Reviewer #1 (Public Review):

In this manuscript, Wang et al provide a pathway required for the production and degradation of exophers - large neuronal extrusions proposed to discard toxic cargo. Exophers were fairly recently described by this group and have now been observed in mammalian neurons, suggesting a broad importance in neuronal health. How exophers were disposed of by surrounding tissues was not known. Here, the authors identify a pathway required for exopher degradation into small debris (starry night), and intriguingly, genes proposed to be required in the degrading cells (hypodermis) for exopher production in neurons.

Strengths of the manuscript include significant new insights into a problem that had not been investigated in mechanistic detail, and the combined use of genetics and cell biology to sort genes into pathways involved in exopher production and degradation. Several differences are found between exopher and cell corpse disposal, highlighting the importance of the study. The findings should be of interest to a broad audience.

Reviewer #2 (Public Review):

Wu Yang et al. investigated how exophers (large vesicles released from neuronal somas) are degraded. They find that the hypodermal skin cells surrounding the neuron break up the exophers into smaller vesicles that are eventually phagocytosed. The neuronal exophers accumulate early phagosomal markers such as F-actin and PIP2, and blocking actin assembly suppressed the formation of smaller vesicles and the clearance of neuronal exophers. They show the smaller vesicles are labeled with various markers for maturing phagosomes, and inhibiting phagosome maturation blocked the breakdown of exophers in to smaller vesicles. Interestingly, they discover that GTPase ARF-6, effector SEC-10/Exocyst, and the phagocytic receptor CED-1 in the hypodermis are required for efficient production of exophers by neurons.

Strength<br /> The study clearly demonstrates that exophers are eliminated via hypodermal cell-mediated phagocytosis. Exophers are broken down into smaller vesicles that accumulate phagocytic markers, and inhibiting this process shows that exophers are not resolved. The paper does a thorough examination of various markers and mutants to demonstrate this process.

The hypodermal cells not only engulf these small vesicles, but they also play a role in the formation of exophers. Exopher production is reduced when ARF-6, SEC-10, or CED-1 are knocked down in the hypodermis. This is intriguing because phagocytosis is a critical step in the final elimination of cells, but in this unique situation, it appears that the neuron fails to extrude the exopher without phagocytes.

Weakness

Non-professional phagocytes engulfing cell corpses and many other types of cellular debris (e.g. degenerating axons) have been shown in multiple systems and the observations here are not surprising. Many of the markers used in the study are well-established phagocytic markers and do not bring forward a new technological advance.

What's interesting is that the breakdown of exophers into smaller vesicles and eventual clearance follows a different sequence of events than macrophages. Exophers appear to undergo phagosomal fission before interacting with lysosomes. This would be difficult to appreciate by a general reader.

While the paper has strengths, it appears that the message is not clear. The title suggests that the reader will learn about how ARF-6 and CED-1 control exopher extrusion. Although this observation is intriguing and maybe the main point of the paper, there does not appear to be a substantial amount of data to support this claim. The only data to back this up is in the final figure and the majority of the paper is focused on how hypodermal cells phagocytose exophers.

To show exopher secretion is dependent on the hypodermal cells-

1. Could authors induce exopher production through other means? And test any involvement of CED-1? For example, authors note exopher production increases under stress conditions including expression of mutant Huntingtin protein. It would be intriguing if loss of CED-1 would be sufficient to block or reduce exopher production in that context and would highlight an exciting role for phagocytic cell types.<br /> 2. It is not clear if the CED-1 localization to the exopher is due to CED-1 expression during phagocytosis or is it involved in the extrusion. Perhaps the basal level of CED-1 is important for the extrusion but the strong expression is important for recognition of the exopher.<br /> 3. While the data with ttr-52 and anoh-1 alleles is compelling, do we know that exophers actually expose PS? Especially since at a certain point, the exopher is still attached to the neuronal soma. Is PS still exposed by exopher in CED-1 background?<br /> 4. What is the fate of a neuron that is unable to produce exophers? Could one look at lifespan of ALMR neuron in CED-1, ARF-6 or Sec-10 allele (potentially with specificity to hypodermis)?

Reviewer #3 (Public Review):

In this paper, the authors examine the fate of exophers ejected from C. elegans neurons overexpressing a presumably aggregated mCherry protein. They show that exophers are taken up by adjacent hypodermal cells, split into smaller fragments, and eventually degraded by lysosome fusion. They identify a number of small GTPases and accessory components, as well as the phagocytic receptor (CED-1) and the likely eat-me signal (phosphatidylserine).

The manuscript follows up on previous exopher work from some members of the current collaboration, and provides a detailed analysis of exopher fate, that will likely be useful for understanding similar events in other settings. The studies are well done, the images and data are convincing, and the interpretations are generally appropriate.

Reviewer #1 (Public Review):

This study investigated the roles of sams-1 and sams-4, two enzymes that generate the major methyl donor SAM, in heat stress response and the associated molecular changes. The authors provided evidence that loss of sams-1 resulted in enhanced resistance to heat stress, whereas loss of sams-4 resulted in heightened sensitivity to heat stress. The authors further showed that whereas the basal level of the histone modification H3K4me3 in intestinal nuclei was substantially reduced in sams-1 loss-of-function mutants, H3K4me3 level greatly increased upon heat stress, and this increase depended on sams-4. Additional RNA-seq results revealed largely distinct heat stress-induced RNA expression changes in the sams-1 mutant and sams-4 knockdown worms. The authors further profiled genomic locations of H3K4me3 in sams-1 mutant and sams-4 knockdown worms. Unfortunately, the lack of sufficient technical detail made it difficult to evaluate the H3K4me3 profiling data.

The paper provided several conceptual advances:<br /> - Uncovering interesting and opposing heat stress phenotype associated with the loss of two related SAM synthases. Thus, even though both SAMS-1 and SAMS-4 produce SAM, the source of SAM production appears to have distinct consequences on the organismal heat stress response.<br /> - Demonstration that SAMS-4 appeared able to compensate for the loss of SAMS-1 upon heat shock, resulting in restoration of the histone mark H3K4me3 in intestinal cells.<br /> - Revealing largely different gene expression changes upon heat shock in animals lacking sams-1 or sams-4. Thus, the gene expression profiles corroborated the differential heat stress response.

This paper describes one of the first adaptations of CUT&TAG in C. elegans, which can be of high impact on the field. Unfortunately, the lack of experimental detail made it difficult to evaluate the quality of the CUT&TAG data and the consequent interpretations.

Overall, the paper reported a number of interesting findings that will be of substantial interest to the field. However, the paper in its current form has substantial shortcomings, particularly related to the difficulty in evaluating the validity of H3K4me3 profiling data. The paper would also benefit from further discussion that attempts to reconcile some of the inconsistent results.

Reviewer #2 (Public Review):

In this manuscript titled "S-adenosylmethionine synthases specify distinct H3K4me3 populations and gene expression patterns during heat stress", the authors Godbole et al investigated how C. elegans SAM synthases, SAMS-1 and SAMS-4, affected gene expression, H3K4 trimethylation (H3K4me3), and the survival under heat stress. They found in this study that SAMS-4 was required for survival during heat shock. They reasoned that SAM supplied by SAMS-4 but not SAMS-1 might be responsible for generating H3K4me3 under heat shock and claimed that the two SAM synthases differentially affected histone methylation and thus gene expression in the heat shock response. This study suggested a stress-responsive mechanism by which the specific isozyme of SAM synthetase provided a specific pool of cellular SAM for H3K4me3. Overall, this study is interesting but descriptive. Lacking necessary controls and mechanistic details weakened the significance of this work.

Strengths: Very interesting survival phenotypes in the loss of different SAM synthetases; technical success in CUT&tag in C. elegans.

Weaknesses: No clear conclusion can be drawn about whether and how SAM synthetases affect H3K4me3.

Reviewer #3 (Public Review):

The manuscript " S-adenosylmethionine synthases specify distinct H3K4me3 populations and gene expression patterns during heat stress " by Godbole et al proposes a novel mechanism by which different S-adenosylmethionine (SAM) synthase enzymes exhibit specificity towards target sequences, thereby providing a layer of control over H3K4 trimethylation (H3K4me3) in Caenorhabditis elegans. The authors detail an extensive investigation of the function of two C. elegans SAM synthase enzymes, SAMS-1 and SAMS-4. They provide evidence that mutation or knockdown of these two enzymes affected gene expression of distinct gene sets and that loss of these enzymes has opposite effects on survival under heat stress. These differential effects are linked to differential effects on histone modification H3K4me3 of specific target gene sets. It is unclear from this work how exactly this specificity may be achieved and some of the data regarding the role of other components of the methylation machinery are somewhat superficial and confusing. Nevertheless, the study suggested a novel mechanism by which H3K4me3 of specific gene sets may be controlled and this mechanism is novel and potentially important.

Reviewer #1 (Public Review):

Castelán-Sánchez et al. analyzed SARS-CoV-2 genomes from Mexico collected between February 2020 and November 2021. This period spans three major spikes in daily COVID-19 cases in Mexico and the rise of three distinct variants of concern (VOCs; B.1.1.7, P.1., and B.1.617.2). The authors perform careful phylogenetic analyses of these three VOCs, as well as two other lineages that rose to substantial frequency in Mexico, focusing on identifying periods of cryptic transmission (before the lineage was first detected) and introductions to and from the neighboring United States. The figures are well presented and described, and the results add to our understanding of SARS-CoV-2 in Mexico. However, I have some concerns and questions about sampling that could affect the results and conclusions:

1) The authors do not provide any details on the distribution of samples across the various Mexican States, making it hard to evaluate several key conclusions. Although this information is provided in Supplementary Data 2, it is not presented in a way that enables the reader to evaluate if lineages were truly predominant in certain regions of the country, or if these results are attributable purely to sampling bias. Specifically, each lineage is said to be dominant in a particular state or region, but it was not clear to me if sampling across states was even at all time points. For example, the authors state that most B.1.1.7 genome sampling is from the state of Chihuahua, but it is not clear if this was due to more sequenced samples from that region during the time that B.1.1.7 was circulating, or if the effects of B.1.1.7 were truly differential across the country. The authors do mention sequencing biases several times but need to be more specific about the nature of this bias and how it could affect their conclusions.

2) It is surprising to see in this manuscript that the B.1.1.7 lineage did not rise above 25% prevalence in the data presented, despite its rapid rise in prevalence in many other parts of the world. This calls into question if the presented frequencies of each lineage are truly representative of what was circulating in Mexico at the time, especially since the coordinated sampling and surveillance program across Mexico did not start until May 2021.

Reviewer #2 (Public Review):

The authors use a series of subsampling methods based on phylogenetic placement and geographic setting, informed by human movement data to control for differences in sampling of SARS-CoV-2 genomes across countries. Of note, the authors show that 2 variants likely arose in Mexico and spread via multiple introductions globally, while other variant waves were driven by repeat introductions into Mexico from elsewhere. Finally, they use human mobility data to assess the impact of movement on transmission within Mexico.

Overall, the study is well done and provides nice data on an under-studied country. The authors take a thoughtful approach to subsampling and provide a very thorough analysis. Because of the care given to subsampling and the great challenge that proper subsampling represents for the field of phylodynamics, the paper would benefit from a more thorough exploration of how their migration-informed subsampling procedure impacts their results. This would not only help strengthen the findings of the paper but would likely provide a useful reference for others doing similar studies. Additionally, I would suggest the authors provide a bit more discussion of this subsampling approach and how it may be useful to others in the discussion section of the paper.

Reviewer #1 (Public Review):

Animal colour evolution is hard to study because colour variation is extremely complex. Colours can vary from dark to light, in their level of saturation, in their hue, and on top of that different parts of the body can have different colours as well, as can males and females. The consequence of this is that the colour phenotype of a species is highly dimensional, making statistical analyses challenging.

Herein the authors explore how colour complexity and island versus mainland dwelling affect the rates of colour evolution in a colourful clade of birds: the kingfishers. Island-dwelling has been shown before to lead to less complex colour patterns and darker coloration in birds across the world, and the authors hypothesise that lower plumage complexity should lead to lower evolutionary rates. In this paper, the authors explore a variety of different and novel statistical approaches in detail to establish the mechanism behind these associations.

There are three main findings: (1) rates of colour evolution are higher for species that have more complex colour phenotypes (e.g. multiple different colour patches), (2) rates of colour evolution are higher on island kingfishers, but (3) this is not because island kingfishers have a higher level of plumage complexity than their mainland counterparts.

I think that the application of these multivariate methods to the study of colour evolution and the results could pave the way for new studies on colour evolution.

I do, however, have a set of suggestions that should hopefully improve the robustness of results and clarity of the paper as detailed below:

1) The two main hypotheses tested linking plumage complexity and island-dwelling to rates of colour evolution seem rather disjointed in the introduction. This section should integrate these two aspects better justifying why you are testing them in the same paper. In my opinion, the main topic of the paper is colour evolution, not island-mainland comparisons. I would suggest starting with colours and the challenges associated with the study of colour evolution and then introducing other relevant aspects.

2) Title: the title refers to both complex plumage and island-dwelling, but the potential effects of complexity should apply regardless of being an island or mainland-dwelling species, am I right? Consider dropping the reference to islands in the title.

3) The results encompass a large variety of statistical results some closely related to the main hypothesis (eg island/mainland differences) tested and others that seem more tangential (differences between body parts, sexes). Moreover, quite a few different approaches are used. I think that it would be good to be a bit more selective and concentrate the paper on the main hypotheses, in particular, because many results are not mentioned or discussed again outside the Results section.

4) Related to the previous section, the variety of analytical approaches used is a bit bewildering and for the reader, it is unclear why different options were used in different sections. Again, streamlining would be highly desirable, and given the novel nature of the analytical approach (as far as I know, many analytical approaches are applied for the first time to study colour evolution) it would be good to properly explain them to the reader, highlighting their strengths and weaknesses.

5) The Results section contains quite a bit of discussion (and methods) despite there being a separate Discussion section. I suggest either separating them better or joining them completely.

6) The main analyses of colour evolutionary rates only include chromatic aspects of colour variation. Why was achromatic variation (i.e. light to dark variation) not included in the analyses? I think that such variation is an important part of the perceived colour (e.g. depending on their lightness the same spectral shape could be perceived as yellow or green, black or grey or white). I realize that this omission is not uncommon and I have done so myself in the past, but I think that in this case, it is highly relevant to include it in the analyses (also because previous work suggests that island birds are darker than their mainland counterparts). This should be possible, as achromatic variation may be estimated using double cone quantum catches (Siddiqi et al., 2004) and the appropriate noise-to-signal ratios (Olsson et al., 2018). Adding one extra dimension per plumage patch should not pose substantial computational difficulties, I think.

7) The methods need to be much better explained. Currently, some methods are explained in the main text and some in the methods section. All methods should be explained in detail in the methods section and I suggest that it would be better to use a more traditional manuscript structure with Methods before Results (IMRaD), to avoid repetition (provided this is allowed by the journal). Whenever relevant the authors need to explain the choice of alternative approaches. Many functions used have different arguments that affect the outcome of the analyses, these need to be properly explained and justified. In general, most readers will not check the R script, and the methods should be understandable to readers that are not familiar with R. This is particularly important because I think that the methodological approach used will be one of the main attractions of the manuscript, and other researchers should be able to implement it on their own data with ease. Judging from the R script, there are quite a few analyses that were not reported in the manuscript (e.g. multivariate evolutionary rates being higher in forest species). This should be fixed/clarified.

Reviewer #2 (Public Review):

In "Complex plumages spur rapid color diversification in island kingfishers (Aves: Alcedinidae)", Eliason et al. link intraspecific plumage complexity with interspecific rates of plumage evolution. They demonstrate a correlation here and link this with the distinction between island and mainland taxa to create a compelling manuscript of general interest on drivers of phenotypic divergence and convergence in different settings.

This will be a fantastic contribution to the literature on the evolution of plumage color and pattern and to our understanding of phenotypic divergence between mainland and island taxa. A few key revisions can help it get there. This paper needs to get, fairly quickly, up to a point where the difference between plumage complexity and color divergence is defined carefully. That should include hammering home that one is an intraspecific measure, while one is an interspecific measure. It took me three reads of the paper to be able to say this with confidence. Leading with that point will greatly improve the paper if that point gets forgotten then the premise of the paper feels very circular.

Also importantly, somewhere early on a hypothesized causal pathway by which insularity, plumage complexity, and color divergence interact needs to be laid out. The analyses that currently follow are good ones, and not wrong, but it's challenging to assess whether they are the right ones to run because I'm not following the authors' reasoning very well here. I think it's possible a more holistic analysis could be done here, but I'll refrain from any such suggestions until I better get what the authors are trying to link.

We also need something near the top that tells us a bit more about the biogeography of kingfishers. Are kingfisher species always allopatric? I know the answer is no, but not all readers will. What I know less well though is whether your insular species are usually allopatric. I suspect the answer is yes, but I don't actually know.

In short, how do the authors think allopatry/sympatry/opportunity for competition link to mainland vs. island link to plumage complexity? And rates of color evolution? Make this clear upfront.

Reviewer #3 (Public Review):

In this article, the authors examined color evolution in the kingfishers, a group of birds that have achieved a spectacular diversity of colors and color patterns as they have diverged across the continents and island chains of the globe. Like many other avian taxa, kingfishers on islands often exhibit color patterns distinct from their close relatives. The authors focus here on putting this informally recognized pattern of evolutionary change to a formal test, asking if plumage color diversity and evolutionary rate are elevated on islands. They also explore whether a notable characteristic of some kingfishers - their simultaneous use of many of the coloration mechanisms available in birds - contributes to the evolutionary lability of their color patterns.

The authors have previously explored how when color varies in birds it is not just in dimensions of color, but also in the distribution of those colors in patches on the body. Summarizing this variation is challenging, and there are statistical obstacles to comparing it in a holistic manner. In this study, the authors use an exceptional set of analyses to study color in total as a multivariate trait. These are the major strengths of the paper. The authors' efforts are somewhat less convincing when they pursue a univariate model fitting on a small number of principal components, but these analyses are not central to the study. And as with all studies using ancestral state reconstruction to test hypotheses, it's an important tool and one that contributes to this study's effectiveness, but we should acknowledge some level of uncertainty with its results.

The authors report two important relationships in this study. They provide convincing evidence that rates of color evolution are elevated in island kingfishers, without convergence towards a particular island phenotype. They also describe a relationship between the complexity of plumage patterns and the rate at which they evolve, which has fundamental implications for our understanding of the tempo of trait evolution.

Islands make up a tiny portion of the earth's surface but are home to a seemingly disproportionate amount of life's diversity. This paper makes an important contribution to our understanding of how this diversity is generated, by showing that the evolutionary rate is elevated on islands for traits relevant to mate choice and recognition. The authors find that "plumage complexity, rather than uniformity, provides more phenotypic traits for natural selection to act upon". Given the number of different coloration mechanisms they express, the kingfishers are a unique group in which to study this issue, so I look forward to reading and hearing more from the authors on this issue in the future.

Reviewer #1 (Public Review):

In this manuscript, Huang et al., assess cognitive flexibility in rats trained on an animal model of anorexia nervosa known as activity-based anorexia (ABA). For the first time, they do this in a way that is fully automated and free from experimenter interference, as apparently experimenter interference can affect both the development of ABA as well as the effect on behaviour. They show that animals that are more cognitively flexible (i.e. animals that had received reversal training) were better able to resist weight loss upon exposure to ABA, whereas animals exposed to ABA first show poorer cognitive flexibility (reversal performance).

Strengths:<br /> - The development of a fully-automated, experimenter-free behavioural assessment paradigm that is capable of identifying individual rats and therefore tracking their performance.<br /> - The bidirectional nature of the study - i.e. the fact that animals were tested for cognitive flexibility both before and after exposure to ABA, so that direction of causality could be established.<br /> - The analyses are rigorous and the sample sizes sufficient.<br /> - The use of touchscreens increases the translational potential of the findings.

Weaknesses<br /> - Some descriptions of methods and results are confusing or insufficiently detailed.<br /> - It seems to me that performance on the pairwise discrimination task cannot be directly (statistically) compared to performance on reversal (as in Figure 4E), as these are tapping into fundamentally different cognitive processes (discrimination versus reversal learning). I think comparing groups on each assessment is valid, however.<br /> - Not necessarily a 'weakness' but I would have loved to see some assessment of the alterations in neural mechanisms underlying these effects, and/or some different behavioural assessments in addition to those used here. In particular, the authors mention in the discussion that this manipulation can affect cholinergic functioning in the dorsal striatum We (Bradfield et al., Neuron, 2013) and a number of others have now demonstrated that cholinergic dysfunction in the dorsomedial striatum impairs a different kind of reversal learning that based on alterations in outcome identity and thus relies on a different cognitive process (i.e. 'state' rather than 'reward' prediction error). It would be interesting perhaps in the future to see if the ABA manipulation also alters performance on this alternative 'cognitive flexibility' task.

Nevertheless, I certainly think the manuscript provides a solid appraisal of cognitive flexibility using more traditional tasks, and that the authors have achieved their aims. I think the work here will be of importance, certainly to other researchers using the ABA model, but perhaps also of translational importance in the future, as the causal relationship between ABA and cognitive inflexibility is near impossible to establish using human studies, but here evidence points strongly towards this being the case.

Reviewer #2 (Public Review):

Huang and colleagues present data from experiments assessing the role of cognitive inflexibility in the vulnerability to weight loss in the activity-based anorexia paradigm in rats. The experiments employ a novel in-home cage touchscreen system. The home cage touch screen system allows reduced testing time and increased throughput compared with the more widely used systems resulting in the ability to assess ABA following testing cognitive flexibility in relatively young female rats. The data demonstrate that, contrary to expectations, cognitive inflexibility does not predispose to greater ABA weight loss, but instead, rats that performed better in the reversal learning task lost more weight in the ABA paradigm. Prior ABA exposure resulted in poorer learning of the task and reversal. An additional experiment demonstrated that rats that had been trained in reversal learning resisted weight loss in the ABA paradigm. The findings are important and are clearly presented. They have implications for anorexia nervosa both in terms of potentially identifying those at risk also in understanding the high rates of relapse.

Reviewer #3 (Public Review):

Activity-based anorexia (ABA), which combines access to a running wheel and restricted access to food, is a most common paradigm used to study anorexic behavior in rodents. And yet, the field has been plagued by persistent questions about its validity as a model of anorexia nervosa (AN) in humans. This group's previous studies supported the idea that the ABA paradigm captures cognitive inflexibility seen in AN. Here they describe a fully automated touchscreen cognitive testing system for rats that makes it possible to ask whether cognitive inflexibility predisposes individuals to severe weight loss in the ABA paradigm. They observed that cognitive inflexibility was predictive of resistance to weight loss in the ABA, the opposite of what was predicted. They also reported reciprocal effects of ABA and cognitive testing on subsequent performance in the other paradigm. Prior exposure to the ABA decreased subsequent cognitive performance, while prior exposure to the cognitive task promoted resistance to the ABA. Based on these findings, the authors argue that the ABA model can be used to identify novel therapeutic targets for AN.

The strength of this manuscript is primarily as a methods paper describing a novel automated cognitive behavioral testing system that obviates the need for experimentalist handling and single housing, which can interfere with behavioral testing, and accelerate learning on the task. Together, these features make it feasible to perform longitudinal studies to ask whether cognitive performance is predictive of behavior in a second paradigm during adolescence, a peak period of vulnerability for many psychiatric disorders. The authors also used machine learning tools to identify specific behaviors during the cognitive task that predicted later susceptibility to the ABA paradigm. While the benefits of this system are clear, the rigor and reproducibility of experiments using this paradigm would be enhanced if the authors provided clear guidelines about which parameters and analyses are most useful. In their absence, the large amount of data generated can promote p-hacking.

The authors use their automated behavioral testing paradigm to ask whether cognitive inflexibility is a cause or consequence of susceptibility to ABA, an issue that cannot be addressed in AN. They provide compelling evidence that there are reciprocal effects of the two behavioral paradigms, but do not perform the controls needed to evaluate the significance of these observations. For example, the learning task involves sucrose consumption and food restriction, conditions that can independently affect susceptibility to the ABA. Similarly, the ABA paradigm involves exercise and restricted access to food, which can both affect learning.

In the Discussion, the authors hypothesize that the ABA paradigm produces cognitive inflexibility and argue that uncovering the underlying mechanism can be used to identify new therapeutic targets for AN. The rationale for their claim of translational relevance is undermined by the fact that the biggest effect of the ABA paradigm is seen in the pair discrimination task, and not reversal learning. This pattern does not fit clinical observations in AN.

In summary, the significance of this manuscript lies in the development of a new system to test cognitive function in rats that can be combined with other paradigms to explore questions of causality. While the authors clearly demonstrate that cognitive flexibility does not promote susceptibility to ABA, the experiments presented do not provide a compelling case that their model captures important features of the pathophysiology of AN.

Reviewer #1 (Public Review):

This article describes simultaneous surface recordings with a transparent electrode array and two-photon calcium imaging in the mouse cortex. The study shows that spiking activity recorded by surface electrodes or imaged layer 2/3 activity is decoupled. Moreover, simulations indicate that this decoupling may be due to a dominance of L1 projecting axons (input to the cortex) in surface spiking activity.

This is a rigorous study capitalizing on the new Windansee surface recording device, which provides extremely useful evidence that surface electrodes may not be able to capture information processed in the cortical layers. Recordings and simulations seem adequately performed. The indication that axons contribute significantly to multiunit activity is extremely important for the interpretation of multiunit activity in surface recordings. Here the claim is limited to surface recording, and one wonders to which extent this conclusion would transpose to recordings made with penetration electrodes.

Reviewer #2 (Public Review):

The manuscript describes a novel transparent electrode array and demonstrates its combination with two-photon calcium imaging in mouse neocortex. Using a computational model, the authors propose that surface multi-unit activity mainly reflects L1 axonal activity and they find a small population of L2/3 neurons that correlates with this activity. While the multi-modal approach with the innovative device in our view is interesting and potentially useful, we have several technical and scientific concerns that should be addressed by the authors.

Strengths:<br /> We find the general scope of this manuscript, to establish a hybrid electrophysiological and optical approach for studying neocortical activity, very interesting and relevant. The authors provide a compelling use case for combined ECoG and two-photon imaging. While extracellular action potentials have been recorded from the cortical surface, the underlying source is unknown and the device and techniques introduced by the authors are appropriate to address this question. The introduced device can be implanted chronically and has good long-term stability, providing longitudinal optical and electrical recordings from the cortex. The authors perform recordings in awake, head-fixed animals which provides the opportunity to relate ECoG and single-cell data to the animal's behavioral state. The combination of empirical data and biophysical modelling is a powerful means by which to answer such questions.

Weaknesses:<br /> The central claim of the paper relies heavily on the computational model and the physiological data could be more completely analyzed. Based on a sample of 136 L2/3 neurons the authors find a small proportion (13%) that correlates with the ECoG MUA (eMUA). Based on this, they use a model to show that ECoG MUA likely reflects axonal spikes. They then posit that these layer 2/3 neurons are tightly correlated to the layer 1 input. The presentation of their data and the specifics of their model makes it difficult to assess the validity of this claim. They do not sufficiently discuss possible confounds in the data, caveats of their model, or alternative explanations of the observed low proportion of L2/3 neurons that correlate with the ECoG MUA.

Most relevantly, the authors do not measure single units with their ECoG. The eMUA is a complex mixture of many neuronal sources, and interpretation is therefore difficult. They relate the calcium transients of small populations of single L2/3 neurons with the aggregate measure of population activity reflected in eMUA. It is possible that the eMUA reflects population activity in the local circuit and might therefore have a low correlation with individual single units. Critically, there is no information on the sensitivity of calcium recordings. Do the imaging data detect single action potentials, or are they biased to bursts of more than 1 AP?

The analysis pipeline and values used for computing the correlation coefficients are counterintuitive. The fluorescence data are first interpolated from 15 Hz to 4 kHz and then both eMUA and imaging data are effectively down-sampled to 2 Hz. A single correlation coefficient is then estimated for each neuron, regardless of behavioral state, even though the authors themselves show that the activity of single neurons and the ECoG signal depend on the state of the animal.

There is also insufficient information on the weight of the implant and its effect on mouse behavior. How does the movement of implanted and non-implanted mice differ? Must mice be singly housed? Finally, the modeling parameters are highly specific, using independently driving spikes, while the activity of neurons can be highly correlated. Likewise, the contribution of tangentially oriented axons that could relate to long-range connections conveying information related to the animal's motion or level of arousal is not considered. The manuscript would benefit from further analysis of the physiological data, consideration of alternative explanations and forthright discussion of limitations and caveats of their device and approach.

Reviewer #3 (Public Review):

The authors have developed a new form of transparent surface multielectrode integrated into an imaging window, enabling simultaneous recording of electrical activity at the surface of the cortex combined with two-photon imaging through the window and electrode. The authors characterise the electrical signals and use simulations to argue that they reflect the activity of axons in layer 1. This is then correlated with calcium imaging signals from layer 2/3 pyramidal cells. A subset of these displayed strong correlations with the layer 1 activity.

The raw electrical recordings appear to be contaminated by large movement artefacts. The authors attempt to decompose the signal into neuronal activity and artefact. The independent component analysis (ICA) employed yields plausible results. However, there is no definitive validation of this procedure.

The simulations strongly suggest that only layer 1 axons will generate significant neuronal signals at the surface, but the authors have not attempted to reconstruct the multiunit activity in the simulations, which could provide additional assurance for their interpretation.

A small fraction of pyramidal cells has activity strongly correlated with the signal at the surface electrode. However, the authors have not examined whether the distance from neuron to the electrode influences the strength of correlation. It remains possible that the differential correlation reflects a distance effect rather than the existence of two populations.

Reviewer #1 (Public Review):

This is an interesting manuscript that highlights the potential for 'clogging' of import channels by mutant proteins to promote mitochondrial dysfunction in disease. One of the challenges with this study is deconvoluting potential loss-of-function phenotypes associated with reductions in ANT1/AAC2 from gain-of-toxicity phenotypes linked to import clogging. This was addressed primarily in yeast, showing that phenotypes associated with overexpression of mutants (e.g., reduced growth on glucose media). The experiment showing that yeast AAC2 clogs import was also convincing including both in vitro and in vivo characterization, although it isn't clear why the proteomic experiments were performed with acute expression of A128P instead of the 'superclogger' double mutant. The extension of this work to mammalian cells and then mice is also admirable. However, the quality of characterization does begin to decline when moving into mammalian models. For example, there is no clear evidence that observed phenotypes can be attributed to gain of toxicity instead of loss of function in mammalian cells and mice. There are similarities to yeast, but this needs to be better defined in my opinion. Lastly, I have questions related to the mouse model, such as how do these phenotypes compare to KO animals and why were homozygous mice used in some situations and heterozygous mice used in others.

Overall, this manuscript is interesting, as it describes a mechanism whereby mutant proteins can lead to import deficiencies in the context of disease. The strengths primarily reside with the yeast work, where the demonstration of import clogging and the functional implications of this clogging are best defined. The transition to mammalian cells and mice is admirable as well, but doesn't reach the same level of characterization, leaving open the possibility that the observed effects could be attributed (at least in part) to loss of function of ANT1.

Reviewer #2 (Public Review):

Mitochondrial dysfunction is now widely recognized as an underlying cause of many human diseases. In many cases, however, very little is known about the molecular etiology of mitochondrial disorders. In this comprehensive study Coyne et al. describe a mechanism by which dominant pathogenic variants of adenine nucleotide translocase Aac2p/ANT1 impair mitochondrial protein import pathway leading to cytotoxicity and mitochondrial dysfunction. By elucidating the fate of this protein in yeast, human cell culture, and murine models, the authors showed that mutant Aac2p variants accumulate the outer membrane translocase TOM complex jamming up mitochondrial protein import and affecting TIM22-mediated carrier import pathway, thus causing proteostatic stress. Furthermore, they showed that the i-AAA protease Yme1p and not the ubiquitin-proteasome system is responsible for proteolytic removal of the mutant Aac2p variants. Finally, the demonstrated that mitochondrial protein import clogging caused by the ANT1 A114P, A123D variant causes severe dominant neurodegenerative phenotype in mice, which resembles neuromuscular disease manifestations in humans. The authors propose this as a candidate pathological mechanism in ANT1-linked human disorders and by extension, to other diseases arising from defects in mitochondrial protein import.

Overall, this is a well-designed and thoroughly executed study that reports on a novel aspect of ANT1 associated dysfunction and provides mechanistic insights into the pathological mechanisms at play.

Reviewer #3 (Public Review):

Dominant pathogenic variants of the Aac2/Ant1 ATP transporter cause disease by an unknown mechanism. In this manuscript the authors aim to reveal how these gain of function mutants impair cellular and mitochondrial health. To characterize the phenotype of Aac2 mutants in yeast, the authors use a series of single and double Aac2 mutations, within the 2nd and 3rd transmembrane domains that are associated with human diseases. Aac2A128P,A137D mutant, which caused high toxicity and damaged the mitochondrial DNA was selected for further analysis. This mutant was not imported efficiently into mitochondria and exhibited an increased association with TOM, suggesting that it clogs the TOM translocase. As a result, expression of Aac2A128P,A137D led to impaired import of other mitochondrial proteins. Several findings suggested that the single mutant Aac2A128P impaired mitochondrial import in a similar manner: 1. mass spec analysis revealed its increased association with cytosolic chaperones, TOM and TIM22 subunits, 2. Aac2A128P overexpression led to global mitochondrial protein import deficiency, demonstrated by HSP60 precursor accumulation and activation of stress responses (transcription of chaperons, proteosome induction, and CIS1).<br /> Parallel mutants of human Ant1 (AntA114P and Ant1A114P,A123D) were ectopically expressed in HeLa cells. The mutants were demonstrated to clog TOM and cause a global defect in mitochondrial protein import. This was confirmed in tissues from Ant1A114P,A123D/+ knock-in mice. The Ant1A114P,A123D/+ mice exhibited decreased maximal mitochondrial respiration in muscles. Examination of the skeletal muscle myofiber diameter and COX and SDH activity revealed that Ant1A114P,A123D expression in heterozygous mice acts dominantly and causes a myopathic phenotype and in some case neurodegeneration.

Major strengths -

The ability of proteins to clog TOM and sequentially disrupt protein import into mitochondria was demonstrated in recent years. However, till now this was achieved using chemicals, artificial cloggers and overexpression of mitochondrial proteins. This study reveals, for the first time, that disease associated variants of native mitochondrial proteins can clog the entry into the organelle. Thus, this work demonstrates that TOM clogging is a physiological relevant phenomenon that is involved in human diseases.

The manuscript is well-written and the experiments are well-designed, presenting convincing data that mostly support the conclusions. The methods used are well-establish and suitable techniques that are often used in the field. This work took advantage of 3 different biological systems/model organism, yeast, cell culture, and mice tissues, to validate the results, show conservation, and exploit the strengths of each system.

Overall, this study is impactful, greatly contributes to the field and should be of interest to the general scientific community. The work sheds light of the mechanisms by which Ant1 pathogenic mutants impact cellular health and provides evidence for the involvement of translocases clogging and impaired protein import in human diseases. The gain of function Aac2/Ant1 mutants will provide a new and powerful tool for future studies of mitochondrial quality control and repair mechanisms.

Major weaknesses -

1. The evidence for clogging of mitochondrial translocases and for general defect in protein import are solid. However, there are not enough evidence to conclude that all phenotype seen in mice and yeast are directly connected to clogging.

2. This work implies that Aac2/Ant1 variants can clogg TOM, TIM22, or both. Clogging of TIM22 is novel and interesting but is not fully discussed in the manuscript, as well as the possibility that clogging of different translocases can result in different defects.

Reviewer #1 (Public Review):

This manuscript presents a comparison between models that may explain psychophysical performance in sensory integration tasks, where a subject essentially has to count stimulus samples and make a motor report about the final count.

The work has many technical strengths:

- The problem of model mimicry is clearly articulated.

- The work shows that the use of discrete sample stimulus (DSS) is key for being able to disambiguate multiple candidate mechanisms that could possibly underlie the observed behavioral data.

- The authors use rigorous model comparison and analysis techniques, some (like the integration maps) newly developed for the current application.

- The model comparison involves both qualitative and qualitative contrasts between alternative models.

- Consistent results are obtained with several data sets involving humans, monkeys, and rats.

- The results provide insight into why the simpler alternative models (the snapshot and extrema detection models) fail.

No glaring weaknesses were found in this manuscript. However, there are some limitations that are worth noting, to put things into context:

- The results are consistent with what has become a well-known principle of operation of sensory-motor circuits, namely, that they are highly effective at integrating sensory evidence over time. Thus, the results are not particularly surprising.

- The results are valuable in that they specifically refute two mechanisms that had been recently proposed as potential alternatives to the more standard temporal integration. To some, these alternative mechanisms may have seemed somewhat far-fetched to begin with, as they would lead to suboptimal performance in general. Nevertheless, settling the question was important.

- Temporal integration and accumulation of evidence have been the focus of many computational studies in systems neuroscience. Although these are certainly important functions, sensory-guided choices require the deployment and coordination of numerous sensory, motor, and cognitive mechanisms, of which integration is just one.

Overall, this is a valuable study that has important theoretical implications in the field of computational neuroscience. It presents a compelling case that temporal integration is a common capability of sensory-motor circuits and that it explains a variety of behavioral data sets much better than two simpler, alternative mechanisms.

Reviewer #2 (Public Review):

The authors' goal is to uncover the most likely method used by mammals to make choices based on a time-limited stream of noisy incoming sensory data. To achieve this, they analyze with great rigor several large datasets obtained from tightly controlled two-alternative forced choice behavioral experiments. The tight control of fluctuating incoming sensory input over a large number of trials allows the authors to extract the influence of different components of that input on the behavioral choice. The conditional analysis, showing the impact of early information on the importance of later information, or vice versa, is an excellent new technique.

They compare three models and find one based on a form of weighted integration of evidence across time is very strongly favored compared to models in which only short segments of the sensory input are used, or the most extreme fluctuations of the sensory input generate a response. Overall, the results clearly do indicate that the integration-like family of models outperforms the other families. The authors succeed well in giving a fair comparison of the different families of models, allowing multiple parameters to be optimized to test different versions of each model.

It should be said that the integration model is a strange type of integration, as the weight of incoming evidence depends on the time at which it arrives-by a factor of 4 in one animal (Fig. 2)-and with an over-weighting of evidence in the middle of the sequence in one case, while the more expected effects of primacy and recency (over-weighting of early or late evidence) in another. It would be nice to see more discussion of how these differences might arise across animals, what it may say about the neural circuit performing such unbalanced integration, and how suboptimal such differential weighting of evidence is. This is important, as in some discussions integration is contrasted with state transitions, which are akin to integration over a barrier, and not necessarily ruled out by the models compared here.

Reviewer #1 (Public Review):

Jordan and Keller investigated the possibility that sensorimotor prediction error (mismatch between expected and actual inputs) triggers locus coeruleus (LC) activation, which in turn drives plasticity of cortical neurons that detect the mismatch (e.g. layer 2/3 neurons in V1), thus updating the internal presentation (expected) to match more the sensory input. Using genetic tools to selectively label LC neurons in mice and in vivo imaging of LC axonal calcium responses in the V1 and motor cortex in awake mice in virtual reality training, they showed that LC axons responded selectively to a mismatch between the visual input and locomotion. The greater the mismatch (the faster the locomotion in relation to the visual input), the larger the LC response. This seemed to be a global response as LC responses were indistinguishable between sensory and motor cortical areas. They further showed that LC drove learning (updating the internal model) despite that LC optical stimulation failed to alter acute cellular responses. Responses in the visual cortex increased with locomotion, and this was suppressed following LC phasic stimulation during visuomotor coupled training (closed loop). In the last section, they showed that artificial optogenetic stimulation of LC permitted plasticity over minutes, which would normally take days in non-stimulated mice trained in the visuomotor coupling mode. These data enhance our understanding of LC functionality in vivo and support the framework that LC acts as a prediction error detector and supervises cortical plasticity to update internal representations.

The experiments are well-designed and carefully conducted. The conclusions of this work are in general well supported by the data. There are a couple of points that need to be addressed or tested.

1) It is unclear how LC phasic stimulation used in this study gates cortical plasticity without altering cellular responses (at least at the calcium imaging level). As the authors mentioned that Polack et al 2013 showed a significant effect of NE blockers in membrane potential and firing rate in V1 layer2/3 neurons during locomotion, it would be useful to test the effect of LC silencing (coupled to mismatch training) on both cellular response and cortical plasticity or applying NE antagonists in V1 in addition to LC optical stimulation. The latter experiment will also address which neuromodulator mediates plasticity, given that LC could co-release other modulators such as dopamine (Takeuchi et al. 2016 and Kempadoo et al. 2016). LC silencing experiment would establish a causal effect more convincingly than the activation experiment.

2) The cortical responses to NE often exhibit an inverted U-curve, with higher or lower doses of NE showing more inhibitory effects. It is unclear how responses induced by optical LC stimulation compare or interact with the physiological activation of the LC during the mismatch. Since the authors only used one frequency stimulation pattern, some discussion or additional tests with a frequency range would be helpful.

Reviewer #2 (Public Review):

The work presented by Jordan and Keller aims at understanding the role of noradrenergic neuromodulation in the cortex of mice exploring a visual virtual environment. The authors hypothesized that norepinephrine released by Locus Coeruleus (LC) neurons in cortical circuits gates the plasticity of internal models following visuomotor prediction errors. To test this hypothesis, they devised clever experiments that allowed them to manipulate visual flow with respect to locomotion to create prediction errors in visuomotor coupling and measure the related signals in LC axons innervating the cortex using two-photon calcium imaging. They observed calcium responses proportional to absolute prediction errors that were non-specifically broadcast across the dorsal cortex. To understand how these signals contribute to computations performed by V1 neurons in layers 2/3, the authors activated LC noradrenergic inputs using optogenetic stimulations while imaging calcium responses in cortical neurons. Although LC activation had little impact on evoked activity related to visuomotor prediction errors, the authors observed changes in the effect of locomotion on visually evoked activity after repeated LC axons activation that were absent in control mice. Using a clever paradigm where the locomotion modulation index was measured in the same neurons before and after optogenetic manipulations, they confirmed that this plasticity depended on the density of LC axons activated, the visual flow associated with running, and the concurrent visuomotor coupling during LC activation. Based on similar locomotion modulation index dependency on speed observed in mice that develop only with visuomotor experience in the virtual environment, the authors concluded that changes in locomotion modulation index are the result of experience-dependent plasticity occurring at a much faster rate during LC axons optogenetic stimulations.

