Reviewer #2 (Public Review):

This is an interesting manuscript with a worthwhile approach to receptor mechanisms. The paper contains an impressive amount of new data. These single molecule concentration response curves have been compiled with care and the authors deserve great credit for obtaining these data. I judge the main result to be that there are different values of the recently-proposed agonist-related quantity "efficiency". These values are clustered into 5 quite closely spaced groups. The authors propose that these groups are the same whether considering mutations in the binding site or different agonists.

It was unclear to me in several places, what new data and what old data are included in each figure. Therefore readers may have difficulty judging the claimed advance. This difficulty is not helped by the discussion, which includes some previous findings as "results".

A further weakness is that it is unclear how general or how specific these concepts are. The authors assert that they are, by definition, completely universal. However, we do not have reference to previous work or current data on any other receptor than the muscle nicotinic. I could not square the concept that "every receptor works like this" with the evident lack of desire to demonstrate this for any other receptor.

On one hand, if the framework can be extended, this can be a very important concept, and in some sense, could be the missing link to understanding concentration response curves. On the other, if it proves not to be general, or not to be generally applicable because of circumstances.

Reviewer #3 (Public Review):

This work attempts to introduce a new attribute of the receptor- efficiency, a fraction of an agonist binding energy consumed by conformational transition of the receptor from resting to active (open) states. Furthermore, the authors use an impressive set of experimental data (single channel recordings with 23 agonists and 53 mutations) to measure the efficiency for each agonist and mutant receptor. All the estimated efficiencies fall into a few groups and inside each of the efficiency groups there is a strong correlation between agonist affinity and receptor opening efficacy.

The main finding in this study is that estimated efficiencies fall into 5 groups. There is no clear description of the method how the efficiencies were allocated into different groups. Most importantly, it is not clear if the method used takes into account the uncertainty of the efficiency estimate. The study does not show any statistical metrics of the efficiency estimates as well as any other calculated variable such as dissociation equilibrium constants to resting or open states. Surely, the uncertainty of the efficiency should matter especially considering how near the efficiency group values are (eg. difference about 10% between 0.51 and 0.56 or 0.41 and 0.45).

All the tested agonists fell into groups according to the efficiency value attributed to them. It is difficult to see why some of the agonists belong to the same group. For example, it is not obvious at all why such agonists as epibatidine, decamethonium and TMP are in the same group. The question, I guess, arises if this grouping based on efficiency has any predictability value. Furthermore, if a series of mutations with the same agonist fall into different groups, the prediction power of this approach is very limited if one attempts to design a new agonist or look for a new mutation.

Reviewer #1 (Public Review):

This manuscript will interest cognitive scientists, neuroimaging researchers, and neuroscientists interested in the systems-level organization of brain activity. The authors describe four brain states that are present across a wide range of cognitive tasks and determine that the relative distribution of the brain states shows both commonalities and differences across task conditions.

The authors characterized the low-dimensional latent space that has been shown to capture the major features of intrinsic brain activity using four states obtained with a Hidden Markov Model. They related the four states to previously-described functional gradients in the brain and examined the relative contribution of each state under different cognitive conditions. They showed that states related to the measured behavior for each condition differed, but that a common state appears to reflect disengagement across conditions. The authors bring together a state-of-the-art analysis of systems-level brain dynamics and cognitive neuroscience, bridging a gap that has long needed to be bridged.

The strongest aspect of the study is its rigor. The authors use appropriate null models and examine multiple datasets (not used in the original analysis) to demonstrate that their findings replicate. Their thorough analysis convincingly supports their assertion that common states are present across a variety of conditions, but that different states may predict behavioural measures for different conditions. However, the authors could have better situated their work within the existing literature. It is not that a more exhaustive literature review is needed-it is that some of their results are unsurprising given the work reported in other manuscripts; some of their work reinforces or is reinforced by prior studies; and some of their work is not compared to similar findings obtained with other analysis approaches. While space is not unlimited, some of these gaps are important enough that they are worth addressing:

1. The authors' own prior work on functional connectivity signatures of attention is not discussed in comparison to the latest work. Neither is work from other groups showing signatures of arousal that change over time, particularly in resting state scans. Attention and arousal are not the same things, but they are intertwined, and both have been linked to large-scale changes in brain activity that should be captured in the HMM latent states. The authors should discuss how the current work fits with existing studies.<br /> 2. The 'base state' has been described in a number of prior papers (for one early example, see https://pubmed.ncbi.nlm.nih.gov/27008543). The idea that it might serve as a hub or intermediary for other states has been raised in other studies, and discussion of the similarity or differences between those studies and this one would provide better context for the interpretation of the current work. One of the intriguing findings of the current study is that the incidence of this base state increases during sitcom watching, the strongest evidence to date is that it has a cognitive role and is not merely a configuration of activity that the brain must pass through when making a transition.<br /> 3. The link between latent states and functional connectivity gradients should be considered in the context of prior work showing that the spatiotemporal patterns of intrinsic activity that account for most of the structure in resting state fMRI also sweep across functional connectivity gradients (https://pubmed.ncbi.nlm.nih.gov/33549755/ ). In fact, the spatiotemporal dynamics may give rise to the functional connectivity gradients (https://pubmed.ncbi.nlm.nih.gov/35902649/ ). HMM states bear a marked resemblance to the high-activity phases of these patterns and are likely to be closely linked to them. The spatiotemporal patterns are typically obtained during rest, but they have been reported during task performance (https://pubmed.ncbi.nlm.nih.gov/30753928/ ) which further suggests a link to the current work. Similar patterns have been observed in anesthetized animals, which also reinforces the conclusion of the current work that the states are fundamental aspects of the brain's functional organization.

Reviewer #2 (Public Review):

In this study, Song and colleagues applied a Hidden Markov Model to whole-brain fMRI data from the unique SONG dataset and a grad-CPT task, and in doing so observed robust transitions between low-dimensional states that they then attributed to specific psychological features extracted from the different tasks.

The methods used appeared to be sound and robust to parameter choices. Whenever choices were made regarding specific parameters, the authors demonstrated that their approach was robust to different values, and also replicated their main findings on a separate dataset.

I was mildly concerned that similarities in some of the algorithms used may have rendered some of the inter-measure results as somewhat inevitable (a hypothesis that could be tested using appropriate null models).

This work is quite integrative, linking together a number of previous studies into a framework that allows for interesting follow-up questions.

Overall, I found the work to be robust, interesting, and integrative, with a wide-ranging citation list and exciting implications for future work.

Reviewer #3 (Public Review):

My general assessment of the paper is that the analyses done after they find the model are exemplary and show some interesting results. However, the method they use to find the number of states (Calinski-Harabasz score instead of log-likelihood), the model they use generally (HMM), and the fact that they don't show how they find the number of states on HCP, with the Schaeffer atlas, and do not report their R^2 on a test set is a little concerning. I don't think this perse impedes their results, but it is something that they can improve. They argue that the states they find align with long-standing ideas about the functional organization of the brain and align with other research, but they can improve their selection for their model.

Strengths:

- Use multiple datasets, multiple ROIs, and multiple analyses to validate their results<br /> - Figures are convincing in the sense that patterns clearly synchronize between participants<br /> - Authors select the number of states using the optimal model fit (although this turns out to be a little more questionable due to what they quantify as 'optimal model fit')<br /> - Replication with Schaeffer atlas makes results more convincing<br /> - The analyses around the fact that the base state acts as a flexible hub are well done and well explained<br /> - Their comparison of synchrony is well-done and comparing it to resting-state, which does not have any significant synchrony among participants is obvious, but still good to compare against.<br /> - Their results with respect to similar narrative engagement being correlated with similar neural state dynamics are well done and interesting.<br /> - Their results on event boundaries are compelling and well done. However, I do not find their Chang et al. results convincing (Figure 4B), it could just be because it is a different medium that explains differences in DMN response, but to me, it seems like these are just altogether different patterns that can not 100% be explained by their method/results.<br /> - Their results that when there is no event, transition into the DMN state comes from the base state is 50% is interesting and a strong result. However, it is unclear if this is just for the sitcom or also for Chang et al.'s data.<br /> - The involvement of the base state as being highly engaged during the comedy sitcom and the movie are interesting results that warrant further study into the base state theory they pose in this work.<br /> - It is good that they make sure SM states are not just because of head motion (P 12).<br /> - Their comparison between functional gradient and neural states is good, and their results are generally well-supported, intuitive, and interesting enough to warrant further research into them. Their findings on the context-specificity of their DMN and DAN state are interesting and relate well to the antagonistic relationship in resting-state data.

Weaknesses:

- Authors should train the model on part of the data and validate on another<br /> - Comparison with just PCA/functional gradients is weak in establishing whether HMMs are good models of the timeseries. Especially given that the HMM does not explain a lot of variance in the signal (~0.5 R^2 for only 27 brain regions) for PCA. I think they don't report their own R^2 of the timeseries<br /> - Authors do not specify whether they also did cross-validation for the HCP dataset to find 4 clusters<br /> - One of their main contributions is the base state but the correlation between the base state in their Song dataset and the HCP dataset is only 0.399<br /> - Figure 1B: Parcellation is quite big but there seems to be a gradient within regions<br /> - Figure 1D: Why are the DMNs further apart between SONG and HCP than the other states<br /> - Page 5 paragraph starting at L25: Their hypothesis that functional gradients explain large variance in neural dynamics needs to be explained more, is non-trivial especially because their R^2 scores are so low (Fig 1. Supplement 8) for PCA<br /> - Generally, I do not find the PCA analysis convincing and believe they should also compare to something like ICA or a different model of dynamics. They do not explain their reasoning behind assuming an HMM, which is an extremely simplified idea of brain dynamics meaning they only change based on the previous state.<br /> - For the 25- ROI replication it seems like they again do not try multiple K values for the number of states to validate that 4 states are in fact the correct number.<br /> - Fig 2B: Colorbar goes from -0.05 to 0.05 but values are up to 0.87<br /> - P 16 L4 near-critical, authors need to be more specific in their terminology here especially since they talk about dynamic systems, where near-criticality has a specific definition. It is unclear which definition they are looking for here.<br /> - P16 L13-L17 unnecessary<br /> - I think this paper is solid, but my main issue is with using an HMM, never explaining why, not showing inference results on test data, not reporting an R^2 score for it, and not comparing it to other models. Secondly, they use the Calinski-Harabasz score to determine the number of states, but not the log-likelihood of the fit. This clearly creates a bias in what types of states you will find, namely states that are far away from each other, which likely also leads to the functional gradient and PCA results they have. Where they specifically talk about how their states are far away from each other in the functional gradient space and correlated to (orthogonal) components. It is completely unclear to me why they used this measure because it also seems to be one of many scores you could use with respect to clustering (with potentially different results), and even odd in the presence of a log-likelihood fit to the data and with the model they use (which does not perform clustering).<br /> - Grammatical error: P24 L29 rendering seems to have gone wrong

Questions:

- Comment on subject differences, it seems like they potentially found group dynamics based on stimuli, but interesting to see individual differences in large-scale dynamics, and do they believe the states they find mostly explain global linear dynamics?<br /> - P19 L40 why did the authors interpolate incorrect or no-responses for the gradCPT runs? It seems more logical to correct their results for these responses or to throw them out since interpolation can induce huge biases in these cases because the data is likely not missing at completely random.

Reviewer #1 (Public Review):

The goal of this study is to identify transcription factors that mediate stem cell transitions during differentiation. To achieve this, the authors examine the type II Drosophila neuroblast lineage, using single-cell RNA sequencing to examine all cell types in the type II lineage. There are known patterns of expression for neurons in this lineage, so they can identify clusters in their data set that are in the developmental state of transitioning from neuroblast to immature intermediate neuronal progenitor. They have outlined a set of expression criteria for transcription factors that are candidates for fine-tuning stem cell fate. They find that an isoform of Fruitless, called FruC, is a candidate transcription factor. Using microscopy and several genetic perturbation conditions the authors find that FruC is expressed in neuroblasts and can alter the number of cells in the lineage. To determine the mechanism that FruC uses to influence stemness the authors examine genomic occupancy of FruC, changes in histone modifications in FruC loss-of-function studies, and examination of DNA occupancy of proteins that function in chromatin modification. The authors argue that FruC functions to promote low-level H3K27me3 enrichment to maintain stemness based on comparisons across these data sets. The identification of transcription factors and the mechanisms used to maintain or differentiate stem cells is an important goal and is still a fundamental question in biology. The Drosophila model is poised for this type of analysis, given the knowledge of gene expression across cell fate that the authors use in this study.

Comments the authors should address:<br /> This is a valuable study that relies on several state-of-the-art genomic data sets to examine the mechanism that drives stemness. However, the authors should be using statical approaches to support their major conclusion regarding FruC and the role of H3K27me3. The study presents peak data in genome browser tracks of a handful of loci in the Notch pathway that show the pattern of reduced HK3K27me3 and not the other modifications they examine. However, it is not clear if the majority of FruC target genes in the genomic analyses have this pattern, though they argue they do. The major conclusion that FruC promotes a stem cell fate is based on the overlap between the list of genes they identify bound by FruC and the lists of genes that have changes in histone modifications (H3K27ac, H3K4me3, and H3K27me3). The limited use of statistical approaches to draw these conclusions is a weakness of the study. The authors do not use statistics to find changes in chromatin modification at loci, instead relying on 2-fold change calculations. Furthermore, the authors don't indicate if the genes with altered histone modification/binding peaks are significantly enriched (or not) with FruC targets, with no quantitative assessments of these data. The data in Figures 5,6 and S4 should have statistics/quantification to support the major conclusions of their study that FruC targets differ in H3K27me3, but not H3K27ac, and H3K4me3.

Reviewer #2 (Public Review):

This study addresses the molecular mechanism by which the FruC transcription factor regulates neurogenesis in Drosophila. The authors combine genetics and genomics to profile FruC genomic binding along with that of trithorax-like (Trl) and Su(H) and several histone modifications including H3K27me3. They propose that Fru acts to fine-tune the expression of Notch effector genes and they show that this regulation does not involve changes in H3K27ac, nor H3K4me3, but rather that FruC-regulated gene expression is correlated with changes in H3K27me3 levels at Fru target genes. While the study is well conducted and combines state-of-the-art techniques, there are several aspects that could be improved. The authors propose that Fru fine-tunes the expression of Notch effector genes, but they do not directly measure gene expression in any of the genetic backgrounds. It is important to do this to have some type of precise measure of transcriptional changes (what does 'fine tune' really mean), as the authors' model is based on subtle changes in H3K27me3. It would be important to quantify and correlate both processes more precisely. Similarly, the authors claim that Fru promotes 'low levels' of H3K27me3 at its bound loci throughout the genome, but they do not describe the criteria that define 'low levels' versus high levels of HK27me3.

In the authors' model, FruC likely functions together with PRC2 to regulate gene expression, and local low-level enrichment of repressive histone marks act to fine-tune gene expression. However, in the absence of experiments directly addressing the molecular mechanisms by which Fru regulates transcription, it would be more accurate to claim that changes in H3K27me3 correlate with altered gene expression.

Reviewer #3 (Public Review):

Rajan et al. used scRNAseq to identify transcription factors responsible for fine-tuning stemness gene expression in neural stem cells (neuroblasts), identifying Fruitless (fru) as a putative regulator of this process. Specifically, loss of the fru isoform C (fruc) results in increased stemness gene expression, while its overexpression leads to the opposite effect. Consistently, overexpression of fruc in a brat-null neuroblast-tumor background is sufficient to partially restore differentiation. Furthermore, by performing extensive genome-wide binding studies, the authors show that Fruc preferentially binds to cis-regulatory elements of stemness genes, with evidence that this transcription factor regulates the Notch-pathway via co-binding with Notch-target genes. The overall impact of FruC on transcription was not assessed.

Their data also shows that instead of regulating the deposition of histone marks associated with active transcription, such as H3K27ac or H3K4me3, loss of fruc results in decreased levels of the repressive mark H3K27me3, namely in the Notch locus or in Notch downstream effector genes, indicating that FruC fine-tunes the expression of their bound genes through maintenance of low-levels of repressive marks at cis-regulatory elements of its target genes. Given the extensive binding profile of FruC the effects promoted by its misexpression in neuroblasts are likely multifactorial.

In addition, the authors also show that PRC2 subunits, Caf1 and Su(z)12, the multisubunit complex responsible for catalyzing H3k27me3 deposition, (1) co-localize with Fru in Fruc-bound regions and (2) their loss partially phenocopies the previous results obtained for fruc depletion. The authors propose a model in which Fruc, via synergistic work with PRC2, is capable of fine-tuning the expression of stemness genes, in particular, Notch and Notch targets in neuroblasts by promoting low levels of transcriptionally repressive histone marks at their target cis-regulatory elements. If FruC and PRC2 functionally interact, and if the recruitment of one factor affects the binding of the other remains unknown.

The authors present an assortment of results that will be useful for those working in transcription and chromatin regulation, namely in the field of Drosophila neural stem cells (neuroblasts). Specifically, the authors provide robust single-cell RNA sequencing results and analysis that can be used by researchers interested in trying to understand the transcriptional state of neuroblasts and their progeny. Additionally, genome-wide binding studies for FruC or PRC2 subunits, together with the profiling of active/repressive histone marks, offer new insights regarding transcription factor or transcriptional repressor binding and the respective read-out in terms of histone modifications. Moreover, the authors propose an interesting model via which transcription regulation of Notch and Notch downstream effectors is rendered via fine-tuning of the transcriptional output. Hence, FruC restrains and limits the levels of its target genes within neuroblasts, avoiding the segregation of high levels of stemness-associated proteins to the progeny, which would incur in fate and differentiation defects. The model proposed here highlights how transcription regulation by histone marks is much more dynamic and layered, other than being dictated only by the mutually exclusive presence of either active or repressive marks.

Reviewer #1 (Public Review):

This study optimized a protocol for analyzing microplastics (MPs) in bovine and human follicular fluid. The authors identified the most common plastic polymers in the follicular fluid and assessed the impact of polystyrene beads on bovine oocyte maturation based on the concentration of MPs in follicular fluid. The authors found a decrease in maturation rate in the presence of polystyrene beads and conducted proteomic analysis of oocytes treated with and without MPs, revealing protein alterations.

Strengths:<br /> • The optimization of the protocol for analyzing MPs in follicular fluid, which is important for future research in this area.<br /> • Investigating the effects of MPs on oocyte maturation and proteomic profiles is significant.

Weaknesses:<br /> • The effects of polystyrene beads on oocyte maturation and proteomic profiles are not directly demonstrated, and insufficient analysis is performed to support the claims made in the manuscript.<br /> • The use of polystyrene beads does not fully mimic the concentration and interaction of MPs in follicular fluid, which warrants careful interpretation and discussion.<br /> • A major weakness is the lack of mechanism. Determining the cause of meiotic arrest (decreased maturation rate) would be needed to strengthen the paper. Are spindle morphology, chromosome morphology/alignment and/or spindle assembly checkpoint mechanism perturbed in MPs-treated oocytes?<br /> • Functional assays to validate one or more of the pathways suggested by the proteomic analysis would be necessary to strengthen the paper.<br /> • The analysis of broken zona pellucida is not sufficiently convincing. Definitely the breakage of zona pellucida is most likely a result of oocyte denudation. However, this may indicate increased fragility of polystyrene beads-treated oocytes. Investigating cytoskeletal components in oocytes treated with or without polystyrene beads would strengthen this paper.<br /> • The percentage of degenerated oocytes in control group is abnormally high which raises concern that the oocytes are not healthy.<br /> • The small font size of the figures (such as Fig. 1C) affects the quality of the manuscript.<br /> • Finally, the authors should cite previous publications on the effects of MPs on female reproduction, as this is not a novel area of research, despite the use of different concentrations. For example, "Polystyrene microplastics lead to pyroptosis and apoptosis of ovarian granulosa cells via NLRP3/Caspase-1 signaling pathway in rats (DOI: 10.1016/j.ecoenv.2021.112012)".

Reviewer #2 (Public Review):

This study presents valuable findings including the use of an improved method of Raman spectroscopy to measure accumulation of microplastics in ovarian follicular fluid obtained from cows and women and demonstration that experimental direct exposure of bovine eggs to biologically relevant levels of polystyrene, a microplastic found in both cows and women's follicular fluid, negatively influenced ova maturation status and the abundance of proteins involved in oxidative stress, DNA damage, apoptosis, and oocyte maturation. The evidence supporting the claims of the authors is solid but inclusion of human population from which the follicular fluid was obtained (e.g., demographics, reason for assisted reproduction), and details about quality control for proteome profiling experiments (i.e., peptide count cut-off for significant proteins) would have strengthened the study. The work will be of interest to exposure scientists, reproductive toxicologists, regulatory scientists, and reproductive health clinicians.

Reviewer #3 (Public Review):

The study from Grechi et al showed that emerging environmental microplastics (MPs) are present in both human and bovine follicular fluid. Moreover, based on the characterization and quantification data, authors treated bovine oocytes with environmentally relevant levels of polystyrene (PS) MPs and found that PS MPs interfered with oocyte maturation in vitro. This study is novel, particularly the first part of MP characterization and quantification, and for the first time confirms the presence of MPs in follicular fluid of humans and large farm animals. These results provide a possible mechanism by which the female infertility rate has been increasing in both humans and large farm animals. The session of exposing MPs to bovine and related oocyte health evaluation can be further improved. For example, authors examined the morphology of the oocyte zona pellucida (ZP) and degeneration and stained oocyte DNA to determine the meiotic maturation status. However, a much more comprehensive oocyte health evaluation can be performed including but not limited to the examination of oocyte spindle morphology, meiotic division, fertilization, early embryo development, mitochondria, and accumulation of ROS. These additional endpoints can provide more robust evidence to determine the impact of MPs on oocyte health. While the oocyte proteomic analysis identified altered proteins, more functional studies and causation experiments can be performed. In addition, authors exposed cumulus-oocyte-complexes (COCs) but not denuded oocytes with MPs, it is crucial to determine whether MPs accumulate in cumulus cells or oocytes or both as well as the compromised oocyte quality is caused by the direct effect of MPs or the indirect impact on somatic cumulus cells to cause a secondary effect on the oocytes.

Reviewer #1 (Public Review):

The authors performed a meta-analysis of GC concentrations and metabolic rates in birds and mammals. They found close associations for all studies showing a positive association between these two traits. As GCs have been viewed with close links to "stress," authors suggest that this overlooks the importance of metabolism and perhaps GC variation does not relate to "stress" per se but an increase in metabolism instead.

This is an important meta-analysis, as most researchers acknowledge the link between GCs and metabolism, metabolism is often overlooked in studies. The field of conservation physiology is especially focused on GCs being a "stress" hormone, which overlooks the importance of GCs in mediating energy balance, i.e., an animal that has high GC concentrations may not be doing that poorly compared to an animal with low GC concentrations, it might just be expending more energy, e.g., caring for young. The results, with overwhelming directionality and strong effect sizes, support the link for a positive association with these two variables.

My main concern lies in that most of the studies come from a few labs, therefore there may be limited data to test this relationship. I would include lab as a random effect to see how strong this effect might be. Furthermore, I would like to see a test of the directionality of the two variables. Authors suggest that changes in metabolism affect GC levels but likely changes in GC levels would affect metabolism. Why not look into studies that have altered GC levels experimentally and see the effect on metabolism? Based on the close link, authors suggest that GCs may not play a role outside of "stress" beyond the stressor's effect on metabolic rate. However, if they were to investigate manipulations of GCs on metabolic rate, the link may or may not be there, which would be interesting to look at. I firmly believe that GCs are tightly linked to metabolism; however, I also think that GCs have a range of effects outside of metabolism as well, depending on the course and strength of the stressor.

This work helps in the thinking that GCs are not the same as a "stress" hormone or labelling hormones with only one function. As hormones are naturally pleiotropic, the view of any one hormone being X is overly simplistic.

Reviewer #2 (Public Review):

Where this study is interesting is that the authors do a meta-analysis of studies in which metabolic rate was experimentally manipulated and both this rate and glucocorticoid levels were simultaneously measured. Unsurprisingly, there are relatively few such studies and many are from the lab of Michael Romero. While the results of the analysis are compelling, they are not surprising. That said, this work is important.

It is worth noting that in this analysis, the majority of the studies, if not all, are dealing with variation in baseline levels of glucocorticoids. That means the hormone is mostly acting metabolically at these lower levels and not as a stress response hormone as it does when levels are much higher. This difference is probably due to differences in receptors being activated. This could be discussed.

Reviewer #1 (Public Review):

The paper titled 'A dual function of the IDA peptide in regulating cell separation and modulating plant immunity at the molecular level' by Olsson Lalun et al., 2023 aims to understand how IDA-HAE/HSL2 signalling modulates immunity, a pathway that has previously been implicated in development. This is a timely question to address as conflicting reports exist within the field. IDL6/7 have previously been shown to negatively regulate immune signalling, disease resistance and stress responses in leaf tissue, however IDA has been shown to positively regulate immunity through the shedding of infected tissues. Moreover, recently the related receptor NUT/HSL3 has been shown to positively regulate immune signalling and disease resistance. This work has the potential to bring clarity to this field, however the manuscript requires some additional work to address these questions. This is especially the case as it contracts some previous work with IDL peptides which are perceived by the same receptor complexes.

Can IDA induce pathogen resistance? Does the infiltration of IDA into leaf tissue enhance or reduce pathogen growth? Previously it has been shown that IDL6 makes plants more susceptible. Is this also true for IDA? Currently cytoplasmic calcium influx and apoplastic ROS as overinterpreted as immune responses - these can also be induced by many developmental cue e.g. CLE40 induced calcium transients. Whilst gene expression is more specific is also true that treatment with synthetic peptides, which are recognised by LRR-RKs, can induce immune gene expression, especially in the short term, even when that is not there in vivo function e.g. doi.org/10.15252/embj.2019103894.

This paper shows that receptors other than hae/hsl2 are genetically required to induce defense gene expression, it would have been interesting to see what phenotype would be associated with higher order mutants of closely related haesa/haesa-like receptors. Indeed recently HSL1 has been shown to function as a receptor for IDA/IDL peptides. Could the triple mutant suppress all response? Could the different receptors have distinct outputs? For example for FRK1 gene expression the hae hsl2 mutant has an enhanced response. Could defence gene expression be primarily mediated by HSL1 with subfunctionalisation within this clade?

One striking finding of the study is the strong additive interaction between IDA and flg22 treatment on gene expression. Do the authors also see this for co-treatment of different peptides with flg22, or is this unique function of IDA? Is this receptor dependent (HAE/HSL1/HSL2)?

It is interesting how tissue specific calcium responses are in response to IDA and flg22, suggesting the cellular distribution of their cognate receptors. However, one striking observation made by the authors as well, is that the expression of promoter seems to be broader than the calcium response. Indicating that additional factors are required for the observed calcium response. Could diffusion of the peptide be a contributing factor, or are only some cells competent to induce a calcium response?<br /> It is interesting that the authors look for floral abscission phenotypes in cngc and rbohd/f mutants to conclude for genetic requirement of these in floral abscission. Do the authors have a hypothesis for why they failed to see a phenotype for the rbohd/f mutant as was published previously? Do you think there might be additional players redundantly mediating these processes?

Can you observe callose deposition in the cotyledons of the 35S::HAE line? Are the receptors expressed in native cotyledons? This is the only phenotype tested in the cotyledons.

Are flg22-induced calcium responses affected in hae hsl2?

Reviewer #2 (Public Review):

Lalun and co-authors investigate the signalling outputs triggered by the perception of IDA, a plant peptide regulating organs abscission. The authors observed that IDA perception leads to a transient influx of Ca2+, to the production of reactive oxygen species in the apoplast, and to an increase accumulation of transcripts which are also responsive to an immunogenic epitope of bacterial flagellin, flg22. The authors show that IDA is transcriptionally upregulated in response to several biotic and abiotic stimuli. Finally, based on the similarities in the molecular responses triggered by IDA and elicitors (such as flg22) the authors proposed that IDA has a dual function in modulating abscission and immunity. The manuscript is rather descriptive and provide little information regarding IDA signalling per se. A potential functional link between IDA signalling and immune signalling remains speculative.

Reviewer #3 (Public Review):

Previously, it has been shown the essential role of IDA peptide and HAESA receptor families in driving various cell separation processes such as abscission of flowers as a natural developmental process, of leaves as a defense mechanism when plants are under pathogenic attack or at the lateral root emergence and root tip cell sloughing. In this work, Olsson et al. show for the first time the possible role of IDA peptide in triggering plant innate immunity after the cell separation process occurred. Such an event has been previously proposed to take place in order to seal open remaining tissue after cell separation to avoid creating an entry point for opportunistic pathogens. The elegant experiments in this work demonstrate that IDA peptide is triggering the defense-associated marker genes together with immune specific responses including release of ROS and intracellular CA2+. Thus, the work highlights an intriguing direct link between endogenous cell wall remodeling and plant immunity. Moreover, the upregulation of IDA in response to abiotic and especially biotic stimuli are providing a valuable indication for potential involvement of HAE/IDA signalling in other processes than plant development.

Strengths:<br /> The various methods and different approaches chosen by the authors consolidates the additional new role for a hormone-peptide such as IDA. The involvement of IDA in triggering of the immunity complex process represents a further step in understanding what happens after cell separation occurs. The Ca2+ and ROS imaging and measurements together with using the haehsl2 and haehsl2 p35S::HAE-YFP genotypes provide a robust quantification of defense responses activation. While Ca2+ and ROS can be detected after applying the IDA treatment after the occurrence of cell separation it is adequately shown that the enzymes responsible for ROS production, RBOHD and RBOHF, are not implicated in the floral abscission.<br /> Furthermore, IDA production is triggered by biotic and abiotic factors such as flg22, a bacterial elicitor, fungi, mannitol or salt, while the mature IDA is activating the production of FRK1, MYB51 and PEP3, genes known for being part of plant defense process.

Weaknesses:<br /> Even though there is shown a clear involvement of IDA in activating the after-cell separation immune system, the use of p35S:HAE-YFP line represent a weak point in the scientific demonstration. The mentioned line is driving the HAE receptor by a constitutive promoter, capable of loading the plant with HAE protein without discriminating on a specific tissue. Since it is known that IDA family consist of more members distributed in various tissues, it is very difficult to fully differentiate the effects of HAE present ubiquitously.<br /> The co-localization of HAE/HSL2 and FLS2 receptors is a valuable point to address since in the present work, the marker lines presented do not get activated in the same cell types of the root tissues which renders the idea of nanodomains co-localization (as hypothetically written in the discussion) rather unlikely.

Reviewer #1 (Public Review):

In this study, Le Moigne and coworkers shed light on the structural details of the Sedoheptulose-1,7-Bisphosphatase (SBPase) from the green algae Chlamydomonas reinhardtii. The SBPase is part of the Calvin cycle and catalyzes the dephosphorylation of sedoheptulose-1,7-bisphosphate (SBP), which is a crucial step in the regeneration of ribulose-1,5-bisphosphate (RuBP), the substrate for Rubisco. The authors determine the crystal structure of the CrSBPase in an oxidized state. Based on this structure, potential active site residues and sites of post-translational modifications are identified. Furthermore, the authors determine the CrSBPase structure in a reduced state revealing the disruption of a disulfide bond in close proximity to the dimer interface. The authors then use molecular dynamics (MD) to gain insights into the redox-controlled dynamics of the CrSBPase and investigate the oligomerization of the protein using small-angle X-ray scattering (SAXS) and size-exclusion chromatography. Despite the difference in oligomerization, disruption of this disulfide bond did not impact the activity of CrSBPase, suggesting additional thiol-dependent regulatory mechanisms modulating the activity of the CrSBPase.

The authors provide interesting new findings on a redox-mechanism that modulates the oligomeric behavior of the SBPase, however without investigating this potential mechanism in more detail. The conclusions of this manuscript are mostly supported by the data, but they should be more carefully evaluated in respect to what is known from other systems as e.g. the moss Physcomitrella patens. This is especially of interest, as SBPase was previously reported to be dimeric, whereas for FBPase a dimer/tetramer equilibrium has been observed.

1.) Given that PpSBPase has been already characterized in detail, the authors should provide a more rigorous comparison to the existing data on SBPases. This includes a more conclusive structural comparison but also the enzymatic assays should be compared to the findings from P. patens. Do the authors observe differences between the moss and the chlorophyte systems, maybe even in regard to the oligomerization of the SBPase?

2.) The authors should include the control experiments (untreated SBPase) and the assays performed with mutant versions of the SBPase, which are currently only mentioned in the text or not shown at all.

3.) The representation of the structure in figures (especially Figures 1 and 3) should be adjusted to match the author's statements. In Figure 1, the angle from which the structure is displayed changes over the entire figure making it difficult to follow especially as a non-structural biologist. Furthermore, important aspects of the structure mentioned in the text are not labeled and should be highlighted, by e.g. a close-up. Same holds true for Figure 3 that currently mostly shows redundant information.

4.) The authors state that mutation of C115 and C120 to serine destabilize the dimer formation, while more tetramer and monomer is formed. As the tetramer is essentially a dimer of dimers, the authors should elaborate how this might work mechanistically. In my opinion, dimer formation is a prerequisite for tetramer formation and the two mutations rather stabilize the tetramer instead of destabilizing the dimer.

Reviewer #2 (Public Review):

The central theme of the manuscript is to report on the structure of SBPase - an enzyme central to the photosynthetic Calvin-Benson-Bassham cycle. The authors claim that the structure is first of its kind from a chlorophyte Chlamydomonas reinhardtii, a model unicellular green microalga. The authors use a number of methods like protein expression, purification, enzymatic assays, SAXS, molecular dynamics simulations and xray crystallography to resolve a 3.09 A crystal structure of the oxidized and partially reduced state. The results are supported by the claims made in the manuscript. One of the main weakness of the work is the lack of wider discussion presented in the manuscript. While the structure is the first from a chlorophyte, it is not unique. Several structures of SBPase are available. As the manuscript currently reads, the wider context of SBPase structures available and comparisons between them is missing from the manuscript. Another important point is that the reported structure of crSBPase is 0.453A away from the alphafold model. Though fleetingly mentioned in the methods section, it should be discussed to place it in the wider context.

Reviewer #1 (Public Review):

The authors used mathematical models to explore the mechanism(s) underlying the process of polar tube extrusion and the transport of the sporoplasm and nucleus through this structure. They combined this with experimental observations of the structure of the tube during extrusion using serial block face EM providing 3 dimensional data on this process. They also examined the effect of hyperosmolar media on this process to evaluate which model fit the predicted observed behavior of the polar tube in these various media solutions. Overall, this work resulted in the authors arriving at a model of this process that fit the data (model 5, E-OE-PTPV-ExP). This model is consistent with other data in the literature and provides support for the concept that the polar tube functions by eversion (unfolding like a finger of a glove) and that the expanding polar vacuole is part of this process. Finally, the authors provide important new insights into the bucking of the spore wall (and possible cavitation) as providing force for the nucleus to be transported via the polar tube. This is an important observation that has not been in previous models of this process.

Reviewer #2 (Public Review):

Microsporidia has a special invasion mechanism, which the polar tube (PT) ejects from mature spores at ultra-fast speeds, to penetrate the host and transfer the cargo to host. This work generated models for the physical basis of polar tube firing and cargo transport through the polar tube. They also use a combination of experiments and theory to elucidate possible biophysical mechanisms of microsporidia. Moreover, their approach also provided the potential applications of such biophysical approaches to other cellular architecture.

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

According to the model 5 (E-OE-PTPV-ExP) in P42 Fig. 6, is the posterior vacuole connected with the polar tube? If yes, how does the nucleus unconnected with the posterior vacuole enter the polar tube? In Fig. 6, would the posterior vacuole become two parts after spore germination? One part is transported via the polar tube, and the other is still in the spore. I recommend this process requires more experiments to prove.

Reviewer #3 (Public Review):

Abstract:

The paper follows a recent study by the same team (Jaroenlak et al Plos Pathogens 2020), which documented the dramatic ejection dynamics of the polar tube (PT) in microsporidia using live-imaging and scanning electron microscopy. Although several key observations were reported in this paper (the 3D architecture of the PT within the spore, the speed and extent of the ejection process, the translocation dynamics of the nucleus during germination), the precise geometry of the PT during ejection remain inaccessible to imaging, making it difficult to physically understand the phenomenon.

This paper aims to fill this gap with an indirect "data-driven" approach. By modeling the hydrodynamic dissipation for different unfolding mechanisms identified in the literature and by comparing the predictions with experiments of ejection in media of various viscosities, authors shows that data are compatible with an eversion (caterpillar-like) mechanism but not compatible with a "jack-in-the-box" scenario. In addition, the authors observe that most germinated spores exhibit an inward bulge, which they attribute to buckling due to internal negative pressure and which they suggest may be a mean of pushing the nucleus out of the PT during the final stage of ejection.

Major strengths:

Probably the most impressive aspect of the study is the experimental analysis of the ejection dynamics (velocity, ejection length) in medium of various viscosities over 3 orders of magnitudes, which, combined with a modeling of the viscous drag of the PT tube, provides very convincing evidence that the unfolding mechanism is not a global displacement of the tube but rather an apical extension mechanism, where the motion is localized at the end of the tube. The systematic classification of the different unfolding scenarios, consistent with the previous literature, and their confrontation with data in terms of energy, pressure and velocity also constitute an original approach in microbiology where in-situ and real time geometry is often difficult to access.

Major weaknesses:

1) While the experimental part of the paper is clear, I had (and still have) a hard time understanding the modeling part. Overall, the different unfolding mechanisms should be much better explained, with much more informative sketches to justify the dissipation and pressure terms, magnifying the different areas where dissipation occurs, showing the velocity field and pressure field, etc. In particular, a key parameter of eversion models is the geometry of the lubrication layers inside and outside the spore (h_sheath, h_slip). Where do the values of h_sheath and h_slip come from? What is the physical process that selects these parameters? For clarity, the figures showing the unfolding mechanics in the different scenario should be in the main text, not in the supplemental materials.

2) The authors compute and discuss in several places "the pressure" required to ejection, but no pressure is indicated in the various sketches and no general "ejection mechanism" involving this pressure is mentioned in the paper. What is this "required pressure" and to what element does it apply? I understand that the article focuses on the dissipation required to the deployment of the PT but I find it difficult to discuss the unfolding mechanism without having any idea on the driving mechanism of the movement. How could eversion be initiated and sustained?

3) Finally, the authors do not explain how pressure, which appears to be a positive, driving quantity at the beginning of the process, can become negative to induce buckling at the end of ejection. Although the hypothesis of rapid translocation induced by buckling is interesting, a much better mechanistic description of the process is needed to support it.

Reviewer #1 (Public Review):

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

The major strengths are that the authors used the cutting-edge technique, whole-exome sequencing, to identify genes associated with male infertility, and used a new model organism, Trypanosoma brucei to validate the findings; together with other high-throughput tools, including comparative proteomics to dissect the protein complex essential for normal sperm formation/function. The major weakness is that limited samples could be collected from the patients for further characterization by other approaches, including western blotting and TEM.

In general, the authors achieved their goal and the conclusion is supported by their results. The findings not only provide another genetic marker for the diagnosis of asthenoteratozoospermia but also enrich the knowledge in cilia/flagella.

Reviewer #2 (Public Review):

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

Reviewer #3 (Public Review):

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

Joint Public Review:

In this study, Porter et al report on outcomes from a small, open-label, pilot randomized clinical trial comparing dornase-alfa to the best available care in patients hospitalized with COVID-19 pneumonia. As the number of randomized participants is small, investigators describe also a contemporary cohort of controls and the study concludes about a decrease of inflammation (reflected by CRP levels) after 7 days of treatment but no other statistically significant clinical benefit.

