Author Response

Reviewer #1 (Public Review):

The goal of this study was to investigate the mechanisms that lead to the release of photosynthetically fixed carbon from symbiotic dinoflagellate alga to their coral host. The experimental approach involved culturing free-living Brevolium sp dinoflagellates under "Normal" and "Low pH" conditions (respective target pH of 7.8 and 5.50) and measuring the following parameters: (Fig.1) cell growth rate over ~28 days, photosynthetic activity, glucose and galactose secretion at day 1; (Fig. 2) Cell clustering, external morphology (using SEM), and internal morphology (using TEM) after 3 weeks; (Fig. 3) Transcriptomic analyses at days 0 and 1; and (Fig. 4) glucose and galactose concentration in Normal culturing medium after 24h incubation with a putative cellulase inhibitor (PSG).

The paper reports decreased growth at Low pH coupled with decreased photosynthetic rates and increased glucose and galactose release in 1-day Breviolum sp. cultures. At this same time point, genes related to cellulase were upregulated, and after 3 weeks morphological changes on the cell wall were reported. The addition of the cellulase inhibitor PSG to cells in pH 7.8 media decreased the release of glucose and galactose.

The paper concludes that acidic conditions mimicking those reported for the coral symbiosome -the intracellular organelle that hosts the symbiotic algae- upregulate algal cellulases, which in turn degrade the algal cell wall releasing glucose and galactose that can be used as a source of food by the coral host. However, there are some methodological issues that hamper the interpretation of results and conclusions.

We appreciate your helpful comments and apologize the confusion caused by insufficient descriptions in the previous manuscript. In the revised manuscript we clarify what we originally intended to demonstrate including the followings:

(1) Most analyses including SEM and TEM were done at day 0 and 1, except for a few, i.e. growth rate over 28 days and cell clumping assay done 3 weeks after the inoculation, which is summarized as a schematic panel and clarified in the revised manuscript.

(2) Inhibitor assay for secreted celluloses was done in pH 5.5.

(3) We do not intend to suggest that low pH medium mimics symbiosomes, as these organelles are far more complex than simple culture media and how symbiosomes are maintained and what the interior environment is like are not fully understood in general. Based on previous studies, presumably they are featured by low pH, high CO2, host-derived nutrients. Among these, we focus on low pH, which is a stressor for dinoflagellates to go through in not only symbiosomes but also natural environments, e.g. animal gut.

In this study, we clarified how algae respond to low pH as an environmental stressor, which can also provide insights into how they interact with the host inside the guts as well as symbiosomes.

Reviewer #2 (Public Review):

Ishii and colleagues investigated the process of monosaccharide release from algae in low-pH environmental conditions, mimicking the acidic lysosomal-like intracellular compartment where the algae reside symbiotically and transfer nutrients to their hosts, namely corals and other animals. Upon exposure of cultured algae to low pH, subsequent physiological changes as well as the increased presence of glucose and galactose were measured in the surrounding media. Concurrently, photosynthetic activity was decreased, and further experiments employing the photosynthetic inhibitor DCMU to cultures also replicated the increased monosaccharide release. Transcriptomic comparison of algae in low pH to controls showed differential expression in glycolytic pathways and, interestingly, a strong upregulation of signal-peptide-containing isoforms of cellulases. Finally, the elegant use of a cellulase inhibitor on the cultured algae revealed a decrease in monosaccharides in the media. This led the authors to propose a pathway of sugar release in which acidic conditions trigger a cellulase-driven cascade of cell wall degradation in the algae and their consequent release of monosaccharides. These results have interesting implications on the molecular mechanisms of coral-algae symbiosis, contributing to the understanding of how these important symbioses function on the cellular level.

Overall the conclusions of this manuscript are supported by the data presented, but clarification and elaboration are needed to fully justify the proposed mechanisms and better situate the results in a broader context of the field.

We thank the reviewer for the positive comments. In the revised the manuscript we show that the results could be better explained with the proposed mechanisms in a broader context.

Author Response

Reviewer #2 (Public Review):

1) Mechanistic details of how FCA regulates FLC have been extensively studied, and both transcriptional and co-transcriptional regulations occur. I understand that FCA affects the 3'end processing of antisense COOLAIR RNAs, which regulate FLC. FCA also physically interacts with COOLAIR RNAs and other proteins, including chromatin-modifying complexes, which establish epigenetic repression of FLC regardless of vernalisation. In addition, FCA appears to function to resolve R-loop at the 3' end FLC, and FLC preferentially interacts with m6A-modified COOLAIR by forming liquid condensates. FCA is also alternatively spliced in an autoregulatory manner, and fca-1 mutant was reported to be a null allele as fca-1 cannot produce the functional form of FCA transcripts (r-form).

However, I could not find any information on the fca-3 allele, which was reported to exhibit a weaker phenotype in terms of flowering time (Koornneef et al., 1991). In this manuscript, the authors showed that the level of FLC expression is lower than fca-1 and higher than Ler WT, but I could not find any other relevant information on the nature of the fca-3 allele. Given the known details on the function of FCA, the authors should explain how fca-3 shows an "intermediate" phenotype, which is highly relevant to the argument for an "analog" mode of regulation in fca-3. Therefore, the nature of the fca-3 mutant should be described in detail.

We thank the reviewers for pointing out this omission. We have added much more information on the genotypes in the methods of the manuscript. We emphasise, however, that the rationale for selecting fca-3 as an intermediate mutant was empirical: namely, it generates an intermediate level of FLC expression (Fig. 1C and Fig. 1S1).

2) The authors used a transgene (FLC-venus) in which an FLC fragment from ColFRI was used. Both fca-1 and fca-3 is Ler background where FLC sequence variations are known. I understand that the authors introgressed the transgenic in Ler background to avoid the transgene effect, but it is not known whether fca-1 or fca-3 mutations have the same impact on Col- FLC.

We tested the expression of both endogenous (Ler) and FLC-Venus (Col-FLC) copies in these mutants by qPCR and found similar results (Fig. 1S1C,D), indicating that the fca-1 and fca-3 mutations have similar effects in both cases.

3) Fig. 3A: I understand that Fig 3A is the qRT-PCR data using whole seedlings, and the gradual reduction of FLC from 7 DAG to 21 DAG was used to test the "analog" vs. "digital" mode of gene regulation in fca-1 and fca-3. I am not sure whether this is biologically relevant.

Indeed, Ler is the only line that has transitioned to flowering during the experiment, with both fca lines being late flowering mutants. We totally agree that for Ler, later timepoints may be biologically irrelevant. It is used in this case as a negative control for the imaging, since FLC in Ler was already mostly OFF from the first timepoint and no biological conclusions are drawn from the later times. We have added a comment to this effect in the results section, also clarifying in the discussion that our focus is on the early regulation of FLC. Therefore, by looking at the young seedling in wildtype Ler, as we and others have previously, we are already looking too late to capture the switching of FLC to OFF. However, we expect that this combination of analog and digital regulation will be highly

relevant to FLC regulation in wild-type plants in different accessions, partly leading to the differences in autumn FLC levels that were shown to be so important in the wild (Hepworth et al. 2020).

3-a) The authors wrote that "This experiment revealed a decreasing trend in fca-3 and Ler (Fig. 3A)". But, I do also see a "decreasing trend" in fca-1 as well (although I understand that they may not be statistically significant). I also noticed that the level of FLC in fca-1 at 7 day has a greater variation. Is there any explanation?

The level of FLC in fca-1 at 7 days is indeed more variable in these experiments. However, in a new second experiment, this is not the case (Fig. 3S2). In addition, a similar effect has not been observed in the ColFRI genotype (Fig. S9F of Antoniou-Kourounioti et al. 2018). Therefore, we believe this greater variation in one data set may simply be due to random fluctuations.

For the decreasing trend in fca-1 in Fig. 3A, as the reviewer says, this is not significant. However, in the second experiment, we again see a decrease, which is now slow but significant. The decrease could be due to a subset of fca-1 ON cells switching off (in tissue that we have not imaged) and we comment on this slow decrease in the text.

3-b) The decreasing trend observed in Ler (although the expression of FLC is already relatively low in Ler) may be the basis for the biological relevance. But Fig. 3D shows that the FLC-venus intensity in Ler root is not "decreasing". The authors interpreted that "root tip cells in Ler could switch off early, while ON cells still remain at the whole plant level that continue to switch off, thereby explaining the decrease in the qPCR experiment." Does this mean that the root tip system with FLC-venus cannot recapitulate other parts of plants (especially at the shoot tip where FLC function is more relevant)?

The authors utilize the root system with transgenes in mutant backgrounds to observe and model the gene repression (transgene repression, to be exact). If the root tip cells behave differently from other parts of plants, how could the authors use data obtained from the root tip system?

We now show that FLC-Venus in Ler, fca-3, fca-1 in young leaves have similar expression patterns to roots, thus validating the root system as an appropriate one to study the switching dynamics, see response to Essential comment 3. Nevertheless, in Fig. 3A, we show that FLC expression declines even in Ler. However, the levels here are low, so if it is indeed a subfraction of late-switching cells that are responsible, these cells cannot form a large proportion of the plant. We now make this clear in the text.

4) I do see both fca-1 and fca-3 can express FCA at a comparable level (Fig. 3B); thus, I guess that the authors are measuring total FCA transcripts and that fca-3 may result in different levels of "functional form" of FCA. But this is not clearly discussed.

We have now added yellow boxes in Fig. 2S3 to show additional examples of short files of ON cells in fca-3 and fca-4. To further improve the interpretation of this image (and all others in the manuscript) we have changed the presentation of the imaging using a different colourmap to enhance clarity.

5) Quantification based on image intensity needs to be carefully controlled. Ideally, a threshold to call "ON" or "OFF" state should be based on the comparison to internal control and it is not clear to me how the authors determined which cells are ON or OFF based on image intensity (especially in fca-3).

For the wild-type and fca-1 situations there is no switching in the model, and hence no dynamical changes in the FLC protein levels. As the FLC levels in the ON or OFF states are simply fit to the data using log-normal distributions, this would simply be a fitting exercise for fca-1 and Ler, and little would be learnt. Hence, we have not pursued this line of analysis.

6) In many parts, I had to guess how the experiments were performed with what kind of tissues/samples. The methods section can benefit from a more thorough description.

We have now gone through and added the missing information.

Related to Public review #2. What is the phenotype (flowering time) of FLC-venus in fca-1 and fca-3? In addition, how many independent lines were used? Do they behave similarly?

It was observed that with the additional FLC gene (in the form of the FLC-Venus), flowering is delayed as expected. However, this was not quantified in this work. Instead, we validated that the expression of the transgene was equivalent to endogeneous between genotypes, as shown in Fig. 1S1, supporting that this is an appropriate readout for FLC expression. One line for each genotype was selected and used in this work. In addition, we also now use fca-4, which has similar expression to fca-3, and where FLC-Venus also behaves similarly to the fca-3 case (Fig. 1S1, 2S3).

Reviewer #3 (Public Review):

1) The way the authors define ON and OFF cells sounds a bit arbitrary to me and, in my understanding, can affect a lot the outcomes and derived conclusions. The authors define ON cells to those cells having more than one transcript, or when they are above the value of 0.5 of the Venus intensity measure - what would it happen if the thresholds are slightly above these levels? And why such thresholds should be the same for the studied lines Ler, fca-3 and fca-1? By looking at the distributions of mRNAs and Venus intensities in Ler and fca-3 plants, one could argue that all cells are in an OFF, 'silent' state, and that what is measured is some 'leakage', noise or simply cell heterogeneity in the expression levels. If there is a digital regulation, I would expect to see this bimodality more clearly at some point, as it was captured in Berry et al (2015) - perhaps cells in fca-1 show at a certain level of bimodality? When seeing bimodality, one could separate ON and OFF states by unmixing gaussians, or something in these lines that makes the definition less arbitrary and more robust.

As explained in Essential comment 5, we have removed arbitrary thresholding from the manuscript and only used absolute thresholds from smFISH (now changed to >3, and shown that our results are robust to varying these thresholds, Fig. 2S2). If all cells are in the OFF state and fca-3 just has higher noise/heterogeneity, then this does not explain the reduction in expression over time. Nor can such heterogeneity explain the short files of ON cells and longer files of OFF cells in Fig. 2S3: the cells should just be a random mix of varying FLC levels. Our results are much more compatible with switching into a heritable silenced state. Finally, with bimodality, this is difficult to see as clearly as before due to the wide levels of expression in fca-3, but we believe it is present: a well-defined OFF state together with a broad ON state. This broadness makes extracting the ON cells quite difficult as a completely rigorous unmixing of the two states is just not possible.

2) The authors use means in all their plots for histograms and data, and perform tests that rely on these means. However, many of these plots are skewed right distributions, meaning that mean is not a good measure of center. I think using median would be more appropriate, and statistical tests should be rather done on medians instead of means. If tests using medians were performed, I believe that some of the pointed results will be less significant, and this will affect the conclusions of this work.

Highly expressing FLC lines and mutants, such as ColFRI and fca-9, often used for vernalization studies, are late flowering, but do eventually flower even with no decrease in FLC levels (and so no switching). This is not an artifact of using roots versus shoots, and presumably arises from there being multiple inputs into the flowering decision which can allow the FLC-mediated flowering inhibition to eventually be overcome.

3) Some data might require more repeats, together with its quantification. For instance, the expression levels for fca-1 in Fig 2E and Fig 3D at 7 days after sowing look qualitatively different to me - not just the mean looks different, but also the distribution; fca-1 in Fig 3D looks more monomodal, while in Fig 2E it looks it shows more a bimodal distribution. Having these two different behaviours in these two repeats indicates that, more ideally, three repeats might be needed, together with their quantification. Fig. 2C would also need some repeats. In Fig 1S1 C and D, it would be good to clarify in which cases there are 2 or more repeats -3 repeats might be needed for those cases in Fig 1S1 C-D that have large error bars.

The data in Figs. 2C and 2E are both based on two independent experiments, with the results combined. The data in Fig. 3D is almost entirely based on three independent experiments. We have now stated this in the legend. The Venus imaging was performed on separate microscopes for Fig. 2 and Fig. 3 and this possibly accounts some of the observed differences. However, we do not think that the data in Fig 2E for fca-1 supports a bimodal distribution: the slight peak at higher levels is, we believe, much more likely to be a statistical fluctuation. For Fig. 1S1 C and D, we now clarify in the legend that n=2 biological replicates for fca-3 and n=3 for others.

Also, when doing the time courses, I find it would be very beneficial to capture an earlier time point for all the lines, to see whether it is easier to capture the digital nature of the regulation. Note that the authors have already pointed that 7 days after sowing might be too late for Ler line to capture the switch.

We agree that capturing earlier time points for Ler in particular is interesting and important. However, we have found that this requires specialist imaging in the embryo and we feel that this is really beyond the scope of this manuscript and will instead form the basis of a future publication.

Author Response

Reviewer #1 (Public Review):

The authors use what is potentially a novel method for bootstrapping sequence data to evaluate the extent to which SARS-CoV-2 transmissions occurred between regions of the world, between France and other European countries, and between some distinct regions within France. Data from the first two waves of SARS-CoV-2 in Europe were considered, from 2020 into January 2021. The paper provides more detail about the specific spread of the virus around Europe, specifically within France, than other work in this area of which I am aware.

First of all, we would like to thank reviewer #1 for their evaluation and their various comments which, in our opinion, have allowed us to considerably improve the manuscript.

An interesting facet of the methodology used is the downsampling of sequence data, generating multiple bootstraps each of around 500-1000 sequences and conducting analysis on each one. This has the strength of sampling, in total, a large number of sequences, while reducing the overall computational cost of analysis on a database that contains in total several hundred thousand sequences. A question I had about the results concerns the extent of downsampling versus the rate of viral migration: If between-country movements are rapid, a reduced sample could be misleading, for example characterising a transmission path from A to B to C as being from A to C by virtue of missing data. I acknowledge that this would be a problem with any phylogeographic analysis relying on limited data. However, in this case, how does the rate of migration between locations compare to the length of time between samples in the reduced trees? Along these lines, I was unclear to what extent the reported proportions of intra- versus inter-regional transmissions (e.g. line 223) would be vulnerable to sampling effects.

This question is indeed a very important one. Between-country movement rate can be high but the contagious period for a SARS-CoV-2-infected individual is short (a bit less than two weeks in average). In our subsamples, the dated trees have a median branch length around 20 days. To ensure that our subsamples did not introduce errors in estimating the exchange events between locations, we conducted a simulation. Briefly, we generated a tree of 1,000,000 tips with a five-states discrete trait. We then took 100 subsampled 1000-leaves trees, reconstructed the ancestry for the discrete trait and assess transitions between states. The error rate is less than 3% on average: it comprises the missing data, as you pointed out, and the errors in reconstructing the ancestry for the trait deeper in the tree.

We think that overall, less than 3% is a satisfying error rate.

The results of this specific simulation were added to the paper (lines 150-157) and as Figure 2—figure supplement 1.

A further question around the methodology was the use of an artificially high fixed clock rate in the phylogenetic analysis so as to date the tree in an unbiased way. Although I understood that the stated action led to the required results, given the time available for review I was unable to figure out why this should be so. Is this an artefact of under-sampling, or of approximations made in the phylogenetic inference? Is this a well-known phenomenon in phylogenetic inference?

We thank reviewer #1, who was, as reviewer #2 and the editor, disturbed by the use of an artificially fast and fixed molecular clock. It was an artifact to correct a mistake in our code that has been fixed. See the answer to point (3) of the editor.

The value of this kind of research is highlighted in the paper, in that genomic data can be used to assess and guide public health measures (line 64). This work elucidates several facts about the geographical spread of SARS-CoV-2 within France and between European countries. The more clearly these facts can be translated into improved or more considered public health action, through the evaluation of previous policy actions, or through the explication of how future actions could lead to improved outcomes, the more this work will have a profound and ongoing impact.

This is a very interesting point to emphasize indeed. We are currently discussing with public health specialists in our institution on how to assess past public health actions using phylodynamics data in a statistically valid manner.

Reviewer #2 (Public Review):

This study represents an important contribution to our understanding of SARS-CoV-2 transmission dynamics in France, Europe and globally during the early pandemic in 2020 and the authors should be congratulated for tackling this important question. Through evaluation of the contributions of intra- and inter-regional transmission at global, continental, and domestic levels, the authors provided compelling, although as of yet correlative and incomplete, evidence towards how international travel restrictions reduced inter-regional transmission while permitting increased transmission intra-regionally. Unfortunately, however this work suffers from a number of serious analytical shortcomings, all of which can be overcome in a major revision and re-analysis.

We would like to thank the reviewer #2 for their evaluation and their various comments. We want to point that reviewer #2 was contacted for advice on strategy for the molecular clock since she performed a study on a similar topic describing SARS-CoV-2 epidemics in Canada during 2020. We strongly believe that all reviewer #2 comments drastically contributed to improve the quality of this work.

With this genomic epidemiology analysis, the authors disentangled the relative contributions of different geographic levels to transmission events in France and in Europe in the first two COVID-19 waves of 2020. By partitioning the analysis into three complementary, but distinct, geographic levels, the migration flows in and out of continents, countries in Europe, and regions in France were inferred using maximum likelihood ancestral state reconstruction. The major strengths of this paper were the inclusion of multiple geographic levels, the comparison of different rate symmetries in the ancestral character estimation, and the comprehensive qualitative descriptions of comparisons over time and geographies. However, there were also major weaknesses that need to be addressed and are described in more detail below. They include summing across replicates that were drawn with replacement and were not independent; inadequate justification for excluding underrepresented geographies; the assertion that positive correlation between intra-regional transmission and deaths validates the accuracy of the analysis; considering the framework the authors have chosen for this analysis the analysis would accommodate and benefit strongly from increasing the size of the sequence sets selected for analysis in each replicate; and the sparsity of quantitative (over qualitative or exploratory) comparisons and statistics in the reporting of results. In particular, it would greatly strengthen the paper if the authors could better evaluate the effect of travel restrictions on importations and exportations by testing hypotheses, quantifying changes in the presence of restrictions, or estimating inflection points in importation rates.

We are grateful for this comprehensive listing of the strengths and weaknesses of our study. Regarding the limitations of this study, these will be detailed specifically for each dedicated remark of the reviewer. We would like to emphasize that all the remarks and limitations reported here by reviewer #2 are in our opinion fully justified. We hence have tried to bring additional analyses (study of the Pango lineages, averaging of the subsamples, simulation study to justify the size of the sampling), a modification of the methodology (in particular concerning the molecular clock) and a thorough rewriting of the “Results” section.

General comments on the Background: Need to elaborate on how this study fits into the big picture in the first paragraph. Should discuss how phylodynamics contributes to understanding of viral outbreaks, SARS-CoV-2 epidemiology and viral evolution.

We have added in the “Introduction” section some elements to better understand why phylodynamics is an important field in the epidemiology of SARS-CoV-2 and its evolution.

The authors should consider a hypothesis driven framework for their analyses, for example considering the geographically central position of France what hypotheses stem from this considering sources of viral importations and destinations of exportations from/to Europe vs other international? Or other a priori expectations.

We agree with reviewer #2 about this remark. Indeed, given the central position of France, we can hypothesize that it has strongly participated in the dissemination of the virus within Europe. This hypothesis has been included in the "Introduction" section of the revised version (lines 102-105).

To address the computational limits of phylogenetic reconstruction, 100 replicates of fewer than 1000 sequences each were sampled for each epidemic wave at each level. The inter- and intra-regional transmissions were averaged and then summed across replicates in order to compare the relative roles played by each geography towards transmission. While we see the logic in using the sum across replicates, this is highly likely to bias results, especially since in the methods, this is described as sampling with replacement between replicates (LX). The validity of summing replicates needs to be discussed and are likely most appropriately presented as mean or median. Also, these samples are quite small considering the computational capacity of the maximum likelihood tools being used. We recommend repeating the analysis with a substantially larger number of sequences per sample.

We thank reviewer #2 for this relevant remark. We initially summed the subsamples, a strategy that may possibly bias the results. In the new version of the manuscript, we averaged the subsamples by region and by week as recommended (and stated in the methods, line 536-537).

About the size of our subsamples, it made no difference to use 1,000, 2,000 or 5,000 genomes in each subsample. To get a more definitive and scientifically sound answer, we performed a simulation assay that has been included in the manuscript and is shown is what is now figure 2 (and figure 2—figure supplement 1). These simulations show that our subsampling strategy allows for an accurate estimate of transition rates for a discrete parameter (lines 107-160).

Author Response

Reviewer #1 (Public Review):

The paper addresses an interesting question - how genetic changes in Y. pestis have led to phenotypic divergence from Y. pseudotuberculosis - and provides strong evidence that the frameshift mutation in rcsD is involved. Overall, I found the data to be clearly presented, and most of the conclusions well supported by the data. The authors convincingly show that (i) the frameshift mutation in rcsD alters the regulation of biofilm formation, (ii) this effect depends upon expression of a small protein that corresponds to the C-terminal portion of RcsD, and (iii) the frameshift mutation in rcsD prevents loss of the pgm locus. I felt that the discussion/conclusions about what phosphorylates/dephosphorylates RcsB and how this impacts biofilm formation are overstated, as there are no experiments that directly address this question. I also felt that the authors' model for what phosphorylates/dephosphorylates RcsB in Y. pestis should be more clearly articulated, even if it is only presented as speculation. Lastly, the authors propose that full-length RcsD is made in Y. pestis and contributes to phosphorylation of RcsB, but the evidence for this is weak (faint band in Figure 2d). It may be that the N-terminal domain of RcsD is functional. I recommend either softening this conclusion or testing this hypothesis further, e.g., by introducing an in-frame stop codon early in rcsD after the frame-shift.

Thanks for your comments. We have provided a model and revised the discussion about phosphorylation/dephosphorylation of RcsB and how this impacts biofilm formation (Figure 8 and Supplementary Figure 4). In addition, we have introduced an in-frame stop codon in rcsD before the frameshift and showed that full-length RcsD is only made in wildtype Y. pestis but not in the rcsDpe-stop mutant (Supplementary Figure 1g).

Reviewer #2 (Public Review):

Guo et al. have investigated the consequences of a frameshift mutation in the rcsD gene in the Yersinia pseudotuberculosis progenitor that is conserved in modern Y. pestis strains. Interestingly, they identify a start codon with a ribosome binding site that enables production of an Hpt-domain protein from the C-terminus in Y. pestis. Targeted deletion of this Hpt-domain increased biofilm production in Y. pestis. They find that the ancestral RcsDpstb (full length) is a positive regulator of biofilm in Y. pestis while the Hpt-domain version (RcsDYP) represses biofilm in vitro. When fleas were infected with Y. pestis expressing the ancestral RcsDPSTB protein, there was no difference in bacterial survival or rate of proventricular blockage. This strain also killed mice the same rate (in a different Y. pestis strain background). However, replacing RcsDYP with RcsYPTB dramatically increases the frequency of pgm locus deletion (containing Hms ECM and yersiniabactin genes) during flea infection. The authors predict that this would reduce the invasiveness of the bacteria in mammals and/or flea blockage in subsequent flea-rodent-flea transmission cycles. They also measured global gene expression differences between RcsDPSTB compared to the wild-type strain. They argue that the frameshift of RcsD maintaining the Hpt-domain (RcsDYP) was needed to regulate biofilm while limiting loss of the pgm locus.

Loss of the pgm locus was not tested in the Y. pestis rcsD mutant strain (lacking the entire gene or just the C-terminal Hpt domain). Therefore, the claim that maintaining the Hpt-domain protein was important lacks convincing evidence. Additionally, it is possible that the population of rcsDpe::rcsDpstb after in vitro growth for 6 days would still be proficient at infecting and blocking fleas, even though many of the bacteria would have lost the pgm locus. Production of Hms polysaccharide by pgm+ could trans-complement those that are pgm-. The nature of the pgm locus loss is assumed to be due to recombination between IS elements. This is certainly the likeliest explanation but not the only one. The authors checked for pgm loss by phenotype (CR binding) and by two sets of primers, one targeting the hmsS gene and another set that is unspecified. Loss of the entire pgm (especially yersiniabactin genes) should be clarified.

Thanks for your comments. We have now provided the data to show that deletion of RcsD-Hpt resulted in increased loss of the pgm locus (Figure 5d) to strengthen the claim that maintenance of the Hpt-domain is significant for retention of the pgm locus. We also agree that 6-day old cultures of a mixture of pgm+ and pgm- rcsDpe::rcsDpstb will still be capable of infecting and blocking fleas. However, these strains will be less efficient at causing disease in the vertebrate host in the absence of the pgm locus. We agree that recombination between IS elements might not be the only cause of loss of the pgm locus. To verify the loss of the pgm locus, we have used two sets of primers. One set targets the hmsS gene and another set targets the upstream and downstream sequences of the pgm locus (Supplementary Table 3). We have clarified this in the revised manuscript (Line 610-613).

Reviewer #3 (Public Review):

The Rcs phosphorelay plays an important role in regulating gene expression in bacteria; most of the current knowledge about the Rcs proteins is from E. coli. Yersinia pestis, carrying mutations in two central components of the Rcs machinery, provides an interesting example of how evolution has shaped this system to fit the life cycle of this bacteria. In bacteria other than Y. pestis, most Rcs activating signals are sensed via the outer membrane lipoprotein RcsF; from there, signalling depends on inner membrane protein IgaA, a negative regulator of RcsD. Histidine kinase RcsC is the source of the phosphorylation cascade that goes from the histidine kinase domain of RcsC to the response regulator domain of RcsC, from there to the histidine phosphotransfer (Hpt) domain of RcsD, and finally to the response regulator RcsB. RcsB, alone or with other proteins, regulates transcription of many genes, both positively and negatively. These authors have previously shown that RcsA, a co-regulator that acts with RcsB at some promoters, is functional in Y. pseudotuberculosis but mutant in Y. pestis, and that this leads to increased biofilm in the flea. The authors also noted that rcsD in Y. pestis contains a frameshift after codon 642 in this 897 aa protein; in theory that should eliminate the Hpt domain from the expressed protein. However, they found evidence that the frame-shifted gene had a role in regulation. This paper investigates this in more depth, providing clear evidence for expression of the Hpt domain (without the N-terminal domain), and demonstrating a critical role for this domain in repressing biofilm formation. The Y. pseudotuberculosis RcsD does not express a detectable amount of the Hpt domain nor does it repress biofilm formation. The ability of the Hpt domain protein to keep biofilm formation low explains most of what is observed for the full-length frame-shifted protein.

1) The authors provide a substantial amount of data supporting the expression of the C-terminus of RcsD is sufficient and necessary for low biofilm levels, and that this is dependent upon the active site His in the RcsD Hpt domain (H844A) as well as other components of the basic phosphorelay (RcsC and RcsB). However, it is only possible to see this protein by Western blot in 100-fold "Enriched" lysates (Figure 2). No small protein was detected in the RcsDpstb strain, although the enriched lysate was not shown for this. Without that experiment, it is not possible to evaluate whether the small protein is also made from the rcsDpstb gene. Either answer would be interesting, and would allow other conclusions to be drawn. Is the RBS and start codon the same for the HPT region of this rcsD gene (it could be added to Supplementary Table 6). If the small protein is made, is its ability to function blocked by the excess full length protein in terms of interactions with RcsC? Or is the expression of the small protein dependent upon loss of overlapping translation from the upstream start?

The small Hpt protein may be produced from expression of the epitope tagged rcsDpstb gene as it can be detected in an enriched isolation of this sample (Supplementary Figure 1f). Because only a small amount of the RcsD-Hpt is produced from the rcsDpstb substitution, it might only function at low levels in the presence of large amounts of RcsDpstb. The RBS and start codon are the same for the RcsD-Hpt in Y. pestis and Y. pseudotuberculosis, we have added them in the Supplementary Table 6. In addition, we have provided a model to show the function and regulation of RcsD and Hpt (Supplementary Figure 4).

2) In many phosphorelays, the protein kinase also acts as a phosphatase, and which direction P flows is critical for regulation. It is often difficult to follow what the model for this is in this paper, and that is important to understand for evaluating the results. Most of this paper uses two assays, biofilm formation and crystal violet staining (also related to biofilm formation) to assess the functioning of the Rcs phosphorelay. Based on the behavior of the rcsB mutant, it would seem that functional Yersinia pestis Rcs (RcsDpe) represses this behavior, and this correlates with RcsB phosphorylation (Figure4). What is the basis (Line 443-44) for saying that RcsD phosphorylates RcsB while RcsDHpt dephosphorylates? Yersinia pseudotuberculosis RcsD(pstb) shows no difference with the rcsB mutant. Doesn't that suggest that RcsDpstb is no longer repressing (phosphorylating)? In the presence of the RcsDpstb as well as multicopy RcsF, an activating signal in other organisms, RcsDpstb seems able to phosphorylate. This all suggests that the full-length protein, like the Hpt domain, is capable of phosphorylating, but that it may be doing nothing in the absence of signal (or dephosphorylating). Given these results, saying that RcsDpstb is positively regulating biofilm formation (Fig.1 title, and elsewhere) is somewhat misleading. What it presumably does is prevent the Hpt domain, expressed from the chromosomal locus in Figure1b, from signalling to RcsB. By itself, it is not clear it is doing anything. Understanding this clearly is important for interpreting this system and the tested mutants. A clear model and how phosphate is flowing in the various situations would help a lot. Currently Supplementary Figure3 seems to reflect the appropriate directional arrows, but the text does not. Moving the rcsB data earlier in the paper (after Figure1, 2, or maybe earlier, before Figure3) would certainly help.

RcsD dephosphorylates RcsB while RcsD-Hpt phosphorylates RcsB. Expression of RcsDpstb in the wild type strain and the N-term deletion mutant resulted in increased biofilm, indicating RcsB is less phosphorylated (Figure 1b and 1c). While over-expression of RcsD-Hpt resulted in decreased biofilm formation, indicating RcsB is more phosphorylated. In addition, the Phos-tag experiments showed that the RcsDpstb strain has a lower level of phosphorylated RcsB (Figure 4b). Expression of RcsDpstb in the wild type strain showed similar results as a rcsB mutant indicating a lower level of phosphorylated RcsB in the presence of RcsDpstb.

It is possible that the RcsDpstb interferes with the ability for RcsD-Hpt to phosphorylate RcsB. However, plasmid expression of the rcsDpstb-H844A mutant in the Y. pestis rcsDN-term deletion mutant formed significantly less biofilm than wild type rcsDpstb indicating H844 might be important for RcsD to dephosphorylate RcsB (Supplementary Figure 2b and Line 180-183). In addition, it is known that RcsD plays a dual role in phosphorylation and dephosphorylation of RcsB in other organisms (Majdalani N, et al., 2005, J. Bacteriol. https://doi.org/10.1128/JB.187.19.6770-6778.2005; Wall EA, et al., 2020, Plos Genetics, https://doi.org/10.1371/journal.pgen.1008610; Takeda S., et al., 2001, Mol. Microbiol., https://doi: 10.1046/j.1365-2958.2001.02393.x). We therefore think it is safe to say that the full length RcsD might function to dephosphorylate RcsB. We have modified the model in the revised manuscript (Supplementary Figure 4 and Figure 8). Regulation of RcsB has been investigated previously. The main finding of our manuscript is regulation of RcsB by the mutated RcsD (RcsD-Hpt). Thus, we have moved the known rcsB deletion mutant data to Figure 1 in the revised manuscript as suggested. We kept the rest of data in Figure 4 the same. We think it might be better to first show the mutation of rcsD alters Rcs signaling and then show how this occurs (by affecting RcsB phosphorylation).

3) The authors show (in their pull-down) that there is a bit of full-length RcsD even in the frame-shifted protein. Is there any clear evidence this does anything here? Does the N-terminus (truncated after the frame-shift) have a function?

We have introduced a stop codon in rcsDpe and showed that full-length RcsD is made by rcsDpe but not by rcsDpe with the stop codon (Supplementary Figure 1g). RcsDN-term seems do not have a function in our tested condition (Figure 1e).

4) While the RNA seq data is useful addition here, it is difficult to interpret without a bit more data on the strain used for the RNA seq, including the biofilm phenotypes of the WT and mutant derivatives, as well as the relevant rcsD sequences, and maybe expression of a few genes or proteins (Hms or hmsT). Are these similar in the parallel strains used earlier in the paper and the one for RNA seq, in WT, rcsB- and the RcsDpstb derivative? It would appear that rcsB- and rcsDpstb have opposite effects, at least at 25{degree sign}C, while in Figure4, these two derivatives have similar effects on biofilm. Is this due to temperature, strains, or biofilm genes that are not shown here? It is certainly possible that the ability of the full-length RcsD changes its kinase/phosphatase balance as a function of temperature, or dependent on other differences in these Y. pestis strains.

The strain used for RNA seq is a derivative of the biovar Microtus strain 201 which has a similar in vitro phenotype as the strain KIM6+ (Line 297-298). We used this strain for RNA seq because it has the virulence plasmid pCD1 and we wanted to analyze the gene expression of this plasmid, which is required for virulence, as well. RNAseq data showed that rcsB- and rcsDpstb have opposite effects on mRNA level of some genes. However, no significant change in expression of biofilm genes was noted in the RNAseq data set. In fact, our previous data has shown that the biofilm related (hmsT and hmsD) genes are only moderately (Less than 2-fold change between wild type and rcsB mutant) regulated by RcsB based on RT-PCR and β-gal analysis (Sun YC, et al., 2012, J. Bacteriol. https:// doi: 10.1128/JB.06243-11and Guo XP, et al., 2015, Sci. Rep. https://doi: 10.1038/srep08412 and Figure 4c).

Author Response

Reviewer #1 (Public Review):

Sex determination and dosage compensation are two fundamental mechanisms in organisms with distinct sexes. These mechanisms vary greatly across the various model organisms in which they have been studied. Comparisons across more closely related members of the same genus have already proven productive in the past, to understand how these essential mechanisms evolve. In this study, the authors compare some aspects of the dosage compensation and sex determination mechanisms across two Caenorhabditis species that diverged ~15-30 MYA.

Previously, the authors have studied dosage compensation and sex determination extensively in C. elegans. Here, they first identify the homologs of some key factors in C. briggsae, a species that independently evolved hermaphroditism. The authors show that some of the key players in these processes play the same roles in C. briggsae as they do in C. elegans. Namely, they show that the nematode-specific SDC-2 protein plays a role in both dosage compensation and sex determination also in C. briggsae, they find the homologs of some of the SMC protein complex that performs dosage compensation also in C. elegans and they study the binding specificity on the X chromosome.

Overall, the work is thorough and compelling and is very clearly presented. The authors generate a number of genetic tools in C. briggsae and the careful genetic analyses together with a number of binding assays in vivo and in vitro, support the authors' main conclusions: that the main players and genetic regulatory hierarchy are conserved between these two nematodes, but the binding sites for the DCC on the X chromosome have diverged and the mode of binding has changed as well. Whereas in C. elegans the DCC binds sites in the X chromosome that contain multiple sequence motifs in a synergistic manner, in briggsae they seem to do so additively. This latter point is supported by the data, but it could be explored a bit more deeply using the available ChIP-seq data that the authors have generated. In addition, it would be interesting to discuss the possible implications of this difference.

One minor weakness of this work is that it could be better put in the context of other related comparisons of these mechanisms. For example, the comparison of sex determination pathway by Haag et al. in Genetics 2008, and the comparison of dosage compensation across Drosophila species (Ellison and Bachtrog, Plos Genetics, 2019), and possibly others. The other point that the authors could provide deeper insight into, is the rate of divergence of proteins like SDC-2 (which is thought to be the protein that contacts DNA), versus some other proteins in the DCC and in general other proteins not involved in sex determination or dosage compensation (this doesn't need to be limited to comparing elegans and briggsae as there are numerous Caenorhabditis genomes available). This would provide a more complete view of the evolution of these processes.

Regarding the comparison of our studies to those of the C. briggsae sex determination pathway described by Haag and others, we have included the following in our revised manuscript:

Pages 8-9. "Within the Caenorhabditis genus, similarities and differences occur in the genetic pathways governing the later stages of sex determination and differentiation (Haag, 2005). For example, three sex-determination genes required for C. elegans hermaphrodite sexual differentiation but not dosage compensation, the transformer genes tra-1, tra-2, and tra-3, are conserved between C. elegans and C. briggsae and play very similar roles. Mutation of any one gene causes virtually identical masculinizing somatic and germline phenotypes in both species (Kelleher et al., 2008). Moreover, the DNA binding motif for both Cel and Cbr TRA-1 (Berkseth et al., 2013), a Ci/GL1 zincfinger transcription factor that acts as the terminal regulator of somatic sexual differentiation (Zarkower and Hodgkin, 1992), is conserved between the two species.

At the opposite extreme, the mode of sexual reproduction, hermaphroditic versus male/female, dictated the genome size and reproductive fertility of Caenorhabditis species diverged by only 3.5 million years (Yin et al., 2018; Cutter et al., 2019). Species that evolved self-fertilization (e.g. C. briggsae or C. elegans) lost 30% of their DNA content compared to male/female species (e.g. C. nigoni or C. remanei), with a disproportionate loss of male-biased genes, particularly the male secreted short (mss) gene family of sperm surface glycoproteins (Yin et al., 2018). The mss genes are necessary for sperm competitiveness in male/female species and are sufficient to enhance it in hermaphroditic species. Thus, sex has a pervasive influence on genome content. In contrast to these later stages of sex determination and differentiation, the earlier stages of sex determination and differentiation had not been analyzed in C. briggsae."

Regarding the comparison to Drosophila dosage compensation, including the work of Ellison and Bachtrog (2019), we included the following in the Discussion of our revised manuscript (page 22) and included related remarks in the abstract.

"In contrast to the divergence of X-chromosome target specificity between Caenorhabditis species, X-chromosome target specificity has been conserved among Drosophila species. A 21-bp GA-rich sequence motif on X is utilized across Drosophila species to recruit the dosage compensation machinery, although it may not be the sole source of X target specificity (Alekseyendo, 2008; Kuzu, 2016, Ellison, 2008; Alekseyendo, 2013)."

Regarding a comparison of our work to that of other rapidly evolving processes, we have made the following revision to our Discussion (page 22):

"Conservation of DNA target specificity among species is also a common theme among developmental regulatory proteins that participate in multiple, unrelated developmental processes, such as Drosophila Dorsal in body-plan specification (Schloop et al., 2020) or Caenorhabditis TRA-1 in hermaphrodite sexual differentiation and male neuronal differentiation (Berkseth et al., 2013; Bayer et al., 2020). Typically, for such multi-purpose proteins, target-site specificity is evolutionarily constrained: protein function is changed far more by changes in the number and location of conserved cis-acting target sequences than by changes in the target sequences themselves (Carroll, 2008; Nitta et al., 2015). Hence, the divergence in X-chromosome target specificity across the Caenorhabditis genus is atypical among developmental regulatory complexes with highly diverse target genes and could have been an important factor for establishing reproductive isolation between species. Our finding is reminiscent of the discovery that centromeric sequences and their corresponding centromere-binding proteins have co-evolved rapidly as a consequence of hybrid incompatibilities (Malik and Henikoff, 2001; Henikoff et al., 2001; Talbert and Henikoff, 2022). Occurrence of rapidly changing DNA targets and their corresponding DNA-binding proteins (see also Lienard et al., 2016; Ting et al., 1998; Ting et al., 2004; Sun et al., 2004) is an increasingly dominant theme contributing to reproductive isolation."

A brief comment about all three comparisons is also made in the beginning of the Discussion on page 18.

Author Response

Reviewer #1 (Public Review):

Following previous publications showing that NR2F2 controls atrial identity in the mouse and human iPS cells, the authors address in the fish the role of the transcription factor Nr2f1a, which is specific to the atrial chamber. This had been initiated in a previous publication (Duong et al, 2018) and is extended in this manuscript. In mutant fish, the atrial chamber is smaller and mispatterned. Markers of the atrioventricular canal and of the pacemaker are expanded. Transcriptomic analyses and electrophysiological measures further support this observation. A putative enhancer of nkx2.5 is identified by ATAC-seq and shown to be repressed in nr2f1a mutants, suggesting that Nkx2.5, a known repressor of pacemaker identity, may be a mediator of Nr2f1a. Overexpression of nkx2.5 delays the appearance of pacemaker cells, and is proposed to partially rescue the absence of nr2f1a.

Overall, this work provides novel insight into the mechanism of atrial chamber patterning in the fish and discusses the conservation of the role of nr2f1a. However, the claim that atrial cells switch their identity into ventricular and pacemaker cells is currently not demonstrated. Alternative hypotheses of mispatterning, cell number changes by proliferation, survival, or ingression are not ruled out by the data presented. The claim that "Nr2f1a maintains atrial nkx2.5 expression" or of a "progressive loss of Nkx2.5 within the ACs" needs to be further supported. The definition of "atrial cells (AC)" varies between figures.

Major comments:

1) The definition of "AC" varies from figure to figure: amhc+ in Fig 1A, amhc+vmhc- in Fig.1S1A, amhc+fgf13a- in Fig. 2 and 5, morphological area in Fig. 3. Please clarify how the atrial chamber is delineated in mutants in Fig. 3 since the avc constriction is not obvious.

a. As stated in the response to Essential Revisions comment 1.B, we have tried to clarify the definitions of the cardiomyocytes populations in the revised text by indicating the specific markers used in the text and the figures. We then provide our interpretation for what this means regarding the different cardiomyocyte populations.

b. Since the analysis of the electrophysiology cannot be performed with markers or the transgenic zebrafish embryos using GFP, we chose areas for analysis closer to the middle of the morphological atrium in the nr2f1a mutant and WT sibling control embryo hearts that would be consistent with having Amhc+ expression and fgf13a:EGFP+ transgenic and Isl1 markers that were found from the analysis with immunohistochemistry. This strategy was schematized in Figure 3A and is now explicitly stated on lines 266 and 267 of the revised manuscript.

2) The claim of a switch in cell identity or transdifferentiation is not demonstrated. This would require cell tracking or single-cell transcriptomics. I don't see how "AVC (..) [is] resolving to ventricular identity", since amhc seems to be maintained throughout the atrial chamber at all stages. The claim that "the number of vmhc+ only cardiomyocytes progressively increased" is not supported by Fig1S1. The expansion of pacemaker cells may result from cell ingression at the arterial pole. This hypothesis is in keeping with the expression of nr2f1a outside the heart tube in putative atrial progenitors (Duong, 2018). The phenotype upon nkx2.5 overexpression may also be interpreted along this line: ingression of pacemaker cells is delayed. The claim that "PC identity progressively expands throughout nr2f1a mutant atria" is not supported by the quantifications of a mean of 12 fgf13a+amhc+ cells at 96hpf (Fig. 2H), which is as many as fgf13a-amhc+ cells (Fig. 2G) and a quarter of the total amhc+ cells in Fig. 1J. The schema in Fig 6 does not reflect quantifications at 96hpf, which indicate the persistence of amhc+vmhc+ cells, amhc+ only, or amhc+fgf13a- in Fig 1S1 and 2G.

"We did not observe effects on cell death or proliferation in the hearts of nr2f1a mutants": please provide the data, since proliferation was shown to be affected in mouse mutants (Wu, 2013).

a. As indicated above in our response to the Essential Revisions comment 1.D, our quantification of cardiomyocytes indicates there are progressively fewer Amhc+/Vmhc+ cardiomyocytes in the nr2f1a mutant hearts (Figure 1J-L). The total number of Vmhc+ cardiomyocytes (Amhc+/Vmhc+ and Amhc-/Vmhc+) cardiomyocytes is increased in the nr2f1a mutant hearts relative to the WT sibling hearts. However, the number of Vmhc+-only (Amhc-/Vmhc+) cardiomyocytes, which reflect the ventricles, does not increase significantly in the n2f1a mutants and are not statistically different than their WT siblings at each of the stages, despite their trending that way (Figure 1 – figure supplement 2C). The total number of cardiomyocytes in the nr2f1a mutant hearts also is not increasing during these stages (Figure 1L). Along with the lack of cardiomyocyte death or proliferation (Figure 1 – figure supplements 3 and 4), this suggests that these hearts have more total Vmhc+ cardiomyocytes and the addition of Vmhc+-only cardiomyocytes is primarily coming from the cardiomyocytes in the Vmhc+/Amhc+ atrioventricular canal progressively losing Amhc expression. As indicated in the response to Essential Revisions comment 1.D, we have provided the individual image channels in a revised Figure 1 – figure supplement 1 and proportions of Vmhc+ cardiomyocytes in Figure 1 – figure supplement 2D to help clarify this issue.

b. Regarding the transdifferentiation vs ingression of newly-differentiating cardiomyocyte hypotheses for the expansion of pacemaker markers, was addressed in the response to Essential Revision comment 2. Please see that comment for how we addressed this concern.

3) The claim that "Nr2f1a maintains atrial nkx2.5 expression" or of a "progressive loss of Nkx2.5 within the ACs" needs to be further supported by quantification of the number of nkx2.5 positive cells in nr2f1a mutants. It seems that some cells in Fig. 4 co-express nkx2.5 and pacemaker markers in the mutant, which questions the repressive role of Nkx2.5. Following the observation of an nkx2.5 enhancer active next to pacemaker cells in control heart but absent in nr2f1a mutants, shouldn't we expect a gap of nkx2.5 expression next to pacemaker cells in mutants? It is unclear why pacemaker cells express nr2f1a (Fig. 6S1) but not nkx2.5. This needs clarification.

a. The repressive role of Nkx2.5 with respect to pacemaker identity has been well documented in zebrafish and mice (Colombo et al., 2018). Nkx2.5 and Isl1 expression at the venous pole of zebrafish hearts are predominantly mutually exclusive, although there are a few cardiomyocytes at their borders that the express both Nkx2.5 and pacemaker markers. We recgonize that there are still some Nkx2.5-expressing cardiomyocytes that overlap with the pacemaker maker cardiomyocytes in the nr2f1a mutant hearts, as shown in Figure 4F. However, the majority of these cardiomyocytes have lower expression than the adjacent cardiomyocytes that form a border and do not have overlapping expression. Furthermore, as shown in Figure 4D-F and Figure 4 – figure supplement 2, the overall effect appears to be a regression of Nkx2.5+ expression in cardiomyocytes and corresponding expansion of pacemaker markers from the venous pole from 48 though 96 hpf in the nr2f1a mutant hearts, consistent with the established role of Nkx2.5 in repressing pacemaker identity. In the revised manuscript, we have provided each of the individual channels for the images in Figure 4 to better allow visualization of the different cardiomyocyte markers and a new supplemental figure showing the predominantly mutually exclusive expression of Nkx2.5 and Isl1 at the venous pole of zebrafish embryo hearts (Figure 4 – figure supplement 1).

b. The expression of Nkx2.5 within the heart, like any gene, is likely controlled by multiple different regulatory elements. It is not clear to us why Reviewer #1 feels one would expect to see a gap in expression between Nkx2.5+ and pacemaker cardiomyocytes in the nr2f1a mutant hearts, unless Nkx2.5 was not required to repress pacemaker identity or there was a significant delay between loss of Nkx2.5 and gain of pacemaker markers. As indicated in the response to Essential Revisions comment 3.C, in the revised manuscript, we show experiments in which we have deleted the putative nkx2.5 enhancer element and found there is a loss of Nkx2.5+ and gain of fgf13a:EGFP+ cardiomyocytes in the atrium, as one might expect if the enhancer promotes or maintains Nkx2.5 expression in atrial cardiomyocytes that border the pacemaker cardiomyocytes. In the revised manuscript, this experiment is described in the Results (lines 348-364 and included in a revised Figure 6 and new Figure 6 – figure supplement 2.

c. Please see our response to Essential Revision comment 3.A regarding the issue of Nr2f1a expression in pacemaker cardiomyocytes.

Author Response

Reviewer #1 (Public Review):

The manuscript by Warren et al., presents evidence suggesting that aberrant Yap signaling plays a role in epithelial progenitor cell dysregulation in lung fibrosis. This work builds on a body of work in the literature that Hippo signaling is aberrantly regulated in idiopathic pulmonary fibrosis. They use a combination of single nuclear and spatial transcriptomics, together with in vivo conditional genetic perturbations of Hippo signaling in mice, to investigate roles for Yap/Taz signaling in alveolar epithelial homeostasis and remodeling associated with exposure to a fibrosing agent, bleomycin. They show that Taz and Tead1/4 are most abundantly expressed by alveolar type 1 (AT1) cells, but Nf2 immunoreactivity (upstream activator of Hippo) is observed predominantly within airway and AT2 cells. Bleomycin exposure was associated with reduced p-Mst in regenerating alveolar epithelium, that inactivation of Yap/Taz arrested AT2>AT1 differentiation, and inactivation of either Nf2 or Mst1/2 promoted AT1 differentiation after bleomycin exposure and reduced matrix deposition/fibrosis. They go on to show that compromised alveolar regeneration resulting from inactivation of Yap/Taz results in enhanced bronchiolization of injured alveoli. Experiments are well designed and include quantitative endpoints where appropriate, data of high quality, and results are generally supportive of conclusions. These studies provide valuable new data relating to roles for the Hippo pathway in regulation of alveolar homeostasis and epithelial regeneration/remodeling in injury/repair and fibrosis.

We thank the reviewer for their enthusiastic and constructive comments.

Reviewer #2 (Public Review):

The authors explored non-redundant, and potentially contrasting, roles of the Hippo effector transcription factors, YAP and TAZ, in the epithelial regenerative response to non-infectious lung injury. The strength of the work is the use of genetic mouse models that explored inducible loss of function of YAP and/or TAZ in an alveolar epithelial type 2 (AT2) specific manner. The main weakness of the work is that gene(s) inactivation was performed prior to lung injury and, therefore, does not take into account the contextual and dynamic nature of YAP/TAZ signaling; for example, work by other groups have shown that YAP/TAZ is activated early following injury followed by a decrease in activity, thus balancing proliferation and differentiation of AT2 cells (for review, see PMID: 34671628).

We thank the reviewer for their enthusiastic and constructive comments.

We agree that knocking out genes prior to injury might not take into account the contextual and dynamic nature of YAP/TAZ signaling. However, the Hippo pathway allows cells to sense changes in their environment. We have published that in the airway epithelium the Hippo pathway becomes inactivated upon naphthalene injury in surviving airway epithelial cells sensing the loss of their neighbors, to induce Wnt7b expression which then induces Fgf10 expression in airway smooth muscle cells to drive airway epithelial regeneration. Normally when regeneration is complete and cell density is restored the Hippo pathway reactivates and the repair cascade is inactivated. Knocking out Mst1/2 in airway epithelium chronically activates this cascade and leads to overproliferation of the airway epithelium. Interestingly, upon inactivation of Mst1/2 in the airway epithelium some airway epithelial cells also turn into AT1 cells.

However, AT1 cells do not proliferate. As such we believe that inactivation of Mst1/2 or Nf2 in AT2 cells will not result in overproliferation but mainly promote AT1 cell differentiation. That being said there are other pathways and molecules that affect Yap/Taz nuclear localization. So inactivation of Mst1/2 or Nf2 in AT2 cells most likely primes/activates AT2 cells to regenerate AT1 cells but this decision is likely not binary.

Reviewer #3 (Public Review):

The manuscript entitled "Hippo signaling impairs alveolar epithelial regeneration in pulmonary fibrosis" is a rigorous and timely report detailing the significance of Hippo signaling, Taz and Yap in AT2/AT1 differentiation and the subsequent impact on the progression of lung fibrosis versus repair/ regeneration. The authors experimental design and results support their conclusions. The identification of the distinct effects of Taz and Yap in these processes highlight the pathway and specific molecules as potential therapeutic targets.

The major strengths of these studies lie in the rigor of the elegant transgenic developmental/adult injuryrepair mouse models combined with spatial transcriptomics and analyses. The weaknesses include a lack of detail presented in the methods, some legends and discussion.

We thank the reviewer for their enthusiastic and constructive comments. And have addressed the issues raised.

Author Response

Reviewer #1 (Public Review):

This is a very interesting paper showing that during amino acid starvation of Neurospora, the general amino acid control factors CPC-1 and CPC-3 are crucial to maintaining circadian rhythm at the levels of rhythmic growth and transcription of the FRQ gene. They show that deleting both genes leads to reduced and arrhythmic cell growth and FRQ transcription that can be accounted for by severely reduced occupancy of the FRQ promoter by the key transcription factor WCC. This defect in turn appears to result from diminished H3 acetylation of the FRQ promoter that was observed at least in the cpc-1 mutant, which is mediated by Gcn5. Thus, they show that Gcn5 occupancy at FRQ is rhythmic and impaired by cpc-1 knock-out, that CPC-1 occupies the FRQ promoter, and provide coIP evidence that Cpc-1 interacts with Gcn5 and Ada2 and, hence, could act directly to recruit these cofactors to the FRQ promoter. Importantly, they show that knock out of GCN5 eliminates rhythmic cell growth and FRQ expression (although surprisingly not FRQ mRNA abundance), as well as reducing H3ac levels and WCC binding at FRQ. They further show that TSA treatment can reverse the effects of histidine starvation on the circadian period in WT cells, and can partially restore rhythmic growth to histidine-starved cpc-3 cells, and that elimination of HDAC Hda1 increases H3ac at FRQ in WT cells. They provide some evidence that transcriptional activation of certain aa biosynthetic genes by CPC-1 is also rhythmic, although the evidence for this is not strong and it's unclear whether CPC-1 occupancy or its activation function would be periodic. They also did not address whether CPC-1 occupancy at FRQ is rhythmic.

This work is important in providing convincing evidence that CPC-1-mediated induction of transcription factor CPC-3 in starved Neurospora cells mediates CPC-1-mediated recruitment of Gcn5 and acetylation of the FRQ promoter, which counteracts the function of histone deacetylase HDA1 to maintain high occupancy of the transcription factor WCC and attendant circadian rhythm of FRQ transcription. Although the work does not identify new regulatory circuits, such as rhythmic transcription of FRQ, the role of Gcn5, Hda1, and promoter histone acetylation in supporting transcriptional activation, and the general amino acid control response to amino acid starvation are all well-established mechanisms, the work is significant in showing how these pathways and mechanisms are integrated to maintain circadian rhythm in the face of amino acid limitation.

There is an abundance of convincing experimental evidence provided to support the key claims just summarized above. However, there are a few instances in which additional experiments might be required to resolve a discrepancy in the data or provide stronger evidence to support a claim.

Thanks for the comments. We have revised the manuscript as suggested.

Reviewer #2 (Public Review):

This study by Liu et al. investigates the mechanism that enables the Neurospora circadian clock to maintain robust molecular and physiological rhythms under conditions of nutrient stress. The authors showed that the nutrient-sensing GCN2 signaling pathway is required to maintain robust circadian clock function and output rhythms under amino acid starvation in the filamentous fungus Neurospora. Specifically, they observed that under amino acid starvation conditions, knocking out GCN2 pathway components GCN4 (CPC-1) and GCN2 (CPC-3) severely disrupts rhythmic transcription of core clock gene frequency (frq) and clock-regulated conidiation rhythm. They provided data to indicate that the observed disruptions are due to reduced binding of the White Collar (WC) complex to the frq promoter stemming from lower histone H3 acetylation levels. This prompted the authors to propose a model in which GCN2 (CPC-3) and GCN4 (CPC-1) are activated upon sensing amino acid starvation, recruit GCN-5 containing SAGA acetyltransferase complex to maintain robust histone acetylation rhythm at the frq promoter. They then performed a battery of assays to show that both GCN-5 and ADA-2 are necessary for maintaining robust H3ac, frq mRNA, and conidiation rhythms under normal conditions. To support that low H3ac level at the frq promoter is the cause for impaired WC binding and frq transcription, they demonstrated they can partially rescue the observed rhythm defects of the knockout mutants under amino acid starvation using an HDAC inhibitor. Finally, the authors used RNA-seq to identify genes and pathways that are differentially activated by GCN4 (CPC-1) under amino acid starvation conditions. Many of these genes are involved in amino acid metabolism and they showed that 3 of them exhibit rhythmic expression in WT but low and non-rhythmic expression in the CPC-1 KO strain.

Strength: The 24-hour period length of the circadian clock is known to be stable over a range of environmental and metabolic conditions because of circadian compensation mechanisms. Whereas temperature compensation (maintenance of circadian period length over a physiological range of temperature) has been studied extensively in multiple model organisms, the phenomenon of nutritional compensation and its underlying mechanisms are poorly understood. This study provides new insights into this important yet understudied area of research in chronobiology. In addition to advancing our understanding of fundamental mechanisms governing clock compensation mechanisms, this study also adds to our understanding of metabolic regulation of rhythmic biology and the relationship between nutrition and healthy biological rhythms. Given that the GCN2 nutrient-sensing pathway is broadly conserved beyond Neurospora, findings from this study will likely be relevant to other eukaryotic systems.

The authors provided strong evidence supporting their claims that the GCN2 signaling pathway is important for maintaining the robustness of the Neurospora clock under conditions of amino acid starvation. The authors performed parallel experiments in normal (no 3-AT) vs amino acid-starved conditions (+3-AT). Their observations of relatively minor disruptions of molecular and conidiation rhythms in cpc-3 and cpc-1 KO strains in normal nutrient conditions compared to starvation conditions support their model that sensing of amino acid starvation by GCN2 pathway-induced changes at the chromatin and transcriptional level that are necessary to maintain a robust frq oscillator. Without the comparison between normal vs amino acid starved conditions, this part of their model will not be as strong.

Previously Karki et al. (2020) showed that rhythmic activation of GCN2 kinase is regulated by the clock, resulting in clock-control rhythmic translation initiation. This study uncovers an additional mechanism through which GCN2 pathway modulates circadian rhythms by regulating histone acetylation of rhythmic genes. RNA-seq as described in Figure 7 provides some potential targets.

Thanks for the comments and suggestions. We have revised the manuscript as suggested.

Weakness:

(1) The authors propose a model (Figure 8) in which the GCN2 pathway is ,activated by amino acid starvation and recruits the SAGA complex to promote histone acetylation level at the frq promoter. There is however no data in this study showing that the GCN2 pathway is activated in amino acid-starved conditions, only that it is required to maintain robust frq and conidiation rhythms. The authors should clarify how they are defining "activation of the GCN2 pathway" in this study. For example, is it recruitment of GCN-5 and SAGA complex to frq promoter?

Thanks for the question. CPC-3, the GCN2 homolog in Neurospora, is the only eIF2α kinase responsible for eIF2α phosphorylation at serine 51(Karki S et al. 2020, PMID: 32355000). As shown in the revised Figure 1-figure supplement 1A, the eIF2α phosphorylation and CPC-1 were induced by 3-AT treatment in the WT but not in the cpc-3KO strain. These results demonstrate that the GCN2 pathway is activated by amino acid starvation, and as a result, the CPC-1 expression is activated to recruit the SAGA complex to the frq promoter.

(2) The experiments to examine the involvement of GCN-5 and ADA-2 were performed in normal conditions (no amino acid starvation). Unlike cpc-1 and cpc-3 KO strains, gcn-5 and ada-2 KO strains showed severely disrupted frq rhythms in normal nutrient conditions, suggesting they are normally required for robust circadian rhythms. If GCN-5 and the SAGA complex are normally involved in regulating H3ac rhythms in the frq loci, how does GCN2 pathway modulates the activity of GCN-5 and SAGA complex in conditions of amino acid starvation? Are the interactions between GCN2/4 with GCN-5 and SAGA complex different in normal vs amino acid starved conditions? The authors should clarify their model.

As mentioned above, our data suggested that GCN-5 and ADA-2 are required for robust circadian rhythms under normal conditions. As suggested, we did detect dampened rhythmic expression of frq in the gcn-5KO and ada-2KO strains under amino acid starvation (Figure 5D and 5E and Figure 5–figure supplement 1E and 1F). We also performed Co-IP to compare the difference of interactions between CPC-1 with ADA-2 and GCN5 with ADA-2 under normal and amino acid starved conditions. The results showed that although the Myc.GCN-5, MYC.CPC-1 or Flag.ADA-2 protein level was repressed by 3 mM 3-AT treatment (likely due to global translational inhibition by induced eIF2α phosphorylation) (Karki S et al. 2020, PMID: 32355000), the interactions between CPC-1 with ADA-2 and GCN-5 with ADA-2 were almost the same under normal and amino acid starved conditions (IP was normalized with Input) (Figure 4B and 4C). These results indicated that amino acid starved conditions had little impact on the protein interactions between CPC-1 with GCN-5 and SAGA complex.

In our model, we proposed that amino acid starvation resulted in compact chromatin structure (due to decreased H3ac) in the frq promoter in the WT strain (Figure 3B), likely due to activation of histone deacetylases or inhibition of histone acetyltransferases. Amino acid starvation activates GCN2 pathway and induces CPC-1 expression. The induced CPC-1 can recruit GCN5-containing SAGA complex to the frq promoter to loosen the chromatin structure, promoting frq rhythmic transcription under starvation conditions. However, in the cpc-3KO mutants, CPC-1 could not effectively recruit GCN5 containing SAGA complex to frq promoter, resulting in arrhythmic frq transcription. We have now clarified our model in the revised discussion.

(3) Given that the GCN2 pathway is important for nutrient sensing, the authors should not disregard the alternative hypothesis that the GCN2 pathway may be important for nutrient compensation and plays a role in maintaining the robustness of rhythms in a range of nutrient conditions.

Thanks for the suggestion. We now discussed the alternative hypothesis in the revised manuscript. “Because GCN2 signaling pathway is important for nutrient sensing, it may be important for nutrient compensation and plays a role in maintaining the robustness of rhythms in a range of nutrient conditions”.

(4) The authors should use circadian statistics to compute the phase and amplitude of the mRNA, DNA binding of the WC complex, and H3Ac rhythms. This will allow them to compare between rhythms and provide statistical significance values, rather than just providing qualitative descriptions. This will be valuable when comparing rhythms between strains and between nutrient conditions.

As suggested, we used CircaCompare to analyze our data.

Reviewer #3 (Public Review):

This is an important paper anchored by the observation that cultures of Neurospora undergoing amino acid starvation lose circadian rhythmicity if orthologs in the classic GCN2/CPC-3 cross-pathway control system are absent. Data convincingly show that Neurospora orthologs of Saccharomyces GCN2 and GCN4 (CPC-3 and CPC-1 respectively) are needed to promote histone acetylation at the core clock gene frequency to facilitate rhythmicity. While the binding of CPC-1 and thereby GCN-5 are plainly rhythmic, the explanation of exactly where rhythmicity enters the pathway is incomplete.

Figure 1 shows that inhibition of the HIS-3 activity affected by 3-AT, which should trigger cross-pathway control, is correlated with a graded reduction in the amplitude of the rhythm, and eventually to arrhythmicity at 3 mM 3-AT. While normalized data are shown in Figure 1B, raw data should also be provided in the Supplement as sometimes normalization hides aspects of the data. Ideally, this would be on the same scale in wt and in mutant strains.

We revised as suggested and added the raw data. The results are now shown in Figure 1–figure supplement 2A and 2B and Figure 5–figure supplement 1B and 1C.

Figure 2. The logical conclusion from Fig 1 is that circadian frq expression driven by the WCC has been impacted by amino acid starvation in the mutants. If so, either WC-1/WC-2 levels might be low, or else they might not be able to bind to DNA. When this was assessed, ChIP assays showed a loss of DNA binding. Although documented, an interesting result is that WCC protein amounts are sharply increased, especially for WC-1. The authors could comment on possible causes for this.

Line 176, "hypophosphorylation of WC-1 and WC-2 (which is normally associated with WC activation . . . )". While the authors are correct that this is often the case it is not always the case and this introduces a potentially interesting caveat. That is, the overall phosphorylation status of WCC does not always reflect its activity in driving frq transcription. This was first noticed by Zhou et al., (2018 PLOS Genetics) who reported that even though WCC is always hyperphosphorylated in ∆csp-6, the core clock maintains a normal circadian period with only minor amplitude reduction. This should be noted, cited, and discussed.

Thanks for the suggestion. We revised the manuscript as suggested, “It should be noted that the overall phosphorylation status of WCC does not always reflect its activity in driving frq transcription, possibly due to the unknown function of multiple key phosphosites on WCC (Wang et al., 2019; X. Zhou et al., 2018)”.

Figure 2 and Figure 2 Suppl. report different gel conditions and show that the sharply increased WC1/WC-2 levels seen in Fig 2 resulting from 3-AT treatment of the cpc pathway mutants are due to the accumulation of hypophosphorylated WC-1/2. The conclusion would be stronger if the gels in the Supplement showed the same degree of difference between wt and mutants as seen in Fig 2. In any case, these hypophosphorylated WC should be active and able to bind DNA but plainly are not based on Fig 2.

Thanks for the comments. It’s correct that WC-1/WC-2 were hypo-phosphorylated and their protein levels were increased (Figure 2 and Figure 2-figure supplement 1). However, the reduced binding of WC-1/WC-2 at the frq promoter explains for the reduced frq transcription in the cpc-1KO or cpc-3KO mutants under amino acid starvation.

Figure 3 correlates the unexpected loss of DNA binding by hypophosphorylated WCC with reduced histone H3 acetylation at frq. The 3 mM 3-AT reported to result in arrhythmicity in cpc mutants in Figures 1 and 2 results in a small (~20%?) and not statistically significant reduction in H3 acetylation in wt, compatible with the sustained rhythms seen in wt in Figure 1, but in a substantial (~5 fold) loss of binding in the ∆cpc-1 background; so CPC-1 is needed for H3 acetylation at frq to sustain the rhythm during amino acid starvation. The simplest explanation here then is that the hypophosphorylated WCC cannot bind to DNA because the chromatin is closed due to decreased AcH3.

Thanks for the comments.

Figure 4. Title:" Figure 4. CPC-1 recruits GCN-5 to activate frq transcription in response to amino acid starvation"; the conditions of amino acid starvation should be mentioned here for the reader's benefit. (In the unlikely case that there was no amino acid starvation here then many things about the manuscript need to be reconsidered.)

Based on the model from yeast where amino acid starvation activates GCN2 (aka CPC-3 in Neurospora) kinase which activates the transcriptional activator GCN4 (aka CPC-1) which recruits the SAGA complex containing the histone acetylase GCN5 to regulated promoters, CPC-1 was tagged and shown by ChIP to bind rhythmically at frq. Co-IP experiments establish the interaction of components of the SAGA complex in Neurospora and Neurospora GCN-5 indeed is bound to frq, likely recruited by CPC-1. This part all follows the Saccharomyces model with the interesting twist that the binding CPC-1 is weakly rhythmic and GCN-5 strongly rhythmic in a CPC-1-dependent manner. Based on the figure legend title, these cultures should always be starved for amino acids (although as noted this should be made explicit in the figure legend). In any case, given this, from where does the rhythmicity in GCN-5-binding arise? This question is developed more below.

Line 224, "low in the cpc-1KO strain, suggesting that CPC-1 rhythmically recruit GCN-5". Because ChIP was done only for a half circadian cycle (DD10-22), it is hard to conclude "rhythmically". The statement should be modified.

To address the concern, we performed the ChIP assay using the CPC-1 antibody instead of Myc antibody (revised Figure 4A). Analysis of the ChIP results with CircaCompare showed that CPC-1 binding at the frq promoter was rhythmic without 3-AT (Figure 4A) or with 3 mM 3-AT treatment (Figure 4-figure supplement 1A). Due to the ADA-2-GCN5 and CPC-1-ADA-2 interactions with/without 3-AT treatment (Revised Figure 4B-C), CPC-1 should be able to recruit GCN-5-containing SAGA complex to activate frq transcription in response to amino acid starvation. We have now clarified this model in the revised manuscript. Please also see response to Reviewer 2/point 5.

It was previously reported that the CPC-3/CPC-1 signaling pathway was rhythmically controlled by circadian clock, as indicated by CPC-3-mediated rhythmic eIF2α phosphorylation at serine 51 (Karki S et al. 2020, PMID: 32355000). Our data showed rhythmic CPC-1 and GCN-5 binding at the frq promoter in the WT strain and decreased GCN-5 binding in the cpc-1KO mutant (Figure 4A and 4D). These results suggested that the circadian clock controlled the CPC-3/CPC-1 signaling pathway rhythmically, which in turn promoted the rhythmic frq transcription through recruiting GCN5 containing SAGA complex under amino acid starvation. We clarified the model and description in the discussion.

As suggested by the reviewer, we modified the statement "suggesting that CPC-1 recruits GCN-5-containing SAGA complex to the frq promoter".

Figure 5 shows that rhythmicity in general and of frq/FRQ specifically requires GCN-5 even under conditions of normal amino acid sufficiency, and that normal levels of H3 acetylation and its rhythm at frq require GCN-5. Not surprisingly, high H3 acetylation at frq correlated with high WC-2 DNA binding, and ADA-2 is required for SAGA functions.

As earlier, raw bioluminescence data corresponding to panel B should be provided in the figure or Supplement.

Also, if CPC-3 and CPC-1 regulate frq transcription through GCN-5, why is the frq level extremely low in the cpc-3KO or cpc-1KO(Fig.1D) but remains normal in gcn-5KO (Fig. 5D)?

Raw bioluminescence data are listed in Figure 5–figure supplement 1B and 1C. For frq transcription in the WT and gcn-5KO mutant, please see response to Essential Revisions point 4.

Figure 6 documents the counter effects of TSA which inhibits histone deacetylation and shortens the period versus 3-AT which decreases (via CPC-3 to CPC-1 to GCN-5) histone acetylation and causes period lengthening or arrhythmicity. HDA-1 is necessary for histone deacetylation at frq.

Thanks for the comments.

Figure 7 documents extensive changes in gene expression associated with 3-AT-induced amino acid starvation and the CPC-3 to CPC-1 pathway. How do these results compare with other previously studied systems, particularly Saccharomyces, where similar experiments have been done? Are the same genes regulated to the same extent or are there some interesting differences?

Thanks for the suggestion. We revised our manuscript by comparing the difference of these genes in Saccharomyces. GCN4/CPC-1 targets are similar. “Similar to Saccharomyces cerevisiae (Natarajan et al., 2001), genes in amino acid biosynthetic pathways, vitamin biosynthetic enzymes, peroxisomal components, and mitochondrial carrier proteins were also identified as CPC-1 targets”.

Figure 8 provides a model consistent with the role of the CPC-3/GCN2 pathway in regulating genes in response to amino acid starvation. It seems this could be any gene responding to amino acid starvation.

Not accounted for in the model is the data from Fig 4 which show the rhythmic binding of CPC-1 and stronger rhythmic binding of GCN-5 to frq, both under amino acid starvation. In the presence of 3-AT, amino acid starvation is constant, which should mean that CPC-3 and CPC-1 would always be "on". Why doesn't CPC-1 recruit GCN5 at the same level at all times leading to constant high H3 acetylation rather than rhythmic H3 acetylation as seen in Figure 3? Perhaps, unlike the statement in lines 345-34, it is WCC that regulates rhythmic GCN-5 binding and facilitates rhythmic histone acetylation at frq. Or perhaps the clock introduces rhythmicity upstream from GCN5. Without an answer to the question of where rhythmicity comes into the pathway, the story is only about how the CPC-3/GCN2 pathway in regulating genes in response to amino acid starvation; without explaining the rhythmicity the story seems incomplete.

It was previously reported that the CPC-3/CPC-1 signaling pathway was rhythmically controlled by circadian clock, as indicated by CPC-3-mediated rhythmic eIF2α phosphorylation at serine 51 (Karki S et al. 2020, PMID: 32355000). Our data showed rhythmic CPC-1 and GCN-5 binding at the frq promoter in the WT strain and decreased GCN-5 binding in the cpc-1KO mutant (Figure 4A and 4D). These results suggested that the circadian clock controlled the CPC-3/CPC-1 signaling pathway rhythmically, which in turn promoted the rhythmic frq transcription through recruiting GCN5 containing SAGA complex under amino acid starvation. We clarified the model and description in the discussion.

Author Response

Reviewer 2 (Public review):

A quasi-experimental before and after design as the methodological intention should be stated in the article. Although there are equally powerful alternatives with arguably less-stringent requirements that are appropriate and well-tested for natural experiments such as that intervened by the COVID-19 pandemic given the simulation methods, as of now obtaining the actual stage distribution of cancer and the cancer-specific mortality rates before and after the pandemic is possible for making scientifically valid conclusions based on observed data to support the simulation study.

We agree with the reviewer that a modelled before-and-after analysis would have been informative. However, the required mortality and cancer stage distribution data to inform this analysis is not yet available for Australia. In future, our modelled predictions can be compared to emergent observed national stage and mortality data. The current paper presents estimates that were modelled during rapid-response modelling commissioned by the Australian Government early in the pandemic. Findings therefore demonstrate what could be done with the information available at that time. We have amended, as shown in bold below, the end of the introduction as follows:

“We demonstrate what could be estimated by a rapid response evaluation based on information available early in the pandemic, and discuss how these estimates relate to subsequent observed disruptions to screening. In future, our modelled predictions can be compared to emergent observed national stage and mortality data.”