The study provides very compelling data on a timely and fascinating topic in neuroscience. The authors carefully designed experiments and corresponding controls to exclude any confounding factors in the interpretation of neuronal activity in LC axons and cortical neurons. The quality of the data and the rigor of the analysis are important strengths of the study. I believe this study will have an important contribution to the field of system neuroscience by shedding new light on the role of a key neuromodulator. The results provide strong support for the claims of the study. However, I also believe that some results could have been strengthened by providing additional analyses and experimental controls. These points are discussed below.

Calcium signals in LC axons tend to respond with pupil dilation, air puffs, and locomotion as the authors reported. A more quantitative analysis such as a GLM model could help understand the relative contribution (and temporal relationship) of these variables in explaining calcium signals. This could also help compare signals obtained in the sensory and motor cortical domains. Indeed, the comparison in Figure 2 seems a bit incomplete since only "posterior versus anterior" comparisons have been performed and not within-group comparisons. I believe it is hard to properly assess differences or similarities between calcium signal amplitude measured in different mice and cranial windows as they are subject to important variability (caused by different levels of viral expression for instance). The authors should at the very least provide a full statistical comparison between/within groups through a GLM model that would provide a more systematic quantification.

Previous studies using stimulations of the locus coeruleus or local iontophoresis of norepinephrine in sensory cortices have shown robust responses modulations (see McBurney-Lin et al., 2019, https://doi.org/10.1016/j.neubiorev.2019.06.009 for a review). The weak modulations observed in this study seem at odds with these reports. Given that the density of ChrimsonR-expressing axons varies across mice and that there are no direct measurements of their activation (besides pupil dilation), it is difficult to appreciate how they impact the local network. How does the density of ChrimsonR-expressing axons compare to the actual density of LC axons in V1? The authors could further discuss this point.

In the analysis performed in Figure 3, it seems that red light stimulations used to drive ChrimsonR also have an indirect impact on V1 neurons through the retina. Indeed, figure 3D shows a similar response profile for ChrimsonR and control with calcium signals increasing at laser onset (ON response) and offset (OFF response). With that in mind, it is hard to interpret the results shown in Figure 3E-F without seeing the average calcium time course for Control mice. Are the responses following visual flow caused by LC activation or additional visual inputs? The authors should provide additional information to clarify this result.

Some aspects of the described plasticity process remained unanswered. It is not clear over which time scale the locomotion modulation index changes and how many optogenetic stimulations are necessary or sufficient to saturate this index. Some of these questions could be addressed with the dataset of Figure 3 by measuring this index over different epochs of the imaging session (from early to late) to estimate the dynamics of the ongoing plasticity process (in comparison to control mice). Also, is there any behavioural consequence of plasticity/update of functional representation in V1? If plasticity gated by repeated LC activations reproduced visuomotor responses observed in mice that were exposed to visual stimulation only in the virtual environment, then I would expect to see a change in the locomotion behaviour (such as a change in speed distribution) as a result of the repeated LC stimulation. This would provide more compelling evidence for changes in internal models for visuomotor coupling in relation to its behavioural relevance. An experiment that could confirm the existence of the LC-gated learning process would be to change the gain of the visuomotor coupling and see if mice adapt faster with LC optogenetic activation compared to control mice with no ChrimsonR expression. Authors should discuss how they imagine the behavioural manifestation of this artificially-induced learning process in V1.

Finally, control mice used as a comparison to mice expressing ChrimsonR in Figure 3 were not injected with a control viral vector expressing a fluorescent protein alone. Although it is unlikely that the procedure of injection could cause the results observed, it would have been a better control for the interpretation of the results.

Reviewer #1 (Public Review):

This is a quite nice work equipped with healthy scientific substance underpinned by a solid mathematical approach.

The authors based on a PGG with the threshold; M (that ranges; 1 < M < N, where N indicates the game size), whether cooperation bringing fruit or not, in which, according to the commonly used parameterization, b and c mean the cooperation fruit and the cost for cooperation. As a kernel in their model, they presumed that an individual will lose his endowment (cooperation fruit in this context) with a probability r, which represents the risk level of collective failure (Eqs. (1 & 2)). Let alone, they presumed a well-mixed and infinite mother-population to ensure their analytical formulation and analysis, and to apply the replicator dynamics. Subsequently, they presumed the co-evolution of cooperation fraction; x, and risk level; r, by introducing another dynamical system for r, of which the general form is defined by Eq. (3).

For a down-to-earth discussion, they presumed two types of concrete forms for non-linear function; U(x,r). Both types premise the so-called logistic type form; containing r*(1 - r). One is what-they-called Linear; Eq. (5). Another is Eq. (7), called Exponential. Up to here, all the modeling approach is well depicted and quite understandable.

By exploring some numerical results backed by their theoretical ground, the authors got phase diagram (Figs. 3 and 5); whether a co-evolutionary destiny evaluated by (x,r) being absorbed by the dominance of unwilling (less cooperative) situation (say, D-dominant); (0,1), or by bi-stable equilibrium (either better state or D-dominant depending on an initial condition) along u (parameter appeared in the dynamical equation for r) and c/b (roughly speaking; it implies dilemma strength).

The result seems interesting and conceivable. As a rough sketch, the two types of U(x,r) seem less different. But the higher absorbing point of (x,r) out of the two cases of bi-stable equilibria is mutually different (yellow region). The authors deliberately illustrated the time-series of properties and trajectory of (x,r) in some representative cases in Figs. 4 and 6.

As a whole, I really evaluate this work as impressive.

Reviewer #2 (Public Review):

Liu, Chen and Szolnoki investigated the coupled dynamics of individual cooperation level and collective risk (i.e. the probability of future loss of all endowment). Their model encapsulates the assumption that not only does risk affect individual decision-making, but that there is also feedback between individual strategies, i.e. the level of individual contributions, and the level of risk. The authors investigate two main forms of this feedback, considering strategies linearly affecting the evolution of risk as well as non-linear (exponential) feedback. They mathematically analyze both these dynamical systems, identifying the fixed points, parametrized by the enhancement rate of defection u and the cost/benefit ratio of cooperation, and analyzing the stability of these points. The results of this systematic analysis show that, while the undesirable equilibrium state of full defection and high risk is always stable independent of the form of the feedback, the coevolutionary dynamics can exhibit a wide range of behaviors. In particular, depending on the initial conditions (frequency of cooperators), sustainable cooperation levels can be reached. This can happen by convergence to a stable fixed point with positive cooperation rates; additionally, the authors also prove that a Hopf bifurcation can take place in the system, such that a stable limit cycle with persistent oscillations in strategy and risk state can appear. Interestingly, the evolutionary outcomes do not depend significantly on the character of the feedback between strategy and risk. These theoretical results are supplemented by representative numerical examples, visualizing the phase plane and temporal dynamics of cooperation and risk for particular initial conditions and parameters.

The main conclusions of the paper are fully supported by the results, as they are directly derived from the comprehensive mathematical analysis of the coevolutionary dynamics and do not rely on external data. Additionally, the stability analysis is clean and the comprehensive numerical examples deepen the reader's understanding. Another strength of the paper is the fact that the considered model is complex enough to be able to still represent somewhat realistic settings while being simple enough to rigorously analyze. One particularly interesting finding is the fact that the exact form of the risk feedback function or its speed does not play a very significant role in the outcome of the dynamics.

The paper hence adds to the literature on the coevolution of environment and strategies in a productive way and will be of interest to various research communities in mathematical biology/ecology and decision-making.

Reviewer #1 (Public Review):

The role of increased temperature on immunity and homeostasis in cold-blooded vertebrates is an understudied yet important field. This work not only examines how immunity is impacted by fever, but also incorporates an infection model and examines resolution of the response. This work can serve as a model for other groups interested in the study of hyperthermia and immunity.

Generally speaking, I agree with the authors' strategy and interpretations of the data.

- In the Introduction, the authors chose to begin with how fever in endotherms impact the immune system. Considering that this work exclusively examines the response of a teleost (goldfish), the authors might consider flipping the way they present this work. After all, cold-blooded vertebrates rely on this response because of their basic physiology.

- I thought the set up of the work in figure 1 was innovative and could provide an example of how to study such a problem.

- Figure 2 was (to me) unexpected. One would not expect such tight response to hyperthermia and infection. This experiment in and of itself was quite interesting, and worth following up in future experiments (by the authors and other groups).

- The other work, on the response to infection and the resolution of infection were unique to this paper, and (sorry to be repetitive) can be an example of how to devise such studies.

- On the other hand, I am not sure this is a study of "fever." That implies how increased temperature impacts immunity and resolution in endotherms. Perhaps the authors could temper the comparisons between cold- and warm-blooded vertebrates regarding the response to hyperthermia.

Reviewer #2 (Public Review):

Fever is an ancient and conserve response to infection from invertebrates to humans. However, the functional benefits of engaging fever responses are not clear, especially when it comes to moderate fever responses where pathogen growth Is not impaired by temperature. This study aims to develop a natural in vivo fever model in fish that overcomes many of the technical challenges to investigate fever in mammals. In ectotherms, fever is manifested as a behavioral response by which animals move to warmer temperatures. By using this new developed in vivo behavioral ring, the present study reveals new functional roles for fever in vertebrates. Additionally, upon infection, sickness behavior did not only consist of fever, but two novel lethargic behaviors not previously described in fish. The experimental evidence is compelling and supports the authors' conclusions. The data presented strongly indicates that moderate fever levels are critical for fine tuning immune responses to pathogens. By triggering earlier but weaker antimicrobial defenses, moderate fever in teleosts results in controlled inflammation and improved wound healing. These exciting results reveal novel roles of fever as a way to minimize the collateral damage that inflammatory responses often cause to the host. This work advances our conceptual view of the evolutionary advantages that fever brings to host-pathogen interactions. The technological development of the annular temperature preference tank can now become the gold standard platform to investigate the consequences of fever during teleost infection.

Reviewer #1 (Public Review):

In this report, Yeung et al studied a mutation in Orai1 channels (L138F) that is associated with constitutive CRAC channel activity and tubular aggregate myopathy (TAM) in humans. They put forth a model whereby substitution with large amino acids at position L138 on TM2 or the neighboring T92 on TM1 causes a steric clash between TM1 and TM2 and elicits a highly Ca2+ selective current in the absence of STIM1, the ER Ca2+ sensor protein that is the physiological activator of Orai channels. The authors went on to study one typical biophysical property of Orai1-mediated CRAC channels which is the fast Ca2+-dependent inactivation (CDI), after the surprising finding of the presence of CDI in CRAC currents mediated by T92 and L138 Orai1 mutants in the absence of STIM1. The authors showed differences in CDI between WT and mutants when using weak vs strong buffers and through computation and experimentation, they show that the Orai1 mutants have enhanced cytosolic Ca2+ sensitivity, which could be normalized when STIM1 was present. The experiments are carefully conducted and the manuscript is clearly written. The study has significant novelty and impact.

Reviewer #2 (Public Review):

The manuscript "A human tubular aggregate myopathy mutation unmasks STIM1-independent rapid inactivation of Orai1 channels" describes the effects of a disease-related gating checkpoint at the TM1-TM2 interface. The authors suggest that the mutation of one of the two oppositely located positions T92 - L138 into a large amino acid leads to constitutive activity due to steric clash. Notably, the mutants also exhibit robust Ca2+ dependent inactivation (CDI) suggesting that this feature is intrinsic to the Orai1 channel, and not as previously thought a key process that is triggered by STIM1. Nevertheless, STIM1 is able to fine-tune Ca2+ selectivity and CDI.

This study provides an extensive electrophysiological characterization of the tubular aggregate myopathy (TAM)-disease-related Orai1 L138F mutation and based on mutational studies provides compelling evidence that constitutive activity is caused by a steric clash between TM1/TM2 Orai helices. Additionally, yet unexpectedly, the constitutive Orai1 mutants exhibit CDI behavior which is thoroughly characterized by experiments using various intracellular Ca2+-buffering reagents. By this, it is proposed that the Orai1 T92W mutant shows increased sensitivity to intracellular Ca2+. This is further revealed in a sophisticated tow step protocol, which would profit from additional control experiments. The unusual behavior of the T92W Orai1 mutant is "corrected" to that of the Orai1 wild-type form by the presence of STIM1.

Reviewer #3 (Public Review):

In this paper, Yeung et al., use patch-clamp electrophysiology measurements combined with structural analyses and mutagenesis to compellingly reveal how the tubular aggregate myopathy (TAM)-associated Orai1 L138F mutation leads to the gain of CRAC channel function. They discover that L138F not only constitutively activates Orai1-composed channels but also enhances Ca2+-dependent inactivation (CDI). The authors find that the L138F gain of function occurs due to a steric clash with T92 from an adjacent subunit and that introduction of a bulky residue at the T92 position similarly activates CRAC channels and enhances CDI in the absence of STIM1. Nevertheless, co-expression of STIM1 with strongly activating T92W or L138F mutants regularized the CDI to wild-type levels. Collectively, the work represents an important conceptual advancement, exposing that STIM1 is not necessary for CDI and that Orai1 likely contains the Ca2+ sensor intrinsically for this phenomenon.

Strengths:<br /> The authors use rigorous and careful electrophysiological measurements to probe how the TAM-related mutation (L138F) affects the biophysical properties of CRAC channels. The extensive and systematic mutagenesis (i.e. substitution to every possible amino acid at the T92 and L138 sites) coupled with these functional assessments reveal a steric clash between L138F and T92 and provide a complete picture of how any residue type at the so-called T92/L138 lever point may contribute to constitutive CRAC and CDI activity. The use of available high-resolution structural data to interpret functional data, rationalize the consequence of new mutations related to the mechanisms of L138F dysfunction, and generate new hypotheses is a strength of the research. Overall, the work provides a considerable conceptual advance in terms of understanding the molecular requirements for CRAC and CDI activity; in particular, the discovery that CDI can occur independently of STIM1 and the notion that Orai1 may contain an intrinsic Ca2+ sensor that regulates CDI are important steps forward for the field.

Weaknesses:<br /> While the work provides a phenomenological advancement regarding CRAC channel regulation and pinpoints new important residues for function, some aspects of the study appear incomplete. It was shown that STIM1 can normalize the enhanced CDI caused by the T92W mutation, but it is not clear how this happens. Further, the authors propose a "push" - "pull" mechanism for the complementary roles L138 and H134 in channel regulation but do not provide any structural dynamics data to support this idea. The authors provide a mathematical explanation for chelator-specific differences in CDI observed for the T92W compared to WT Orai1 but do not show any fitted data to accompany and support the model. Finally, the authors show that a considerable portion of the CDI can be eliminated after a C-terminal Orai1 deletion (i.e. residues 267-301) and probe the idea that N-terminal W76, Y80, and R83 residues may contribute to the residual CDI effect; however, after W76E, Y80E, R83E mutations showed enhanced CDI (rather than suppressed) in the context of the T92W mutation, no further experiments were pursued to account for the residual CDI.

Overall, the strengths far outweigh the weaknesses of this study, and the conclusions drawn based on the data are compelling. The work represents an important conceptual advancement as future studies can now steer towards identifying the STIM-independent Ca2+ sensor underlying the CDI of CRAC channels and revealing structural mechanisms by which Ca2+ sensing leads to pore closure.

Reviewer #1 (Public Review):

FLOWERING LOCUS C (FLC) is a key repressor of flowering in Arabidopsis thaliana. FLC expression creates a requirement for vernalization which is the acquisition of competence to flower after exposure to the prolonged cold of winter. Vernalization in Arabidopsis and other Brassicas results in the suppression of FLC expression.

How exposure to winter cold initiates the vernalization process (i.e., the silencing of FLC) is not fully understood. It is known that cold exposure causes several long non-coding RNAs, including COOLAIR and COLDAIR, to be transcribed from FLC. this work shows that COOLAIR induction by cold results requires the binding of CRT/DRE-binding factors (CBFs) to their cognate recognition elements which reside at the 3' end of the FLC locus. The authors demonstrate this regulation in many ways including studying the effect on vernalization of knocking out all CBFs and also by showing that constitutive CBF expression causes COOLAIR levels to be elevated even without cold exposure. Intriguingly, plants with genetic alterations that eliminate COOLAIR expression (loss of CBF activity and FLC deletion mutants that eliminate COOLAIR expression) do not have a significant impairment in becoming vernalized.

The work appears to be done properly and provides much important information about how this remarkable environmentally-induced epigenetic switch operates.

Reviewer #2 (Public Review):

Here the authors questioned the regulation and functional roles of anti-sense transcripts at the 3'end of an important flowering-time regulator FLC.

The authors present compelling genetic, molecular biology, transgene, and biochemical data on the molecular details of how COOLAIR is induced by cold temperatures. They report that cold-induction of COOLAIR is mediated by C-repeat/dehydration-responsive elements (CRT/DREs) at the 3'-end of the FLC and relatively small deletions of the CRT/DREs prevent cold-induction of COOLAIR. They also report that long-term cold results in an increase in the expression of CRT/DRE BINDING FACTORs (CBFs) that bind to the CRT/DREs and result in the activation of genes containing CRT/DREs.

Interestingly, in lines in which COOLAIR is not induced the vernalization proceeds normally with respect to flowering behavior and cold-mediated FLC chromatin changes, a result that is at odds with some publications but consistent with other reports.

The major strength of this research is the comprehensive battery of relevant assays used to address their aim. Using ChIP they demonstrate CBF3 directly binds to the 3'end of FLC in vivo, and of less interest, but still very relevant, CBF3 binds to a CRT/DRE motif containing oligo-nucleotides in vitro using an EMSA. Using CRISPR-mediated genetic deletion of these sequences in vivo, they demonstrated that the downstream antisense transcripts are no longer transcribed. Interestingly, in these CRISPR mutants or genetic mutants of higher-order CBF mutants, the vernalisation response (chromatin modifications) is not impaired. They also show that CBF mRNA transcription occurs in at least two waves, an early peak, and over a prolonged cold period.

While the CRISPR genetic motif mutants are relatively small, a few hundred base pairs, ideally they would have been smaller if only encompassing the CRT/DRE motif.

The authors clearly achieved their aims and the presented results strongly support their conclusions. The compelling data clearly questions a widely held view in the vernalisation field. The presented methods can be widely transferable to a broader research community.

Reviewer #3 (Public Review):

The authors start by examining the COOLAIR promoter and identifying a CRT/DRE motif that is bound by the CBF transcription factor family that is involved in the short-term cold. This is confirmed by gel shift assays and chromatin immunoprecipitation. However, it should be noted that the gel shift assays are an in vitro assay and the chromatin immunoprecipitation is carried out with plants over-expressing CBF3-myc from the pSuper promoter and so do not necessarily reflect the native state. The authors then examine COOLAIR expression in lines over-expressing each of the three CBF proteins of Arabidopsis and found COOLAIR expression elevated in the warm in all three, but with small differences in the variants of COOLAIR that are expressed. Examination of the expression of COOLAIR after short-term cold shows that transcript abundance increases after 6 hours, this expression was not observed in the cbfs-1 where all three CBFs are knocked out. Taken together this provides good evidence that COOLAIR transcription is rapidly induced via CBFs on exposure to cold.

The authors then go on to look at the roles of CBFs in longer-term cold. COOLAIR has previously been shown to increase during long-term cold (multiple weeks duration), so the question was whether this increase is CBF-dependent. The increase in COOLAIR abundance is similar to other CBF targets but does begin to decline with 40-day cold periods, presumably reflecting the shutdown of the FLC locus. The lack of COOLAIR expression in the cbfs-1 mutant is good evidence that increased COOLAIR expression is CBF-dependent. The authors also present evidence that CBFs are required for COOLAIR induction by the first seasonal frost, which is consistent with this being a short-term cold response.

The authors then examine deletions of the COOLAIR promoter. In agreement with the hypothesis that CBFs regulate COOLAIR transcription via the CRT/DREs in the COOLAIR promoter, deletions that include the two elements do not show cold induction of COOLAIR, while one that contains them does. It should be noted that these deletions are relatively coarse so could include other elements than the CRT/DREs.

The authors then use the finding that COOLAIR is not induced in the cbfs-1 mutant or in the deltaCOOLAIR1 and 3 lines to ask whether COOLAIR is required for the repression of FLC in the vernalization response. The data in Figures 6 and 7 show that these lines don't show different responses to vernalization treatment at the FLC expression, FLC chromatin modifications, or flowering time/leaf number to flowering. This supports the conclusion that the COOLAIR transcript does not play an essential role in the vernalization response.

The Discussion is well-balanced and considers previous publications in this area and highlights differences with this study. The conservation of COOLAIR in other brassica species suggests that it does have a biological function, but the data here suggest it isn't an essential component of the vernalization response. Whether there is a function in more natural conditions where the temperature fluctuates in a diurnal manner during the vernalization period is a possibility that is considered. When the data presented here are taken with other publications, the precise biological role of COOLAIR remains enigmatic.

Joint Public Review:

In this study, the authors transcriptomically characterize TIL from head and neck cancers and associate their transcriptional programs with overall survival as a function of HPV positivity. Specifically, they study the impact of CDK4 inhibition on TIL from these tumors. They find an exhausted T cell subset that preferentially expresses CDK4. They then perform some in vitro studies to test the function of exhausted T cells and the impact of CDK4 inhibition on different TIL subsets from head and neck tumors. Understanding the functional impact of different cancer therapies on cells in the TME is of high importance and interest to the field.

1. Line 215: The authors state that pairing TCRseq with RNAseq reflects the magnitude of TCR signaling. This is absolutely not the case. TCR sequencing does not reflect TCR signaling strength.<br /> 2. A lot of discussion around "activation" is presented, but there is no evidence to support which genes or gene programs are associated with "activation".<br /> 3. Line 249: It is unclear why the authors are indicating that TCRseq was used in pseudotime analysis. This type of analysis does not take TCRs into account but rather looks at the proportion of spliced mRNA of individual genes from the DGE data.<br /> 4. There is no way to know if the differences in proliferation and cell viability shown in Figs. 4a and b, respectively, are meaningful or not. Proper controls or replicates should be provided to fully understand if this difference is biologically meaningful. Likewise, what is the evidence that P-Tex cells are self-renewing rather than expanding?

!- Book Review : Free Range Activist !- Title : ‘Running on Emptiness – The Pathology of Civilisation’ !- Author : John Zerzan (2002) !- Website : http://www.fraw.org.uk/blog/reviews/023/index.shtml

• All religions have problems with ‘unbelievers’, but that response is insignificant compared to their visceral hatred of ‘apostates’.

Reviewer #1 (Public Review):

This study reports the results of a computational and EEG analysis of altruistic decision making. The authors intend to examine whether fundamentally different mechanisms operate to drive altruistic decision making in different contexts, which they here manipulate by examining choices in the realm of advantageous and disadvantageous inequality. The authors find that changes in self payoff are encoded in opposite manners in the two contexts, but that a similar evidence accumulation mechanism leading up to the time of response seems to operate equally in both. In addition, they find that individual differences in generosity are predicted more by differences in sensitivity to change in the other's payoff in the disadvantageous inequality condition, and by stronger phase coupling between sensors related to this delta-other signal and sensors related to the evidence accumulation signal.

This study makes a valuable contribution by combining a sophisticated suite of modelling and neurophysiological analyses to shed light on the decision parameter adjustments that inform altruistic decisions in different contexts. The conclusions regarding those adjustments appear well supported by the data. One aspect that could be clarified is that there is an apparent discrepancy between the cross-condition bound adjustments identified by the modelling and the absence of any corresponding neural evidence accumulation signal amplitude difference.

One of the stated overarching goals of this study is to determine whether the neural mechanisms and circuits for altruistic decisions are context-specific or general. The manuscript would benefit from greater clarity on this point, in particular defining what is meant by 'mechanisms' and what qualitative and quantitative criteria should be applied when identifying them as distinct versus common. As all decisions in this study are reported via the same manual actions it seems implausible that there would be no overlap at all in the circuits and mechanisms involved. In addition, the prior literature has demonstrated that even individual neurons can trace different computations depending on the circumstances. Therefore, it is necessary to clarify whether the authors are searching for context-dependence in the brain areas/signals that are recruited and/or in the computations that are performed within a brain area.

Reviewer #2 (Public Review):

Recent work in the neurosciences has suggested that decision making in most domains consists of computations at multiple stages. In value-based choices, initial evidence is perceived, categorized, and evaluated, then accumulated over time in a process that essentially compares the relative value of two options, until the accumulated evidence passes a threshold for choice. Although previous work has shown that this basic structure also applies to decisions in the domain of prosocial choice, it has remained unclear at what stage of this decision process variation in prosocial choices arises. The authors aimed to resolve this issue by using a combination of computational modeling and EEG, applied to a choice paradigm that evokes variation in altruistic behavior through two distinct routes: exogenous variation in the inequality context of a choice (i.e., advantageous vs. disadvantageous inequality), and endogenous variation as a function of individual differences in prosocial preferences.

One of the strengths of this approach (particularly the use of EEG) over previous studies is that the authors can use the timing and nature of the EEG signals to disentangle both HOW preferences evolve, and WHEN differences evoked by context or individual preference emerge. This work very clearly shows that late-stage choice comparison processes, locked to the time of response (i.e., the evidence accumulation phase of a choice) are likely NOT where variations in altruistic choice arise. Instead, the evidence points to a set of distinct signals that occur time-locked to the onset of an option that enables participants to make a choice, which implies that the computations driving choice behavior likely occur at the perceptual and/or valuation stage. This is not wholly surprising, but is interesting and important to verify.

A second potential strength of this approach is that the methodology allows the authors to determine whether the observed signals more strongly resemble encoding of the overall magnitude of outcomes to self and others, or instead are more related to signals sensitive to distributional values (i.e., inequality/fairness). The evidence here paints a quite intriguing, but somewhat mixed picture, in my view, and I think needs to come with more caveats than the authors currently acknowledge. The authors claim that their evidence supports the idea that people are making choices by considering inequality, rather than by computing outcomes for self or other directly. The lack of a consistently-signed association between EEG signals and either self or other outcome magnitude across contexts is not consistent with the idea that values are encoded in terms of self and other, which has sometimes been argued from fMRI data. However, I also do not think they are fully consistent with the authors' claims that they are observing signals related directly to fairness considerations either. Fairness/inequality, as typically defined by economic models of social preferences, involves computing the differences between self and other payoffs. The authors find ERP signals scaling with payoff changes for self but not other. Those signals do move in opposite directions in the two inequality contexts, which is why the authors interpret this as meaning that these ERP signals represent some calculation related to fairness. But there is no sensitivity of these signals to payoff change for the other, suggesting that these signals are not precisely driven by fairness as it is canonically conceived. Instead, it seems that these signals might reflect something about how people orient to self outcomes differently in the two contexts. This actually is an intriguing finding, but is somewhat difficult to interpret, since it is not wholly clear what these ERP signals represent (i.e., are they related to perception, valuation, attention, etc.?). Moreover, as the authors acknowledge in their discussion, the design of the study, with its presentation of a first option that determines the inequality context and a second option that determines the relative values of the options, means that it is difficult to know when and how one would expect to see raw self and other values as opposed to comparative value signals related to differences in self and other. Finally, the sensitivity (or lack thereof) of EEG to more subcortical signals means that it is not clear one is getting a whole picture of the computations driving choice. Thus, I think the conclusion that behavior is related to inequality processing rather than to a focus on self- and other-payoffs directly, while intriguing, needs to be tempered a bit.

What also seems somewhat puzzling is that the behavioral and neural signals do not always seem fully consistent with one another, or with prior research. For example, behaviorally people seem to put more weight on others' payoff changes in the advantageous inequality context. And in other work (Morishima et al., 2012), it is behavioral variation in the advantageous context that correlates with neural (anatomical) variation. Yet here, there are no EEG signals that encode changes in other outcomes as a main effect in the advantageous context, and it is individual variation in encoding of others' payoffs in the DISADVANTAGEOUS context that relate to individual differences in equality-seeking in that context. Thus, it is actually in the context where one would expect people to be paying *less* attention to other outcomes (based on the modeling parameters) that neural signals seem to be *more* sensitive to those outcomes. This doesn't mean that these signals aren't interesting, but it does point to a need to more fully understand what they represent before coming to firm conclusions about what they actually mean, computationally and psychologically.

Thus, I think this paper will likely have an impact on the field largely for the intriguing questions it raises about how people make altruistic choices rather than for providing definitive answers. This is an important contribution and researchers will, I expect, find this paper thought-provoking.

Reviewer #1 (Public Review):

In this study, the authors examine the function of Tomosyn, in dense core vesicle fusion using CRE-mediated deletion in neuronal cultures from mice expressing conditional alleles of tomosyn and tomosyn-2. Tomosyn is a large soluble SNARE protein, where earlier work in multiple species suggested that it functions as a competitive inhibitor of cognate SNARE interactions impairing fusion. The authors show that while loss of tomosyns did not affect dense core vesicle exocytosis, it reduced the expression of several key dense core cargos, including BDNF. Limited (if anything opposite) impact of tomosyn loss-of-function on intracellular vesicle trafficking or Golgi function.

The authors concluded that tomosyns regulate neuropeptide and neurotrophin secretion by regulating dense core vesicle cargo production but not exocytosis.

Reviewer #2 (Public Review):

The authors provide here a very careful and thorough analysis of the effects of tomosyn elimination in neurons, in relation to dense-core vesicles. They find strong effects on vesicle generation (size, protein composition), but not on vesicle exocytosis, in spite of tomosyn's known interaction with the exocytosis SNAREs.

Reviewer #3 (Public Review):

Based on studies over the last two decades, tomosyns participate in processes as diverse as synaptic SNARE complex stability (Yu H et al., 2014), dendritic spine density (Saldate JJ et al., 2018), mossy fiber synaptic plasticity (Ben-Simon Y et al., 2015), inhibition of mast cell degranulation (Madera-Salcedo IK et al., 2018), insulin-stimulated GLUT4 exocytosis by adipocytes (Wang S, et al., 2020), and both basal and stimulated secretion by PC12 cells (Williams et al., 2011). In yeast, which lacks storage granules, two tomosyn orthologs control the formation of post-Golgi vesicles. The actions of tomosyn are cell-type specific and subject to regulation by phosphorylation and the ubiquitin-proteasome system (Saldate JJ et al., 2018; Williams et al., 2011; Madera-Salcedo IK et al., 2018). In beta-cells, the ability of tomosyn to decrease insulin secretion by binding syntaxin1A requires its SUMOylation (Ferdaoussi M, et al., 2017). The carefully designed and validated mouse line developed by the authors will facilitate detailed, mechanistic studies of the diverse, cell-type specific actions of tomosyns.

Using cultures derived from the hippocampi of this new mouse strain, multiple differences were observed between two-week-old WT and DKO (double knockout of tomosyn-1 and -2) cultures. Analysis of dense core vesicle release by single neurons revealed no change in their exocytosis, but identified a decrease in levels of the dense core vesicle reporter, leading to the discovery of a decrease in levels of two endogenous dense core vesicle proteins, BDNF and IA-2. In contrast, levels of two lysosomal/endocytic markers were unaltered, demonstrating granule specificity.

WT and DKO cultures were compared using mass spectrometry. Significant changes in the levels of 3% of the proteins were identified. Strikingly, levels of several additional dense core vesicle proteins were decreased in DKO cultures. In contrast, levels of multiple mitochondrial proteins were greatly increased in DKO cultures. In addition, significant increases in VGLUT2 (a marker of glutamatergic neurons) and in GAD67, GAT1, and GAT3 (GABAergic markers) confirmed the presence of widespread differences in hippocampal cultures that matured in the absence of tomosyns. Focusing on BDNF and other dense core vesicle proteins, qPCR studies revealed decreases in mRNA levels for a subset of dense core vesicle proteins.

The use of multiple culture systems allowed the authors to employ different approaches, ranging from monitoring the release of single granules expressing a dense core vesicle reporter to quantifying the accelerated trafficking of a tagged cargo protein from the ER through the TGN and into DCVs in the absence of tomosyns. While no changes in synaptic complex formation were observed, both electron microscopy and analysis of single vesicles expressing a dense core vesicle reporter revealed a decrease in granule diameter.

Weaknesses of methods and results. Within 8 h of plating, hippocampal cultures prepared from a single litter were transduced with a lentivirus encoding active or inactive mCherry-tagged Cre-recombinase, generating WT and DKO cultures; expression of Cre-recombinase was limited to neurons using the synapsin promoter. Cultures were generally examined after two weeks. Culture conditions were varied to allow comparison of dense core vesicle exocytosis by single neurons (a neuron on a glial microisland) or protein and mRNA levels in dense neuronal networks plated on coated plastic without a glial feeder layer in WT vs. DKO cultures. Whether cultures allowed to develop under these vastly different conditions respond to the absence of tomosyns in a different manner is unknown. No attempt was made to rescue any of the differences observed by expressing tomosyn in DKO neurons. Successful rescue experiments would alleviate concerns about the effects of developmental differences on the phenotypes observed.

Immunocytochemical studies revealed an approximately two-fold drop in BDNF protein levels in the soma and neurites of DKO neurons. In contrast, BDNF, which was detectable in WT cultures using mass spectrometry, was not detectable using mass spectrometry to analyze DKO cultures. No explanation for this discrepancy between immunocytochemistry and mass spectrometry is offered. Despite the fact that neither BDNF secretion nor BDNF degradation was assessed, the authors state in their Abstract that "tomosyns regulate neuropeptide and neurotrophin secretion via control of DCV cargo production".

The authors do not adequately refer to the rich literature discussing the many secretory pathways used by different cell types, referring only to synaptic vesicles and dense core vesicles. Golgi by-pass pathways are known to take membrane proteins to dendrites and tomosyns are known to play a role in the trafficking of GLUT4 from endocytic compartments to the plasma membrane. Soluble cargo proteins such as BDNF are released both constitutively and in response to stimuli. Cargo proteins (proinsulin, proANP, and growth hormone, for example) can drive the appearance of dense core vesicles.

The mass spectrometry data presented in Fig. 3 are not well incorporated into the Discussion. KIF6, which plays a role in retrograde Golgi to ER traffic, is detectable in DKO cultures, but not in WT cultures and could contribute to the accelerated trafficking phenotype observed using RUSH. Coordinate control of the expression of dense core vesicle genes has been studied in a variety of systems, ranging from mammals to C. elegans to D. melanogaster. Levels of these gene products could have been assessed using existing mass spectrometric data or by additional qPCR studies. The diminished levels of dense core vesicle reporters observed in Fig.1 remain unexplained. Intracellular degradation and increased basal secretion, neither of which was assessed, could contribute to this observation.<br /> The authors did not take advantage of the structure/function studies used to dissect the roles of the beta-propeller and SNARE-domains of tomosyns. In yeast, loss of SR07/SR077, tomosyn orthologs which lack a SNARE-like domain, causes a defect in the exocytosis of post-Golgi vesicles and the accumulation of secretory vesicles with altered composition (Forsmark et al., 2011).

Are claims and conclusions justified by data: The title of the manuscript, "SNARE protein tomosyn regulates dense core vesicle composition but not exocytosis in mammalian neurons" is misleading. The authors present no evidence that the SNARE-domain of tomosyn is necessary for its effects on dense core vesicle composition. The yeast orthologs of tomosyn, which lack a SNARE domain, affect post-Golgi vesicular trafficking via their beta-propeller domains. Hippocampal neurons are not representative of all "mammalian" neurons. In rat sympathetic neurons, tomosyn depletion results in a decrease in neurotransmitter release. A key conclusion is that tomosyns regulate neuropeptide and neurotrophin secretion by controlling cargo production, not cargo release - this conclusion is not supported by the data presented.

Likely impact of work on the field: The mouse line developed for these studies will be of great use in mechanistic studies of the multiple roles of tomosyns. The authors identified a range of parameters that are altered in hippocampal neurons which develop in the absence of tomosyns. Additional mechanistic studies are needed to directly assess the manner in which the absence of tomosyns contributes to these changes.

Reviewer #1 (Public Review):

These findings for the first time provide a comprehensive multiscale assessment of the arrhythmogenic potential of elite exercise training.

The authors trained canines using a treadmill over 16 weeks, and compared these animals (n=12) to sedentary animals (n=13). The authors found global evidence of electrophysiologic remodeling ECG indices and heart rate, as well as repolarization variability in trained animals relative to controls.

The authors also demonstrate a range of effects of ventricular cardiomyocyte ion channels and fibrosis. Finally, using an induction protocol, the authors show enhanced risk for ventricular fibrillation as well as spontaneous arrhythmias in trained dogs.

The authors conclude that structural and electrophysiologic remodeling of ventricles in elite trained athletes is associated with ventricular arrhythmogenesis.

First, this is a difficult study to achieve given the logistical challenges of managing a large animal set up as utilized in this study. Further protocols that involve in vivo and subsequently in vitro studies of tissues from large animals are challenging to accomplish. Finally, the multimodal assessments undertaken in this study to achieve these comprehensive objectives are an additional strength.

Weaknesses include the descriptive nature of the work and somewhat low level of rigor in presenting the observed data. The presentation of the data in the text could also be improved. Finally, some of the counterintuitive/conflicting findings e.g. enhanced HCN4 expression with reduced heart rate.

Reviewer #2 (Public Review):

In this manuscript, Polyák et al. report detailed and systematic functional, electrocardiographic, electrophysiologic (both in vivo and in vitro experiments) and histological analysis in a large animal (canine) model of exercise to assess risk of ventricular arrhythmia susceptibility. They find that exercise-trained dogs have a slower heart rate (not accounted by heightened vagal tone alone and consistent with recent work from Denmark), an increased ventricular mass and fibrosis, APD lengthening due to repolarisation abnormality, enhanced HCN4 expression and decreased outward potassium channel density together with increased ventricular ectopic beats and ventricular fibrillation susceptibility (open-chest burst pacing). The authors suggest these changes as underlying the risk of VA in athletes, and appropriately caution against consigning the beneficial effects of exercise. In general, this study is well done, reasonably well-written, with reasonable conclusions, supported by the data presented and is much needed. There are some methodological, however, given the paucity of experimental data in this area, I think it would still be additive to the literature.

Strengths<br /> 1. This is an area with very limited experimental data- this is an area of need.<br /> 2. The study, in general seems to be well-conducted with two clear groups<br /> 3. The use of a large animal model is appropriate<br /> 4. The study findings, in general, support the authors conclusions<br /> 5. The authors have shown some restraint in their conclusions and the limitations section is detailed and well written.

Weaknesses<br /> 1. There are some methodological issues:<br /> a. Authors should explain what the conditioning protocol was and why it was necessary.<br /> b. The rationale for the exercise parameters chosen needs to be presented.<br /> c. Open chest VF induction was a limitation, and it was unnecessary.<br /> d. A more refined VT/VF induction protocol was required. This is a major limitation to this work.<br /> e. The concept of RV dysfunction has not been considered in the study and its analysis.<br /> f. The lack of a quantitative measure for fibrosis is a limitation.<br /> 2. Statistical analysis requires further detail (checking of normality of the data/appropriate statistical test).<br /> 3. The use of Volders et al. study as a corollary in the discussion does not seem justified given that this study used AV block induced changes as an acquired TdP model.

Reviewer #3 (Public Review):

This is a well-designed and well conducted study on the effect of 4 months sustained exercise on atrioventricular function and cardiac remodeling in a clinically relevant large animal (canine) model. All methods are well described with proper controls. The findings support the conclusion. Potential limitations are the study are clearly stated. The findings advance the field and provide clear evidence for the susceptibility of ventricular arrhythmia in the canine model of endurance training.

Reviewer #4 (Public Review):

In the manuscript the author tried to find the cellular level mechanism that causes sudden cardiac death in elite athletes. They found that there are more ventricular fibrosis, ventricular extrasystole burden, longer action potential duration, higher ventricular fibrillation (VF) inducibility, higher HCN4 expression and decreased Ito in sustained trained dog model.

The author successfully conducted large animal training model, showed bradycardia and ventricular fibrosis as a finding similar in athletes and demonstrated the increased ventricular arrhythmia susceptibility to electrical stimulation. The finding of increased action potential duration can be postulated to be a factor of sudden cardiac death in these athletes. However, the interpretation of these findings should be cautious just like all the animal studies. Human has a more complex interaction with the environment and individual variabilities. Will the higher susceptibility of VF to electrical stimulation be the same in athletes is still hard to answer.

Still, it is the first study to provide a large animal model of sustained training mimicking trained athletes and to give insights into the cellular level of change in an athlete's heart. The young death of this special group is a tragedy and the importance of these studies cannot be overemphasized.

Reviewer #1 (Public Review):

This article is aimed at constructing a recurrent network model of the population dynamics observed in the monkey primary motor cortex before and during reaching. The authors approach the problem from a representational viewpoint, by (i) focusing on a simple center-out reaching task where each reach is predominantly characterised by its direction, and (ii) using the machinery of continuous attractor models to construct network dynamics capable of holding stable representations of that angle. Importantly, M1 activity in this task exhibits a number of peculiarities that have pushed the authors to develop important methodological innovations which, to me, give the paper most of its appeal. In particular, M1 neurons have dramatically different tuning to reach direction in the movement preparation and execution epochs, and that fact motivated the introduction of a continuous attractor model incorporating (i) two distinct maps of direction selectivity and (ii) distinct degrees of participation of each neuron in each map. I anticipate that such models will become highly relevant as neuroscientists increasingly appreciate the highly heterogeneous, and stable-yet-non-stationary nature of neural representations in the sensory and cognitive domains.

As far as modelling M1 is concerned, however, the paper could be considerably strengthened by a more thorough comparison between the proposed attractor model and the (few) other existing models of M1 (even if these comparisons are not favourable they will be informative nonetheless). For example, the model of Kao et al (2021) seems to capture all that the present model captures (orthogonality between preparatory and movement-related subspaces, rotational dynamics, tuned thalamic inputs mostly during preparation) but also does well at matching the temporal structure of single-neuron and population responses (shown e.g. through canonical correlation analysis). In particular, it is not clear to me how the symmetric structure of connectivity within each map would enable the production of temporally rich responses as observed in M1. If it doesn't, the model remains interesting, as feedforward connectivity between more than two maps (reflecting the encoding of many more kinematic variables) or other mechanisms (such as proprioceptive feedback) could well explain away the observed temporal complexity of neural responses. Investigating such alternative explanations would of course be beyond the scope of this paper, but it is arguably important for the readers to know where the model stands in the current literature.