Suggestions to the authors:<br /> • The RCT does not follow CONSORT statement and reporting guidelines<br /> • The authors have chosen a primary outcome that cannot be at least considered as clinically relevant or interesting. After 3 years of the pandemic with so much research, why investigate if a drug reduces CRP levels as we already have marketed drugs that provide beneficial clinical outcomes such as dexamethasone, anakinra, tocilizumab and baricitinib.<br /> • Please provide in Methods the timeframe for the investigation of the primary endpoint<br /> • Why day 35 was chosen for the read-out of the endpoint?<br /> • The authors performed an RCT but in parallel chose to compare also controls. They should explain their rationale as this is not usual. I am not very enthusiastic to see mixed results like Figures 2c and 2d.<br /> • Analysis is performed in mITT; this is a major limitation. The authors should provide at least ITT results. And they should describe in the main manuscript why they chose mITT analysis.<br /> • It is also not usual to exclude patients from analysis because investigators just do not have serial measurements. This is lost to follow up and investigators should have pre-decided what to do with lost-to-follow-up.<br /> • In Table 1 I would like to see all randomized patients (n=39), which is missing. There are also baseline characteristics that are missing, like which other treatments as BAT received by those patients except for dexamethasone.<br /> • In the first paragraph of clinical outcomes, the authors refer to a cohort that is not previously introduced in the manuscript. This is confusing. And I do not understand why this analysis is performed in the context of this RCT although I understand its pilot nature.<br /> • Propensity-score selected contemporary controls may introduce bias in favor of the primary study analysis, since controls are already adjusted for age, sex and comorbidities.<br /> • The authors do not clearly present numerically survivors and non-survivors at day 34, even though this is one of the main secondary outcomes.<br /> • It is unclear why another cohort (Berlin) was used to associate CRP with mortality. CRP association with mortality should (also) be performed within the current study.

Reviewer #1 (Public Review):

Levy and Hasselmo investigated the representational codes of dorsal hippocampus neurons in episodic memory and spatial navigation. Specifically, how new learning affects previously acquired spatial memory. They asked if the hippocampal representational codes evolve in a different manner when two tasks governed by different rules are learnt in a single environment vs. when each rule is learnt in a separate environment. The two rules they used were based on the classical Packard & McGaugh (1996) experiment. In the original 1996 experiment there was a striatal-dependent response-based task vs. a hippocampal-dependent map-based task. In the current paper they either trained the two types of rules (response vs. map based) in two different contexts (Two-Maze), or in a single context (One-Maze). They found that the remapping of the second time in the response-based rule task was greater in the One-Maze variant of the experiment than in the Two-Maze variant, and they interpreted this by suggesting that in the One-Maze variant, the different intermediate map-based task interfered with the representation in the second response-based task, while in the Two-Maze variant no such interference occurred, and thus the hippocampal map remained more stable.

The results of this paper are well supported by data; however, we believe the conclusion of paper should be different than the conclusion the authors have arrived at.

Major issue:<br /> 1. The main claim is that a new behavioral rule in a familiar environment leads to an increase in the level of remapping of hippocampal activity when returning to the original rule in the same environment. However, we are worried that the result is not due to the interference by a different task in the same environment, but rather by the fact that the mouse spent more time in the environment, causing a larger representational drift. Consider, for example, the change in correlation in Figure 4E over days. In all cases, there is a maze-dependent reduction in correlation from day to day. This reduction continues in the One-maze case also when changing the rule, suggesting that what determined the larger reduction is the time spent in each context, and not the actual change of rule or behavior. Thus it is probable that the fact that the mouse was longer in the first maze in the one-maze variant was enough to create a difference in correlation. See also Khatib et al., bioRxiv, 2022 on the issue of context-dependent drift. To actually control for that, we suggest that the mouse spends twice the time in the first maze during the first Turn-Right session, in the Two-maze variant, and then the comparison will be more valid, by equating the amount of time spent in the first maze in-between comparisons, in the two types of experiments.

Additional points:<br /> 2. Figure 1.d: While behaving differently, is there a difference in the representation? (e.g. mouse 2 on 7th day showed in the beginning very bad behavior). What is the relation between the reduction in performance and the change in representation?<br /> 3. Figure 3.c: We suggest to get a better estimate of the significance of the effect here using shuffling. Specifically, it could be a good idea to distinguish between signal correlations (derived from the overlapping spatial fields) vs. noise correlations. To what extent are the correlations dependent on spatial overlap? It could be worthwhile to determine the type of correlation: Is it due to the fact that the maps are similar for overlapping place cells, or is there noise correlations between these cells?<br /> 4. Figure 4.a: What is the explanation for the reduction in correlation between days 5 and 6?<br /> 5. Supplementary Figure 2: Higher correlations in all arms - note the higher correlation in all arms in the Two-Maze vs. the One-Maze, suggesting again that the effect is related to the longer time in the context, and not so much to the rule-change.<br /> 6. Methods: the researchers note that the animals were previously used in a different study. This should be stated clearly also in the results.

Reviewer #2 (Public Review):

In this study, the authors design a study to examine how place cell representations in the hippocampus change when the rules of a navigational task change. In one group of animals (group 1), the rules change in the same environment as the initial task was performed, and in the second group of animals (group 2), the task with the new rules is presented in a different environment, and then the animals are returned to the first environment with the original rule. (Briefly, on a cross maze, animals first learned to turn right, then the task rule changed to require turning east, and then the rule changed back to turning right). Broadly, using one photon calcium imaging with head mounted mini microscopes, the authors show that, at both the single cell and population level, more remapping occurs in group 1 animals in the initial environment than in group 2 animals.

This work is bolstered by the unique and rigorous way in which the authors track cells across days, in which they compare the rotation angles of crossed-registered groups of cells-I will definitely be using this in the future! The work also benefits from the extensive analysis of both temporal and spatial correlations of cellular activity. However, there are several shortcomings of the behavioral setup and learning conditions that need to be addressed in order to fully support the conclusions of the authors:

First, group 1 animals spend significantly more time in maze 1 than group 2 animals, since group two animals were switched to a different maze when the rule was changed. It is thus difficult to make direct comparison between the two groups, particularly in the last phase of experimentation when although both groups are in the first environment with the task rule, group 1 has experienced maze one for 6 days while group 2 has only experienced in for 3 days. It is therefore potentially difficult to disentangle differences caused by task changes versus length of environmental exposure.

Secondly, and similarly, during the task period, group 1 animals only have exposure to one environment while group 2 animals have exposure to 2 environments. Ideally, group 1 animals would also be exposed to environment 2, to rule out any potential effects of experiencing a novel environment may have on place cell representations, otherwise this cannot be disentangled from the effect of a task rule change.

Third, two concerns about how the animals are trained: First, if I am interpreting the methods correctly, both Group 1 and Group 2 animals are trained so turn-right is on one maze and turn east is on another way. As such, both groups thus have an "original understanding" that different rules are associated with different mazes. This seems potentially confounding given that it is consistent with the future training of Group 2 but not Group 1 mice. Additionally confounding is the fact that, because of the pretraining, group 1 mice have actually experienced the task in 3 different environments; I am unclear if and how this might be expected to affect results. Additionally, it is methodically unclear why pre-training occurs in a different environment than testing does, and what the criterion is for switching the animals from pre-training to training.

It would additionally be useful to discuss the results of this study in the context of spatial and non-spatial tasks. The authors, usefully, spend a significant portion of the paper comparing their results to results seen during fear extinction. It might be worth contextualizing the differences in how fear conditioning has a contextual "background" (i.e., the animals are conditioned to the context) while in their experiment the entire task is based entirely on navigation.

Overall, this is an interesting manuscript that attempts to address how contextual representations change as task parameters change. While the paper contains thorough statistical analysis but could benefit from more discussion of behavior in the context of learning as well as more rigorous behavioral controls. This work will be of interest to researchers studying hippocampus, navigation, and learning.

Reviewer #3 (Public Review):

The fundamental question that the authors address in this work is how our brain encodes when two events occur in the same context as against two events occurring in different contexts. Often in life, we encounter situations where it is difficult to alter the memory specifically associated with a place, for e.g. when we try to find our favourite brand of soap after the shopkeeper rearranges the shelves. Here the authors hypothesise that the acquisition of new memory, bound by a context /space, results in extensive remapping. Presumably, such remapping is manifested as an increased difficulty in acquiring new memories that are linked through space, especially when we have to remember both the old and the new. Using a combination of a modified behavioural task and in vivo imaging of neuronal activity, the authors test this hypothesis in mice. The spatial task requires the animal to learn two navigational rules of when to make a turn. Rule 1 requires the animal to make a turn with respect to itself (turn right), and Rule 2 requires the animal to turn with respect to the outside world (turn East). This is achieved by training the mice in two distinct contexts (mazes). Having trained the mice, they acquire the neuronal activation data and analysis through i) correlation matrices and ii) population vectors they test and show that the hypothesis is true. The manuscript is well-written and easy to follow in general. One of the important aspects of this manuscript is the clarity and detail with which the methods are described. The descriptions are unambiguous and complete in detail. This needs to be appreciated.

One of the soft spots of the study is the following: The animal learns to perform the task in two different contexts. It could also be interpreted as a change in context triggering the change in rule rather than a specific context predicting a specific rule as interpreted. I would like to know the authors' views on this. Additionally, the data is from one experiment with six mice, and the data is analyzed through different frameworks to glean information. This is both the boon and bane of the study. Independent/additional cross-validation of the overall effect would be nice to establish the observed phenomena. For e.g., the use of IEGs to identify the ensembles across the two scenarios, and/or inactivation of CA1 to show that rule change is affected or the first memory is also affected.

Reviewer #1 (Public Review):

In their manuscript, Brischigliaro et al. show that the disruption of respiratory complex assembly results in Drosophila melanogaster results in the accumulation of respiratory supercomplexes. Further, they show that the change in the supercomplex abundance does not impact respiratory function suggesting that the main role of supercomplex formation is structural. Overall, the manuscript is well written and the results and conclusion are supported. The D. melanogaster system, in which the abundance of supercomplexes can be altered through the genetic disruption of the assembly of the individual complexes, will be important for the field to discover the role of the supercomplexes. This manuscript will be of broad interest to the field of mitochondrial bioenergetics. The findings are valuable and the evidence is convincing.

Strengths:

The system developed in which the relative levels of SCs can be varied will be extremely useful for studying SC physiology.

The experiments are clearly described and interpreted.

Weaknesses:

The statement in the abstract regarding low amounts of SCs in "insect tissues" needs further support or should be narrowed. I am only aware of detailed characterization of the mitochondrial SC composition from D. melanogaster, which is insufficient to make a broad statement about the large and diverse category of insects. This should be rewritten.

In the introduction (line 76) and discussion (line 283), the authors reference the CoQ binding sites in CI and CIII2 being "too far apart" to allow for substrate channeling. The distance between the active sites, though significant, is insufficient to rule out substrate channeling. A stronger argument arises from the fact that the CoQ sites of both CI and CIII2 are open to the membrane and that there are no clear barriers for the free exchange of CoQ with the membrane pool.

Line 195, the slight elevation in CI amounts referred to here, does not appear to be statistically significant and therefore should not be discussed a being altered relative to the control.

Figure 4H, the assignments of the observed larger bands seem incorrect. The largest band (currently assigned as SC I1+III2+IV1) represents too large of a shift for only the addition of CIV and the band currently assigned at SC I1+III2 appears to also contain CIV. The identity of these bands should be reevaluated and additional experiments are needed to definitively prove their identity. This uncertainty should be addressed experimentally or made more explicit in the text.

Line 302, the authors state that the structural basis for less SC in D. melanogaster is "due to a more stable association of the NDUFA11 subunit..." However, this would not result is a less stable SC association and only explains why NDUFA11 is more stably associated with CI in the absence of CIII2. The more likely structural reason for the observation of less SC in D. melanogaster is the N-terminal truncation of Dm-NDUFB4 relative to mammalian NDUFB4. This truncation results in the loss of a major SC interaction site between CI and CIII2 in the matrix.

Reviewer #2 (Public Review):

Respiratory chain complexes assemble in higher-ordered structures termed supercomplexes or respirasomes. The functional significance of these assemblies is currently investigated, there are two main hypothesis tested, namely that supercomplexes provide kinetic advantages or structural stability. Here, the authors use the fruitfly to reveal that, while the respiratoy chain in the organism normally does not form higher-order assemblies, it does so under conditions when their assembly is impaired. Because the rather moderate increase in supercomplex formation does not change oxygen consumption stimulated by CI or CII substrate, the authors conclude that supercomplex formation has more a structural than a functional role. The main strength of this work is that the technical quality of the experiments is high and that the authors induced defects in respiratory chain assembly through sets of well-controlled genetic models. The obtained data are mostly descriptive using standard approaches and are very well executed. The authors claim that their experiments allow to conclude that the role of supercomplex formation is restricted to a structural role and, hence, exclude a function directly related to electron transport efficiency. However, while the authors can show convincingly that supercomplexes form in the mutants, but not in the wild type, their main claim is not well supported by data and both the structural mechanism of supercompelx formation and their significance remain unknown. While the supercomplex formation observed only in mitochondrial mutants per se is interesting, it would be good to great to define structural aspects of supercomplex formation and their potential impact on the stability of the respiratory chain complexes in these mutants.

Reviewer #2 (Public Review):

Yanagihara and colleagues investigated the immune cell composition of bronchoalveolar lavage fluid (BALF) samples in a cohort of patients with malignancy undergoing chemotherapy and with with lung adverse reactions including Pneumocystis jirovecii pneumonia (PCP) and immune-checkpoint inhibitors (ICIs) or cytotoxic drug induced interstitial lung diseases (ILDs). Using mass cytometry, their aim was to characterize the cellular and molecular changes in BAL to improve our understanding of their pathogenesis and identify potential biomarkers and therapeutic targets. In this regard, the authors identify a correlation between CD16 expression in T cells and the severity of PCP and an increased infiltration of CD57+ CD8+ T cells expressing immune checkpoints and FCLR5+ B cells in ICI-ILD patients.

The conclusions of this paper are mostly well supported by data, but some aspects of the data analysis need to be clarified and extended.

1) The authors should elaborate on why different set of markers were selected for each analysis step. E.g., Different set of markers were used for UMAP, CITRUS and viSNE in the T cell and myeloid analysis.

2) The authors should state if a normality test for the distribution of the data was performed. If not, non-parametric tests should be used.

3) The authors should explore the correlation between CD16 intensity and the CTCAE grade in T cell subsets such as EMRA CD8 T cells, effector memory CD4, etc as identified in Figure 1B.

4) The authors could use CITRUS to better assess the B cell compartment.

Reviewer #3 (Public Review):

The authors collected BALF samples from lung cancer patients newly diagnosed with PCP, DI-ILD or ICI-ILD. CyTOF was performed on these samples, using two different panels (T-cell and B-cell/myeloid cell panels). Results were collected, cleaned-up, manually gated and pre-processed prior to visualisation with manifold learning approaches t-SNE (in the form of viSNE) or UMAP, and analysed by CITRUS (hierarchical clustering followed by feature selection and regression) for population identification - all using Cytobank implementation - in an attempt to identify possible biomarkers for these disease states. By comparing cell abundances from CITRUS results and qualitative inspection of a small number of marker expressions, the authors claimed to have identified an expansion of CD16+ T-cell population in PCP cases and an increase in CD57+ CD8+ T-cells, FCRL5+ B-cells and CCR2+ CCR5+ CD14+ monocytes in ICI-ILD cases.

By the authors' own admission, there is an absence of healthy donor samples and, perhaps as a result of retrospective experimental design, also an absence of pre-treatment samples. The entire analysis effectively compares three yet-established disease states with no common baseline - what really constitutes a "biomarker" in such cases? The introduction asserts that "y characterizing the cellular and molecular changes in BAL from patients with these complications, we aim to improve our understanding of their pathogenesis and identify potential therapeutic targets" (lines 82-84). Given these obvious omissions, no real "changes" have been studied in the paper. These are very limited comparisons among three, and only these three, states.

Even assuming more thorough experimental design, the data analysis is unfortunately too shallow and has not managed to explore the wealth of information that could potentially be extracted from the results. CITRUS is accessible and convenient, but also make a couple of big assumptions which could affect data analysis - 1) Is it justified to concatenate all FCS files to analyse the data in one batch / small batches? Could there be batch effects or otherwise other biological events that could confuse the algorithm? 2) With a relatively small number of samples, and after internal feature selection of CITRUS, is the regression model suitable for population identification or would it be too crude and miss out rare populations? There are plenty of other established methods that could be used instead. Have those methods been considered?

Colouring t-SNE or UMAP (e.g. Figure 6C) plots by marker expression is useful for quick identification of cell populations but it is not a quantitative analysis. In a CyTOF analysis like this, it is common to work out fold changes of marker expressions between conditions. It is inadequate to judge expression levels and infer differences simply by looking at colours.

The relatively small number of samples also mean that most results presented in the paper are not statistical significant. Whilst it is understandable that it is not always possible to collect a large number of patient samples for studies like this, having several entire major figures showing "n.s." (e.g. Figures 3A, 4B and 5C), together with limitations in the comparisons themselves and inadequate analysis, make the observations difficult to be convincing, and even less so for the single fatal PCP case where N = 1.

It would also be good scientific practice to show evidence of sample data quality control. Were individual FCS files examined? Did the staining work? Some indication of QC would also be great.

This dataset generated and studied by the authors have the potential to address the question they set out to answer and thus potentially be useful for the field. However, in the current state of presentation, more evidence and more thorough data analysis are needed to draw any conclusions, or correlations, as the authors would like to frame them.

Reviewer #1 (Public Review):

Cytotoxic agents and immune checkpoint inhibitors are the most commonly used and efficacious treatments for lung cancers. However their use brings two significant pulmonary side-effects; namely Pneumocystis jirovecii infection and resultant pneumonia (PCP), and interstitial lung disease (ILD). To observe the potential immunological drivers of these adverse events, Yanagihara et al. analysed and compared cells present in the bronchoalveolar lavage of three patient groups (PCP, cytotoxic drug-induced ILD [DI-ILD], and ICI-associated ILD [ICI-ILD]) using mass cytometry (64 markers). In PCP, they observed an expansion of the CD16+ T cell population, with the highest CD16+ T proportion (97.5%) in a fatal case, whilst in ICI-ILD, they found an increase in CD57+ CD8+ T cells expressing immune checkpoints (TIGIT+ LAG3+ TIM-3+ PD-1+), FCRL5+ B cells, and CCR2+ CCR5+ CD14+ monocytes. Given the fatal case, the authors also assessed for, and found, a correlation between CD16+ T cells and disease severity in PCP, postulating that this may be owing to endothelial destruction. Although n numbers are relatively small (n=7-9 in each cohort; common numbers for CyTOF papers), the authors use a wide panel (n=65) and two clustering methodologies giving greater strength to the conclusions. The differential populations discovered using one or two of the analytical methods are robust: whole population shifts with clear and significant clustering. These data are an excellent resource for clinical disease specialists and pan-disease immunologists, with a broad and engaging contextual discussion about what they could mean.

Strengths:<br /> • The differences in immune cells in BAL in these specific patient subgroups is relatively unexplored.<br /> • This is an observational study, with no starting hypothesis being tested.<br /> • Two analytical methods are used to cluster the data.<br /> • A relatively wide panel was used (64 markers), with particular strength in the alpha beta T cells and B cells.<br /> • Relevant biomarkers, beta-D-glucan and KL-6 were also analysed<br /> • Appropriate statistics were used throughout.<br /> • Numbers are low (7 cases of PCP, 9 of DI-ILD, and 9 of ICI-ILD) but these are difficult samples to collect and so in relative terms, and considering the use of CyTOF, these are good numbers.<br /> • Beta-D-glucan shows potential as a biomarker for PCP (as previously reported) whilst KL-6 shows potential as a biomarker for ICI-ILD (not reported before). Interestingly, KL-6 was not seen to be increased in DI-ILD patients.<br /> • Despite the relatively low n numbers and lack of matching there are some clear differentials. The CD4/CD8+CD16+HLA-DR+CXCR3+CD14- T cell result is striking - up in PCP (with EM CD4s significantly down) - whilst the CD8 EMRA population is clear in ICI-ILD and 'non-exhausted' CD4s, with lower numbers of EMRA CD8s in DI-ILD.<br /> • The authors identify 17/31 significantly differentiated clusters of myeloid cells, eg CD11bhi CD11chi CD64+ CD206+ alveolar macrophages with HLA-DRhi in PCP.<br /> • With respect to B cells, the authors found that FCRL5+ B cells were more abundant in patients with ICI-ILD compared to those with PCP and DI-ILD, suggesting these FCRL5+ B cells may have a role in irAE.<br /> • One patient's extreme CD16+ T cell (97.5% positive) and death, led the authors to consider CD16+ T cells as an indicator of disease severity in PCP. This was then tested and found to be correct.<br /> • Authors discuss results in context of literature leading them to suggest that CD16+ T cells may target endothelial cells and wonder if anti-complement therapy may be efficacious in PCP.<br /> • Great discussion on auto-reactive T cell clones where the authors suggest that in ICI-ILD CD8s may react against healthy lung, driving ILD.<br /> • An observation of CXCR3 in different CD8 populations in ICI-ILD and PCP lead the authors to hypothesise on the chemoattractants in the microenvironment.<br /> • Excellent point suggesting CD57 may not always be a marker of senescence on T cells - reflective of growing change within the community.<br /> • Well considered suggestion that FCRL5+ B cells may be involved in ICI-ILD driven autoimmunity.<br /> • The authors discuss the main weaknesses in the discussion and stress that the findings detailed in the paper "demonstrate a correlation rather than proof of causation".<br /> • Figures and legends are clear and pleasing to the eye.

Weaknesses:<br /> • This is an observational study, with no starting hypothesis being tested.<br /> • Only patients who were able to have a lavage taken have been recruited.<br /> • One set of analysis wasn't carried out for one subgroup (ICI-ILD) as PD1 expression was negative owing to the use of nivolumab.<br /> • Some immune cell subsets wouldn't be picked up with the markers and gating strategies used; e.g. NK cells.<br /> • Some immune cells would be disproportionately damaged by the storage, thawing and preparation of the samples; e.g. granulocytes.<br /> • Numbers are low (7 cases of PCP, 9 of DI-ILD, and 9 of ICI-ILD), sex, age and adverse event matching wasn't performed, and treatment regimen are varied and 'suspected' (suggesting incomplete clinical data) - but these are difficult samples to collect. These numbers drop further for some analyses e.g. T cell clustering owing to factors such as low cell number.<br /> • The disease comparisons are with each other, there is no healthy control.<br /> • Samples are taken at one time point.<br /> • The discussion on probably the stand out result - the CD16+ T cells in PCP - relies on two papers - leading to a slightly skewed emphasis on one paper on CD16+ cells in COVID. There are other papers out there that have observed CD16+ T cells in other conditions. It is also worth being in mind that given the markers used, these CD16+ T cell may be gamma deltas.<br /> • The discussion on ICI patient consistently showing increased PD1, could have been greater, as given the ICI is targeting PD1, one would expect the opposite as commented on, and observed, in the methods section.

Reviewer #1 (Public Review):

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

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

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

Reviewer #2 (Public Review):

The authors developed 11 key measures of clinical activity in primary care and measured changes in the frequency of these measures throughout the first 1.5 years of the COVID-19 pandemic. The biggest strength of the study is the data source, which comprises records from 99% of general practices in England. The biggest limitation lies in the analysis of the data: The authors used only descriptive statistics for the investigation of time trends and have not accounted for long-term time trends (only one "control year" was considered). Still, owing to the large study size, the time trends observed are convincing. The work is of high significance to the field because the OpenSAFELY platform will enable the continuous and real-time monitoring of primary care activity.

Reviewer #3 (Public Review):

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

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

Strengths:

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

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

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

Weaknesses:

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

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

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

Reviewer #1 (Public Review):

In this study, Fang H et al. describe a potential pathway, ITGB4-TNFAIP2-IQGAP1-Rac1, that may involve in the drug resistance in triple negative breast cancer (TNBC). Mechanistically, it was demonstrated that TNFAIP2 bind with IQGAP1 and ITGB4 activating Rac1 and the following drug resistance. The present study focused on breast cancer cell lines with supporting data from mouse model and patient breast cancer tissues. The study is interesting. The experiments were well controlled and carefully carried out. The conclusion is supported by strong evidence provided in the manuscript. The authors may want to discuss the link between ITGB4 and Rac 1, between IQGAP1 and Rac1, and between TNFAIP2 and Rac1 as compared with the current results obtained. This is important considering some recent publications in this area (Cancer Sci 2021, J Biol Chem 2008, Cancer Res 2023).

Reviewer #3 (Public Review):

In this manuscript, Fang and colleagues found that IQGAP1 interacts with TNFAIP2, which activates Rac1 to promote drug resistance in TNBC. Furthermore, they found that ITGB4 could interact with TNFAIP2 to promote TNBC drug resistance via the TNFAIP2/IQGAP1/Rac1 axis by promoting DNA damage repair.

This work has good innovation and high potential clinical significance.

Reviewer #2 (Public Review):

Breast cancer is the most common malignant tumor in women. One of subtypes in breast cancer is so called triple-negative breast cancer (TNBC), which represents the most difficult subtype to treat and cure in the clinic. Chemotherapy drugs including epirubicin and cisplatin are widely used for TNBC treatment. However, drug resistance remains as a challenge in the clinic. The authors uncovered a molecular pathway involved in chemotherapy drug resistance, and molecular players in this pathway represent as potential drug targets to overcome drug resistance. The experiments are well designed and the conclusions drawn mostly were supported by the data. The findings have potential to be translated into the clinic.

Reviewer #1 (Public Review):

Current experimental work reveals that brain areas implicated in episodic and spatial memory have a dynamic code, in which activity representing familiar events/locations changes over time. This paper shows that such reconfiguration is consistent with underlying changes in the excitability of cells in the population, which ties these observations to a physiological mechanism.

Delamare et al. use a recurrent network model to consider the hypothesis that slow fluctuations in intrinsic excitability, together with spontaneous reactivations of ensembles, may cause the structure of the ensemble to change, consistent with the phenomenon of representational drift. The paper focuses on three main findings from their model: (1) fluctuations in intrinsic excitability lead to drift, (2) this drift has a temporal structure, and (3) a readout neuron can track the drift and continue to decode the memory. This paper is relevant and timely, and the work addresses questions of both a potential mechanism (fluctuations in intrinsic excitability) and purpose (time-stamping memories) of drift.

The model used in this study consists of a pool of 50 all-to-all recurrently connected excitatory neurons with weights changing according to a Hebbian rule. All neurons receive the same input during stimulation, as well as global inhibition. The population has heterogeneous excitability, and each neuron's excitability is constant over time apart from a transient increase on a single day. The neurons are divided into ensembles of 10 neurons each, and on each day, a different ensemble receives a transient increase in the excitability of each of its neurons, with each neuron experiencing the same amplitude of increase. Each day for four days, repetitions of a binary stimulus pulse are applied to every neuron.

The modeling choices focus in on the parameter of interest-the excitability-and other details are generally kept as straightforward as possible. That said, I wonder if certain aspects may be overly simple. The extent of the work already performed, however, does serve the intended purpose, and so I think it would be sufficient for the authors to comment on these choices rather than to take more space in this paper to actually implement these choices. What might happen were more complex modeling choices made? What is the justification for the choices that are made in the present work?

The two specific modeling choices I question are (1) the excitability dynamics and (2) the input stimulus. The ensemble-wide synchronous and constant-amplitude excitability increase, followed by a return to baseline, seems to be a very simplified picture of the dynamics of intrinsic excitability. At the very least, justification for this simplified picture would benefit the reader, and I would be interested in the authors' speculation about how a more complex and biologically realistic dynamics model might impact the drift in their network model. Similarly, the input stimulus being binary means that, on the single-neuron level, the only type of drift that can occur is a sort of drop-in/drop-out drift; this choice excludes the possibility of a neuron maintaining significant tuning to a stimulus but changing its preferred value. How would the use of a continuous input variable influence the results.

Result (1): Fluctuations in intrinsic excitability induce drift<br /> The two choices highlighted above appear to lead to representations that never recruit the neurons in the population with the lowest baseline excitability (Figure 1b: it appears that only 10 neurons ever show high firing rates) and produce networks with very strong bidirectional coupling between this subset of neurons and weak coupling elsewhere (Figure 1d). This low recruitment rate need may not necessarily be problematic, but it stands out as a point that should at least be commented on. The fact that only 10 neurons (20% of the population) are ever recruited in a representation also raises the question of what would happen if the model were scaled up to include more neurons.

Result (2): The observed drift has a temporal structure<br /> The authors then demonstrate that the drift has a temporal structure (i.e., that activity is informative about the day on which it occurs), with methods inspired by Rubin et al. (2015). Rubin et al. (2015) compare single-trial activity patterns on a given session with full-session activity patterns from each session. In contrast, Delamare et al. here compare full-session patterns with baseline excitability (E = 0) patterns. This point of difference should be motivated. What does a comparison to this baseline excitability activity pattern tell us? The ordinal decoder, which decodes the session order, gives very interesting results: that an intermediate amplitude E of excitability increase maximizes this decoder's performance. This point is also discussed well by the authors. As a potential point of further exploration, the use of baseline excitability patterns in the day decoder had me wondering how the ordinal decoder would perform with these baseline patterns.

Result (3): A readout neuron can track drift<br /> The authors conclude their work by connecting a readout neuron to the population with plastic weights evolving via a Hebbian rule. They show that this neuron can track the drifting ensemble by adjusting its weights. These results are shown very neatly and effectively and corroborate existing work that they cite very clearly.

Overall, this paper is well-organized, offers a straightforward model of dynamic intrinsic excitability, and provides relevant results with appropriate interpretations. The methods could benefit from more justification of certain modeling choices, and/or an exploration (either speculative or via implementation) of what would happen with more complex choices. This modeling work paves the way for further explorations of how intrinsic excitability fluctuations influence drifting representations.

Reviewer #2 (Public Review):

In this computational study, Delamare et al identify slow neuronal excitability as one mechanism underlying representational drift in recurrent neuronal networks and that the drift is informative about the temporal structure of the memory and when it has been formed. The manuscript is very well written and addresses a timely as well as important topic in current neuroscience namely the mechanisms that may underlie representational drift.

The study is based on an all-to-all recurrent neuronal network with synapses following Hebbian plasticity rules. On the first day, a cue-related representation is formed in that network and on the next 3 days it is recalled spontaneously or due to a memory-related cue. One major observation is that representational drift emerges day-by-day based on intrinsic excitability with the most excitable cells showing highest probability to replace previously active members of the assembly. By using a day-decoder, the authors state that they can infer the order at which the reactivation of cell assemblies happened but only if the excitability state was not too high. By applying a read-out neuron, the authors observed that this cell can track the drifting ensemble which is based on changes of the synaptic weights across time. The only few questions which emerged and could be addressed either theoretically or in the discussion are as follows:

1. Would the similar results be obtained if not all-to-all recurrent connections would have been molded but more realistic connectivity profiles such as estimated for CA1 and CA3?<br /> 2. How does the number of excited cells that could potentially contribute to an engram influence the representational drift and the decoding quality?<br /> 3. How does the rate of the drift influence the quality of readout from the readout-out neuron?

Reviewer #3 (Public Review):

The authors explore an important question concerning the underlying mechanism of representational drift, which despite intense recent interest remains obscure. The paper explores the intriguing hypothesis that drift may reflect changes in the intrinsic excitability of neurons. The authors set out to provide theoretical insight into this potential mechanism.

They construct a rate model with all-to-all recurrent connectivity, in which recurrent synapses are governed by a standard Hebbian plasticity rule. This network receives a global input, constant across all neurons, which can be varied with time. Each neuron also is driven by an "intrinsic excitability" bias term, which does vary across cells. The authors study how activity in the network evolves as this intrinsic excitability term is changed.

They find that after initial stimulation of the network, those neurons where the excitability term is set high become more strongly connected and are in turn more responsive to the input. Each day the subset of neurons with high intrinsic excitability is changed, and the network's recurrent synaptic connectivity and responsiveness gradually shift, such that the new high intrinsic excitability subset becomes both more strongly activated by the global input and also more strongly recurrently connected. These changes result in drift, reflected by a gradual decrease across time in the correlation of the neuronal population vector response to the stimulus.

The authors are able to build a classifier that decodes the "day" (i.e. which subset of neurons had high intrinsic excitability) with perfect accuracy. This is despite the fact that the excitability bias during decoding is set to 0 for all neurons, and so the decoder is really detecting those neurons with strong recurrent connectivity, and in turn strong responses to the input. The authors show that it is also possible to decode the order in which different subsets of neurons were given high intrinsic excitability on previous "days". This second result depends on the extent by which intrinsic excitability was increased: if the increase in intrinsic excitability was either too high or too low, it was not possible to read out any information about past ordering of excitability changes.

Finally, using another Hebbian learning rule, the authors show that an output neuron, whose activity is a weighted sum of the activity of all neurons in the network, is able to read out the activity of the network. What this means specifically, is that although the set of neurons most active in the network changes, the output neuron always maintains a higher firing rate than a neuron with randomly shuffled synaptic weights, because the output neuron continuously updates its weights to sample from the highly active population at any given moment. Thus, the output neuron can readout a stable memory despite drift.

Strengths:<br /> The authors are clear in their description of the network they construct and in their results. They convincingly show that when they change their "intrinsic excitability term", upon stimulation, the Hebbian synapses in their network gradually evolve, and the combined synaptic connectivity and altered excitability result in drifting patterns of activity in response to an unchanging input (Fig. 1, Fig. 2a). Furthermore, their classification analyses (Fig. 2) show that information is preserved in the network, and their readout neuron successfully tracks the active cells (Fig. 3). Finally, the observation that only a specific range of excitability bias values permits decoding of the temporal structure of the history of intrinsic excitabililty (Fig. 2f and Figure S1) is interesting, and as the authors point out, not trivial.

Weaknesses:<br /> 1) The way the network is constructed, there is no formal difference between what the authors call "input", Δ(t), and what they call "intrinsic excitability" Ɛ_i(t) (see Equation 3). These are two separate terms that are summed (Eq. 3) to define the rate dynamics of the network. The authors could have switched the names of these terms: Δ(t) could have been considered a global "intrinsic excitability term" that varied with time and Ɛ_i(t) could have been the external input received by each neuron i in the network. In that case, the paper would have considered the consequence of "slow fluctuations of external input" rather than "slow fluctuations of intrinsic excitability", but the results would have been the same. The difference is therefore semantic. The consequence is that this paper is not necessarily about "intrinsic excitability", rather it considers how a Hebbian network responds to changes in excitatory drive, regardless of whether those drives are labeled "input" or "intrinsic excitability".

2) Given how the learning rule that defines input to the readout neuron is constructed, it is trivial that this unit responds to the most active neurons in the network, more so than a neuron assigned random weights. What would happen if the network included more than one "memory"? Would it be possible to construct a readout neuron that could classify two distinct patterns? Along these lines, what if there were multiple, distinct stimuli used to drive this network, rather than the global input the authors employ here? Does the system, as constructed, have the capacity to provide two distinct patterns of activity in response to two distinct inputs?

Impact:<br /> Defining the potential role of changes in intrinsic excitability in drift is fundamental. Thus, this paper represents a potentially important contribution. Unfortunately, given the way the network employed here is constructed, it is difficult to tease apart the specific contribution of changing excitability from changing input. This limits the interpretability and applicability of the results.

Reviewer #1 (Public Review):

Qin et al. set out to investigate the role of mechanosensory feedback during swallowing and identify neural circuits that generate ingestion rhythms. They use Drosophila melanogaster swallowing as a model system, focusing their study on the neural mechanisms that control cibarium filling and emptying in vivo. They find that pump frequency is decreased in mutants of three mechanotransduction genes (nompC, piezo, and Tmc), and conclude that mechanosensation mainly contributes to the emptying phase of swallowing. Furthermore, they find that double mutants of nompC and Tmc have more pronounced cibarium pumping defects than either single mutants or Tmc/piezo double mutants. They discover that the expression patterns of nompC and Tmc overlap in two classes of neurons, md-C and md-L neurons. The dendrites of md-C neurons warp the cibarium and project their axons to the subesophageal zone of the brain. Silencing neurons that express both nompC and Tmc leads to severe ingestion defects, with decreased cibarium emptying. Optogenetic activation of the same population of neurons inhibited filling of the cibarium and accelerated cibarium emptying. In the brain, the axons of nompC∩Tmc cell types respond during ingestion of sugar but do not respond when the entire fly head is passively exposed to sucrose. Finally, the authors show that nompC∩Tmc cell types arborize close to the dendrites of motor neurons that are required for swallowing, and that swallowing motor neurons respond to the activation of the entire Tmc-GAL4 pattern.

Strengths:<br /> -The authors rigorously quantify ingestion behavior to convincingly demonstrate the importance of mechanosensory genes in the control of swallowing rhythms and cibarium filling and emptying<br /> -The authors demonstrate that a small population of neurons that express both nompC and Tmc oppositely regulate cibarium emptying and filling when inhibited or activated, respectively<br /> -They provide evidence that the action of multiple mechanotransduction genes may converge in common cell types

Weaknesses:<br /> -A major weakness of the paper is that the authors use reagents that are expressed in both md-C and md-L but describe the results as though only md-C is manipulated<br /> -Severing the labellum will not prevent optogenetic activation of md-L from triggering neural responses downstream of md-L. Optogenetic activation is strong enough to trigger action potentials in the remaining axons. Therefore, Qin et al. do not present convincing evidence that the defects they see in pumping can be specifically attributed to md-C.<br /> -GRASP is known to be non-specific and prone to false positives when neurons are in close proximity but not synaptically connected. A positive GRASP signal supports but does not confirm direct synaptic connectivity between md-C/md-L axons and MN11/MN12.<br /> -As seen in Figure Supplement 2, the expression pattern of Tmc-GAL4 is broader than md-C alone. Therefore, the functional connectivity the authors observe between Tmc expressing neurons and MN11 and 12 cannot be traced to md-C alone

Overall, this work convincingly shows that swallowing and swallowing rhythms are dependent on several mechanosensory genes. Qin et al. also characterize a candidate neuron, md-C, that is likely to provide mechanosensory feedback to pumping motor neurons, but the results they present here are not sufficient to assign this function to md-C alone. This work will have a positive impact on the field by demonstrating the importance of mechanosensory feedback to swallowing rhythms and providing a potential entry point for future investigation of the identity and mechanisms of swallowing central pattern generators.

Reviewer #2 (Public Review):

In this manuscript, the authors describe the role of cibarial mechanosensory neurons in fly ingestion. They demonstrate that pumping of the cibarium is subtly disrupted in mutants for piezo, TMC, and nomp-C. Evidence is presented that these three genes are co-expressed in a set of cibarial mechanosensory neurons named md-C. Silencing of md-C neurons results in disrupted cibarial emptying, while activation promotes faster pumping and/or difficulty filling. GRASP and chemogenetic activation of the md-C neurons is used to argue that they may be directly connected to motor neurons that control cibarial emptying.

The manuscript makes several convincing and useful contributions. First, identifying the md-C neurons and demonstrating their essential role for cibarium emptying provides reagents for further studying this circuit and also demonstrates the important of mechanosensation in driving pumping rhythms in the pharynx. Second, the suggestion that these mechanosensory neurons are directly connected to motor neurons controlling pumping stands in contrast to other sensory circuits identified in fly feeding and is an interesting idea that can be more rigorously tested in the future.