The screening disruption is the only concerned parameter in modelling the change of cancer progression in this study. But delayed diagnosis after screening as another concern could be possibly affected by the pandemic. This should be taken into consideration in the simulation. The authors also claimed the cancer treatment could also be affected by the pandemic, the evaluation on mortality is therefore not feasible. However, the impacts of COVID-19 pandemic on the delayed treatment and cancer treatment are important issues which should be covered by simulation study.

We clearly state that this is a limitation of the current study. We have added the following sentence to the discussion, lines 377-379.

‘These effects will be incorporated in future modelled evaluations, after careful calibration and validation to observed data, with a view to extending the modelled outcomes to mortality estimates.’

By simulations, the confident intervals for the outcomes should be provided as the requirement to determine the required reliability for the estimates.

The manuscript aims to present indicative estimates for a range of scenarios, with numerous simplifying assumptions as indicated. In this context, generating meaningful uncertainty intervals is not feasible or appropriate.

Author Response

Reviewer #1 (Public Review):

There has been a lot of work showing that multi-peaked tuning curves contain more information than single peaked ones. If that's the case, why are single-peaked tuning curves ubiquitous in early sensory areas? The answer, as shown clearly in this paper, is that multi-peaked tuning curves are more likely to produce catastrophic errors.

This is an extremely important point, and one that should definitely be communicated to the broader community. And this paper does an OK job doing that. However, it suffers from two (relatively easily fixable) problems:

I) Unless one is an expert, it's very hard to extract why multi-peaked tuning curves lead to catastrophicerrors.

II) It's difficult to figure out under what circumstances multi-peaked tuning curves are bad. This isimportant, because there are a lot of neurons in the sensory cortex, and one would like to know whether multi-peaked tuning curves are really a bad idea there.

And here are the fixes:

I) Fig. 1c is a missed opportunity to explain what's really going on, which is that on any particular trialthe positions of the peaks of the log likelihood can shift in both phase and amplitude (with phase being more important). However Fig. 1c shows the average log likelihood, which makes it hard to understand what goes wrong. It would really help if Fig. 1c were expanded into its own large figure, with sample log likelihoods showing catastrophic errors for multi-peaked tuning curves but not for single peaked ones. You could also indicate why, when multi-peaked tuning curves do give the right answer, the error tends to be small.

We thank the reviewer for this suggestion. We have now split the first figure into two.

In the new Figure 1, we provide an intuitive explanation of local vs catastrophic errors and single-peaked / periodic tuning curves. We have also added smaller panels to illustrate how the distribution of errors changes with decoding time (using a simulated single-peaked population).

The new Figure 2 shows sampled likelihoods for 3 different populations. We hope this provides some intuitive understanding of the phase shifts. Unfortunately, it proved difficult not to normalize the “height” of each module’s likelihood as they can differ by several orders of magnitude across the modules. However, due to the multiplication, the peak likelihood values can (approximately) be disregarded in the ML-decoding. Lastly, we have also added more simulation points (scale factors) compared to what we had in the earlier version of the figure (see panels d-e).

II) What the reader really wants to know is: would sensory processing in real brains be more efficient ifmulti-peaked tuning curves were used? That's certainly hard to answer in all generality, but you could make a comparison between a code with single peaked tuning curves and a good code with multi-peaked tuning curves. My guess is that a good code would have lambda_1=1 and c around 0.5 (you could use the module ratio the grid cell people came up with -- I think 1/sqrt(2) -- although I doubt if it matters much). My guess is that it's the total number of spikes, rather than the number of neurons, that matters. Some metric of performance (see point 1 below) versus the contrast of the stimulus and the number of spikes would be invaluable.

We thank the reviewer for this comment and the suggestions. We agree, ideally such an expression would be useful. However, as you note it is a very challenging task due to the large parameter space (number of neurons, peak amplitude, spontaneous firing rate, width of tuning, stimulus dimensionality etc) and is beyond the scope of the present study. We have instead included a new figure (see Figure 7 in the manuscript) detailing the minimal decoding times for various choices of parameter values. We believe this gives an indication to how minimal decoding time scales with various parameters.

Author Response:

Reviewer #1 (Public Review):

[…] This novel system could serve as a powerful tool for loss-of-function experiments that are often used to validate a drug target. Not only this tool can be applied in exogenous systems (like EGFRdel19 and KRASG12R in this paper), the authors successfully demonstrated that ARTi can also be used in endogenous systems by CRISPR knocking in the ARTi target sites to the 3'UTR of the gene of interest (like STAG2 in this paper).

We thank the referee for highlighting the novelty and potential of the ARTi system.

ARTi enables specific, efficient, and inducible suppression of these genes of interest, and can potentially improve therapeutic target validations. However, the system cannot be easily generalized as there are some limitations in this system:

• The authors claimed in the introduction sections that CRISPR/Cas9-based methods are associated with off-target effects, however, the author's system requires the use CRISPR/Cas9 to knock out a given endogenous genes or to knock-in ARTi target sites to the 3' UTR of the gene of interest. Though the authors used a transient CRISPR/Cas9 system to minimize the potential off-target effects, the advantages of ARTi over CRISPR are likely less than claimed.

We thank the reviewer for raising these very valid concerns about potential off-target effects related to the CRISPR/Cas9-based gene knockout or engineering of endogenous ARTi target sites. In contrast to conventional RNAi- and CRISPR-based approaches, such off-target effects can be investigated prior to loss-of-function experiments through comparison between parental and engineered cells, which in the absence of CRISPR-induced off-target events are expected to be identical. Subsequent ARTi experiments provide full control over RNAi-induced off-target activities through comparison of target-site engineered and parental cells. However, we agree that undetected CRISPR/Cas9-induced off-target events cannot be ruled out in a definitive way, which we will point out in our revised manuscript.

• Instead of generating gene-specific loss-of-function triggers for every new candidate gene, the authors identified a universal and potent ARTi to ensure standardized and controllable knockdown efficiency. It seems this would save time and effort in validating each lost-of-function siRNAs/sgRNAs for each gene. However, users will still have to design and validate the best sgRNA to knock out endogenous genes or to knock in ARTi target sites by CRISPR/Cas9. The latter is by no-means trivial. Users will need to design and clone an expression construct for their cDNA replacement construct of interest, which will still be challenging for big proteins.

We fully agree that the required design of gene-specific sgRNAs and subsequent CRISPR-engineering steps are by no means trivial. However, we believe that decisive advantages of the method, in particular the robustness of LOF perturbations and additional means for controlling off-target activities, can make ARTi an investment that pays off. In our experience, much time can be lost in the search for effective LOF reagents, and even when these are found, questions about off-target activity remain. While ARTi overcomes many of these challenges by providing a standardized experimental workflow, we do not propose to replace all other LOF approaches by this method. Instead, we would position ARTi as a unique orthogonal approach for the stringent validation and in-depth characterization of candidate target genes, as we will highlight in our revised discussion.

• The approach of knocking-out an endogenous gene followed by replacement of a regulatable gene can also be achieved using regulated degrons, and by tet-regulated promoters included in the gene replacement cassette. The authors should include a discussion of the merits of these approaches compared with ARTi.

We thank the reviewer for pointing out these alternative LOF methods. We had already included a brief discussion of chemical-genetic LOF methods based on degron tags. While we certainly share the current excitement about degron technologies, they inevitably require changes to the coding sequence of target proteins, which can alter their regulation and function in ways that are hard to control for. In our revised discussion, we will add a brief comparison to conventional tet-regulatable expression systems, which unlike ARTi require the use of ectopic tet-responsive promoters. Overall, we would position ARTi as an orthogonal tool that enables inducible and reversible LOF perturbations without changing the coding sequence and the endogenous transcriptional control of candidate target genes.

Reviewer #2 (Public Review):

[…] The system is very innovative, likely easy to be established and used by the scientific community and thus very meaningful.

We thank the reviewer for their enthusiasm about ARTi.

Author Response

Reviewer #1 (Public Review):

Starrett, Gabriel et al. investigated 43 bladder cancers (primary tumors), 5 metastases and 14 normal tissues from 43 solid organ transplant recipients of 5 Transplant Cancer Match Study participating registries (US) for the presence of viral genetic signatures, their host genome integration and possible contribution in carcinogenesis. They isolated DNA and RNA from FFPE tissues to perform state of the art whole genome and transcriptome sequencing. They find that 20 of the primary tumors, 3 of the metastases and 7 of the normal tissues harbor viral signatures with BKPyV and JCPyV being the most prevalent viruses detected. The bulk of the experiments focuses on the 9 BKPyV-positive primary tumors. They report that several of the BKPyV-positive tumors show host genome integration of BKPyV with associated focal amplifications of adjacent host chromosome regions, with chromosome 1 being the most prevalent. Furthermore, BKPyV-positive tumors show a distinct transcriptomic signature with gene expression changes related to DNA damage responses, cell cycle progression, angiogenesis, chromatin organization, mitotic spindle assembly, chromosome condensation/separation and neuronal differentiation. The authors only touch the features of other virus-positive tumors, e.g. those with JCPyV and HPV signals, without offering further detail or thought. The overall mutation signature analysis reveals no clear correlation between presence of viral sequences and tumor mutation burden suggesting that many different, virus-unrelated, factors possibly contribute to bladder cancer genesis and progression. Most striking are cases potentially linked to aristolochic acid, ABOBUCK3 and SBS5. Thus, while the approach is state-of-the-art, the causality of viral signatures and oncogenesis and vice versa remains unsolved.

Strengths:

1) The study assesses 43 primary tumors, 5 metastases and 14 normal tissues from 43 solid organ transplants of different kinds (24x kidney, 4x liver, 14x heart and/or lung, 1x pancreas) rather than just focusing on a particular organ.

2) The study makes use of whole genome sequencing and transcriptomics and the assayed material is extracted from FFPE tissue, which shows a high level of practical, technical and computational skills and expertise.

Weaknesses:

1) There have been multiple inconsistencies in sample number and figure references throughout the publication. Is it 19 or 20 cases that have viral sequences detected? A comprehensive checker board table showing all cases, the available tissue samples and respective analyses would be in order.

We would like to thank the reviewer for their detailed assessment of the manuscript. A checkerboard table of all samples tissues and analysis has been added as supplemental table 1 (Supplementary file 1a).

2) The overall low coverage of the whole genome sequencing, which the authors mention, and the relatively big variation in coverage in both datasets (WGS, transcriptomics) are major limitations of the study. Possibly, this was done to increase specificity, but sorting out and discarding reads may also be problematic. Please comment.

Besides performing quality and adapter trimming as described in the methods, we did not discard any reads. Experimental design and analysis were conducted to be as inclusive as possible considering the rarity of these specimens.

Reviewer #2 (Public Review):

Starrett et al performed whole genome and transcriptome sequencing of bladder cancers from 43 organ transplant recipients. They found that most of these tumors contained DNA from one of four viruses (BKPyV, JCPyV, HPV, and TTV). Viral genomes are most often integrated into the genomes of these tumor cells and the authors provide evidence that the integration utilized the POL theta-mediated end joining pathway. In most cases, viral RNA was detected in tumors with viral DNA. This suggests that the viruses are actively altering the cellular environment. Frequently, this resulted in similarities for overall gene expression patterns in the tumors that were grouped by the type of virus present in the tumor. Moreover, the changes in expression linked with viral gene expression were found in genes relevant to tumorigenesis. Immunohistochemical detection of viral proteins in these tumors also demonstrated active viral gene expression. However, the presence of viral proteins was heterogenous within the tumor, with between 1 and 100% of the tumor staining positive for BKPyV large T antigen. An analysis of mutational signatures in these tumors indicate that the viruses are also shaping the tumor genome by inducing mutations. Evidence that specific viruses are contributing to tumorigenesis in organ transplant patients has fundamental implications for preventing tumorigenesis in these patients.

The conclusions of this paper are generally well supported by the data provided. Indeed, there is little doubt that viral infections are more likely in these tumors. However, there are aspects of the paper that could be improved and or clarified. Most importantly, despite the strong evidence that the viruses are altering the tumor cell environment, it is unclear if these changes are necessary for tumorigenesis or less excitingly the result of an even more immune suppressive environment within the tumor. The heterogeneity of the LT expression suggests that the presence of the viral DNA and RNA may not be enough to assess whether it is actively contributing to the tumor. Is an increased frequency of viral protein staining linked with any evidence of an active contribution to tumorigenesis (fewer tumor-suppressor/oncogene mutations). that they reduced mutations in tumor suppressors. This might be easiest to assess with the tumors that have oncogenic HPV DNA. If those tumors lacked p53 and RB mutations, it would support a causative role for the virus.

We thank the reviewer for their thoughtful review. Indeed, in Figure 6 we show that no BKPyV-positive or HPV-positive tumor harbored mutations in RB1. Additionally, only one BKPyV-positive tumor and none of the HPV-positive tumors had a mutation in TP53. We have added further emphasis to this point on page 14, “None of the HPV-positive tumors with WGS harbored mutations in TP53 or RB1. Similarly, none of the polyomavirus-positive tumors harbored mutations in RB1 and only TBC08 had a frameshift mutation in TP53.”

Author Response

Reviewer #1 (Public Review):

In mammals, a small subset of genes undergoes canonical genomic imprinting, with highly biased expression in function of parent of origin allele. Recent studies, using polymorphic mouse embryos and tissues, have reevaluating the number of allele-specific expressed genes (ASE) to 3 times more than previously thought, however with most of these novel genes showing a very low ASE (50%-60% bias toward one parental allele). Here, the authors undergo a comparison of 4 datasets and complete bioinformatic reanalysis of 3 recent allele specific RNAseq to study potential novel imprinted genes, using recently released iSoLDE pipeline. Very few genes have been confirmed with true ASE in the different studies and/or validated by pyrosequencing analysis, However, the authors show that most of the newly discovered ASE genes are lying in close proximity of already known imprinted loci and could be co-regulated by these imprinted clusters. This is important to understand how and to which extent imprinted control regions control gene expression.

This manuscript highlights the number of potential false discovered imprinted genes in previous datasets that could result to either lack of replicates, weak allelic ratio or low gene expression and lack of read depth. But the lack of overlap in the ASE called genes (at the exception to the known imprinted genes) between the different datasets is worrying and important to discuss, as the authors did. I would have appreciated more details into the differences between the different datasets that could explain the lack of reproducibility : library preparation protocol, sequencer technology, SNP calling, number of reads per SNP, bioinformatics pipeline.

We agree and a comparison of all the studies is included in the methods section. In particular, we have now included more information on SNP calling and sequencer technology.

Studying allele specific expression of lowly expressed genes is difficult by technology based on PCR amplification (library preparation, pyrosequencing) and could result on a bias expression only due to the random amplification of a small pool of molecules. Could the author compare the level of expression of their different classes of genes? The more robust ASE genes in their study could be the more highly expressed? Several genes were identified only in one or two of the previous studies, were they expressed in the other studies when not define as ASE? This would also allow defining a threshold of expression to study allelic bias in the future. To conclude, this study is an important resource for the epigenetic field and better understand genomic imprinting.

We thank-you for this suggestion. We have now taken all RNAseq data that we had run through the ISoLDE pipeline and extracted the transcripts per million (TPM) expression levels for each of the genes called in the original studies. We find no over representation of lowly expressed genes in the novel biased genes compared with known imprinted genes. We also looked specifically at the expression levels of the genes tested by pyrosequencing in these datasets and saw no relationship between validation and expression levels. Expression levels are consistent between studies, especially in the same tissue, indicating the lack of reproducibility between studies is not due to differing expression. These observations have been added to the manuscript.

Reviewer #2 (Public Review):

This work aims to understand genomic imprinting in the mouse and provide further insight to challenges and patterns identified in previous studies.

Firstly, genomic imprinting studies have been surrounded by controversy especially ~10 years ago when the explosion of sequencing data but immature methods to analyze it lead to highly exaggerated claims of widespread imprinting. While the methods have improved, clear standards are not set and results still have some inconsistencies between studies. The authors first do a meta-analysis of previous studies, comparing their results and doing a useful reanalysis of the data. This provides some valuable insights into the reasons for inconsistencies and guides towards better study designs. While this work does not exactly set a common standard for the field, or provide a full authoritative catalog of imprinted loci in mouse tissues, it provides a step in that direction. I find these analyses relatively simple and straightforward, but they seem solid.

Previous studies have described a relatively common pattern of subtle expression bias towards one parental allele, rather than the classical imprinting pattern of fully monoallelic expression. This work digs deeper into this phenomenon, using first the meta-analysis data and then also targeted pyrosequencing analysis of selected loci. The analysis is generally well done, although I did not understand why gDNA amplification bias was not systematically corrected in all cases but only if it was above a given (low) threshold. I doubt this would affect the results much though. To some extent the results confirm previously observed patterns (bimodal distribution of either subtle or full bias, and effect of distance from the core of the imprinted locus). The novel insights mostly concern individual loci, with discovery and validation of some novel genes, typically with a subtle or context-specific parental bias.

The study also provides some insights into mechanisms, especially by analysis of existing mouse models with a deletion of the ICR of specific loci. The change in the parental bias pattern was then used to infer potential methylation and chromatin-related mechanisms in these imprinted loci, including how the subtle bias further away is achieved. There are interesting novel findings here, as well as hypotheses for further research. However, this is an area where the conclusions rely quite heavily on published research especially as this study doesn't include single-cell resolution, and it's not entirely clear how much of e.g. the Figure 7 mechanisms part is based on discoveries of this study.

We agree that Figure 7 does not illustrate models based exclusively on data generated in this study: instead, it serves as hypotheses to be tested in the coming years

Imprinting is a fascinating phenomenon that can be informative of mechanisms of genome regulation and parental effects in general. It is a bit of a niche area though, and the target audience of this study is likely going to be limited to specialists doing research on this specific topic. As the authors point out, the functional importance of the findings is unknown.

Author Response:

Reviewer #1 (Public Review):

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

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

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

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

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

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

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

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

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

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

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

Reviewer #2 (Public Review):

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

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

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

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

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

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

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

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

Author Response

Reviewer #1 (Public Review):

This paper describes the accrual of RSV mutations in a severely immunocompromised child with persistent infection and demonstrates that ribavirin increases the observed mutation rate with base pair changes (C to U and G to A) compatible with its known mechanism. The paper utilizes a mathematical model to explain the counterintuitive finding that viral load does not decrease despite loss of viral fitness and clinical improvement. Positive selection is observed but does not keep pace with deleterious mutations induced by ribavirin. Overall, though the data is restricted and limited to a single person, the analysis is rigorous and supports the paper's interesting conclusions.

The paper is fascinating, but its generalizability is somewhat limited by the single study participant. Nevertheless, comparisons of therapy-induced deleterious mutations versus adaptive mutations over time is potentially important for multiple viruses.

We thank the reviewer for their comments. Although we acknowledge that this is only a single case of infection, we believe that it is an interesting case, and we are keen to share our findings with the broader scientific community.

Reviewer #2 (Public Review):

In this work, Illingworth et al. investigate the effectiveness of ribavirin and favipiravir on the treatment of a paediatric patient with chronic RSV. These drugs cause mutations and the authors tested whether they could observe this effect through deep sequencing viruses from nasal aspirates over the course of treatment. They found an increase in mutations caused by ribavirin but favipiravir appeared to have no additional mutagenic effect. Despite the lack of change in viral load, the authors suggest that the ribavirin reduced viral fitness and did not lead to adaptive escape mutations. The authors modelled how generation time and fitness interacted with mutational load. They also estimated fitness for different haplotypes generated from the mutational data.

Strengths of the paper:

Using mutagenic drugs to treat viruses is generally accepted but results have been mixed with severe viral infections and specific evidence of the precise effects of the drugs is often lacking. This paper is especially valuable for demonstrating that despite in vitro evidence that favipiravir had some effect against RSV, there was no evidence for favipiravir having an effect in a patient. This differs from the authors previous work showing a clear clinical benefit to favipiravir in treating influenza. This paper also appears to be the first to sequence RSV from a patient having been exposed to ribavirin which is important for demonstrating that the drug is having a measurable effect.

Weaknesses in the paper:

I think there is a conceptual problem with the paper. Ribavirin is supposed to increase the mutational rate of the virus which would increase the mutational load. Mutational load has been calculated by summing up the frequencies of minor alleles. However, if a particular mutation rises in frequency, it does not mean that ribavirin has caused additional mutations at the same site but rather viruses containing the mutation have risen in frequency. If a subpopulation containing mutations rises through drift or selection to a relatively high percentage that will bias the mutational load. The authors provide ~75 mutations which were at significant percentages across multiple different timepoints. It seems that these mutations contribute significantly to the mutational load but changes in mutation percentages between samples do not reflect changes in mutational events but changes in viral haplotypes/subpopulations. In a previous study Lumby et al. 2020, the authors removed mutations at >5% from their analysis but there is no indication that they performed this step similarly here. Summing many small changes will give an indication of background mutational rate (though counting only a single mutation at each locus is perhaps the only method to remove the effect of viral clonal expansion).

The mutational load is defined as the mean number of mutations per virus with respect to the consensus, equal to the sum of minor allele frequencies across the genome. We filter variant frequencies prior to calculating mutational load to compensate for noise arising from genome sequencing.

We use a deterministic model of mutation-selection balance to describe the overall dynamics of mutational load, but are conscious that the dynamics of individual variants are complex. Genetic drift could contribute to these dynamics, as might hidden structure in the viral population, with stochastic observations of viruses from distinct subpopulations. As we make clear, our key assumption regarding mutational load is that all variants from the consensus are at least mildly deleterious; under this assumption calculating the sum of allele frequencies is an appropriate measurement of mutational load. Our model accounts for the possible presence of variants under stronger and weaker selection being observed at lower and higher frequencies respectively.

We note that, in a case where distinct variants occurred in subpopulations, these variants would be observed in a mixture at lower frequencies than they existed in the subpopulations. This would lead to the observation of more variants overall, with each variant being at a reduced frequency. While stochastic effects would alter the frequencies of mutations in individual samples, if mutational load acted equally on each subpopulation, the total mutational load would be preserved across samples. The existence of subpopulations would not of itself invalidate the calculation of mutational load as we have performed it.

Our previous study Lumby et al, 2020 considered a case where favipiravir was given for a short period of time in a case of influenza B infection. In that case we did not make an assessment of the total mutational load in a population, although we did remove mutations at >5% when considering the spectrum of mutations i.e. the proportion of mutations of each type C to T, G to A, etc. We are still working on different approaches to measuring mutational load, but we are not convinced that removing high frequency mutations is always a good idea when evaluating the total mutational load. Cutting out higher frequencies is potentially a useful means to study mutational spectra under viral mutagenesis, but in a measurement of mutational load it could exclude deleterious mutations.

While ribavirin appears to have shown an effect, many questions remain. Why does the mutational load only increase for 3 points before plateauing? The authors would likely argue that this is the new saturation point for mutation load but they don't test it. Sequencing points from after the cessation of treatment would be expected to show lower mutational load but this data was not collected. Furthermore, questions remain over the methodology. It is thought that Ribavirin should only increase transitions and a transition/transversion ratio for the different samples would have been helpful. The absolute numbers of many mutation classes appear to have increased including transversions e.g AU. There isn't a good reason why nucleoside analogues should have caused this effect and perhaps it is an artefact.

Ribavirin has been shown to increase C to T and G to A mutations; these are both transitions, but T to C and A to G mutations are also transitions; the proportion of these was found to decrease under treatment. We have included a new figure showing Ts/Tv ratios but we do not find a significant pattern of change in these statistics over time.

The plateauing of the observed mutational load is consistent with the theory of mutationselection balance. Following a change in the mutation rate we would expect a shift to a new equilibrium U/s.

Sequencing was conducted as part of an investigation that was secondary to treatment of the patient: All of the samples that were collected were sequenced. We agree that upon the cessation of mutagenic drugs we would expect to see a fall in mutational load.

I don't think that the authors can reasonably determine how many haplotypes there are in the population from short read sequencing data. I think that the sequencing data very clearly shows subpopulations due to the large changes in mutation frequencies between different time points. The authors say that their analysis assumes a well-mixed population which is clearly not the case. Therefore, determining fitness of different haplotypes or mutations is likely not accurate.

Although we have short read sequencing data, some of the reads we have span more than one locus, providing some information about linkage between variants. As noted in the Methods section our inference approach provides a minimal reconstruction of haplotypes: Our reconstruction details the smallest set of distinct haplotypes necessary to explain the data.

Where we use a haplotype-based model to reconstruct the within-host evolution of the population, we neglect the potential presence of subpopulations by assuming a well-mixed population, then fully discuss the implications of this assumption for our result.

Our basic question is whether within-host adaptation leads to a gain in viral fitness in excess of the loss of fitness imposed by an increase in mutational load. In this comparison we make a conservative (i.e. low) estimate for the extent of the loss of fitness through mutational load.

When we look at within-host evolution our assumption of a well-mixed population attributes all of the systematic change in the viral population to the effects of selection. If some of this change arises through stochastic differences in emissions from a structured population, the influence of selection would be less than our inference. Thus, our estimate of the gain in fitness through within-host adaptation is a high estimate. As our high estimate of within-host fitness gain is less than a low estimate of the fitness lost through mutational load, our result is robust to our assumption.

The authors construct a model to estimate viral fitness and suggest that viral fitness decreased with the drug. This is somewhat problematic to me as viral load has not changed so it would be reasonable to say that viral fitness was likely unaffected by the drug. The authors define fitness in terms of the number of mutations that each virus likely has and assumes that these mutations are deleterious. The authors then use this to claim that mutagenic drugs reduce fitness. This seems very circular to me. If the drugs reduce fitness, it should be observed as a property of the virus population. As the only measure was viral load, which didn't change, it is difficult to claim ribavirin reduced viral fitness. There are other reasons why there could be an increase in the number of mutations e.g. sequencing more subpopulations which would have nothing to do with fitness.

We have discussed our assumption that variants in the viral population are deleterious; this lies behind the use of a model of mutation-selection balance. Under this assumption, the accumulation of a greater number of mutations following ribavirin treatment is indicative of a loss of viral fitness, although we cannot precisely quantify the magnitude of this loss. The link between an increased mutation rate and lower viral fitness is intrinsic to the concept of mutagenic drugs; our data show an increase in mutational load coincident with the therapeutic use of ribavirin.

A change in viral fitness does not necessarily lead to a substantial and clearly observable drop in viral load; we say more about this in the response to comments below.

At various points, the paper assumes that there is no selection taking place but immunoglobulin was being applied weekly and palivizumab monthly. The timing of when these drugs were given should be included. How did the palivizumab affect selection? The K272E mutation seems to go up and down but it is not clear if this was in response to drug infusion timing or if this mutation was present in a subpopulation.

Our approach assumes that selection could act at two distinct levels: Firstly, we assume that the observed increase in mutational load correlates to a reduction in viral fitness; the link between viral fitness and mutational load is intrinsic to the equation of Haldane. Secondly we use a haplotype-based model to infer how selection is acting on the level of higherfrequency mutations; under the assumption of a well-mixed model we identify a signal of within-host adaptation.

We have added details of the timing of palivizumab treatment to Figure 1. Immunoglobulin was given throughout; details of treatment have been given in Supporting Data. As we have now clarified in the Methods, our identification of potentially selected alleles was a two stage process, with the first assessing the level of noise in the data. Our model of noise envisages nonuniformity arising from multiple sources, including a situation whereby the viral population was divided in subpopulations, and in which reads comprised stochastic samples from these subpopulations. Given our model for noise, the observation of the K272E mutation at generally higher frequencies in earlier samples and generally lower frequencies in later samples was sufficient to call this as a potentially selected variant. We did not explore more complex models of drug-dependent selection.

I think the main impact of the paper will be that favipiravir will not be used in the future to treat RSV. Given that the EC50 of favipiravir against RSC is ~100x that of influenza, favipiravir was unlikely to reach a therapeutic level in the patient. Nucleoside analogues have a mixed record at treating serious viral infections. Hopefully, this work will spur on future studies to precisely measure the effect that ribavirin has on RSV.

Favipiravir was used in this patient following its successful experimental use against a case of influenza B infection (Lumby et al., 2020). We would be happy if our work inspires future research in this area.

Author Response

Reviewer #1 (Public Review):

This manuscript explores how biliary epithelial cells respond to excess dietary lipids, an important area of research given the increasing prevalence of NAFLD. The authors utilize in vivo models complemented with cultured organoid systems. Interesting, E2F transcription factors appear important for BEC glycolytic activation and proliferation.

We thank this reviewer for his/her comments and for finding the E2F-mediated mechanism of interest.

Much of the work utilizes the BEC-organoid model, which is complicated by the fact that liver cell organoid models often fail to maintain exclusive cell identity in culture. The method used by the authors (Broutier et al., 2016) can lead to organoids with a mixture of ductal and hepatocyte markers. It would be helpful for the authors to further demonstrate the cholangiocyte identity of the organoid cells.

We understand the concern of this reviewer. Indeed, this method can give rise to biliary cells or more hepatocyte-like cells. However, this choice depends on the culture media used. Our experiments used BEC-organoids in an undifferentiated state with a biliary expression profile. Please see point 1 above for a detailed answer.

The authors suggest that BECs form lipid droplets in vivo by detecting BODIPY immunofluorescence of liver cryosections. While confocal microscopy would ensure that the BODIPY fluorescence signal is within the same plane as the cell of interest, the authors use a virtual slide microscope that cannot exclude fluorescence from a different focal plane. The conclusion that BECs accumulate lipids does not seem to be fully supported by this analysis.

We fully agree with this criticism. To address this concern, we decided to use FACS analysis, a quantitative and independent method, to further confirm our initial findings. To this end, we stained sorted EPCAM+ BECs isolated from livers of CD- or HFD-fed mice with BODIPY, quantified the number of BODIPY+/EPCAM+ BECs in each experimental condition, and confirmed that these cells accumulate more lipids after HFD feeding (New Figure 1I, page 5, lines 112-115, and see also reply rebuttal to point 4).

Several mouse experiments rely heavily on rare BEC proliferation events with the median proliferation event per bile duct being 0-1 cell. While the proliferative effect appears consistent across experiments, a more quantitative approach, such as performing Epcam+ BEC FACS and flow cytometry-based cell cycle analyses, would be helpful.

Following this suggestion, we quantified proliferative EdU+ BEC cells by FACS in a new cohort of C57BL/6J mice fed CD or HFD. These data, now included in the revised manuscript (New Figure 2G, page 7, lines 143-147), strongly confirm that immunofluorescence quantification mirrors the FACS quantification and reinforce the initial finding that EPCAM+ BECs proliferate more in the livers of HFD-fed mice. Please see point 6 above for a detailed answer.

Finally, it is not yet clear how relevant the findings in this study are to ductular reaction, which is a non-specific histopathologic indicator of liver injury in the context of severe liver disease. In NAFLD, the ductular reaction is uncommon in benign steatosis, and if seen at all, occurs in the setting of substantial liver inflammation and fibrosis (Gadd et al., Hepatology 2014). The authors use a dietary model containing 60 kcal% fat, which causes adipose lipid accumulation as well as subsequent liver lipid accumulation. This diet does not cause overt inflammation or fibrosis that would represent experimental NASH, which typically requires the addition of cholesterol in dietary lipid NASH models (Farrell et al., Hepatology, 2019). While the E2F-driven proliferation may be important for physiologic bile duct function in the setting of obesity, the claim that E2Fs mediate DR initiation would require an additional pathophysiologic model or human data to demonstrate relevance. The authors could clarify this point in their discussion.

We agree with this reviewer that 15 weeks of HFD on C57BL/6J feeding are insufficient to trigger a ductular reaction. For this purpose, we used the term “BEC activation” in our manuscript, which refers to the first mandatory step for the ductular reaction to initiate. We apologize if our initial manuscript did not sufficiently emphasize this point. However, as suggested by the reviewer we investigated the ductular reaction in our model. In order to further characterize the livers after 15 weeks of CD or HFD feeding, we stained the bile ducts for pancytokeratin (PANCK) and osteopontin (OPN) and asked a pathologist (Dr. Christine Gopfert at EPFL) to evaluate these sections with a particular focus on the bile ducts. She concluded that the livers of HFD-fed mice showed steatosis and inflammation but no apparent fibrosis (New Figure 1 – figure supplement 1E). The shape of bile ducts was similar in the livers of CD- and HFD-fed mice (New Figure 1 – figure supplement 1I), concomitant with the absence of portal fibrosis and inflammation. In addition, we checked the expression levels of several established markers of ductular reaction in our RNA sequencing data and observed that, of all these genes, only Ncam1 was significantly upregulated with HFD feeding in EPCAM+-BEC cells (New Figure 2 – figure supplements 1D and 1E, Page 6, lines 127-131). Overall, these data support our conclusion that HFD triggers BEC activation without signs of an established ductular reaction and might suggest Ncam1 as a marker for this initial BEC activation process. Please see point 3 above for a detailed answer.

Reviewer #2 (Public Review):

The manuscript by Yildiz et al investigates the early response of BECs to high fatty acid treatment. To achieve this, they employ organoids derived from primary isolated BECs and treat them with a FA mix followed by viability studies and analysis of selected lipid metabolism genes, which are upregulated indicating an adjustment to lipid overload. Both organoids with lipid overload and BECs in mice exposed to a HFD show increased BEC proliferation, indicating BEC activation as seen in DR. Applying bulk RNA-sequencing analysis to sorted BECs from HFD mice identified four E2F transcription factors and target genes as upregulated. Functional analysis of knock-out mice showed a clear requirement for E2F1 in mediating HFD induced BEC proliferation. Given the known function of E2Fs the authors performed cell respiration and transcriptome analysis of organoids challenged with FA treatment and found a shift of BECs towards a glycolytic metabolism. The study is overall well-constructed, including appropriate analysis. Likewise, the manuscript is written clearly and supported by high-quality figures.

We appreciate that this reviewer finds our study well-constructed, clear, and with high-quality figures.

My major point is the lack of classification of the progression of DR, since the authors investigate the early stages of DR associated with lipid overload reminiscent of stages preceding late NAFLD fibrosis. How are early stages distinguished from later stages in this study? Molecularly and/or morphologically? While the presented data are very suggestive, a more substantial description would support the findings and resulting claims.

We thank the reviewer for the suggestion. We would like to emphasize that instead of ductular reaction, we used the term “BEC activation” in our revised manuscript, referring to the first mandatory step for initiating the ductular reaction. Both reviewers criticized the poor characterization of the ductular reaction process in the first version of our study; we put substantial effort into further clarifying this point. Our response to this point can be read in our reply to the last comment of reviewer 1 and point 3 of the rebuttal.

Author Response

Reviewer #1 (Public Review):

IRF8 is a key transcription factor in the differentiation of hematopoietic cell lineages including dendritic cell (DC) and monocyte/macrophage lineages. The promoter and enhancer regions of Irf8 have been a focus of intense research in recent times. In the submitted study Xu H. et. Al., have first time reported a lncRNA transcribed specifically in the pDC subtype from +32Kb which is also the region for the enhancer for Irf8 specifically in the cDC1 subtype. Authors have employed modern-day tools for an in-depth understanding of the role of lncIrf8, its promoter region, and crosstalk with Irf8 promoter to identify that it is not the lncIRF8 itself but its promoter region is crucial for pDC and cDC1 differentiation conferring feedback inhibition of Irf8 transcription. In the attempt to decipher the crosstalk between the promoter regions of IRF8 and lncIRF8 by employing various in vitro artificial systems, the study falls short of identifying the real significance of the lncIRF8 which is specifically expressed in pDC subtype.

We appreciate the public review made by the reviewer. We agree with the reviewer that most of the experiments on the identification of the negative feedback regulation of IRF8 via the lncIRF8 promoter element were carried out in vitro. But we would like to point out also our in vivo work: (i) transplantation lncIRF8 promoter KO cells into mice demonstrates that pDC and cDC1 development were compromised (Figure 3); (ii) lncIRF8 is expressed in in vivo BM and spleen pDC (new Figure 1-figure supplement 3). We also would like to emphasize that (i) in vivo studies on the identification of the negative feedback regulation of IRF8 via the lncIRF8 promoter element and (ii) mechanistic studies with CRISPR activation and CRISPR interference would have been difficult to perform in vivo with current tools available in mice.

According to our current understand lncIRF8 act as an indicator of +32 kb enhancer activity and we agree with the reviewer that further potential functions of lncIRF8 still need to be explored. We added a sentence on page 13, lines 427 and 428 on potential additional functions of lncIRF8:

"However, lncIRF8 might have additional functions in DC biology, which are not revealed in the current study and remain to be identified."

Reviewer #2 (Public Review):

The manuscript of Xu and colleagues examines in detail the regulation of the important transcription factor IRF8 in dendritic cell (DC) subsets. They identify a long noncoding RNA arises from the +32kb enhancer of IRF8 specifically in plasmacytoid DCs (pDCs)and show clearly that this lncIRF8 marks the activity of a region of this enhancer but the RNA itself does not appear to have any function. Deletion of the promoter of the lncIRF8 ablated cDC1 and pDC differentiation using an in vitro cell differentiation model. The authors propose an innovative model that the lncIRF8 promoter sequences act to limit IRF8 expression in cDC1, but are inactive in pDCs, resulting in their characteristically very high IRF8 expression.

This is a conceptually interesting study that makes excellent use of an extensive set of genomic data for the DC subsets. There has been a lot of recent research investigating the regulation of the IRF8 gene in hematopoiesis and this study provides an important new aspect to the work. The use of an in vitro model of DC differentiation is a powerful practical approach to investigating IRF8 regulation, as is the innovative use of CRISPR technology. Perhaps the biggest limitation of this study is that the authors have not conformed to the in-cell system data by creating a mouse strain lacking the lncIRF8 element. Such approaches by others, most notably the Murphy lab, have been instrumental in pushing this field forward. Nevertheless, Xu et al. significantly add to our current knowledge of the regulation of IRF8, a critical step in forming the dendritic cell network.

We appreciate the public review made by the reviewer and the positive assessment of our work. We agree with the review that extending our in-cell system data to lncIRF8 promoter KO mice will further strengthen our data and this will be subject of our future work.

Author Response:

We thank the reviewers and editor for their feedback, which we will carefully consider as we revise the manuscript. We aim to provide more detail on how this technique could be used with other probes, ideally showing experimental data to support this use. We will add further detail of the histology from our ex vivo ovine and porcine and in vivo porcine testing. We will also provide a more thorough comparison of our technique to other recently developed lesioning techniques. In order to provide more complete evidence that our technique perturbs local neuron populations, we will refine the action potential analysis presented before and after lesions in non-human primates. In addition to providing further clarity of the method, we will include more non-human primate data where possible.

Author Response:

We are very glad that the reviewers found our paper of broad interest to the community of population, evolutionary, and ecological genetics. We thank them for their positive feedback and insightful comments and suggestions. We are preparing a revision of the preprint that will address these points. 

One issue raised by the reviewers was that it is important to acknowledge possible limitations of the demographic model used in simulation in capturing different aspects of genomic variation. In particular, different demographic models inferred for the same species using different methods or sets of samples may have different strengths and weaknesses, and this should be considered when selecting a demographic model for simulation. This is an important point that we intend to discuss in the revised version of our manuscript. We also plan to expand the documentation of the stdpopsim catalog to include more information about  the type of data used to fit every demographic model. Below we provide an outline of our thoughts on the topic.

First of all, it is important to acknowledge that demographic models inferred from genomic data cannot fully capture all aspects of the true demographic changes in the history of a species. As a result, these models do a good job in capturing some aspects of genetic variation, but not all of them. This is primarily determined by two factors: the method used for demographic inference, and the samples whose genomes were used in inference. Regardless of the method applied, the inferred demographic model can only reflect the genealogical ancestry of the sampled individuals, and this will typically make up a small portion of the complete genealogical ancestry of the species (albeit the genealogy of any set of sampled individuals includes many ancestors). Thus, demographic models inferred from larger sets of samples from diverse ancestry backgrounds may provide a more comprehensive depiction of genetic variation within a species, as long as a sufficiently realistic demographic model can be fit. That said, the choice of samples used for inference will mostly influence recent changes in genetic variation. This is because the genealogy of even a single individual consists of numerous ancestors in each generation in the deep past (which is the premise behind PSMC-style inference methods).

The computational method used for inference also affects the way genetic variation is reflected by the demographic model, because different methods derive their inference from different features of genomic variation. Some methods make use of the site frequency spectrum at unlinked single sites (e.g., dadi, Stairway plot), while other methods use haplotype structure (e.g., PSMC, MSMC, IBDNe). This, in turn, may influence the accuracy of different features in the inferred demography. For example, very recent demographic changes, such as recent admixture or bottlenecks, are difficult to infer from the site frequency spectrum, but are more easily inferred by examining shared long haplotypes (as demonstrated by the demographic model inferred for Bos Taurus by MacLeod et al. (2013)). There have been several studies that compare different approaches to demography inference (e.g., Biechman et al. (2017); Harris and Nielsen (2013)), but unfortunately, there is currently no succinct handbook that describes the relative strengths and weaknesses of different methods. Indeed, we hope that the standardized simulations provided by stdpopsim will facilitate systematic comparisons between methods, which will, in turn, provide valuable insights for researchers when selecting demographic models for simulation.

It is important to note that inclusion of a demographic model in the stdpopsim catalog does not involve any judgment as to which aspects of genetic variation it captures. Any model that is a faithful implementation of a published model inferred from genomic data can be added to the stdpopsim catalog. Thus, potential users of stdpopsim should use the implemented models with the appropriate caution, keeping in mind the limitations discussed above. Scientists contributing a new model to the catalog are required to write a brief summary, which is added to the documentation page of the catalog: https://popsim-consortium.github.io/stdpopsim-docs/ latest/catalog.html. This summary includes a graphical description of the model (such as the one shown for Anopheles gambiae in Fig. 2B of the paper), as well as a description of the data and method used for inference. We will mention this in the revised manuscript to help users of stdpopsim navigate through this resource.

Author Response:

First of all, we would like to thank the reviewers for their work. We appreciate the constructive review comments and useful suggestions to further improve our article.

The main criticism on our manuscript, from both reviewers, is that the cryo-EM structures are of low resolution and that the fit of the crystallographic structures of the PAD and the stalk domain into these low-resolution structures is questionable. We would like to point out that the cryo-EM data, and the conclusions from it, are not essential for the main conclusions of the article. All mutants that we made in this study were designed based on the structural data obtained from the high-resolution X-ray structures, with no input from the low-resolution cryo-EM docked models. We chose to include the cryo-EM data since it allowed us to speculate about the interaction between the PAD and the stalk domain of PrgB, domains that we have separately determined the structures of via X-ray crystallography. We agree with the reviewers that further experiments are needed to verify this potential interaction. Therefore, we will perform additional biochemical assays to investigate the proposed interaction. We will also try to optimize the cryo-EM data to hopefully allow for a more reliable fit of our high-resolution crystallographic structures. Once that is done, we will submit a revised version of the manuscript.

On behalf of all authors,

Ronnie Berntsson

Author Response:

We’d like to thank the three reviewers for reviewing our work in depth and providing insightful comments and suggestions.

Reviewer 1

1. The in vivo efficacy of MS023 does not seem to be very great. The mice treated with MS023 display a very small reduction in ADMA levels and a small increase in SDMA levels (Fig S6A).

REPLY: We have quantified proteins with ADMA and SDMA by Western blotting tail clippings from mice treated with vehicle (n=6) and MS023 (n=6). These were normalized for equal loading to b-actin levels. The average ADMA relative expression was 0.92 for vehicle treated mice and 0.86 for MS023 treated mice (p < 0.044). The average SDMA relative expression was 0.89 for vehicle treated mice and 0.98 for MS023 treated mice (p < 0.000019). These whole-body measurements show MS023 promotes the decrease of proteins with ADMA and increasing proteins with SDMA, as observed before with inhibition of PRMT1 (Dhar et al, 2013).

Reviewer 2

1. Two weaknesses are noted which lie in overstatements of the findings. There are six type I PRMTs (PRMT1, 2, 3, 6, 8, and CARM1), all of which are inhibited by MS023. While the authors demonstrate that their observations are not due to the inhibition of CARM1, they do not demonstrate that it is due to the inhibition of PRMT1, as they suggest. 

REPLY: MS023 has been shown to have in vitro activity for several type I enzymes (Eram et al, 2016) and the same goes for GSK3368712 (Fedoriw et al, 2019). MS023 IC50 in vitro 30nM PRMT1, 119 nM PRMT3, 83 nM CARM1, 4 nM PRMT6, and 5 nM PRMT8 (Eram et al., 2016).  It was documented early that PRMT1 is the major cellular type I enzyme (Pawlak et al, 2000) and this is why PRMT1 and PRMT5, major type II, are embryonic lethal in mice (Guccione & Richard, 2019). In vivo data using MS023 is paralleled by using siPRMT1 (Gao et al, 2019; Plotnikov et al, 2020; Wu et al, 2022; Zhu et al, 2019). Thus in vivo, MS023 targets the main type I PRMT, PRMT1. Further, in support of our claim that MS023 targets PRMT1 in MuSCs is our previous observation that deleting PRMT1 stimulates MuSC proliferation. Since this effect was irreversible (Blanc et al, 2016) we pursued studies with the reversible MS023, the only compound to have significant activity towards PRMT1 in vivo. For these reasons, we are convinced that the effect of MS023 is mainly mediated by inhibiting PRMT1 in the MuSC.

To be thorough we should test all other type I PRMT inhibitors as they become available. CARM1 was shown to be a player in MuSC (Kawabe et al, 2012), but we excluded it using a CARM1 inhibitor TP-064 (Nakayama et al, 2018). PRMT6 mice that we generated are perfectly viable without overt phenotypes, suggesting PRMT6 is not involved (Neault et al, 2012), and PRMT8 is brain specific (Taneda et al, 2007).

2. Furthermore, this study suggests that the switch and elevated cellular metabolism in muscle stem cells due to MS023 enhanced self-renewal and engraftment capabilities but does not demonstrate this fact directly as stated. 

REPLY: Agreed. The link between cellular metabolism and MS023 enhanced self-renewal and engraftment capabilities is correlative and we will edit the revised text to reflect this.

Reviewer 3

1. However, the proposed underlying mechanism, which is claimed to rely on the expansion of MuSC and 'reprograming' of MuSCs towards a "unique and previously uncharacterized identity" is not sufficiently supported. The extent of the description of scRNA-seq data is inappropriate. Some conclusions from the scRNA-seq data appear to be overinterpreted or are rather trivial.

REPLY: We presented the top marker genes for each subpopulation that was identified in our scRNAseq to aid the reader in establishing a broad view of whether a given subpopulation was quiescent-like, proliferating, or differentiating. M1-M5 clusters were all enriched for cell cycle markers (Mki67, Cdk1, etc), indicating a proliferative identity. The unique finding in our data is that treatment with MS023 resulted in a shift in identity as compared to the DMSO-treated proliferating MuSCs (M1, M2 and M4), creating transcriptionally distinct M3 and M5 clusters. M3 and M5 had elevated markers for metabolism (E.g. Eno1, Atp5k, etc) and early activation (E.g. Fos, Jun), while the untreated MuSCs in clusters M1, M2 and M4 did not. Furthermore, M3 and M5 had higher baseline levels of Pax7 expression when compared to untreated cells. Together, these findings describe a transitional subpopulation of MuSCs unique to MS023 treatment which not only harbour stem like/early activation markers Pax7, Fos and Jun, but also elevated proliferative markers related to cell cycle and energy metabolism. This particular combination of characteristics is unique to the MS023-treated MuSCs, thus identifying a novel subtype of MuSC identity. In accordance with our scRNAseq data, we validated experimentally that MS023-treated cells have higher energy metabolism and increased self-renewal potential, thereby confirming that the unique transcriptomic signature of these cells also lead to a different cell fate decision.

2. It remains completely unclear whether the MS023-stimulated increase of metabolic pathway activity (OXPHOS, glycolysis) plays any role for preserving stem cell properties of MuSC during expansion and improves engraftment. Additional functional and mechanistic studies are required to explore the underlying molecular processes.

REPLY: Agreed. The link between cellular metabolism and MS023 enhanced self-renewal and engraftment capabilities is correlative and we will edit the revised text to reflect this.

3. Furthermore, it remains completely unclear whether the acclaimed increase in grip and tetanic strength of mdx mice after MS023 treatment relies on enhanced expansion of MuSC mediated by PRMT1 inhibition. 

REPLY: Agreed. We cannot exclude if the effect is mediated by an expansion of the MuSC pool or by an effect on other cell types, such as a direct impact on the myofibers. The goal of this figure was to provide a therapeutic perspective for the use of type I PRMT inhibitor for the treatment of DMD. Muscle wasting/weakness in DMD is a complex and multifactorial process (e.g., myofiber fragility, MuSC defects, chronic inflammation, fibrofatty accumulation). If MS023 can target multiple aspects of the physiopathology of the disease it would increase its therapeutic applicability. Further studies will be needed to determine the exact mechanism by which MS023 mediate its beneficial effect. The manuscript will be modified to reflect this.

References

• Blanc RS, Vogel G, Li X, Yu Z, Li S, Richard S (2016) Arginine methylation by PRMT1 regulates muscle stem cell fate. Mol Cell Biol 37: e00457-00416

• Dhar S, Vemulapalli  V, Patananan AN, Huang GL, Di Lorenzo A, Richard S, Comb MJ, Guo A, Clarke SG, Bedford MT (2013) Loss of the major Type I arginine methyltransferase PRMT1 causes substrate scavenging by other PRMTs. Scientific reports 3: 1311

• Eram MS, Shen Y, Szewczyk M, Wu H, Senisterra G, Li F, Butler KV, Kaniskan HU, Speed BA, Dela Sena C et al (2016) A Potent, Selective, and Cell-Active Inhibitor of Human Type I Protein Arginine Methyltransferases. ACS Chem Biol 11: 772-781

• Fedoriw A, Rajapurkar SR, Brien SO, Gerhart SV, Lorna H, Pappalardi B, Shah N, Laraio J, Liu Y, Butticello M et al (2019) Anti-tumor activity of the first-in-class type I PRMT inhibitor, GSK3368715, synergizes with PRMT5 inhibition through MTAP loss. Cancer cell XX: XX

• Gao G, Zhang L, Villarreal OD, He W, Su D, Bedford E, Moh P, Shen J, Shi X, Bedford MT et al (2019) PRMT1 loss sensitizes cells to PRMT5 inhibition. Nucleic acids research 47: 5038-5048

• Guccione E, Richard S (2019) The regulation, functions and clinical relevance of arginine methylation. Nat Rev Mol Cell Biol 20: 642-657

• Kawabe Y, Wang YX, McKinnell IW, Bedford MT, Rudnicki MA (2012) Carm1 regulates Pax7 transcriptional activity through MLL1/2 recruitment during asymmetric satellite stem cell divisions. Cell Stem Cell 11: 333-345

• Nakayama K, Szewczyk MM, Dela Sena C, Wu H, Dong A, al. e (2018) TP-064, a potent and selective small molecule inhibitor of PRMT4 for multiple myeloma. Oncotarget 9: 18480-18493

• Neault M, Mallette FA, Vogel G, Michaud-Levesque J, Richard S (2012) Ablation of PRMT6 reveals a role as a negative transcriptional regulator of the p53 tumor suppressor. Nucleic acids research 40: 9513-9521

• Pawlak MR, Scherer CA, Chen J, Roshon MJ, Ruley HE (2000) Arginine N-Methyltransferase 1 Is Required for Early Postimplantation Mouse Development, but Cells Deficient in the Enzyme Are Viable. Mol Cell Biol 20: 4859-4869

• Plotnikov A, Kozer N, Cohen G, Carvalho S, Duberstein S, Almog O, Solmesky LJ, Shurrush KA, Babaev I, Benjamin S et al (2020) PRMT1 inhibition induces differentiation of colon cancer cells. Scientific reports 10: 20030

• Taneda T, Miyata S, Kousaka A, Inoue K, Koyama Y, Mori Y, Tohyama M (2007) Specific regional distribution of protein arginine methyltransferase 8 (PRMT8) in the mouse brain. Brain Res 1155: 1-9

• Wu Q, Nie DY, Ba-Alawi W, Ji Y, Zhang Z, Cruickshank J, Haight J, Ciamponi FE, Chen J, Duan S et al (2022) PRMT inhibition induces a viral mimicry response in triple-negative breast cancer. Nature chemical biology 18: 821-830

• Zhu Y, He X, Lin YC, Dong H, Zhang L, Chen X, Wang Z, Shen Y, Li M, Wang H et al (2019) Targeting PRMT1-mediated FLT3 methylation disrupts maintenance of MLL-rearranged acute lymphoblastic leukemia. Blood 134: 1257-1268

Author Response

Reviewer #1 (Public Review):

This manuscript describes a relatively novel approach to discovering combinations of herbal medications that may help modulate immune responses, and in turn help treat diseases such as cancer. The authors use breast plasma call mastitis as a disease in which they present results from a non-blinded clinical trial with modest results. The main shortcomings are a lack of rigor around standardizing the control group given steroids versus the treatment group given the combinations of herbal medications. There needs to be a detailed statistical analysis of the comparison in tumor size, stage, invasiveness, etc. as well as consideration of confounding disease states (autoimmune disease, prior cancers, diabetes, etc.). While the results are interesting in that the use of herbal medications is often overlooked in Western medicine, the manuscript needs great detail in the clinical comparison in order to provide convincing evidence for an effect.

Many thanks for your very kind words about our work. We are excited to hear that you think our manuscript is relatively novel with considerable translational impact to the field of herbal medications. We are grateful for your valuable time and efforts you have spent to provide your very insightful comments, which are of great help for our revision.

Reviewer #2 (Public Review):

The work is rather interesting and novel because for the first time, the authors employed knowledge graph, a cutting-edge technique in the domain of artificial intelligence, to identify a novel herbal drug combination for the treatment of PCM. The results of the clinical trial study clearly demonstrated that the drug combination is effective to ameliorate the symptoms of PCM patients and improve the general health status of the patients. Overall, the strategy of this manuscript may provide a paradigm for the design of drug combination towards many other human disorders.

We are truly grateful for your very kind words about our work. It is very encouraging to know that you think our work is novel and of significance for the field. We sincerely appreciate the valuable time and kind efforts that you have spent on the thorough review of our manuscript.

Reviewer #3 (Public Review):

The major merit of the manuscript is that the authors introduced the concept of knowledge graph into the domain of herbal drugs or TCM. Namely, the authors designed a knowledge graph towards systematic immunity or immunotherapy based on massive data mining techniques. The authors successfully identified an herbal drug combination for PCM with the help of a scoring system. Moreover, the authors conducted a clinical trial study and the clinical data showed that the herbal drug combination holds great promise as an effective treatment for PCM. The weakness of the manuscript is that some details for the herbal drug combination and the clinical trial study are missing.

Many thanks for your very kind words about our work. We are excited to hear that you think our work is relatively novel and holds great promise as an effective remedy for PCM. We are truly thankful for your valuable time and efforts you have spent to provide your very insightful comments, which are of great help for our revision.

Author Response

Reviewer #1 (Public Review):

After giving a very accessible introduction to cellular processes during brain development, the authors present the computational model used in this study. It combines the kinematics of cell proliferation with the mechanic of brain tissue growth and is essentially equal to their model presented in Zarzor et al (2021), but extended for the outer subventricular zone (OSVZ), see for example Figs. 2 in the present manuscript and in Zarzor et al (2021). This zone, which is specific to humans, provides a second zone of cell proliferation. The division rate in the OSVZ is smaller and at most equal to that in the ventricular zone.

The authors present two main findings: The distance between sulci in the cortex is decreased whereas the cell density in the ventricular zone is increased in presence of the OSVZ. Furthermore, the "folding evolution", which is the ratio between the outer perimeter at time t and the initial perimeter increases in presence of the OSVZ. The strongest effect is seen, when division rates in both proliferating zones are equal. The authors compare the cases of varying and constant cortical stiffness, which they had also done in Zarzor et al (2021). Finally, they consider the feedback of cortical folding on OSVZ thickness.

The computational model provides a sound description of how cell proliferation and migration combined with tissue mechanics yield cortical folding patterns. However, only a few parameter values are varied in a limited range. Also, it remains unclear to me, how important the specific functional dependencies of, for example, the cell division rate on the radial coordinate are. This point seems of particular importance because the effect of the presence of the OSVZ on the folding patterns seems rather minute, see Fig. 5. The authors do not propose experiments that could be used to test their description and results. Finally, the analysis is restricted to 2 dimensions.

Thank you very much for the valuable suggestions. We agree that we are only able to show limited parameter studies in the manuscript. Therefore, we have now implemented a user interface that can be downloaded from Github (https://github.com/SaeedZarzor/BFSimulator) and will allow interested readers to directly change the parameter values and run the simulations.

To better emphasize the effect of the presence of the OSVZ on the folding patterns, we have edited the corresponding section and figure in the revised manuscript to include a quantification of the distance between sulci:

“In general, the distance between neighboring sulci decreases with increasing Gosvz, as marked in Figure 7. For the displayed cases, the distance decreases from d = 8.796 mm for Gosvz = 0 to d = 8.67 mm for Gosvz = 10 and finally d = 8.2 mm for Gosvz = 20. Interestingly, the cortical thickness and effective stiffness ratio at the first instability point (denoted by w in Figure 5) are the same for all these cases. Therefore, we attribute the observed differences to the faster increase in the cell density and thus cortical growth, cortical stiffness and the effective stiffness after the instability has been initiated.”

In addition, we have added a new figure to show that the observed trends also hold true for 3D simulations:

“Figure 8 demonstrates that the observed trends also hold true when extending the model to 3D. For the case of varying stiffness with a stiffness ratio of 3, a growth ratio of 3, and an initial division rate in the ventricular zone Gvz = 600, the folding complexity increases with increasing initial division rate in the OSVZ Gosvz.”

Reviewer #2 (Public Review):

Weaknesses

• To account for the complexity of biological phenomena, the model relies on a large number of ad hoc choices whose consequences are difficult to predict.

We fully agree that there are quite a number of model assumptions that we have to make. Still, we have achieved great agreement with the data from fetal brain sections, which in our opinion justified the assumptions made.

To better explain the choice of parameters, we have now included the following paragraph in the manuscript: “The mechanical and diffusion parameters are adapted from the literature Budday et al. (2020); de Rooij and Kuhl (2018), while the geometry parameters are estimated based on histologically stained human brain sections and magnetic resonance images. For instance, to determine the MST factor, we measured the relative distance between the ISVZ and OSVZ in histologically stained images. The final value adopted is the result of dividing the measured distance by the expected time. When determining the growth problem parameters, numerical stability and algorithm convergence were major criteria.”

• The physical model description is highly technical and out of reach for a non-specialist.

Thank you for making this point! We have now adapted the model description to better emphasize the main features of the model and the feedback mechanisms between the mechanical growth problem and the cell density problem:

“...is the Cauchy stress tensor formulated in terms of the elastic deformation tensor, as only the elastic deformation induces stresses. The Cauchy stress describes the three dimensional stress state in the spatial (grown and deformed) configuration and is computed by deriving the strain energy function…”

“Through Equation 6, the cell density problem controls the effective stiffness ratio between cortex and subcortex (as the cortical stiffness changes while the subcortical stiffness remains constant) and thus also the emerging cortical folding pattern Budday et al. 2014; Zarzor et al. 2021.”

“Through Equation 8, the amount of growth is directly related to the cell density - the higher the cell density, the more growth.”

“The vector n represents the normalized orientation of radial glial cell fibers in the spatial configuration and controls the migration direction of neurons. As the brain grows and folds, the fiber direction changes. Through this feedback mechanism, the mechanical growth problem affects how neurons migrate and the cell density evolves locally.”

“By applying Equation 16 for the VZ, we ensure that the division rate decreases from its initial value G_vz to a smaller value as the maximum stretch value s in the domain increases, i.e., with increasing gestational age. This constitutes an additional feedback mechanism between the mechanical growth problem and the cell density problem: As the maximum stretch and thus the deformation increases due to constrained cortical growth, the division rate in the VZ decreases, resulting in less newborn cells” and “G^s_osvz is the division rate in the OSVZ that decreases with increasing maximum stretch s in the domain”

• The description of neurogenesis shows three zones of cell proliferation, each inhabited by a specific cell type. Despite its realism, the proposed model does not take into account the ISVZ where the intermediate progenitors operate.

Indeed, in our model we have focused on two original sources of the cells which are radial glial cells and ORGCs. As we know so far, the intermediate progenitor cells are produced from those two cell types, so they are indirectly included in the model as a resulting cell density.

• The experiment of comparing several regimes derived from the relative importance of proliferation in the VZ and OSVZ is not very clear. It leads to the observation of the evolution of cell density maxima over time, which seems insufficient to conclude the importance of the OSVZ for folding. One wonders whether the key parameter that leads to folding is the rate of OSVZ proliferation or simply the total quantity of neurons generated by the two or even the three zones.

Thank you for this remark. We fully agree with the Reviewer that a key factor is the total quantity of neurons generated. However, the major question we intend to address here is where these neurons originate from and how the different proliferating zones interact. In other words, we do not question the existence of the OSVZ, but we are trying to build a computational model that can mimic all relevant cellular processes during brain development - to then study their individual effect on cortical folding. Therefore, we do not argue that the OSVZ is necessary for folding, but that it plays a crucial role in the speed of generating these folds and their complexity in the Conclusion section:

“Our results show that the existence of the OSVZ particularly triggers the emergence of secondary mechanical instabilities leading to more complex folding patterns. Furthermore, the proliferation of outer radial glial cells (ORGCs) reduces the time required to induce the mechanical instability and thus cortical folding.”

• The experiment on the heterogeneity of proliferation in the OSVZ is a bit frustrating. I would like to see a set-up corresponding to the mosaics found in ferrets and closely associated with folding patterns.

This is a valuable point, thank you! We have now added new results showing a more distinct regional variation of the OSVZ and have adapted our conclusions regarding this point:

“Also in the ferret brain, where a region close in structure to the primate's OSVZ was found, this region shows a unique mosaic-like structure Fietz et al. (2010b); Reillo and Borrell (2012). In this section, we aim to assess the effect of regional proliferation variations in the OSVZ on the emerging cortical folding pattern. We discuss two different heterogeneous patterns here, but have included more variations online through our user interface on GitHub, as described in the Data availability section. In the first case, the OSVZ division rate gradually decreases along the circumferential direction. In the second case, the division rate varies in a more random pattern. Figures 13 and 14 show how cortical folds develop in both cases for the varying cortical stiffness case, a division rate in the VZ of G_vz = 120 and an initial division rate in the OSVZ of G_osvz = 20. As expected, the evolving folding patterns slightly differ. In both cases, the first folds appear, where the cell proliferation rate is highest. Expectedly, those regions also show a higher cell density in the cortex than regions nearby. However, both cases lead to final patterns with similar distances between sulci and folding complexity (one period doubling pattern). In addition, gyri and sulci are distributed equally -- regardless of the division rate. Therefore, we may conclude that inhomogeneous cell proliferation in the OSVZ controls the location of first gyri and sulci but does not necessarily affect the distance between sulci (also referred to as folding wavelength) and the overall complexity of the emerging folding pattern. This agrees well with our previous finding that the characteristic wavelength of folding remains relatively stable for inhomogeneous cortical growth patterns Budday and Steinmann (2018). The simulation results are also consistent with the previously found remarkable surface expansion above the regions with higher proliferation in the OSVZ Llinares-Benadero and Borrell (2019).”

“Finally, our simulations reveal that inhomogeneous cell proliferation patterns in the OSVZ can control the location of first gyri and sulci but do not necessarily affect the distance between sulci and the overall complexity of the emerging folding pattern.”

Furthermore, in our code, we have added a user interface with multiple options for different OSVZ regional variations. The link to the code with the user interface shown below is now updated in the Data availability section.

• It would be interesting to elaborate a little on the possibility of extending the model in 3D, which seems imperative to evaluate the nature of the folding pattern generated. Comparing them to reality is an essential step in gauging the credibility of the model. For instance, it would be interesting to test to which extent the model can father the type of variability observed in the general population (Mangin et al.). It will also be particularly interesting to work on the inverse model between the real folding patterns and the heterogeneous proliferation maps that can generate them.

We fully agree with the Reviewer. Unfortunately, to the best of the Author’s knowledge, there is currently no data set providing both the 3D evolution of the folding pattern and the corresponding distribution of the cell density. Therefore, the validation of 3D results is difficult. Promisingly, our model achieved good agreement with data from histologically stained fetal brain sections regarding the local gyrification index, final cortical thickness, and cell density distribution, as presented in Zarzor, et al (2021). We have indeed initiated the collection of additional data, ideally for the 3D validation. However, this will take some time and is out of the scope of the current work. It is also a great suggestion to compare our 3D simulation results with the variability found in the general population. Indeed, we plan to do such work in the future but consider this out of the scope of the current work, which focuses more on the OSVZ.

To still show that our model can be extended to 3D, we have now included the following results: “Figure 8 demonstrates that the observed trends also hold true when extending the model to 3D. For the case of varying stiffness with a stiffness ratio of 3, a growth ratio of 3, and an initial division rate in the ventricular zone G_vz = 600, the folding complexity increases with increasing initial division rate in the OSVZ G_osvz.”

Reviewer #3 (Public Review):

Zarzor et al. developed a new multifield computational model, which couples cell proliferation and migration at the cellular level with biological growth at the organ level, to study the effect of OSVZ on cortical folding. Their approach complements the classical experimental approach in answering open questions in brain development. Their simulation results found the existence of OSVZ triggers the emergence of secondary mechanical instabilities that leads to more complex folding patterns. Also, they found that mechanical forces not only fold the cortex but also deepen subcortical zones as a result of cortical folding. Their physics-based computational modeling approach offered a novel way to predictively assess the links between cellular mechanisms and cortical folding during early human brain development, further shedding light on identifying the potential controlling parameters for reverse brain study.

Strengths:

The newly developed physics-based computational model has several advantages compared to previous existing computational brain models. First, it breaks the traditional double-layer computational brain model, gray matter layer and white matter layer, by introducing the outer subventricular zone. Second, it develops multiscale computational modeling by bringing the cellular level features, cell diffusion, and migration, into the macroscale biological growth model. Third, it could provide a cause-effect analysis of cortical folding and axonal fiber development. Finally, their approach could complement, but not substitute, sophisticated experimental approaches to answer some open questions in brain science.

Weaknesses:

The cellular diffusion and migration seem determined and controlled by a single variable, cell density, which is one-way coupled with the deformation gradient of the brain model. However, cell migration and diffusion should be potentially coupled with stress and vice versa. Also, the current computational model can be improved by extending it to a 3D model. Finally, they can further improve the study of regional proliferation variation by introducing fully-randomized heterogenous cell density and growth in their model.

Thank you. We apologize for the lack of clarity in the original submission. There are indeed more coupling mechanisms, which we have now better emphasized when introducing the model:

“Through Equation 6, the cell density problem controls the effective stiffness ratio between cortex and subcortex and thus also the emerging cortical folding pattern Budday et al. 2014; Zarzor et al. 2021.”

“Through Equation 8, the amount of growth is directly related to the cell density - the higher the cell density, the more growth.”

“The vector n represents the normalized orientation of radial glial cell fibers in the spatial configuration and controls the migration direction of neurons. As the brain grows and folds, the fiber direction changes. Through this feedback mechanism, the mechanical growth problem affects how neurons migrate and the cell density evolves locally.”

“By applying Equation 16 for the VZ, we ensure that the division rate decreases from its initial value Gvz to a smaller value as the maximum stretch value s in the domain increases, i.e., with increasing gestational age. This constitutes an additional feedback mechanism between the mechanical growth problem and the cell density problem: As the maximum stretch and thus the deformation increases due to constrained cortical growth, the division rate in the VZ decreases, resulting in less newborn cells” and “Gosvzs is the division rate in the OSVZ that again decreases with increasing maximum stretch s in the domain”

In addition, we have added a new figure to show that the observed trends also hold true for 3D simulations:

“Figure 8 demonstrates that the observed trends also hold true when extending the model to 3D. For the case of varying stiffness with a stiffness ratio of 3, a growth ratio of 3, and an initial division rate in the ventricular zone Gvz = 600, the folding complexity increases with increasing initial division rate in the OSVZ Gosvz.”

Finally, we have added new results showing a more distinct regional variation of the OSVZ. Furthermore, in our code, we have added a user interface with multiple options for different OSVZ regional variations. The link to the code with user interface is available in the paper:

“Also in the ferret brain, where a region close in structure to the primate's OSVZ was found, this region shows a unique mosaic-like structure Fietz et al. (2010b); Reillo and Borrell (2012). In this section, we aim to assess the effect of regional proliferation variations in the OSVZ on the emerging cortical folding pattern. We discuss two different heterogeneous patterns here, but have included more variations online through our user interface on GitHub, as described in the Data availability section. In the first case, the OSVZ division rate gradually decreases along the circumferential direction. In the second case, the division rate varies in a more random pattern. Figures 13 and 14 show how cortical folds develop in both cases for the varying cortical stiffness case, a division rate in the VZ of G_vz = 120 and an initial division rate in the OSVZ of G_osvz = 20. As expected, the evolving folding patterns slightly differ. In both cases, the first folds appear, where the cell proliferation rate is highest. Expectedly, those regions also show a higher cell density in the cortex than regions nearby. However, both cases lead to final patterns with similar distances between sulci and folding complexity (one period doubling pattern). In addition, gyri and sulci are distributed equally -- regardless of the division rate. Therefore, we may conclude that inhomogeneous cell proliferation in the OSVZ controls the location of first gyri and sulci but does not necessarily affect the distance between sulci (also referred to as folding wavelength) and the overall complexity of the emerging folding pattern. This agrees well with our previous finding that the characteristic wavelength of folding remains relatively stable for inhomogeneous cortical growth patterns Budday and Steinmann (2018). The simulation results are also consistent with the previously found remarkable surface expansion above the regions with higher proliferation in the OSVZ Llinares-Benadero and Borrell (2019).”

Author Response

Reviewer #1 (Public Review):

The authors developed a new concept: Skeletal age, which is chronological age + years lost due to suffering a low-energy fracture. There seem to be conceptual problems with this concept: It is not known if the years lost are lost due to the fracture or co-morbidities.

The Reviewer raises an important point, and we are happy to discuss it as follows. While it is not possible to show the causal relationship between a fragility fracture and excess mortality, it has been shown repeatedly that a fracture is associated with an increased risk of pre-mature mortality after accounting for comorbidities and frailty. Indeed, we and others have found that comorbidities contribute little to the increased risk10,11. Moreover, in a previous study using the ‘relative survival analysis’ technique12, we have shown that hip and proximal fractures were associated with reduced life expectancy after accounting for time-related changes in background mortality in the population, suggesting that hip and proximal fractures are an independent clinical risk factor for mortality.

In this study, we used a multivariable Cox’s proportional hazards model to adjust for confounding effects of age and severity of comorbidities, and our result clearly indicated that a fracture is associated with years of life lost. Moreover, comorbidities were considered a factor in an individual's risk profile for estimating skeletal age. As a result, skeletal age reflects the common real-world scenario that the combination of comorbidities and proximal or lower leg fractures compounded post-fracture excess mortality, much greater than each alone13.

Technically, there are two steps to individualise skeletal age for each individual with a specific risk profile. First, we used the statistical approach recommended for the individualisation of survival time prediction using statistical models14 to individualise specific mortality risk for each participant with a specific risk profile. Specifically, we calculated the prognostic risk index as a single-number summary of the combined effects of his/her specific risk profile of a specific fracture site and the severity of comorbidity. His/her individualised fracture-mortality association was then computed as the difference between his/her prognostic index and the mean prognostic index of “typical” people in the general population. In the second step, we used the Gompertz law of mortality and the Danish national lifetable data to transform the individualised association into life expectancy loss as a result of a fracture15.

We have modified part of the description of the methodology as follows:

“For the second aim, we determined skeletal age for individual based on the individual’s specific risk profile. First, we calculated the prognostic risk index as a single-number summary of the combined effects of his/her specific fracture site and the severity of comorbidity51. The prognostic index is a linear combination of the risk factors with weights derived from the regression coefficients. The individualised fracture-mortality association for an individual with a specific risk profile is then the difference between the individual's prognostic index and the mean prognostic index of 'typical' people in the general population51. In the second step, we used the Gompertz law of mortality and the Danish national lifetable data to transform the excess mortality into life expectancy loss as a result of a fracture49.”.

In addition, with the possible exception of zoledronate after hip fracture, we have no evidence that this increased risk of mortality can be changed with interventions.

We agree that there is a lack of strong evidence from randomised controlled trials supporting the benefit of anti-resorptive therapy on post-fracture survival. As mentioned above, the mention of zoledronic acid was simply for illustrating the use of skeletal age to convey a treatment benefit. We have decided to remove the section related to the benefit of pharmacological treatment on post-fracture mortality.

Furthermore, it is not clear why the authors think that patients and doctors will better understand the implications of older "skeletal age", on future fracture risk and the need for prevention, for example, the 10-year risk of MOF? Knowing that my bones are older than me, could make a patient feel even more fragile and afraid of being physically active. The treatment will reduce the risk of future fractures, but this study provides no information about the effect on mortality of preventing the subsequent fracture or the risk of mortality associated with recurrent fractures.

The risk of fracture is typically conveyed to patients and the public in terms of absolute risk metric (e.g., probability) or relative risk metrics (e.g., risk ratio). However, patients and doctors often struggle to comprehend probabilistic statements such as 'Your risk of death over the next 10 years is 5% if you have suffered from a bone fracture'. The underappreciation of post-fracture mortality's gravity has caused patients to be hesitant towards treatment and prevention, contributing to the current crisis of osteoporosis treatment.

We consider that skeletal age will make doctor-patient risk communication more intuitive and probably more effective. For example, for the same 2-fold increased mortality risk of hip fracture, telling a 60-year man with a hip fracture that his skeletal age would be 66 years old, equivalent to a 6-year loss of life is much more intuitive. The patient might be thus more likely to accept the recommended pharmacological treatment, ultimately improving health benefits. However, we have not had RCT evidence for the effectiveness of skeletal age, and this will be one of our future research focus. We would like to point out that there is RCT evidence that effective age (such as 'Heart Age', 'Lung Age') could improve the uptake of preventive actions. For example, informing patients about their heart age, as shown by Lopez-Gonzalez et al16 was found to better improve their cardiovascular risk compared to informing the Framingham probabilistic risk score.

Introduction:

The statement that treatment reduces the risk of dying, needs modification as the majority of clinical trials have not demonstrated reduced mortality with treatment.

We have modified the statement as follows: “In randomised controlled trials, treating high-risk individuals with bisphosphonates or denosumab reduces the risk of fracture4, though whether the reduction translates into reduced mortality risk remains contentious5, 6.”

It is not clear how the skeletal age captures the risk of a future fracture. The other difference between the idea of "skeletal age" and for example "heart age" is that there are treatments available for heart disease that reduce the risk of mortality, as mentioned above this has not been shown consistently in clinical trials in osteoporosis.

We take the Reviewer's point, but we would like to point out that there are at least two RCTs on zoledronic acid showing that treating patients with a fragility fracture reduces their risk of mortality17,18.