Below is a summary of my view on the main strengths and weaknesses of the paper:

1. From a theoretical perspective, this is a great paper that makes an interesting use of the multi-map attractor model of Romani & Tsodyks (2010), motivated by the change in angular tuning configuration from the preparatory epoch to the movement execution epoch. Continuous attractor models of angular tuning are often criticised for being implausibly homogeneous/symmetrical; here, the authors address this limitation by incorporating an extra dimension to each map, namely the degree of participation of each neuron (the distribution of which is directly extracted from data). This extension of the classical ring model seems long overdue! Another nice thing is the direct use of data for constraining the model's coupling parameters; specifically, the authors adjust the model's parameters in such a way as to match the temporal evolution of a number of "order parameters" that are explicitly manifested (i.e. observable) in the population recordings.

I believe the main weakness of this continuous attractor approach is that it - perhaps unduly - binarises the configuration of angular tuning. Specifically, it assumes that while angular tuning switches at movement onset, it is otherwise constant within each epoch (preparation and execution). I commend the authors for carefully motivating this in Figure 2 (2e in particular), by showing that the circular variance of the distribution of preferred directions is higher across prep & move than within either prep or move. While this justifies a binary "two-map model" to first order, the analysis nevertheless shows that preferred directions do change, especially within the preparatory epoch. Perhaps the authors could do some bootstrapping to assess whether the observed dispersion of PDs within sub-periods of the delay epoch is within the noise floor imposed by the finite number of trials used to estimate tuning curves. If it is, then this considerably strengthens the model; otherwise, the authors should say that the binarisation reflects an approximation made for analytical tractability, and discuss any important implications.

2. While it is great to constrain the model parameters using the data, there is a glaring "issue" here which I believe is both a weakness and a strength of the approach. The model has a lot of freedom in the external inputs, which leads to relatively severe parameter degeneracies. The authors are entirely forthright about this: they even dedicate a whole section to explaining that depending on the way the cost function is set up, the fit can land the model in very different regimes, yielding very different conclusions. The problem is that I eventually could not decide what to make of the paper's main results about the inferred external inputs, and indeed what to make of the main claim of the abstract. It would be great if the authors could discuss these issues more thoroughly than they currently do, and in particular, argue more strongly about the reasons that might lead one to favour the solutions of Fig 6d/g over that of Fig 6a. On the other hand, I see the proposed model as an interesting playground that will probably enable a more thorough investigation of input degeneracies in RNN models. Several research groups are currently grappling with this; in particular, the authors of LFADS (Pandarinath et al, 2018) and other follow-up approaches (e.g. Schimel et al, 2022) make a big deal of being able to use data to simultaneously learn the dynamics of a neural circuit and infer any external inputs that drive those dynamics, but everyone knows that this is a generally ill-posed problem (see also discussion in Malonis et al 2021, which the authors cite). As far as I know, it is not yet clear what form of regularisation/prior might best improve identifiability. While Bachschmid-Romano et al. do not go very far in dissecting this problem, the model they propose is low-dimensional and more amenable to analytical calculations, such that it provided a valuable playground for future work on this topic.

3. As an addition to the motor control literature, this paper's main strengths lie in the model capturing orthogonality between preparatory and movement-related activity subspaces (Elsayed et al 2016), which few models do. However, one might argue that the model is in fact half hand-crafted for this purpose, and half-tuned to neural data, in such a way that it is almost bound to exhibit the phenomenon. Thus, some form of broader model cross-validation would be nice: what else does the model capture about the data that did not explicitly inspire/determine its construction? As a starting point, I would suggest that the authors apply the type of CCA-based analysis originally performed by Sussillo et al (2015), and compare qualitatively to both Sussillo et al. (2015) and Kao et al (2021). Also, as every recorded monkey M1 neuron can be characterized by its coordinates in the 4-dimensional space of angular tuning, it should be straightforward to identify the closest model neuron; it would be very compelling to show side-by-side comparisons of single-neuron response timecourses in model and monkey (i.e., extend the comparison of Fig S6 to the temporal domain).

4. The paper's clarity could be improved.

Reviewer #2 (Public Review):

The authors study M1 cortical recordings in two non-human primates performing straight delayed center-out reaches to one of 8 peripheral targets. They build a model for the data with the goal of investigating the interplay of inferred external inputs and recurrent synaptic connectivity and their contributions to the encoding of preferred movement direction during movement preparation and execution epochs. The model assumes neurons encode movement direction via a cosine tuning that can be different during preparation and execution epochs. As a result, each type of neuron in the model is described with four main properties: their preferred direction in the cosine tuning during preparation (denoted by θ_A) and execution (denoted by θ_B) epochs, and the strength of their encoding of the movement direction during the preparation (denoted by η_A) and execution (denoted by η_B) epochs. The authors assume that a recurrent network that can have different inputs during the preparation and execution epochs has generated the activity in the neurons. In the model, these inputs can both be internal to the network or external. The authors fit the model to real data by optimizing a loss that combines, via a hyperparameter α, the reconstruction of the cosine tunings with a cost to discourage/encourage the use of external inputs to explain the data. They study the solutions that would be obtained for various values of α. The authors conclude that during the preparatory epoch, external inputs seem to be more important for reproducing the neuron's cosine tunings to movement directions, whereas during movement execution external inputs seem to be untuned to movement direction, with the movement direction rather being encoded in the direction-specific recurrent connections in the network.

Major:

1) Fundamentally, without actually simultaneously recording the activity of upstream regions, it should not be possible to rule out that the seemingly recurrent connections in the M1 activity are actually due to external inputs to M1. I think it should be acknowledged in the discussion that inferred external inputs here are dependent on assumptions of the model and provide hypotheses to be validated in future experiments that actually record from upstream regions. To convey with an example why I think it is critical to simultaneously record from upstream regions to confirm these conclusions, consider two alternative scenarios: I) The recorded neurons in M1 have some recurrent connections that generate a pattern of activity that is based on the modeling seems to be recurrent. II) The exact same activity has been recorded from the same M1 neurons, but these neurons have absolutely no recurrent connections themselves, and are rather activated via purely feed-forward connections from some upstream region; that upstream region has recurrent connections and is generating the recurrent-like activity that is later echoed in M1. These two scenarios can produce the exact same M1 data, so they should not be distinguishable purely based on the M1 data. To distinguish them, one would need to simultaneously record from upstream regions to see if the same recurrent-like patterns that are seen in M1 were already generated in an upstream region or not. I think acknowledging this major limitation and discussing the need to eventually confirm the conclusions of this modeling study with actual simultaneous recordings from upstream regions is critical.

2) The ring network model used in this work implicitly relies on the assumption that cosine tuning models are good representations of the recorded M1 neuronal activity. However, this assumption is not quantitatively validated in the data. Given that all conclusions depend on this, it would be important to provide some goodness of fit measure for the cosine tuning models to quantify how well the neurons' directional preferences are explained by cosine tunings. For example, reporting a histogram of the cosine tuning fit error over all neurons in Fig 2 would be helpful (currently example fits are shown only for a few neurons in Fig. 2 (a), (b), and Figure S6(b)). This would help quantitatively justify the modeling choice.

3) The authors explain that the two-cylinder model that they use has "distinct but correlated" maps A and B during the preparation and movement. This is hard to see in the formulation. It would be helpful if the authors could expand in the Results on what they mean by "correlation" between the maps and which part of the model enforces the correlation.

4) The authors note that a key innovation in the model formulation here is the addition of participation strengths parameters (η_A, η_B) to prior two-cylinder models to represent the degree of neuron's participation in the encoding of the circular variable in either map. The authors state that this is critical for explaining the cosine tunings well: "We have discussed how the presence of this dimension is key to having tuning curves whose shape resembles the one computed from data, and decreases the level of orthogonality between the subspaces dedicated to the preparatory and movement-related activity". However, I am not sure where this is discussed. To me, it seems like to show that an additional parameter is necessary to explain the data well, one would need to compare fit to data between the model with that parameter and a model without that parameter. I don't think such a comparison was provided in the paper. It is important to show such a comparison to quantitatively show the benefit of the novel element of the model.

5) The model parameters are fitted by minimizing a total cost that is a weighted average of two costs as E_tot = α E_rec + E_ext, with the hyperparameter α determining how the two costs are combined. The selection of α is key in determining how much the model relies on external inputs to explain the cosine tunings in the data. As such, the conclusions of the paper rely on a clear justification of the selection of α and a clear discussion of its effect. Otherwise, all conclusions can be arbitrary confounds of this selection and thus unreliable. Most importantly, I think there should be a quantitative fit to data measure that is reported for different scenarios to allow comparison between them (also see comment 2). For example, when arguing that α should be "chosen so that the two terms have equal magnitude after minimization", this would be convincing if somehow that selection results in a better fit to the neural data compared with other values of α. If all such selections of α have a similar fit to neural data, then how can the authors argue that some are more appropriate than others? This is critical since small changes in alpha can lead to completely different conclusions (Fig. 6, see my next two comments).

6) The authors seem to select alpha based on the following: "The hyperparameter α was chosen so that the two terms have equal magnitude after minimization (see Fig. S4 for details)". Why is this the appropriate choice? The authors explain that this will lead to the behavior of the model being close to the "bifurcation surface". But why is that the appropriate choice? Does it result in a better fit to neural data compared with other choices of α? It is critical to clarify and justify as again all conclusions hinge on this choice.

7) Fig 6 shows example solutions for 2 close values of α, and how even slight changes in the selection of α can change the conclusions. In Fig. 6 (d-e-f), α is chosen as the default approach such that the two terms E_rec and E_ext have equal magnitude. Here, as the authors note, during movement execution tuned external inputs are zero. In contrast, in Fig. 6 (g-h-i), α is chosen so that the E_rec term has a "slightly larger weight" than the E_ext term so that there is less penalty for using large external inputs. This leads to a different conclusion whereby "a small input tuned to θ_B is present during movement execution". Is one value of α a better fit to neural data? Otherwise, how do the authors justify key conclusions such as the following, which seems to be based on the first choice of α shown in Fig. 6 (d-e-f): "...observed patterns of covariance are shaped by external inputs that are tuned to neurons' preferred directions during movement preparation, and they are dominated by strong direction-specific recurrent connectivity during movement execution".

8) It would be informative to see the extreme case of very large and very small α. For example, if α is very large such that external inputs are practically not penalized, would the model rely purely on external inputs (rather than recurrent inputs) to explain the tuning curves? This would be an example of the hypothetical scenario mentioned in my first comment. Would this result in a worse fit to neural data?

9) The authors argue in the discussion that "the addition of an external input strength minimization constraint breaks the degeneracy of the space of solutions, leading to a solution where synaptic couplings depend on the tuning properties of the pre- and post-synaptic neurons, in such a way that in the absence of a tuned input, neural activity is localized in map B". In other words, the use of the E_ext term, apparently reduces "degeneracy" of the solution. This was not clear to me and I'm not sure where it is explained. This is also related to α because if alpha goes toward very large values, it would be like the E_ext term is removed, so it seems like the authors are saying that the solution becomes degenerate if alpha grows very large. This should be clarified.

10) How do the authors justify setting Φ_A = Φ_B in equation (5)? In other words, how is the last assumption in the following sentence justified: "To model the data, we assumed that the neurons are responding both to recurrent inputs and to fluctuating external inputs that can be either homogeneous or tuned to θ_A; θ_B, with a peak at constant location Φ_A = Φ_B ≡ Φ". Does this mean that the preferred direction for a given neuron is the same during preparation and movement epochs? If so, how is this consistent with the not-so-high correlation between the preferred directions of the two epochs shown in Fig. 2 c, which is reported to have a circular correlation coefficient of 0.4?

Reviewer #3 (Public Review):

In this work, Bachschmid-Romano et al. propose a novel model of the motor cortex, in which the evolution of neural activity throughout movement preparation and execution is determined by the kinematic tuning of individual neurons. Using analytic methods and numerical simulations, the authors find that their networks share some of the features found in empirical neural data (e.g., orthogonal preparatory and execution-related activity). While the possibility of a simple connectivity rule that explains large features of empirical data is intriguing and would be highly relevant to the motor control field, I found it difficult to assess this work because of the modeling choices made by the authors and how the results were presented in the context of prior studies.

Overall, it was not clear to me why Bachschmid-Romano et al. couched their models within a cosine-tuning framework and whether their results could apply more generally to more realistic models of the motor cortex. Under cosine-tuning models (or kinematic encoding models, more generally), the role of the motor cortex is to represent movement parameters so that they can presumably be read out by downstream structures. Within such a framework, the question of how the motor cortex maintains a stable representation of movement direction throughout movement preparation and execution when the tuning properties of individual neurons change dramatically between epochs is highly relevant. However, prior work has demonstrated that kinematic encoding models provide a poor fit for empirical data. Specifically, simple encoding models (and the more elaborate extensions [e.g., Inoue, et al., 2018]) cannot explain the complexity of single-neuron responses (Churchland and Shenoy, 2007), and do not readily produce the population-level signals observed in the motor cortex (Michaels, Dann, and Scherberger, 2016) and cannot be extended to more complex movements (Russo, et al., 2018).

In both the Introduction and Discussion, the authors heavily cite an alternative to kinematic encoding models, the dynamical systems framework. Here, the correlations between kinematics and neural activity in the motor cortex are largely epiphenomenal. The motor cortex does not 'represent' anything; its role is to generate patterns of muscle activity. While the authors explicitly acknowledge the shortcomings of encoding models ('Extension to modeling richer movements', Discussion) and claim that their proposed model can be extended to 'more realistic scenarios', they neither demonstrate that their models can produce patterns of muscle activity nor that their model generates realistic patterns of neural activity. The authors should either fully characterize the activity in their networks and make the argument that their models better provide a better fit to empirical data than alternative models or demonstrate that more realistic computations can be explained by the proposed framework.

Major Comments<br /> 1. In the present manuscript, it is unclear whether the authors are arguing that representing movement direction is a critical computation that the motor cortex performs, and the proposed models are accurate models of the motor cortex, or if directional coding is being used as a 'proof of concept' that demonstrates how specific, population-level computations can be explained by the tuning of individual neurons.<br /> If the authors are arguing the former, then they need to demonstrate that their models generate activity similar to what is observed in the motor cortex (e.g., realistic PSTHs and population-level signals). Presently, the manuscript only shows tuning curves for six example neurons (Fig. S6) and a single jPC plane (Fig. S8). Regarding the latter, the authors should note that Michaels et al. (2016) demonstrated that representational models can produce rotations that are superficially similar to empirical data, yet are not dependent on maintaining an underlying condition structure (unlike the rotations observed in the motor cortex).<br /> If the authors are arguing the latter - and they seem to be, based on the final section of the Discussion - then they need to demonstrate that their proposed framework can be extended to what they call 'more realistic scenarios'. For example, could this framework be extended to a network that produces patterns of muscle activity?

2. Related to the above point, the authors claim in the Abstract that their models 'recapitulate the temporal evolution of single-unit activity', yet the only evidence they present is the tuning curves of six example units. Similarly, the authors should more fully characterize the population-level signals in their networks. The inferred inputs (Fig. 6) indeed seem reasonable, yet I'm not sure how surprising this result is. Weren't the authors guaranteed to infer a large, condition-invariant input during movement and condition-specific input during preparation simply because of the shape of the order parameters estimated from the data (Fig. 6c, thin traces)?

3. In the Abstract and Discussion (first paragraph), the authors highlight that the preparatory and execution-related spaces in the empirical data and their models are not completely orthogonal, suggesting that this near-orthogonality serves an important mechanistic purpose. However, networks have no problem transferring activity between completely orthogonal subspaces. For example, the generator model in Fig. 8 of Elsayed, et al. (2016) is constrained to use completely orthogonal preparatory and execution-related subspaces. As the authors point out in the Discussion, such a strategy only works because the motor cortex received a large input just before movement (Kaufman et al., 2016).

Reviewer #1 (Public Review):

DeRisi and colleagues used a new phage-display peptide platform, with 238,068 tiled 62-amino acid peptides covering all known P falciparum coding regions (and numerous other entities), to survey seroreactivity in 198 Ugandan children and adults from two cohorts. They find that breadth of responses to repeat-containing peptides was twofold higher in children living in the high versus moderate exposure setting, while no such differences were observed for peptides without repeats. Additionally, short motifs associated with seroreactivity were extensively shared among hundreds of antigens, with much of this driven by motifs shared with PfEMP1 antigens.

Malaria immunity is complex, and this new platform is a potentially valuable addition to the toolkit for understanding humoral responses. The two cohorts differed in fundamental ways: 1) high versus moderate exposure to infective bites; 2) samples drawn at the time of malaria for most donors in the high zone versus ~100 days after the last malaria episode in the moderate zone. The effect of acute malaria to boost short-term cross-reactive antibodies can confound the ability to draw inferences when comparing the two cohorts, and this should be further explored to understand its role in the patterns of seroreactivity observed.

Reviewer #2 (Public Review):

This work profiles naturally acquired antibodies against Plasmodium falciparum proteins in two Ugandan cohorts, at incredibly high resolution, using a comprehensive library of overlapping peptides. These findings highlight the ubiquity and importance of intra- and inter-protein repeat elements in the humoral immune response to malaria. The authors discuss evidence that repeat elements reside in more seroreactive proteins, and that the breadth of immunity to repeat-containing antigens is associated with transmission intensity in children.

A key strength and value added to publicly available data are the breadth of proteome coverage and unprecedented resolution from using tiling peptides. The authors point out that a known limitation of PhIP-seq is that conformational and discontinuous-linear epitopes cannot be detected with short linear peptides. In addition, disulfide linkages and post-translational modifications would be absent in the T7 representations.

Several significant conclusions drawn from the results in this study are based on the humoral response to repeat elements that are present in multiple locations, including different genes. If antibodies to these regions are cross-reactive as described, it is not clear how the assay can differentiate antibodies that were developed against one or many of these loci. This potential confounding could change the conclusions about inter-protein motifs.

Reviewer #3 (Public Review):

This work provides a new tool, a comprehensive PhIP-seq library, containing 238,068 individual 62-amino acids peptides tiled every 25-amino acid peptide covering all known 8,980 proteins of the deadliest malaria parasite, Plasmodium falciparum, to systematically profile antibody targets in high resolution. This phage display library has been screened by plasma samples obtained from 198 Ugandan children and adults in high and moderate malaria transmission settings and 86 US controls. This work identified that repeat elements were commonly targeted by antibodies. Furthermore, extensive sharing of motifs associated with seroreactivity indicated the potential for extensive cross-reactivity among antigens in P. falciparum. This paper provides a new proteome-wide high-throughput methodology to identify antibody targets that have been investigated by protein arrays and alpha screens to date. Importantly, only this methodology (PhIP-seq library) is able to investigate repeat-containing antigens and cross-reactive epitopes in high resolution (25-amino acid resolution).

Strengths:<br /> 1) Novel technology<br /> Firstly, the uniqueness of this study is the use of novel technology, the PhIP-seq library. This PhIP-seq library in this study contains >99.5% of the parasite proteome and is the highest coverage among existing proteome-wide tools for P. falciparum. Moreover, this library can identify antibody responses in high resolution (25 amino acids).<br /> Secondly, the PhIP-seq converts a proteomic assay (ie. protein array and alpha screen) into a genomic assay, leveraging the massive scale and low-cost nature of next-generation short-read sequencing.<br /> Thirdly, the phage display system is the ability to sequentially enrich and amplify the signal to noise.<br /> Finally, a high-quality strategic bioinformatic analysis of PhIP-seq data was applied.

2) Novel findings<br /> The major findings of this study were obtained only by using this novel technology because of its full-proteome coverage and high resolution. Repeat elements were the common target of naturally acquired antibodies. Furthermore, extensive sharing of motifs associated with seroreactivity was observed among hundreds of parasite proteins, indicating the potential for extensive cross-reactivity among antigens in P. falciparum.

3) Usefulness for the future research<br /> Importantly, plasma samples from longitudinal cohort studies will give the scientific community important insights into protective humoral immunity which will be important for the identification of vaccine and exposure-marker candidates in the near future.

Weaknesses:<br /> Although the paper does have strengths in principle, the weaknesses of the paper are the insufficient description of the selected parasite proteins and seroreactivity ranking of the selected proteins such as TOP100 proteins.

Reviewer #1 (Public Review):

Osteoclasts, giant multinucleated bone-resorbing cells, are crucial regulators of bone homeostasis and pathology. An underestimated aspect of their biology is that they are very heterogeneous, with at least 2 sub-populations (inflammatory osteoclasts and tolerogenic osteoclasts) existing, and exerting different actions, especially in the context of inflammatory bone loss. In this report, Madel, Halper (co-first authors), and colleagues present an interesting report investigating this heterogeneity, and showing that the probiotic yeast S. boulardii (probably through β-glucans) may be useful in managing inflammation-mediated bone loss, including oestrogen deprivation-mediated osteoporosis, as the authors show in vivo using an OVX mouse model.

The authors first evaluate the differences in the transcriptional landscape of tolerogenic vs inflammatory osteoclasts with RNAseq, and then they evaluate the differences in miRNA expression between the two. Finding that some of the pathways/genes that vary are related to pattern recognition receptors (PRRs), specialized in recognizing non-self antigens including those arising from bacteria and yeasts, they wonder if the probiotic yeast S. boulardii could influence the balance between tolerogenic and inflammatory osteoclasts. Indeed, when the authors treated OVX mice, characterised by an increase in inflammatory osteoclasts and estrogen deprivation/inflammation-induced bone loss, with the probiotic, the bone loss is avoided and inflammatory osteoclasts are reduced. This challenges the classical way in which osteoclast-mediated bone loss is treated, since targeting specifically the inflammatory osteoclasts could allow the good osteoclasts to keep working and improving bone health and immunity, while only the bad osteoclasts are targeted. Current treatments are not able to distinguish between the two, which can cause a paradoxical degradation in bone health and atypical fractures. The report is therefore potentially very important for the field, and although quite focused on a specific strain, it can pave the way to treating bone diseases with probiotics, or specific molecules derived from them including beta-glucans.

Reviewer #2 (Public Review):

The authors apply their previously developed concept that osteoclasts exist in at least two flavors, tolerogenic and inflammatory osteoclasts towards the treatment of osteoporosis. They suggest that selectively targeting inflammatory osteoclasts attenuates ovariectomy-induced bone loss by agonists of pattern recognition receptors (PRR) that are higher expressed on inflammatory osteoclasts. The vision would be that the tolerogenic osteoclasts are still functioning, allowing bone remodeling with high bone quality, while the strong resorbing inflammatory osteoclasts are resorbed. By expression profiling, they detected PPR differentially expressed and confirmed these by flow cytometry and RT-QPCR. The activation of the Tlr2, Dectin-1, and Mincle reduced inflammatory osteoclast generation in vitro and affected their resorptive activity. Dendritic syk cell-specific deletion abrogated the differentiation of this osteoclast subset as well. The application of yeast Saccharomyces boulardii (Sbb) into mice attenuated trabecular bone loss (but not cortical) and seemed to inhibit in vitro the generation of inflammatory osteoclasts.

Strength:<br /> - The expression profiling between very defined in vitro generated osteoclasts, which are somehow extreme phenotypes, provides a good tool to discern gene signatures on the osteoclast level.<br /> - The candidate of PPR were evaluated in their expression at the protein level by flow cytometry and their function was evaluated by loss of function studies.<br /> - The effect of S.b. treatment is striking and exploiting such probiotic fungi could be an elegant way to treat osteoporosis.

Weakness:<br /> - The osteoclasts are generated in vitro in the presence of M-CSF to induce tolerogenic osteoclasts or GM-CSF / Il-4 to generate inflammatory osteoclasts. The demonstration of these cell populations in the S.b. treated mice in vivo is not present, despite the challenge to do this. The author tried to tackle this, by analyzing the differentiation potential of bone marrow progenitor cells of S.b. treated animals, which provides some information.<br /> - The effect on tolerogenic osteoclasts could have been further evaluated, whether they are not affected at all, or whether there are also effects.

The authors strikingly show that agonists for PPR are affecting strongly GM-CSF/IL-4 progenitor-derived osteoclasts. They show that t-Ocl and i-Ocl differ in their gene signature and convincingly show the differential expression of the PPR, with exception of mincle which is clearly acknowledged. The molecular mechanism of how Sb treatment acts via the receptors remains obscure since it might act via changes in the gut permeability or by components directly released by the fungus. The kinase syk could play a role, at least some data in vitro suggest this.

Conceptionally the authors tried to utilize the previously generated knowledge by the group published 2016 and 2020 into an approach. If the use of a probiotic fungus would be beneficial indeed this could be a suitable drug with few side effects much superior to current treatments of osteoporosis.<br /> For me, an intriguing question arises from this study, in case these i-Ocl express these receptors and are thus so "vulnerable" to the agonists to decrease their activity, evt. a negative feedback to prevent overshooting reactions?

Reviewer #3 (Public Review):

The general objective of this work is the dissection of osteoclast diversity; in particular, the authors intend to identify the specific features and properties that distinguish inflammatory and steady-state (tolerogenic) osteoclasts. To this end, the authors perform a transcriptional analysis of inflammatory and tolerogenic osteoclasts and identify the pattern recognition receptors TLR2, Dectin-1, and Mincle as differentially expressed genes. Agonists of these receptors or yeast probiotics regulating the elicited mechanisms in vitro and in vivo caused a specific inhibition of the differentiation of inflammatory rather than tolerogenic osteoclasts, thus highlighting the preferential use of different differentiation pathways by the two distinct osteoclast populations.

The project is based on the previous knowledge and know-how of the authors on this peculiar skeletal cell population. The work is well conceived; the experiments are clearly designed and exploit state-of-the-art technologies. The results confirm the heterogeneity of osteoclasts and provide new insights in this respect. The in vitro and in vivo studies suggest that osteoclast heterogeneity can be purposedly modulated; which might be useful and advisable for therapeutic purposes. Overall, the work provides hints for further implementation and future broad applications to diseases featuring pathological bone loss.

Reviewer #1 (Public Review):

This well-done platform trial identifies that ivermectin has no impact on SARS-CoV-2 viral clearance rate relative to no study drug while casirivimab lead to more rapid clearance at 5 days. The figures are simple and appealing. The study design is appropriate and the analysis is sound. The conclusions are generally well supported by the analysis. Study novelty is somewhat limited by the fact that ivermectin has already been definitively assessed and is known to lack efficacy against SARS-CoV-2. Several issues warrant addressing:

1) Use of viral load clearance is not unique to this study and was part of multiple key trials studying paxlovid, remdesivir, molnupiravir, and monoclonal antibodies. The authors neglect to describe a substantial literature on viral load surrogate endpoints of therapeutic efficacy which exist for HIV, hepatitis B and C, Ebola, HSV-2, and CMV. For SARS-CoV-2, the story is more complicated as several drugs with proven efficacy were associated with a decrease in nasal viral loads whereas a trial of early remdesivir showed no reduction in viral load despite a 90% reduction in hospitalization. In addition, viral load kinetics have not been formally identified as a true surrogate endpoint. For maximal value, a reduction in viral load would be linked with a reduction in a hard clinical endpoint in the study (reduction in hospitalization and/or death, decreased symptom duration, etc...). This literature should be discussed and data on the secondary outcome, and reduction in hospitalization should be included to see if there is any relationship between viral load reduction and clinical outcomes.

2) The statement that oropharyngeal swabs are much better tolerated than nasal swabs is subjective. More detail needs to be paid to the relative yield of these approaches.

3) The stopping rules as they relate to previously modeled serial viral loads are not described in sufficient detail.

4) The lack of blinding limits any analysis of symptomatic outcomes.

5) It is unclear whether all 4 swabs from 2 tonsils are aggregated. Are the swabs placed in a single tube and analyzed?

6) In supplementary Figure 7, both models do well in most circumstances but fail in the relatively common event of non-monotonic viral kinetics (multiple peaks, rebound events). Given the importance of viral rebound during paxlovid use, an exploratory secondary analysis of this outcome would be welcome.

Reviewer #2 (Public Review):

This manuscript details the analytic methods and results of one arm of the PLATCOV study, an adaptive platform designed to evaluate low-cost COVID-19 therapeutics through enrollment of a comparatively smaller number of persons with acute COVID-19, with the goal of evaluating the rate of decrease in SARS-CoV-2 clearance compared to no treatment through frequent swabbing of the oropharynx and a Bayesian linear regression model, rather than clinical outcomes or the more routinely evaluated blunt virologic outcomes employed in larger trials. Presented here, is the in vivo virologic analysis of ivermectin, with a very small sample of participants who received the casirivimab/imdevimab, a drug shown to be highly effective at preventing COVID-19 progression and improving viral clearance (during circulation of variants to which it had activity) included for comparison for model evaluation.

The manuscript is well-written and clear. It could benefit however from adding a few clarifications on methods and results to further strengthen the discussion of the model and accurately report the results, as detailed below.

Strengths of this study design and its report include:<br /> 1. Selection of participants with presumptive high viral loads or viral burden by antigen test, as prior studies have shown difficulty in detecting effect in those with a lower viral burden.<br /> 2. Adaptive sample size based on modeling- something that fell short in other studies based on changing actuals compared to assumptions, depending on circulating variant and "risk" of patients (comorbidities, vaccine state, etc) over time. There have been many other negative studies because the a priori outcomes assumptions were different from the study design to the time of enrollment (or during the enrollment period). This highlight of the trial should be emphasized more fully in the discussion.<br /> 3. Higher dose and longer course of ivermectin than TOGETHER trial and many other global trials: 600ug/kg/day vs 400mcg/kg/day.<br /> 4. Admission of trial participants for frequent oropharyngeal swabbing vs infrequent sampling and blunter analysis methods used in most reported clinical trials<br /> 5. Linear mixed modeling allows for heterogeneity in participants and study sites, especially taking the number of vaccine doses, variant, age, and serostatus into account- all important variables that are not considered in more basic analyses.<br /> 6. The novel outcome being the change in the rate of viral clearance, rather than time to the undetectable or unquantifiable virus, which is sensitive, despite a smaller sample size<br /> 7. Discussion highlights the importance of frequent oral sampling and use of this modeled outcome for the design of both future COVID-19 studies and other respiratory viral studies, acknowledging that there are no accepted standards for measuring virologic or symptom outcomes, and many studies have failed to demonstrate such effects despite succeeding at preventing progression to severe clinical outcomes such as hospitalization or death. This study design and analyses are highly important for the design of future studies of respiratory viral infections or possibly early-phase hepatitis virus infections.

Weaknesses or room for improvement:

1. The methods do not clearly describe allocation to either ivermectin or casirivimab/imdevimab or both or neither. Yes, the full protocol is included, but the platform randomization could be briefly described more clearly in the methods section.<br /> 2. The handling of unquantifiable or undetectable viruses in the models is not clear in either the manuscript or supplemental statistical analysis information. Are these values imputed, or is data censored once below the limits of quantification or detection? How does the model handle censored data, if applicable?<br /> 3. Did the study need to be unblinded prior to the first interim analysis? Could the adaptive design with the first analysis have been done with only one or a subset of statisticians unblinded prior to the decision to stop enrolling in the ivermectin arm?<br /> 4. Can the authors comment on why the interim analysis occurred prior to the enrollment of 50 persons in each of the ivermectin and comparison arms? Even though the sample sizes were close (41 and 45 persons), the trigger for interim analysis was pre-specified.<br /> 5. The reporting of percent change for the intervention arms is overstated. All credible intervals cross zero: the clearance for ivermectin is stated to be 9% slower, but the CI includes + and - %, so it should be reported as "not different." Similarly, and more importantly for casirivimab/imdevimab, it was reported to be 52% faster, although the CI is -7.0 to +115%. This is likely a real difference, but with ten participants underpowered- and this is good to discuss. Instead, please report that the estimate was faster, but that it was not statistically significant. Similarly, the clearance half-life for ivermectin is not different, rather than "slower" as reported (CI was -2 to +6.6 hours). This result was however statistically significant for casirivimab/imdevimab.<br /> 6. While the use of oropharyngeal swabs is relatively novel for a clinical trial, and they have been validated for diagnostic purposes, the results of this study should discuss external validity, especially with respect to results from other studies that mainly use nasopharyngeal or nasal swab results. For example, oropharyngeal viral loads have been variably shown to be more sensitive for the detection of infection, or conversely to have 1-log lower viral loads compared to NP swabs. Because these models look for longitudinal change within a single sampling technique, they do not impact internal validity but may impact comparisons to other studies or future study designs.<br /> 7. Caution should be used around the term "clinically significant" for viral clearance. There is not an agreed-upon rate of clinically significant clearance, nor is there a log10 threshold that is agreed to be non-transmissible despite moderately strong correlations with the ability to culture virus or with antigen results at particular thresholds.<br /> 8. Additional discussion could also clarify that certain drugs, such as remdesivir, have shown in vivo activity in the lungs of animal models and improvement in clinical outcomes in people, but without change in viral endpoints in nasopharyngeal samples (PINETREE study, Gottlieb, NEJM 2022). Therefore, this model must be interpreted as no evidence of antiviral activity in the pharyngeal compartment, rather than a complete lack of in vivo activity of agents given the limitations of accessible and feasible sampling. That said, strongly agree with the authors about the conclusion that ivermectin is also likely to lack activity in humans based on the results of this study and many other clinical studies combined.

Reviewer #3 (Public Review):

This is a well-conducted phase 2 randomized trial testing outpatient therapeutics for Covid-19. In this report of the platform trial, they test ivermectin, demonstrating no virologic effect in humans with Covid-19.

Overall, the authors' conclusions are supported by the data.

The major contribution is their implementation of a new model for Phase 2 trial design. Such designs would have been ideal earlier in the pandemic.

Reviewer #1 (Public Review):

In the study, Zhao et al. investigated loop conformational changes in the active site of L1 Metallo-beta-lactamase. Antibiotic resistance is on the rise and beta-lactamases are enzymes that cleave a lactam ring. Authors investigate class B3 MBLs since these could be used for designing drugs for treating antibacterial resistance. Authors find specific loops that act as gates to the shape and access to the active site of the enzymes. They study these loops via MD simulations, Markov state models, and CVAE-based deep learning to experimentally reveal how each residue affects activity as well as remodeling of the active site.

Strengths<br /> - The authors make a good case for why MD is important for this scaffold and protein class. The study performs MD simulations coupled with Markov State Models - this coupled with CVAE to understand the different states the protein exists in shapes the state-of-the-art study. Authors are able to isolate three different states that the protein exists in and pinpoint which interactions cause a reshaping of the active site.<br /> - Furthermore, they isolate the likely states that also correspond with lower free energy indicating why these states might be more populated. This study adds to the depth of their work.

Weaknesses<br /> - Overall, the impact of work on the currently used antibiotic classes is unclear since the total market presence of all antibiotics is discussed not the carbapenem-based antibiotics class. Statistics related to broad antibiotic class reduce the impact statement instead of improving it.<br /> - Finally in the experimental testing only a few variants at each position were tested, leading to limited learning of the impact of active site interactions.<br /> - Authors state from previous studies on TEM-1 that disruption of the salt bridge between the two loops would alter the binding site, thus reducing antibiotic resistance. The authors continue on to hypothesize that this would hold true for the structure in consideration for this paper as well. While a good hypothesis, this cannot be inferred until we see experimental evidence for the same or a sequence comparison discussing how similar TEM1 is to the L1 MBL in question.<br /> - The authors do not explain how different splits of this data in terms of splitting (80:20 vs 70:30 or others) and reducing interaction matrix lower than 22 x 22 residues can impact their results. Also, the effect of changing the distance shell (8A) for matrix generation is not described. This variation is unaccounted for and can enable authors to pressure test their method and learnings.

Reviewer #2 (Public Review):

Zhao, Shen et al. ran molecular dynamics simulations, followed by the application of Markov State Model analysis and deep machine learning dimensional reduction, to study the dynamical behavior of two loops close to the catalytic site of L1 Metallo-β-lactamase (MBL).

The simulations are carefully executed and of sufficient length to build a representative kinetic model. Using a dimensional reduction of the loop conformational sampling based on backbone dihedral features followed by tICA embedding, the authors obtain a Markov state model that identifies the main conformational states of the loops in the absence of bound ligands and provides estimates of the timescales for the transitions between them. Next, the authors employ an alternative way to cluster the conformations of the loops, using unsupervised dimensional reduction, implemented as a convolutional VAE applied to residue distances followed by tSNE embedding. This second step gives results that are not consistent with the clustering used for the kinetic modeling (for instance, supplement 2 of figure 4 shows that the 7 macrostates obtained by the MSM analysis don't always correspond to different areas in the CVAE+tSNE embedding).

Moreover, an inspection of the results from both analysis techniques just confirms the role of interactions that are readily observed in the available crystal structure stabilising the most populated, closed conformation of the loops. The sophisticated computational analysis does not elucidate much of the role of the loop dynamics beyond the intuitive conclusion that disruption of the key interactions keeping the loops in the closed state would affect the function. For instance, it does not clarify what is the role of the other observed metastable states.

Finally, the authors propose and test mutations that would likely disrupt the stability of the closed state and find that they have variable effects on the ability of the enzyme to contrast the antibiotic effect of a panel of substrates. These experimental results look useful and can potentially be used to elucidate the role of the loops in the recognition and activity of the enzyme, and for the design of inhibitors. However, no additional attempt is made to clarify the experimental results based on the mechanistic model of loop dynamics: why do different mutations have different effects? why do some mutations affect all substrates, other mutations only some substrates, and for others, no substrate is affected? What is the role of the tetrameric arrangement?

Reviewer #3 (Public Review):

The authors provide a molecular dynamics (MD)-based detailed evaluation of the contribution of the two elongated loops (alpha3-beta7 and beta12-alpha5), present near each active site of the tetrameric Stenotrophomonas maltophilia class B Metallo-beta-lactamase (MBL) L1, towards the L1's lactamase activity with the premise that a better understanding of the categorical conformational states sampled by the loops would ultimately help in the design of a better lactamase inhibitor. This is to then ultimately alleviate the public health crisis arising from β-lactam antibiotic resistance. Using enhanced sampling MD, Markov state modeling (MSM), and convolutional variation autoencoder (CAVE)-based deep learning, the authors identify five key interacting residues in these two loops which contribute to the conformational states of loops.

The major strength of the study is that the authors carry out a detailed study (e.g., enhanced sampling MD, Markov state modeling, and convolutional variation autoencoder-based deep learning) of the conformational landscape of an important enzyme as these findings would help further experimental studies (e.g., NMR dynamics) for ligand binding, better design of inhibitory ligands of an important class of enzyme. One weakness would be that MBL L1 is a good representative of the class of MBL enzymes or not needs clarification.

The authors achieve the goal of capturing the various conformational states of the L1 enzyme loops and their computational results support the conclusion about the various loop conformations sampled during the dynamics. However, how the mutagenesis experiment supports the existence of different conformational states will likely benefit from more clarification. Further clarification on how detecting the existence of multiple conformers benefits better inhibitor design will be very beneficial.

Since details on macromolecular motion are often neglected in macromolecular experimental studies, the detailed MD methods described here will be a very useful companion in experimental studies of proteins and their interactions.

A discussion on how the study of one particular enzyme could benefit in understanding the molecular properties of a class of enzymes would enhance the generality of the study.

Reviewer #1 (Public Review):

Bornstein and colleagues address an important question regarding the molecular makeup of the different cellular compartments contributing to the muscle spindle. While work focusing on single components of the spindle in isolation - proprioceptors, gamma-motor neurons, and intrafusal muscle fibres - have been recently published, a comprehensive analysis of the transcriptome and proteome of the spindle was missing and it fills an important gap considering how local translation and protein synthesis can affect the development and function of such a specialised organ.

The authors combine bulk transcriptome and proteome analysis and identify new markers for neuronal, intrafusal, and capsule compartments that are validated in vivo and are shown to be useful for studying aspects of spindle differentiation during development. The methodology is sound and the conclusions in line with the results. I feel a bit more analysis regarding the specificity and developmental expression profiles of the identified markers would be a great addition. In particular:

- Are any of the proprioceptive sensory neurons markers specific for fibres innervating the muscle spindles or also found in Golgi tendon organs?

- On the same line are any of the gamma motor neurons markers found also in alpha?

- How early expression of ATP1A3 is found in neurons at the spindle or fibres starting to innervating the muscle? A couple of late embryonic timepoints would be great.

- Given that the approach used allows to obtain insights on whether local translation plays a major role into the differentiation of the spindle it would be interesting to assess whether the proprioceptor and gamma motor neuron markers identified are also found in the cell body or exclusively at the spindle.

Altogether, this is a novel and important work that will benefit scientists studying the neuromuscular and musculoskeletal systems by pushing the field toward an holistic understanding of the muscle spindle. These datasets in combination with the previous ones can be used to develop new genetic and viral strategies to study muscle spindle development and function in healthy and pathological states by analysing the roles and relative contributions of different components of this fascinating and still mysterious organ.

Reviewer #2 (Public Review):

The data presented are of high quality. Through complementary experiments involving the isolation of masseter muscle spindles, the authors perform RNA-seq and proteomic analysis, and identify genes and proteins that are differentially expressed in the muscle spindle versus the adjacent muscle fiber, and proteins that accumulate specifically in capsule cells and nerve endings. These data, while essentially descriptive, provide important information about the developmental framework of the sensory apparatus present in each muscle that accounts for its tension/contraction state. The data presented thus allow for a better characterization of muscle spindles and provide the community with a set of new markers for better identification of these structures. Analysis of the expression pattern of the Tomato reporter in transgenic animals under the control of Piezo2-CRE, Gli1-CRE and Thy1-YFP reporter reinforces the findings and the specificity of the expression pattern of the specific genes and proteins identified by the multi-omics approach and further validated by immunohistochemistry.

Reviewer #1 (Public Review):

The authors devised a new mRNA imaging approach, MASS, and showed that it can be applied to investigate the activation of gene expression and the dynamics of endogenous mRNAs in the epidermis of live C. elegans. The approach is potentially useful, but this manuscript will benefit by addressing the following questions:

Major comments:

1. In Figure 1-figure supplement 1, the authors claimed that MASS could verify the lamellipodia-localization of beta-actin mRNAs. However, the image showed the opposite of the authors' claim as the concentration of beta-actin mRNA was lower in lamellipodia than the rest of the cytosol. This result disagreed with ref. 17 (Katz, Z.B. et al., Genes and Development, 2012). Hence, the authors cannot make the statement that "MASS can be readily used to image RNA molecules in live cells without affecting RNA subcellular localization". To thoroughly test this notion, the authors should image beta-actin mRNA using MASS and the conventional MS2 system side by side and calculate the polarization index in the same way as shown in Katz, Z.B. et al., Genes and Development, 2012.