At the same time, there are several shortcomings that limit the scope of the paper and the confidence in some claims. These include:

a) the MN-LexA lines used for GRASP experiments are not characterized in any other way to demonstrate specificity. These were generated for this study using Phack methods, and their expression should be shown to be specific for MN11 and MN12 in order to interpret the GRASP experiments.

b) There is also insufficient detail for the P2X2 experiment to evaluate its results. Is this an in vivo or ex vivo prep? Is ATP added to the brain, or ingested? If it is ingested, how is ATP coming into contact with md-C neuron if it is not a chemosensory neuron and therefore not exposed to the contents of the cibarium?

c) In Figure 3C, the authors claim that ablating the labellum will remove the optogenetic stimulation of the md-L neuron (mechanosensory neuron of the labellum), but this manipulation would presumably leave an intact md-L axon that would still be capable of being optogenetically activated by Chrimson.

d) Average GCaMP traces are not shown for md-C during ingestion, and therefore it is impossible to gauge the dynamics of md-C neuron activation during swallowing. Seeing activation with a similar frequency to pumping would support the suggested role for these neurons, although GCaMP6s may be too slow for these purposes.

e) The negative result in Figure 4K that is meant to rule out taste stimulation of md-C is not useful without a positive control for pharyngeal taste neuron activation in this same preparation.

In addition to the experimental limitations described above, the manuscript could be organized in a way that is easier to read (for example, not jumping back and forth in figure order).

Reviewer #3 (Public Review):

Swallowing is an essential daily activity for survival, and pharyngo-laryngeal sensory function is critical for safe swallowing. In Drosophila, it has been reported that the mechanical property of food (e.g. Viscosity) can modulate swallowing. However, how mechanical expansion of the pharynx or fluid content sense and control swallowing was elusive. Qin et al. showed that a group of pharyngeal mechanosensory neurons, as well as mechanosensory channels (nompC, Tmc, and Piezo), respond to these mechanical forces for regulation of swallowing in Drosophila melanogaster.

Strengths:<br /> There are many reports on the effect of chemical properties of foods on feeding in fruit flies, but only limited studies reported how physical properties of food affect feeding especially pharyngeal mechanosensory neurons. First, they found that mechanosensory mutants, including nompC, Tmc, and Piezo, showed impaired swallowing, mainly the emptying process. Next, they identified cibarium multidendritic mechanosensory neurons (md-C) are responsible for controlling swallowing by regulating motor neuron (MN) 12 and 11, which control filling and emptying, respectively.

Weaknesses:<br /> While the involvement of md-C and mechanosensory channels in controlling swallowing is convincing, it is not yet clear which stimuli activate md-C. Can it be an expansion of cibarium or food viscosity, or both? In addition, if rhythmic and coordinated contraction of muscles 11 and 12 is essential for swallowing, how can simultaneous activation of MN 11 and 12 by md-C achieve this? Finally, previous reports showed that food viscosity mainly affects the filling rather than the emptying process, which seems different from their finding.

Reviewer #4 (Public Review):

A combination of optogenetic behavioral experiments and functional imaging are employed to identify the role of mechanosensory neurons in food swallowing in adult Drosophila. While some of the findings are intriguing and the overall goal of mapping a sensory to motor circuit for this rhythmic movement are admirable, the data presented could be improved.

The circuit proposed (and supported by GRASP contact data) shows these multi-dendritic neurons connecting to pharyngeal motor neurons. This is pretty direct - there is no evidence that they affect the hypothetical central pattern generator - just the execution of its rhythm. The optogenetic activation and inhibition experiments are constitutive, not patterned light, and they seem to disrupt the timing of pumping, not impose a new one. A slight slowing of the rhythm is not consistent with the proposed function.

The mechanosensory channel mutants nompC, piezo, and TMC have a range of defects. The role of these channels in swallowing may not be sufficiently specific to support the interpretation presented. Their other defects are not described here and their overall locomotor function is not measured. If the flies have trouble consuming sufficient food throughout their development, how healthy are they at the time of assay? The level of starvation or water deprivation can affect different properties of feeding - meal size and frequency. There is no description of how starvation state was standardized or measured in these experiments.

The brain is likely to move considerably during swallow, so the GCaMP signal change may be a motion artifact. Sometimes this can be calculated by comparing GCaMP signal to that of a co-expressed fluorescent protein, but there is no mention that this is done here. Therefore, the GAaMP data cannot be interpreted.

Reviewer #1 (Public Review):

In this preprint, Zhang et al. describe a new tool for mapping the connectivity of mouse neurons. Essentially, the tool leverages the known peculiar infection capabilities of Rabies virus: once injected into a specific site in the brain, this virus has the capability to "walk upstream" the neural circuits, both within cells and across cells: on one hand, the virus can enter from a nerve terminal and infect retrogradely the cell body of the same cell (retrograde transport). On the other hand, the virus can also spread to the presynaptic partners of the initial target cells, via retrograde viral transmission.

Similarly to previously published approaches with other viruses, the authors engineer a complex library of viral variants, each carrying a unique sequence ('barcode'), so they can uniquely label and distinguish independent infection events and their specific presynaptic connections, and show that it is possible to read these barcodes in-situ, producing spatial connectivity maps. They also show that it is possible to read these barcodes together with endogenous mRNAs, and that this allows spatial mapping of cell types together with anatomical connectivity.

The main novelty of this work lies in the combined use of rabies virus for retrograde labeling together with barcoding and in-situ readout. Previous studies had used rabies virus for retrograde labeling, albeit with low multiplexing capabilities, so only a handful of circuits could be traced at the same time. Other studies had instead used barcoded viral libraries for connectivity mapping, but mostly focused on the use of different viruses for labeling individual projections (anterograde tracing) and never used a retrograde-infective virus.

The authors creatively merge these two bits of technology into a powerful genetic tool, and extensively and convincingly validate its performance against known anatomical knowledge. The authors also do a very good job at highlighting and discussing potential points of failure in the methods.

Unresolved questions, which more broadly affect also other viral-labeling methods, are for example how to deal with uneven tropism (ie. if the virus is unable or inefficient in infecting some specific parts of the brain), or how to prevent the cytotoxicity induced by the high levels of viral replication and expression, which will tend to produce "no source networks", neural circuits whose initial cell can't be identified because it's dead. This last point is particularly relevant for in-situ based approaches: while high expression levels are desirable for the particular barcode detection chemistry the authors chose to use (gap-filling), they are also potentially detrimental for cell survival, and risk producing extensive cell death (which indeed the authors single out as a detectable pitfall in their analysis). This is likely to be one of the major optimisation challenges for future implementations of these types of barcoding approaches.

Overall the paper is well balanced, the data are well presented and the conclusions are strongly supported by the data. Impact-wise, the method is definitely going to be useful for the neurobiology research community.

Reviewer #2 (Public Review):

Although the trans-synaptic tracing method mediated by the rabies virus (RV) has been widely utilized to infer input connectivity across the brain to a genetically defined population in mice, the analysis of labeled pre-synaptic neurons in terms of cell-type has been primarily reliant on classical low-throughput histochemical techniques. In this study, the authors made a significant advance toward high-throughput transcriptomic (TC) cell typing by both dissociated single-cell RNAseq and the spatial TC method known as BARseq to decode a vast array of molecularly-labeled ("barcoded") RV vector library. First, they demonstrated that a barcoded-RV vector can be employed as a simple retrograde tracer akin to AAVretro. Second, they provided a theoretical classification of neural networks at the single-cell resolution that can be attained through barcoded-RV and concluded that the identification of the vast majority (ideally 100%) of starter cells (the origin of RV-based trans-synaptic tracing) is essential for the inference of single-cell resolution neural connectivity. Taking this into consideration, the authors opted for the BARseq-based spatial TC that could, in principle, capture all the starter cells. Finally, they demonstrated the proof-of-concept in the somatosensory cortex, including infrared connectivity from 381 putative pre-synaptic partners to 31 uniquely barcoded-starter cells, as well as many insightful estimations of input convergence at the cell-type resolution in vivo. While the manuscript encompasses significant technical and theoretical advances, it may be challenging for the general readers of eLife to comprehend. The following comments are offered to enhance the manuscript's clarity and readability.

Major points:<br /> 1. I find it difficult to comprehend the rationale behind labeling inhibitory neurons in the VISp through long-distance retrograde labeling from the VISal or Thalamus (Fig. 2F, I and Fig. S3) since long-distance projectors in the cortex are nearly 100% excitatory neurons. It is also unclear why such a large number of inhibitory neurons was labeled at a long distance through RV vector injections into the RSP/SC or VISal (Fig. 3K). Furthermore, a significant number of inhibitory starter cells in the somatosensory cortex was generated based on their projection to the striatum (Fig. 5H), which is unexpected given our current understanding of the cortico-striatum projections.

2. It is unclear as to why the authors did not perform an analysis of the barcodes in Fig. 2. Given that the primary objective of this manuscript is to evaluate the effectiveness of multiplexing barcoded technology in RV vectors, I would strongly recommend that the authors provide a detailed description of the barcode data here, including any technical difficulties or limitations encountered, which will be of great value in the future design of RV-barcode technologies. In case the barcode data are not included in Fig. 2, I would suggest that the authors consider excluding Fig. 2 and Fig. S1-S3 in their entirety from the manuscript to enhance its readability for general readers.

3. Regarding the trans-synaptic tracing utilizing a barcoded RV vector in conjunction with BARseq decoding (Fig. 5), which is the core of this manuscript, I have a few specific questions/comments. First, the rationale behind defining cells with only two rolonies counts of rabies glycoprotein (RG) as starter cells is unclear. Why did the authors not analyze the sample based on the colocalization of GFP (from the AAV) and mCherry (from the RV) proteins, which is a conventional method to define starter cells? If this approach is technically difficult, the authors could provide an independent histochemical assessment of the detection stringency of GFP positive cells based on two or more colonies of RG. Second, it is difficult to interpret the proportion of the 2,914 barcoded cells that were linked to barcoded starter cells (single-source, double-labeled, or connected-source) and those that remained orphan (no-source or lost-source). A simple table or bar graph representation would be helpful. The abundance of the no-source network (resulting from Cre-independent initial infection of the RV vector) can be estimated in independent negative control experiments that omit either Cre injection or AAV-RG injection. The latter, if combined with BARseq decoding, can provide an experimental prediction of the frequency of double-labeled events since connected-source networks are not labeled in the absence of RG. Third, I would appreciate more quantitative data on the putative single-source network (Fig. 5I and S6) in terms of the distribution of pre- and post-synaptic TC cell types. The majority of labeling appeared to occur locally, with only two thalamic neurons observed in sample 25311842 (Fig. S6). How many instances of long-distance labeling (for example, > 500 microns away from the injection site) were observed in total? Is this low efficiency of long-distance labeling expected based on the utilized combinations of AAVs and RV vectors? A simple independent RV tracing solely detecting mCherry would be useful for evaluating the labeling efficiency of the method. I have experienced similar "less jump" RV tracing when RV particles were prepared in a single step, as this study did, rather than multiple rounds of amplification in traditional protocols, such as Osakada F et al Nat Protocol 2013.

Reviewer #3 (Public Review):

The manuscript by Zhang and colleagues attempts to combine genetically barcoded rabies viruses with spatial transcriptomics in order to genetically identify connected pairs. The major shortcoming with the application of a barcoded rabies virus, as reported by 2 groups prior, is that with the high dropout rate inherent in single cell procedures, it is difficult to definitively identify connected pairs. By combining the two methods, they are able to establish a platform for doing that, and provide insight into connectivity, as well as pros and cons of their method, which is well thought out and balanced.

Overall the manuscript is well-done, but I have a few minor considerations about tone and accuracy of statements, as well as some limitations in how experiments were done. First, the idea of using rabies to obtain broader tropism than AAVs isn't really accurate - each virus has its own set of tropisms, and it isn't clear that rabies is broader (or can be made to be broader). Second, rabies does not label all neurons that project to a target site - it labels some fraction of them. Third, the high rate of rabies virus mutation should be considered - if it is, or is not a problem in detecting barcodes with high fidelity, this should be noted. Fourth, there are a number of implicit assumptions in this manuscript, not all of which are equally backed up by data. For example, it is not clear that all rabies virus transmission is synaptic-specific; in fact, quite a few studies argue that it is not (e.g., detection of rabies transcripts in glial cells). Thus, arguments about lost-source networks and the idea that if a cell is lost from the network, that will stop synaptic transmission, is not clear. There is also the very real propensity that, the sicker a starter cell gets, the more non-specific spread of virus (e.g., via necrosis) occurs. Fifth, in the experiments performed in Figure 5, the authors used a FLEx-TVA expressed via a retrograde Cre, and followed this by injection of their rabies virus library. The issue here is that there will be many (potentially thousands) of local infection events near the injection site that TVA-mediated but are Cre-dependent (=off-target expression of TVA in the absence of Cre). This is a major confound in interpreting the labeling of these cells. They may express very low levels of TVA, but still have infection be mediated by TVA. The authors did not clearly explore how expression of TVA related to rabies virus infection of cells near the rabies injection site. A modified version of TVA, such as 66T, should have been used to mitigate this issue. Otherwise, it is impossible to determine connectivity locally. The authors do not go to great lengths to interpret the findings of these observations, so I am not sure this is a critical issue, but it should be pointed out by the authors as a caveat to their dataset. Sixth, the authors are making estimates of rabies spread by comparison to a set of experiments that was performed quite differently. In the two studies cited (Liu et al., done the standard way, and Wertz et al., tracing from a single cell), the authors were likely infecting with a rabies virus using a high multiplicity of infection, which likely yields higher rates of viral expression in these starter cells and higher levels of input labeling. However, in these experiments, the authors need to infect with a low MOI, and explicitly exclude cells with >1 barcode. Having only a single virion trigger infection of starter cells will likely reduce the #s of inputs relative to starter neurons. Thus, the stringent criteria for excluding small networks may not be entirely warranted. If the authors wish to only explore larger networks, this caveat should be explicitly noted.

Overall, if the caveats above are noted and more nuance is added to some of the interpretation and discussion of results, this would greatly help the manuscript, as readers will be looking to the authors as the authority on how to use this technology.

Reviewer #1 (Public Review):

The manuscript describes an interesting experiment in which an animal had to judge a duration of an interval and press one of two levers depending on the duration. The Authors recorded activity of neurons in key areas of the basal ganglia (SNr and striatum), and noticed that they can be divided into 4 types.

The data presented in the manuscript is very rich and interesting, however, I am not convinced by the interpretation of these data proposed in the paper. The Authors focus on neurons of types 1 & 2 and propose that their difference encodes the choice the animal makes. However, I would like to offer an alternative interpretation of the data. Looking at the description of task and animal movements seen in Figure 1, it seems to me that there are 4 main "actions" the animals may do in the task: press right lever, press left lever, move left, and move right. It seems to me that the 4 neurons authors observed may correspond to these actions, i.e. Figure 1 shows that Type 1 neurons decrease when right level becomes more likely to be correct, so their decrease may correspond to preparation of pressing right lever - they may be releasing this action from inhibition (analogously Type 2 neurons may be related to pressing left lever). Furthermore, comparing animal movements and timing of activity of neurons of type 3 and 4, it seems to me that type 3 neurons decrease when the animal moves left, while type 4 when the animal moves right.

I suggest Authors analyse if this interpretation is valid, and if so, revise the interpretation in the paper and the model accordingly.

Reviewer #2 (Public Review):

In this valuable manuscript Li & Jin record from the substantial nigra and dorsal striatum to identify subpopulations of neurons with activity that reflects different dynamics during action selection, and then use optogenetics in transgenic mice to selectively inhibit or excite D1- and D2- expressing spiny projection neurons in the striatum, demonstrating a causal role for each in action selection in an opposing manner. They argue that their findings cannot be explained by current models and propose a new 'triple control' model instead, with one direct and two indirect pathways. These findings will be of broad interest to neuroscientists, but lacks some direct evidence for the proposal of the new model.

Overall there are many strengths to this manuscript including the fact that the empirical data in this manuscript is thorough and the experiments are well-designed. The model is well thought through, but I do have some remaining questions and issues with it.

Weaknesses:<br /> 1. The nature of 'action selection' as described in this manuscript is a bit ambiguous and implies a level of cognition or choice which I'm not sure is there. It's not integral to the understanding of the paper really, but I would have liked to know whether the actions are under goal-directed/habitual or even Pavlovian control. This is not really possible to differentiate with this task as there are a number of Pavlovian cues (e.g. lever retraction interval, house light offset) that could be used to guide behavior.<br /> 2. In a similar manner, the part of the striatum that is being targeted (e.g. Figures 4E,I, and N) is dorsal, but is central with regards to the mediolateral extent. We know that the function of different striatal compartments is highly heterogeneous with regards to action selection (e.g. PMID: 16045504, 16153716, 11312310) so it would have been nice to have some data showing how specific these findings are to this particular part of dorsal striatum.<br /> 3. I'm not sure how I feel about the diagrams in Figure 4S. In particular, the co-activation model is shown with D2-SPNs represented as a + sign (which is described as "having a facilitatory effect to selection" in the caption), but the co-activation model still suggests that D2-SPNs are largely inhibitory - just of competing actions rather than directly inhibiting actions. Moreover, I am not sure about these diagrams because they appear to show that D2-SPNs far outnumbers D1-SPNs and we know that this isn't the case. I realize the diagrams are not proportionate, but it still looks a bit misrepresented to me.<br /> 4. There are a number of grammatical and syntax errors that made the manuscript difficult to understand in places.<br /> 5. I wondered if the authors had read PMID: 32001651 and 33215609 which propose a quite different interpretation of direct/indirect pathway neurons in striatum in action selection. I wonder if the authors considered how their findings might fit within this framework.<br /> 6. There is no direct evidence of two indirect pathways, although perhaps this is beyond the scope of the current manuscript and is a prediction for future studies to test.

Joint Public Review:

In this work, Jain and colleagues have created two libraries of the AAV2 rep gene - either expressed separately from a strong heterologous promoter or embedded in the viral wild-type context - containing all possible single codon mutations. The libraries were cleverly made through a cloning process that ensured each mutant was attached to an exactly known 20-nt barcode included in each mutagenic oligo. This allowed the authors to confidently observe nearly all rep variants in their experiments, resulting in a comprehensive map between Rep protein variants and AAV production. Interrogation of these libraries identified several variants that improved AAV production, including mutations not observed in natural AAV isolates thus far, as independently verified through a conventional AAV vector production protocol. These benefits were also conserved across multiple natural AAV capsid variants including the heterologous AAV5 serotype.

While many other groups have previously created and interrogated individual point mutants of the AAV rep gene/protein or domain swapping mutants, this study is distinguished and excels by its degree of comprehensiveness and the complexity of the two complementary libraries. This reflects the next step in the field's efforts to better understand the natural biology of AAV and, as a result, to improve the production of recombinant AAV gene transfer vectors. Considering the rapidly increasing momentum of these vectors in the clinics and as approved drugs on the gene therapy market, and considering that the individual validation experiments reported in this work support the conclusions, this work including the reported resources and technologies is likely to have a critical impact on current and future research on AAV biology and vector development.

However, there are a few areas in which the study could be expanded for even greater impact. For instance, the authors may consider testing the selected rep variants in the context of a self-complementary AAV genome, which has different biology compared to the single-stranded genomes used in this study, and which is widely used granted its compatibility with the transgene of choice (which should be <2.5 kb). Likewise, it would be important to study the functionality of the selected rep variants with at least one AAV genome of regular size, considering that the two tested here seem rather unusual in length (2.9 kb, which is very small, or 5.0 kb, which is borderline large). Last but not least, despite the fact that the AAV2 ITRs are by far most commonly used in the field, it will also be interesting to test these rep variants in combination with ITRs derived from other AAV serotypes, considering that numerous groups have previously cloned and analyzed them, and that they can provide several benefits over the AAV2 ITRs.

Furthermore, in interpreting the results of this study, the reader should bear in mind that what has been measured and validated in this work is the production of intact genome-containing AAVs. Production is a precondition to functional AAVs that can transduce cells but is not equivalent to it. While the two are likely well correlated, further studies are needed to determine how well the effects of Rep protein variants on AAV production translate to their ability to then transduce cells. The more relevant property for gene therapy is the efficiency by which an AAV preparation transduces cells. For example, might production-enhancing Rep protein variants change the ratio of empty capsids to genome-containing capsids in a way that influences transduction efficiency of the corresponding AAV preparations? Does this influence reduce or enhance the production benefit? This particular scenario of empty capsid ratios influencing transduction represents a population effect that is not possible to capture in the multiplex assay, but it seems like a good idea to at least test transduction of some individual variants because transduction is the important function of AAV for gene therapy.

One additional aspect that may warrant further consideration is the assumption, as mentioned in Figure 2's legend, that synonymous mutations are neutral and can serve as controls for normalizing the production rate. However, Figures S5-6 and Figures S11-12 suggest that synonymous mutations are not necessarily neutral, as their distribution is similar to that of nonsynonymous mutations. Thus, a deeper examination of the impacts of synonymous mutations on the genotype-phenotype landscape could provide more nuanced insights into AAV2 rep gene function.

Reviewer #1 (Public Review):

In this study, the authors identify an insect salivary protein participating viral initiate infection in plant host. They found a salivary LssaCA promoting RSV infection by interacting with OsTLP that could degrade callose in plants. Furthermore, RSV NP bond to LssaCA in salivary glands to form a complex, which then bond to OsTLP to promote degradation of callose.

The story focus on tripartite virus-insect vector-plant interaction, and is interesting. However, the study is too simple and poor-conducted. The conclusion is also overstated due to unsolid findings.

Major comments:<br /> 1. The key problem is that how long the LssCA functioned for in rice plant. Author declared that LssCA had no effect on viral initial infection, but on infection after viral inoculation. It is unreasonable to conclude that LssCA promoted viral infection based on the data that insect inoculated plant just for 2 days, but viral titer could be increased at 14 day post-feeding. How could saliva proteins, which reached phloem 12-14 days before, induce enough TLP to degrade callose to promote virus infection? It was unbelievable.

2. Lines 110-116 and Fig. 1, the results of viruliferous insect feeding and microinjection with purified virus could not conclude the saliva factor necessary of RSV infection, because these two tests are not in parallel and comparable. Microinjection with salivary proteins combined with purified virus is comparable with microinjection with purified virus.

The second problem is how many days post viruliferous insect feeding and microinjection with purified virus did author detect viral titers? in Method section, authors declared that viral titers was detected at 7-14 days post microinjection. Please demonstrate the days exactly.

The last problem is that how author made sure that the viral titers in salivary glands of insects between two experiments was equal, causing different phenotype of rice plant. If not, different viral titers in salivary glands of insects between two experiments of course caused different phenotype of rice plant.

3. The callose deposition in phloem can be induced by insect feeding. In Fig. 5H, why was the callose deposition increased in the whole vascular bundle, but not phloem? Could the transgenic rice plant directional express protein in the phloem? In Fig.5, why was callose deposition detected at 24 h after insect feeding? In Fig. 6A, why was callose deposition decreased in the phloem, but not all the cells of the of TLP OE plant? Also in Fig.6A and B, expression of callose synthase genes was required.

Reviewer #2 (Public Review):

There is increasing evidence that viruses manipulate vectors and hosts to facilitate transmission. For arthropods, saliva plays an essential role for successful feeding on a host and consequently for arthropod-borne viruses that are transmitted during arthropod feeding on new hosts. This is so because saliva constitutes the interaction interface between arthropod and host and contains many enzymes and effectors that allow feeding on a compatible host by neutralizing host defenses. Therefore, it is not surprising that viruses change saliva composition or use saliva proteins to provoke altered vector-host interactions that are favorable for virus transmission. However, detailed mechanistic analyses are scarce. Here, Zhao and coworkers study transmission of rice stripe virus (RSV) by the planthopper Laodelphax striatellus. RSV infects plants as well as the vector, accumulates in salivary glands and is injected together with saliva into a new host during vector feeding.

The authors present evidence that a saliva-contained enzyme - carbonic anhydrase (CA) - might facilitate virus infection of rice by interfering with callose deposition, a plant defense response. In vitro pull-down experiments, yeast two hybrid assay and binding affinity assays show convincingly interaction between CA and a plant thaumatin-like protein (TLP) that degrades callose. Similar experiments show that CA and TLP interact with the RSV nuclear capsid protein NT to form a complex. Formation of the CA-TLP complex increases TLP activity by roughly 30% and integration of NT increases TLP activity further. This correlates with lower callose content in RSV-infected plants and higher virus titer. Further, silencing CA in vectors decreases virus titers in infected plants. Interestingly, aphid CA was found to play a role in plant infection with two non-persistent non-circulative viruses, turnip mosaic virus and cucumber mosaic virus (Guo et al. 2023 doi.org/10.1073/pnas.2222040120), but the proposed mode of action is entirely different.

While this is an interesting work, there are, in my opinion, some weak points. The microinjection experiments result in much lower virus accumulation in rice than infection by vector inoculation, so their interpretation is difficult. Also, the effect of injected recombinant CA protein might fade over time because of degradation or dilution. The authors claim that enzymatic activity of CA is not required for its proviral activity. However, this is difficult to assess because all CA mutants used for the corresponding experiments possess residual activity. It remains also unclear whether viral infection deregulates CA expression in planthoppers and TLP expression in plants. However, increased CA and TLP levels could alone contribute to reduced callose deposition.

Reviewer #1 (Public Review):

Bacteria can adapt to extremely diverse environments via extensive gene reprogramming at transcriptional and post-transcriptional levels. Small RNAs are key regulators of gene expression that participate in this adaptive response in bacteria, and often act as post-transcriptional regulators via pairing to multiple mRNA-targets.

In this study, Melamed et al. identify four E. coli small RNAs whose expression is dependent on sigma 28 (FliA), involved in the regulation of flagellar gene expression. Even though they are all under the control of FliA, expression of these 4 sRNAs peaks under slightly different growth conditions and each has different effects on flagella synthesis/number and motility. Combining RILseq data, structural probing, northern-blots and reporter assays, the authors show that 3 of these sRNAs control fliC expression (negatively for FliX, positively for MotR and UhpU) and two of them regulate r-protein genes from the S10 operon (again positively for MotR, and negatively for FliX). UhpU also directly represses synthesis of the LrhA transcriptional regulator, that in turn regulates flhDC (at the top of flagella regulation cascade). Based on RILseq data, the fourth sRNA (FlgO) has very few targets and may act via a mechanism other than base-pairing.

As r-protein S10 is also implicated in anti-termination via the NusB-S10 complex, the authors further hypothesize that the up-regulation of S10 gene expression by MotR promotes expression of the long flagellar operons through anti-termination. Consistent with this possible connection between ribosome and flagella synthesis, they show that MotR overexpression leads to an increase in flagella number and in the mRNA levels of two long flagellar operons, and that both effects are dependent on the NusB protein. Lastly, they provide data supporting a more general activating and repressing role for MotR and FliX, respectively, in flagellar genes expression and motility.

This study brings a lot of new information on the regulation of flagellar genes, from the identification of novel sigma 28-dependent sRNAs to their effects on flagella production and motility. It represents a considerable amount of work; the experimental data are clear and solid and support the conclusions of the paper. Even though mechanistic details underlying the observed regulations by MotR or FliX sRNAs are lacking, the effect of these sRNAs on fliC, several rps/rpl genes, and flagellar genes and motility is convincing.<br /> The connection between r-protein genes regulation and flagellar operons is exciting and raises a few questions. First, from the RILseq data, chimeric reads with mRNA for r-proteins (including rpsJ) are not restricted to the sigma 28-dependent sRNAs (e.g. rpsJ-sucD3'UTR, rpsF-DicF, rplN-DicF, rplK-ChiX, rplU-CyaR, rpsT-CyaR, rpsK-CyaR, rpsF-MicA...), suggesting that regulation of r-protein synthesis by sRNAs is not necessarily related to flagella/motility. Second, it would be interesting to know if the flagellar operons are more sensitive than other long operons to antitermination following MotR overexpression? In other words, does pMotR similarly affect antitermination in rrn or other long operons?

The general effect of pMotR or pFliX on the expression of multiple middle and late flagellar genes is also interesting even though the mechanism is not clear. While it may be difficult to fully address it, testing whether some of these regulatory events depend on the control of fliC and/or the S10 operon could be relevant (by analyzing the effects in strains deleted for fliC or nusB for instance).

Reviewer #2 (Public Review):

This manuscript discusses the posttranscriptional regulation of flagella synthesis in Escherichia coli. The bacterial flagellum is a complex structure that consists of three major domains, and its synthesis is an energy-intensive process that requires extensive use of ribosomes. The flagellar regulon encompasses more than 50 genes, and the genes are activated in a sequential manner to ensure that flagellar components are made in the order in which they are needed. Transcription of the genes is regulated by various factors in response to environmental signals. However, little is known about the posttranscriptional regulation of flagella synthesis. The manuscript describes four UTR-derived sRNAs (UhpU, MotR, FliX, and FlgO) that are controlled by the flagella sigma factor σ28 (fliA) in Escherichia coli. The sRNAs have varied effects on flagellin protein levels, flagella number, and cell motility, and they regulate different aspects of flagella synthesis.<br /> UhpU corresponds to the 3´ UTR of uhpT.

UhpU is transcribed from its own promoter inside the coding sequence of uhpT.

MotR originates from the 5´ UTR of motA. The promoter for motR is within the flhC CDS and is also the promoter of the downstream motAB-cheAW operon.

FliX originates from the 3´ UTR of fliC. Probably processed from parental mRNA.

FlgO originates from the 3´ UTR of flgL. Probably processed from parental mRNA.

This is a very interesting study that shows how sRNA-mediated regulation can create a complex network regulating flagella synthesis. The information is new and gives a fresh outlook at cellular mechanisms of flagellar synthesis. The presented work could benefit from additional experiments to confirm the effect of endogenous sRNAs expressed at natural level.

Reviewer #3 (Public Review):

Flagella are crucial for bacterial motility and virulence of pathogens. They represent large molecular machines that require strict hierarchical expression control of their components. So far, mainly transcriptional control mechanisms have been described to control flagella biogenesis. While several sRNAs have been reported that are environmentally controlled and regulate motility mainly via control of flagella master regulators, less is known about sRNAs that are co-regulated with flagella genes and control later steps of flagella biogenesis.

In this carefully designed and well-written study, the authors explore the role of four E. coli σ28-dependent 3' or 5' sUTR-derived sRNA in regulating flagella biogenesis. UhpU and MotR sRNAs are generated from their own σ28(FliA)-dependent promoter, while FliX and FlgO sRNAs are processed from the 3'UTRs of flagella genes under control of FliA. The authors provide an impressive amount of data and different experiments, including phenotypic analyses, genomics approaches as well as in-vitro and in-vivo target identification and validation methods, to demonstrate varied effects of three of these sRNAs (UhpU, FliX and MotR) on flagella biogenesis and motility. For example, they show different and for some sRNAs opposing phenotypes upon overexpression: While UhpU sRNA increases flagella number and motility, FliX has the opposite effect. MotR sRNA also increases the number of flagella, with minor effects on motility.

While the mechanisms and functions of the fourth sRNA, FlgO, remain elusive, the authors provide convincing experiments demonstrating that the three sRNAs directly act on different targets (identified through the analysis of previous RIL-seq datasets), with a variety of mechanisms. The authors demonstrate, UhpU sRNA, which derives from the 3´UTR of a metabolic gene, downregulates LrhA, a transcriptional repressor of the flhDC operon encoding the early genes that activate the flagellar cascade. According to their RIL-seq data analyses, UhpU has hundreds of additional potential targets, including multiple genes involved in carbon metabolism. Due to the focus on flagellar biogenesis, these are not further investigated in this study and the authors further characterize the two other flagella-associated sRNAs, FliX and MotR. Interestingly, they found that these sRNAs seem to target coding sequences rather than acting via canonical targeting of ribosome binding sites. The authors show FliX sRNA represses flagellin expression by interacting with the CDS of the fliC mRNA. Both FliX and MotR sRNA turn out to modulate the levels of ribosomal proteins of the S10 operon with opposite effects. MotR, which is expressed earlier, interacts with the leader and the CDS of rpsJ mRNA, leading to increased S10 protein levels and S10-NusB complex mediated anti-termination, promoting readthrough of long flagellar operons. FliX interacts with the CDSs of rplC, rpsQ, rpsS-rplV, repressing the production of the encoded ribosomal proteins. The authors also uncover MotR and FliX affect transcription selected representative flagellar genes, with an unknown mechanism.

Overall, this comprehensive study expands the repertoire of characterized UTR derived sRNAs and integrate new layers of post-transcriptional regulation into the highly complex flagellar regulatory cascade. Moreover, these new flagella regulators (MotR, FliX) act non-canonically, and impact protein expression of their target genes by base-pairing with the CDS of the transcripts. Their findings directly connect flagella biosynthesis and motility, highly energy consuming processes, to ribosome production (MotR and FliX) and possibly to carbon metabolism (UhpU).

Specific points to be considered:

- The authors use a crl- hyper-motile strain as WT strain for the study and sometimes also a crl+ strain is used. Can the authors comment on potential reasons why some phenotypes (e.g., UhpU and MotR effects on motility) are only detectable in the crl+ strain or vice versa? Is σS regulation important for the function of these sRNAs?

- In several experiments, a variant of MotR sRNA, MotR* that harbors a 3 nt mutation upstream of the seed sequence is used and seems to mediate stronger phenotypes (impact on flagellar number) upon overexpression compared to WT or phenotypes not retrieved for WT MotR (increased flagellin expression). It would be helpful to have some more clarification throughout the text, why this variant was used, even when OE of WT MotR already has impact on the target and how these three mutated nucleotides impact target regulation. For example, does MotR* show increased RNA stability or Hfq binding compared to MotR? Does the mutation in MotR* impact MotR structure (e.g., based on secondary structure predictions) or increase the complementarity with selected targets at potential secondary binding sites (e.g., based on target predictions)? For example, Fig. S7 shows additional regions of interaction between MotR and fliC mRNA beside the seed sequence. It is also suggested that MotR might have multiple interaction sites on rpsJ mRNA. Additional structure probing or biocomputational predictions could clarify these points.

- It is suggested that UphU impacts on motility via regulation of LrhA, which represses transcription of flhDC, and therefore the flagellar cascade. While LhrA-mediated regulation by UphU is validated based on reporter genes, the effect of UhpU OE on FlhDC levels is not directly examined (Fig. 3). Furthermore, as deletion of LrhA de-represses the flagellar cascade and UhpU was also shown to increase motility, the conclusions could be further strengthened by examining flhDC levels and/or the effect of ∆UhpU (if the sRNA part can be deleted) on motility (reduction) due to relieved down-regulation of LrhA.

-This study provides many opportunities for future follow-work. Now that the four sRNAs and some of their targets and opposing effects on flagella biogenesis have been identified, it will be interesting to see how the sRNAs themselves are temporally regulated throughout the flagella biogenesis cascade and which other targets are regulated by them. Future studies could also provide insights into the mechanism and function of FlgO sRNA, which seems to act via a different mechanism than base-pairing to target RNAs, as well as the global effects of regulation of ribosomal genes via FliX and MotR.

Reviewer #1 (Public Review):

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

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

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

Reviewer #2 (Public Review):

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

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

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

Reviewer #3 (Public Review):

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

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

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

Reviewer #2 (Public Review):

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

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

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

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

Consensus Public Review:

Ottenheimer et al., present an interesting study looking at the neural representation of value in mice performing a pavlovian association task. The task is repeated in the same animals using two odor sets, allowing a distinction between odor identity coding and value coding. The authors use state-of-the-art electrophysiological techniques to record thousands of neurons from 11 frontal cortical regions to conclude that 1) licking is represented more strongly in dorsal frontal regions, 2) odor cues are represented more strongly in ventral frontal regions, 3) cue values are evenly distributed across regions. They separately perform a calcium imaging study to track coding across days and conclude that the representation of task features increments with learning and remains stable thereafter.Overall, these conclusions are interesting and well supported by the data.

The authors use reduced-rank kernel regression to characterize the 5332 recorded neurons on a cell-by-cell basis in terms of their responses to cues, licks, and reward, with a cell characterized as encoding one of these parameters if it accounts for at least 2% of the observed variance (while at first this seemed overly lenient, the authors present analyses demonstrating low false-positives at this threshold and that the results are robust to different cutoffs).

Having identified lick, reward, and cue cells, the authors next select the 24% of "cue-only" neurons and look for cells that specifically encode cue value. Because the animal's perception of stimulus value can't be measured directly, the authors created a linear model that predicts the amount of anticipatory licking in the interval between odor cue and reward presentations. The session-average-predicted lick rate by this model is used as an estimate of cue value and is used in the regression analysis that identified value cells. (Hence, the authors' definition of value is dependent on the average amount of anticipatory behavior ahead of a reward, which indicates that compared to the CS+, mice licked around 70% as much to the CS50 and 10% as much to the CS-.) The claim that this is an encoding of value is strengthened by the fact that cells show similar scaling of responses to two odor sets tested. Whereas the authors found more "lick" cells in motor regions and more "cue" cells in sensory regions, they find a consistent percentage of "value" cells (that is, cells found to be cue-only in the initial round of analysis that is subsequently found to encode anticipatory lick rate) across all 11 recorded regions, leading to their claim of a distributed code of value.

In subsequent sections, the authors expand their model of anticipatory-licking-as-value by incorporating trial and stimulus history terms into the model, allowing them to predict the anticipatory lick rate on individual trials within a session. They also use 2-photon imaging in PFC to demonstrate that neural coding of cue and lick are stable across three days of imaging, supported by two lines of evidence. First, they show that the correlation between cell responses on all periods except for the start of day 1 is more correlated with day 3 responses than expected by chance (although the correlation is low, the authors attribute this to inherent limitations of the data), and that response for a given neuron is substantially better correlated with its own activity across time than random neurons. Second, they show that cue identity is able to capture the highest unique fraction of variance (around 8%) in day 3 cue cells across three days of imaging, and similarly for lick behavior in lick cells and cue+lick in cue+lick cells. Nonetheless, their sample rasters for all imaged cells also indicate that representations are not perfectly stable, and it will be interesting to see what *does* change across the three days of imaging.

Reviewer #1 (Public Review):

This work describes a novel high-throughput approach to diverse transgenesis which the authors have named TARDIS for Transgenic Arrays Resulting in Diversity of Integrated Sequences. The authors describe the general approach: the generation of a synthetic 'landing pad' for transgene insertion (as previously reported by this group) that has a split selection hygromycin resistance gene, meaning that only perfect integration with the insert confers resistance to the otherwise lethal hygromycin drug. The authors then demonstrate two possible applications of the technology: individually barcoded lineages for lineage tracing and transcriptional reporter lines generated by inserting multiple promoters. In both cases, the authors did a limited 'proof of concept' study including many important controls, showcasing the potential of the method. The conclusions for applications of this method in C. elegans are supported by the data and the authors discuss important observations and considerations. In the discussion, the discuss the potential application of the method beyond C. elegans, although this remains speculative at this point given that a nontrivial aspect of the success of the method in worms is the self-assembly of DNA into heritable extrachromosomal arrays (a feature that is absent in most other systems).

Reviewer #2 (Public Review):

This paper explores the possibility of integrating diverse and multiple DNA fragments in the genome taking advantage of plasmids in arrays, and CRISPR. Since the efficiency of integration in the genome is low, they, as others in the field, use selection markers to identify successful events of integration. The use of these selection markers is common and diverse, but they use a couple of distinct strategies of selection to:

- Introduce bar codes in the genome of individuals at one specific genomic site (gene for Hygromycin resistance with bar code in an intron with homology arms to complete a functional gene);

- Introduce promoters at two specific genomic landing pads downstream of fluorescent reporters.

The strengths of the study are the clever design of the selection markers, which enrich the collection of this type of markers. While the work is not methodologically novel - it adds to other recent studies, e.g. from Nonet, Mouridi et al., and Malaiwong et al, that use the integration of single and multiple/diverse DNA sequences in the C. elegans genome - it provides a protocol for doing so and tool to make it practical. A limited number of experiments using the method are presented here, and the real test of this method will be its use to address biological questions.