Because the risk profile that is associated with a post-fracture mortality is also associated with the risk of fracture, skeletal age can be seen as a measure of the decline of the skeleton due to a fracture or exposure to risk factors that raise the risk of fracture. Thus, a 60-year-old with a skeletal age of 66 is in the same risk category as a 66-year-old with 'favourable risk factors' or at least the ones that are potentially modifiable. Hence, an older skeletal age means a greater risk of fracture.

Neither the “Skeletal Age” nor the “Heart Age”16,19,20 has the treatment intervention incorporated into its calculator. We have added details to explain how the assessment of skeletal age would provide the conceptual risk of both fracture and post-fracture mortality as follows:

“Unlike the current fracture risk assessment tools17 which estimate the probability of fracture over a period of time using probability-based metrics, such as relative risk and absolute risk, skeletal age quantifies the consequence of a fracture using a natural frequency metric. A natural frequency metric has been consistently shown to be easier and more friendly to doctors and patients than the probability-based metrics9 11 30. It is not straightforward to appreciate the importance of the two-fold increased risk of death (i.e., relative risk = 2.0) without knowing the background risk (i.e., 2 folds of 1% would remarkably differ from 2 folds of 10%). By contrast, for the same 2-fold mortality risk of hip fracture, telling a 60-year man with a hip fracture that his skeletal age would be 66 years old, equivalent to a 6-year loss of life, is more intuitive. The skeletal age can also be interpreted as the individual being in the same risk category as a 66-year-old with 'favorable risk factors' or at least the ones that are potentially modifiable. Hence, an older skeletal age means a greater risk of fracture.”.

Discussion:

The prevalent comorbidities; cardiovascular diseases, cancer, and diabetes, suggest that fracture patients die from their comorbidities and not their fractures.

Please refer to the above response for more detail. Briefly, the multivariable Cox’s proportional hazards regression adjusted for the confounding effect of age and the severity of comorbidities, indicating the association between fracture and mortality was independent of aging and comorbidity severity. On the other hand, skeletal age is a measure of excess mortality related to either fracture or co-morbidities or both.

The discussion should be more balanced as there is a number of clinical trials demonstrating reductions in vertebral and non-vertebral fractures without effect on mortality. There may be specific effects of zoledronate on mortality, but that has not been shown for the vast majority of treatments.

Please refer to the above response for more detail. Specifically, as the study primarily aimed at introducing skeletal age as a new metric for risk communication, we have decided to omit the paragraph discussing the potential benefit of zoledronic acid on post-fracture mortality risk in order to maintain the clarity and focus of the study.

It is not correct that FRAX does not take mortality into account? It does not tell you specifically how high the risk of dying and how high the risk of a fracture is but integrates the two. "Skeletal age" does not provide either information, it just tells you that your skeleton is older than your chronological age - most patients and doctors will not associate that with an increased risk of dying - only of frailty.

Although it is commonly believed that FRAX accounts for competing risk of death, it does not provide the risk of post-fracture mortality. Indeed, none of the current fracture risk assessment tools was designed to provide post-fracture mortality risk5. Skeletal age fills the gap by providing the excess mortality following a fracture for an individual with specific risk profile.

The statement that zoledronate reduces the "skeletal age" by 3 years, has not been demonstrated and it is not clear how this can be demonstrated by the analysis reported here. As the reduced mortality has only been shown for the Horizon RFT, this cannot be inferred for other treatments and other fracture types. The information provided by the "skeletal age" is only that the fracture you already had took x years of your remaining lifetime. With the exception of perhaps zoledronate after hip fracture, we have no indication from clinical trials that the treatment of osteoporosis will change this.

The current study was not designed to examine the effectiveness of an intervention. The statement related to the survival benefit of zoledronate is used to illustrate how skeletal age is used to convey the treatment benefit in real-world doctor-patient risk communication. Given the hazard ratio of 0.72 for zoledronate-mortality association17, a patient might find the statement “Zoledronic acid treatment helps a patient with a hip fracture gain (back) 3 years of life” much easier to understand and probably more persuasive than the traditional statement of “Zoledronic acid treatment reduced the risk of death by 28%”.

Reviewer #2 (Public Review):

The paper of Tran et al. introduces the concept of 'skeletal age' as a means of conveying the combined risk of fracture and fracture-associated mortality for an individual. Skeletal age is defined as the sum of chronological age and the number of years of life lost associated with a fracture. Using the very comprehensive Danish national registry and employing Cox's proportional hazards model they estimated the hazard of mortality associated with a fracture. Skeletal age was estimated for each age and fracture site stratified by gender. The authors propose to replace the fracture probability with skeletal age for individualized fracture risk assessment.

Strengths of the study lie in the novelty of the concept of 'skeletal age' as an informative metric to internalize the combined risks of fracture and mortality, the very large and well-described Danish National Hospital Discharge Registry, the sophisticated statistical analysis and the clear messages presented in the manuscript. The limitations of the study are acknowledged by the authors.

We appreciate your positive remark that captures the essence of our work.

References:

1. Lujic S, Simpson JM, Zwar N, Hosseinzadeh H, Jorm L. Multimorbidity in Australia: Comparing estimates derived using administrative data sources and survey data. PloS one 2017; 12(8): e0183817.
2. Andersen TF, Madsen M, Jorgensen J, Mellemkjoer L, Olsen JH. The Danish National Hospital Register. A valuable source of data for modern health sciences. Dan Med Bull 1999; 46(3): 263-8.
3. Vestergaard P, Mosekilde L. Fracture risk in patients with celiac Disease, Crohn's disease, and ulcerative colitis: a nationwide follow-up study of 16,416 patients in Denmark. Am J Epidemiol 2002; 156(1): 1-10.
4. Hundrup YA, Hoidrup S, Obel EB, Rasmussen NK. The validity of self-reported fractures among Danish female nurses: comparison with fractures registered in the Danish National Hospital Register. Scand J Public Health 2004; 32(2): 136-43.
5. Beaudoin C, Moore L, Gagne M, et al. Performance of predictive tools to identify individuals at risk of non-traumatic fracture: a systematic review, meta-analysis, and meta-regression. Osteoporos Int 2019; 30(4): 721-40.
6. Spiegelhalter D. How old are you, really? Communicating chronic risk through 'effective age' of your body and organs. BMC Med Inform Decis Mak 2016; 16: 104.
7. Vestergaard P, Rejnmark L, Mosekilde L. Osteoporosis is markedly underdiagnosed: a nationwide study from Denmark. Osteoporos Int 2005; 16(2): 134-41.
8. Roerholt C, Eiken P, Abrahamsen B. Initiation of anti-osteoporotic therapy in patients with recent fractures: a nationwide analysis of prescription rates and persistence. Osteoporos Int 2009; 20(2): 299-307.
9. Cummings SR, Lui LY, Eastell R, Allen IE. Association Between Drug Treatments for Patients With Osteoporosis and Overall Mortality Rates: A Meta-analysis. JAMA Int Med 2019; 179(11): 1491-500.
10. Chen W, Simpson JM, March LM, et al. Comorbidities Only Account for a Small Proportion of Excess Mortality After Fracture: A Record Linkage Study of Individual Fracture Types. J Bone Miner Res 2018; 33(5):795-802
11. Vestergaard P, Rejnmark L, Mosekilde L. Increased mortality in patients with a hip fracture-effect of pre-morbid conditions and post-fracture complications. Osteoporos Int 2007; 18(12): 1583-93.
12. Tran T, Bliuc D, Hansen L, et al. Persistence of Excess Mortality Following Individual Nonhip Fractures: A Relative Survival Analysis. J Clin Endocrinol Metab 2018; 103(9): 3205-14.
13. Tran T, Bliuc D, Ho-Le T, et al. Association of Multimorbidity and Excess Mortality After Fractures Among Danish Adults. JAMA Netw Open 2022; 5(10): e2235856.
14. Henderson R, Keiding N. Individual survival time prediction using statistical models. J Med Ethics 2005; 31(12): 703-6.
15. Kulinskaya E, Gitsels LA, Bakbergenuly I, Wright N. Calculation of changes in life expectancy based on proportional hazards model of an intervention. Insur Math Econ 2020; 93: 27-35. 16 Lopez-Gonzalez AA, Aguilo A, Frontera M, et al. Effectiveness of the Heart Age tool for improving modifiable cardiovascular risk factors in a Southern European population: a randomized trial. Eur J Prev Cardiol 2015; 22(3): 389-96.
16. Lyles KW, Colon-Emeric CS, Magaziner JS, et al. Zoledronic acid and clinical fractures and mortality after hip fracture. N Engl J Med 2007; 357(18): 1799-809.
17. Reid IR, Horne AM, Mihov B, et al. Fracture Prevention with Zoledronate in Older Women with Osteopenia. N Engl J Med 2018; 379(25): 2407-16.
18. Bonner C, Batcup C, Cornell S, et al. Interventions Using Heart Age for Cardiovascular Disease Risk Communication: Systematic Review of Psychological, Behavioral, and Clinical Effects. JMIR Cardio 2021; 5(2): e31056.
19. Svendsen K, Jacobs DR, Morch-Reiersen LT, et al. Evaluating the use of the heart age tool in community pharmacies: a 4-week cluster-randomized controlled trial. Eur J Public Health 2020; 30(6): 1139-45.
20. Suissa S. Immortal time bias in pharmaco-epidemiology. Am J Epidemiol 2008; 167(4): 492-9.

Author Response

Reviewer #1 (Public Review):

I noticed 2 weaknesses, the first related to the killing assays: considering that WT IgG less efficiently promotes complement-mediated phagocytosis of bacteria, one would assume that the ingested bacteria (to be killed) would be lower in neutrophils exposed to this IgG, to begin with - which is not accounted for in the analyses shown.

We now included a better explanation of our opsonophagocytic killing assay.

A second weakness in my mind pertains to the in vivo experiment: the model used obviously requires a very high number of bacteria (the inoculum), somehow indicating that this specific bacterial strain does not lead to progressive infection (i.e. with replicating bacteria) but mice experience a severe acute inflammatory response followed by the rapid elimination of bacteria. This explains the high mortality - and indicates that mice succumb to acute inflammation, rather than the progressive replication of bacteria. To conclusively prove the therapeutic value of those modified antibodies, a clinically more relevant S. pneumoniae model would be helpful.

The inoculum used in our mouse model was based on a dose finding study. Although the initial starting dose was 5x106 bacteria (based on previously published mouse infection models with S. pneumoniae serotype 6A), we needed a higher dose (1x108 bacteria) to reach 80-100% mortality. While we agree that the final dose was relatively high, this does not mean that capsule type 6 is not a clinically relevant strain. It is well known that clinically relevant serotypes in humans are not always invasive in mice (doi: 10.1128/iai.60.1.111-116.1992). This is the exact reason why we chose to perform in vivo experiments with serotype 6A, which is known to be more invasive in mice (while serotype 6B is more virulent in humans). Of course, while our in vivo data provide an important proof-of-concept for the capacity of hexamer-enhancing mutations to improve protection by anti-capsular antibodies, future studies are needed to verify the potential use of mAbs against other serotypes.

A third aspect, which should be addressed in the discussion, unless tested and not shown, is how anti-pneumococcal IgM antibodies compare to hexamerized IgGs. Is there any advantage, or do they perform similarly with regards to complement activation?

We have now generated and tested IgM against CPS6 (Figure 2g). Although anti-CPS6 IgM can induce complement-dependent phagocytosis to some extent, but IgM was less potent than IgG variants with hexamer-enhancing mutations. This suggests that complement activation via pre-assembled IgM oligomers was less effective than via IgG hexamers that are formed after target binding.

These new data are now included in the revised manuscript as figure 2g, supplemental figure 9 and commented in results section lines 172-179.

Reviewer #2 (Public Review):

The results are intriguing, and one consideration is whether enhancing complement activation is beneficial or harmful for a therapeutic antibody. Based on these results is there the possibility of a natural selection against strong levels of complement activation?

We appreciate the positive feedback to our presented work. Indeed, it is believed there is a natural selection against these mutations to avoid uncontrolled complement activation by naturally occurring IgGs in solution. It is important to realize that formation of IgG hexamers is a surface-dependent process. When IgG molecules bind to surface-bound antigens (via Fab), they can organize into higher-ordered hexamers via Fc-Fc interactions. The specific point mutations used in this paper increase hexamer formation after antigen binding on the cell surface. However, at high concentrations of IgG (as those occurring in our blood (>10 mg/ml), IgG hexamers might be formed independent of target binding (van Kampen et al Journal of Pharmaceutical Sciences Volume 111, Issue 6, June 2022, Pages 1587-1598). If naturally occurring IgGs would have hexamer-enhancing mutations, IgG hexamers could be formed in solution resulting in massive complement activation and depletion of the complement system.

The study clearly shows that the introduction of the hexamerisation mutations affects the ability of the antibodies to bind and activate complement. The studies in Fig 2 examining the role of Fc are particularly elegant. One issue is that it is surprising that the WT IgG1 and IgG3 monoclonals have a minimal capacity to fix and activate complement, despite IgG1/3 to other antigens being efficient isotypes at fixing complement. In the absence of data showing whether IgG1/3 from normal human sera against capsule fixes complement then it is difficult to contextualise these results or to assess if other changes, such as in glycosylation, contribute to the results presented. Related to this, there is reasonable evidence that antibodies induced to capsules can be protective yet the data in Fig 5 suggests that without the mutations then the monoclonals are not effective at all for 6B and only effective at the highest concentration for 19A.

As mentioned in Essential revision 3 our data with S. aureus antibodies demonstrate that this is not a consequence of how these mAbs are produced or differences in their Fc glycosylation profile. We agree with the fact that there are reasonable evidence that antibodies induced to capsules can be protective. However, not all vaccine serotypes are able to induce a strong immune protection. Serotype 6B, for instance, which is covered by current vaccines, is found to be poorly immunogenic (manuscript lines 101-103). For further studies, it would be really interesting to find out what makes this difference between mAbs and, specifically in our case between anti-CPS antibodies.

The adoptive transfer experiments demonstrate that the antibodies can moderate bacteraemia. The mechanism of this is not explored and the importance of hexamerisation and complement activation not demonstrated, especially as it is not clear if human antibodies and mouse complement are a productive combination in this context.

We have now included additional phagocytosis assays with mouse sera (supplemental figure 15) that demonstrate that human antibodies and mouse complement are a productive combination.

Author Response

Reviewer #1 (Public Review):

The manuscript by Silva et al. "Evaluation of the highly conserved S2 hairpin hinge as a pan-coronavirus target" seeks to evaluate a new epitope target on the S2 domain of SARS-CoV2 Spike protein and evaluate its potential as a pan-coronavirus target. This is an impressive combination of extensive structural, HDXMS-based dynamics and antibody engineering approaches. What is missing is a detailed correlation of HDXMS with Spike dynamics. The authors have not examined the allosteric effects of 3A3 binding to the Spike trimer, specifically cooperativity in antibody binding. Does binding of one Fab positively or negatively impact the subsequent binding of antibody? In this regard, readers would benefit from HDXMS spectral envelopes in figures, at least for the epitope locus peptides. Further, what is the effect of the intrinsic ensemble behavior of the Spike protein on 3A3 interactions? In a broader sense antibody binding is assisted by intrinsic trimer ensemble behavior, as observed by the lowered binding to the omicron variant- but are there induced binding effects? It would help to better integrate HDXMS with cryo-EM and antibody engineering. It is a novel, less explored epitope target on the S2 domain. Overall, a more definitive mechanistic conclusion for how targeting the S2 hinge can advance future pan-coronavirus strategies is missing.

1) Given that the authors have demonstrated ensemble switching behavior from 4 ℃ to 37 ℃ (Costello et al. (2021)) why is this not factored in how the HDXMS is carried out? The samples were stored, frozen at -80 ℃, thawed, and equilibrated for 20 min at 20 ℃ with or without antibody present and analyzed by HDXMS. However, the reported t1/2 for trimer tightening at 37 ℃ is t1/2 = 2.5 h (Supplementary Fig. 7). The samples should ideally be analyzed under standardized conditions with the stable conformer. Sample heterogeneity from HDXMS is likely due to any of the following contributing factors:

i) Intrinsic ensemble heterogeneity (Costello et al. (2021)), Kinetics of RBD- up and down conformational switching

ii) Cooperativity of Fab binding.

iii) Partial occupancy of trimer epitopes with bivalent IgG.

iv) Combination of cooperativity effects and partial binding effects

I would predict for any of the above reasons, it is intriguing why are there no bimodal kinetics of deuterium exchange reported. Partial occupancy should be evident from HDXMS paratope analysis.

2) Pan-coronavirus neutralization potential is clearly evident. It is intriguing that the antibodies were isolated after immunization with an authentic MERS S2 domain but showed better selectivity to full-length 6P-engineered Spike. How is cooperativity built into antibody binding, given that the epitope site is occluded to various extents by the S1 domain and access is contingent upon RBD up-down kinetics?

3) I am surprised that there is no allostery described for 3A3 (Supplementary figures 5, 6).

The HDX-MS experiments presented in this work were carried out by the D’Arcy lab and published in a preprint on bioRxiv (originally posted on February 1, 2021) prior to publication of Costello et al. (first posted to bioRxiv July 11, 2021, epub March 2, 2022). Indeed, our bioRxiv posting inspired the Marqusee lab to request 3A3 for inclusion in their work focused on the conformational heterogeneity of the spike protein. Without prior knowledge of the conformational heterogeneity, we carried out these epitope mapping experiments at 25Ç, which allowed us to successfully mapped the epitope without determining which conformation the antibody prefers.

The data presented in Costello et al. further confirms the location of 3A3’s epitope presented here and provides additional information about its preference for different conformational states within the spike protein. We have included an additional comment in the methods section (lines 660-661) stating, “The location of the 3A3 epitope was confirmed in a separate experiment carried out over the temperature range of 4 to 37 °C (Costello et al. 2022).”

This is a clear example of the value of pre-prints to stimulate timely scientific collaboration. While Costello et al. used 3A3 as a tool to probe spike dynamics, here we highlight the original work that identified the epitope.

Spectral envelopes have been provided (Supplementary Fig. 4b and Supplementary Table 3).

The HDX-MS data provides limited insight into possible cooperative or allosteric binding of the 3A3 antibody because of other sources of heterogeneity such as spike dynamics and partial occupancy of the spike epitopes. However, no difference in occupancy was detected when HDX-MS with 3A3 Fab was compared to the same experiment with bivalent 3A3 IgG. It should be noted that in this HDX system, the antibody is not bound so tightly that the spectra are bimodal, showing the exchange of bound and unbound populations separately. Though HDX-MS experiments were performed in slight Fab or IgG excess of 1:1 Fab:spike monomer stoichiometry, the absolute stoichiometry in the context of the spike trimer is unclear.

Reviewer #2 (Public Review):

The authors report a conserved spike S2 hinge epitopes and two conformationally selective antibodies that help elucidate spike behavior. This work defines a third class of S2 antibody and provides insights into the potency and limitations of targeting this S2 epitope for future pan-coronavirus strategies.

Thank you for your review of this manuscript.

Reviewer #3 (Public Review):

The study by Silva et al details the discovery and evaluation of a third class of broadly cross-reactive anti-Spike antibody that binds a conserved hinge region in the S2 domain. After immunizing mice with a stabilized S2 protein from MERS and generating scFv phage libraries, the authors were able to identify antibody 3A3, which showed broad cross-reactivity with SARS2 (including Omicron BA.1), SARS1, MERS, and HKU1 spike proteins. Using a combination of a low-resolution cryo-EM structure and HDX mass spectrometry, the authors were able to map amino acids in the antibody paratope and spike epitope, the latter of which is the hinge region of the Spike S2 domain (residues 980-1005) that plays a critical role in pre- to -post-fusion conformational changes. Through well-executed and comprehensive mutagenesis, binding, and functional assays, the authors further validated critical residues that lead to antibody escape, which centered around the 2P residues and diminished viral entry. While 3A3 and an affinity-enhanced engineered version, RAY53, did not show potent in vitro neutralization against the authentic virus, the antibody was shown to recruit Fc effector functions for viral clearance, in vitro.

Overall, the conclusions of this paper are well supported by the data, but the usefulness of such antibodies is likely limited. The work can be strengthened by extending the analysis of 3A3-like antibodies in the context of human immune responses and in vivo effectiveness.

1) Isolation of 3A3 was achieved after the generation of scFv-phage libraries following immunization with a MERS S2-domain immunogen in a mouse model. The fact that 3A3 binds well to 2P-stabilized sequences and binding/neutralization is diminished upon reversion of 2P mutations back to the native spike sequence (Figures 3a, 4c, and 5b), suggest that such antibodies would likely not arise from natural infection. This contrasts the isolation of fusion peptide and stem helix-directed antibodies, which were isolated from both immunized animals and convalescent individuals. To make their results more solid regarding the use of such antibodies in future vaccine strategies, the authors should provide evidence that 3A3-like antibodies can be identified in human donors. For example, they could enrich donor-derived S2-specific antibodies that bind both MERS and SARS2 S2 domains and evaluate the fraction of antibodies that recognize the hinge-epitope using competition binding assays (either ELISA or BLI), which have commonly been used to map epitope-specific sera responses. This could also be achieved with nsEMPEM of polyclonal IgGs bound to S2 proteins.

2) The authors speculate in the discussion that strategies to enhance access to the hinge epitope, which may include ACE2-mimicking antibodies, could promote enhanced viral clearance. In addition to ACE2-mimicking antibodies, several antibodies have been described that bind the RBD and promote S1 shedding (see for instance mAb S2A4 - Piccoli et al, 2020, Cell). Several 2nd generation vaccine platforms utilize RBD-only immunogens that are likely to induce high titers of ACE2-mimicking and cross-reactive S1-shedding antibodies. Thus, adding in vitro neutralization and ADCC experiments to assess synergy between 3A3/RAY53 and such antibodies would booster this speculative claim and be of interest to many in the field developing strategies for pan-coronavirus therapies.

3) The authors provide in vitro evidence in Figure 5c,d for Fc-mediated viral clearance. While in vivo data to show effectiveness in animal models is ideal, additional in vitro data that utilize engineered constructs that modulate effector function (e.g., DLE (+) or LALA (-)) would boost the authors' claims regarding Fc-mediated viral clearance mechanisms by 3A3/RAY53.

1) Though we do not plan to isolate 3A3-like antibodies from human donors, there is evidence that these antibodies are elicited in infected humans via analysis of polyclonal responses in Claireaux et al 2022. We also know of several studies on naturally occurring S2 hinge targeting antibodies from colleagues that are in preparation. Understanding the therapeutic role of this antibody class is relevant to the study of broadly-reactive S2 antibodies, even if that role is limited.

2) We agree that synergy between S2 hinge epitope binding antibodies and ACE2 mimicking antibodies will be very interesting to investigate. We hope to pursue this in future work.

3) We agree these are excellent controls to include, in addition to isotype controls already shown. In accordance with the eLife COVID research policy, we minimized our claims around Fc-effector functions elicited by RAY53 and stated that further experiments to confirm our preliminary findings are needed.

The existing description of the effector function experiments states in lines 392-392 “These results indicate that RAY53 binding is compatible with ADCP and ADCC,” which is already a very limited claim.

We also added in line 450 that S2 core-binding antibodies “require further validation” of their ability to recruit effector functions.

We appreciate the importance of controls providing effector function modulation and will include the LALAPG mutations as a standard component of our future ADCC evaluation. However, given our focus on the relevance of the epitope and consistency of the Fc regions across the antibodies, we felt that the isotype and positive control antibodies (target binding controls) were the most relevant controls to include in this study.

Author Response

eLife assessment

Germline inactivation of NPHP2, which encodes a protein that localizes to the transition zone at the base of the primary cilium, results in infantile kidney cysts and fibrosis. In this study, the authors provide solid evidence that increased cell proliferation and fibrosis precede cyst formation in Nphp-2 mouse models, that mutant renal epithelial cells are responsible for the phenotype, and that genetic inhibition of ciliogenesis in this model reduces disease severity. They also show that valproic acid, a drug that affects a number of cellular targets and is used to treat other human conditions, slows disease progression. One limitation of the study is that it provides limited insights into the mechanisms responsible for any of its interesting observations.

To our knowledge, our study is the first to pinpoint defective epithelial cells as the main driver for both epithelial cysts and interstitial fibrosis in a NPHP model. The discovery that abnormal signaling from epithelial cells triggered a profibrotic response in the absence of cyst formation is also novel. Our Ift88 Nphp2 double mutant results, combined with tissue-specific function of NPHP2, suggest that NPHP2 functions as a negative regulator of a profibrotic and pro-cystic pathway that interacts with cilia-mediated signaling in epithelial cells and that abnormal signaling from epithelial cells triggers interstitial fibrosis. Moreover, we identified the HDAC inhibitor VPA as a potential candidate drug for treating NPHP. Although the precise molecular function of NPHP2 remains undefined, our results suggest that epithelial specific function and epithelial-stromal crosstalk underlie NPHP like phenotypes in Nphp2 mutant kidneys. Furthermore, although whether NPHP2 interacts with polycystin-mediated signaling remains an outstanding question, our results ruled out the involvement of NPHP2 in ciliary localization of PC2.

Reviewer #1 (Public Review):

Nephronophthisis (Nphp) is a multigenic, recessive disorder of the kidney presenting in childhood that is characterized by cysts predominantly at the cortico-medullary junction and progressive fibrosis. An infantile form of the disease presents earlier with more diffuse cystic change. The condition is considered a ciliopathy because most of the genes linked to the condition encode proteins involved in ciliary biogenesis or function. Germline mutations in NPHP2 are associated with a particularly severe, infantile form of the disease. Given that interstitial fibrosis is a more prominent feature of Nphp compared to many other forms of polycystic kidney disease, the authors sought to determine the mutant cell types responsible for the phenotype.

In the current study, the authors generated and characterized mouse lines with Nphp2 selectively inactivated in either renal epithelial cell or stromal cell lineages and found that inactivation in renal epithelial cells was both necessary and sufficient to cause disease. They further showed that markers of interstitial fibrosis and proliferation increase in mutants prior to the onset of histologically evident cystic disease, suggesting that aberrant epithelial-stromal cell signaling is an early and primary feature of the condition (Figures 1-4). The study design was straightforward and appropriate to address the question, and the results support their conclusions.

They next tested whether the cilia-dependent cyst-activating pathway (CDCA) that is "unmasked" by loss of other PKD-related genes is similarly active in Nphp2 mutants by generating Nphp2/Ift88 double mutants. Their studies found that the severity of cystic disease and markers of proliferation and fibrosis was attenuated in double-mutants (Fig 5, 6). These studies were also appropriate for testing the hypothesis and the results were similarly consistent with their interpretation.

In the last set of studies, they tested whether valproic acid (VPA), a drug that has multiple modes of action including acting as a broad inhibitor of HDACs and previously used by the investigators in other forms of polycystic kidney disease, would have similar effects in Nphp2 mutants. The authors tested daily injection from days P10 through P28 in both control and Nphp2 mutant mice with VPA or an appropriate vehicle control and found that VPA was beneficial (Fig 7). The study design was acceptable and the results generally support their conclusions. The one perplexing result is shown in Fig 7B. The Nphp2 mutants, regardless of treatment status, have body weights (BW) that are significantly lower than the controls, with treated mutants even trending lower than their untreated mutant counterparts. This is unexplained and should be addressed. In the mutants with more widespread epithelial cell knock-out of Nphp2 (Ksp-Cre, Fig 1), total body weight decreased as mice became more severely cystic with renal impairment. In the milder form of disease produced with the Pkhd1- Cre (Fig 7), total body weight is inexplicably approx. 2g lower on average despite having much more modestly elevated KBWs and BUNs. Moreover, one might have expected that mutants treated with VPA would have had BWs intermediate between untreated mutants and controls since the severity of the disease was moderately attenuated. These differences raise the question as to whether body weight differences are due to factors independent of disease status, the most likely of which would be that the controls were not littermates. This prompted a careful review of the text for descriptions of the control mice. Throughout the study, the investigators describe selecting animals from the same "cohort", but this term is imprecise.

There is little information provided about background strains, whether any of the lines were congenic, or whether any of the studies were done using littermate controls. This must be addressed. It would help if the investigators identified the litter status in their plots. This would clearly show relationships between animals and the number of litters that had animals with these properties. If littermates were not used for each study, the authors must explain both why they didn't do so and how they then selected which animals to use. This information is especially important for interpreting the results of their genetic interaction (fig 5) and drug treatment studies (fig 7).

We thank the reviewer for the multiple positive comments.

To address the issue of body weight, we examined the time course of body weight change more carefully and added Figure 7-figure supplement 1 to present the results. Although Nphp2flox/flox;Pkhd1-Cre mice displayed reduced body weight at P28 in comparison to controls, this reduction was more moderate than that of Nphp2flox/flox;Ksp-Cre mice (Figure 7-figure supplement 1A). Notably, the trend of body weight difference started at around P21 in both Nphp2flox/flox;Pkhd1-Cre and Nphp2flox/flox;Ksp-Cre mice, coinciding with weaning (Figure 7-figure supplement 1B). It is possible that mutants with compromised kidney function were less capable to thrive and gain weight at around this transition time. In terms of VPA treatment, body weight trended down in both wild type and mutant mice subjected to the treatment, although the difference did not reach statistical significance (Fig. 7B). We cannot rule out the possibility that side effect of VPA contributed to weight loss in treated mice. In addition, VPA may affect body weight increase through HDAC: the HDAC inhibitor Trichostatin A was shown to inhibit adipogenesis (PMID: 34232916) and 4-hexylresorcinol, another HDAC inhibitor, reduced body weight in treated rats (PMID: 34445640). To include the additional data and references, we added the following in the Results section:

"We analyzed body weight change of Nphp2flox/flox;Pkhd1-Cre mice in more detail and compared it to Nphp2flox/flox;Ksp-Cre mice. At P28, the reduction of body weight in Nphp2flox/flox;Pkhd1-Cre mice in comparison to control mice was more moderate than that in Nphp2flox/flox;Ksp-Cre mice (Figure 7-figure supplement 1)."

" However, the reduced body weight phenotype in mutant mice was not suppressed by VPA treatment (Fig. 7B). We cannot rule out the possibility that the side effects of VPA contributed to weight loss in treated mice. In addition, VPA may reduce body weight through inhibiting HDAC during the growth period: the HDACI Trichostatin A was shown to inhibit adipogenesis (51)."

Regarding genetic background, all mice analyzed in figures 5 and 7 are in the same genetic background (C57/BL6J). We added more detailed description of genetic background in the Materials and Methods section. Littermate status is now also indicated in figure legends.

In Figure 5, multiple genotypes (i.g. Nphp2flox/flox;Ksp-Cre, Nphp2flox/flox;Ift88flox/flox;Ksp-Cre and Ift88flox/flox;Ksp-Cre) were analyzed. Because of the limited number of animals per litter and low yield of desired genotypes, non-littermates had to be included in some cases. Littermate status is now highlighted by colors in the data tables of Figure 5 source data.

In Figure 7, because of the limited number of animals per litter and the need to subject each genotype to VPA and vehicle treatment, non-littermates had to be included in some cases. Littermate status is now indicated by highlight colors in the data tables of Figure 7 source data.

Several other considerations. The authors state that the effects of VPA are mediated through the drug's inhibition of HDACs and suggest that future studies could be directed at refining the specific HDAC. While this is certainly possible, the authors should acknowledge that VPAs have been reported to have numerous pharmacologic effects and targets and which of these is mediating the effects in their model is unknown (text). They would need mechanistic studies to show this, though it doesn't discount their possible efficacy as a therapy for PKD.

We agree that it is an important point to clarify and added in Discussion: "It is also worth noting that VPA could affect targets other than HDACs and testing newly approved HDACIs will provide useful insight."

The authors also state in their abstract that their double knock-out studies "support a significant role of cilia in Nphp2 function in vivo." It is not clear to me how their studies show this nor how they can exclude that ciliary activity is operating in an Nphp2-independent, parallel fashion that modulates some common downstream pathways.

We agree with the reviewer that our results do not exclude the possibility that NPHP2 and ciliary activity feed into a common downstream pathway, i.e., a cilia-dependent cyst-activating pathway could operate outside of cilia. We changed the sentence in abstract to "supporting a significant interaction of cilia and Nphp2 function in vivo." In addition, we added "Although cilia-dependent, the downstream pathway could potentially operate outside of cilia and receive parallel signals from both ciliary activity and Nphp2." to Discussion to clarify and reflect the results and model more precisely.

Reviewer #2 (Public Review):

The manuscript by Li et al demonstrates the role of Nphp2/Invs in renal epithelia in preventing NPHP-like phenotypes, such as epithelial/stromal proliferation and stromal fibrosis, in mice. Previously, mutants of the Nphp2 allele in mice, generated by insertional mutagenesis, showed severe cystic kidney disease and fibrosis in neonates.

The authors nicely show that the NPHP-like phenotypes in mutant kidneys arise from abnormal signaling specifically within and from renal epithelial cells. Furthermore, the fibrotic response and abnormal increase of cell proliferation precede cyst formation and could be initiated independently of cyst formation. The authors also show that the removal of cilia reduces the severity of Nphp2 phenotypes. The authors suggest that similar to polycystins, NPHP2 inhibits a cilia-dependent cyst and fibrosis-activating pathway. Finally, the histone deacetylase (HDAC) inhibitor valproic acid (VPA) reduces these phenotypes and preserves kidney function in Nphp2 mutant mice, supporting HDAC inhibitors as potential candidate drugs for treating NPHP.

Overall, understanding the mechanisms driving NPHP phenotypes is important and drugging relevant pathways in treating this disease is an important unmet need in patients. The authors have provided insights into both these aspects in this study. The manuscript is nicely written, and the assays shown are rigorous and insightful.

We thank the reviewer for the positive comments.

Reviewer #3 (Public Review):

In this manuscript, Li et. al, investigate whether epithelial or stromal Nphp2 loss, a gene causative of nephronophthisis (NPHP), drives polycystic kidney disease (PKD) and kidney fibrosis in a novel floxed model of Nphp2. The authors found that only epithelial and not stromal Nphp2 loss results in NPHP-like phenotypes in their mouse model. In addition, the authors show that concurrent cilia, via Ift88 loss, and Nphp2 loss within the kidney epithelium as well as HDAC inhibition results in less severe PKD/kidney fibrosis, as has been shown in mouse models of other non-syndromic forms of PKD, such as autosomal dominant PKD caused by mutations to Pkd1 or Pkd2.

The authors aimed to understand (1) whether the published NPHP phenotype (kidney cysts and fibrosis), known from the global Nphp2 knockout mouse, is driven by the function of NPHP2 in the kidney epithelium or stromal cells; (2) if kidney fibrosis in NPHP is linked to kidney damage caused by cysts, or independent and preceding of the PKD phenotype; (3) whether cilia are required, causative, or prohibitive of NPHP cystogenesis; and (4) if a broad spectrum HDAC inhibitor is a potential therapeutic approach for NPHP.

With the provided results, the authors established that epithelial Nphp2 loss is likely a predominant driver of PKD in their model; however, they cannot exclude that stromal NPHP2 does not play a role in cysts growth post-initiation because the authors failed to directly compare their cell type-specific models to a global cre knockout (e.g. Cagg-cre).

We agree with the reviewer that we cannot rule out the possibility that stromal NPHP2 plays a role post cyst initiation and added "However, our result does not rule out functional significance of interstitial cells once a pro-cystic and fibrotic response is triggered in mutant epithelial cells." to the Discussion section.

A direct comparison between epithelial specific and global knockout models is an attractive idea, but technically challenging. For an interpretable comparison, it is essential that the stage and knockout efficiency in epithelial cells are equivalent between the two models. However, Ksp-Cre is expressed in the distal nephron specifically, sparing epithelial cells in other segments, while epithelial cells in all segments would be affected by Cagg-Cre. In addition, global knockout of Nphp2 leads to peri-natal lethality. Inducible Cagg-Cre could potentially be used to bypass earlier functional requirements. But matching stage and knockout efficiency in renal epithelial cells between Ksp-Cre and inducible Cagg-Cre mediated knockout remains challenging. These factors make a direct comparison problematic. Finally, our results revealed the role of defective epithelial cells in triggering the phenotypes but did not rule out a role of interstitial cells once abnormal signaling is initiated in epithelial cells. To clarify this point, we added " However, our result does not rule out functional significance of interstitial cells once a pro-cystic and fibrotic response is triggered in mutant epithelial cells." to the Discussion section.

In addition, it is possible that cyst initiation/growth upon stromal Nphp2 loss occurs substantially slower compared to epithelial Nphp2 loss. However, it seems the authors did not look at kidney phenotypes beyond 28 days of age. Publications from the ADPKD field suggest, that stromal Pkd1 loss initiates cystogenesis much slower than epithelial Pkd1 loss.

We have expanded our analysis to 8-week-old mice. We now show that Nphp2flox/flox;Foxd1-Cre mice show normal kidney weight, kidney/body weight ratio, kidney function and histology at P56, supporting our original conclusion that deletion of Nphp2 in interstitial cells fails to trigger obvious renal phenotypes, up to young adult stage. These results were presented in Figure 4- figure supplement 1 and the Results section.

Further, while the authors suggest that kidney fibrosis precedes cyst development, the results supporting this conclusion are limited to one time point, analyzing IF staining of a single marker that can be compared between non-cystic and cystic time points. These analyses need to be extended to make any firm conclusions.

At the precystic kidney stage (P7), we analyzed SMA and vimentin levels via immunostaining. Their mRNA levels were additionally quantified via RT-qPCR. We have now analyzed vimentin levels at multiple timepoints (P9, 14 and 21) and results were added to Figure 2. Combined, these data support the initiation of a fibrotic response prior to cyst formation.

The most interesting finding of the manuscript, and likely most impactful to the field, is, that loss of cilia within the setting of epithelial Nphp2 loss reduces PKD severity. This finding parallels published findings for Pkd1 and Pkd2 which are suggested to function in a cilia- dependent cyst-activation mechanism. Unfortunately, the here shown studies, do not add to the mechanistic insight beyond showing the descriptive finding. Most importantly, it remains unclear whether NPHP2 functions in the same pathway as polycystin-1 or -2 (the Pkd1, Pkd2 gene products) or in a separate independent pathway.

Our Ift88 Nphp2 double mutant results, combined with tissue-specific function of NPHP2, which to our knowledge is completely novel in a NPHP model, suggest that NPHP2 functions as a negative regulator of a profibrotic and pro-cystic pathway that interacts with cilia-mediated signaling in epithelial cells and that abnormal signaling from epithelial cells triggers interstitial fibrosis. We agree with the reviewer that whether NPHP2 functions in the same pathway as polycystins is an interestingly question. However, we feel it is out of the scope of this manuscript and would pursue this research direction in our future studies.

With respect to the HDAC preclinical studies, the authors show supporting data that a broad- spectrum HDAC inhibitor may be suitable for slowing cyst growth in their model of NPHP. Overall, these studies are not novel to the field, as HDAC inhibition has been shown to slow PKD progression in various models of PKD al while not in NPHP specifically. Further, the studies seem like an add-on, which does not directly link to the prior cell type-specific studies of NPHP2, and no mechanisms linking the two concepts are provided.

Although we and others showed that HDACIs slow cyst progression in other PKD models, this study is the first to show its impact on a NPHP model. Given the current lack of treatment for NPHP, we feel it important to communicate the results to the research community even though the molecular mechanism remains to be defined.

Author Response

Reviewer #1 (Public Review):

The article "Identification of a weight loss-associated causal eQTL in MTIF3 and the effects of MTIF3 deficiency on human adipocyte function" explored the functional roles of MTIF3 during adipocyte differentiation. In persons living with obesity, genetic variation at the MTIF3 locus associates with body mass index and responses to weight loss interventions. MTIF3 regulates mitochondrial protein expression and gene knockouts cause cardiomyopathy in mice. This paper provides insight into the impacts of MTIF3 knockout on adipocyte differentiation and the expression effects of the eQTL on MTIF3 levels. The authors implement a CRISPR/Cas9 gene editing approach coupled with an in vitro platform to detect influences of MTIF3 on adipocyte glucose metabolism and gene expression. This method may serve as a platform to explore knockouts in human cell lines, so it may allow the discovery of new gene x environment influences on in vitro outcomes related to differentiation, growth, and metabolism.

The conclusions of this paper are mostly well supported by data, but some experimental conditions and data analysis needs to be clarified and extended.

1) The authors use CRISPR/Cas9 to generate the rs1885988 variant in the human white adipocyte cell line and performed a comprehensive validation analysis of gene editing (Figure 1). qPCR analysis showed reduced MTIF3 expression during human adipocyte differentiation (Figure 1E, F). To expand the importance of the rs1885988 variant, the authors should have provided target gene measurements to verify the canonical differentiation profile (e.g., FABP4, ADIPOQ) and help readers understand the overall impact of gene editing at the MTIF3 locus.

Thank you for your suggestions. As you requested, we have quantified several adipocyte differentiation markers in the allele-edited cells after 12 days of adipogenic differentiation. The data (Figure 1-figure supplement 1) shows no significant difference between cells with the different genotypes. We have added more information about this in lines 100-101, and also in another context in lines 105-116.

Notably, the intra-group variation of the marker gene expression is large (Figure 1-figure supplement 1), which makes it difficult to clearly state how much the allele editing, as opposed to random variation resulting from single cell cloning, contributes to the differentiation outcome. However, if we also consider MTIF3 knockout cells (that do not need to be single-cell cloned), their differentiation marker expression also appears unaffected (Figure 3-figure supplement 1). Taken together then, it is unlikely the allele editing with the consequent effect on MTIF3 expression affects adipogenic differentiation in our experiments. We mention the absence of effect of MTIF3 knockout on differentiation in the paragraph starting on line 137.

2) The direct mechanistic influences of MTIF3 on adipocyte function remain unclear. MTIF3 regulates the translation initiation of mitochondrial protein synthesis. Western blots of OXPHOS proteins do not per se underscore supercomplex formation, which is also a process mediated by MTIF3. Blue native gel electrophoresis may prove a better method to establish the effects of MTIF3 loss-of-function on supercomplex formation.

As suggested, we have run blue native gel electrophoresis to detect the formation of OXPHOS respiration complexes. In the revised manuscript (lines: 158-168 and Figure 4 E,F), we show how MTIF3 knockout indeed interferes with the complex formation, with lower abundance of complexes V/III2+IV1, III2/IV2 and IV1. Additionally, although the blot signal for complex I+III2+IVn is diffuse, it appears higher in scrambled control cells than in MTIF3 knockout cells. Interestingly, complex II content is slightly higher in MTIF3 knockouts, which may result from a compensatory regulation mechanism, as none of the subunits of complex II is encoded by mitochondrial DNA. We also found several faster-migrating (“undefined bands” in the figure) in the MTIF3 knockout samples, although it is hard to determine whether those are single chain proteins, or degradation or mistranslation products. Overall though, the native gel blots show impaired OXPHOS complex assembly in MTIF3 knockout samples.

In addition, we performed western blots for other mitochondrial proteins, including COX II (subunit of OXPHOS complex IV), ND2 (subunit of OXPHOS complex I), ATP8 (subunit of OXPHOS complex V), and CYTB (subunit of OXPHOS complex III). The data (Figure 4 A,B), show decreased ND2 and COX II, trending decrease of CYTB, and unaffected ATP8 content in MTIF3 knockout adipocytes.

The methods (paragraph starting at line 479), results (paragraph starting at line 145), and discussion (lines: 261-263, 274-277) were incorporated in the revised manuscript.

3) Based on the findings, the authors argue that MTIF3 knockout alters the function of adipocytes. However, many of the experiments show fairly small effect sizes (Figure 5A, Figure 6A). How does the MTIF3 knockout explicitly perform functions related to body weight regulation? Gene editing in vivo would have helped to substantiate the authors' conclusions.

In the paper we are looking at the consequences of MTIF3 deficiency in one cell type, over short time, in vitro. The outcome of body weight regulation, e.g. during weight loss, would result from long-term effects of MTIF3-altered metabolism in more than one tissue. We envisage that small changes in energy metabolism in not only fat, but also in e.g. muscle, would make a substantial difference over time in vivo (this, we cannot capture in in vitro models). We have added this discussion to lines 294-311.

As for in vivo genomic editing, the alleles of interest are specific to the human genome. Ideally, a genotype-based recall study in humans would be appropriate, but due to time and resource limitation, we are not able to conduct such a study at the moment (although we certainly hope to perform such a study in the future). As for modeling the MTIF3 deficiency in mice – the MTIF3 knockout mice are not viable [1], and certainly other options (e.g. overexpression, tissue-specific knockouts) are possible and tempting to investigate. This, however, would require considerable additional work which we could only perform in a future project.

4) In several instances, the authors refer to 'feeding' cells with glucose (line 206, line 171). Feeding experiments often imply complex nutrient interventions in animal models and people, which cannot be easily recapitulated in cell culture. The in vitro experiments simply alter levels of glucose and more precise language would state the specific challenges accurately.

In the revised manuscript, we have substituted “feeding” for exact glucose concentration, or “glucose concentration” where appropriate. (paragraph starting at line 215, and lines 577-578, 597, 873-879)

Reviewer #2 (Public Review):

Huang Mi, et al. investigated the role of MTIF3, the mitochondrial translation initiation factor 3, in the function of adipocytes. They first detected the expression of the obesity-related MTIF3 variants based on the GTEx database and found two variants lead to an increase in MTIF3 expression. Then they knockout MTIF3 in differentiated hWAs adipocytes and characterized the mitochondrial function. They found loss of MTIF3 decrease mitochondrial respiration and fatty acid oxidation. They further treated cells with low glucose medium to mimic weight loss intervention and found MTIF3 knockout adipocytes lose fewer triglycerides than control adipocytes. This paper provides new information about MTIF3 in adipocytes and the potential functional role of MTIF3 in mitochondrial function.

1) The authors provided sufficient data to show those two genetic variants increase MTIF3 expression. Their CRISPR/Cas9 knockin cell line is also convincing. But they didn't show if the genetic variants affect adipogenesis. Adipogenesis is an important process for weight gain and fat deposition. In lines 103-107, the authors mentioned that the "allele-edited cells have some problem in differentiated state, e.g. triglyceride or mitochondrial content", so they used an inducible Cas9 system. However, the issue of differentiated allele-edited cells may be the functional effect of MTIF3 genetic variants, such as interrupting adipogenesis, decreasing triglyceride, or affecting mitochondrial number. The authors should provide that information.

Thank you for all your suggestions. We think we were not clear regarding this issue. We did not mean that the allele-edited cells have problem in differentiated state, which then definitely could be (as you point out) due to the functional effect of MTIF3 genetic variants. The problem relates to the process of single-cell cloning itself, which inherently introduces random variation. As a consequence, the data on adipogenic differentiation in allele-edited cells has relatively high intra-group variation. We have added more clarifying text in lines 104-116.

To provide the data on this, per your request, in the revised manuscript we include the results for the rs67785913-edited cells in Figure 1-figure supplement 1. As shown, we observed no differences in the expression of adipogenic markers (ADIPOQ, PPARG, CEBPA, SREBF1 and FABP4) or in mitochondrial content between the two rs67785913 genotypes. Since the intra-group variation is often high, it is hard to conclude how much the rs67785913 eQTL affects the quantified variables. Much of the variation could instead be ascribed to the effects of single cell cloning.

The cloning per se introduces random variation, but is required to obtain homozygous allele-edited cells. Because of this dilemma, and to clarify how much MTIF3 expression can actually influence adipogenic differentiation, we have, during the revision, also used the hWAs-iCas9 cells to generate MTIF3 knockouts at the preadipocyte stage and then tested their differentiation capacity. As we show in Figure 3-figure supplement 1, we found no apparent differences in adipogenic marker gene expression between scrambled control and MTIF3 knockout cells (we mention that in lines 137-144). Taken together, our results may indicate that the rs67785913 genotype, through affecting MTIF3 expression, is unlikely to regulate adipogenic differentiation.

2) In Figure 4, the author mentioned that MTIF3 knockout does not affect the expression of adipogenic differentiation markers. They need to provide more evidence to prove their point. Oil-red O staining is a clearer way to quantify adipocyte differentiation in cell culture. In addition, in Fig. 4B western blot, the author should include MTIF3 as a control to show the knockout efficiency. It is not clear the meaning of plus and minus in that panel. The author should also compare the total triglyceride levels in MTIF3 knockout cells and control cells.

We have now included Oil-red O staining results and total triglyceride levels (Figure 3 F,G), which show no apparent differences between scrambled control and MTIF3 knockout cells (method: lines 427-431; results: lines 137-144). We also added the MTIF3 blots to figure 4A as a control, showing high and consistent MTIF3 knockout efficiency in independent experiments. In the original manuscript, the plus and minus referred to control and knockout, respectively. To clarify that, we have changed the expression to SC and KO in the revised manuscript.

With regards to Oil-red O vs. quantification of adipogenic markers, we actually prefer the latter method, as it gives more accurate and less variable results than Oil-red O (at least in the cell line we use). We have, however, performed Oil-red O as well to address your question.

3) MTIF3 is a translation initiation factor in mitochondria and is involved in the protein synthesis of mitochondrial DNA-encoding genes. The authors should check protein levels rather than the mRNA levels of mitochondrial DNA-encoding genes (Fig. 6E). It's interesting to see the increase of mRNA levels of ND1 and ND2, which might be feedback of lower translation. Since ND1 and ND2 are in OXPHOS complex I, the expression levels of complex I in MTIF3 KO cells would be worth checking. Additionally, the author should also check the mitochondria copy number.

As suggested, we have detected several mitochondrial encoding proteins which are subunits of each mitochondrial OXPHOS complex. As shown in figure 4A, ND2 (subunit of OXPHOS complex I) and COX II (subunit of OXPHOS complex IV) expression were significantly reduced, CYTB (subunit of OXPHOS complex V) expression tended to decrease, and ATP8 expression was not affected in the MTIF3 knockout adipocytes. We also detected the formation of the OXPHOS respiration complex in extracted mitochondrial proteins and found MTIF3 perturbation affect mitochondrial complex assembly. The detailed methods (lines: 479-490), results (lines: 145-169) and discussion (lines: 260-262, 274-277) were incorporated in the revised manuscript.

We have also added the mitochondrial copy number data (Figure 3A), showing that MTIF3 knockout has lower mitochondrial content (methods: lines 491-500; results: 156-157)

4) MTIF3 knockout adipocytes retain more triglycerides under glucose restriction is interesting. It may link to the previous result of lower fatty acid oxidation in MTIF3 knockout adipocytes. However, the authors then showed there is no difference in lipolysis. The author should discuss those results in the manuscript.The authors could also check lipolysis in glucose restriction conditions. It's also necessary to include the triglyceride levels of KO cell lines at full medium

We have now examined the glycerol release in glucose restriction condition, and found no differences between control and MTIF3 knockouts (Figure 6-figure supplement 1). Interestingly, in 1 mM glucose, both genotypes released less glycerol than at 25 mM glucose, and this has been observed before in SGBS cell line [2] According to your suggestion, we have added the total triglyceride content at 25 mM glucose condition (Figure 6C), which also was not different between control and MTIF3 knockout cells. We speculate the higher retention of triglycerides in the knockouts could be due to higher re-esterification of lipolytically released fatty acids, since, as we observed, fatty acid oxidation is impaired in the knockouts. In the revised manuscript, we added that to the discussion (lines: 289-293).

References

1. Rudler, D.L., et al., Fidelity of translation initiation is required for coordinated respiratory complex assembly. Sci Adv, 2019. 5(12): p. eaay2118.
2. Renes, J., et al., Calorie restriction-induced changes in the secretome of human adipocytes, comparison with resveratrol-induced secretome effects. Biochim Biophys Acta, 2014. 1844(9): p. 1511-22.

Author Response

Reviewer #2 (Public Review):

The idea that decidualization is related to or evolved from wound healing, including fibroblast activation, is old, going back all the way to Creighton 1878 who pointed to the similarity between granulation tissue and decidual tissue, and is supported by the fact that embryo implantation is a compensated form of the endometrial lesion. Nevertheless, the mechanistic connection between FB activation and decidualization is an important fact necessary for understanding decidualization, a fact that is reflected in previous work, for instance, Kim et al., 1999 (Hum Reprod 14 Suppl 2), their reference 20, and Oliver et al., 1999 (Humn Reprod 14), their reference 56 a.o.m. More specifically, a recent single-cell study of in vitro decidualization has shown that a myofibroblast-like cell state is a transient state in the process of decidualization, i.e. decidual cells themselves are not so much activated fibroblasts, but rather decidual cells differentiate after endometrial stromal fibroblasts undergo a FB activation like process, and the decidual re-programming happens from these activated FB like states (Stadtmauer et al., 2021, Biol. of Reprod. 1-18).

Yes, the paper from Stadtmauer DJ and Wagner GP (2022) was cited in revised version.

The above assessment of how the current study fits into the conceptual landscape of mammalian reproductive biology does not diminish the importance of the paper under consideration. The study contributes a large amount of observational and experimental facts to the understanding of how FB activation and decidualization are related. The authors suggest, in particular, that blastocyst-derived TNF activates the cLPA- producing Arachidonic acid (AA), activating PGI2 and PPARd signaling pathway (more about this later).

Other major comments:

The authors suggest that luminal epithelial cells signal through the release of arachidonic acid (AA) in response to TNF. That is interesting and supported by in vitro experiments inducing decidualization and FB activation by AA. What makes this conclusion a little problematic is that it is known that luminal epithelial cells also express COX2/PTGS2 and thus the synthesis of prostaglandins is already starting in the LE and thus LE can also signal to the stoma via PGE2, PGI2 as well as PGL2 rather than AA directly. The in vitro experiments can not exclude the possibility that the ESF is producing some prostaglandin and then having an autocrine effect.

Yes, we agree with you. It is possible that PGI2 and PGE2 from luminal epithelial cells may also induce fibroblast activation. Based on the data from in situ hybridization, COX-2, mPGES, PGIS and PPARδ are mainly expressed in subluminal stromal cells at mouse implantation site on day 5 of pregnancy (Lim et al, 2000; Ni et al, 2002; Wang et al, 2004). Therefore, PGI2 from stromal cells should be the dominant one compared to that from luminal epithelial cells. In the future, we will examine the effects of AA on COX-2, mPGES and PGIS in luminal epoithelial cells.

Lim H, Dey SK. PPAR delta functions as a prostacyclin receptor in blastocyst implantation. Trends Endocrinol Metab. 2000 May-Jun;11(4):137-42.

Ni H, Sun T, Ding NZ, Ma XH, Yang ZM. Differential expression of microsomal prostaglandin e synthase at implantation sites and in decidual cells of mouse uterus. Biol Reprod. 2002 Jul;67(1):351-8.

Wang H, Ma WG, Tejada L, Zhang H, Morrow JD, Das SK, Dey SK. Rescue of female infertility from the loss of cyclooxygenase-2 by compensatory up-regulation of cyclooxygenase-1 is a function of genetic makeup. J Biol Chem. 2004 Mar 12;279(11):10649-58.

344: here the authors report that PGE2 has no effect on FB activation marker expression, but the problem with that is, that (at least in human ESF), progesterone is causing a change in the expression of the PGE2 receptors from EP4 to EP2, and it is only the EP2 receptor that activates cAMP/PKA pathway.

Yes, we agree with you. PGES is highly expressed in stromal cells at implantation site. Previous studies also show that PGE2 is important during decidualization. In our study, PGES showed no significant changes after stromal cells were treated with AA. PGE2 also had no significant effects on fibroblast activation. Therefore, we focused on PGI2-PPAR pathway. It is possible that PGE2 may regulate decidualization through an alternative way rather than fibroblast activation.

The fact that the authors show an effect of PGI2 is interesting because PGI2 receptors are among the strongest expressed PTG receptors in mammalian ESF. Prostacyclin receptor is a GPCR rather than a nuclear receptor. So the question is really why the authors have not pursued the role of prostacyclin receptor and instead have focused on PPARd?

Yes, we agree with you. When mouse stromal cells were treated with AA, there was no significant change for the protein level of prostacyclin receptor (Figures 4E, 4F). When mouse stromal cells were treated with the agonist SELEXIPAG of prostacyclin receptor, the markers of fibroblast activation showed lower changes compared with treatments with PPARδ (Figure 3D). Therefore, we focused on PPARδ. Yes, we agree with you. Although prostacyclin receptor is less responsive than PPARδ in activating fibroblast activation, it should contribute to fibroblast activation. In the future, we will pursue the effect of prostacyclin receptor on fibroblast activation. Thank you very much for your suggestion.

Reviewer #3 (Public Review):

This manuscript postulates that uterine stroma cells undergo a stage of activation between the resting state and the differentiated decidual state in order to support embryo implantation. Using in vivo mouse and in vitro mouse and human stroma cells they demonstrate that during decidualization the stroma cells express the marker genes for activated stroma. They then trace an axis from the embryo-producing TNF to prostaglandin production and activin A that is required for this process. They propose data to show that activation of the stroma is altered in infertility due to fetal trisomy 16.

The strengths of this manuscript are:

1) This is a comprehensive study using both in vivo and in vitro studies and in both mouse and human stroma cells.

2) The experiments use a combination of ligands, agonists, and inhibitors to map the signaling axis regulating stroma activation.

3) The data shown support the conclusions in this manuscript.

The weaknesses of this manuscript are:

1) The conclusion that Acitvin A is the regulator of stroma activation as mentioned by this manuscript is correlative. What is needed is a knockdown of Activin A and then assess stroma activation to prove Activin A is the major regulator and not one of many TGFb family members.

Yes, the data from Activin A knockdown were provided.

2) The use of uterine epithelial cells is problematic. The in vitro co-culture approach is not a state-of-the-art co-culture. Removal of epithelial cells from the uterus results in loss of the epithelial phenotype. If the manuscript used an epithelial organoid stroma cell coculture approach it may better reflect the role of the epithelial cells in this process. Otherwise, it is not clear that the epithelial cells are actual participants in the signaling axis. The treatments could be directly on the stroma cells.

Yes, we agree with you. According to your suggestions, we established a culture system for epithelial organoid. When the epithelial organoids were treated with TNF, a similar response was obtained compared with in vitro cultured mouse epithelial cells.

3) Ishikawa cells are endometrial cancer cells. They do not really reflect uterine epithelium and it is not clear that any epithelial cell could be substituted for these cells.

Thank you very much for your comments. It is true that Ishikawa cells should be different from in vivo endometrial epithelial cells. However, several studies showed that Ishikawa cell line possess apical adhesiveness to JAR trophoblast cells and expresses many of the same enzymes and structural proteins found in normal human endometrium (Castelbaum AJ et al, 1997).. Because both estrogen and progesterone receptors are expressed in Ishikawa cells, Ishikawa cells show a good response to both estrogen and progesterone (Castelbaum AJ et al, 1997). Therefore, Ishikawa cells are used as a model for receptive endometrial epithelial cells (Hannan NJ et al, 2010).

Castelbaum AJ, Ying L, Somkuti SG, Sun J, Ilesanmi AO, Lessey BA. Characterization of integrin expression in a well differentiated endometrial adenocarcinoma cell line (Ishikawa). J Clin Endocrinol Metab 1997; 82:136-142.

Hannan NJ, Paiva P, Dimitriadis E, Salamonsen LA. Models for study of human embryo implantation: choice of cell lines? Biol Reprod. 2010; 82:235-245.

Lessey BA, Ilesanmi AO, Castelbaum AJ, Yuan L, Somkuti SG, Chwalisz K, Satyaswaroop PG. Characterization of the functional progesterone receptor in an endometrial adenocarcinoma cell line (Ishikawa): progesterone-induced expression of the alpha1 integrin. J Steroid Biochem Mol Biol. 1996; 59:31-39.

4) The activation of stroma cells in the fetal trisomy 16 experiments at the end is very superficial. Data should show that these cells decidualize with decidual markers. This appears to be an experiment to show the translational value of the signaling axis. This experiment, again, is not well developed, does not add much to the manuscript, and should be omitted.

Yes, we agree with you. The description on human trisomy 16 was deleted.

In summary, the concept of stroma cell activation as part of decidualization is nicely developed and will add to the field. Normally investigators consider decidualization a mesenchymal to epithelial transition while some consider it stromal activation. This manuscript demonstrates that stroma cell activation is a critical part of the process of decidualization.

Author Response

Reviewer #1 (Public Review):

The authors screen large libraries of small proteins to identify three proteins of <50 aa that rescue the growth of an auxotrophic serB deletion Escherichia coli strain. They convincingly show that the growth rescue is due to the small proteins increasing expression of the his operon by reducing transcriptional attenuation. The authors argue that the small proteins function by directly binding the his RNA 5' UTR to alter RNA secondary structure.

The conclusion that the three small proteins reduce his operon attenuation is well supported by the data. A previous study suggested this mechanism for a somewhat larger, randomly selected protein, but the current study extends this prior work by firmly establishing that the proteins modulate attenuation. The suggestion that the small proteins function by directly binding the his RNA is less well supported by the data. The RNase T1 mapping data are not straightforward to interpret, and there is no assessment of protein-RNA interactions in vivo.

Major comments:

1) The RNase T1 probing data are not straightforward to interpret, and hence are insufficient to conclude that Hdp1 binding to the his 5' UTR is the mechanism by which it reduces attenuation. Specifically, G96 has reduced cleavage in the presence of Hdp1, inconsistent with the antiterminator conformation. The authors argue that G96 could be within the site of Hdp1 binding. This is certainly possible but would require additional experimental evidence to draw a confident conclusion. Also, the increased cleavage of bases around the start codon and Shine-Dalgarno sequence is inconsistent with a shift from the terminator to the antiterminator conformation. One confounding issue here is the lack of replicates and the lack of quantification. Additional probes could be tested, which would provide complementary structural information.

We agree that the RNase T1 probing data alone does not provide sufficient resolution to fully assess changes in terminator/anti-terminator conformations. Therefore, we have clarified our interpretation of the data, addressed its limitations, and have softened the conclusions that can be drawn from it in the text (lines 419-431). We have also included two additional T1 probing experimental replicates in Supplementary Fig. S11 which are in agreement with the cleavage patterns presented in the main text Figure 3D. Based on the revised conclusions and the consistency of the cleavage patterns between the experimental replicates, we do not think that quantification of the probing data would provide any additional information.

2) There are no experiments to test whether Hdp1 binds the his RNA in vivo. The in vitro data show that Hdp1 can bind the his RNA, but they do not show that this occurs in vivo, or that this is the mechanism by which Hdp1 regulates the expression of the his operon.

As addressed in the Essential Revisions section, we have now performed and included data from co- immunoprecipitation assays, in which we were able to successfully detect and demonstrate enrichment of his operator-regulated RNA transcripts in HA-tagged Hdp1 pull-down samples. We were also able to demonstrate less enrichment (i.e. reduced interaction/specificity) for thr operator-regulated RNA transcripts in the Hdp1 pull-downs as well as lower enrichment for all his operator-regulated target RNA transcripts in pull-downs performed with the HA-tagged Hdp1 L27Q mutant. These data are presented in Fig. 3A and discussed in lines 313-337.

Reviewer #2 (Public Review):

In this work, Babina et al. address a central question in molecular evolution that is only partially answered: how does cellular novelty emerge in evolution? The authors focus here on small proteins, whose importance to various cellular functions has become more appreciated recently. Babina et al. ask if functional small proteins can emerge from random sequences, a question that is mostly unresolved with only a small number of examples in the published literature for such functions. In this study, the authors demonstrate that proteins selected from random, synthetic libraries can rescue auxotrophy in E. coli. Namely, the authors find three small, random proteins (<50 amino acids) that allow E. coli cells with a ΔserB genetic background to grow in a medium without the amino-acid serine. They then show that this rescue is based on the up-regulation of HisB, an enzyme that can compensate for the serB deletion. Finally, using different molecular biology techniques, the authors propose a model in which up-regulation of HisB is achieved by physical interactions between the random proteins and the his operator that regulates the transcription of the his operon in E. coli.

Notably, as the authors themselves point out, a previous study has already shown that semi-random proteins can result in up-regulation of HisB levels to rescue ΔserB cells. Thus, most of the novelty comes from the attempt to figure out the molecular mechanism of the three random proteins. The idea that a random protein binds the 5' of an mRNA which results in up-regulated expression levels is interesting and can benefit the field. However, some clarification on existing data and additional control experiments are needed to support the authors' claims:

1) Growth data are not presented in the current form of the manuscript, which makes it impossible to evaluate many of its claims. Especially, the extent of rescue and fitness gain achieved by these random proteins compared to cells harboring the serB gene.

We thank the reviewer for pointing out this discrepancy. We have now added all relevant growth data under non-permissive conditions (Figure 1G, Supplementary Figures S2, S3, S5) and have also included data on the fitness effects exerted by Hdp expression in cells harboring serB under permissive conditions (LB medium), to allow for comparison with the empty plasmid control strain (Supplementary Figure S1).

2) The authors have screened their library on other auxotrophic strains, however, they could only find random proteins that rescue growth in the ΔserB background. Currently, they do not address this point, but it might be relevant to the molecular mechanism of those random proteins.

The reviewer raises an interesting point. We have added a paragraph to our Discussion addressing why we believe that the serB-model with a complementary enzyme is an ideal target for the selection of de novo genes (lines 536-543).

3) Central to the authors' claims is the up-regulation of HisB, however, they mostly work with an alternative LacZ system to assess the effects of the random proteins on expression. The paper will benefit from some more work measuring actual HisB levels as expressed by the various constructs used along the paper. The authors did provide an important proteomic analysis to show that HisB (along with other proteins in the his operon) is up- regulated as a result of the expression of one of the random proteins. However, it is unclear if the reported ~3- fold increase in HisB levels is enough to allow the growth of ΔserB cells in a medium without serine.

We thank the reviewer for raising this concern and allowing the opportunity to clarify. It is well established that upregulation of HisB can rescue growth of a SerB-deficient strain on minimal medium (for examples, see Patrick, et al. PMID: 17884825, Digianantonio and Hecht PMID: 26884172). We have now performed additional proteomics analyses that show a specific upregulation of the his operon upon expression of Hdp1 and Hdp3. We have also added a control experiment overexpressing HisB from our expression vector, showing that it restores growth of the auxotrophic ΔserB mutant. It is also clear that histidine starvation itself does not de-repress HisB sufficiently to allow growth of a ΔserB mutant, as this strain does not grow on minimal medium lacking histidine (such as M9 minimal medium that was used for the functional selection in our study). In addition to upregulation of HisB, we show that the rescue is dependent on presence of HisB and provide additional experiments showing a specific interactions in vitro and in vivo of Hdp1 with the his operator RNA. Our results clearly show that rescue depends on HisB and that Hdp expression upregulates HisB, and we do believe our central claim is substantiated beyond reasonable doubt. The reviewer’s main concern, that it is unclear if expression levels of HisB are high enough to allow growth is, in our opinion, resolved by the observation that Hdp-dependent upregulation of HisB does restore growth.

We respectfully disagree with the reviewer’s suggestion that an exact determination of the level of upregulation is relevant and needed, as outlined above. In addition, we would like to point out that it is not possible to measure HisB upregulation compared to an empty plasmid control strain under non- permissive conditions. Comparing HisB levels in a ΔserB strain expressing Hdp vs. the empty plasmid control in minimal medium is not possible, since the empty plasmid control strain is not able to grow, and the corresponding baseline of HisB expression cannot be determined in a non-growing strain. To circumvent this, we determined HisB levels in rich medium, which does not necessarily reflect the exact amount of upregulation occurring under non-permissive conditions, but still allows us to detect a physiological activity. Alternative experimental setups, such as comparing HisB levels in a strain carrying serB in minimal medium also suffer severe shortcomings as it no longer reflects the cellular physiology of the auxotoph under non-permissive conditions, where growth is dependent on HisB upregulation.

4) It is unclear how noisy and statistically significant some of the critical experiments in the manuscript are, especially the EMSA and T1-digestion experiments. The authors should try to find a different operator with a similar RNA structure and attenuation function, but a different nucleotide sequence, to the his operator, and show that this control operator is unaffected by the random proteins. Demonstrating the lack of phenotypes using the LacZ system, EMSA experiments, and T1-digestion patterns will much support the authors' claims.

We thank the reviewer for suggesting this important control and agree that its inclusion significantly strengthens our claims. We used the threonine operon (thr) operator, which is regulated by terminator/anti-terminator formation similar to that of to the his operon with the his operator. We show that Hdp1 does not cause de-repression of this operator using a lacZ reporter construct. Strongly supporting this is the fact that our whole proteome analysis showed specific upregulation of the his operon. Any other off target de-repression would be detected in this assay. Furthermore, we now include the thr operator RNA as a control in the EMSAs, which demonstrates reduced binding with Hdp1 in comparison to the his operator RNA. We also added an in vivo pull-down experiment using tagged Hdp1, showing marked enrichment of his operator-regulated RNA transcripts, whereas the observed enrichment of the control thr RNA transcripts is substantially less.

Author Response

Reviewer #1 (Public Review):

Thakkar et al describe the immune effects of 3rd and 4th doses of COVID-19 monovalent vaccines in a diverse cohort of immunocompromised cancer patients. They describe augmentation of anti-Spike antibodies after dose 3, especially seroconversion in 57% of patients, followed by a durable response over six months. The fourth dose was associated with increased anti-Spike antibodies in 67% of patients. T-cell responses were seen in 74% and 94% of patients after the third and fourth doses respectively. Strikingly, neutralization of Omicron was absent in all patients after the third dose but increased to 33% after the fourth dose.

Strengths:

Diverse cohort (34% Caucasian, 31% AA, 25% Hispanic 8% Asian) including 106 cancer patients after dose 3, of which 47 patients were longitudinally assessed for six months, as well as eighteen patients assessed after the fourth dose. Seronegative as well as seropositive patients benefit from a third dose of vaccination. Assessment of cellular (T cell) immune responses and viral neutralization against wild-type as well as Omicron variant is commendable.

Weaknesses:

The efficacy of the bivalent vaccine (Omicron specific) is not studied here, since the fourth dose of vaccine was a monovalent vaccine. This should be clarified in the discussion.

We have added text in the discussion section regarding this comment, lines 470-472

“The bivalent COVID-19 vaccine was introduced after the enrollment for our study was closed however it is reassuring to see that the bivalent vaccine has better neutralization activity against Omicron sub-variants”

The authors describe an increase in anti-S titers after monoclonal antibodies. Were any of the patients receiving IVIG, and what was the effect, if any on Anti-S antibodies? Characteristics of breakthrough infections, particularly if they had prolonged duration, would be important to include.

We have added text in the results section for IVIG (lines 382-383) and characteristics of breakthrough infections (lines 341-344)

“No patients were on intravenous immunoglobulin (IVIG) at the time of study participation” “Of the 4 breakthrough infections, 1 patient had no symptoms, and 3 had mild symptoms”

Reviewer #2 (Public Review):

In this manuscript, Thakkar and colleagues evaluate the immunogenicity of 3rd and 4th doses of SARS-CoV2 vaccinations in patients with cancer. The authors find that additional vaccine doses are able to seroconvert a subset of patients and that antibody levels correlate with T-cell responses and viral neutralization.

The main strengths of this manuscript are:

1) The authors systemically performed a broad array of immunological assessments, including assessments of antibody levels, T cell activity, and neutralization assays, in a large cohort of patients with cancer receiving 3rd and 4th doses of COVID vaccines.

2) The authors recruited an ethnically diverse cohort of patients with diverse cancer types, though enrolled participants were enriched for hematological malignancies.

3) Prior to FDA/CDC guidance supporting a 4th vaccine dose, the authors recruited participants with no or inadequate responses into a prospective clinical trial of a 4th dose, the results of which are outlined here.

4) The authors' findings that patients with hematologic malignancies and those receiving anti-CD20/BTK inhibitors have lower immunological responses to SARS-CoV-2 vaccines are consistent with multiple prior studies, including prior studies from these authors.

5) The authors also find that 3rd and 4th COVID vaccine doses are able to seroconvert a subset of patients with no or "inadequate" responses, though it's unclear whether seroconversion is enough for true protection from SARS-CoV-2 infection.

The main weaknesses of the manuscript include:

1) The study cohorts disproportionately enrolled patients with hematological malignancies who have been previously shown to mount lower immunological responses to COVID-19 vaccines; thus, the findings may not be representative of a typical oncology patient population.

We have clarified this in the discussion (lines 465-466)

“However, caution should be exercised in generalizing these results to the broader immunosuppressed population given the small sample size of our cohort and the disproportionately high representation of hematologic malignancy patients”

2) The subgroup analyses were relatively small.

The discussion text in line 464-465 is in concordance with this observation

“However, caution should be exercised in generalizing these results to the broader immunosuppressed population given the small sample size of our cohort and the disproportionately high representation of hematologic malignancy patients”

3) The nomenclature used in the manuscript was confusing when it came to "baseline" assessments and boosters versus additional doses of vaccines.

We have clarified the nomenclature throughout the manuscript

4) Ultimately, the major limitation of this manuscript is that antibody levels/T-cell responses/neutralization are surrogates for immune protection against SARS-CoV-2, but it's unclear what defines the ideal cutoffs for protection. Simply seroconverting may still be insufficient. The authors don't provide data showing antibody levels as relates to breakthrough infection, likely because they are underpowered for this analysis.

We have added text to expand on this further lines 475-482

“Further efforts are also needed to better determine cut-off values at which anti-S antibody levels provide protection from symptomatic COVID-19. At the present time, this data exists only for neutralizing antibody titers[36, 44] and the commercially available anti-S antibody assays are quite heterogenous with efforts being made to improve equivalency in titer reporting[45]. Our study while providing a correlation between anti-S antibody titer and neutralizing antibody titer supports that the higher the titer, the better neutralization is expected and by extrapolation, less likelihood of symptomatic infection however this needs to be confirmed in larger, systematic studies”.

Author Response

Reviewer #3 (Public Review):

Zhang, Q. et al. developed a two-photon fluorescence microscope (2PFM) by incorporating direct wavefront sensing adaptive optics (AO), which is optimized for mouse in vivo retinal imaging. By using the same 2PFM with the option of using or not using the incorporated AO system, this team compared the in vivo retinal images and convincingly demonstrated that AO correction acquired brighter and higher resolution images of retinal ganglion cells (RGCs) and their axons in both densely and sparse labeled transgenic mouse lines, normal and defected capillary vasculatures, and RGC spontaneous activities detected by genetic Ca2+ sensor. Interestingly and importantly, this team found that a global correction by removing the common aberration from the entire FOV enhances imaging signals throughout the entire large FOV, indicating a preferable AO imaging strategy for large FOVs. The potential applications of the in vivo retinal imaging techniques and strategies developed by this study will certainly inspire further investigation of the dynamic morphological and functional changes of retinal vasculatures and neurons during disease progression and before and after treatments. It would be beneficial to the manuscript and the readers if the authors can elaborate on optic design a little bit more. For example, whether the incorporation of AO adversely affects the 2PFM optic design? If the 2PFM can be further optimized by uncompromised optic design without incorporating AO, the quality of in vivo images will comparable to the AO-2PFM or not?

We thank the reviewer for these thoughtful questions.