2. The experiments that validate this new RNA imaging method are not sufficient. The authors need to systematically compare MASS and the MS2 system, including their RNA signal intensity, signal-to-background ratio.

3. In line with this, does beta-actin mRNA display the same behavior as in (Figure 1C-F) when the mRNA was imaged with the MS2 system? The movies do not indicate the type of motility expected of mRNA. For instance, it seems that almost all of the GFP dots, which are presumably single beta-actin mRNAs, stayed stationary over a time course of tens of seconds (Movie 1). This seems to be very different from what has been observed before. It's not clear that the dots are real mRNAs molecules. This further stresses the importance for them to compare their new imaging system with the conventional MS2 application.

4. The authors claimed that a major advantage of MASS is that it has only 8xMS2 stem loops (350 nt) and overcomes "the previous obstacle of the requirement of inserting a long 1,300 nt 24xMS2". This statement lacks experimental support in this manuscript. The authors need to quantitatively compare the genomic tagging efficiency of 8xMS2 and 24xMS2.

5. MASS has the same strategy as SunRISER (Guo, Y. & Lee, R.E.C., Cell Reports Methods, 2022). Both methods use Suntag to amplify signals of MS2- or PP7-tagged RNA. The authors need to elaborate the discussions and describe the similarities and differences of the two studies. In particular, the Guo paper needs to be properly referenced.

6. In Guo, Y. & Lee, R.E.C., Cell Reports Methods, 2022, they showed that 8XPP7 with 24XSunTag configuration led to fewer mRNA per cell (Figure 5B of the Cell Reports Methods paper). Does MASS, which has 8xMS2 with 24xSunTag, similarly lead to few mRNAs? The authors should compare the number of mRNAs detected by MASS and the conventional MS2, or by FISH.

Reviewer #2 (Public Review):

Hu et al. developed a new reagent to enhance single mRNA imaging in live cells and animal tissues. They combined an MS2-based RNA imaging technique and a Suntag system to further amplify the signal of single mRNA molecules. They used 8xMS2 stem-loops instead of the widely-used 24xMS2 stem-loops and then amplified the signal by fusing a 24xSuntag array to an MS2 coat protein (MCP). While a typical 24xMS2 approach can label a single mRNA with 48 GFPs, this technique can label a single mRNA with 384 GFPs, providing an 8-fold higher signal. Such high amplification allowed the authors to image endogenous mRNA in the epidermis of live C. elegans. While a similar approach combining PP7 and Suntag or Moontag has been published, this paper demonstrated imaging endogenous mRNA in live animals. Data mostly support the main conclusions of this paper, but some aspects of data analysis and interpretation need to be clarified and extended.

Strengths:<br /> Because the authors further amplified the signal of single mRNA, this technique can be beneficial for mRNA imaging in live animal tissues where light scattering and absorption significantly reduce the signal. In addition, the size of an MS2 repeat cassette can be reduced to 8, which will make it easier to insert into an endogenous gene. Also, the MCP-24xSuntag and scFv-sfGFP constructs can be expressed in previously developed 24xMS2 knock-in animal models to image single mRNAs in live tissues more easily.

The authors performed control experiments by omitting each one of the four elements of the system: MS2, MCP, 24xSuntag, and scFV. These control data confirm that the observed GFP foci are the labeled mRNAs rather than any artifacts or GFP aggregates. And the constructs were tested in two model systems: HeLa cells and the epidermis of C. elegans. These data demonstrate that the technique may be used across different species.

Weaknesses:<br /> Although the paper has strength in providing potentially useful reagents, there are some weaknesses in their approach.

Each MCP-24xSunTag is labeled with 24 GFPs, providing enough signal to be visualized as a single spot. Although the authors showed an image of a control experiment without MS2 in Figure 1B, the authors should at least mention this potential problem and discuss how to distinguish mRNA from MCP tagged with many GFPs. MCP-24xSunTag labeled with 24 GFPs may diffuse more rapidly than the labeled mRNA. Depending on the exposure time, they may appear as single particles or smeared background, but it will certainly increase the background noise. Such trade-offs should be discussed along with the advantage of this method.

Also, more quantitative image analysis would be helpful to improve the manuscript. For instance, the authors can measure the intensity of each GFP foci, show an intensity histogram, and provide some criteria to determine whether it is an MCP-24xSuntag, a single mRNA, or a transcription site. For example, it is unclear if the GFP spots in Figure 2D are transcription sites or mRNA granules.

Another concern is that the heavier labeling with 24xSuntag may alter the dynamics of single mRNA. Therefore, it would be desirable to perform a control experiment to compare the diffusion coefficient of mRNAs when they are labeled with MCP-GFP vs MCP-24xSuntag+scFv-sfGFP.

The authors could briefly explain about the genes c42d4.3 and mai-1. Why were these specific genes chosen to study gene expression upon wound healing? Did the authors find any difference in the dynamics of gene expression between these two genes?

Reviewer #3 (Public Review):

It is a brilliant idea to combine the MS2-MCP system with Suntag. As the authors stated, it reduces the copies of the MS2 stem loops, which can create challenges during cloning process. The Suntag system can easily amplify the signal by several to tens of folds to boost the signal for live RNA tagging. One of the best ways to claim that MASS works better than the MS2 system by itself is to compare their signal-to-noise ratios (SNRs) within the same model system, such as HeLa cells or the C. elegans epidermis. Because the authors' main argument is that they made an improvement in live RNA tagging method, it is necessary to compare it with other methods side-by-side. The authors claim that MASS can significantly improves the efficiency of CRISPR by reducing the size of the insert, it still requires knocking in several transgenes, which can be even more challenging in some model systems where there are not many selection markers are available. Another possible issue is that the bulky, heavy tagging (384 scFv-sfGFP along with 24xSuntag) can affect the mobility or stability of the target mRNAs. If it also tags preprocessed RNA in the nucleus, it may affect the RNA processing and nuclear export. A few experiments to address these possibilities will strengthen the authors' arguments. I am proposing some experiments below in detailed comments.

1. For the experiments with HeLa cells, it is not clear whether the authors used one focal plane or the whole z-stack for their assessment of mRNA kinetics, such as fusion, fission, and anchoring. If it was from one z-plane, it was possible that many mRNAs move along the z-axis of the images to assume kinetics. If the kinetics is true, is it expected by the authors? Are beta-actin mRNAs bound to some RNA-binding proteins or clustered in RNP complexes?<br /> 2. Some quantifications on beta-actin mRNA kinetics, such as a plot of their movement speed or fusion rate, etc., would help readers better understand the behaviors of the mRNAs and assess whether the MASS tagging did not affect them.<br /> 3. Using another target gene for MASS tagging would further confirm the efficacy of the system. Assuming the authors generated a parental strain of HeLa cell, where MCP-24xSuntag and scFv-sfGFP are already stably expressed (shown in Fig. 1B), CRISPR-ing in another gene should be relatively easy and fast.<br /> 4. Adding a complementary approach to the data presented in Fig. 1, such as qRT-PCR for beta-actin, with or without the MASS system would ensure the intense tagging did not affect the mRNA expression or stability.<br /> 5. For experiments with the C. elegans epidermis, including at least one more MASS movie clip for c42d4.3 and a movie for mai-1 would be helpful for readers to appreciate the RNA labeling and its dynamics.<br /> 6. The difference between Fig. 2D and Fig. 2-fig supp. 3 is unclear. The authors should address the different patterns of RNA signal propagation. Is it due to the laser power used too much, resulting in photobleach in Fig. 2D?<br /> 7. Movie 7 is the key data the authors are presenting, but there are a few discrepancies between their arguments and what is seen from the movie. The authors say the RNAs are "gradually spread" (the line 120 in the manuscript). However, it seems that the green foci just appear here and there in the epidermis and the majority of them stay where they were throughout the timelapse. This pattern seems to be different from the montage in Fig. 2-fig supp. 3, which indeed looks like the mRNA spots are formed around the lesion and spread overtime. Additional explanation on this will strengthen the arguments. Given the dramatic increase of c42d4.3 mRNA abundance 1 min. after the laser wounding, there must be a tremendous boost of transcription at the active transcription sites, which should be captured as much bigger and fewer green foci that are located inside the nucleus. Is this simply because those nuclear sites are out of focus or in a similar size as mRNA foci? Regardless, this should be addressed in the discussion.<br /> 8. One clear way to confirm that MASS labels mRNAs and does not affect their stability/localization is to compare the imaging data with single-molecule RNA fluorescence in situ hybridization (smFISH) that the Singer lab developed decades ago. The authors can target the endogenous c42d4.3 or mai-1 RNAs using smFISH and compare their abundance and subcellular localization patterns with their data.<br /> 9. One of the main purposes to live image RNAs is to assess their dynamics. Adding some more analyses, such as the movement speed of the foci, would be helpful to show how effective this system is to assess those dynamics features.

Reviewer #4 (Public Review):

Hu et al introduced the MS2-Suntag system into C. elegans to tag and image the dynamics of individual mRNAs in a live animal. The system involves CRISPR-based integration of 8x MS2 motifs into the target gene, and two transgene constructs (MCP-Suntag; scFv-sfGFP) that can potentially recruit up to 384 GFP molecule to an mRNA to amplify the fluorescent signal. The images show very high signal to background ratio, indicating a large range of optimization to control phototoxicity for live imaging and/or artifacts caused by excessive labeling. The use of epidermal wound repair as a case study provides a simplified temporal context to interpret the results, such as the initiation of transcription upon wounding. The preliminary results also reveal potentially novel biology such as localization of mRNAs and dynamic RNP complexes in wound response and repair. On the other hand, the system recruits a large protein complex to an mRNA molecule, an immediate question is to what extent it may interfere with in vivo regulation. Phenotypic assays, e.g., in development and wound repair, would have been a powerful argument but are not explored. In all, C. elegans is powerful system for live imaging, and the genome is rich in RNA binding proteins as well as miRNAs and other small RNAs for rich posttranscriptional regulation. The manuscript provides an important technical progress and valuable resource for the field to study posttranscriptional regulation in vivo.

Reviewer #1 (Public Review):

Auxin-induced degradation is a strong tool to deplete CHK-2 and PLK-2 in the C. elegans germ line. The authors strengthen their conclusions through multiple approaches, including rescuing mutant phenotypes and biochemical analyses of CHK-2 and PLK-2.

The authors overcame a technical limitation that would hinder in vitro analysis (low quantity of CHK-2) through the clever approach of preventing its degradation via the proteasome. In vitro phosphorylation assays and mass spectrometry analysis that establishes that CHK-2 is a substrate of PLK-2 nicely complement the genetic data.

The authors argue that the inactivation of CHK-2 by PLK-2 promotes crossover designation; however, the data only indicate that PLK-2 promotes proper timing of crossover designation.

It is not clear whether the loss of CHK-2 function with the S116A and T120A mutations is the direct result of the inability to phosphorylate these residues or whether it is caused by the apparent instability of these proteins, as their abundance was reduced in IPs compared to wild-type.

The mechanism of CHK-2 inactivation in the absence of PLK-2 remains unclear, though the authors were able to rule out multiple candidates that could have played this role.

Reviewer #2 (Public Review):

In this manuscript, Zhang et al., address the role of Polo-like kinase signaling in restricting the activity of Chk2 kinase and coordinating synapsis among homologous chromosomes with the progression of meiotic prophase in C. elegans. While individual activities of PLK-2 and CHK-2 have been demonstrated to promote chromosome pairing, and double-strand break formation necessary for homologous recombination, in this manuscript the authors attempt to link the function of these two essential kinases to assess the requirement of CHK-2 activity in controlling crossover assurance and thus chromosome segregation. The study reveals that CHK-2 acts at distinct regions of the C. elegans germline in a Polo-like kinase-dependent and independent manner.

Strengths:<br /> The study reveals distinct mechanisms through which CHK-2 functions in different spatial regions of meiosis. For example, it appears that CHK-2 activity is not inhibited by PLK's (1 and 2) in the leptotene/zygotene meiotic nuclei where pairing occurs. This suggests that either CHK-2 is not phosphorylated by PLK-2 in the distal nuclei or that it has a kinase-independent function in this spatial region of the germline. These are interesting observations that further our understanding of how the processes of meiosis are orchestrated spatially for coordinated regulation of the temporal process.

Weaknesses:<br /> While the possibilities stated above are interesting, they lack direct support from the data. A key missing element in the study is the actual role of PLK-2 signaling in controlling CHK-2 activity and thus function. I expand on this below.

Throughout the manuscript, the authors test the role of each of the kinases (CHK-2 or PLK-1, or 2) using auxin-induced degradation, which would eliminate both phosphorylated and unphosphorylated pools of proteins. This experiment thus does not test the role of PLK-2 signaling in controlling CHK-2 function or the role of CHK-2 activation. To test the role of signaling from PLK-2 or CHK-2, the authors need to generate appropriate alleles such as phospho-mutants or kinase-dead mutants. The authors do generate unphosphorylatable and phosphomimetic versions of CHK-2, however, they find that the protein level for both these alleles is lower than wild-type CHK-2 (which the authors state is already low). The authors conclude that the lower level of protein in the CHK-2 phospho-mutants is because the mutations cause destabilization of the protein. I am sympathetic with the authors since clearly these results make interpretations of actual signaling activity more challenging. But there needs to be some evidence of this activity, for example through the generation of a phosphor-specific antibody to phosphorylated CHK-2. While not functional, at least the phosphorylation status of CHK-2 would provide more information on its spatial pattern of activation and inactivation. In addition, it would still be of interest to the readership to present the data on these phosphor-mutant alleles with crossover designation and COSA-1::GFP. Is the phenotype of the WT knockin, and each of the phosphomutant knock-ins similar to auxin-induced degradation of CHK-2?

Given that the CHK-2 phosphomutants did not pan out for assessing the signaling regulation of PLK-2 on CHK-2, to directly assess whether PLK-2 activity restricts CHK-2 function in mid-pachytene but not leptotene/zygotene, the authors should generate PLK-2 kinase dead alleles. These alleles will help decouple the signaling function of PLK-2 from a structural function.

Similarly, to assess the potentially distinct roles of CHK-2 in leptotene/zygotene and mid-pachytene it would be important to assess CHK-2 kinase-dead mutant alleles. At this time, all of the analysis is based on removing both active CHK-2 and inactive CHK-2 (i.e. phosphorylated and unphosphorylated pool) using auxin-induced degradation. The kinase-dead alleles will help infer the role of the kinase more directly. The authors can then superimpose the auxin-induced degradation and assess the impact of complete removal of the protein vs only loss of its kinase function. These experiments may help clarify the role of signaling outcomes of these proteins, vs their complete loss. For example, what does kinase dead PLK-2 recruitment to the synapsed chromosomes appear like? Are their distinct activities for active and inactive PLK-2 that are spatially regulated? The same can be tested for CHK-2.

Reviewer #3 (Public Review):

In this paper, Zhang et al. investigated the regulation of the meiotic checkpoint kinase CHK-2, whose inactivation is a necessary step in ensuring that chromosomes have synapsed and received crossovers before progression to later events of meiotic prophase. Using mass spectrometry, biochemistry, and cytological analysis of mutant and transgenic strains, they show that CHK-2 is phosphorylated and that CHK-2 activity is attenuated in a manner dependent on recruitment of the kinase PLK-2 to a conserved docking motif on the synaptonemal complex, which forms between pairs of homologous chromosomes. The results plausibly explain how CHK-2 can remain active and prolong the events of early prophase chromosome dynamics in response to delays in synapsis since unsynapsed chromosomes will not recruit PLK-2 to inactivate CHK-2 locally. While molecular details remain to be worked out (e.g., why the loss of crossover intermediates can also extend CHK-2 activity; why PLK-2 does not inactivate CHK-2 at pairing centers), this work provides an elegant explanatory unification of several disparate observations.

The authors made extensive use of the auxin-inducible degron system combined with the spatiotemporal arrangement of the C. elegans germline to examine the effect of conditional depletion of proteins in cells where the depleted protein was required for earlier events. This is a powerful approach that can give stronger evidence than an examination of genetic mutant backgrounds, especially when, as in this paper, controls are performed to confirm the timing of depletion by loss of immunofluorescence signal. The method of measuring the proportion of the gonad occupied by nuclei with bright COSA-1 foci is generally robust, but the criteria for demarcation could be more strictly defined. For example, does a single nucleus with a single bright COSA-1 spot suffice to mark the beginning of a zone?

A weakness of this paper is that the non-phosphorylatable alleles constructed to provide a functional test of CHK-2 phosphorylation, unfortunately, had severe meiotic defects, so the importance of CHK-2 phosphorylation in its deactivation remains uncertain. While the results overall point towards direct phosphorylation of CHK-2 by PLK-2 (and possibly PLK-1), the authors are careful to point out that this is not the only possible explanation. In this regard, the mass spectrometry data should be given a statistical analysis to see whether they are best explained by in vitro phosphorylation of CHK-2 by PLK-2.

Reviewer #1 (Public Review):

In this manuscript, Li et al identify sleep and circadian regulatory role for ecdysone signaling in cortex glia. Prior to this report, numerous studies have linked ecdysone to sleep regulation, though these have primarily focused on its function in neurons. The manuscript is of high interest for a number of reasons. First, it provides a systematic analysis of how NHRs impact sleep. Second, the identification of ecdysone as a critical regulator of both sleep and circadian neurons provides new avenues to study how glia regulate sleep. Finally, the link to lipid accumulation is interesting, but perhaps preliminary. The manuscript is well written, and the data are clearly presented with appropriate controls. Overall this is an exciting manuscript that opens up new directions for the field.

Reviewer #2 (Public Review):

This is an interesting manuscript establishing a role for Ecdysone signaling in the control of sleep. The authors show that the Ecdysone receptor EcR is required primarily in cortex glia for the control of sleep and that its target E75 is also involved in sleep regulation. This is a novel function for both cortex glia and steroid signaling in Drosophila. The authors also present evidence that Ecdysone signaling would be important for response to starvation, and that lipid droplet mobilization would mediate the effect of ecdysone on sleep. This work is certainly innovative. However, the main conclusions need to be strengthened. In particular: variability in sleep amounts in certain strains could complicate interpretation, the idea that ecdysone modulates sleep response to starvation is not sufficiently well supported, and genetic evidence for mobilization of lipid droplets being the mechanism linking steroid signaling to sleep is currently quite weak.

Major concerns:

1) I have concerns with the variability observed with the GS drivers (whether nSyb or repo). This is particularly striking in figure S3 when comparing experiments conducted with EcR-c and the Ecl RNAi. Daytime is most affected, but even nighttime looks significantly different. Definitely, nighttime quantification should be shown in addition to total sleep in figure S3. However, I feel that confirming the key results of this study with an additional driver would be reassuring. Could repo-GAL4 combined with GAL80ts be used to drive EcR RNAi, instead of repo-GS? The same combination could help determine whether glia is responsible for the 20E-mediated increase in sleep after starvation (figure S4A).<br /> 2) The idea that ecdysone might suppress the response to starvation is interesting, but the results are not convincing. First, there is an important control missing. It is important to test the effect of Ecdysone on fed flies, to ensure that Ecdysone does not simply make flies sleepy. Second, it is not clear that EcR RNAi has a specific effect on starved flies. Starvation reduces sleep, but is this reduction really exaggerated in flies expressing EcR RNAi than in control flies? It seems to me that starvation reduces sleep by the same amount when comparing results in panels 3D and E. The effect of EcRNAi and starvation might be simply additive, which would suggest that 20E impacts sleep independently of starvation.<br /> 3) The material and method section needs to be improved. In particular, it is not clear to me how the starvation/ecdysone feeding assay was done. There are some additional explanations in the figure legend, but the approach is still not clear to me. Indicate clearly when the flies were starved, and when they were exposed to Ecdysone.<br /> 4) I am not convinced that the Lsd2 results necessarily support the idea that this gene is required for the effect of 20E on sleep. Sleep is dramatically reduced during the day in the Lsd2 mutant. This is actually an interesting observation, but this strong effect on baseline sleep might be masking the ability of 20E to modulate sleep.

Reviewer #1 (Public Review):

The manuscript entitled "Endo-lysosomal assembly variations among Human Leukocyte 1 Antigen class I (HLA-I) allotypes" by Eli Olson and co-workers reports an interesting observation that HLA-I alleles known not to require the standard peptide-loading complex for assembly and egress from the endoplasmic reticulum, may assemble with peptide ligands within the endo/lysosomes and are more adept at antigen cross-presentation to the CD8+ subset of T cells.

The strengths of the work are (a) a novel hypothesis that HLA-I allotype variations caused by HLA-I gene polymorphisms control endo/lysosomal HLA-I assembly and antigen cross-presentation even though there is ample evidence for cross-presentation using the endo/lysosomal pathway; and (b) new evidence to support this hypothesis.

Weaknesses are (a) the use of qualitative serologic assays in which specificity of broadly reactive antibodies such as the anti-Bw6 antibody cannot be easily controlled; (b) poor resolution of co-localization micrographs and quantification based on such data; (c) evidence that endo/lysosomal pathway dominates in cross-presentation by B35.1 allotype is weak as the data suggest a significant role for the standard cytoplasmic pathway itself in this immunologic process; and (d) narrow focus on a single member of the B7 supertype that is prevalent at low frequency in the African American (AA: 0.05) and White American (WA: 0.07) populations. These weaknesses can be addressed by using (a) more quantitative biochemical assays; (b) high resolution microscopy; & (c) extending cellular biochemical studies to one or more additional allotypes in the B7 superfamily-e.g., B7.2 (AA: 0.08; WA: 0.155) itself &/or B35.3 (AA: 0.005; WA: 0.027) or B53.1 (AA: 0.133; WA: 0.004).

Reviewer #2 (Public Review):

The manuscript reports on the complex variability of expression, trafficking, assembly/stability, and peptide loading among different MHC I haplotypes. In particular by analyzing two distinct MHC I molecules as representative members of groups of allotypes, that favor canonical or non-canonical assembly modes, the PI reports on preferential cytosolic or endo-lysosomal MHC I loading. Overall, the data shed light on the intersection between MHC I conformation and subcellular sites of peptide loading and help explain MHC I immunosurveillance at a different subcellular location.

In the first series of experiments the authors report an uneven surface expression of HLA-B vs HLA-A, and C on circulating monocytes, with HLA-B being expressed 4 times higher, also they report that as compared to the TAP-dependent allotype B*08:01 the TAP-independent allotype B*35:01 has a lower surface half-life and if often present as an empty molecule. These data set the basis for the author's hypothesis that B*35:01 could traffic in Rab11+ compartment and be involved in cross-presentation, which indeed is demonstrated in a series of pulse-chase peptide experiments and using cathepsin inhibitors.

Overall, the experiments could be improved by performing subcellular fractionation and organelle purification to conclusively demonstrate the differential trafficking of B*08:01 vs B*35:01, as well as quantitative mass spectrometry to determine cytosolic vs endosomal processing for one selected epitope presented by the different haplotypes.

Reviewer #3 (Public Review):

The work by Olson and colleagues provides novel, fundamental insights into the role of HLA polymorphisms in the processing of exogenous antigens via the non-canonical vacuolar and cytosolic pathways. The choice of the two exemplar HLA-B allotypes leverages a significant amount of background work done both by the Raghavan lab and others, together with a series of novel and very elegant in vitro assays to elucidate a trend where differences in peptide binding preferences and other molecular features can have a drastic effect on non-canonical processing of exogenous antigens. Finally, using two related cell types (monocytes and monocyte-derived DCs) it highlights important differences in endo-lysosomal assemble within different cell types, an aspect of the non-canonical antigen processing that has not been sufficiently addressed in previous studies. While the number of allotypes and cell types utilized in this study is small (n=2 in each case), it provides an elaborate view into the vacuolar processing pathway and motivates further studies on a more expanded set of alleles in future studies. Finally, it underscores the importance of defining the expression of HLA expression levels in the context of specific cell types, setting a standard for future studies in the field.

Moreover, the work outlined in this study is technically sound, with sufficient attention to detail, adequate control experiments, and a rigorous statistical analysis of the resulting data when needed. Overall, the conclusions are well supported by the data. The manuscript is written in a clear, succinct manner to comprehend by a wide audience of readers.

One shortcoming of the paper is a lack of molecular characterization of the peptide-receptive MHC-I species at different stages of their assembly and trafficking process. For instance, while the authors utilize a monoclonal antibody (HC10) to probe empty MHC-I conformers and their dynamics, they don't provide further analysis of interactions with the light chain, a component of the complex that is known to be critical for regulating the internalization and peptide-loading process, both on the cell surface and at different intracellular compartments. Finally, while the overall effects on the cross-presentation of specific EBV antigens by the two allotypes are well described, what is lacking is a more quantitative analysis of the number of molecules, their densities, and distribution on the cell surface, all of which are known to have important consequences for T cell stimulation.

Reviewer #1 (Public Review):

This study used MEG to investigate the neural changes induced by two weeks of reading instruction in pre-literate children. The study addresses a topic of great importance, measuring neural changes resulting from learning to read. While there have been several previous studies investigating this question, this may be the first study to use a truly experimental approach (i.e., involving random assignment).

There are some weaknesses in the current presentation of results that limit the conclusions that can be drawn. First, there is no control region (e.g., the right FFA or object-selective LO) to show that learning to read specifically affected tuning in a region corresponding to the VWFA, as hypothesized. This is important also to exclude more general differences between conditions (e.g., increased attention to letters after two weeks of training to recognize letters). Second, the statistical pattern of results is closely linked to the specific time window of interest (135-235 ms after stimulus onset) but there is no evidence that this time window is selective for words in this age group. For comparison, the face-selective response in children of this age is only observed at around 250 ms after onset (Taylor et al., Clinical Neurophysiology 1999). Third, the power analysis is very optimistic, with an estimated effect size of d=4.65. Considering the between-subjects design, the relatively low SNR of data in young children, and the multiple comparisons that are inherent to neuroimaging data, the study may be underpowered to detect the likely subtle effects of the 2 weeks of training.

Reviewer #2 (Public Review):

Yeatman and colleagues used MEG in pre-literate children following a literacy intervention program to investigate changes in cortical responses to visual images of words, faces, and objects. Children who participated in a literacy intervention program showed improvements in letter knowledge and increased neural responses to words relative to an object category. The authors interpret these findings in the framework of the neuronal recycling hypothesis proposed by Dehaene and colleagues. This is important work. The opportunity to use a causal manipulation to study neural and behavioral development in humans is rare. The finding of neural changes from just 2 weeks of intervention is striking. The scope of the work extends beyond understanding brain development and has potential relevance for social and educational policies. The study appears well-designed and includes an important control group. Overall, I am enthusiastic about this work. However, it is unclear whether the results are specific to the area of interest - the visual word form area. The increased response to words from the intervention appears quite widespread cortically (Figure 5). These issues are central to the idea of neuronal recycling and the authors' proposal that training leads to increased modularization. Thus, the results currently only provide modest support for the conclusions. Additionally, aspects of the analysis need clearer motivation/justification.

Reviewer #3 (Public Review):

Yeatman et al. tested whether the emergence of brain regions that selectively process novel visual stimuli like words occur at the expense of cortical representations of other stimuli like faces and objects. They conducted a randomized controlled trial with preschool children (five years of age) that were either taught reading or oral language skills. They found that being taught reading versus oral language skills induced different patterns of change in category-selective regions of the visual cortex. Their main conclusion is that reading instruction enhanced the response to text but did not diminish the response to other categories.

The main novelty of this study seems to be that they conducted a randomized controlled trial. The study is well crafted and executed. However, based on the current methodology, it is unclear if they shed novel light on the cortical recycling hypothesis.

Reviewer #1 (Public Review):

This study comprehensively categorizes the olivocochlear efferents, using single nucleus RNA-sequencing and 3D reconstructions of individual fibers and their pre-synaptic contacts onto target neurons in the cochlea.

The major strengths of the methods and results are the gene expression studies, which reveal 5 clusters of olivocochlear neurons. Traditionally, efferents have been divided into two groups, medial olivocochlear neurons that terminate on outer hair cells, and the lateral olivocochlear neurons, that terminate on spiral ganglion neurons postsynaptic to the auditory hair cells. The analyses here revealed 3 main clusters, one large cluster of medial olivocochlear neurons, and two clusters of lateral olivocochlear neurons.

In a second major strength, the study shows changing patterns of physiologically relevant gene expression over development. The authors further showed changes in the neuropeptide expression in the lateral olivocochlear neurons days after acoustic trauma.

There are no significant weaknesses, barring the issue of a gap between gene and protein expression. This is mitigated by a close match with previous protein expression studies.

Thus, the authors have achieved their aims to characterize the phenotypes and arborization patterns of the cochlear efferents. They have confirmed and enlarged upon what has previously known about this important population.

Since these neurons have been difficult to characterize, and are important for auditory function, particularly in noise, the likely impact of the work on the field is high.

Reviewer #2 (Public Review):

The molecular characteristics of OCNs in normal or ototoxic conditions are poorly understood before. The strength of this study is that it provides the first single-cell RNA-seq database of OCNs as well as surrounding facial branchial motor neurons. By thoroughly analyzing the database, they found high heterogeneities within OCN populations and identified distinct markers that are enriched in different OCN subtypes. Furthermore, a few previously unknown neuropeptides are revealed, including Npy which is more enriched in the LOC-2 located on the medial side. They also found that neuropeptide expression levels and distributions are subjected to hearing experience and noise exposure. On the other hand, the weakness of the study is that the numbers of single-cell RNA-seq are not sufficient, and may underscore the MOC heterogeneity (Figure 3A). Moreover, the physiological functions of the LOC-2 are not revealed in this study, and no specific markers in one OCN subtype are identified that can predict the morphological or projecting axon features. Those might be addressed in the following studies.

Reviewer #3 (Public Review):

The research question is highly relevant as far too little is known about the efferent olivocochlear system, and the methods are state-of-the-art. This is high-quality work both for molecular analysis as well as for LOC physiology. The study is well-designed and executed, the manuscript is elegantly prepared. The high-quality gene expression data from a region of the ventral brainstem at 3 different postnatal time points (P1, P5, P26-28) is impactful in terms of development, heterogeneity, and physiological relevance of OCNs. I expect the data of this study to become instrumental for future functional studies on the lateral efferent olivocochlear system.

One issue inherent to transcriptomics studies is the challenge of linking RNA levels to protein levels for functional interpretation. I would ask the authors to acknowledge this and (still more) carefully draw conclusions. For example, name the differentiation of LOC from MOC based on collagen (Col4a4) expression or Gad2 vs. Htr2c for differentiating OCNs from FMNs. Moreover, the lack of physiological differences in soma recordings would seem to suggest a rather homogeneous phenotype but certainly does not exclude the postulated different presynaptic functions of LOC2 and LOC1 neurons.

I am worried that the NPY-based identity in the sparse labeling experiment meant to selectively report LOC2 might not be such a safe approach. This is even more concerning considering that the NPY identity of presynaptic terminals varies within a given axon. Therefore, wonder why the authors did not perform more immunohistochemical labeling of LOC2 and LOC1 markers in the cochlea. Also, it would be great to see how LOC subtype specification changes in genetically deaf and noise-deafened mice.

Reviewer #1 (Public Review):

In this study, the authors describe an elegant genetic screen for mutants that suppress defects of MCT1 deletions which are deficient in mitochondrial fatty acid synthesis. This screen identified many genes, including that for Sit4. In addition, genes for retrograde signaling factors (Rtg1, Rtg2 and Rtg3), proteins influencing proteasomal degradation (Rpn4, Ubc4) or ribosomal proteins (Rps17A, Rps29A) were found. From this mix of components, the authors selected Sit4 for further analysis. In the first part of the study, they analyzed the effect of Sit4 in context of MCT1 mutant suppression. This more specific part is very detailed and thorough, the experiments are well controlled and convincing. The second, more general part of the study focused on the effect of Sit4 on the level of the mitochondrial membrane potential. This part is of high general interest, but less well developed. Nevertheless, this study is very interesting as it shows for the first time that phosphate export from mitochondrial is of general relevance for the membrane potential even in wild type cells (as long as they live from fermentation), that the Sit4 phosphatase is critical for this process and that the modulation of Sit4 activity influences processes relying on the membrane potential, such as the import of proteins into mitochondria. However, some aspects should be further clarified.

1. It is not clear whether Sit4 is only relevant under fermentative conditions. Does Sit4 also influence the membrane potential in respiring cells? Fig. S2D shows the membrane potential in glucose and raffinose. Both carbon sources lead to fermentative growths. The authors should also test whether Sit4 levels influence the membrane potential when cells are grown under respirative conditions, such in ethanol, lactate or glycerol. Even if deletions of Sit4 affect respiration, mutants with altered activity can be easily analyzed.<br /> 2. The authors should give a name to the pathway shown in Fig. 4D. This would make it easier to follow the text in the results and the discussion. This pathway was proposed and characterized in the 90s by George Clark-Walker and others, but never carefully studied on a mechanistic level. Even if the flux through this pathway cannot be measured in this study, the regulatory role of Sit4 for this process is the most important aspect of this manuscript.<br /> 3. To further support their hypothesis, the authors should show that deletion of Pic1 or Atp1 wipes out the effect of a Sit4 deletion. In these petite-negative mutants, the phosphate export cycle cannot be carried out and thus, Sit4, should have no effect.<br /> 4. What is the relevance of Sit4 for the Hap complex which regulates OXPHOS gene expression in yeast? The supplemental table suggests that Hap4 is strongly influenced by Sit4. Is this downstream of the proposed role in phosphate metabolism or a parallel Sit4 activity? This is a crucial point that should be addressed experimentally.<br /> 5. The authors use the accumulation of Ilv2 precursors as proxy for mitochondrial protein import efficiency. Ilv2 was reported before as a protein which, if import into mitochondria is slow, is deviated into the nucleus in order to be degraded (Shakya,..., Hughes. 2021, Elife). Is it possible that the accumulation of the precursor is the result of a reduced degradation of pre-Ilv2 in the nucleus rather than an impaired mitochondrial import? Since a number of components of the ubiquitin-proteasome system were identified with Sit4 in the same screen, a role of Sit4 in proteasomal degradation seems possible. This should be tested.

Reviewer #2 (Public Review):

This study reports interesting findings on the influence of a conserved phosphatase on mitochondrial biogenesis and function. In the absence of it, many nucleus-encoded mitochondrial proteins among which those involved in ATP generation are expressed much better than in normal cells. In addition to a better understanding of th mechanisms that regulate mitochondrial function, this work may help developing therapeutic strategies to diseases caused by mitochondrial dysfunction. However there are a number of issues that need clarification.

1) The rationale of the screening assay to identify genes required for the gene expression modifications observed in mct1 mutant is not clear. Indeed, after crossing with the gene deletion libray, the cells become heterozygote for the mct1 deletion and should no longer be deficient in mtFAS. Thank you for clarifying this and if needed adjust the figure S1D to indicate that the mated cells are heterozygous for the mct1 and xxx mutations.

2) The tests shown in Fig. S1E should be repeated on individual subclones (at least 100) obtained after plating for single colonies a glucose culture of mct1 mutant, to determine the proportion of cells with functional (rho+) mtDNA in the mct1 glucose and raffinose cultures. With for instance a 50% proportion of rho- cells, this could substantially influence the results of the analyses made with these cells (including those aiming to evaluate the MMP).

3) The mitochondria area in mct1 cells (Fig.S1G) does not seem to be consistent with the tests in Fig. 1C. that indicate a diminished mitochondrial content in mct1 cells vs wild-type yeast. A better estimate (by WB for instance) of the mitochondrial content in the analyzed strains would enable to better evaluate MMP changes monitored with Mitotracker since the amount of mitochondria in cells correlate with the intensity of the fluorescence signal.

4) Page 12: "These data demonstrate that loss of SIT4 results in a mitochondrial phenotype suggestive of an enhanced energetic state: higher membrane potential, hyper-tubulated morphology and more effective protein import." Furthermore, the sit4 mutant shows higher levels of OXPHOS complexes compared to WT yeast.

Despite these beneficial effects on mitochondria, the sit4 deletion strain fails to grow on respiratory substrates. It would be good to know whether the authors have some explanation for this apparent contradiction.

Reviewer #3 (Public Review):

In this study, the authors investigate the genetic and environmental causes of elevated Mitochondrial Membrane Potential (MMP) in yeast, and also some physiological effects correlated with increased MMP.

The study begins with a reanalysis of transcriptional data from a yeast mutant lacking the gene MCT1 whose deletion has been shown to cause defects in mitochondrial fatty acid synthesis. The authors note that in raffinose mct1del cells, unlike WT cells, fail to induce expression of many genes that code for subunits of the Electron Transport Chain (ETC) and ATP synthase. The deletion of MCT1 also causes induction of genes involved in acetyl-CoA production after exposure to raffinose. The authors therefore conduct a screen to identify mutants that suppress the induction of one of these acetyl-CoA genes, Cit2. They then validate the hits from this screen to see which of their suppressor mutants also reduce expression in four other genes induced in a mct1del strain. This yielded 17 genes that abolished induction of all 5 genes tested in an mct1del background during growth on raffinose.

The authors chose to focus on one of these hits, the gene coding for the phosphatase SIT4 (related to human PP6) which also caused an increase in expression of two respiratory chain genes. The authors then investigated MMP and mitochondrial morphology in strains containing SIT4 and MCT1 deletions and surprisingly saw that sit4del cells had highly elevated MMP, more reticular mitochondria, and were able to fully import the acetolactate synthase protein Ilv2p and form ETC and ATP synthase complexes, even in cells with an mct1del background, rescuing the low MMP, fragmented mitochondria, low import of Ilv2 and an inability to form ETC and ATP synthase complexes phenotypes of the mct1del strain. Surprisingly, the authors find that even though MMP is high and ETC subunits are present in the sit4del mct1del double deletion strain, that strain has low oxygen consumption and cannot grow under respiratory conditions, indicating that the elevated MMP cannot come from fully functional ETC subunits. The authors also observe that deleting key subunits of ETC complex III (QCR2) and IV (COX5) strongly reduced the MMP of the sit4del mutant, which would suggest that the majority of the increase in MMP of the sit4del mutant was dependant on a partially functional ETC. The authors note that there was still an increase in MMP in the qcr2del sit4del and cox4del sit4del strains relative to qcr2del and cox4del strains indicating that some part of the increase in MMP was not dependent on the ETC.

The authors dismiss the possibility that the increase in MMP could have been through the reversal of ATP synthase because they observe that inhibition of ATP synthase with oligomycin led to an increase of MMP in sit4del cells. Indicating that ATP synthase is operating in a forward direction in sit4del cells.

Noting that genes for phosphate starvation are induced in sit4del cells, the authors investigate the effects of phosphate starvation on MMP. They found that phosphate starvation caused an increase in MMP and increased Ilv2p import even in the absence of a mitochondrial genome. They find that inhibition of the ADP/ATP carrier (AAC) with bongkrekic acid (BKA) abolishes the increase of MMP in response to phosphate starvation. They speculate that phosphate starvation causes an increase in MMP through the import and conversion of ATP to ADP and subsequent pumping of ADP and inorganic phosphate out of the mitochondria.

They further show that MMP is also increased when the cyclin dependent kinase PHO85 which plays a role in phosphate signaling is deleted and argue that this indicates that it is not a decrease in phosphate which causes the increase in MMP under phosphate starvation, but rather the perception of a decrease in phosphate as signalled through PHO85. Unlike in the case of SIT4 deletion, the increase in MMP caused by the deletion of pho85 is abolished when MCT1 is deleted.

Finally they show an increase in MMP in immortalized human cell lines following phosphate starvation and treatment with the phosphate transporter inhibitor phosphonoformic acid (PFA). They also show an increase in MMP in primary hepatocytes and in midgut cells of flies treated with PFA.

The link between phosphate starvation and elevated MMP is an important and novel finding and the evidence is clear and compelling. Based on their experiments in various mammalian contexts, this link appears likely to be generalizable, and they propose and begin to test an interesting hypothesis for how MMP might occur in response to phosphate starvation in the absence of the Electron Transport Chain.

The link between phosphate starvation and deletion of the conserved phosphatase SIT4 is also interesting and important, and while the authors' experiments and analysis suggest some connection between the two observations, that connection is still unclear.

Major points

Mitotracker is great fluorescent dye, but it measures membrane potential only indirectly. There is a danger when cells change growth rates, ion concentrations, or when the pH changes, all MMP indicating dyes change in fluorescence: their signal is confounded Change in phosphate levels can possibly do both, alter pH and ion concentrations. Because all conclusions of the manuscript are based on a change in MMP, it would be a great precaution to use a dye-independent measure of membrane potential, and confirm at least some key results.

Mitochondrial MMP does strongly influence amino acid metabolism, and indeed the SIT4 knockout has a quite striking amino acid profile, with histidine, lysine, arginine, tyrosine being increased in concentration. http://ralser.charite.de/metabogenecards/Chr_04/YDL047W.html<br /> Could this amino acid profile support the conclusions of the authors? At least lysine and arginine are down in petites due to a lack of membrane potential and iron sulfur cluster export.- and here they are up. Along these lines, according to the same data resource, the knock-outs CSR2, ASF1, SSN8, YLR0358 and MRPL25 share the same metabolic profile. Due to limited time I did not re-analyse the data provided by the authors- but it would be worth checking if any of these genes did come up in the screens of the authors.

One important claim in the manuscript attempts to explain a mechanism for the MMP increase in response to phosphate starvation which is independent of the ETC and ATP synthase.

It seems to me the only direct evidence to support this claim is that inhibition of the AAC with BKA stops the increase of mitotracker fluorescence in response to phosphate starvation in both WT and rho0 cells (Figs 4B and 4C). It would strengthen the paper if the authors could provide some orthogonal evidence.

Introduction/Discussion The author might want to make the reader of the article aware that the 'reversal' of the ATP synthase directionality -i.e. ATP hydrolysis by the ATP synthase as a mechanism to create a membrane potential (in petites), has always been a provocative idea - but one that thus far could never be fully substantiated. Indeed some people that are very familiar with the topic, are skeptical this indeed happens. For instance, Vowinckel et al 2021 (PMID: 34799698) measured precise carbon balances for peptide cells, and found no evidence for a futile cycle - peptides grow slower, but accumulate the same biomass from glucose as peptides that re-evolve at a fast growth rate . Perhaps the manuscript could be updated accordingly.

