goal of this manuscript, as it extensively discusses the claims made by Zhu et al. and presents a point-to-point rebuttal, including counter evidence. This may narrow the interest of this work to a small audience of specialists. Nevertheless, to best evaluate the current work, it is necessary to review the Zhu et al. paper and compare individual analyses and conclusions of the two studies. 

      In doing so, I found that the two articles have distinct scopes that appear similar but not actually inline. To a large extent, the current work does not constitute actual rebuttal to the points made by Zhu et al. In contrast, some of the analyses presented in the current work support those by Zhu et al., despite being interpreted in a different way. For the claims that directly contest Zhu et al., I do not see sufficient evidence that they are supported by the analyses. 

      Below is a summary of the comparison, which I will explain point-by-point in later paragraphs. 

      - Moody et al. assessed AB branch length, while Zhu et al. assessed AB evolutionary distance (which is different). <br> - Moody et al. evaluated the phylogeny indicated by a small number of core markers, while Zhu et al. evaluated the genome average using hundreds of global markers. <br> - Zhu et al.'s results also showed that gene non-verticality, substitutional saturation, and site-homogeneous models shorten the AB distance, which is consistent with Moody et al.'s. <br> - However, Zhu et al. found that some core markers are outliers in the genome-wide context, and the long AB distance indicated by them cannot be compensated for by the aforementioned effects. Moody et al. hasn't addressed this. <br> Therefore, the novelty and potential impact of the current work is less compelling: It used a classical method (a few dozen core genes) and found a pattern that has been found many times by some of the same authors and others (including Zhu et al., who also analyzed core genes). 

      AB distance metric:

      There is a subtle but critical difference between the scopes of the two papers: The Zhu et al. paper "reveals evolutionary proximity between domains Bacteria and Archaea". By stating "evolutionary proximity", it investigated two metrics: <br> The length of the branch separating Archaea from Bacteria in the phylogenetic tree, i.e., the "AB branch". This was the main focus of the current work. 

      The average tip-to-tip distance (sum of branch lengths) between pairs of Archaea and Bacteria taxa in the tree. A significant proportion of the Zhu et al. work was discussing this metric, and it led to several important conclusions (e.g., Figs. 4F, 5). The current work has not explored this metric. 

      These two metrics implicate distinct research strategies: For 1), HGTs and paralogy are usually considered problematic (as the current and many previous works argued). However, 2) is naturally compatible with the presence (and prevalence) of HGTs and paralogy. 

      Authors of the current work equate "genetic distance" to "branch length" (line 70), and only investigated the latter. This equation is misleading. If organism groups A and B diverged early, but then exchanged many genes post-divergence, then this is indisputable evidence that their "genetic distance" is close. This point needs to be clearly explained in the manuscript. 

      Core vs genome:

      This difference between "AB distance" and "AB branch length" is relevant to a more fundamental question: What defines the "evolutionary distance" between two groups of organisms? Both papers did not explicitly discuss this topic. It likely cannot be resolved in one article (as many scholars have continuously attempted on related topics in the past decades). But the discordance in understanding led to very different research strategies in the two papers, and rendering them incongruent in methodology. 

      Specifically, the current work (and multiple previous works) based phylogenetic inference on only genes that demonstrate a strong pattern of vertical evolution. HGTs were considered deleterious, and needed to be excluded from the analysis. This left a few dozen genes at most, and many are spatially syntenic and functionally related (e.g. ribosomal proteins). In this work, the final number is 27. Previous critiques of this methodology have suggested that this is not a tree of life, but a "tree of one percent" (Dagan and Martin, Genome Biol. 2006). 

      In contrast, Zhu et al. (and related previous works) attempted to evaluate the evolution of whole genomes by "maximizing the included number of loci.". They used a "global" set of 381 genes. They faced the challenge of "reconciling discordant evolutionary histories among different parts of the genome", because "HGT is widespread across the domains". To resolve this, they adopted the gene tree summary method ASTRAL. 

      Therefore, the "AB distance" estimated by Zhu et al. is a genome-level distance, calculated by merging conflicting gene evolutions (which itself can be disputed, see below). Whereas the "AB branch" evaluated in this work is strictly the branch length in the core gene evolution. Therefore, the results presented in the two papers do not necessarily conflict, because of the different scopes. 

      The expanded marker set:

      The authors made a valid critique (line 121-135) that many of the 381 genes in the "expanded marker set" adopted by Zhu et al., are under-represented in Archaea. According to the PhyloPhlAn paper (Segata et al. Nat Commun. 2013) which originally developed the 400 markers (a superset of the 381 markers), these genes were selected from ~3,000 bacterial and archaeal genomes available in IMG at that time time (note that it was 2013). Zhu et al. also admitted, in the discussion section, that this marker set falls short in addressing some questions (such as the placement of DPANN). What is important in the current context, is that they were not specifically selected to address the AB distance question. 