Whether the incorporation of AO adversely affects 2PFM optical design may be a matter of perspective. As we demonstrated in the retina and elsewhere, AO substantially improves the achievable spatial resolution. Its incorporation does not reduce the temporal resolution of the system, as the ocular aberrations are temporally stable in the anesthetized mouse due to the lack of eye movement and do not require repeated aberration measurements throughout the imaging session. Signal enhancement by AO can increase the frame rate by reducing pixel dwell time required to achieve desired signal-to-noise ratio (SNR). The deformable mirror used for wavefront correction has high reflectivity, thus does not reduce the power throughput of the 2PFM. Using similar lenses for conjugation of the AO path to those employed by the 2PFM itself, we also maintain the scanning field of view size.

However, the incorporation of AO, including the direct wavefront sensing module (the “L10-L11-SH-sensor” path in Fig. 1A) and the deformable mirror (together with a pair of lenses for optical conjugation), does increase the complexity of the imaging system. Maintaining the optimal performance of AO also requires advanced optical knowledge that may not be possessed by most biological users.

For this reason, we carefully designed the 2PFM path for optimal imaging performance without AO, characterized its performance (“AO two-photon fluorescence microscope (AO-2PFM)” and “System correction” sections of Materials and Methods, Fig. S1), and optimized sample preparation including designing our own contact lens (“In vivo imaging” section of Materials and Methods, Fig. S2). Our efforts, which we believe to have led to the best possible performance of a 2PFM sans AO, allowed us to resolve retinal capillaries and cell bodies (in 2D) in vivo. Therefore, our 2PFM (sans AO) design and sample preparation procedure should benefit users who do not plan to implement AO.

Hypothetically, if the ocular aberrations of all mouse eyes were similar, it would be possible to add a static corrective element to a conventional 2PFM to improve image resolution (in the same spirit as the non-prescription reading glasses for far-sighted human eyes). However, as shown in Fig. S6 (“Zernike decompositions and corrective wavefronts for all experiments”), ocular aberrations are variable. These variabilities may arise from alignment differences (e.g., different angles between the optical axis of the ocular optics and the optical axis of the 2PFM), which can be minimized by establish a procedure to reproducibly position the eyes of different mice in similar ways. In this case, a static corrective element may be designed for substantial aberration reduction. However, the variations also arise from optical differences in the ages [1] or strains [2] of the mice. To have a 2PFM that always performs at the diffraction limit, an adaptive element as employed by AO is necessary to maintain optimal performance regardless of the specifics of the sample.

References

1. C. Cheng, J. Parreno, R. B. Nowak, S. K. Biswas, K. Wang, M. Hoshino, K. Uesugi, N. Yagi, J. A. Moncaster, W.-K. Lo, B. Pierscionek, and V. M. Fowler, "Age-related changes in eye lens biomechanics, morphology, refractive index and transparency," Aging (Albany. NY). 11(24), 12497–12531 (2019).
2. C. Tan, H. na Park, J. Light, K. Lacy, and M. Pardue, "Strain differences in mouse lens refractive indices when measured with OCT," Invest. Ophthalmol. Vis. Sci. 54(15), 1917 (2013).

Authoor Response

Reviewer #1 (Public Review):

This manuscript investigates the question of how polylysogeny impacts competition with a sensitive non-lysogen, and how this is shaped by phage resistance. This is an important and timely question, as lysogeny can be a strategy to invade new niches, and prophages are important vehicles for the acquisition of a range of virulence factors by pathogens including Klebsiella. The authors use a polylysogenic Klebsiella clone in competition with a non-lysogen that is sensitive to at least some of the prophages produced by the polylysogen. They compete these strains over a 30-day period and measure host population dynamics and evolution of phage resistance and lysogenic conversion in the (initially) sensitive competitor. Overall, the experiment shows that lysogen formation is relatively rare and short-lived. Instead, phage resistance through complete loss of the capsule is the primary mechanism evolving, but other resistant capsule mutants, with more subtle mutations affecting capsule expression, emerge as well. The authors have collected a very impressive amount of data and made some very interesting observations.

My main problem with this paper is that the manuscript lacks a clear narrative, making it very hard to extract the key message this paper wants to convey. Related to this, (some of) the conclusions that the authors make do not appear to be well supported by the data. For example, the authors conclude that selection favours more subtle capsule mutations because they are less costly than capsule-loss mutants (lines 497-500). However, there are no data to support this conclusion, as fitness costs of the various resistance phenotypes analysed were not measured. Apart from the genotypes, the data that are presented in this show that these subtle mutants have more subtle decreases in capsule production compared to the mutants that show a complete loss of capsule. But this does not tell us their relative cost. It also doesn’t tell us how the emergence of these different mutants relates to phage pressure, because whilst bacterial population dynamics data are monitored meticulously, phage dynamics data are missing (I have not found them in the supplemental information either). This makes it impossible to directly relate the emergence of the various resistance mechanisms to phage infection pressure during the coevolution experiment, even though this appears to be a hypothesis the authors wish to test.

Overall I think the overarching question of the manuscript is important and the model system is a very relevant one to study this question, but in my view, the current data don’t support the conclusions of the paper. Apart from these criticisms, the manuscript is very well written and the figures are overall easy to interpret.

We thank the reviewer for the critical assessment of our work and the time invested in the process. We have modified our manuscript following the recommendations, provided new data and we are convinced that our main results are now fully supported by the data.

Reviewer #2 (Public Review):

This manuscript presents data on multiple experiments regarding the co-evolution of poly-lysogenic and phage-susceptible Klebsiella pneumoniae strains. In particular, the manuscript aimed to determine the mechanisms of resistance that would shape bacterial competition over co-evolutionary timescales. The major finding is that the potential for lysogenization as a phage resistance mechanism is narrow and only likely to occur given certain circumstances. Moreover, the manuscript again reinforces the importance of receptor changes -initially loss, but modification in structure or expression over longer time scales- as a major mechanism of phage resistance that influences bacterial competition.

Strengths

A major strength of this manuscript is the care in designing experiments and conducting follow-up experiments to isolate the essential elements to support each of the conclusions. This includes using orthogonal methods such as sequencing and modeling to support or expand the findings from culturing and experimental evolution. The study features results that were beautifully replicated (e.g. Figure 3) lending confidence to the findings.

Weaknesses

Two weaknesses of the manuscript in its current form are: 1) a need to discuss other studies that also have found context-dependent results and 2) more focus on delivering the key overall "message" of the paper to the reader. Finally, not a weakness, but a (necessary) limitation is the study system, but this manuscript sets a bar for other groups to test in their systems to probe the generality of the findings.

The support for the conclusions is compelling. The findings were counter to the initial expectation (lysogenization as a major feature) and the manuscript does an admirable job of supporting the unexpected conclusion with thorough experimental work, supplemented with modeling.

This manuscript will be of great significance in microbial evolution, both for its implications in limiting the scope of lysogenization as a viable phage resistance mechanism in the long term and for its significant experimental rigor, particularly with regard to the co-evolutionary timescale studied. The study has very important implications for the evolution of antimicrobial resistance and phage therapy.

We thank the reviewer for the time spent and enthusiasm towards our experimental set-up.

Author Response

Reviewer #1 (Public Review):

The authors conducted a thorough analysis of the correlation between height and measures of cognitive abilities (what are essentially IQ test components) across four cohorts of children and adolescents in the UK measured between 1957 and 2018. The authors find the strength of the association between height and cognitive measures declined over this time frame--for example, among 10- and 11-year-olds born in 1958, height explained roughly 3% of the variation in verbal reasoning scores; this dropped to approximately 0.6% among those born in 2001. These associations were further attenuated after accounting for proxy measures of social class.

The authors' analyses were performed carefully and their observations regarding declining height / cognitive measure associations are likely to be robust if we interpret their results with an important caveat: these results reflect measurements aimed at assessing cognition rather than cognition itself. The importance of this distinction is evidenced by the changing correlation structure of the cognitive measures over time. For example, age 11 verbal / math scores were correlated at >= 0.75 at the first two time points but dropped to 0.33 at the most recent time point. Similar patterns are present for the other cognitive measures and time points. The authors' conclude that such changes are unlikely to impact their primary findings, but I'm less certain. For example, one interpretation of this finding is that older cognitive measures were simply worse at indexing distinct cognitive domains and instead reflected a combination of cognitive ability together with non-specific factors relating to opportunity, health, class, etc. Further, height was historically a stronger proxy for class and economic status than it is today (e.g., by capturing adequate nutritional intake, risk for childhood disease, etc.). Together, then, previously high height / cognitive measure correlations might reflect the fact that both phenotypes previously indexed socio-economic factors to a greater extent than they might today (which is still non-negligible).

We agree, it is possible that our results could in principle be explained by changes to the measures. We have provided further analysis to attempt to inform the likelihood of this suggestion and have expanded our discussion of this issue (Discussion, explanation of findings section; copied below).

First, we conducted additional sensitivity analysis repeating our main analysis using cognition measures in which the number of response options was set to be the same for each test (the lowest common denominator across all cohorts). This was tested in two separate approaches: 1) by reducing the number of categories to the same number in each cohort; and 2) or by picking a random sample of question items for each category. Our main findings were unchanged: described in “Additional and sensitivity analyses” section, Figs S20-S21.

Regarding the suggestion that “high height / cognitive measure correlations might reflect the fact that both phenotypes previously indexed socio-economic factors to a greater extent than they might today” – we sought to account for this by adjustment for measured indicators of socioeconomic position, and found the trend remained after adjustment (Fig 1 panel 2). As in other observational studies we cannot fully rule out the possibility of residual confounding however (Discussion, Explanation of findings paragraph 2).

“The multi-purpose and multidisciplinary cohorts used cognition tests which differed slightly in each cohort. It is therefore possible that differences in testing could have either: 1) entirely generated the pattern of results we observed, such that if identical tests were used the association between cognition and height would otherwise have been identical in each cohort; in contrast to previous findings which reported using identical tests20; or 2) biased our results, such that if identical tests were used the decline in association between cognition and height would have been less marked than we reported. While we cannot directly falsify this alternative hypothesis given our reliance on historical data sources, a number of lines of reasoning suggest that the first scenario is unlikely. First, our results were similar when using 4 different cognitive tests (spanning mathematical and verbal reasoning); any bias which generated the results we observed should be similarly present across all 4 tests. Other things being equal, one would expect that more discriminatory tests (i.e., those with a greater number of responses) would have higher accuracy and thus better index cognition. Our results were similar when the youngest cohort had similar numbers of unique scores in cognitive tests compared with the oldest cohort (Verbal @ 11 years: n=41 in 1946c, n=40 in 2001c) and fewer unique scores (Maths @ 7/11: n=51 in 1946c, n=21 in 2001c). Our results were also similar in sensitivity analyses in which the number of response options were set to be the same in each cohort. Higher random measurement error in the independent variable (cognition) would lead to weakened observed associations with the outcome (height),52 yet we do not a-priori anticipate that this such error was higher in younger across all tests in such a manner that would have led to the correlation we observed. Ensuring comparability of exposure is a major challenge across such large timespans. Reassuringly, our results are consistent with those from a previous study which reported consistent tests being used (from 1939-1967).20 However, even seemingly identical require modification across time (e.g., for verbal reasoning/vocabulary there is typically a need to adapt question items due to societal and cultural changes over time in vocabulary and numerical use); further, changes to education such as increases in testing may have led to increasing preparedness and familiarity with testing than in the past even where identical tests are used.

Interestingly, we observed a marked reduction in the correlation between cognitive tests across time (e.g., between verbal and maths scores). This trend has been reported in previous studies53 54 and warrants future investigation; it is consistent with evidence that IQ gains across time seemingly differ by cognitive domain,45 potentially capturing differences across time in cognitive skill use and development in the population. Previous studies using three (1958-2001c) of the included cohorts have also reported changing associations between cognition (verbal test scores at 10/11 years) and other traits: a declining negative association with birth weight19 and a change in direction of association with maternal age (from negative to positive);55 each finding has plausible explanations based on changes across time in relevant societal phenomena (improved medical conditions19 and changes in parental characteristics,55 respectfully), yet also cannot conclusively falsify the notion that differences in tests used influences the results obtained. In this paper, we used multiple tests and sensitivity analyses to attempt to address this.”

Additionally, their findings add an interesting data point to a collection of recent results suggesting that the relationship between cognitive and anthropometric measures is complex and difficult to interpret. For example, studies using genetic markers to examine shared genetic bases have virtually all relied on methods assuming mating is random, which is not the case empirically. Howe et al. (doi.org/10.1038/s41588-022-01062-7) recently reported that the ostensible genetic correlation of -.32 between years of education and BMI attenuates to -.05 when using direct-effect estimates, which should theoretically be immune to the effects of non-random mating and other confounding variables. Likewise, Keller et al. (doi.org/10.1371/journal.pgen.1003451) and Border et al. (doi.org/10.1101/2022.03.21.485215) used very different approaches to arrive at the same conclusion that ~50% of the nominal genetic correlation between IQ and height could be attributed to bivariate assortative mating rather than shared causal biological factors. Given that assortative mating on both IQ measures and height involves many other traits (not just two as assumed in such bivariate models), the true extent to which height / IQ correlations reflect causal factors is plausibly even lower than these estimates suggest. For these reasons, I do not entirely agree with the authors' review of previous findings in the introduction, where they write "recent studies have suggested that links between higher cognition and taller height can be largely explained by genetic factors", though it is certainly true that this claim has been made.

We have revised our introduction to better reflect the complexity of previous findings and to note that this claim.

Reviewer #2 (Public Review):

The authors use birth cohorts with extensive cognitive assessments and height measurements along with data on parental height and socioeconomic status. The authors estimate that the correlation between height and cognitive ability has approximately halved in the last 60 years.

Quantile regression results suggest that this is due to a stronger association between low cognitive ability and short stature in older cohorts, potentially due to environmental factors that cause both and that have been removed by improvements in the environment in the last 60 years.

While this is a plausible hypothesis, the evidence presented in the manuscript is unable to rule out alternative hypotheses, such as changes in assortative mating.

The results in the manuscript will be of interest to researchers investigating how genetics and environment lead to correlations between cognitive and physical/health traits, and to researchers interested in the relationship between social and health inequalities.

While my sense of the evidence presented is that there is fairly solid statistical evidence for a trend where the correlation between cognitive ability and height declines over time, there is no formal quantification of this trend nor measurement of the uncertainty in the trend.

We now include additional statistical tests to compare estimates in each cohort (Fig S6). We have opted to include this in supplemental material given the large number of tests included already.

Similarly, the quantile regression plots in Figure 2 appear to show a trend across the height deciles for the two oldest cohorts, but no quantification of how strong this is nor what uncertainty exists is calculated. Furthermore, if the apparent trend in the quantile regression plots is true, wouldn't this imply a non-linear association between height and cognitive ability for the older cohorts? Can this be seen in the scatterplots or in a non-linear regression?

We included 95% confidence intervals in our quantile regression analyses which provide an indication of uncertainty. We believe that given the substantial amount of analyses (across 4 historical cohorts and 4 cognition tests; 23 supplemental results) further work would be best placed to undertake additional statistical exploration of both quantile regression and non-linear associations. We would be happy to reconsider this if requested.

I think the authors could have done more with their data to investigate the contribution of assortative mating to the observed trend. Looking at Figure S4, it looks like the correlation between mother's education and father's height in the 2001 cohort is substantially lower than for previous cohorts. While cognitive ability may not be available for parents, one could look at, for example, father's education and mother's height across the cohorts and see if there is a downward trend in correlation.

We now include in Figure S5 cross-cohort investigation of the correlation between parental height and maternal education. We find that the correlation is similar across 1946c, 1958c, and 1970c, yet is weaker in 2001c (Fig S5). We comment on this in the paper (see revised discussion, explanation of findings section). Interpretation of these results is complicated by measurement error in parental education (typically reported for both parents by mothers). Further, interpretation may be further complicated by reductions in the socioeconomic patterning of height across time (see https://www.thelancet.com/journals/lanpub/article/PIIS2468-2667(18)30045-8/fulltext). Future would which focuses on assortative mating could investigate these issues.

Reviewer #3 (Public Review):

A difficulty with the paper is the different cognitive tests used in the different cohorts; the authors address this at some length in the discussion. However, I am afraid that this matter makes the results hard or impossible to interpret along the lines of their research question. One would need to know that, if these cognitive tests were administered in a single cohort at one time, they would have the same correlation with height.

Please see our responses to Reviewer 1 and our revised Discussion. We are reliant upon imperfect historical data to make inferences on long-run trends, in the absence of ideal data for this paper (eg, the same tests used in all cohorts born in 1946, 1958, 1970 and millennium; though even in this instance some changes would be required (eg, to the words chosen in verbal reasoning tasks; see Discussion, explanation of findings section)).

I judge that the main limitation of the method is the fact that different cognitive tests are used in the different cohorts. The tests in themselves are valid tests of cognitive functions. However, given that the focus of the study is on the change in correlations across time, then it is a worry that the tests are different; that is, the authors have the burden of proving to us that, if the environmental/social changes had NOT been operative across time, then the height-cognitive test correlations would be the same. What can the authors do to prove to us that if, say, all of these different-cohort verbal tests had been given to a single cohort on a single occasion, then they would have the same correlations with height? The same goes for the mathematics based tests. I note the tests' somewhat different distributions in Figure 1, but that is not the only thing that could lead to different correlations with, say, height. I am aware that all cognitive tests tend to correlate positively and that they all have loadings on general intelligence; however, different tests will not necessarily have the same correlations with outside variables (e.g. height). This will depend on things such as their content, their reliability/internal consistency etc.

In the Results the authors state: "Cognitive test scores were strongly-moderately positively correlated with each other, with the size of the correlation weakening across time." That's true, but perhaps, also a major concern for this study. One possible reason for the decline in verbal-maths test correlations across cohorts (old to recent) is that the nature of these tests has changed across time, either/both in terms of content (what capabilities are assessed) or something such as reliability/internal consistency/ceiling-or-floor effects (how well the capabilities are assessed). That is, given that the height-cognitive test correlations show a similarly declining pattern of correlations over cohorts, it could be that the tests' contents (of the different tests) is partly or wholly responsible. I raise that as a possibility only, and I appreciate that it might be correct, as the authors prefer, that there is an inherent lowering of intelligence-height correlations over time, but I do not think that one can rule out-with the present study's design-that it might have been due to the change in tests. For example, a reading-math correlation of 0.74 in 1946 lowered to a correlation of .32 in 2001, in the face of different tests. To show that this is not due to the different tests being used would require more information. If this is a true result, it is big news.

Please see our responses to Reviewer 1. This includes additional analysis and an expanded discussion of this possible cause of bias. We hope our manuscript now provides further evidence and discussion to inform the likelihood of this possibility.

I have a suggestion: if the authors wish to rule out the possibility that the lowering intelligence-height correlations across cohorts are due to different cognitive tests being used, they should take all the cognitive tests used here and apply them cross-sectionally to single-year-born samples (of 11- and 16-year olds) that have also been measured for height. If the cognitive tests all correlate at the same level with height within each of these two samples (they needn't do so across the 11- and 16-year olds), then one could proceed more safely with between-cohorts (1946, 1958, 1970, 2001) comparisons of the correlations.

We thank the reviewer for this suggestion. However we are unsure that we understood the suggested analysis or whether it was tractable given our data—the cohorts we used were born in either 1946, 1958, 1970, or around 2000. We do not have cross-sectional samples of 11 and 16 year olds at the same time.

Author Response:

Dear eLife Editorial Board, dear reviewers, dear readers,

We very much thank the eLife editors and reviewers for their overall very positive review and encouraging assessment of our manuscript, and for highlighting our study’s innovation and relevance for using genomic approaches for the conservation of biodiversity.

We very much thank the reviewers for pointing out parts of the manuscript that could be described more clearly or in more detail to make the study fully reproducible, and have therefore rewritten parts of the manuscript. We importantly follow reviewer 1’s specific recommendation to focus the main text on clearly understandable results, and therefore now only showcase the application of selective nanopore sequencing (aka adaptive sampling) to one soil sample, which we hope will make the flow of the manuscript easier to understand.

We further agree that parts of the study could have been conducted more extensively (e.g. include more samples and thereby showcase the broad applicability of the approach), which was unfortunately not feasible since I as the lead author left New Zealand to take up another position abroad. We are, however, following up on this work with another controlled large-scale study.  

We further agree that both qPCR and metabarcoding have their advantages and disadvantages. Metabarcoding approaches, however, importantly deliver more information about the biodiversity of a location than just the presence of a single species; this, in our case, includes other endangered species and evidence of kākāpō predators. We further show that the chosen marker gene region (12S rRNA) is species-specific enough to distinguish kākāpō from its two closest relatives. While qPCR has been shown to be more sensitive for some species, the difference is often minimal (see e.g., Harper et al., Ecol Evol. 2018 Jun; 8(12): 6330–6341), and for some species has been shown to be equally sensitive (Schneider et al., PLoS ONE 2016, 11, e0162493). qPCR approaches further require the careful design of species-specific primers, and herewith the access to samples and DNA of the target species and of closely related species – all of which are not necessarily at hand, especially not for conservationists who want to use these approaches regularly in the future, and in countries like New Zealand where genomic work with material from any “treasured” species has to be approved in a long and detailed process according to national regulations and the Nagoya Protocol. Given all these reasons, and the general good performance of our metabarcoding approach (also in detecting our species of interest), we do not see the necessity of applying a qPCR approach in this study.

To avoid any confusion, we now also describe the samplings sites in more detail and use their labels consistently throughout the manuscript. Briefly, the sites were always sampled directly at the site, and at 4m and 20m distance, and all in replicates, as described in detail in the manuscript. Specifically, the “abandoned nests” had only been abandoned ~30 days before sampling, as described in the Methods, and this is why kākāpō DNA is still present.

We further thank reviewer 2 for suggesting to discuss the impact of selective nanopore sequencing on pore efficiency in more depth, and added a respective sentence to the Discussion. We in general added more references and the broader scientific context to the Discussion.

Thank you again for this very helpful review of our work.

With best regards,<br /> Lara Urban

Author Response:

We are grateful for the detailed feedback provided by the two anonymous reviewers. We provide a point-by-point response to their reviews below:

Reviewer #1 (Public Review):

Medwig-Kinney et al perform the latest in a series of studies unraveling the genetic and physical mechanisms involved in the formation of C. elegans gonad. They have paid particular attention to how two different cell fates are specified, the ventral uterine (VU) or anchor cell (AC), and the behaviors of these two cell types. This cell fate choice is interesting because the anchor cell performs an invasive migration through a basement membrane. A process that is required for correct C. elegans gonad formation and that can act as a model for other invasive processes, such as malignant cancer progression. The authors have identified a range of genes that are involved in the AC/VC fate choice, and that imparts the AC cell with its ability to arrest the cell cycle and perform an invasive migration. Taking advantage of a range of genetic tools, the authors show that the transcription factor NHR-63 is strongly expressed in the AC cell. The authors also present evidence that NHR-63 is could function as a transcriptional repressor through interactions with a Groucho and also a TCF homolog, and they also suggest that these proteins are forming repressive condensates through phase separation.

The authors have produced an extensive dataset to support their two primary claims: that NHR-67 expression levels determine whether a cell is invasive or proliferative, and also that NHR-67 forms a repressive complex through interactions with other proteins. The authors should be commended for clearly and honestly conveying what is already known in this area of study with exhaustive references. But absent data unambiguously linking the formation and dissolution of NHR-67 condensates with the activation of downstream genes that NHR-67 is actively repressing, the novelty of these findings is limited.

Response 1.1: We thank the reviewer for recognizing the extensive dataset we provide in this manuscript in support of our claims that, (1) NHR-67 expression levels are important for distinguishing between AC and VU cell fates, and (2) NHR-67 interacts with transcriptional repressors in VU cells. We acknowledge that a complete mechanistic understanding of the functional significance of NHR-67 puncta is not possible without knowing direct targets of NHR-67 in the AC. Unfortunately, tools to identify transcriptional targets in individual cells or lineages in C. elegans do not exist, and generation of such tools would be beyond the scope of this work. This is evidenced by the fact that the first successful attempt to transcriptionally profile the AC was only posted as a preprint one month ago (Costa et al., doi: 10.1101/2022.12.28.522136). It is our hope that the findings we present here can be integrated with future AC- and VU-specific profiling efforts to provide a more complete picture of the functional significance of NHR-67 subnuclear organization.

Reviewer #2 (Public Review):

Medwig-Kinney et al. explore the role of the transcription factor NHR-67 in distinguishing between AC and VU cell identity in the C. elegans gonad. NHR-67 is expressed at high levels in AC cells where it induces G1 arrest, a requirement for the AC fate invasion program (Matus et al., 2015). NHR-67 is also present at low levels in the non-invasive VU cells and, in this new study, the authors suggest a role for this residual NHR-67 in maintaining VU cell fate. What this new role entails, however, is not clear. The model in Figure 7E shows NHR-67 switching from a transcriptional activator in ACs to a transcriptional repressor in VUs by virtue of recruiting translational repressors. In this model, NHR-67 actively suppresses AC differentiation in VU cells by binding to its normal targets and acting as a repressor rather than an activator. Elsewhere in the text, however, the authors suggest that NHR-67 is "post-translationally sequestered" (line 450) in nuclear condensates in VU cells. In that model, the low levels of NHR-67 in VU cells are not functional because inactivated by sequestration in condensates away from DNA. Neither model is fully supported by the data, which may explain why the authors seem to imply both possibilities. This uncertainty is confusing and prevents the paper from arriving at a compelling conclusion. What is the function, if any, of NHR-67 and so-called "repressive condensates" in VU cells?

Response 2.1: As the reviewer correctly notes, we present two possible models in this manuscript. The interaction between NHR-67 and the Groucho/TCF complex in the VU cells could (1) switch the role of NHR-67 from a transcriptional activator to a transcriptional repressor, or (2) sequester NHR-67 away from its transcriptional targets. Indeed, we cannot definitively exclude the possibility of either model. In our resubmission, we will attempt to make this more clear in the text and by presenting both possible models in the summary figure (Fig. 7E).

Below we list problems with data interpretation and key missing experiments:

1) The authors report that NHR-67 forms "repressive condensates" (aka. puncta) in the nuclei of VU cells and imply that these condensates prevent VU cells from becoming ACs. Fig. 3A, however, shows an example of an AC that also assemble NHR-67 puncta (these are less obvious simply due to the higher levels of NHR-67 in ACs). The presence of NHR-67 puncta in the AC seems to directly contradict the author's assumption that the puncta repress the AC fate program. Similarly, Figure 5-figure supplement 1A shows that UNC-37 and LSY-22 also form puncta in ACs. The authors need to analyze both AC and VU cells to demonstrate that NHR-67 puncta only form in VUs, as implied by their model.

Response 2.2: The puncta formed by NHR-67 in the AC are different in appearance than those observed in the VU cells and furthermore do not exhibit strong colocalization with that of UNC-37 or LSY-22. The Manders’ overlap coefficient between NHR-67 and UNC-37 is 0.181 in the AC, whereas it is 0.686 in the VU cells. Likewise, the Manders’ overlap coefficient between NHR-67 and LSY-22 is 0.189 in the AC compared to 0.741 in the VU cells. We speculate that the areas of NHR-67 subnuclear enrichment in the AC may represent concentration around transcriptional targets, but testing this would require knowledge of direct targets of NHR-67.

2) While a pool of NHR-67 localizes to "repressive condensates", it appears that a substantial portion of NHR-67 also exists diffusively in the nucleoplasm. This would appear to contradict a "sequestration model" since, for such a model to work, a majority of NHR-67 should be in puncta. What proportion of NHR-67 is in puncta? Is the concentration of NHR-67 in the nucleoplasm lower in VUs compared to ACs and does this depend on the puncta?

Response 2.3: The proportion of NHR-67 localizing to puncta versus the nucleoplasm is dynamic, as these puncta form and dissolve over the course of the cell cycle. However, we estimate that approximately 25-40% of NHR-67 protein resides in puncta based on segmentation and quantification of fluorescent intensity of sum Z-projections. We also measured NHR-67 concentration in the nucleoplasm of VU cells and found that it is only 28% of what is observed in ACs (n = 10). We disagree with the notion that the majority of NHR-67 protein should be located in puncta to support the sequestration model. As one example, previously published work examining phase separation of endogenous YAP shows that it is present in the nucleoplasm in addition to puncta (Cai et al., 2019, doi: ​​10.1038/s41556-019-0433-z). In our system, it is possible that the combination of transcriptional downregulation and partial sequestration away from DNA is sufficient to disrupt the normal activity of NHR-67.

3) The authors do not report whether NHR-67, UNC-37, LSY-22, or POP-1 localization to puncta is interdependent, as implied in the model shown in Fig. 7.

Response 2.4: It is difficult to test whether localization of these proteins to puncta is interdependent, as perturbation of UNC-37, LSY-22, and POP-1 result in ectopic ACs. Trying to determine if loss of puncta results in VU-to-AC transdifferentiation or vice versa becomes a chicken-egg argument. It is also possible that UNC-37 and LSY-22 are at least partially redundant in this context. We based our model, shown in Fig. 7E, on known or predicted protein-protein interactions, which we confirmed through yeast two-hybrid analyses (Fig. 7D; Fig. 7-figure supplement 1).

4) The evidence that the "repressor condensates" suppress AC fate in VUs is presented in Fig. 4D where the authors deplete the presumed repressor LSY-22. First, the authors do not examine whether NHR-67 forms puncta under these conditions. Second, the authors rely on a single marker (cdh-3p::mCherry::moeABD) to score AC fate: this marker shows weak expression in cells flanking one bright cell (presumably the AC) which the authors interpret as a VU AC transformation. The authors, however, do not identify the cells that express the marker by lineage analyses and dismiss the possibility that the marker-positive cells could arise from the division of an AC-committed cell. Finally, the authors did not test whether marker expression was dependent on NHR-67, as predicted by the model shown in Fig. 7.

Response 2.5: For the auxin-inducible degron experiments, strains contained labeled AID-tagged proteins, a labeled TIR1 transgene, and a labeled AC marker. Thus, we were limited by the number of fluorescent channels we could co-visualize and therefore could not also visualize NHR-67 (to assess for puncta formation) or another AC marker (such as LAG-2). We could have generated an AID-tagged LSY-22 strain without a fluorescent protein, but then we would not be able to quantify its depletion, which this reviewer points out is important to measure. We did visualize NHR-67::GFP expression following RNAi-induced  knockdown of POP-1 and observed consistent loss of puncta in ectopic ACs. However, this again becomes a chicken-egg argument as far as whether cell fate change or loss of puncta causes the other.

5) Interaction between NHR-67 and UNC-37 is shown using Y2H, but not verified in vivo. Furthermore, the functional significance of the NHR-67/UNC-37 interaction is not tested.

Response 2.6: We attempted to remove the intrinsically disordered region found at the C-terminus of the endogenous nhr-67 locus, using CRISPR/Cas9, as this would both confirm the NHR-67/UNC-37 interaction in vivo and allow us to determine the functional significance of this interaction. However, we were unable to recover a viable line after several attempts, suggesting that this region of the protein is vital.

6) Throughout the manuscript, the authors do not use lineage analysis to confirm fate transformation as is the standard in the field.

Response 2.7: The timing between AC/VU cell fate specification and AC invasion (the point at which we look for differentiated ACs) is approximately 10-12 hours at 25 °C. With our imaging setup, we are limited to approximately 3-4 hours of live-cell imaging. Therefore, lineage tracing was not feasible for our experiments. Instead, we relied on visualization of established markers of AC and VU cell fate to determine how ectopic ACs arose. In Fig. 6B,C we show that the expression of two AC markers (cdh-3 and lag-2) turn on while a VU marker (lag-1) get downregulated within the same cell. In our opinion, live-imaging experiments that show in real time changes in cell fate via reporters was the most definitive way to observe the phenotype.

There are 4 multipotential gonadal cells with the potential to differentiate into VUs or ACs. Which ones contribute to the extra ACs in the different genetic backgrounds examined was not determined, which complicates interpretation. The authors should consider and test the following possibilities: disruption of NHR-67 regulation causes 1) extra pluripotent cells to directly become ACs early in development, 2) causes VU cells to gradually trans-fate to an AC-like fate after VU fate specification (as implied by the authors), or 3) causes an AC to undergo extra cell division(s)?? In Fig. 1F, 5 cells are designated as ACs, which is one more that the 4 precursors depicted in Fig. 1A, implying that some of the "ACs" were derived from progenitors that divided.

Response 2.8: When trying to determine the source of the ectopic ACs, we considered the three possibilities noted by the reviewer: (1) misspecification of AC/VU precursors, (2) VU-to-AC transdifferentiation, or (3) proliferation of the AC. We eliminated option 3 as a possibility, as the ectopic ACs we observed here were invasive and all of our previous work has shown that proliferating ACs cannot invade and that cell cycle exit is necessary for invasion (Matus et al., 2015; Medwig-Kinney & Smith et al., 2020; Smith et al., 2022). Specifically, NHR-67 is upstream of the cyclin dependent kinase CKI-1 and we found that induced expression of NHR-67 resulted in slow growth and developmental arrest, likely because of inducing cell cycle exit. For our experiment using hsp::NHR-67, we induced heat shock after AC/VU specification. For POP-1 perturbation, we explicitly acknowledged that misspecification of the AC/VU precursors could also contribute to ectopic ACs (Fig. 6A; lines 364-402). We could not achieve robust protein depletion through delayed RNAi treatment, so instead we utilized timelapse microscopy and quantification of AC and VU cell markers (Fig. 6B,C; see response 2.7 above).

In conclusion, while the authors report on interesting observations, in particular the co-localization of NHR-67 with UNC-37/Groucho and POP-1 in nuclear puncta, the functional significance of these observations remains unclear. The authors have not demonstrated that the "repressive condensates" are functional and play a role in the suppression of AC fate in VU cells as claimed. The colocalization data suggest that NHR-67 interacts with repressors, but additional experiments are needed to demonstrate that these interactions are specific to VUs, impact VU fate, and sequester NHR-67 from its targets or transform NHR-67 into a transcriptional repressor.

Response 2.9: We agree that, at this time, we cannot pinpoint the precise mechanism through which NHR-67 puncta function (i.e., by sequestering NHR-67 from DNA or switching the role of NHR-67 from activating to repressing). However, identification of NHR-67 puncta and their colocalization with UNC-37, LSY-22, and POP-1 in VU cells allowed us to discover an undescribed role for the Groucho/TCF complex in maintaining VU cell fate. This, combined with our evidence demonstrating that NHR-67 transcriptional regulation is important for distinguishing between AC and VU cell fate, are the main contributions of our study.

Author Response:

Reviewer #1 (Public Review):

Vaparanta et al propose a new bioinformatic algorithm for pathway discovery from multi-omics data sources at one time point, and validate some of their algorithm's predictions using functional experiments. The authors should be commended for their detailed experimental work and comprehensive data collection around TYRO3 signaling in melanoma, which will likely be of value to that field. They also provide a mature software package that is well documented for implementing their bioinformatic methods. The reviewer's experience with the software was that it is computationally efficient/fast with well written code. The biological data (both multiomics and functional validation studies) will be of interest to melanoma research as well as scientists interested in TYRO3 signaling.

The authors wish to thank the Reviewer for the positive comments.

At this time, however, the bioinformatics algorithm proposed is of unclear utility to the broader multiomics community for the following reasons:

First, the algorithm itself has numerous hyperparameters, which can make it challenging to use and potentially highly sensitive to these user inputs. Just the regulatory complex inference step has 10 hyperparameters/settings required to be selected.

We have now reduced the number of parameters in the code by automating the choice for 2 of the parameters. The manuscript is now accompanied by a sensitivity analysis on all the key parameters in the code (new Supplementary Figures 5-11) and we have created a script to inform the choice of the key parameter S (suggest parameter S value for regulatory complex inference, new Supplementary Figure 10). We have additionally thoroughly revised the accompanying documentation in helping the user choose the right settings for their datasets (available in Mendeley data: https://data.mendeley.com/datasets/m3zggn6xx9/draft?a=71c29dac-714e-497e-8109-5c324ac43ac3).

Second, the algorithm is presented in an ad hoc manner without mathematical/statistical justifications of the many design decisions and steps in the analysis. For example, the authors write "The inference of regulatory complexes from the combined score follows the nearest neighbor principle, assuming that while a single high combined score can be random chance, the combination of combined scores between 3 cell signaling molecules would be predictive". It is mathematically unclear that this is true…

We have now tested the effect of the design decisions of the algorithm on the ability to discover known associations in omics datasets (new Supplementary Figure 4). Adhering to the design decision of the algorithm greatly improves the amount of known associations found in real omics data.

…and thus this reviewer attempted to test the algorithm using simulated uncorrelated Gaussian noise (see code/outputs at end of the review) in 10K genes and 10 samples using a best attempt at hyperparameter selection per the code comments and documentation. It appears that nearly 1/3 of all genes (i.e., 3205 of 10K) were erroneously grouped into complexes (assuming no mistakes in reviewer's usage of the code). In general, "unbiased" pathway analysis in multiomics that is not relying on prior knowledge will require solving the extraordinarily challenging task of estimating a very large covariance matrix from statistically small sample sizes. This puts the method at high risk of producing spurious results.

The Reviewer raises an important topic that should be considered in de novo analyses. However, the test dataset the reviewer used is not truly representative of the omics datasets that should be used to evaluate the performance of the algorithm. First, the algorithm should be only used with positive expression values due to the way the stoichiometry score is calculated. This is now more clearly indicated in the accompanying documentation (available in Mendeley data: https://data.mendeley.com/datasets/m3zggn6xx9/draft?a=71c29dac-714e-497e-8109-5c324ac43ac3). The Gaussian noise used by the reviewer does not represent any positive expression values of any omics datasets.

Second, the way the algorithm is constructed it will try to find an association to all features in the dataset if so instructed by the parameters. To this end, we have now added a new parameter (parameter S) into the algorithm to better control this setting. If correctly used in the test dataset used by the reviewer the algorithm now returns 0 complexes. The authors also wish to point out that they strongly believe that the amount of features in the dataset that have no real association with other features in real omics data is very low since most intracellular molecules have common upstream regulators. This poses a problem only if the dataset has a very limited amount of features.

Third, it seems to the authors that instead of testing the limits of the algorithm with totally randomized data, it would be more valuable to assess whether the algorithm can find true positives among randomized data. To this end we estimated the true positive and false positive rate with normally, negative binomial and beta distributed simulated data (new Supplementary Figures 7-9). Indeed, the algorithm can discover only the true positives among the false positives as long as the S parameter is not set too low. We now provide a separate script (suggest parameter S value for regulatory complex inference, new Supplementary Figure 10) that will help the user to choose the parameter S for their data so that the amount of false positives in the inference is minimized.

Fourth, the data produced by the standard normal distribution has a relatively low variance, already 68% values fall between -1 and 1 and 95% values between -2 and 2. If you simulate 10000 random rows with a sample size of 10 of such low variance parameter you are at high chance of creating highly correlating rows that actually would be representative of true positives in the dataset due to the generally high variation within omics data. Therefore, it is exceedingly hard to interpret whether the features were erroneously assigned into complexes or not because the chosen simulation method could have by chance created associations that represent true positives in the dataset.

Fifth, we also analyzed the standard normal distributed simulated data with WGCNA, which is still the most widely used module discovery method. WGCNA assigned almost all the features into modules. However, I think it is clear due to the wide us that the analysis still can offer valuable insight into biological processes. Therefore, the authors are not sure how concerned they should be about the results of this test.

Third, pathway analysis has long been a bioinformatic goal in the literature, with the authors citing a landmark paper for the WGCNA method from 2008. As such, there are numerous and long-standing discussions in the literature regarding challenges of pathway analysis (i.e., omics data often has dimensionality D far larger than sample size N, and correlation matrix estimation requires D^2 >> N parameters to be estimated) and its potential for spurious correlations. Some authors use sophisticated statistical tools (e.g., "Biological network inference using low order partial correlation" 2014, "Learning Large‐Scale Graphical Gaussian Models from Genomic Data" 2005, "Incorporating prior knowledge into Gene Network Study" 2013) to attempt to deal with this issue.

The authors agree that if by spurious the Reviewer means non causal indirect associations like in the paper by Zuo et al. (Zuo et al., 2014. Biological network inference using low order partial correlation. Methods 69:266-73. doi: 10.1016/j.ymeth.2014.06.010.), then, indeed, the algorithm has not been designed to find directed networks. Instead, the algorithm has been designed to find common upstream regulators.

Furthermore, the authors indicate that their approach is the first to attempt pathway analysis in multi-omics setting, stating "Integrative approaches combining more than one robust molecular association measure, however, have not been explored", but one can find attempts such as "An Integrative Transcriptomic and Metabolomic Study of Lung Function in Children With Asthma" to build on WGCNA for work in multiomics datasets.

Indeed, the Reviewer is correct that correlation networks and WGCNA have been previously used with multi-omics datasets. What the authors meant to convey is that these previous approaches rely only on one measure of molecular association, which in the case of correlation networks is correlation and WGCNA covariation, while our method is the first that combines two measures of molecular association, the correlation and stoichiometry score. We have now amended the sentence in the manuscript (lines 51-52).

The 2020 review paper "Metabolomics and Multi-Omics Integration: A Survey of Computational Methods and Resources" seems to identify multiple published methods dealing with pathway estimation in multiomics datasets. As the paper stands, this reviewer cannot adequately assess the impact of the proposed bioinformatic algorithm and its results against the existing body of literature for pathway inference.

We have now benchmarked our method against existing module discovery, network and multi-omics integration methods and provide evidence that our method outperforms these methods (new Figure 4).

Reviewer #2 (Public Review):

The authors describe a bioinformatic platform that allows for unbiased pathway analysis from multiomics data. The concept is based on correlation, stoichiometry scores and their combination to evidence interaction between two proteins, transcripts or phosphosites in an omic dataset. This platform was developed and validated on both previously published and in house omics data. I really appreciate that the paper is well written and clear, and I would like to acknowledge the amount of work generated to produce the in-house dataset.

The authors wish to thank the Reviewer for the encouraging words.

Author Response:

Reviewer #1 (Public Review):

The authors' conclusions presented herein are supported by a well-established mouse genetic conditional approach and an extensive array of phenotypic analyses.

Strengths:

1. The authors utilized well-described genetic tools, AdipoQCre, to target preadipocyte-like progenitor cell populations in bone marrow, as well as Csf1 floxed alleles. They further sifted through the cell population by showing that mature lipid-laden adipocytes express Csf1 at a much lower level, and determined that AdipoQCre-marked progenitor cell population presents a major cellular source of M-CSF,

2. The reanalysis of published scRNAseq datasets in Figure 1, as well as the following phenotypic analyses of the mutant mice are well-conducted. The analyses include a broad range of experiments both in vivo (3DmicroCT, histology, flow cytometry) and ex vivo (osteoclastogenesis assay in bone marrow cell culture). The confidence of the reported findings is high.

3. The data presented in this manuscript are of very high quality.

Weaknesses:

1. The role of AdipoQ-lineage progenitors as a source of M-CSF is overstated. The authors claim in many instances that "mature bone adipocytes do not express M-CSF", "These cells however do not produce Csf1", "...these peripheral AdipoQ+ cells nearly do not produce M-CSF". However, the authors' qPCR experiments only show four times differences in Csf1 expression. Therefore, the claim that AdipoQ-lineage progenitors are an exclusive source of M-CSF is not well substantiated. In line with this, some of the recent literature reporting conditional deletion of M-CSF in other bone cells (JBMR Plus. 4:e10080., Nature. 590:457-462) are not included.

We thank the reviewer for this important question. We have performed the below experiments to further clarify and support our conclusion:

1) We increased the replicates of each group cells in Fig. 3A (the old Fig. 1E) to five/group and based on reviewer 3’ recommendation on housekeeping gene usage, we found that the mRNA expression of Csf1 in bone marrow AdipoQ-lineage progenitor cells is 20-30 fold higher than those in mature adipocytes. This result has been updated in Fig. 3A.

2) We further performed immunofluorescence staining of M-CSF on bone slices, and found that the majority of bone marrow AdipoQ-expressing progenitor cells express M-CSF (Fig. 3B, 1865 cells out of 2001 cells counted, n=3 mice, 93.2%). In contrast, M-CSF expression was not detected in mature bone marrow adipocytes (Perilipin1+) (Fig. 3C, 0 cells out of 115 cells counted, n=3 mice, 0%), indicating that mature bone marrow adipocytes are unlikely a significant source of M-CSF.

3) We performed western blot to analyze M-CSF protein expression in peripheral adipose. As shown in Fig. 3D, the stromal vascular fraction (SVF) cells in adipose, which contain multiple cell populations including adipogenic progenitors, express M-CSF. On the contrary, M-CSF was nearly undetectable in the peripheral mature adipocytes isolated from adipose (Fig. 3D).

These data collectively support that mature adipocytes are not a significant source of M-CSF as evidenced by nearly undetectable M-CSF expression compared to the Adipoq-lineage progenitors. The results were described on pg. 5. However, the reviewer’s comment on ‘exclusive source’ is well taken as osteocytes and osteo lineage also express certain levels of M-CSF. We deleted ‘exclusive source’ in the manuscript, have added relevant literature and discussion in the Results and Discussion section on pp. 5 and 9.

2. Some of the phenotypic analyses are still incomplete. The authors did not report whether CHet (AdipoQCre Csf1(flox/+)) showed any bone phenotype. Further, the authors did not show that Csf1 mRNA or M-CSF protein is expressed in AdipoQ-lineage progenitors using histological methods. Current evidence is only based on scRNAseq and qPCR of isolated cells. Whether there was any change in circulating bone resorption markers in CKO mice was not shown. Cortical bone parameters were not included in the 3D-microCT analyses. These missing pieces of information would be important to correctly interpret the phenotypes.

The het mice (Csf1f/+;AdipoQ Cre) do not show abnormal bone phenotype, which is now shown in Fig. 4-figure supplement 4. We performed immunofluorescence staining of M-CSF on bone slices, and found that the majority of bone marrow AdipoQ-expressing progenitor cells express M-CSF (Fig. 3B, 1865 cells out of 2001 cells counted, n=3 mice, 93.2%). We tested serum TRAP level in mice, and found that the Csf1 deficiency in Csf1∆AdipoQ mice significantly decreased the TRAP level in serum, compared to that in the WT control mice (Fig. 5B). Csf1∆AdipoQ mice do not exhibit abnormal cortical bone phenotype. The cortical bone parameters are now included in Fig. 4G.

3. Which bone marrow cell population(s) are marked by AdipoQCre remain largely unclear. It is possible that AdipoQCre also marks at least part of MSPC-osteo cluster in addition to MSPC-adipo. Adipo-lineage progenitors may not stay entirely as adipoprogenitors and drift toward osteoblasts or their precursor cells.

We thank the reviewer for the insightful comment on this interesting mystery and complicated question, which is drawing more attention in the field.

In addition to Adipoq-lineage progenitors, Adipoq Cre also labels other clusters. However, the expression levels of Adipoq and frequency of Adipoq+ cells in other cell populations are relatively low. For example, the integrated scRNAseq dataset we analyzed shows that Adipoq is expressed at a low level (with scaled mean expression at 0.68, (27)) in a small proportion of MSPC-osteo cells (Fig. 1), and small amounts (31, 37) (about 4%) of osteoblasts in 8 or 12-week-old mice are Adipoq-lineage. A recent report found that in 24-week-old mice, about 15-40% of osteoblasts are marked with Adipoq Cre (37). This raises a few important possibilities that will need to be distinguished in future work. One possibility is that the Adipoq-lineage cells (adipo-CAR cells/MALPs) have minor or latent osteogenic potential that may become more evident under specific conditions, such as in older animals. However, balanced against this is the alternative that Adipoq-cre could primarily target a population of solely adipogenic adipo-CAR cells but that its specificity is imperfect, leading to progressive low levels of deletion in a separate population expressing very low levels of Adipoq, such as osteo-CAR cells. An additional possibility is that the Adipoq-lineage cells may themselves actually be further subdivided into multiple component cell types, including a major adipogenic and a separate minor osteogenic subpopulation. Ultimately, at the root of these issues is that Adipoq cre primarily defines one or possibly more lineages of cells rather than a cell type within those lineages. Therefore, application of further markers to fractionate the adipoq-lineage into its component cell types will be needed to resolve these possibilities, focusing on whether any potential osteogenic activity present can be fractionated away from the primary adipogenic activity present.

Of note, the Adipoq expression level and positive cell proportion are much higher in bone marrow Adipoq lineage progenitors than the levels seen in osteoblast lineage (Fig.1, Fig.2, (22, 27, 31)) or endothelial cells in bone marrow (38, 39). For example, the MSPC-Adipo cluster (Adipoq-lineage progenitors) has 6441 cells with the highest level (scaled mean expression level at 3.01 per (27) at Single Cell Portal) of Adipoq seen among bone marrow cells analyzed. In contrast, the MSPC-osteo cluster consists of 2247 cells with a very low Adipoq expression level (scaled mean expression level at 0.68 per (27) at Single Cell Portal). Taken together with both average expression level and cell numbers in each cluster, the relative overall contribution to Adipoq expression by MSPC-osteo vs the Adipoq-lineage progenitors is 7.8% ((2247 x 0.68)/(6441 x 3.01)). Therefore, the expression of Adipoq in MSPC-osteo cluster is marginal compared to that in the Adipoq-lineage progenitors. These data make Adipoq as an important marker to identify bone marrow Adipoq lineage progenitors. Overall, our work not only validates prior research identifying adipoq-lineage cells, identified as MALPs (22, 31), as a key osteoclast regulatory population, but also further extends the scope of their functions to encompass M-CSF production and regulation of macrophages.

These points have been added to the Discussion sections on pp. 9-10.

4. The OVX data in Figure 5 are not very well explained. The data do not seem to support the authors' conclusion that M-CSF deficiency in AdipoQ-lineage progenitors alleviates estrogen-deficiency induced osteoporosis. The CKO mice lose bone mass almost to the same extent as WT mice upon OVX.

To address the reviewer’s question, we calculated the changes of the uCT parameter values between Sham and OVX groups in the WT control and Csf1∆AdipoQ mice. Significant changes were identified between the control and Csf1∆Adipoq mice in several μCT parameters. For example, a decrease in trabecular BV/TV after OVX: 35.1% in the control vs 20.9% in Csf1∆Adipoq mice; a decrease in Tb. N after OVX:11.34% in the control vs 7.97% in Csf1∆Adipoq mice; a decrease in Conn-Dens after OVX: 39.7% in the control vs 14.56% in Csf1∆Adipoq mice; an increase in Tb. Sp after OVX: 12.51% in the control vs 1.97% in Csf1∆Adipoq mice. These results support our conclusion that M-CSF deficiency in AdipoQlineage progenitors alleviates estrogen-deficiency induced osteoporosis. These value changes have been included in Fig. 7C and discussed on pg. 7.

Reviewer #3 (Public Review):

Macrophage colony-stimulating factor (M-CSF) plays key roles in the differentiation of myeloid-lineage cells, including monocytes, macrophages and osteoclasts. The latter mediate bone resorption, which is important for physiological bone remodelling but, unrestrained, contributes to bone loss in conditions such as in post-menopausal osteoporosis. M-CSF production within the bone marrow is implicated in the maintenance of myeloid and skeletal homeostasis, but the cellular source of bone marrow M-CSF has remained elusive. In this study, Inoue et al address this issue through advanced transcriptomic and gene targeting approaches. They conclude that a population of Adipoq-expressing progenitors within the bone marrow, designated "AdipoQ-lineage progenitors", is the key cellular source of M-CSF. Consistent with this, they find that transgenic deletion of M-CSF from these cells disrupts macrophage and osteoclast development, leading to osteopetrosis and possibly preventing bone loss following ovariectomy. However, they have not adequately addressed the possibility that M-CSF production from other cell types, particularly adipocytes in peripheral adipose tissues, may also be influencing these phenotypes. Specific strengths and weaknesses are as follows:

Strengths:

1. The manuscript is written in a clear, succinct manner and the data are generally nicely presented. It is therefore a pleasure to read.

2. The analysis of single-cell transcriptomic data is clear and convincing, and the skeletal phenotyping has been done to a high standard.

Weaknesses:

1. The authors underplay the potential contribution of M-CSF production from other cell types, particularly from adipocytes in peripheral adipose tissues. They show that M-CSF expression from these cells is lower than from the bone marrow progenitors that they focus on; however, based on this they allude to "no expression" of M-CSF from these other adipocytes. This overlooks the findings of other studies showing that peripheral adipocytes produce M-CSF and that this has biological functions. Whether their knockout model alters M-CSF expression in peripheral adipose tissue, whether for whole tissue or for isolated adipocytes, has not been tested.

We performed western blot to analyze M-CSF protein expression in peripheral adipose. As shown in Fig. 3D, the stromal vascular fraction (SVF) cells in adipose, which contain multiple cell populations including adipogenic progenitors, express M-CSF. On the contrary, M-CSF was nearly undetectable in the peripheral mature adipocytes isolated from adipose (Fig. 3D). These data collectively support that mature adipocytes are not a significant source of M-CSF as evidenced by nearly undetectable M-CSF expression compared to the Adipoq-lineage progenitors. However, we understand that current techniques may have limitation in identification of trace amount of M-CSF. We thus deleted descriptions such as ‘exclusive’ or ‘do not produce/express…’ in the revised manuscript.

2. The decreases in M-CSF have been assessed at the transcript level, but not for M-CSF protein. Whether their knockout model

We performed immunofluorescence staining of M-CSF on bone slices, and found a drastic decrease in M-CSF protein in bone marrow AdipoQ+ cells in Csf1∆AdipoQ mice compared to the WT control mice. The results are shown in Fig. 4B, and Fig. 3B-D.

3. It is also unclear if the Adipoq-lineage progenitors consist exclusively of adipogenic cells, or if osteogenic progenitors are also part of this population.

We thank the reviewer for the insightful comment on this interesting mystery and complicated question, which is drawing more attention in the field.

In addition to Adipoq-lineage progenitors, Adipoq Cre also labels other clusters. However, the expression levels of Adipoq and frequency of Adipoq+ cells in other cell populations are relatively low. For example, the integrated scRNAseq dataset we analyzed shows that Adipoq is expressed at a low level (with scaled mean expression at 0.68, (27)) in a small proportion of MSPC-osteo cells (Fig. 1), and small amounts (31, 37) (about 4%) of osteoblasts in 8 or 12-week-old mice are Adipoq-lineage. A recent report found that in 24-week-old mice, about 15-40% of osteoblasts are marked with Adipoq Cre (37). This raises a few important possibilities that will need to be distinguished in future work. One possibility is that the Adipoq-lineage cells (adipo-CAR cells/MALPs) have minor or latent osteogenic potential that may become more evident under specific conditions, such as in older animals. However, balanced against this is the alternative that Adipoq-cre could primarily target a population of solely adipogenic adipo-CAR cells but that its specificity is imperfect, leading to progressive low levels of deletion in a separate population expressing very low levels of Adipoq, such as osteo-CAR cells. An additional possibility is that the Adipoq-lineage cells may themselves actually be further subdivided into multiple component cell types, including a major adipogenic and a separate minor osteogenic subpopulation. Ultimately, at the root of these issues is that Adipoq cre primarily defines one or possibly more lineages of cells rather than a cell type within those lineages. Therefore, application of further markers to fractionate the adipoq-lineage into its component cell types will be needed to resolve these possibilities, focusing on whether any potential osteogenic activity present can be fractionated away from the primary adipogenic activity present.

Of note, the Adipoq expression level and positive cell proportion are much higher in bone marrow Adipoq lineage progenitors than the levels seen in osteoblast lineage (Fig.1, Fig.2, (22, 27, 31)) or endothelial cells in bone marrow (38, 39). For example, the MSPC-Adipo cluster (Adipoq-lineage progenitors) has 6441 cells with the highest level (scaled mean expression level at 3.01 per (27) at Single Cell Portal) of Adipoq seen among bone marrow cells analyzed. In contrast, the MSPC-osteo cluster consists of 2247 cells with a very low Adipoq expression level (scaled mean expression level at 0.68 per (27) at Single Cell Portal). Taken together with both average expression level and cell numbers in each cluster, the relative overall contribution to Adipoq expression by MSPC-osteo vs the Adipoq-lineage progenitors is 7.8% ((2247 x 0.68)/(6441 x 3.01)). Therefore, the expression of Adipoq in MSPC-osteo cluster is marginal compared to that in the Adipoq-lineage progenitors. These data make Adipoq as an important marker to identify bone marrow Adipoq lineage progenitors. Overall, our work not only validates prior research identifying adipoq-lineage cells, identified as MALPs (22, 31), as a key osteoclast regulatory population, but also further extends the scope of their functions to encompass M-CSF production and regulation of macrophages.

These points have been added to the Discussion section on pp. 9-10.

If these weaknesses are addressed then this work has potential to yield firm conclusions and new insights into the regulation of myeloid and skeletal homeostasis, both in normal physiology and in clinically relevant conditions.

Yes, we have addressed the above 3 major questions.

Author Response

Reviewer #1 (Public Review):

The current study proposed a drug discovery pipeline to accelerate the process of identifying drug candidates for LCA10 patients using cells from mouse retinal organoid for initial screening, human patient iPSC-derived retinal organoid for further testing, and then mouse mutants for in vivo validation. Reserpine was identified as the top candidate, possibly through modulating proteostasis and autophagy to promote cilium assembly. The study was with high translational value. However, the rationale using dissociated cells from mouse retinal organoid for initial drug screening needs to be justified. In addition, the consistency of phenotypic characteristics in human patient iPSC-derived retinal organoid needs to be reported. It was unclear if the rescued phenotypic changes were from the drug effects or a result of phenotypic variations in organoids.

We thank the reviewer for the comments and suggestions. Please see the response provided in the “Essential Revisions” earlier. Briefly, the use of single-cell cultures for screening is to compensate for the variations of the Nrl-GFP signal in rd16 organoids so that each compound was present to homogenous cells. In addition, we performed a large-scale screening with 11 concentrations and 2 duplicates of over 6000 compounds. It was thus not feasible to manually perform the screening. We used a semi-automatic electronic dispenser to set up the screens in 1536-well plates and a liquid handling system to add the compounds. Intact mouse retinal organoids are too big to be dispensed and would be damaged during the process. They are also too big to fit into one well of a 1536-well plate or even in a 384-well plate. Therefore, single-cell cultures outweigh intact organoids in this application. We understand the potential pitfalls and thus the positive hits were verified in intact organoids in the secondary assays.

We have now tested reserpine on retinal organoids derived from 2 clones of each (a total of 4) of LCA1 and LCA2 patients. As suggested by the reviewers, we quantified the fluorescence intensity of rod marker rhodopsin staining in multiple sections of at least two batches of differentiation (Figure 3C and Figure 3—figure supplement 2). Although showing variability as predicted, reserpine treatment significantly increased the fluorescence intensity of rhodopsin in retinal organoids differentiated from multiple lines (Figure 3C), further validating the rescue effect of reserpine.

Reviewer #2 (Public Review):

In this manuscript, a drug discovery pipeline was developed using a human iPSC derived organoid-based high-throughput screening platform to be used to identify drug candidates for maintaining photoreceptor survival in LCA10 retinopathies. Reserpine proved effective in patient organoids and in mutant mouse retina in vivo to improve photoreceptor survival and outer segment structure. Protein homeostasis was restored after reserpine treatment by increasing p62 levels, decreasing the 20S proteasome, and increasing proteasome activity. The manuscript is clearly written, contains a large amount of valuable and high-quality data and demonstrates that rebalancing proteostasis can stabilize photoreceptor overall homeostasis in the presence of a mutation that causes retinal degeneration.

The manuscript may lack functional in vivo data on the treatment by reserpine in RD16 mice such as ERG measurements or other functional tests (the authors also refer to it as future direction). Nevertheless, in my view, the study provides a solid and convincing set of data and substantially advances our understanding on the neuroprotective effects of reserpine beyond the scope of the retina and therefore can be expected to have widespread influence on a readership interested in the principles of neuroprotection rebalancing proteostasis.

We sincerely thank the reviewer for the positive comments and suggestions. This study has taken many years to materialize. We agree and have now performed full-field electroretinogram (ERG) of untreated and reserpine-treated rd16 retina (as stated in response to an earlier comment). Scotopic a-wave was only marginally increased, yet scotopic b-wave displayed a significant higher amplitude, suggesting improved rod photoreceptor function (Figure 6D).

Reviewer #3 (Public Review):

Chen et al. perform an innovative screen using retinal organoids derived from rd16 mice to identify small molecules to treat CEP290 hypomorphic mutations linked to ciliopathies such as LCA. The authors identify reserpine which promotes photoreceptor development and viability in retinal organoids derived from LCA patient iPSCs and rd16 mouse retinas. The authors finally propose a mechanistic model where reserpine restores proteostasis thereby improving ciliogenesis.

The authors present a highly effective drug screen that utilizes the benefits of retinal organoids while also accounting for the inherent variability of retinal organoids by performing a screen on 2D cultures derived from the organoids. This is an innovated approach to using retinal organoids in drug screens and is of interest to the greater community. The success of the screen is reflected in the effectiveness of reserpine in the in vivo rd16 mouse retinal model where it promotes photoreceptor survival. However there are multiple issues with the LCA patient organoid screen that must be resolved.

We are grateful to the reviewer for generous comments. We have incorporated the suggestions and performed additional work to resolve the issues, as mentioned earlier in this response as well as below.

The patient derived iPSC lines are not controlled sufficiently enough to make conclusions stated in the manuscript. The authors rely on single iPSC clones from disease patients to perform experiments, and it is not clear whether karyotyping to validate normal chromosomal integrity was performed. In the case of the RNAseq experiment one patient clone does not show any differences calling into question the findings from the other clone. Patient derived iPSC studies would benefit from the use of multiple independently derived iPSC clones per patient, or rescuing the LCA10 mutation using CRISPR editing to validate the correlation of the mutation with the differences observed.

This study could be strengthened by parallel RNAseq studies is the rd16 mouse retina and patient iPSC retinal organoids.

Thanks for the suggestions. As mentioned earlier in “Essential Revisions” and response to other reviewers, we have performed additional experiments using multiple iPSC clones and from three patients (2 each from LCA1 and LCA2). These iPSC lines have been characterized previously (Shimada et al. 2017). We have now provided more details on iPSC derivation, iPSC maintenance, and differentiation. Karyotypes of all human and mouse iPSC lines were provided in Figure 1—figure supplement 1. Retinal organoids were generated using iPSC lines within 10 passages of test cells.

The purpose of the RNA-seq data is to provide primers on the signaling pathways modulated by reserpine treatment. The rescue effect of reserpine suggests that these pathways might be implicated in disease pathogenesis. Based on our RNA-seq data, we have validated the dysregulation of proteostasis pathway in patient-derived retinal organoids and in vivo rd16 retina. Further investigations are needed to validate other pathways but are beyond the scope of this manuscript. Although RNA-seq studies have advantages, more detailed molecular and functional assays are needed to validate the findings of RNA-seq studies and therefore we argue that performing additional RNA-seq on different clones or cell lines or mouse retina would provide more solid information.

According to our quantification of rhodopsin staining intensity (Figure 3C and Figure 3—figure supplement 2), LCA1 organoids are more responsive to reserpine compared to LCA2, which is not surprising based on the variations of patient responsiveness to drug treatments in previous clinical studies. We note that reserpine is not a transcription factor, thus the differentially expressed genes in reserpine treatments are secondary effects and the change of gene profiles upon reserpine treatment could vary in time and intensity, which could explain the few differentially expressed genes observed in LCA-2. Nevertheless, the action mechanisms of reserpine we found based on LCA1 could be validated on LCA2 (Figure 5—figure supplement 3), further strengthening our findings.

The reason why we performed RNA-seq on treated organoids but not treated mice was to identify the signaling pathways modulated by reserpine in a well-controlled manner in order to catch the small changes. Compared to reserpine treatment on organoid cultures, in which the organoids have stable and constant contact with reserpine, intravitreal injection of reserpine into P7 mice is technically challenging and leads to substantial variations. In this case, some small changes might be missed and masked by the variations.

Author Response

Reviewer #2 (Public Review):

The authors sought to be able to examine what cellular mechanisms underlie increases in mature blood cell production upon immune challenge. To this end they devised a new in vitro organ culturing system for the lymph gland, the main hematopoietic organ of the fruit fly Drosophila melanogaster; the fly serves as an excellent model for studying fundamental questions in immunology, as it allows live imaging combined with genetic manipulation, and the molecular pathways and cellular functions of its innate immune system are highly conserved with vertebrates.

The authors provide compelling evidence that the cultured lymph gland shows a similar time scale, dynamics, and capacity for cell division as was observed in vivo, and does not undergo undue oxidative stress in their optimized culture conditions. This technique will prove extremely useful to the large community studying the fly lymph gland, and potentially vertebrate immunologists seeking to expand the models they utilize.

In these cultured glands, the authors identify progenitors undergoing symmetric cell divisions and provide some evidence that is consistent with, but does not prove, that these two cells maintain their proliferative capacity. They detect equivalent levels in the two equally sized daughter cells of dome-Meso-GFP, a marker for JAK-STAT activity; however, this could be due to an equal inheritance of the protein from the mother, not an equivalent maintenance of a proliferative capacity.

This is an interesting question. A close look at the our movie (Video 4) of the dome-Meso-GFP marker shows the following sequence of events: the marker is nuclear, the mother cell divides and the nuclear envelope breaks down, cell division is completed, the dome-Meso-GFP re-accumulates at the nucleus of the daughter cells. This sequence of events implies that JAK-STAT is still active in the daughter cells. But as the reviewer points out there is a possibility of inheritance of the signal from the mother. If one of the cells were to differentiate, we would expect two things to occur, a differentiation marker to turn on in one of the daughter cells, and likely a slow decrease in the signal level of dome-Meso-GFP in one of the cells over time. We failed to mention that we accounted for both of those possibilities in our experiments such as the one shown in Video 5. We did this by first, including the eater-dsRed in the genetic background (see Figure 2 figure legend) in which these experiments were undertaken, if differentiation took place dsRed level would go up, an occurrence which we did not observe. Second, long-term tracking of dome-Meso-GFP levels for extended periods of time after completion of cell division did not show divergence or significant decrease of protein levels in the two daughter cells (Figure 2 - figure supplement 2). In any case, to directly make readers aware of this important caveat raised by the reviewer concern we added to the Results section in line 225-230 an explanation mentioning the possibility of inheritance of the marker and why we did not think this was the case.

The authors develop a technique to conduct tracking of progenitor cell size over time in the cultured lymph glands and identify a switch increase in growth after division, as well as two orientations of the divisions, with the main one occurring 90% of the time.

They show that bacterial infection results in a significant decrease in the division of Blood progenitors and the elimination of the minor orientation of division, but no obvious change in the rate of division.

By imaging two markers, Dome-GFP for the progenitor state and Eater dsRed for the differentiated one, they examine the trajectories by which differentiation occurs in the wild-type lymph gland. They describe two main categories of fate transitions. In one that they call linear, the blood cells express high levels of the differentiation marker along with the progenitor marker before turning off the progenitor marker. The dynamics of how these progenitor cells get to the state of expressing both the differentiation and progenitor marker at high levels is not described. In the other, which they call sigmoidal, cells express only high levels of the progenitor marker, and the differentiation marker increases after or as the progenitor marker decreases. The authors show that upon infection there is a large increase in the amount of the linear type of differentiation. But how this change in the type of differentiation upon infection explains the increased amount of differentiation is not clear.

A potential explanation comes from an aspect of their data that the authors don't comment upon. In their live analysis of lymph glands at a distinct time point in the uninfected state (Fig 7M-N), 95% of the cells they analyze traversing the sigmoidal path are in the intermediate step. This would predict that the cells on this path spend a much longer time stuck in this intermediate state before traversing to the final differentiated one, or that only a small fraction of the cells that become sigmoidal intermediate cells progress onwards to full differentiation. But this does not match the trajectories observed in the real-time analysis for uninfected cultured lymph glands (Fig 7A'-D') marker. Perhaps their algorithm discarded traces from the live imaging in which the differentiation marker did not come up quickly and was thus not analyzed in the trajectories.

If my interpretation of the single time point analysis is true, this would argue that the linear path is actually much faster/more fruitful than the sigmoidal one and this would explain why a higher level of total progenitor differentiation infection is the result of infection-inducing more differentiation by the linear path. Otherwise, I don't understand how their data explains that observation.

We understand the reviewer concern here and would like to state categorically that we did not use an algorithm to “discard” traces. As the reviewer outlines there is a large concentration of cells in the Dome-Meso-GFP (low expressing), eater-dsRed (low expressing) state. This is an intermediate state for the sigmoid differentiation trajectory. The reviewer suggests two scenarios to explain this. The first scenario is that this is the slowest (and thus rate limiting) step in the sigmoid differentiation trajectory. But, also as the reviewer notes, our tracking of individual cell trajectories doesn't show that cells spend a lot of time in this state. This leaves the second scenario the reviewer outlines, that only a small fraction of the cells that are in the Dome-Meso-GFP (low expressing), eater-dsRed (low expressing) state go on to differentiate (at least in the larval stage). We favor this model, because it is consistent with our observations, mainly that manipulating the sigmoid pathway is not a good way to induce the production of mature blood cells following infection, compared to manipulating the linear pathway. As the reviewer correctly points out the linear pathway provides a powerful way to change the rate of production of mature blood cells, with a few hours of infection the number of cells that are found in the intermediate state for this trajectory (Dome-Meso-GFP (high expressing), eater-DSred (high expressing)) increases 5-6 times. We now mention this specifically in the Discussion in line 532-539.

Author Response

Reviewer #1 (Public Review):

Single-cell sequencing technologies such as 10x, in conjunction with DNA barcoded multimeric peptide MHCs (pMHCs) has enabled high throughput paring of T cell receptor transcript with antigen specificity. However, the data generated through this method often suffers from the relatively high background due to ambient DNA barcodes and TCR transcripts leaking into "productive" GEMs that contain a 10X bead and a T cell decorated with antigen-specific barcoded proteins. Such contaminations can affect data analysis and interpretation and have the potential to lead to spurious results such as an incorrect assessment of antigen-TCR pairs or TCR cross-reactivity. To address this problem, Povelsen and colleagues have described a data-driven algorithm called "Accurate T cell Receptor Antigen Pairing through data-driven filtering of sequencing information from single-cells" (ATRAP) that supplies a set of filtering approaches that significantly reduces background and allows for accurate pairing of T cell clonotypes with cognate pMHC antigens.

This paper is rigorously conducted and will be useful for the field - there are some areas where further clarifications and comparisons will benefit the reader.

Strengths:

1) Povelsen and colleagues have systematically evaluated the extent to which parameters in the experimental metadata can be used to assess the likelihood of a GEM to correctly identify the antigen specificity of the associated T cell clonotype.

2) Povelsen and colleagues have provided elegant data-driven scoring metrics in the form of concordance score, specificity score, and an optimal ratio of pMHC UMI counts between different pMHCs on a GEM, which allows for easy identification of poor quality data points.

3) Based on the experimental goals, ATRAP allows for customizable filters that could achieve appropriate data quality while maximizing data retention.

Weakness:

1) The authors mention that 100% of the 6,073 "productive" GEMs contained more than one sample hashing barcode, and 65% contained pMHC multiplets. While the rest of the paper elaborates on the steps taken to deal with pMHC multiplets issue, not much is said about the extent of multiplet hashing issue and how was it dealt with when assigning cells to individual donors. How is this accounted for? Even a brief explanation would be beneficial.

We agree that the issue of multiplet hashing was only very briefly discussed in the manuscript. The reason for this is that although cell hashing multiplets exist for every GEM, it is generally a much simpler issue to solve than pMHC multiplets, because one hashing entry most often has much higher counts compared to the others (see supplementary fig. 3). Moreover, in the experimental design, only one hashing antibody is added to each sample. It is therefore given that only a single hashing signal should be associated with each GEM, i.e. this does not mirror the complex nature of the pMHC data, where cross-reactivity could result in more than one pMHC being a true binder to a given TCR. Given the simplicity associated with the hashing signal, we have here opted for utilizing an existing tool to annotate cell hashing. We have elaborated the description of this in the revised manuscript (line 384).

2) It would be helpful for the authors to describe how experimental factors such as the quality of the input MHC protein may affect the outputted data (where different proteins may have different degrees of non-specific binding), and to what degree the ATRAP approach is robust to these changes. As an example, the authors mention that RVR/ A03 was present at high UMI counts across all GEMs and RPH/ B07 was consistently detected at low levels. Are these observations the property of the pMHCs or the barcoded dextran reagent? Furthermore, are there differences in the frequency of each of these multimers in the starting staining library which manifests in consistent high vs low read counts for the pMHC barcodes?

We understand the reviewers' concern. We have extensive experience from staining with large libraries of different pMHCs in a bulk setting (Bentzen et al 2016), where it is part of the routine analyses to include an aliquot of the barcoded pMHC library taken prior to incubation with cells (input sample). From this data, we know that even if pMHCs are present in uneven amounts prior to cell incubation, this unevenness is not translated to the final output. I.e. if a given barcode (associated with a specific pMHC) is present at levels up to 2x higher than the remaining barcodes, this does not result in that barcode also being enriched after cell incubation if T cells do not recognize the corresponding pMHC. And vice versa, a barcode present at lower levels in the input can still be enriched after incubation with cells.. From the same type of data, we also have experience with differences in the background associated with different MHC/HLA molecules, i.e. a general higher level of background related to a certain MHC irrespectively of the peptide bound in this. We agree that this potentially could be a confounding factor influencing our results (as it will influence any other results related to the potential different background signal associated with different MHC/HLA molecules). We are currently in other studies investigating in a broader sense whether these differences reflect a biological inherent MHC association or are experimental artifacts. In the current work, we have opted for not defining pHLA specific UMI count threshold to ensure that any biological relevance remains unmasked, but still ensure that we can at the same time filter the data to identify the most likely true pMHC specific interaction.

3) It would be helpful for the authors to further explain how ATRAP handles TCRs that may be present in only one (or a small number) of GEMs, as seen in Figure 7b, and potentially for the large number of relatively small clonotypes observed for the RVR/A03 peptide in Figure 6 (it is difficult to know if the long tail of clonotypes for RVR is in the range of 1 or 10 GEMs based on the scale bar). Beyond that, is there any effect on expected (or observed) clonal expansion on these data analyses, for example, if samples are previously expanded with a peptide antigen ex vivo or not?