In the introduction and conclusion there is discussion of MMP set points. In particular the authors state:

"Critically, we find that cells often prioritize this MMP setpoint over other bioenergetic priorities, even in challenging environments, suggesting an important evolutionary benefit."

This does not seem to be consistent with the central finding of the manuscript that MMP changes under phosphate starvation. MMP doesn't seem so much to have a 'set point' but rather be an important physiological variable that reacts to stimuli such as phosphate starvation.

The authors suggest that deletion of Pho85 causes an increase in MMP because of cellular signaling. However, they also state in the conclusion:

"Unlike phosphate starvation, the pho85D mutant has elevated intracellular phosphate concentrations. This suggests that the phosphate effect on MMP is likely to be elicited by cellular signaling downstream of phosphate sensing rather than some direct effect of environmental depletion of phosphate on mitochondrial energetics."

The authors should cite the study that shows deletion of PHO85 causes increased intracellular phosphate concentrations. It also seems possible that the 'cellular signaling' that causes the increase in MMP could be a result of this increase in intracellular phosphate concentrations, which could constitute a direct effect of an environmental overload of phosphate on mitochondrial energetics.

Related to this point, in the conclusion, the authors state:

"We now show that intracellular signaling can lead to an increased MMP even beyond the wild-type level in the absence of mitochondrial genome."

In sum, the data shows that signaling is important here- but signaling alone is only the message - not the biophysical process that creates a membrane potential. The authors then could revise this slightly.

The authors state in the conclusion that

"We first made the observation that deletion of the SIT4 gene, which encodes the yeast homologue of the mammalian PP6 protein phosphatase, normalized many of the defects caused by loss of mtFAS, including gene expression programs, ETC complex assembly, mitochondrial morphology, and especially MMP (Fig. 1)"

The data shown though indicates that a defect in mtFAS in terms of MMP, deletion of SIT4 causes a huge increase (and departure away from normality) whether or not mct1 is present (Fig 1D)

The language "SIT4 is required for both the positive and negative transcriptional regulation elicited by mitochondrial dysfunction" feels strong. SIT4 seems to influence positive transcriptional regulation in response to mitochondrial dysfunction caused by MCT1 deletion (but may not be the only thing as there appears to be an increase in CIT2 expression in a sit4del background following a further deletion of MCT1). In terms of negative regulation, SIT4 deletion clearly affects the baseline, but MCT1 deletion still causes down regulation of both examples shown in Fig 1B, showing that negative transcriptional regulation can still occur in the absence of SIT4. The authors might consider showing fold change of expression as they do in later figures (Figs 4B and C) to help the reader evaluate the quantitative changes they demonstrate.

The authors induce phosphate starvation by adding increasing amounts of potassium phosphate monobasic at a pH of 4.1 to phosphate dropout media supplemented with potassium. The authors did well to avoid confounding effects of removing potassium. The final pH of YNB is typically around 5.2. Is it possible that the authors are confounding a change in pH with phosphate starvation? One would expect the media in the phosphate starvation condition to have a higher pH than the phosphate replacement or control media. Is a change in pH possibly a confounding factor when interpreting phosphate starvation? Perhaps the authors could quantify the pH of the media they use for the experiment to understand how much of a factor that could be. One needs to be careful with Miotracker and any other fluorescent dye when pH changes. Albeit having constraints on its own, MitoLoc as a protein rather than small molecule marker of MMP might be a good complement.

Reviewer #1 (Public Review):

The authors optimize a live cell imaging method based on the detection of FAD/NAD(P)H adopted from the fast-growing field of live metabolic imaging. They build upon a method described by KreiB et al 2020 that used metabolic ratio and collagen fiber second harmonic generation imaging. They follow by combining metabolic imaging with morphologic measurements to train a machine-learning model that is able to identify cell types accurately. Upon visualization, authors detected structures hypothesized and then proven to resemble the "goblet cell associated antigen passages" previously studied in intestinal epithelia.

STRENGTHS<br /> - The manuscript is succinct, well written, and overall done rigorously.<br /> - The optimization of the method at multiple levels to the point of identifying both common and rare cell types is impressive.<br /> - Describes the elegant implementation of a sorely needed method in epithelial biology.<br /> - Provides an approach to studying the cholinergic response in epithelial cells, a poorly understood phenomenon despite broad clinical use for diagnosis and treatment.

WEAKNESSES<br /> A) For what is in large part a methods-development paper, the methods are not explained or shared in a manner that facilitates reproducibility. For example:<br /> A.1.) The training and validation datasets seem to come from the same sample (or the source is not clearly described). Therefore, it is not clear whether the "96% accuracy" refers to accuracy within the sample measured, or whether it can extrapolate to other samples.<br /> A.2.) It is unclear whether the model needs to be re-trained within each new sample measured, or if it's applicable to others. This has implications for method adoption by others. Either way is useful but needs to be clarified.<br /> A.3.) Code was only listed in a PDF file, which makes reproducing the analysis very cumbersome.

B) Whereas the optimization to improve cell type detection is very well described, the implementability of the approach could benefit from exploration (using the data already obtained) of the minimal set of measurements needed to identify cell types. For example, is the FAD/NAD(P)H ratio necessary? Or could just morphologic measurements achieve the same goal?

C) Whereas the conclusions are overall supported by the data, need small adjustments in some cases:<br /> C.1.) For example, P3L80: Claims autofluorescence imaging is more specific than "functional markers", however, this is done in the setting of a very specific intervention that massively affects a protein often used as a secretory cell marker (CCSP aka SCGB1A1), which is known to be secreted (and depleted) in secretory cells upon stimulation.<br /> C.2.) Relatedly, it is unclear how the method's accuracy would be affected in conditions that affect redox/metabolic state; the approach may be highly affected in inflammation and injury, for example.

D) The data used to describe "SAPs" is very cursory.<br /> D.1.) Unclear if FITC dextran uptake occurs in other cells too, or in secretory cells prior to methacholine stimulation, or induced nonspecifically due to epithelia manipulation. Secretory and goblet cells are very sensitive to stimulation and often considered minimal, for example, see the paper by Abdullah et al DOI:10.1007/978-1-61779-513-8_16 in which extreme care had to be applied to prevent any secretion at all.<br /> D.2.) A single image is provided for the SAP timeline (Figure 5C), which appears to be the same cell shown in the supplementary video.

IMPACT AND UTILITY<br /> This is well-done work with high potential for widespread adoption within the epithelial biology community, particularly if the methods and code are shared in better detail.

Reviewer #2 (Public Review):

Shah and colleagues tackle a significant impediment to exploiting tissue culture systems that enable prospective ex vivo experimentation in real-time. Namely, the ability to identify and track dynamic and coordinated activities of multiple composite cell types in response to experimental perturbations. They develop a clever label-free approach that collects biologically-encoded autofluorescence of epithelial cells by 2-photon imaging of mouse tracheal explant culture over 2 days. They report the ability to distinguish 7 cell types simultaneously, including rare ones, by developing a machine-learning approach using a combination of fluorescence and cytologic features. Their algorithm demonstrates high accuracy by Mathew's Correlation Coefficient when applied to a test set. Lastly, they show the ability of their approach to visualize the dynamic uptake and expulsion of fluorescently-tagged dextran by individual secretory cells. Overall, the results are intriguing and may be very useful for specific applications.

Reviewer #1 (Public Review):

This study used GWAS and RNAseq data of TCGA to show a link between telomere length and lung cancer. Authors identified novel susceptibility loci that are associated with lung adenocarcinoma risk. They showed that longer telomeres were associated with being a female nonsmoker and early-stage cancer with a signature of cell proliferation, genome stability, and telomerase activity.

Major comments:

1. It is not clear how are the signatures captured by PC2 specific for lung adenocarcinoma compared to other lung subtypes. In other words, why is the association between long telomeres specific to lung adenocarcinoma?

2. The manuscript is lacking specific comparisons of gene expression changes across lung cancer subtypes for identified genes such as telomerase etc since all the data is presented as associations embedded within PCs.

3. It is not clear how novel are the findings given that most of these observations have been made previously i.e. the genetic component of the association between telomere length and cancer.

Reviewer #2 (Public Review):

The manuscript of Penha et al performs genetic correlation, Mendelian randomization (MR), and colocalization studies to determine the role of genetically determined leukocyte telomere length (LTL) and susceptibility to lung cancer. They develop an instrument from the most recent published association of LTL (Codd et al), which here is based on n=144 genetic variants, and the largest association study of lung cancer (including ~29K cases and ~56K controls). They observed no significant genetic correlation between LTL and lung cancer, in MR they observed a strong association that persisted after accounting for smoking status. They performed colocalization to identify a subset of loci where LTL and lung cancer risk coincided, mainly around TERT but also other loci. They also utilized RNA-Seq data from TCGA lung cancer adenocarcinoma, noting that a particular gene expression profile (identified by a PC analysis) seemed to correlate with LTL. This expression component was associated with some additional patient characteristics, genome stability, and telomerase activity.

In general, most of the MR analysis was performed reasonably (with some suggestions and comments below), it seems that most of this has been performed, and the major observations were made in previous work. That said, the instrument is better powered and some sub-analyses are performed, so adds further robustness to this observation. While perhaps beyond the scope here, the mechanism of why longer LTL is associated with (lung) cancer seems like one of the key observations and mechanistically interesting but nothing is added to the discussion on this point to clarify or refute previous speculations listed in the discussion mentioned here (or in other work they cite).

Some broad comments:

1. The observations that lung adenocarcinoma carries the lion's share of risk from LTL (relative to other cancer subtypes) could be interesting but is not particularly highlighted. This could potentially be explored or discussed in more detail. Are there specific aspects of the biology of the substrata that could explain this (or lead to testable hypotheses?)

2. Given that LTL is genetically correlated (and MR evidence suggests also possibly causal evidence in some cases) across a range of traits (e.g., adiposity) that may also associate with lung cancer, a larger genetic correlation analysis might be in order, followed by a larger set of multivariable MR (MVMR) beyond smoking as a risk factor. Basically, can the observed relationship be explained by another trait (beyond smoking)? For example, there is previous MR literature on adiposity measures, for example (BMI, WHR, or WHRadjBMI) and telomere length, plus literature on adiposity with lung cancer; furthermore, smoking with BMI. A bit more comprehensive set of MVMR analyses within this space would elevate the significance and interpretation compared to previous literature.

3. In the initial LTL paper, the authors constructed an IV for MR analyses, which appears different than what the authors selected here. For example, Codd et al. proposed an n=130 SNP instrument from their n=193 sentinel variants, after filtering for LD (n=193 >>> n=147) and then for multi-trait association (n=147 >> n=130). I don't think this will fundamentally change the author's result, but the authors may want to confirm robustness to slightly different instrument selection procedures or explain why they favor their approach over the previous one.

4. Colocalization analysis suggests that a /subset/ of LTL signals map onto lung cancer signals. Does this mean that the MR relationships are driven entirely by this small subset, or is there evidence (polygenic) from other loci? Rather than do a "leave one out" the authors could stratify their instrument into "coloc +ve / coloc -ve" and redo the MR analyses.

Mainly here, the goal is to interpret if the subset of signals at the top (looks like n=14, the bump of non-trivial PP4 > 0.6, say) which map predominantly to TERT, TERC, and OBFC1 explain the observed effect here. I.e., it is biology around these specific mechanisms or generally LTL (polygenicity) but exemplified by extreme examples (TERT, etc.). I appreciate that statistical power is a consideration to keep in mind with interpretation.

Reviewer #1 (Public Review):

The authors set out to answer the standing mystery of an origin of a unique and complex system that is hagfish slime. They formulated a cogent scenario for the co-option of epidermal thread cells and mucous cells into slime and slime glands. Both histology and EM images back this up. It is a delight to see detailed and careful morphological analysis of both the cells and the secretion.

The weakness of the manuscript lies in: a) the absence of an alternative hypothesis (therefore the lacking sense of hypothesis testing); and b) oversimplification and insufficient description of results in transcriptomic and phylogenetic comparison. These are both key elements of the narrative. Because all the data "support" the only scenario considered in this paper, it could risk giving the impression of a just-so story. My reading of the results of their transcriptomic and phylogenetic analyses is more nuanced than explained in the paper. For example, the authors didn't explain in sufficient detail how the data summary in Fig. 5 "demonstrate" that the epidermal thread cells are "ancestral", and that the diversity of alpha and gamma thread biopolymer genes is a prerequisite to slime (without a functional analysis), or that the gene duplication events facilitated the origin of hagfish slime.

This work stands unique. I am not aware of any other study that attempted to explain the origin of this truly bewildering adaptation in hagfish with such a multifaceted approach.

Reviewer #2 (Public Review):

The study is a careful investigation of the physical properties of hagfish slime and the underlying cellular framework that enables this extraordinary evolutionary innovation. I appreciate the careful and detailed measurements and images that the authors provide. The results presented here will surely be extremely important for researchers working on this particular organism and those interested in understanding the evolution, biomedical relevance, and biochemistry of mucus. However, I had difficulty contextualizing the findings in broader biological questions (e.g., the evolution of functional novelty, the adaptive processes, and the links between genetic and phenotypic evolution). I also think that the conclusions on the evolutionary origins and underlying genetics of hagfish slime based on comparative transcriptomic data may be premature.

Reviewer #1 (Public Review):

The manuscript aims to provide a comprehensive insight into the development of the tuberal hypothalamus of the chick by carefully analyzing the expression patterns of a plethora of proteins involved and perturbation of BMP signaling.

Strengths:<br /> This manuscript presents the results of an in-depth analysis aimed to unravel the expression of a variety of transcription factors, and the role of signaling molecules, in particular BMP, SHH and Notch, and, and the role of BMP for the development of the tubular hypothalamus. For this, the authors applied a variety of methods, including in-situ RNA hybridizations to chick embryos, fate mapping, explant cultures, and loss and gain-functions studies in embryos, complemented by carefully mining previously performed scRNA-Seq data. From the data they derive a model, which explains the dynamic changes of expression of signaling molecules and transcription factors from anterior to posterior during chick development. In addition, they show that fate specification and growth occur concomitantly.<br /> Overall, the data provide a plethora of information on expression patterns and consequences of BMP signaling perturbation, which will be valuable for scientists interested in the events taking place during the development of the chick tubular hypothalamus.

Weaknesses:<br /> The plethora of data presented makes it very difficult for a reader, who is not familiar with this system, to follow the major conclusions from each of the panels. This difficulty is enhanced by the lack of a concise, simple and focused summary at the end of most chapters, which, from my point of view, still contains too many details. Similarly, the discussion too often refers to details presented in the figures of the Results section, rather than giving a broader and focused summary and pointing out to novel conclusions.

I also suggest that the authors check the Materials and Methods section, which does not always contain the information required. For example, in the chapter on "Chicken HCR": I guess they used the HCR IHC kit from Molecular Instruments? What kind of "modification" of the Molecular Instruments protocol did they introduce?

Reviewer #2 (Public Review):

Chinnaiya et al. integrated recent scRNA transcriptomics with high-resolution multiplexing in situ hybridization, fate mapping and tissue explants to unravel the spatiotemporal development of early chick tuberal hypothalamus. They show that a wave of BMP signaling passes through anterior and posterior regions sequentially. Interestingly, they showed that neuroepithelial-intrinsic BMPs drive and maintain tuberal hypothalamus late development. Using bioinformatical and in situ profiling, the authors indicated the potential of the tuberal progenitors transferring into radial glia-like cells.

This is a remarkable piece of work and I commend the authors for their bold endeavor to decipher the complex developmental of the tuberal hypothalamus.

Reviewer #1 (Public Review):

In this work, Zhang et al. test neutralizing antibody immunity elicited by a primary vaccination series and homologous boosting with the Sinovac-inactivated COVID-19 vaccine CoronaVac.

While the interpretation of the data is complicated by how some of the experiments were done, it seems that boosting with CoronaVac has only a marginal effect on Omicron (BA.1 subvariant) neutralization.

After primary vaccination comprising two doses, SARS-CoV-2 neutralization, as assessed by a live virus neutralization assay and pseudovirus neutralization assay, are low in absolute terms at peak response 1-month post-second dose (~GMT ~20), then wane. Boosting with a third dose of CoronaVac results in neutralization levels about an order of magnitude higher relative to 1-month post-primary vaccination. However, neutralizing capacity against Omicron (subvariant BA.1) is very limited even at the peak response from the boost, and the great majority of participant samples neutralize less than 50% of Omicron infection with relatively concentrated plasma (1:50).

Form an immunogenicity perspective, puts the utility of homologous boosting with CoronaVac into question given the current Omicron circulating subvariants.

While the strength of this study lies in the implications of the results, a weakness is how the pseudovirus results were done, and these are key to interpreting the data. For these, the authors did not fit a dose-response curve to a dilution series but rather used one plasma concentration (1:50 dilution) and measured percent inhibition. Not doing the measurement with multiple dilutions make the results less accurate and conclusions weaker.

Reviewer #2 (Public Review):

This manuscript shows that two doses of the live attenuated Coronavac vaccine induce neutralising antibodies in the majority of individuals, though neutralisation is modest for Omicron BA.1 even after 1 month post-dose two, and substantial waning at 12 months is noted. Boosting achieves higher neutralisation than for prior doses.

Strengths of the work are the significant sample size in the cross-sectional part and a smaller prospective part which adds value to the study as a whole.

The assays used are appropriate, with PV bearing Wu-hu-1, Delta, and Omicron spike proteins.

Weakness includes the fact that the cross-sectional aspect recruits at different sites at different time points, introducing the fact that observed differences in vaccine response may be related to the underlying population differences.

In addition, the data on third-dose boosting do not appear to include VOC. This is important because data from other vaccines suggest broadening of neutralisation with the third dose.

Reviewer #1 (Public Review):

In this paper Lei et al analyzed the interaction between HIV-1 Gag and the viral RNA packaging signal Psi in living cells using the CLIP-seq method. The authors convincingly showed that NC alone is not sufficient to bind specifically to Psi sequence, while CANC does. They further showed that CANC mutants that are deficient in CA multimerization failed to bind specifically to the Psi sequence. The results indicate that correct assembly of Gag is required for specific binding of the protein to the Psi sequence.

Most of the data are convincing and support the conclusions. My only concern is that the authors analyzed the binding of some CA mutant proteins to the Psi sequence, but it is not so clear whether the specific binding of these mutants would lead to effective packaging of the RNA into virions. Measuring RNA/Gag ratios of some of the mutants in Figure 4 might help to address this concern.

Reviewer #2 (Public Review):

To advance the understanding of the initial events in recognition of HIV-1 genome by the viral structural protein Gag, in this study, the authors examined the involvement of the CA domain in the specific interaction between Gag and the viral genomic RNA. Previous studies including a study from the same group (Kutluay et al 2010) showed that the CA C-terminal domain plays a role in Gag binding to viral genomic RNA. In the current study, they analyzed a panel of CA mutants using a modified PAR-CLIP RNA sequencing, which allows identification of Gag binding sites in the viral genome, and a chemical crosslinking approach, which allows assessment of the multimerization status of Gag in cells. They found that substitutions of CA residues at the CA dimer, trimer, or hexamer interfaces, which reduce Gag multimerization as expected, also reduce the Ψ sequence-specific viral RNA binding, whereas substitutions elsewhere in CA have no impact. They further found that substitutions of the Lys residues important for IP6 binding, which disrupt Gag lattice formation, reduce the Ψ-specific RNA binding, whereas a second-site mutation that restores virus assembly in these Lys substitution mutants restores the RNA binding. These results strongly support the authors' conclusion that Gag lattice formation driven by CA plays an important role in NC-mediated recognition of the Ψ sequence.

The strengths of the work include the application of the modified PAR-CLIP method to the analysis of a large panel of CANC constructs. This provided the detailed information on the specific molecular features in CA required for interactions between Gag and the Ψ sequence, which was not obtainable in the previous studies. The absence of the MA domain in these constructs allowed the authors to focus on the cytoplasmic interactions. The data obtained with oligomer-forming NC constructs and CANC constructs that differ in the IP6 dependence also add support to the authors conclusion that CA-mediated lattice formation of CANC and not just NC oligomerization plays a key role in Gag-vRNA binding. Overall, the data support the conclusion that the ability to form the CANC lattice is essential for the initial NC-vRNA interaction.

The only notable weakness is that previous work by this group and others have already shown that CA and/or its interaction interfaces plays an important role in the Gag-vRNA interaction. Therefore, the current work can be regarded as a refinement of the previously presented concept rather than a conceptual breakthrough. Nonetheless, these mechanistic details are likely to help the retrovirology community gain a clearer grasp of the early steps of infectious particle formation.

Reviewer #3 (Public Review):

The authors' aim was to examine the early stages of the HIV-1 packaging process inside cells, with specific focus upon how the Gag protein and its cognate domains mediate the initial interaction with the packaging signal on the genomic RNA. The technique that has generated the majority of results in the paper is a modified version of CLIP. The authors have achieved this aim well, with data that clearly support the importance of Capsid, as well as the importance of two different aspects of RNA structure, the IP6 binding site, and various sites that help to form the dimer, trimer and hexamer interfaces on Gag. The major conclusions of the paper, that an immature Gag lattice is needed to form, that NC alone is insufficient to mediate specific recognition of the packaging signal within cells, and that various aspects of Capsid are necessary, are clearly supported by the data.

A particular strength of the paper is the way in which the viral protein and RNA are expressed within cells - these derive from the same construct, which is essentially the proviral genome with mutations to enable the authors to study the various truncations/mutations of Gag and/or the RNA structure. The authors could instead have transfected separate packaging signal/gRNA and viral protein plasmids, but in ensuring that the viral proteins are translated from the same RNA molecule that can also be packaged, they recapitulate the native viral situation in a state of the art experimental form. This is important in terms of the conclusions they can draw, because although HIV-1 can co-package some other lentiviruses, and HIV-1 packaging can occur in trans (ie where 2 gRNA molecules are packaged by molecules of Gag that have not been translated from them), the experiments determining copackaging ability are sometimes not performed in a competitive or limiting system, so it is difficult to say whether there is indeed some remaining importance of co-translational packaging in the very early stages of HIV-1 Gag-psi recognition. Expression of gRNA and protein from the same construct also ensures a balance in stoichiometry within the cytoplasm that is representative of a native infection.

The weakness within the paper is the lack of consideration of how Gag concentration within the cytosol may affects its binding kinetics, both with itself and with the RNA. The CLIP experiments are internally controlled in that they measure binding to the packaging signal relative to the rest of the genome; however, the authors do not appear to have checked that all constructs were expressing at roughly equivalent amounts. This is especially important when interpreting data from a protein such as Gag, which undergoes very complex multimerization, and when considering that the RNA also multimerizes. Both of these multi-step events may alter according to the actual concentrations of both Gag and RNA, and not just the stoichiometric ratio of the two. Some of the data that are needed to provide this evidence are present within the paper already, as western blots analysing multimerization of Capsid mutants, and look to broadly support the expression of the constructs at similar levels. More consideration of this point would strengthen the paper.

The authors place their findings in the context of the field very well. They appear to have considered multiple lines of evidence and to have accounted broadly for previous work done. I do find the discussion of Capsid mutants, and the dimer, trimer and hexamer interfaces quite protein-centric though. I wonder whether there might be a larger role for the RNA structure and structural changes in bringing together the precise Gag lattice structure in some sort of step-wise fashion.

Overall, the manuscript is of great value to the retroviral research community, as it provides data from a highly relevant biological setting. Such data has largely been lacking within the field.

Reviewer #1 (Public Review):

There is growing precedent for the utility of GWAS-type analyses in elucidating otherwise cryptic genotypic associations with specific Mtb phenotypes, most commonly drug resistance. This study represents the latest instalment of this type of approach, utilizing a large set of WGS data from clinical Mtb isolates and refining the search for DR-associated alleles by restricting the set to those predicted (or known) to be phenotypically DR. This revealed a number of potential candidate mutations, including some in nucleotide excision repair (uvrA, uvrB), in base excision repair (mutY), and homologous recombination (recF). In validating these leads functional assays, the authors present evidence supporting the impact of the identified mutations on antibiotic susceptibility in vitro and in macrophage and animal infection models. These results extend the number of candidate mutations associated with Mtb drug resistance, however the following must be considered:

(i) The GWAS analysis is the basis of this study, yet the description of the approach used and presentation of results obtained is occasionally obscure; for example, the authors report the use of known drug resistance phenotypes (where available) or inferences of drug-resistance from genotypic data to enhance the potential to identify other mutations that might be implicated in enabling the DR mutations, yet their list of known DR mutations seem to be predominantly rare or unusual mutations, not those commonly associated with clinical DR-TB. In addition, the distribution of the identified resistance-associated mutations across the different lineages need to be explained more clearly.

(ii) By combining target gene deletions with different complementation alleles, the authors provide compelling microbiological evidence supporting the inferred role of the mutY and uvrB mutations in enhanced survival under antibiotic treatment. The experimental work, however, is limited to assessments of competitive survival in various models, with/without antibiotic selection, or to mutant frequency analyses; there is no direct evidence provided in support of the proposed mechanism.

(iii) The low drug concentrations used (especially of rifampicin against M. smegmatis) suggest the identified mutations confer low-level resistance to multiple antimycobacterial agents - in turn implying tolerance rather than resistance. If correct, it would be interesting to know how broadly tolerant strains containing these mutations are; that is, whether susceptibility is decreased to a broad range of antibiotics with different mechanisms of action (including both cidal and static agents), and whether the extent of the decrease be determined quantitatively (for example, as change in MIC value).

Reviewer #2 (Public Review):

This interesting manuscript uses a collection of whole genome sequences of TB isolates to associate specific sequence polymorphisms with MDR/XDR strains, and having found certain mutations in DNA repair pathways, does a detailed analysis of several mutations. The evaluation of the MutY polymorphism reveals it is loss of function and TB strains carrying this mutation have a higher mutation frequency and enhanced survival in serial passage in macrophages. The strengths of the manuscript are the leveraging of a large sequence dataset to derive interesting candidate mutations in DNA repair pathway and the demonstration that at least one of these mutations has a detectable effect on mutagenicity and pathogenesis. The weaknesses of the manuscript are a lack of experimental exploration of the mechanism by which loss of a DNA repair pathway would enhance survival in vivo. The model presented is that these phenotypes are due to hypermutagenicity and thereby evolution of enhanced pathogenesis, but this is not actually directly tested or investigated. There are also some technical concerns for some of the experimental data which can be strengthened.

This paper presents the following data:

- Analyzed whole-genome sequences 2773 clinical strains: 160 000 SNPs identified<br /> - 1815 drug-susceptible/422 MDR/XDR strains: 188 mutations correlated with Drug resistance.<br /> - Novel mutations associated with the drug resistance have been found in base excision repair (BER), nucleotide excision repair (NER), and homologous recombination (HR) pathway genes (mutY, uvrA, uvrB, and recF).<br /> - Specific mutations mutY-R262Q and uvrB-A524V were studied.<br /> - mutY-R262Q and uvrB-A524V mutations behave as loss of function alleles in vivo, as measured by non-complementation of the increased mutation frequency measured by resistance to Rif and INH.<br /> - The mutY deletion and the mutY-R262Q mutation increase Mtb survival over WT in macrophages when Mtb has not been submitted to previous rounds of macrophage infection.<br /> - This advantage is exacerbated in presence of antibiotic (Rif and Cipro but not INH).<br /> - The MutY deletion and the MutY-R262Q mutation result in an enhanced survival of Mtb during guinea pig infection.

Major issues:

The finding that mutations in MutY confers an advantage during macrophage infection is convincing based on the macrophage experiments, but it is premature to conclude that the mechanism of this effect is due to hypermutagenesis and selection of fitter bacterial clones. It is described in E. coli (Foti et al., 2012) and recently in mycobacteria (Dupuy et al., 2020) that the MutY/MutM excision pathways can increase the lethality of antibiotic treatment because of double-strand breaks caused by Adenine/oxoG excisions. The higher survival of the mutY mutant during antibiotic treatment could more be due to lower Adenine/oxoG excision in the mutant rather than acquisition of advantageous mutations, or some other mechanism. The same hypothesis cannot be excluded for the Guinea pig experiments (no antibiotics, but oxidative stress mediated by host defenses could also increase oxoG) and should at least be discussed. Experiments that would support the idea that the in vivo advantage is due to hypermutagenesis would be whole genome sequencing of the output vs input populations to directly document increased mutagenesis. Similarly, is the ΔmutY survival advantage after rounds of macrophage infections dependent on macrophage environment? What happens if the ΔmutY strain is cultivated in vitro in 7H9 (same number of generations) before infecting macrophages?

- It would be useful to present more data about the strain relatedness and genome characteristics of the DNA repair mutant strains in the GWAS. For example, the model would suggest that strains carrying DNA repair mutations should have higher SNP load than control strains. Additionally, it would be helpful to know whether the identified DNA repair pathway mutations are from epidemiologically linked strains in the collection to deduce whether these events are arising repeatedly or are a founder effect of a single mutant since for each mutation, the number of strains is small.

- Some of the mutation frequency, survival and competition data could be strengthened by more experimental replicates. Data Lines 370-372 (mutation frequency), lines 387-388 (Survival of strains ex vivo), line 394 (competition experiment) : "Two biologically independent experiments were performed. Each experiment was performed in technical triplicates. Data represent one of the two biological experiments." Two biological replicates is insufficient for the phenotypes presented and all replicates should be included in the analysis. In addition, the definition of "technical triplicates" should be given, does this mean the same culture sampled in triplicate?

- MutY phenotypes. One caveat to the conclusion that the MutY R262Q mutant is nonfunctional is the lack of examination of the expression of the complementing protein. I would be informative to comment on the location of this mutation in relation to the known structures of MutY proteins. Similarly, for the UvrB polymorphism, this null strain has a clear UV sensitivity phenotype in the literature, so a fuller interrogation for UV killing would be informative re: the A524V mutation.

Reviewer #3 (Public Review):

STRENGTHS

• This ambitious study is broad in scope, beginning with a bacterial GWAS study and extending all the way to in vivo guinea pig infection models.

• Numerous reports have attempted to identify Mtb strains with elevated mutation rates, and the results are conflicting. The present study sets out to thoroughly evaluate one such mutation that may produce a mutator phenotype, mutY-Arg262Gln.

WEAKNESSES

• While the authors follow-up experiments with the mutY-Arg262Gln allele are all consistent with the conclusion that this mutation elevates the mutation rate in Mtb and thus could promote the evolution of drug resistance, further work is needed to unambiguously demonstrate this link.

• The authors highlight five mutations in genes associated with DNA replication and or repair from their GWAS analysis:

o dnaA-Arg233Gln: as the authors note in the Discussion, Hicks et al. associate SNPs in dnaA with low-level isoniazid resistance, as a result of lowered katG expression. Since this is unrelated to their focus on DNA repair genes whose mutation could elevate mutation rates, I would consider removing this allele from the Table.

o mutY-Arg262Gln: querying publicly available whole genome sequences of clinical Mtb isolates, this SNP appears to be restricted to lineage 4.3 (L4.3). All of these L4.3 strains appear to be drug-resistant. How many times did the mutY-Arg262Gln mutation evolve in the authors dataset? If there is evidence of homoplastic evolution, this would strengthen their case. If not, it doesn't mean the authors findings are incorrect, but does elevate that risk that this mutation could be a passenger (i.e. not driver) mutation. To address this, the authors could attempt to date when the mutY-Arg262Gln arose. If it was before the evolution of drug-resistance conferring alleles in these L4.3 strains, that is consistent with (but not proof of) a driver mutation. If mutY-Arg262Gln arose after, this is much more consistent with a passenger mutation.

o uvrB-Ala524Val: curiously we don't see this SNP in our dataset of publicly available whole genome sequences of clinical Mtb isolates (~45,000 genomes).

o uvrA-Gln135Lys: this SNP also appears to be restricted to lineage 4.3. Same question as for mutY-Arg262Gln.

o recF-Gly269Gly: this is a very common mutation, is it unique to lineage 2.2.1? Same question as for mutY-Arg262Gln.

• The CRYPTIC consortium recently published a number of preprints on biorxiv detailing very large GWAS studies in Mtb. Did any of these reports also associate drug resistance with mutY? If yes, this should be stated. If not, the potential reasons for this discrepancy should be discussed.

• Based on the authors follow-up studies in vivo, MutY-Arg262Gln is presumed to be a loss-of-function allele. If the authors could convincingly demonstrate this biochemically with recombinant proteins, this would significantly strengthen their case.

• If the authors are correct and mutY-Arg262Gln strains have elevated mutation rates, presumably there would be evidence of this in the clinical strain sequencing data. Do mutY-Arg262Gln containing strains have elevated C→G or C→A mutations in their genomes? Presumably such strains would also have a higher number of SNPs than closely related strains WT for mutY- is this the case?

• While more work, mutation rates as measured by Luria-Delbruck fluctuation analysis are more accurate than mutation frequencies. I would recommend repeating key experiments by Luria-Delbruck fluctuation analysis. It is also important to report both drug-resistant colony counts and total CFU in these sorts of experiments. Given the clumpy nature of mycobacteria, mutation rates can appear to be artificially elevated due to low total CFU and not an increase in the number of drug-resistant colonies.

• Figure 4 would appear to measuring drug tolerance not resistance? Are the elevated CFU in the presence of drugs in the mutY-Arg262Gln strain due to an increase in the number of drug resistant strains or drug sensitive strains? This could be assessed by quantifying resulting CFU in the presence or absence the indicated drugs.

Reviewer #1 (Public Review):

In this manuscript, Wang et al focused on defining the importance of the ER proteostasis factor HSP47 in regulating the folding, assembly, trafficking, and activity of GABAA receptors. Previous mass spectrometry-based interactomics identified HSP47 as the most enriched GABAA interacting chaperone in HEK293T cells. Here, the authors expand this study, demonstrating that HSP47 interacts with GABAA subunits in mouse brain homogenates and in vitro, demonstrating that HSP47 binds the alpha1 GABAA subunit with high affinity. They went on to show that depletion of HSP47 reduces the surface expression and activity of GABAA receptors in primary hippocampal neurons. Alternatively, overexpression of HSP47 increased the trafficking of GABAA receptors in HEK293T cells. Interestingly, chemical or genetic disruption of critical disulfide bonds within the alpha1 subunit of GABAA decreased interactions with HSP47, while increasing interactions with the ATP-dependent ER chaperone BiP, suggesting that HSP47 binds folded GABAA subunits in the ER lumen and promoting receptor assembly. Consistent with this, the authors employed a FRET-based system and biochemical assays to demonstrate that HSP47 enhances the assembly of GABAA receptors. Further, they demonstrate that overexpression of HSP47 could enhance the trafficking and surface activity of the epilepsy-associated alpha1(A322D) GABAA mutant. Finally, the authors show that HSP47 promotes the assembly and activity of other Cys-loop receptors including nAchR.

Overall, this work expands our understanding of how membrane receptors including GABAA and nAchRs are folded and assembled. In particular, the demonstration that HSP47 works after canonical ATP-dependent chaperones such as BiP in promoting the assembly of GABAA receptors is intriguing, as it is beginning to demonstrate the sequence of events important for ER quality control of these membrane proteins. The use of multiple biochemical and genetic approaches to manipulate GABAA receptors and following the assembly, trafficking, and activity of these receptors is also a strength. However, one outstanding question is how does manipulation of HSP47 (with either overexpression or depletion) influence overall ER proteostasis and function. Can these effects be specifically attributed to HSP47 or does this reflect more global impairment of ER function induced by altered signaling through pathways such as the UPR? This is important because, while the authors do demonstrate direct interactions with HSP47, the direct importance of these interactions on the assembly and activity of Cys loop receptors remains somewhat unclear. Additional efforts addressing the specific impact of HSP47 manipulation on overall ER proteostasis should address this comment and allow for a more complete understanding of the role of HSP47 in regulating the assembly and trafficking of these proteins. Regardless, this is a solid manuscript that reviews new insights into membrane protein quality control and the importance of HSP47 in regulating ER proteostasis and function.

Reviewer #2 (Public Review):

Wang and colleagues previously characterized the protein interactome for GABA subunits and identified HSP47 chaperone as a top interacting protein. Here, they follow up to assess the function of this HSP47-GABA interaction. Using primarily HEK293 cells, they provide evidence that the ER-resident HSP47 chaperone promotes the folding of GABA receptor subunits and the assembly of GABA subunits into multimeric ion channels. Interestingly, they demonstrate HSP47 can rescue the folding and function of a missense mutant A332D epilepsy-associated GABA subunit. They also demonstrate similar enhanced folding/function for acetylcholine receptor assembly. Overall, the experimental data are well-presented and provide insight into new ion channel clients whose folding and assembly are dependent on the HSP47 chaperone. The study also identifies HSP47 expression as a potential strategy to target and enhance the function of misfolded ion channels, and this may have broader biomedical therapeutic significance beyond GABA channels.

Reviewer #3 (Public Review):

Wang et al. show a new role for the small heat-shock protein Hsp47 in the assembly and plasma membrane trafficking of GABAA receptors and other heptameric neuroreceptors. Hsp47 (SERPINH1) is primarily known as a collagen-specific molecular chaperone, but it has been increasingly recognized as important for other protein clients. In a prior mass spectrometry study from the same group, Hsp47 was identified as the most enriched interaction partner of GABAA neurotransmitter-gated ion channels. In this study, the authors now follow up on the functional role of Hsp47 for the GABAA heteromer assembly and its cell-surface trafficking.

Strengths:<br /> The authors show convincingly that Hsp47 plays an important role in promoting the cell surface expression and activity of GABAA receptors. Knockdown of Hsp47 in rat primary neurons decreases endogenous GABAA protein subunits on the cell surface and GABA-induced currents. Overexpression of Hsp47 in HEK293T increases abundance and cell surface trafficking of exogenously expressed GABAA subunits. Importantly, the overexpression of Hsp47 also rescues cell surface expression and channel currents of epilepsy-associated mutant GABAA receptors (alpha1 A332D), which could point to a future avenue to ameliorate pathogenic misfolding. The authors use a variety of experimental approaches to glean the mechanism by which Hsp47 promotes GABAA cell surface expression. In vitro GST pulldown experiments confirm a direct interaction between Hsp47 and the alpha1 and beta2 subunits. Site-directed mutagenesis and DTT addition indicate that the formation of a disulfide bond in the alpha1 subunits is critical for the Hsp47 interactions, leading the authors to conclude that Hsp47 is likely to bind to a more folded state of the subunit. In contrast, the ER Hsp70 chaperone BiP binds more strongly when the disulfide bond is disrupted, which corresponds to a more misfolded state as indicative of more alpha1 in the insoluble fraction. FRET assays and non-reducing gels to monitor GABAA receptor assembly again show that Hsp47 overexpression promotes the formation of the alpha1-beta2 complex. However, while these experiments are generally carried out thoroughly and the data is presented well, the results are interpreted too narrowly to only support their proposed models without considering alternative possibilities (see more below). Lastly, the authors show that Hsp47 overexpression also enhances the cell-surface expression and peak currents of another heteropentameric Cys-loop superfamily neuroreceptor, namely the a4b2 nicotinic acetylcholine receptor.

Weaknesses:<br /> The authors propose a compelling model in Figure 7 by which Hsp47 binds to a late-stage, largely folded alpha1 or beta2 subunit essentially acting as a holdase to promote assembly into larger dimers or other folding intermediates. However, the data in the manuscript would also support alternative models that the authors should more carefully consider. For instance, Hsp47 overexpression leads to a buildup of additional alpha1 and beta2 subunits (as described in lines 256-258 and seen in Fig. 4C), suggesting that Hsp47 may instead prevent subunits from getting degraded. Conclusions about Hsp47 binding after BiP to a largely folded state are indirectly based on shifts in the steady population of WT or misfolded GABAA subunits, but Hsp47 overexpression may in turn influence this equilibrium. Without any experiments examining the kinetics of protein interactions, degradation, or cell surface expression conclusions are difficult to interpret. Lastly, most experiments are carried out in HEK293T, which does not endogenously express GABAA or other neuroreceptors. There is a disconnect between the knockdown studies in rat primary hippocampal neurons and the overexpression experiments in HEK293T cells. The loss of GABAA receptor trafficking and function in the neurons could result from the secondary effect of the Hsp47 knockdown.

Overall, the study provides valuable new insights into the client scope of the ER small heat shock protein Hsp47, advances our understanding of neuroreceptor proteostasis, and provides potential corrective strategies to enhance the expression of epilepsy-associated mutations through targeting Hsp47. Hence, the paper should have broader relevance for a readership interested in proteostasis, membrane protein trafficking, and neuroreceptor signaling. However, I recommend addressing the following comments, mainly because the study in its current form only incompletely corroborates the authors' conclusions about the mechanism by which Hsp47 facilitates the neuroreceptor subunit assembly:

• For the in vitro experiments in Fig. 1, it would be important to show controls that the recombinantly expressed alpha1(ERD) adopts a well-folded state. Similarly, how did the authors ensure that the alpha1- and beta2-GST proteins adopt a folded (or near-folded) conformation?<br /> • In several experiments (e.g. Fig. 2A, Fig. 4B-C, Fig. 5B) IF staining or Western blots for the alpha1 and beta2 subunits are taken as a proxy for full GABA receptor assembly. Are the other subunits (e.g. gamma2) present and can they be detected?<br /> • Does Hsp47 knockdown in the primary hippocampal neurons leads to other changes in proteostasis network composition, e.g. UPR activation? This will be important to quantify to ensure that the reduced GABAA function can be directly attributed to the loss of Hsp47.<br /> • How are the Hsp47 knockdown and overexpression phenotype in the 2 different cell lines connected? If Hsp47 abundance is a limiting factor for GABAA proteostasis, it would be helpful to show (e.g. by lentivirus transduction) that additional Hsp47 can increase GABAA surface expression in the primary neurons.<br /> • Increased alpha1 and beta2 monomers in Fig. 4C suggest that the increase in receptor complex formation is likely due to more subunits being present when Hsp47 is overexpressed. Does Hsp47 prevent the degradation of excess or misfolded subunits? This can be easily tested with cycloheximide-chase or pulse-chase assays.<br /> • Does Hsp47 overexpression lead to more alpha1(A332D) monomer build up in cells (similarly to the WT alpha1)? The total level of alpha1(A332D) should be quantified for Fig. 5B. Similarly in Fig. 6A, does Hsp47 overexpression stabilize the abundance of nAChR subunits? The authors could easily quantify the abundance of individual subunits by Western blot.<br /> • Did the authors test the effect of Hsp47 overexpression on the trafficking of other misfolding-prone GABAA subunit variants? For therapeutic purposes, it will be important to evaluate a broader set of variants. Even if Hsp47 only restores select variants, these results would be useful for pinpointing a mechanism by which Hsp47 binds to the receptor subunits.