Reviewer #1 (Public Review):

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

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

Reviewer #2 (Public Review):

This study reports a novel role of thalamic activity in the late components of a cortical event related potential (ERP). To show this association, the authors used high-density EEG together with multiple deep electrophysiological recordings combined with electrical stimulation of superficial and deep cortical layers. Stimulation of deep layers elicits a late ERP component that is closely related to bursts of thalamic activity during quiet wakefulness. This relationship is quite noticeable when deep layers of the cortex are stimulated, and it does depend on arousal state, being maximal during quiet wakefulness, diminished during active wakefulness, and absent during anesthesia.

The study is very well performed, with a high number of subjects and appropriate methodology. Performing simultaneous recording of EEG and several neuropixels probes together with cortical microstimulation is no small feat considering the size of the mouse head and the fact that mice are freely behaving in many of the experiments. It is also noticeable how the authors use a seemingly outdated technique (electrical microstimulation) to produce compelling and significant research. The conclusions regarding the thalamic contributions to the ERP components are strongly supported by the data.

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

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

Reviewer #1 (Public Review):

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

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

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

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

3. Does the platform allow the observation of non-synchronized beating when testing with compounds? This can be extremely important as the intended applications of this platform are drug testing and cardiac disease modeling. The author should elaborate on the method in the manuscript and explain the obtained results in detail.

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

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

Reviewer #2 (Public Review):

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

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

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

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

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

Reviewer #1 (Public Review):

Kou and Kang et al. investigated the role of Notch-RBP-J signaling in regulating monocyte homeostasis. Specifically, they examined how a conditional knockout of Rbpj expression in monocytes through a Rbpjfl/fl Lyz2cre/cre mouse affects the homeostasis of Ly6Chi versus Ly6Clo monocytes. They discovered that Rbpj deficiency did not affect the percentage of Ly6Chi monocytes but instead, led to an accumulation of Ly6Clo monocytes in the peripheral blood. Using a comprehensive number of in vivo techniques to investigate the origin of this increase, the authors revealed that the accumulation of Rbpj deficient Ly6Clo monocytes was not due to an increase in bone marrow egress and that this defect was cell intrinsic. However, EdU-labelling and apoptosis assays revealed that this defect was not due to an increase in proliferation nor conversion of Ly6Chi to Ly6Clo monocytes. Interestingly, it was revealed that Rbpj deficient Ly6Clo monocytes had increased expression of CCR2 and ablation of CCR2 expression reversed the accumulation of these cells in the periphery. Lastly, they discovered that Rbpj deficiency also led to downstream effects such as an accumulation of Ly6Clo monocytes in the lung tissue and increased CD16.2+ interstitial macrophages, presumably due to dysregulated monocyte differentiation and function.

Their findings are interesting and highlight a previously unexplored mechanistic link between Notch-RBP-J signaling and CCR2 expression in monocyte homeostasis, providing further insight into the distinct pathways that regulate Ly6Chi vs Ly6Clo monocyte subsets individually.

The conclusions of this paper are mostly well substantiated from the experimental data. The strengths of this paper include the use of multiple conditional genetic knock out mouse models to explore their hypothesis and the use of sophisticated in vivo techniques to study the major mechanisms involved in monocyte homeostasis.

Reviewer #2 (Public Review):

The authors provide compelling data to demonstrate that the Notch-related transcription factor RBP-J can influence the number of circulating and recruited monocytes. The authors first delete the Rbpj gene in the myeloid lineage (Lyz2) and show that, as a proportion, only Ly6Clo monocytes are increased in the blood. The authors then attempted to identify why these cells were increased but ruled out proliferation or reduced apoptosis. Next, they investigated the gene signature of Rbpj null monocytes using RNA-sequencing and identified elevated Ccr2 as a defining feature. Crossing the Rbpj null mice to Ccr2 null mice showed reduced numbers of Ly6Clo monocytes compared with Rbpj null alone. Finally, the authors identify that an increased burden of blood Ly6Clo monocytes is correlated with increased lung recruitment and expansion of lung interstitial macrophages.

The main conclusion of the authors, that there is a 'cell intrinsic requirement of RBP-J for controlling blood Ly6CloCCR2hi monocytes' is strongly supported by the data. However, other claims and aspects of the study require clarification and further analysis of the data generated.

Strengths<br /> The paper is well written and structured logically. The major strength of this study is the multiple technically challenging methods used to reinforce the main finding (e.g. parabiosis, adoptive transfer). The finding reinforces the fact that we still know little about how immune cell subsets are maintained in situ, and this study opens the way for novel future work. Importantly, the authors have generated an RNA-sequencing dataset that will prove invaluable for identifying the mechanism - they have promised public access to this data via GEO.

Weaknesses - The main weakness of the study, is that although the main result is solidly supported, as written it is mostly descriptive in nature. For instance, there is no given mechanism by which RBP-J increases Ly6Clo monocytes. The authors conclude this is dependent on CCR2, however CCR2 deletion has a global effect on monocyte numbers and importantly in this study, it does not remove the Ly6Clo bias of cell proportions, if anything it seems to enhance the difference between the ly6C low and high populations in Rbpj null mice (figure 5C). This oversight in data interpretation likely occurred because this experiment is missing a potentially important control (Lyz2cre/cre Ccr2RFP/RFP or RBP-J variations). In general, there seemed to be a focus on the Ly6C low cells, where the mechanism may be more identifiable in their precursors - likely the Ly6C high monocytes.

Other specific weaknesses were identified:<br /> 1) The confirmation of knockout in supplemental figure 1A shows only a two third knockdown when this should be almost totally gone. Perhaps poor primer design, cell sorting error or low Cre penetrance is to blame, but this is below the standard one would expect from a knockout.<br /> 2) Many figures (e.g. 1A) only show proportional data (%) when the addition of cell numbers would also be informative<br /> 3) Many figures only have an n of 1 or 2 (e.g. 2B, 2C)<br /> 4) Sometimes strong statements were based on the lack of statistical significance, when more n number could have changed the interpretation (e.g. 2G, 3E)<br /> 5) There is incomplete analysis (e.g. Network analysis) and interpretation of RNA-sequencing results (figure 4), the difference between the genotypes in both monocyte subsets would provide a more complete picture and potentially reveal mechanisms<br /> 6) The experiments in Figures 5 and 7 are missing a control (Lyz2cre/cre Ccr2RFP/RFP or the Rbpj+/+ versions) and may have been misinterpreted. For example if the control (RBP-J WT, CCR2 KO) was used then it would almost certainly show falling Ly6C low numbers compared to RBP-J WT CCR2 WT, but RBP-J KO CCR2 KO would still have more Ly6c low monocytes than RBP-J WT, CCR2 KO - meaning that the RBP-J function is independent of CCR2. I.e. Ly6c low numbers are mostly dependent on CCR2 but this is irrespective of RBP-J.<br /> 7) Figure 6 was difficult to interpret because of the lack of shown gating strategy. This reviewer assumes that alveolar macrophages were gated out of analysis<br /> 8) The statements around Figure 7 are not completely supported by the evidence, i) a significant proportion of CD16.2+ cells were CCR2 independent and therefore potentially not all recently derived from monocytes, and ii) there is nothing to suggest that the source was not Ly6C high monocytes that differentiated - the manuscript in general seems to miss the point that the source of the Ly6C low cells is almost certainly the Ly6C high monocytes - which further emphasises the importance of both cells in the sequencing analysis<br /> 9) The authors did not refer to or cite a similar 2020 study that also investigated myeloid deletion of Rbpj (Qin et al. 2020 - https://doi.org/10.1096/fj.201903086RR). Qin et al identified that Ly6Clo alveolar macrophages were decreased in this model - it is intriguing to synthesise these two studies and hypothesise that the ly6c low monocytes steal the lung niche, but this was not discussed

Reviewer #3 (Public Review):

In this study, the authors investigate the role of the Notch signalling regulator RBP-J on Ly6Clow monocyte biology starting with the observation that RBP-J-deficient mice have increased circulating Ly6low monocytes. Using myeloid specific conditional mouse models, the authors investigate how RBP-J deficiency effects circulating monocytes and lung interstitial macrophages.

A major strength of this study is that it describes RBP-J as a novel, critical factor regulating Ly6Clow monocyte cell frequency in the blood. The authors demonstrate that RBP-J deficiency leads to increased Ly6Clow monocytes in the blood and lung and CD16.2+ interstitial macrophages in steady state. The authors use a number of different techniques to confirm this finding including bone marrow transplantation experiments and parabiosis.

There are several critical weaknesses that need to be assessed to improve the manuscript, in summary the data presented in the current manuscript are highly descriptive and without mechanistic insight. The inclusion of more mechanistic insight would greatly improve the manuscript.

The authors begin to explore the potential mechanism underlying why Ly6Clow monocytes are increased in the absence of RBP-J - is it through increased survival, increased conversion from Ly6C+ monocytes, increased proliferation or increased egress from the bone marrow. The majority of the data they present here is negative. Whilst I applaud the authors for including negative data, I think that their exploration into how RBP-J leads to increased monocytes does not go far enough and it is critical to understand the mechanism by which RBP-J increases circulating monocytes. Low n-numbers in multiple figures mean that the claims made are not fully supported.

The current title of the paper "RBP-J regulates homeostasis and function of circulating Ly6Clo monocytes" does not fully reflect the manuscript in its current form - there is no exploration of Ly6Clow monocyte functionality in the paper as it stands.<br /> Given that targeting monocytes and macrophages in a range of inflammatory diseases is an attractive yet elusive therapeutic option, understanding the underlying biology that regulates monocyte biology are critically important. This manuscript has the potential to add to our current knowledge of how Ly6Clow monocyte biology is regulated and potentially opens novel avenues for preferentially enhancing Ly6Clow monocytes without influencing Ly6C+ monocytes. This is an attractive proposition for many inflammatory conditions however, considerably more in-depth analysis is required to understand the role of RBP-J in monocyte biology.

Reviewer #1 (Public Review):

The manuscript entitled, "Loss of PTPMT1 limits mitochondrial utilization of carbohydrates and leads to muscle atrophy and heart failure," by Zheng, et al., is focused on assessing the role of deletion of PTPMT1, a mitochondria-based phosphatase, in mitochondrial fuel selection. Authors show that the utilization of pyruvate, a key mitochondrial substrate derived from glucose, is inhibited, whereas fatty acid utilization is enhanced. Importantly, while the deletion of PTPMT1 does not impact development of skeletal muscle or heart, the metabolic inflexibility leads to muscular atrophy, heart failure, and sudden death. Mechanistically, authors claim that the prolonged substrate shift from carbohydrates to lipids causes oxidative stress and mitochondrial dysfunction, leading to accumulation of lipids and muscle cell and CM damage in the KO. Interestingly, PTPMT1 deletion from the liver or adipose tissue does not generate any local or systemic defects. Authors conclude that PTPMT1 plays an important role in maintaining mitochondrial flexibility and that the balanced utilization of carbohydrates and lipids is essential for skeletal muscle and heart. While interesting and authors did a ton of experiments for this project, several major concerns exist. First, because both the CKMM- and the MYHC-Cre express early, during development , it seems the effects of the deletion of PTPMT1 are more likely be specific to defects in muscle and cardiac development rather than postnatal, especially since loss of PTPMT1 affects mTOR activity; indeed, previous reports have shown that selective deletion of mTOR or raptor in skeletal muscle during embryonic development leads to a reduction in postnatal growth and the development of late-onset myopathy and premature death around 6 to 8 months of age. The effects of the deletion in muscle seem eerily similar and therefore likely mechanistically function the same -embryonically, as has been previously suggested. This is also true for cardiac abnormalities, where developmental defects can manifest in mice as they age after at least 3-4 months and decreased mTOR activity can lead to significant cardiac dysfunction and failure (similarly to the effects observed here by the authors). To prove one way or another, authors should show developmental data providing evidence that the effects are not occurring at this stage. It is a lot of work, but the right way to differentiate pre- vs post- development functions of PTPMT1 in the muscle and heart, otherwise cannot verify mechanistically what the precise cause for the phenotype may be. Authors could consider generating mice that have inducible Cre drivers. In addition, how is it that the effects of loss of PTPMT1 are similar between muscle and heart given the differences in energy usage and utilization between these two tissues? Increases in AMPK are usually associated with better metabolic outcomes, particularly in the heart. Increased AMPK activation has also been shown to help reduce fat storage, increase insulin sensitivity, reduce cholesterol/triglyceride production, and suppress chronic inflammation. In addition, increases in carnitines are associated with enhanced metabolic function. Carnitines facilitate transport of long-chain fatty acids into the mitochondrial matrix, triggering cardioprotective effects through reduced oxidative stress, inflammation and necrosis of cardiac myocytes. All of these factors are positive, so how do authors explain this discrepancy in their findings which suggest opposing outcomes- as above, I suggest the explanation is that it is due to developmental effects of deletion of PTPMT1.

Authors attribute much of the pathology in the muscle and heart due to increased lipid accumulation in these tissues; but how do authors explain how hearts and muscle have more fat when the mice are smaller than wt? Is there a difference in energy expenditure in the mice? What about changes in white fat, core temperature or browning of fat? Authors do not mechanistically prove that lipid accumulation is the cause of death in these animals. Rescue experiments should be considered.

Reviewer #2 (Public Review):

Zheng et al. have investigated the effects of PTPMT1 Knock-out on cellular metabolic flexibility. Using several types of appropriate tissue-specific mouse models, the authors have generated data that are both reasonable and broadly significant. While the central mechanism driving the metabolic fuel preference and flexibility remains elusive as the author mentioned in the main text, the finding that the absence of PTPMT1 inhibits glucose (pyruvate) utilization and promotes FAO, resulting in cellular stress and damage, particularly in skeletal and cardiac muscle cells, is intriguing and has practical implications for further research. However, some quantitative data are lacking and certain explanations may be misleading, warranting revisions.

Public Review:

In this manuscript, Karl et al. explore mechanisms underlying the activation of the receptor tyrosine kinase FGFR1 and stimulation of intracellular signaling pathways in response to FGF4, FGF8, or FGF9 binding to the extracellular domain of FGFR1. Quantitative binding experiments presented in the manuscript demonstrate that FGF4, FGF8, and FGF9 exhibit distinct binding affinities towards FGFRs. It is also proposed that FGF8 exhibits "biased ligand" characteristics that is manifested via binding and activation FGFR1 mediated by "structural differences in the FGF- FGFR1 dimers, which impact the interactions of the FGFR1 trans membrane helices, leading to differential recruitment and activation of the downstream signaling adapter FRS2".

Major points:

1. Previous studies have demonstrated that the variability of signal transduction stimulated by different FGF family members originates from their preferential activation of different members of the FGFR family (Ornitz et al., 1996). For example, it was previously shown that members of the FGF8 subfamily preferentially activate FGFR3c, whereas members of the FGF4 subfamily activate FGFR1c more potently than other FGFs. Moreover, it was shown that FGF18, a member of the FGF8 subfamily, preferentially binds to and activates the FGFR3c isoform. Indeed, this can be seen in the data shown in Figure 3 in this manuscript, where maximum levels of FGFR1 pY653/4 and pFRS2 are reached at different concentrations when stimulated with increasing concentrations of each ligand in HEK293T cells. In order to be sure that the 'biased agonist' described in this manuscript for FGF8 binding is not caused by binding preference towards different FGFR members, the authors should present data comparing cell signaling via FGFR3c stimulated by FGF4, FGF8, and FGF9.

2. It is well-established that FGFR signaling by canonical FGF family members including FGF4, FGF8, and FGF9 is dependent on interactions of heparin or heparan sulfate proteoglycans (HSPG) to the ligand the receptors. Differential contributions of heparin to cell signaling mediated by FGF4, FGF8, and FGF9 binding and activation of different FGFRs expressed in RCS cells as this cell express endogenous HSPG molecules. This question should be addressed by comparing cell signaling via FGFRs ectopically expressed in BAF/3 cells (which do not possess endogenous FGFRs and HSPG) stimulated by FGF4, FGF8, and FGF9 in the absence or presence of different heparin concentrations. This approach has been applied many times in the past to explore and establish the role of heparin in control of ligand induced FGFR activation. It is impossible to interpret the FGFR binding characteristics and cellular activates of FGF4, FGF8, and FGF9 in the absence of information about the role of heparin in their binding and activation.

3. It is not clear how some of the experimental data were analyzed. Blots in Figures 3A and 3B should include controls (total FGFR1 for pY653/4 and total FRS for pFRS2). How are the data shown in Figure 3C normalized? It does look like the level of phosphorylation was all normalized against the strongest signals irrespective of which ligand was used. Each data representing each ligand should be separately normalized.

4. In page 6, authors used the plot shown in Figure 3 for 'FGFR downregulation' to conclude that "the effect of FGF4 on FGFR1 downregulation is smaller when compared to the effects of FGF8 and FGF9. However, it is unclear how the data shown in the plot was normalized - none of the data seem to reach "1.0". Moreover, the plot seems to suggest that FGF4 can strongly downregulate FGFR as it can downregulate FGFR with higher potency.

5. The structural basis of FGFR1 ligand bias and the different dimeric configurations and interactions between the kinase domain of FGFR1 dimers are not warranted (Figure 6). In the absence of any structural experimental data of different forms of FGFR dimers stimulated by FGF ligands the model presents in the manuscript is speculative and misleading.

Reviewer #1 (Public Review):

I feel that this study has potentially high public health significance and should be made known to the public, especially the usefulness of a natural chemical product, oligomeric proanthocyanidins, in preventing SARS-CoV2 infection. The studies are very well designed, using the first 5 figures to compare carefully the effects of tannic acid, punicalagin, and oligomeric proanthocyanidins in disrupting the interaction of the virus with host cells and in inhibiting the enzymatic activity of transmembrane serine protease 2 required for viral entry. I am especially impressed by the work done in Figures 6 and 7 in which the investigators put their efforts into quantitating the amounts of oligomeric proanthocyanidins, tannic acid, and punicalagin present in the grape seed, peel, flesh as well as juice. I also appreciate the translational application in which the investigators prepared grape seed extract capsules (200 mg and 400 mg), recruited healthy human subjects to take these capsules once or twice, and showed that the sera from randomized human subjects taking grape seed extract capsules indeed exert does-dependent and time-dependent activities in suppressing the infection rate of various SARS-CoV2 variants using in vitro studies. The study in Figure 7 is indeed very well-designed and quite elegant. The manuscript is also well-written.

Reviewer #1 (Public Review):

The authors of this manuscript are interested in identifying the molecular mechanisms underlying antidepressant action. Though most antidepressants target the serotonin system, regulation of glutamate neurotransmission has been associated with rapid treatment response. Here the authors find that monoaminergic targeted antidepressants are associated in some patients with expression of a small nucleolar RNA that they go on to show results in alterations to glutamate neurotransmission in a mouse model. These data offer a molecular mechanism that can link traditional monoaminergic targeted antidepressants with glutamatergic regulation and could offer a new way to estimate the efficacy of these drugs.

Reviewer #1 (Public Review):

The authors generated detailed anatomical descriptions and images of the coronary vasculature of mice, quails, zebrafish, Japanese tree frogs, Japanese fire belly newt, African clawed frogs, salmon sharks, Japanese sleeper rays and bird-beak dogfish. Using this data, they are able to show anatomical similarities in the origination points of evolutionary distant vertebrates from the third to fourth brachial arch. Additionally, the authors highlight the additional presence of a coronary vascular plexuses as a unique amniote trait, since it is seen in quail and mice but not xenopus frogs. Based on the presence of the possible homologies, the authors propose that the early developmental amniotic coronary artery is a derived from the ancestral hypobrachial artery. The methods for labeling and imaging the cardiac vessels are robust and congruent with previous studies describing these structures in mice and zebrafish. The study also presents an intriguing hypothesis; however, it could benefit from a more expansive survey of vertebrate coronary diversity using an increased number of species and developmental time points. A more exhaustive surveying of vertebrate diversity is required to demonstrate that the coronary vasculature anatomy observed is from common ancestral states or novel adaptations. The author's claim that a primitive vascular plexus represents a novel amniote phenotype, is reasonable, but could benefit from further confirmation using additional species.

Reviewer #2 (Public Review):

In this manuscript, Mizukami et al. investigate the differences in coronary vasculature morphology across several diverse species to investigate the transition of extrinsic coronary arteries existing on the outflow track in non-amniotes to arteries presenting on the ventricle surface itself in amniotes. They use various visualization techniques, including resin-filling, tissue staining, and fluorescence microscopy to compare the gross morphology and orifice locations of the aortic subepicardial vessels (ASVs) between several amniotes and non-amniotes. Intriguingly, the authors show that the embryonic amniotes rely on a similar ASV structure to adult non-amniotes, but this primitive structure is lost during development in favor of the formation of true coronary arteries on the ventricle surface. While these data intend to show that the difference in coronary artery structure exists between amniotes and non-amniotes, the authors only investigated mice and quail as amniote representatives. Without the inclusion of an ectothermic reptile species as an additional amniote representative, it is entirely possible that the difference in coronary artery structure may instead exist across the endotherm-ectotherm axis as opposed to amniotes and non-amniotes. Despite these concerns, Mizukami et al. show intriguing evolutionary differences between coronary artery structure that draw parallels to changes observed during amniote development.

Reviewer #3 (Public Review):

Mizukami et al. compare the structure of the coronary arteries in multiple species of amniotes, amphibians, and fish. By selecting species from each of these taxa, the authors were able to evaluate modifications to the coronary arteries during key evolutionary transitions. In mice and quail, they show two populations of vessels that are visible on the developing heart-true coronary arteries on the ventricle and a second population of vessels on the outflow tract known as the ASV., They found that in amphibians, outflow tract vessels were present but ventricular coronary arteries were completely absent. In zebrafish (a more ancestral species) an arterial branch off the rostral section of the hypobranchial artery was shown to have similar anatomical features to outflow tract vessels found in higher organisms. These zebrafish outflow tract arteries also appeared conserved in several chondriichthyes specimens. The authors conclude that rearrangement of the outflow tract vasculature or hypobranchial arteries in fish during evolution, could be homologous to the ASV population of coronary arteries in amphibians and amniotes. These data give new insight into the evolutionary origins of the coronary vasculature.

Major Points

1. The manuscript presents important data on the coronary vascular structure of several different species. However, these data alone do not conclusively demonstrate whether the developmental origins of ASV like vessels are homologous. Therefore, care should be taken when concluding that the outflow tract vessels found in all different species are conserved features. While this is a reasonable hypothesis and should be presented, the manuscript could be improved by also discussing alternate explanations. For example, ASVs in mice originate during embryonic development, while in fish and amphibians outflow tract vessels are formed only in mature animals.

2. Figure 3 A-D: The authors state that "the ASV ran through the outflow tract, then entered the aortic root before reaching the ventricle to form a secondary orifice". Do the authors have serial sections to conclude that the vessel branching off the carotid runs the length of the aorta and is continuous with an orifice at the aortic root? The endothelial projection off the aorta in panel C could reasonably be an independent projection. For example, Chen et al., described similar looking projections in the base of the aorta that were not attached to external vessels. A whole mount approach would be the most convincing to show the attachments of the ASV vessel.

3. Figure 3E: Similar as above, how is it concluded that the orifice is continuous with the ASV and that this projection is not the coronary artery stem?

4. The discussion section could be improved by making some statements more consistent, using more precise or appropriate terminology accepted in the field, and being more cognizant of how the authors' findings fit within the history of the field. For example, when referring to coronary arteries, please clarify whether this refers to ASV/ outflow tract coronary arteries, or true ventricular coronary arteries. In addition, the first sentence of the discussion makes it seem like the origins of coronary arteries were unknown prior to this study, however, their origins have been described in multiple papers previously. The authors could revise their statement to acknowledge these previous findings.

Reviewer #1 (Public Review):<br /> <br /> Lobanov et al. investigated the effects of spatial structure in microbial communities that interact via secreted metabolites. The work builds up on a previous theoretical model by the authors that considered well-mixed populations in which different bacterial species secrete and consume different sets of metabolites, and metabolites in turn modify the growth rates of species. The model considers communities that are periodically exposed to dilutions, and the authors focus on the regime in which bacterial densities do not reach saturation before the next dilution. Analyzing the stable outcome of these dynamics through comparison with well-mixed scenarios, the authors found that space can favor species richness, especially in the case of communities with prevalent facilitative interactions. This positive effect on species coexistence is also more pronounced in situations in which species produce more kinds of metabolites than they consume. On the other hand, the positive effects on coexistence can be reversed when bacterial dispersal becomes relevant over the timescale of the simulations, as well as in cases in which the diffusion of metabolites is too slow - which could even result in less coexistence than in well-mixed scenarios. These results add to an ongoing discussion on the different ways in which spatial effects can impact microbial community dynamics and species richness.

The conclusions of this paper are mostly well supported by the data, but some aspects of the methodology and analysis need to be clarified and extended.

1) This is a model with many parameters and the manuscript should be clearer about how these parameters were used in different scenarios. It is probably a matter of rewriting the text, but I found it hard to understand which parameter values remained the same in scenarios with or without space, as well as how the strength of interactions was assigned, among a few other examples. In other cases, additional analysis (e.g. on how the spatial impact on coexistence depends on the average strength of interactions) would make the work more comprehensive.<br /> 2) To assess stable coexistence and richness, the authors use a criterium in which species have to be almost equally abundant (above 90% of the abundance of the fastest-growing species). It is not clear if the results would change significantly if potentially less abundant species would be classified as coexisting ones.<br /> 3) The majority of the results consider scenarios in which bacteria cannot disperse very effectively so bacterial dynamics is mostly driven by the growth of the initial populations at each region. Expanding on the analysis of higher dispersal rates would be valuable in order to analyze additional realistic scenarios of how bacteria grow and disperse in space.

Reviewer #2 (Public Review):

In this work, the authors extend a mathematical model that they previously developed. Their original paper (Niehaus..Momeni, Nature Comm., 2019) models species interactions using mediators (i.e. metabolites) that species produce and that can affect other species' growth rates. Here, they extend the original model, which was well-mixed, to study communities in space. To do this, here they assume that species grow on a 1D grid, that species can possibly overlap in the same grid spot, and that species and mediators can diffuse in space. They find that spatial structure promotes the coexistence of species when interactions are more facilitating than inhibiting, and when species dispersal is low. Both of these features separately allow for species to self-organize in a way that allows them to be closer in space to partners that facilitate their growth. Properties of the metabolic interactions, such as the amount of metabolites produced and consumed, consumption and production rates, and metabolite diffusion also have effects on species coexistence.

Strengths: The authors extend their previously published model (Niehaus..Momeni, Nature Comm., 2019) to study the role of space in maintaining species diversity. The authors have the goal of modeling realistic bacterial communities; they in fact claim that the model's motivation is to "capture situations in which microbes can disperse inside a matrix", such as the mucosal layer of the digestive or intestinal tract, yogurt or cheese. To do this, the authors add relevant spatial aspects to their previous well-mixed model: species grow on a grid (even though 1D), where they can possibly overlap in the same grid spot, and species and mediators can diffuse in space. The advantage of the model they develop here is that it is simple enough for it to be used to explore general features of systems for which the assumptions of the model are justified. The authors perform a thorough investigation of the effect of spatial structure on the diversity that is maintained in the system. Their investigation includes the role of different types of interactions (facilitation and inhibition), species dispersal, and a range of properties of the metabolic interactions (number of mediators consumed and produced, consumption and production rates, mediator diffusion). Every scenario is compared to the well-mixed scenario to highlight the role of space.

Weaknesses: We are not convinced about some assumptions the authors make when extending their model from well-mixed (Niehaus..Momeni, Nature Comm., 2019) to spatial (this manuscript). The authors want to model a spatially structured system, with a framework that resembles the metacommunity framework, to which they add specific biophysical processes, such as the diffusion of metabolites. However, when adding these specific biophysical processes, the authors use parameters that seem to be unrealistic. One example is the packing of cells: 10^9, which implies a ratio between cells and the environment of 1:1000 volume-wise. Another example is the diffusion of molecules, which is 10 times slower than stated in the literature. With these parameters, the authors aim at describing physical processes in their model, but overall the parameters seem to be far from real values. Thus we suggest either changing these parameters to realistic values, discussing why the chosen parameters are meaningful or reframing the model as an heuristic model.

Overall, we think that the contribution of the paper is to extend a previously published work (Niehaus..Momeni, Nature Comm., 2019) to model spatial communities. It is thus fundamental that the assumptions made by the authors to model the spatial dynamics are well justified. Several physical parameters are chosen to values that do not represent realistic values for spatially structured communities. The authors should discuss if the results hold also for more realistic values.

Reviewer #3 (Public Review):

The authors develop and analyze a novel model of microbial communities that considers both space and chemical mediator dynamics explicitly, with the goal of understanding the impact of spatial structure on coexistence. The authors' primary method for assessing the impact of space is to compare numerical simulations of their spatial model to simulations of an equivalent well-mixed model. They explore how spatial structure changes coexistence over a wide range of parameter space, varying parameters such as the ratio of facilitative to inhibitory interactions and the degree of mediator diffusion. They find that spatial structure can have variable effects on richness (the number of cell types within a community), in contrast to existing intuition in the field that spatial structure increases diversity.

Overall, I think the approach that the authors have taken is sound. A very interesting aspect of this model is that the diffusion of mediators and microbes can occur at different rates. In other spatial systems, such as the classic Turing model of pattern formation, differences in diffusion timescales are the key ingredient needed for interesting spatial dynamics. However, while the authors have thoroughly characterized the impact of model parameters on ecological richness, their focus on this single metric provides a somewhat limited view of coexistence in their models. For example, richness considers neither the population composition nor the spatial patterns of coexistence emerging from the model. I also have some concerns about the implementation of the carrying capacity in the model, which in its current form may lead to non-physical outcomes in a small part of the phase space.

Reviewer #1 (Public Review):

In this manuscript, Castrillon et al. analyze the heterogeneity of B cells exiting spontaneous germinal center reactions by scRNA-seq in a new mouse model of autoimmunity. In this model, they track the fate of wild-type Aid-Cre ERT2-EYFP B cells in the presence of 564 lgi B cells harboring a BCR specific for RNP. Throughout the manuscript, the authors compared the results obtained in the autoimmune model with those obtained after acute immunization with NP/OVA in Alum. They found extensive clonal overlap among dark/light zone germinal centers, memory B cells, and antibody-secreting cells (ASC). Within the ASC compartment, they found seven clusters. Through pseudotime analysis, they conclude the presence of two early ASC clusters, three intermediate ASC clusters, and two terminal ASC clusters. The two late ASCs have different patterns of gene expression (CD28, Itga4 among them), isotype expression (ASC_Late_1 mostly class-switched while ASC_Late_2 mostly IgM), and potentially different antibody-secreting capacity and metabolic program based on Ig counts and OXPHOS signature. Regarding memory B cells, they found four clusters of memory B cells with similar isotype expression (except for MemB2 which expresses more IgM) but different gene expression patterns (CD83, Fcrl5, Vim, Fcer2a). Finally, the authors found that FCRL5+ and CD23+ memory B cells are located in different areas of the spleen based on confocal microscopy analysis and their accessibility to blood after anti-CD45 iv administration. The data provided by the authors are very attractive and interesting. Yet, I found that the manuscript over relies on scRNA-seq. It will be important that authors back up some of their conclusions made from the scRNA-seq analysis with functional experiments, like measuring the differential antibody-secreting capacity of both terminal ASC subsets or profiling their metabolic status through one of the many metabolic techniques available.

Reviewer #2 (Public Review):

This paper uses single-cell RNA sequencing to assess the B cell response in a mouse model of autoimmunity. The authors find that the B cell response is transcriptionally similar to the response induced by protein immunization. They further determine that the memory B cell response is composed of transcriptionally distinct subsets that may have distinct spatial distributions.

A major strength of this manuscript is the author's use of an elegant model of autoimmunity in which self-reactive B cells can escape negative selection to become activated and participate in the germinal center response. This system allows the author's a system to study the development of B cells in an autoimmune setting without restricting the repertoire of those cells though the use of BCR transgenes. This single-cell data generated in this study is also likely to be useful to individuals interested in understanding the differences in the B cell response between autoimmune and protein immunization settings.

One weakness of this study is that its main findings do not seem to represent a major conceptual advancement. There are already many published single-cell RNA-seq data sets that show that heterogeneity exists within B cell subsets. Therefore, the author's data primarily extends these findings to indicate that heterogeneity also exists in their model of autoimmunity.

Another major weakness of this study is that the authors only analyze about 13K cells in their single cell RNA-seq experiment with only 3.3K coming from the immunized mice. This low number of cells likely prevents the authors from identifying differences between specific B cell subsets between the two disease settings because there are likely very few cells in many of the clusters in the immunized group.

Finally, the author's data in which they seek to validate their use of Fcrl5 and CD23 to identify memory B cell subsets is not convincing. The flow cytometry gating used to distinguish the memory B cell subsets seem somewhat arbitrary with there not being a clear separation between the four populations shown using the author's gating strategy. This strategy also causes many CD23+ cells to not be analyzed in Fig. 6G.

The imaging data is also not clear as it is not apparent whether the S1pr2-expressing cells indicated by the authors express Fcrl5 since Fcrl5 does not encircle the indicated cell. The authors also do not quantify their images. While the authors do see a difference between the populations following in vivo labeling, it is not clear why the CD45+ population among the Fcrl5+ cells have a higher staining intensity than the Cd23+ cells. It is expected that cells that are exposed to circulation would have a similar staining intensity. Therefore, it is possible that there may be a technical issue with this data. Finally, it is not clear whether the results in figure 6 were repeated with several of the plots only having three mice per group limiting the conclusions that can be drawn from this data.

Reviewer #1 (Public Review):

This manuscript reports new findings about the role of the glutamate transporter EAAC1 in controlling neural activity in the striatum. The significance is two-fold - it addresses gaps in knowledge about the functional significance of EAAC1, as well as provides a potential explanation for how EAAC1 mutations contribute to striatal hyperexcitability and OCD-associated behaviors. The manuscript is clearly presented, and the well-designed experiments are rigorously performed and analyzed. The main results showing that EAAC1 deletion increases the dendritic arbor of MSN D1 neurons and increases excitatory synaptic connectivity, as well as reduces D1-to-D1 mediated IPSCs are convincing. These results clearly demonstrate that EAAC1 deletion can alter excitatory and inhibitory synaptic function. Modelling the potential consequences for these changes on D1 MSN neural activity, and the behavior changes are interesting. Minor weaknesses include incomplete support for the conclusions about how EAAC1 regulates GABAergic transmission.

Reviewer #1 (Public Review):

This manuscript made use of a biologically realistic neuronal network model of cortico-basal ganglia-thalamic (CBGT) circuits and a cognitive drift-diffusion model (DDM) to account for both behavioural and functional neuroimaging (fMRI) data and to understand how change in reward contingency in the environment can affect different decision dynamics. They found that the rate of evidence accumulation was most affected, allowing explorative behaviour with a lower drift rate during likely contingency change and exploitative behaviour with a higher drift rate when contingency was likely similar. The multi-pronged approach presented in the manuscript is commendable. The biophysical model was sufficiently realistic with varying ramping firing rates of spiny projection neurons linked to the varying drift rates in the DDM. However, there are a few concerns regarding this work.

The model's cortical neurons had no contralateral encoding, unlike their neuroimaging data. Another concern with this work is that it was unclear why the spiking neuronal network model with so many model parameters was used to account for coarse-scale fMRI data - a simple firing-rate neural population model would perhaps do the work. Moreover, the activity dynamics of the fMRI were not shown. It would have been more rigorous to show the fMRI (BOLD) signals in different (particularly CBGT) brain regions and compare that with the CBGT model simulations.

The association between classier uncertainty and drift rate (by participants) was an order of magnitude difference between the simulated and actual participants (compare Figure 2E with Figure 4B). There was also a weak effect on human reaction times (Supp. Fig. 2).

There were only 4 human participants that performed the experiment - the results would perhaps be better with more human participants.

For such a complex biophysical computational model, there could perhaps have been more model predictions provided.

Overall, this work is interesting and could potentially be a good contribution in the area of computational modelling and neuroscience of adaptive choice behaviour.

Reviewer #2 (Public Review):

In this paper, Bond et al. build on previous behavioral modelling of a reversal-learning task. They replicate some features of human behavior with a spiking neural network model of cortical basal ganglia thalamic circuits, and they link some of these same behavioral patterns to corresponding areas with BOLD fMRI. I applaud the authors for sharing this work as a preprint, and for publicly sharing the data and code.

While the spiking neural network model offers a helpful tool to complement behavior and neuroimaging, it is not very clear which predictions are specific to this model (and thus dissociate it from, or go beyond, previous work). Thus, the main strength of this work (combining behavior, brain, and in silico experiments) is not fully fleshed out and could be stronger in the conclusions we can draw from them.

It would be helpful to know more about which features of the spiking NN model are crucial in precisely replicating the behavioral patterns of interest (and to be more precise in which behaviors are replicated from previous work with the same task, vs. which ones are newly acquired because the task has changed - or the spiking CBGT model has afforded new predictions for behavior). Throughout, I am wondering if the authors can compare their results to a reasonable 'null model' which can then be falsified (e.g. Palminteri et al. 2017, TICS); this would give more intuition about what it is about this new CBGT model that helps us predict behavior.

The same question about model comparison holds for the behavior: beyond relying on DIC score differences, what features of behavior can and cannot be explained by the family of DDMs?

Reviewer #1 (Public Review):

In this manuscript, Modi et al present a novel method to analyze brain oscillations. Traditional approaches are typically based on analyzing spectral features on individual oscillations (univariate methods) or the power and phase relationship between two oscillations (bivariate methods). The authors take a different, multivariate, approach to simultaneously analyze interactions between multiple oscillations. This is a better way to study dynamics interactions in a complex system than the more traditional 'reductionist' approach and, so far, few methods exist that allow such multivariate analysis of neural oscillations. The method is well demonstrated in the paper, including several application cases. Several aspects of the results need to be better characterized, a clear discussion of the caveats and limitations of the method is lacking and the advantages over existing methods need to be outlined more clearly. Provided these issues are corrected I believe this would be an important contribution to the field that may have multiple applications.

Reviewer #2 (Public Review):

Modi and colleagues describe a multivariate framework to analyze local field potentials, which is specifically applied to CA1 data in this work. Multivariate approaches are welcome in the field and the effort of the authors should be appreciated. However, I found the analyses presented here are too superficial and do not seem to bring new insights into hippocampal dynamics. Further, some surrogate methods used are not necessarily controlling for confounding variables. These concerns are further detailed below.

1. The authors in reality do not analyze oscillations themselves in this manuscript but only the power of signals filtered at determined frequency bands. This is particularly misleading when the authors talk about "spindles". Spindles are classically defined as a thalamico-cortical phenomenon, not recorded from hippocampus LFPs. Thus, the fact that you filter the signal in the same frequency range matching cortical spindles does not mean you are analyzing spindles. The terminology, therefore, is misleading. I would recommend the authors to change spindles to "beta", which at least has been reported in the hippocampus, although in very particular behavioral circumstances. However, one must note that the presence of power in such bands does not guarantee one is recording from these oscillations. For example, the "fast gamma" band might be related to what is defined as fast gamma nested in theta, but it might also be related to ripples in sleep recordings. The increase of "spindle" power in sleep here is probably related to 1/f components arising from the large irregular activity of slow wave sleep local field potentials. The authors should avoid these conceptual confusions in the manuscript, or show that these band power time courses are in fact matching the oscillations they refer to (for example, their spindle band is in fact reflecting increased spindle occurrence).