ITRAP removes any GEM that does not meet the criteria of the selected filters. Small clones are only removed if all GEMs in a clone fail to meet the selected filter criteria. As ITRAP is based on combinations of filters which are user-defined, one can choose to filter away singlet specificities, i.e. a TCR-pMHC pair only observed in a single GEM. However, this might not be relevant in all cases. We believe that it is a strength of the method that it is flexible and adaptable to the needs of individual users. This also allows for additional filters to be imposed by the user, if one for instance wishes to remove clones of fewer than a certain number of GEMs. With respect to figure 6, we agree that it was difficult to estimate the number of clonotypes within a given peptide plateau, and have updated the figure to include a clonotype count in the x-axis. In relation to the effect on clonotype expansion, we would first like to refer to figure 7. Here, we in figure a) and b) display the observed T cell frequencies towards the individual pMHCs as obtained by the two different experiment approaches a) conventional fluorescent multimer staining, and b) GEMs counts as obtained using the single-cell pipeline described here. This analysis demonstrates a very high concordance between the two approaches of the T cell populations, reflected by the vast majority of the responses detected by fluorescent multimer staining also being captured in the single-cell screening, (recall of 0.95). This result suggests that sensitivity of the SC approach, in the context of the current pMHC epitope set, is comparable to that of conventional fluorescent multimer staining. With regard to clonotype expansion, we would next like to refer back to figure 3. Even though we have not expanded the clones in vitro, this figure shows how the specificity of a TCR clone can be more confidently assigned when there are more GEMs mapped to a given TCR clone. Hence, to identify a single TCR-pMHC match, it could in many cases be valuable to expand a given clone prior to the experiments. However, since the 10x pipeline can only include a limited number of cells, we argue that it is valuable to identify pMHC TCR pairs on unexpanded/unmanipulated material to include as many different pairs as possible.

4) The authors mention a second method, ICON, for conducting these types of analyses, and that the approach leads to significantly more data loss. However, given there could be differences in dataset quality themselves, and given the dataset, ICON is publicly available, it would be helpful for a more explicit cross-comparison to be conducted and presented as a figure in the paper.

We have conducted such a comparative analysis in a separate manuscript (available at BioRxiv doi.org/10.1101/2023.02.01.526310). The overall conclusion is that both methods allow for effective denoising of the provided data, with an overall advantage in favor of iTRAP. We have extended the discussion in the current manuscript with a brief summary of the main findings from this study.

Reviewer #2 (Public Review):

The study by Povlsen, Bentzen et al. describes certain computational pipelines authors used to analyze the results from a single-cell sequencing experiment of pMHC-multimer stained T cells. DNA-barcoded pMHC multimers and single-cell sequencing technologies provide an opportunity for the high-throughput discovery of novel antigen-specific TCRs and profiling antigen-specific T-cell responses to multiple epitopes in parallel from a single sample. The authors' goal was to develop a computational pipeline that eliminates potential noise in TCR-pMHC assignments from single-cell sequencing data. With several reasonable biological assumptions about underlying data (absence of cross-reactivity between these epitopes, same specificity for different T-cells within a clonotype, more similarity for TCRs recognizing the same epitope, HLA-restriction of T cell response) authors identify the optimal strategy and thresholds to filter out artifacts from their data.

It is not clear If the identified thresholds are optimal for other experiments of this kind, and how the violation of authors' assumptions (for example, inclusion of several highly similar pMHC-multimers recognized by the same clone of cross-reactive T cells) will impact the algorithm performance and threshold selection by the algorithm. The authors do not discuss several recent papers featuring highly similar experimental techniques and the same data filtering challenges:

https://www.science.org/doi/10.1126/sciimmunol.abk3070

https://www.nature.com/articles/s41590-022-01184-4

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9184244/

As described above, we have investigated the use of ITRAP on the large data set provided by 10X Genomics, and here further compared the result to that obtained by ICON in an independent publication [BioRxiv doi.org/10.1101/2023.02.01.526310]. We have included a brief summary of the findings in study in the current manuscript. The overall results and conclusions between the two studies align very well. UMI count filtering and donor-HLA matching are in both cases driving the strongly denoising signal. However, when it comes to the identified UMI thresholds, they were found to differ between the two data sets. As stated above, this we however believe to be a strength of the ITRAP framework, since it demonstrates that the tools can be robustly applied to data originating from very different technical and/or biological settings.

We acknowledge that ITRAP is highly dependent on the data containing a set of “large” clonotypes for which a single pMHC target can be assigned using the statistical approach outlined in the manuscript. This since the UMI filtering thresholds are defined based on these clonotypes and associated peptide annotations. However, other than this, the method does not exclude identification of cross-reactive TCR (in contrast to for instance ICON). We have expanded the discussion to make this point more clear.

When it comes to the papers mentioned by the reviewer, these are clearly of high interest to us, and we are currently in the process of analyzing these data using the ITRAP framework. We however believe these analyses are beyond the score of the current publication, in particular since we have conducted the parallel benchmark study on the 10X Genomics data mentioned above.

Unfortunately, I was unable to validate the method on other datasets or apply other approaches to the authors' data because neither code nor raw or processed data were available at the moment of the review.

All data sets and code has been made publicly available at https://services.healthtech.dtu.dk/suppl/immunology/ITRAP

One of the weaknesses of this study is that the motivation for the experiment and underlying hypothesis is unclear from the manuscript. Why these particular epitopes were selected, why these donors were selected, are any of the donors seropositive for EBV/CMV/influenza is unclear. Without particular research questions, it is hard to evaluate pipeline performance and justify a particular filtering strategy: for some applications, maximum specificity (i.e. no incorrect TCR specificity assignments) is crucial, while for others the main goal is to retain as many cells as possible.

We understand this concern and have elaborate our motivation for the experimental design in the text. The overall motivation for this study was to generate TCR-pMHC data complementing what was available in the public domain at the start of the project. This with the purpose of generating novel data for training of TCR specificity prediction models. This is also the reason why we explicitly “deselected” T cells specific for the 3 negative control peptides, since these already are covered with large amounts of TCR sequences in the public databases.

We do not know the serostatus of the donors included, but have determined the antigen-specificities present in the donors prior to initiating the study (evaluated for T cell recognition against 945 common viral specificities, using barcoded pMHC multimers in a bulk setting). The 945 peptides were selected from prevalent epitopes within IEDB. This means that the T cell specificities for the donors selected to be included in the current study was known a priori. We have updated the motivation for performing the study (lines 122-126).

Author Response

Reviewer #2 (Public Review):

The manuscript "Optimal Cancer Evasion in a Dynamic Immune Microenvironment Generates Diverse Post-Escape Tumor Antigenicity Profiles" by George and Levine describes TEAL - a mathematical model for the dynamics of cancer evolution in response to immune recognition. The authors consider a process in which tumor cells from one clone are characterized by a set of neoantigens that may be recognized by the immune system with a certain probability. In response to the recognition, the tumor may adapt to evade immune recognition, by effective removal of recognizable neoantigens. The authors characterize the statistics of this adaptive process, considering, in particular, the evasion probability parameter, and a possibility of an adaptive strategy when this parameter is optimized in each step of the evolution. The dynamics of the latter process are solved with a dynamic programming approach. In the optimal case, the model captures the tradeoff between a cancer population's need for adaptability in hostile immune microenvironments and the cost of such adaptability to that population. Additionally, immune recognition of neoantigens is incorporated. These two factors, antitumor vs pro-tumor IME as quantified by the Beta penalty term, and the level of immune recognition as quantified by the rate q, form the basis of a characterization of tumors as 'hot' or 'cold'.

I think this framework is a valuable attempt to formally characterize the processes and conditions that result in immunologically hot vs cold tumors. The model and the analytical work are sound and potentially interesting to a major audience. However, certain points require clarification for evaluation of the relevance of the model:

1) Tumor clonality

My main concern is about the lack of representation of the evolutionary process in the model and that the heterogeneity of the tumor is just glossed over.

The single mention of the problem occurs in Section 2, p2: "Our focus is on a clonal population, recognizing that subclonal TAA distributions in this model may be studied by considering independent processes in parallel for each clone."

I don't think this assumption resolves the impact of tumor heterogeneity on the immune evasion process. Furthermore, I would claim that the process depicted in Fig 1A is very rare and that cancers rarely lose recognizable neoantigens - typically it would be realized via subclonal evolution, with an already present cancer clone without the neoantigens picking up. Similarly, the adaptation of a tumor clone is an evolutionary process - supposedly the subclones that manage to escape recognition via genetic or epigenetic changes are the ones that persist. It is not clear what the authors assume about the heterogeneity of the adapting/adapted population between different generations, n->(n+1). Is the implicit assumption that the n+1 generation is again clonal, i.e. that the fitness advantage of the resulting subclone was such that the remaining clones were eliminated? Or does the model just focuses on the fittest subclone? A discussion on whether these considerations are relevant to the result would clarify the relevance of the result.

We thank the reviewer for these helpful clarifying points. Empirical evidence in lung cancer exists for genomic changes manifesting as lost neoantigens in treatment-resistant clones (and Anagnostou et al. Cancer Discovery 2017) showed that those lost antigens were also shown to generate functional immune responses). Similar results for melanoma have also been shown (Verdegaal et al. Nature 2016), with loss of neoantigens associated with reactivity in TILs. Recent observations (Jaeger et al. Clinical Cancer Research 2020) even show that mutated peptides may be hid by protein stabilization, in addition to reduced expression patterns. We however do wish to clarify that our model implicitly equates antigen loss and the progression of a subpopulation currently adapted to evade immune targeting – either by direct pruning of the fittest subclone or by stochastic emergence and subsequent growth of a new one lacking the targeted antigens – as equivalent.

Because we for foundational understanding studied the case where a single clonal signature was tracked in time, we under-explained the implementation of such a model in more complicated cases. As mentioned previously, the next most complicated scenario involves a heterogeneous population of cancer cells with disjoint neoantigen profiles. In this case, a parallel process can be studied wherein the effects of recognition in one environment are decoupled from the other (relevant to, for example, spatially distinct sub-populations). This description however misses the case where such disparate populations evolve to express shared antigens, or in the case where there are both clonal and subclonal antigen targets. Here, our model can still be applied in parallel to study distinct clones but requires additional structure. Namely, in this case we would need to incorporate non-trivial coupling between the possible recognition/selection against certain antigens shared across clones. For example, control of a population with clonal antigens {a,b} but having unique subclones having either antigens {w,x} or {y,z} could be considered by studying the process in parallel, and control in the next periods would require recognition/selection against either 1) at least one of {w,x} and at least one of {y,z}, or 2) at least one of {a,b}. In this more general framework, the arrival of new subclones with distinct features from the parent clone in question could also be incorporated and studied across time periods. This strategy of subdividing more complicated evolutionary structures has now been further elaborated on in the Methods section, and we have expounded these points in the discussion (see additions given under Editor Comment 2).

2) Time scales

Section 2, p2: "We assume henceforth that the recognition-evasion pair consists of the T cell repertoire of the adaptive immune system and a cancer cell population, recognizable by a minimal collection of s_n TAAs present on the surface of cancer cells in sufficient abundance for recognition to occur over some time interval n.".

How do the results depend on the duration of interval n? The duration should be long enough to allow for recognition and, up to some limiting duration, proportional to the TAA recognition probability q. However, it should not be so long that the state of the system can change significantly. A clarification on this point is needed.

We agree with the reviewer that these points should be elaborated upon when discussing the time interval. Very briefly, we opted for a discrete-time model tracking a cancer population under selective immune pressure. In order for 𝒒 to represent the total recognition probability of an immune system against a particular TAA, the time interval 𝚫𝒏 in question is a coarse-grained feature representing the time between the earliest chance that the adaptive immune system may identify a cancer clone and the latest point after which such a recognition event would no longer be able to prevent cancer escape. This time period may vary substantially across cancer subtypes and depends on the cancer per-cell division rate, for example (George, Levine. Can Res 2020). As the reviewer pointed out, in implementing such a model there is an asymmetric risk to considering 𝚫𝒏 too large, as the future state of the system may not be well-reflected by the simple loss and addition of new TAAs. On the other hand, considering small time intervals 𝚫𝒏, while possible, would require the incorporation of additional intermediate states ending in neither cancer elimination nor cancer escape.

We have clarified the points that the reviewer has brought up by adding them to the discussion section: In this discrete-time evolutionary model, the intertemporal period considered represents the time period between the earliest moment that the adaptive immune system may identify a cancer clone and the latest point after which such a recognition event would no longer be able to prevent cancer escape (George, Levine. Can Res 2020). This effectively gives 𝒒 a probabilistic representation for the total rate of opportunity to recognize a given TAA during cancer progression. Implementing this model in cancer subtype-specific contexts thus requires a consideration of the per-cell division rates, for example.

Reviewer #3 (Public Review):

Cancer cell populations co-evolve under the pressure exerted by the recognition of tumor-associated antigens by the adaptive immune system. Here, George and Levine analyze how cancers could dynamically adapt the rate of tumor-associated antigen loss to optimize their probability of escape. This is an interesting hypothesis that if confirmed experimentally could potentially inform treatments. The authors analyze mathematically how such optimally adapting tumors gain and lose tumorassociated antigens over time. By simplifying the complex interplay of immune recognition and tumor evolution in a toy model, the authors are able to study questions of practical interest analytically or through stochastic simulations. They show how different model parameters relating to the tumor microenvironment and immune surveillance lead to different dynamics of tumor immunogenicity, and more immunologically hot or cold tumors.

Simple models are important because they allow an exhaustive study of dynamical regimes for different parameters, such as has been done elegantly in this study. However, in this quest for simplification, the authors have not considered biological features that are likely to be of importance for understanding the process of cancer immune co-evolution in generality: tumor heterogeneity and immune recognition that only stochastically results in cancer elimination. In this sense, this paper might be seen as the opening act in a series of more sophisticated models, and the authors discuss avenues towards such further developments.

We share the reviewer’s credence in foundational modeling for comprehensive predictions on available dynamical behavior for the important problem at hand. The reviewer also correctly points out that that future model refinement will be needed to further develop the foundational model developed in this work. In an attempt to illustrate one of the more reasonable generalizations, which is to include nontrivial sub-clonal heterogeneity in tumor antigens, we now describe how one would go about enhancing the existing model to address this, which has been added to the Methods and Discussion sections (see additions given under Editor Comment 2).

Author Response

Reviewer #1 (Public Review):

N1-methyladenosine (m1A) is a rather intriguing RNA modification that can affect gene expression and RNA stability etc. The manuscript presented the exploration of RNAs m1A modification in normal and OGD/R-treated neurons and the effects of m1A on diverse RNAs. The authors showed that m1 modification can mediate circRNA/LncRNA-miRNA-mRNA mechanism and 3'UTR methylation of mRNAs can disturb miRNA-mRNA binding.

The manuscript provides evidence for the following,

1) The OGD/R can have impacts on various functions of m1A mRNAs and neuron fates.

2) The m1A methylation of mRNA 3'UTRs disturbs the miRNA-mRNA binding.

3) The authors identified three possible patterns of m1A modification regulation in neurons.

The main merit of the manuscript is that the authors identified some critical features and patterns of m1A modification and in neurons and OGD/R-treated neurons. Moreover, the authors identified m1A modifications on different RNAs and explored the possible effects of m1A modification on the functions of different RNAs and the overall posttranscriptional regulation mechanism via an integrated approach of omics and bioinformatics. The major weakness of the manuscript is that technique details for many results are missing. Moreover, language inconsistences can be found throughout the manuscript. My general feeling about the manuscript is that some conclusions are rather superficial and therefore require validation and discussion.

We appreciate your endorsement and constructive opinion concerning our work. Our study provides a comprehensive exploration of the characteristics of m1A modifications in neurons. According to your suggestions, we have specified the technique details in the revised manuscript have included our perspectives on some of the conclusions in the Discussion section. In addition, we have made changes to language inconsistences throughout the manuscript. We hope that the revisions made are acceptable and meet your requirements.

Reviewer #2 (Public Review):

In this manuscript, investigators explore the m1A modification, an important post-transcriptional regulatory mechanism, in primary normal neuron and OGD/R treated neuron. As far as I know, the regulatory m1A modification remains poorly characterized in neuron. This is an interesting topic in the context of epitranscriptomics. This paper not only provided us with a landscape of m1A modifications in neuron, but also explored the impact of m1A modifications on the biological functions of different RNA (mRNA, lncRNA, circRNA). In addition, the argument that m1A modification affects miRNA binding to other RNAs is of interest to reader, and the authors have performed a dual luciferase validation here to add feasibility to this conclusion.

Thank you for your careful review of our study, and thank you for your appreciation on our work. The aim of this work was to explore the characteristics of m1A modification in neurons. We believe that incorporating your advice into the revised manuscript has enhanced the quality of our article.

Reviewer #3 (Public Review):

Overall, this is an interesting and well performed study that described a comprehensive landscape of m1A modification in primary neuron and investigated the role of m1A in the circRNA/lncRNA‒miRNA-mRNA regulatory network following OGD/R. The focus on the two different complex regulatory networks for differential expression and differential methylation is important and it will be a valuable resource for the research community that focuses on epitranscriptomics and central nerve system diseases. Collectively, the authors present an exciting piece of work that certainly adds to the literature regarding epitranscriptomic features in neuron. While interesting results obtained and the paper is nicely written, I have the following suggestions for minor revisions to improve the paper.

We are grateful for your many positive comments and recognition of the potential of our work. Due to your suggestion, we found some shortcomings in our current manuscript. These suggestions were introduced and added value to our article. Our future research will continue to explore some conclusions obtained from this work. And we will continue to contribute our research outcomes in this field. Thank you again for your excellent suggestions!

1) The authors have explored the role of m1A modification in neuron, but it would have been helpful if the authors described the significance of these findings in depth in some sections (Figure 5 and Figure 6) to enhance the value of the article.

Thank you for your insightful suggestion. We agree to the comment that the significance of these findings should be described in detail. As such, we have added corresponding content to the Results (line 407-424) and Discussion (line 532-550) sections respectively.

2) The authors should describe in detail the current research state of m1A modification and the significance of this study to the field of epitranscriptomics in the introduction and Discussion section.

Thank you for your insightful suggestion. There is relatively little knowledge in the m1A modification area. It is really important to summarize the existing knowledge and research progress in a comprehensive and detailed manner. We conducted a comprehensive latest literature search and added corresponding content to the Introduction (line 78-83) and Discussion section (line 505-511, line 532-562) as you suggested.

Author Response

Reviewer 1 (Public Review):

Protein oligomerization is essential to their in vivo function, and it is generally challenging to determine the distribution of oligomeric states and the corresponding conformational ensembles. By combining coarse-grained molecular dynamics simulations and experimental small-angle X-ray scattering profiles at different protein concentrations, the authors have established a robust approach to self-consistently determine the oligomeric state(s) and the conformational ensemble. The approach has been applied specifically to the speckle-type POZ protein (SPOP) and generated new insights into the conformational ensemble and structural features that determine the ensemble. The model was further tested by the analysis of several relevant mutants as well as models with different types of structural restraints. The results also support the isodesmic selfassociation model, with KD values comparable to those measured from independent experiments in the literature. The approach is potentially applicable to a broad set of systems.

We thank the reviewer for taking the time to assess our work.

Reviewer 2 (Public Review):

This manuscript applied the SAXS data analysis of protein selfassembly by implementing the simultaneous fitting of intra- and intermolecular motions/conformations against SAXS data at a series of oligomerization states/concentrations. Despite several major assumptions hinted, a diverse pool of conformational and oligomeric candidates was generated from CG simulations, and more importantly, these candidates were fitted into these SAXS data to reach a reasonable agreement, suggesting a somewhat convergence (even if the ensemble-fitting could well be at a local minimal). This is considered a technical advance, given the fairly large numbers of both the oligomer fraction phi_i (i=1, ..., N) and the conformational weight w_k (k=1, ..., n), where N is the number of oligomers and n is the number of internal conformational states.

We thank Prof. Yang for taking the time to assess our work.

Central is optimizing phi_i and w_k, simultaneously. The former has been illustrated in Fig. 4 and SI-Fig. 7 for the total number of 60mers. The latter relies on an overfitting-preventing strategy, as shown in SI_Fig. 1, where an effective fraction cutoff was used from 0.1 to 1.0, as opposed to the number of conformational states. What are the numbers of conformational states for these oligomers? This should be quantifiable, e.g., defining the conformational differences by chi_2.

The reviewer is correct that the entropy-based term for preventing overfitting is a key aspect of the method. In contrast to some of the other methods to combine experiments with simulations, our approach does, however, not require us to define individual conformational states. Instead, the weights in the entropy term refer to individual configurations rather than states, and we can thus integrate the SAXS experiments and simulations without, for example, clustering the conformations. Indeed, for most of the collective variables that we have calculated from the ensembles, such as the radii of gyration, end-to-end distances, and MATH-MATH distances, we observe continuous monomodal probability distributions, which suggests that it might be difficult to define a few distinct conformational states. For the MATH-BTB/BACK distance, we observe a trimodal distribution, and these distinct conformational states are shown as overlaid structures in Fig. 4i. Thus, while these “states” change populations during reweighting, this is the result from changing weights of the individual configurations.

Reviewer 3 (Public Review):

Molecular-level interpretations of SAXS data are challenging, especially for oligomeric systems of variable length with intrinsic flexibility and the possibility of multiple association interfaces. In order to make this challenge tractable, a number of assumptions are made here: 1) There is a single pathway by which individual domains associate first into homodimers and then into longer oligomers; 2) the association kinetics is isodesmic, which allows the direct calculation of oligomer distributions based on the given value of a single dissociation constant; 3) the internal dynamics within dimers is restricted essentially to relative domain-domain motions, that are sampled comprehensively via MD simulations. As a result, excellent fits to the SAXS data are obtained and the underlying conformational ensembles are highly plausible. The resulting models are useful to further understand SPOP function, especially in the context of liquidliquid phase separation.

We thank the reviewer for taking time to read our work and for their various suggestions.

Author Response

Reviewer #1 (Public Review):

This work provides a new general framework for estimating missing data on cervical cancer epidemiology, including sexual behavior, HPV prevalence, and cervical cancer incidence. These data are useful to determine impact projections of cervical cancer prevention. The authors suggest a three-step approach: 1) a clustering method applied on registries with an intermediate level of data availability to cluster cervical cancer incidence based on a Poisson-regression-based CEM algorithm, 2) a classification method applied on registries with a low level of data availability to classify cervical cancer incidence based on a Random Forest, 3) a projection method applied on missing data based on the mean of available data. The authors use India as a case study to implement this new methodology. Results indicate that two patterns of cervical cancer incidence are identified in India (high and low incidence), classifying all Indian states with missing data to a low incidence. From this classification, missing data is approximated using the mean of the available data within each cluster.

A strength of this approach is that this methodology can be applied to regions with missing data, although a minimum set of information is needed. This makes it possible to have individual data for each unit in the region.

One of the weaknesses of this methodology is the need for a minimum set of epidemiological data to enable impact projections. It is true that when epidemiological cervical cancer data is not available, authors mentioned that general indicators (e.g., human development index, geography) can be used but projections will be probably less realistic. As observed with other techniques, countries with fewer resources have less data available and cannot benefit from these types of techniques to have more adequate guidelines.

Imputation of missing data is always a challenging issue. The technique proposed in this manuscript is an interesting new approach to missing data imputation that could be applied with a minimum set of available data. However, we must focus on obtaining reliable data from each region of the world to help local health authorities implement better preventive measures for the local population.

We thank the reviewer for the considerate comments and suggestions and have tried to incorporate them as much as possible in the revised manuscript.

As the reviewer has pointed out, the applicability of the proposed methodology depends on the available data. In our opinion, it is a general challenge for approximating missing data, rather than a weakness particular to our methodology. In fact, we believe that our framework is flexible to address missing data in many situations. To clarify this point, we have included the following sentences in the Discussion (lines 363-376, page 18): “It is important to note that, in general, the applicability the proposed framework depend on the actual amount of data available. However, in our opinion, it is a general challenge for approximating missing data, rather than a weakness particular to our methodology. By allowing possible adaptations, we believe that our framework is sufficient flexible to address missing data in many situations.”

Finally, we fully agree with the reviewer that we should continue our effort to collect more data for countries where these are not available. The proposed framework should be considered as a solution to the situation in which collection of additional data is not or not yet possible.

Reviewer #2 (Public Review):

The burden of cervical cancer worldwide is well recognized. While prevention strategies, including vaccination against human papillomavirus (HPV), cervical cancer screening, and pre-cancer treatment, can reduce the burden of cervical cancer, access to these measures is still limited, especially in low- and middle-income countries. Since the impact of prevention strategies is heavily dependent on the disease's burden on a particular population, we need to know the latter to assess the impact of these context-specific prevention strategies.

However, epidemiological data on cervical cancer are not always available for all geographical areas. This paper uses India as a case study to propose a framework called "Footprinting" to comprehensively evaluate the burden of cervical cancer. The authors applied a three-step analytical strategy to impute cervical cancer epidemiological data in states where this information was unavailable using data from cervical cancer incidence, HPV prevalence, and sexual behaviour from other regions. The findings suggest a high and low incidence of cervical cancer incidence in different parts of India; all Indian states with missing data were classified as low incidence.

The proposed analytical strategy presents an important solution for imputing data from geographic areas of a country where data are missing.

We thank the reviewer for the considerate comments and suggestions and have tried to incorporate them as much as possible in the revised manuscript.

One conceptual limitation of this work is the lack of explanation or evidence that sexual behaviour can be used to approximate cervical cancer and/or HPV rates.

A similar comment was raised by Reviewer #1. It is well established that sexual contact is the only transmission route of carcinogenic HPV infection, and hence necessary for the occurrence of cervical cancer [ref #26 Vaccerella 2006, Muñoz 1992 Int J Cancer 52, 743-749].

We have included sexual behaviour variables that have previously been shown to be risk factors of HPV infection and cervical cancer risk, e.g., age of sexual debut and number of sexual partners [ref #26 Vaccerella 2006, ref #27 Schulte-Frohlinde 2021]. Furthermore, we used variables that are commonly available so that the analyses can be easily applied to other settings.

As far as we know, there is no established set of sexual behaviour variables for predicting the patterns of HPV prevalence and cervical cancer incidence. The good prediction performance in the India case study shows that using the selected set is sufficient. As sexual behaviour variables are highly correlated, including more variables might even risk overfitting.

To clarify these points we have included the following paragraph in the Discussion (lines 319-325, page 16): “In our analysis of classifying clusters of cervical cancer incidence, we only included some of the sexual behaviour variables available in the NACO report [15]. We selected variables that were previously shown to be risk factors of HPV infection and cervical cancer risk and that are commonly available so that the analyses can be easily applied to other settings, e.g., age of sexual debut and number of sexual partners [26, 27]. As far as we know, there is no established set of sexual behaviour variables for predicting the patterns of HPV prevalence and cervical cancer incidence. The good prediction performance shows that using the selected set is sufficient. As sexual behaviour variables are highly correlated, including more variables might even risk overfitting.”

Also, full information on the three main indicators is only available in two states. This is used to impute the values for the other states.

Indeed, HPV prevalence data were only available for two states. While we acknowledge that this affects the certainty in the imputed HPV prevalence, we considered the imputed results to be satisfactory based on the good accordance with the cervical cancer incidence data we found in the validation step (lines 286-23, page 14). We verified that the ratio of HPV prevalence between the high-and low-incidence cluster (1.7-fold) was very similar to the ratio of age-standardized cervical cancer incidence (1.9-fold).

Furthermore, we note that previous modelling works on India relied on even less data, namely one source of HPV prevalence and cervical cancer incidence data [ref #29 Brisson 2020, Diaz 2008 Br J Cancer].

Moreover, the available data used in this study also present some limitations; for example, cervical cancer incidence data were from 2012 to 2016, while sex behaviour data were from 2006. This large gap is likely to have a significant cohort effect, especially given changes in sexual norms in Western countries over the last few decades, which may have gradually influenced other countries, especially in this age of the internet and social media.

In our opinion, for the purpose of modelling the natural history of cervical cancer, it is not necessarily more adequate to use the most recent data of sexual behaviour data. Arguably, as sexual behaviour is the “exposure” for the “outcome” cervical cancer, calibration of HPV transmission and cervical cancer model is best done with data of sexual behaviour and cervical from the same cohorts, hence, sexual behaviour data from an earlier period than the cervical cancer data.

In addition, if changes of sexual behaviour occur across the country, it should not affect the clustering much.

Finally, due to delay in reporting, cervical cancer incidence from the period 2012-2016 is the most recent edition at the moment of writing. Regarding sexual behaviour data, there is at the moment no later edition of the NACO report published after that of year 2006.

Finally, it would be interesting to validate this methodology to confirm its utility.

We agree that it would be very interesting to validate this proposed methodology in other regions. Unfortunately, it was beyond the scope of this work. Currently, we are working on a project in which we try to apply footprinting to a collection of low- and middle-income countries.

The proposed framework's strength is difficult to evaluate because the steps and justification for the model variables were not clearly presented, nor were the models validated.

We acknowledge that the framework could be more clearly presented and have added additional explanation in the following places to do so:

• Concerning the framework steps, in Method (144-163, pages 7-8): “For convenience of explanation, we assumed earlier that data availability occurs hierarchically. However, the framework can also be applied with less stringent data requirements. First, the source of Footprint data needs not necessarily cover all geographical units. It is still possible to train a classifier in the classification step with Footprint data available for only a part of clustered geographical units. Second, if none of the key cervical cancer epidemiological data (sexual behavior, HPV prevalence, and cervical cancer incidence data) have large enough coverage to serve as Footprint data, alternatives indicators of similarity, such as human development index and geographical distance, could also be used as substitute. However, the resulting classification performance might be suboptimal, as we expect these indicators to correlate less well with cervical cancer risk. Third, for the projection step, data of cervical cancer incidence, sexual behavior, and HPV prevalence needed for calibration of projection models need not necessarily belong to the same geographical unit. Calibration can be performed as long as the three types of data are available within each cluster.

With these less stringent data requirements, the proposed framework should sufficient flexible to be applied to many situations. However, one should still be cautious in applying the framework when there are little data. This means that, in some cases, we might need to exclude from the analysis some geographical units with too little data or redefine bigger geographical units if the data are not granular enough. Furthermore, we should assess the goodness-of-fit of the obtained clustering, performance of classification, correlation of data within different clusters, and calibration fits to ensure the validity of the final impact projections.”

• Concerning selection of model variables (lines 319-325, page 16): “In our analysis of classifying clusters of cervical cancer incidence, we only included some of the sexual behaviour variables available in the NACO report [15]. We selected variables that were previously shown to be risk factors of HPV infection and cervical cancer risk and that are commonly available (e.g., age of sexual debut and number of sexual partners) so that the analyses can be easily applied to other settings [26, 27]. In the India case study, the good classification performance shows that using the selected set is sufficient. As sexual behaviour variables are highly correlated, including more variables might even risk overfitting.”

Based on the authors' interpretation of the framework findings, this framework may help extrapolate data from one country to another. I'm curious as to whether this framework could be applied across states and countries.

We thank the reviewer for this comment. Currently, we are working on a multi-year projects in which we try to apply the framework to all low- and middle-income countries.

Author Response:

eLife assessment

This work is an attempt to establish conditions that accurately and efficiently mimic a drought response in Arabidopsis grown on defined agar-solidified media - an admirable goal as a reliable experimental system is key to conducting successful low water potential experiments and would enable high-throughput genetic screening (and GWAS) to assess the impacts of environmental perturbations on various genetic backgrounds. The authors compare transcriptome patterns of plant subjected to water limitation imposed using different experimental systems. The work is valuable in that it lays out the challenges of such an endeavor and points out shortcomings of previous attempts. However, a lack of water relations measurements, incomplete experimental design, and lack of critical evaluation of these methods in light of previous results render the proposed new methodology inadequate.

We thank eLife for the initial assessment and comments to our work. In our revised manuscript we plan to address the main concerns raised by reviewers. Specifically, we plan to perform water relations measurements for all our treatment assays, as well as explore the separate effects agar hardening and nutrient concentration have in our low-water agar assay. We will also provide a more in depth critical review of our results compared to previously published results.

Reviewer #1 (Public Review):

High-throughput genetic screening is a powerful approach to elucidate genes and gene networks involved in a variety of biological events. Such screens are well established in single-celled organisms (i.e. CRISPR-based K/O in tissue culture or unicellular organisms; screens of natural variants in response to drugs). It is desirable to extend such methodology, for example to Arabidopsis where more than 1000 ecotypes from around the Northern hemisphere are available for study. These ecotypes may be locally adapted and are fully sequenced, so the system is set up for powerful exploration of GxE. But to do so, establishing consistent "in vitro" conditions that mimic ecologically relevant conditions like drought is essential. 

The authors note that previous attempts to mimic drought response have shortcomings, many of which are revealed by 'omics type analysis. For example, three treatments thought to induce osmotic stress; the addition of PEG, mannitol, or NaCl, fail to elicit a transcriptional response that is comparable to that of bonafide drought. As an alternative, the authors suggest using a low water-agar assay, which in the things they measure, does a better job of mimicking osmotic stress responses. The major issues with this assay are, however, that it introduces another set of issues, for example, changing agar concentration can lead to mechanical effects, as illustrated nicely in the work of Olivier Hamant's group.

We thank the reviewer for their comments. We hypothesize that our low-water agar assay is able to replicate drought gene expression patterns through a combination of hardened agar and higher nutrient concentration. However, we did not explore the separate effects each of these factors may play in eliciting such responses. Thus, in our revised manuscript, we will explore what role the mechanical effects of changing agar concentration has on root gene expression. However, we suspect that the mechanical effects introduced by hard agar does not introduce another issue per se, but in fact may help with replicating the transcriptional effects seen under drought.

Reviewer #2 (Public Review):

[…] The authors have not always considered literature that would be relevant to their topic. For example, there is a number of studies that have reported (and deposited in the public database) transcriptome analysis of plants on PEG-plates or plants exposed to well-controlled, moderate severity soil drying assays (for the latter, check the paper of Des Marais et al. and others, for the former, Verslues and colleagues have published a series of studies using PEG-agar plates). They also overlook studies that have recorded growth responses of wild type and a range of mutants on properly prepared PEG plates and found that those results agree well with results when plants are exposed to a controlled, partial soil drying to impose a similar low water potential stress. In short, the authors need to make such comparisons to other data and think more about what may be wrong with their own experimental designs before making any sweeping conclusions about what is suitable or not suitable for imposing low water potential stress. 

To solve the problem of using these other systems to impose low water potential stress, the authors propose the seemingly logical (but overly simplistic) idea of adding less water to the same mix of nutrients and agar. Because the increased agar concentration does not substantially influence water potential (the agar polymerizes and thus is not osmotically active), what they are essentially doing is using a concentrated solution of macronutrients in the growth media to impose stress. This is a rediscovery of an old proposal that concentrated macronutrient solutions could be used to study the osmotic component of salt stress (see older papers of Rana Munns). There are also effects of using very hard agar that is of unclear relationship to actual drought stress and low water potential. Thus, I see no reason to think that this would be a better method to impose low water potential. 

We thank the reviewer for their comments. In our revised manuscript, we will address points regarding plant and soil water potential; similar concerns were also raised by Reviewer 1 and 3. We note that we report vermiculite water content in Supplementary Table 4.

We would like to clarify that both the PEG media and overlay solution were buffered - we did not include this within the written description in the methods, but will do in our revised manuscript.

We agree with the reviewer’s concern that it may be problematic to compare the transcriptomic profiles of seedling and mature plants. In light of this, we plan to explore what effects our treatment media has on mature rosettes.

We note that we do not claim that PEG is unable to produce low-water potential responses similar to partial soil drying. Indeed, we indicate that it is a good technique for eliciting phenotypes comparable to drought at the physiological level (line 48). Rather, we claim that PEG is unable to produce gene expression responses that are sufficiently similar to partial vermiculite drying.

Reviewer #3 (Public Review):

[…] The authors observed that gene expression responses of roots in their 'low-water agar' assay resembled more closely the water deficit in pots compared to the PEG, mannitol, and salt treatments (all at the highest dose). In particular, 28 % of PEG led to the down-regulation of many genes that were up-regulated under drought in pots. Through GO term analysis, it was pointed out that this may be due to the negative effect of PEG on oxygen solubility since downregulated genes were over-represented in oxygen-related categories. The data also shows that the treatment with abscisic acid on plates was very good at simulating drought in roots. Gene expression changes in shoots showed generally a high concordance between all treatments at the highest dose and water deficit in pots, with mannitol being the closest match. This is surprising, since plants grow in plates under non-transpiring conditions, while a mismatch between water loss by transpiration on water supply via the roots leads to drought symptoms such as wilting in pot and field-grown plants. The authors concluded that their 'low-water agar' assay provides a better alternative to simulate drought on plates. 

Strengths: 

The development of a more robust assay to simulate drought on plates to allow for high-throughput screening is certainly an important goal since many phenotypes that are discovered on plates cannot be recapitulated on the soil. Adding less water to the media mix and thereby increasing agar strength and nutrient concentration appears to be a good approach since nutrients are also concentrated in soils during water deficit, as pointed out by the authors. To my knowledge, this approach has not specifically been used to simulate drought on plates previously. Comparing their new 'low-water agar' assay to popular treatments with PEG, mannitol, salt, and abscisic acid, as well as plants grown in pots on vermiculite led to a comprehensive overview of how these treatments affect gene expression changes that surpass previous studies. It is promising that the impact of 'low-water agar' on the shoot size of 20 diverse Arabidopsis accessions shows some association with plant fitness under drought in the field. Their methodology could be powerful in identifying a better substitute for plate-based high-throughput drought assays that have an emphasis on gene expression changes. 

Weaknesses: 

While the authors use a good methodological framework to compare the different drought treatments, gene expression changes were only compared between the highest dose of each stress assay (Fig. 2B, 3B). From Fig. 1F it appears that gene expression changes depend significantly on the level of stress that is imposed. Therefore, their conclusion that the 'low-water agar' assay is better at simulating drought is only valid when comparing the highest dose of each treatment and only for gene expression changes in roots. Considering how comparable different levels of stress were in this study leads to another weakness. The authors correctly point out that PEG, mannitol, and salt are used due to their ability to lower the water potential through an increase in osmotic strength (L. 45/46). In soils, water deficit leads to lower water potential, due to the concentration of nutrients (as pointed out in L. 171), as well as higher adhesion forces of water molecules to soil particles and a decline in soil hydraulic conductivity for water, which causes an imbalance between supply and demand (see Juenger and Verslues, The Plant Cell 2022 for a recent review). While the authors selected three different doses for each treatment that are commonly used in the literature, these are not necessarily comparable on a physiological level. For example, 200 mM mannitol has an approximate osmotic potential of around -5 bar (Michel et al. Plant Physiol. 1983) whereas 28 % PEG has an osmotic potential closer to -10 bar (Michel et al. Plant Physiol. 1973). It also remains unclear how the increase in agar concentration versus the increase in nutrient concentration in the 'low-water agar' affect water potentials. For these reasons it cannot be known whether a better match of the 'low-water agar' at the 28% dose to water deficit in pots for roots in comparison to the other treatments is due to a good match in stress levels with the 'low-water agar' or adverse side-effect of PEG, mannitol, or and salt on gene regulation. Lastly, since only two biological replicates for RNA sequencing were collected per treatment, it is not possible to know how much variance exists and if this variance is greater than the treatments themselves. 

We thank the reviewer for their comments. In our statistical analyses, we found that dose-responsive genes (as fit by a linear model) were very similar to those genes found differentially expressed at the highest dose. Thus, for clarity, we decided to simply present the genes differentially expressed at the highest dose. We see now that this might have been an oversimplification. In our revised manuscript, we will present genes that are dose responsive across the range of treatment doses, thus providing more evidence that lower doses of low-water agar are also capable of simulating drought (as is suggested by overlap analysis of Figure 2A).

Additionally, we will also explore the osmotic potential of each of our different assays to provide a better benchmark of how comparable each of our treatments are (as similarly requested by Reviewer 1 and 2). Lastly, to address concerns regarding the size of variance in gene expression, we will sequence a 3rd replicate of RNA.

Author Resposnse

Reviewer #2 (Public Review):

This manuscript reassesses the strength of evidence for rapid human germline mutation spectrum evolution, using high coverage whole genome sequencing data and paying particular attention to the potential impact of confounders like biased gene conversion. The authors also refute some recently published arguments that historical changes in the age of reproduction might explain the existence of such mutation spectrum changes. My overall impression is that the paper presents a useful new angle for studying mutation spectrum evolution, and the analysis is nicely suited to addressing whether a particular model such as the parental age model can explain a set of observed polymorphism data. My main criticism is that the paper overstates certain weaknesses of previously published papers on mutation spectrum evolution as well as the generation time hypothesis; correcting these oversimplifications would more accurately capture what the paper's new analyses add to the state of knowledge in these areas.

As part of the motivation for the current study, the introduction states in lines 97-99 that "it thus remains unclear if the numerous observed [mutation spectrum] differences across human populations stem from rapid evolution of the mutation process itself, other evolutionary processes, or technical factors." This seems to overstate the uncertainty that existed prior to this study, given that Speidel, et al. 2021 found elevated TCC>TTC fractions in ancient genomes from a specific ancient European population, which seems like pretty airtight evidence that this historical mutation rate increase really happened. In addition, earlier papers (Harris 2015, Mathieson & Reich 2016, Harris & Pritchard 2017) already presented analyses rejecting the hypothesis that biased gene conversion or genetic drift could explain the reported patterns-in fact, the Mathieson & Reich paper reports one mutation spectrum difference between populations that they conclude is an artifact caused by the Native American population bottleneck, but they conclude that other mutation spectrum differences appear more robust.

We completely agree with the reviewer that there has been compelling evidence from multiple independent groups supporting transient elevation of TCC>TTC mutation rate in Europeans. Beyond the TCC signal, however, the mechanisms underlying the observed differences in mutation spectrum across populations remain unclear. In particular, several biological and technical factors impact the mutation spectrum and none of the previous studies have investigated their effects, independently or altogether. Thus, it remains unclear if the mutation rate is evolving rapidly across populations, or if one or more factors (like biased gene conversion) differ across groups or over evolutionary time. Our analysis framework attempts to control these effects together to more reliably investigate the effects of various factors and examine when and how often there has been evolution of mutation rate over the course of human evolution.

As the authors acknowledge in the discussion of their own results, biased gene conversion and non-equilibrium demography are difficult confounders to deal with, and neither previous papers nor the current paper are able to do this in a way that is 100% foolproof. The current manuscript makes a valuable contribution by presenting new ways of dealing with these issues, particularly since previous papers' work on this topic was often confined to supplementary material, but it seems appropriate to acknowledge that earlier papers discussed the potential impacts of biased gene conversion and demographic complexity and presented their own analyses arguing that these phenomena were poor explanations for the existence of mutation spectrum differences between populations.

For the most part, I found the paper's introduction to be a useful summary of previous work, but there are a few additional places where the limitations of previous work could be described more clearly. I'd suggest noting that the data artifacts discovered by Anderson-Trocmé, et al. were restricted to a few old samples and that the large differences the current manuscript focuses on were never implicated as potential cell line artifacts. In addition, when the authors mention that their new approach includes "minimiz[ing] confounding effects of selection by removing constrained regions and known targets of selection" (lines 106-107), they should note that earlier papers like Harris & Pritchard 2017 also excluded conserved regions and exons.

We agree with the reviewer that some of the previous work also attempted to account for the contributions of selection or other factors in post hoc ways; we now acknowledge this in the Results section more explicitly. However, we note that our contribution is in introducing a framework to account for these effects a priori and then assess if there are differences in mutation spectrum across populations and over the course of human evolution. In particular, an innovation of our framework is to better control for the effect of gBGC, which has not been done in previous studies.

One innovative aspect of the current paper's approach is the use of allele ages inferred by Relate, which certainly has advantages over using allele frequencies as a proxy for allele age. Though the authors of Relate previously used this approach to study mutation spectrum evolution, they did not perform such a thorough investigation of ancient alleles and collapsed mutation type ratios. I like the authors' approach of building uncertainty into the use of Relate's age estimates, but I wonder about the validity of assuming that the allele age posterior probability is distributed uniformly between the upper and lower confidence bounds. Can the authors address why this is more appropriate than some kind of peaked distribution like a beta distribution?

The lower and upper bounds of the allele age reported by Relate reflect the start and end points of the branch that the mutation falls on in the reconstructed genealogical tree. If Relate does a perfect job in reconstructing the tree and estimating the branch lengths, the mutation age should be uniformly distributed in the inferred interval. It is unrealistic that Relate can perform perfectly in tree building, and there is likely considerable uncertainty and even bias in the time to endpoints of the branch. Unfortunately, Relate does not report the uncertainty in the lower and upper bounds of the mutation age, so we were not able to model the posterior distribution of the allele age properly. However, assuming a uniform distribution of the mutation age between the upper and lower confidence bounds should be valid to first approximation.

I would also argue that the statement on line 104 about Relate's reliability is not yet supported by data-there is certainly value in using Relate ages to investigate mutation spectrum change over time and compare this to what has been seen using allele frequencies, but I don't think we know enough yet to say that the Relate ages are definitely more reliable. Relate's estimates might be biased by the same processes like selection and demography that make allele frequencies challenging to interpret. The paper's statements about the limitations of allele frequencies are fair, but there is always a tradeoff between the clear drawbacks of simple summary statistics and the more cryptic possible blind spots of complicated "black box" algorithms (in the case of Relate, an MCMC that needs to converge properly). DeWitt, et al. 2021 noted that the demographic history inferred by Relate doesn't accurately predict the underlying data's site frequency spectrum, indicating that the associated allele ages might have some problems that need to be better characterized. While testing Relate for biases is beyond the scope of this work, the introduction should acknowledge that the accuracy and precision of its time estimates are still somewhat uncertain.

We agree with the reviewer and have now added a paragraph in the Discussion highlighting some issues of Relate regarding mutation age estimation and ancestral allele polarization.

The paper's results on C>T mutations in Europeans versus Africans are a nice confirmation of previous results, including the observation from Mathieson & Reich that neither SBS7 nor SBS11 is a good match for the mutational signature at play. More novel is the ancient mutational signature enriched in Africa and the interrogation of the ability of parental age to explain the observed patterns. I just have a few minor suggestions regarding these analyses:

1) I like the idea of using maternal age C>G hotspots to test the plausibility of the maternal age as an explanatory factor, but I think this would be more convincing with the addition of a power analysis. Given two populations that have average maternal ages of 20 and 40, and the same population sample sizes available from 1000 Genomes, can the authors calculate whether the results they'd predict are any different from what is observed (i.e. no significant differences within the maternal hotspots and significant differences outside of these regions)?

We thank the review for this suggestion. We performed simulations to estimate the power of observing significant inter-population differences within and outside the maternal C>G mutation hotspots, under the assumption that all differences in the mutation spectrum between the two populations are related to the parental age (i.e., generation time). We found that, because of the extraordinarily strong maternal age effects in the maternal mutation hotspots, the power for detecting variation in C>G/T>A ratio due to change in generation age is much greater within maternal hotspots than outside, despite the smaller total size of the maternal hotspot regions (and hence fewer SNPs; Figure 3 – figure supplement 4). For example, even with an age difference of five years, there is nearly 100% power to detect significant differences in the maternal hotspots, compared to <12% for regions outside the maternal hotspots. In other words, if inter-population differences in the mutation spectrum are driven by differences in maternal age across populations, we should have enough power to observe a signal in the maternal hotspot regions alone, the lack of which (Figure 2C) strongly suggests that maternal age is not driving these signals.

2) Is it possible that the T>C/T>G ratio is elevated in all variants above a certain age but shows up as an African-specific signal because the African population retains more segregating variation in this age range, whereas non-African populations have fixed or lost more of this variation? Since Durvasula & Sankararaman identified putative tracts of super-archaic introgression within Africans, is it possible to test whether the mutation spectrum signal is enriched within those tracts?

The observation that the T>C / T>G signal is driven by TpG>CpG mutations (which might be mis-polarized CpG transitions) casts a doubt on the signal. Given the unresolved technical issue, we have now removed any discussion of the biological explanations behind the signal and instead focus on describing the challenges with ancestral allele polarization under context-dependent mutation rate variation.

3) Although Coll Macià, et al. argued that generation time is capable of explaining all mutation spectrum differences between populations, including the excess of TCC>TTC in Europeans, Wang et al. argue something slightly different. They exclude TCC>TTC and the other major components of the European signature from their analysis and then argue that parental age can explain the rest of the differences between populations. I think the analysis in this paper convincingly refutes the Coll Macià, et al. argument, but refuting the Wang, et al. version would require excluding the same mutation types that are excluded in that paper.

Although we did not present an analysis that explicitly excludes TCC>TTC mutations, our analysis still shows that generation time alone cannot explain the remaining variations in the mutation spectrum observed (Figure 4). Specifically, the temporal trend of T>C/T>G ratio would suggest a decreasing generation time of Europeans with time, whereas the C>G/T>A ratio suggests the opposite. In addition, the power analysis for C>G maternal hotspots (suggested by the reviewer) further supports that the inter-population differences observed cannot be entirely driven by differences in parental ages. These observations, which do not involve TCC>TTC mutations, strongly suggest that generation time is not the sole or primary driver of differences in mutation spectrum across populations. Further, our analysis shows that several technical issues and biological processes, in addition to changes in life history traits can lead to changes in the mutation spectrum of polymorphisms. Therefore, inferring generation time using changes in mutation spectrum is not straightforward as Wang et al. proposed, because generation time is not the only or dominant factor impacting mutation spectrum.

Author Response

Reviewer #2 (Public Review):

This study identifies the neural circuits inhibited by activation of opioid receptors using complex experimental approaches such as electrophysiology, pharmacology, and optogenetics and combined them with retrograde and anterograde tracings. The authors characterize two key regions of the brainstem, the preBötzinger Complex, and the Kolliker-Fuse, and how these neuronal populations interact. Understanding the interactions of these circuits substantially increases our understanding of the neural circuits sensitive to opioid drugs which are critical to understand how opioids act on breathing and potentially design new therapies.

Major strengths.

This study maps the excitatory projections from the Kolliker-Fuse to the preBötzinger Complex and rostral ventral respiratory group and shows that these projections are inhibited by opioid drugs. These Kolliker-Fuse neurons express FoxP2, but not the calcitonin gene-related peptide, which distinguishes them from parabrachial neurons. In addition, the preBötzinger Complex is also hyperpolarized by opioid drugs. The experiments performed by the authors are challenging, complex, and the most appropriate types of approaches to understanding pre- and post-synaptic mechanisms, which cannot be studied in vivo. These experiments also used complex tracing methods using adenoassociated virus and cre-lox recombinase approaches.

Limitations.

(1) The roles of the mechanisms identified in this study have not been established in models recording opioid-induced respiratory depression or respiratory activity. This study does not record, modulate, or assess respiratory activity in-vitro or in-vivo, without or with opioid drugs such as fentanyl or morphine.

(2) Experiments are performed in-vitro which do not mimic the effects of opioids observed in-vivo or in freely-moving animals. However, identification of pre- and post- synaptic mechanisms, as well as projections, cannot be performed in-vivo, so the authors use the right approaches for their experiments.

We agree with both of these points. We hope this study lays the groundwork for future studies assessing the impact of these projections on respiratory activity in vitro and in vivo.

(3) The type of neurons projecting from KP to preBötzinger Complex or ventral respiratory group have not been identified. Although some of these cells are glutamatergic, optogenetic experiments could have been performed in other cre-expressing cell populations, such as neurokinin-1 receptors.

There are indeed many different cell populations that could be interrogated. In addition to the optogenetic identification of glutamatergic projections, we identified immunohistochemically that at least some opioid receptor-expressing, medullary-projecting KF neurons express FoxP2, and not CGRP. Further dissection of other cell populations, such as Lmx1b and Phox2b, are excellent future directions.

Reviewer #3 (Public Review):

This manuscript reveals opioid suppression of breathing could occur via multiple mechanisms and at multiple sites in the pontomedullary respiratory network. The authors show that opioids inhibit an excitatory pontomedullary respiratory circuit via three mechanisms: 1) postsynaptic MOR-mediated hyperpolarization of KF neurons that project to the ventrolateral medulla, 2) presynaptic MOR mediated inhibition of glutamate release from dorsolateral pontine terminals onto excitatory preBötC and rVRG neurons, and 3) postsynaptic MOR-mediated hyperpolarization of the preBötC and rVRG neurons that receive pontine glutamatergic input.

This manuscript describes in detail a useful method for dissecting the relationship between the dorsolateral pons and the rostral medulla, which will be useful for various researchers. It's also great to see how many different methods have been applied to improve the accuracy of the results.

1. Relationship between the dorsolateral pons and rostral ventrolateral medulla.

The method of this paper is a good paper to show a very precise relationship between the presence of opioid receptors and the dorsolateral pons and rostral ventrolateral medulla, and for opioid receptors, based on the expression of Oprm1, the use of genetically modified mice with anterograde or retrograde viruses with additional fluorescent colors showed both anterograde and retrograde projections, revealing a relationship between the dorsolateral pons and rostral ventrolateral medulla.

For example, to visualize dorsal pontine neurons expressing Oprm1, Oprm1Cre/Cre mice were crossed with Ai9tdTomato Cre reporter mice to generate Ai9tdT/+ oprm1Cre/+ mice (Oprm1Cre/tdT mice) expressing tdTomato on neurons that also express MOR at any point during development, and the retrograde virus encoding Cre-dependent expression of GFP (retrograde AAV-hSIN-DIO-eGFP was injected into the respiratory center of Oprm1Cre/+ mice and into the ventral respiratory neuron group, showing that KF neurons expressing Oprm1 project to the respiration-related nucleus of the ventrolateral medulla.

However, although the authors have also corrected it, the virus may spread to other places as well as where they thought it would be injected, and it is important to note that it is injected accordingly to mark the injection site with an anterograde virus encoding a different fluorescent color mCherry, and the extent of the injection is quantified, which is excellent as a control experiment.

In addition, the respiratory center seems to be related not only to preBötC but also to pFRG recently, so if the relation with it is described, it is important from the viewpoint of the effect on the respiratory center and the effect on the rhythm.

Our injections centered in preBotC, rVRG or BötC did not spread extensively to slices containing 7N/pFRG (Figure 2C and Figure 2-supplement 1D, Bregma -6.0 to -6.4, shaded region labeled 7N).

Author Response:

eLife assessment

This manuscript analyzes large-scale Neuropixels recordings from visual areas and hippocampus of mice passively viewing repeated clips of a movie and reports that neurons respond with elevated firing activities to specific, continuous sequences of movie frames. The important results support a role of rodent hippocampal neurons in general episode encoding and advance understanding of visual information processing across different brain regions. The strength of evidence for the primary conclusion is solid, but some technical limitations of the study were identified that merit further analyses.

We thank the editors and reviews for the assessment and reviews. We have provided clarifications and updated the manuscripts to address the seeming technical limitations that are perhaps due to some misunderstanding, please see below. We provide additional results that isolate the contribution of pupil diameter, sharpwave ripple and theta power to show that movie tuning cannot be explained by these nonspecific effects. Nor are these mere time cells or some other internally generated patterns due to many differences highlighted below.

Reviewer #1 (Public Review):

Taking advantage of a publicly available dataset, neuronal responses in both the visual and hippocampal areas to passive presentation of a movie are analyzed in this manuscript. Since the visual responses have been described in a number of previous studies (e.g., see Refs. 11-13), the value of this manuscript lies mostly on the hippocampal responses, especially in the context of how hippocampal neurons encode episodic memories. Previous human studies show that hippocampal neurons display selective responses to short (5 s) video clips (e.g. see Gelbard-Sagiv et al, Science 322: 96-101, 2008). The hippocampal responses in head-fixed mice to a longer (30 s) movie as studied in this manuscript could potentially offer important evidence that the rodent hippocampus encodes visual episodes.

We have now included citations to Gelbard-Sagiv et al. Science 2008 paper and many other references too, thank you for pointing that out. There are major differences between that study and ours.

• The movies used in previous study contained very familiar, famous people and famous events, and the experiment was about the patient’s ability to recall those famous movie episodes. In our case the mice had seen this movie clip only twice before.

• They did not look at the fine structure of neural responses below half a second whereas we looked at the mega-scale representations from 30ms to 30s.

• The movie clips in that study were in full color with audio, we used an isoluminant, black-and-white, silent movie clip.

• Their movie clips contained humans and was observed by humans, whereas our study mice observed a movie clip with humans and no mice or other animals.

The analysis strategy is mostly well designed and executed. A number of factors and controls, including baseline firing, locomotion, frame-to-frame visual content variation, are carefully considered. The inclusion of neuronal responses to scrambled movie frames in the analysis is a powerful method to reveal the modulation of a key element in episodic events, temporal continuity, on the hippocampal activity. The properties of movie fields are comprehensively characterized in the manuscript.

Thank you.

Although the hippocampal movie fields appear to be weaker than the visual ones (Fig. 2g, Ext. Fig. 6b), the existence of consistent hippocampal responses to movie frames is supported by the data shown. Interestingly, in my opinion, a strong piece of evidence for this is a "negative" result presented in Ext. Fig. 13c, which shows higher than chance-level correlations in hippocampal responses to same scrambled frames between even and odd trials (and higher than correlations with neighboring scrambled frames). The conclusion that hippocampal movie fields depend on continuous movie frames, rather than a pure visual response to visual contents in individual frames, is supported to some degree by their changed properties after the frame scrambling (Fig. 4).

Yes, hippocampal selectivity is not entirely abolished with scrambled movie, as we show in several figures (Fig 4d,g and Extended Data Fig. 16), but it is greatly reduced, far more than in the afferent visual cortices. The fraction of tuned cells for scrambled movies dropped to 4.5% in hippocampus, which is close to the chance level of 3%. In contrast, in visual areas selectivity was still above 80%.

Significant overlap between even and odd trials is to be expected for the tuned cells. Without a significant overlap, i.e. a stable representation, they will not be tuned. Despite this, the correlation between even and odd trials for the (only 4.5% of) tuned cells in the hippocampus was more than 2-fold smaller than (more than 80% of) cells in visual cortices. This strongly supports our hypothesis that unlike visual cortices, hippocampal subfields depended very strongly on the continuity of visual information. We will clarify this in the main text.

However, there are two potential issues that could complicate this main conclusion.

One issue is related to the effect of behavioral variation or brain state. First, although the authors show that the movie fields are still present during low-speed stationary periods, there is a large drop in the movie tuning score (Z), especially in the hippocampal areas, as shown in Ext. Fig. 3b (compared to Ext. Fig. 2d). This result suggests a potentially significant enhancement by active behavior.

There seems to be some misunderstanding here. There was no major reduction in movie tuning during immobility or active running. As we wrote in the manuscript, the drop in selectivity during purely immobile epochs is because of reduction in the amount of data, not reduction in selectivity per se. Specifically, as the amount data reduces, the statistical strength of tuning (z-scored sparsity) reduces. For example, if we split the total of 60 trials worth of data into two parts, the amount of data reduces to about half in each part, leading to a seeming reduction in selectivity in both halves. Extended figure 2B shows nearly identical tuning in all brain regions during immobility and equivalent subsamples chosen randomly from the entire data, including mobility and immobility. We will include additional data in the revised manuscript to demonstrate this more clearly. Please see below for more details.

Second, a general, hard-to-tackle concern is that neuronal responses could be greatly affected by changes in arousal or brain state (including drowsy or occasional brief slow-wave sleep state) in head-fixed animals without a task. Without the analysis of pupil size or local field potentials (LFPs), the arousal states during the experiment are difficult to know.

In the revised manuscript we will that the behavioral state effects cannot explain movie tuning. Specifically:

• We compare sessions in which the mouse was mostly immobile versus sessions in which the mouse was mostly running. Movie tuned cells were found in both these cases (Extended Data Fig. 7).

• b. We detect and remove all data around sharp-wave ripples (SWR). Movie tuning was unchanged in the remaining data.

• c. As a further control, we quantified arousal by two standard metrics. First within a session, we split the data into two groups, segments with high theta power and segments with low theta power. Significant movie tuning persisted in both.

• d. Finally, pupil dilation is another common method to estimate arousal, so data within a session were split into two parts: those with pupil dilation versus constriction. Movie tuning remained significant in both parts. See the new Extended Data Fig. 7.

Many example movie fields in the presented raw data (e.g., Fig. 1c, Ext. Fig. 4) are broad with low-quality tuning, which could be due to broad changes in brain states. This concern is especially important for hippocampal responses, since the hippocampus can enter an offline mode indicated by the occurrence of LFP sharp-wave ripples (SWRs) while animals simply stay immobile. It is believed that the ripple-associated hippocampal activity is driven mainly by internal processing, not a direct response to external input (e.g., Foster and Wilson, Nature 440: 680, 2006). The "actual" hippocampal movie fields during a true active hippocampal network state, after the removal of SWR time periods, could have different quantifications that impact the main conclusion in the manuscript.

We included the broadly tuned hippocampal neurons to demonstrate the movie-field broadening compared to those in visual areas. We will include more examples with sharp movie fields in the hippocampal regions (Main figure 1a-d right column, 2d and h, Extended Data Fig 5 and 8). Further, as stated above, we detected sharp-wave ripples and removed one second of data around SWR. Move tuning was unchanged in the remaining data. Thus, movie tuning is not generated internally via SWR (Extended Data Fig. 6). See also Extended Data 7 and 8 and the response above.

Another issue is related to the relative contribution of direct visual response versus the response to temporal continuity in movie fields. First, the data in Ext. Fig. 8 show that rapid frame-to-frame changes in visual contents contribute largely to hippocampal movie fields (similarly to visual movie fields).

There seems to be some misunderstanding here. That figure showed that the frame-toframe changes in the visual content had the highest effect on visual areas MSUA and much weaker in hippocampus (Extended Data Fig. 8, as per previous version). For example, the depth of modulation (max – min) / (max + min) for MSUA was 21% and 24% for V1 but below 6% for hippocampal regions. Similarly, the MSUA was more strongly (negatively) correlated with F2F correlation for visual areas (r=0.48 to 0.56) than hippocampal (0.07 to 0.3). Similarly, comparing the number of peaks or their median widths, visual regions showed stronger correlation with F2F, and largest depth of modulation than hippocampal regions, barring handful exceptions (like CA3 correlation between F2F and median peak duration). This strongly supports our claim that visual regions generated far greater response of the frame-to-frame changes in the movie than hippocampal regions.

Interestingly, the data show that movie-field responses are correlated across all brain areas including the hippocampal ones.

The changes in multiunit activity are strongly correlated only between visual areas and some of the hippocampal region pairs. The correlation is much weaker for hippocampal areas, or hippocampal-visual area pairs. This will be quantified explicitly in the revised text Extended Data Fig. 11 with an additional correlation matrix. Further, in Fig 3c we compared the MSUA responses with normalization between brain regions. Amongst the 21 possible brain region pairs, 5 were uncorrelated, 7 were significantly negatively correlated and 9 were significantly correlated.

This could be due to heightened behavioral arousal caused by the changing frames as mentioned above, or due to enhanced neuronal responses to visual transients, which supports a component of direct visual response in hippocampal movie fields.

As shown in Extended data 7 and 8 and described above, the effect of arousal as quantified by theta power of pupil diameter cannot explain the results in hippocampal areas and the correlations in multiunit responses are unrelated across many brain areas.

Second, the data in Ext. Fig. 13c show a significant correlation in hippocampal responses to same scrambled frames between even and odd trials, which also suggests a significant component of direct visual response.

This is plausible. The fraction of hippocampal cells which were significantly tuned for the scrambled presentation (4.5%) was close to chance level (3%), and this small subset of cells was used to compute the population overlap between even and odd trials in Ext Fig. 13 (old numbering). As described above, this significant but small amount of tuning could generate significant population overlap, which is to be expected by construction.

Is there a significant component purely due to the temporal continuity of movie frames in hippocampal movie fields? To support that this is indeed the case, the authors have presented data that hippocampal movie fields largely disappear after movie frames are scrambled. However, this could be caused by the movie-field detection method (it is unclear whether single-frame field could be detected).

As described in the methods section, the movie-field detection algorithm had a resolution of 3.3ms resolution, which ensured that we could detect single frame fields. As reported, we did find such short movie fields in several cells in the visual areas. The sparsity metric used is agnostic to the ordering of the responses, and hence single frame field, and the resultant significant movie-tuning, if present, can be detected by our methods.

Another concern in the analysis is that movie-fields are not analyzed on re-arranged neural responses to scrambled movie frames. The raw data in Fig. 4e seem quite convincing. Unfortunately, the quantifications of movie fields in this case are not compared to those with the original movie.

We saw very few (3.6-4.9%) cells with significant movie tuning for scrambled presentation in the hippocampus. Hence, we did not quantify this earlier. This is now provided in new Extended Data Fig. 16. The amount of movie tuning for the scrambled presentation taken as-is, or after rearranging the frames is below 5% for all hippocampal brain regions.

Reviewer #2 (Public Review):

[…] The authors have concluded that the neurons in the thalamo-cortical visual areas and the hippocampus commonly encode continuous visual stimuli with their firing fields spanning the mega-scale, but they respond to different aspects of the visual stimuli (i.e., visual contents of the image versus a sequence of the images). The conclusion of the study is fairly supported by the data, but some remaining concerns should be addressed.

1) Care should be taken in interpreting the results since the animal's behavior was not controlled during the physiological recording.

This was done intentionally since plenty of research shows that task demand (e.g., Aronov and Tank, Nature 2017) can not only modulate hippocampal responses but also dramatically alter them. We have now provided additional figures (Extended Data Fig. 6 and 7) where we quantified the effects of the behavioral states (sharp wave ripples, theta power and pupil diameter), as well as the effect of locomotion (Extended Data Fig. 4). Movie tuning remained unaffected with these manipulations. Thus, movie tuning cannot be attributed to behavioral effects.

It has been reported that some hippocampal neuronal activities are modulated by locomotion, which may still contribute to some of the results in the current study. Although the authors claimed that the animal's locomotion did not influence the movie-tuning by showing the unaltered proportion of movie-tuned cells with stationary epochs only, the effects of locomotion should be tested in a more specific way (e.g., comparing changes in the strength of movie-tuning under certain locomotion conditions at the single-cell level).

Single cell analysis of the effect of locomotion and visual stimulation is underway, and beyond the scope of the current work. As detailed in the (Extended Data Fig. 4), we have ensured that in spite of the removal of running or stationary epochs, as well as removal of sharp wave ripple events (Extended Data Fig. 6) movie tuning persists. Further, we will provide examples of strongly tuned cells from sessions with predominantly running or predominantly stationary behavior (Extended Data Fig. 7).

2) The mega-scale spanning of movie-fields needs to be further examined with a more controlled stimulus for reasonable comparison with the traditional place fields. This is because the movie used in the current study consists of a fast-changing first half and a slow-changing second half, and such varying and ununified composition of the movie might have largely affected the formation of movie-fields. According to Fig. 3, the mega-scale spanning appears to be driven by the changes in frame-to-frame correlation within the movie. That is, visual stimuli changing quickly induced several short fields while persisting stimuli with fewer changes elongated the fields.

Please note that a strong correlation between the speed at which the movie scene changed across frames was correlated with movie-field width in the visual areas, but that correlation was much weaker in the hippocampal areas (see above). Please see Extended Data Fig. 11 and the quantification of correlation between frame-to-frame changes in the movie and the properties of movie fields.

The presentation of persisting visual input for a long time is thought to be similar to staying in one place for a long time, and the hippocampal activities have been reported to manifest in different ways between running and standing still (i.e., theta-modulated vs. sharp wave ripple-based). Therefore, it should be further examined whether the broad movie-fields are broadly tuned to the continuous visual inputs or caused by other brain states.

As shown in Extended Data Fig. 6, movie field properties are largely unchanged when SWR are removed from the data, or when the effect of pupil diameter or theta power were factored for (Extended Data Fig.7).

3) The population activities of the hippocampal movie-tuned cells in Fig. 3a-b look like those of time cells, tiling the movie playback period. It needs to be clarified whether the hippocampal cells are actively coding the visual inputs or just filling the duration.

Tiling patterns would be observed when the maximal are sorted in any data, even for random numbers. This alone does not make them time cells. The following observations suggest that movie fields cannot be explained as being time cells.

• a. Time cells mostly cluster at the beginning of a running epoch (Pastalkova et al. Science 2008, MacDonald et al. Neuron 2011) and they taper off towards the end. Such large clustering is not visible in these tiling plots for movie tuned cells.

• b. Time fields become wider as the temporal duration progresses (Pastalkova et al. Science 2008, MacDonald et al. Neuron 2011) as the encoded temporal duration increases. This is not evident in any movie fields.

• c. Widths of movie fields in visual areas, and to a smaller extent in the hippocampal areas, were clearly modulated by the visual content, like the change from one frame to the next (F2F correlation, Extended Data Fig. 11).

• d. Tiling pattern of movie fields was found in visual areas too, with qualitatively similar pattern as hippocampus. Clearly, visual area responses are not time cells, as shown by the scrambled stimulus experiment. Here, neural selectivity could be recovered by rearranging them based on the visual content of the continuous movie, and not the passage of time.

The scrambled condition in which the sequence of the images was randomly permutated made the hippocampal neurons totally lose their selective responses, failing to reconstruct the neural responses to the original sequence by rearrangement of the scrambled sequence. This result indirectly addressed that the substantial portion of the hippocampal cells did not just fill the duration but represented the contents and temporal order of the images. However, it should be directly confirmed whether the tiling pattern disappeared with the population activities in the scrambled condition (as shown in Extended Data Fig. 11, but data were not shown for the hippocampus).

As stated above for the continuous movie, tiling pattern alone does not mean those are time cells. Further, tuning, and tiling pattern remained intact with scrambled movie in the visual cortices but not in hippocampus.

Reviewer #3 (Public Review):

[…] The paper is conceptually novel since it specifically aims to remove any behavioral or task engagement whatsoever in the head-fixed mice, a setup typically used as an open-loop control condition in virtual reality-based navigational or decision making tasks (e.g. Harvey et al., 2012). Because the study specifically addresses this aspect of encoding (i.e. exploring effects of pure visual content rather than something task-related), and because of the widespread use of video-based virtual reality paradigms in different sub-fields, the paper should be of interest to those studying visual processing as well as those studying visual and spatial coding in the hippocampal system. However, the task-free approach of the experiments (including closely controlling for movement-related effects) presents a Catch-22, since there is no way that the animal subjects can report actually recognizing or remembering any of the visual content we are to believe they do.

Our claim is that these are movie scene evoked responses. We make no claims about the animal’s ability to recognize or remember the movie content. That would require entirely different set of experiments. Meanwhile, we have shown that these results are not an artifact of brain states such as sharp wave ripples, theta power or pupil diameter (Extended Data Fig. 6 and 7) or running behavior (Extended Data Fig. 4). Please see above for a detailed response.

We must rely on above-chance-level decoding of movie segments, and the requirement that the movie is played in order rather than scrambled, to indicate that the hippocampal system encodes episodic content of the movie. So the study represents an interesting conceptual advance, and the analyses appear solid and support the conclusion, but there are methodological limitations.

It is important to emphasize that these responses could constitute episodic responses but does not prove episodic memory, just as place cell responses constitute spatial responses but that does not prove spatial memory. The link between place cells and place memory is not entirely clear. For example, mice lacking NMDA receptors have intact place cells, but are impaired in spatial memory task (McHugh et al. Cell 1996), whereas spatial tuning was virtually destroyed in mice lacking GluR1 receptors, but they could still do various spatial memory tasks (Resnik et al. J. Neuro 2012). The experiments about episodic memory would require an entirely different set of experiments that involve task demand and behavioral response, which in turn would modify hippocampal responses substantially, as shown by many studies. Our hypothesis here, is that just like place cells, these episodic responses without task demand would play a role, to be determined, in episodic memory. We will emphasize this point in the main text (Ln 432-436 in the revised manuscript).

Major concerns:

1) A lot hinges on hinges on the cells having a z-scored sparsity >2, the cutoff for a cell to be counted as significantly modulated by the movie. What is the justification of this criterion?

The z-scored sparsity (z>2) corresponds to p<0.03. This would mean that 3% of the results could appear by chance. Hence, z>2 is a standard method used in many publications. Another advantage of z-scored sparsity is that it is relatively insensitive to the number of spikes generated by a neuron (i.e. the mean firing rate of the neuron and the duration of the experiment). In contrast, sparsity is strongly dependent on the number of spikes which makes it difficult to compare across neurons, brain regions and conditions (See Supplement S5 Acharya et al. Cell 2016). To further address this point, we compared our z-scored sparsity measure with 2 other commonly used metrics to quantify neural selectivity, depth of modulation and mutual information (Extended Data Fig. 3). Comparable movie tuning was obtained from all 3 metrics, upon z-scoring in an identical fashion.

It should be stated in the Results. Relatedly, it appears the formula used for calculating sparseness in the present study is not the same as that used to calculate lifetime sparseness in de Vries et al. 2020 quoted in the results (see the formula in the Methods of the de Vries 2020 paper immediately under the sentence: "Lifetime sparseness was computed using the definition in Vinje and Gallant").

The definition of sparsity we used is used commonly by most hippocampal scientists (Treves and Rolls 1991, Skaggs et al. 1996, Ravassard et al. 2013). Lifetime sparseness equation used by de Vries et al. 2020, differs from us by just one constant factor (1-1/N) where N=900 is the number of frames in the movie. This constant factor equals (1- 1/900)=0.999. Hence, there is no difference between the sparsity obtained by these two methods. Further, z-scored sparsity is entirely unaffected by such constant factors. We will clarify this in the methods of the revised manuscript.

To rule out systematic differences between studies beyond differences in neural sampling (single units vs. calcium imaging), it would be nice to see whether calculating lifetime sparseness per de Vries et al. changed the fraction "movie" cells in the visual and hippocampal systems.

As stated above, the two definitions of sparsity are virtually identical and we obtained similar results using two other commonly used metrics, which are detailed in Extended Data Fig. 3.

2) In Figures 1, 2 and the supplementary figures-the sparseness scores should be reported along with the raw data for each cell, so the readers can be apprised of what types of firing selectivity are associated with which sparseness scores-as would be shown for metrics like gridness or Raleigh vector lengths for head direction cells. It would be helpful to include this wherever there are plots showing spike rasters arranged by frame number & the trial-averaged mean rate.

As shown in several papers (Aghajan et al Nature Neuroscience 2015, Acharya et al., Cell 2016) raw sparsity (or information content) are strongly dependent on the number of spikes of a neuron. This makes the raw values of these numbers impossible to compare across cells, brain regions and conditions. (Please see Supplement S5 from Acharya et al., Cell 2016 for details). Including the data of sparsity would thus cause undue confusion. Hence, we provide z-scored sparsity. This metric is comparable across cells and brain regions, and now provided above each example cell in Figure 1 and Extended Data Fig. 2.

3) The examples shown on the right in Figures 1b and c are not especially compelling examples of movie-specific tuning; it would be helpful in making the case for "movie" cells if cleaner / more robust cells are shown (like the examples on the left in 1b and c).

We did not put the most strongly tuned hippocampal neurons in the main figures so that these cells are representative of the ensemble and not the best possible ones, so as to include examples with broad tuning responses. We have clarified in the legend that these cells are some of the best tuned cells. Although not the cleanest looking, the z-scored sparsity mentioned above the panels now indicates how strongly they are modulated compared to chance levels. Additional examples, including those with sharply tuned responses are shown in Extended Data Fig. 5 and 8.