Reviewer #1 (Public Review):

This is an outstanding manuscript that takes a comprehensive approach to studying allosteric modulation at the M4R. I think it is an important addition to the literature and provides important insights into allosteric modulation. Overall the pharmacological approaches are very rigorous and are the types of analyses that need to be performed to move this field forward.

Reviewer #2 (Public Review):

Overall, the manuscript is clearly written and remarkably comprehensive, presenting a very large amount of data. Experimental methods are well-documented and rigorous, and I have no significant technical concerns about any of the work presented. There are some points where the presentation might be improved by modifications to the text or figures, particularly with the goal of making this important work accessible to a broad audience.

Reviewer #3 (Public Review):

The authors aimed to study and describe allosteric modulation of the pharmacologically important muscarinic acetylcholine receptor 4 (M4R). Developing orthosteric ligands (agonists and antagonists) has had limited success in the past, due to the conserved binding pocket of acetylcholine across all (five) homologous receptors. The study uses a broad spectrum of experimental results, using binding and signaling assays, structure determination by cryoEM, as well as some mutational studies to study species selectivity. These results were combined with expansive MD simulations, to correlate receptor 'rigidity' with binding affinities, as well as signaling. The main strength of this paper is the sheer breadth of results to study the important aspect of allosteric modulation from any possible angle. I do not see any noteworthy weaknesses in the manuscript. The work presented here will be an important reference for future drug discovery efforts.

Reviewer #1 (Public Review):

The authors set out to extend modeling of bispecific engager pharmacology through explicit modelling of the search of T cells for tumour cells, the formation of an immunological synapse and the dissociation of the immunological synapse to enable serial killing. These features have not been included in prior models and their incorporation may improve the predictive value of the model.

The model provides a number of predictions that are of potential interest- that loss of CD19, the target antigen, to 1/20th of its initial expression will lead to escape and that the bone marrow is a site where the tumour cells may have the best opportunity to develop loss variants due to the limited pressure from T cells.

A limitation of the model is that adhesion is only treated as a 2D implementation of the blinatumomab mediated bridge between T cell and B cells- there is no distinct parameter related to the distinct adhesion systems that are critical for immunological synapse formation. For example, CD58 loss from tumours is correlated with escape, but it is not related to the target, CD19. While they begin to consider the immunological synapse, they don't incorporate adhesion as distinct from the engager, which is almost certainly important.

While the random search is a good first approximation, T cell behaviour is actually guided by stroma and extracellular matrix, which are non-isotropic. In a lymphoid tissue the stroma is optimised for a search that can be approximated as brownian, or more accurately, a correlated random walk, but in other tissues, particularly tumours, the Brownian search is not a good approximation and other models have been applied. It would be interesting to look at observations from bone marrow or other sites to determine the best approximating for the search related to BiTE targets.

Reviewer #2 (Public Review):

This mechanistic PK/PD model simultaneous characterized several important factors, including formation of immunological synapses synapse variants, target/tumor cell densities, target CD3/tumor antigen expression levels, tumor antigen escape and the associated cancer relapse, in a unified model structure.

This model has the potential to be used in optimization dosage of T cell redirecting bispecific treatment towards best clinical outcome.

Reviewer #3 (Public Review):

Liu et al. combined mechanistic modeling with in vitro experiments and data from a clinical trial to develop an in silico model to describe response of T cells against tumor cells when bi-specific T cell engager (BiTE) antigens, a standard immunotherapeutic drug, are introduced into the system. The model predicted responses of T cell and target cell populations in vitro and in vivo in the presence of BiTEs where the model linked molecular level interactions between BiTE molecules, CD3 receptors, and CD19 receptors to the population kinetics of the tumor and the T- cells. Furthermore, the model predicted tumor killing kinetics in patients and offered suggestions for optimal dosing strategies in patients undergoing BiTE immunotherapy. The conclusions drawn from this combined approach are interesting and are supported by experiments and modeling reasonably well. However, the conclusions can be tightened further by making some moderate to minor changes in their approach. In addition, there are several limitations in the model which deserves some discussion.

Strengths

A major strength of this work is the ability of the model to integrate processes from the molecular scales to the populations of T cells, target cells, and the BiTE antibodies across different organs. A model of this scope has to contain many approximations and thus the model should be validated with experiments. The authors did an excellent job in comparing the basic and the in vitro aspects of their approach with in vitro data, where they compared the numbers of engaged target cells with T cells as the numbers of the BiTE molecules, the ratio of effector and target cells, and the expressions of the CD3 and CD19 receptors were varied. The agreement with the model with the data were excellent in most cases which led to several mechanistic conclusions. In particular, the study found that target cells with lower CD19 expressions escape the T cell killing.

The in vivo extension of the model showed reasonable agreements with the kinetics of B cell populations in patients where the data were obtained from a published clinical trial. The model explained differences in B cell population kinetics between responders and non-responders and found that the differences were driven by the differences in the T cell numbers between the groups. The ability of the model to describe the in vivo kinetics is promising. In addition, the model leads to some interesting conclusions, e.g., the model shows that the bone marrow harbors tumor growth during the BiTE treatment. The authors then used the model to propose an alternate dosage scheme for BiTEs that needed a smaller dose of the drug.

Weaknesses

There are several weaknesses in the development of the model. Multiscale models of this nature contain parameters that need to be estimated by fitting the model with data. Some these parameters are associated with model approximations or not measured in experiments. Thus, a common practice is to estimate parameters with some 'training data' and then test model predictions using 'test data'. Though Supplementary file 1 provides values for some of the parameters that appeared to be estimated, it was not clear which dataset were used for training and which for test. The confidence intervals of the estimated parameters and the sensitivity of the proposed in vivo dosage schemes to parameter variations were unclear.

The model appears to show few unreasonable behaviors and does not agree with experiments in several cases which could point to missing mechanisms in the model. Here are some examples. The model shows a surprising decrease in the T cell-target cell synapse formation when the affinity of the BiTEs to CD3 was increased; the opposite should have been more intuitive. The authors suggest degradation of CD3 could be a reason for this behavior. However, this probably could be easily tested by removing CD3 degradation in the model. Another example is the increase in the % of engaged effector cells in the model with increasing CD3 expressions does not agree well with experiments (Fig. 3d), however, a similar fold increase in the % of engaged effector cells in the model agrees better with experiments for increasing CD19 expressions (Fig. 3e). It is unclear how this can be explained given CD3 and CD19 appears to be present in similar copy numbers per cell (~104 molecules/cell), and both receptors bind the BiTE with high affinities (e.g., koff < 10-4 s-1).

The model does not include signaling and activation of T cells as they form the immunological synapse (IS) with target cells. The formation IS leads to aggregation of different receptors, adhesion molecules, and kinases which modulate signaling and activation. Thus, it is likely the variations of the copy numbers of CD3, and the CD19-BiTE-CD3 will lead to variations in the cytotoxic responses and presumably to CD3 degradation as well. Perhaps some of these missing processes are responsible for the disagreements between the model and the data shown in Fig. 3. In addition, the in vivo model does not contain any development of the T cells as they are stimulated by the BiTEs. The differences in development of T cells, such as generation of dysfunctional/exhausted T cells could lead to the differences in responses to BiTEs in patients. In particular, the in vivo model does not agree with the kinetics of B cells after day 29 in non-responders (Fig. 6d); could the kinetics of T cell development play a role in this?

Addressing these concerns and a discussion of the limitations will make the conclusions of the study stronger and will provide cues for extending the approach for future studies.

Reviewer #1 (Public Review):

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

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

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

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

Reviewer #2 (Public Review):

This study by Yang & al. explores the mechanism of X dosage compensation in the nematode species C. briggsae; which is a close relative of C. elegans. The mechanism is well described in C. elegans, and the authors have asked whether the same condensin-like complex (DCC) is responsible for the silencing of the X and which motifs on the X are responsible for this binding specificity in C. briggsae. They discovered that although the general principle of X inactivation is conserved between these 2 species, and ortholog proteins of the pathway (xol-1, sdc-2, and the genes encoding the DCC complex) are conserved, the sequences on the X that are recognized by the DCC complex have evolved very rapidly. The motifs of C. briggsae are not recognized by the C. elegans proteins and vice versa. The authors have accumulated very solid data, both in vitro and in vivo, to support this conclusion.

Overall, the results are very convincing and extremely interesting, for the chromatin field but also from an evolutionary perspective. This finding is comparable to the discovery that centromeric sequences and centromere proteins, despite their essential function in cells, evolve extremely rapidly. The reason is that they are involved in genetic conflicts, are a perfect target to generate hybrid incompatibilities during crosses, and therefore, under such selective pressure, evolve super fast. Most examples of hybrid incompatibilities rely on chromatin conflicts, and with this study, it appears that the dosage compensation system could be one other way to generate hybrid breakdown.

Reviewer #3 (Public Review):

In this manuscript, Meyer and colleagues characterized the conserved dosage compensation complex (DCC) and its recruitment mechanisms to X chromosomes in C. briggsae. This paper features comparative analyses of the dosage compensation mechanisms between C. briggsae and C. elegans, which are separated by 15-30 million years in evolution. While the dosage compensation machinery and the regulatory hierarchy are conserved, the target specificity of the DCC complex, the density of the recruiting motifs, and the mode of recruitment have diverged between the two species. The authors speculated that the divergence of the X chromosome DCC target sites could have been a factor for nematode speciation.

Overall, this is a thorough work demonstrating how the dosage compensation mechanisms in C. briggsae compare with those in C. elegans. By employing a series of complementary assays, the authors provided compelling evidence, establishing how C. briggsae and C. elegans have diverged DCC recruitment sites and motifs, while the composition of the DCC and the regulatory hierarchy are conserved. The manuscript is clearly written, and all the experiments are rigorously performed with proper controls. The figures are also effective and nicely illustrate the experimental designs and the results. The conclusions drawn from the current work are compelling, and I have no major concerns.

Reviewer #1 (Public Review):

Several new observations on the fascinating marine midge system are provided. The results are robust and have broad interest. First, multiple polymorphic chromosomal inversions are shown to be segregating in the study populations but these inversions are not strongly associated with ecotype differentiation. At least 4 QTL are detected that together explain a large part of the timing difference between co-located populations that emerge at full and new moon. Good candidate genes under these QTL are identified, with unusually high differentiation between populations. These are involved in the circadian clock (period) and in nervous system rewiring. The involvement of period suggests a link between the circadian and circalunar clocks.

The major context provided by the authors is the idea of 'magic traits' that influence multiple components of reproductive isolation and so are potentially important in population differentiation because associations between traits do not need to be built and maintained in the face of recombination. This idea is not described very clearly in the current MS. In particular, the authors suggest that magic traits are expected to have simple genetic basis, an expectation that they say should be re-evaluated on the basis of their results. However, there is no strong justification for this expectation. It tends to confuse the magic trait idea with the possibility of pleiotropy and with the advantage of recombination suppression where local adaptation is opposed by gene flow. The latter is relevant here because of the involvement of inversions, which might be expected to capture multiple alleles contributing to local adaptation but here apparently do not do so.

Reviewer #2 (Public Review):

Briševac et al. investigate the genetic architecture of an exceptional ecological system where Clunio marinus populations have diverged in their timing of reproduction, controlled by a circalunar clock. These loci may be important in sympatric speciation and/or rapid evolution of reproductive isolation but there are some issues that need to be resolved. I outline these below:

1) The QTL mapping relies on a modest number of individuals and there are important details missing. The manuscript is missing information on heritability of the trait which is important for interpretation. While the variance explained by the QTL is hight, for the estimates of QTL effect sizes from such small samples, there is a common issue known as the Beavis effect that can inflate the effect size of individual QTL.

2) My major concern with the paper is the interpretation of divergence within the inversion as linked causally to the genes underlying ecological divergence. As the authors observe, divergence will vary within an inverted region. This can be traced to myriad factors, including variation in mutation rate, variation in constraint, patterns of ancestral polymorphism within this region, and variation in gene conversion within the inversion. Given this, I do not think it is valid to interpret the regions of high differentiation as the causal drivers of the ecological differentiation.

3) The authors imply in the discussion based on historical results that the ecotype evolved in situ in the last ~60 years. This seems substantially less likely to me than a number of alternative hypotheses including missing the phenotype in previous samples, plasticity in the phenotype causing it to be missed or migration from populations where the phenotype already existed.

Reviewer #1 (Public Review):

The manuscript is of importance for vaccine design and understanding tissue microenvironmental influence on the functionality of monocytes and DCs to respiratory viruses such as Influenza A virus or SARS-CoV-2 virus. The methods used were mainly flow cytometry, ELISA, Luminex as well as TNFα secretion Assay Detection. The authors wanted to evaluate if and how the tissue microenvironment might impact DC and monocyte subset presence and functionality during Influenza A virus infection.

Strength:<br /> The study is summarizing a large cohort of human samples of blood, nasal swabs and nasopharyngeal aspirates. This is very uncommon as most of the time studies focus on the blood and serum of patients. Within the study, 3 monocyte and 3 DC subsets have been followed in healthy and Influenza A virus-infected persons. The study also includes functional data on the responsiveness of Influenza A virus-infected DC and monocyte populations. The authors achieved their aims in that they were able to show that the tissue microenvironment is important to understand subset specific migration and activation behavior in Influenza A virus infection and in addition that it matters with which kind of agent a person is infected. Thus, this study also impacts a better understanding of vaccine design for respiratory viruses.

Weakness:<br /> In the described study, the authors used a different nomenclature to introduce the DC subsets. This is confusing and the authors should stick to the nomenclature introduced by Guilliams et al., 2014 (doi.org/10.1038/nri3712) and commented in Ginhoux et al., 2022 (DOI: 10.1038/s41577-022-00675-7 ) or at least should introduce the alternative names (cDC1, cDC2, expression markers XCR1, CD172a/Sirpa). Further, Segura et al., 2013 (doi: 10.1084/jem.20121103) showed that all three DC subpopulations were able to perform cross-presentation when directly isolated. Overall, a more up-to-date introduction would be useful.<br /> As the data of this was already obtained in 2016-2018 it is clear that the FACS panel was not developed to study DC3. If possible, the authors might be able to speculate about the role of this subset in their data set. Moreover, there were other studies on SARS-CoV-2 infection and DC subset analyses in blood (line 87, and line 489) e.g. Winheim et al., (DOI: 10.1371/journal.ppat.1009742 ), which the authors should introduce and discuss in regard to their own data. Taken together, although the data are very important and very interesting, my overall impression of the manuscript is that in the era of RNA seq and scRNA seq analyses the study lacks a bit of comprehensiveness.

Reviewer #2 (Public Review):

This study aims to describe the distribution and functional status of monocytes and dendritic cells in the blood and nasopharyngeal aspirate (NPA) after respiratory viral infection in more than 50 patients affected by influenza A, B, RSV and SARS-CoV2. The authors use flow cytometry to define HLA-DR+ lineage negative cells, and within this gate, classical, intermediate and non-classical monocytes and CD1c+, CD141+, and CD123+ dendritic cells (DC). They show a large increase in classical monocytes in NPA and an increase in intermediate monocytes in blood and NPA, with more subtle changes in non-classical monocytes. Changes in intermediate monocytes were age-dependent and resolution was seen with convalescence. While blood monocytes tended to increase in blood and NPA, DC frequency was reduced in blood but also increased in NPA. There were signs of maturation in monocytes and DC in NPA compared with blood as judged by expression of HLA-DR and CD86. Cytokine levels in NPA were increased in infection in association with enrichment of cytokine-producing cells. Various patterns were observed in different viral infections suggesting some specificity of pathogen response. The work did not fully document the diversity of human myeloid cells that have arisen from single-cell transcriptomics over the last 5 years, notably the classification of monocytes which shows only two distinct subsets (intermediate cannot be distinguished from classical), distinct populations of DC1, DC2 and DC3 (DC2 and 3 both having CD1c, but different levels of monocyte antigens), and the lack of distinction provided by CD123 which also includes a precursor population of AXL+SIGLEC6+ myeloid cells in addition to plasmacytoid DC. Furthermore, some greater precision of the gating could have been achieved for the subsets presented. Specifically, CD34+ cells were not excluded from the HLA-DR+ lineage- gate, and the threshold of CD11c may have excluded some DC1 owing to the low expression of this antigen. Overall, the work shows that interesting results can be obtained by comparing myeloid populations of blood and NPA during viral infection and that lineage, viral and age-specific patterns are observed. However, the mechanistic insights for host defense provided by these observations remain relatively modest.

Reviewer #1 (Public Review):

In this manuscript, the authors propose a model to explain how oxysterols provide protection against bacteria and viruses by modulating a cell's "accessible cholesterol". The paper concentrates on one particular oxysterol, 25-hydroxycholesterol (25HC) which provides an example of a wider group of side-chain oxysterols. Previous studies have shown that 25HC can protect cells against microbial infection and have suggested that this is achieved by depleting "un-sequestered" or "accessible" cholesterol from the plasma membrane. Here, Heisler et al provide convincing evidence that this is achieved initially by activation of the enzyme acyl coenzyme A: cholesterol acyltransferase (ACAT also known as Sterol O-acyltransferase, SOAT, not to be confused with acetyl-coenzyme A acetyltransferase) to rapidly reduce plasma membrane accessible cholesterol by conversion of cholesterol to its cholesteryl ester. This is followed by 25HC-induced inhibition of the processing of SREBPs, the master transcription factors for genes of the cholesterol biosynthesis pathway and also the LDL receptor, to maintain cholesterol depletion.

The data presented throughout are solid, however, some of the structures drawn of the oxysterols in Figure 1 are not chemically correct. 24(S)HC is drawn as 24(R)HC and visa versa, also the oxysterol sulfate should have a bond between C-3 and the O of OSO3H. It would also help the reader if the vehicle for oxysterol additions was clarified.<br /> The data presented in Figures 2 and 3 show that inhibition of SREBP processing by 25HC is important for the long-term maintenance of depletion of plasma membrane accessible cholesterol, but I wonder if activation of LXR may also be important here. I appreciate that the data in Figure 2 points against LXR being involved in the rapid depletion of accessible cholesterol in HEK293 cells, but perhaps it is important for the long-term depletion of accessible cholesterol. Could there be some cell type specificity here?<br /> Something that always concerns me when the antimicrobial activity of 25HC is discussed is the fact that 25HC is usually a minor side-chain oxysterol compared to 24(S)HC and 27HC (and 22(R)HC in steroidogenic tissue), except for a short time after infection. Perhaps any long-term antimicrobial activity, and diminishment of accessible cholesterol, results from these other side-chain oxysterols. This may be worthy of some additional discussion.

In summary, the authors present a convincing model for the depletion of accessible cholesterol by oxysterols and their involvement in antimicrobial activities.

Reviewer #2 (Public Review):

The paper describes a fairly complete set of experiments describing a mechanism by which 4-hour treatment with 25HC can provide reductions in plasma membrane cholesterol for up to 22 hours. The basic finding is that 25HC depletes the ER of cholesterol by stimulating esterification and that SREBP activation is also inhibited. This effect is associated with the slow loss of 25HC from the cells.

The paper describes detailed studies of the long-lasting effects of a 4-hour exposure to 25HC on the loss of plasma membrane cholesterol. The paper characterizes the effects on SREBP processing to account for this. The possible long-lasting effects of ACAT stimulation were not investigated but may play an equal role.

The paper presents data that the effects on plasma membrane cholesterol can account for the inhibitory effects on some bacterial toxins and viruses.

Reviewer #3 (Public Review):

The paper uses multiple approaches in cultured cells to show that the rapid depletion of accessible plasma membrane cholesterol by 25-hydroxycholesterol is mediated by the activation of the cholesterol-esterifying enzyme acylCoA:cholesterol acyltransferase (ACAT). They carefully consider and exclude other potential mechanisms that could explain the effects of 25-OH cholesterol on the plasma membrane cholesterol pool, such as decreased cholesterol biosynthesis or activation of LXR transcription factors. Cell lines with mutations in ACAT and in cholesterol homeostatic factors are used in an ingenious fashion to support the role of ACAT and exclude these other mechanisms. The in vivo relevance of accessible membrane cholesterol and ACAT is then demonstrated for toxic cytolysin binding to cells, Listeria infection in vivo, and Zika and Coronavirus infections of cultured liver cells. Overall, the evidence is exceptional that ACAT modulates the plasma membrane accessible cholesterol pool as a strategy of the host to protect against various infectious agents. The discussion of the paper could be broadened to include other mechanisms that are known concerning the role of 25-OH cholesterol in infectious processes and the body's responses.

Reviewer #1 (Public Review):

This work provides a new multimodal blastocyst evaluation method utilising both blastocyst images and patient couple's clinical features (e.g., maternal age, hormone profiles, endometrium thickness, and semen quality) to predict live birth outcomes.<br /> The manuscript was reviewed using the checklist from the "Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): The TRIPOD statement" (https://www.equator-network.org/reporting-guidelines/tripod-statement/ ). Generally, the authors have achieved their aims, and the results support their conclusions.

The major study strengths are as follows:

The study dataset consists of a huge amount (17,580) of blastocysts with known live birth outcomes, as well as blastocyst images, and data included the clinical features of couples.<br /> The authors developed a new artificial intelligence model consisting of a convolutional neural network to process blastocyst images and a multilayer perceptron to process patient couple's clinical features. This model demonstrated an AUC of 0.77 for live birth prediction, which is significantly higher than that achieved by the previously developed models. The conclusions of this paper are mainly well supported by the data.

Nevertheless, there are some weaknesses:

Regarding testosterone, the method of testosterone assessment is essential. The statistical significance of testosterone as a predictor could change when calculated free T or bioavailable testosterone is used.

According to the data presented in Supplementary Table 1, there are more than 15 statistically significant predictors of live birth. However, the value of predictive significance is presented only for 15 (Fig. 3).

Reviewer #2 (Public Review):

In this article, a multi-modal strategy for live birth prediction is proposed using blastocyst images and clinical features. The CNN architecture is used for the imaging dataset, while an MLP is built for the clinical features, and the final model is developed by concatenating CNN and MLP features. 17,580 samples are used for training and testing the model. The proposed model performed significantly better than the previous ones, with an AUC of 0.77.

By creating activation maps in both scenarios: I) when imaging and clinical features were used, and II) when only imaging data was used, authors highlight the parts of images that are crucial for predictions. Their results confirm the benefits of utilizing multi-modal datasets.

However, the manuscript is currently lacking crucial methodological information that is necessary to judge the validity of various claims.<br /> Furthermore, it lacks discussion of the potential applications of the proposed model in clinical settings.

Reviewer #1 (Public Review):

Chen and colleagues report the results of 3 experiments assessing how one or both eyes open under a patch influence resting EEG activity, contrast sensitivity, and binocular balance in normally sighted subjects. They found that keeping an eye open (as opposed to closed) under a patch enhances contrast sensitivity and evoked responses through the unpatched eye as well as interocular shifts in contrast sensitivity and binocular balance in favor of the patched eye once the patch is removed. These results suggest that the state of eye opening temporarily, but significantly, influences shifts in ocular dominance with relevance for treatment of binocular visual disorders such as amblyopia that are treated with periodic monocular occlusion.

Strengths:<br /> 1. Elegant simplicity in study design.<br /> 2. Well-designed and executed psychophysical assessments of contrast sensitivity and binocular balance. More than one assay for binocular balance is used.<br /> 3. Cross-modality relationships are analyzed and support the underlying hypothesis.

Weaknesses:<br /> 1. The investigators demonstrate an effect of eye open status under the patch on occipital oscillatory activity, but subsequent results cannot be directly attributed to these changes since occipital oscillation are not directly manipulated. Therefore, the extent to which the oscillatory activity in visual cortex mediates differential open versus closed eye effects on contrast sensitivity and binocular balance cannot be concluded based on these data alone.<br /> 2. Long-term effects produced or enabled through open or closed eye patching are not reported, limiting translational potential for visual disorders such as amblyopia.

Reviewer #2 (Public Review):

There are fundamental differences in resting state with eyes open or eyes closed regardless of visual stimulation. Without visual stimulation, these differences are attributed to the switching of involuntary attention from internal (eyes closed) to external (eyes open). The authors employ a monocular deprivation paradigm by patching one eye (with it either open or closed) to induce differences in alpha amplitude that are similar to differences measured with both eyes open or closed. They then examine how these differences from monocular deprivation impact after-effects in contrast sensitivity and binocular balance.

The authors pose an interesting and well-supported hypothesis based on prior knowledge that internal oscillations (i.e. alpha waves) can be modulated with eyes open vs eyes closed. The presented experiments build well upon one another and the authors clearly describe how relevant findings from experiment 1 contribute to the design of the following monocular deprivation experiments. The authors also combine several metrics including EEG, SSVEP and contrast sensitivity to assess both neural activity and perception in tandem.

Despite these strengths, the reported data in the first experiments only shows a modest difference between conditions. In experiment one, the authors make the assumption that differences in alpha measured with binocular eyes open vs closed translates to differences in alpha noted with a patched eye open or closed. Although changes in alpha amplitude appear comparable under monocular and binocular viewing, the differences in perceptual contrast sensitivity between the patched eye open and closed condition are quite modest. The authors do not report differences in contrast sensitivity in the binocular condition, so it is difficult to assess if these are comparable (contrast sensitivity changes in binocular (both eyes open vs closed) and monocular (patched eye open vs closed). The authors also employ their results to make claims about neuroplasticity, however this may be too general a claim. It seems as though the authors are specifically using an adaptation paradigm to elicit short-term changes (within 30 minutes from deprivation). While technically, the visual system is changing, it may be slightly misleading to refer to these neuroplastic changes given there are no measured long-term effects. The authors also fail to explain differences in binocular paradigm, noting recovery of binocularity in their phase combination paradigm, but persistent changes in their rivalry assessment. The authors also may overstate the implications of this in the discussion, as they provide no direct evidence that their reported changes after monocular deprivation are attributed to GABA interactions in primary visual cortex.

This work is important to our understanding of not only endogenous modulators of visual perception, but may have implications in how this knowledge is applied in clinical practice, specifically the treatment of amblyopia with patching.

Reviewer #1 (Public Review):

The contribution of disease-specific factors to the capacity of iPSCs for chondrogenic differentiation is unknown. A better understanding of the underlying mechanism will facilitate approaches to design more effective therapies and interventions to benefit cartilage regeneration.<br /> The authors adequately characterized the stemness of OA-derived iPSC clones compared with previously generated healthy iPSC (AC-iPSCs) based on accepted molecular markers, progenitor properties, and chondrogenic potential pointing to undifferentiated pluripotent phenotype. Clones from AC and OA-iPSCs were then successfully differentiated into mesenchymal progenitor intermediates and displayed similar phenotype characteristics. Immunophenotypic analyses were also performed and confirmed the expression of typical MSC markers in both population progenitors and the lack of hematopoietic and endothelial markers. In terms of multipotency, both iMSCs differentiated into OBs, adipocytes, and chondrocytes, although AC-iPSCs displayed enhanced chondrogenic potential compared with OA-iPSCs. This was confirmed in the chondrogenic differentiation assay using the pellet culture method and 3D-micromass culture wherein iPSCs derived from healthy chondrocytes displayed significantly higher chondrogenic potential compared with OA-iPSCs. The authors logically concluded that the reduced ECM generation by OA-iMSCs is likely due to retention (or memory) of OA phenotype of the original cell source.

RNA-seq analysis of the transcriptome of both AC and OA-iPSCs revealed significant differences between the two cell clones. Similarly, PC analysis suggested that the two populations are genomically distinct. Enrichment of GO terms and KEGG pathway analysis revealed metabolic pathways, epigenetic regulation, and chromatin organization are mostly enriched in AC-iPSCs. These findings suggested that metabolic and epigenetic pathways in AC cells support enhanced chondrogenic differentiation. It should be also noted that the profile of metabolic and epigenetic gene networks exhibited significant differences not only in the terminally differentiated cells but also in the undifferentiated state, further highlighting their distinct chondrogenic potential.

Altogether, using advanced pan-transcriptomic analyses, the authors convincingly demonstrate that distinct expression signature of epigenetic and metabolic marks was detected in healthy iPSCs different from OA-derived iPSCs.

The Implication of epigenetic pre-disposition in OA-iPSC is critically important for designing appropriate strategies to control chondrogenesis and potential cartilage regenerative approaches.

Reviewer #2 (Public Review):

The manuscript compares the chondrogenic potential of iPSCs derived from human chondrocytes isolated from healthy and osteoarthritic AC tissue. Both iPSCs derived from healthy and osteoarthritic AC tissue exhibit markers of pluripotency and were able to give rise to mesenchymal progenitors, although they had distinct differences in metabolic and chromatin modifier genes, as found by RNA seq analysis. The impact of these transcriptome signatures was functionally reflected in a lower chondrogenic potential of the MSCs derived from OA iPSCs compared to healthy donor (AC) iPSCs. This was assessed based on the reduced expression of hyaline cartilage markers and the reduced deposition of the glycoprotein-rich ECM matrix upon chondrogenic differentiation of day 21 micromass cultures from OA patients compared to healthy donors. The distinct gene expression profiles of OA chondrocytes were also found to be consistent with publicly available RNA-seq data performed on healthy and OA cartilage tissues further confirming that the newly identified differences in epigenetic and metabolic signatures are imprint from healthy and OA-chondrocytes.

Reviewer #3 (Public Review):

The current manuscript undoubtedly demonstrates that gene expression associated with healthy or diseased donor cartilage used to derive iPSCs influences the iPSCs potential to differentiate to functional chondrocytes. Using comprehensively designed and described experimental approaches they have shown that even though AC-iPSC and OA-iPSC have similar characteristics in terms of stemness and pluripotency, they vary significantly in terms of their chondrogenic differentiation potential. Further, they showed that AC-iMSC and OA-iMSC which are derived from the AC and OA-iPSCs also show similar phenotypic characteristics but differ significantly in terms of their chondrogenic differentiation. The pan-transcriptional analysis confirmed that the AC and OA-iMSC preserve their epigenetic and metabolism-associated transcriptional memory from AC or OA donor cells which in turn regulate their differentiation to chondrocytes. In summary, these findings have significant implications for designing new approaches to enhance the differentiation potential of iPSCs to desired cells for regenerative research.

A paper recommended in the presentation "William Rowlandson - Image, Imagination And The Imaginal" filmed at Breaking Convention 2017.

Seems to be a different take on the "imaginal" than John Vervaeke's suggestion that the "imaginal" is using imagination for the sake of training and enhancing sensory awareness.

A term recommended by Eve regarding an interdisciplinary approach that accounts for multiple feedback loops within complex systems. Need to confer complex systems science to see if ADHD is already addressed in that domain.

Reviewer #1 (Public Review):

Sorkac et al. devised a genetically encoded retrograde synaptic tracing method they call retro-Tango based on their previously developed anterograde synaptic tracing method trans-Tango. The development of genetically encoded trans-synaptic tracers has long been a difficult stumbling block in the field, and the development of trans-Tango a few years back was a breakthrough that was immediately, widely, and successfully applied. The recent development of the retrograde tracer method BActrace was also exciting for the field, but requires lexA driver lines and required by its design the test of candidate presynaptic neurons instead of an unbiased test for connectivity.

Retro-Tango now provides an unbiased retrograde tracer. They cleverly used the same reporter system as for trans-Tango by reversing the signaling modules to be placed in pre-synaptic neurons instead of post-synaptic neurons. Therefore, synaptic tracing leads to the labeling of pre-synaptic neurons under the regulation of the QUAS system. Using visual, olfactory as well sexually dimorphic circuits authors went about providing examples of specificity, efficiency, and usefulness of the retro-Tango method. The authors successfully demonstrated that many of the known pre-synaptic neurons can be successfully and specifically labelled using the retro-Tango method.

Most importantly, because it is based on the most used, very well tested and widely adopted trans-Tango method, retro-Tango promises to not just be a clever development, but a really widely and well-used technique as well. This is an outstanding contribution.

Reviewer #2 (Public Review):

Tools that enable labeling and genetic manipulations of synaptic partners are important to reveal the structure and function of neural circuits. In a previous study, Barnea and colleagues developed an anterograde tracing method in Drosophila, trans-TANGO, which targets a synthetic ligand to presynaptic terminals to activate a postsynaptic receptor and trigger nuclear translocation of a transcription factor. This allows the labeling and genetic manipulation of cells postsynaptic to the ligand-expressing starter cells. Here, the same group modified trans-TANGO by targeting the ligand to the dendrites of starter cells to genetically access pre-synaptic partners of the starter cells; they call this method retro-TANGO. The authors applied retro-TANGO to various neural circuits, including those involved in escape response, navigation, and sensory circuits for sex peptides and odorants. They also compared their retro-TANGO data with synaptic connectivity derived from connectivity obtained from serial electron microscopy (EM) reconstruction and concluded that retro-TANGO can allow trans-synaptic labeling of presynaptic neurons that make ~ 17 synapses or more with the starter cells.

Overall, this study has generated and characterized a valuable retrograde transsynaptic tracing tool in Drosophila. It's simpler to use than the recently described BAcTrace (Cachero et al., 2020) and can also be adapted to other species. However, the manuscript can be substantially strengthened by providing more quantitative data and more evidence supporting retrograde specificity.

Reviewer #3 (Public Review):

This is a valuable addition to the currently available arsenal of methods to study the Drosophila brain.

There are many positives to the present manuscript as it is:<br /> (i) The introduction makes a clear and fair comparison with other available tracing methods.<br /> (ii) The authors do a systematic analysis of the factors that influence the labeling by retro-tango (age, temperature, male versus female, etc...)<br /> (iii) The authors acknowledge that there are some limitations to retro-TANGo. For example, the fact that retro-T does not label all the expected neurons as indicated by the EM connectome. This is fine because no technique is perfect, and it is very laudable that the authors did a serious study of what one should expect from retro-tango (for example, a threshold determined by the number of synapses between the connected neurons).

Reviewer #1 (Public Review):

The authors in this manuscript investigate the effect of co-substrate cycling on the metabolic flow. The main finding is that this cycling can limit the flux through a pathway. The authors examine implications of this effect in different simple configurations to highlight the potential impact on metabolic pathways. Overall, the manuscript follows logical steps and is accessible. Once the main point-reduction in flux of a pathway with limited pool of a cycled co-substrate-is established, some of the following steps become expected (e.g. the fraction of the flux in a branched pathway). Nevertheless, it is understandable that the authors have picked a few simple examples of the metabolic network motifs to highlight the implications. The results presented in the manuscript overall support the conclusions. One weakness is that some of the details of the assumptions (e.g. the choices of rates) are not explicitly spelt out in the manuscript. This work is impactful because it brings into light how cycling of some of the intermediates in a pathway can influence metabolic fluxes and dynamics. This is a factor in addition to (and separate from) reaction rates which are often considered as the main driver of metabolic fluxes.

Reviewer #2 (Public Review):

The cycling of "co-substrates" in metabolic reactions is possibly a very important but often overlooked determinant of metabolic fluxes. To better understand how the turnover dynamics of co-substrates affect metabolic fluxes the authors dissect a few metabolic reaction motifs. While these motifs are necessarily much simpler than real metabolic networks with dozens or hundreds of reactions, they still include important characteristics of the full network but allow for a deeper mathematical analysis. I found this mathematical approach of the manuscript convincing and an important contribution to the field as it provides more intuitive insights how co-substrate cycling could affect metabolic fluxes. In the manuscript, the authors stress particularly how the pool sizes of co-substrates and the enzymes involved in the cycling of those can constrain metabolic fluxes but the presented results also go substantially beyond this statement as the authors further illustrate how turnover characteristics of substrates in branches/coupled reactions can affect the ratio of produced substrates.

The authors further present an analysis of previously published experimental data (around Figure 3). This is a very nice idea as it can in principle add more direct proof that the cycling of co-substrates is indeed an important constraint shaping fluxes in real metabolic networks and (instead of being merely a theoretical phenomena which occurs only in unphysiological parameter regimes). However, the way currently presented, it remained unclear to which extent the data analysis is adding convincing support that co-cycling substantially constrains metabolic fluxes. Particularly, it remains unclear for which organisms and conditions the used experimental dataset holds, how it has been generated, and with what uncertainty different measured values come. For example, the comparison requires an estimation of v_max. How can these values determined in-vivo? Are (expected) uncertainties sufficiently low to allow for the statement that fluxes are higher than what enzyme kinetics predict? Furthermore, I am wondering to which extent the correlations between co-substrate pool levels and flux is supporting the idea that co-substrate cyling is important. The positive relation between ATP/AMP/ADP levels for example, is a nice observation. However, it remains a correlation which might occur due to many other factors beyond the limitations of co-substrate cycling and which might change with provided conditions.

Reviewer #3 (Public Review):

In the study, the authors present a mathematical framework and data analysis approach that revisits an "old" idea in cell physiology: The role of co-substrate cycling as potential key determinant of reaction flux limits in enzyme-catalyzed reaction systems. The aim of the study is to identify metabolic network properties that indicate potential global flux regulatory capacities of co-substrate cycling.

The authors approached this aim in two steps. First, a mathematical framework, which is based on ODEs was developed and which reflects small abstract metabolic pathways including kinetic parameters of the involved reactions. While the modeled pathways are abstract, the considered pathway motifs are motivated by structures of known existing pathways such as glycolysis (as example of a linear pathway) and certain amino acid biosynthesis pathways (as example of branched pathways). The developed ODE-based models were used for steady state analysis and symbolic and numerical simulations of flux dynamics. As a main result of the first step, the authors highlight that co-substrate cycling can act as mechanism which limits specific metabolic fluxes across the metabolic network and that co-substrate cycling can facilitate flux regulation at branching points of the network. Second, the authors re-analyzed data on flux rates (experimental measurements and flux-balance-analysis predictions) from previous publications in order to assess whether the predicted role of co-substrate cycling could explain the observed flux distributions. In this data analysis, the author provide evidence that the fluxes of specific reactions in central metabolism could be constrained by co-substrate cycling, because their observed fluxes are often lower than expected by the kinetics of the corresponding enzymes.

A particular strength of the study is that the authors highlight that co-substrates are not limited to ATP and NAD(P)H, but could include a range of other metabolites and which could also be organism-specific. Building on this broad definition of co-substrates, the authors developed an abstract mathematical framework that can be used to study the general potential 'design principle' of co-substrate cycling in cellular metabolism and to adapt the framework to study different co-substrates in specific organisms in future works.

Experimental data (i.e. measured fluxes using mass-spectrometry data and labeled substrates) that is available to date is limited and therefore also limits the broad evaluation of the developed mathematical framework across various different organisms and environmental conditions. However, with advances in metabolomics and derived metabolic flux measurements, the mathematical framework will serve as a valuable resource to understand the potential role of co-substrate cycling in more biological systems. The framework might also guide new experiments that generate data for a systematic evaluation of when and to what extent co-substrate cycling governs flux distributions, e.g. depending on growth rates or response to environmental stress.

Reviewer #1 (Public Review):

This study analyses associations between different blood groups and 1,312 hospital diagnosis codes, among >480,000 Danish patients who had their blood type determined in hospitals. While biological relationships between blood types and disease are of substantial interest, unfortunately, the analyses do not adjust for ethnicity (which is correlated with both blood types and many diseases). Thus it is unclear to which extent disease associations represent relationships with the blood types, as opposed to possible differences in disease incidence or severity between people with different ethnic backgrounds (which could also be due to socioeconomic differences as well as any other factors correlated with ethnicity).

Reviewer #2 (Public Review):

The authors are building on previous work by Dahlén et al testing for phenome-wide associations between ABO/RhD blood groups. This is important for identifying potential disease mechanisms related to the blood groups, and for identifying blood groups that may be at higher risk of certain diseases. As we begin to create predictive models across diseases for precision medicine approaches in clinical care, this type of information informs the inclusion of blood groups as predictors in these models.

Notably, this study looks at each subset of A, B, AB, and O versus the remaining groups as compared to other studies which focus on comparing O and non-O blood groups. This paper successfully estimates the incidence rate ratios for 1,312 phecodes for A, AB, B, O, and RhD blood groups. The authors also tested for associations between the age of diagnosis and blood groups. The study's conclusions largely summarize these associations, which are important for the community to browse and interpret. However, the conclusion that ABO/RhD groups are the result of selective pressure driven partially by robustness to disease is not well founded simply from the significant association statistics within the paper.

As in all studies, there are inherent limitations in the data. The Danish National Patient Registry (DNPR) is a population-level cohort, so findings may be generalizable to Denmark or European countries. However, ascertainment biases may exist from what subset of the DNPR also had blood group determination (patients who may need blood transfusions during their hospital stay) and from the use of diagnoses from a hospital setting (most severe diseases) rather than the primary care setting.

The statistical model used to identify these associations is sound, although additional sensitivity analyses and rationale descriptions would add clarity to the appropriateness of this model and variable selection. The authors carefully note that, based on the study design, any associations here are not to be causally interpreted. The study is well powered with nearly 500,000 patients and a median follow-up time of 40.8 years. Multiple testing burden is accounted for using FDR-adjusted p-values. The established method of phecode mapping is used for this phenome-wide approach.

Reviewer #3 (Public Review):

This article analyzes retrospective follow-up data from 482914 patients in the Danish National Patient Registry, with the goal of characterizing the association between blood type, as measured by the ABO and RhD blood group systems, and the incidence of ICD-based phenotypes ('phecodes'). The primary statistical tool employed is a log-linear model, fit separately for each phecode, with the outcome being the number of recorded phecodes per person over the follow-up period. Because the ABO blood group systems contains four subgroups, the authors choose to compare each subgroup - one at at time - against all others. The primary findings are described in Manhattan plots (one for each subgroup), which visually identify statistically significant associations between that blood group and the phecode.

This study has a number of strengths. By using the Danish National Patient Registry, the study population is better characterizable than most phenome-wide association studies. The statistical models employed are appropriate. And the findings are clearly and concisely communicated.

A weakness of the underlying approach is that, by separately modeling each ABO blood subgroup one at a time and collapsing the remaining subgroups, the interpretation of the resulting estimated rate ratio is based upon an assumption that the remaining subgroups have a common incidence. But this cannot be simultaneously true unless all four subgroups have a common incidence, i.e. unless the null scenario holds everywhere. The number of statistically significant phecodes in each of the ABO subgroups reflects the underlying prevalence of each subgroup (more cases allows for greater precision in estimation and therefore smaller p-values) but does not necessarily reflect actual differences in the incidence.