2. The shuffling procedure to control for the occupancy difference between awake and sleep does not seem to be sufficient. From what I understand, this shuffling is not controlling for the autocorrelation of each band which would be the main source of bias to be accounted for in this instance. Thus, time shifts for each band would be more appropriate. Further, the controls for trial durations should be created using consecutive windows. If you randomly sample sleep bins from distant time points you are not effectively controlling for the difference in duration between trial types. Finally, it is not clear from the text if the UMAP is recomputed for each duration-matched control. This would be a rigorous control as it would remove the potential bias arising from the unbalance between awake and sleep data points, which could bias the subspace to be more detailed for the LFP sleep features. It is very likely the results will hold after these controls, given it is not surprising that sleep is a more diverse state than awake, but it would be good practice to have more rigorous controls to formalize these conclusions.

3. Lots of the observations made from the state space approach presented in this manuscript lack any physiological interpretation. For example, Figure 4F suggests a shift in the state space from Sleep1 to Sleep2. The authors comment there is a change in density but they do not make an effort to explain what the change means in terms of brain dynamics. It seems that the spectral patterns are shifting away from the Delta X Spindle region (concluding this by looking at Fig4B) which could be potentially interesting if analyzed in depth. What is the state space revealing about the brain here? It would be important to interpret the changes revealed by this method otherwise what are we learning about the brain from these analyses? This is similar to the results presented in Figure 5, which are merely descriptions of what is seen in the correlation matrix space. It seems potentially interesting that non-REM seems to be split into two clusters in the UMAP space. What does it mean for REM that delta band power in pyramidal and lm layers is anti-correlated to the power within the mid to fast gamma range? What do the transition probabilities shown in Figures 6B and C suggest about hippocampal functioning? The authors just state there are "changes" but they don't characterize these systematically in terms of biology. Overall, the abstract multivariate representation of the neural data shown here could potentially reveal novel dynamics across the awake-sleep cycle, but in the current form of this manuscript, the observations never leave the abstract level.

Reviewer #3 (Public Review):

Modi et al. developed a novel data-driven computational framework to investigate interactions between multiple brain oscillations and validated this approach in hippocampal CA1 utilizing well-studied changes in oscillations across CA1 layers. This approach provides a new way to investigate complex interactions between diverse neural oscillations during different behaviors. In contrast to standard approaches that classify LFP recordings into a few different oscillatory states which simplify patterns in the LFP, this approach maps a complex state space. The essential idea behind the method is novel and interesting with the potential to expand to other studies of other brain regions or interactions between regions. The authors provide a comprehensive analysis showing how this state space relates to traditional oscillatory states (like delta, theta, and gamma). Among the reported results, it is sometimes unclear what is a validation of their approach versus a novel scientific finding (in the context of the larger literature) and the significance of the finding. Although the overall results seem convincing, the paper is a lacking a demonstration that shows why this approach is of high physiological significance. Furthermore, more evidence showing the specific advantages of using this method in LFP data from a single CA1 layer would make this approach more readily adoptable for the community.

Major concerns:<br /> 1. My primary concern is to provide clear evidence that this approach will provide key insights of high physiological significance, especially for readers who may think the traditional approaches are advantageous (for example due to their simplicity). I think the authors' findings of distinct sleep state signatures or altered organization of the NLG3-KO mouse could serve this purpose. However, right now the physiological significance of these results is unclear. For example, do these sleep state signatures predict later behavior performance, or is altered organization related to other functional impairments in the disease model? Do neurons with distinct sleep state signatures form distinct ensembles and code for related information?<br /> 2. For cells with different mean firing rates during exploration: is that because they are putative fast-spiking interneurons and pyramidal cells? From the reported mean firing rates, I think some of these cells are interneurons. Since mean firing rates are well known to vary with cell type, this should be addressed. For example, the sleep state signatures may be distinct for different putative pyramidal cells and interneurons. This would be somewhat expected considering prior work that has shown different cell types have different oscillatory coupling characteristics. I think it would be more interesting to determine if pyramidal cells had distinct sleep state signatures and, if so, whether pyramidal cells from the same sleep state signature have similar properties like they code for similar things or commonly fire together in an ensemble. It seems the number of cells in Fig. 8 may be limited for this analysis. The authors could use the hc-11 data in addition, which was also tested in this work.<br /> 3. Example traces are needed to show how LFPs change over the state-space. Example traces should be included for key parts of the state-space in Figures 2 and 3.<br /> 4. What is the primary rationale for 200ms time bins? Is this time scale sufficient to capture the slow dynamics of delta rhythm (1-5Hz) with a maximum of 1s duration?<br /> 5. Since oscillatory frequency and power are highly associated with running speed, how does speed vary over the state space. Is the relationship between speed and state-space similar to the results of previous studies for theta (Slawinska and Kasicki, Brain Res 1998; Maurer et al, Hippocampus 2005) and gamma oscillations (Ahmed and Mehta J. Neurosci 2012; Kemere et al PLOS ONE 2013), or does it provide novel insights?<br /> 6. The separation of 9 states (Fig. 6ABC) seems arbitrary, where state 1 (bin 1) is never visited. I suggest plotting the density distribution of the data in Fig. 2A or Fig. 6A to better determine how many states are there within the state space. For example, five peaks in such a density plot might suggest five states. Alternately, clustering methods could be useful to determine how the number of states.<br /> 7. The results in Fig. 4G are very interesting and suggest more variation of sub-states during nonREM periods in sleep1 than in sleep2. What might explain this difference? Was it associated with more frequent ripple events occurring in sleep2?<br /> 8. The state transition results in Fig. 6 are confusing because they include two fundamentally different timescales: fast transitions between oscillatory states and slow dynamics of sleep states. I recommend clarifying the description in the results and the figure caption. Furthermore, how can an animal transition between the same sleep state (Fig. 6EF)? Would they both be in a single sleep state?

Reviewer #1 (Public Review):

This manuscript by Bohannon et al. continues a line of work from the Larsson laboratory with fundamental contributions describing the effects of polyunsaturated fatty acids (PUFAs) on the cardiac delayed rectifier potassium channel (IKs) formed by Kv7.1 and KCNE1 heteromers. Although the activating effect of PUFAs on these specific channels has been previously described, the authors now present a novel finding related to PUFAs containing large aromatic tyrosine head groups, showing significant activation effects on IKs, larger than other PUFAs previously studied. A combination of site-directed mutagenesis, electrophysiological and pharmacological approaches are used to dissect the different molecular mechanisms and sites involved in the functional interactions. The main conclusions are: 1) PUFA analogues with Tyr head groups are strong activators of the cardiac IKs channel by action on two previously described mechanisms: left-shift of the voltage-activation curve (by interaction with the voltage-sensor region of Kv7.1); and increased Gmax (by interacting with the pore region). 2) the underlying molecular interactions between PUFA and Kv7.1 are not cation-pi, as shown by the lack of effect of different chemical variations that disrupt the electrostatic surface potential. 3) the presence of electronegative groups on the aromatic ring favors increases in the maximal conductance. 4) the generation of a hydrogen bond with the -OH on the Tyr group seems to selectively impact on IKs voltage dependence of activation. 4) Kv7.1 sites involved in interactions with aromatic PUFAs are similar to the ones previously described for non-aromatic PUFAS, that is: R231 in S4 and K326 in S6. 5) residue T224 is newly identified as a potential site forming a hydrogen bond between the Tyr in the aromatic PUFA and Kv7.1.

The manuscript is very well written and structured. The experiments are solid and lead to mostly well-grounded conclusions. There are some aspects that would benefit from some clarification, which are mainly related to the different effects of the aromatic PUFA variants on IKs voltage dependence and/or conductance.

Reviewer #2 (Public Review):

In the present study, Briana M. Bohannon et al. expand on the study of the effect of Polyunsaturated fatty acids (PUFAS) on Iks (KV7.1 + KCNE1), a delayed rectifier potassium channel of critical relevance in cardiac physiology. PUFAs are amphipathic molecules that activate IKs channels by interacting with positively charged residues on the voltage sensor domain and in the channel's pore. The authors aim to characterize the molecular mechanisms behind the Iks activation by PUFA analogs that contains a tyrosine head group instead of the carboxyl or sulfonyl group present in other PUFAs.

The authors present a well-written manuscript with clear data and well-presented figures. The authors describe the effects of various tyrosine-PUFA analogs and unveil the mechanistic nature of their interactions with the channel. The focus is the N -(alpha-linolenoyl) Tyrosine (NALT), a potent activator by shifting the channel G-V by more than 50mV facilitating the opening of the channel, although the authors tested other tyrosine-PUFA analogs. Remarkably, the hydroxyl group in the tyrosine head is essential to shift the voltage-dependence of activation due to an H-bond with a threonine from the S3-S4 linker that helps coordinate the PUFA together with an electrostatic interaction with arginine in the S4. Furthermore, to test whether the aromatic ring from the tyrosine had a role in the interaction, the authors took a fascinating and exciting approach by modifying it and making the ring more electronegative by adding negatively charged atoms. Interestingly, they discovered that an electronegative-modified aromatic PUFA could increase the channel's conductance, an effect mediated by a specific interaction with a Lysine at the top of the S6 helix.

Although the question addressed in the manuscript is fascinating due to the possible use of these tyrosine-PUFA analogs as IKs modulators, the presented work is very mechanistic and specialized. While the effect of tyrosine-PUFA analogs is robust, the authors could improve the story by highlighting their interest in them and discussing whether they have potential therapeutic uses.

Due to the relevance of IKs currents in cardiac physiology and Long QT syndrome, the discovery and characterization of activators are highly relevant. The present manuscript presents a group of potent IKs channel activators that have the potential to impact the cardiac physiology field dramatically if they can perform under pathophysiological conditions or in the presence of disease-causing mutations.

Reviewer #3 (Public Review):

Bohannon and colleagues demonstrate that aromatic PUFA analogues positively modulate delayed rectifier potassium channel (Iks) currents, identifying new compounds that could be useful for the treatment of long QT syndrome. The data suggest that aromatic PUFA analogues have two modulatory effects that occur by distinct mechanisms involving hydrogen bonds and ionic interactions. However, the exact determinants of these molecular interactions remain unclear.

Strengths of the study include the following:<br /> 1) By examining a large panel of aromatic PUFA analogues, the study provides a thorough understanding of the relationship between the structure of these analogues and the modulatory effect. Of note, these aromatic PUFA analogues are more efficacious than previously characterized PUFAs such as DHA and N-AT. This knowledge will be important for the design of PUFA analogues for the modulation of IKs current, which could be a strategy for the treatment of long QT syndrome.<br /> 2) By examining the effect of mutations previously shown to disrupt two mechanisms of PUFA modulation, the results suggest that aromatic PUFAs act through the same mechanisms. Furthermore, the effects of the different analogues shed light on the determinants of these binding sites such as the presence of additional hydrogen bonds and electrostatic interactions between the aromatic PUFAs and ion channels.

One limitation of the study is that the structure-activity relationships and effects of the mutations do not provide a complete molecular understanding of how the aromatic PUFA analogues are interacting with the channel. This understanding will require additional studies to examine PUFA analogue binding combined with more extensive mutagenesis. Specifically, the model in Figure 5 suggests that the effect of aromatic PUFAs on the voltage dependence of activation depends on an electrostatic interaction with R231 and a hydrogen bond interaction possibly with T224. Similarly, the effect on channel conductance depends on an electrostatic interaction with K326 through the carboxylate anion of the aromatic PUFA as well as an additional electrostatic interaction with some other part of the protein. It is unclear what residues mediate these interactions. Additionally, the authors propose that T224 is forming a hydrogen bond interaction with the hydroxyl group of NALT, but there appears to be a relatively similar effect of the T224V mutation on NAL-phe, only that the spread in the data makes this effect statistically insignificant. Therefore, the conclusion that T224 mediates NALT action by forming a hydrogen bond with the hydroxyl group (a chemical moiety that is absent in NAL-phe) is not fully supported by the data. A structural model to indicate that T224 is well-positioned to form a hydrogen bond with NALT when it is also interacting with R231 would strengthen this model.

Reviewer #1 (Public Review):

In this study the authors investigate whether a presumably allosteric P2RX7 activating compound that they previously discovered reduces fibrosis in a bleomycin mouse model. They chose this particular model as publicly available mRNA data indicate that the P2XR7 pathway is downregulated in idiopathic pulmonary fibrosis patients compared to control individuals. The authors first demonstrate that two proxies of lung damage, Ashcroft score and collagen fibers, are significantly reduced in the bleomycin model on HEI3090 treatment. Additional data implicate specific immune cell infiltrates and cytokines, namely inflammatory macrophages and damped release of IL-17A, as potential mechanistic links between their compound and reduced fibrosis. Finally, the researchers transplant splenocytes from WT, NLRP3-KO, and IL-18-KO mice into animals lacking the P2XR7 receptor to specifically ascertain how the transplanted splenocytes, which are WT for P2XR7 receptor, respond to HEI3090 (a P2XR7 agonist). Based on these results, the authors conclude that HEI3090 enhanced IL-18 production through the P2XR7-NLRP3 inflammasome axis to dampen fibrosis.

These findings could be interesting to the field, as there are conflicting results as to whether NLRP3 activation contributes to fibrosis and if so, at what stage(s) (e.g., acute damage phase versus progression). However, major weaknesses of the study include inconsistent and small effect sizes in key outcomes used to measure fibrosis, small animal cohorts that do not empower results, and lack of key experimental controls. For example, damage indicators for the vehicle-treated mice transplanted with splenocytes of various genetic background are markedly different, and there are no statistical tests of these effects because the data are presented as separate graphs. Moreover, the fundamental assumption that HEI3090 acts specifically through the P2XR7 pathway in this model is questionable, as P2XR7 knockout mice are not included in the initial key experiments. These issues must be addressed as stimulating an inflammasome response might lead to pathogenic inflammation, which could counterproductively exacerbate fibrosis in the clinic and harm people.

Experimental concerns:

1. Ashcroft method quantification throughout is outdated and prone to bias. The methods describing quantification are lacking, and only include a citation: there should be mention of researcher blinding, etc. In general, please re-quantify using an automated classifier, and consider staining for additional markers of lung damage that are appropriate in the field.

2. For Figure 2, P2XR7 knockout mice, and an additional P2XR7 activator, should be included (e.g, A74003, AZ10606120, others), to support the hypothesis that HEI3090 acts through this pathway to alleviate fibrosis. Moreover, these data are especially important as the author's conclusions are directly opposed to a previous study demonstrating that the P2XR7 receptor is required for inflammation/fibrosis in this model system (PMID: 20522787). Two-way ANOVA or similar statistical tests on all groups should be examined to see whether genetic knockout of this DAMP receptor alone is protective or exacerbates fibrosis (e.g., comparing the vehicle-alone groups), and whether compounds exert a specific effect through this receptor.

3. Fig. 3A: Please show the individual IFN/IL-17A plots in the supplement, as a ratiometric result might mask variance. Moreover, please conduct a statistical test for the outlier in the HEI3090 condition (to potentially remove it), as this sole data point might skew the entire mean, causing the observed statistical difference between means despite a very modest change. If the results are still significant, please comment on effect size.

4. Fig. 3: How is IL-17A measured and what is the abbreviation GMFI?

5. Fig. 3E: It's unclear how the left and right figures align-it looks like the gates are 45.8 % and 25 %, respectively, but the means on the right are between 2-3%. Also, is this effect size (2 versus 3 %) significant biologically?

6. For Figure 4B-G, the Ashcroft scores for the vehicle mice treated with HEI3090 are at entirely different starting points following adoptive transfer of cells with different genetic background. In Fig. 1, WT mice have starting scores of around 3 following the induction of fibrosis, with a modest decrease of about 0.8 following HEI3090 treatment. Here, there is a much greater effect of the genetic background itself rather than the treatment, with the IL-18 knockout mice having a much lower baseline "vehicle" score (~1) compared to Fig. 1F (both of which are 14 day treatments). In fact, adoptive transfer of WT splenocytes start at a baseline of 1.8 here, which is much lower than Fig. 1F, and NLRP3-KO splenocytes score nearly the same as Fig. 1F following BLM treatment, with a modest reduction following treatment with HEI3090. Please analyze all of these groups together with appropriate multiple hypothesis testing to examine the effect of the genetic background, and please comment on why IL-18-knockout splenocytes might be protective at vehicle baseline while NLRP3-knockout splenocytes might exacerbate the phenotype at vehicle baseline.

7. The variance on Supplemental Figure 5C is quite large. These data have a decrease in mean Ashcroft score between untreated and HEI3090 treatment of around 0.8, which is similar to the WT mice in Figure 1. This is very concerning, as the underlying assumption is that KO of the protein required for HEI3090's on-target effect would completely ablate response, and this would be required for the subsequent adoptive transfer experiments in Figure 4. Please conduct power analysis, comment, and provide additional evidence (other than Ashcroft score).

8. Figure 4: Should quantify collagen fibers or have an additional quantitative metric for lung damage, as in Fig. 2C/J.

9. Figure 4: Should group the quantification of C/E/G and perform a 2-way Anova to assess effects of genetic background versus treatment.

10. Fig. 4H, Supplemental Fig. 6D: Is it reasonable to expect differences in IL-1beta and IL-18 in sera compared to in lung tissue itself?

Reviewer #2 (Public Review):

In the study by Hreich et al, the potency of P2RX7 positive modulator HEI3090, developed by the authors, for the treatment of Idiopathic pulmonary fibrosis (IPF) was investigated. Recently, the authors have shown that HEI3090 can protect against lung cancer by stimulating dendritic cell P2RX7, resulting in IL-18 production that stimulates IFN-γ production by T and NK cells (DOI: 10.1038/s41467-021-20912-2). Interestingly, HEI3090 increases IL-18 levels only in the presence of high eATP. Since the treatment options for IPF are limited, new therapeutic strategies and targets are needed. The authors first show that P2RX7/IL-18/IFNG axis is downregulated in patients with IPF. Next, they used a bleomycin-induced lung fibrosis mouse model to show that the use of a positive modulator of P2RX7 leads to the activation of the P2RX7/IL-18 axis in immune cells that limits lung fibrosis onset or progression. Mechanistically, treatment with HEI3090 enhanced IL-18-dependent IFN-γ production by lung T cells leading to a decreased production of IL-17 and TGFβ, major drivers of IPF. The major novelty is the use of the small molecule HEI3090 to stimulate the immune system to limit lung fibrosis progression by targeting the P2RX7, which could be potentially combined with current therapies available. However, there is the lack of information on the reproducibility of data, especially for the data presented in Figures 3 and 4, and related supplementary figures, as well as the lack of support data for experiments that emphasize the role of P2RX7 expressed on immune cells (e.g. frequency of transferred cells compared to endogenous cells).

Reviewer #3 (Public Review):

Idiopathic pulmonary fibrosis (IPF) is an aggressive interstitial lung disease with progressive and irreversible deterioration of respiratory functions that lacks curative therapies. The authors investigated a new therapeutic approach to treat idiopathic pulmonary fibrosis by targeting P2RX7/IL-18/IFNG axis.

The current data are mainly based on P2RX7 activator HEI3090 and genetic experiments are lacking to support the primary claim that activation P2RX7/IL-18/IFNG axis is beneficial for IPF.

- Parenteral systemic administration of IFN-γ failed in clinical trials (INSPIRE; NCT00075998). However, this study used i.p. administration of P2RX7 activator HEI3090 to activate P2RX7/IL-18/IFNG axis.

- Activation of P2RX7 NLRP3 inflammasome triggers cell death and the current experiments do not explore IL-18 as a potential therapy that would avoid harmful cell death as a consequence of P2RX7/NLRP3 inflammasome activation.

- Reciprocal bone marrow chimera model is needed to demonstrate the requirement of a hematopoietic compartment for HEI3090's antifibrotic effect.

- There is no evidence to show whether P2RX7 interferes with bleomycin during the generation of the IPF model. Independent IPF models would validate the therapeutic effect of P2RX7.

Reviewer #1 (Public Review):

In this study, Satake and colleagues endeavored to explore the rates and patterns of somatic mutations in wild plants, with a focus on their relationship to longevity. The researchers examined slow- and fast-growing tropical tree species, demonstrating that slow-growing species exhibited five times more mutations than their fast-growing counterparts. The number of somatic mutations was found to increase linearly with branch length. Interestingly, the somatic mutation rate per meter was higher in slow-growing species, but the rate per year remained consistent across both species. A closer inspection revealed a prevalence of clock-like spontaneous mutations, specifically cytosine-to-thymine substitutions at CpG sites. The author suggested that somatic mutations were identified as neutral within an individual, but subject to purifying selection when transmitted to subsequent generations. The authors developed a model to assess the influence of cell division on mutational processes, suggesting that cell-division independent mutagenesis is the primary mechanism.

The authors have gathered valuable data on somatic mutations, particularly regarding differences in growth rates among trees. Their meticulous computational analysis led to fascinating conclusions, primarily that most somatic mutations accumulate in a cell-division independent manner. The discovery of a molecular clock in somatic mutations significantly advances our comprehension of mutational processes that may generate genetic diversity in tropical ecosystems. The interpretation of the data appears to be based on the assumption that somatic mutations can be effectively transmitted to the next generation unless negative selection intervenes. However, accumulating evidence suggests that plants may also possess "effective germlines," which could render the somatic mutations detected in this study non-transmittable to progeny. Incorporating additional analyses/discussion in the context of plant developmental biology, particularly recent studies on cell lineage, could further enhance this study.

Specifically, several recent studies address the topics of effective germline in plants. For instance, Robert Lanfear published an article in PLoS Biology exploring the fundamental question, "Do plants have a segregated germline?" A study in PNAS posited that "germline replications and somatic mutation accumulation are independent of vegetative life span in Arabidopsis." A phylogenetic-based analysis titled "Rates of Molecular Evolution Are Linked to Life History in Flowering Plants" discovered that "rates of molecular evolution are consistently low in trees and shrubs, with relatively long generation times, as compared with related herbaceous plants, which generally have shorter generation times." Another compelling study, "The architecture of intra-organism mutation rate variation in plants," published in PLoS Biology, detected somatic mutations in peach trees and strawberries. Although some of these studies are cited in the current work, a deeper examination of the findings in relation to the existing literature would strengthen the interpretation of the data.

Reviewer #2 (Public Review):

In this manuscript, the authors used an original empirical design to test if somatic mutation rates are different depending on the plant growth rates. They detected somatic mutations along the growth axes of four trees - two individuals per species for two dipterocarp tree species growing at different rates. They found here that plant somatic mutations are accumulated are a relatively constant rate per year in the two species, suggesting that somatic mutation rates correlate with time rather than with growth, i.e. the number of cell divisions. The authors then suggest that this result is consistent with a low relative contribution of DNA replication errors (referred to as α in the manuscript) to the somatic mutation rates as compared to the other sources of mutations (β). Given that plants - in particular, trees - are generally assumed to deviate from the August Weismann's theory (a part of the somatic variation is expected to be transmitted to the next generation), this work could be of interest for a large readership interested by mutation rates as a whole, since it has implications also for heritable mutation rates too. In addition, even if this is not discussed, the putatively low contribution of DNA replication errors could help to understand the apparent paradox associated to trees. Indeed, trees exhibit clear signatures of lower molecular evolution (Lanfear et al. 2013), therefore suggesting lower mutation rates per unit of time. Trees could partly keep somatic mutations under control thanks to a long-term evolution towards low α values, resulting in low α/β ratios as compared to short-lived species. I therefore consider that the paper tackles a fundamental albeit complex question in the field.

Overall, I consider that the authors should clearly indicate the weakness of the studies. For instance, because of the bioinformatic tools used, they have reasonably detected a small part of the somatic mutations, those that have reached a high allele frequency in tissues. Mutation counts are known to be highly dependent on the experimental design and the methods used. Consequently, (i) this should be explicit and (ii) a particular effort should be made to demonstrate that the observed differences in mutation counts are robust to the potential experimental biases. This is important since, empirically, we know how mutation counts can vary depending on the experimental designs. For instance, a difference of an order of magnitude has been observed between the two papers focusing on oaks (Schmid-Siegert et al. 2017 and Plomion et al. 2018) and this difference is now known to be due to the differences in the experimental designs, in particular the sequencing effort (Schmitt et al. 2022).

Having said that, my overall opinion is that (i) the authors have worked on an interesting design and generated unique data, (ii) the results are probably robust to some biases and therefore strong enough (but see my comments regarding possible improvements), (iii) the interpretations are reasonable and (iv) the discussion regarding the source of somatic mutations is valuable.

Reviewer #3 (Public Review):

In animals, several recent studies have revealed a substantial role for non-replicative mutagenic processes such as DNA damage and repair rather than replicative error as was previously believed. Much less is known about how mutation operates in plants, with only a handful of studies devoted to the topic. Authors Satake et al. aimed to address this gap in our understanding by comparing the rates and patterns of somatic mutation in a pair of tropical tree species, slow-growing Shorea lavis and fast-growing S. leprosula. They find that the yearly somatic mutation rates in the two species is highly similar despite their difference in growth rates. The authors further find that the mutation spectrum is enriched for signatures of spontaneous mutation and that a model of mutation arising from different sources is consistent with a large input of mutation from sources uncorrelated with cell division. The authors conclude that somatic mutation rates in these plants appears to be dictated by time, not cell division numbers, a finding that is in line with other eukaryotes studied so far.

In general, this work shows careful consideration and study design, and the multiple lines of evidence presented provide good support for the authors' conclusions. In particular, they use a sound approach to identify rare somatic mutations in the sampled trees including biological replicates, multiple SNP-callers and thresholds, and without presumption of a branching pattern. By applying these methods consistently across both species, the authors provide confidence in the comparative mutation rate results. Further steps could be taken to ensure the validity of the results; however, these issues are relatively minor and should minimally impact the overall findings.

Some of the identified somatic mutations (primarily those in individual F1) appear to require two mutation events-one on each chromosome-to be generated and should be either removed or accounted for. Also, while the authors provide estimates of their false positive rate at different filtering thresholds, an assessment of the false negative rate is absent and would help assure readers that the differing number of somatic mutations found is not due to differences in statistical power.

The authors compare the mutation rate per meter of growth, demonstrating that the rate is higher in slow-growing S. laevis: a key piece of evidence in favor of the authors' conclusion that somatic mutations track absolute time rather than cell division. To estimate the mutation rate per unit distance, they regress the per base-pair rate of mutations found between all pairwise branch tips against the physical distance separating the tips (Fig. 2a). While a regression approach is appropriate, the narrowness of the confidence interval is overstated as the points are not statistically independent: internal branches are represented multiple times. (For example, all pairwise comparisons involving a cambium sample will include the mutations arising along the lower trunk.) Regressing rates and lengths of distinct branches might be more appropriate. Judging from the data presented, however, the point estimates seem unlikely to change much.

The most obvious drawback of this study is the low sample size with only two individuals of each species sequenced. To eliminate lingering doubts, it would be helpful to include a more in-depth discussion about stray factors that might affect the authors' conclusions. For example: Could an error in estimation of the trees' ages affect the yearly mutation rate comparisons? If mutations are replicatively driven, could the 30% species difference in the number of cell divisions per meter be sufficient to explain the results?

This work deepens our understanding of how mutation operates at the cellular level by adding plants to the list of eukaryotes in which many mutations appear to derive from non-replicative sources. Given these results, it is intriguing to consider whether there is a fundamental mechanism linking mutation across distantly related species. Plants, generally, present a unique opportunity in the study of mutation as the germline is not sequestered, as it is in animals, and thus the forces of both mutation and selection acting throughout an individual plant's life could in principle affect the mutations transmitted to seed. The authors touch on this aspect, finding no evidence for a reduction in non-synonymous somatic mutations relative to the background rate, but more work-both experimental and observational-is needed to understand the dynamics of mutation and cell-competition within an individual plant. Overall, these results open the door to several intriguing questions in plant mutation. For example, is somatic mutation age-dependent in other species, and do other tropical plants harbor a high mutation rate relative to temperate genera? Any future inquiries on this topic would benefit from modeling their approach for identifying somatic mutations on the methods laid out here.

https://www.theparisreview.org/blog/2017/09/12/free-pencil-day/

A list of quotes from famous writers about pencils in The Paris Review.

Reviewer #1 (Public Review):

The manuscript by Hayes et al. explored the potential of combining chromosomal instability with macrophage phagocytosis to enhance tumor clearance of B16-F10 melanoma. However, the manuscript suffers from substandard experimental design, some contradictory conclusions, and a lack of viable therapeutic effects.

The authors suggest that early-stage chromosomal instability (CIN) is a vulnerability for tumorigenesis, CD47-SIRPa interactions prevent effective phagocytosis, and opsonization combined with inhibition of the CD47-SIRPa axis can amplify tumor clearance. While these interactions are important, the experimental methodology used to address them is lacking.

Reviewer #2 (Public Review):

Harnessing macrophages to attack cancer is an immunotherapy strategy that has been steadily gaining interest. Whether macrophages alone can be powerful enough to permanently eliminate a tumor is a high-priority question. In addition, the factors making different tumors more vulnerable to macrophage attack have not been completely defined. In this paper, the authors find that chromosomal instability (CIN) in cancer cells improves the effect of macrophage targeted immunotherapies. They demonstrate that CIN tumors secrete factors that polarize macrophages to a more tumoricidal fate through several methods. The most compelling experiment is transferring conditioned media from MSP1 inhibited and control cancer cells, then using RNAseq to demonstrate that the MSP1-inhibited conditioned media causes a shift towards a more tumoricidal macrophage phenotype. In mice with MSP1 inhibited (CIN) B16 melanoma tumors, a combination of CD47 knockdown and anti-Tyrp1 IgG is sufficient for long term survival in nearly all mice. This combination is a striking improvement from conditions without CIN.

Like any interesting paper, this study leaves several unanswered questions. First, how do CIN tumors repolarize macrophages? The authors demonstrate that conditioned media is sufficient for this repolarization, implicating secreted factors, but the specific mechanism is unclear. In addition, the connection between the broad, vaccination-like IgG response and CIN is not completely delineated. The authors demonstrate that mice who successfully clear CIN tumors have a broad anti-tumor IgG response. This broad IgG response has previously been demonstrated for tumors that do not have CIN. It is not clear if CIN specifically enhances the anti-tumor IgG response or if the broad IgG response is similar to other tumors. Finally, CIN is always induced with MSP1 inhibition. To specifically attribute this phenotype to CIN it would be most compelling to demonstrate that tumors with CIN unrelated to MSP1 inhibition are also able to repolarize macrophages.<br /> Overall, this is a thought-provoking study that will be of broad interest to many different fields including cancer biology, immunology and cell biology.

Reviewer #1 (Public Review):

The study by Ding et al demonstrated that microbial metabolite I3A reduced western diet induced steatosis and inflammation mice. They showed that I3A mediates its anti-inflammatory activities through AMP-activated protein kinase (AMPK)-dependent manner in macrophages. Translationally, they proposed that I3A could be a potential therapeutic molecule in preventing the progression of steatosis to NASH.

Major strengths<br /> • Authors clearly demonstrated that the Western Diet (WD)-induced steatosis and I3A treatment reduced steatosis and inflammation in pre-clinical models. Data clearly supports these statements.<br /> • I3A treatment rescued WD-altered bile acids as well partially rescued the metabolome, proteome in the liver.<br /> • I3A treatment reduced the levels of enzymes in fatty acid transport, de novo lipogenesis and β-oxidation<br /> • I3A mediates its anti-inflammatory activities through AMP-activated protein kinase (AMPK)-dependent manner in macrophages.

Minor Weakness<br /> Although data strongly support the notion that I3A reduced WD-induced steatosis and I3A treatment reduced steatosis and inflammation, the following concerns need to be addressed.<br /> • Authors suggested that I3A anti-inflammatory activities do not require AhR by using AhR-inhibitor in RAW cell lines. In the literature, studies do show that RAW cells do respond to AhR ligands such as TCDD and FICZ.<br /> • AhR-dependency needs to be confirmed by bone marrow derived macrophages isolated from AhR+/+ and AhR-/- or siRNA/ShRNA knockdown experiments.<br /> • Utilization of known AhR ligands as controls will strengthen the interpretation of the conclusions.

Reviewer #2 (Public Review):

This article examines the ability of dietary supplementation with indole-3-actetate (I3A) to attenuate western diet-induced fatty liver disease. The experiments are appropriately described, and convincing data are provided that I3A can attenuates fat accumulation in the liver. Several possible mechanisms of action were explored and one likely mechanism, an alteration in AMPK signaling pathway was observed, and is likely involved in the observed phenotype. However, I3A has already been shown to yield similar data in a high fat diet mouse model system (PMID: 31484323), although the I3A was administered through IP injection, not in the drinking water. In both studies the effects seen may well be due to activation of PPAR-alpha. Another study (PMID: 19469536) gave acetic acid in the drinking water and obtained data similar to this manuscript, supporting that the effect seen in this study may not be specific to I3A. These references should be included and discussed. Overall, the data and experimental approach taken support the stated conclusions.

Reviewer #3 (Public Review):

Ding et al. address the experimental question of whether the microbially derived I3A can exert pro-metabolic effects in an experimental model of diet induced obesity/hepatic steatosis. This was based on previous findings by the authors that high fat diet alters levels of I3A, and that I3A can exert anti-steatotic and anti-inflammatory effects in vitro. The data are robust and the authors provide a plethora of omics-based platforms including proteomics and metabolomics under a variety of treatment paradigms. By performing these studies in vivo in mouse liver tissue, these atlases of proteomic and metabolomic datasets would be of interest to the field of metabolism for future analysis. However, there are several weaknesses identified within this manuscript. Primarily, weaknesses in the interpretation and organization of presented data overshadow the robust data presented and make it difficult for the reader to draw any new biological conclusions. Specifically, this manuscript in its current form is primarily of descriptive nature and does not distill any of the complex datasets presented into digestable conclusions that shed new insight into regulation of hepatic metabolism and inflammation by I3A. In essence, this manuscript in its current form is an in vivo extension to the author's previous in vitro assessment of I3A on liver function. Finally, there is a flaw in the model presented (Supplemental Fig. 9) with regards to the authors linking the anti-inflammatory effects of I3A with the metabolic effects. In fact, the authors present data (Fig. 1&2) that show the opposite of this interpretation in which inflammation is uncoupled from the metabolic effects of I3A in the low dose treatment group. While the authors achieved their main goal of addressing the metabolic effects of I3A in vivo, the organization and interpretation of the data presented in its current form is likely to result in a modest impact on the field.

Reviewer #1 (Public Review):

This is an interesting study by Pinos and colleagues that examines the effect of beta carotene on atherosclerosis regression. The authors have previously shown that beta carotene reduces atherosclerosis progress and hepatic lipid metabolism, and now they seek to extend these findings by feeding mice a diet with excess beta carotene in a model of atherosclerosis regression (LDLR antisense oligo plus Western diet followed by LDLR sense oligo and chow diet). They show some metrics of lesion regression are increased upon beta carotene feeding (collagen content) while others remain equal to normal chow diet (macrophage content and lesion size). These effects are lost when beta carotene oxidase (BCO) is deleted. The study adds to the existing literature that beta carotene protects from atherosclerosis in general, and adds new information regarding regulatory T-cells. However, the study does not present significant evidence about how beta-carotene is affecting T-cells in atherosclerosis. For the most part, the conclusions are supported by the data presented, and the work is completed in multiple models, supporting its robustness. However there are a few areas that require additional information or evidence to support their conclusions and/or to align with the previously published work.

Specific additional areas of focus for the authors:<br /> The premise of the story is that b-carotene is converted into retinoic acid, which acts as a ligand of the RAR transcription factor in T-regs. The authors measure hepatic markers of retinoic acid signaling (retinyl esters, Cyp26a1 expression) but none of these are measured in the lesion, which calls into question the conclusion that Tregs in the lesion are responsible for the regression observed with b-carotene supplementation.

There does not appear to be a strong effect of Tregs on the b-carotene induced pro-regression phenotype presented in Figure 5. The only major CD25+ cell dependent b-carotene effect is on collagen content, which matches with the findings in Figure 1 +2. This mechanistically might be very interesting and novel, yet the authors do not investigate this further or add any additional detail regarding this observation. This would greatly strengthen the study and the novelty of the findings overall as it relates to b-carotene and atherosclerosis.

The title indicates that beta-carotene induces Treg 'expansion' in the lesion, but this is not measured in the study.

Reviewer #2 (Public Review):

Pinos et al present five atherosclerosis studies in mice to investigate the impact of dietary supplementation with b-carotene on plaque remodeling during resolution. The authors use either LDLR-ko mice or WT mice injected with ASO-LDLR to establish diet-induced hyperlipidemia and promote atherogenesis during 16 weeks, and then they promote resolution by switching the mice for 3 weeks to a regular chow, either deficient or supplemented with b-carotene. Supplementation was successful, as measured by hepatic accumulation of retinyl esters. As expected, chow diet led to reduced hyperlipidemia, and plaque remodeling (both reduced CD68+ macs and increased collagen contents) without actual changes in plaque size. But, b-carotene supplementation resulted in further increased collagen contents and, importantly, a large increase in plaque regulatory T-cells (TREG). This accumulation of TREG is specific to the plaque, as it was not observed in blood or spleen. The authors propose that the anti-inflammatory properties of these TREG explain the atheroprotective effect of b-carotene, and found that treatment with anti-CD25 antibodies (to induce systemic depletion of TREG) prevents b-carotene-stimulated increase in plaque collagen and TREG.

An obvious strength is the use of two different mouse models of atherogenesis, as well as genetic and interventional approaches. The analyses of aortic root plaque size and contents are rigorous and included both male and female mice (although the data was not segregated by sex). Unfortunately, the authors did not provide data on lesions in en face preparations of the whole aorta.

Overall, the conclusion that dietary supplementation with b-carotene may be atheroprotective via induction of TREG is reasonably supported by the evidence presented. Other conclusions put forth by the authors (e.g., that vitamin A production favors TREG production or that BCO1 deficiency reduces plasma cholesterol), however, will need further experimental evidence to be substantiated.

The authors claim that b-carotene reduces blood cholesterol, but data shown herein show no differences in plasma lipids between mice fed b-carotene-deficient and -supplemented diets (Figs. 1B, 2A, and S3A). Also, the authors present no experimental data to support the idea that BCO1 activity favors plaque TREG expansion (e.g., no TREG data in Fig 3 using Bco1-ko mice).

As the authors show, the treatment with anti-CD25 resulted in only partial suppression of TREG levels. Because CD25 is also expressed in some subpopulation of effector T-cells, this could potentially cloud the interpretation of the results. Data in Fig 4H showing loss of b-carotene-stimulated increase in numbers of FoxP3+GFP+ cells in the plaque should be taken cautiously, as they come from a small number of mice. Perhaps an orthogonal approach using FoxP3-DTR mice could have produced a more robust loss of TREG and further confirmation that the loss of plaque remodeling is indeed due to loss of TREG.

Reviewer #1 (Public Review):

In this study, Kim et al. investigated the mechanism by which uremic toxin indoxyl sulfate (IS) induces trained immunity, resulting in augmented pro-inflammatory cytokine production such as TNF and IL-6. The authors claim that IS treatment induced epigenetic and metabolic reprogramming, and the aryl hydrocarbon receptor (AhR)-mediated arachidonic acid pathway is required for establishing trained immunity in human monocytes. They also demonstrated that uremic sera from end-stage renal disease (ESRD) patients can generate trained immunity in healthy control-derived monocytes.

These are interesting results that introduce the important new concept of trained immunity and its importance in showing endogenous inflammatory stimuli-induced innate immune memory. Additional evidence proposing that IS plays a critical role in the initiation of inflammatory immune responses in patients with CKD is also interesting and a potential advance of the field. This study is in large part well done, but some components of the study are still incomplete and additional efforts are required to nail down the main conclusions.