4) The scrambled movie condition is an essential control which, along with the stability checks in Supplementary Figure 7, provide the most persuasive evidence that the movie fields reflect more than a passive readout of visual images on a screen. However, in reference to Figure 4c, can the authors offer an explanation as to why V1 is substantially less affected by the movie scrambling than it's main input (LGN) and the cortical areas immediately downstream of it? This seems to defy the interpretation that "movie coding" follows the visual processing hierarchy.

This is an important point, one that we find very surprising as well. Perhaps this is related to other surprising observations in our manuscript, such as more neurons appeared to be tuned to the movie than the classic stimuli. A direct comparison between movie responses versus fixed images is not possible at this point due to several additional differences such as the duration of image presentations and their temporal history. The latency required to rearrange the scrambled responses (60ms for LGN, 74ms for V1, 91ms for AM/PM) supports the anatomical hierarchy. The pattern of movie tuning properties was also broadly consistent between V1 and AM/PM (Fig 2). However, all metrics of movie selectivity (Fig 2) to the continuous movie showed a consistent pattern that was the exact opposite pattern of the simple anatomical hierarchy: V1 had stronger movie tuning, higher number of movie fields per cell, narrower movie-field widths, larger mega-scale structure, and better decoding than LGN. V1 was also more robust to the scrambled sequence than LGN. One possible explanation is that there are other sources of inputs to V1, beyond LGN, that contribute significantly to movie tuning. This is an important insight and we will modify the discussion to highlight this.

Relatedly, the hippocampal data do not quite fit with visual hierarchical ordering either, with CA3 being less sensitive to scrambling than DG. Since the data (especially in V1) seem to defy hierarchical visual processing, why not drop that interpretation? It is not particularly convincing as is.

The anatomical organization is well established and an important factor. Even when observations do not fit the anatomical hierarchy, it provides important insights about the mechanisms. All properties of movie tuning (Fig 2) –the strength of tuning, number of movie peaks, their width and decoding accuracy firmly put visual areas upstream of hippocampal regions. But, just like visual cortex there are consistent patterns that do not support a simple feed-forward anatomical hierarchy. We have pointed out these patterns so that future work can build upon it.

5) In the Discussion, the authors argue that the mice encode episodic content from the movie clip as a human or monkey would. This is supported by the (crucial) data from the scrambled movie condition, but is nevertheless difficult to prove empirically since the animals cannot give a behavioral report of recognition and, without some kind of reinforcement, why should a segment from a movie mean anything to a head-fixed, passively viewing mouse?

We emphasize once again that our claim is about the nature of encoding of the movie across these neurons. We make no claims about whether this forms a memory or whether the mouse is able to recognize the content or remember it. Despite decades of research, similar claims are difficult to prove for place cells, with plenty of counter examples (See the points above). The important point here is that despite any cognitive component, we see remarkably tuned responses in these brain areas. Their role in cognition would take a lot more effort and is beyond the scope of the current work.

Would the authors also argue that hippocampal cells would exhibit "song" fields if segments of a radio song-equally arbitrary for a mouse-were presented repeatedly? (reminiscent of the study by Aronov et al. 2017, but if sound were presented outside the context of a task). How can one distinguish between mere sequence coding vs. encoding of episodically meaningful content? One or a few sentences on this should be added in the Discussion.

Aronov et al 2017, found the encoding of an audio sweep in hippocampus when the animals were doing a task (release the lever at a specific frequency to obtain a reward). However, without a task demand they found that hippocampal neurons did not encode the audio sequence beyond chance levels. This is at odds with our findings with the movie where we see strong tuning despite any task demand or reward. These results are consistent with but go far beyond our recent findings that hippocampal (CA1) neurons can encode the position and direction of motion of a revolving bar of light (Purandare et al. Nature 2022). Please see Ln 414-420 for related discussion.

These responses are unlikely to be mere sequence responses since the scrambled sequence was also fixed sequence that was presented many times and it elicited reliable responses in visual areas, but not in hippocampus. Hence, we hypothesize that hippocampal areas encode temporally related information, i.e. episodic content. We will modify the discussion to address these points.

Author Response:

We thank the eLife editorial board and the reviewers for the assessment of our article. We look forward to thoroughly addressing their comments and concerns. We would like to correct one factual error in the consensus public review:

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

The imaging sessions in which we identify value coding cells were in fact performed on separate days: Experimental Days 6 and 7 (see Figure 1b), which is evidence of the stability of value coding across consecutive days. Days 6 and 7 correspond to the third day of Odor Set 1 and the third day of Odor Set 2, respectively, which is why we referred to them both as “Day 3” in the manuscript, and this may have led to the confusion about the temporal relationship between these sessions. We will clarify this terminology in the revised manuscript.

Author Response

Reviewer #1 (Public Review):

In this well-written manuscript, Afshar et al demonstrated the significant transcriptional and proteomic differences between cultured human umbilical vein endothelial cells (HUVECs) and those freshly isolated from the cords. They showed that TGFbeta and BMP signaling target genes were enriched in cord cells compared to those in culture. Extracellular matrix (ECM) and cell cycle-related genes were also different between the two conditions. Because master regulators of EC shear stress response genes, KLF2 and KLF4, were downregulated in culture, the authors sought to restore the in vivo transcriptional profile with the application of shear stress in an orbital shaker and dextran-containing media for various time periods. They showed that after 48 hours of shear stress the transcriptional profile of sheared cells correlated with in vivo transcriptional profile more significantly than static cultures. They also showed, using single cell RNAseq, that EC-smooth muscle cell cocultures resulted in changes in TGFbeta and NOTCH signaling pathways and rescued 9% of the in vivo transcriptional signatures.

This is an important study that was elegantly executed. The authors should also be commended for making their data public; thereby, creating a valuable resource for vascular biologists.

We much appreciate the comments and thank the reviewer for the time and effort evaluating the study.

Reviewer #2 (Public Review):

The authors profiled the transcriptome and proteome of human umbilical vein endothelial cells freshly isolated from in vivo and compared that with the same cells exposed to in vitro culture under different conditions, including static culture, flow, and co-culture with smooth muscle cells. The experiments were properly designed and performed. The authors also provided a reasonable and sound interpretation of their findings. This study provides valuable insights into how the culturing conditions impact on gene expression, encouraging the field to select their in vitro work setting appropriately. Overall, the manuscript is well-written and easy to follow.

Several notable strengths include:

1. Parallel transcriptome- and proteome-wide profiling of endothelial cells enabling the unbiased interrogation of gene expression and a genome-wide view of the impact of in vitro culture on endothelial transcriptome.

2. The innovative experimental design and comparisons were done with genetically identical ECs (from the same donors) in vivo and in vitro.

3. The analyses were robust and provided novel information on flow-dependent and cell context-dependent gene regulation, with the native freshly isolated cells as a baseline.

4. The donor samples used in this study were diverse including Asian, White, Black, Latino, and American Indian samples which reduce racial background bias.

Some points that can strengthen the study:

A clear description of experimental and analytical details (e.g. how the comparisons were made) and more in-depth interpretation and discussion of the results, e.g. the complete genes that are rescued by flow and co-culture and potential synergy of these factors.

We thank the reviewer for highlighting the strengths and appreciate the comments on experimental and analytical details which have been now addressed in this revised manuscript. Specifically, we have expanded the discussion and included synergy and additional comments on the rescued genes. A clear description of experimental and analytical details (e.g. how the comparisons were made) and more in-depth interpretation and discussion of the results, e.g. the complete genes that are rescued by flow and co-culture and potential synergy of these factors are now included.

Reviewer #3 (Public Review):

Afshar et al. performed RNA-seq and LC-MS of in vivo and in vitro HUVECs to identify the role of culture conditions on gene expression. Given the widespread use of HUVECs to study EC biology, these findings are interesting and can help design better in vitro experiments. There have been previous papers that compared in vivo and in vitro HUVECs, however, the depth of sequencing and analysis in this manuscript identifies some novel effects which should be accounted for in future in vitro experiments using ECs.

Strengths:

1. Major findings of distinct pathways affected by cell culture are novel and interesting. The authors identify major effects on TGFb and ECM gene expression. They also corroborate previous findings of flow response pathways, namely KLF2/4 and Notch pathway regulation.

2. Use of multiple genomic methods to profile effects of culture conditions. The LC-MS data showed a significant correlation with RNA-seq, however, the data were not as strong so not used for subsequent analyses.

3. Use of scRNA-seq to show the dynamic effects of co-culture and shear stress on ECs is very novel. However, the heterogeneity in the EC populations is not discussed in this manuscript.

We would like to thank the reviewer for the in-depth analysis of our study and for highlighting the novelty and strength of the data. Note that we included comments in relation to EC heterogeneity as part of the limitations of this study (in the Discussion).

Weaknesses:

1. The physiological relevance of these changes in gene expression is not demonstrated in the manuscript. The authors claim the significance of their data is to improve in vitro culture to better represent in vivo biology. Is this the case with orbital shear stress? Do they rescue some functional effects in ECs with long-term shear stress? An angiogenesis, barrier function, or migration assay for HUVECs exposed to different conditions would help answer this question. A similar assay for cells after EC-VSMC co-culture would validate the importance of these stimuli.

The reviewer is correct, our manuscript did not expand into physiological read outs, we have now clearly acknowledged this as part of the limitations of the study. Notably, there is already extensive literature on the effects of different types of flow on several physiological parameters. For example, others have shown that laminar shear stress (by orbital or other means) reduces proliferation and migration (PMID: 31831023; PMID: 22012789, PMID: 12857765, PMID: 21312062, PMID: 15886673; PMID: 17323381), reduces inflammation (PMID: 34747636; PMID: 32951280), and improves barrier function (PMID: 20543206; PMID: 32457386 ; PMID: 12577139, PMID: 27246807; PMID: 31500313 ).

From the onset, our objective was to bring granularity to transcriptional changes associated with the transition from in vivo to in vitro. Further, it was our goal to identify the cohorts of transcripts that could and those that could not be rescued by altering culture conditions. Because we had transcriptional information from the identical samples at a time that they were in the vessel, we have been able to fulfill our goal. We feel this is important, and currently missing data, that will be of value to many investigators.

1. One explanation for the increased expression of ECM genes in vivo is that these cells are contaminated with VSMCs/fibroblasts. This could be very likely given that cells were not sorted or purified upon isolation. Expression of other VSMC or fibroblast-specific markers (i.e. CNN1, MYH11, SMTN, DCN, FBLN1) would help determine if there is some level of non-EC contamination.

We thank the reviewer for this comment and prompted by this, we have included a new figure (Supplemental Figure 1 and new panels in Supplemental Figure 5) that directly address this concern.

Amongst the several pieces of data, we included scRNAseq from cells that were immediately obtained from umbilical vein – three independent experiments sequenced together and showed in one UMAP (Supplemental Figure 1C). As can be appreciated, the very large majority of cells are endothelial and the only other cell types present were blood cells (erythrocytes and CD45+ cells). No smooth muscle cells or fibroblasts were detected. These three examples are indeed representative of a large number of scRNAseq datasets (35 from cords and cultures for this and other projects). Furthermore, our cultures are also routinely evaluated by FACS (one example has been provided in Supplemental Figure 1E). We do not find, as illustrated in that example, cells that are not positive for CD31 and VE-Cadherin.

We hope this information reveals the rigor of our studies and convinces the reviewer that the transcriptional changes observed are from endothelial cells.

1. The use of scRNA-seq in Figure 4 is interesting. There appear to be 2 distinct EC populations in the co-cultured ECs. What are the marker genes for the 2 populations?

Indeed, we and others (Kalluri et al., 2019) have noticed two distinct populations in the in vivo and also in cultured ECs, as pointed by the reviewer. Evaluation as to these two subpopulations reflect two transcriptionally distinct groups or different states of cyclic expression patterns, requires more thorough analysis and lineage tracing studies and distinct from the focus of this manuscript. Nonetheless, we have made a point in the revised manuscript to highlight these possibilities.

Reference: Kalluri, AS, Vellarikkal, SK, Edelman, ER, Nguyen, L, Subramanian, A, Ellinor PT, Regev, A, Kathiresan, S, Gupta, RM. Single Cell Analysis of the Normal Mouse Aorta Reveals Functionally Distinct Endothelial Cell Populations. Circulation, 2019. 140:147-163.

1. The modest shifts in gene expression with shear stress and co-culture could be attributed to the batch effect. The authors describe 1 batch correction method (ComBat) in the bulk RNA-seq, but no mention of batch correction was noted in the scRNA-seq methods. The authors should ensure that batch effect correction in all data is adequate, and these results should be added to the manuscript.

We thank the reviewer for this comment. Indeed, batch effects are a particularly important consideration when samples are prepared separately and/or sequenced at distinct times, note this was not the case in this study.

For the scRNA-seq analysis, we removed the low-quality cells, but did not use batch-effect correction methods because the samples were prepared and run at the same time. Meaning, isolation was performed in parallel, generation of cDNA libraries was done concurrently, and sequencing was run in the same gel. The quality of the data (and lack of batch effect) was subsequently verified when the two mono-culture biological replicates were evaluated by Seurat and were found to overlap on the UMAP (Figure 4), the same applies to the two co-culture biological replicates. These results clearly indicate that there’s no batch effect (as the samples were not process in distinct batches) among these samples.

1. Table 1 shows ATAC-seq was done, however, no data from these experiments are provided in the manuscript.

As mentioned (reviewer 2), we had performed ATACseq but decided to remove from the manuscript for several reasons and apologize for missing reference to Table 1. We have now corrected this error.

1. Shear stress was achieved with an orbital shaker, which the accompanying citation states introduces significant heterogeneity in the ECs. This is based on the location of the culture dish. Was this heterogeneity seen in the scRNA-seq data?

Correct. We only use the 2/3 peripheral area of the plates and discard the central aspect of the plate. We have added clarifying language to the Methods > Shear stress application to reflect this: “Orbital shear stress (130 rpm) was applied to confluent cell cultures by using an orbital shaker positioned inside the incubator as previously discussed (32). The shear stress within the cell culture well corresponds to arterial magnitudes (11.5 dynes/cm2) of shear stress. To reduce issues associated with uniformity of shear stress, the endothelial cell monolayers in 6-well plates were lysed after removing center region using cell scraper (BD Falcon #35-3085) and washing with 1X HBSS (Corning #21-022-CV). The 1.8cm blade was circumferentially used in the center of the 6-well plate to remove the center of the monolayer that did not see the higher shear stress.”

1. It would be important to know whether the authors reproduce the findings from other papers that CD34 expression is reduced in cultured HUVECs:

Muller AM, Cronen C, Muller KM, Kirkpatrick CJ: Comparative analysis of the reactivity of human umbilical vein endothelial cells in organ and monolayer culture. Pathobiology 1999;67:99-107. Delia D, Lampugnani MG, Resnati M, Dejana E, Aiello A, Fontanella E, Soligo D, Pierotti MA, Greaves MF: Cd34 expression is regulated reciprocally with adhesion molecules in vascular endothelial cells in vitro. Blood 1993;81:1001-1008.

Thank you for this suggestion. Supplemental Excel 4 allows the reader to review single genes that are modulated by condition and in fact, consistent with all previous literature, CD34 expression is one of the most significantly decreased genes in cultured HUVECs (0.9, p=1E-5).

Author Response

Reviewer #1 (Public Review):

1) I was confused about the nature of the short-term plasticity mechanism being modeled. In the Introduction, the contrast drawn is between synaptic rewiring and various plasticity mechanisms at existing synapses, including long-term potentiation/depression, and shorter-term facilitation and depression. And the synaptic modulation mechanism introduced is modeled on STDP (which is a natural fit for an associative/Hebbian rule, especially given that short-term plasticity mechanisms are more often non-Hebbian).

Indeed, because of its associative nature, the modulation mechanism was envisioned to be STDP-like, i.e. on faster time scales than the complete rewiring of the network (via backpropagation) but slower time scales than things like STSP which, as the reviewer points out, are usually not considered associative. One thing we do want to highlight is that backpropagation and the modulation mechanism are certainly not independent of one another. During training, the network’s weights that are being adjusted by backpropagation are experiencing modulations, and said modulations certainly factor into the gradient calculation.

We have edited the abstract and introduction to try to make the distinction of what we are trying to model clearer.

1) cont: On the other hand, in the network models the weights being altered by backpropagation are changes in strength (since the network layers are all-to-all), corresponding more closely to LTP/LTD. And in general, standard supervised artificial neural network training more closely resembles LTP/LTD than changing which neurons are connected to which (and even if there is rewiring, these networks primarily rely on persistent weight changes at existing synapses).

Although we did not highlight this particular biological mechanism because we wanted to keep the updates as general as possible, one could view the early versus late LTP. We have added an additional discussion of how the associative modulation mechanisms and backpropagation might biologically map into this mechanism in the discussion section.

1) cont: Moreover, given the timescales of typical systems neuroscience tasks with input coming in on the 100s of ms timescale, the need for multiple repetitions to induce long-term plasticity, and the transient nature/short decay times of the synaptic modulations in the SM matrix, the SM matrix seems to be changing on a timescale faster than LTP/LTD and closer to STP mechanisms like facilitation/depression. So it was not clear to me what mechanism this was supposed to correspond to.

We note that although the structure of the tasks certainly resembles known neuroscience experiments that happen on shorter time scales (and with the introduction of the 19 new NeuroGym tasks, even more so), we did not have a particular time scale for task effects in mind. So each piece of “evidence” in the integration tasks may indeed occur over significantly slower time scales and could abstractly represent multiple repetitions in order to induce (say) early phase LTP.

Given that the separation between the two plasticity mechanisms may be clearer for STSP, and indeed many of the tasks we investigate may more naturally be mapped to tasks that occur on time scales more relevant to STSP, we have introduced a second modulation rule that is only dependent upon the presynaptic firing rates. See our response to the Essential Revisions above for additional details on these new results.

2) A number of studies have explored using short-term plasticity mechanisms to store information over time and have found that these mechanisms are useful for general information integration over time. While many of these are briefly cited, I think they need to be further discussed and the current work situated in the context of these prior studies. In particular, it was not clear to me when and how the authors' assumptions differed from those in previous studies, which specific conclusions were novel to this study, and which conclusions are true for this specific mechanism as opposed to being generally true when using STP mechanisms for integration tasks.

We have added additional works to the related works sections and expanded the introduction to try to better convey the differences with our work and previous studies. Briefly, mostly our assumptions differed from previous studies in that we considered a network that relied only on synaptic modulations to do computations, rather than a network with both recurrence and synaptic modulations. This allowed us to isolate the computational power and behavior of computing using synaptic modulations alone.

It is hard to say which of the conclusions are generally true when using STP mechanisms for integration tasks without a comprehensive comparison of the various models of STP on the same tasks we investigated here. That being said, we believe we have presented in this work conclusions that are not present in other works (as far as we are aware) including: (1) a demonstration of the strength of computing with synaptic connection on a large variety of sequential tasks, (2) an investigation into the dynamics of such computations how they might manifest in neuronal recordings, and (3) a brief look at how these different dynamics might be computational beneficial in neuroscience-relevant areas. We also note that one reason for the simplicity of our mechanism is that we believe it captures many effects of synaptic modulations (e.g. gradual increase/decrease of synaptic strength that eventually saturates) with a relatively simple expression, and so we believe other STP mechanisms would yield qualitatively similar results. We have edited the text to try to clarify when conclusions are novel to this study and when we are referencing results from other works.

Reviewer #2 (Public Review):

On the other hand, the general principle appears (perhaps naively) very general: any stimulus-dependent, sufficiently long-lived change in neuronal/synaptic properties is a potential memory buffer. For instance, one might wonder whether some non-associative form of synaptic plasticity (unlike the Hebbian-like form studied in the paper), such as short-term synaptic plasticity which depends only on the pre-synaptic activity (and is better motivated experimentally), would be equally effective. Or, for that matter, one might wonder whether just neuronal adaptation, in the hidden layer, for instance, would be sufficient. In this sense, a weakness of this work is that there is little attempt at understanding when and how the proposed mechanism fails.

We have tried to address if the simplicity of the tasks considered in this work may be a reason for the MPN’s success by training it on 19 additional neuroscience tasks (see response to Essential Revisions above). Across all these additional tasks, we found the MPN performs comparable to its RNN counterparts.

To address whether associativity is necessary in our setup we have introduced a version of the MPN that has modulation updates that are only presynaptic dependent. We call this the “MPNpre” and have added several results across the paper addressing its computational abilities (again, additional details are provided above in Essential Revisions). We find the MPNpre has dynamics that are qualitatively the same as its MPN counterpart and has very comparable computational capabilities.

Certainly, some of the tasks we consider may also be solvable by introducing other forms of computation such as neuronal adaptation. Indeed, we believe the ability of the brain to solve tasks in so many different ways is one of the things that makes it so difficult to study. Our work here has attempted to highlight one particular way of doing computations (via synapse dynamics) and compared it to one particular other form (recurrent connections). Extending this work to even more forms of computation, including neuronal dynamics, would be very interesting and further help distinguish these different computational methods from one another.

Reviewer #3 (Public Review):

Because the MPN is essentially a low-pass filter of the activity, and the activity is the input - it seems that integration is almost automatically satisfied by the dynamics. Are these networks able to perform non-integration tasks? Decision-making (which involves saddle points), for instance, is often studied with RNNs.

We have tested the MPN on 19 additional supervised learning tasks found in the NeuroGym package (Molano-Mazon et. al., 2022), which consists of several decision-making-based tasks and added these results to the main text (see response to Essential Revisions above, and also Figs. 7i & 7j). Across all tasks we investigated, we found the MPN performs at comparable levels to its RNN counterparts.

Manuel Molano-Mazon, Joao Barbosa, Jordi Pastor-Ciurana, Marta Fradera, Ru-Yuan Zhang, Jeremy Forest, Jorge del Pozo Lerida, Li Ji-An, Christopher J Cueva, Jaime de la Rocha, et al. “NeuroGym: An open resource for developing and sharing neuroscience tasks”. (2022).

The current work has some resemblance to reservoir computing models. Because the M matrix decays to zero eventually, this is reminiscent of the fading memory property of reservoir models. Specifically, the dynamic variables encode a decaying memory of the input, and - given large enough networks - almost any function of the input can be simply read out. Within this context, there were works that studied how introducing different time scales changes performance (e.g., Schrauwen et al 2007).

Thank you for pointing out this resemblance and work. In our setup, the fact that lamba is the same for the entire network means all elements of M decrease uniformly (though the learned modulation updates may allow for the growth of M to be non-uniform). One modification that we think would be very interesting to explore is the effects on the dynamics of non-uniform learning rates or decays across synapses. In this setting, the M matrix could have significantly different time scales and may even further resemble reservoir computing setups. We have added a sentence to the discussion section discussing this possibility.

Another point is the interaction of the proposed plasticity rule with hidden-unit dynamics. What will happen for RNNs with these plasticity rules? I see why introducing short-term plasticity in a "clean" setting can help understand it, but it would be nice to see that nothing breaks when moving to a complete setting. Here, too, there are existing works that tackle this issue (e.g., Orhan & Ma, Ballintyn et al, Rodriguez et al).

Thank you for pointing out these additional works, they are indeed very relevant and we have added them all to the text where relevant.

Here we believe we have shown that either recurrent connections or synaptic dynamics alone can be used to solve a wide variety of neuroscience tasks. We don’t believe a hybrid setting with both synaptic dynamics and recurrence (e.g. a Vanilla RNN with synaptic dynamics) would “break” any part of this setup. Since each of the computational mechanisms could be learned to be suppressed the network could simply solve the task by relying on only one of the two mechanisms. For example, it could use a strictly non-synaptic solution by driving eta (the learning rate of the modulations) to zero or it could use a non-recurrent solution by driving the influence of recurrent connections to be very small. Orhan & Ma mention they have a hard time training a Vanilla RNN with Hebbian modulations on the recurrent weights for any modulation effect that goes back more than one time step, but unlike our work they rely on a fixed modulation strength.

Indeed, we think how networks with multiple computational mechanisms will solve tasks is a very interesting question to be further investigated, and a hybrid solution may be likely. We believe our work is valuable in that it illuminates one end of the spectrum that is relatively unexplored: how such tasks could be solved using just synaptic dynamics. However, what type of solution a complete setup ultimately lands on is likely largely dependent upon both the initialization and the training procedure, so we felt exploring the dynamics of such networks was outside the scope of this work.

One point regarding biological plausibility - although the model is abstract, the fact that the MPN increases without bounds are hard to reconcile with physical processes.

Note although the MPN expression does not have explicit bounds, in practice the exponential decay eventually does balance with the SM matrix updates, and so we observe a saturation in its size (Fig. 4c, except for the case of lamba=1.0, which is not considered elsewhere in the text). However, we explicitly added modulation bounds to the M matrix update expression and did not find it significantly changed the results (see comments on Essential Revisions above for details).

Author Response

Reviewer #2 (Public Review):

Here I will mainly comment on the biology of adipocytes, which is my specialty.

In this manuscript, it has been very convincingly shown that O-GlcNAc acts as an important regulator of MSC differentiation in mice, and given previous studies in which O-GlcNAc is regulated by aging and nutritional status, it makes sense that this PTM determines differentiation and BM niche.

The point that O-GlcNAc regulates adipocyte differentiation is convincing, but there are already previous studies using 3T3-L1 (e.g., Biochemical and Biophysical Research Communications 417 (2012) 1158-1163), and a more step-by-step demonstration of the molecular mechanism would make this an excellent paper that can be extended to adipocyte research in general, not just BM.

While O-GlcNAc has been demonstrated in regulating many aspects of metabolic physiology, our understanding of its role in adipogenesis has been limited so far. As the reviewer pointed out, there was an in vitro report on its inhibition of adipogenesis in 3T3-L1 cells (Ji et al., 2012). Two recent publications from Dr. Xiaoyong Yang’s group revealed the profound role of mature white adipocytes OGT in regulating lipolysis and obesity (Li et al., 2018; Yang et al., 2020). To my knowledge, our manuscript is the first attempt to address the regulation of adipogenesis by O-GlcNAc in vivo. While using the BMSCs as a non-conventional model, we speculate our molecular mechanisms (i.e., O-GlcNAc inhibition of C/EBPβ) could be conserved in peripheral adipose organs, including white and brown adipose tissues. Future experiments are warranted in the lab to extend the current knowledge to these adipocyte progenitors. Nonetheless, I would also like to point out that, due to the broad actions of OGT and the current lack of adipocyte progenitor specific Cre animal tools, such efforts might be futile as results can be confounded by defects in other organs/cells.

It is somewhat unclear whether or not the authors' in vitro experiments using 10T1/2 cells accurately reflect what is happening in vivo in knockout mice. The PDGFRa+VCAM1+ population of adipocyte progenitors shown by the authors is upregulated by about 30% by knockout of Ogt (Figure 4C). How significant is this difference? Rather, might the expression of Pparg, which indicates lineage commitment, be the underlying mechanism? In any case, this manuscript is highly impactful in the sense that the differentiation of adipocytes forming the BM niche can be controlled using tissue-specific knockouts of the Ogt gene.

We agree with the reviewer that the role of OGT in BMSC fate determination and adipogenesis might be multifaceted. The 30% increase in PDGFRa+VCAM1+ BM adipose progenitors cannot fully explain the massive adipogenesis observed in OgtΔOsx animals (Fig. 4A). Indeed, we provided in vitro evidence that genetic deletion or chemical inhibition of OGT activates adipogenesis (Fig. 4D-I). Mechanistically, we found the O-GlcNAcylation of C/EBPβ protein (but not PPARγ) is responsible in the inhibition, which leads to reduced expression of adipogenic genes, including Pparg (Fig. 4H).

Author Response

Reviewer #1 (Public Review):

The paper states that they observed a combined total of 77,017 single-nucleotide variants (SNVs) and 12,031 insertion/deletions (In/Dels) across all tissue, age, and intervention groups. Collectively, these data represent the largest collection of somatic mtDNA mutations obtained in a single study to date. However, A study with more somatic mtDNA mutations by the LostArc method (PMID 32943091) revealed 35 million deletions (~ 470,000 unique spans) in skeletal muscle from 22 individuals with and 19 individuals without pathogenic variants in POLG. Thus, the authors should reword this part to say that this study represents the largest collections of mouse mtDNA point mutations detected, but not the largest amount of mutations (deletions exceed this number).

Thank you for pointing this out. When we wrote that sentence, we were more referring to small polymerase-based errors, as opposed to larger structural variants that likely arise from a different mechanism. However, the distinction between these two event classes is poorly defined. We have amended our statement and have added a citation to Lujan et al. Our statement now reads “We observed a combined total of 77,017 single-nucleotide variants (SNVs) and 12,031 small insertion/deletions (In/Dels) (≲15bp in size) across all tissue, age, and intervention groups. Collectively, these data represent the largest collection of somatic mtDNA point mutations obtained in a single study to date and is second only to Lujan et al. in terms overall In/Del counts (Lujan et al., 2012).” (Lines 252-256)

What is the theoretical limit of pt mutations in the mitochondrial genome, assuming only one pt mutation per genome? Doesn't 77000 detected independent pt mutations approach that limit? Can the authors estimate how many molecules contained two or more pt mutations? Did the analysis reveal any un-mutated regions implying an essential function? For example, on p.9 can the authors provide an explanation of why OriL and other G/C-rich regions were not uniformly covered as compared to the rest of the genome?

This is an interesting question and one we’ve given some thought to. In fact, this basic question was the inspiration for our recent Nucleic Acids Research paper (PMC8565317) where we asked how mutations were distributed in the genome. The short answer is that we likely exceed the limit for only dG site mutations (and only for G>A mutations, at that), but not the other reference sites. The reason is that there are only 2013 dG sites and the mutation spectrum is heavily skewed toward G>X (there are 47,680 dG site mutations, 42,924 of which are G>A). In comparison, we observe only 4,421 A>X, 9,277 T>X, and 15,632 C>X mutations, but with 5,629, 4,681, and 3,976 dA, dT, and dC genomic sites, respectively. Assuming the mutations are uniformly distributed along the genome (which they are not; see our NAR paper), then random binomial sampling would require a fair amount more mutations in order to reach saturation for the other genomic sites. The uneven distribution increases this number further.

With regard to the second question, we can’t actually do this estimation with this data set. The reason is because the ~77,000 mutations aren’t found in a single sample, but are distributed across may independent or semi-independent (i.e. different organs within a mouse), which means that most, if not all, of the mutations are necessarily on different mtDNA molecules.

With regard to the OriL and G/C rich regions, these presumably have some sort of secondary structure that prevents the sequencer from obtaining any useful information. However, this is all speculative and we don’t know why. Interestingly, human mtDNA doesn’t show this dip at the OriL, despite a similar function and location in the mtDNA.

Given that mitochondrial disease usually doesn't present until >60% of the genomes are affected, the very low level of detected pt mutations observed in the mouse (and presumably similar to human) would mean that they are well below a physiological level. Thus, these low-level pt mutations are well tolerated. Can the authors estimate a theoretical age of the mouse (well beyond their life span) where over 50% of the genomes carry at least one pt mutation?

The reviewer brings up a frequent noted point in mitochondrial biology that is very much worth addressing in this manuscript. The often-cited statistic that mitochondrial disease doesn’t present until ~60% of genomes are affected is, while true, only pertinent to overt mitochondrial diseases, such as LHON, MERRF, etc, where all or nearly all cells in an individual are affected by the mutation. However, the impact of mtDNA mutations is not only contingent on how many cells have the mutation, but also the fraction of mtDNA molecules within a cell that harbor the variant. Because the deleterious effects of a mtDNA mutation act at the level of individual cells, it is important to know both how many cells harbor a mutation as well as what the heteroplasmic level is within the cell before making claims on their pathological impact.

To date, nearly all studies on mtDNA mutations rely on bulk DNA analysis from thousands to millions of cells, which necessarily decouples variant phasing information between any two reads, resulting in a loss of important biological information such as the heteroplasmic level within any given cell. As such, with bulk sequencing it is impossible to tell the difference between a homoplasmic mutation in a small subset of cells and heteroplasmic mutation in all cells. In the first case, the cells harboring this mutation would be negatively impacted, whereas in the second example, it is unlikely. One can imagine a scenario where every cell contains a different homoplasmic pathogenic mutation which would negatively affect cellular function for every cell. In this case, mutations would be highly prevalent (100% of cells), yet individually rare. However, bulk sequencing would give the appearance that no mutation comes close to exceeding the phenotypic threshold. We highlight this issue in a recent review (Sanchez-Contreras and Kennedy, 2022; PMC8896747).

The point that the review brings up is extremely important, so we have added a section in the discussion related to heteroplasmy versus clones.

Also, the problem with this low level of pt mutations is that they are not physiological, the effect of the drug treatment causing a reduction in ROS-mediated transversions would not be expected to have a detectable effect on mitochondria. The improvement on mitochondrial seen by others is most likely independent of the mutations in the genome. There needs to be a cause and effect here and I don't see one.

It is important to note that we do not make the claim (no do we want to imply) that the reduction of mutations is the reason behind the improvements in mitochondrial function by these interventions. Instead, we believe that loss of ROS-linked mutations is a consequence of the mechanism by which these interventions work. We do hypothesize that the reduction in ROS-linked mutations suggests that “there is tissue specificity in how cells repair and/or destroy oxidatively damaged mitochondria and/or mtDNA resulting in a steady-state of ROS-linked mutations.” (Lines 551-553) and that “We propose that rather than the incidence and impact of ROS damage on mtDNA being minimal, recognition and removal of ROS-linked mutations are maintained at a steady state during aging.” (Lines 572-574).

In addition, as noted above, how “low level” these mutations are and their impact on cellular function is not easily determined in bulk sequencing studies, so a strong link between cause and effect is not an answerable relationship with this data set.

There's no mention in this paper and methodology about how point mutations in nuclear-encoded mtDNA (NUMTs) are excluded from the reads and I'm worried that these errors are being read as rare errors in the mtDNA genome. While NUMTs have been documented for decades, a recent report in Science (PMID: 36198798) documents how frequently and fluidly NUMTs occur. Can the authors provide a clear explanation of how mutations in NUMTs are excluded?

The reviewer is absolutely correct to call attention to this important aspect of mitochondrial biology. We don’t believe NUMTs are an important confounder in our data set for several reasons.

1) We used isogenic inbred C57Blk6/J which, frequently, were litter mates (siblings). Therefore, any mutations from NUMTS that are there would be expected to be uniform across samples, especially between tissues from a single sample animal. Unknown and variations of NUMTS would certainly be a potentially strong confounder in an outbred population, but the use of one isogenic inbred line for this study likely eliminates this confounder.

2) We used the mm10 reference genome which is based on the C57Blk6/J strain so any NUMTS derived variants present in our mtDNA data should preferentially align against the NUMT. Therefore, we perform a BLAST step of all reads containing at least one variant against the mm10. BLAST is much more sensitive to sequence variation compared to bwa but is far slower, so it is impractical to run as the initial aligner. We then reassign the read based to whatever genomic location has the lower e-score. The result is typically around a dozen reads are removed, demonstrating that NUMTS are not likely a major source of false mutations.

3) Because NUMTS are inherited, then any variants would be found across all the tissues and animals we used in this study. As part of our processing, we mark and remove variants shared between multiple individual samples.

We have made edits to the Methods section (Lines 198-206) to more explicitly highlight the filtering steps and the logic behind them. In addition, we have added a paragraph in the discussion that addresses NUMTs (Starting on line 642).

Reviewer #2 (Public Review):

A common problem in mutation analysis is that DNA damage (present in one strand) is difficult to separate from real mutations (present in both strands). One of the approaches to solve this problem based on independent tagging of the two strands by different unique molecular identifiers was developed by the authors about 10 years ago. This study summarizes the application of this method to a wide range of mouse tissues, ages, and drug treatment regimes. Much of the results confirm previous conclusions from this laboratory. This involves overall mutational levels of somatic mtDNA mutations (~10-6-10-5), their accumulation with age, the prevalence of GA/CT transitions, and their clonality. Although these results were not new, it is important that these were confirmed in a single study with high confidence in a huge number of independent mutations.

We thank the reviewer for the comment and really hope this data set will be of significant use to other researchers given its breadth of sample types and large number of mutations.

What really sets this study apart from other studies is the detection of a large proportion of transversion mutations, primarily of the C>A/G>T and C>G/G>C types. Transversions are traditionally considered 'persona non grata' in mtDNA mutational spectra and are typically associated with errors of mutational analysis (which they in fact are). The presence of these mutations in both strands of the duplex makes a good case that these mutations are real, rather than converted damage. However, because this is such a novel discovery and because regular controls do not work (I mean, for example, that these mutations never clonally expand. If there is a clonal expansion, then the mutation is real, only real mutation can expand. But in the case of non-expandable C>A/G>T and C>G/G>C this control does not help to validate these mutations), it would be nice to provide extra assurances that this is not some kind of artifact that somehow slipped through the ds sequencing procedure. I would recommend including in the supplement the data on the abundance of single-stranded base changes as detected by ds sequencing (i.e., changes confirmed in one and not in the other strand of a given molecule). An unusually high presence of such single-stranded changes of the C>A/G>T and C>G/G>C type would be a red flag for me. If ratios of single and double-stranded mutations were similar for transitions and transversions - that would reassure me and hopefully the reader.

Furthermore, a similar excess of C>A/G>T and C>G/G>C has been observed in a recent paper by Abascal 2021 (cited in the manuscript). In that paper, a UMI- free, but otherwise very similar ds sequencing approach in nuclear DNA (BotSeqS) was demonstrated to suffer from an artifact causing (among other effects) an excess of C>A/G>T and C>G/G>C transversions. This artifact is related to end repair and nick-translation of DNA fragments during library preparation. Because BotSeqS is very similar to ds sequencing, we expect that same artifact may be taking place in the study under review. We recommend running checks similar to those undertaken by Abascal et al (which include, at the very minimum, checking the distribution of the C>A/G>T and C>G/G>C transversions within the reads (artifacts tend to be concentrated towards the ends of the reads).

The reviewer is absolutely correct to bring up this extremely important point. We have addressed these concerns in two ways that are addressed on Lines 332-361. 1) by performing an analysis of the single-stranded consensus data, which is a measure of PCR artifacts that frequently arise as a function of DNA damage, across all the tissues of the aged cohort. We noted no differences between tissues, which indicates that the amount of ROS-induced PCR artifacts is no different between the tissues. Thus, it would require a different rate at which ROS artifacts lead to false “Duplex consensus” variants that is tissue specific. The analysis is presented in Figure 3-figure supplement 2. 2) we have included an experiment in which we show that treatment of post-fragmented DNA with FPG, a glycosylase that targets Fapy-dG and 8-oxo-dG, does not differ from untreated control DNA. Because Duplex-Seq requires that both strands of a parent DNA molecule be present to form a final Duplex Consensus Sequence, the scission of one strand by the lyase activity of FPG would prevent the formation of this final consensus and prevent this sort of error from “bleeding through”. This analyses can now be found in a Figure 3-figure supplement 3.

Of note, even if transversions detected in this study prove to be artifacts of the Abascal type (likely) they still may reflect real ss damage in mtDNA (not instrumental artifacts, like sequencing errors or in vitro DNA damage). This is supported by the strong variation in the levels of transversions across tissues and as a result of the ameliorating drug intervention. Artifacts, in contrast, would be expected to be at a constant level. This logic, however, does not differentiate between real ds mutations and ss damage. So UMI-based ds sequencing evidence remains the only (though very strong) independent proof. So, in my view, whereas the jury may be still out on whether the observed transversions are true ds mutations or some kind of single-stranded damage, this is a critically important observation. The evidence of ss damage greatly varied between tissues and detected with such precision on a single molecule level is a very important finding as well.

Out of caution, I would recommend mentioning the above-stated uncertainty and noting that more research is needed to fully confirm that C>A/G>T and C>G/G>C changes detected in this study are indeed double-stranded mutations.

We agree. Together with comments from Reviewer #1 regarding NUMTs (Comment #5), we have added a paragraph in the Discussion about potential alternative explanations for our observations.

Author Response

Reviewer #1 (Public Review):

Reviewer 1 confirmed the view that your paper provides new insight into YTHDC1 function in regulating SC activation/proliferation but added that some of the data could be improved to fully support the conclusions. Specifically:

The title "Nuclear m6A Reader YTHDC1 Promotes Muscle Stem Cell Activation/Proliferation by Regulating mRNA Splicing and Nuclear Export" seems a bit overstated. Their data are not sufficient to show YTHDC1 regulating nuclear export. From figure 6 we could see some mRNAs export was inhibited upon YTHDC1 loss but intron retention also occurs on these mRNAs, for example, Dnajc14. Since intron retention could lead to mRNA nuclear retention, the mRNA export inhibition may be caused by splicing deficiency. From the data they provided we could not draw the conclusion that YTHDC1 directly affects mRNA export. I think they could not emphasize this point in the title.

Thanks for the suggestion. It is true that in our initial submission, we had more data to support YTHDC1 regulation of mRNA splicing but not enough on nuclear export. It will take substantial amount of time and efforts to have thorough dissection on both mechanisms. Nevertheless, we argue that our data does provide evidence on YTHDC1 regulation of nuclear export. For example, in Figures 6 C, H, and M, only ~20% of the target mRNAs (such as Dnaj14) showed alteration in both splicing and export upon YTHDC1 loss while the majority of the export targets showed no splicing deficiency. For example, Btbd7 and Tiparp in Figure 6 N showed no intron retention. In addition, we have now performed Co-IP experiments to validate the interaction between YTHDC1 and THOC7 (new result added in Figure 7L), which provides extra evidence to support YTHDC1 function in regulating mRNA nuclear export. We thus would like to keep the original title in order to reflect the multifaceted function of YTHDC1 in muscle stem cells.

The mechanism of YTHDC1 promoting muscle stem cell activation/proliferation is not solidified. The authors could strengthen their evidence through bioinformatics analysis or give more discussion. Besides, the previous work done by Zhao and colleagues (Zhao et al,. Nature 542, 475-478 (2017).) reported another m6A reader Ythdf2 promotes m6A-dependent maternal mRNA clearance to facilitate zebrafish maternal-to-zygotic transition. Does YTHDC1 regulate mRNA clearance during SC activation/proliferation? The authors should explore this possibility by deep-seq data analysis and give some discussion.

Thanks for the critical comment. For the first concern, we think YTHDC1 promotes muscle stem cell activation/proliferation through the multi-level gene regulatory capabilities of YTHDC1 on both transcriptional and post-transcriptional processes and the myriads of targets regulated by YTHDC1. In addition, with the newly added data, we believe that YTHDC1’s function is largely dependent on its synergism with hnRNPG (Figure 7 K). We have added the discussion in lines 421-427 of the revised text. For the second question, our data showed that YTHDC1 predominantly localizes in the nucleus of SCs and myoblasts (Figure 1 F&G), thus it may not have a role in regulating mRNA clearance in the cytoplasm like YTHDF2. Nevertheless, there are a few existing reports1, 2 suggesting its possible role in mRNA degradation and stability which may arise from its transient shuttling to cytoplasm of cells. We have now added this point in lines 469-472 of the revised text.

Reviewer #2 (Public Review):

Reviewer 2 was similarly positive stating that several tour-de-force techniques were used to examine m6A and the biological consequence in satellite cells and that there was a large amount of data supporting the conclusions with only a few minor weaknesses.

General points: The main body is lengthy, and some content can be reduced or condensed. For example, RNA-seq was used to determine gene expression in WT and cKO cells, but the purpose of this is not well justified given that YTHDC1 mainly functions to regulate splicing and nuclear expert of mRNA rather than controlling their expression levels. Does the RNA-seq data suggest that YTHDC1 may also regulate gene expression independent of m6A reader function?

Thanks for the comment. We have now revised the entire text to condense the content. Nevertheless, we must point out that the purpose of the RNA-seq is to provide extra evidence for the proliferation defect of the YTHDC1 KO cells but not to search for the underlying mechanism. We have now revised in lines 159-160 to clarify this.

Reference:

1. Shima, H., Matsumoto, M., Ishigami, Y., Ebina, M., Muto, A., Sato, Y., Kumagai, S., Ochiai, K., Suzuki, T. & Igarashi, K. S-Adenosylmethionine Synthesis Is Regulated by Selective N(6)-Adenosine Methylation and mRNA Degradation Involving METTL16 and YTHDC1. Cell Rep 21, 3354-3363 (2017).
2. Zhang, Z., Wang, Q., Zhao, X., Shao, L., Liu, G., Zheng, X., Xie, L., Zhang, Y., Sun, C. & Xu, R. YTHDC1 mitigates ischemic stroke by promoting Akt phosphorylation through destabilizing PTEN mRNA. Cell Death Dis 11, 977 (2020).
3. He, P.C. & He, C. m(6) A RNA methylation: from mechanisms to therapeutic potential. EMBO J 40, e105977 (2021).
4. Widagdo, J., Anggono, V. & Wong, J.J. The multifaceted effects of YTHDC1-mediated nuclear m(6)A recognition. Trends Genet 38, 325-332 (2022).
5. Sheng, Y., Wei, J., Yu, F., Xu, H., Yu, C., Wu, Q., Liu, Y., Li, L., Cui, X.L., Gu, X., Shen, B., Li, W., Huang, Y., Bhaduri-Mcintosh, S., He, C. & Qian, Z. A Critical Role of Nuclear m6A Reader YTHDC1 in Leukemogenesis by Regulating MCM Complex-Mediated DNA Replication. Blood (2021).
6. Cheng, Y., Xie, W., Pickering, B.F., Chu, K.L., Savino, A.M., Yang, X., Luo, H., Nguyen, D.T., Mo, S., Barin, E., Velleca, A., Rohwetter, T.M., Patel, D.J., Jaffrey, S.R. & Kharas, M.G. N(6)-Methyladenosine on mRNA facilitates a phase-separated nuclear body that suppresses myeloid leukemic differentiation. Cancer Cell 39, 958-972 e958 (2021).
7. Chen, C., Liu, W., Guo, J., Liu, Y., Liu, X., Liu, J., Dou, X., Le, R., Huang, Y., Li, C., Yang, L., Kou, X., Zhao, Y., Wu, Y., Chen, J., Wang, H., Shen, B., Gao, Y. & Gao, S. Nuclear m(6)A reader YTHDC1 regulates the scaffold function of LINE1 RNA in mouse ESCs and early embryos. Protein Cell 12, 455-474 (2021).
8. Xiao, W., Adhikari, S., Dahal, U., Chen, Y.S., Hao, Y.J., Sun, B.F., Sun, H.Y., Li, A., Ping, X.L., Lai, W.Y., Wang, X., Ma, H.L., Huang, C.M., Yang, Y., Huang, N., Jiang, G.B., Wang, H.L., Zhou, Q., Wang, X.J., Zhao, Y.L. & Yang, Y.G. Nuclear m(6)A Reader YTHDC1 Regulates mRNA Splicing. Mol Cell 61, 507-519 (2016).
9. Webster, M.T., Manor, U., Lippincott-Schwartz, J. & Fan, C.M. Intravital Imaging Reveals Ghost Fibers as Architectural Units Guiding Myogenic Progenitors during Regeneration. Cell Stem Cell 18, 243-252 (2016).
10. Yankova, E., Blackaby, W., Albertella, M., Rak, J., De Braekeleer, E., Tsagkogeorga, G., Pilka, E.S., Aspris, D., Leggate, D., Hendrick, A.G., Webster, N.A., Andrews, B., Fosbeary, R., Guest, P., Irigoyen, N., Eleftheriou, M., Gozdecka, M., Dias, J.M.L., Bannister, A.J., Vick, B., Jeremias, I., Vassiliou, G.S., Rausch, O., Tzelepis, K. & Kouzarides, T. Small-molecule inhibition of METTL3 as a strategy against myeloid leukaemia. Nature 593, 597-601 (2021).
11. Otto, A., Schmidt, C., Luke, G., Allen, S., Valasek, P., Muntoni, F., Lawrence-Watt, D. & Patel, K. Canonical Wnt signalling induces satellite-cell proliferation during adult skeletal muscle regeneration. J Cell Sci 121, 2939-2950 (2008).
12. Liu, J., Gao, M., He, J., Wu, K., Lin, S., Jin, L., Chen, Y., Liu, H., Shi, J., Wang, X., Chang, L., Lin, Y., Zhao, Y.L., Zhang, X., Zhang, M., Luo, G.Z., Wu, G., Pei, D., Wang, J., Bao, X. & Chen, J. The RNA m(6)A reader YTHDC1 silences retrotransposons and guards ES cell identity. Nature 591, 322-326 (2021).
13. Xu, W., Li, J., He, C., Wen, J., Ma, H., Rong, B., Diao, J., Wang, L., Wang, J., Wu, F., Tan, L., Shi, Y.G., Shi, Y. & Shen, H. METTL3 regulates heterochromatin in mouse embryonic stem cells. Nature 591, 317-321 (2021).
14. Roberson, P.A., Romero, M.A., Osburn, S.C., Mumford, P.W., Vann, C.G., Fox, C.D., McCullough, D.J., Brown, M.D. & Roberts, M.D. Skeletal muscle LINE-1 ORF1 mRNA is higher in older humans but decreases with endurance exercise and is negatively associated with higher physical activity. J Appl Physiol (1985) 127, 895-904 (2019).
15. Mumford, P.W., Romero, M.A., Osburn, S.C., Roberson, P.A., Vann, C.G., Mobley, C.B., Brown, M.D., Kavazis, A.N., Young, K.C. & Roberts, M.D. Skeletal muscle LINE-1 retrotransposon activity is upregulated in older versus younger rats. Am J Physiol Regul Integr Comp Physiol 317, R397-R406 (2019).

Author Response

Reviewer #1 (Public Review):

Laurent et al. generate genotyping data from 259 individuals from Cabo Verde to investigate the histories and patterns of admixture in the set of islands that make up Cabo Verde. The authors had previously studied admixture in an earlier study but in a smaller set of individuals from two cities on one island (from Santiago) in Cabo Verde. Here, the authors sample from all the islands of Cabo Verde to study admixture in these islands and reveal that there is a varied picture of admixture in that the demographic histories are distinct amongst this set of islands.

I found the article interesting and clearly written, and I like that it highlights that admixture is a dynamic process that has manifested differently in distinct geographical regions, which will be of broad interest. It also highlights how genetic ancestry patterns are correlated with the populations that were in power/enslaved during colonial times and proposes that certain social practices (e.g. legally enforced segregation) might have affected the distribution/length of runs of homozygosity.

We thank the reviewer for this positive and encouraging appreciation of our work.

My main suggestion is that the authors provide a set of hypotheses regarding admixture that may explain their observations, and it would be nice to see if at least one of these has some support using simulations. Could the authors run simulations under their proposed demographic model for populations in Cabo Verde vs what we would expect in a pseudo-panmictic population with two sources of admixture? The authors probably already have simulations they could use. And then see how pre/post admixture founding events change patterns of ancestry.

As suggested by the reviewer, in the revised version of the manuscript, we conducted the same MetHis-ABC scenario-choice and posterior parameter inference considering the 225 Cabo Verde-born individuals as a single random-mating population, in addition to our main results considering each island of birth separately. Most interestingly, we find that our ABC inferences fail to accurately reconstruct the detailed admixture history of Cabo Verde when considered as a whole instead of per each island of birth separately. This is due to admixture histories substantially differing across islands of birth of individuals, also consistent with the significantly differentiated genetic patterns within Cabo Verde obtained from ADMIXTURE, local-ancestry inferences, ROH, and isolation-by-distance analyses. These results are now implemented throughout the revised version of the manuscript and in supplementary figures and tables. See in particular Results L758-769, and Appendix1-figures and tables, Figure7-figure supplement 1-3, and Appendix 5-table 10.

Reviewer #2 (Public Review):

In this article, the authors leveraged patterns on the empirical genomic data and the power of simulations and statistical inferences and aimed to address a few biologically and culturally relevant questions about Cabo Verde population's admixture history during the TAST era. Specifically, the authors provided evidence on which specific African and European populations contributed to the population per island if the genetic admixture history parallels language evolution, and the best-fitting admixture scenario that answers questions on when and which continental populations admixed on which island, and how that influenced the island population dynamics since then.

Strengths

1) This study sets a great example of studying population history through the lens of genetics and linguistics, jointly. Historically most of the genetic studies of population history either ignored the sociocultural aspects of the evidence or poorly (or wrongly) correlated that with genetic inference. This study identified components in language that are informative about cultural mixture (strictly African-origin words versus shared European-African words), and carefully examined the statistical correlation between genetic and linguistic variation that occurred through admixture, providing a complete picture of genetic and sociocultural transformation in the Cabo Verde islands during TAST.

We thank the reviewer for this very enthusiastic and encouraging comment on our work.

2) The statistical analyses are carefully designed and rigorously done. I especially appreciate the careful goodness-of-fit checking and parameter error rates estimation in the ABC part, making the inference results more convincing.

Again, we thank the reviewer for this positive comment.

Weaknesses

1) Most of the methods in the main analyses here were previously developed (eg. MDS, MetHis, RF/NN-ABC). However, when being introduced and applied here, the authors didn't reinstate the necessary background (strength and weakness, limitations and usage) of these methods to make them justifiable over other methods. For example, why ADS-MDS is used here to examine the genetic relationship between Cabo Verde populations and other worldwide populations, rather than classic PCA and F-statistics?

As mentioned in the answer to the general comments, we extensively modified our manuscript in both Results and Material and Methods, to clarify and justify our reasoning for each one of the analyses conducted, and to discuss pros and cons of the methods used. We warmly thank the reviewers for this request, as we believe it allowed us to strongly improve the accessibility of our work in particular for the less specialized audience, as well as equally crucially improve replicability of our work for specialists. See in particular Results L185-193, L245-250, L368-371, L380-386, L495-511, L567-571, L606-621, and the corresponding Material and Methods sections.

For the particular example of PCA raised by the reviewer: see Results L185-193.

For that of F-statistics, see Results L368-386. Note that we added the F-stat analysis suggested by the reviewer to the revised version of our manuscript (see detailed answers below), Figure 3-figure supplement 2.

We believe that these changes strongly strengthen our manuscript and enlarged its potential readership, and we thank, again, the reviewer for this request.

2) The senior author of this paper has an earlier published article (Verdu et al. 2017 Current Biology) on the same population, using a similar set of methods and drew similar conclusions on the source of genetic and linguistic variation in Cabo Verde. Although additional samples on island levels are added here and additional analyses on admixture history were performed, half of the main messages from this paper don't seem to provide new knowledge than what we already learned from the 2017 paper.

We substantially modified the text of the revised version of the manuscript to address the concern raised by the reviewer in numerous locations of the Abstract, Introduction and Results and Discussion sections, thus hoping to highlight better what we think is the profound novelty brought by this study. In particular, see Introduction L128-153.

3) Furthermore, there are a few essential factors that could confound different aspects of the major analyses in this article that I believe should be taken into account and discussed. Such factors include the demographic history of source populations prior to admixture, different scenarios of the recipient population size changes, differences in recombination rates across the genome and between African and European populations, etc.

We thank the reviewer for these comments which allowed us to improve the clarity of our manuscript and rise very interesting discussion points that we had overlooked. As indicated in part in the general answer to reviewers above:

1) We clarified our methods’ design and discussed extensively its limitations with respect to ancestral populations’ sizes mis-specifications. Indeed, ancestral source population sizes are not modelized in our MetHis-ABC approach. Instead, we consider that the observed proxy source populations from Africa and Europe are at the drift-mutation equilibrium and are large since the initial and recent founding of Cabo Verde in the 1460’s, and thus use observed genetic variation patterns in these populations to build virtual gamete reservoirs for the admixture history of Cabo Verde with the MetHis-ABC framework. Therefore, while we cannot evaluate explicitly the influence of ancestral source population sizes differences on our inferences in Cabo Verde, as we now state in the revised version of our manuscript: “we nevertheless implicitly take the real demographic histories of these source populations into account in our simulations, as we use observed genetic patterns themselves the product of this demographic history to create the virtual source populations at the root of the admixture history of each Cabo Verdean island.”. We then discuss the outcome of such an approach which mimics satisfactorily the real data for ABC inference. See in particular the revised versions of the Material and Methods L1454-1491 novel section “Simulating the admixed population from source-populations for 60,000 independent SNPs with MetHis”, and Results L637-649.

2) Concerning the possibilities for population-size changes in the admixed population in our simulations and ABC inferences, we clarified our Material and Methods and explanations of our Results to better show that we readily consider various possible scenarios (for each island separately). Indeed, with our MetHis simulation design, given values of model-parameters correspond either to a constant, a linearly increasing, or a hyperbolic increase in reproductive size in the admixed population over time. We further clarified our Results and Discussion pointing out that we find, a posteriori, indeed, different demographic regimes among islands.

Nevertheless, reviewers are right that we did not test the possibility for bottlenecks. We thus substantially expanded the Results and Discussion sections in multiple locations to highlight this limitation and the challenges involved in overcoming it in future work. See in particular Material and Methods L1386-1404 section “Hyperbolic increase, linear increase, or constant reproductive population size in the admixed population”, Results L739-742, and Discussion L934-941, and Perspectives.

3) Finally, concerning recombination rate, we considered only independent SNPs in our simulation and inference process, as is now clarified in multiple locations throughout the text. Otherwise, we further discuss matters of recombination concern regarding specifically our ROH analyses, as suggested in the detailed reviewer’s comments. In brief, we note that in Figure 8 Pemberton 2012 (AJHG 91:275-292) shows that occurrence of long ROH at the same genomic location across individuals is correlated with low recombination rates, although the effect is relatively weak unless in extreme recombination cold spots. Unless there were many extreme recombination cold spots that were different among the islands or ancestral populations, we anticipate fine-scale recombination rate differences not to matter very much for total ROH levels in these data. Similarly, we do not expect large genome-wide differences in mutation rate, and therefore we don’t anticipate minor local variation in mutation rates to make a systematic difference in total ROH levels. We now refer to these important points in the revised version of our Results L414-415.

Overall, the paper is of interest to the field of human evolutionary genetics - that not only does it tell the story of a historically important population, but also the methodology behind this paper sets a great example for future research to study genetic and sociocultural transformations under the same framework.

We would like to thank the reviewer for this very encouraging conclusion and for the detailed revision of our work which, we believe, helped us to substantially improve our manuscript.

Author Response

Reviewer #1 (Public Review):

1) The heat shock effect in the drosophila lines was not understood in the study. Why did some lines show phenotypes only at 29C but not 22C? The study showed data that ubiquilin 2 expression was not impacted by 29C, then what caused the phenotypic differences? In addition, the method section did not describe clearly whether a temperature sensitive promoter was used in the flies.

The heat inducibility of the UBQLN2 transgenes is likely attributed to heat shock elements in the UAS promoter as noted in on page 6, line 4-14. The heat inducibility of dUbqln is interesting and may reflect transcriptional and/or posttranscriptional mechanisms. While it is possible that increased UBQLN2 contributes to the severe phenotypes in UBQLN24XALS flies reared at 29C; this is not seen for UBQLN2WT and UBQLN2P497H flies. Instead, we postulate that heat stress synergizes with the misfolded UBQLN24XALS protein to disrupt proteostasis and/or endolysosomal function. This clarification has been added to paragraph 2 of the Discussion (page 16, line 15-25) section of the revised MS: “The reason for enhanced toxicity of UBQLN24XALS is unclear; however, its enhanced aggregation potential may overwhelm cellular proteostasis machinery and/or accelerate disease mechanisms that are slow to manifest in neurons harboring ALS point mutations. This is consistent with the fact that UBQLN24XALS toxicity in flies was unmasked by HS, which is a well-known inducer of proteotoxicity.” We have also explicitly state the HS inducibility of the UAS-Gal4 in the revised Materials and methods (page 20, line 24-25).

2) The study showed data on male and female flies separately in some but not all experiments. In addition, the manuscript largely avoided discussing whether there was a sex difference in those experiments.

We showed separate male and female eye phenotypes in Figure 1 to clearly demonstrate that UBQLN24XALS toxicity is not sex dependent. Subtle sex differences were seen in the longevity and climbing assays and were reported in figures 4A and 4D. In Figure 4D, Unc-5 silencing extended the lifespan of Elav>Gal4 female control flies but not Elav>Gal4 male control flies. In Figure 4A, an Unc-5 KK RNAi line rescued climbing of D42>UBQLN24XALS male flies, but not female flies (a second Unc-5 RNAi line rescued both males and females). The reasons for sex differences in these specific experiments is unclear.

3) Some data appear to be peripheral with no significant contribution to the main findings. Moreover, some data were introduced but were not explained. For instance, the RNA-Seq analysis (Fig 2) did not contribute much to the study. The rescue effect of UBA* (F594A mutant) in Fig 1-Supplemental 1B was interesting but was not elaborated or followed up. FUS flies in Fig 6-Supplement 2 were abrupted introduced with little discussion.

We understand the reviewer’s point or the reviewer’s point is well taken. Appreciating the reviewer’s comment, we moved both figures to the supplementary data.

RNA-Seq (Fig. 2)

Although not essential, the RNA-Seq adds experimental rigor to the study by providing strong molecular correlates to eye degeneration phenotypes across different UBQLN2 genotypes. It shows the unique toxicity of UBQLN24XALS and reinforces phenotypic similarity between UBQLN2WT and UBQLN2P497H flies, which likely reflects non-specific toxicity of overexpressed UBQLN2 proteins. We have carried out additional data analyses requested by the reviewer and moved the RNA-Seq data to Figure 1-figure supplement 2.

UBA mutant (Figure1-figure supplement 1)

Both aggregation and toxicity of UBQLN24XALS were abolished by an inactivating F594A mutation in the UBA domain. While this implicates Ub binding in the biochemical mechanism of UBQLN2 toxicity, we have not followed up on the finding in either fly or iMN models and have chosen to remove the data (Figure1-figure supplement 1) from the revised MS.

Lack of genetic interaction between FUS and Unc-5 (Figure 3-figure supplement 1).

This data was included to show that shUnc-5 is not a general suppressor of eye toxicity in Drosophila. This contrasts with lilliputian, whose mutation rescues toxicity phenotypes elicited by FUS, TDP-43, and UBQLN2. We believe that the FUS control data enhances experimental rigor and have retained the data in the revised MS, with some additional clarification on page 10, line 5-8.

4) The main quadrupole (4XALS) mutation used in the study was not found in patients. The relevance of the findings needs to be thoroughly justified.

The use of combinatorial mutants—either in the same gene or same pathway—can sometimes be used to enhance neurodegenerative phenotypes in cellular and rodent models for neurodegenerative diseases, most notably, Alzheimer’s Disease. In the case of the 4XALS mutant, we reasoned that its enhanced aggregation might drive stronger phenotypes than those elicited by UBQLN2 clinical alleles, whose toxicity is barely discernible in flies (relative to overexpressed UBQLN2WT) or in iMNs. We have clarified the rationale for testing the 4XALS mutant and articulated its potential strengths and weaknesses in Results (page 5, line 14-page 6, line 2) and Discussion (page 16, line 15-25) sections.

5) ALS and FTD are age-related neurodegenerative diseases, whereas the involvement of axon guidance genes in indicative of disruptions during the developmental stage. The manuscript did not discuss this potential caveat.

We have inserted the following sentence in the discussion to note this caveat: “Consistent with this notion, UNC5B has been linked to neurodegeneration in the 6-OHDA model of Parkinson’s Disease (PD) and UNC5C has been nominated as a risk allele in late-onset Alzheimer’s Disease. Defining the contributions of pathologic UNC5 signaling to the development or progression of ALS-dementia awaits further study.” on Page 20, line 2-6. We have added a similar sentence to the Limitations paragraph at the end of the Discussion: “Third, it is possible that axon guidance genes are only relevant to UBQLN2 toxicity in the context of the developing nervous system”.

Author Response

Reviewer #1 (Public Review):

This work describes a new method, Proteinfer, which uses dilated neural networks to predict protein function, using EC terms and GO terms. The software is fast and the server-side performance is fast and reliable. The method is very clearly described. However, it is hard to judge the accuracy of this method based on the current manuscript, and some more work is needed to do so.

I would like to address the following statement by the authors: (p3, left column): "We focus on Swiss Prot to ensure that our models learn from human-curated labels, rather than labels generated by electronic annotation".

There is a subtle but important point to be made here: while SwissProt (SP) entries are human-curated, they might still have their function annotated ("labeled") electronically only. The SP entry comprises the sequence, source organism, paper(s) (if any), annotations, cross-references, etc. A validated entry does not mean that the annotation was necessarily validated manually: but rather that there is a paper backing the veracity of the sequence itself, and that it is not an automatic generation from a genome project.

Example: 009L_FRG3G is a reviewed entry, and has four function annotations, all generated by BLAST, with an IEA (inferred by electronic annotation) evidence code. Most GO annotations in SwissProt are generated that way: a reviewed Swissprot entry, unlike what the authors imply, does not guarantee that the function annotation was made by non-electronic means. If the authors would like to use non-electronic annotations for functional labels, they should use those that are annotated with the GO experimental evidence codes (or, at the very least, not exclusively annotated with IEA). Therefore, most of the annotations in the authors' gold standard protein annotations are simply generated by BLAST and not reviewed by a person. Essentially the authors are comparing predictions with predictions, or at least not taking care not to do so. This is an important point that the authors need to address since there is no apparent gold standard they are using.

The above statement is relevant to GO. But since EC is mapped 1:1 to GO molecular function ontology (as a subset, there are many terms in GO MFO that are not enzymes of course), the authors can easily apply this to EC-based entries as well.

This may explain why, in Figure S8(b), BLAST retains such a high and even plateau of the precision-recall curve: BLAST hits are used throughout as gold-standard, and therefore BLAST performs so well. This is in contrast, say to CAFA assessments which use as a gold standard only those proteins which have experimental GO evidence codes, and therefore BLAST performs much poorer upon assessment.

We thank the reviewer for this point. We regret if we gave the impression that our training data derives exclusively, or even primarily, from direct experiments on the amino acid sequences in question. We had attempted to address this point in the discussion with this section:

"On the other hand, many entries come from experts applying existing computational methods, including BLAST and HMM-based approaches, to identify protein function. Therefore, the data may be enriched for sequences with functions that are easily ascribable using these techniques which could limit the ability to estimate the added value of using an alternative alignment-free tool. An idealised dataset would involved training only on those sequences that have themselves been experimentally characterized, but at present too little data exists than would be needed for a fully supervised deep-learning approach."

We have now added a sentence in the early sentence of of the manuscript reinforcing this point:

"Despite its curated nature, SwissProt contains many proteins annotated only on the basis of electronic tools."

We have also removed the phrase "rather than labels generated by a computational annotation pipeline" because we acknowledge that this could be read to imply that computational approaches are not used at all for SwissProt which would not be correct.

While we agree that SwissProt contains many entries inferred via electronic means, we nevertheless think its curated nature makes an important difference. Curators as far as possible reconcile all known data for a protein, often looking for the presence of key residues in the active sites. There are proteins where electronic annotation would suggest functions in direct contradiction to experimental data, which are avoided due to this curation process. As one example, UniProt entry Q76NQ1 contains a rhomboid-like domain typically found in rhomboid proteases (IPR022764) and therefore inputting it into InterProScan results in a prediction of peptidase activity (GO:0004252). However this is in fact an inactive protein, as discovered by experiment, and so is not annotated with this activity in SwissProt. ProteInfer successfully avoids predicting peptidase activity as a result of this curated training data. (For transparency, ProteInfer is by no means perfect on this point: there are also cases in which UniProt curators have annotated single proteins as inactive but ProteInfer has not learnt this relationship, due to similar sequences which remain active).

We had also attempted to address this point by comparing with phenotypes seen in a specific high-throughput experimental assay ("Comparison to experimental data" section).

We have now added a new analysis in which we assess the recall of GO terms while excluding IEA annotation codes. We find that at the threshold that maximises F1 score in the full analysis, our approach is able to recall 60-75% (depending on ontology) of annotations. Inferring precision is challenging due to the fact that only a very small proportion of the possible function*gene combinations have in fact been tested, making it difficult to distinguish a true negative from a false negative.

"We also tested how well our trained model was able to recall the subset of GO term annotations which are not associated with the "inferred from electronic annotation" (IEA) evidence code, indicating either experimental work or more intensely-curated evidence. We found that at the threshold that maximised F1 score for overall prediction, 75% of molecular function annotations could be successfully recalled, 61% of cellular component annotations, and 60% of biological process annotations."

Pooling GO DAGs together: It is unclear how the authors generate performance data over GO as a whole. GO is really 3 disjoint DAGs (molecular function ontology or MFO, Biological Process or BPO, Cellular component or CCO). Any assessment of performance should be over each DAG separately, to make biological sense. Pooling together the three GO DAGs which describe completely different aspects of the function is not informative. Interestingly enough, in the browser applications, the GO DAG results are distinctly separated into the respective DAGs.

Thank you for this suggestion. To answer the question of how we were previously generating performance data: this was simply by treating all terms equivalently, regardless of their ontology.

We agree that it would be helpful to the reader to split out results by ontology type, especially given clear differences in performance.

We now provide PR-curve graphs split by ontology type.

We have also added the following text:

"The same trends for the relative performance of different approaches were seen for each of the direct-acyclic graphs that make up the GO ontology (biological process, cellular component and molecular function), but there were substantial differences in absolute performance (Fig S10). Performance was highest for molecular function (max F1: 0.94), followed by biological process (max F1:0.86) and then cellular component (max F1:0.84)."

Figure 3 and lack of baseline methods: the text refers to Figures 3A and 3B, but I could only see one figure with no panels. Is there an error here? It is not possible at this point to talk about the results in this figure as described. It looks like Figure 3A is missing, with Fmax scores. In any case, Figure 3(b?) has precision-recall curves showing the performance of predictions is the highest on Isomerases and lowest in hydrolases. It is hard to tell the Fmax values, but they seem reasonably high. However, there is no comparison with a baseline method such as BLAST or Naive, and those should be inserted. It is important to compare Proteinfer with these baseline methods to answer the following questions: (1) Does Proteinfer perform better than the go-to method of choice for most biologists? (2) does it perform better than what is expected given the frequency of these terms in the dataset? For an explanation of the Naive method which answers the latter question, see: ( https://www.nature.com/articles/nmeth.2340 )

We apologise for the errors in figure referencing in the text here. This emerged in part from the two versions of text required to support an interactive and legacy PDF version. We had provided baseline comparisons with BLAST in Fig. 5 of the interactive version (correctly referenced in the interactive version) and in Fig. S7 of the PDF version (incorrectly referenced as Fig 3B).

We have now moved the key panel of Fig S7 to the main-text of the PDF version (new Fig 3B), as suggested also by the editor, and updated the figure referencing appropriately. We have also added a Naive frequency-count based baseline. This baseline would not appear in Fig 3B due to axis truncation, but is shown in a supplemental figure, new Fig S9. We thank the reviewer and the editor for raising these points.

Reviewer #2 (Public Review):

In this paper, Sanderson et al. describe a convolutional neural network that predicts protein domains directly from amino acid sequences. They train this model with manually curated sequences from the Swiss-Prot database to predict Enzyme Commission (EC) numbers and Gene Ontology (GO) terms. This paper builds on previous work by this group, where they trained a separate neural network to recognize each known protein domain. Here, they train one convolutional neural network to identify enzymatic functions or GO terms. They discuss how this change can deal with protein domains that frequently co-occur and more efficiently handle proteins of different lengths. The tool, ProteInfer, adds a useful new tool for computational analysis of proteins that complements existing methods like BLAST and Pfam.

The authors make three claims:

1) "ProteInfer models reproduce curator decisions for a variety of functional properties across sequences distant from the training data"

This claim is well supported by the data presented in the paper. The authors compare the precision-recall curves of four model variations. The authors focus their training on the maximum F1 statistic of the precision-recall curve. Using precision-recall curves is appropriate for this kind of problem.

2) "Attribution analysis shows that the predictions are driven by relevant regions of each protein sequence".

This claim is very well supported by the data and particularly well illustrated by Figure 4. The examples on the interactive website are also very nice. This section is a substantial innovation of this method. It shows the value of scanning for multiple functions at the same time and the value of being able to scan proteins of any length.

3) "ProteInfer models create a generalised mapping between sequence space and the space of protein functions, which is useful for tasks other than those for which the models were trained."

This claim is also well supported. The print version of the figure is really clear, and the interactive version is even better. It is a clever use of UMAP representations to look at the abstract last layer of the network. It was very nice how each sub-functional class clustered.

The interactive website was very easy to use with a good user interface. I expect will be accessible to experimental and computational biologists.

The manuscript has many strengths. The main text is clearly written, with high-level descriptions of the modeling. I initially printed and read the static PDF version of the paper. The interactive form is much more fun to read because of the ability to analyze my favorite proteins and zoom in on their figures (e.g. Figure 8). The new Figure 1 motivates the work nicely. The website has an excellent interactive graphic showing how the number of layers in the network and the kernel size change how data is pooled across residues. I will use this tool in my teaching.

We are grateful for these comments. We are excited that the reviewer hopes to use this figure for teaching, which is exactly the sort of impact we hoped for this interactive manuscript. We agree that the interactive manuscript is by far the most compelling version of this work.

The manuscript has only minor weaknesses. It was not clear if the interactive model on the website was the Single CNN model or the Ensemble CNN model.

We thank the reviewer for pointing out the ambiguity here. The model shown on the website is a Single CNN model, and is chosen with hyperparameters that achieve good performance whilst being readily downloadable to the user's machine for this demonstration without use of excessive bandwidth. We have added additional sentences to address this better in the manuscript.

" When the user loads the tool, lightweight EC (5MB) and GO model (7MB) prediction models are downloaded and all predictions are then performed locally, with query sequences never leaving the user's computer. We selected the hyperparameters for these lightweight models by performing a tuning study in which we filtered results by the size of the model's parameters and then selected the best performing models. This approach uses a single neural network, rather than an ensemble. Inference in the browser for a 1500 amino-acid sequence takes < 1.5 seconds for both models "

Overall, ProteInfer will be a very useful resource for a broad user base. The analysis of the 171 new proteins in Figure 7 was particularly compelling and serves as a great example of the utility and power of ProteInfer. It completes leading tools in a very valuable way. I anticipate adding it to my standard analysis workflows. The data and code are publicly available.

Reviewer #3 (Public Review):

In this work, the authors employ a deep convolutional neural network approach to map protein sequence to function. The rationales are that (i) once trained, the neural network would offer fast predictions for new sequences, facilitating exploration and discovery without the need for extensive computational resources, (ii) that the embedding of protein sequences in a fixed-dimensional space would allow potential analyses and interpretation of sequence-function relationships across proteins, and (iii) predicting protein function in a way that is different from alignment-based approaches could lead to new insights or superior performance, at least in certain regimes, thereby complementing existing approaches. I believe the authors demonstrate i and iii convincingly, whereas ii was left open-ended.

A strength of the work is showing that the trained CNNs perform generally on par with existing alignment based-methods such as BLASTp, with a precision-recall tradeoff that differs from BLASTp. Because the method is more precise at lower recall values, whereas BLASTp has higher recall at lower precision values, it is indeed a good complement to BLASTp, as demonstrated by the top performance of the ensemble approach containing both methods.