      However, note that Zhu et al.'s Fig. 5A, B presented the AB distance informed by 161 out of the 381 genes. These genes have at least 50% taxa represented in both domains - the same threshold discussed in the current work (line 132). Even with those sufficiently represented genes, they still found that ribosomal proteins and a few other core genes are "outliers" in the far end of the AB distance spectrum. 

      Domain monophyly in gene trees:

      The authors' efforts in manually checking the gene trees are appreciable (Table S1), considering the number and size of those trees. They found (line 147) "Archaea and Bacteria are recovered as reciprocally monophyletic groups in only 24 of the 381 published (Zhu et al., 2019) maximum likelihood (ML) gene trees of the expanded marker set." 

      The domain monophyly check was valid, however the result could be misleading because any sporadical A/B mixture was considered evidence of non-monophyly for the entire gene tree. As the taxon sampling grows, the opportunity of observing any A/B mixture also increases. For example, in Puigbò et al. J. Biology. 2009, 56% (a much higher ratio) of nearly universal genes trees had perfect domain monophyly based on merely 100 taxa. This is because even the "perfect" marker genes (such as ribosomal proteins) are not completely free from HGTs (e.g., Creevey et al. Plos One. 2011), let alone the fact that there are many artifacts in the published reference genomes (Orakov et al. Genome Biol. 2021). 

      Therefore, to have an objective assessment of this topic, it would be better to have a metric that allows some imperfection and reports an overall "degree" of separation (also see below). 

      AB branch by gene: correlation and outliers

      Figure 1 is the single most important result in this work, because it argues that the short AB branch observed in Zhu et al. is an artifact due to "inter-domain gene transfer and hidden paralogy" (line 202). This argument is based on the observation that the indicated AB branch length is negatively correlated with "verticality" (measured by ΔLL and split score) of the gene. 

      However, Zhu et al. also investigated the impact of verticality on AB distance, and they also found that they are negatively correlated (Fig. 5E). Therefore, the current result does not appear to deliver new information (as do multiple other analyses, see below). 

      An important finding in Zhu et al., which is largely not discussed in the current work, is that a handful of "core" genes are outliers in the spectrum of AB distance, as compared to the majority of the genome (Fig. 5A). The AB distance indicated by these core genes is so long compared with the genome average that it cannot be compensated for by the impact of non-verticality, substitutional saturation, site-homogeneous model, etc (see below). 

      Fig. 1A of the current work also clearly shows that many long-AB branch genes are outliers compared with the majority of the genome (the bottom of the blue bar). 

      Figs. 3 and 4 attempted to show that ribosomal proteins are not outliers, but that analysis was based on a very small set of core genes, and the figures clearly show that there are outliers even in this small set (to be further discussed below). 

      Verticality is not causative of short AB branch:

      In spite of the outlier question, there is an important logic problem in these analyses: The authors observed that gene verticality (measured by negative ΔLL) is correlated with AB branch length (Fig. 1), and concluded that HGTs and paralogy shortened the AB branch (line 202). However, they did not directly assess the rate of evolution in this model. It is totally possible that the most vertical genes happen to be those that evolved faster at the AB split. In order to support the claim made in this work, it is important to separate the effect of the rate of evolution from the effect of HGT / paralogy. 

      The ideal solution would be to include ALL genes (not just "good" ones), build gene trees, identify parts of the gene trees that once experienced HGT or paralogy, and prune off these PARTS, instead of excluding the entire gene tree. The remaining data are thus free of HGT / paralogy, based on which one can quantify the "true" AB branch length, and further assess how much it is correlated with "verticality", and whether there are still "outliers". This solution is likely not trivial in implementation, though. However, without such assessment, the observed short AB branch still only applies to the "tree of one percent", not the "tree of life". 

      Differential metric for verticality:

      In spite of the similarity between the current result and Zhu et al.'s (see above), the two works approached this goal using different metrics. 

      First, the authors attempted to quantify the AB branch length in individual gene trees, including those that do not have Archaea and Bacteria perfectly separated. To do so they performed a constrained ML search (line 210). I am wary of this treatment because it could force distinct sequences (due to HGT or paralogy) to be grouped together, and the resulting branch length estimates could be highly inaccurate. 

      In contrast, Zhu et al. quantifies the average taxon-to-taxon phylogenetic distance between the two domains, regardless of the overall domain monophyly. That method was free of this concern, although it computed a different metric. 