Specific comments:<br /> 1) Of greatest concern, there are concerns about the rigor of these experiments, whether the interpretation and conclusions are fully supported by the data. 1) Although many experiments have been sporadically conducted in many fields such as epigenetic, metabolic regulation, and AhR signaling, the causal relationship between each mechanism is not clear. 2) Throughout the manuscript, no distinction was made between the group treated with IS for 6 days and the group treated with the second LPS (addressed below). 3) Besides experiments using non-specific inhibitors, genetic experiments including siRNA or KO mice should be examined to strengthen and justify central suggestions.<br /> 2) The authors showed that IS-trained monocytes showed no change in TNF or IL-6, but increased the expression levels of TNF and IL-6 in response to the second LPS (Fig. 1B). This suggests that the different LPS responsiveness in IS-trained monocytes caused altered gene expression of TNF and IL-6. However, the authors also showed that IS-trained monocytes without LPS stimulation showed increased levels of H3K4me3 at the TNF and IL-6 loci, as well as highly elevated ECAR and OCR, leading to no changes in TNF and IL-6. Therefore, it is unclear why or how the epigenetic and metabolic states of IS-trained monocytes induce different LPS responses. For example, increased H3K4me3 in HK2 and PFKP is important for metabolic rewiring, but why increased H3K4me3 in TNF and IL6 does not affect gene expression needs to be explained.<br /> 3) The authors used human monocytes cultured in human serum without growth factors such as MCSF for 5-6 days. When we consider the short lifespan of monocytes (1-3 days), the authors need to explain the validity of the experimental model.<br /> 4) The authors' ELISA results clearly showed increased levels of TNF and IL-6 proteins, but it is well established that LPS-induced gene expression of TNF and IL-6 in monocytes peaked within 1-4 hours and returned to baseline by 24 hours. Therefore, authors need to investigate gene expression at appropriate time points.<br /> 5) It is a highly interesting finding that IS induces trained immunity via the AhR pathway. The authors also showed that the pretreatment of FICZ, an AhR agonist, was good enough to induce trained immunity in terms of the expression of TNF and IL-6. However, from this point of view, the authors need to discuss why trained immunity was not affected by kynurenic acid (KA), which is a well-known AhR ligand accumulated in CKD and has been reported to be involved in innate immune memory mechanisms (Fig. S1A).<br /> 6) The authors need to clarify the role of IL-10 in IS-trained monocytes. IL-10, an anti-inflammatory cytokine that can be modulated by AhR, whose expression (Fig. 1E, Fig. 4D) may explain the inflammatory cytokine expression of IS-trained monocytes.<br /> 7) The authors need to show H3K4me3 levels in TNF and IL6 genes in all conditions in one figure. (Fig. 2B). Comparing Fig. 2B and Fig. S2B, H3K4me3 does not appear to be increased at all by LPS in the IL6 region.<br /> 8) The authors need to address the changes of H3K4me3 in the presence of MTA.<br /> 9) Interpretation of ChIP-seq results is not entirely convincing due to doubts about the quality of sequencing results. First, authors need to provide information on the quality of ChIP-seq data in reliable criteria such as Encode Pipeline. It should also provide representative tracks of H3K4me3 in the TNF and IL-6 genes (Fig. 2F). And in Fig. 2F, the author showed the H3K4me3 track of replicates, but the results between replicates were very different, so there are concerns about reproducibility. Finally, the authors need to show the correlation between ChIP-seq (Fig. 2) and RNA-seq (Fig. 5).<br /> 10) AhR changes in the cell nucleus should be provided (Fig. 4A).<br /> 11) Do other protein-bound uremic toxins (PBUTs), such as PCS, HA, IAA, and KA, change the mRNA expression of ALOX5, ALOX5AP, and LTB4R1? In the absence of genetic studies, it is difficult to be certain of the ALOX5-related mechanism claimed by the authors.<br /> 12) Fig.6 is based on the correlated expression of inflammatory genes or AA pathway genes. It does not clarify any mechanisms the authors claimed in the previous figures.

Reviewer #2 (Public Review):

Manuscript entitled "Uremic toxin indoxyl sulfate (IS) induces trained immunity via the AhR-dependent arachidonic acid pathway in ESRD" presented some interesting findings. The manuscript strengths included use of H3K4me3-CHIP-Seq, AhR antagonist, IS treated cell RNA-Seq, ALOX5 inhibitor, MTA inhibitor to determine the roles of IS-AhR in trained immunity related to ESRD inflammation and trained immunity.

Reviewer #3 (Public Review):

The manuscript entitled, "Uremic toxin indoxyl sulfate induces trained immunity via the AhR-dependent arachidonic acid pathway in ESRD" demonstrates that indoxyl sulfate (IS) induces trained immunity in monocytes via epigenetic and metabolic reprogramming, resulting in augmented cytokine production. The authors conducted well-designed experiments to show that the aryl hydrocarbon receptor (AhR) contributes to IS-trained immunity by enhancing the expression of arachidonic acid (AA) metabolism-related genes such as arachidonate 5-lipoxygenase (ALOX5) and ALOX5 activating protein (ALOX5AP). Overall, this is a very interesting study that highlights that IS mediated trained immunity may have deleterious outcomes in augmented immune responses to the secondary insult in ESRD. Key findings would help to understand accelerated inflammation in CKD or RSRD.

Reviewer #1 (Public Review):

The manuscript by Park et. al. examines the interaction of macrophages with SARS-CoV-2 spike protein and subsequent inflammatory reactions. The authors demonstrate that following intranasal delivery of spike it rapidly accumulates in alveolar macrophages. Inflammation associated with internalized spike recruits neutrophils to the lung, where they undergo a cell death process consistent with NETosis. The authors demonstrate that modifications spike to contain high mannose reduces uptake of spike protein and limits the inflammation induced. This finding could have implications on vaccine development, as vaccines containing modified spike could be safer and better tolerated.

The authors use a number of different techniques, including in vivo modeling, imaging, human and murine systems to interrogate their hypotheses. These systems provide robust supporting information for their conclusions. There are two key aspects from the current manuscript which would add key evidence. The authors suggest that neutrophils exposed to spike protein undergo a process of NETosis. To confirm this hypothesis inhibitors of NETosis should be used to demonstrate that the cell death is prevented. Additionally, vaccination of a murine model with the modified spike protein would add additional support to the conclusion that modified spike protein would be less inflammatory while maintaining its utility as a vaccine antigen.

Reviewer #2 (Public Review):

The paper describes the various types of immune cells interacting with SARS-CoV-2 spike protein and undergoing pathological changes upon different routes of administration into mice mainly in the absence of human ACE-2. Multiple murine cell types in the lungs, the cremaster muscle and surrounding tissues, and the liver were studied. The spike interactions with various cells from the human peripheral blood ex vivo and in cultures were also examined. This study focused on hACE-2-independent effects of the spike protein in vivo in mice and in vitro on human leukocytes and touched upon the potential involvement of sialic-acid-binding lectins (Siglec) as non-hACE-2 receptors for spike. Hence, a multitude of aspects about spike-cell interactions was studied, although each was covered without significant depths and the key findings are difficult to parse through. Many inconsistencies are not explained and the critical experimental parameters and controls are missing. Ultimately, the main message of the study is buried among supporting vs confounding data.

Reviewer #3 (Public Review):

The study focuses on in vivo and in vitro cellular responses intranasal instillation of glycoforms and mutants of SARS-CoV2 spike trimer or spike bearing VLP. Collectively, the experiments suggest that SARS-CoV2 spike has pro-inflammatory roles through increase M1 macrophage associated cytokines and induction of neutrophil netosis, a proinflammatory cell death mechanisms. These effects seem largely independent of hACE2 interaction and partly depend upon interactions with scavenger receptors on macrophages and neutrophils. A strength of the study is that a number sophisticated methods are used, including intravital microscopy in the cramaster and liver as well as acute lung slice models, to look at uptake of the spike proteins and immune cell dynamics. The weakness is that some of the reagents maybe contaminated with uncharacterized glycoforms and some important controls, such as control spike protein and control VLP are unevenly applied or not included. Given the breadth of the studies, it would be ideal for the authors to prioritise strengthening the most important in vivo results in the best animal models with the strongest controls to be able to realise the full impact of the results.

Reviewer #1 (Public Review):

In the present work the authors explore the molecular driving events involved in the establishment of constitutive heterochromatin during embryo development. The experiments have been carried out in a very accurate manner and clearly fulfill the proposed hypotheses.

Regarding the methodology, the use of: i) an efficient system for conversion of ESCs to 2C-like cells by Dux overexpression; ii) a global approach through IPOTD that reveals the chromatome at each stage of development and iii) the STORM technology that allows visualization of DNA decompaction at high resolution, helps to provide clear and comprehensive answers to the conclusion raised.

The contribution of the present work to the field is very important as it provides valuable information on chromatin-bound proteins at key stages of embryonic development that may help to understand other relevant processes beyond heterochromatin maintenance.

The study could be improved through a more mechanistic approach that focuses on how SMARCAD1 and TOPBP1 cooperate and how they functionally connect with H3K9me3, HP1b and heterochromatin regulation during embryonic development. For example, addressing why topoisomerase activity is required or whether it connects (or not) to SWI/SNF function and the latter to heterochromatin establishment, are questions that would help to understand more deeply how SMARCAD1 and TOPBP1 operate in embryonic development.

Reviewer #2 (Public Review):

The manuscript by Sebastian-Perez describes determinants of heterochromatin domain formation (chromocenters) at the 2-cell stage of mouse embryonic development. They implement an inducible system for transition from ESC to 2C-like cells (referred to as 2C+) together with proteomic approaches to identify temporal changes in associated proteins. The conversion of ESCs to 2C+ is accompanied by dissolution of chromocenter domains marked by HP1b and H3K9me3, which reform upon transition back to the 2C-like state. The innovation in this study is the incorporation of proteomic analysis to identify chromatin-associated proteins, which revealed SMARCAD1 and TOPBP1 as key regulators of chromocenter formation.

In the model system used, doxycycline induction of DUX leads to activation of EGFP reporter regulated by the MERVL-LTR in 2C+ cells that can be sorted for further analysis. A doxycycline-inducible luciferase cell line is used as a control and does not activate the MERVL-LTR GFP reporter. The authors do see groups of proteins anticipated for each developmental stage that suggest the overall strategy is effective.

The major strengths of the paper involve the proteomic screen and initial validation. From there, however, the focus on TOPBP1 and SMARCAD1 is not well justified. In addition, how data is presented in the results section does not follow a logical flow. Overall, my suggestion is that these structural issues need to be resolved before engaging in comprehensive review of the submission. This may be best achieved by separating the proteomic/morphological analyses from the characterization of TOPBP1 and SMARCAD1.

Reviewer #3 (Public Review):

The manuscript entitled "SMARCAD1 and TOPBP1 contribute to heterochromatin maintenance at the transition from the 2C-like to the pluripotent state" by Sebastian-Perez et al. adopted the iPOTD method to compare the chromatin-bound proteome in ESCs and 2C-like cells generated by Dux overexpression. The authors identified 397 chromatin-bound proteins enriched only in ESC and 2C- cells, among which they further investigated TOPBP1 due to its potential role in controlling chromocenter reorganization. SMARCD1, a known interacting protein of TOPBP1, was also investigated in parallel. The authors observed increased size and decreased number of H3K9me3-heterochromatin foci in Dux-induced 2C+ cells. Interestingly, depletion of TOPBP1 or SMARCD1 also led to increased size and decreased number of H3K9me3 foci. However, depletion of these proteins did not affect entry into or exit from the 2C-like state. Nevertheless, the authors showed that both TOPBP1 and SMARCD1 are required for early embryonic development.

Although this manuscript provides new insights into the features of 2C-like cells regarding H3K9me3-heterochromatin reorganization, it remains largely descriptive at this stage. It does not provide new insights into the following important aspects: 1) how SMARCD1 associates with H3K9me3 and contributes to heterochromatin maintenance, 2) how TOPBP1 regulates the expression of SMARCD1 and facilitates its localization in heterochromatin foci, 3) whether the remodelling of chromocenter is causally related to the mutual transitions between ESCs and 2C-like cells. Furthermore, some results are over-interpreted. Additional experiments and analyses are needed to increase the strength of mechanistic insights and to support all claims in the manuscript.

Reviewer #1 (Public Review):

The paper from Hsu and co-workers describes a new automated method for analyzing the cell wall peptidoglycan composition of bacteria using liquid chromatography and mass spectrometry (LC/MS) combined with newly developed analysis software. The work has great potential for determining the composition of bacterial cell walls from diverse bacteria in high-throughput, allowing new connections between cell wall structure and other important biological functions like cell morphology or host-microbe interactions to be discovered. In general, I find the paper to be well written and the methodology described to be useful for the field. However, there are areas where the details of the workflow could be clarified. I also think the claims connecting cell wall structure and stiffness of the cell surface are relatively weak. The text for this topic would benefit from a more thorough discussion of the weak points of the argument and a toning down of the conclusions drawn to make them more realistic.

Specific points:

1) It was unclear to me from reading the paper whether or not prior knowledge of the peptidoglycan structure of an organism is required to build the "DBuilder" database for muropeptides. Based on the text as written, I was left wondering whether bacterial samples of unknown cell wall composition could be analyzed with the methods described, or whether some preliminary characterization of the composition is needed before the high-throughput analysis can be performed. The paper would be significantly improved if this point were explicitly addressed in the main text.

2) The potential connection between the structure of different cell walls from bifidobacteria and cell stiffness is pretty weak. The cells analyzed are from different strains such that there are many possible reasons for the change in physical measurements made by AFM. I think this point needs to be explicitly addressed in the main text. Given the many possible explanations for the observed measurement differences (lines 445-448, for example), the authors could remove this portion of the paper entirely. Conclusions relating cell wall composition to stiffness would be best drawn from a single strain of bacteria genetically modified to have an altered content of 3-3 crosslinks.

Reviewer #2 (Public Review):

The authors introduce "HAMA", a new automated pipeline for architectural analysis of the bacterial cell wall. Using MS/MS fragmentation and a computational pipeline, they validate the approach using well-characterized model organisms and then apply the platform to elucidate the PG architecture of several members of the human gut microbiota. They discover differences in the length of peptide crossbridges between two species of the genus Bifidobacterium and then show that these species also differ in cell envelope stiffness, resulting in the conclusion that crossbridge length determines stiffness.

The pipeline is solid and revealing the poorly characterized PG architecture of the human gut microbiota is worthwhile and significant. However, it is unclear if or how their pipeline is superior to other existing techniques - PG architecture analysis is routinely done by many other labs; the only difference here seems to be that the authors chose gut microbes to interrogate.

I do not agree with their conclusions about the correlation between crossbridge length and cell envelope stiffness. These experiments are done on two different species of bacteria and their experimental setup therefore does not allow them to isolate crossbridge length as the only differential property that can influence stiffness. These two species likely also differ in other ways that could modulate stiffness, e.g. turgor pressure, overall PG architecture (not just crossbridge length), membrane properties, teichoic acid composition etc.

Reviewer #1 (Public Review):

In this manuscript, "Diminishing neuronal acidification by channelrhodopsins with low proton conduction" by Hayward and colleagues, the authors report on the properties of novel optogenetic tools, PsCatCh2.0 and ChR2-3M, that minimize photo-induced acidification. The authors point out that acidification is an undesirable side-effect of many optogenetic approaches that could be minimized using the new tools. ChRs are known to acidify cells, while Arch are known to alkalize cells. This becomes particularly important when optical stimulation is prolonged and pH changes can become significant. pH is known to affect neuronal excitability, vesicular release, and more. To develop novel optogenetic tools with minimal proton conductances, the authors combined channelrhodopsin stimulation with a red-shifted pH sensor to measure pH during optogenetic stimulation. The authors report that optogenetic activation of CheRiff caused slow cellular acidification. 150 seconds of illumination caused a 3-fold increase in protons or approximately a 0.6 unit pH change that returned to baseline very slowly. They also found that pH changes occurred more rapidly, and recovered more rapidly, in dendrites. The authors go on to robustly characterize PsCatCh2.0 and ChR2-3M in terms of their proton conductances, photocurrent, kinetics, and more. They convincingly show that these constructs induced reduced acidification while maintaining robust photocurrents. In sum, this manuscript shows important findings that convincingly characterizes 2 optogenetic tools that have reduced pH artifacts that may be of broad interest to the field of neuroscience research and optogenetic therapies.

Reviewer #2 (Public Review):

In this paper, the authors utilize optogenetic stimulation and imaging techniques with fluorescent reporters for pH and membrane voltage to examine the extent of intracellular acidification produced by different ion-conducting opsins. The commonly used opsin CheRiff is found to conduct enough protons to alter intracellular pH in soma and dendrites of targeted neurons and in monolayers of HEK293T cells, whereas opsins ChR2-3M and PsCatCh2.0 are shown to produce negligible changes in intracellular pH as their photocurrents are mostly carried by metal cations. The conclusion that ChR2-3M and PsCatCh2.0 are more suited than proton conducting opsins for optogenetic applications is well supported by the data.

Reviewer #1 (Public Review):

The blood-brain barrier separates neural tissue from blood-borne factors and is important for maintaining central nervous system health and function. Endothelial cells are the site of the barrier. These cells exhibit unique features relative to peripheral endothelium and a unique pattern of gene expression. There remains much to be learned about how the transcriptome of brain endothelial cells is established in development and maintained throughout life.

The manuscript by Sadanandan, Thomas et al. investigates this question by examining transcriptional and epigenetic changes in brain endothelial cells in embryonic and adult mice. Changes in transcript levels and histone marks for various BBB-relevant transcripts, including Cldn5, Mfsd2a and Zic3 were observed between E13.5 and adult mice. To perform these experiments, endothelial cells were isolated from E13.5 and adult mice, then cultured in vitro, then sequenced. This approach is problematic. It is well-established that brain endothelial cells rapidly lose their organotypic features in culture (https://elifesciences.org/articles/51276). Indeed, one of the primary genes investigated in this study, Cldn1, exhibits very low expression at the transcript level in vivo but is strongly upregulated in cultured ECs.

(https://elifesciences.org/articles/36187 ; https://markfsabbagh.shinyapps.io/vectrdb/)

This undermines the conclusions of the study.

An additional concern is that for many experiments, siRNA knockdowns are performed without validation of the efficacy of the knockdown.

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

Reviewer #2 (Public Review):

Sadanandan et al describe their studies in mice of HDAC and Polycomb function in the context of vascular endothelial cell (EC) gene expression relevant to the blood-brain barrier, (BBB). This topic is of interest because the BBB gene expression program represents an interesting and important vascular diversification mechanism. From an applied point of view, modifying this program could have therapeutic benefits in situations where BBB function is compromised.

The study involves comparing the transcriptomes of cultured CNS ECs at E13 and adult stages and then perturbing EC gene expression pharmacologically in cell culture (with HDAC and Polycomb inhibitors) and genetically in vivo by EC-specific conditional KO of HDAC2 and Polycomb component EZH2.

This reviewer has several critiques of the study.

First, based on published data, the effect of culturing CNS ECs is likely to have profound effects on their differentiation, especially as related to their CNS-specific phenotypes. Related to this, the authors do not state how long the cells were cultured.

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

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

Reviewer #1 (Public Review):

The paper describes a robotic system that can be used for prolonged recording of forced activity in crawling Drosophila larvae. This is mostly intended to be a proof of principle description of a tool potentially useful for the community. The system - whose value lies completely in its reproducibility and adoption - is only superficially described in the paper, but a more detailed description is made available through Github, along with the software used for the collection and analysis of data.

There is good, convincing evidence this can work as some sort of "larval conveyor belt", used to artificially prolong food crawling behaviour in the animals. More could be said about the ecological implications of the assay (for instance: how relevant is it to an animal's natural behaviour? Does the system introduce artifactual distortions in the analysis, driven by the fact that animals crawl greater distances than they would normally crawl in nature? Will this extensive activity affect their development to pupation or adulthood?).

Reviewer #2 (Public Review):

This study describes the development of a robotic system that allows investigators to track the movements of Drosophila larvae for extremely long time durations. Prior studies were limited by the fact that tracking of larval movements needed to be stopped whenever the animal reached the edge of a behavioral arena. This new study overcomes this limitation with a robot arm that gently picks up the larvae when they reach the edge of the arena and then gently releases them again so that tracking can be resumed. The very long periods of data acquisition are performed with a video camera that provides a low-resolution 64x64 pixel representation of the larvae. Nevertheless, the authors are able to extract postural information from the animals using a sophisticated machine vision based neural network. The authors use this system to continuously track the behaviors of individual larvae for six hours in the presence or absence of a thermal gradient. They argue that high inter-animal variability in a navigation index occurs in the presence of a thermal gradient but not in its absence. The intra-animal mean navigation also appears to be bimodal, apparently switching between "non-navigating" and "strongly navigating" states (not the authors' words). Interestingly, when only the population means are investigated a single mode is indicated with an overall weak navigation index. This comparison very nicely illustrates the power of this method to reveal richness in the data that leads to insights that cannot be observed with short-term measurements. Another impressive feature of the robotic system design is that it is capable of delivering small droplets of food to individual larvae. This allowed the authors to track a single larva for a remarkable 30 hours in which it is seen to crawl for more than 48 meters. Overall, the robotic system presented here will allow the researchers to investigate behaviors of larvae in long-term experiments in ways that were previously unimaginable.

Reviewer #3 (Public Review):

"Continuous, long-term crawling behavior characterized by a robotic transport system" by Yu et al. presents their new robotic device to track, reposition, and feed Drosophila larvae as they crawl on an arena. By using a water droplet (or if necessary, suction) to transport larvae from the edge of the arena to the middle, long behavior trajectories can be recorded without losing larvae from the arena or camera field of view. The picker robot is also able to dispense small amounts of apple juice at precise locations to keep larvae alive for extended periods although the food was not sufficient to trigger molting and the development to the next instar stage.

The approach is interesting, but the authors could provide more details on why the approach is necessary for non-expert readers. For example, what are the advantages of using the robot picker compared to simply confining larvae in a closed arena? It's not obvious (to me) that being picked back to the center of the arena is a smaller perturbation compared to running into a chamber wall and changing direction.

The first paragraph of the introduction emphasizes the multiple time scales that are relevant for behavior from rapid stimulus response up to developmental times. This is to set the context of the authors' contribution but I'm not sure it's a fair representation of the state of the art. For example, the authors state that high-bandwidth measurement over long times is prohibitive and cite three Drosophila papers, but there are home-cage monitoring systems that allow continuous recording of mouse behavior over long times with high resolution. At the other end of the spectrum, there have been some long-term behaviour experiments done on worm behaviour with reasonably high time resolution (e.g Stern et al. 10.1016/j.cell.2017.10.041).

The authors train a neural network to segment and track the larvae, however, little information is given on the training process and I don't think it would be possible to reproduce the model based on the description. More details of the network, hyperparameters, and training data would be required to evaluate it.

The authors also state several times that larval identity is maintained throughout the recording, but this isn't quantified. It's not clear whether identity is maintained across collisions of two or more animals by the tracking algorithm or whether these collisions simply don't happen in their data because density is low.

The environment is nominally isotropic, but once larvae have been crawling on the surface for hours, including periodic feeding, there will likely be multiple gradients the larvae may sense. This may not be observable in the data, but should perhaps be mentioned in the text.

The authors show that the picking action results in a small but detectable increase in speed. The degree of perturbation overall depends on the picking frequency so some quantification of the inter-pick time interval would help to interpret whether this perturbation is relevant for a particular experiment. Is there a difference in excitation when larvae are picked successfully on the first try compared to when multiple tries or suction are required?

From the reconstructed trajectory in Figure 4, this interval looks very long compared to speed increase after picking. When reconstructing the trajectory, how are the segments joined? Is it simply by resetting the xy position or also updating rotating to match the previous direction of travel? (I'm guessing the larva can rotate during transport?)

The authors present a simple model in Figure 6 to illustrate the differences between individuals that can be hidden when looking at population distributions. However, the differences they show in the simulation don't seem relevant to the differences they observe in the experiments. Specifically, Fig. 6A and B show a contrast between individuals with similar mean speeds compared to individuals with different (but still unimodal) mean speeds. In contrast, the experimental data in Fig. D shows individual distributions that are quite similar but that are bimodal. So, there is indeed a difference between the individual distributions that is obscured in the population distribution, but is there evidence of larval personality types (line 444)? Similarly, the sentence beginning line 381 doesn't seem right either.

Reviewer #1 (Public Review):

This manuscript provides a structural analysis of bushy cells in the mouse cochlear nucleus. The analysis uses volume electron microscopy techniques to describe bushy cell-auditory nerve synapses and bushy cell dendrites. The analysis takes a morphological analysis of bushy cells to a new level, and the computational modeling is well done. The models are used to predict busy cell behavior, which leads to a major concern. The authors make reasonable assertions, but all of these need to be validated by electrophysiological studies before they can be treated as fact. Instead, they should be treated as predictions. For example, in the conclusions from the model section, that endbulb size does not strictly predict synaptic efficacy should be modified from an assertion to a prediction.

Reviewer #2 (Public Review):

This is an interesting manuscript in which the authors demonstrate the power of serial section reconstruction at the EM level of a volume within the anterior ventral cochlear nucleus (aVCN) containing bushy cells and their large afferent synapses - the endbulbs of Held. Integration of this information with compartmental modelling of the neuronal excitability is then used to make observations about the form and function of these neurons and their synaptic inputs. While this is technically impressive (in regards to both the structure and modelling) there are significant weaknesses because this integration makes massive assumptions and lacks a means of validation; for example, by checking that the results of the structural modelling recapitulate the single-cell physiology of the neuron(s) under study. This would require the integration of in vivo recorded data, which would not be possible (unless combined with a third high throughput method such as calcium imaging) and is well beyond the present study. The authors need to be more open about the limitations of their observations and their interpretations and focus on the key conclusions that they can glean from this impressive data set. The manuscript would be considerably improved by re-writing to focus the science on the most important results and provide clear declarations of limitations in interpretation.

Reviewer #3 (Public Review):

Bushy cells are one of the principal neurons in the cochlear nucleus that provide temporal information to higher auditory nuclei to compare sound signals from both ears. One prominent feature in the auditory processing of bushy cells is that they show enhanced temporal responses compared to the auditory nerve (AN) inputs, thus providing a better temporal representation of the acoustic signals. Another feature of the AN-bushy cell circuit is that AN fibers form large synapses termed "endbulbs" around the soma of bushy cells. Scientists have proposed that the temporal enhancement can be due to the coincidence detection of subthreshold convergent AN inputs, or a first-spike latency-based detection of convergent supra-threshold inputs. However, testing these hypotheses requires knowledge of the detailed anatomical arrangement of the AN inputs onto bushy cells. This paper provides a first look at the 3-D organizations of the pre- and postsynaptic structures of mouse bushy cells at a nanoscale resolution. Furthermore, the authors create a morphology-constrained biophysical model to examine how these structures may affect synaptic integration and auditory processing.

The main finding of the paper is that the authors found two input motifs in the AN-bushy cell circuit: one with all small, subthreshold endbulb inputs (all < 180 um2), and the other with 1-2 large, suprathreshold endbulb inputs (> 180 um2) plus other smaller endbulb inputs. Using modeling, the authors argue that the former group correlates with a physiological phenotype of "coincidence detection", and the latter correlates with a phenotype termed "mixed-mode detection". "Coincidence detection" cells require the coincident firing of many subthreshold presynaptic inputs to evoke a postsynaptic spike; "mixed-mode" cells can either have postsynaptic spikes evoked by the largest input(s) alone, or by the coincident firing of small (plus large) inputs. Interestingly, the authors found that even though the large inputs alone can trigger spikes in the "mixed mode" cells, smaller inputs can further enhance the temporal precision of the spikes. The structural data are of very high quality and clearly show the endbulb inputs comprise various sizes. Whether these inputs are really supra/sub-threshold remains to be explored physiologically, but nevertheless, the model provides a hypothesis for the functional roles of the endbulb of different sizes.

In addition to the finding of "two convergent motifs', the authors also report a first complete map of synaptic inputs to a single bushy cell, and structures that have not been observed before, such as synapses at axon-hillock and axon initial segment, dendritic "hub", "braided" dendrites, non-innervated dendrites, etc. These data, like the previous "two input motifs" observation, are also of very high quality and can be useful resources for the ultrastructural study of the bushy cells.

Strengths:<br /> The strengths of this paper are that the authors obtained unprecedented high-resolution 3-D images of the AN-bushy cell circuit, and they implemented a biophysical model to simulate the neural processing of AN inputs based on these structural data. The 3-D reconstruction of the pre- (input organization) and post- (dendrites and axons) synaptic structures of bushy cells are of high quality, as exemplified by the high-resolution figures and animations. The biophysical modeling, although lacking comparison with in vivo physiological data due to the chosen species (mice), is also solid and well documented. The combination of high-resolution imaging and structure-based modeling, together with the detailed documentation, provides rich information for not only auditory scientists but non-auditory scientists who want to use similar techniques to explore neural circuits.

Weaknesses:<br /> Despite the high quality of the data, the paper is marred by the species they chose: there are very few published in vivo single-unit results from mouse bushy cells, so it is hard to evaluate how well the model predictions fit the real-world data, and how the structural findings address the "fundamental questions" in physiology. If we look at data from other animals such as cats and gerbils, it is true that high-frequency (globular) bushy cells show envelope phase locking, but compared to ANs they are at best only moderately enhanced (gerbils: Frisina et al. 1990: Fig 7 and 10; cats: Joris and Yin 1998 Fig 4); the most prominent enhancement is actually to the temporal fine structures of low-frequency bushy cells (cells tuned to < 1 kHz), which mice lack. Furthermore, the temporal modulation transfer function (tMTF, i.e. the vector strengths vs modulation frequency plots in Fig 7O of the paper) of (globular) bushy cells are mostly low-pass filtered, with a cutoff frequency close to 1 kHz, and the highest vector strength rarely surpasses 0.9 (cats: Rhode 1994 Fig 9, 16, Rhode 2008 Fig 8G, Joris and Yin 1998 Fig 7; and there's one report from mice: Kopp-Scheinpflug et al 2003 Fig 8). Thus, the band-pass tMTFs tuned to 100-200 Hz with vector strengths > 0.9 or 0.95 in this paper (Fig 7O, Fig 8M) do not really match known physiology (in non-mouse species). Again, we know very little about in vivo physiology of mouse (globular) bushy cells and there is of course a possibility that responses in mice may be closer to the predictions of this paper. No rationale (e.g. use of molecular tools or in vitro physiology) is given why the authors focus on the mouse. It seems that the analyses provided here could as well have done on a species with good low-frequency hearing, which may have provided a much more interesting case for understanding the spectacular temporal transformation performed by bushy cells.

Reviewer #4 (Public Review):

The authors have collected an impressive array of physiological data and provided some beautiful 3D images of SBCs with dendrites. These are clearly strengths. The computational models for mechanisms of SBC responses, however, are made to fit what may be inadequate anatomical data. Instead of conclusions, perhaps they need to reword their discussions to refer to the anatomy as hypothetical substrates.

Reviewer #1 (Public Review):

Ichinose et al., utilize a mixture of cultured hippocampal neurons and non-neuronal cells to identify the role of the transmembrane protein teneurin-2 (TEN-2) in the formation of inhibitory synapses along the dendritic shaft. First, they identify distinct clusters of gephyrin that are either actin-rich, microtubule-rich or contain neither actin nor microtubules and find that TEN-2 is enriched in microtubule-rich gephyrin clusters. This leads the authors to hypothesize that TEN-2 recruits microtubules (MTs) through the plus end binding protein EB1 when successfully matched with a pre-synaptic partner, and perform a variety of experiments to test this hypothesis. The authors then extend this finding to state quite strongly throughout the paper, including in the title, that TEN-2 acts as a signpost for the unloading of cargo from motor proteins without providing any supporting evidence. They use previous work to justify this conclusion, but without actual experiments to back up the claim, it seems like a reach.

The strength of the paper lies in the various lines of evidence that the authors employ to assess the role of TEN-2 in MT recruitment and synaptogenesis. They have also been very thorough in validating the expression and functionality of various knock-in constructs, knock-down vectors and antibodies that were generated during the study. However, there are some discrepancies in the findings that have not been addressed satisfactorily, as well as some instances where the data presented is not of sufficient quality to support the conclusions derived from them.<br /> 1. The emphasis placed on the clustering analysis presented in figure 1 and the two associated supplementary figures is puzzling, since the conclusion derived from the results presented would be that Neuroligin 2 (NLGN2) is the strongest candidate to test for a relationship to MT recruitment at inhibitory post synapses. Instead, the authors cite prior evidence to exclude NLGN2 from subsequent analysis and choose to focus on TEN2 instead.<br /> 2. It is difficult to reach the same conclusion as the authors from the images and intensity plot shown on Figure 2 E and F. While there seems to be an obvious reduction in expression levels between the TEN2N-L and TEN2TM constructs, neither seem to co-localize with EB1.<br /> 3. The authors mimic the activity of TEN-2 at the inhibitory post synapse in non-neuronal cells by immobilizing HA- tagged TEN constructs in COS-7 cells as a proxy for synaptic partner matching. Using this model, they find that by immobilizing TEN2N-L, which contains EB1 binding motifs, MTs are excluded from the cell periphery (Figure 3D). This contradicts their conclusion that MTs are recruited through EB1 by TEN-2 on synaptic partner matching. Later in the paper, when they use the same TEN2N-L construct as a dominant negative in neuronal cells, they find that MTs are recruited the membrane, even if TEN2N-L is not immobilized by synaptic partner matching (Figure 6C). Taken together, these findings call into question the sequence of events driven by TEN-2 during synaptogenesis.<br /> 4. It is unclear how the authors could conclude that TEN-2 is at the semi-periphery (?) of inhibitory post synapses from the STORM data that is presented in the paper. Figure 4D and 4F show comparisons of Bassoon and TEN-2 localization vs TEN-2 and gephyrin, but the image quality is not sufficient to adequately portray a strong distinction in the distance of center of mass, which is also only depicted for the TEN2-Gephyrin pair and not the TEN2-Bassoon pair in Figure 4J.<br /> 5. The authors do not satisfactorily explain why gephyrin appears to have completely disappeared in the TEN2N-L condition (Figure 6A), instead of appearing uniformly distributed as one would expect if MTs are indiscriminately recruited to the membrane by the dominant negative construct that remains unanchored.<br /> 6. In a similar critique to that of Figure 2E and F, the distinction that the authors wish to portray between the effect of TEN2TM and TEN2N-L constructs on EGFP-TEN-2 and MAP2 colocalization (Figure 6 E and F) appear to be driven by a difference in overall expression levels of EGFP-TEN2 rather that a true difference in localization of TEN-2 and MTs.

Reviewer #2 (Public Review):

Maturation of inhibitory synapses requires multiple vital biological steps including, i) translocation of cargos containing GABAARs and scaffolds (e.g. gephyrin) through microtubules (MTs), ii) exocytosis of inhibitory synapse proteins from cargo followed by the incorporation to the plasma membrane for lateral diffusion, and iii) incorporation of proteins to inhibitory synaptic sites where gephyrin and GABAARs are associated with actin. A number of studies have elucidated the molecular mechanisms for GABAARs and gephyrin translocation in each step. However, the molecular mechanisms underlying the transition between steps, particularly from exocytosis to lateral diffusion of inhibitory proteins, still need to be elucidated. This manuscript successfully characterizes three stages of inhibitory synapses during maturation, cluster1: an initial stage that receptors are being brought in and out by the MT system; cluster2: lateral diffusion stage; cluster 3: matured postsynapses anchored by gephyrin and actin, by quantifying the abundance of MAP2 or Actin in inhibitory synapse labeled by gephyrin. Importantly, the authors' findings suggest that TEN2, a trans-synaptic adhesion molecule that has two EB1 binding motifs, plays an important role in the transition from clusters 1 to 2, and inhibitory synapse maturation. The imaging results are impressive and compelling, these data will provide new insights into the mechanisms of protein transport during synapse development. However, the present study contains several loose ends preventing convincing conclusions. Most importantly, (1) it remains more TEN2 domain characterization on inhibitory synapse maturation, (2) further validation of the HA knock-in TEN2 mouse model is required, and (3) it requires additional physiology data that complement the authors' findings.

Reviewer #3 (Public Review):

In this paper, Ichinose et al. examine mechanisms that contribute to building inhibitory synapses through differential protein release from microtubules. They find that tenurin-2 plays a role in this process in cultured hippocampal neurons via EB1 using a variety of genetic and imaging methods. Overall, the experiments are generally designed well, but it is unclear whether their findings offer a significant advance. The experimental logic flow and rational difficult for readers to follow in the manuscript's current form.

Strengths:<br /> 1) The experiments are generally well designed overall, and appropriate to the questions posed.<br /> 2) Several experimental methods are combined to validate key results.<br /> 3) Use of cutting-edge technologies (i.e. STORM imaging) to help answer key questions in the paper.

Weakness:<br /> 1) Simplifying the text and story line would go a long way to ensure the study results are more effectively communicated. Additional specific suggestions are provided in the recommendations for the authors.<br /> 2) The introduction overall would benefit from simplification so that the reader is given only the information they need to know to understand the question at hand.<br /> 3) MT dynamics are important for paper results, but the background in the paper does not appropriately introduce this topic.<br /> 4) It is a bit unclear from the abstract and introduction how the findings of this paper have significantly advanced the field or taught something fundamentally new about how inhibitory synapses are regulated.<br /> 5) Figure 1 - Line 109, it is obscure why "it was found appropriate" to divide the data into three clusters. This section would better justified by starting with cellular functions and then basing the clusters on these functions.<br /> 6) The proteomic screen and candidate selection is not well justified and the logic steps for arriving at TEN2 are a bit weak. Again, less is more here.<br /> 7) Fig. 2 - The authors should consider whether EB1 overexpression would have functional consequences that alter the results and colocalization.<br /> 8) Fig. 3 - Is immobilization of COS cells using HA tag antibodies a relevant system for study of these questions?<br /> 9) Fig. 4 - The authors should confirm post-synaptic localization in vivo (brain).<br /> 10) Figure 4D-E - The way the STORM results are presented is confusing. The authors state is shows that TEN2 is postsynaptic but before this say that the Abs are the same size as the synaptic cleft so that the results cannot be considered conclusive. This issue should be resolved.<br /> 11) Figure 5 -The authors should examine the levels of gephyrin relative to the levels of knockdown given the knockdown variability.<br /> 12) Functional validation of a reduction in inhibition following TEN2 manipulation would elevate the paper.<br /> 13) Figure 6E - The expression levels of TEN2TM and TEN2NL are important to the outcome of these experiments. How did the authors ensure that the levels of two proteins were the same to begin with?

Reviewer #1 (Public Review):

Wu et al. sought to investigate the biological role of GPR110 in modulating hepatic lipid metabolism. The authors demonstrate a pathological role of GPR110 in promoting hepatic steatosis and generalized metabolic syndrome in a mouse model of diet-induced obesity. Furthermore, the authors identify enhanced SCD1 expression as an underlying mechanism promoting GPR110-induced metabolic dysfunction. Finally, the authors provide clinically relevant human data demonstrating a positive correlation between GPR110 expression and degree of hepatic steatosis. The strengths of this study include the rigorous design and execution of experiments, the utilization of gain and loss of function as well as pharmacological and genetic approaches, and the clinically relevant human data presented. The claims are supported by robust data. These findings have the potential to impact the field of metabolism in general, given their findings indicate targeting GPR110 can reverse diet induced obesity and metabolic syndrome. Only minor weaknesses were noted in regard to further interpretation of the data.

Reviewer #2 (Public Review):

In this manuscript, the authors have proposed that the suppression of hepatic GPR110, known as a tumorigenic gene, could improve non-alcoholic fatty liver disease (NALFD). With AAV-mediated GPR110 overexpression or a GalNAc-siGPR110 experiment, they have suggested that GPR110 could increase hepatic lipids through SCD1.