Another strength of the work is its emphasis on usability and interpretability, as demonstrated in the graphical interface, use of class activation mapping for sub-sequence attribution, and the analysis of hierarchical functional clustering when projecting the high-dimensional embedding into UMAP projections.

We thank the reviewer for highlighting these points.

However, a main weakness is the premise that this approach is new. For example, the authors claim that existing deep learning "models cannot infer functional annotation for full-length protein sequences." However, as the proposed method is a straightforward deep neural network implementation, there have been other very similar approaches published for protein function prediction. For example, Cai, Wang, and Deng, Frontiers in Bioengineering and Biotechnology (2020), the latter also being a CNN approach. As such, it is difficult to assess how this approach differs from or builds on previous work.

We agree that there has been a great deal of exciting work looking at the application of deep learning to protein sequences. Our core code has been publicly available on GitHub since April 2019 , and our preprint has now been available for more than a year. We regret the time taken to release a manuscript and for it to reach review: this was in part due to the SARS-CoV-2 pandemic, which the first author was heavily involved in the scientific response to. Nevertheless, we believe that our work has a number of important features that distinguish it from much other work in this space.

● We train across the entire GO ontology. In the paper referenced by the reviewer, training is with 491 BP terms, 321 MF terms, and 240 CC terms. In contrast, we train with a vocabulary of 32,102 GO labels, and the majority of these are predicted at least once in our test set. ● We use a dilated convolutional approach. In the referenced paper the network used is instead of fixed dimensions. Such an approach means there is an upper limit on how large a protein can be input into the model, and also means that this maximum length defines the computational resources used for every protein, including much smaller ones. In contrast, our dilated network scales to any size of protein, but when used with smaller input sequences it performs only the calculations needed for this size of sequence.

● We use class-activation mapping to determine regions of a protein responsible for predictions, and therefore potentially involved in specific functions.

● We provide a TensorFlow.JS implementation of our approach that allows lightweight models to be tested without any downloads

● We provide a command-line tool that provides easy access to full models.

We have made some changes to bring out these points more clearly in the text:

"Since natural protein sequences can vary in length by at least three orders of magnitude, this pooling is advantageous because it allows our model to accommodate sequences of arbitrary length without imposing restrictive modeling assumptions or computational burdens that scale with sequence length. In contrast, many previous approaches operate on fixed sequence lengths: these techniques are unable to make predictions for proteins larger than this sequence length, and use unnecessary resources when employed on smaller proteins."

We have added a table that sets out the vocabulary sizes used in our work (5,134 for EC and 32,109 for GO):

"Gene Ontology (GO) terms describe important protein functional properties, with 32,109 such terms in Swiss-Pr ot (Table S6) that cov er the molecular functions of proteins (e.g. DNA-binding, amylase activity), the biological processes they are involved in (e.g. DNA replication, meiosis), and the cellular components to which they localise (e.g. mitochondrion, cytosol)."

A second weakness is that it was not clear what new insights the UMAP projections of the sequence embedding could offer. For example, the authors mention that "a generalized mapping between sequence space and the space of protein functions...is useful for tasks other than those for which the models were trained." However, such tasks were not explicitly explained. The hierarchical clustering of enzymatic proteins shown in Fig. 5 and the clustering of non-enzymatic proteins in Fig. 6 are consistent with the expectation of separability in the high-dimensional embedding space that would be necessary for good CNN performance (although the sub-groups are sometimes not well-separated. For example, only the second level and leaf level are well-separated in the enzyme classification UMAP hierarchy). Therefore, the value-added of the UMAP representation should be something like using these plots to gain insight into a family or sub-family of enzymes.

We thank the reviewer for highlighting this point. There are two types of embedding which we discuss in the paper. The first is the high-dimensional representation of the protein that the neural network constructs as part of the prediction process. This is the embedding we feel is most useful for downstream applications, and we discuss a specific example of training the EC-number network to recognise membrane proteins (a property on which it was not trained): "To quantitatively measure whether these embeddings capture the function of non-enzyme proteins, we trained a simple random forest classification model that used these embeddings to predict whether a protein was annotated with the intrinsic component of membrane GO term. We trained on a small set of non-enzymes containing 518 membrane proteins, and evaluated on the rest of the examples. This simple model achieved a precision of 97% and recall of 60% for an F1 score of 0.74. Model training and data-labelling took around 15 seconds. This demonstrates the power of embeddings to simplify other studies with limited labeled data, as has been observed in recent work (43, 72)."

As the reviewer points out, there is a second embedding created by compressing this high-dimensional down to two dimensions using UMAP. This embedding can also be useful for understanding the properties seen by the network, for example the GO term s highlighted in Fig. 7 , but in general it will contain less information than the higher-dimensional embedding.

The clear presentation, ease of use, and computationally accessible downstream analytics of this work make it of broad utility to the field.

Author Response

Reviewer #1 (Public Review):

The manuscript by Kschonsak et al. describes the rational structure-based design of novel hybrid inhibitors targeting human Nav1.7 channel. CryoEM structure of arylsulfonamide (GNE-3565) - VSD4 NaV1.7-NaVPas channel complex confirmed binding pose observed in x-ray structure GX-936 - VSD4 Nav1.7-NavAb channel. Remarkably, cryoEM structure of acylsulfonamide (GDC-0310) - VSD4 NaV1.7-NaVPas channel complex revealed a novel binding pocket between the S3 and S4 helices, with the S3 segment adopting a distinct conformation compared to the arylsulfonamide (GNE-3565) - VSD4 NaV1.7-NaVPas channel complex. Creatively, the authors designed a novel class of hybrid inhibitors that simultaneously occupy both the aryl- and acylsulfonamide binding pockets. This study underscores the power of structure-guided drug design to target transmembrane proteins and will be useful to develop safer and more effective therapeutics.

We thank this Reviewer for the very positive feedback and for highlighting the importance of our work in utilizing structure-based drug design to target key membrane targets.

Reviewer #2 (Public Review):

In this manuscript, the authors identify a critical unmet need for the (structure-based) drug design of human Nav channels, which are of clinical interest. They cleverly rationalized a hybrid strategy for developing target-specific small molecule inhibitors, which integrate binding mechanisms of two drug candidates that act orthogonally on the VSD4 of Nav 1.7. Thus, the authors illustrate a promising outlook on pharmaceutical intervention on Nav channels.

Overall, the cryo-EM structures of the ligand-bound Nav channels are convincing, with a clear indication of the site-specific, distinct density of the small molecules. At the moment, it is difficult to tell how innovative the pipeline is compared to conventional cryo-EM structure determination.

We thank this Reviewer for this positive comments and for the very helpful suggestions. We are addressing the concerns regarding our cryoEM pipeline.

Reviewer #3 (Public Review):

This is an excellent manuscript, describing a few lines of discoveries:

1. Establishment of a structural biological pipeline for iterative structural determination of an engineered Nav1.7;

2. Illumination of the novel compound binding mode;

3. Structure-based development of the hybrid compounds, which led to the novel Nav1.7 inhibitor;

The cryo-EM study on the engineered Nav1.7 consistently reveals the map at the mid to low 2 Å range, which is unprecedented and impressive, thus, demonstrating the high value of this workflow. The further strength of this study is that the authors were able to develop a new compound by combining structural information gained from the two Nav1.7 structures complexed to two different compounds with different binding modes. Overall, the depth and quality of this study are excellent.

We thank this Reviewer for highlighting the importance of this manuscript and specifically recognizing our accomplishments in enabling iterative high-resolution structure for this target which allowed us to perform SBDD and design a new series of hybrid compounds. We are also grateful for indicating the excellence of our studies.

Author Response

Reviewer #1 (Public Review):

In this manuscript, McQuate et al. use serial block face SEM to provide a high resolution, 3D analysis of mitochondrial structure in hair cells and surrounding supporting cells of the zebrafish lateral line. They first demonstrate that hair cells have a higher mitochondrial volume as compared to supporting cells, which likely reflects the high metabolic load of these sensory cells. Their deeper analysis of mitochondrial morphology in hair cells reveals that the base of the hair cell - near the presynapse is dominated by a large, networked mitochondrion, while the apex of the cell is dominated by many small mitochondria. By examining hair cells at different stages of development, the authors show that specialized features of hair cell mitochondria are gradually established over the course of development. Finally, by examining hair cells in mutants that lack mechanosensation or presynaptic calcium responses, McQuate et al. reveal that cellular activity contributes to the development of appropriate mitochondrial morphology and localization within hair cells. This dataset, which will be made publicly available, is an immense resource to the community and will facilitate the generation of novel hypotheses about hair cell mitochondrial function in health and disease.

Strengths:

1. The painstaking acquisition and analysis of hair cell EM data in a genetically tractable system that is easily accessible for in vivo functional experiments to address hypotheses that emerge from this work.

2. The use of multiple datasets and analysis methods to cross-validate results.

3. The thoughtful, careful analysis of the data highlights the richness of the dataset.

4. The use of both wild-type and mutant animals substantially adds to the manuscript, providing significantly more insight than wild-type data alone.

Weaknesses:

1. The manuscript could more strongly highlight the utility of this dataset and facilitate its future use by providing a summary table that lists each sample together with salient details.

2. The authors examine an opa-1 mutant with altered mitochondrial fission (which consequently has changes in mitochondrial morphology and organization) to suggest that aberrant mitochondrial architecture negatively impacts mitochondrial function. However, mitochondrial fusion is thought to be critical for mitochondrial health beyond just altered architecture. Because fusion has other roles, it is difficult to use this manipulation to conclude that it is simply disruptions in mitochondrial architecture that alters function.

3. Although the work of acquiring and reconstructing EM data is labor-intensive, ideally, multiple fish would be examined for each genotype. Readers should take into consideration that one of the mutant datasets is derived from just one animal.

We thank Reviewer 1 for pointing out the “painstaking acquisition” that went into this study, the “thoughtful, careful analysis,” and the “richness of the dataset.” We believe we have addressed the aforementioned weaknesses.

Reviewer #2 (Public Review):

Sensory hair cells have high metabolic demands and rely on mitochondria to provide energy as well as regulate homeostatic levels of intracellular calcium. Using high-resolution serial block face SEM, the authors examined the influences of both developmental age and hair cell activity on hair cell mitochondrial morphology. They show that hair cell mitochondria develop a regionally specific architecture, with the highest volume mitochondria localized to the basolateral presynaptic region of hair cells. Data obtained from mutants lacking either mechanotransduction or presynaptic calcium influx provide evidence that hair cell activity shapes regional mitochondrial morphology. These observed specializations in mitochondrial morphology may play an important role in mitochondrial function, as mutants showing disrupted hair cell mitochondrial architecture showed depolarized mitochondrial potentials and impaired evoked mitochondrial calcium influx.

This work provides novel and intriguing evidence that mechanotransduction and presynaptic calcium influx play important roles in shaping subcellular mitochondrial morphology in sensory hair cells. Yet there was a lack of consistency in the analysis and presentation of the data which made it difficult to contextualize and interpret the results. This study would be greatly strengthened by i) consistent definitions for hair cell maturation, ii) comparable data analysis of cav1.3a mutant and cdh23 mutant mitochondrial morphologies, and iii) more detailed descriptions and interpretations of the UMAP analysis.

We thank Reviewer #2 for thinking the work is “novel and intriguing”. We have addressed the weaknesses raised.

Reviewer #3 (Public Review):

McQuate et al have succeeded in reconstructing 3D images of mitochondria and discovered unique structural features of mitochondria in zebrafish hair cells. Compared to the other cell types, such as central and peripheral support cells, Hair cells have many elongated and connected mitochondria and they seem to be involved in hair cell and ribbon synapses development. These findings will contribute to understanding the mechanisms for mitochondrial network regulation.

Using the SBFSEM technique, the authors provide clear 3D images of hair cells and the technique improves the resolution of the image to understand the structural parameters of not only mitochondria but also ribbon synapses compared to typical fluorescent imaging. These results are very attractive and have the high potential to broadly apply to 3D imaging of any type of organelles, cells, and tissues. On the other hand, however, the authors provide the data from a small sample size, and the functional experiments to make a conclusion are lacking. Some missing representative images and the nonunified methods of grouping for the analysis make the reviewer concerned.

We thank the Reviewer for thinking the results are “very attractive and have the high potential to broadly apply to 3D imaging of any type or organelles, cell, and tissues.” We agree. We have addressed the weaknesses raised

Author Response

Reviewer #1 (Public Review):

The article from Dumoux et al. shows the use of plasma-based focused ion beams for volume imaging on cryo-preserved samples. This exciting application can potentially increase the throughput and quality of the data acquired through serial FIB-SEM tomography on cryo-preserved and unstained biological samples. The article is well-written, and it is easy to follow. I like the structure and the experimental description, but I miss some points in the analyses, without which the conclusions are not adequately supported.

The authors state the following: "the application of serial FIB/SEM imaging of non-stained cryogenic biological samples is limited due to low contrast, curtaining, and charging artefacts. We address these challenges using a cryogenic plasma FIB/SEM (cryo-pFIB/SEM)".

Reading the article, I do not find that the challenges are addressed; it appears that some of these are evaluated when the samples are prepared using plasma-based beams. To support the fact that charging, contrast, and curtaining are addressed, a comparison should be made with the current state of the art, or it is otherwise impossible to determine whether these systems bring any advantage.

Charging is an issue that is not described in detail, nor has it been adequately analysed. The effect of using plasma beams is independent of the presented algorithm for charging suppression, which is purely image processing based, although very interesting. Given that the focus of the work is on introducing the benefit of using plasma ion beams (from the title) and given that a great deal of data is presented on the effect of the multiple ion sources, one would expect to have comparable images acquired after the surfaces have been prepared with the different beams. This should also be compared against the current state-of-the-art (gallium) to provide a baseline for different beams' benefits. I realise that this requires access to another microscope and that this also imposes controls on the detector responses on each instrument to have a normalised analysis. Still, it also provides the opportunity to quantify the benefits of each instrumentation.

We have provided a response to the charging comments outlined here in the main rebuttal above. The SEM we used in this study was selected based on its optimal performance at low electron voltages due to its immersion field. The low kV capability is particularly of interest in the case of charging (cross over energy). There is the possibility the interaction of the sample surface with chemically inert or reactive ion species could change the surface potential (either positively or negatively). The Vero cells imaged during a serial pFIB/SEM using nitrogen plasma still exhibit charging as well as the argon plasma we canonically used, suggesting that charging is ion beam independent.

Regarding Gallium, this would require prolonged access to another very bespoke microscope for a like-for-like comparison, and indeed there are studies (e.g. Schertel et al. 2013 and Scher et al, 2021) that show SEM data of cryogenic sample surfaces milled with gallium. Therefore, we consider such a study outside of the scope of this manuscript.

The curtaining scores. This is a good way to explain the problem, though a few aspects need to be validated. For example, curtains appear over time when milling, and it would be useful to understand how different sources behave over time in FIB/SEM tomography sessions. The score is currently done from individual windows milled, which gives a good indication of the performance. However, it would make sense to check that the behaviour remains identical in an imaging setting and with the moving milling windows (or lines). This will show the counteracting effect to the redeposition and etching effect reported when imaging with the E-beam the milled face.

Please see our response in the main rebuttal points.

No detail about the milling resolution has been reported. Since different currents and beams have different cross-sections, it is expected to affect the z-resolution achievable during an imaging session. It would be useful to have a description of the beam cross-sections at the various conditions used and how or whether these interfere with the preparation.

Please see our response in the main rebuttal points.

Contrast. No analysis of plasma FIBs' benefits on image contrast compared to the current state of the art has been provided. Measuring contrast is complex, especially when this value can change in response to the detector settings. Still, attempts can be made to quantify it through the FRC and through the analysis of the image MTF (amplitude and fall off), given that membranes are the only most prominent and visible features in cryoFIB/SEM images of biological samples.

We agree that measuring contrast is complex, and therefore the following parameters as stated on page 6, line 6 to 7 were kept consistent throughout data collection: voltage, current, line integration, exposure, detectors voltage offset and gain. We also decided to keep constant or vary the working distance (focus) in Figure 4 and compared the FRC as well as the contrast. As discussed above, a like-for-like comparison with the state of the art (gallium) is not currently possible, making this experiment/analysis outside the scope of this manuscript.

Figure S4 points out that electrons that hit the sample at normal incidence give better signal/contrast or imaging quality than when the sample is imaged at a tilt. This fact is expected to significantly affect large areas as the collection efficiency will vary across the sample, particularly as regions get further away from the optimal location. The dynamic focusing option available on all SEM will compensate for the focal change but not the collection efficiency. Even though this is a fact, the authors show a loss of resolution, which is not explained by the tilt itself. In particular, the generation of secondary electrons is known to increase with the increased tilt, and to consider that the curtains (that are the prominent feature on the surface) are running along the tilt direction, it would be expected to see no contrast difference between the background and the edge of each curtain as the generation of secondary electrons will increase with tilt for both the edges and the background. Therefore, the contrast should be invariant, at least on the curtains.

Looking at the images presented in the figure, they appear astigmatic and not properly focused when imaged at a tilt. As evidence of this claim, the cellular features do not measure the same, and the sharpness of the edge of the curtains is gone when tilted. This experience comes from improper astigmatism correction, which in turn, in scanning systems, leads to the impossibility of focusing. The tilt correction provides not only dynamic focusing but also corrects for the anisotropy in the sampling due to the tilt. If all imaging is set up correctly, the two images should show the imaged features with the exact sizes regardless of the resolution (which, in the presented case, is sufficient), and the sharpness of the curtain edges should be invariant regardless of the tilt, at least while or where in focus. Only at that point, the comparison will be fair.

Please see our response in the main rebuttal points.

Finally, the resolution measurements presented in the last supplementary figures have no impact or relation to the use of plasma FIB/SEM. It is an effect related to the imaging conditions used in the SEM regardless of the ion beam nature. The distribution of the resolution within images appears predominantly linked to local charging and the local sample composition (from fig8). Given the focus is aimed at introducing or presenting the use of the plasma-based beams the results should be presented in that optic in mind with a comparison between beams.

This figure is to present the absence of degradation in image quality over the dataset. As the stage is moving during the imaging at 90 it would be possible for the focus to be lost throughout a longer data acquisition session. However, this figure demonstrates that the focus is well adjusted throughout the data acquisition. We also considered potential beam damage accumulation which does not seem to be detectable with our method.

Reviewer #2 (Public Review):

The authors present a manuscript highlighting recent advancements in cryo-focused ion beam/scanning electron microscopy (cryo-FIB) using plasma ion sources as an alternative to positively-charged gallium sources for cryo-FIB milling and volumetric SEM (cryo-FIB/SEM) imaging. The authors benchmark several sources of plasma and determine argon gas is the most suitable source for reducing undesirable curtaining effects during milling. The authors demonstrate that milling with an argon source enables volumetric imaging of vitrified cells and tissue with sufficient contrast to gleam biological insight into the spatial localization of organelles and large macromolecular complexes in both vitrified human cells and in high-pressure frozen mouse brain tissue slices. The authors also show that altering the sample angle from 52 to 90 degrees relative to the SEM beam enhances the contrast and resolution of biological features imaged within the vitrified samples. Importantly, the authors also demonstrate that the resolution of SEM images after serial milling with argon and nitrogen plasma sources does not appear to significantly affect resolution, suggesting that resolution does not vary over an acquisition series. Finally, the authors test and apply a neural network-based approach for mitigating image artifacts caused by charging due to SEM imaging of biological features with high lipid content, such as lipid droplets in yeast, thereby increasing the clarity and interpretability of images of samples susceptible to charging.

Strengths and Weaknesses:

The authors do a fantastic job demonstrating the utility of plasma sources for increased contrast of biological features for cryo-FIB/SEM images. However, they do not specifically address the lingering question of whether or not it is possible to use this plasma source cryo-FIB/SEM volumetric imaging for the specific application of localizing features for downstream cryo-ET imaging and structural analyses. As a reader, I was left wondering whether this technique is ideally suited solely for volumetric imaging of cryogenic samples, or if it can be incorporated as a step in the cellular cryo-ET workflow for localization and perhaps structure determination. Another biorxiv paper (doi.org/10.1101/2022.08.01.502333) from the same group establishes a plasma cryo-FIB milling workflow to generate lamella of sufficient quality to elucidate sub-nanometer reconstructions of cellular ribosomes. However, I anticipate the real impact on the field will be from the synergistic benefits of combining both approaches of volumetric cryo-FIB/SEM imaging to localize regions of interest and cryo-ET imaging for high-resolution structural analyses.

Additional experiments were undertaken to demonstrate that serial cryo pFIB/SEM can be used in a variety of correlative imaging workflows, including follow-on cryoET. However, we have yet to carefully determine the consequences for downstream high spatial frequencies of such imaging modalities e.g., for sub volume averaging. The role of the SEM imaging, ion beam damage, etc has yet to be analysed or optimised in detail. This work is outside of the scope of this manuscript.

Another weakness is the lack of demonstration that the contrast gained from plasma cryo-FIB/SEM is sufficient to apply neural network-based approaches for automated segmentation of biological features. The ability to image vitrified samples with enhanced contrast is huge, but our interpretation of these reconstructions is still fundamentally limited in our ability to efficiently analyze subcellular architecture.

We have demonstrated that the segmentation of subcellular features such as mitochondria within a serial pFIB-SEM data set of heart tissue can be automated using SuRVos2 – a neural network based automated segmentation software. These comparisons are included in an additional figure (Figure 11).

Author Response

Reviewer #2 (Public Review):

1) My main reservation is the presentation of the work. The writing style is conversational and expansive, which makes it challenging for the reader. Furthermore, long paragraphs shift from one topic to the next rather than using separate paragraphs with strong topic sentences to cover each topic. I suggested a few places to start new paragraphs, but many more paragraphs could be divided.

We have also made significant efforts to reduce the text of the manuscript in each section, with more compact phrasing (including the headlines for the different results sections), and more short paragraphs to make the paper more readable. This has resulted in an overall reduction in the total number of words in the manuscript from ~11.000 to 9.000 (including Abstract, Introduction, Results, Discussion, Materials and Methods, and Figure legends sections), equivalent to approximately four pages of typed text.

2) Most of the figures are also overly complicated. I did not attempt to edit one of them, but I am sure that findings will be much clearer with about half of the panels moved to supplemental materials, so the reader can concentrate on the most important data.

As recommended by the reviewer, we have significantly reduced the number of panels within the figures in the revised manuscript. Accordingly, the total number of panels in the modified figures compared to the original version is as follows: Figure 1 (7 vs 8); Figure 2 (8 vs 10); Figure 3 (7 vs 10); Figure 4 (7 vs 12); Figure 5 (6 vs 11); Figure 6 (4 vs 8).

The remaining panels, including quantitative data such as cable-to-patch ratios, or percentages of septated/multiseptated cells, among others, have been moved to existing and new supplementary figures. The total number of supplementary figures is now 9 versus 6 in the original version.

Author Response

Reviewer #1 (Public Review):

This study combines the biologging method with captive experiments and DNA metabarcoding to detail the hunting behavior of a bat species in the wild. Specifically, it shows that bats use two foraging strategies (echolocating small prey in the air and capturing large ground prey with passive listening) with different success rates and energetic gains. This result highlights that a species believed to be a specialist forager can, in fact, have mixed strategies depending on the condition and environment.

The detailed foraging behavior they show for such a small animal is impressive. A combination of several different methods, including captive experiments, is a major strength of the paper. I especially like the mastication sound analysis, although I don't know how new it is. However, I have a major concern about the presentation of this study. The manuscript is apparently written for a bat community, and it's hard to understand the significance of the results in the field of animal ecology.

Thank you for your helpful feedback. We agree that the framing of the ms was too narrow for the audience of eLife, and we have framed the introduction for a broader audience of animal ecology.

Reviewer #2 (Public Review):

This paper has huge potential for influencing the way we think about bats as foragers. But, I think that it can be improved.

Specifically, there is no clearly articulated hypothesis underlying the work. Second, there should be specific testable predictions arising from the hypothesis. This change, while relatively minor, will vastly improve the focus of the work, and hence its impact on the reader.

Thank you highlighting the need for clear hypotheses. We have added three specific hypotheses to guide the reader (line: 54-56) in the introduction. We have also reformatted the discussion section to address each hypothesis in succession using subheadings with clear take home messages (line: 223-224, 271-272, 293, 318)

Reviewer #3 (Public Review):

The study addresses a tough question in the study of wild bats: what and where they eat, using both acoustic bio-logging and DNA metabarcoding. As a result, it was found that greater mouse-eared bats made more frequent attack attempts against passively gleaning prey with lower predation success but higher prey profitability than aerial hawking with higher predation success. This is a precious study that reveals essential new insights into the foraging strategies of wild bats, whose foraging behavior has been challenging to measure. On the other hand, the detection of capture attempts, success or failure of predation, and whether it was by passively gleaning prey or aerial hawking were determined from the audio and triaxial accelerometer analysis, and all results of this study depend entirely on the veracity of this analysis. Also, although two different weights and a tag nearly 15% of its weight were used, it is essential for the results of this data that there be no effect on foraging behavior due to tag attachment. Since this is an excellent study design using state-of-the-art methods and very valuable results, readers should carefully consider the supplemental data as well.

Thank you for the kind words. We agree that it is critically important that the two foraging strategies are un-affected by tagging effects. In the revised ms, we have added tag weights, tag types and change in body weight during instrumentation as explanatory factors in out statistical models and found no effect of the tag weight on our results. We have also addressed this important issue in the method section (model 1: line 520-539, model 3: 568-590).

Author Response

Reviewer #1 (Public Review):

Zeng and colleagues investigated the neural underpinnings of visual-vestibular recalibration. Specifically, they measured changes in three monkeys' perception of unisensory heading cues as well as associated changes in neuronal responses to these cues in three different cortical areas following prolonged exposure to systematic visual-vestibular discrepancies. Behavioral responses in a motion direction discrimination task indicate unisensory perceptual shifts in opposite directions that account for the cross-modal discrepancy the monkeys were exposed to. Neuronal firing patterns, related to motion discrimination judgments by means of neurometric functions indicated analogous shifts in neuronal tuning in areas MSTd and PIVC. In contrast, in area VIP tuning for visual heading stimuli shifted in the same direction as tuning for vestibular stimuli and thus in contradiction to the observed perceptual shifts.

The shifts observed in MSTd and PIVC fit nicely with existing theories and results regarding cross-modal recalibration and substitute claims that activity in these areas might underlie perceptual decisions. The shift of visual tuning in VIP is surprising and will certainly spark further investigation.

Overall the results are really interesting, yet, the manuscript in its current form needs revisions along two dimensions, 1) data analysis and 2) writing.

We thank the reviewer for the positive comments and thoughtful suggestions, which have greatly helped us improve the data analysis and writing. Also, thank you for the thorough list of specific suggestions for improved writing and phrasing. This considerably helped us clarify these aspects in our manuscript.

Reviewer #2 (Public Review):

The manuscript by Zeng and colleagues aims to investigate how neural representations of sensory cues in two modalities (visual and vestibular) change when conflicts are introduced between the cues. The manuscript convincingly demonstrates that this recalibration process differs between areas MSTd (a multisensory region), where sensory responses recalibrated differently for visual and vestibular cues, following each modality's conflict, and area VIP ( a higher-level region), where responses follow the vestibular cue. More limited insights are present for area PIVC, where visual responses are limited.

The analyses generally support the conclusions of the authors, but I have two major suggestions to strengthen the statistical robustness of the manuscript:

1) The analysis about the lack of visual recalibration in area PIVC would have been more convincing if the authors had used Bayesian statistics instead of regular t tests. In this way it would have been possible to estimate if the lack of visual recalibration in this area, for those few neurons that show visual tuning, can be taken as evidence for the absence of an effect or not. In the absence of this additional analysis, it is in fact difficult to properly interpret the results about area PIVC. Is PIVC more in line with MSTd, in view of the lack of visual responses? Or is there actually no visual recalibration, in contrast to both MSTd and VIP?

In response to this comment, we calculated the Bayesian Pearson correlation for visual recalibration in area PIVC, with the alternative hypothesis (H1) of a correlation between neuronal shifts and perceptual shifts and the null hypothesis (H0) of no correlation: Pearson's r = 0.26, and BF10 = 0.49. Thus, the evidence neither supports H1 nor H0. The lack of support for or against visual recalibration in PIVC primarily reflects the lack of robust tuning to visual heading stimuli in PIVC. Accordingly, in the manuscript, we do not argue for or against the recalibration of visual heading tuning in PIVC. Rather, we highlight that neurons in PIVC respond strongly to vestibular signals, but not so to visual heading stimuli and that the vestibular responses undergo recalibration. We agree that the lack of evidence for (or against) visual recalibration in PIVC primarily reflects the lack of robust tuning to visual heading stimuli. We interpret the observed shifts in vestibular tuning in PIVC as lower-level, sensory, recalibration (similar to MSTd) based on the broader understanding that PIVC encodes lower-level vestibular signals, with transient time-courses, and impoverished visual tuning (Chen et al., 2016; Chen et al., 2021). Our results are in line with this interpretation, and there is no reason to suspect that PIVC reflects more complex multisensory recalibration (like VIP). Nonetheless, the data could also be in line with alternative interpretations. Therefore, in the revised manuscript we now more explicitly explain this argument and have added limitations thereof, and alternative interpretations to the Discussion (in subsection “Limitations and future directions”, paragraph 2).

2) For all statistical analyses, multi-level statistics would have been more appropriate than simple t-tests. In fact, since recordings come from few subjects, which in turn have relatively few recording sessions, there is a risk that the results are influenced by one subject and do not represent the full population. Admittedly, this is unlikely in view of the apparently large effect size and low p values. Nonetheless, a more appropriate statistical analysis would make the results more robust and convincing.

Thank you. We agree with this suggestion and have now: 1) added summary statistics for the individual monkeys, and 2) performed linear mixed model (LMM) analyses (please see our response to Essential Revisions Comment #1, for further details).

Once these issues are addressed, I believe that the manuscript would provide relevant evidence supporting the hypothesis that multisensory processing in the cortex is an area-specific phenomenon, and that effects observed in one area cannot be simply expected to operate elsewhere. This will therefore elucidate the mechanisms of multimodal plasticity.

Reviewer #3 (Public Review):

This study documents an empirical investigation of a fundamental brain process: adaptation to systematic cross-sensory discrepancies. The question is important, the experiment is carefully designed, and the results are striking. Following an unsupervised recalibration block, perceptual judgments of self-motion on the basis of visual and vestibular cues are systematically altered. These behavioral effects are mirrored by changes in the response properties of single neurons in areas MSTd and PIVC (provided that neurons in these areas exhibited selectivity for the sensory cue). Remarkably, neurons in downstream area VIP adjust their response properties in a very different manner, seemingly exclusively reflecting vestibular recalibration (which is opposite in direction to visual perceptual shifts). In the former two areas, the neural-behavior association follows the stimulus dynamics. In VIP, this association remains high beyond the life span of the stimulus. VIP typically exhibits strong choice signals. These decreased in strength after recalibration (an effect unique to area VIP). Together, these findings further dissociate VIP's functional role from that of MSTd and PIVC, without however, fully revealing what that role may be. These results offer a novel perspective on the neural basis of cross-sensory recalibration and will inspire future modeling studies of the neural basis of perception of self-motion.

We thank the reviewer for the supportive comments.

Author Responses

Reviewer #1 (Public Review):

The authors present a very detailed short report on a previously undocumented behaviour where flying squirrels are believed to have created grooves in various species of nuts to aid their secure storage in the crotch or forks of twigs. The behaviour is suggested to have evolved as an adaptive strategy in this population of flying squirrels because of the challenges for nut caching in a rainforest environment.

Thanks

Using detailed photographs, GPS locations, measurements and camera trap videos, the authors describe the behaviour in great depth providing a useful base for comparative and future studies. However, the weakest point of this study is that the authors did not detect any squirrels making the grooves and only monitored nuts once they were cached. Therefore more research needs to be done to ascertain who, how and where the grooves are produced in the first place.

Three new videos are attached to show that two squirrel species are rotate and carving the nuts to create the grooves. By the new videos, we can also observe that squirrels re-fixed the nuts between the twigs by carving the nuts. These direct observations can support the claim better. See Supplementary Media files 6-8.

This work will be of great interest to scholars of animal behaviour and cognition and draws attention to a novel behaviour that warrants further study in similar species.

Yes, it is. Thanks

Reviewer #2 (Public Review):

The authors describe observations of an innovative food caching behavior attributed to two species of flying squirrels and likened the behavior to architectural joints used by humans. The discovery of nuts stored in the crook of shrub branches, facilitated by indented rings seemingly carved by squirrels, possibly represents an interesting food handling innovation that may function to prevent spoilage in a damp tropical ecosystem.

Thanks!

I applaud the efforts to survey the area multiple times after the initial discovery, and the use of trail cameras to try capture evidence of animal associations. For what is in essence a natural history note, the authors did a great job of trying to gather a variety of supporting evidence. The videos capturing squirrels visiting and retrieving the cached nuts were compelling, and the shaking of the shrubs demonstrating the difficulty in dislodging the nuts helps build the case that the nuts are cached effectively.

Thanks!

The most glaring gap in the evidence is that there is no direct observation of the squirrels actually performing this nut carving behavior, only associating with the nuts after they have been cached.There must be more documentation provided to explicitly link the causality between squirrels and this caching innovation.

We have included three additional videos to demonstrate that squirrels of both species rotate and carve the nuts to create the grooves. These new videos also show that squirrels can fit the nuts between twigs by carving the nuts. We think that these direct observations clearly support our claim, but agree that it was oversight not to included them in the first draft. See Supplementary Media files 6-8.

The second major weakness is more to do with writing style and could be addressed with significant revisions to phrasing and development of ideas. This is namely to do with the claim that this is somehow an evolved behavior, without providing evidence that 1) it is indeed the squirrels performing this behavior, 2) that is confers some kind of fitness benefit, and 3) hard evidence that this caching method does indeed prevent decomposition/germination in comparison to the more traditional caching methods of these species. Given the limited geographic range of the observations, I wonder how much of this is actually attributable to learning and/or innovation by these individuals. These ideas are not developed fully, and sometimes the writing wanders among learning and evolution without exploring the deep links among the two concepts.

1) As above, three new videos establish that the squirrels do, in fact, carve the nuts. See Supplementary Media files 6-8.

2) We added more description to suggest how this behavior likely confers fitness benefit in the discussion. At this point, however, it is correct to say that we have no hard evidence to demonstrate this, and thus, we’ve attempted to ‘tighten up’ the discussion accordingly so that our arguments (and its limitations) are more understandable.

3) We revised the statistics about the proportion of nuts that were fresh during each of the surveys, and added some references about how long is required for the nuts to germinate in natural conditions. L163-172.

Third, the connection to architecture is attention-grabbing, but I'd like to see this fleshed out a bit more with more text description (and a visual here would help immensely).

We added more description about how the grooving, caching and checking processes were performed by squirrels and how the principles of this suspension are similar to the mortise-tenon joint as employed by humans. L186-202. As above, three new videos are attached.

Ultimately this work stands to potentially contribute a fascinating piece of evidence into the growing literature on animal cognition, spatial awareness, caching behavior, innovation, and adaptation, but currently, the claims are unsupported by the evidence presented.

Thank you for your comments about the potential importance of our work on this interesting system. In this version we try to focus more tightly on the aspects for which we have new information to interpret.

Reviewer #3 (Public Review):

The authors were trying to describe and document the grooving behaviour of nuts in two species of flying squirrels (Hylopetes Phayrei electilis and H. alboniger) as well as related such behaviour to tool use or that the squirrels are smart. To achieve these objectives, the authors conducted three field surveys. They also set out a camera later to capture animal species that interacted with these nuts. They found that these nuts with grooves are fixed between twigs and can be found in different small plant species. Both species of squirrels made grooves a nut. More shallow grooves are found in nuts that are fixed on alive than dead trees. Ellipsoid nuts have deeper grooves than oblate nuts. They concluded that these nut grooving behaviours are evolved or learned in those flying squirrel populations, and related these behaviours to tool use as well as that the squirrels are smart.

Thanks!

One strength of this work is that the data were collected in the field, which may provide hard evidence with video footage showing the two flying squirrel populations made grooves on nuts as well as fixing them between twigs. This evidence will induce new interests to understand the causes and consequences of such nut grooving behaviour. It may be bold to claim that such behaviour involves advance cognition or cognitive process without proper, systematic, experiments. Accordingly, whether the squirrels are 'smart' remains unclear. The authors did well in describing and documenting the nut grooving behaviours of the two species of flying squirrels, which has achieved their first aim. However, as mentioned above, whether such behaviour is 'smart' will need more systematic investigations.

We have removed the description about cognition or cognitive process in the paper, and the paper is focused on the grooving behavious. “Smart” is also removed, with other words used instead.

Author Response

Reviewer #3 (Public Review):

1) (Schichl et al. 2011 JBC 286:38466). This publication is not cited in the current version of the manuscript. The results of Schichl et al. seem particularly relevant for the interpretation of some of the results presented here and should be considered in the final discussion and conclusions of the present work.

This reference and related text was added in the discussion section in the revised manuscript (lines 508-517).

2) The ubiquitination of endogenous TTP has not been demonstrated.

New data assessing the ubiquitination of endogenous TTP was added as Figure 1 – figure supplement 1D.

3) The type of ubiquitination detected on the overexpressed version of TTP is not characterized. This seems important in view of the results of Schichl et al. who showed non-degradative ubiquitination (K63) of TTP.

New data with the detection of K48- or K63-linked poly-ubiquitin chain by specific antibodies was added as Figure 1 – figure supplement 1G. These data show that recombinant poly-ubiquitin chains can be readily detected with both antibodies, but that only K48-linked chains were detected on TTP IPed from cells.

4) The half-life of the non-ubiquitinated mutant of TTP (K→R) was not precisely compared to the half-life of the wild-type TTP protein (similar to the experiment presented in 1B).

New data from TTP-KtoR chase experiments was added as Figure 1 – figure supplement 1E. The half-life was increased substantially from 1.4 h for wtTTP to 5.7 h for the mutant.

5) The effect of the E1 ubiquitin ligase TAk-243 on endogenous TTP levels was not tested.

New data assessing the effect of TAK-243 on endogenous TTP was added as Figure 1 – figure supplement 1B. Consistent with our data with exogenously expressed TTP, treatment with the inhibitor increased the abundance of endogenous TTP.

6) While they demonstrate that TTP-HA is efficiently degraded after 3 to 7h of LPS stimulation (Fig 1B) and that the stronger decrease in mCherry-TTP fusion level occurs between 4 and 6h of LPS stimulation the screen for identification of TTP modulators is performed 16h of LPS stimulation (Fig 2A). The rationale behind this experimental setting is not explicitly described.

We found that endogenous TTP and mCherry-TTP levels were substantially lower at 16 h post-LPS stimulation compared to 6 h. (see Fig. 1D), and reasoned that this would yield the best genetic screen window in which to identify mutant cells with non-functional degradation mechanisms.

7) The authors did not directly test the effect of HUWE1 inactivation on endogenous TTP accumulation after blocking protein synthesis. This control seems important as data presented in figure 2E could result both from an effect of Huwe1 level on LPS-induced TTP synthesis and TTP degradation.

New data from chase experiments with endogenous TTP have been added as Fig. 2G. Consistent with the data presented in Fig. 2E, TTP levels declined during the chase period in sgROSA control cells, with an estimated half-life of 3.7 h. In contrast, TTP levels did not significantly decline during the CHX chase period in Huwe1 KO cells, resulting in an estimated TTP protein half-life of ~20 h in this genotype.

8) In the data presented in figure 2, it is not entirely clear what exactly the authors are referring to as "endogenous TTP". In Figure 2C endogenous TTP is detected by western blot on cells transfected with an mCherry-TTP fusion. In this case, the size difference allows unambiguous identification of the endogenous form of TTP (although one could not exclude that overexpressing a TTP fusion protein might affect the level of the endogenous protein). However, TTP and mCherry-TTP cannot be distinguished by FACS (Fig2 D and E). If cells used in the experiments shown in 2C and 2D-E are distinct, this should be mentioned more explicitly in the legend of Fig. 2. Otherwise, the detection of endogenous TTP should be performed on cells that do not express mCherry-TTP.

Results from Fig. 2D/E are indeed from cells that do not express mCherry-TTP. Endogenous TTP is detected in these cells by intracellular antibody staining. The figure legend text has been updated to reflect that panel 2C is with the RAW264.7-Dox-Cas9-mCherry-TTP cell line, and D-E is with the RAW264.7-Dox-Cas9 cell line.

9) The third part of the manuscript aims to demonstrate that loss of Huwe1 decreases the half-life of pro-inflammatory mRNAs controlled by TTP. In my opinion, this conclusion is reliably supported by the data presented in Figure 3 and Supplementary Figure 3. As the conclusion of this paragraph refers to the effect of TTP on the stability of these mRNAs, the measurement of TNF mRNA stability (Fig. sup. 3C) should be presented in the main part of Fig. 3.

The TNF mRNA stability figure panel was moved to the main figures as Fig. 3C.

10) Fig 4E aims to identify kinases and phosphatases potentially involved in TTP stability (line 277, line 298). However, the approach used here (a measure of intracellular TTP level) cannot distinguish between increased production of TTP or a decrease in TTP degradation.

One of the main points of this experiment was to assess whether the steady-state increase in TTP in HUWE1 KO cells, which stems for an important part from increased stability (Fig. 2G), was influenced by TTP phospho-status. Thus, while we do not explicitly measure TTP protein half-life in this particular assay, it is very likely to reflect changes in TTP protein stability. This idea is consistent with the fact that treatment with p38i, MK2i, and CaclycA affected TTP steady-state levels consistent with their previously reported effects on TTP protein stability.

11) Also, the result presented in fig. 4E, are not totally consistent with the results presented in 4A. Fig4D shows a similar level of endogenous TTP accumulating after 2h of LPS stimulation in Huwe1 KO and control cells while a clear difference in TTP level is observable in the same condition in fig. 4A. Could the difference in the TTP detection method (Western vs intracellular FACS) be responsible for this discrepancy?

We do not exactly know, but agree that this could indeed be influenced by the measurement method per se, as well as small variations in cell density, or total sample numbers in a particular experiment (as this may increase the time outside of the incubator for handling/stimulations). The much larger sample size of the experiment from panel 6E, and having multiple different stimulations, may have contributed to a slightly delayed timing of the Huwe1-dependent phenotype. It is important to note, that we have consistently demonstrated with different measurement methods, that TTP is initially stabilized post-LPS treatment (2-3 h, insensitive to Huwe1 KO), followed by TTP degradation (6-16h, sensitive to Huwe1 KO).

12) These experiments and data presented in Fig.5D show that the level of the TTP paralog ZFP36L1 accumulates in huwe1 KO cells but do not demonstrate that HUWE1 affects ZFP36L1 protein stability.

We agree, and changed all instances in the text that claimed ZFP36L1 ‘stabilization’ to ‘increase in abundance’.

13) Based on data presented in fig. 6 B and sup. 6B the authors conclude that residues S52 and 178, previously identified as regulators of TTP stability, are unlikely to be involved in HUWE1-dependent TTP accumulation. The data are only based on 2 independent experiments, one of which (fig 6B) shows a difference in TTP S52/S178 mutant in Huwe1 deficient cells as compared to wt TTP. These results seem therefore too preliminary to reliably exclude the implication of S52 and 178 on the HUWE1 accumulation of TTP.

Additional new data with the S52/178 TTP mutant of six biological replicates has been added to the manuscript as Figure 6 – figure supplement 1C. Data from these experiments are consistent with our other results, and show that protein levels similarly increase for both wtTTP and the S52/178A mutant in Huwe1 KO cells.

14) From these data, the authors conclude (line 416) that N-terminal deletion does not affect the TTP protein level. However, TTP accumulation in Huwe1 KO cells seems mostly lost in mutant N4. As mentioned above the limited number of replicates (n=2) and the absence of a statistical test makes the interpretation of this result difficult.

Additional new data with the Δ4 mutant of two biological replicates has been added to the manuscript as Figure 6 – figure supplement 1E. Data from these experiments are consistent with our other results, and show that protein levels similarly increase for the Δ4 mutant in Huwe1 KO cells.

15) Several TTP C-terminal mutants show a HUWE1-independent accumulation when compared to the wt protein (Fig6. D). Is this region identical to the unstructured region identified by Ngoc (line 1255) as a potent regulator of TTP degradation? If relevant this point should be discussed.

Ngoc showed that fusion to GFP of either the N-terminal TTP part, or the TTP Cterminal part (aa 214-436), destabilized GFP in cells. Thus, the GFP destabilization was seemingly indiscriminate, and possibly caused by the disordered nature of the fusion construct per se. Since the C-terminal TTP part fused to GFP by Ngoc included aa 214-436, we cannot rule out that part of this effect was HUWE1-dependent. However, the discrepancy with our finding that the TTP N-terminus does not contribute to HUWE1-dependent TTP regulation, may suggest that the GFP fusions by Ngoc were destabilized by more general protein principles, rather than HUWE1-specific effects. Additional text conveying this notion was added to the Discussion section (line 490-497).

Author Response

Reviewer #1 (Public Review):

Understanding the evolution of nitrogenases is a very important problem in the field of evolutionary biogeochemistry. Ancestral sequence reconstruction at least in theory could offer insights into how this planet alerting activity evolved from ancestors that did not reduce nitrogen. But the very many components of the nitrogenase enzyme system make this a very challenging question to answer.

This paper now demonstrates the first empirical resurrection of functional ancestral nitrogenases both in vivo and in vitro. The nodes that are resurrected are very shallow in the nitrogenase tree and do not help answer how these proteins evolved. The authors' reasoning for choosing these nodes is that they are likely compatible with the metal cluster assembly machinery of their chosen host organism, A. vinelandii. The reader is left to wonder if deeper, more interesting nodes were tried but didn't yield any activity. As the paper stands, it proves that relatively shallow nitrogenase ancestors can be resurrected, but these nodes do not yet teach us anything very fundamental about how these enzymes evolved.

Technically, this work was no doubt challenging. Genome engineering in A vinelandii is very difficult and time-consuming. This organism was chosen because it is an obligate aerobe, which makes it easier to handle than the many anaerobic bacteria and archaea that harbor nitrogenases. It does make one wonder if this choice of organism is wise: the authors themselves note that it probably has a set of specialized proteins that allow the nitrogenase to be assembled and function in the presence of oxygen. This may limit A. vinelandii's potential future ancestral reconstructions deeper in the tree, which according to the authors' reasoning probably requires different assembly machinery.

The ancestral sequence reconstruction is done in two different ways: Two out of three reconstructions are carried out with what appears to be an incorrect algorithm implemented in older versions of RaxML. This algorithm is not a full marginal reconstruction, because it only considers the descendants of the node of interest for the reconstruction. The full algorithm (implemented e.g. in PAML and the newest versions of RaxML) considers all tips for a marginal reconstruction. The fact that this was called a marginal ancestral sequence reconstruction in RaxML's manual is unfortunate - as far as I understand it is in fact just the internal labelling of nodes produced by the pruning algorithm, which is not equivalent to a marginal reconstruction. In this specific case, it is unlikely that this has led to any fundamental issues with the reconstructions (as all are functional nitrogenases, which is to be expected in this part of the tree). For the shallower of the two nodes, the authors in fact verify that they get the same experimental results if they use PAML's full implementation of a marginal reconstruction (which yields a somewhat different sequence for this node). It would have been helpful to point this RaxML-related issue out in the methods, so as to prevent others from using this incorrect implementation of the ASR algorithm.

One other slightly confusing aspect of the paper is that it contains two different maximum likelihood trees, which were apparently inferred using the same dataset, model, and version of RaxML. It is unclear why they have different topologies. This probably indicates a lack of convergence. Again, this does not cast any doubt on the uncontroversial findings of this paper that shallow nodes within the nitrogenases are also nitrogenases.

We thank the reviewer for their careful appraisal of our article, and their helpful recommendations for improving its quality. We appreciate the reviewer’s comment regarding the experimental challenges associated with nitrogenase engineering and genetic studies of our bacterial model, Azotobacter vinelandii. The complexity of nitrogen fixation machinery does indeed present several experimental obstacles, though, as we note in our revised article, this feature also makes the systems-level approach we have implemented here ideal for evolutionary studies of nitrogenases and their associated network.

The reviewer focuses on three central points: 1) the relevance of the targeted ancestral nodes for addressing fundamental questions concerning nitrogenase origins, 2) the applicability of our bacterial model for older reconstructions, and 3) issues associated with the different trees/methods for ancestral sequence reconstruction.

Addressing the first point, we concede that targeting relatively shallow nodes cannot specifically test hypotheses concerning the earliest stages of nitrogenase evolution (e.g., “how this planet altering activity evolved from ancestors that did not reduce nitrogen”). Our central result is that a specific, enzymatic mechanism for dinitrogen binding reduction (established for three modern nitrogenases to date) extends back through nitrogenase ancestry over the studied timeline. More broadly, a conserved nitrogenase mechanism in the only surviving family of nitrogenase families suggests that life may have been constrained in its available strategies for achieving this challenging biochemical reaction. By comparison, multiple abiotic pathways for nitrogen fixation are feasible, and another, ecologically vital metabolism, carbon fixation, can proceed by at least seven pathways. Deeper investigations into these possible evolutionary constraints and across deeper portions of the nitrogenase tree will require continued study, which we anticipate will be facilitated by the experimental approach presented in this article.

Concerning the applicability of our bacterial model, we agree that it is possible that older reconstructions may require different host organisms so as to provide a compatible genetic background. Similar considerations we have outlined in our article, including a systematic evaluation of the genetic components that likely accompanied nitrogenase ancestors in their ancient hosts, will likely be necessary. Nevertheless, we foresee that the general, systems-level approach that we have built for Azotobacter can be adapted for additional microbial models, and that these efforts will be worthwhile given the significance of biological nitrogen fixation to evolutionary biogeochemistry and microbial engineering applications.

Finally, we thank the reviewer for noting the differences in the ancestral sequence reconstruction algorithms of RAxML v.8 and PAML and welcome an explanation of these issues in our revised article. We confirm that RAxML v.8 does not perform full marginal reconstruction (in contradiction to its description in the RAxML manual). Due to this concern, we repeated our ancestral sequence reconstruction with PAML, which, like newer versions of RAxML, does implement the full algorithm. Here, ancestors reconstructed by RAxML v.8 and PAML from equivalent phylogenetic nodes yield comparable experimental results, indicating that the algorithm differences have not significantly impacted the major outcomes of our study. In the second analysis, we repeated the entire phylogenetic ancestral sequence reconstruction workflow, though did not trim the alignment as we did in the first case (this has now been clarified). This likely explains the differences in our trees, as the reviewer notes. We have included these details in the Materials and Methods section of our revised article.

In addition to expanding upon the points outlined above throughout the revised article, we have included additional text in the Discussion that elaborates on the limitations of our study, and in particular, the need to explore deeper portions of the nitrogenase tree in future work.

Reviewer #2 (Public Review):

The authors convincingly show that their reconstructed ancestral nitrogenases are active both in vivo and in vitro, and show similar inhibitory effects as extant/wild-type enzymes.

The conclusion that, evolutionarily, there is a "single available mechanism for dinitrogen reduction" is not well explored in the paper. This suggests a limitation of using ancestral sequence reconstruction in this instance.

We thank the reviewer for their comments and appreciate their assessment that the core experimental results are conclusively demonstrated, including in vivo/in vitro activity of ancestral nitrogenase enzymes and that they all exhibit the specific mechanism for dinitrogen binding and reduction, evidenced by hydrogen inhibition.

We note the reviewer’s concern regarding the evolution of the dinitrogen reduction mechanism described above. Our primary conclusion is that this mechanism is conserved in the studied nitrogenase ancestors, which, together with previous demonstrations of this mechanism in the different nitrogenase isozymes (Mo, V, Fe) of Azotobacter vinelandii, suggests that this is an early evolved feature of the nitrogenase family. These enzymes have thus not only been performing an ecologically vital, metabolic function, but have likely been achieving this challenging biochemical reaction in the same manner for billions of years. We discuss the resulting implications as they relate to evolutionary constraints on biological nitrogen fixation strategies. We clarify that our presented paleomolecular approach cannot directly evaluate alternate evolutionary scenarios that did not persist and were not preserved in extant genomic sequences, as ancestral sequence reconstruction is fundamentally informed by extant sequence diversity. Our approach is a powerful tool for defining the contours of ancestral nitrogenase sequence-function space, which can serve as a basis for engineering and evaluating alternate scenarios. We have clarified these points in our Discussion.

Reviewer #3 (Public Review):

In this work, the authors attempt to probe the constraints on the early evolution of nitrogen fixation, the development of which presented a key metabolic transition. Given that life on Earth evolved only once (to our knowledge) which aspects were necessary and which may have taken a different course are open questions. Are there alternative forms of life, metabolic networks, or even enzymatic mechanisms that could have replaced the ones we see today, or is the space of possible biologies limited? This manuscript tests the ability of ancestrally-reconstructed molybdenum-dependent nitrogenase complexes to support diazotrophic growth in Azotobacter vinelandii, as well as in vivo and in vitro activity, which all point towards a conserved mechanism for nitrogen reduction at least since proteobacteria divergence.

This is an ambitious project, requiring multiple techniques, systems, and approaches, and the successful combination of these is one of the major strengths of this work. Using parallel techniques is an important way to be certain that the overall results are robust, and an appropriate mix of in vivo and in vitro experiments is chosen here. The manuscript should serve as a useful model for how to combine phylogenetics and biochemistry.

The nature of ASR means that a solid phylogeny and/or understanding of how robust the results are to uncertainty in reconstructed states is essential since all results flow from there. The overall phylogenetic methods used are appropriate and the system is an apt one for the technique, but there is not quite enough detail in the methods to be certain of the results. Given that only the single maximum a posteriori sequence is assayed at every 3 nodes, this may have compounding results in that the sensitivity to uncertainty in the reconstruction is increased. The authors appropriately make qualitative rather than quantitative inferences, but some hesitation towards the overall results still exists.

The assumption that the Anc1A/B and Anc2 nodes correspond to ancestral states might be undermined by horizontal gene transmission, which has been reported for nif clusters. In particular, there may be different patterns of transmission for each element of the cluster. By performing reconstruction with a concatenated alignment, the phylogenetic signal is potentially maximized, but with the assumption that each gene has an identical history. Discordant transmission may cause an incorrect topology to be recovered.

Finally, I am unsure if ASR is the most appropriate approach to answer questions of contingency and alternative pathways for protein evolution. ASR may tell what nitrogenase millions or billions of years ago looked like, but it can only say what has already existed. If there are different mechanisms or metabolic pathways enabling nitrogen fixation that simply never came to pass, via contingency and entrenchment or simple chance, ASR would say nothing about them. It is true that a conserved mechanism would point towards a constrained space for evolving nitrogen fixation, but that does not directly address it.

Overall, despite these issues, the manuscript is compellingly written and the figures are attractive and clear, and help get the major narrative across. This work will be of interest to protein biochemists of evolutionary bent and microbial physiologists with an interest in the origins of life.

We thank the reviewer for their evaluation of our study and appreciate their comments regarding the experimental effort involved and scientific significance. We have carefully considered their recommendations to improve our article.

The reviewer’s critical comments concern 1) the level of detail regarding the phylogenetic methodology, 2) the impact of horizontal gene transfer on phylogenetic reconstructions, and 3) the appropriateness of ancestral sequence reconstruction for accessing alternate evolutionary scenarios in the emergence of biological nitrogen fixation.

We have addressed the first point by including additional methodological details regarding our phylogenetic analyses in our Materials and Methods section, including alignment and model testing tools, as well as our rationale for using two ancestral sequence reconstruction methods, RAxML and PAML.

Regarding the second point, we acknowledge that horizontal gene transfer has played a significant role in the evolution and distribution of biological nitrogen fixation, which has been established and explored in previous work by others. We have included in our Discussion an additional paragraph which addresses potential impact of horizontal gene transfer in nitrogenase evolution. Though we do not expect horizontal transfer to contribute a significant source of uncertainty in the timeline studied for the reasons discussed in the revised manuscript, we agree that it is an important consideration for future work and that may impact reconstructions in other lineages within the nitrogenase phylogeny.

Finally, in new text within the Discussion, we also acknowledge that ancestral sequence reconstruction cannot yet directly test alternate historical scenarios. We have clarified our language concerning conservation and constraints in the evolution of biological nitrogen fixation. Because ancestral sequence reconstruction is informed by modern sequences, it is limited to exploring the historical sequence space within their shared ancestry. It is therefore possible that, early in the history of life, there were multiple enzymatic strategies for fixing nitrogen, and that they were outcompeted and thus have left no trace in modern genomes. Another possibility is that these alternate strategies simply never evolved.

In the present study, we have identified a pattern of conservation with regard to a specific mechanism for dinitrogen binding and reduction, suggesting a level of evolutionary constraint that can be further interrogated. For example, ancestral sequence reconstruction, as implemented in our nitrogenase resurrection strategy, can be used to empirically investigate the underlying sources of these constraints. We note that despite decades of research in this domain, a full understanding of how nitrogenases perform this remarkable metabolic step, both today and in the past, remains elusive (as other reviewers of the present study have also noted). Evolutionarily informed studies of nitrogenase function enabled by ASR can reveal the design principles that have shaped its direct ancestry, which can potentially serve as a basis for engineering alternative molecular strategies for nitrogen fixation. The power of the molecular paleogenetic approach here is in extending functional investigations beyond the sequence space occupied by modern nitrogenase and identifying patterns in their functional variation through their evolutionary histories.

Author Response

Reviewer #1 (Public Review):

The study's primary motivating goal of understanding how nutrigenomic signaling works in different contexts. The authors propose that OGT- a sugar-sensing enzyme- connects sugar levels to chromatin accessibility. Specifically, the authors hypothesize that the OGT/Plc-PRC axis in sweet taste neurons interprets the sugar levels and alters chromatin accessibility in sugar-activated neurons. However, the detailed model presented by authors on OGT/PRC/Pcl Rolled in regulating nutrigenomic signaling relies on pharmacological treatments and overexpression of transgenes to derive genetic interactions and pathways; these approaches provide speculative rather than convincing evidence. Secondly, evidence is absent to show that PRC occupancy remains the same in other neurons (non-sweet taste neurons) under varied sugar levels or OGT manipulations. Hence, the claim that OGT-mediated access to chromatin via PRC-Plc is a key regulatory arm of nutrigenomic signaling needs further substantiation.

We thank the reviewer for their thoughtful reading of the manuscript and their suggestions. We disagree with the reviewer’s assessment that our work only relies solely on overexpression and pharmacological treatments and that this provides only “speculative” evidence. Indeed, both of the other two reviewers praised our approach:

Reviewer 2: “This is an elegant group of experiments revealing mechanisms for how nutrigenomic signaling triggers cellular responses to nutrients”

Reviewer 3: “Strengths: Good genetically targeted interventions; Thorough exploration of the epistatic relationships between different players in the system … The conclusions in this manuscript are mostly well or at least reasonably supported by data.

All of our experiments combine genetic manipulations in combination with dietary and/or pharmacological treatments to show that molecular, neural, and behavioral taste phenotypes arise only in specific contexts, so no single phenotype occurs due to nonspecific manipulations. Without this approach, most of these epistatic relationships would be largely inaccessible in this system. We have also used a combination of both genetic and pharmacological tools to implicate not only genes but also their function (i.e., enzymatic activity) to nutrient-specific effects. Third, we established causality and relationship by inducing and rescuing the molecular, behavioral, and electrophysiological phenotypes. Thus, our model is based on a combination of direct and indirect data (genetic manipulations are by nature inferential) obtained from a controlled and careful set of experiments. Limitations of our approach were laid out under the “Limitation” section of the discussion, as well as alternative interpretations or possibilities. In the manuscript's revised version, we added additional genetic experiments to further support and validate our model and expanded data analyses as suggested by the reviewer.

Reviewer #2 (Public Review):

Nutrigenomics has advanced in recent years, with studies identifying how the food environment influences gene expression in multiple model organisms. The molecular mechanisms mediating these food-gene interactions are poorly understood. Previous work identified the enzyme O-GlcNAC (OGT) in mediating the decreased sensitivity in sweet-taste cells when exposed to a high-sugar diet. The present study, using fly gustatory neurons as a model, provides mechanistic insight into how nutrigenomic signaling encodes nutritional information into cellular changes. The authors expand previous work by showing that OGT is associated with neural chromatin at introns and transcriptional start sites, and that diet-induced changes in chromatin accessibility were amplified at loci with presence of both OGT and PRC2.1. The work also identifies Mitogen Activated Kinase as a critical mediator in this pathway. This is an elegant group of experiments revealing mechanisms for how nutrigenomic signaling triggers cellular responses to nutrients.

We thank the reviewer for their thoughtful reading of the manuscript and their positive and actionable suggestions. We have addressed these in the revised manuscript.

Reviewer #3 (Public Review):

This paper dissects the molecular mechanisms of diet induced taste plasticity in Drosophila. The authors had previously identified two proteins essential for sugar-diet derived reduction of sweet taste sensitivity - OGT and PRC2.1. Here, they showed that OGT, an enzyme implicated in metabolic signaling with chromatin binding functions, also binds a range of genomic loci in the fly sweet gustatory receptor neurons where binding in a subset of those sites is diet composition dependent. Furthermore, a minority of OGT binding sites overlapped with PRC2.1 recruiter Pcl, where collectively binding of both proteins increased under sugar-diet while chromatin accessibility decreased. The authors demonstrate, that the observed taste plasticity requires catalytic activity of OGT, which impacts chromatin accessibility at shared OGT x Pcl but not diet induced occupancy. In an effort to identify transcriptional mechanisms that instantiate the plastic changes in sensory neuron functions the authors looked for transcription factors with enriched motifs around OGT binding sites and identified Stripe (Sr) as a transcription factor that yielded sugar taste phenotypes upon gain and loss of function experiments. In follow-up overexpression experiments, they show that this results in reduced taste sensitivity and reduced taste evoked spiking in gustatory receptor neurons. Notably the effects of Sr on taste sensitivity also depend on OGT catalytic activity as well as PRC2.1 function. Finally, they explore the function of rolled (rl) - an extracellular-signal regulated kinase (ERK) ortholog in Drosophila, suggested to function upstream of Sr - in diet induced gustatory plasticity. The authors showed that the overexpression of the constitutively active form of rl kinase results in reduced neuronal and behavioral responses to sucrose which was dependent on OGT catalytic activity. In sum, these findings reveal several new players that link dietary experience to sensory neuron plasticity and open up clear avenues to explore up- and downstream mechanisms mediating this phenomenon.

Strengths:

• Good genetically targeted interventions

• Thorough exploration of the epistatic relationships between different players in the system• Identification of several new signaling systems and proteins regulating diet derived gustatory plasticity

Weaknesses:

• The GO term enrichment analyses with little functional follow up has limited explanatory power• ERK/rl data is a bit hard to interpret since any imbalance in this system appears to reduce gustatory sensitivity.

The conclusions in this manuscript are mostly well or at least reasonably supported by data.

We appreciate the reviewer’s thoughtful read of the manuscript and their feedback. We were pleased to read the reviewer’s positive comments on the experimental treatment of epistatic relationships and the identification of new pathways; we have addressed the reviewer’s comments and suggestions in the revised manuscript.

We agree with the reviewer about the limited explanatory power of the GO term analysis. We have expanded our computation analysis of the OGT/PRC2 genes in Figure 5 and selected several of these genes for functional analysis. In the revised version of the manuscript, we show that several of the genes affected by diet via this nutrigenomic pathway impact sugar taste sensation as measured by PER. We also agree with the reviewer that the Erk data are harder to interpret than those from OGT or PRC2; this effect is somewhat expected, given the reported action of this kinase in neural activity and plasticity. Importantly, the epistatic interactions between ERK/Sr and OGT/PRC2 we discovered are intriguing and may be involved in other cellular processes beyond taste.

Below are a few recommendations for improvement:

• The paper claims to address cell-type-specific nutrigenomic regulatory mechanisms. However, this work only explores nutrigenomic mechanisms in a single cell type (Gr5a+ sweet sensing cells) and we don't really learn whether these nutrigenomic mechanisms exist in all other cell types or just Gr5a+ cells. It would be valuable to see how specific OGT and PRC2.1 binding locations and effects on chromatin accessibility are in a different cell type - e.g. bitter sensing Gr66a. This would reveal how global in nature these findings are and or which aspects of nutrigenomic signaling are specific for sweet sensory cells.

This study is a cell-specific investigation of nutrigenomic mechanisms in the Gr5a+ sweet taste neurons, which is what we outlined to do. It was not our intention for this study to examine mechanisms across different cell types. However, we can understand the reviewer’s comment after rereading the abstract and introduction. As such, we have rewritten part of the manuscript to better introduce the rationale behind the study as the integration of metabolic signaling and cellular contexts. We hope this is now an improved framing for the study rationale.

(As in response to the author’s recommendations): About analyzing the effects of diet on other cells; no doubt this is an interesting question. However, this also signifies embarking on a completely separate project that would take, optimistically speaking, at least one year to complete and require a budget of ~ $130,000 (see breakdown). Thus, this suggestion doesn’t seem in line with the peer review and editorial philosophy of eLife. Carrying out this new project would result in an additional 6-7 figures but would not fundamentally change the conclusion of the current work; in fact, it may even take away from the targeted integration of molecular biology and neuroscience we have tried to achieve. Beyond this, we do not have such an unallocated budget, and so this new project would require us first to generate preliminary data on the bitter neurons to write then a grant proposal to fund it; as you can appreciate, this would take longer than a year, especially since we do not even know if the bitter gustatory neurons are affected by a high-sugar diet. Beyond this, looking at the bitter neurons would do little to prove specificity. If we found no effects of this pathway on the activity of the bitter neurons, it wouldn’t establish that the changes in the sweet taste neurons are specific. In fact, the same pathway could be acting in some of the other thousands of fly circuits that were not investigated (Black swan effect). If we did find that OGT/PRC2/Sr play a role in the bitter neurons, it would also do little to disprove specificity since their targets would likely be different because the sets of genes expressed in these two sensory neurons are different. By analogy, the protein sensor mTOR is expressed and active in every cell, where it modulates some of the same targets (i.e., S6K); however, the effects of the pathway may be different due to the distinct metabolic and genetic idiosyncrasies of cells, as well as cellular compartments. This lack of specificity doesn’t mean that mTOR is not important. Finally, we would like to note that we have tested the effects of manipulating OGT levels in other neurons (dopamine and Mushroom Body Output Neurons) without effects on behavior or neural responses (May et al. 2020; Pardo-Garcia et al. 2022); based on these, OGT doesn’t seem to affect neurons indiscriminately.

Budget = $129,000

Salary and benefit for PD for 10 calendar months: (2 months behavior experiments, 2 months training for molecular biology experiments and troubleshooting in new neurons, 4 months growing flies and conducting experiments, 2 months data analysis and visualization)= $75,000. DAM ID: Pcl:dam and OGT:dam in CD and SD, with and without OSMI x 4 biological replicates per condition= 32 samples @ $500 per sample (UM Genomics core) $16,0000

TRAP: Pcl mutant and OSMI in CD and SD x 4 biological replicates per condition + sequencing input = 32 samples @ $500 per sample (UM Genomics core) $16,0000

Animals: $500 per person/10 months = $5,000

Reagents: including sequencing kit (32 reactions =$6,000) x 2 = $12,000, and other reagents such as drugs and plastic = $17,000

Note that this PD would have to be hired and retrained. The first author of the manuscript who carried out the molecular experiments graduated in Dec 2021 but failed to pass on the technical knowledge due to COVID restrictions at the UM: we were completely shut down until July 2020, and at 20% capacity from March 2020 to July 2021 (people couldn’t also work together to show techniques), and no new people joined the lab in 2020-2022 (most of the 2021 grad student class deferred to 2022).

● Behavioral data from the screen identifying Sr is missing. Which other candidates were screened and what were the phenotypes?

We have now added the screen data in Fig. 5-Supplemental Fig. 1C. We targeted RNAi and OE transgenes against the candidate transcription factors (or control RNAi) to the Gr5a+ neurons and measured PER to 30, 20, and 5% sucrose in fasted flies on a control diet.

● Go terms analysis for Figure 4

We selected a dozen DEGs dependent on OGT and PRC2.1 (purple circle in Fig. 4E) and tested the effects on PER when these were overexpressed or knocked down (depending on the direction of changes in the SD). In Fig. 4F we show the effects of a handful of them on proboscis responses to sucrose.

Author Response

Reviewer #2 (Public Review):

The ability of the model to recreate one non-trivial aspect of the crossover distribution is not sufficient to rule out other possible models, which would be necessary to consider this work a significant advance. However, if the authors are able to provide additional, non-trivial predictions relating to this and to other experimental conditions, this would dramatically elevate their ability to claim that a coarsening-based mechanism is indeed the most plausible one to explain crossover distribution. Some of these conditions could involve experimental perturbation of key parameters in the model: HEI10 levels, the number of DSBs or recombination intermediates (the 'substrate' that ends up resulting in crossovers), the length of time coarsening is allowed to proceed, or the volume of the nucleus.

As discussed above, we have now included additional experiments and modelling investigating the patterning of late-HEI10 foci in a pch2 mutant, which exhibits partial synapsis. We have also demonstrated that the nucleoplasmic coarsening model can explain the recently published massive elevation of COs in zyp1 + HEI10 overexpressor lines (Durand et al., 2022). We hope that these additional results, explaining other non-trivial aspects of CO patterning, sufficiently elevates this work to be considered as a significant advance within the field.

Reviewer #3 (Public Review):

The new model assumes the possibility of loading HEI10 directly from the nucleoplasm, which of course is logical considering the phenotype of the zyp1 mutant in Arabidopsis. However, in a situation where the SC is fully functional, should not we expect some level of nucleoplasmic coarsening in addition to the dominant SC-mediated coarsening? Should the original model not be corrected, and if it is not necessary (e.g., because it included this effect from the very beginning, or the effect is too weak and therefore negligible), the authors should discuss it. With reference to this observation, it would be worthwhile to compare different characteristics of both types of coarsening (e.g., time course).

We agree with this reviewer that it seems intuitive and likely that some small amount of nucleoplasmic coarsening will persist even in the wild-type situation. As mentioned above, we have now explicitly modelled a combined version of the coarsening model than incorporates aspects of SC and nucleoplasm-mediated coarsening and compared this to simulation outputs from our original coarsening model (which did not incorporate nucleoplasmic recycling). The effects and implications of combining the two models on coarsening dynamics are now discussed.

Recently, a preprint from the Raphael Mercier group has been released, in which the authors show a massive increase in crossover frequency in zyp1 mutants overexpressing HEI10. I think this is a great opportunity to check to what extent the parameters adopted by the authors in the nucleoplasmic coarsening model are universal and can correctly simulate such an experimental set-up. Therefore, can the authors perform such a simulation and validate it against the experimental data in Durand et al. doi.org/10.1101/2022.05.11.491364? Can CO sites identified by Durand et al. be used instead of MLH1 foci for the modeling?

As mentioned above, we have now incorporated additional modelling demonstrating that the nucleoplasmic coarsening model can reproduce the massive increase in COs observed in zyp1 + HEI10 overexpressor lines (Durand et al., 2022). We have compared our model simulations against cytological data from this study (MLH1 counts from male Col-0 plants) as we feel this is the most appropriate data to compare our model against. The remaining CO patterning data in the Durand et al., paper is from genetic experiments, which are not optimal for comparing model simulations against for two main reasons. Firstly, the metric of interference (and coarsening) is microns of axis/SC length and not, for example, Mbp and we feel that (due to the non-uniform compaction of chromatin along pachytene chromosomes) the coarsening model cannot currently be reliably used to explain genetic mapping data. Secondly, genetic CO data includes both class I and class II COs, whereas the coarsening model only simulates class I CO patterning. Therefore, we strongly feel that, for now, it is better to exclusively rely on cytological data to fit our model against.

Author Response

Reviewer #2 (Public Review):

By now, the public is aware of the peculiarities underlying the omicron variants emergence and dissemination globally. This study investigates the mutational biography underlying how mutation effects and epistasis manifest in binding to therapeutic receptors.

The study highlights how epistasis and other mutation effect measurements manifest in phenotypes associated with antibody binding with respect to spike protein in the omicron variant. It rigorously tests a large suite of mutations in the omicron receptor binding domain, highlighting differences in how mutation effects affect binding to certain therapeutic antibodies.

Interestingly, mutations of large effect drive escape from binding to certain antibodies, but not others (S309). The difference in the mutational signature is the most interesting finding, and in particular, the signature of how higher-order epistasis manifests in the partial escape in S309, but less so in the full escape of other antibodies.

The results are timely, the scope enormous, and the analyses responsible.

My only main criticisms walk the stylistic/scientific line: many of the others have pioneered discussions and methods relating to the measurement of epistasis in proteins and other biomolecules. While I recognize that the purpose of this study is focused on the public health implications, I would have appreciated more of a dive into the peculiarity of the finding with respect to epistasis. I think the authors could achieve this by doing the following:

a) Reconciling discussions around the mutation effects in light of contemporary discussions of global epistasis "vs" idiosyncratic epistasis, etc. Several of the authors of the manuscript have written other leading manuscripts of the topic. I would appreciate it if the authors couched the findings within other studies in this arena.

We added a discussion related to global epistasis at the end of the “Epistasis Analysis” methods section. We tried to highlight that the cause and relevance of global epistasis phenomena are quite different at molecular and at organismic level.

B) While the methods used to detect epistasis in the manuscript make sense, the authors surely realize that methods used to measure is a contentious dimension of the field. I'd appreciate an appeal/explanation as to why their methods were used relative to others. For example, the Lasso correction makes sense, but there are other such methods. Citations and some explanation would be great.

We added more context and justification in the methods section (Epistasis Analysis). We used Lasso correction not particularly to obtain a sparser representation of the epistasis coefficients (an assumption that is not always valid, particularly within proteins) but rather to reduce instabilities created by the Tobit model inference. In this inference, the model coefficients are unbounded. Thus, if one mutation causes a complete binding loss, all epistatic terms associated with this mutation are not constrained and can become very large in magnitude. A Lasso term with a small coefficient constrains these coefficients but will have a limited influence on the other coefficients.

Lastly (somewhat relatedly), I found myself wanting the discussion to be bolder and more ambitious. The summary, as I read it, is on the nose and very direct (which is appropriate), but I want more: What do the findings say for greater discussions surrounding evolution in sequence space? For discussions of epistasis in proteins of a certain kind? In, my view, this data set offers fodder for fundamental discussion in evolutionary biology and evolutionary medicine. I recognize, however, the constraints: such topics may not be within the scope of a single paper, and such discussions may distract from the biomedical applications, which are more relevant for human health.

But I might say something similar about the biomedical implications: the authors do a good job outlining exactly what happened, but what does this say about patterns (the role of mutations of large effect vs. higher-order epistasis) in some traits vs others? Why might we expect certain patterns of epistasis with respect to antibody binding relative to other pathogenic virus phenotypes?

We agree that these are interesting questions, and have added a paragraph in the discussion to explore these points.