Reviewer #1 (Public Review):

It has recently been shown that the HIV-1 protease can cleave and activate the inflammasome-forming sensor CARD8 upon treatment of infected cells with non-nucleoside reverse-transcriptase inhibitors (Wang et al., Science 2021). Here, Kulsuptrakul and colleagues show that the high susceptibility to proteolytic activation by the HIV-1 protease is a specific feature of human CARD8. They show that changes in human-specific F-F motif render the CARD8 protein of non-human primates largely resistant to cleavage. Interestingly, the protease of SIVcpz the direct precursor of pandemic HIV-1 strains are also capable of cleaving human but not chimpanzee CARD8. Thus, the authors propose that a human-specific CARD8 motif may contribute to the increased levels of inflammation and disease progression in HIV-infected humans compared to non-human primates that are naturally infected with SIV.

Strengths of the study are that the authors convincingly show that a single human-specific amino acid change in CARD8 determines its susceptibility to cleavage by the HIV-1 protease and that the results shown are well controlled and presented. It is also interesting that SIVcpz can cleave human CARD8 and activate an inflammatory response. The major weakness is that it remains unclear whether HIV-1 of SIVcpz may induce CARD8-dependent inflammatory responses in primary CD4+ T cells or macrophages. The most relevant setting in the study was the infection of THP-1 cells with the T cell line-adapted X4-tropic HIV-1 LAI molecular clone. However, the effects on cell death were modest (Figure 3A) and on IL-1ß secretion was not dose-dependent (Figure 3B). Altogether, stronger effects were observed with VSV-G-pseudotyped HIV-1 and only those were used in subsequent experiments involving human CARD8 cleavage mutants (Figure 4). Additional evidence that primary HIV-1 molecular clones and/or SIVcpz may indeed induce CARD8-dependent inflammatory responses in primary viral target cells would greatly increase the significance of the study. In the absence of such data, conclusions about the potential role of CARD8 sensing of the viral protease for the pathogenesis of AIDS should be cautioned throughout.

Reviewer #2 (Public Review):

The HIV inflammasome sensor CARD8 senses intracellular HIV-1 protease activity through direct cleavage by HIV-1 protease between the F59 and F60 positions in the human CARD8 protein. The authors show that the F60 position is variable across non-human primate species that show varying levels of cleavage efficacy. They also posit that inflammasome induction may be dependent upon Toll-like receptor signaling.

Strengths: The authors are able to show that both HIV-1 and HIV-2 cleave and activate the human CARD8 inflammasome. The authors also demonstrate that changes to the 60th position of CARD8 cause a decrease in cleavage efficacy in vitro by HIV-1.

Weaknesses: The study is limited to the introduction of a few mutations in human CARD8 and their cleavage and activation by HIV. The physiological relevance remains unclear without direct investigation of different versions of simian CARD8 protein and SIVs in T cells and macrophages.

Reviewer #3 (Public Review):

The work provides interesting information on human CARD8 for its role in sensing HIV-1 infection and subsequent inflammasome activation as a possible cause of HIV pathogenesis. Proteolytic cleavage at the N-terminus of human CARD8 was confirmed by western blotting of HEK293T cells co-transfected with a CARD8-expression vector and HIV proviral constructs. This analysis also allowed the definition of substrate/enzyme specificity - only human CARD8 is susceptible to proteases derived from HIV and SIV; CARD8s of other gibbons and Old-World monkeys are not due to a single amino acid variation at the P1' position. One thing to note is that the efficiency of this cleavage reaction appeared fairly low because this product (33 kD in Figure 1B) only consisted of a small portion of total CARD8 antibody reactive proteins. To define the correlation between HIV infection and CARD8-mediated inflammasome activation in THP-1 model cells, authors used cell death by propidium iodide staining and IL-1β secretion as inflammasome activation biomarkers. However, cell death measured by propidium iodide staining could be caused by a variety of factors/pathways and thus not specific for pyroptosis resulting from CARD8-mediated inflammasome activation, complicating data interpretation. With IL-1β secretion as an indicator, authors concluded that TLR2 priming (by Pam3CSK4) is required for inflammasome activation by HIV infection, which raises a question of whether HIV infection alone is sufficient at CARD8 activation in THP-1 cells. Data obtained with clonal CARD8 knockout THP cells by CRISPR/cas9 provide clean results confirming that CARD8-mediated inflammasome activation contributes to IL-1β secretion and cell death in parallel with other inflammasome pathways. Data obtained from CARD8KO cells complemented with CARD8 proteins with various substrate sequences provided vital evidence showing that proteolysis at the N-terminus of human CARD8 by HIV-1 protease contributed to CARD8-mediated inflammasome activation although at levels much lower than VbP-stimulated inflammasome activation that appeared to be independent of HIV PR catalyzed N-terminus cleavage. Taken together, this report presents evidence that supports the involvement of human CARD8-mediated inflammasome activation via the N-terminus cleavage by HIV PR, which added valuable information to advance the understanding of pathogenesis caused by HIV infection. However, how much it contributes to HIV-1 pathogenesis remains to be further defined as the contributions are expected to be diverse among cell types and homeostatic stages of infected cells.

Reviewer #1 (Public Review):

In this study, Moulana et al. measured the binding affinity of four antibodies, Ly-CoV016, REGN10987, Ly-CoV555, and S309, against ~30,000 RBD variants that represent the possible intermediates between the ancestral SARS-CoV-2 RBD and Omicron BA.1 RBD. Mutational pathway to antibody escape and the importance of epistasis in antibody binding were examined. By comparing the data here with a previous study by the same authors (ref 23), the authors also conclude that escape mutations with deleterious effects on ACE2 affinity can be compensated by Q498R and N501Y. Overall, the results are clearly presented and provide important insights into antibody escape.

Reviewer #2 (Public Review):

By now, the public is aware of the peculiarities underlying the omicron variants emergence and dissemination globally. This study investigates the mutational biography underlying how mutation effects and epistasis manifest in binding to therapeutic receptors.

The study highlights how epistasis and other mutation effect measurements manifest in phenotypes associated with antibody binding with respect to spike protein in the omicron variant. It rigorously tests a large suite of mutations in the omicron receptor binding domain, highlighting differences in how mutation effects affect binding to certain therapeutic antibodies.

Interestingly, mutations of large effect drive escape from binding to certain antibodies, but not others (S309). The difference in the mutational signature is the most interesting finding, and in particular, the signature of how higher-order epistasis manifests in the partial escape in S309, but less so in the full escape of other antibodies.

The results are timely, the scope enormous, and the analyses responsible.

My only main criticisms walk the stylistic/scientific line: many of the others have pioneered discussions and methods relating to the measurement of epistasis in proteins and other biomolecules. While I recognize that the purpose of this study is focused on the public health implications, I would have appreciated more of a dive into the peculiarity of the finding with respect to epistasis. I think the authors could achieve this by doing the following:

a) Reconciling discussions around the mutation effects in light of contemporary discussions of global epistasis "vs" idiosyncratic epistasis, etc. Several of the authors of the manuscript have written other leading manuscripts of the topic. I would appreciate it if the authors couched the findings within other studies in this arena.

B)While the methods used to detect epistasis in the manuscript make sense, the authors surely realize that methods used to measure is a contentious dimension of the field. I'd appreciate an appeal/explanation as to why their methods were used relative to others. For example, the Lasso correction makes sense, but there are other such methods. Citations and some explanation would be great.

Lastly (somewhat relatedly), I found myself wanting the discussion to be bolder and more ambitious. The summary, as I read it, is on the nose and very direct (which is appropriate), but I want more: What do the findings say for greater discussions surrounding evolution in sequence space? For discussions of epistasis in proteins of a certain kind? In, my view, this data set offers fodder for fundamental discussion in evolutionary biology and evolutionary medicine. I recognize, however, the constraints: such topics may not be within the scope of a single paper, and such discussions may distract from the biomedical applications, which are more relevant for human health.

But I might say something similar about the biomedical implications: the authors do a good job outlining exactly what happened, but what does this say about patterns (the role of mutations of large effect vs. higher-order epistasis) in some traits vs others? Why might we expect certain patterns of epistasis with respect to antibody binding relative to other pathogenic virus phenotypes?

In summary: rigorous and important work, and I congratulate the authors.

Reviewer #1 (Public Review):

The therapeutic effects of NSC transplantation is limited by the low survival and differentiation rates of NSCs due to the harsh environment in the brain after ischemic stroke. To solve this technical challenge, the authors employed NSCs derived from human induced pluripotent stem cells (iPSCs) together with NSC-derived exosomes extracted from NSCs to treat cerebral ischemia induced by middle cerebral artery occlusion/reperfusion (MCAO/R) in mice. In the current study, the authors attempt to demonstrate that NSC-derived exosomes could act as a supportive adjuvant for NSC transplantation after stroke. They showed that NSC-derived exosomes significantly reduced the inflammatory response, alleviated oxidative stress after NSC transplantation, and facilitated NSCs differentiation in vivo. The combination of NSCs with exosomes ameliorated the injury of brain tissue including cerebral infarct, neuronal death and glial scarring, and promoted the motor function recovery. To explore the underlying mechanisms, they analyzed the miRNA profiles of NSC-derived exosomes and the potential downstream genes. Overall, the study provided solid rationale supporting the application of exosomes during stem cell-based therapy. The data quality is good and convincingly supports the major claims. The impact is high for the NSC-transplantation for treating various neurological diseases.

Reviewer #2 (Public Review):

In the submitted manuscript under the title "NSC-derived exosomes enhance therapeutic effects of NSC transplantation on cerebral ischemia in mice", Zhang et al. applied human induced pluripotent stem cells (iPSCs) together with exosomes extracted from NSCs to treat cerebral ischemia induced by middle cerebral artery occlusion/reperfusion (MCAO/R) in mice. They reported that NSC-derived exosomes can ease the inflammatory response, alleviated oxidative stress after NSC transplantation, and facilitated NSCs differentiation in mouse brain. Using the NSC together with their exosomes can ameliorate the injury of brain tissue including cerebral infarct, neuronal death and glial scarring, and promoted the motor function recovery. Finally, they speculated that the miRNA(s) in the exosomes is the key factor to improve the treatment of the NSC transplantation for the stroke. This is an interesting study that contributes important findings which will be of interest to the researcher in the field of the NSC transplantation. However, there are some key points should be further explained.

Major points:<br /> 1). This study does not provide any evidence about the cell death of the transplanted cells. The immunostaining of the Caspase-3 or TUNEL staining should be used to address this issue.

2). The authors showed that the neurological functions (evaluated by balance beam, ladder lung, rotarod test and Modified Neurological Severity Score (mNSS) up to 8 weeks after treatment (Figure 1C)) were significantly improved in the NES+Exo group compared to their control groups. However, these cells (transplanted cells) are progenitors (Nestin+) or undifferentiated cells (Tuj1+) at this stage (Figure 3). Thus, I was curious about that how can the immature neurons play neurological functions? This point should be explained.

3). The authors used the Golgi staining to show the NES+Exo can improve dendritic density and length. How do you know these neurons are transplanted cells?

4). The cell morphology of tdTomato+ cells is fuzzy and it is difficult to distinguish the cell body. It looks like that these cells out of whack.

Reviewer #3 (Public Review):

This study investigates the efficacy of exosomes of neuronal stem cells (NSC) derived from human iPSCs) in improving NSC therapy for neuroprotection in mouse stroke model. The results show that at one-week post-stroke, administration of NSCs through lateral ventricle injections in combination with exosomes significantly improved post-stroke survival, neurological function recovery and brain lesion attenuation in mice at 8-week post treatment. The strengths of this study include: 1) the positive outcomes from this combinatory treatment delivered at the subacute phase; 2) multiple assessments of neurological function impairments; 3) non-invasive, unbiased assessment of brain lesion with MRI. However, the evaluation of the possible underlying mechanisms is weak, which included reduction of reactive astrocytes, increased NeuN+ cells, and possible roles of anti-inflammatory miRNA profiles of exosomes from NSCs in the study. Further strengthening of the relationship in the above phenomena will be beneficial for developing cell therapy for ischemic stroke.

Reviewer #1 (Public Review):

RNA-based self-replication systems might have been concentrated and compartmentalized with peptides by forming droplet-like complexes before the emergence of cellular organisms enveloped by lipid membranes. This report clearly shows that the physical properties of such droplets (phenotypes) can be affected by the activities of a ligase ribozyme in the droplets. This suggests that sequences of such ribozymes (genotypes) might have been selected not only for their direct activities (e.g., elongation of RNA) but also for their indirect effects on the droplet phenotypes (e.g., more viscous/solid droplets formed by the elongated RNAs) on the ancient earth. However, the exact requirements (e.g., average/maximum length of the ligated RNAs + double strand formation) for such phenotypic changes are not assessed in this report. It is not demonstrated whether the droplet property changes caused by the ribozyme activity can be advantageous for the survival of the RNA-based system (e.g., for the ribozyme activity itself). Follow-up studies would be desired to clarify these points and the true values of this report.

Reviewer #2 (Public Review):

To understand the origins of life, it is often necessary to establish synthetic molecular systems that model how primitive cells might have operated. Adopting this approach, here Le Vay et al. tackle one of the mysteries of early cells: how could primitive biomolecules have controlled the behavior of the compartments they inhabited? By forming coacervate droplets from polylysine peptides and ribozymes (catalytic RNAs), they observe changes in droplet properties driven by ribozyme activity and propose a route to form an integrated protocellular system that allows the evolution of biomolecules based on compartment behavior, modeling potential early life processes.

Polymers of opposite charge can phase-separate into coacervate droplets in equilibrium with surrounding aqueous phases. Such condensates are thought to act as subcellular compartments mediating some cellular functions. Coacervates, though, are also of interest as model compartments for biomolecules at the origins of life. A number of studies have shown how the properties and behavior of coacervates can be modulated based on external biological or physicochemical changes. There remains a key question: for coacervates to serve as a vessel for biology at the origin of life, can coacervate behavior be controlled from within? Previously this has been shown possible in some systems of membranous vesicles, an alternative model of primordial compartments.

Proteinaceous enzymes have been deployed to transform precursor compounds into potential coacervate components and induce the formation of condensed-phase microdroplets, but such enzymes are not thought to have been available at the origins of life. Instead, ribozymes are thought to have catalysed key reactions in early biology. Here, by using a ribozyme ligase to concatenate RNA molecules when together in a polylysine coacervate, the authors clearly demonstrate that coacervate properties change, showing a more rounded droplet shape and reduced fusion tendencies. Interestingly, the authors find that this distinctive behavior emerges when the reaction occurs in the coacervate phase, instead of before coacervate formation.

This influence of sequence-encoded phenotype on compartment properties has few precedents and has long been a target of origin of life research. The authors propose that it could serve as the basis for the establishment of coacervate droplets as units of selection and evolution. For this, a trio of critical challenges must be overcome and the authors begin to shed light on these.

First, the droplets must support ribozyme activity, without overly inhibiting it (or the droplet becomes an unfavorable habitat for these catalysts). Other ribozymes have often suffered inhibition due to conformation effects or substrate availability when mixed in a coacervate. The authors show here that the ligase ribozyme maintains activity (and may even be accelerated) in the coacervate. However, it appears to operate under single-turnover conditions and it is not yet clear whether multiple-turnover catalysis is possible in the coacervate.

Second, the droplet properties must be responsive to the activity of the biomolecules inside. The authors' observations of changes in coacervate behavior are robust, and they make some suggestions as to how such changes might be leveraged to establish selection pressure to drive the evolution of content molecules. In this study, though, the ribozyme comprises a substantial fraction (~1/2) of the coacervate negatively charged components, and in an evolutionary situation (with fewer molecules of RNA catalyst per compartment) it is not known whether the resulting droplet phenotype will change impactfully.

Third, the droplet must hold together its contents and avoid mixing with the contents of other droplets, to hold a molecular species together and defend against molecular parasites. Though there may still be some exchange of smaller molecules, the authors demonstrate that the lengthening of RNAs by ribozyme ligases in a coacervate can prevent fusion with other similar droplets (which otherwise occurs in the absence of RNA ligation) and preserve droplet identity. To use the coacervate as an evolutionary unit, droplets with active ribozyme will also need to be resistant to fusion with inactive droplets.

Putting such a system together based on the phenomenon observed by the authors would be a breakthrough in modeling primordial biology. A range of compartments have been proposed to act as habitats for early molecular biology, including porous rocks, mineral surfaces, ice phases, aerosols as well as membranous vesicles, and a key challenge is demonstrating how internal biological activities can influence compartment behavior. Establishing coacervates as genetically-controllable habitats for biomolecules will add to experimental models of such "life but not as we know it" and provide a new view of early biology.

Reviewer #3 (Public Review):

The study investigates the consequences of mixing a ligase ribozyme, its substrates, and oligo(Lys) peptides of different lengths in the context of a coacervate droplet protocell in a 'Nucleic Acid World' as an early stage of life. The study shows convincingly several very interesting results that are certain to have an impact on origins-of-life studies: First, the activity of ribozymes in the coacervate droplets - the formation of longer RNAs - affects the size of the droplets, with inactive ribozymes leading to more droplet fusion. Second, this behavior is reflected in the adhesion to hydrophobic surfaces, showing that not only the size but also the physical properties of the droplets are changed by ribozyme catalysis. Third, the exchange rate of material between droplets is also affected by ribozyme catalysis, which has important implications for coacervates as model systems for early life forms.

More detailed information should be provided in the text that ribozyme catalysis actually proceeds in/on the coacervates, a discussion section needs to be devoted to the implication of ribozyme catalysis affecting the measured material exchange rates on the coupling of genotype/phenotype, molecular parasites, and the inflow/outflow of metabolites, and the importance of the system with longer peptides needs to be clarified and perhaps toned down.

Reviewer #1 (Public Review):

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

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

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

Reviewer #2 (Public Review):

Gyrencephaly has been linked to the split of the subventricular zone (SVZ) and the formation of an outer subventricular zone (OSVZ) during neurogenesis. This paper proposes a convincing multizone computational model of neurogenesis allowing exploration of the role of this OSVZ in the folding dynamics. This model is a bridge between knowledge of cell proliferation and migration and the physics of growth.

Strengths<br /> • The computational model described in this paper is probably the most ambitious to date. It succeeds in translating the complexity of microscopic biological phenomena that describe cell proliferation and migration into physical phenomena from continuum mechanics. It is truly a tour de force.<br /> • The description of neurogenesis is particularly clear, within the reach of a naive reader despite its complexity. The figure illustrating the chronology of the phenomena at work is a success.<br /> • The paper builds on impressive efforts to estimate from real human brain sections some of the complex parameters of the model such as the density of cells at different stages of migration.<br /> • The physical model is able to show ripples in the deep zones of proliferation that seem induced by the folding of the cortex. This observation is consistent with feedback from folding on the organization of the migration, as these ripples are not part of the model. I do not know to what extent these ripples have been demonstrated in reality.<br /> • The model shows that significant proliferation in the OSVZ leads to a doubling of the frequency of folding, a phenomenon observed in reality in large brains, which gives rise to allometric laws between folding and brain size (see Toro et al., Germanaud et al.)<br /> • The paper includes an experiment based on heterogeneous proliferation in the OSVZ, which is difficult to model in more classical models such as Tallinen's one. This is a particularly interesting possibility for modelling spatial heterogeneity in the expression of genes that modulate neurogenesis (see Llinares-Benadero et al.).

Weaknesses<br /> • To account for the complexity of biological phenomena, the model relies on a large number of ad hoc choices whose consequences are difficult to predict.<br /> • The physical model description is highly technical and out of reach for a non-specialist.<br /> • The description of neurogenesis shows three zones of cell proliferation, each inhabited by a specific cell type. Despite its realism, the proposed model does not take into account the ISVZ where the intermediate progenitors operate.<br /> • The experiment of comparing several regimes derived from the relative importance of proliferation in the VZ and OSVZ is not very clear. It leads to the observation of the evolution of cell density maxima over time, which seems insufficient to conclude the importance of the OSVZ for folding. One wonders whether the key parameter that leads to folding is the rate of OSVZ proliferation or simply the total quantity of neurons generated by the two or even the three zones.<br /> • The experiment on the heterogeneity of proliferation in the OSVZ is a bit frustrating. I would like to see a set-up corresponding to the mosaics found in ferrets and closely associated with folding patterns.<br /> • It would be interesting to elaborate a little on the possibility of extending the model in 3D, which seems imperative to evaluate the nature of the folding pattern generated. Comparing them to reality is an essential step in gauging the credibility of the model. For instance, it would be interesting to test to which extent the model can father the type of variability observed in the general population (Mangin et al.). It will also be particularly interesting to work on the inverse model between the real folding patterns and the heterogeneous proliferation maps that can generate them.

Conclusion

The computational model of neurogenesis described in this paper is the most sophisticated model proposed to date. It is a convincing step towards a model that could one day simulate perturbations of neurogenesis that may give rise to the gyration abnormalities observed in certain developmental pathologies. A better understanding of the genesis of these anomalies could contribute to their use as a signature of hidden deleterious events occuring during neurogenesis.

References

Toro, R., Perron, M., Pike, B., Richer, L., Veillette, S., Pausova, Z., & Paus, T. (2008). Brain size and folding of the human cerebral cortex. Cerebral cortex, 18(10), 2352-2357.<br /> Germanaud, D., Lefèvre, J., Toro, R., Fischer, C., Dubois, J., Hertz-Pannier, L., & Mangin, J. F. (2012). Larger is twistier: spectral analysis of gyrification (SPANGY) applied to adult brain size polymorphism. NeuroImage, 63(3), 1257-1272.<br /> Tallinen, T., Chung, J. Y., Rousseau, F., Girard, N., Lefèvre, J., & Mahadevan, L. (2016). On the growth and form of cortical convolutions. Nature Physics, 12(6), 588-593.<br /> Llinares-Benadero, C., & Borrell, V. (2019). Deconstructing cortical folding: genetic, cellular and mechanical determinants. Nature Reviews Neuroscience, 20(3), 161-176.<br /> Mangin, J. F., Le Guen, Y., Labra, N., Grigis, A., Frouin, V., Guevara, M., ... & Sun, Z. Y. (2019). "Plis de passage" deserve a role in models of the cortical folding process. Brain topography, 32(6), 1035-1048.

Reviewer #3 (Public Review):

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

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

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

Reviewer #1 (Public Review):

In this work, Roche et al. study a 13-year long time series of microbiome samples from wild baboons from Kenya. The data used in this work challenge a previous finding from the same authors that temporal dynamics in microbiome changes are largely individualized. Using a multinomial logistic-normal modeling approach, the authors detect that co-variance in temporal dynamics in microbial pair-wise associations among individuals occurs more frequently between relatives. Furthermore, the authors identify that microbial phylogenetic proximity is associated with consistent co-abundance changes over time and that their metric of universal microbial relationships is robust across hosts and is detected even in human longitudinal data. The authors conduct a thorough statistical revision of publicly available results, highlighting this time (e.g. compared to Björk et al, doi: 10.1038/s41559-022-01773-4) the consistently shared microbial properties between individuals, rather that the individual microbial signatures highlighted in their previous work.

Strengths:<br /> This work is foundational in its compelling effort to generate a rigorous method to evaluate co-abundance dynamics in longitudinal microbiome data. The approach taken will likely inspire developments that will sharpen the capacity to extract co-varying microbial features, taking into account seasonality, diet, age, relatedness, and more. To the best of my understanding, their hierarchical model integrated into the Gaussian process to analyze microbial dynamics is reasonably robust and they clearly explain the implementation. Furthermore, this work introduces and defines the concept of a universality score for microbial taxon pairs.<br /> Overall, the work presented is clear and convincing and provides tools for the community to benefit from both methods and results. Furthermore, conceptually, this work stresses the value of consistent and shared microbial dynamics in groups, which enriches our understanding of host-associated microbial ecology, otherwise understood to be largely dependent on external fluctuations.

Weakness:<br /> It is not entirely clear the extent to which the presented results revise, refute, or support the previously published analysis performed by the authors on the same dataset (doi: 10.1038/s41559-022-01773-4), which was more focused on individuality.

Reviewer #2 (Public Review):

The authors of this paper identify a knowledge gap in our understanding of the generalizability of ecological associations of gut bacteria across hosts. Theoretically, it is possible that ecological associations between bacteria are consistent within a host organism but differ between hosts, or that they are universal across hosts and their environmental gradients. The authors utilize longitudinal data with a unique temporal resolution, on Amboseli baboons, 56 individuals who were sampled for gut microbiome hundreds of times over a decade. This data allows disentangling ecological dynamics within and across individuals in a way that as far as I know has never been done before. The authors show that ecological relationships among baboon gut bacteria, measure through a correlation based on covariation, are largely universal (similar within and across host individuals) and that the most universally covarying taxa are almost always positively associated with each other. They also compare these results with two sets of human data, finding similar patterns in one human data set but not in the other.

The main aim of this paper is to establish whether gut microbial ecologies are universal across hosts, and this the authors generally show to be true in a thorough and convincing way. However, some re-assessment or re-assurance on the solidity of their chosen method of estimating co-variation would be needed to fully assess the robustness of subsequent results. Specifically, the authors measure the correlation between microbial taxa from data on their abundance co-variation across samples. While necessary steps have been taken to validate the estimates across spurious correlations due to the compositional nature and autocorrelation structures present in the data, I worry that the sparsity of the data might influence the estimation of positive and negative correlations in a slightly different manner. There exist more microbial taxa than samples in the data and some taxa are present in as few as 20% of the samples, meaning that the covariation data will have a large amount of 0-0 pairs. I worry that the abundance of 0-0 pairs in the data might inflate the measures of positive co-variation, making taxa seem highly positively correlated in abundance when they in fact are missing from many samples. Of course, mutual absence is also a form of biologically meaningful covariation but taking the larger number of taxa than samples and the inability of sequencing technology to detect all low-abundance taxa in a sample, I am currently not convinced that all of the 0-0 pairs are modeled as a realistic and balanced way as a continuum of the other non-zero co-variation between taxa in the data. This may become problematic when positive and negative relationships are compared: The authors state that even though most associations between taxa were negative, the most universally correlated taxa pairs (taxa pairs with strongest correlations in abundance both within and between hosts) were enriched in positive associations. It may be possible that this is influenced by the fact that zero inflation in the data lends more weight to positive links than negative links. Whether these universal positive correlations are driven by positive non-zero abundance covariation or just 0-0 links in the data is currently unclear.<br /> Another additional result that would benefit from a more clear context is the result that taxa correlation patterns were more similar between phylogenetically close taxa and between genetically close host individuals. The former notion is to be expected if taxa abundances are driven by environmental (or host physiology-related) selective forces that favor bacteria with similar phenotypes. This yields more support to the idea that covariation is environmentally driven rather than driven by the ecological network of the bacteria themselves, and this could be more clearly emphasized. The latter notion of covariation being more similar in genetically related hosts is currently impossible to disentangle from the notion that covariation patterns were more similar with individuals harboring a more similar baseline microbiome composition since microbiome composition and genetic relatedness were apparently correlated. To understand if something about relatedness was actually influential over correlation pattern similarity, one would need to model that effect on top of the baseline similarity effect. Currently, it is not clear if this was done or not.

The authors also slightly overemphasize the generalizability of their results to humans, taking that only one of the human data sets they compare their results to, shows similar patterns. While they mention that the other human data set (that was not similar in patterns to theirs) was different in some key aspects (sampling frequency was much higher), the other human data set was also dissimilar to the other two (it only contained infants, not adults). Furthermore, to back up the statement that higher sampling frequency would be the reason this data set had dissimilar covariation between taxa, one would need to show that the temporal variation in this data set was different from the baboon one and show that these covariation patterns were sensitive to timescale by subsampling either data to create mock data sets with different sampling frequency and see how this would change the inference of ecological associations.

To the extent that the results are robust, particularly regarding to the main result of the universality of gut microbial ecological associations, the impact of this paper is not small. This question has never been so thoroughly and convincingly addressed, and the results as they stand have the power to strongly influence the expectations of gut microbial ecology across many different systems. Moreover, as the authors point out, evidence for universal gut microbial ecology is important for the future development of probiotics. An important point here, under-emphasized by the authors, is that universal gut microbe ecologies will allow specific interventions that use gut microbe ecology to manipulate emergent community properties of microbiomes to be more beneficial for the host, rather than just designing compositional cocktails that should fit all. In addition to the main finding of this study, the unique data set and the methods developed as part of this study (e.g. the universality score, the enrichment measures, the model of log-ratio dynamics, the assessment of covariation from time-ordered abundance trajectories) will doubtlessly be translatable to many other studies in the future.

Reviewer #3 (Public Review):

This is a well-executed study, offering thorough analysis and insightful interpretations. It is well-written, and I find the conclusions interesting, important, and well-supported.

Reviewer #1 (Public Review):

The synaptonemal complex (SC) is a meiosis-specific tripartite chromosome structure for chromosome synapsis and regulates crossover formation essential for proper chromosome segregation during meiosis. In this interesting paper, the authors studied the dynamic behavior of two components of SC central regions, SYP-2 and SYP-3, in both oocytes and spermatocytes in C. elegans and also the effect of the dosage of the proteins on meiotic recombination and found sex-dimorphic and/or SYP-2/3 dosage sensitive dynamics of the SC components and proteins involved in crossover formation on the meiotic chromosomes, suggesting the intimate relationship between SC central regions and meiotic recombination.

Reviewer #2 (Public Review):

The synaptonemal complex (SC) is a ladder-like structure that is assembled between homologous chromosomes during meiotic prophase I. This structure is critical for accurate chromosome segregation as it is required for both crossover formation and regulating crossover frequency. In this study, the composition of the SC throughout meiotic prophase, SC dynamics, and its contribution to crossover formation is compared between male meiosis and female meiosis using C. elegans. Although the SC is found in both sexes, many aspects of meiosis, including recombination initiation and formation, differ between sexes. Whether sex-specific differences extend to the SC and how this influences recombination events has not been investigated. The authors use fluorescently-tagged SC central region proteins (SYP-2 and SYP-3) to quantify the amount of SC protein accumulation per nucleus. The data indicate that the composition of the SC is dynamic throughout the meiotic prophase with sexually dimorphic properties. In addition, by examining and quantifying the number of proteins that mark different recombination intermediates, the authors found that not only does the SC regulate different aspects of recombination, but the regulation is sex-specific. Overall, the assays and quantification in this manuscript are of high quality.

Overall, the manuscript is largely descriptive and doesn't test possible mechanisms behind the observed sex-specific differences. However, this study is of high interest as these sexually dimorphic phenotypes have not been previously studied. The data presented in this paper set a nice foundation for future work. The manuscript is mostly well-written and the data is presented well but lacks explanations for some of the observed phenotypes. Some minor textual revisions would provide insights into some of the male-specific phenotypes that were noted without explanation (e.g. Why might SYP-3 be more dynamic in early pachytene in spermatocytes?). In addition, the introduction could be revised to provide a more coherent flow and to highlight the significance of sexual dimorphic aspects of meiosis.

Reviewer #3 (Public Review):

Cahoon set out to demonstrate that sexual dimorphic outcomes of meiosis are caused by different regulations of the synaptonemal complex (SC). In the employed model organism C. elegans it has been shown that the SC consists of at least 6 different proteins (SYP-1-6) and that their assembly into this intricate structure is mutually dependent and that crossover formation is drastically, if not completely abolished, in the absence of individual SC mutants (SYP-5 and SYP-6 are functionally redundant).

The authors employ FRAP analysis and examine the rate of reincorporation of the synapsis components SYP-2 and SYP3 in three different regions of the gonad and compare the incorporation after photobleaching in hermaphrodite and male gonads. They find that SYP-2 dynamics is increased in spermatocytes, whereas in oocytes SYP-3 dynamics is increased. They also found differing profiles of incorporation during the progression of prophase I for those two synapsis components in the two sexes.

Furthermore, the authors show that syp-2/+ and syp-3/+ show signs of haploinsufficiency, as demonstrated by increased embryonic lethality and the missegregation of the X chromosome. In these mutants, the authors examined the kinetics of the appearance of recombination foci, where they used RAD-51 as a measure for progress of homologous recombination and repair pathway choice (repair via the sister versus the homolog and/or non-homologous end joining), MSH-5 for stabilisation of the strand invasion product and COSA-1 as a marker for crossover designation.<br /> The authors show that in the hypomorphs the behaviour of some recombination markers change. The counts of the numbers of COSA-1 are not explaining the missegregation of the X chromosome. The localisation of the crossovers shifts towards the pairing centre chromosome ends in the hypomorphs.

The manuscript is descriptive and the link that dimorphic incorporation rates of SYP-2 and SYP-3 are causative for recombination dimorphisms is not substantiated by the shown experiments. The observed phenomena in the heterozygous syp mutants could be due to general SC defects and not the lack of a critical amount at a specific point during recombination. Overall, the FRAP experiments do not address the possible different synthesis rates of the employed markers (it would be more meaningful to examine the incorporation under protein synthesis inhibitory conditions) or use a photoconvertible tag, that allows the assessment of new synthesis. It has been well documented that in the more distal regions of the gonad gene expression is upregulated. It is not clear what the contribution of differing gene expression of the examined synapsis proteins to the different dynamic behaviour actually is.

Reviewer #1 (Public Review):

The authors set out to achieve two overarching objectives: 1) to demonstrate that BAFF is a bona fide senescence-associated factor and 2) to expand the field's understanding of senescence outcomes across cell types and models. By inducing senescence in a multitude of ways and across cell culture and animal models, the authors demonstrate that BAFF is robustly upregulated in response to senescence induction. Beyond mere association, by knocking down, overexpressing, or ectopically adding BAFF, the authors demonstrate that various senescence-associated phenotypes can be altered, suggesting that it is an effector of the senescent state. Moreover, by comparing transcriptomic and proteomic profiles in two very different types of cells-diploid WI-38 human fibroblasts and cancerous THP-1 monocytes-the authors identify two parallel trajectories, one involving p53 and one involving NF-kB.

Although trajectory differences may stem from cell type differences, it is possible that the cancerous vs non-cancerous status of the cell lines used may be a more important variable in this case. One question the reader may be left with is: would the two trajectories be different if non-cancerous monocytes with intact p53 were profiled?

Regardless, this study establishes a precedent for characterizing senescence responses in additional cell types of either healthy or diseased origin. Though a number of technical and statistical issues exist in the current version of the manuscript (i.e. use of only a single reference gene for RT-qPCR and inconsistent fold change thresholding in RNA-seq analyses), the results appear robust enough to remain statistically significant after modification. Moreover, many analyses are carried out at both the transcriptomic and proteomic levels with consistent results, highlighting the robustness of their observations.

Ultimately, the results strongly suggest that BAFF plays a senomorphic role in senescence, modulating downstream senescence-associated phenotypes, and may be an interesting candidate for senomorphic therapy.

Reviewer #2 (Public Review):

The study by Rossi et al. is focused on the role of the SASP factor BAFF as a key regulator of senescent cell biology. Using both in vivo and in vitro studies, the authors show that BAFF, particularly in the monocytic cell line THP-1, is a type I interferon-induced gene. The authors further showed, using both proteomics, transcriptomics, and genetic studies that while BAFF does not play a role in the cell viability or cell cycle arrest of senescent cells, BAFF does regulate other aspects of senescent cell Biology. This includes SA-B-GAL activity and inflammatory gene expression of multiple SASP genes. Lastly, the authors demonstrate that via potential autocrine/paracrine signaling mechanisms BAFF can likely bind to multiple BAFF receptors upregulated in senescent cells, and affect both the p53 and NF-kB pathways to control inflammatory gene expression.

In summary, we find the manuscript to be both well written and organized, and a nice study on the role of BAFF as a key regulator of senescent cell biology. We believe this finding will be of significant interest to both the senescent cell field and the aging field in general.

Reviewer #3 (Public Review):

The authors examine the role of secreted BAFF in senescence phenotypes in THP1 AML cells and primary human fibroblasts. In the former, BAFF is found to potentiate the inflammatory phenotype (SASP) and in the latter to potentiate cell cycle arrest. This is an important study because the SASP is still largely considered in generic and monolithic terms, and it is necessary to deconvolute the SASP and examine its many components individually and in different contexts.

Although the results show differences for BAFF in the two cell models, there are many places where key results are missing and the results over-interpreted and/or missing controls.

1. Figure 1. Test whether the upregulation of BAFF is specific to senescence, or also in reversible quiescence arrest.

2. Figure 1, Supplement 1G. Show negative control IgG for immunofluorescence.

3. All results with siRNA should be validated with at least 2 individual siRNAs to eliminate the possibility of off-target effects.

4. To confirm a role for IRF1 in the activation of BAFF, the authors should confirm the binding of IRF1 to the BAFF promoter by ChIP or ChIP-seq.

5. Key antibodies should be validated by siRNA knockdown of their targets, for example, TACI, BCMA, and BAFF-R in Figure 5. Note that there is an apparent discrepancy between BCMA data in Figure 5B vs 5C.

6. Figure 5E. Negative/specificity controls for this assay should be shown.

7. Hybridization arrays such as Figure 5H, Figure 6 - Supplement 1I, and Figure 6H should be shown as quantitated, normalized data with statistics from replicates.

8. Figure 6B - Supplement 1. Controls to confirm fractionation (i.e., non-contamination by cytosolic and nuclear proteins) should be shown.

9. Figure 6A. Knockdown of BAFF should be shown by western blot.

10. Figure 6G. Although BAFF knockdown decreases the expression of p53, p21 increases. How do the authors explain this?

Reviewer #1 (Public Review):

Cryo-EM structures of respiratory complex I have in recent years have a large impact on our understanding of its mechanism, regulation, assembly, and evolution. However, the coupling mechanism of complex I is still not clear, and controversies exist about whether certain conformations are part of the catalytic cycle or arise from the deactivation of the enzyme. Padavannil and colleagues now add to the story with the first structures of insect complex I, from the model organism Drosophila melanogaster. One of the rationales for choosing this organism is that it lacks the active-to-deactive (A-to-D) transition that prevents the enzyme from going in reverse, which should make the interpretation of any different conformations more straightforward.

The authors showed that the A-D transition seen in mammals and fungi was indeed not present in Drosophila complex I and they determined the cryo-EM structure. In contrast to especially mammalian complex I, which is often found in an "open" and a "closed" state, there was only a single conformation. Drosophila complex I has lost two accessory subunits compared to the mammalian complex, and several other subunits have lost or gained elements, with possible implications for the assembly, stability, or regulation of the complex. The interface of the two peripheral and membrane arms was poorly resolved. A focused classification on this region yielded distinct structures, differing in the angle of the two arms and in the presence or absence of an alpha helix at the N terminus of subunit NDUFS4 (the "lock helix"), a region that is not present in mammalian or yeast complex I. The authors observe a transition between two states named "closed" and "locked open" and speculate that the transition constitutes a deactivation mechanism in insect complex I.

The conclusions of the paper are for the most part solid and supported by the data. Only the interpretation of the significance of the "lock helix" is not convincing: without any evidence, it is assumed to be a regulatory element responsible for an off-pathway deactive state. The nomenclature "closed" and "locked open" is unfortunate, as most of the structural features that differ between the states are reversed compared to the mammalian closed and open states: the disorder of several loops in the quinone binding regions and the presence of absence of a π bulge in helix 4 of the ND6 subunit. Thus, the "locked open" state, which the authors assign as an off-pathway resting state, shares the features of the mammalian closed state, which in all catalysis models is considered an "active" state. An especially important feature in the closed state is the alpha-helical conformation of ND6-helix 4, which has been shown to support a water wire connection from the Q site to the membrane arm, suggesting a role in proton transfer. Conversely, all structures considered as possible D states in mammalian or yeast complex I are open and show disordered loops and a π bulge. These features as shared by the "closed" state of Drosophila, which is however assumed to be on-pathway.

Reviewer #2 (Public Review):

In this work, the authors present a high-resolution cryo-EM structure of mitochondrial complex I, which was isolated from the model protostomian Drosophila melanogaster. Although multiple structures of related complexes have been published earlier, this system is particularly interesting as it seems not to adopt a so-called off-pathway "deactive" (D) state in contrast to the complex from Deuterostomia (including mammals) and therefore may provide novel mechanistic insights into complex I. The work is interesting, as it provides a novel contribution to the current discussion about the assignment of structural conformations to states in the catalytic cycle and/or in the active/deactive state transition of the complex.

Reviewer #3 (Public Review):

This paper by Padavannil et al. presents a new cryo-EM structure of mitochondrial complex I from Drosophila melanogaster. This is a timely and important study - the new structure and comparative analysis would allow new insights into mitochondrial complex I mechanism and regulation. The major strength is the advanced CryoEM analysis and structure resolution. The manuscript is well-written and scientifically sound, but a clear weakness is the lack of classical enzyme kinetic analysis of the A/D transition, even though this is supposed to be the foundation for the main conclusion of the manuscript. However, the interpretation of the data is rational and scientifically justified.

Reviewer #1 (Public Review):

Most previous studies about burn injuries only considered systemic inflammation with analyses of blood specimens from patients. The current study is unique in the fact that it utilizes skin samples. The authors used single-cell analyses by flow cytometry and RNA-seq to characterize in detail the different T-cell populations. The differences are striking. Burned skins have higher degrees of CD69-negative T cells, which indicates that these are recruited from probably blood circulation. They are also substantially more responsive to stimulation by producing higher amounts of immunologic molecules, such as IFNG and TNFA. The results are compelling because they indicate that following burn injury, T cells infiltrate the lesions to potentially protect the damaged tissue from secondary infections.

However, there is an important aspect missing. What does induce T-cell infiltration into the burned skins? A potential explanation is that resident myeloid cells directly or indirectly promote chemokine-mediated recruitment of T cells.

Another important consideration is the impact on other leukocyte populations. While the study is well focused on T cells, the immune system consists of a complex network of cells and molecules that interact with each other. The study does not address myeloid cells and innate lymphoid cells, which could also play important roles and display altered functions in burned injuries.

Nevertheless, the study provides important information about the "activation" statuses of several T cell populations following burned injuries and could help guide the development of better treatments.