      Second, the authors assessed "marker gene verticality" using two metrics: a) AU test result (rejected or not) (Fig. 1A), c) ΔLL, the difference in log likelihood between the constrained ML tree and ML gene tree (line 222, Fig. 1B, C). I am concerned that they are sensitive to taxon sampling and stochastic events, as I explained above regarding domain monophyly. It is possible that a single mislabeling event would cause the topology test to report a significant result. In addition, they evaluate how severely domain monophyly is violated, but they do not account for intra-domain HGTs and other artifacts, which are also part of "verticality", and they can potentially distort the AB branch as well. 

      I did not find the ΔLL values of individual markers in any supplementary table. I also did not find any correlation statistics associated with Fig. 1B. 

      Statistical test:

      Line 157: "For the remaining 302 genes, domain monophyly was rejected (p < 0.05) for 232 out of 302 (76.8%) genes." Did the authors perform multiple hypothesis correction? If not, they probably should. 

      Line 217: "This result suggests that inter-domain gene transfers reduce the AB branch length when included in a concatenation." and Fig. 1A. If I understand correctly, this analysis was performed on individual gene trees, rather than in a concatenated setting. Therefore, the result does not directly support this conclusion. 

      Line 224: "Furthermore, AB branch length decreased as increasing numbers of low-verticality markers were added to the concatenate (Figure 1(c))". While this statement is likely true, Zhu et al. also presented similar results (Fig. 5) despite using a different metric, and they concluded that the impact is moderate and cannot explain the status of some core genes as outliers. 

      Concatenation and branch length:

      The authors pointed out that "Concatenation is based on the assumption that all of the genes in the supermatrix evolve on the same underlying tree; genes with different gene tree topologies violate this assumption and should not be concatenated because the topological differences among sites are not modelled, and so the impact on inferred branch lengths is difficult to predict." (line 187). 

      This argument is valid. In my opinion, this is the one most important potential issue of Zhu et al.'s analysis. In that work, they inferred genome tree topology through ASTRAL, which resolves conflicting gene evolutions. However ASTRAL does not report branch lengths in the unit of number of mutations. Therefore, they plugged the concatenated alignment into this topology for branch length estimation, hoping that it will "average out" the result. That workaround was apparently not ideal. 

      However, the practice of molecular phylogenetics is complicated. Theoretically, every gene, domain, codon position and site may have its unique evolutionary process, and there have been efforts to develop better partition and mixture models to address these possibilities. But there is a trade off; these technologies are computationally demanding and have the risk of overfitting. It is plausible that in some scenarios, the gain of concatenating many loci (despite conflicting phylogeny) may outweigh the cost of having unpredictable effects. 

      But this dilemma needs to be analyzed rather than just being discussed. The Zhu et al. paper did not assess the impact of such concatenation on branch length estimation. The best answer is to conduct an analysis to show that concatenating genes with conflicting phylogeny would result in an AB branch that is shorter than the mean of those genes, and the reduction of AB branch length is correlated with the amount of conflict involved. The current work has not done this. 

      Divergence time estimation:

      The manuscript dedicates one section (line 230-266) to argue that the divergence time estimation analysis performed by Zhu et al. was not good evidence for marker gene suitability. Zhu et al. showed congruence of the expanded marker set with geological records whereas ribosomal proteins were conflicting with the geologic record.To support their argument, the authors estimated divergence times using the top 20 most "vertical" genes measured by ΔLL. 

      It would be good to clarify which genes they are, and it would be important to check whether they include some of the most "AB-distant" ones found by Zhu et al. Their Fig. 5A shows that there are genes that divide the two domains several folds further than the ribosomal proteins (such as rpoC). If they are among the 20 genes, it will not be surprising that the estimated AB split is older than it should be. 

      Overall, I think this section is logically questionable. Zhu et al. suggested that "They show the limitation of using core genes alone to model the evolution of the entire genome, and highlight the value in using a more diverse marker gene set.". The current work showed that using another set of a few genes (I do not know if they include multiple "core" genes, as discussed above, but it is plausible) also did not work well. This does not refute Zhu et al.'s claim. 

      What's important in Zhu et al.'s analysis is this: they demonstrated that using a small set of genes in DTE may cause artifacts due to them significantly violating the molecular clock at certain stages of evolution. Instead, using a larger set of markers that represent a portion of the entire genome would help to "smooth out" these artifacts. This of course is not the ideal solution, likely because concatenating conflicting genes and modelling them uniformly is not the best idea (see above). But as an operational workaround, it was not challenged by the analysis in the current work. 

      Finally, I agree with the authors' statement that more and reliable calibrations are the best way to improve divergence time estimation. 