Major comments<br /> 1. Although the authors claimed that GPR110 could enhance SCD1-mediated hepatic de novo lipogenesis, the level of GPR110 expression was decreased in obese mice (Figure 1E-F). However, it has been reported that the levels of de novo lipogenic genes, including SCD1, are upregulated in HFD-fed mice (PMID: 18249166, PMID: 31676768). Thus, they should show the levels of hepatic lipids and lipogenic gene expression, including SCD-1, in liver tissues from NCD vs. HFD-fed mice, which will provide insights between GPR110 level and hepatic lipogenic activity.

2. In Figure 2, the authors have characterized metabolic phenotypes of hepatic GPR110 overexpression upon HFD, exhibiting significant phenotypes (including GTT, ITT, HOMA-IR, serum lipids, and hepatic lipid level). However, it is likely that these phenotypes could stem from increased body weight gain. Since they cannot explain how hepatic GPR110 overexpression could increase body weight, it is hard to conclude that the increased hepatic lipid level would be a direct consequence of GPR110 overexpression. Also, given the increased fat mass in GPR110 overexpressed mice, they should test whether GPR110 overexpression would affect adipose tissue. Along the same line, they have to carefully investigate the reason of increased body weight gain in GPR110 overexpressed mice (ex., food intake, and energy expenditure).

3. GPR110 enhances hepatic lipogenesis via SCD1 expression (Figures 5 and 6). To verify whether GPR110 would specifically regulates SCD1 transcript, they have to provide the expression levels of other lipogenic genes, including Srebf1, Chrebp, Acaca, and Fasn. Also, measurement of de novo lipogenic activity using primary hepatocyte with GPR110 overexpression or knockdown would be valuable to affirm the authors' proposed model.

4. In Figure 6, the author should provide the molecular mechanisms how GPR110 signaling could enhance SCD-1 transcription.

5. Figure 9C shows the increased level of GPR110 with NAFLD severity. They should test whether the levels of hepatic GPR110 and SCD-1 might be elevated in a severe NAFLD mouse model. If it is the case, it would be better to show the beneficial effects of GPR110 suppression against NAFLD progression using a severe NAFLD (ex., NASH) mouse model.

Reviewer #3 (Public Review):

In this study, the authors examined the expression of GPR110 in a HFD-fed mouse model and validated their findings in human samples. They then performed both gain- and loss-of-function studies on the cellular and systemic metabolic effects of manipulating the levels of GPR110. They further demonstrated that SCD-1 was a downstream effector of GPR110, and the effects of GPR110 could be mediated by SCD-1. This study provides a novel target in NAFLD. Overall the data and analyses well performed and convincing. As the GPR110-SCD1-lipid metabolic phenotype axis is a central theme of the study, I would suggest that the authors further discuss the connection between GPR110 and SCD1, especially the persistent upregulation of SCD1 at late stage of HFD-fed mice (obese mouse model) when GPR110 is very low, for example, whether another regulator plays a more relevant role at this time point.

Reviewer #1 (Public Review):

The first defined that FAM76B inhibited the NF-κB-mediated inflammation by modulating translocation of hnRNPA2B1 to cytosol, where hnRNPA2B1 bound to IKK and released active NFkB that translocated into nuclear and initiated inflammation.

Reviewer #2 (Public Review):

This manuscript describes a study of a novel role of FAM76B in regulation of NF-kB-mediated inflammation, specially in neuroinflammation both in animal model and human brain disease. This study was logically designed and laid out and data from gene knockdown and knockout cell line and animals strongly support the note that FAM76B is involved in the neuroinflammatory diseases. This notion was further confirmed in patients with brain inflammatory diseases. Importantly, the authors further dissected the cellular molecular action of FAM76B in regulation of NF-kB pathway through binding to the hnRNPA2B1. However, it is still unclear how the FAM76B regulates/or affects the cytoplasmic translocation of hnRNPA2B1 in brain cells after a variety of inflammatory stimuli or injuries. Nonetheless, this study greatly enhances our understanding of the mechanisms of the brain inflammation and inflammation related brain degeneration.

Reviewer #1 (Public Review):

The authors have investigated the effect of aerobic exercise on the decline in cerebral blood flow and cognitive function in old mice. Using appropriately two-photon microscopy and optical coherence tomography they found that aerobic exercise restored capillary blood flow and oxygenation in the white matter more than in the grey matter in old female mice. Interestingly, this aerobic exercise also ameliorated cognitive performance in these mice. The data obtained strongly supports the hypothesis and supports the conclusion of the study. Nevertheless, it would be important to compare the effects of aerobic exercise in old mice to its effects in young animals. It will be also interesting to know if the protective effect of exercise is similar in male mice.

This work brings new insights into the comprehension of the age-associated changes in cerebral microcirculation and in the protective effects of aerobic exercise.

Reviewer #2 (Public Review):

While aging is known to cause cerebral blood flow deficits, some studies suggested that exercise could reverse - at least partially - these deficits. In this study, the authors used technically-challenging techniques and approaches to test the hypothesis that 5 months of voluntary exercise reverses impairments in cerebrovascular function and cognition. Overall, I find the evidence for a favorable impact of exercise on microvascular perfusion and oxygenation convincing. The impact of exercise was most evident in the white matter and deep cortical tissues, which I believe to be a major finding of the study. The methods are very well-detailed and easy to follow. It is not clear, however, why the authors chose to study only one sex (female mice). This is an important consideration given that age-dependent hormonal changes could play a role in the findings. There are a few instances where it is unclear whether the number of vessels or animals were used for statistical analyses. It'd be very useful for the reader to understand why whisker stimulation led to a reduction in detected light intensity that reflects hyperemia as previously published by the authors (Sencan et al., 2022 JCBFM).

Reviewer #3 (Public Review):

The manuscript by Shin et al, "Aerobic exercise reverses aging-induced depth-dependent decline in cerebral microcirculation", addresses fundamental questions on the mechanism by which aerobic exercise can reverse several age-related dysfunctions in the cerebral vasculature. This work is solid as they use a wide range of complementary in vivo imaging modalities including two-photon fluorescent imaging, optical coherence tomography, and measurements of PO2 as well as behavioral tests. The experiments specifically examined region-specific differences in the young and aged vasculature and the response to aerobic exercise in superficial cortical areas and importantly in deeper white matter areas. This is a solid contribution because it provides additional understanding of age-related changes in the white matter microcirculation, a brain region where our understanding is incomplete. This work effectively sets the stage to further examine aging-related white matter degeneration, how aerobic exercise ameliorates the vascular decline in aging, and will potentially lead to novel interventions targeting the white matter.

Reviewer #1 (Public Review):

The manuscript by Huang, Li, et al. describes the identification of variants in the gene coding for p31 comet, a protein required for silencing the spindle assembly checkpoint or SAC, in women with recurrent pregnancy loss upon IVF. In three families mutations affecting splicing or expression of full-length protein were identified. The authors show that oocytes of the patients arrest in meiosis I, are most likely to fail to inactivate the SAC without a fully functional p31 comet. Indeed, the metaphase I arrest occurring in mouse oocytes upon overexpression of Mad2 can be rescued by overexpression of wild-type p31 comet, but not a truncated version. Injection of wt p31 comet into 6 human oocytes from one patient rescued the meiosis I arrest.

Main points:

The fact that inactivation of the SAC is required for anaphase I onset in human oocytes is not novel. Biallelic mutations of TRIP13 were shown to lead to the same phenotype (Zhang et al. Am J. Hum Gen., 2020).

No new mechanistic insights are obtained.

The authors propose a role for female fertility, however, also a male patient with a p31 comet variant is sterile.

The fact that the C-terminus of p31 comet is required for interaction with Mad2 and hence, turning off the SAC, is already known.

Reviewer #2 (Public Review):

In this manuscript by Huang et al. the authors explore the genetic underpinnings that may cause human oocyte meiotic arrest. The meiotic arrest of oocytes can cause female infertility leading patients to seek treatment at IVF clinics to assist in having genetically related babies. However, because oocytes fail to develop to MII, oocytes from these patients cannot be fertilized, leaving no current options for genetically related babies for patients with this pathology. Huang et al identified 50 IVF patients with this phenotype, and after the whole exome sequence, 3 patients had mutations in a spindle assembly checkpoint regulator, Mad1bp1. This study describes these mutations in detail, shows how these mutations affect Mad1bp1 expression, evaluates gross function in mouse oocytes, and explores therapeutic treatment in human oocytes. Overall, this is an important translational study that adds to the growing body of literature that genetic mutations impact oocyte quality and fertility.

In its current form, I find that the strengths exist in the analysis of the patients' genomes and pedigree information. This is unique data and is important for the field. The expression in oocytes, structure modeling, and conservation in evolution, while not essential for this study, add interesting information for the reader to consider. I sometimes find these distracting in manuscripts, but appreciate them here in this context. The conclusion using human oocytes to propose possible treatment takes the study to completion and is not an easy approach to carry out.

I do find some weaknesses that weaken the conclusions. The conclusion described is that the SAC is not satisfied in oocytes from these patients. The authors attempt to show this by analysis of mouse oocytes using polar body extrusion and its timing as an assay. There could be many reasons contributing to arrest, therefore a singular assay is not ideal to justify the conclusions. While I do suspect the authors are correct, an intact SAC should be shown at the molecular level to fully justify this conclusion. There are many assays routinely performed in mouse oocytes that the authors can consider (check papers by authors from Wassmann, FitzHarris, and Schindler labs for example).

Reviewer #1 (Public Review):

This work reports an important demonstration of how to predict the mutational pathways to antimicrobial resistance (AMR) emergence, particularly in the enzyme DHFR (dihydrofolate reductase). Epistasis, or non-additive effects of mutations due to their background dependence, is a major confounding factor in the predictability of protein evolution, including proteins that confer antimicrobial resistance. In the first approach, they used the Rosetta to predict the mutant DHFR-drug binding affinity and the resulting selection coefficient, which then became inputs to a population genetics model. In the second approach, they use the observed clinical/environmental frequency of the variants to estimate the selection coefficient. Overall, this work is a compelling demonstration that a mechanistic model of the fitness landscape could recapitulate AMR evolution; however, considering that the number of mutations and pathways is small, a more compelling description of the robustness of the results and/or limitations of the model is needed.

Major strengths:<br /> 1. This is a compelling multi-disciplinary work that combines a mechanistic fitness landscape of DHFR (previously articulated in literature and cited by the authors), Rosetta to determine the biophysical effects of mutations, and a population genetics model.<br /> 2. The study takes advantage of extensive data on the clinical/environmental prevalence of DHFR mutations.<br /> 3. Provides a careful review of the surrounding literature.

Major weakness:<br /> 1. Considering that the number of mutations and pathways being recapitulated is rather small, I would suggest a more detailed description of the robustness of the results. For example:<br /> a. Please report the P-value for the correlation of the predicted DDG_{binding, theory} and DDG_{binding, experimental}. If interested in showing the correct assignment of mutational effects, perhaps use a contingency matrix to derive a P-value.<br /> b. Although the DDG_binding calculation in Rosetta seems to converge (Appendix figures 3 and 4), I do not think the DDG values before equilibration should be included in the final DDG estimate. In practice, there is a "burn in" number of runs where the force field optimizes the calculation to account for potential clashes in the structure, etc. This is particularly important since the starting structures are modeled from homology. Consequently, the distributions of DDG that include the equilibration runs are multimodal (Appendix figure 2), which means that calculating an average may be inappropriate.<br /> 2. The geographical areas over which the mutational pathways are independently estimated are not isolated, allowing for the potential that an AMR variant in one region arose due to "migration" from another area. For example, the S58R-S117N is the most frequent double mutant of PvDHFR in geographically proximate Southern/Southeastern Asia (Fig. 4). To a certain extent, similar mutational patterns occur for PfDHFR in Southern/Southeastern Asia (Fig. 3). Although accounting for mutant migration in the model may be beyond the scope of the study, a clear argument for the validity of the "isolated island" assumption is needed.

Reviewer #2 (Public Review):

In this work the authors use a simple biophysical model to predict evolutionary trajectories of resistance to pyrimethamine - inhibitor of PfDHFR from P. falciparum and PvDHFR from P. vivax - pathogens causing malaria which presents a worldwide health concern. The authors use a simple fitness model that posits that selection coefficient -relative change in fitness between WT and mutant strains is determined by the fraction of unbound (to antibiotic inhibitor) DHFR. The population genetics simulations use the Kimura formula which is applicable to low mutation high selection regime where populations are monoclonal. The authors use computational tool Rosetta Flex ddG to assess binding of the antibiotic ligand to WT and mutant protein and compare their predicted evolutionary trajectories with lab evolution and data on naturally evolved variants worldwide and find semi-quantitative agreement, albeit sith significant variation in detail.

The paper is of potential interest as it presents one of the first (but not the first) attempts to compare evolutionary dynamics based on biophysics inspired fitness model with laboratory evolution and natural data for very important problem of emergence and fixation of antibiotic resistant alleles. As such it can be a useful starting point for more detailed and biophysical realistic models of evolution of resistance against anti-DHFR drugs.

Reviewer #1 (Public Review):

This work develops new and improved methods for tracking and quantifying yeast cells in time-lapse microscopy. Overall, the manuscript presents exceedingly clever solutions to many practical data analysis problems that arise in microfluidics, some of which may be useful in other image analysis settings.

I find the manuscript is at times very dense and technical and is missing context for a general audience. Hard to know what are the most important contributions, and the authors assume the reader is familiar with many details of their previous work/field. Claims are made with little explanation, context or scientific logic.

Reviewer #2 (Public Review):

Microfluidics-assisted live-cell imaging is often the method of choice to gain insight into the growth behavior of single cells, in particular unicellular organisms with simple shapes. While growth rate measurements of symmetrically dividing and rod-shape organisms such as E.coli or fission yeast are simplified by their geometry, measurements of the common model organism budding yeast are more complicated due to growth in three dimensions and asymmetric 'budding'. As a consequence, analysis of live-cell imaging experiments typically still requires time-consuming manual work, in particular, to correct automated segmentation and tracking, assign mother-bud pairs, and determine the time point of cell division. In the present manuscript, Pietsch et al. aim to address this important issue by developing deep-learning-based analysis software named BABY for the automated extraction of growth rate measurements performed with microfluidic traps that are designed to keep mother cells, but quickly lose newborn daughters.

To achieve this, Pietsch et al. introduce several innovative approaches. 1.) In contrast to previous deep-learning segmentation tools they allow 3D data (z-stacks) as inputs and allow for overlapping segmentation masks. 2.) By introducing 3 different object categories based on their size, they can take more specified approaches for each category and for the segmentation of overlapping objects 3.) By using cell edges and bud necks as additional predicted channels, they facilitate downstream post-processing of segmentation masks and mother-bud pairing, respectively. 4.) By using machine learning to predict tracking and mother-bud pairs from multiple features, they develop a novel approach to automate these steps. Using their automated analysis pipeline, the authors then study the growth behavior in different mutants and propose a novel mechanism in which growing buds are regulated by a combination of a 'sizer' and a 'timer' mechanism.

This manuscript introduces exciting steps towards a fully automated analysis of bright-field microscopy data of growing yeast cells, which makes this manuscript an important contribution to the field. However, in part the quantitative reporting on the actual performance is not sufficient. For example, what is the actual overall success-rate in predicting mother-bud pairs? How accurately can cell cycle durations be predicted? This lack of information makes it hard to evaluate how appropriate using fully automated BABY actual is. In addition, the experiments supporting the major biological insight, i.e. the sizer-timer transition for bud growth are rather limited, and further experiments would be needed to strengthen this conclusion.

Reviewer #3 (Public Review):

In the work presented in "A label-free method to track individuals and lineages of budding cells", Pietsch et al. use multiple machine learning approaches to identify, delineate, and track yeast cells in microscopy images.

I commend the authors for putting a lot of work into this manuscript and coming up with many new ideas to solve their problem of interest. However, throughout the manuscript, I felt that this manuscript does not work well as a 'methods' paper. Maybe it should have been a paper about the biology, which I find very interesting. My main reason for finding this manuscript not well-suited for a methods paper is that their approach as well as the goal are so specific that it may not be readily adopted by others. I would like to list the number of limitations and particularities of their set-up to support this conclusion:

- The whole problem of small cells not being in focus in a single plane is to a large part due to the high ceiling of the authors' microfluidic chips (6 um according to Crane et al.). Other microfluidic chips have much lower ceilings, keeping cells essentially in 2D. If Pietsch et al. used a lower ceiling, small cells would presumably not be out of focus so frequently nor appear to overlap with other cells, and the usual single z-stack approach would suffice. (Another configuration in which cells appear to overlap is in wells, e.g., 96-well plates, which are similarly not ideal for imaging.) Thus, for the problem of interest to Pietsch et al., I would have used a different chip first and then seen what remains of the identification, segmentation, and tracking problem.

- The method requires a number of z-stacks (although I read somewhere how many z-stacks the method needs, I now cannot find that information any more, which highlights a general problem with the presentation, which I will get to below). This means that the already large amount of data that needs to be acquired with regular 2D images now is multiplied by "n" for each z-stack. More importantly, initially, z-stacks have to be individually labeled for training the neural network. That is n times what other segmentation methods require. So, one would presumably only invest this amount of work if one really cared about the tiniest buds because that is, from what I understand, the main selling point of the method. But how many labs do care about this question and going about it in the exact same way as Pietsch et al.? For example, to just find the exact time a bud appears, most people could just extrapolate the size of a new bud in time to zero or simply use a fluorescent budneck marker. Somebody would have to want to measure the growth rates of the smallest buds without fluorescent labels, which the authors do in this present work. But unless someone wants to repeat this exact measurement, say, with other mutants, I do not see who else would invest a large amount of time and resources for this. Other quantities such as fluorescent protein levels cannot be measured with this approach anyway, i.e., by going through z-stacks with a widefield microscope. One would presumably have to use a confocal microscope.

- Could the problem have been simplified by taking z-stacks but analyzing each as a regular 2D image with existing segmentation methods? If a new bud is detected in any of the z-stacks, it is counted as a new cell. This would allow one to use existing 2D training sets and methods and only add a few images of one's own, whether taken in a single z-stack or not. It would only involve tweaking or augmenting existing methods slightly.

- While a 3D image needs to be fed to the neural network, ultimately, all measurements in this manuscript are 2D measurements, e.g., all growth rates are in units of um^2/h. (Somewhat unexpectedly, the authors use a Myo1-GFP construct to identify the budded phase of cells in Fig. 4, i.e., exactly what this method was designed to avoid.) Thus, the effort of going to 3D is only to make the identification of buds more accurate. So, we are not really dealing with a method that goes from 2D to 3D and reconstructs, for example, the shape of cells in 3D. So, while z-stacks go in, it is not 3D annotations that come out.

- The authors may argue that they want to use their high-ceiling chips because they want to follow aging cells. Or, they may argue that indeed, this method is going to be used more widely because people want to study the growth rates of tiny buds in various mutants. However, then the limitation of their method to convex shapes or shapes that can be represented in cylindrical coordinates is a problem since old cells and many mutants can have strange shapes. In this way, the authors have gone a step back methodologically for reasons that I do not understand.

- Given that the method is tailored to detecting small buds, I also do not understand why the authors do not use a higher magnification objective, e.g., a 100x objective instead of 60x? Maybe the problem becomes much easier that way?

- It is unclear how well the tracking method generalizes for other configurations. Here, the tracking problem is somewhat special because there are only a few cell in and around the traps and frequently cells are washed away. For a method paper, the tracking method would need to be compared and contrasted with others for different kinds of experiments. Since tracking is in the title of the manuscript, it is presumably an important selling point of the manuscript.

- The same applies to the segmentation problem. The traps in the authors' microfluidic chips only keep a small number of cells, avoiding problems that emerge when many cells of similar sizes abut.

Reviewer #1 (Public Review):

Extracellular vesicles (EVs) are emerging as important mediators of cell-to-cell signaling. In this paper the authors aim to demonstrate that Stranded at second (Sas), a Drosophila cell surface protein, binds to dArc1 and Ptp10D to mediate intercellular transport of dArc1 via EVs. dArc1 protein has been shown to form virus-like capsids that carry dArc1 mRNA from neurons to muscle, but little is known about this new intercellular communication pathway. Similarly, not much is known generally about how EVs are targeted to specific cell types, or how specific EV cargo can be delivered. Thus, this work is of interest to cell biologists and neuroscientists. However, the jumbled description of the results and general lack of rigor of experiments diminish the impact and interpretability of the conclusions. Moreover, almost all experiments rely on gain-of-function and over-expression of Sas, thus the relevance to normal physiological signaling is unclear.

Major strengths:<br /> 1. The data showing that Sas is released into EVs and delivered to cells is strong.<br /> 2. The EM data showing Sas localization to EVs is clear.

Major weaknesses:<br /> The description of the results omits some data in the figures and is not in a logical order. This made it hard to read and follow. There is also a lack of rigor and quantification in some experiments. Specifically:

1. Figure 2: Description of dArc1 putative capsids is absent from the results section (2f,g) until describing fig 4 data (line 362). Given that there is no immuno-EM labeling of dArc1 protein, it is not clear if Sas and dArc1 are localized to the same EVs. Nor is it clear if the double membrane EVs are actually EVs that contain capsids. Overall, the EM data lacks quantification. How many EVs on average show Sas labeling? How many EVs have double membranes? The dense protein staining surrounding EVs seems unusual, is this due to artifact of the purification? EV kits are generally non-specific and isolate non-EV membranes, corroboration using ultracentrifugation or size exclusion chromatography methods would be beneficial. SAS-FL overexpression results in more EVs, which confounds subsequent experiments suggesting that Sas targets EVs to specific cell types/regions.

2. Figure 3: There are no data showing the expression of Sas in SG cells using the GAL4 lines. Is this expression restricted to just SG cells? The results jump from a-b to f-g. c-e are out of order. The quantification in g should be broken into two and paired with the actual data (c-e, and f). It is not clear how the quantification in g was performed. How many WBs were analyzed? There seems to be a bubble in the first lane of f, which would preclude quantification. Why is d not quantified and there seems to be an overall increase in background staining in e that is not specific to discs. The source data files are not labelled and these data should be incorporated into annotated supplemental figures. Is transfer in a-b due to Ptp10D? How many WBs were quantified in g?

3. Figure 4: C and d, IP data has no inputs for IPs, no sizing markers, and no IgG controls for antibody specificity. These data would also be more convincing if done with FL Sas and included co-Ips from cell lysates.

4. In general, the WBs in the figures show very white backgrounds with high contrast, which suggests the images may have been manipulated. Total protein controls are also missing.

5. Figure 5: Ashley et al (Cell 2018) showed that dArc1 mRNA transfer required the 3'UTR so it is puzzling that the authors used heterologous UTRs. The results using FISH on endogenous dArc1 mRNA are dramatic. The authors should show definitively that their probe does not pick up over-expressed dArc1.

6. Many of the conclusions would be strengthened by the loss of function experiments, especially showing a requirement for Sas in dArc1 transfer.

Reviewer #2 (Public Review):

The manuscript addresses the important question of how EVs are targeted to their recipient cells once they are produced and released.

The present manuscript contains 4 messages:<br /> First, it shows that the transmembrane protein Sas gets incorporated into EVs and that this protein binds to its receptor Ptp10D on target cells, thus targeting the EVs. Second, the manuscript shows that the Sas cytoplasmic domain ICD binds to dARC1 protein (and perhaps darc1 RNAs), which are incorporated into EVs where they form capsids, before being targeted to recipient cells. dARC1 is important for neuron development in flies! Interestingly the motif in the Sas ICD is conserved in mammalian APP that also binds ARC1, suggesting a conserved mechanism of targeting EVs in mammalian neural development. Third, exposure of target cells (ex vivo wing discs) to EVs positive to FL Sas leads to its increased targetting when the target cells also expressed Ptp10D and Numb, which are acting as Sas receptors in a synergetic manner. Fourth, dARC1 ORF expression in the EV-producing cells (SG) leads to the increased expression of dARC1 protein and mRNAs in the recipient cells in vivo (Trachea). Many techniques are used, including IEM, fly genetics, S2 cells, and Ips. It is broad, and well executed, and the questions are interesting.

However, the manuscript should be strengthened. It is a lot of data and techniques but because there are so many messages in the paper, each needs more substances and controls.

1: Use of more extensive fly genetics using specific Ptp10D LOF in wing discs and trachea (to show the converse of the GOF).<br /> Does Ptp10D acts as the MAIN receptor to FL Sas? Numb LOF, a combination of LOF and GOF?<br /> does Ptp10D GOF compensate for Numb and vice versa?

2: What is the specificity for FL Sas? The expression of short Sas should not lead to its incorporation in EVs and their overnight addition should not lead to the same effect (Figure 3). This should be better investigated as short Sas is a good control for FL Sas.

3: A better quantitative analysis should be provided. For instance, there is no quantitative data for Figure 5.

4: All experiments are done with flies. There is no data on mammalian neurons in culture. This is missing. Exposure of neurons with SAS-positive EVs (or APP)

5: Are the capsid reconstitution with purified dARC1 and 2 performed in the presence of darc1 rRNA? Any RNA (figure 2).

6: The dAC1 increased expression in the target cells upon dARC1 increased production in SG(Figure 5) becomes an important part of the paper (and the model) but is not investigated!<br /> How does it work? Does the delivery of darc1 mRNAs packaged in capsids simply lead to more dARC1 translation? Is it proportional?<br /> OR is there also stimulation of darc1 transcription? Is there also an increase in the mRNA level (I cannot see the SG control of 5o (sage>+) supporting the authors' claim on line 562!).

7: Most (all) experiments are performed with overexpression of FL Sas or ICD. Does endogenous Sas bind endogenous Ptp10D and dARC1? ICDs? Also full-length APP?

Reviewer #3 (Public Review):

Lee et al. identify the Stranded at second (Sas) cell surface protein as an extracellular vesicle (EV) component in Drosophila. They first show that different isoforms of Sas exhibit differential tissue distribution in vivo, with the EV-enriched full-length Sas isoform exhibiting distribution at distant sites away from its cells of origin. They show that Sas is present in EVs purified from Drosophila S2 cells, as assessed using exosome isolation kits and via immuno-electron microscopy. Their data suggest that Sas-bearing EVs preferentially target cells expressing Ptp10D, a receptor tyrosine phosphatase that is a known binding partner of Sas, both in the context of S2 cells and imaginal discs engineered to overexpress Ptp10D and the endocytosis regulatory protein Numb. Through immunoprecipitation (IP) of Sas from S2 cell EVs, as well as validation co-IPs and peptide binding assays, the authors found that Sas can interact with the dArc1 protein (i.e. the orthologue of mammalian Arc, which has the ability to form capsid-like structures) via a conserved protein motif of Sas. Finally, they show that Sas increases the transfer of dArc1 protein and mRNA from Sas-expressing cells to Ptp10D-enriched tissues in vivo. The authors conclude that Sas facilitates the delivery of dArc1 capsids that carry dArc1 mRNA to recipient cells that express Ptp10D.

General Strengths: The in vivo and in vitro data conveying the selective targeting of the full-length Sas isoform to EVs, and the impact on the delivery of dArc1 to distant Ptp10D-expressing cells, are generally strong and supportive of the proposed model. The authors also show convincing data confirming the interaction of Sas with dArc1 by IP-MS and binding assays.

General Weakness: It is not clear if the major biological function of the endogenous Sas-Ptp10D interaction is mediated via EVs. The inclusion of additional data evaluating dArc1 mRNA EV-mediated transfer to the trachea in Sas and/or Ptp10D null mutant flies would strongly enhance the paper and support the role of these proteins in tissue-specific EV targeting in vivo. Moreover, throughout the paper, there are several controls and quantifications missing that would be required in order to strengthen the general conclusions and proposed regulatory model. For instance, it is not clear to what extent Sas and dArc1 proteins are co-enriched within purified EV specimens. Immuno-EM studies or nanoparticle analysis strategies should be implemented to address this aspect. Several of the IF- and FISH-based labeling experiments lacked controls. Also, there are few if any quantifications provided as to the number of tissue specimens that were examined in the various assays as a basis for making specific conclusions.

Reviewer #1 (Public Review):

The authors of this manuscript address the question of whether vagal and sacral neural crest make distinct contributions to the enteric nervous system (ENS). The ENS regulates intestinal motility and many intestinal homeostatic functions; mutations in genes involved in ENS development lead to defects that can range from mild to catastrophic. The best studied of the ENS neuropathies is Hirschsprung disease, which is thought to result from failure of vagal neural crest cells to migrate properly into the distal intestine to differentiate into ENS neurons and glia. However, sacral neural crest cells are known to contribute to the distal ENS and have to migrate a considerably shorter distance. Thus, understanding whether there are distinct vagal and sacral contributions to the ENS provides insights into basic ENS biology as well as the basis of human disease. Previous transplantation and ablation studies have already revealed that vagal and sacral neural crest have differing ENS developmental potentials, although this has not been directly correlated with discrete cell types. Here the authors combine single cell RNA sequencing and a viral lineage tracing technique that is new to avians to gain insight into the different ENS cell types generated by vagal and sacral neural crest along the length of the intestine. They find that vagal and sacral neural crest exhibit distinct transcriptional profiles and contribute both similar and different progeny to the ENS. For example, both vagal and sacral crest contribute to progenitor cells, connective tissue and neurons, but most secretomotor neurons are vagal crest-derived whereas most adrenergic neurons and melanocytes in the distal intestine are sacral-crest derived. The authors also suggest a role of the local environment in determining the fate of vagal and sacral derivatives. The data presented in this manuscript provide a multitude of hypotheses about similarities and differences between vagal and sacral derived ENS cells. However, a shortcoming of the manuscript is that all of these hypotheses remain untested.

Reviewer #2 (Public Review):

The manuscript by Tang et al investigates the potential difference between the enteric nervous system derived from different axial regions of chicken embryos. By applying single cell RNA-sequencing (scRNA-seq) analysis of virally traced enteric cell populations, the authors conclude that vagal and sacral neural crest may contribute to different neural subtypes and non-neural cells in the sub-umbilical ENS. Confirming previous studies, their method also demonstrates the exact axial levels of the GI-tract populated by sacral neural crest. The analysis suggests that NPY/VIP+ neurons mainly arise from vagal neural crest in both the pre- and postumbilical ENS, while sacral neural crest mainly contribute with Th/Dbh/Ddc+ neurons. Sacral neural crest also appears to generate a greater proportion of schwann cell-like cells and melanocytes to the gut.

While early studies in the chicken model (combined with quail) founded many of the key principles underlying the emergence of the ENS from different neural crest sources, the chicken model currently lags behind in the implementation of modern transcriptomic and neurophysiological approaches. This paper provides a long-saught comprehensive scRNA-seq datasets of the chicken ENS which is clearly lacking in the ENS field. The elegant viral delivery allows targeting of both vagal and sacral neural crest in the same embryo offering clear advantages to other commonly used model systems (including the mouse). However, analytical approaches are in the current form preliminary and not enough to draw firm biological conclusions. While the datasets are large (which is highly appreciated), they represent a relatively early stage of ENS development and possible differences between vagal and sacral-derived populations could partially be attributed to difference in maturity. Maturity will surely not explain the whole difference observed but needs to be factored into the interpretation. As scRNA-seq datasets from the mature chicken ENS are lacking (as well as detailed IHC-based neural classification system) the inference made in the paper between molecular classes and functional types are premature.

Specific concerns:<br /> 1) Analysis of scRNA-sequenced sacral- versus vagal-derived ENS reveals clusters consistent with a non-ENS identity (endothelial, muscle, vascular and more). Previous studies in mouse using the neural crest tracing line Wnt1-Cre has not demonstrated such diverse progenies of neural crest from any region. An exception being a small population of mesenchymal-like cells (Ling and Sauka-Spengler, Nat Cell Biol. 2019; Zeisel et al., Cell 2018; Morarach et al., 2021; Soldatov et al., Science 2019). Therefore, the claimed broad potential of neural crest giving rise to diverse gut cell populations warrants more validating experiments.

2) Several earlier studies have revealed that parts of the ENS is derived from neural crest that attach to nerve bundles, obtain a schwann cell precursor-like identity and thereafter migrate into the gut (Uesaka et al. J Neurosci 2015 and Espinosa-Medina et al, PNAS 2017). The current work in chicken needs to be interpretated in the light of these findings and the publications should be discussed in relevant sections of the introduction and discussion.<br /> 3) The analysis indicates the presence of melanocytes. It is not clear why they are part of the GI-tract preparations. Could they correspond to another cell type, with partially overlapping gene expression profile as melanocytes?

4) As evident, the sacral- and vagal-derived ENS are not clonally related. To decipher differentiation paths and relations between clusters, individual analysis of the different datasets are needed. With only one UMAP representing the merged datasets combined with little information on markers, it is hard to evaluate the soundness of the conclusions regarding cell-identities of clusters and lineage differentiation.

5) E10 is a relatively early stage in chicken ENS development. Around E7, the intestines do not contain differentiated neurons even. The relative high expression of Hes5 (marking mature enteric glia in the mouse; Morarach et al., 2021) in the vagal neural crest population might be explained by the more mature state of vagal versus sacral ENS. As also outlined below, Th/Dbh are known to be transiently expressed in the developing ENS why they could indicate the relative immaturity of sacral neural crest rather than differential neural identities. These issues need to be taken into account when interpreting biology from scRNA-seq data.

6) Unlike the guineapig, and to some extent pig and murine ENS, the physiology of chicken enteric neurons has not been well characterized yet. Therefore, it is highly advisable to refrain from a nomenclature of clusters designating functions. Several key molecular markers are known to differ between murine, guineapig, rat and human systems. IPANs are a good example where differential expression is seen (SST in human but not mice; CGRP labels some IPANS in mouse, but not in guineapig, where Tac1 instead is expressed). IPANs are not defined in the chicken very well, and molecular markers found in other species may not be valid. Adrenergic and noradrenergic neurons have not been validated in the ENS (although, TH and Dbh have been observed in the especially in the submucosal ENS). Cholinergic neurons are also mentioned in the text, but do not appear in the figures as a defined group. Another reason to refrain from functional nomenclature is that a rather early stage is analysed in the present study, without possibilities to compare with scRNA-seq data from the mature chicken ENS (which was performed in Morarach et al, 2021 for the mouse). Recent data suggest that considerable differentiation may occur even in postmitotic neurons, and several markers are known to display a transient expression pattern (TH, DBH and NOS1; Baetge and Gershon 1990; Bergner et al., 2014; Morarach et al., 2021) why caution should be taken to infer neuronal identities to clusters.

7) The immunohistochemical analysis (Figure 5,6) is an essential complementary addition and validation of scRNA-seq. However, it is very difficult to discern staining when magenda and red are combined to display co-expression.

8) To give more information to the field and body of evidence for claims made, quantifications relating to the analysis in Figures 5 and 6 are warranted as well as an expanded set of marker genes that align with the scRNA-seq results.

9) Correlations between genes and functions/neuron class are in many cases wrong (including Grm3, Gad1, Nts, Gfra3, Myo9d, Cck and more).

10) Attempts to subcluster neuronal populations are needed (Figure 7). However, to understand the biology, it is important to address which cells are sacral versus vagal-derived. Additionally, related to previous comment, as the vagal and sacral neurons are not clonally related, it would be important to make separate analysis of neurons relating to each region.

Reviewer #1 (Public Review):

In this project, the authors used a single-cell RNA sequencing technique, created a cell atlas of normal and diseased human anterior cruciate ligaments of 49,356 cells from 8 patients, explored the variations of the cell subtypes' spatial distributions, and found their associations with ligamental degeneration. Using the single-cell RNA sequencing data, the authors identified fibroblast subsets unique to normal and diseased tissues, revealed two processes of acute and chronic disease outcome in ligamental degeneration and found immune cell and stromal cell subclusters changed the extracellular matrix in ligament and contributed to the disease. Combined with spatial transcriptome sequencing, they found the spatial distribution of immune and stromal cells associated with the disease and demonstrated cell-cell communications among endothelial cells, macrophages, and fibroblasts.

Reviewer #2 (Public Review):

In this study, Yang et al. used single-cell technology to construct the cell profiles of normal and pathological ligaments and identified the critical cell subpopulations and signaling pathways involved in ligament degeneration. The authors identified four major cell types: fibroblasts, endothelial cells, pericytes, and immune cells from four normal and four pathological human ligament samples. They further revealed the increased number of fibroblast subpopulations associated with ECM remodelling and inflammation in pathological ligaments. In addition, the authors further resolved the heterogeneity of endothelial and immune cells and identified an increase in pericyte subpopulations with muscle cell characteristics and macrophages in pathological ACL. Ligand-receptor interaction analysis revealed the involvement of FGF7 and TGFB signaling in interactions between pathological tendon subpopulations. Spatial transcriptome data analysis also validated the spatial proximity of disease-specific fibroblast subpopulations to endothelial and macrophages, suggesting their interactions in pathological ligaments. This study offers a comprehensive atlas of normal and pathological cells in human ligaments, providing valuable data for understanding the cellular composition of ligaments and screening for critical pathological targets. However, more in-depth analyses and experimental validation are needed to enhance the study.

1) In this study, the authors performed deconvolution analysis between bulk RNA sequencing results and scRNA-seq results (L204-L208). However, the analysis of this section is not sufficiently in-depth and the authors failed to present the proportion of different cell subpopulations of the bulk sequencing samples to further increase the reliability of the results of the single cell data analysis.<br /> 2) In results 5, the authors should clearly describe whether the analysis is based only on pathological subpopulations of ligament cells or includes a mixture of normal and pathological subpopulations; the corresponding description should also be indicated in Figure 5. Besides, Although the authors claimed that "the TGF-β pathway was involved in many cell-cell interactions among fibroblasts subpopulations and macrophages", Figure 5C displayed that the CD8+NKT-like cells displayed the most TGFB signaling interactions with fibroblasts subpopulations.<br /> 3) In result 6, the authors performed spatial transcriptome sequencing, however, the sample numbers were relatively limited, with only one sample from each group; in addition, the results of this part failed to correlate and correspond well with the single-cell results. The subgroups labelled in L382 and L384 should be carefully checked. Besides, expression data of FGF7 and TGFB ligand and receptor molecules based on the spatial transcriptomes should be added to further confirm the critical signalling pathway in regulating the cellular interactions in pathological ACL.

Reviewer #3 (Public Review):

In this manuscript, Yang et al. claimed the creation of a single-cell atlas of the human anterior cruciate ligament (ACL) using scRNA-seq, spRNA-seq, and transcriptomic profiling. Upon analysis of about 25K cells from healthy and degenerated human ACL, the authors reported the existence of fibroblasts, endothelial cells, pericytes, and immune cells in healthy ACL. Their ratios altered in the degenerative ACL, featuring an increase in fibroblasts and immune cells, as demonstrated by the UMAP. Further characterization revealed the presence of subclusters in each of the four major types of cells. The evolution trajectory, spatial transcriptome, and signaling pathways that may contribute to biphasic ACL degeneration were also explored. These data are valuable, to some extent, in improving the current knowledge regarding ACL cellular heterogeneity, homeostasis, and ligamental degeneration. However, the abovementioned findings are purely derived from computational modeling; the authors haven't validated any of them experimentally in vitro and in vivo, particularly regarding whether there are multiple fibroblast subclusters in the ACL with distinct biology. The spatial transcriptomic analysis is also superficial, and few novel insights were generated. The reported work seems like a window show of fancy technologies rather than a hypothesis-driven investigation. Some figures were not clearly labeled, and figure legends were too brief to follow up the studies. Therefore, the significance of this work and its value as a cell atlas of ACL are compromised.