In summary: rigorous and important work, and I congratulate the authors.

Author Response

Reviewer #1 (Public Review):

In this work, the authors investigate a means of cell communication through physical connections they call membrane tubules (similar or identical to the previously reported nanotubes, which they reference extensively). They show that Cas9 transfer between cells is facilitated by these structures rather than exosomes. A novel contribution is that this transfer is dependent on the pair of particular cell types and that the protein syncytin is required to establish a complete syncytial connection, which they show are open ended using electron microscopy.

The data is convincing because of the multiple readouts for transfer and the ultrastructural verification of the connection. The results support their conclusions. The implications are obvious, since it represents an avenue of cellular communication and modifications. It would be exciting if they could show this occurring in vivo, such as in tissue. The implication of this would be that neighboring cells in a tissue could be entrained over time through transfer of material.

Thank the reviewer for his/her comments and suggestion. It’s possible that the thick tubular connections found in this study also exist in vivo. A previous study reported that TNT-like structures were found in mouse or human primary tumor cells (PMID: 34494703; PMID: 34795441). Our transfer assays could be adopted to evaluate such transfer in primary cultures and in vivo. We anticipate this for future work.

Reviewer #2 (Public Review):

There is a lot of interest in how cells transfer materials (proteins, RNA, organelles) by extracellular vesicles (EV) and tunneling nanotubes (TNTs). Here, Zhang and Schekman developed quantitative assays, based on two different reporters, to measure EV and direct contact-dependent mediated transfer. The first assay is based on transfer of Cas9, which then edits a luciferase gene, whose enzymatic activity is then measured. The second assay is based on a split-GFP system. The experiments on EV trafficking convincingly show that purified exosomes, or any other diffusible agent, are unable to transfer functional Cas9 (either EV-tethered or untethered) and induce significant luciferase activity in acceptor cells. The authors suggest a plausible model by which Cas9 (with the gRNA?) gets "stuck" in such vesicles and is thus unable to enter the nucleus to edit the gene.

To test alternative pathways of transfer, e.g. by direct cell-cell contact, the authors co-cultured donor and acceptor cells and detect significant luciferase activity. The split GFP assay also showed successful transfer. The authors further characterize this process by biochemical, genetic and imaging approaches. They conclude that a small percentage of cells in the population produce open-ended membrane tubules (which are wider and distinct from TNTs) that can transfer material between cells. This process depends on actin polymerization but not endocytosis or trogocytosis. The process also seems to depend on endogenously expressed Syncytin proteins - fusogens which could be responsible for the membrane fusion leading to the open ends of the tubules.

The paper provides additional solid evidence to what is already known about the inefficiency of EV-mediated protein transport. Importantly, it provides an interesting new mechanism for contact-dependent transport of cellular material and assigns valuable new information about the possible function of Syncytins. However, the evidence that the proteins and vesicles transfer through the tubules is incomplete and a few more experiments are required. In addition, certain inconsistencies within the paper and with previous literature need to be resolved. Finally, some parts of the text, methods and the figures require re-writing or additional information for clarity.

Major comments

1) In Figure 1F, the authors compare the function of exosome-transported SBP-Cas9-GFP vs. transient transfection of SBP-Cas9-GFP. It is not clear if the cells in the transiently transfected culture also express the myc-str-CD63 and were treated with biotin. It is important to determine if CD63-tethering itself affects Cas9 function.

Thank the reviewer for his comments and suggestions. We now show in Figure 1- figure supplement 1D that CD63-tethering itself does not affect Cas9 function.

2) The authors do not rule out that TNTs are a mode of transfer in any of their experiments. Their actin polymerization inhibition experiments are also in-line with a TNT role in transfer. This possibility is not discussed in the discussion section.

Yes, the results in this study do not rule out a role for TNTs in the transfer. At present, we are not aware of conditions that would functionally distinguish transfer mediated by TNTs and thick tubules. We have now included this in the Discussion section.

3) Issues with the Split GFP assay:

a) On page 4, line 176, the authors claim that "A mixture of cells before co-culture should not exhibit a GFP signal". However, this result is not presented.

The results of mixture experiment are included in Figure 2-figure supplement 1D, E.

b) The authors show in Figure 2C and F that in MBA/HEK co-culture or only HEK293T co-culture, there are dual-labeled, CFP-mCherry, cells. First - what is the % of this sub-population? Second, the authors dismiss this population as cell adhesion (Page 5, line 192) - but in the methods section they claim they gated for single particles (page 17, line 642), supposedly excluding such events. There is a simple way to resolve this - sort these dual labeled cells and visualize under the microscope. Finally - why do the authors think that the GFP halves can transfer but not the mature CFP or mCherry?

The plot in the Figure 2C and F are displayed in an all-cell mode, not in singlet mode. The percentage of dual-labeled CFP-mCherry in singlet was 0-0.2%. Thus, most of the signal was from doublet, or cell adhesion. We did not claim that the mature CFP or mCherry cannot be transferred. We suggested that the GFP signal of split-GFP recombination may be a more accurate reflection of cytoplasmic transfer between cells. In contrast, mature CFP or mCherry may simply attach to the cell surface but not enter into the other cells.

c) In the Cas9 experiments - the authors detect an increase in Nluc activity similar in order of magnitude that that of transient transfection with the Cas9 plasmid - suggesting most acceptor cells now express Nluc. However, only 6% of the cells are GFP positive in the split-GFP assay. Can the authors explain why the rate is so low in the split-GFP assay? One possibility (related to item #2 above) is that the split-GFP is transferred by TNTs.

The Cas9-based Nluc activity assay is more sensitive as it measures an enzyme with a very high turnover number. The split-GFP assay requires a transfer of GFP fragments to produce intact GFP molecules where the signal is not amplified. We think this explains the dramatic increase in a signal once Cas9 is transferred. Our cell sorting results suggest that at least 6% of the receptor cells are transferred in the co-cultures. Of course, nothing in either analysis rules out a role for TNTs in this transfer.

4) The membrane tubules, the membrane fusion and the transfer process are not well characterized:

a) The suggested tubules are distinct from TNTs by diameter and (I presume, based on the images) that they are still attached to the surface - whereas TNTs are detached. However, how are these structures different from filopodia except that they (rarely) fuse?

We used TIRF microscopy and found that the thick tubules are not attached to the surface (not shown). Filopodia are much closer in diameter to TNTs (0.1-0.4 micron). The thick tubules we observe are much thicker (2-4 micron in diameter).

b) Figure 5E shows that the acceptor cells send out a tubule of its own to meet and fuse. Is this the case in all 8 open-ended tubules that were imaged? Is this structure absent in the closed-ended tubules (e.g. as seen in Figures 6 & 8)?

Around half of open-ended tubules appeared to emanate from acceptor cells. Likewise, for closed-ended tubules, for example, in Figure 6E where a recipient HEK293T cell projected a short tubule.

c) The authors suggest a model for transport of the proteins tethered to vesicles (via CD63 tethering). However, the data is incomplete.

i) They show only a single example of this type of transport, without quantification. How frequent is this event?

The transport of the proteins tethered to vesicles (via CD63 tethering) were found in all 8 open-ended tubules that we detected in this study.

ii) Furthermore, the labeling does not conclusively show that these are vesicles and not protein aggregates. Labeling of the vesicle - by dye or protein marker will be useful to determine if these are indeed vesicles, and which type.

In Figure 4B, the moving punctum in a tubular connection appears to contain SBP-Cas9-GFP, Streptavidin-CD63-mCherry, and the cell surface WGA conjugate that may have been internalized into a donor cell endosome, which indicates that the moving punctum is vesicle type. Nonetheless, in general we cannot distinguish the forms of Cas9 that are transferred and become localized to the nucleus of target cells and we make no claim other than to suggest this possibility that Cas9 may be transferred as an aggregate.

iii) The data from Figure 2 suggest (if I understand correctly) transfer of the CD63-tethered half-GFP, further strengthening the idea of vesicular transfer. However, the authors also show efficient transfer of untethered Cas9 protein (Figure 2A and other figures). Does this mean that free protein can diffuse through these tubules? The Cas9 has an NLS so the un-tethered versions should be concentrated in the nucleus of donor cells. How, then, do they transfer? The authors do not provide visual evidence for this and I think it is important they would.

Based on the results using the Cas9-based luciferase assay (His- or SBP-tagged Cas9) (Figure 2A) and split-GFP assay (free GFP1-10) (Figure 2G), we suggest that free protein could be transferred between cells. Our current imaging approach is not designed to quantify protein diffusion. However, we are able to detect from images that Cas9-GFP does not colocalize exclusively with CD63 or concentrate in the nucleus, but also appears in the cytoplasm. These data indicate that both vesicle association and free diffusion may mediate the transfer through tubules. We thank the referee for emphasizing this issue which we will consider for future work to distinguish the transfer types through tubules.

iv) In Figures 6 & 8, where transfer is diminished, there are still red granules in acceptors cells (representing CD63-mcherry). Does this mean that vesicles do transfer, just not those with Cas9-GFP? Is this background of the imaging? The latter case would suggest that the red granule moving from donor to acceptor cells in figure 4 could also be "background". This matter needs to be resolved.

There are a few red puncta in the acceptor cell in Figure 6B. Since the acceptor cell is close to and overlapped with other donor cells containing CD63-mCherry, the red signal may, as the reviewer suggests, be from donor cells and not as a result of transfer through tubular connections. However, donor-acceptor cultures of HEK293T where transfer is not observed, little CD63-mCherry signal, for example, in Figure 6a, was seen in acceptor cells, even during several hours of observation (Figure 6- figure supplement video). A minor red signal could arise from exosomes secreted by donor cells that are internalized by acceptor cells. Images of single-culture receptor cells were added in Figure 4- figure supplement 1.

For Figure 8, we used MDA-MB-231 syncytin-2 knock-down cells containing Fluc:Nluc:mCherry as the receptor cell, thus in these experiments the red signal most likely represents mCherry expressed in the acceptor cells.

In Figure 4, we observed moving punctum in a tubular connection which contained co-localized green, red, and purple signals, corresponding to SBP-Cas9-GFP, streptavidin-CD63-mCherry, and the WGA conjugate, respectively. The video of punctum transport (Figure 4-figure supplement video) suggests that the red signal is not “background”.

5) Why do HEK293T do not transfer to HEK293T?

a) A major inexplicable result is that HEK293T express high levels of both Syncytin proteins (Figure 7 - supp figure 1A) yet ectopic expression of mouse Syncytin increases transfer (Figure 7E). Why would that be? In addition, Fig 3A shows high transfer rates to A549 cells - which express the least amount of Syncytin. The authors suggest in the discussion that Syncytin in HEK293T might not be functional without real evidence.

We cannot yet explain why the basal level of syncytin expressed in HEK293 cells is insufficient to promote open-ended tubular connections between these cells. It could be that the proteins are not well represented in a processed form at the cell surface. Nonetheless, ectopic expression of mouse syncytin-A in HEK293T produced some increased transfer but less than when syncytin-A is ectopically expressed in MDA-MB-231 cells (up to 4-fold vs. 30-fold change of Nluc/Fluc signal) (Figure 7E). Furthermore, we have added new results which show that apparent furin-processed forms of syncytin-A, -1 and -2 can be detected by cell surface biotinylation in transfected MDA-MB-231 cells (Figure 8-figure supplement 1D). All we demonstrate is that syncytin in the acceptor cell is required for fusion and we make no claim that it is the only protein or lipid at the cell surface in the acceptor cell required for fusion. Clearly, more work is essential to establish the complexity of this fusion reaction.

For A549 cells, syncytin-1 is highly expressed in A549 cells, thus it is possible that syncytin-1 in A549 plays crucial roles in the process.

b) In addition - previous publications (e.g. PMID: 35596004; 31735710) show that over expression of syncytin-1 or -2 in HEK293T cells causes massive cell-cell fusion. The authors do not provide images of the cells, to rule out cell-cell fusion in this particular case.

Overexpression of syncytin-1 or -2 in cells indeed causes massive cell-cell fusion, while overexpression of syncytin-A induced much less cell fusion than syncytin-1, or -2. We have now added new images shown in Figure 8-figure supplement 1A-C to document these observations. It may be that overexpressed human syncytins are better represented in a furin-processed form in both cell types. In contrast, we did not observe donor-acceptor cell fusion at basal levels of expression of syncytin in HEK293T and MDA-MB-231. For example, the Figure 4-figure supplement video shows that tubular structures were seen to form and break during the course of visualization with a tubule fusion event but no cell fusion to form heterokaryons.

Reviewer #3 (Public Review):

In this manuscript, Zhang and Schekman investigated the mechanisms underlying intercellular cargo transfer. It has been proposed that cargo transfer between cells could be mediated by exosomes, tunneling nanotubes or thicker tubules. To determine which process is efficient in delivering cargos, the authors developed two quantitative approaches to study cargo transfer between cells. Their reporter assays showed clearly that the transfer of Cas9/gRNA is mediated by cell-cell contact, but not by exosome internalization and fusion. They showed that actin polymerization is required for the intercellular transfer of Cas9/gRNA, the latter of which is observed in the projected membrane tubule connections. The authors visualized the fine structure of the tubular connections by electron microscopy and observed organelles and vesicles in the open-ended tubular structure. The formation of the open-ended tubule connections depends on a plasma membrane fusion process. Moreover, they found that the endogenous trophoblast fusogens, syncytins, are required for the formation of open-ended tubular connections, and that syncytin depletion significantly reduced cargo Cas9 protein transfer.

Overall, this is a very nice study providing much clarity on the modes of intercellular cargo transfer. Using two quantitative approaches, the authors demonstrated convincingly that exosomes do not mediate efficient transfer via endocytosis, but that the open-ended membrane tubular connections are required for efficient cargo transfer. Furthermore, the authors pinpointed syncytins as the plasma membrane fusogenic proteins involved in this process. Experiments were well designed and conducted, and the conclusions are mostly supported by the data. My specific comments are as follows.

1) The authors showed that knocking down actin (which isoform?) in both donor and acceptor cells blocked transfer, and more so in the acceptor cells perhaps due to the greater knockdown efficiency in these cells. However, Arp2/3 complex knockdown in donor cells, but not recipient cell, reduced Cas9 transfer. It would be good to clarify whether the latter result suggests that the recipient cells use other actin nucleators rather than Arp2/3 to promote actin polymerization in the cargo transfer process. Are formins involved in the formation of these tubular connections?

We thank the reviewer for his/her comments and suggestions. Beta-actin was knocked down in this study. We tried a formin inhibitor, SMIFH2 which resulted in a decrease the Cas9 transfer between cells (Figure 3F).

2) The authors provided convincing evidence to show that the tubular connections are involved in cargo transfer. Intriguingly, in Figure 4-figure supplement video (upper right), protein transfer appeared to occur along a broad cell-cell contact region instead of a single tubular connection. How often does the former scenario occur? Is it possible that transfer can happen as long as cells are contacting each other and making protrusions that can fuse with the target cell?

In the Figure 4-figure supplement video (upper right), it may be that several membrane tubes from several different donor cells contact at sites close to one another on the recipient cell resulting in the appearance a broad cell-cell contact. This was a rare observation. In our quantification, only 8 connections were open-ended in 120 cell-cell contact junctions. Once open-ended, or plasma membrane fused, cargo transfer is observed.

3) The requirement of MFSD2A in both donor (HEK293T) and recipient (MDA-MB-231) cells is consistent with a role for syncytin-1 or 2 in both types of cells. Since HEK293T cells contain both syncytins and MFSD2A but cargo transfer does not occur among these cells, does this suggest that syncytins and/or MFSD2A are only trafficked to the HEK293T cell membrane in the presence of MDA-MB-231 cells?

A proper answer to this question requires the visualization of syncytins and MFSD2A. The commercial syncytin antibodies were inadequate for immunofluorescence. In advance of the more detailed effort required to tag the genes for endogenous syncytin 1 and 2, we performed live cell imaging and surface biotin labeling of cells transiently transfected to express fluorescently-tagged forms of syncytin-1, -2 and -A. We now show that syncytin-A, -1, and -2 partially localize to the plasma membrane or the cell surface of MDA-MB-231 and at points of cell-cell contact. In fact, overexpression of codon-optimized human syncytin-1, and -2 induced dramatic HEK293T cell-cell fusion. However, at basal levels of syncytin expression, HEK293T could not form open-ended tubular connections, which may be because the basal level of syncytins are not well represented in a processed form at the cell surface or their activity is limited by unknown factors.

As an independent test of cell surface localization, we used surface biotinylation to show that a fraction of the syncytins can be labeled externally (Figure 8-figure supplement 1D). This fraction shows evidence of proteolytic processing consistent with furin cleavage whereas the overwhelming majority of transfected syncytins detected in a blot of lysates suggests that most remain in the unprocessed precursor form, consistent with the punctate and reticular fluorescence images (Figure 8-figure supplement 1A-C).

We used IF and GFP-tagged MFSD2A and found this protein partially localized to the plasma membrane of HEK293T cells (Figure 9E, F). Given the results reveal that cargos could be transferred among MDA-MB-231 cells (Figure 2G), syncytin and its receptor appear to function in transfer among these cells.

Author Response

Reviewer #1 (Public Review):

1) The authors show that there are several classes of Snf1 targets (Fig. 3e), most notably some that are phosphorylated immediately after Snf1 activation by glucose (<5 min) and others that are only phosphorylated after 15 min. In a simple view, all direct Snf1 targets should be phosphorylated immediately after Snf1 activation. Is that the case? What is the overlap between the direct targets found using the OBIKA assay and the slow and fast responding in vivo targets? What about the phosphorylation motif, does it differ between the groups? These points are not discussed in the text except to point out that the direct Snf1 target Msn4 is among the slowly phosphorylated group.

This is a very good point and we have performed the suggested analysis, which resulted in an interesting finding that we describe now in the text as follows:

“Notably, of the 145 confirmed target sites, 81 (i.e. 72%) were significantly regulated after both 5 min and 15 min. Of the remaining 64 sites, 32 responded only after 5 min, while the other 32 responded only after 15 min. Some of the former residues are located within Snf1 itself, the -subunit of the Snf1 complex (i.e. Sip1), the Snf1-targeting kinase Sak1, or Mig1, while some of the latter are located within the known Snf1-interacting proteins such as Gln3, Msn4, and Reg1. These observations indicate that Snf1-dependent phosphorylation initiates, as expected, within the Snf1 complex and then progresses to other effectors. Interestingly, based on the residues that responded exclusively after 5 min, we retrieved a perfect Snf1 consensus motif (i.e. an arginine residue in the -3 position and a leucine residue in the +4 position; Supplementary figure 2A). The one retrieved for the residues that respond exclusively at 15 min, in contrast, significantly deviated from this consensus motif (Supplementary figure 2B). The slight temporal deferral of Snf1 target phosphorylation may therefore perhaps in part be explained by reduced substrate affinity due to consensus motif divergence.”

2) The data showing that Snf1-dependent phosphorylation of Pib2 plays a key role in triggering inhibition of TORC1 is convincing but is entirely dependent on a rescue of the TORC1 inhibition defect seen in cells where Snf1 is inhibited. That is, TORC1 is normally inactivated during glucose starvation; this does not occur when Snf1 is inhibited by 2nm-pp1 but does occur when Snf1 is inhibited in a strain carrying a phosphomimetic version of Pib2 (Pib2SESE). This indicates that Pib2 phosphorylation is sufficient to replace Snf1 signaling and inhibit TORC1 during glucose starvation. However, in a simple model, a phosphodead version of Pib2 (SASA) should have the opposite effect. That is TORC1 should remain active during glucose starvation in the Pib2SASA strain-but that is not the case (Fig. 4g). This point is not discussed in the paper; why do the authors think that TORC1 is inhibited normally in the SASA mutant inhibits TORC1 normally?

We fully agree with this statement and have highlighted and discussed this issue now in the last paragraph of the results section (where we think this fits best) as follows:

“In contrast, the separated and combined expression of Sch9S288A and Pib2S268A,S309A showed, as predicted, no significant effect in the same experiment. Unexpectedly, however, the latter combination did not result in transient reactivation of TORC1, like we observed in glucose-starved, Snf1-compromised cells. This may be explained if TORC1 reactivation would rely on specific biophysical properties of the non-phosphorylated serines within Sch9 and Pib2 that may not be mimicked by respective serine-to-alanine substitutions. Alternatively, Snf1 may employ additional parallel mechanisms (perhaps through phosphorylation of Tco89, Kog1, and/or other factors; see above) to prevent TORC1 reactivation even when Pib2 and Sch9 cannot be appropriately phosphorylated. While such models warrant future studies, our current data still suggest that Snf1-mediated phosphorylation of Pib2 and Sch9 may be both additive and together sufficient to appropriately maintain TORC1 inactive in glucose-starved cells”

Reviewer #2 (Public Review):

1) Because PIB2 is a major focus of the manuscript, I was surprised that it was not discussed in the introduction. I think it would be appropriate to discuss prior evidence linking this protein to TORC1.

We thank the reviewer for this suggestion. Pib2 and its role in TORC1 control is now described in the introduction.

2) The authors introduce mutations into PIB2 at two sites determined to be phosphorylated by SNF1, at S268 and S309. Somewhat confusing results are obtained, in that the PIB2 null and phosphomimic mutants (S268E and S309E) confer a similar TORC1 phenotype, compared to the S268A S308A mutant. These results require further explanation than simply that "TORC1 inactivation defect in SNF1-compromised cells is due to a defect in PIB1 phosphorylation". This is particularly intriguing given that the opposite results are observed with the SCH9 mutants, where the null and alanine mutants confer a similar phenotype compared to the S to E mutants.

The finding that both loss of Pib2 and expression of the phosphomimetic allele yield the same phenotype is indeed counterintuitive. Hence, we fully agree with the criticism put forward here. We believe that the underlying reason for our observation is based on the unique property of Pib2 in having both a C-terminal TORC1-activating domain (CAD) and an-N-terminal TORC1-inhibitory domain (NID). We have addressed this point briefly in the discussion ("Our current data favor a model according to which Snf1-mediated phosphorylation of the Kog1-binding domain in Pib2 weakens its affinity to Kog1 and thereby reduces the TORC1-activating influence of Pib2 that is mediated by the C-terminal TORC1-activating (CAD) domain via a mechanism that is still largely elusive"), but now also address this issue in the results section as suggested.

3) The authors conclude, based on the co-IP data in Figure 4H, that interactions between KOG1 and PIB2 are direct. However, it remains possible that interactions between these proteins are mediated by other components of TORC1 or within cells. This should be addressed.

Please note that the Kog1-Pib2 interaction has previously been demonstrated by different methods. Accordingly, Pib2 has not only been shown to interact with Kog1 (or TORC1) in co-IP studies in vivo (PMID: 30485160, PMID: 29698392), but also by co-IP studies in vitro (PMID: 29698392, PMID: 28483912, PMID: 34535752). In addition, the interaction between Kog1-Pib2 has also been dissected (down to defined domains) by classical two hybrid analyses (PMID: 28481201). All of these studies are cited now in the introduction where Pib2 is discussed.

4) The authors demonstrate convincingly that the PIB2 and SCH9 SNF1-specific phospho-site mutants have a detectable effect on TORC1, primarily by examining TORC1-dependent phosphorylation of SCH9. What is unclear is whether phosphorylation at these sites has a significant physiological impact on cells. It appears that the rapamycin hyper-sensitivity displayed in Figure 6E is the only data presented to address this question. It would be appropriate for the authors to comment further on the significance of SNF1-dependent phosphorylation of these two substrates.

To further address the physiological role of the Snf1-dependent phosphorylation of Sch9 and Pib2 combined, we newly assessed the growth rate of the strain that expresses the Sch9SE and Pib2SESE alleles combined. Accordingly, we found the snf1as pib2SESE sch9SE strain to exhibit a significantly higher doubling time than the snf1as strain on both low-nitrogen-containing media and standard synthetic complete media. This is now included in the text (results section).

Reviewer #3 (Public Review):

1) Conceptually, the manuscript shows that Snf1 activity is important for the acute inhibition of TORC1 during glucose starvation. However, this is mainly restricted to 10 and 15 minutes of glucose starvation. After 20 minutes, TORC1 is inhibited by some unknown mechanisms independent of Snf1 (Hughes Hallet et al). This raises concern regarding the physiological relevance of Snf1-mediated TORC1 inhibition during acute glucose stress. The authors show that this regulation is important for the survival of cells under TORC1 inhibition. How do the authors envision that the acute role of Snf1 plays an important long-term physiological relevance during rapamycin treatment? Providing more support for the physiological relevance of this regulation will make this study of interest to a broad readership.

Please see our response to point 4 of reviewer #2.

2) Another major concern of the manuscript is the inconsistencies between the various representative immunoblots and their quantifications. The effect of AMPK activity on TORC1 signaling under glucose starvation seems very subtle. A few specific concerns are mentioned below:

a) In figure 1A, the increase in TORC1 activity upon inhibition of analogue sensitive Snf1as by 2NM-PP1 is very marginal. Although quantification shows a significant increase, a representative western blot figure should be shown.

We have replaced the original immunoblots with more representative ones in Figure 1A.

b) Does deleting Snf1 itself have any effect on TORC1 activity? Lane 4 of figure 1A shows reduced activity compared to lane 1.

TORC1 activity is generally assessed as the ratio between phosphorylated Sch9 and total Sch9 (see also below under (e)). Accordingly, based on the quantification of 6 blots (we added two more experiments to address this point; Figure 1B), loss of Snf1 has no significant impact on TORC1 activity in exponentially growing cells, as we expected.

c) To show the effect of Snf1 on the repression of TORC1, the time-course experiments are run on two separate gels in figure 1C. Hence, it is difficult to compare the effect of Snf1 on unscheduled reactivation of TORC1 under glucose starvation.

Please note that the data of the two blots were cross-normalized to the sample from exponentially growing cells (labeled “Exp”; i.e. the same sample was loaded on the two blots) in order to compare and quantify the effects of Snf1.

d) In figure 1E, the effect of Reg1 deletion on TORC1 activity seems minor as both phospho- and total levels of Sch9 are reduced.

As correctly pointed out by this reviewer, we consistently found the total Sch9 levels to be lower in reg1Δ cells when compared to wild-type cells. To assess TORC1 activity, we therefore always determine the ratio between phosphorylated Sch9 and total Sch9, and the respective ratio is significantly different in reg1∆ cells when compared to wild-type cells. We speculate that the reduced Sch9 levels in this mutant are caused by the reduced growth rate (PMID: 22140226) and hence lower protein synthesis rate (to which translation of SCH9 mRNA may be specifically sensitive).

Since further mechanistic insights are based on these initial findings of figure 1, solidifying these observations is very important.

3) In figure S1, the analogue sensitive Snf1as shows significant reduction in its activity (reduced S79 phosphorylation of ACC1-GFP). This raises the concern of whether this genetic background is an ideal system to resolve the mechanism of TORC1 suppression.

The Snf1as allele is indeed hypomorphic, which we acknowledge appropriately in the text. We would like to point out however, that we took great care in each experiment to include the DMSO control that allowed us to unequivocally assign any observed effects to the specific drug-mediated inhibition of Snf1as. Importantly, we think that the hypomorphic nature of the Snf1as allele (which allows normal growth on non-fermentable carbon sources) represents a minor trade-off when compared to the advantages that this allele provides over the use of a snf1∆ strain, which exhibits a fundamentally reprogrammed transcriptome/proteome (PMID: 17981722). Accordingly, this allele allows the assessment of Snf1 inhibition on very short time scales while minimizing confounding large-scale proteome rearrangements that may indirectly affect the studies. Moreover, use of the Snf1as allele also allowed us to compare our results more directly with other phosphoproteome studies that used the same allele (PMID: 25005228, PMID: 28265048). Finally, please also note that our main conclusions (on Snf1-mediated control of TORC1) are corroborated by additional genetic data such as the ones in Figure 1A/E where we use snf1∆ and reg1∆ cells.

4) In figure 2, during glucose restimulation, there is increased retention of Snf1as-pThr210 in the presence of 2NM-PP1. This suggests that the upstream glucose sensing pathway as well as Snf1 might be more active than in DMSO-treated cells. This also raises concerns regarding the suitability of the genetic background for the study. Can authors comment on why this phosphorylation persists? Does the phosphoproteomic analysis give any hint for this phenotype?

This is a very good point. In fact, we forgot to mention in the text that the observed effect of the 2NM-PP1 treatment on Snf1-Thr210 phosphorylation has already been studied and mechanistically explained earlier (PMID: 23184934). Accordingly, the entry of the drug into the broader catalytic cleft of the Snf1as mutant causes the catalytic domain to be stabilized in a conformation, which prevents dephosphorylation of pThr210 by the dedicated Glc7-Reg1 phosphatase heterodimer. This can be observed each time when we compared 2NM-PP1- and DMSO-treated cells and probed for Snf1-Thr210 phosphorylation. This is, in fact, an independent control for proper 2NM-PP1 functioning. We have now added a sentence (including reference) that pinpoints this issue in the text.

5) In figure 4H, where authors claim reduced binding of Kog1 to Pib2SESE, levels of Kog1 in input are also reduced. Can authors provide further support using colocalization studies? Also, does Pib2SESE has any defect in forming Kog1 bodies?

We took great care to load equal amounts of IPed Pib2-myc variants and then normalized the co-IPed Kog1-HA on the IPed Pib2-myc variant levels. The Kog1-HA input levels vary a bit between the 4 experiments, but they are on average not significantly lower in Pib2SESE-myc-expressing cells when compared to WT cells. In addition, in our Co-IP experiments, the beads are saturated with Pib2-myc variants and Kog1-HA levels are generally not limiting. We therefore deem it fair to say that the Pib2SESE has a reduced affinity for Kog1. Based on our experience with other co-localization studies of membrane-bound proteins and protein complexes (e.g. TORC1 versus EGOC), we find it extremely difficult to quantify local interactions by fluorescence microscopy (unless they are close to all or nothing). In this case, where we have a partial defect in the interaction between Kog1 and Pib2SESE, we anticipate that such analyses will not allow us to draw additional conclusions.

Regarding the issue of Kog1/TORC1-body formation: all of our mutations in PIB2 and SCH9 were introduced (by CRISPR-Cas9) in the genome of our snf1as strain, which was used throughout this study. To analyze Kog1/TORC1-bodies, we have therefore first tried to C-terminally tag KOG1 with GFP in the genome of our strain background (similarly as was done in the original description of Kog1 bodies; PMID: 26439012). However, because all our attempts failed to create KOG1-GFP in our strain, we assumed that this construct may be lethal in our strain background. This is not completely unexpected, as it is known that the Kog1-GFP allele is hypomorphic and temperature sensitive (PMID: 19144819). In an alternative approach, we have therefore set out to study TORC1 body formation in our strains by using a GFP-TOR1 allele that can be integrated into the genome and that expresses functional TORC1 (PMID: 25046117). As we have described earlier, the respective GFP-Tor1 construct localized on vacuolar membranes and on foci that we previously have shown to correspond to signaling endosomes (PMID: PMID: 30732525, 30527664). Unexpectedly, however, when we starved the respective cells for glucose, the number of GFP-Tor1 foci did only marginally increase (20%) in our strain background over a period of up to 1 hour. Given these various unexpected issues, we prefer to not include any of these preliminary data in the current version of our manuscript, but to rather follow up on these observations in a separate study. We deem this particularly justified as the current literature on TORC1-body and TOROID formation also appears controversial and may need further clarification. For instance, while TORC1-body formation has been suggested to represent a Snf1-dependent process that is dispensable for TORC1 inhibition (PMID: 30485160), TOROID formation has been suggested to represent a Snf1-independent process that is mechanistically linked to TORC1 inhibition (PMID: 28976958).

6) In figure 5F, where the authors claim the Sch9SE mutant has lower TORC1 activity, the difference is very minor. Furthermore, corresponding lanes also show reduced levels of Snf1as expression. Hence, improved blots are required here. Also, an in vitro kinase assay with full-length Sch9 KD with and without the Ser288 mutation could solidify the observation that phosphorylation of Ser288 indeed affects TORC1-mediated phosphorylation.

We have replaced the blots in Figure 5F with an alternative set that more clearly highlights the (statistically significant) differences, while also exhibiting more equal levels of Snf1as levels. Regarding the in vitro kinase assays: we have repeatedly tried to perform TORC1 kinase assays on full length Sch9KD without success. We currently believe that proper TORC1-mediated phosphorylation of Sch9 may have to occur on membranes to which both TORC1 and Sch9 are tethered through phospholipid interactions (PMID: 29237820). We are trying to set up such a system on liposomes, but we assume that this will be a major effort that cannot be resolved in due time.

7) In figure 6E, the Sch9SE mutant shows no effect in the presence of rapamycin. Thus, in vivo, phosphorylation at Ser288 may not be perturbing the phosphorylation of Sch9 by TORC1.

When cells are grown on glucose where TORC1 is highly active (as in Fig. 6E or 6A/B in Exp), expression of Sch9SE has no significant effect indeed. However, in glucose-starved cells, where TORC1 activity is low, expression of the Sch9S288E allele clearly and significantly contributes to inhibition of Sch9-Thr737 phosphorylation by TORC1 (Figure 6A/B and Figure 5F/G).

8) According to the author's proposed mechanism, TORC1 activity in Pib2SASA or Pib2SASA/Sch9SA backgrounds should be higher during glucose starvation compared to the control strains. However, glucose starvation shows a similar level of reduction in TORC1 activity in these backgrounds. This raises concern regarding the proposed mechanism. The authors mainly base their conclusions on Ser to Glutamate mutants. The authors should be cautious that Ser to Glutamate changes may also affect the protein structure which can confer similar phenotypes. How do the authors justify this discrepancy?

Please see our response to point 2 of reviewer #1.

Author Response

Reviewer #1 (Public Review):

The authors sequence some of the oldest maize macroremains found to date, from lowland Peru. They find evidence that these specimens were already domesticated forms. They also find a lack of introgression from wild maize populations. Finally, they find evidence the Par_N16 sample already carried alleles for lowland adaptation.

Overall I think this is an interesting topic, the study is well-written and executed for the most part. I have a variety of comments, most important of which revolve around methodological clarity. I will give those comments first.

1) The authors should say in the Results section how "alleles previously reported to be adaptive to highlands and lowlands, specifically in Mesoamerica or South America" were identified in Takuno et al. 2015. What method was used? I see this partly comes in the Discussion eventually, but it would help to have it in the Results with more detail. The answer to this question would help a skeptical reader decide the appropriateness of the resource, given that many selection scans have been performed on maize genomes, the choice would ideally not be arbitrary.

This was explained in more detail in the Material and Methods section, to keep the Results and Discussion sections more concise. However, we agree that adding a brief explanation in the Results section would be useful and we have modified the revised version accordingly. Now the relevant part of the section Specific adaptation to lowlands in Mesoamerica and South America reads as follows: “To assess this, we identified in Par_N16 all covered SNPs with alleles previously reported to be adaptive to highlands and lowlands, specifically in Mesoamerica or South America by Takuno and coworkers (Takuno et al., 2015). These authors used genome-wide SNP data from 94 Mesoamerican and South American landraces and identified SNPs with significant FST values to infer which allele was likely adaptive. For example, those SNPs showing significant FST only in Mesoamerica, were characterized as adaptive for lowlands if they were at high frequency in the lowland population and at low frequency in the highland population, and vice versa. The same was applied for South America (Takuno et al., 2015). They identified 668 Mesoamerican and 390 South American previously reported adaptive SNPs, from which 32 and 20 were covered in Par_N16, respectively.”

2) How were the covered putative adaptive SNPs distributed in the genome? Were any clustered and linked? The random sampled SNPs should be similarly distributed to give an appropriate null.

The SNPs in Takuno et al. (2015) are in general at a median distance of 353 bp from each other. The 20 adaptive sites covered in Par_N16 for South America (SA) are at a median distance of 8,301,843 bp (approximately 8.3 Mbp), while the 32 for Mesoamérica (MA) are at a median distance of 24,295,968 bp (approximately 24.3 Mbp). SNPs in five pairs from Mesoamerica are closer than 100 bp between them, but each pair is at a considerable distance (beyond 1 cM) from each other and from other SNPs covered in Par_N16. This same happens for only one SNP pair from South America. Then, in general, the covered adaptive SNPs are not clustered. For our random samples, the range of genomic distances between SNPs is similar to those of adaptive SNPs. This shows that our null distributions are adequate for our statistical purposes. The genomic positions of covered adaptive sites in Par_N16 are now included in a new Table in the revised version (Supplementary File 2). We have included these observations in the main text (section Specific adaptation to lowlands in Mesoamerica and South America), as follows: “In general, adaptive SNPs represented in Par_N16 were not clustered. The 20 South American adaptive SNPs are at a median distance of 8,301,843 bp, while the 32 Mesoamerican SNPs are at a median distance of 24,295,968 bp (Supplementary File 2). SNPs in five pairs from MA are closer than 100 bp between them, but each pair is at a considerable distance (beyond 1 cM) from each other and from other SNPs. This same happens for only one SNP pair from SA. Then, although at low proportions, the adaptive SNPs in Par_N16 are a bona fide representation of different genomic responses to selection pressures...” and “We analyzed some of these random samples and observed a similar behavior as the adaptive SNPs regarding the range of distances between SNPs (Fig, S18).”

3) How is genetic similarity calculated? It should be briefly described in the Results.

This is formally explained in the Material and Methods section, but now we have included a brief description in the Results section (Specific adaptation to lowlands in Mesoamerica and South America) as follows: “The allelic similarity is the average of the frequencies of the Par_N16 alleles in the intersected sites with each test population (see Material and Methods).”

4) It would help for the authors to state why they focus on Par_N16, I did not see this in my reading. Presumably, the analyses done are because of the higher quality data, but it would also help to mention why Par_N16 was sequenced in an additional run.

Indeed, Par_N16 has an endogenous DNA content of 1.1 %, while the other two samples presented a very low DNA content (0.2%). Therefore, we decided to invest more in the best sample, as a cost/benefit decision for additional sequencing. We have included brief explanations of this in the revised text. In the Results section Paleogenomic characterization of ancient maize samples, it reads as follows: “Due to its higher endogenous DNA content (one order of magnitude larger, we further sequenced the Par_N16 library, obtaining 459M additional reads, to generate a total of 851M for this sample (Table 2).” and “To determine if the specific elimination of C to T and G to A modifications could bias the results in favor of maize rather than teosinte alleles, an additional database was generated in which all transitions were eliminated (i.e., only transversions were included) in Par_N16 only, because it was the only sample with enough sequencing data to conduct this experiment.” While in the section Tests of gene flow from mexicana, is as follows: “Par_N16 was the only sample with enough DNA sequence data to perform this analysis. All the samples showed the same phylogenetic position; therefore, Par N 16 was considered to be representative of ancient Paredones maize.”

5) In the sections on phylogenetic analysis, introgression, and D statistics, the authors could do a better job specifically indicating how the results support their conclusions.

Precise indications of how our results support our conclusions are given in the Discussion section. Nevertheless, we added relevant sentences in the specified sections. In the section Relationship between ancient maize, extant landraces, and Balsas teosinte, we added the following: “Thus, based on genome-wide relatedness, Paredones maize clusters with extant domesticated Andean landraces, supporting both, a single origin for maize and that these Peruvian samples were already domesticated.” In the section on introgression and D-statistics (Tests of gene flow from mexicana), we improved the last sentence as follows: “These results consistently show the absence of significant gene flow between Par_N16 and mexicana, implying that the lineage that gave rise to Paredones maize left Mesoamerica without relevant introgressions from this teosinte.”

Reviewer #2 (Public Review):

In this foundational article, the authors conduct an ancient DNA characterization of maize unearthed in archaeological contexts from Paredones and Huaca Prieta in the Chicama river valley of Peru. These maize specimens were recovered by painstakingly controlled excavation. Their context would appear to be beyond reproach though the individual radiocarbon determinations should be subject to further scrutiny.

1) Radiocarbon determination for at least one of the maize cobs analyzed for aDNA is not a direct date, but dates associated material. The authors should provide a table of the direct dates on the specimens that were analyzed for ancient DNA. They should also specify the type and quantity of material sent and whether the cob, glumes, pith, or husks were submitted for dates. Include δ13C determinations for each cob with laboratory analysis numbers because there is justifiable concern that at least one of these cob dates has a δ13C value suggesting the material dated is not maize. Generally, the δ13C for maize ranges from -14 to -7. One or more of the specimens subjected to ancient DNA analysis in this paper have δ13C values far outside of this confidence interval.

The indirect radiocarbon date on a maize cob was derived from a single piece of wood charcoal in a hearth directly associated with the analyzed cob, both embedded in a thin intact floor in Unit 20 at the Paredones site. The assay on the charcoal and the floor are in an undisturbed stratigraphic context and are in agreement with assays on other maize and charcoal remains in floors both above and below the hearth. We have included this information in Table 1 in the revised version. The information sought by Reviewer 2 on the studied cobs was published previously in Grobman et al. 2012 and in Dillehay 2017. Since details of the cobs were published, we decided to submit only what we thought were pertinent data for this manuscript.

As for the δ13C reading of one cob outside of the confidence interval for maize, the dated specimen with this value is a maize husk fragment. Both the macro- and micro-morphology and the ancient DNA analysis of the husk demonstrated it was maize. We do not understand what affected the δ13C value for this specimen. Similarly, three human skeletons from deeper site levels have δ13C values greater than the expected range for human remains.

2) From the perspective of future scientists being able to repeat the analyses performed here, I would hope that all details of specimen treatment, extraction methods, read length and quality would need to be assiduously described. Routine analytical results should be reported so that comparisons with earlier and future results are facilitated, and not made difficult to decipher or search for.

The general procedures for accurate ancient DNA extraction were described in Vallebueno-Estrada et al. 2016 and we do not see the need to repeat this information in this article. Specific aspects of sample treatment and DNA extraction of the samples analyzed here are described in the Material and Methods, section on Extraction and sequencing of ancient samples. Results on quality (percentage of endogenous DNA, quality-filtered reads, mapped reads to either repetitive or unique regions, amount of sequence mapped, mapping Phred scores, estimated error rates, percentage of deamination, fragment median lengths, percentage of sites with signatures of molecular damage, number of unique genomic sites covered and their corresponding average sequencing depth) are described in the Results, section Paleogenomic characterization of ancient maize samples. This section also includes the number of SNPs in relation to the reference and the number of intersected SNPs between our samples and the HapMap3 database. In addition, complementary information to this section is included in Tables 2-4 and supplementary Figures S2-S6, as properly referenced in the last mentioned section.

3) The aDNA analysis may or may not be affected by the anomalous δ13C values but one would anticipate that standard aDNA extraction and analysis protocols would provide a means by which the specimen's preservation of the specimens could be ascertained, for example, perhaps deamination and fragmentation rates could be compared or average read length evaluated with modern-contemporary materials so that preservation of the Paredones samples relative to that of maize in the CIMMYT germplasm bank and the San Marcos specimens investigated by the same researchers can be evaluated.

Average read length from contemporary material depends more on the sequencing platform than sample preservation. For example, Illumina can only read fragments of hundreds of base pairs, while MinIon or PacBio can read fragments in the order of kb. Also, deamination is not an issue in DNA extracted from modern material (unless bisulfite is used for methylation detection). Comparison with San Marcos samples indicates that Paredones samples are heavily degraded, although this is not a function of time only (humidity, temperature, and pH are among other relevant factors). Therefore, to avoid misleading interpretations, we are not including a comparison with San Marcos samples in the revised version.

4) The size and shape of the cobs depicted are similar to specimens occurring much later in Mesoamerican assemblages. For example, the approximate rachis diameter of the San Marcos specimens depicted by Valle-Bueno et al. (2016: Fig.1) averages less than 0.5cm while the specimens depicted in Valle-Bueno et al. (this manuscript) average 1.0 cm. The former - San Marcos - specimens are dated at 5300-4970 BP cal while the larger - Paredones - specimens date roughly 6777 - 5324 BP cal. The considerable disparity among the smaller more recent specimens compared to the very much larger putatively older specimens suggests the Paredones specimen's radiocarbon determinations are equivocal. The authors point this out but repeatedly state these cobs are the most ancient; a conundrum that should be resolved.

Radiocarbon determinations in Paredones are not equivocal, on the contrary, they are perfectly in agreement with and supported by the unimpeachable stratigraphy of the site and by more than 150 other radiocarbon and OSL dates from Paredones and nearby excavated contexts. The difference in morphology between the more recent samples from Tehuacan and the more ancient samples from Paredones is exactly the paradox we try to address. Our results indicate that the rapid migration and adaptation of maize to the coast of Peru in comparison with a slower migration and adaptation to Tehuacan lands explains this apparent conundrum. This rapid movement and migration allowed the presence of more “modern” maize in Peru than in Tehuacan on the respective dates. This more rapid maize development also coincides with more rapid and advanced socio-cultural transformations in Peru, including proto-urbanism (i.e, first cities), early religious symbolism, long-distance irrigation canals, and other major innovations that far exceed what was happening in Mesoamerica at the time.

5) I would suggest the authors consider redating these three specimens and if they do, hope that they will prepare the laboratory personnel with depositional environment information. MacNeish was skeptical about late dates on maize at Tehuacan, at first. Adovasio was initially certain about maize's associated dates from Meadowcroft. One would prefer to be reasonably certain the foundation this article creates is solid; the author's repeated reference to these cobs as the most ancient in the Americas should be reaffirmed so retraction will not be necessary.

As discussed in Grobman et al. 2012 and in Dillehay 2017, we do not confide in C14 dating of unburned corn remains due to the possible intrusion of fungi in the soft cellular structure of cobs. The chrono-stratigraphically acceptable dates on cobs and other maize remains were taken on burned and hard tissue remains, such as husks. See detailed discussion in Supplementary Materials.

MacNeish and Adovasio were excavating cave and rock shelter sites, which are known to often have areas of stratigraphically disturbed deposits. Paredones, Huaca Prieta, SR-18 and other Preceramic sites excavated in the study area here contain late to early varieties of maize and radiocarbon assays that are in chrono-stratigraphic agreement. As noted in the main text and in prior publications, these sites are open air localities with clear stratigraphy defined by intact floor and fill sequences, with no tree root, animal burrowing, or other major taphonomic disturbances.There were occasional hearths and pits (i.e., human burials) that intruded into deeper floor-fill sequences but none of the assayed and studied maize samples were derived from these contexts. Once again, we encourage readers to examine the stratigraphy shown in the main text and in Grobman et al. (2012) and Dillehay (2017). Moreover, as noted in the text, there is a growing number of Preceramic sites in South America that date between 6800 and 6000 years ago and later that contain micro-maize remains (see Kistler et al., 2018). Not all of these sites are well-dated and present reliable contexts, but several have good chrono-stratigraphic settings and micro-evidence (e.g., phytoliths, starch grains) indicative of a maize presence at or prior to 6000 years ago.

Author Response

Reviewer #3 (Public Review):

The only substantial point I raise relates to the sexual selection (mate choice) part of the work. While it has no major effect on the overall conclusion, I think their interpretation needs to be reconsidered.

When reporting the results of mate choice experiment (L219ff), the authors state that males of wild and Klara type preferred wild-type females, because 75% of laid eggs belonged to wild-type females. However, another possibility is that Klara females had reduced fecundity, and the lower share of eggs had nothing to do with mate choice. In the same way, "90% of eggs were fertilized by wild-type males" (L223) is used to conclude that they were preferred by females (active mate choice). However, male success in N. furzeri is largely driven by male dominance (and not female mate choice) and it is more likely (and more precise to state) that wild-type males were more successful in male-male competition for access to females (and fertilize their eggs). This is especially so because wild-type males were larger (L. 322) and body size plays a major role in establishing dominance between N. furzeri males. This is then also pertaining to interpretation in discussion (L 318).

Concerning fecundity, we analyzed quantity and quality of eggs obtained from either klara or wild type breeding groups. As shown in Figure 3A we did not observe differences between klara and wild type fish. Thus, we conclude that fecundity is not reduced in klara females. Regarding males, we did not observe a size difference between the klara and wild type animals in this experiment (Fig. 3C), however, weight was different. As noted by the reviewer, this might influence male dominance and breeding success. We have been more explicit on this in the discussion of the revised version.

Author Response

Reviewer #1 (Public Review):

This paper presents the results of two fragment screens of PTP1B using room-temperature (RT) crystallography, and compares these results with a previously published fragment screen of PTP1b using cryo-temperature crystallography. The RT screen identified fewer fragment hits and lower occupancy compared to the cryo screen, consistent with prior publications on other proteins. The authors attempted to identify additional hits by applying two additional layers of data processing, which resulted in a doubling in the number of possible hits in one of the screens. Because I am not an expert in panDDA modeling, however, I am unable to evaluate the reproducibility and potential potency of these fragment hits as protein binders or their potential use as starting points for follow-up chemistry.

The fragment library used in this study was larger than those used in previously published RT crystallography experiments. Among the cryo hits that bound in RT, most fragments bound in the same manner as they did in cryo, while some bound in altered orientations or conformations, and two bound at different locations in RT compared to cryo. This level of variability is not surprising. However, one fragment was observed to bind covalently to lysines in RT, even though it showed no density in the cryo crystallization attempt. It is unclear from the provided information whether this fragment decayed during storage or if the higher temperatures accelerated the covalent chemistry. The authors also observed temperature-dependent changes in the solvation shell, and modifications to the protein structure upon fragment binding, including a distal modification.

We thank the reviewer for the thorough summary of our manuscript.

Regarding reproducibility of fragment hits, cryo structures are more variable than RT structures for proteins themselves (Keedy et al., Structure, 2014). Thus the variability of repeated cryo-temperature crystallography experiments is a relevant consideration when comparing cryo to RT structures for protein-ligand interactions. However, to our knowledge, no published papers have explored this issue. Our previous cryo fragment screen (Keedy, Hill, et al., eLife, 2018), as with many others, was focused on breadth (many fragments), not depth (replicates). Unpublished work by some of the authors of the present study suggests that fragment poses are robust in replicate cryo experiments; however, future studies focused on fragment reproducibility in terms of binding occupancy, pose, and site at cryo temperature would be useful contributions to the field.

Regarding follow-up chemistry, there is growing evidence from multiple successful fragment-based inhibitor design studies (COVID Moonshot Consortium et al., bioRxiv, 2022; Gahbauer, Correy, et al., PNAS, 2023; etc.) that, although fragments usually bind too weakly to impact function on their own, they offer rich information to seed the design of high-affinity, potent functional modulators of proteins. As our study is the first to report many structures of fragments bound to proteins at RT, we cannot yet comment as to whether they offer unique advantages over cryo fragments in downstream fragment-based drug design efforts, but this is an open area for future study.

Regarding the covalent lysine binder, we agree with the reviewer on this point; our manuscript includes a note to this effect. Unfortunately we were unable to obtain the original fragment sample for mass spectrometry analysis. Returning to the point above about follow-up chemistry, the path forward for this fragment hit is promising and clear, and includes confirming chemistry using the original nominal compound vs. what is observed in the electron density, fragment linking and/or expansion, functional assays, and structural biology, all hopefully leading to a potent covalent inhibitor of wildtype PTP1B.

The current version of the paper is somewhat repetitive in its presentation of the results and could be clearer in its presentation of the variations and comparisons of the two different protocols. It would be helpful to have a more concise summary of the differences between the two protocols in the current paper, as well as a discussion of how they compare to the protocol used in the previously published cryo-temperature fragment screen.

We agree that it would be helpful to cut down on any redundant text and more straightforwardly compare/contrast the different room-temperature screen methods vs. the previous cryo-temperature screen method. To address this suggestion, we deleted the Discussion paragraph about the strengths and weaknesses of the two methods relative to serial approaches, deleted the text in the Introduction that introduces the two screens, and placed new text at the start of the Results section in the subsection titled “Two crystallographic fragment screens at room temperature” to provide a concise summary in one location of the manuscript.

While I appreciate the speculative nature of the discussion at the end of the paper, the evidence presented by the authors does not instil confidence that these results will correspond to meaningful binders that could be used to train future machine learning models. However, depending on the intended use, it may be acceptable to train ML models to predict expected densities under typical experimental conditions.

Indeed, this part of the Discussion is speculative, and seeks to place our results into a possible broader context. The definition of “meaningful binders” in the context of fragment screening is a difficult one. As noted above in response to the comment about follow-up chemistry, one important measure of meaningfulfulness is the ability to successfully seed structure-based design of analogs that have potent functional effects, and many fragments do meet this definition. Regarding potential applications to machine learning, we agree it is not self-evident that structural data for small-molecule fragments will be readily translatable to AI/ML methods aimed at larger compounds. The reviewer’s point about predicting densities is an intriguing one, and is in line with the fragment screening ethos, including existing experimental as well as computational (e.g. Greisman, Willmore, Yeh*, et al., bioRxiv, 2022) approaches to mapping ligandable surface sites and regions. The number of RT structures we report here is high relative to most crystallography studies, but still is likely insufficient to explore questions about AI/ML training, and at any rate would be beyond the scope of the current report. However, it seems equally true that AI/ML methods trained on structures based on data from nonphysiological cryogenic conditions, with associated structural artifacts, may have some (previously unrecognized) limitations, and thus RT crystal structures can play a useful role in AI/ML training sets in the future. We have added new text to the Discussion paragraph in question to convey these points.

Reviewer #2 (Public Review):

The authors set out to understand how a room-temperature X-Ray crystallography-based chemical-fragment screen against a drug target may differ from a cryo screen. They carried out two room-temperature screens and compared the results with that of a cryo screen they previously performed. With a substantial set of crystallographic evidence they showed that the modes of protein-fragment binding are affected by temperature. The conclusion of the work is compelling. It suggests that temperature provides another dimension in X-ray crystallography-based fragment screening. In a practical sense, it suggests that room-temperature fragment screen is a promising new avenue for hit identification in drug discovery and for obtaining insights into the fragment binding. Room-temperature screening carries unique advantage over cryo screening. This work is confirmative to the notion, which seems not yet universally considered, that very weak protein-small molecule binding may be inherently fluid structurally, and that crystal structures of such weak binding, especially cryo structures, cannot be taken for granted without cross validation.

We thank the reviewer for their clear summary and positive comments about our manuscript.

Author Response

Reviewer #2 (Public Review):

In this study, The authors developed a mouse model to specifically investigate whether GC B cells that present nuclear protein (NucPr) could be specifically suppressed by Tfr cells. Most current mouse models that have been used in investigating Tfr functions are based on the overall readout of autoantibody production in the scenario of loss-of-function of Tfr cells. The proposed model of gain-of-function of Tfr cells is novel and valuable.

The authors mainly compared two boosting immunizations by Strepatividin (SA) alone or SA-conjugated with nuclear proteins (SA-NucPr) and demonstrated SA-NucPr boosting immunization was able to expand Tfr cells, suppress overall and SA-specific GC/memory/plasma cell responses. The results are mostly convincing.

One major concern is the conditions and controls used in the study. The control group (SA boosting immunization) would have enhanced T and B cell responses by this boosting. Unfortunately, there was no non-boosting control group so the level was unclear. It is therefore to strictly match such boosting condition in the SA-NucPr group. Notably, both SA and SA-NucPr were used at 10ug for boosting immunization. Considering NucPr were comparable or much larger (Nucleosome, about 200KDa) than SA (about 60KDa), the dose of SA in the SA-NucPr group was far less than that in the SA group. Due to this cavity, it is difficult to judge the difference between two groups was due to less SA boosting immunization or NucPr-induced Tfr function. This was a fundamental issue weakens the conclusion.

The single cell analyses clearly demonstrated the expansion of Tfr clones. It remains unclear why other Treg populations other than Tfr cells were not expanded? The Treg cells in the CXCR5intPD-1int population were recently activated and should be able to respond to the boosting immunization. On an alternative explanation, the changes in Tfr cells could be indirectly driven by the changes in Tfh cells. For example, Tfh can produce IL-21 and restrict Tfr expansion (Jandl C, et al.2017). This could be the case of the reduction in Tfr cells in the SA-OVA group as compared to the SA group.

As the reviewer, we were surprised not to detect significant increase in the levels of CXCR5intPD-1int Tregs in the original experiment after the boosting with SA-NucPrs(Fig.1). Our interpretation of this result was that the fraction of NucPr-specific CXCR5intPD-1int Tregs was small as compared to the total CXCR5intPD-1int Tregs and proliferation of this small fraction of cells would not be detectable by flow cytometry analysis of the total CXCR5intPD-1int Tregs numbers. Alternatively, the observed rapid accumulation of Tfrs was due to proliferation of the NucPr-specific Tfrs that may be abundant after a standard immunization with foreign antigen.

In single cell analysis we have used only presorted CXCR5highPD1high follicular T cells so majority of CXCR5intPD-1int Treg population was excluded from the analysis.

Author Response

Reviewer #1 (Public Review):

The authors optimize a live cell imaging method based on the detection of FAD/NAD(P)H adopted from the fast-growing field of live metabolic imaging. They build upon a method described by KreiB et al 2020 that used metabolic ratio and collagen fiber second harmonic generation imaging. They follow by combining metabolic imaging with morphologic measurements to train a machine-learning model that is able to identify cell types accurately. Upon visualization, authors detected structures hypothesized and then proven to resemble the "goblet cell associated antigen passages" previously studied in intestinal epithelia.

STRENGTHS

• The manuscript is succinct, well written, and overall done rigorously.

• The optimization of the method at multiple levels to the point of identifying both common and rare cell types is impressive.

• Describes the elegant implementation of a sorely needed method in epithelial biology.

• Provides an approach to studying the cholinergic response in epithelial cells, a poorly understood phenomenon despite broad clinical use for diagnosis and treatment.

WEAKNESSES

A) For what is in large part a methods-development paper, the methods are not explained or shared in a manner that facilitates reproducibility. For example:

A.1.) The training and validation datasets seem to come from the same sample (or the source is not clearly described). Therefore, it is not clear whether the "96% accuracy" refers to accuracy within the sample measured, or whether it can extrapolate to other samples.

In order to avoid any confusion, we further clarify that the machine learning training and validation data sets come from the same sample. We had split the total data set into 2 separate subsets for this purpose. This has been laid out in the text as follows:

“In order to assess the performance of machine learning algorithms designed to distinguish cell types, we divided our data set into training and testing subsets. We utilized 75% of the total cells (154 cells) for machine learning training, leaving 25% (52 cells) for subsequent validation.”

A.2.) It is unclear whether the model needs to be re-trained within each new sample measured, or if it's applicable to others. This has implications for method adoption by others. Either way is useful but needs to be clarified.

This is a very interesting point and one that we further clarify in the Discussion noting that in both disease and non-diseased states the model needs to be re-trained in each particular experimental regime.

A.3.) Code was only listed in a PDF file, which makes reproducing the analysis very cumbersome.

We hope that all can utilize the code made for this methodology and have uploaded it to a publicly available GitHub account:

https://github.com/vss11/Label-free-autofluorescence

B) Whereas the optimization to improve cell type detection is very well described, the implementability of the approach could benefit from exploration (using the data already obtained) of the minimal set of measurements needed to identify cell types. For example, is the FAD/NAD(P)H ratio necessary? Or could just morphologic measurements achieve the same goal?

This is an excellent point, and we appreciate the Reviewer’s suggestion for this analysis. We have added Figure 3 Supplement 5 where we perform modeling without autofluorescence data. This analysis reveals a dramatic reduction in accuracy with a Matthew’s correlation coefficient ranging from 0.66 to 0.78. This provides additional justification for the use of autofluorescence for cell type identification. Morphologic measurements are not sufficient for cell type identification alone.

We also have determined the relative contribution of each characteristic to the cell type identification by the Xgboost algorithm in Figure 3 Supplement 4, which shows that autofluorescence signatures are amongst the top contributing characteristics to cell type identification by machine learning.

C) Whereas the conclusions are overall supported by the data, need small adjustments in some cases:

C.1.) For example, P3L80: Claims autofluorescence imaging is more specific than "functional markers", however, this is done in the setting of a very specific intervention that massively affects a protein often used as a secretory cell marker (CCSP aka SCGB1A1), which is known to be secreted (and depleted) in secretory cells upon stimulation.

We agree with the Reviewer that secretory cell identification is a prime example where autofluorescence imaging may be superior to conventional staining, specifically due to the point the Reviewer makes regarding CCSP secretion. We discuss this concept in the Discussion while giving examples of CCSP staining being reduced in asthma, COPD, and smokers. It could be that these cells are missed due to depletion of CCSP. Indeed, we clarify that our methodological approach may be less affected by the loss of the category of specific markers that change with cell state. There are, of course, caveats with utilizing this approach in disease states, and we elaborate on this further below and add this point to the discussion.

C.2.) Relatedly, it is unclear how the method's accuracy would be affected in conditions that affect redox/metabolic state; the approach may be highly affected in inflammation and injury, for example.

As suggested by the Reviewer, we re-analyzed the data after Antimycin A + Rotenone and FCCP to determine if autofluorescence ratio is sufficiently different to identify ciliated and secretory cells and included this data in Figure 2 Supplement 1. This is an example where the redox/metabolic state is indeed altered. Though the autofluorescence ratio is affected, it is still useful for cell type identification after intervention as the ciliated and secretory cells have statistically different ratios.

However, different disease states, particularly infection and inflammation may result in a more profound effect on autofluorescence signatures. For instance, previous work by Dilipkumar et. al, 2019 found changes in autofluorescence over days in repeated measurements in a mouse model of inflammatory bowel disease. Therefore, it is likely that the cell type identification methodology will need to be re-optimized for different experiments and diseased tissues. We include commentary to this effect in the discussion.

D) The data used to describe "SAPs" is very cursory.

To further elaborate on our description of SAPs we have included the following:

1) SAP formation occurs in secretory cells in both stimulated and unstimulated conditions. We performed additional analysis of Figure 4C and determined that SAP formation does occur at baseline prior to stimulation in 9% of secretory cells. Methacholine addition results in 78% of secretory cells forming SAPs (Figure 4 Supplement 1). We have added Figure 5C to demonstrate that SAP formation occurs in the absence of stimulation and is enhanced after methacholine stimulation.

2) We demonstrate that SAPs can uptake both FITC-dextran and FITC-ovalbumin in Figure 5E, and Figure 5 Supplement 2. We also now show that immune cells (CD11c antigen presenting cells) associate with SAPs containing FITC-dextran and FITC-ovalbumin in Figure 5E and Figure 5 Supplement 2. We have expanded the Discussion of SAPs.

3) We now show 3 video examples and an XZ optical cross section of ALI that demonstrate uptake and secretion of FITC-dextran in Figure 5 Supplemental Videos 1-3 and Figure 5 Supplement 1.

D.1.) Unclear if FITC dextran uptake occurs in other cells too, or in secretory cells prior to methacholine stimulation, or induced nonspecifically due to epithelia manipulation. Secretory and goblet cells are very sensitive to stimulation and often considered minimal, for example, see the paper by Abdullah et al DOI:10.1007/978-1-61779-513-8_16 in which extreme care had to be applied to prevent any secretion at all.

Our autofluorescence methodology revealed the formation of “voids” of autofluorescence forming in secretory cells and we focused our experiments on this phenomenon. Based on the reviewer question, we generated Figure 5C to better characterize SAP formation. Figure 5C illustrates that SAP formation occurs in both unstimulated and methacholine stimulated conditions, but is dramatically increased following methacholine stimulation. This is analogous to the behavior of GAPs in the intestine (Knoop et al., 2015). Furthermore, we have reanalyzed Figure 4C to identify SAPs prior to stimulation and found that these structures are present in 9% of secretory cells. After methacholine stimulation this percentage increases to 78%.

D.2.) A single image is provided for the SAP timeline (Figure 5C), which appears to be the same cell shown in the supplementary video.

We now provide numerous example videos and optical XZ cross section of ALI demonstrating SAP uptake and secretion in Supplementary Videos 1-3 and Figure 5 Supplement 1.

IMPACT AND UTILITY

This is well-done work with high potential for widespread adoption within the epithelial biology community, particularly if the methods and code are shared in better detail.

We indeed hope that this methodology can be utilized by others. We have posted analysis code, raw data, MATLAB algorithm, and other necessary files onto a publicly available GitHub link. https://github.com/vss11/Label-free-autofluorescence

Reviewer #2 (Public Review):

Shah and colleagues tackle a significant impediment to exploiting tissue culture systems that enable prospective ex vivo experimentation in real-time. Namely, the ability to identify and track dynamic and coordinated activities of multiple composite cell types in response to experimental perturbations. They develop a clever label-free approach that collects biologically-encoded autofluorescence of epithelial cells by 2-photon imaging of mouse tracheal explant culture over 2 days. They report the ability to distinguish 7 cell types simultaneously, including rare ones, by developing a machine-learning approach using a combination of fluorescence and cytologic features. Their algorithm demonstrates high accuracy by Mathew's Correlation Coefficient when applied to a test set. Lastly, they show the ability of their approach to visualize the dynamic uptake and expulsion of fluorescently-tagged dextran by individual secretory cells. Overall, the results are intriguing and may be very useful for specific applications.

We thank the reviewers for their assessment and indeed hope that the methodology is useful and the discovery of the dynamics of SAP formation have important implications for airway mucosal immunology.

Author Response

Reviewer #1 (Public Review):

Animal colour evolution is hard to study because colour variation is extremely complex. Colours can vary from dark to light, in their level of saturation, in their hue, and on top of that different parts of the body can have different colours as well, as can males and females. The consequence of this is that the colour phenotype of a species is highly dimensional, making statistical analyses challenging.

Herein the authors explore how colour complexity and island versus mainland dwelling affect the rates of colour evolution in a colourful clade of birds: the kingfishers. Island-dwelling has been shown before to lead to less complex colour patterns and darker coloration in birds across the world, and the authors hypothesise that lower plumage complexity should lead to lower evolutionary rates. In this paper, the authors explore a variety of different and novel statistical approaches in detail to establish the mechanism behind these associations.

There are three main findings: (1) rates of colour evolution are higher for species that have more complex colour phenotypes (e.g. multiple different colour patches), (2) rates of colour evolution are higher on island kingfishers, but (3) this is not because island kingfishers have a higher level of plumage complexity than their mainland counterparts.

I think that the application of these multivariate methods to the study of colour evolution and the results could pave the way for new studies on colour evolution.

We appreciate this positive comment about our manuscript.

I do, however, have a set of suggestions that should hopefully improve the robustness of results and clarity of the paper as detailed below:

1) The two main hypotheses tested linking plumage complexity and island-dwelling to rates of colour evolution seem rather disjointed in the introduction. This section should integrate these two aspects better justifying why you are testing them in the same paper. In my opinion, the main topic of the paper is colour evolution, not island-mainland comparisons. I would suggest starting with colours and the challenges associated with the study of colour evolution and then introducing other relevant aspects.

We implemented this suggestion by reorganizing the introduction to introduce color/and challenges with studying it (para 1), then we discuss plumage complexity (para 2). We follow this with a paragraph about the importance of islands in testing evolutionary hypotheses (para 3), and onto kingfishers as a model system (para 4) and our hypothesis/predictions (para 5).

2) Title: the title refers to both complex plumage and island-dwelling, but the potential effects of complexity should apply regardless of being an island or mainland-dwelling species, am I right? Consider dropping the reference to islands in the title.

We removed “island” from the title.

3) The results encompass a large variety of statistical results some closely related to the main hypothesis (eg island/mainland differences) tested and others that seem more tangential (differences between body parts, sexes). Moreover, quite a few different approaches are used. I think that it would be good to be a bit more selective and concentrate the paper on the main hypotheses, in particular, because many results are not mentioned or discussed again outside the Results section.

We removed analyses that we felt were distracting from our main point (e.g., MCMCglmm) and streamlined our approach to use PGLS methods for both rates (phylolm) and multivariate color patterns (d-PGLS). The relevance of sex differences in coloration is also made more clear, as we added details about how we tested for a relationship between male and female coloration and that we use this strong correlation as a justification for averaging color by species (e.g., see lines 369-375).

4) Related to the previous section, the variety of analytical approaches used is a bit bewildering and for the reader, it is unclear why different options were used in different sections. Again, streamlining would be highly desirable, and given the novel nature of the analytical approach (as far as I know, many analytical approaches are applied for the first time to study colour evolution) it would be good to properly explain them to the reader, highlighting their strengths and weaknesses.

We appreciate the suggestion and have now included a workflow diagram, as suggested (see Figure 1). We further added considerable detail to the Methods (old length = 502 words, new length = 1355 words) and mention caveats of the approaches we have taken (e.g., line 308: “We used photosensitivity data for the blue tit (Hart et al., 2000) due to the limited availability of sensitivity data for other avian species”).

5) The Results section contains quite a bit of discussion (and methods) despite there being a separate Discussion section. I suggest either separating them better or joining them completely.

We appreciate this. We were following other eLife articles that include more discussion within the Results, therefore we would prefer to leave these aspects in place. However, we did move a considerable amount of information from the Results section to the Methods section. In addition, we also reorganized the Results to better match the logical flow of the Introduction. The end result, we hope, is a Results section that is considerably more streamlined.

6) The main analyses of colour evolutionary rates only include chromatic aspects of colour variation. Why was achromatic variation (i.e. light to dark variation) not included in the analyses? I think that such variation is an important part of the perceived colour (e.g. depending on their lightness the same spectral shape could be perceived as yellow or green, black or grey or white). I realize that this omission is not uncommon and I have done so myself in the past, but I think that in this case, it is highly relevant to include it in the analyses (also because previous work suggests that island birds are darker than their mainland counterparts). This should be possible, as achromatic variation may be estimated using double cone quantum catches (Siddiqi et al., 2004) and the appropriate noise-to-signal ratios (Olsson et al., 2018). Adding one extra dimension per plumage patch should not pose substantial computational difficulties, I think.

We incorporated this suggestion and we have now fully integrated achromatic color variation into all of our analyses. These new analyses let us compare results to previous work showing that island birds are darker than mainland counterparts. We further discuss the caveats of chromatic and achromatic channels (e.g., lines 313-317: “Although it is possible, in theory, to combine chromatic and achromatic channels of color variation in a single analysis (Pike, 2012), we opted to analyze them separately, as these different channels are likely under different selection pressures (Osorio and Vorobyev, 2005).”).

7) The methods need to be much better explained. Currently, some methods are explained in the main text and some in the methods section. All methods should be explained in detail in the methods section and I suggest that it would be better to use a more traditional manuscript structure with Methods before Results (IMRaD), to avoid repetition (provided this is allowed by the journal). Whenever relevant the authors need to explain the choice of alternative approaches. Many functions used have different arguments that affect the outcome of the analyses, these need to be properly explained and justified. In general, most readers will not check the R script, and the methods should be understandable to readers that are not familiar with R. This is particularly important because I think that the methodological approach used will be one of the main attractions of the manuscript, and other researchers should be able to implement it on their own data with ease. Judging from the R script, there are quite a few analyses that were not reported in the manuscript (e.g. multivariate evolutionary rates being higher in forest species). This should be fixed/clarified.

We clarified several methodological details in the manuscript (e.g., added package versions throughout, mention the permutation option used for compare.evol.rates, cited RPANDA) and modified the Methods section considerably to make logical connections among the sections. We also checked and cleaned up the R markdown file to ensure the analyses were in sync with the manuscript analyses.

Reviewer #2 (Public Review):

In "Complex plumages spur rapid color diversification in island kingfishers (Aves: Alcedinidae)", Eliason et al. link intraspecific plumage complexity with interspecific rates of plumage evolution. They demonstrate a correlation here and link this with the distinction between island and mainland taxa to create a compelling manuscript of general interest on drivers of phenotypic divergence and convergence in different settings.

This will be a fantastic contribution to the literature on the evolution of plumage color and pattern and to our understanding of phenotypic divergence between mainland and island taxa. A few key revisions can help it get there. This paper needs to get, fairly quickly, up to a point where the difference between plumage complexity and color divergence is defined carefully. That should include hammering home that one is an intraspecific measure, while one is an interspecific measure. It took me three reads of the paper to be able to say this with confidence. Leading with that point will greatly improve the paper if that point gets forgotten then the premise of the paper feels very circular.

We hope our considerable modifications throughout–including explicitly mentioning that complexity is an intraspecific measure whereas rates are interspecific (e.g., see lines 65, 140, 170, 667)–have made the premise of the paper more clear. We also added a new workflow figure (Figure 1) that includes example species pairs showing cases in which intraspecific plumage complexity and interspecific color divergence could show a negative relationship, rather than a positive one as we predict in the manuscript. We discuss this detail in lines 159-161 (“However, this is not necessarily the case, as there are examples within kingfishers that show simple plumages yet high color divergence, as well as complex plumages with little evolutionary divergence (Figure 1B).”).

Also importantly, somewhere early on a hypothesized causal pathway by which insularity, plumage complexity, and color divergence interact needs to be laid out. The analyses that currently follow are good ones, and not wrong, but it's challenging to assess whether they are the right ones to run because I'm not following the authors' reasoning very well here. I think it's possible a more holistic analysis could be done here, but I'll refrain from any such suggestions until I better get what the authors are trying to link.

We overhauled the Introduction. This included adding lines that connect the ideas of complexity and insularity (lines 65-58: “intraspecific plumage complexity (i.e., the degree of variably colored patches across a bird's body) could be a key innovation that drives rates of color evolution in birds and should be considered alongside ecological and geographic hypotheses.”) and insularity and color divergence (lines 69-85). We also rethought the analyses and now include PGLS analyses using tip-based rates that allow us to account for both insularity and complexity in the same analysis.

We also need something near the top that tells us a bit more about the biogeography of kingfishers. Are kingfisher species always allopatric? I know the answer is no, but not all readers will. What I know less well though is whether your insular species are usually allopatric. I suspect the answer is yes, but I don't actually know.

Great point. We have added details to the manuscript to clarify this (e.g., line 214: “The number of sympatric lineages ranged from 1–9 on islands, and 6–38 for mainland taxa.”).

In short, how do the authors think allopatry/sympatry/opportunity for competition link to mainland vs. island link to plumage complexity? And rates of color evolution? Make this clear upfront.

We believe our revised introduction makes these connections much clearer.

Author Response

Reviewer #2 (Public Review):

The molecular characteristics of OCNs in normal or ototoxic conditions are poorly understood before. The strength of this study is that it provides the first single-cell RNA-seq database of OCNs as well as surrounding facial branchial motor neurons. By thoroughly analyzing the database, they found high heterogeneities within OCN populations and identified distinct markers that are enriched in different OCN subtypes. Furthermore, a few previously unknown neuropeptides are revealed, including Npy which is more enriched in the LOC-2 located on the medial side. They also found that neuropeptide expression levels and distributions are subjected to hearing experience and noise exposure. On the other hand, the weakness of the study is that the numbers of single-cell RNA-seq are not sufficient, and may underscore the MOC heterogeneity (Figure 3A). Moreover, the physiological functions of the LOC-2 are not revealed in this study, and no specific markers in one OCN subtype are identified that can predict the morphological or projecting axon features. Those might be addressed in the following studies.

We agree that this study does not allow us to make conclusions about MOC heterogeneity or LOC2 functions. These are certainly interesting avenues to pursue in the future.