      AB branch by ribosomal and non-ribosomal genes:

      Two figures (Figs. 3 and 4) are two sections (line 270-303) dedicated to the argument that ribosomal markers do not indicate a longer AB branch than a non-ribosomal one. However, this is a small scale test (38 ribosomal markers vs. 16 non-ribosomal markers) compared with the similar analysis in Zhu et al. (30 ribosomal markers vs. 381 global markers). A closer look at Figs. 3 and 4 suggests that while the AB lengths indicated by the ribosomal markers are within a relatively narrow range, those by the non-ribosomal ones are very diverse, including ones that are several folds longer than the ribosomal average. This result is in accordance with that of Zhu et al.'s Fig. 5A, although the latter was describing a different metric. Do these genes also overlap the ones found by Zhu et al.? 

      Nevertheless, this analysis does not falsify Zhu et al.'s, because it compared a different, much smaller, and deliberately chosen group of genes. 

      Substitutional saturation:

      The comparative analysis of slow- and fast-evolving sites is interesting. The result (Fig. 5) is visually impactful. In my view, this analysis is valid, and the conclusion is supported. It would be better to explain the rationale with more detail to facilitate understanding by a general audience. 

      Zhu et al. also tested the impact of substitution saturation on the AB branch, using a more traditional approach (Fig. S19). They also found that the inter-domain distance is more influenced by potential substitution saturation, but the difference is minor. They concluded that (AB distance) "is not substantially impacted by saturation." 

      Like other analyses, these two analyses involved very different locus sampling (27 most "vertical" genes vs. 381 expanded genes). They also differ by the metric being measured (AB branch length vs. average distance between AB taxa). Therefore, the analysis in the current work does not falsify the analysis by Zhu et al. In contrast, it is inline with (though not in direct support of) Zhu et al. and others' suggestion that there was "accelerated evolution of ribosomal proteins along the inter-domain branch" (line 25) in the 27 core genes (of which 15 are ribosomal proteins). 

      Evolutionary model fit:

      The authors compared the AB branch length indicated by the standard, site-homogeneous model LG+G4+F vs. the site-heterogeneous model LG+C60+G4+F, and found that the latter recovered a longer AB branch (2.52 vs. 1.45). The author's reasoning for using a site-heterogeneous model is valid, and this analysis is sound. 

      However, Zhu et al. also analyzed their data using the site-heterogeneous model C60 -- the same as in this work, but through the PMSF (posterior mean site frequency) method. Zhu et al. also compared it with two site-homogeneous models (Gamma and FreeRate). The results were extensively presented and discussed (Figs. 3, 4E, F, S23, S24, Note S2). They also found that C60+PMSF elongated the AB branch compared with the site-homogeneous models (Fig. S24A). As for the average AB distance (another metric evaluated by Zhu et al., as discussed above), C60+PMSF increased this metric when using ribosomal proteins, but not much when using the expanded marker set (Fig. S25A). And overall, the elongation by C60+PMSF with the expanded markers cannot compensate for the long branch indicated by the ribosomal proteins. 

      Therefore, similar to the point I made above, this analysis is sound but it does not logically falsify the conclusion made by Zhu et al., as it only concerns a small set of markers, and it recovered a previously described pattern. 

      The manuscript also did not clarify what the phrase "poor model fit" refers to (line 34 and line 304). If this is addressing the Gamma model evaluated by the authors, then this claim is valid though not novel (but see my previous comment on the trade-off). If that is a general reference to Zhu et al.'s methodology, then the authors should at least include the C60+PMSF model in the analysis, and show that C60 indicates a significantly longer AB branch than C60+PMSF does (if that's the case, which is doubtful). Admittedly, C60+PMSF is cheaper than the native C60 in computation, but "In some empirical and simulation settings PMSF provided more accurate estimates of phylogenies than the mixture models from which they derive." (Wang et al. Syst Biol. 2018). 

      Finally, Zhu et al. also performed an analysis using the native C60 model on a further reduced taxon set. That result was not presented in the published paper, but it can be found in the "Peer Review File" posted on the Nature Communications website. That tree also recovered a short AB distance, and placed CPR at the base of Bacteria, and showed that this placement was not impacted by the removal of Archaea. 

      Taxon sampling:

      My final comment is about taxon sampling. Zhu et al. developed an algorithm for less biased taxon sampling, and they argued that extensive taxon sampling is important in resolving the early evolution of life. They presented evidence showing that reduced taxon sampling changed overall topology and basal relationships (Figs. S13, S14, S23, Note S2). The analyses were performed in combination with the assessment of site sampling, locus sampling, substitution model and other factors. The importance of less biased and/or extensive taxon sampling was also noted by previous works, esp