Reviewer #2 (Public Review):

In this study, Labuz and collaborators characterize the impact of burn injury on the T cell populations of the human skin. The authors use multiparametric flow cytometry and single-cell transcriptomics to analyze the numbers and the transcriptional profile of conventional and unconventional T cell populations in samples collected from patients with acute burn injury, late burn injury, and without burn injury. Their results show that burn injury disturbs the balance of T cell subtypes by increasing the percentage of CD4 T cells and decreasing the percentage of CD8 T cells. Both CD4 and CD8 T cells in the burn tissue presented lower expression of CD69 and higher expression of CD38, IFN-gamma, and TNF-alpha. The percentage of gamma delta T cells and MAIT cells positive for TNF-alpha and IFN-gamma also increased in the burn tissue. The authors then use single-cell RNA sequencing to gain further insights into how burn injury impacts the overall functions of skin T cells. This unbiased transcriptional profiling confirmed their previous observations that both conventional and unconventional T cells in the skin are replaced by new clusters that express lower levels of "tissue-resident" signature genes such as CD69 and higher levels of homing markers such as SELL and S1PR1. CD8 T cells of the burn skin samples show a clear reduction in the expression of cytotoxic molecules such as GZMK, GZMH, and GNLY, and this contrasts with the upregulation of cytotoxic molecules that are observed in the populations of unconventional T cell populations.

This is a relatively simple and descriptive work that will likely be an important resource for future studies investigating the role of T cell responses in skin wound healing and the maintenance of skin barrier function against pathogens following burn injury. A broader and more unbiased analysis of scRNA-seq data is necessary to better understand the biological processes and cellular responses that are being affected by the transcriptional changes observed in each T cell population as well as the possible implications of their findings.

Reviewer #1 (Public Review):

The authors studied Eurasian perch in an experimental setup facilitated by a nuclear cooling plant to provide a natural laboratory. The heated area of the ecosystem raised in temperature by 8 degrees centigrade, while a reference area remained unheated. The authors provide a thorough and convincing description that the two areas are segregated such that individuals could not escape from one area to another prior to 2004, and such use data only until 2003 to test their hypotheses. The authors used both length-at-catch and age-increment data in a series of Bayesian mixed effects models to estimate the growth rate and length-at-age. They find that in the warmed area, both younger, smaller fish and older adults grew faster, contrary to the prediction of the temperature-size rule as well as many predictions and observations from other systems that fish reach smaller terminal body sizes in warmer environments due to increased metabolic demands. The authors furthermore combine the estimated body sizes with a mortality rate to determine the size-spectrum slope for both areas and determine the increased growth and increased mortality combine to essentially leave the size-spectrum slope observed in the ecosystem unchanged.

This is a thorough and interesting paper presented clearly and succinctly. These authors present a strong and thorough analysis of how temperature affects growth when all other ecosystem factors remain unchanged in a population. The dataset is a powerful one to support this type of analysis, and the statistical analysis methods the authors used appear to be robust and thorough. The diagnostics and visualizations are complete and inspire confidence in the convergence and accuracy of the modeling approach. The use of the size spectrum exponent to roll up individual-level changes across the population into a single metric was useful and interesting.

The estimates of the von Bertalanffy growth parameters in the results and discussion are less convincing than the growth increment and length-at-age estimates which seem much more robust. The presentation of estimates of the von Bertalanffy growth parameters in Figure S6 exhibit the high negative correlation between the k and L infinity parameters that are typical whenever multiple VBGF models are fit to subsets of data. It is difficult to determine which changes in parameters correspond to actual differences in early vs late life stage growth when, in any given year, if k is estimated low, L infinity will skew high simply due to the model structure. An example of this can be seen in 1995-1997 where L infinity is quite high but k is estimated quite low concurrently - in this case, it seems more reasonable to conclude the likelihood surface is quite flat between different parameter values than that fish suddenly reached a larger asymptotic size in these three years than all of the rest. The data in this case so strongly show larger growth in the heated area even without the VBGF results, and it would be more credible to base the discussion and results of this paper on the growth rate or observed length-at-age (e.g. Figure S4) estimates which are so clear.

Reviewer #2 (Public Review):

With warming, fishes are generally expected to grow faster to smaller adult body sizes, as described by the temperature-size rule and other similar theories. However, the generality of this shrinking and the patterns among age classes within a species remain major research questions made all the more urgent by the rapid warming faced by many aquatic ecosystems. In this manuscript, the authors take advantage of an artificially heated ecosystem to investigate the impacts of warming on an unharvested population of fish and investigate patterns of growth, size structure, and mortality in an unexploited fish population. Surprisingly, while faster growth rates in juveniles are demonstrated, as would be expected, adult size remains higher in the heated habitat compared to a nearby non-heated habitat. This unexpected result will be of broad interest.

Strengths

The semi-natural experiment provided by the artificial warming from the power plant is a very nice design. While it is not the only place this type of study could be conducted, this system seems to have an unusually high degree of heating, that fact and the unexpected results make for a very interesting study that should be of broad interest. The study is also presented in a clear and concise manuscript and the conclusions are well-supported.

Weaknesses

In certain sections, it seems like the paper would benefit from a more thorough consideration of alternative explanations for the higher body size in the warmed population, like the release from density dependence or altered prey availability, and how those alternative explanations do or do not fit with the result that mortality was higher for the heated population. The consideration of mortality is a strength of the paper, but this result and how it fits with the result that heated adults did not shrink could be discussed in greater depth. It is unfortunate that factors other than the heat that might influence mortality, like predation rates, remain unknown in this system, but then they are rarely well understood in real-world settings like whole ecosystems.

Reviewer #1 (Public Review):

Caetano and colleagues describe the changes caused by periodontal inflammation in terms of tissue structure and provide additional evidence to understand the involvement of fibroblasts in altering the immune microenvironment.

While interesting and a concise study, the authors should improve their work on two major points:

1. To improve the resolution, the authors introduced a method that addresses improving the resolution by combining more information from the neighbour structure and the existing database. This raises the question of whether the lack of previous gingival tissue spatial transcriptome sequencing results weakens the reliability of this method. Does it miss the identification of some gingival tissue-specific cells? Is the failure to match two populations of fibroblasts between single-cell sequencing and spatial transcriptome sequencing of gingival tissue fibroblasts related to this?

2. Although the authors did the identification of the captured tissues, the results seem to require more analysis. Take Figure 5A as an example, there is a clear overlap between endothelial cells and basal cells. In addition, it is suggested that the authors indicate the specific location of the 10 clusters of cells in Figures 1D and 2C.

Reviewer #2 (Public Review):

This is an interesting study. In this study, the authors have linked single-cell RNA sequencing, spatial transcriptomic, and multiplex fluorescence in situ hybridization to characterize human oral mucosa in health and oral chronic inflammatory disease. They defined highly specialized epithelial and stromal compartments and spatially mapped a rare pathogenic fibroblast population likely responsible for lymphocyte recruitment and angiogenesis. They highlighted that the most dramatic variation in transcriptional/cellular spatial variability corresponds to oral mucosal tissue depth. The comparison of the list of genes with altered expression in gingival inflammation with the ones highlighted from the GWAS analysis related to patients with periodontitis is very interesting and will help to generate new hypotheses for future studies. Together with the recent publication from Williams et al., 2021, these studies are of particular interest and a valuable resource for researchers who study oral mucosa, especially gingiva in healthy conditions and periodontal diseases.

Reviewer #1 (Public Review):

In this manuscript, the authors employed an adult-trained variational autoencoder deep learning model on a relatively large sample (over 700) of human fetal-neonatal resting fMRI data to enhance the individual non-linear compression of functional activity patterns of baby brains. This approach showed better performance in the reconstruction of functional fluctuation maps, age prediction accuracy, and age prediction generalizability in fetal and neonatal fMRI data compared with conventional linear models such as spatial independent component analysis. This method also revealed distinct baby brain functional networks spanning primary and high-level systems.

This is an inspired attempt to represent non-linear changes in fetal-neonatal brain fMRI data. Considering the high noises and inconsistent functional spatial distributions in baby fMRI images, stable and sensitive feature extraction approaches are urgently needed in the field of early brain studies. This work is well designed and well written in general.

Reviewer #2 (Public Review):

In the paper, the authors aimed to repurpose a previously developed Variational Autoencoder (VAE) trained on adult rsfMRI data to characterise the in vivo foetal-neonatal brain development. Although the attempts to understand both healthy and aberrant early functional development are becoming increasingly popular, the processing and interpretation of the foetal-neonatal rsfMRI remain challenging due to methodological difficulties and the extremely fast and complex nature of the early brain development itself. For this reason, the non-linear computational models, such as the proposed VAE, have the potential to represent the rsfMRI data and capture the early neurodevelopmental trajectories with higher accuracy compared to more prevalent linear methods such as ICA.

In this vein, the authors successfully apply the adult-trained VAE to compress the spatial representation of foetal-neonate rsfMRI cortical patterns into 256 latent features. Due to the non-linear nature of the VAE, this latent representation has the potential to yield more informative brain representations of rsfMRI data compared to other available methods making it a strength of the article.

Nevertheless, one important limitation is that the direct application of the model trained on adult data to early functional connectome and more importantly, the interpretation of the reconstructed latent space-based maps rests on a strong assumption that the adult connectome features are stable and recognisable in the very early period. Moreover, such a model trained on the adult data would also be incapable to reveal possible network structures that would be present in the developing but not in the adult brain.

The attempt to validate the method and assess its generalisability on two independent, fairly large datasets that include foetuses, and preterm- and term-born infants is commendable. However, the interpretation of the results in light of the subject, image acquisition, and processing (which is widely recognised to be very difficult, especially in foetuses) heterogeneity requires caution. For example, the VAE reconstruction error is positively correlated with the age at scan in dHCP, and DBI full-terms, but the relationship is very strong in the reverse direction in DBI foetuses. This suggests differences between the subgroups of subjects which might be driven by factors other than age. Thus, we cannot exclude the possibility that the high age-predictive power of the models based on the latent features is partly driven by those differences in addition to the age-dependant features of the infant functional connectome.

The approach for the extraction and mapping of the group-level brain resting state networks is interesting and has the potential to uncover new insights into the early connectome. However, some of the current results are rather surprising and put into question their biological plausibility. For example, the authors suggest observing the precursor of the default-mode network in the DBI but not the dHCP dataset. This is rather strange given the DBI subjects (including foetuses) were on average scanned earlier than the dHCP subjects. Also, the pattern similarity of the best matched extracted independent component ('brain network') in the full-term dHCP vs full-term DBI comparison is 0.6 which is rather low if expecting the same networks to be extracted in the age-matched comparison. Additionally, the network visualisations show large heterogeneity of the distribution of activation/deactivations within extracted independent components between the datasets (even after ordering them for pattern similarity) which contradicts the expectation that the extracted networks (if real) should be stable, if not along the whole development, then at least between the narrower age ranges within the datasets.

Overall, the interpretation of the current work is somewhat limited, and careful analysis of the latent representations derived from foetal-neonate data might be required to dissociate the effects of potential confounders from biological/developmental mechanisms. This might be difficult in the context of the highly complex and mostly black-box strategy such as VAE (this applies not only to the current method but to all novel methods proposed to study rsfMRI). Despite these limitations, the proposed approach could be very interesting methodologically with a potential impact on the future analysis of rsfMRI data. Overall, the authors achieved their aim of applying a novel VAE method to foetal-neonatal functional data and demonstrated that the extracted latent variables are predictive of brain age. However, careful evaluation of the latent representations and differences in predictive results and the mapped networks between the two datasets might be necessary to support the conclusion that the VAE-derived representations of foetal-neonatal rsfMRI carry informative neural signatures.

Reviewer #3 (Public Review):

In this manuscript, Kim et al. use a deep generative model (a Variational Auto Encoder previously applied to adult data) to characterize neonatal-fetal functional brain development. The authors suggest that this approach is suitable given the rapid non-linear development taking place in the human brain across this period. Using two large neonatal and one fetal datasets, they describe that the resultant latent variables can lead to improved characterization of prenatal-neonatal development patterns, stable age prediction and that the decoder can reveal resting state networks. The study uses already accessible public datasets and the methods have been also made available.

The manuscript is clearly written, the figures excellent and the application in this group novel. The methods are generally appropriate although there are some methodological concerns which I think would be important to address. Although the authors demonstrate that the methods are broadly generalisable across study populations - however, I am unsure about the general interest of the work beyond application of their previously described VAE approach to a new population and what new insight this offers to understanding how the human brain develops. This is a particular consideration given that the major results are age prediction (which is easily done with various imaging measures including something as simple as whole brain volume) and recapitulation of known patterns of functional activity in neonates. As such, the work will be of interest to researchers working in fMRI analysis methods and deep learning, but perhaps less so to a wider neuroscience/clinical readership.

Specific comments:<br /> 1. If I understand correctly, the method takes the functional data after volume registration into template space and then projects this data onto the surface. Given the complexities of changing morphology of the development brain. would it not be preferable to have the data in surface space for standard space alignment (rather than this being done later?). This would certainly help with one of the concerns expressed by the authors of "smoothing" in the youngest fetuses leading to a negative relationship between age and performance.<br /> 2. A key limitation which I feel is important to consider if the method is aiming to be used for fetuses is the effects of the analysis being limited only to the cortical surface - and therefore the role of subcortical tissue (such as developmental layers in the immature white matter and key structures like the thalami) cannot be included. This is important, as in the fetal (and preterm neonatal) brain, the cortex is still developing and so not only might there be not the same kind of organisation to the activity, but also there is likely an evolving relationship with activity in the transient developmental layers (like the subplate) and inputs from the thalamus.<br /> 3. As the authors correctly describe, brain development and specifically functional relationships are likely evolving across the study time window. Beyond predicting age and a different way of estimating resting state networks using the decoding step, it is not clear to me what new insight the work is adding to the existing literature - or how the method has been specifically adapted for working with this kind of data. Whilst I agree that these developmental processes are indeed likely non-linear, to put the work in context, I think the manuscript would benefit from explaining how (or if) the method has been adapted and explicitly mentioning what additional neuroscientific/biological gains there are from this method.<br /> 4. The unavoidable smoothing effect of VAE is very noticeable in the figures - does this suggest that the method will be relatively insensitive to the fine granularity which is important to understand brain development and the establishment of networks (such as the evolving boundaries between functional regions with age) - reducing inference to only the large primary sensory and associative networks? This will also be important to consider for the individual "reconstruction degree" - (which it would likely then overstate - and would need careful intersubject comparison also) if it was to be used as a biomarker or predictor of cognition as suggested by the authors.

Reviewer #2 (Public Review):

This study combines molecular analysis of human melanoma cells with in vivo functional experiments in zebrafish. ChIP-seq analysis of A375 melanoma cells stimulated with TGFB revealed a TGFB enhancer. The human enhancer was a clone and a zebrafish transgenic line driving GFP by this enhancer (TIE:EGFP) revealed that TIE:EGFP was only expressed in late melanomas. TIE:EGFP+ cells showed downregulated IFN response but upregulation of novel chronic GHB target genes. AP-1 transcription factor is required for the activation of this enhancer. Expression of the chromatin remodeller SATB2 promoted activation of TIE:EGFP in early melanomas. Finally, in vivo imaging, flow cytometry and scRNA seq showed that macrophages preferentially phagocytosed TIE:EGFP+ melanoma cells.

The identification of this novel TGFB enhancer is important since most studies focused on acute TGFB effects in melanoma. However, the present study identified a set of chronic TGFB target genes that may be relevant in melonoma and probably other tumors. Therefore, this study paves the way for future studies aiming at revealing the importance of this enhancer in different tumor histotypes and the novel identified chronic TGFB target genes.

Most conclusions are supported by the data, with the exception of the ones related to macrophages that are not fully convincing.

Reviewer #1 (Public Review):

This study by Noonan et al. explores the role of TGFb signaling in melanoma. TGFb signaling in melanoma and in the tumor microenvironment is complex, acting as both a tumor suppressor and tumor promoter, as well as an immune suppressor. The authors identified a human TGFb-responsive genomic regulatory element that is activated in TGFb-treated melanoma cell lines. This human genomic regulatory element also functions in a zebrafish melanoma model (TIE:EGFP) in specific regions of advanced melanoma. The enhancer region is bound by SMAD2/3, JunB, and ATF3. The proposed model that TIE:EGFP+ melanoma cells are preferentially phagocytosed by macrophages suggests there is some signal specific to this subset of melanoma cells. How this subset of melanoma cells is phagocytosed by macrophages is still poorly understood and will require further investigation. In addition, the authors found that SATB2 overexpression drives the early onset of the TIE:EGFP reporter in melanoma. This novel zebrafish TGFb reporter line has provided unique insights into the dynamic in vivo interactions between melanoma cells and the microenvironment, as well as immune cells. This study will be of interest to researchers looking for novel signaling mechanisms of melanoma progression.

Reviewer #3 (Public Review):

Noonan et al. developed a clever reporter of TGFbeta signaling using human A375 melanoma cells to identify a TGFbeta-induced enhancer and generated a zebrafish transgenic line to monitor TGFbeta activation during the development of melanoma. They found that few discrete cells in advanced melanoma express the TIE:EGFP reporter, and used single-cell sequencing to identify differences in gene expression between these TGFbeta-responsive melanoma cells and the remaining population. They found that these cells downregulate interferon signaling and upregulate a gene signature compatible with chronic TGFbeta signaling that favours metastasis and requires AP-1 binding to regulatory elements of the target genes. Then they overexpressed SATB2, a known inducer of TGFbeta activation, in whole melanoma to increase the amount of TIE:EGFP positive cells for better characterization. Among the TIE:EGFP positive cells they retrieved a population of macrophages (Marco positive in single-cell analysis) and interpreted this observation as due to the phagocytic activity of macrophages that preferentially phagocytose TIE:EGFP positive melanoma cells. Since melanoma cells expressing TGFbeta upregulate a chronic TGFbeta signature that favours metastasis, downregulate interferon signaling, and are preferentially phagocytosed by macrophages that, as a consequence, turn on M2 markers (immunosuppressive), the authors conclude that this work highlights the need for the identification of a chronic TGFbeta biomarker signature to predict patient response to TGFbeta inhibitors.

The conclusions of this paper on melanoma cells are mostly well supported by data, while the data concerning macrophages and their interpretation need strengthening with better images and additional data analysis.

Reviewer #1 (Public Review):

By performing immunopeptidomics of macrophages infected with virulent M. tuberculosis, the authors were able to appropriately address whether Mtb proteins are able to enter the MHC-I antigen processing pathway. Their interrogation provides convincing evidence that substrates of Mtb's type VII secretion systems (T7SS) are a significant contributor to the Mtb-derived peptides presented on MHC-I. Compelling data are provided to demonstrate that ESX-1 activity is required for the MHC-1 presentation of these newly identified peptides.

Strength:

Employing a virulent strain of Mtb for infection of human monocyte-derived macrophages to identify Mtb proteins that access the MHC-I antigen processing pathways and the associated mechanisms.

Weakness:

The immunogenicity of at least some of the identified peptides should have been evaluated.

Reviewer #1 (Public Review):

The accessory protein Orf3a from severe acute respiratory syndrome coronavirus (SARS-CoV-1 or SARS-CoV-2) was initially suggested to be a viroporin and function as a cation channel. In this study, Miller et.al performed a comprehensive structural and functional investigation of SARS-CoV-2 Orf3a utilizing a multidisciplinary approach, including extensive electrophysiological analysis using different systems and determination of multiple single-particle EM structures of the protein under different conditions. Their findings demonstrated that Orf3a has no channel function and is unlikely to be a viroporin. In addition, they tried, but failed to record any channel activity of Orf3a claimed in other studies. They demonstrated that large single-channel currents measured from vesicle-reconstituted Orf3a are due to transient membrane leakiness caused by high protein/lipid ratio and/or channel contamination. Furthermore, they found that SARS-CoV-2 Orf3a, but not SARS-CoV-1 Orf3a, interacts with VPS39, a host HOPS protein involved in autophagosome/late endosome fusion with the lysosome. They proposed that the interaction between SARS-CoV-2 Orf3a and VPS39 may function to assist with SARS-CoV-2 exit and host intracellular immune evasion. This is a meticulously executed research work. I appreciate the tremendous effort the authors spent in the study to clarify some misconceptions related to the role and function of Orfsa from coronavirus.

Reviewer #2 (Public Review):

There is currently much discussion about the function of several viral proteins hypothesized to be "viroporins", especially specific proteins within SARS-CoV-1 and CoV-2, such as Orf3a. While some prior studies suggest that Orf3a exhibits ion channel activity, others disagree on this important topic. In the present study, compelling evidence is presented that Orf3a does not function as an ion channel, and suggestions are made as to its actual function. The study combines imaging to delineate Orf3a location in the cell, extensive functional analyses that demonstrate a lack of ion channel activity beyond endogenous currents, and compelling structural evidence that Orf3a does not take the form of an ion channel - lacking a clear conduction pathway and also having a basic aqueous vestibule that would not be predicted to support cation channel activity. Finally, co-assembly with trafficking proteins suggest, instead, functioning of Orf3a as a host cell trafficking disruptor that could contribute to immune cell evasion or even viral exit.

The authors present exhaustive, high-quality data to support their conclusions that Orf3a proteins from SARS-CoV-1 and SARS-CoV-2 do not exhibit ion channel activity. They clearly show Orf3a at the cell membrane and fail to detect ion channel activity using multiple modalities. I believe this work closes the book on the question of Orf3a as a viroporin. It is difficult to find any deficiencies in the experimental work. The parts about a role disrupting trafficking are a little more speculative but nevertheless appropriate and serve as a guidepost for future studies to fully elucidate the true role of Orf3a.

Reviewer #3 (Public Review):

This study combines data from cryo-electron microscopy, electrophysiology and cellular localization studies to provide insight into the structure and potential function of two orthologues of the membrane protein Orf3a from the corona viruses SARS-CoV-1 and SARS-CoV-2. The work follows up on previous studies, which assigned these proteins as viral ion channels (viroporins). By using patch-clamp electrophysiology in different cellular systems and from reconstituted protein, the authors provide convincing evidence that these proteins do likely not function as ion channels and that previous conclusions in this direction were presumably based on experimental artifacts. The lack of functional evidence is supported by structures of both proteins in different lipid environments, which concur with previous structures of the same system, and which do not show characteristic features of an ion channel. Instead, the authors describe the localization of both proteins on the plasma membrane and endo-lysosomal compartments, and they show specific interactions of the orthologue from SARS-CoV2 but not SARS-CoV1 with the protein VPS39, which as part of the HOPS complex is involved in the fusion of late endosomes and autophagosomes with lysosomes.

The strength of this manuscript relies on the wealth of high-quality data and its careful analysis, which refutes the presumed function of the viral membrane protein Orf3a as viroporin. Instead, the work provides conclusive evidence for its involvement in a different process. The electrophysiology data is very well carried out and the authors make a convincing case that the observed lack of specific currents renders a role of Orf3a as ion channel as highly unlikely. Similarly, the structural data and the cellular studies are of high quality.

The main weakness of the study, which should be considered minor in light of the strong results, relates to the unclear relevance of structural features of Orf3a to the still poorly defined function of the protein. In this respect, I regard the discussion of potential lipid density at the cytoplasmic side as exaggerated. The only region that was assigned a functional importance in mediating interactions with the protein VPS39 is unstructured and only found in one of the two orthologs. Although the data describing the interaction between SARS-CoV-2 Orf3a and VPS39 is conclusive, a function of Orf3a that is common to both viral orthologs is still missing. These weaknesses can be addressed by some revision of the text whereas the clarification of the role of Orf3a is beyond the scope of the current study and should be addressed in future work.

Reviewer #1 (Public Review):

The idea that because the hippocampal code generates responses that match the most needed variable for each task (time or distance) makes it a predictive code is not fully proved with the analyses provided in the manuscript. For example, in the elapsed time task, there are also place cells and in the fixed-distance travel there are also cells that encode other features. This, rather than a predictive code, can be a regular sample of the environment with an overrepresentation of the more salient variable that animals need to get in order to collect rewards. In addition, the analysis provided in the manuscript are rather simple, and better controls could be provided. Improving the analytical quantification of the results is necessary to support the main claim.

- What is the relationship of each type of cell with the speed of the animal?<br /> - What is the relationship with the n of trial that the animal has run (first 10 trials, last 10 trials..)?<br /> - What is the average firing rate of each neuron? Is there any relationship between intrinsic firing rate and the type of coding that the cell develops in each task?<br /> - What is the relation of the units of each type with LFP features (theta phase, ripple recruitment)?

Reviewer #2 (Public Review):

The manuscript presents a very simple and clear result. It demonstrates that neither place-cell nor time-cell presence is a constant in the rat hippocampus, but that both of these modes of activity are engaged flexibly depending on task demands. This result fits into a growing body of published work showing similar examples of flexibility in hippocampal representations; the authors do a fair job in relating their results to these studies. The innovative aspect of their manuscript is that it specifically addresses place cells and time cells, which have been different, and sometimes confusing, ways of thinking about hippocampal activity. By showing that the hippocampus shifts between distance and time encoding, the authors fit place cells and time cells into a more general framework of flexible representations.

The manuscript uses somewhat unusual and not very well-motivated criteria for classifying cells as distance or time cells. To detect the timing of neural activity on each trial, the authors look at the earliest onset of firing prior to the peak. It seems that this method would be highly susceptive to noise, and it is unclear why it would be better than the more standard methods like detecting the actual peak of firing or fitting a stretchable template to the entire firing pattern on each trial. This is a minor weakness of the manuscript, since the main conclusion shouldn't depend on the exact method used to classify cells. The difference between fixed-time and fixed-distance trials reported by the manuscript appears to be large and statistically robust.

Reviewer #3 (Public Review):

In some contexts, individual neurons in the hippocampus of rodents, called time cells, can spike selectively after a specific amount of time following a triggering event. Hippocampal neurons can also encode the traversal of a specific amount of distance (for example, running on a treadmill). Some hippocampal neurons also appear to represent mixtures of these features in addition to classical representations of place selectivity. In this manuscript, Abramson et al. hypothesize that the formation of these representations might be influenced by the task which the animal is performing in the context of the recording. To test this hypothesis, they exploit data from a previous maze-running study (Kraus et al., 2013) in which rats were trained to run on a treadmill across several trials of a session at experimentally-varied velocities. (This study had originally been done to tease apart potential confounds in the questions regarding representations of time versus distance.) In the Kraus et al. study, these walks occurred in one of two contexts or "session types." In a "fixed time" condition, on the other hand, the animal ran on the treadmill for a fixed amount of time before leaving the treadmill. In a "fixed-distance" condition, the animal ran on the treadmill for a "fixed-distance" (in the sense of self-motion). Abramson et al. conjectured that hippocampal pyramidal cells would be biased to represent elapsed time (from entering the treadmill) in the fixed-time condition, whereas they would be biased to represent elapsed distance in the fixed-distance condition. This conjecture appears to be due to the fact that the reward structure of the task motivates the prediction of elapsed time in the fixed time condition, whereas it motivates the prediction of elapsed distance in the fixed distance condition.

To test this hypothesis, the authors use the velocity of the treadmill in each trial to predict the onset of a cell's spiking activity after entering the treadmill. Such predictions would have quite different forms depending on whether the cell's representation correlates with time vs. distance, for example. The authors then use a comparison of the error in each of those two predictors, parametrically formulated, to build a classifier that predicts session type from the spiking onsets of a cell across the trials in that session. The classifier is fit to the Kraus et al. data and optimized to maximize rate of classification as distance cells in the fixed-distance sessions, and minimize rate of classification as time cells in distance sessions. By this metric, they find that 69% of cells in fixed-distance sessions are classified as distance cells, and 68% of cells in the fixed-time sessions are classified as time cells. Applying these results to a parametric hypothesis test, the null hypothesis that session type is independent of cell classifications is strongly rejected. Two other classifiers, based on similar comparisons, found similar results.

The authors conjecture that these findings may be due to the fact that the structure of the task was such that anticipation of reward would depend on "distance" traversed in the fixed-distance sessions, whereas it would depend on time elapsed in the fixed-time sessions. Thus the results are aimed to provide evidence supportive of widely-discussed theories which view the selectivity observed in hippocampal firing patterns as exemplars of predictive coding.

Weaknesses:

The original study of Kraus et al. consisted of 3 rats for which all sessions, including both training and recording, were of one type. Another 3 rats had a hybrid mixture of distance and time sessions. This is mentioned very briefly in the main text. It would appear that the theory of reward might lead to different predictions that could be verified by comparing these animals session to session at a finer grain. For example, are there examples of cells switching or transforming their "predictive" representations when a large number of trials in on session type is followed by a large number of trials of the opposite type? For another example, the transition from training to recording could give similar opportunities. It seems at least possible that ignoring these issues could cause a loss of power.

Some circularities in the construction and interpretation of the time-cell and distance-cell classifiers are not clearly addressed. The classifiers currently appear to be fit to predict the type of session a cell's response patterns are observed within. But it is tautological to use the session type to define the cell type. I sense this is ultimately reasonable because of how the classifier is built, but this concern is not addressed or explained.

Less parametric statistical thinking could be more convincing. Partly this could be a matter of explaining how and why the three classifiers were constructed and their respective scientific motivations. The strong literal finding is the rejection of the hypothesis of independence between cell response properties and session type. A measure of the strength of this effect is missing.

Reviewer #1 (Public Review):

In this manuscript, the authors describe a one-step genome editing method to replace endogenous EB1 with their previously-developed light-sensitive variant, in order to examine the effect of acute and local optogenetic inactivation of EB1 in human neurons. They then attempt to assess the effects of EB1 inactivation on microtubule growth, F-actin dynamics, and growth cone advance and turning. They also perform these experiments in neurons that are lacking EB3, in order to determine whether EB1 can function in a direct and specific way without possible EB3 redundancy.

First, the experiments depicting the methodology are rigorous and compelling. Most previous studies of +TIP function use knockout or knockdown studies in which the proteins are inactivated over many hours or days in non-human systems. This is the first study to acutely and locally inactivate a +TIP in human neurons. While this group previously published the effects of replacing endogenous EB1 with the light-sensitive variant, the novelty in this current study is that they use a one-step gene editing replacement method (using CRISPR/Cas9) along with using human neurons derived from iPSCs. After proving their new experimental system works, the authors next seek to test the effect that acutely inactivating EB1 (alongside chronic EB3 knockdown) has on microtubule dynamics, and they observe a marked reduction in MT growth and MT length. They then seek to investigate whether F-actin dynamics are immediately affected by EB1 inactivation. While measured F-actin flow rates are not significantly affected, which leads the authors to conclude that EB1 inactivation does not have any immediate effect, the included figures and movies show a different phenotype, which is not discussed. Finally, they examine the effect of EB1 inactivation on growth cone advance and growth cone turning, and find that both are affected. However, the lack of certain controls in these final experiments (specifically for Figures 3, 4, and 5) reduces the strength of their findings.

Thus, the first part of this paper describing the new methodology is very compelling and should be of interest to a wide readership, while the second part describing the functional analysis is mostly solid, with very high-quality imaging data. However, additional analysis and controls would be needed to increase confidence in their conclusions.

1) Analysis of F-actin dynamics is not thorough and their claim is not completely supported by the data. Figure 3 only depicts F-actin dynamics data from growth cones of π-EB1 EB3-/- i3Neurons and does not control growth cones (to compare dark and light conditions). While their conclusion is that F-actin dynamics are not affected, there do appear to be immediate changes in the F-actin images, other than flow rates. For example, the F-actin bundles do not appear to emanate straight out with the light condition, compared to the dark condition. There also appears to be more F-actin intensity in the transition domain of the growth cone, compared to the dark condition. If the reason is due to the effects of four minutes of blue light exposure, this would be made clear by doing this experiment with control growth cones as well.

2) Analysis of the effect of EB1 inactivation on growth cone advance and growth cone turning. Figure 4C, showing the neurite unable to cross the blue light barrier, is potentially quite compelling data, but it would be even more convincing if there were also data showing that the blue light barrier has no effect on a control neurite. Given that a number of previous recent studies have shown a detrimental effect of blue light on neurons, it seems important to include these negative controls in this current study.

3) This concern also holds true for the final experiment, in which the authors examine whether localized blue light would lead to growth cone turning. The authors report difficulty with performing this technically challenging experiment of accurately targeting the light to only a localized region of the growth cone. Thus, the majority of the growth cones (72%) were completely retracted, and so only a small subset of growth cones showed turning. However, this data would be more compelling if there were also a control condition of blue light with neurons that are not expressing the light-inactivated EB1. Another useful control would be to examine whether precise region-of-interest blue light leads to localized loss of EGFP-Zdk1-EB1C on MT plus-ends within the growth cone, or if the loss extends throughout the growth cone. Either outcome would be helpful to potential readers.

Reviewer #2 (Public Review):

In their manuscript "Growth cone advance requires EB1 as revealed by genomic replacement with a light-sensitive variant", Dema et al. showcase a CRISPR-based strategy to introduce a photo activatable EB1 variant into cultured cells in a single genome engineering step. Upon photoactivation this EB1 variant, which they term π-EB1, dissociates, thus severing the connection between the microtubule tip and +TIP proteins. They demonstrate this technique in human induced pluripotent stem cells, verifying that this genetic engineering procedure neither influences the cells beyond the EB1 gene nor hinders their ability to differentiate into neurons. Subsequently, they nicely verify that dissociation of π-EB1 leads to hindered microtubule growth, which subsequently leads to growth cone retraction. Accordingly, π-EB1 expressing axons cannot grow into an area illuminated with blue light, demonstrating the system's usefulness in circuit engineering. Finally, the authors try to specifically redirect growth cones by illuminating defined sections of the growth cone. This however leads mainly to growth cone retraction, in 70% of axons as the authors note, but succeeds in the remaining axons. Sadly, the authors do not further investigate the mechanism at the bottom of the observed axon retraction. Nevertheless, this study adds a valuable tool to the optogenetic toolbox of neurobiologists in the axon growth as well as circuit engineering fields.

Besides a few small writing and figure-editing faux pas, the study is well-written and robustly designed. The conclusions drawn by the authors are well supported by the data, which itself is technically well-prepared and controlled.

Reviewer #3 (Public Review):

The major strength of the study was the approach of using photosensitive protein variants to replace endogenous protein with the 1-step Crispr-based gene editing, which not only allowed acute manipulation of protein function but also mimicked the endogenous targeted protein. However, the same strategy has been used by the same first author previously in dividing cells, somewhat reducing the novelty of the current study. In addition, the results obtained from the study were the same as those from previous studies using different approaches. In other words, the current study only confirmed the known findings without any novel or unexpected results. As a result, the study did not provide strong evidence regarding the advantage of the new experimental approach in our understanding of the function of EB1. Some specific comments are listed below.

1. In Figure 1, to show that the photosensitive EB1 variant did not affect stem cell properties and their neuronal differentiation, Oct4 staining and western blot of KIF2C and EB3 were not strong evidence. Some new experiments more specifically related to stem cell properties or iPSC-derived neurons are necessary. In addition, the effect of EB1 inactivation on microtubule growth was quantified in stem cells but not in differentiated neurons, which supposed to be the focus of the study. In Figure S2D, quantification is needed to show the effect of blue light-induced EB1 inactivation in growth cones.

2. In Figure 2, the effect of blue light on microtubule retraction in the control cells was examined, showing little effect. However, it is still unclear if the blue light per se would have any effect on microtubule plus end dynamics, a more sensitive behavior than that of retraction. In Figure 2C, the length of individual microtubules in different growth cones was presented, showing microtubule retraction after blue light. Quantification and statistical analysis are necessary to draw a strong conclusion.

The results showed that EB3 did not seem to contribute to stabilizing microtubules in growth cones. It was discussed that EB3 might have a different function from that of EB1 in the growth cone, although they are markedly up-regulated in neurons. In the differentiated neuronal growth cones examined in the study, does EB3 actually bind to the microtubule plus ends? In the EB3 knockout cells without the blue light, the microtubules were stable, indicating that EB3 had no microtubule stabilization function in these cells. Is such a result consistent with previous studies? If not, some explanation and discussion are needed.

3. In Figure 3, for the potential roles of EB1 on actin organization and dynamics, only the rates of retrograde flow were measured for 5 min. and no change was observed. However, based on the images presented, it seemed that there was a reduced number of actin bundles after blue light and the actin structure was somewhat disrupted. Some additional examination and measurement of actin organization are necessary to get a clear result.

4. In Figure 4, the effect of blue light and EB1 inactivation on neurite extension need to be quantified in some way, such as the neurite length changes in a fixed time period, and the % of growth cones passing the blue light barrier compared with growth cones of the control cells.

5. For the quantification of growth cone turning, a control condition is needed to show that blue light itself has no effect on turning.

Reviewer #1 (Public Review):

The authors initiated the study motivated by the lack of knowledge about the molecular events downstream of the polarity effector Emx2 in the mammalian inner ear, hypothesizing that some of those molecular players will be found by sequencing cells that normally express Emx2 in ears from Emx2-mutant mice.

The hypothesis is sound, the technologies used are standard and well-established, and the presented data is of high quality. The results largely support the authors' conclusions. However, the authors have not formally demonstrated that Stk32A is a transcriptional target of Emx2. It is clear that it is positioned downstream of the events triggered by Emx2, and that it can reverse Emx2 activity, but the data do not support the claim that the kinase is under direct transcriptional control of Emx2.

The revelation that Stk32A has two separate functions in planar polarity is significant.

The results will have a significant impact in the field because it provides one of the more persuasive molecular links between Emx2 and the polarization machinery.

Reviewer #2 (Public Review):

Previous studies have shown that the transcription factor Emx2 controls mirror-image PCP along the line of polarity reversal (LPR) by regulating the trafficking of an orphan receptor GPR156. However, the underlying mechanism is unknown. Here, the authors provide evidence that Emx2 represses transcription of Stk32a, which, in turn, negatively regulates GPR156 surface expression, thereby coupling cell-intrinsic and tissue-level PCP in the vestibular sensory epithelia.

Overall, the data are clearly presented and largely convincing. Using RNA-seq and ISH and both loss- and gain-of-Emx2 in vivo, the authors show that Stk32a is expressed in a complementary domain to Emx2 via Emx2-mediated repression. Gain- and loss-of-Stk32a experiments demonstrate that Stk32a is required for hair cell PCP in the Emx2-negative regions and is sufficient to reorient PCP in the Emx2-positive region. Moreover, Stk32a negatively regulates GPR156 localization to apical junctions without affecting core PCP proteins or Emx2 expression. However, there are several notable weaknesses, including a) because transcripts of the Stk32a mutant allele were still present, the nature of the Stk32a mutation is unclear; b) Mechanisms by which Emx2 represses Stk32a transcription were not addressed or discussed; c) Mechanisms by which Stk32a regulates GPR156 surface expression were not addressed. Addressing these issues at least partially would provide stronger support for the proposed model and improve the paper's impact.

Reviewer #3 (Public Review):

The manuscript by Jia, Ratzan et al. is elegant and makes an important contribution to the hair cell and PCP field. Using a subtractive approach involving deep sequencing of the mouse Emx2 mutant and control mice, they identified Stk32a as a candidate gene regulated by EMX2. Next, they made a Stk32a mouse mutant and showed that STK32a is necessary/sufficient to determine hair bundle orientation in the vestibule. Moreover, they show that STK32A governs GPR156. The images are compelling. I have no major concerns.

Reviewer #1 (Public Review):

This is a nicely written, very compelling manuscript, comprehensive in scope, that reaches new molecular and mechanistic conclusions on metal transport by Nramp on the basis of extensive crystallographic, molecular dynamics, and metal binding/transport assays. The higher resolution of the structures reported here provides new insights into metal (both Mn and Cd) coordination chemistry along the transport pathway which was generally missing (or incomplete) from previous structural analysis of this well-studied model bacterial system. The findings are strongly topical and likely applicable to other Nramps that are present in higher eukaryotes.

The new crystallography coupled with the molecular dynamics provides support for the overall transport pathway model. The conclusions are by and large strongly supported by the data. The figures are absolutely outstanding, and readily accessible even to the non-specialist. The authors identify a lower affinity "external" site which may function as an Mn transfer site that kinetically enhances Mn-binding to the cognate "orthosteric" site essential for transport across the membrane.

Minor weaknesses are the ITC experiments in general. The authors use these experiments to estimate binding affinities of the external and orthosteric sites in a variety of conformations. Although these data are extensive (there are many titrations here), the robustness of the fits to these data is not apparent from what is provided. Clearly the stoichiometry, and thus the binding model (one site vs. two independent sites) was assumed prior to the data fitting; the uncertainties in K are then quite large.

Reviewer #2 (Public Review):

This study combines data from different experiments to provide a detailed and conclusive mechanism of how transition metal ions are transported by a prokaryotic member of the SLC11 family. Although insight into this process was already provided in previous investigations, the novelty here concerns the presentation of X-ray structures at high resolution which, in combination with previously determined structures of the same protein, show three relevant conformations on the transport cycle in the presence and absence of substrate. For the interpretation of mechanisms, the conclusions derived from these structures are supported by complementary functional experiments from isothermal titration calorimetry and transport assays. Finally, a series of molecular dynamics simulations illustrate the stability of the investigated conformations and the interaction network that was proposed to be relevant for conformational transitions.

The strength of the manuscript lies in the thoughtful experimental design of the study and the high quality of the data. The X-ray structures are as good as they probably can get for a delicate membrane protein and the interaction with ions was confirmed by anomalous scattering experiments. Although the structure of the outward-facing conformation has relied on a mutation that stabilizes this state, the conformation is similar to known outward-facing conformations of other family members. The presented complementary ITC experiments are of high quality and the experimental design is intriguing.

A comparably smaller weakness concerns a shortage in the critical assessment of the data and their relation to previous findings in the field. This is in no way meant to question major conclusions drawn from this study, but it might help the reader to better understand the limits of the results and their interpretation. This weakness can be addressed by better documentation of the data and some revision of the text.

Reviewer #3 (Public Review):

The manuscript by Ray et al. reports a massive body of work targeting the transport cycle of a class of LeuT-fold transporters that specializes in metal transport, the Nramps. The Gaudet laboratory has published extensively on this family of proteins and here they ask the question of how Nramps can transport one of their physiological substrates Mn2+ and how that differs structurally from a toxic metal like Cd2+. The authors capitalize on previously published mutations to trap the transporter in three states with and without Mn2+. Together with ITC data and MD simulations, they put together a plausible, albeit oversold, model of transport. I am not an expert on the details of the technical elements but overall given they appear sound and the corresponding author is a noted expert in crystallography. The structures recapitulate previously seen conformational changes. Nevertheless, the mechanistic story is new and of interest.