Reviewer #1 (Public Review):

It has been previously shown that defective autophagy and disorganized microtubule network contribute to the pathogenesis of Duchenne muscular dystrophy (DMD). The authors previously reported that nitrite oxide synthase 2 (NOX2) regulates these alterations. It was also shown that acetylated tubulin facilitates autophagosome-lysosome fusion and thus autophagy. In the present study, the authors showed that autophagy is differentially regulated by redox and acetylation modifications in dystrophic mdx mice. The ablation of Nox2 in mdx mice activated the autophagosome maturation but not its fusion with the lysosome. On the other hand, the inhibition of histone acetylase 6 (HDAC6) restored microtubule acetylation, promoted autophagosome-lysosome fusion, and improved muscle function in mdx mice. The strength of this paper is the combination of different approaches to decipher the mechanism, including the evaluation of the level and interaction of several proteins involved in the maturation of autophagosomes and in the fusion between autophagosomes and lysosomes.

This study reveals an important molecular mechanism by which increasing microtubule acetylation improves autophagy and muscle function in dystrophic mice. This has a translational impact on several diseases in which autophagy is impaired. The improvement of autophagosome-lysosome fusion with HDAC6 inhibitor is supported by several data, but some parts merit further analysis:

1) To add appropriate controls (e.g. without antibodies) to support protein-protein interaction for all co-immunoprecipitation assays.<br /> 2) The simple evaluation of the protein levels of p62 and LC3-II is not sufficient to claim autophagy improvement after HDAC6 inhibition. It would be good to evaluate the autophagic flux in vivo in all groups of mice (to treat the mice with or without autophagy inhibitor and evaluate whether the difference in the level of LC3-II between the two conditions is higher with HDAC6 inhibitor than without in the mdx mice).

Reviewer #2 (Public Review):

Agrawal et al. propose an interesting model in which the autophagy pathway in adult mouse skeletal muscle fibers is orchestrated by two independent mechanisms: a) the activity of the NADPH oxidase (Nox) 2 enzyme necessary for autophagosome biogenesis and maturation and b) the level of acetylation of the microtubule (MT) network more selectively responsible for the fusion of the autophagosomes to the lysosomes. Using the well-known mdx mouse, a model for Duchenne muscular dystrophy, the authors perform a quite impressive (but rather traditional) biochemical characterization of the autophagy pathway and found that biogenesis and maturation of the autophagosomes are impaired in mdx mice muscle fibers by means of altered expression of components of the class III phosphatidylinositol 3-kinase complex (PI3K) such as Beclin, VPS15 (both upregulated in mdx mice), ATG14L and VPS34 (both downregulated), and by the reduced expression of JNK and JIP-1, required for the formation of the heterodimer between Beclin and ATG14L-VPS34. In mdx mice, defective nucleation of the phagophore appears to be coupled to altered elongation and expansion as confirmed by decreased expression of WIPI-1, an early marker of autophagosome formation, required for the assembly of the ATG5-12 complex. Clearance of sequestered cytosolic components necessitates the fusion of the autophagosome with the lysosome, a process that the authors found impaired in mdx mice due to altered formation of the SNARE tertiary complex (STX17-SNAP29-VAMP8), as a result of the marked reduction of STX17 expression.

In a previous work (Pal et al., Nat Commun 2014), the same group described the generation of an mdx-based mouse model where Nox2 activity was abolished by genetic ablation of the p47phox component. These mice presented with a better outcome in terms of dystrophic pathophysiology by means of reduced oxidative stress and improved autophagy. Further characterization of these mice in the present study reveals that in p47-/-/mdx mice abolishment of Nox2 activity restores autophagosome nucleation and maturation thanks to the increased expression of p-JNK, JIP-1 and improved stability of the Beclin-ATG14L complex, but no amelioration is observed on the formation of the SNARE tertiary complex indicating that the biogenesis of autophagosomes is dependent on Nox2 activity but not the fusion between autophagosomes and lysosomes. Given the existing body of evidence in non-muscle cells pointing at alpha-tubulin acetylation as a regulator of MT activity facilitating the fusion of autophagosomes to lysosomes, the authors thought to investigate the level of MT acetylation in mdx mice muscle fibers and found that acetylation is reduced but can be restored by inhibiting the HDAC6 enzyme via the FDA-approved, highly selective pharmacological inhibitor Tubastatin A (Tub A). Treatment of mdx mice at 3 weeks of age (before the onset of pathological manifestations) with Tub A not only restored the normal level of alpha-tubulin acetylation (without altering the organization and density of the MT network) but also curbed the intracellular redox status and improved the autophagic flux by stabilizing the SNARE tertiary complex. Interestingly, treatment of dystrophic mice with Tub A results in substantial improvement of the dystrophic phenotype as confirmed by a reduced level of apoptosis, diminished tissue inflammation, improved sarcolemma integrity, and superior force generation capacity in ex vivo experiments using the diaphragm and Extensor Digitorum Longus (EDL) muscle fibers of Tub A-treated mdx mice compared to untreated mdx and healthy counterparts.

The in-depth characterization of the steps orchestrating the autophagy pathway in the mdx mouse model on the one hand, and the comprehensive evaluation of the phenotype of the mdx mice treated with the HDAC6 inhibitor Tubastatin A on the other, support the conclusions proposed by the authors. Nonetheless, some aspects deserve consideration.

1) The effect of increased alpha-tubulin acetylation by means of genetic and pharmacological strategies (i.e., in vivo overexpression of alpha-tubulin acetyltransferase-aTAT1 and treatment with Tubacin or Tubastatin A, respectively) has been previously explored in isolated cardiomyocytes and skeletal muscle fibers and revealed that augmented MT acetylation, due to selective inhibition of HDAC6, increases cytoskeletal stiffness and favors Nox2 activation (Coleman et al., J Gen Physiol 2021).

2) Altered organization and density of the MT network in mdx FDB muscle fibers with loss of vertical directionality is not a novelty as well and it has been reported by others (see Randazzo et al., Hum Mol Genet 2019), who also observed that overexpression of a single beta-tubulin (tubb6) in normal Flexor Digitorum Brevis (FDB) muscle fibers mimic the disruption to the MT network of mdx FDB fibers, increases the level of detyrosinated tubulin and increases Nox2 activity (through elevated expression of gp91phox). Conversely, downregulation of the same beta-tubulin restores normal MT organization in mdx FDB. Previous work from the authors (Loehr et al., eLife 2018) reported that in p47-/-/mdx mice MT organization in diaphragm muscle fibers is normalized and autophagy improved. Accordingly, it is puzzling that increased alpha-tubulin acetylation determines such a wide range of ameliorations in terms of physiological and morphological aspects in dystrophic skeletal muscle fibers treated with Tubastatin A whereas no improvement in the overall MT organization is observed, as reported by Agrawal and colleagues.

3) Given that p47-/-/mdx mice present with levels of acetylated alpha-tubulin and HDAC6 expression comparable to mdx while showing significant improvement of the dystrophic phenotype despite partial rescue of the autophagic flux (as reported in Loehr et al., eLife 2018), it would have been of great interest to investigate the effect of HDAC6 inhibition in p47-/-/mdx mice as well.

Reviewer #1 (Public Review):

The purpose of this study was to investigate within the diverse Multiethnic Cohort (MEC) study on how COVID-19 impacted access to cancer screenings and treatment through a cross-sectional survey in this study population.

Major strengths were leveraging existing participants in a cohort study that contained a diverse population. The MEC cohort participants have been studied since the 90's. The investigators used a well-designed survey and performed analysis on responses focused on cancer screening attendance. Weaknesses of this study are low response rates that make the results not generalizable to other populations, especially younger populations, and possible bias of specific types of individuals responding.

This study found associations with racial/ethnic, age, comorbidities, and education to be key factors associated with cancer-related screening and healthcare seeking during the COVID-19 pandemic.

Whether the associations observed by the investigators would remain over time is unknown, as health care seeking changed as the pandemic evolved and prevention tools (including mass testing and vaccination) became available. It is important to note that this is a snapshot in time, so while it is informative, it will be important to monitor whether certain groups/populations that may be at high risk for cancer may need to be targeted for early diagnosis and screening.

Reviewer #1 (Public Review):

This study provided evidence to interpret and understand the aging and developmental processes in children. The main strength of the study is it measures a set of biological age measures and a set of developmental measures, thus providing multi-faceted evidence to explain the associations between aging and development in children. The main weakness of this study is that how to measure and test the aging hypothesis of "a buildup of biological capital model" and "wear and tear" is not well-explained. Why the observed associations between biological age measures and developmental measures could support the aforementioned aging theories?

1. Abstract - conclusion: The aging hypothesis of "a buildup of biological capital model" and "wear and tear" were mentioned in the conclusion without an explanation of these theories in the previous section. Readers who are not experts in the field may not understand the logic.<br /> 2. Result - Biological age marker performance: the correlation between transcriptome age and chronological age is very strong (r =0.94). I am afraid that very little age-independent information could be captured by the transcriptome age. Is it possible to down-regulate the age dependency of the transcriptome age in the training process?<br /> 3. The study population comes from several cohorts, which might influence the results. How the cohort effects were controlled for in the analyses?<br /> 4. Figure 3 only showed the number of p values. Can the author also provide the number of point estimates and 95% confidence intervals, perhaps in the supplemental table?

Reviewer #2 (Public Review):

The study had an especially relevant aim for aging research and utilized various data types in an especially interesting human population. Multi-omics perspective adds great value to the work. The researchers aimed to evaluate how different indicators of biological age (BA) behave in children during their developmental stage. In the analysis, relationships between indicators of BA, health risk factors, and developmental factors were assessed in cross-sectional data comprising children aged 5-12 years. The manuscript is well-written and easy to follow. The methodology is good. The authors succeeded to reach the aim in most parts.

In the study, previously known and unknown biological age indicators were used. Known indicators included telomere length and Horvath's epigenetic age. Unknown (novel) indicators, transcriptomic and immunometabolic clocks, were developed in the present study and they showed a strong correlation with calendar age in this population, also in the validation data set. Although the transcriptomic and immunometabolic clocks have the potential of being true indicators of biological age, they are still lacking scientific evidence of being such indicators in adults. That is, their associations with age-related diseases and mortality are yet to be shown. Thus, the major remark of the study relates to the phrasing: these novel transcriptomic and immunometabolic clocks should be presented as BA indicator candidates waiting for the needed evidence.

Reviewer #1 (Public Review):

In the manuscript, titled "Comparative single-cell profiling reveals distinct cardiac resident macrophages essential for zebrafish heart regeneration," Wei et al. perform bulk and single-cell RNA-sequencing on uninjured and injured zebrafish hearts with or without prior macrophage depletion by clodronate. For the single-cell RNA sequencing, the authors sort macrophages and neutrophils prior to sequencing by using fluorescent reporters for each of the two lineages. The authors characterize the differential gene expression between injured and uninjured hearts with and without prior macrophage depletion. The single-cell analyses allow the characterization of nine discrete subpopulations of macrophages and two distinct neutrophil types. The manuscript is largely descriptive with lots of discussion of specific differentially expressed genes. The authors conclude that tissue-resident macrophages are important for heart regeneration through the remodeling of the microenvironment and by promoting revascularization. Circulating monocyte-derived macrophages cannot adequately replace the resident macrophages even after recovery from clodronate depletion.

The manuscript presents a very large catalog of useful gene expression data and further characterizes the diversity of macrophages and neutrophils in the heart following injury. Although the conclusions that resident macrophages are important for regeneration and that circulating macrophages cannot adequately substitute for them are not particularly novel, this manuscript provides additional support for those ideas and extends that work by providing a wealth of gene expression data from the different macrophage sub-populations in the zebrafish and how they respond to and promote regeneration. The authors also present a nice analysis supporting the interactions of macrophages with neutrophils via comparing receptors and ligands (from gene expression data) on the two populations - this should be a useful resource.

Reviewer #2 (Public Review):

Wei et al. analysed the composition of immune cells, mostly macrophages, and neutrophils, in the context of zebrafish cardiac injury while utilizing clodronate liposomes (CL) to inhibit regeneration via alteration of the immune response. This work is a direct continuation of Shih-Lei et al. which compared the regenerative outcomes of zebrafish vs the non-cardiac regenerative medaka. In that work, the authors used CL to pre-deplete macrophages and showed significant effects on neutrophil clearance, revascularization, and cardiomyocyte proliferation. In this work, the authors used the same pre-depletion method to study the dynamics, composition, and transcriptomic state of macrophages and neutrophils, to overall assess the effect on cardiac regeneration. Using bulk RNA-seq at CL vs PBS treated hearts 7 and 21 days post cryo injury (dpci) a delayed\altered immune response was evident. Single-cell analysis at 1,3 and 7 dpci showed a wide range of immune populations in which most diverse are the macrophage populations. Pre-depletion using CL, altered the composition of immune cells resulting in the complete removal of a single resident macrophage population (M2) or dramatically reducing the overall numbers of other resident populations, while other populations were retained. Looking at the injury time course and distribution of macrophage populations, the authors identified several macrophage populations and neutrophil population 1 as pro-regenerative as their presence compared to CL-treated hearts correlates with regeneration. CL-treated hearts also show a marked sustained neutrophil retention suggesting that interaction with depleted macrophage populations is required for neutrophil clearance. As the marked reduction in populations 2 and 3 occurs after CL treatment, the authors tested whether early CL treatment (8 days or 1 month prior to injury) could reduce the non-recoverable populations and affect regenerative outcomes and indeed they observed a reduction in key genes characterizing M2 and M3 which caused marked reduction in revascularization, CM proliferation, neutrophil retention, and overall higher scaring of the heart.

The findings of this paper could be broadly separated into the characterization of myeloid cells after injury and in non-regenerating animals and assessing the effects of early pre-depletion of macrophages on various cardiac functions involved in regeneration. Both parts draw conclusions that are supported by the facts however several questions remain to be clarified.

1. In figures 2 and 3 the main claim is that the main resident macrophage populations, M2 and M3 are depleted and are largely unable to replenish after injury, similar to resident macrophages in mice 1. However, as the identification of this population is made solely using scRNA-seq, an alternative explanation would be that these cell populations do replenish but are sufficiently changed due to CL treatment (directly or indirectly) and thus would be a part of another cluster. To address this, we suggest:<br /> A. Run trajectory analysis to ascertain whether the different cell clusters are due to differentiating states of the cells<br /> B. Create a reporter line for M2 and M3 macrophages and assess whether they are indeed depleted or changing.

2. One of the major findings of this paper is that some macrophage populations can persist throughout injury and promote the regenerative response. Considering that macrophages have a half-life of less than a day in tissue 2 (although could be different in zebrafish and in this population), we estimate that the resident populations should be proliferative. As there is only a single proliferating macrophage population (M5) we speculate that it is a combination of several populations which are clustered together due to the high expression of cell cycle genes. To verify whether the resident populations are proliferating we suggest:<br /> A. Perform cell-cycle scoring and regression (found in Seurat package) and assess whether after regressing out cell cycle genes there are contributions of M5 to other clusters.<br /> B. Perform EDU labelling experiments with cell cycle identifiers (staining for hbaa1, Timp4.3) and assess their proliferative dynamics.

3. In connection to the previous point if indeed these resident macrophage populations are proliferative, even a smaller portion of remaining cells should be sufficient to partly replenish given sufficient time after CL 1. However as seen in Fig. 3B, the M2 population has a similar proportion of cells on days 1 and 3 after CL treatment and by day 7 it declines in numbers. Given that CL should not be present anymore, we expect this population to increase in numbers over time.

4. In Figure 6 the authors show a reduction in mpeg+ population however a persistent, large population ({plus minus}70% of the original mpeg+) is retained. The authors suggest that this population is comprised of other, non-macrophage, cell types however as this method is the very core of the paper and the persistence of macrophages could alter our understanding of the results, it must be verified.

Dick, S. A. et al. Self-renewing resident cardiac macrophages limit adverse remodeling following myocardial infarction. Nature Immunology 20, 29-39, doi:10.1038/s41590-018-0272-2 (2019).<br /> 2 Leuschner, F. et al. Rapid monocyte kinetics in acute myocardial infarction are sustained by extramedullary monocytopoiesis. J Exp Med 209, 123-137, doi:10.1084/jem.20111009 (2012).

Reviewer #3 (Public Review):

Macrophages play an important role during heart regeneration. This has been shown in the mouse and zebrafish for example by treating the animals with clodronate liposomes to eliminate phagocytic cells.<br /> The manuscript follows up on a previous observation by the authors performing these experiments in the zebrafish (Lai et al eLife 2017). When comparing regenerative vs non-regenerative teleosts zebrafish resp Medaka they found that macrophages and neutrophils were the cell types more differentially responding in these two species to a cardiac injury.

Here the authors anaylse in extenso neutrophil and macrophage populations using single-cell RNA-seq at different stages of regeneration. They perform FAC sorting of the two populations using specific reporter lines. They also assess the change in these populations upon clodronate treatment. They find that clodronate treatment affects the gene expression profiles of different subsets of macrophages and neutrophils as well as their abundance.

They also show that chlodronate treatment performed several days before cryoinjury depleted macrophages from the heart but after injury overall macrophage number recovers. However, heart regeneration does not. Cardiomyocyte is the only parameter that is not affected, but vasculogenesis and scar resolution is impaired.

The authors conclude that (1) there are different subsets of macrophages and neutrophils, (2) that they interact with each other during regeneration through specific ligand and receptor pairs, and (3) that a cardiac resident population rather than a circulating macrophage population is important for heart regeneration.

The transcriptomic characterization of the two immune cell populations is very exhaustive and rigorous. No functional validation of subpopulation marker genes was performed, but the data as it stands will already be of great value to the community. The figure quality is outstanding.

Reviewer #1 (Public Review):

In the current work, the authors aimed to investigate the genetic and non-genetic factors that impact structural asymmetry.

A major strength is the number of data samples included in the study to assess brain structural asymmetry. A consequence of the inclusion of many samples is then also the sample size. Given that the authors also work with longitudinal data, it would be nice to be able to appreciate the individual effects across time points, this is now a little unclear. A possible less well-developed approach is the genetic basis, as this was stated as the main question, here the investigations are not that deep and may only touch upon the question. Moreover, the association with cognition, handedness, sex, and ICV is somewhat interesting yet seems also a bit minimal to fully grasp its implications.

To some extent, the aim of the study could still be written with more clarity. However, the authors have in part achieved their aims - assuming it is found a consensus on the brain asymmetry patterns in humans as is stated in the abstract. Overall the results support the conclusions, yet the strong interpretation of early life factors in particular is not empirically investigated as far as I gather.

Overall this is a nice and thorough work on asymmetry that may inform further work on brain asymmetry, its genetic basis, development, environmentally induced change, and link to behavioural variation.

Reviewer #1 (Public Review):

This manuscript puts forward the concept that there is a specific time window during which YAP/TAZ driven transcription provides feedback for optimal endothelial cell adhesion, cytoskeletal organization and migration. The study follows up on previous elegant findings from this group and others which established the importance of YAP/TAZ-mediated transcription for persistent endothelial cell migration. The data presented here extends the concept at two levels: first, the data may explain why there are differences between experimental setups where YAP/TAZ activity are inhibited for prolonged times (e.g. cultures of YAP knockdown cells), versus experiments in which the transient inhibition of YAP/TAZ and (global) transcription affects endothelial cell dynamics prior to their equilibrium.

All experiments are convincing, clearly visualized and quantified. I have some questions that the authors may address to strengthen this exciting new concept:

• Point for more elaborate discussion: Apparently the timescale of negative feedback signals is conserved between endothelial cell migration in vitro (with human cells) and endothelial migration during the formation of ISVs in zebrafish. What do you think might be an explanation for such conserved timescales? Are there certain processes within cytoskeletal tension build up that require this quantity of time to establish? Or does it relate to the time that is needed to begin to express the YAP/TAZ target genes that mediate feedback?<br /> • Do you expect different timescales for slower endothelial migratory processes (e.g. for instance during fin vascular regeneration which takes days) ?<br /> • Is the ~4hrs and 8hrs feedback time window a general property or does it differ between specific endothelial cell types? In the veins the endothelial cells generate less stress fibers and adhesions compared to in the arteries. Does this mean that there might be a difference in the feedback time window, or does that mean that certain endothelial cell types may not have such YAP/TAZ-controlled feedback system?<br /> • The experiments are based on perturbations to prove that transcriptional feedback is needed for endothelial migration. What would happen if the feedback systems is always switched on? An experiment to add might be to analyse the responsiveness of endothelial cells expressing constitutively active YAP/TAZ.<br /> • To investigate the role of YAP-mediated transcription in an accurate time-dependent manner the authors may consider using the recently developed optogenetic YAP translocation tool: https://doi.org/10.15252/embr.202154401

Reviewer #2 (Public Review):

Here the effect of overall transcription blockade, and then specifically depletion of YAP/TAZ transcription factors was tested on cytoskeletal responses, starting from a previous paper showing YAP/TAZ-mediated effects on the cytoskeleton and cell behaviors. Here, primary endothelial cells were assessed on substrates of different stiffness and parameters such as migration, cell spreading, and focal adhesion number/length were tested upon transcriptional manipulation. Zebrafish subjected to similar manipulations were also assessed during the phase of intersegmental vessel elongation. The conclusion was that there is a feedback loop of 4 hours that is important for the effects of mechanical changes to be translated into transcriptional changes that then permanently affect the cytoskeleton.

The idea is intriguing, but it is not clear how the feedback actually works, so it is difficult to determine if the events needed could occur within 4 hrs. Specifically, it is not clear what gene changes initiated by YAP/TAZ translocation eventually lead to changes in Rho signaling and contractility. Much of the evidence to support the model is preliminary. Some of the data is consistent with the model, but alternative explanations of the data are not excluded. The fish washout data is quite interesting and does support the model. It is unclear how some of the in vitro data supports the model and excludes alternatives.

Major strengths: The combination of in vitro and in vivo assessment provides evidence for timing in physiologically relevant contexts, and rigorous quantification of outputs is provided. The idea of defining temporal aspects of the system is quite interesting.

Major weaknesses: The evidence for a "loop" is not strong; rather, most of the data can also be interpreted as a linear increase in effect with time once a threshold is reached. Washout experiments are key to setting up a time window, yet these experiments are presented only for the fish model. A major technical challenge is that siRNA experiments take time to achieve depletion status, making precise timing of events on short time scales problematic. Also, Actinomycin D blocks most transcription so exposure for hours likely leads to secondary and tertiary effects and perhaps effects on viability. No RNA profiling is presented to validate proposed transcriptional changes.

Reviewer #3 (Public Review):

The authors examined mechanotransductive feedback dynamics that govern endothelial cell motility and vascular morphogenesis. They investigated endothelial cell morphology, migration speed, cell shape, cytoskeletal and focal adhesion maturation in human derived ECFC. To substantiate their in vitro data set, they imaged intersegmental vessel development in zebrafish embryos treated with various inhibitors of translation and acto-myosin remodelling . They conclude that the transcriptional regulators, YAP and TAZ, are activated by mechanical cues to transcriptionally limit cytoskeletal and focal adhesion maturation, forming a conserved mechanotransductive feedback loop that mediates endothelial cell motility. Mechanistically, YAP and TAZ induced transcriptional suppression of myosin II activity to maintain dynamic cytoskeletal equilibria. Such transcriptional feedback loop may be necessary for persistent endothelial cell migration and vascular morphogenesis. The authors addressed an interesting aspect of vascular development and I have some comments and suggestions that are listed below.

Comments:

The authors used ECFC - endothelial colony forming cells (circulating endothelial cells that activate in response to vascular injury).

Q: did the authors characterize these cells and made sure that they are truly endothelial cells - for example examine specific endothelial markers, arterial-venous identity markers & Notch signalling status, overall morphology etc prior to the start of the experiment. How were ECFC isolated from human individuals, are these "healthy" volunteers - any underlying CVD risk factors, cells from one patient or from pooled samples, what injury where these humans exposed to trigger the release of the ECPFs into the circulation, etc. The materials & methods on ECFC should be expanded.

The authors suggest that loss of YAP/TAZ phenocopies actinomycin-D inhibition - "both transcription inhibition and YAP/TAZ depletion impaired polarization, and induced robust ventral stress fiber formation and peripheral focal adhesion maturation". However, the cell size of actinomycin-D treated cells (Fig. 1B, top right panel), differs from the endothelial cell size upon siYAP/TAZ (Fig. 1E, top right panel) - and vinculin staining seems more pronounced in actinomyocin-D treated cells (B, bottom right) when compared to siYAP/TAZ group. Cell shape is defined by acto-myosin tension.

Q: besides Fraction of focal adhesion >1um; focal adhesion number did the authors measure additional parameters related to cytoskeleton remodelling / focal adhesions that can substantiate their statement on similarity between loss of YAP/TAZ and actinomycin-D treatment. Would it be possible to make a more specific genetic intervention (besides YAP/TAZ) interfering with the focal adhesion pathway as opposed to the broad spectrum inhibitor actinomyocin-D.<br /> Q: does the actinomycin-D treatment affect responsiveness to Vegf? induce apoptosis or reduce survival of the ECFC?<br /> Q: Which mechanism links ECM stiffness with endothelial surface area in the authors scenario. In zebrafish, activity of endothelial guanine exchange factor Trio specifically at endothelial cell junctions (Klems, Nat Comms, 2020) and endoglin in response to hemodynamic factors (Siekmann, Nat Cell Biol 2017) have been show to control EC shape/surface area - do these factors play a role in the scenario proposed by the authors.<br /> Q: the authors report that EC migrate faster on stiff substrate, and concomitantly these cells have a larger surface area. What is the physiological rationale behind these observations. Did the authors observe such behaviors in their zebrafish ISV model? How do these observation integrate with the tip - stalk cell shuffling model (Jakobsson&Gerhardt, Nat Cell Biol, 2011) and Notch activity in developing ISVs.

The authors examined the formation of arterial intersegmental vessels in the trunk of developing zebrafish embryos in vivo. They used a variety of pharmacological inhibitors of transcription and acto-myosin remodelling and linked the observed morphological changes in ISV morphogenesis with changes in endothelial cell motility.<br /> Q: reduced formation and dorsal extension of ISVs may have several reasons, including reduced EC migration and proliferation. The Tg(fli1a:EGFP) reporter however is not the most suitable line to monitor migration of individual endothelial cells. Can the authors repeat the experiments in Tg(fli1a:nEGFP); Tg(kdrl:HRAS-mCherry) double transgenics to visualise movement-migration of the individual endothelial cells and EC proliferation events, in the different treatment regimes.<br /> ISV formation is furthermore affected by Notch signalling status and a series of (repulsive) guidance cues.<br /> Q: Does de novo blockade of gene expression with Actinomycin D affect Notch signalling status, expression of PlexinD - sFlt1, netrin1 or arterial-venous identify genes.

Remark: the authors may want to consider using the Tg(fli1:LIFEACT-GFP) reporter for in vivo imaging of actin remodelling events.

Remark: the authors report "As with broad transcription inhibition, in situ depletion of YAP and TAZ by RNAi arrested cell motility, illustrated here by live-migration sparklines over 10 hours: siControl: , siYAP/TAZ: (25 μm scale-bar: -)". Can the authors make a separate figure panel for this, how many cells were measured?<br /> Remark: in the wash-out experiments, exposure to the inhibitors is not the same in the different scenarios - could it be that the longer exposure time induces "toxic" side effect that cannot be "washed out" when compared to the short treatment regimes?

Reviewer #1 (Public Review):

The authors set out to develop an organoid model of the junction between early telencephalic and ocular tissues to model RGC development and pathfinding in a human model. The authors have succeeded in developing a robust model of optic stalk(OS) and optic disc(OD) tissue with innervating retinal ganglion cells. The OS and OD have a robust pattern with distinct developmental and functional borders that allow for a distinct pathway for pathfinding RGC neurites.

This study falls short on a thorough analysis of their single cell transcriptomics (scRNAseq). From the scRNAseq it is unclear the quality and quantity of the targeted cell types that exist in the model. A comparative analysis of the scRNAseq profiles of their cell-types with existing organoid protocols, to determine a technical improvement, or with fetal tissue, to determine fidelity to target cells, would greatly improve the description of this model and determine its utility. This is especially necessary for the RGCs developed in this protocol as they recommend this as an improved model to study RGCs.

Future work targeting RGC neurite outgrowth mechanisms will be exciting.

Reviewer #2 (Public Review):

The study by Liu et al. reports on the establishment and characterization of telencephalon eye structures that spontaneously form from human pluripotent stem cells. The reported structures are generated from embryonic cysts that self-form concentric zones (centroids) of telencephalic-like cells surrounded by ocular cell types. Interestingly, the cells in the outer zone of these concentric structures give rise to retinal ganglion cells (RGCs) based on the expression of several markers, and their neuronal morphology and electrophysiological activity. Single-cell analysis of these brain-eye centroids provides detailed transcriptomic information on the different cell types within them. The single-cell analysis led to the identification of a unique cell-surface marker (CNTN2) for the human ganglion cells. Use of this marker allowed the team to isolate the stem cell-derived RGCs.

Overall, the manuscript describes a method for generating self-forming structures of brain-eye lineages that mimic some of the early patterning events, possibly including the guidance cues that direct axonal growth of the RGCs. There are previous reports on brain-eye organoids with optic nerve-like connectivity; thus, the novel aspect of this study is the self-formation capacity of the centroids, including neurons with some RGC features. Notably, the manuscript further reports on cell-surface markers and an approach to generating and isolating human RGCs.

via Andy

Andy indicates that this review functionality isn't specific to zettelkasten, but it still sits in the framework of note taking. Given this, are there really "other" ways available?

Reviewer #3 (Public Review):

This study focuses on defining the specific importance of HSP90 isoforms in stress-resistance. Specifically, addressing the importance of the two HSP90 isoforms alpha and beta in adapting cells to chronic stress. Noting that chronic stresses of different types can induce increases of cellular size, the authors investigated the role of HSP90a/b in this process. Intriguingly, they found that KO of either of these isoforms did not influence chronic stress-dependent increases of cell size. However, they did find that HSF1 plays an important role in this process through undefined mechanisms. The authors go on to show that this increase in cell size appears to be correlated with enhanced protein synthesis during conditions of stress, which allow cells to maintain protein density in the enlarged cell. Intriguingly, this correlation is disrupted in HSP90a/b KO cells, where cell size increases, but there is a deficiency in recovery of protein synthesis following the initial insult. This appears to involve sustained ISR signaling that does not resolve in HSP90-deficient cells. Using a number of different compounds that increase (e.g., CDKi) or inhibit (e.g., rapamycin) cell size changes, the authors demonstrate that protection against chronic stress correlates with cell size and protein density, linking cell expansion to stress resistance.

Overall, this is an observational study that heavily relies on correlation to define a proposed stress responsive signaling mechanism termed the 'rewiring stress response' to explain the coordinated increase in cell size and protein translation in protection against chronic stress.

Due to the reliance on correlation, there remains many questions unanswered related to this work. For example. What is the specific role for HSP90a/b in regulating protein translation during chronic stress through the ISR or related pathways? The authors indicate that the induction of the eIF2a phosphatase GADD34 is not impacted in HSP90-deficient cells, so what role does HSP90 have in this process. Is HSP90 required for proper folding of GADD34? Would you see similar effects in protein translation recovery if other ISR activators are used in HSP90-deficient cells? Addressing this central unanswered question that would significantly enhance the current study. While the authors are undoubtedly pursuing this in subsequent studies, it is difficult to fully gauge the impact of this work without more clarity on that point specifically.

Along the same lines, another critical unanswered question is 'Are similar effects observed in non-dividing cells?' Does chronic stress lead to increases of size and regulation of protein translation in primary cell models that are not undergoing division.

Ultimately, this is an interesting study that does a good job of establishing correlations between increases in cell size and protein translation, but does not get to the really intriguing questions related to this coordination. As this study is extended through either revisions to this manuscript or subsequent papers, the importance of this rewiring stress response in the context of cellular stress and pathologic conditions (e.g., age-associated disease) will become increasing apparent.

Reviewer #1 (Public Review):

The manuscript describes that cultured mammalian cells adapt to chronic stress by increasing their size and protein translation through Hsp90. The authors extensively use Hsp90 knockout cells and mass spectrometry to provide solid evidence that chronic heat shock response is accompanied by cell size changes and stress resistance in large cells. The major strength of the work is the authors ability to document the heat shock response in detail, while the main weakness is that the cell size changes appear not to be quantitative making it difficult to assess how much the cell density is changed in chronic stress. Nevertheless, the increased stress resistance of large cells is conceptually important and provides one potential explanation why cells need to control their size. This work adds to our understanding of how cellular stress is managed, and while stress responses have been observed previously in relation to cell size, this work provides evidence for increased stress resistance in larger cells.

Reviewer #2 (Public Review):

The paper by Maiti et al. reporting a highly interesting, previously un-noticed, phenomenon of cell size increase as part of the response to chronic proteotoxic stresses, such as heat shock, which the authors term "rewiring stress response". Furthermore, they establish that it is mediated via HSF1, and, strikingly, necessitates a certain threshold levels of HSP90. Dwelling deeper into the underlying mechanisms, they find that HSP90 help scale protein synthesis with the increased cell sizes, and when diminished, this scaling is impaired, and also cell viability in chronic stress is also compromised. These findings correspond with a previous study by this group on the lethality of HSP90 deficient mice, and moreover, have implications to our understanding of cellular adaptation to stress, and generate interesting hypotheses about the possible links of this mechanism to the impairments of the ability to cope with stress during aging and senescence.

This is an excellent study, with highly novel and important findings, which illuminate a new phenomenon related to cellular adaptation to chronic stress. I have only one major concern, about some technical aspects, specifically over-crowding effects, which could confound the results, which should be answered by the authors. Other than that, further details which I think are pertinent to the study most likely already exist in the experiments performed, and most could be answered with additional simple experiments and by further analyses of the proteomics data which has already been performed, but which results are not sufficiently shown in detail.

Reviewer #2 (Public Review):

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

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

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

Reviewer #1 (Public Review):

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

Results:

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

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

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

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

Methods:

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

Reviewer #1 (Public Review):

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

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

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

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

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

Reviewer #2 (Public Review):

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

To test this hypothesis, they first set out to investigate the presence/absence of putative functional orthologs of CDCA7, HELLS and DNMTs across major eukaryotic clades. They succeed in identifying homologs of these genes in all clades spanning 180 species. To annotate putative functional orthologs, they use similarity over key functional domains and residues such as ICF related mutations for CDCA7 and SNF2 domains for HELLS. Using established eukaryote phylogenies, the authors conclude that the CDCA7-HELLS-DNMT axis arose in the last common ancestor to all eukaryotes. Importantly, they found recurrent loss events of CDCA7-HELLS-DNMT in at least 40 eukaryotic species, most of them lacking DNA methylation.

Having identified these factors, they successfully identify signatures of co-evolution between DNMTs, CDCA7 and HELLS using CoPAP analysis - a probabilistic model inferring the likelihood of interactions between genes given a set of presence/absence patterns. As a control, such interactions are not detected with other remodelers or chromatin modifying pathways also found across eukaryotes. Expanding on this analysis, the authors found that CDCA7 was more likely to be lost in species without DNA methylation.

In conclusion, the authors suggest that the CDCA7-HELLS-DNMT axis is ancestral in eukaryotes and raise the hypothesis that CDCA7 becomes quickly dispensable upon the loss of DNA methylation and/or that CDCA7 might be the first step toward the switch from DNA methylation-based genome regulation to other modes.

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

Strengths:

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

Weaknesses:

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

Reviewer #1 (Public Review):

The manuscript focused on roles of a key fatty-acid synthesis enzyme, acetyl-coA-carboxylase 1 (ACC1), in the metabolism, gene regulation and homeostasis of invariant natural killer T (NKT_ cells and impact on these T cells' roles during asthma pathogenesis. The authors presented data showing that the acetyl-coA-carboxylase 1 enzyme regulates the expression of PPARg then the function of NKT cells including the secretion of Th2-type cytokines to impact on asthma pathogenesis. The results are clearcut and data were logically presented.

Major concerns:

1. This study heavily relied on the CD4-CreACC1fl/fl mice. While using of a-GalCer stimulation and Ja18KO mice mitigated the concern, it is still a major concern that at least some of the phenotype were due to the effect on conventional CD4 T cells. For example, the deletion of ACC1 gene seems also decreased the numbers of conventional CD4 T cells (Fig. 2D, Fig. S1D). Previously there were reports showing ACC1 gene in conventional CD4 T cells also plays a role in lung inflammation (Nakajima et al., J. Exp. Med. 218, 2021). If the authors believe the phenotype observed was mainly due to iNKT cells, rather than conventional CD4 T cells, a compare/contrast of the two studies should be discussed to explain or reconcile the results.

2. The overall significance of the manuscript is related to the potential clinical suppression of ACC1 in human asthma patients. However, the authors only showed the elevated ACC1 genes in these patients, not even in vitro data demonstrating that suppression of ACC1 genes in the iNKT cells from patients could have potential therapeutic effect or suppression of the relevant cytokines.

3. The authors report that a-GalCer administration can induce the AHR, however, in the cited paper (Hachem et al., Eur J. Immunol. 35, 2793, 2005), iNKT cell activation seems to have the opposite effect to inhibit AHR. Did the authors mean to cite different papers?

Reviewer #2 (Public Review):

In this study the authors sought to investigate how the metabolic state of iNKT cells impacts their potential pathological role in allergic asthma. The authors used two mouse models, OVA and HDM-induced asthma, and assessed genes in glycolysis, TCA, B-oxidation and FAS. They found that acetyl-coA-carboxylase 1 (ACC1) was highly expressed by lung iNKT cells and that ACC1 deficient mice failed to develop OVA-induced and HDM-induced asthma. Importantly, when they performed bone marrow chimera studies, when mice that lacked iNKT cells were given ACC1 deficient iNKT cells, the mice did not develop asthma, in contrast to mice given wildtype NKT cells. In addition, these observed effects were specific to NKT cells, not classic CD4 T cells. Mechanistically, iNKT cell that lack AAC1 had decreased expression of fatty acid-binding proteins (FABPs) and peroxisome proliferator-activated receptor (PPAR)γ, but increased glycolytic capacity and increased cell death. Moreover, the authors were able to reverse the phenotype with the addition of a PPARg agonist. When the authors examined iNKT cells in patient samples, they observed higher levels of ACC1 and PPARG levels, compared to healthy donors and non-allergic-asthma patients.

Reviewer #1 (Public Review):

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

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

Reviewer #1 (Public Review):

Original review:

This manuscript by Walker et. al. explores the interplay between the global regulators HapR (the QS master high cell density (HDC) regulator) and CRP. Using ChIP-Seq, the authors find that at several sites, the HapR and CRP binding sites overlap. A detailed exploration of the murPQ promoter finds that CRP binding promotes HapR binding, which leads to repression of murPQ. The authors have a comprehensive set of experiments that paints a nice story providing a mechanistic explanation for converging global regulation. I did feel there are some weak points though, in particular the lack of integration of previously identified transcription start sites, the lack of replication (at least replication presented in the manuscript) for many figures, some oddities in the growth curve, and not reexamining their HapR/CRP cooperative binding model in vivo using ChIP-Seq.

Review of revised version:

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

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

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

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

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

Reviewer #2 (Public Review):

This manuscript by Walker et al describes an elegant study that synergizes our knowledge of virulence gene regulation of Vibrio cholerae. The work brings a new element of regulation for CRP, notably that CRP and the high density regulator HapR co-occupy the same site on the DNA but modeling predicts they occupy different faces of the DNA. The DNA binding and structural modeling work is nicely conducted and data of co-occupation are convincing. The work seeks to integrate the findings into our current state of knowledge of HapR and CRP regulated genes at the transition from the environment and infection. The strength of the paper is the nice ChIP-seq analysis and the structural modeling and the integration of their work with other studies. The weakness is that it is not clear how representative these data are of multiple hapR/CRP binding sites or how the work integrates as a whole with the entire transcriptome that would include genes discovered by others. Overall this is a solid work that provides an understanding of integrated gene regulation in response to multiple environmental cues.